{"id": "c4ffc09af49072d5", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: ABSTRACT\nType: text\n\nThis manuscript was handled by Emmanouil Anagnostou, Editor-in-Chief.\n\nDeglaciation due to atmospheric warming has led to the formation and expansion of numerous glacial lakes, especially in the eastern Himalaya. Many of these glacial lakes are susceptible to glacial lake outburst floods (GLOFs), which can cause far-reaching impacts on downstream infrastructure and livelihoods. This study is a comprehensive assessment of GLOF susceptibility, hazard, exposure, vulnerability, and risk for four potentially dangerous glacial lakes (Bechung Tsho, Raphstreng Tsho, Thorthomi Tsho, and Lugge Tsho) located in the Lunana glacier complex of the Phochu basin in Bhutan. Exposure and risk assessments were based on modelled GLOF hydrodynamics, infrastructure data, population and housing census data. Among the four glacial lakes, Thorthormi Tsho and Lugge Tsho are relatively more susceptible to outburst floods than Raphstreng Tsho and Bechung Tsho. Outflow flood volumes from these lakes range between  $6 \\times 10^5$  and  $3 \\times 10^8$  m<sup>3</sup> which can potentially impact over 16,000 people, two hydropower projects, numerous other infrastructures, and agricultural land up to 150 km downstream of the lakes. The GLOF exposed elements are largely in Punakha and Wangdue Phodrang districts, which are located 90 and 100 km downstream of the Lunana glacier complex respectively. Among 17 subdistrict blocks within the basin, one (Lunana) lies in a very high GLOF risk area, while 9 others are in the high GLOF risk zone. The study highlights the importance of multi-source data in improving the knowledge of downstream GLOF risk and serves as a base for improving GLOF risk reduction strategies in high mountain regions.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 7, "token_count_estimate": 464, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "d63f29cf486248f0", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 1. Introduction\nType: text\n\nGlacial lakes can be potential hazard sources as outburst floods originating from these lakes can cause far-reaching damage to infrastructure and people (Allen et al., 2015; Zheng et al., 2021b). Glacial lakes are often dammed by unstable moraine or glacier ice and are surrounded by destabilizing permafrost slopes or hanging glaciers (Otto, 2019; Richardson and Reynolds, 2000). Slope failure originating in the vicinity of a glacial lake can result in a mass movement that can enter a lake, capable of displacing the lake's water creating an impulse wave that overtops the frontal dam. The instability of slopes and glacial lakes\n\nare further aggravated in young and seismically active alpine mountains (Armbruster et al., 1978), such as Himalaya, where hazard chains/cascades are common (Kirschbaum et al., 2019; Sharma et al., 2022). The rapid expansion of glacial lakes resulting from glacier retreat increases the exposure of the lakes to glacial lake outburst flood (GLOF) triggers from the surrounding catchment, where slopes are destabilized due to permafrost degradation making it more susceptible to slope failures and rockfalls (Grämiger et al., 2017). In addition, as lakes grow depending on the basin geometry, the risk of overtopping failures may increase. Thus, lake growth increases the probability of GLOFs being triggered (Rounce et al., 2017, 2016) and increases the potential flood volume\n\nE-mail address: guoqing.zhang@itpcas.ac.cn (G. Zhang).\n\n<sup>\\*</sup> Corresponding author.\n\n(Fujita et al., 2013), which are among the main factors contributing to increased GLOF hazard (Westoby et al., 2015). These intrinsic and extrinsic hazard drivers can cause largely unpredictable, rapid, and deadly draining of lake water, which may transform into debris or hyper-concentrated flow as the flood entrains sediment (Richardson and Reynolds, 2000; Sattar et al., 2022).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 29, "token_count_estimate": 536, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "375409022b6ce1bb", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 1. Introduction\nType: text\n\n, 2016 ) and increases the potential flood volume E - mail address : guoqing . zhang @ itpcas . ac . cn ( G . Zhang ) . < sup > \\ * < / sup > Corresponding author . ( Fujita et al . , 2013 ) , which are among the main factors contributing to increased GLOF hazard ( Westoby et al . , 2015 ) . These intrinsic and extrinsic hazard drivers can cause largely unpredictable , rapid , and deadly draining of lake water , which may transform into debris or hyper - concentrated flow as the flood entrains sediment ( Richardson and Reynolds , 2000 ; Sattar et al . , 2022 ) .\n\nSince GLOFs are high-magnitude and largely unpredictable catastrophic events, they can cause immense damage and disruption to the lives and infrastructures in the downstream settlements (Allen et al., 2015; Carey et al., 2011; Zheng et al., 2021b). It is estimated that 15 million people globally are exposed to the threat of GLOF, with populations in High Mountain Asia the most exposed, with around 1 million people living within 10 km of a glacial lake (Taylor et al. 2023). Numerous GLOF events have occurred in the past across the world, causing damage to infrastructures and the loss of thousands of lives (Carrivick and Tweed, 2016; Veh et al., 2022). For example, the Jinweng Co GLOF event in 2020 in the southeastern Tibetan Plateau destroyed ten residential houses, washed away eight bridges, and 43.9 km of road, also causing damage to several other structures and agricultural land (Zheng et al., 2021b). In 2013, the debris flow resulting from Chorabari lake combined with cloud-burst-induced landslides and flooding in the Indian state of Uttarakhand caused over 6,000 deaths and heavy damage to infrastructure (Allen et al., 2015). In the Peruvian Cordillera Blanca, GLOFs in 1941 and 1945 killed over 6,000 people, washed away  $\\sim$ 35 % of Huaraz city and other essential infrastructures, including hydroelectric power stations (Carey et al., 2011). Bhutan alone has recorded at least 18 GLOF events since the 1950s, the latest being the breaching of subsidiary lake II of Thorthormi Tsho on 20 June 2019 (Gurung et al., 2017; Komori et al., 2012; National Center for Hydrology and Meteorology [NCHM], 2019b; Rinzin et al., 2021). At least seven of these past GLOF events in Bhutan have caused varying degrees of infrastructure damage and loss of lives in the downstream settlements. The most serious was the 1994 GLOF event of Lugge Tsho, amongst best-known contemporary GLOF events in the Himalaya (Veh et al., 2019b), which led to around 21 fatalities, seriously damaged Punakha Dzong, the capital administrative center of Bhutan, and around 90 houses (Komori et al., 2012; Rinzin et al., 2021; Watanbe and Daniel, 1996). All these tragic and disastrous past GLOF experiences underpin the need for systemic flood disaster risk reduction (DRR) activities for which detailed downstream GLOF hazard and risk assessment provides the necessary first step.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 29, "token_count_estimate": 804, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "af5584851bbc4c5f", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 1. Introduction\nType: text\n\nloss of lives in the downstream settlements . The most serious was the 1994 GLOF event of Lugge Tsho , amongst best - known contemporary GLOF events in the Himalaya ( Veh et al . , 2019b ) , which led to around 21 fatalities , seriously damaged Punakha Dzong , the capital administrative center of Bhutan , and around 90 houses ( Komori et al . , 2012 ; Rinzin et al . , 2021 ; Watanbe and Daniel , 1996 ) . All these tragic and disastrous past GLOF experiences underpin the need for systemic flood disaster risk reduction ( DRR ) activities for which detailed downstream GLOF hazard and risk assessment provides the necessary first step .\n\nGLOFs often involve a cascade of different processes, variably termed a process chain or hazard cascade (Mergili et al., 2020; Zheng et al., 2021b), which can increase the spatial reach and destructive power of the event (Sattar et al., 2022). In the Himalaya, GLOFs frequently start with rapid mass movement into the lake, often due to slope failure from the mountain flank above the lake, leading to displacement waves, overtopping the dam (Komori et al., 2012; Nie et al., 2018). The overtopping waves cause moraine dam incision and dam failure resulting in a sudden discharge of lake water. As the flood propagates farther downstream, it transforms into debris flow and hyper-concentrated or secondary debris flow, depending on the geologic and topographic characteristics of the river channel and available sediment (GAPHAZ, 2017; Schneider et al., 2014; Westoby et al., 2014; Worni et al., 2014). These complex GLOF process chains present manifold challenges to accurately capturing its physical characteristics by numerical modelling and to a growing extent, the whole hazard cascade. However, emerging models can either better represent phase changes or sediment-laden events and their application to past events has increasingly enabled a better understanding of GLOFs. Some examples of commonly employed mass movement models include RAMMS (Christen et al., 2010), r.avaflow (Mergili et al., 2017), IBER (Bladé et al., 2014), FLOW3D (Flow Science, 2012), D-Claw (Iverson and George, 2014) and, primarily for clear water floods, HEC-RAS (CEIWR-HEC, 2021). Two or more of these models can be combined appropriately to model each process chain separately and sequentially (Frey et al., 2018; Schneider et al., 2014;\n\nWorni et al., 2014), or the entire process chain can be integrated into a single model framework (Mergili et al., 2017). However, due to computation cost and uncertainty of input parameters for practical application of numerically sophisticated models (Westoby et al., 2014), computationally less complex models like HEC-RAS 2D are often used to capture first-order hydrodynamics and impacts of GLOF (Maskey et al., 2020; Sattar et al., 2021a; Sattar et al., 2021b).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 29, "token_count_estimate": 755, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "8349010f48379d74", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 1. Introduction\nType: text\n\nseparately and sequentially ( Frey et al . , 2018 ; Schneider et al . , 2014 ; Worni et al . , 2014 ) , or the entire process chain can be integrated into a single model framework ( Mergili et al . , 2017 ) . However , due to computation cost and uncertainty of input parameters for practical application of numerically sophisticated models ( Westoby et al . , 2014 ) , computationally less complex models like HEC - RAS 2D are often used to capture first - order hydrodynamics and impacts of GLOF ( Maskey et al . , 2020 ; Sattar et al . , 2021a ; Sattar et al . , 2021b ) .\n\nPopulation and infrastructures continue to grow in GLOF-prone areas despite the increasing threat from GLOFs (Allen et al., 2022, Carrivick and Tweed, 2016, Taylor et al. 2023). This is especially true for Bhutan, where most of the settlements (National Statistics Bureau [NSB], 2018) and major hydropower projects (the mainstay of the country's economy) are concentrated along the glacier-fed river valleys; for example, Punatsangchu hydroelectric dams (PHP-I and -II) are fed by meltwater from glaciers in the Lunana region (Ministry of Economic Affairs [MOEA], 2021). Although the positive trend in the frequency of GLOFs has decayed since the 1970s (Veh et al., 2022), some studies have forecasted a 3 to 7-fold increase in GLOF hazard and downstream risk in the Himalaya due to future deglaciation (Allen et al., 2016; Zheng et al., 2021a). The assessment at the global scale has pointed out that Bhutan and Nepal have the greatest national-level economic impact from GLOF (Carrivick and Tweed, 2016), and in absolute normalized GLOF risk scores, Bhutan is ranked 13th in the world (Taylor et al. 2023).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 29, "token_count_estimate": 490, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "c8c821fa660eb339", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 1. Introduction\nType: text\n\n2021 ) . Although the positive trend in the frequency of GLOFs has decayed since the 1970s ( Veh et al . , 2022 ) , some studies have forecasted a 3 to 7 - fold increase in GLOF hazard and downstream risk in the Himalaya due to future deglaciation ( Allen et al . , 2016 ; Zheng et al . , 2021a ) . The assessment at the global scale has pointed out that Bhutan and Nepal have the greatest national - level economic impact from GLOF ( Carrivick and Tweed , 2016 ) , and in absolute normalized GLOF risk scores , Bhutan is ranked 13th in the world ( Taylor et al . 2023 ) .\n\nThe increasing GLOF threat coupled with growing downstream exposure merits an evaluation of systematic downstream risk combining GLOF hydraulics and population and census housing data. While ample existing studies offer comprehensive information about the GLOF process chains and its effects on downstream communities (Frey et al., 2018; Kougkoulos et al., 2018; Sattar et al., 2022; Sattar et al., 2021b; Schneider et al., 2014), these studies have not included the social vulnerability, due to which the GLOF downstream risk has remained far from being understood. Although Allen et al. (2016) have included the social vulnerability to evaluate GLOF downstream risk in the Indian state of Himachal Pradesh, the GLOF risk was evaluated based on the rough estimation of GLOF paths in a GIS environment, falling short of including GLOF hydraulics and intensity. Taylor et al. (2023) assessed GLOF risk, including exposure and vulnerability, but for consistency used open access simplified data and assumptions, for example, populations within a 1 km buffer of river centerlines, and GLOF reach as a fixed 50 km downstream of a lake. Lunana glacier complex, located in the headwater of Punatsangchu has five proglacial lakes, of which four were reported to have high to very high outburst susceptibility in terms of both potential trigger mechanisms and flood volume (Nagai et al., 2017; Rinzin et al., 2021). Therefore, for the first time, this study offers a detailed GLOF risk assessment, including underlying components of hazard, vulnerability, and exposure, using multi-source data including lake bathymetry, OpenStreetMap, and population and housing census data, to improve confidence in the knowledge of GLOF susceptibility and downstream risk from these lakes. This study is guided by the following three main objectives: i) to evaluate the potential GLOF hazard under different moraine-breaching scenarios from the glacial lakes in the Lunana glacier complex; ii) to identify the exposed population, infrastructures, and agricultural land in the valley; and iii) to conduct a detailed downstream risk assessment by considering socio-economic factors along the potentially inundated areas of Punatsangchu and Phochu river basins.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 29, "token_count_estimate": 704, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "045e80bb4e8b7709", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 2. Study area: significance of the Lunana glacier complex\nType: text\n\nBhutan is a small landlocked country in the Himalaya, where the landscapes are largely dominated by rugged terrains and high mountains. The Himalaya stretch along the entire northern part of the country and host large reserves of glaciers. A total of 1583 ( $\\sim$ 1487 km²) glaciers, of which 219 ( $\\sim$ 951 km²) debris-covered, and 1364 ( $\\sim$ 536 km²) cleanice glaciers have been reported in the Bhutan Himalaya (Nagai et al., 2016). Bhutanese glaciers have undergone significant area and mass loss\n\nsince the 1980s. For example, the glacier area shrunk by  $23.3 \\pm 0.9$  % during 1980–2010 (Bajracharya et al., 2014). Further, between 2000 and 2020, glacier mass loss occurred at the rate of 0.47 m w.e.yr $^{-1}$ , higher than in other neighboring eastern Himalayan ( $\\sim$ 0.33 m w.e.yr $^{-1}$ ) and Nyainqentanglha ( $\\sim$ 0.46 m w.e.yr $^{-1}$ ) regions (Hugonnet et al., 2021). Under the future global 1.5 °C warming scenario, the eastern Himalaya, where Bhutan is located, is projected to warm by  $\\sim$  1.9 °C, which is predicted to cause a  $\\sim$  60 % of glacier mass loss by the end of 21st century (Kraaijenbrink et al., 2017).\n\nRegion-wide studies such as the Himalayan scale (Nie et al., 2018), High Mountain Asia (Wang et al., 2020), and Third Pole (Zhang et al., 2015), all have indicated that the eastern Himalaya is one of the regions with the highest concentration of glacial lakes. Using high-resolution ALOS PRISM and AVNIR-2 satellite imagery from 2006 to 2011, Nagai et al. (2017) documented 733 glacial lakes (~83.6 km²) in the Bhutan Himalaya, identified 226 lakes with positive potential flood volume (PFV) (0.04–33.7  $\\times$  106 m³), 33 as having larger PFV (>5  $\\times$  106 m³), and two with very large-scale PFV (>30  $\\times$  106 m³). A more recent inventory (Rinzin et al., 2021) using Sentinel-1, Sentinel-2, and declassified Corona KH-4 data reported 2574 (156.63  $\\pm$  7.9 km²) glacial lakes in 2020, from which 1118 (~82.92 km²) were classified as glacier-fed lakes. Their inventory also revealed that Bhutanese glacial lakes have expanded by ~26.5 km² (+17 %) since the 1960s (Rinzin et al., 2021). Further, 65 lakes were found to have high outburst susceptibility based", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "2. Study area: significance of the Lunana glacier complex", "section_headings": ["2. Study area: significance of the Lunana glacier complex"], "chunk_type": "text", "line_start": 31, "line_end": 41, "token_count_estimate": 704, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "83931b0eba9d1c57", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 2. Study area: significance of the Lunana glacier complex\nType: text\n\n30 $ \\ times $ 106 m³ ) . A more recent inventory ( Rinzin et al . , 2021 ) using Sentinel - 1 , Sentinel - 2 , and declassified Corona KH - 4 data reported 2574 ( 156 . 63 $ \\ pm $ 7 . 9 km² ) glacial lakes in 2020 , from which 1118 ( ~ 82 . 92 km² ) were classified as glacier - fed lakes . Their inventory also revealed that Bhutanese glacial lakes have expanded by ~ 26 . 5 km² ( + 17 % ) since the 1960s ( Rinzin et al . , 2021 ) . Further , 65 lakes were found to have high outburst susceptibility based\n\non multiple attributes such as lake area and the likelihood of mass movement into the lake (Rinzin et al., 2021). The two inventories reported different numbers and statistics of glacial lakes in the Bhutan Himalaya due to differences in the approach and criteria employed in mapping glacial lakes and defining potentially dangerous glacial lakes. However, there is a common consensus that the Phochu basin, where the Lunana glacier complex is located contains the largest proportion of glacial lakes and the majority of potentially dangerous glacial lakes (PDGLs). For example, Rinzin et al. (2021) reported the highest proportion (22.6 %) of glacier-fed lakes in the Phochu basin and identified it as the inland basin with the highest number of PDGLs. The two glacial lakes with larger-scale potential flood volume identified by Nagai et al. (2017) are both located in the Phochu basin. Likewise, nearly half (8) of 17 PDGLs in Bhutan, first identified by ICIMOD (Mool et al., 2001) and later verified by NCHM (2019) are located in the Phochu basin. Bhutan is a dense cluster of glacial lakes in the Himalaya, and the Phochu basin is a global hotspot of potential GLOF risk (Taylor et al., 2023).\n\nFour proglacial lakes, namely, Bechung Tsho, Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho in the Lunana glacier complex of Phochu basin in the Bhutan Himalaya were selected for detailed GLOF susceptibility evaluation and downstream hazard and risk assessment (Fig. 1). These four glacial lakes are the primary source of water for the Phochu river, which coalesces with Mochu and forms Punatsangchu, one of the biggest rivers in Bhutan. A first-order hazard assessment had", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "2. Study area: significance of the Lunana glacier complex", "section_headings": ["2. Study area: significance of the Lunana glacier complex"], "chunk_type": "text", "line_start": 31, "line_end": 41, "token_count_estimate": 629, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "f4442672b8ccd09d", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 2. Study area: significance of the Lunana glacier complex\nType: figure\nFigure\n\nImage /page/2/Figure/6 description: A detailed topographical map of the Lunana glacier complex and downstream settlements in Bhutan. The main map, labeled (a), shows a mountainous region with elevation ranging from 352 to 8590 meters. It displays rivers, lakes, and Gewog boundaries. Key settlements along a river are numbered: 1. Lunana, 2. Punakha, 3. Wangdue Phodrang, 4. PHP-I, and 5. PHP-II. Two inset maps in the top-left corner show the location of the area within High Mountain Asia and Bhutan. On the right, four panels, (b) through (e), show close-ups of specific lakes from the Lunana complex with their respective areas: (b) Area: 0.5 km², (c) Area: 1.3 km², (d) Area: 4.3 km², and (e) Area: 1.6 km². The map includes latitude and longitude lines and a scale bar for 20 km.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "2. Study area: significance of the Lunana glacier complex", "section_headings": ["2. Study area: significance of the Lunana glacier complex"], "chunk_type": "figure", "figure_caption": null, "line_start": 42, "line_end": 42, "token_count_estimate": 264, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "9622b554c0ead1fc", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 2. Study area: significance of the Lunana glacier complex\nType: text\n\nFig. 1. Lunana glacier complex and downstream settlements. Map (a) shows settlements downstream of the Lunana glacier complex along the Phochu and Punatsangchu basins. Downstream settlements are presented following the subdistrict block administrative boundary. Panels (b–e) show four glacial lakes, which we studied here: Bechung Tsho, Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho, respectively. Background image: ALOS-PALSAR DEM.\n\nalready revealed that all these lakes are highly (susceptibility level higher than 0.8) or very highly (susceptibility level higher than 0.6) susceptible to outburst flood (Rinzin et al., 2021). The Lunana glacier complex is also the catchment where at least three past GLOF events have been recorded since 1990, for example, the most devastating 1994 GLOF from Lugge Tsho (Watanbe and Daniel, 1996), and the recent breaching of subsidiary lake II of Thorthormi Tsho in July 2019 (National Center for Hydrology and Meteorology [NCHM], 2019a). Although the artificial draining of Thorthormi Tsho initiated in 2008 reduced the lake water level by 5 m, the lake is still deemed as highly dangerous (NCHM, 2019b). The investigation with persistent scatterer interferometry (PSI) has revealed an unstable left moraine wall for Thorthormi Tsho and significant movement of the side moraine wall of Lugge Tsho (Wangchuk et al., 2022). Although assessment with PSI by Wangchuk et al. (2022) has found no significant movement of the thin side moraine between Thorthormi Tsho and Raphstreng Tsho, a detailed field investigation in 2019 has reported active sliding of this thin sidemoraine (NCHM, 2019a), suggesting a threat of a future cascading flood from Thorthormi Tsho and Raphstreng Tsho. Lunana, the only community-dwelling within the proximity of glaciers in Bhutan, is located about 5-20 km away from these glacial lakes. Punakha Dzong, the former capital, with significant cultural heritage, is situated at the confluence of Phochu and Mochu, about 90 km downstream. Punatsangchu basin also hosts the two largest hydroelectric projects, Punatsangchu hydroelectric project (PHP)-I (1200 MW) and -II (1020 MW), at 110 and 125 km downstream respectively which are expected to be commissioned soon (MOEA, 2021). Taylor et al. (2023) state that the Punatsangchu catchment is the 4<sup>th</sup> most at risk from GLOF catchments in the world. Located along the Phochu and Punatsangchu riverbanks there are also two district capital towns: Khuruthang town (90 km downstream) for Punakha and Bajo town (100 km downstream) for Wangdue Phodrang districts. This high GLOF potential combined with high downstream exposure means that investigating the hazard and risk status becomes critically important (Fig. 1).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "2. Study area: significance of the Lunana glacier complex", "section_headings": ["2. Study area: significance of the Lunana glacier complex"], "chunk_type": "text", "line_start": 43, "line_end": 47, "token_count_estimate": 741, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "feaa7bd0fd60dedf", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.1. Remote sensing and field data\nType: text\n\nDeclassified satellite data, Corona KH-4 and KH-9 were used to manually digitize glacier and glacial lake boundaries in the 1960s and 1970s, respectively (Table 1). To correct the image distortion of Corona and Hexagon data, the co-registration using ground control points from Sentinel-2 imagery was performed (see Rinzin et al., 2021). A Landsat series (TM, ETM, and OLI) were used to map glacial lakes and glaciers from 1990 to 2020. Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) DEM with 12.5 m spatial resolution was used as a source of terrain information for the GLOF routing. We modified terrain data along the river flow path by adding a line vector and cutting a channel through the artificial flood plain using the Terrain Modification Tool in the RAS Mapper (see supplementary material for detail). The ALOS-PALSAR DEM was also used to map the lake surrounding topographic potential for ice and rock avalanching and steepness of the moraine dam. The bathymetry data for three glacial lakes (Bechung Tsho, Rapshtreng Tsho, and Lugge Tsho) in the Lunana glacier complex were collected during the field expedition from 15 September to 20 October 2021. A Hummingbird 999SI combo (echo sounder) with an inbuilt Global Positioning System was used to collect the bathymetry data. The echo sounder transmits sound waves coupled with coordinate information into the lake bottom and records the returning echoes on a sonar chart with depth information. The lake depth data collected along the transverse transects were interpolated to the entire lake surface. The field-measured lake bathymetry was used to calculate the flood breach volume and determine the bed topography of the lakes.\n\nWe used the Hindu Kush Himalaya-scale Landsat-based land cover", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.1. Remote sensing and field data", "section_headings": ["3. Data and method", "3.1. Remote sensing and field data"], "chunk_type": "text", "line_start": 51, "line_end": 57, "token_count_estimate": 487, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["999SI"]}}
{"id": "82264a291e807227", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.1. Remote sensing and field data\nType: table\nTable: Table 1 Summary of all data used for lake outburst flood susceptibility assessment, downstream hazard, exposure, vulnerability, and risk assessment.\n\n| No. | Period         | Source                        | Resolution (m) | Purpose                                                                                                        |\n|-----|----------------|-------------------------------|----------------|----------------------------------------------------------------------------------------------------------------|\n| 1   | 1965–1968      | Corona KH-4                   | ~2.7           | Map glacial lakes and glaciers in the 1960 s                                                                   |\n| 2   | 1973–1974      | Hexagon KH-9                  | 6–9            | Map glacial lakes and glaciers in the 1970 s                                                                   |\n| 3   | 1990–2020      | Landsat (OLI, ETM, TM)        | 30             | Map glaciers and glacial lakes during 1990–2020                                                                |\n| 4   | 2006–2011      | ALOS PALSAR                   | 12.5           | Source of terrain data for multi-scenario flood modelling and lake surrounding topography analysis |\n| 5   | 2022/02/ 18 | OpenStreetMap                 | NA             | Quantify structures exposed to the simulated GLOFs                                                       |\n| 6   | 2000 & 2018 | Land use and land cover    | 30             | Determine Manning's coefficient and area of agricultural land ( ICIMOD, 2021)                         |\n| 7   | 2017           | Population and housing census | NA             | Determine downstream settlement social vulnerability level ( NSB, 2018)                               |\n| 8   | 2010–2020      | Glacier mass balance       | 100            | Determine glacier mass balance (Hugonnet et al., 2021)                                                   |\n| 9   | 2000–2018      | Glacier velocity data      | 240            | Determine glacier flow velocity (Gardner et al., 2019)                                                   |\n| 10  | 2021           | Lake bathymetry               | NA             | Calculate potential flood volume                                                                            |", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.1. Remote sensing and field data", "section_headings": ["3. Data and method", "3.1. Remote sensing and field data"], "chunk_type": "table", "table_caption": "Table 1 Summary of all data used for lake outburst flood susceptibility assessment, downstream hazard, exposure, vulnerability, and risk assessment.", "columns": ["No.", "Period", "Source", "Resolution (m)", "Purpose"], "table_row_start": 1, "table_row_end": 10, "line_start": 58, "line_end": 69, "token_count_estimate": 486, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c6062ec33622a83", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.1. Remote sensing and field data\nType: text\n\ndata of 2018 (from ICIMOD) to quantify the area of agricultural land inundated by the modelled GLOF (ICIMOD, 2021). The same data was also used to derive pixel-based Manning's n value for flood routing. We used time-series of Land Ice Velocity and Elevation (ITS\\_LIVE) (Gardner et al., 2019) to calculate the flow velocity of glaciers. The glacier mass loss was calculated using the global-scale glacier mass loss dataset available at a  $1^{\\circ} \\times 1^{\\circ}$  grid scale (Hugonnet et al., 2021). The global-scale consensus ice thickness datasets and corresponding surface DEM (Farinotti et al., 2019) were also used to calculate glacier bed overdeepening and determine future glacial lake extent.\n\nOpenStreetMap data was used to count the number of buildings exposed to the multiple GLOF scenarios modelled in the study (data as of 15 June 2022). Bhutan population and housing 2017 census data were used to determine the social vulnerability level of the exposed population (NSB, 2018). The 2020 livestock statistics from the Ministry of Agriculture and Forestry, Bhutan (Renewable Natural Resources Statistics Division [RNR-SD], 2020) were used to quantify the number of livestock exposed to the hazard from GLOFs. The granularity of these data will be further discussed below.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.1. Remote sensing and field data", "section_headings": ["3. Data and method", "3.1. Remote sensing and field data"], "chunk_type": "text", "line_start": 70, "line_end": 74, "token_count_estimate": 373, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "ea12e8e6a67947b6", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.2. Glacial lake outburst susceptibility assessment\nType: text\n\nThe GLOF susceptibility assessment for the lakes in the Lunana glacier complex was performed based on a total of 18 conditioning and triggering factors, broadly categorized into atmospheric, cryospheric, geotechnical, and geomorphic factors (after GAPHAZ, 2017) (see Supplementary Table S1). Each factor was either observed or quantified using the presented data, including remote sensing, DEM, and field observation.\n\nThe topographic potential of the surrounding slopes for landslide/rockfall and ice/snow avalanche (slope greater than or equal  $30^{\\circ}$  and run-out trajectory slope greater than or equal to  $14^{\\circ}$ ) were mapped using a GIS-based modelling approach (Allen et al., 2019; Romstad et al., 2008) (Fig. 2). Steep Lakefront Area (SLA) (depression angle  $>10^{\\circ}$  from", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.2. Glacial lake outburst susceptibility assessment", "section_headings": ["3. Data and method", "3.2. Glacial lake outburst susceptibility assessment"], "chunk_type": "text", "line_start": 76, "line_end": 80, "token_count_estimate": 260, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "551a04e44cf5aeea", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.2. Glacial lake outburst susceptibility assessment\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A methodological flowchart is presented in six numbered sections, detailing the process for assessing Glacial Lake Outburst Flood (GLOF) susceptibility, hazard, exposure, vulnerability, and risk.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.2. Glacial lake outburst susceptibility assessment", "section_headings": ["3. Data and method", "3.2. Glacial lake outburst susceptibility assessment"], "chunk_type": "figure", "figure_caption": null, "line_start": 81, "line_end": 81, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea99aae1ce67d592", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.2. Glacial lake outburst susceptibility assessment\nType: text\n\n1. Glacial lake outburst flood susceptibility: This flowchart shows that data from Landsat series, Hexagon KH-9, Corona KH-4, ALOS-PALSAR DEM, Google Earth Imagery, and Field photos are used to analyze factors like glacier & lake area change, mass balance, glacier velocity (leading to cryospheric factor), topographic potential area, moraine dam stability, and catchment area. These analyses inform intermediate factors such as calving front, avalanche, rockfall/landslide, and geotechnical & geomorphological factors, which all contribute to determining GLOF susceptibility.\n\n2. Dam breach and GLOF routing: This section shows that ALOS-PALSAR DEM and Bathymetry Survey data are used to calculate peak discharge, flood velocity, and flood depth, which together determine the flood intensity.\n\n3. Hazard: A 2x3 matrix shows the hazard level based on Intensity (High, Medium) and Probability (High, Medium, Low). The combinations result in High (H), Medium (M), or Low (L) hazard levels, represented by red, orange, and green colors respectively. For example, High Intensity and High Probability result in a High Hazard level.\n\n4. Exposure: A flowchart indicates that flood intensity affects people, buildings, agricultural land, motorways, and bridges. The overall exposure is rated as High (H), Medium (M), or Low (L), shown in a red, orange, and green bar.\n\n5. Vulnerability: This section shows that population and housing census data are used to create a social vulnerability index, which is then rated as High (H), Medium (M), or Low (L) in a red, orange, and green bar.\n\n6. Risk: A 3x3 matrix determines the risk level based on Exposure (High, Medium, Low) and Hazard (High, Medium, Low). The matrix also aligns Vulnerability levels with Exposure levels. The resulting risk is categorized as High (H), Medium (M), or Low (L), represented by dark red, orange, and green squares. For instance, High Exposure and High Hazard result in a High Risk level.\n\nFig. 2. Methodological flow chart adopted for GLOF susceptibility, downstream hazard, exposure, vulnerability, and risk assessments for the potentially dangerous glacial lakes in the Lunana glacier complex of the Bhutan Himalaya: downstream GLOF hazard, vulnerability, exposure, and risk are categorized into three classes; high (H), medium (M) and low (L). For the hazard computation, the GLOF intensity used here is translated from the modelled flood depth: high (more than 1 m) and medium (less than 1 m) (after GAPHAZ, 2017).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.2. Glacial lake outburst susceptibility assessment", "section_headings": ["3. Data and method", "3.2. Glacial lake outburst susceptibility assessment"], "chunk_type": "text", "line_start": 82, "line_end": 100, "token_count_estimate": 681, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "150161855216b130", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.2. Glacial lake outburst susceptibility assessment\nType: text\n\nExposure and High Hazard result in a High Risk level . Fig . 2 . Methodological flow chart adopted for GLOF susceptibility , downstream hazard , exposure , vulnerability , and risk assessments for the potentially dangerous glacial lakes in the Lunana glacier complex of the Bhutan Himalaya : downstream GLOF hazard , vulnerability , exposure , and risk are categorized into three classes ; high ( H ) , medium ( M ) and low ( L ) . For the hazard computation , the GLOF intensity used here is translated from the modelled flood depth : high ( more than 1 m ) and medium ( less than 1 m ) ( after GAPHAZ , 2017 ) .\n\nthe flat lake surface), which provides information on the steepness of the moraine dam, was computed following the approach of Fujita et al. (2013). Hydrological characteristics such as total area of catchment and stream density were calculated from AlOS-PALSAR DEM. Other attributes, such as calving potential, which require finer resolution observation, were validated based on expert interpretation of Google Earth imagery and field photos (Fig. 3). The parent glacier extent was vectorized by modifying RGI v6.0 glacier outlines (RGI Consortium, 2017) and through our expert observation in a false-color composite of Landsat series imagery (OLI, TM, and ETM) for 1990–2020, Corona KH-4 for the 1960s, and Hexagon KH-9 for the 1970s. The same scenes were also used for digitizing the glacial lake boundaries manually. The glacier bed overdeepenings, as an indicator of future lake extent, were calculated by subtracting glacier ice thickness from the surface DEM (Farinotti et al., 2019) following Linsbauer et al. (2016).\n\nBased on the degree to which these factors can condition or trigger a GLOF from a given lake, inferred through either expert judgment or calculated value, the above factors were classified into high, medium, and low susceptibility levels (Allen et al., 2022; GAPHAZ, 2017) with the corresponding score of 3, 2, 1, respectively. The total GLOF susceptibility score of the lake was calculated as the sum of scores across all factors. The susceptibility score was finally used to compare the relative outburst susceptibility level of the four lakes considered here.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.2. Glacial lake outburst susceptibility assessment", "section_headings": ["3. Data and method", "3.2. Glacial lake outburst susceptibility assessment"], "chunk_type": "text", "line_start": 82, "line_end": 100, "token_count_estimate": 597, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e1207e1bcff0aff5", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.3. Dam breach and GLOF routing\nType: text\n\nHydrologic Engineering Center's River Analysis System (HEC-RAS v\n\n6.0), a 2-dimensional (2D) dam breach model was used to simulate a series of potential GLOF events from the lakes in the Lunana glacier complex. Although limited to clear-water flow assumptions, HEC-RAS has been widely used for hydrodynamic modelling of GLOF in the Himalaya, such as in Nepal (Khanal et al., 2015; Sattar et al., 2021b), Tibetan Plateau (Wang et al., 2015), India (Sattar et al., 2021a) and Bhutan (Maurer et al., 2020; Osti et al., 2013). The model solves the 2D Saint Venant equation to calculate the unsteady flow hydraulics of GLOFs (CEIWR-HEC, 2021). The sine wave progression breach model was employed where initial breach forms slowly and with a further gradual incision the outflow velocities and shear stress change which leads to an increase in the outflow discharge (Sattar et al., 2021b). For the majority of moraine-dammed lakes, the outburst events are caused by displacement waves from mass movement followed by accompanying breach development and dam break (GAPHAZ, 2017). Therefore, we assumed that the dam breach was instigated by overtopping due to the mass movement into the lake, with subsequent dam incision and degradation leading to the outburst event. In HEC-RAS this is implemented with the dam failure mode considered as overtopping, where dam breach starts at the top of the dam and progresses toward the base of the moraine as erosion occurs (CEIWR-HEC, 2021). Breach parameters like breach width and formation time for each GLOF scenario were calculated using Froehlich (1995) (Eqs. (1) and (2)). This set of equations has been used previously in calculating breach parameters for GLOF modelling (Anacona et al., 2015; Sattar et al., 2021b; Wang et al.,", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.3. Dam breach and GLOF routing", "section_headings": ["3. Data and method", "3.3. Dam breach and GLOF routing"], "chunk_type": "text", "line_start": 102, "line_end": 106, "token_count_estimate": 509, "basins": [], "subbasins": [], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": []}}
{"id": "f4c7a8932331b5eb", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.3. Dam breach and GLOF routing\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A figure displaying four photographs of different glacial lakes, each with highlighted geological hazards. A legend at the top defines the colored, dashed boxes used in the photos: black for 'Calving front', blue for 'Avalanche', orange for 'Landslide/Rockfall', and purple for 'Unstable moraine'.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.3. Dam breach and GLOF routing", "section_headings": ["3. Data and method", "3.3. Dam breach and GLOF routing"], "chunk_type": "figure", "figure_caption": null, "line_start": 107, "line_end": 107, "token_count_estimate": 133, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "144cbe674208e31d", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.3. Dam breach and GLOF routing\nType: text\n\nThe top-left photo is of 'Bechung Tsho' on 11/10/2021, showing highlighted areas for avalanches and landslides/rockfalls, with an inset image pointing to the calving front.\n\nThe top-right photo is of 'Lugge Tsho' on 8/10/2021, indicating a calving front and a landslide/rockfall area, with an inset showing another view with avalanche and landslide zones.\n\nThe bottom-left photo is of 'Raphstreng Tsho' on 11/10/2021, with several areas marked for potential landslides/rockfalls and an avalanche.\n\nThe bottom-right photo is of 'Thorthormi Tsho' on 11/10/2021, showing potential avalanche and landslide/rockfall zones, with an inset highlighting an unstable moraine.\n\nFig. 3. Field photographs show the surrounding conditions that make lakes susceptible to GLOF, including calving front, avalanche, landslide/rockfall, and unstable moraine dam. Photo courtesv: NCHM. Bhutan.\n\n$$B_{w} = 0.1803k_{0}(V_{w})^{0.32}(h_{b})^{0.14}$$\n(1)\n\n$$T_f = 0.00254 (V_w)^{0.53} (h_b)^{-0.9}$$\n (2)\n\nwhere  $B_w$  is breach width,  $T_f$  is breach formation time,  $V_w$  is breach volume, and  $h_b$  is breach height.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.3. Dam breach and GLOF routing", "section_headings": ["3. Data and method", "3.3. Dam breach and GLOF routing"], "chunk_type": "text", "line_start": 108, "line_end": 134, "token_count_estimate": 388, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["00254"]}}
{"id": "02be9f500e190df7", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.3. Dam breach and GLOF routing\nType: text\n\nlandslide / rockfall , and unstable moraine dam . Photo courtesv : NCHM . Bhutan . $ $ B_ { w } = 0 . 1803k_ { 0 } ( V_ { w } ) ^ { 0 . 32 } ( h_ { b } ) ^ { 0 . 14 } $ $ ( 1 ) $ $ T_f = 0 . 00254 ( V_w ) ^ { 0 . 53 } ( h_b ) ^ { - 0 . 9 } $ $ ( 2 ) where $ B_w $ is breach width , $ T_f $ is breach formation time , $ V_w $ is breach volume , and $ h_b $ is breach height .\n\nFor the forward modelling, a series of 12 GLOF scenarios were simulated, considering large, moderate, and small magnitude events originating from each lake: Bechung Tsho, Raphstreng Tsho, Thorthormi Tsho, and Lugge Tsho. For each simulation, a unique set of parameters were considered: breach width  $(B_w)$ , breach formation time  $(T_f)$  and breach volume  $(V_w)$ , and breach height  $(h_b)$  while keeping the downstream valley geomorphology and river hydrological conditions constant. For most past GLOF events in the Himalaya, it is unlikely that the entire moraine fails in case of an outburst event (Maurer et al., 2020; Veh et al., 2019a). Therefore, for the high magnitude GLOF scenario routing, we constructed lake breach models assuming  $h_b$  starting at the crest of the dam down to the mean depth of the lakes (calculated based on the field-measured bathymetry). The  $h_b$  for the moderate magnitude is assumed to be half of the large-magnitude  $h_b$ . Likewise, the smallmagnitude  $h_b$  was calculated as half of the moderate magnitude. Breach volumes were calculated from the field bathymetries as the volume of the water above the lowest point of breaching following the conservative trend in the previous studies, such as of Sattar et al. (2021b). However, owing to the lack of bathymetry data, the  $V_w$  for Thorthormi Tsho was calculated as the product of area and potential lowering height (Hp) following (equation (3)) Fujita et al. (2013). Hp is the anticipated water lowering height after SLA is removed due to moraine dam failure (Fujita et al., 2013). Here, it is worth noting that the GLOF was simulated as a clear water flood and did not account for the complex process chain of a GLOF, such as debris loading from erosion and sediment deposition along the downstream channel. We used pixelbased Manning's n value according to the latest Landsat-derived land use and land cover data (LULC) (ICIMOD, 2021). Depending on different classes of LULC, the pixel-based Manning's *n* ranges from 0.035 to 0.120.\n\n$$PFV = \\min[Hp; Dm]A \\tag{3}$$\n\nwhere, *PFV* is potential flood volume, *Hp* is potential height lowering, *Dm* is the mean depth of the lake, and A is a lake area.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.3. Dam breach and GLOF routing", "section_headings": ["3. Data and method", "3.3. Dam breach and GLOF routing"], "chunk_type": "text", "line_start": 108, "line_end": 134, "token_count_estimate": 835, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["00254"]}}
{"id": "719bdeb7bb3e6270", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.3. Dam breach and GLOF routing\nType: text\n\nand sediment deposition along the downstream channel . We used pixelbased Manning ' s n value according to the latest Landsat - derived land use and land cover data ( LULC ) ( ICIMOD , 2021 ) . Depending on different classes of LULC , the pixel - based Manning ' s * n * ranges from 0 . 035 to 0 . 120 . $ $ PFV = \\ min [ Hp ; Dm ] A \\ tag { 3 } $ $ where , * PFV * is potential flood volume , * Hp * is potential height lowering , * Dm * is the mean depth of the lake , and A is a lake area .\n\nWe reconstructed the 1994 GLOF event of Lugge Tsho to understand the GLOF hydrodynamics in the Punatsangchu basin before conducting the multi-scenario modelling from the lakes in the Lunana glacier complex. There are limited field and eyewitness information on the 1994 GLOF event, so we drew upon sparse data available in the previous literature to define essential input parameters and initial conditions. For example, the moraine dam breach height was set to 18 m based on the height estimated by Fujita et al. (2008). Likewise, the dam breach width was set to 105 m and the dam crest width to 28 m both of which were constrained by Koike and Takenaka (2012). Combining field measurements and remote sensing observations, Fujita et al. (2008) have estimated a GLOF volume of 17.2  $\\pm$  5.3  $\\times$  10<sup>6</sup> m<sup>3</sup>. Accordingly, we simulated six scenarios of GLOF by changing flood volume between 15  $\\times$  10<sup>6</sup> and 20  $\\times$  10<sup>6</sup> m<sup>3</sup> (each time increasing by 1 million m<sup>3</sup>) and keeping all other initial breach parameters constant. Other essential information available for the 1994 Lugge GLOF is the Peak flow at Wangdue Phodrang hydrological station (1800–2500 m<sup>3</sup>/s) (Yamada, 2000; Osti et al., 2013), and flood wave arrival time at Punakha through eyewitness (5.1 hr) and estimated using seismometer (4.75 hr) (Maurer et al., 2020; Watanbe and Daniel, 1996). So, we measured peak flow at Wangdue Phodrang hydrological station and flood reach time at Punakha.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.3. Dam breach and GLOF routing", "section_headings": ["3. Data and method", "3.3. Dam breach and GLOF routing"], "chunk_type": "text", "line_start": 108, "line_end": 134, "token_count_estimate": 629, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "02c3a0e6664a24f9", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.4. Downstream risk assessment\nType: text\n\nThe risk from any disaster event is represented as a function of hazard, exposure, and vulnerability (IPCC, 2014). Here, GLOF modelling\n\nresults were first translated into a hazard map. In doing so, the flow height from each scenario modelled GLOF was converted into two classes of intensity: high intensity (more than 1 m) and medium intensity (less than 1 m) (Frey et al., 2018; GAPHAZ, 2017). Later, the hazard map was computed by combining these intensities with the probability of occurrence (low, medium, and high corresponding to potential large, moderate, and small magnitude events, respectively). We combined intensity and probability maps by using the simple matrix approach (GAPHAZ, 2017). Since all the lakes examined here are situated in the same basin and pose a threat to the downstream regions, we evaluate the combined hazard for all four glacial lakes in the Lunana glacier complex. This final hazard map was created by initially producing four GLOF hazard maps (one for each lake) with three hazard levels given as high, medium, and low. Later, a simple matrix technique was used to merge the individual lake's hazard map. The combined hazard map is classified into four hazard levels: very high (when all (four) high hazard level from each lake intersects), high (when at least three high hazard level intersect or two high hazard level and two medium levels intersect), medium (at least two medium hazard level intersect) and low (when all low hazard levels intersect).\n\nExposure is the presence of people, livelihood, environmental services, and other infrastructure along the flood plain (Allen et al., 2019). Flood exposure calculation involves two components: delineating the flood hazard map and the population, structures, livestock, and agricultural land intersecting with that flood hazard map (Tate et al., 2021). The OpenStreetMap data were used to quantify any buildings, roads, and bridges that intersect with the extent of flood hazard under different scenarios. While there was good coverage of OpenStreetMap in our study area, we manually updated 75 buildings based on high-resolution Google Earth imagery. To estimate the GLOF exposed population we divided the total population by the number of buildings in the 17 identified subdistrict blocks to roughly appraise the number of people per exposed building, following Kougkoulos et al. (2018). The population data were extracted from Bhutan population and housing census 2017 data (NSB, 2018). The same approach was used to calculate the total number of exposed livestock using Bhutan's annual livestock statistics 2020 from the Ministry of Agriculture and Forestry (RNR-SD, 2020). The inundated agricultural land was delineated by overlaying GLOF extent on the 2018 Landsat 8-based LULC (ICIMOD, 2021). The exposure level for each subdistrict block was classified into three classes depending mainly on the number of exposed buildings as high (more than 50), medium (10–50), and low (up to 10 buildings with agricultural land, road, and bridge damage). We identified buildings as highly important since their destruction often directly relates to human casualties during disasters like GLOF (Allen et al., 2016).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.4. Downstream risk assessment", "section_headings": ["3. Data and method", "3.4. Downstream risk assessment"], "chunk_type": "text", "line_start": 136, "line_end": 144, "token_count_estimate": 793, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "5702f17c48935cdd", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > 3.4. Downstream risk assessment\nType: text\n\nfrom the Ministry of Agriculture and Forestry ( RNR - SD , 2020 ) . The inundated agricultural land was delineated by overlaying GLOF extent on the 2018 Landsat 8 - based LULC ( ICIMOD , 2021 ) . The exposure level for each subdistrict block was classified into three classes depending mainly on the number of exposed buildings as high ( more than 50 ) , medium ( 10 – 50 ) , and low ( up to 10 buildings with agricultural land , road , and bridge damage ) . We identified buildings as highly important since their destruction often directly relates to human casualties during disasters like GLOF ( Allen et al . , 2016 ) .\n\nBased on Bhutanese population and housing census 2017 data, 15 social vulnerability indicators, and their values were selected, representing a wide range of social and economic conditions of the people (NSB, 2018) (Table 2). Here, we did not differentiate the indicator into positive or negative dependence, since we uniformly chose indicators with positive dependence, where increasing indicator value increases vulnerability. For example, we chose the percentage of households without a car for the indicator, household ownership of the car. Each indicator value of the subdistrict block (Table S2) was normalized to a value ranging from 0 (low vulnerability) to 1 (high vulnerability) with impartial weighting. The individual vulnerability index (VI) of each subdistrict block was calculated as the average across all normalized scores. The vulnerability index for each subdistrict block was also classified into very high (0.75-1), high (0.5-0.75), medium (0.25-0.5), and low (0-0.25). Finally, hazard, vulnerability, and exposure were combined using the simple matrix method to produce the risk level of each subdistrict block (Fig. 2). Here, we treated hazard, exposure, and vulnerability equally and no weighting was used.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > 3.4. Downstream risk assessment", "section_headings": ["3. Data and method", "3.4. Downstream risk assessment"], "chunk_type": "text", "line_start": 136, "line_end": 144, "token_count_estimate": 471, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69bc7e569c26d182", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > Table 2\nType: text\n\nList of indicators and corresponding represented components considered for calculating the social vulnerability index for the downstream settlements. The indicators are extracted from the Bhutan Population and Housing Census 2017 data (NSB, 2018), following the convention used by Allen et al. (2016) and Cutter et al. (2003). The absolute and normalized values for each indicator are also provided in supplementary Table S2.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > Table 2", "section_headings": ["3. Data and method", "Table 2"], "chunk_type": "text", "line_start": 146, "line_end": 148, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "3097c5a12a2757c2", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 3. Data and method > Table 2\nType: table\nTable\n\n| No. | Social vulnerability indicator                                            | Component represented                                    |\n|-----|---------------------------------------------------------------------------|----------------------------------------------------------|\n| 1   | Old age dependence ratio                                                  | Sensitivity, capacity to prepare, respond and recover |\n| 2   | Child dependence ratio                                                    | Sensitivity, capacity to prepare, respond and recover |\n| 3   | Disability prevalence rate                                                | Sensitivity, capacity to prepare, respond and recover |\n| 4   | Female population                                                         | Sensitivity, capacity to prepare, respond and recover |\n| 5   | Household without high school education attainment (adequate literacy) | Capacity to prepare, respond and recover              |\n| 6   | Household with insufficiency in food                                      | Sensitivity, capacity to prepare, respond and recover |\n| 7   | Rented household                                                          | Capacity to recover                                      |\n| 8   | Household without a reliable source of water                           | Capacity to prepare, respond and recover              |\n| 9   | Household without an improved sanitation                               | Capacity to recover                                      |\n| 10  | Household with more than a 30-minute walk to the road point            | Capacity to prepare, respond and recover              |\n| 11  | Household without a smartphone                                            | Capacity to prepare and respond                       |\n| 12  | Household living in the house without housing concrete wall            | Capacity to recover                                      |\n| 13  | Household without a car                                                   | Capacity to prepare, respond and recover              |\n| 14  | Household without a TV                                                    | Capacity to prepare and respond                       |\n| 15  | Household without electricity as a main source of energy               | Capacity to prepare and respond                       |", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "3. Data and method > Table 2", "section_headings": ["3. Data and method", "Table 2"], "chunk_type": "table", "table_caption": null, "columns": ["No.", "Social vulnerability indicator", "Component represented"], "table_row_start": 1, "table_row_end": 15, "line_start": 149, "line_end": 165, "token_count_estimate": 441, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff9393dcf218702a", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.1. Outburst susceptibility of glacial lake\nType: text\n\nThe total GLOF susceptibility score (Sc) for the Thorthormi Tsho (Sc = 47) and Lugge Tsho (Sc = 45) was comparatively higher than the Bechung Tsho (Sc = 37) and Raphstreng Tsho (Sc = 34), suggesting that the former two lakes have higher chances of producing GLOF than the latter two. Since all lakes are located within the small catchment (99.3 km<sup>2</sup>), they had uniform susceptibility scores of 8 resulting from atmospheric factors. Seven of the 18 susceptibility factors considered here were cryospheric factors. With a total score of 17, Thorthormi Tsho had the highest susceptibility score from cryospheric factors, followed by Lugge Tsho with 16. This high score is because, firstly, the likelihood of ice avalanche, an important cryospheric factor, was highest for the Thorthormi Tsho as its total area susceptible to avalanche (4.3 km<sup>2</sup>) is higher than other lakes, almost by 10-fold. Likewise, for the Lugge Tsho, the meltwater ponds on the moraine indicated a possible degradation of the ice core or permafrost in its terminal moraine dam. Lugge Tsho is also in direct contact with the parent glacier and has a calving front measuring up to 360 m (Fig. 3) indicating the high possibility of impact from the calving. The Thorthormi glacier surface flow velocity (9.37 m a<sup>-1</sup>), especially in the lower terminus, is almost 2–3-fold higher than other glaciers. The other cryospheric factors, such as frontal glacier retreat, mass loss, and lake expansion, were also higher for Thorthormi and Lugge Tsho (Fig. 4 and Supplementary Table S1). Additionally, the modelling of glacier bed topography suggested that Lugge Tsho will expand up to 1.83 km<sup>2</sup> (+11.6 %) Supplementary Fig. S3).\n\nThe total susceptibility scores across nine geotechnical and geomorphologic factors ranked Lugge Tsho with the highest GLOF susceptibility level, followed by Thorthormi Tsho with a score of 23 and 20, respectively. Again, this higher score is because most of the calculated", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.1. Outburst susceptibility of glacial lake", "section_headings": ["4. Results", "4.1. Outburst susceptibility of glacial lake"], "chunk_type": "text", "line_start": 170, "line_end": 174, "token_count_estimate": 581, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5026609eaca9c104", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.1. Outburst susceptibility of glacial lake\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: This is a figure from a scientific paper, labeled \"Fig. 4\", illustrating glacier retreat and glacial lake expansion between 1966 and 2020 for four different locations: Rapstreng, Thorthormi, Lugge, and Bechung. The figure is organized into four rows, one for each location. Each row contains a series of four satellite images from different years (1966, 1990, 2010, 2020, with specific dates in 1994 for Lugge) and a corresponding line graph. The images visually show the decrease in glacier size (outlined in blue) and the increase in lake size (outlined in yellow) over time. The line graphs plot Lake area (km²) in green on the left y-axis and Glacier length (km) in blue on the right y-axis, against the year on the x-axis. For Rapstreng, the lake area grows from near 0 to 1.25 km², while the glacier length shrinks from about 5.5 to 4.2 km. For Thorthormi, the lake area increases from 0 to about 3.5 km², and the glacier length decreases from about 9.2 to 6.2 km. For Lugge, the graph shows a sharp drop in lake area on 7/10/1994 due to an outburst flood, after which it begins to increase again. For Bechung, the lake area grows from 0 to 0.4 km², while the glacier length decreases from about 6.6 to 5.8 km. A legend at the bottom clarifies that the blue outline represents the glacier and the yellow outline represents the lake.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.1. Outburst susceptibility of glacial lake", "section_headings": ["4. Results", "4.1. Outburst susceptibility of glacial lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 175, "line_end": 175, "token_count_estimate": 404, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13cccb3918b5305b", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.1. Outburst susceptibility of glacial lake\nType: text\n\nFig. 4. Glacier retreat and glacial lake expansion between 1966 and 2020 in the Lunana glacier complex. Lugge Tsho area recession after the 1994 outburst flood is also shown. The background image: Corona KH-4 (1968) and Landsat series (1990–2020).\n\nvalues and expert judgment for these factors were in favour of Lugge Tsho and Thorthormi Tsho, as discussed below. All four glacial lakes in the Luanna complex have the most imminent threat from probable mass movements from the surrounding slopes (clearly visible in field photos; Fig. 3). By comparison, the total area susceptible to the mass movement for Thorthormi Tsho is  $10.8 \\text{ km}^2$  which is 2–4-fold higher than other lakes making it relatively more susceptible to the impact of the mass movement (Supplementary Table S1). The individual catchment area for the lake ranges from 11.8– $46.2 \\text{ km}^2$ . However, owing to the largest catchment area ( $46.2 \\text{ km}^2$ ) and high stream order, Lugge Tsho appears to be most exposed, allowing us to assign higher scores for the Lugge Tsho (Supplementary Table S1). Likewise, the field photos showed that Thorthormi Tsho's moraine dam width, especially toward Raphstreng Tsho (right side), is very thin (30–40 m) (Fig. 3). On the other hand,\n\nRaphstreng Tsho's moraine width is thick (500 m) as observed in Google Earth high-resolution imagery. Moraine damming Bechung Tsho seems very wide (700 m) with a good vegetation cover, seemingly the most stable among the lakes in the Lunana glacier complex.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.1. Outburst susceptibility of glacial lake", "section_headings": ["4. Results", "4.1. Outburst susceptibility of glacial lake"], "chunk_type": "text", "line_start": 176, "line_end": 182, "token_count_estimate": 445, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b65a2cdc8b44e241", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex\nType: text\n\nThe glacial lakes in the Lunana glacier complex have potential flood volumes ranging from  $6\\times 10^5$  to  $3\\times 10^8$  m³, which could produce floods with a peak discharge of 200–76,000 m³/s. Thorthormi Tsho has a comparatively higher GLOF magnitude than the other three lakes (Fig. 5). The resulting peak flow mainly depends on the flood magnitude at the starting point and gradually decreases as the flood routes", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex", "section_headings": ["4. Results", "4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex"], "chunk_type": "text", "line_start": 184, "line_end": 186, "token_count_estimate": 162, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d1bda5dab70b88aa", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A figure from a scientific paper illustrating breach parameters and outflow hydrographs for four different glacial lakes (Tsho) under three different scenarios: Large, Moderate, and Small. The figure is a 4x3 grid where rows represent the lakes (Thorthormi Tsho, Lugge Tsho, Raphstreng Tsho, Bechung Tsho) and columns represent the scenarios. Each cell contains a schematic of a dam breach showing breach width (Bw) and height (Hb), along with a corresponding hydrograph of discharge versus time. For Thorthormi Tsho, the parameters for Large, Moderate, and Small scenarios are respectively: V=303.5, 151.5, 75.8 (x10⁶ m³); T=1.7, 2.2, 2.9 hr; Bw=293.7, 206.2, 144.8 m; Hb=70.8, 35.4, 17.7 m; with peak discharges around 75,000, 22,000, and 9,000 m³/s. For Lugge Tsho, the parameters are: V=80.2, 40.1, 20.1 (x10⁶ m³); T=1.2, 1.7, 2.0 hr; Bw=178.1, 126.0, 87.9 m; Hb=48, 24, 12 m; with peak discharges around 30,000, 12,000, and 6,000 m³/s. For Raphstreng Tsho, the parameters are: V=61.1, 30.5, 15.3 (x10⁶ m³); T=1.1, 1.4, 1.8 hr; Bw=161.6, 113.5, 79.7 m; Hb=45.4, 22.7, 11.4 m; with peak discharges around 25,000, 10,000, and 5,000 m³/s. For Bechung Tsho, the parameters are: V=4.9, 2.4, 0.6 (x10⁶ m³); T=1.1, 1.4, 1.8 hr; Bw=54.5, 41.0, 26.9 m; Hb=10.4, 5.2, 2.6 m; with peak discharges around 1200, 450, and 200 m³/s. The hydrographs for Large and Moderate scenarios span 200 minutes, while those for the Small scenario span 400 minutes.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex", "section_headings": ["4. Results", "4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex"], "chunk_type": "figure", "figure_caption": null, "line_start": 187, "line_end": 187, "token_count_estimate": 550, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "078c39b5b064d61e", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex\nType: text\n\n**Fig. 5.** Illustration of breach parameters: breach width  $(B_w)$ , breach height  $(H_b)$ , breach formation time  $(T_f)$ , and outflow hydrograph at the dam for four typical proglacial lakes in the Lunana glacier complex under three different outbursts GLOF magnitude scenarios: large, moderate, and small.\n\ndownstream. For example, peak flow from Thorthormi Tsho at Lunana, 10 km from the Lunana glacier complex, is 9000–55,000 m<sup>3</sup>/s while it decreases to 4200-20,000 m<sup>3</sup>/s when the flood arrives PHP-II, about 125 km downstream (Fig. 6). The flood from Bechung is the smallest of all, attenuating before reaching Toedwang in Punakha, the first major settlement after Lunana. So, the flood hydraulics hereafter is presented only for the other three lakes (Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho). Unlike peak flow, the disparities in inundation depth and velocity at different downstream sites were also influenced by the channel topography and flood plain (Fig. 7). All 12 simulated GLOFs arrive at Lunana (which is about 10 km downstream) between 0.5 hr (Thorthormi large scenario) to 4.5 hr (Bechung small scenario) (Table 3), and depth ranges between 1 m and 25 m, while flow velocity is 3–14 m s<sup>-1</sup> (Table 3). The nine GLOFs (3 each from Raphstreng Tsho, Lugge Tsho, and Thorthormi Tsho) take ~2.5 to 4.75 hr to reach Punakha Dzong and flow with a velocity of 4.7–8.0 m s<sup>-1</sup> and flow depth of 6.9-27.8 m (Table 3). The flood arrival time increases to 3.0-6.0 hr as the floods arrive at Wangdue Phodrang hydrological station. Here, due to the low gradient (slope =  $\\sim 0.02^{\\circ}$ ) flood path, the floods flow with the minimum velocity  $(1.7-3.2 \\text{ m s}^{-1})$  while their depth becomes the maximum (8.0-29.2 m) (Table 3). The flood proceeds farther downstream and arrives at PHP-I within 4.0 to 8.25 hr with a flow velocity of 1.7– $7.0~m~s^{-1}$  and depth of 9.0–26.2~m~(Table~3). Taking around 4.5-10.5 hr, the flood flows at 3.7 m s<sup>-1</sup> at PHP-II while their depth ranges between 7.0 and 29.0 m (Table 3). The detailed flow velocity and arrival time across various stations along the river centreline is also indicated in Fig. 7.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex", "section_headings": ["4. Results", "4.2. GLOF discharge and hydraulics: scenario modelling of the lakes in the Lunana glacier complex"], "chunk_type": "text", "line_start": 188, "line_end": 192, "token_count_estimate": 742, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "16787a0094394531", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: text\n\nGLOF downstream risk was computed by combining hazard, exposure, and vulnerability. Fig. 8 is the excerpt of the hazard map from Lunana and Wangdue Phodrang. The simulated flood (up to 150 km downstream) from the lakes in the Lunana glacier complex showed that the flood would inundate about 42.8 km<sup>2</sup> downstream area, out of which 15.6 % fall under the very high hazard zone while 42.4 %, 14.5 %, and 27.4 % fall in the high, medium, and low hazard levels, respectively. In terms of total land area, Lunana suffers the maximum (11.3 km<sup>2</sup>) inundation, followed by Toedwang (5.1 km<sup>2</sup>). Subdistrict blocks were classified into different GLOF hazard units based on which hazard level map intersected with them. Lunana, Kazhi, and Toedwang lie in very high hazard level zones, so three of them were classified as very high GLOF hazard level subdistrict blocks. The 13 others overlap with the high-hazard level map, so, these 13 were assigned high-hazard levels. Only one (Nahi) does not intersect with the hazard map, given a zerohazard level (Fig. 9a).\n\nThe GLOF exposure assessment showed that the simulated clearwater floods from the glacial lakes in the Lunana glacier complex can cause substantial inundation to two hydropower projects (PHP-I and PHP-I) and numerous structures and agricultural land across different downstream sites. The floods expose up to 2,721 buildings which convert to 16,062 people and 6,157 livestock. Besides 11.8 km² of agricultural land, 101.4 km of roads (including highways, farms, and feeder roads) and 71 bridges are also exposed to the different hazard levels of the simulated floods. Of the total, only 4 buildings (all in\n\nJournal of Hydrology 619 (2023) 129311", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "text", "line_start": 194, "line_end": 200, "token_count_estimate": 498, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["129311"]}}
{"id": "fe232788f23e47ca", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A figure, labeled Fig. 6, displays a grid of flow hydrographs for three different Glacial Lake Outburst Flood (GLOF) scenarios. The grid has four columns, representing four proglacial lakes (Thorthormi Tsho, Lugge Tsho, Raphstreng Tsho, and Bechung Tsho), and five rows, representing downstream sites (Lunana, Punakha dzong, Wangdue hydro. sta, PHP-I, and PHP-II). Each graph plots Flow (m³/s) on the y-axis against Time (min) on the x-axis, from 0 to 1000 minutes. Three scenarios are shown in each plot: Large (red line), Moderate (black line), and Small (blue line). Generally, as the flood wave moves downstream (from top to bottom row), the peak flow decreases and the time to peak increases. For the Thorthormi Tsho lake, the large scenario peak flow decreases from about 55,000 m³/s at Lunana to 18,000 m³/s at PHP-II. For Lugge Tsho, it decreases from 20,000 m³/s to 5,000 m³/s. For Raphstreng Tsho, it decreases from 18,000 m³/s to about 3,500 m³/s. For the Bechung Tsho lake, a flood wave is only shown at the Lunana site, with a peak of about 850 m³/s for the large scenario; for all downstream sites, a note indicates 'Flood wave terminates before reaching here'.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "figure", "figure_caption": null, "line_start": 201, "line_end": 201, "token_count_estimate": 404, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4b534ab9e99a17e3", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: text\n\nFig. 6. Flow hydrograph of three different scenarios of the GLOF magnitudes: large, moderate and small at each site downstream of the four typical proglacial lakes.\n\nLunana), 0.01 km<sup>2</sup> of agricultural land, and 4 bridges (all in Lunana) were located within the very high-hazard flood zone; none of the roads fall within the very high-hazard zone (detail in Table 4). These exposed structures and agricultural land were primarily located in the subdistrict blocks of Wangdue Phodrang and Punakha districts, about 80–100 km\n\ndownstream of the Lunana glacier complex. For example, the highest number of buildings are exposed in Guma (753), followed by Thedtsho (639). Guma also suffers from the longest stretch (15.5 km) of road inundation. Likewise, Dzomi is modelled to likely suffer maximum (2.1 km²) damage to agricultural land, while the highest number of bridges", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "text", "line_start": 202, "line_end": 208, "token_count_estimate": 262, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "532de67194ef69a3", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A figure with eight graphs, labeled (a) through (h), arranged in two columns and four rows. The figure illustrates flood reach time and velocity for three different Glacial Lake Outburst Flood (GLOF) scenarios (Large, Moderate, and Small) from four different lakes: Bechung tsho, Raphstreng tsho, Lugge tsho, and Thorthormi tsho. A legend indicates that the Large scenario is represented by a red line, Moderate by a blue line, and Small by a green line.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "figure", "figure_caption": null, "line_start": 209, "line_end": 209, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aff40aedc5622579", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: text\n\nThe left column of graphs (a, b, c, d) shows Flood Reach Time. The x-axis represents 'Station: distance from the lakes (m)' from 0 to 150,000. The y-axis represents 'Time (hr)' from 0 to 20, with the values increasing downwards.\n- Graph (a) for Bechung tsho shows the flood traveling less than 100,000 m in over 20 hours.\n- Graphs (b) for Raphstreng tsho, (c) for Lugge tsho, and (d) for Thorthormi tsho show the flood traveling the full 150,000 m. In these cases, the 'Large' scenario is the fastest, taking approximately 8-11 hours, while the 'Small' scenario is the slowest, taking 12-18 hours.\n\nThe right column of graphs (e, f, g, h) shows Velocity. The x-axis is also 'Station: distance from the lakes (m)' from 0 to 150,000. The right y-axis is 'Velocity (m s⁻¹)' from 0 to 25. The left y-axis, shown in graph (e), is 'Elevation m a.s.l.' from 0 to 4000. Graph (e) also includes a light green line showing the elevation profile, which decreases from over 4000 m to near 0 m over the 150,000 m distance. These graphs have two vertical grey shaded bands.\n- Graph (e) for Bechung tsho shows velocities generally below 15 m/s.\n- Graphs (f) for Raphstreng tsho, (g) for Lugge tsho, and (h) for Thorthormi tsho show fluctuating velocities along the river path, with peaks exceeding 25 m/s, particularly within the first 25,000 m. Generally, the 'Large' scenario has the highest velocity and the 'Small' scenario has the lowest.\n\nFig. 7. Flood reach time (a–d) and velocity (e–h) for every 300 m along the river centreline of three different GLOF scenarios (large, moderate, and small) from the lakes in the Lunana glacier complex. Plotting the elevation profile (see e) along the river centreline shows how flow velocity changes depending on the elevation gradient at different places (shaded with grey color in e–h).\n\n(11) exposure would occur in Rubisa (Table 4). Although no road is exposed in Lunana since it is only connected by non-vehicle access, 11.8 km of the footpath is being inundated. There are, however, current plans to extend the road network towards Lunana which will change exposure and risk. For display purposes, we classified exposure into high, medium, and low levels for each subdistrict block. Based on our classification approach (see method), 10 subdistrict blocks were classified as highly exposed, while 3 (Athang, Rubisa, and Patshaling) and 1 (Gase Tshowongm) were labeled as medium and low, respectively. The exposure level was zero for the other three, including Kazhi, Largyab,\n\nand Nahi, since no structure or agricultural land intersects with the flood hazard map (Fig. 9b).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "text", "line_start": 210, "line_end": 229, "token_count_estimate": 776, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f594bd01a2341338", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: text\n\nare , however , current plans to extend the road network towards Lunana which will change exposure and risk . For display purposes , we classified exposure into high , medium , and low levels for each subdistrict block . Based on our classification approach ( see method ) , 10 subdistrict blocks were classified as highly exposed , while 3 ( Athang , Rubisa , and Patshaling ) and 1 ( Gase Tshowongm ) were labeled as medium and low , respectively . The exposure level was zero for the other three , including Kazhi , Largyab , and Nahi , since no structure or agricultural land intersects with the flood hazard map ( Fig . 9b ) .\n\nThe calculated normalized score for the social vulnerability index for the 17 subdistrict blocks ranges from 0.3 to 0.7. A total of six, including Lunana, Largyab, Dzomi, Kabisa, Nahi, and Kazhi, had high vulnerability levels. Only Gase Tshowongm had a low vulnerability level, while the other 10 exhibited medium-level vulnerability (Fig. 9c). The risk assessment produced by combining hazard, exposure, and vulnerability revealed that only Lunana gewog is located in a very high-risk zone. Nine of the other subdistrict blocks lie in high-risk categories, and four\n\nFlood arrival time, flow velocity, and depth at five downstream sites under three scenarios: large (L), moderate (M), and small (S) GLOF magnitudes.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "text", "line_start": 210, "line_end": 229, "token_count_estimate": 388, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f6b402144ea57944", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: table\nTable: Table 3\n\n| Flood hydraulics             | Lake       | Magnitude | Downstream | site    |                     |       |        |\n|------------------------------|------------|-----------|------------|---------|---------------------|-------|--------|\n|                              |            |           | Lunana     | Punakha | Wangdue Phodrang | PHP-I | PHP-II |\n| Flood wave arrival time (hr) | Thorthormi | L         | 0.5        | 2.5     | 3.0                 | 4.0   | 4.5    |\n|                              |            | M         | 0.75       | 3.0     | 4.0                 | 5.0   | 6.0    |\n|                              |            | S         | 1.0        | 4.25    | 5.25                | 7.0   | 8.0    |\n|                              | Lugge      | L         | 1.0        | 3.75    | 4.5                 | 5.75  | 7.0    |\n|                              |            | M         | 1.25       | 4.75    | 5.5                 | 7.25  | 8.75   |\n|                              |            | S         | 1.25       | 5.5     | 6.5                 | 8.75  | 10.75  |\n|                              | Raphstreng | L         | 0.75       | 3.5     | 4.25                | 5.75  | 7.0    |\n|                              |            | M         | 0.75       | 4.25    | 5.25                | 7.0   | 8.75   |\n|                              |            | S         | 1.0        | 4.75    | 6.0                 | 8.25  | 10.5   |\n|                              | Bechung    | L         | 1.4        |         |                     |       |        |\n|                              | -          | M         | 2.1        |         |                     |       |        |\n|                              |            | S         | 3.25       |         |                     |       |        |\n| Flood velocity (m/s)         | Thorthormi | L         | 14.2       | 8.0     | 3.2                 | 7.0   | 8.9    |\n| •                            |            | M         | 10.2       | 6.1     | 2.2                 | 5.3   | 7.4    |\n|                              |            | S         | 7.4        | 5.0     | 1.7                 | 3.5   | 6.3    |\n|                              | Lugge      | L         | 12.3       | 7.1     | 3.2                 | 3.2   | 6.0    |\n|                              | 00         | M         | 9.0        | 6.0     | 2.4                 | 2.5   | 5.2    |\n|                              |            | S         | 7.4        | 5.0     | 2.0                 | 2.0   | 5.0    |\n|                              | Raphstreng | L         | 12.3       | 6.7     | 3.0                 | 3.1   | 5.2    |\n|                              | r 0        | M         | 9.7        | 5.6     | 2.2                 | 2.2   | 4.3    |\n|                              |            | S         | 8.0        | 4.7     | 1.7                 | 1.7   | 3.7    |\n|                              | Bechung    | L         | 6.0        |         |                     |       |        |\n|                              |            | M         | 4.0        |         |                     |       |        |\n|                              |            | S         | 3.0        |         |                     |       |        |\n| Flood depth (m)              | Thorthormi | L         | 25.0       | 28.0    | 29.2                | 26.2  | 29.1   |\n| 1                            |            | M         | 14.7       | 18.0    | 20.4                | 18.7  | 20.0   |\n|                              |            | S         | 10.0       | 11.4    | 14.0                | 13.3  | 12.8   |\n|                              | Lugge      | L         | 13.0       | 13.4    | 15.3                | 16.6  | 15.0   |\n|                              | - 00 -     | M         | 9.2        | 10.0    | 11.0                | 12.6  | 10.0   |\n|                              |            | S         | 7.2        | 7.3     | 8.6                 | 10.1  | 7.0    |\n|                              | Raphstreng | L         | 12.4       | 12.1    | 13.6                | 14.0  | 13.4   |\n|                              | 1 0        | M         | 9.2        | 9.0     | 1.0                 | 10.6  | 9.3    |\n|                              |            | S         | 6.0        | 7.0     | 8.0                 | 9.0   | 7.0    |\n|                              | Bechung    | L         | 2.5        | ,       |                     |       |        |", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "table", "table_caption": "Table 3", "columns": ["Flood hydraulics", "Lake", "Magnitude", "Downstream", "site", "", "", ""], "table_row_start": 1, "table_row_end": 35, "line_start": 230, "line_end": 268, "token_count_estimate": 1084, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "70b4ba6b8879db39", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: table\nTable: Table 3\n\n| Flood hydraulics             | Lake       | Magnitude | Downstream | site    |                     |       |        |\n|------------------------------|------------|-----------|------------|---------|---------------------|-------|--------|\n|                              |            | M         | 1.7        |         |                     |       |        |\n|                              |            | S         | 1.0        |         |                     |       |        |", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "table", "table_caption": "Table 3", "columns": ["Flood hydraulics", "Lake", "Magnitude", "Downstream", "site", "", "", ""], "table_row_start": 36, "table_row_end": 37, "line_start": 230, "line_end": 268, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6e73d922663e893", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 4. Results > 4.3. Downstream hazard, exposure, and risk\nType: text\n\nare in the medium-risk zone. The remaining 3 (Kazhi, Largyab, and Nahi) were classified as zero-risk areas since they have zero exposure and hazard (Fig. 9d).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "4. Results > 4.3. Downstream hazard, exposure, and risk", "section_headings": ["4. Results", "4.3. Downstream hazard, exposure, and risk"], "chunk_type": "text", "line_start": 269, "line_end": 271, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b892197705419d43", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: text\n\nThe comprehensive analysis of wide-ranging cryospheric, geotechnical, and geomorphological factors showed that Thorthormi Tsho and Lugge Tsho are relatively more susceptible to GLOF than Raphstreng Tsho and Bechung Tsho. This finding resonates well with the earlier GLOF susceptibility assessments undertaken in the Bhutan Himalaya. The first-order GIS-based susceptibility assessment by Rinzin et al. (2021) ranked Thorthormi Tsho as the most dangerous glacial lake in the Bhutan Himalaya. Also, the detailed field investigation by NCHM (2019a) has reported active sliding of a thin moraine between Thorthormi Tsho and Raphstreng Tsho, thus, increasing the susceptibility to GLOF events in the future. The comparatively higher velocity of the Thorthormi glacier, especially in the frontal area, indicates active sliding of ice, the same being identified as the likely cause of Thorthormi subsidiary lake-II draining in 2019 according to NCHM (2019a). Calving of ice into the lake would further increase its hydrostatic gradient and GLOF volume (GAPHAZ, 2017; Rounce et al., 2016) and calving activities can create displacement waves as the calved ice impacts the lake. The high GLOF susceptibility of Lugge Tsho also relates well with the\n\nfindings from the earlier studies (NCHM, 2019a; Rinzin et al., 2021; Wangchuk et al., 2022). This is evident as Lugge Tsho has produced the most catastrophic past GLOF events in the Bhutan Himalaya (Komori et al., 2012; Maurer et al., 2020). There is evidence of multiple cases of GLOF events from the same lake in the Himalaya, e.g., Cirenma Co in the Central Himalaya produced three GLOFs in 1964, 1981, and 1983, respectively (Wang et al., 2015). Such experience from the Himalaya combined with the high GLOF susceptibility of Lugge Tsho suggests that GLOF occurrence in the future cannot be neglected and is a subject of further investigation. It is also an actively growing glacial lake in the region, with the potential to grow in the future (Linsbauer et al., 2016), implying that it is likely to become more exposed with time to surrounding slope failure (Rounce et al., 2017; Rounce et al., 2016). Also, Thorthormi Tsho and Lugge Tsho both being in contact with their parents' glaciers means they would exacerbate the melting of the corresponding glacier through increasing mechanical calving activities and subaqueous melting, which in turn would cause enhanced expansion of the lake through glacier-lake interaction. This interaction would make Thorthormi Tsho and Lugge Tsho more susceptible to GLOF while also increasing potential flood volume (King et al., 2018, 2019). Thus, we highly recommend continuous monitoring of Thorthormi Tsho and Lugge Tsho. Nevertheless, we cannot discount the GLOF susceptibility of Raphstreng Tsho and Bechung Tsho entirely since they also have one or more factors indicating high outburst susceptibility (e.g., high potential flood volume of Raphstreng Tsho). Bechung Tsho is also expected to grow in the future by expanding towards its upstream direction and can", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "text", "line_start": 275, "line_end": 279, "token_count_estimate": 801, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "541e8f8c737b1dcb", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: figure\nFigure\n\nImage /page/12/Figure/2 description: The image displays two satellite maps, labeled (a) and (b) in the caption, showing hazard levels in different river valleys. The top map, (a), shows a barren, mountainous area around Thanza village. It is marked with longitude from 90°11'30\"E to 90°13'0\"E and latitude from 28°4'30\"N to 28°5'0\"N. A scale bar indicates 0.5 km. A color-coded overlay shows hazard levels, with a legend defining green as Low, yellow as Medium, orange as High, and dark red as Very high. The 'Very high' and 'High' zones are concentrated in the main river channel, with 'Medium' and 'Low' zones extending to the banks. The bottom map, (b), shows the area around Wangdue Phodrang, which has more vegetation and a visible settlement. It is marked with longitude from 89°53'0\"E to 89°54'0\"E and latitude from 27°29'30\"N to 27°31'30\"N, with a similar 0.5 km scale bar. The hazard overlay here shows 'High' (orange) in the river channel, with 'Medium' (yellow) and 'Low' (green) zones on the banks, partially covering the settlement. The 'Very high' level is not present in this map. The caption identifies map (a) as Thanza village (7 km from the lakes) and map (b) as Wangdue Phodrang (100 km from the lakes).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "figure", "figure_caption": null, "line_start": 280, "line_end": 280, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51e5283e08624c31", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: text\n\nFig. 8. The excerpt of the hazard map from Thanza village of Lunana (7 km from the lakes) (a) and Wangdue Phodrang (100 km from the lakes) (b) is derived from multiple GLOF scenarios originating from the Lunana glacier complex. It should be noted that the GLOF from the Bechung Tsho ultimately terminates before arriving Wangude Phodrang area, and so no very high hazard level is identified in this area based on our classification approach. The background image: ESRI Basemap Imagery.\n\nJournal of Hydrology 619 (2023) 129311", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "text", "line_start": 281, "line_end": 285, "token_count_estimate": 174, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["129311"]}}
{"id": "abdcb6b6522d108c", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: figure\nFigure\n\nImage /page/13/Figure/2 description: A figure from a scientific paper by S. Rinzin et al., published in the Journal of Hydrology, showing four maps labeled (a), (b), (c), and (d). These maps illustrate the hazard, exposure, vulnerability, and risk levels in 2020 for 17 subdistrict blocks in a mountainous region. The maps are gridded with latitude lines from 27°0'N to 28°0'N and longitude lines from 90°0'E to 90°30'E, and include a 50 km scale bar.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "figure", "figure_caption": null, "line_start": 286, "line_end": 286, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13f381d4c0772ba3", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: text\n\nMap (a) shows the Hazard level. The legend indicates 'High' (orange) and 'Very high' (dark red). The subdistricts LN, TW, and KZ are colored dark red for 'Very high' hazard, while the rest are orange for 'High' hazard.\n\nMap (b) shows the Exposure level. The legend has four categories: 'No exposure' (white), 'Low' (green), 'Medium' (yellow), and 'High' (orange). Subdistricts KZ and LM have 'No exposure'. GW has 'Low' exposure. AT and PT have 'Medium' exposure. The remaining subdistricts have 'High' exposure.\n\nMap (c) shows the Vulnerability level. The legend has three categories: 'Low' (green), 'Medium' (yellow), and 'High' (orange). Subdistrict GW has 'Low' vulnerability. TW, CH, DZ, AT, and PT have 'Medium' vulnerability. The rest of the subdistricts have 'High' vulnerability.\n\nMap (d) shows the Risk level. The legend has four categories: 'No risk' (white), 'Medium' (yellow), 'High' (orange), and 'Very high' (dark red). Subdistricts KZ and LM have 'No risk'. GW, AT, and PT have 'Medium' risk. Subdistrict LN has 'Very high' risk. The remaining subdistricts have 'High' risk.\n\nFig. 9. The hazard (a), exposure (b), vulnerability (c), and risk (d) levels in 2020 of 17 subdistrict blocks downstream of the Lunana glacier complex under three different scenarios of GLOF magnitude (large, medium, and small) from each lake. The risk is calculated as a function of hazard, exposure, and vulnerability. The 17 subdistrict blocks are: Lunana (LN), Kazhi (KZ), Toedwang (TD), Chhubu (CH), Guma (GM), Dzomi (DZ), Lingmukha (LM), Kabisa (KB), Thedtsho (TD), Nahi (NH), Gase Tshogongm (GG), Gase Tshowongm (GW), Rubisa (RB), Darkar (DK), Athang (AT), Patshaling (PT), Largyab (LG). Background image: ALOS-PALSAR DEM.\n\nbecome more exposed to GLOF triggers like an avalanche and landslide/rockfall in the future (Richardson and Reynolds, 2000). Therefore, attention might be needed to monitor Raphstreng Tsho and Bechung Tsho. The NCHM has reported an increasing temperature trend in Bhutan since the 1990s. There has been a decreasing trend in precipitation, but Bhutan experienced high precipitation variability over the\n\nsame period (Dorji and Tamang, 2019), leading to numerous extreme weather events in Bhutan (NCHM, 2022). This increasing frequency of extreme precipitation (Dorji and Tamang, 2019) added to increasing temperature would further amplify the GLOF susceptibility level of glacial lakes in the Lunana complex (GAPHAZ, 2017). However, since all lakes are located in the same basin, if the most dangerous glacial lakes\n\nBuildings, people, livestock, road, bridges, and agricultural land within flood hazard level: very high (VH), high (H), medium (M) and low (L).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "text", "line_start": 287, "line_end": 304, "token_count_estimate": 834, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "59fcaa6a784af713", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: table\nTable: Table 4\n\n| No. | Name        | Building (count) |     |     |      | People (count) |      |      |      | Agricultural land (km2) |      |      |      | Livestock (count) |       |       |       | Bridge (count) |    |    |    | Road (km) |     |     |     |\n|-----|-------------|------------------|-----|-----|------|----------------|------|------|------|-------------------------|------|------|------|-------------------|-------|-------|-------|----------------|----|----|----|-----------|-----|-----|-----|\n|     |             | VH               | H   | M   | L    | VH             | H    | M    | L    | VH                      | H    | M    | L    | VH                | H     | M     | L     | VH             | H  | M  | L  | VH        | H   | M   | L   |\n| 1   | Athang      | 0                | 8   | 7   | 11   | 0              | 48   | 42   | 66   | 0                       | 0.4  | 0.1  | 0.2  | 0                 | 18.4  | 16.1  | 25.3  | 0              | 4  | 0  | 1  | 0         | 1.3 | 0.8 | 2.0 |\n| 2   | Chhubu      | 0                | 5   | 6   | 97   | 0              | 30   | 36   | 582  | 0                       | 0.4  | 0.2  | 0.5  | 0                 | 11.5  | 13.8  | 223.1 | 0              | 3  | 1  | 2  | 0         | 1.3 | 1.4 | 4.2 |\n| 3   | Darkar      | 0                | 49  | 42  | 50   | 0              | 294  | 252  | 300  | 0                       | 0.5  | 0.2  | 0.3  | 0                 | 112.7 | 96.6  | 115   | 0              | 7  | 1  | 2  | 0         | 5.3 | 3.2 | 5.0 |\n| 4   | Dzomi       | 0                | 152 | 56  | 75   | 0              | 912  | 336  | 450  | 0                       | 0.8  | 0.3  | 1.0  | 0                 | 349.6 | 128.8 | 172.5 | 0              | 3  | 2  | 1  | 0         | 7.9 | 1.9 | 1.0 |\n| 5   | Gase Tgongm | 0                | 36  | 22  | 30   | 0              | 216  | 132  | 180  | 0                       | 0.3  | 0.1  | 0.1  | 0                 | 82.8  | 50.6  | 69    | 0              | 3  | 2  | 1  | 0         | 3.9 | 1.8 | 2.5 |\n| 6   | Gase Twongm | 0                | 0   | 2   | 3    | 0              | 0    | 12   | 18   | 0                       | 0.05 | 0.03 | 0.04 | 0                 | 0     | 4.6   | 6.9   | 0              | 1  | 0  | 1  | 0         | 0.3 | 0.4 | 1.5 |\n| 7   | Guma        | 0                | 172 | 127 | 454  | 0              | 1032 | 762  | 2724 | 0                       | 0.4  | 0.2  | 1.1  | 0                 | 395.6 | 292.1 | 1044  | 0              | 2  | 0  | 0  | 0         | 3.4 | 3.1 | 9.0 |\n| 8   | Kabisa      | 0                | 22  | 11  | 37   | 0              | 132  | 66   | 222  | 0                       | 0.3  | 0.1  | 0.3  | 0                 | 50.6  | 25.3  | 85.1  | 0              | 0  | 0  | 0  | 0         | 2.1 | 0.7 | 1.2 |\n| 9   | Kazhi       | 0                | 0   | 0   | 0    | 0              | 0    | 0    | 0    | 0                       | 0    | 0    | 0    | 0                 | 0     | 0     | 0     | 0              | 0  | 0  | 0  | 0         | 0   | 0   | 0   |\n| 10  | Largyab     | 0                | 0   | 0   | 0    | 0              | 0    | 0    | 0    | 0                       | 0    | 0    | 0    | 0                 | 0     | 0     | 0     | 0              | 0  | 0  | 0  | 0         | 0   | 0   | 0   |\n| 11  | Lingmukha   | 0                | 73  | 39  | 101  | 0              | 438  | 234  | 606  | 0                       | 0    | 0    | 0    | 0                 | 167.9 | 89.7  | 232.3 | 0              | 1  | 0  | 0  | 0         | 0   | 0   | 0   |\n| 12  | Lunana      | 1                | 17  | 20  | 21   | 6              | 102  | 120  | 126  | 0                       | 0.2  | 0.05 | 0.1  | 2.3               | 39.1  | 46    | 48.3  | 3              | 0  | 1  | 0  | 0         | 4.5 | 1.0 | 1.2 |\n| 13  | Nahi        | 0                | 0   | 0   | 0    | 0              | 0    | 0    | 0    | 0                       | 0    | 0    | 0    | 0                 | 0     | 0     | 0     | 0              | 0  | 0  | 0  | 0         | 0   | 0   | 0   |\n| 14  | Patshaling  | 0                | 8   | 3   | 23   | 0              | 48   | 18   | 138  | 0                       | 0.1  | 0.01 | 0.01 | 0                 | 18.4  | 6.9   | 52.9  | 0              | 2  | 1  | 0  | 0         | 0.5 | 0.1 | 0.5 |\n| 15  | Rubisa      | 0                | 5   | 9   | 18   | 0              | 30   | 54   | 108  | 0                       | 0.8  | 0.2  | 0.2  | 0                 | 11.5  | 20.7  | 41.4  | 0              | 10 | 3  | 2  | 0         | 4.0 | 1.2 | 2.0 |\n| 16  | Thedtsho    | 0                | 199 | 116 | 324  | 0              | 1194 | 696  | 1944 | 0                       | 0.7  | 0.2  | 0.4  | 0                 | 457.7 | 266.8 | 745.2 | 0              | 2  | 1  | 0  | 0         | 5.0 | 2.8 | 5.9 |\n| 17  | Toedwang    | 3                | 109 | 59  | 55   | 18             | 654  | 354  | 330  | 0.01                    | 0.4  | 0.1  | 0.3  | 6.9               | 250.7 | 135.7 | 126.5 | 1              | 4  | 2  | 1  | 0.03      | 3.6 | 1.1 | 3   |\n|     | Aggregate   | 4                | 855 | 519 | 1299 | 24             | 5130 | 3114 | 7794 | 0.01                    | 5.35 | 1.78 | 4.64 | 9.2               | 1967  | 1194  | 2988  | 4              | 42 | 14 | 11 | 0         | 43  | 19  | 39  |\n|     | Total       | 2677             |     |     |      | 16,062         |      |      |      | 11.8                    |      |      |      | 6157              |       |       |       | 71             |    |    |    | 101.4     |     |     |     |", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "table", "table_caption": "Table 4", "columns": ["No.", "Name", "Building (count)", "", "", "", "People (count)", "", "", "", "Agricultural land (km2)", "", "", "", "Livestock (count)", "", "", "", "Bridge (count)", "", "", "", "Road (km)", "", "", ""], "table_row_start": 1, "table_row_end": 20, "line_start": 305, "line_end": 326, "token_count_estimate": 1915, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab7097d4949a8d38", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.1. Glacial lakes outburst susceptibility\nType: text\n\nare monitored (e.g., Thorthormi Tsho), and proper disaster management plans are implemented in the downstream settlement, possible downstream damage from the floods from any lakes in the basin could be highly reduced.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.1. Glacial lakes outburst susceptibility", "section_headings": ["5. Discussion", "5.1. Glacial lakes outburst susceptibility"], "chunk_type": "text", "line_start": 327, "line_end": 329, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "43d608c19aa888e0", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.2. GLOF risk on downstream settlement\nType: text\n\nOur simulation of the 1994 GLOF by setting breach volume at  $20.0 \\times 10^6$  yielded a peak flow of 2,129 m³/s at Wangdue Phodrang, within the range of estimated peak flow (1800 to 2500 m³/s) of the 1994 GLOF at the same station (Osti et al., 2013). Likewise, our simulated GLOF takes 4.4 hr to reach Punakha, which also relates well with the arrival time noted by eyewitnesses and the reconstructed arrival time (4.75 hr) using a seismometer (Maurer et al., 2020). Simulations with varying breach times between 0.25 and 1 hr result in a significant difference in peak discharge at the dam, but both arrival time and peak flow remain fairly consistent by the time the flood wave reaches Punakha and Wangdue Phodrang (Supplementary Fig. S2). This model calibration with the GLOF events of 1994 in the same basin reasonably indicates that our model setup is suitable for modelling different scenarios of future GLOF for risk assessment purposes.\n\nThe study combines hazard, exposure, and vulnerability to determine the risk posed by the potentially dangerous lakes in the Lunana glacier complex to the downstream region along the Phochu and Punatsangchu banks. The study reaffirms the earlier studies such as Osti et al. (2013) and Koike and Takenaka (2012) that Bhutanese people are inevitably highly exposed to the GLOF. Most of the modelled GLOFs (8/12) from the lakes produce a flood of magnitude higher than the 1994 GLOF of Lugge Tsho (Fujita et al., 2008; Maurer et al., 2020; Osti et al., 2013; Watanbe and Daniel, 1996). Although there is no history of floods of such high magnitudes in Bhutan, with climate change-induced deglaciation and cryospheric system change in the high mountains across the world in recent decades (GAPHAZ, 2017), these potential events in the future cannot be disregarded. Our results offer new insights into the impacts of large-magnitude GLOF events in a typical basin in the Himalaya.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.2. GLOF risk on downstream settlement", "section_headings": ["5. Discussion", "5.2. GLOF risk on downstream settlement"], "chunk_type": "text", "line_start": 331, "line_end": 341, "token_count_estimate": 535, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "bcc5b8a8931b3d44", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.2. GLOF risk on downstream settlement\nType: text\n\n/ 12 ) from the lakes produce a flood of magnitude higher than the 1994 GLOF of Lugge Tsho ( Fujita et al . , 2008 ; Maurer et al . , 2020 ; Osti et al . , 2013 ; Watanbe and Daniel , 1996 ) . Although there is no history of floods of such high magnitudes in Bhutan , with climate change - induced deglaciation and cryospheric system change in the high mountains across the world in recent decades ( GAPHAZ , 2017 ) , these potential events in the future cannot be disregarded . Our results offer new insights into the impacts of large - magnitude GLOF events in a typical basin in the Himalaya .\n\nOur study showed that one among 17 subdistrict blocks (Lunana) lies in the very high GLOF risk zone and 9 others in a high-risk zone. Some of the villages in Lunana, like Thanza village of Lunana, are located very close to the lakes in the Lunana glacier complex (just about 2-7 km). The large and medium scenario GLOFs from Thorthormi Tsho take only 15-30 min to reach some part of Lunana. This also means that the Lunana region is likely impact by all GLOF events with little warning, suggesting high-magnitude damage to the exposed structures (most of which are not built with the concrete wall (NSB, 2018). The capacity to prepare for, respond to, and recover from such a disaster is challenging for Lunana because of its remote location and high social vulnerability level. The nine high-risk subdistrict block clusters (Fig. 9) are located in the Punakha and Wangdue Phodrang districts, about 80-100 km from the Lunana glacier complex. The long travel time for GLOF allows these subdistrict blocks to potentially have more warning time in a GLOF event. Even the large scenario GLOF from the Thorthormi Tsho takes about 2.5 and 3 hr to arrive in Punakha and Wangdue Phodrang, respectively (Table 2). But this is only possible if there is a flood early warning system installed along the riverbank and if authorities and communities are well trained in response procedures, or it becomes entirely reliant on ad-hoc community-to-community communication. Flood early warning instruments have been installed along the banks of Phochu and Punatsangchu (NCHM, 2021), and continuous monitoring and maintenance of these instruments are highly recommended (Huggel et al., 2020). However, due to the high density of settlement, the inundation of infrastructures including buildings and roads is very high in Punakha and Wangdue Phodrang areas; 85 % of exposed buildings and 59 % of the bridge are located within these nine subdistrict blocks (Table 4). Our results suggest that the Lunana subdistrict block needs the highest level of attention for GLOF disaster risk reduction. Also, the\n\nfarther downstream settlements in the Punakha and Wangdue Phodrang districts, such as Guma and Thedtsho need to be prioritized (Fig. 9).", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.2. GLOF risk on downstream settlement", "section_headings": ["5. Discussion", "5.2. GLOF risk on downstream settlement"], "chunk_type": "text", "line_start": 331, "line_end": 341, "token_count_estimate": 748, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "d6e995c7f89458b3", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.2. GLOF risk on downstream settlement\nType: text\n\nand continuous monitoring and maintenance of these instruments are highly recommended ( Huggel et al . , 2020 ) . However , due to the high density of settlement , the inundation of infrastructures including buildings and roads is very high in Punakha and Wangdue Phodrang areas ; 85 % of exposed buildings and 59 % of the bridge are located within these nine subdistrict blocks ( Table 4 ) . Our results suggest that the Lunana subdistrict block needs the highest level of attention for GLOF disaster risk reduction . Also , the farther downstream settlements in the Punakha and Wangdue Phodrang districts , such as Guma and Thedtsho need to be prioritized ( Fig . 9 ) .\n\nInvestigations elsewhere in the world have revealed that eventual damage from disasters not only depend on the magnitude of the hazard (GLOF intensity in this case) but also on the social vulnerability level (crucial for resilience, preparedness, and response capacity of the people) (Cutter and Finch, 2008; Flanagan et al., 2018). Similarly, the importance of data from multiple sources (GLOF hazard data, subdistrict level socio-economic condition, and settlement density) for the downstream GLOF risk assessment is clearly portrayed in this study. Gase Tshogongm falls in a high-hazard zone, but with low vulnerability and exposure level, so it has been classified as a medium GLOF-risk subdistrict block. Likewise, Kazhi, Nahi, and Largyab have high social vulnerability and fall in a high GLOF hazard zone; however, all of them have zero exposure, which allowed us to rule out the GLOF risk in these subdistrict blocks under current conditions. These findings help us improve the GLOF risk knowledge in the Punatsangchu and Phochu basins, which is an essential preliminary assessment for the implementation of an early warning system (Huggel et al., 2020) and risk reduction prioritization. The GLOF early warning system has been put in place by National Centre for Hydrology and Meteorology and is mainly based on experience from the 1994 GLOF event of Lugge Tsho (NCHM, 2021). By contrast, our downstream risk assessment is based on the multiple GLOF scenario modelling results, most of which have a higher magnitude than the 1994 GLOF event. Thus, our result provides new insights for consolidating and improving the existing early warning system. To this end, this study is valuable for the comprehensive and careful design of GLOF risk management strategy, both hard engineering schemes such as improving structural measures (e.g., construction of flood retention walls, identification of safe/evacuation zone, lowering lake levels) and enhancing the response and recovery capacity (e.g., GLOF risk advocacy programs, training populations for GLOF evacuation) in the affected downstream community.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.2. GLOF risk on downstream settlement", "section_headings": ["5. Discussion", "5.2. GLOF risk on downstream settlement"], "chunk_type": "text", "line_start": 331, "line_end": 341, "token_count_estimate": 696, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb8103715370abe7", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.3. GLOF impact on the hydroelectric project\nType: text\n\nPunatsangchu basin hosts the two largest hydroelectric power projects, PHP-I (1200 MW) and PHP-II (1020 MW), both of which are at the advanced stage of construction (MOEA, 2021). With a cost estimate of 1.74 (Nu. 133.20) billion USD for PHP-I and 0.95 (Nu. 72.9) billion USD for PHP-II; these two projects are record-high investment projects in Bhutan. In the latest inventory, Punatsangchu headwaters have a total of 180 glacial lakes of larger than 0.05 km<sup>2</sup> (Rinzin et al., 2021). The glacial lakes present an imminent threat of outburst flood to these two hydroelectric projects. Potential GLOFs can impact these hydroelectric power projects and lead to significant economic loss and potentially endanger human lives. While the potential impact of GLOFs on the hydroelectric project has been undertaken at a large scale (Schwanghart et al., 2016), this study presents a pilot-based detailed assessment of the dangerous glacial lakes and their impacts on hydropower projects in Bhutan. Except for GLOFs from the Bechung Tsho, all potential GLOFs intercept the hydroelectric projects and may present a significant risk in the future, e.g., at PHP-II, the peak ranges from 870 m<sup>3</sup>/s (Raphstreng small magnitude) to 19000 m<sup>3</sup>/s (Thorthormi large magnitude). These hydroelectric power projects are inevitably exposed to GLOFs from the lakes in the Lunana glacier complex. However, their risk will depend on the design standard of the dams. The feasibility assessment done before the commencement of these projects by the Japan International Cooperation Agency (2001) has considered peak flow up to 2176 m<sup>3</sup>/s. So, a high flood magnitude (>20,000 m<sup>3</sup>/s) from Thorthormi Tsho is an order of magnitude higher than the design flood of the hydropower projects. A flood of such magnitude may therefore cause severe damage to the reservoir and dam, causing overtopping, excessive sedimentation, outages, equipment damage, and cascading impacts on the low-lying settlements. The high concentration of sediment in the Punatsangchu river from the 1994 GLOF event of Lugge Tsho is said to have persisted up to a", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.3. GLOF impact on the hydroelectric project", "section_headings": ["5. Discussion", "5.3. GLOF impact on the hydroelectric project"], "chunk_type": "text", "line_start": 343, "line_end": 347, "token_count_estimate": 595, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["19000"]}}
{"id": "dcc933b3cccd0898", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.3. GLOF impact on the hydroelectric project\nType: text\n\npeak flow up to 2176 m < sup > 3 < / sup > / s . So , a high flood magnitude ( > 20 , 000 m < sup > 3 < / sup > / s ) from Thorthormi Tsho is an order of magnitude higher than the design flood of the hydropower projects . A flood of such magnitude may therefore cause severe damage to the reservoir and dam , causing overtopping , excessive sedimentation , outages , equipment damage , and cascading impacts on the low - lying settlements . The high concentration of sediment in the Punatsangchu river from the 1994 GLOF event of Lugge Tsho is said to have persisted up to a\n\nmonth after the event (Watanbe and Daniel, 1996). The massive landslide-induced flood cascade in 2003 from the Tsatichhu river, a north-easterly flowing tributary of the Kurichu river in eastern Bhutan, resulted in an estimated loss of 1.1 million USD due to the disruption of the 60 MW Kurichu hydroelectric power plant (Dunning et al., 2006). The disruption of PHP-I and PHP-II due to GLOFs from the Lunana glacier complex could result in greater financial implications since their installed capacity is about 2-order magnitudes greater than the Kurichu hydroelectric power plant. Several past GLOF events in the Himalaya have caused implacable damage to the hydroelectric power downstream, including GLOFs that occurred in the neighbouring regions. For example, the 1985 GLOF of Dig Tsho in eastern Nepal (Vuichard and Zimmermann, 1987) and the 1981 GLOF of Cirenma Co (Wang et al., 2015), and the 2016 Gongbatongsha GLOF (Sattar et al., 2022) in the Poiqu River basin of the central Himalaya. All these experiences from the neighbouring Himalaya and Bhutan suggest that GLOFs from the lakes in the Lunana glacier complex may also cause potential damage to PHP-I and PHP-II.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.3. GLOF impact on the hydroelectric project", "section_headings": ["5. Discussion", "5.3. GLOF impact on the hydroelectric project"], "chunk_type": "text", "line_start": 343, "line_end": 347, "token_count_estimate": 496, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "8f32627508582035", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.4. GLOF modelling approach and uncertainty\nType: text\n\nThe input dam breach parameters are the essential initial condition determining the magnitude of flood hydraulics and the translated hazard and risk maps (Sattar et al., 2021a; Wahl, 2004). Previous studies have found outflow hydraulics like peak discharge are highly sensitive to breach formation time ( $T_f$ ) (Basheer et al., 2017; Sattar et al., 2021a). While there are no models to predict the uncertainty of GLOF breach parameters, previous studies (Sattar et al., 2021a; Somos-Valenzuela et al., 2015) have used embankment dam-based equations (Wahl, 2004) to predict uncertainty ranges of moraine dam breach parameters. When Wahl (2004) is used here, the value of upper bound uncertainty dam breach width for the Thorthormi Tsho is more than double (704.5 m) the actual calculated value (293.7 m), while the lower bound decreases to less than half (116.7 m). There is an extensive range of uncertainty in other breach parameters like  $T_f$  (see supplementary Table 2 for detail). Also, due to the lack of a clear historical record, scenario definition for GLOFs cannot typically depend on the site-specific frequency-magnitude relationship, commonly used in other natural hazards scenario definitions (Frey et al., 2018; GAPHAZ, 2017). As a result, the scenario definition is another primary source of uncertainty in GLOF modelling. Here we accounted for these uncertainties by considering multiple potential GLOF scenarios with varying magnitudes (3 from each lake) following the established trend in the existing literature (Frey et al., 2018; Sattar et al., 2021a; Sattar et al., 2021b). This allowed us to define 12 unique scenarios (3 for each lake), all flowing along the same trajectory. However, for disaster risk reduction worst-case GLOFs scenarios can be considered, although it could be argued that an inability to mitigate against the highest magnitude events should not remove the focus from smaller events that could, and should have mitigation schemes.\n\nThe overtopping breach failure mode in the HEC-RAS dam breach model is a gradually progressive process of dam erosion due to an overflow of water, which contrasts with overtopping resulting from a large displacement wave induced by a rapid mass movement into the lake. These differences could have underestimated the peak discharge and flood arrival time in the current study (Allen et al. 2022). Here we do not model the process chain or the trigger processes, however, we assume breach scenarios of varied dimensions to assess the downstream impact. Moreover, GLOF is a highly complex process, involving several cascading events, usually initiated by mass movement and wave overtopping, followed by debris flow, hyper-concentrated flow, and secondary debris flow (GAPHAZ, 2017; Schneider et al., 2014; Worni et al., 2014). However, these complex modelling approaches are usually applied in the context where run-out distance is relatively short (Frey et al., 2018), which is not the case in Bhutan, especially in the Punatsangchu basin. Here, most simulated floods (8/12) can reach up to 150", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.4. GLOF modelling approach and uncertainty", "section_headings": ["5. Discussion", "5.4. GLOF modelling approach and uncertainty"], "chunk_type": "text", "line_start": 349, "line_end": 359, "token_count_estimate": 798, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "001475b9462328c2", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.4. GLOF modelling approach and uncertainty\nType: text\n\nassess the downstream impact . Moreover , GLOF is a highly complex process , involving several cascading events , usually initiated by mass movement and wave overtopping , followed by debris flow , hyper - concentrated flow , and secondary debris flow ( GAPHAZ , 2017 ; Schneider et al . , 2014 ; Worni et al . , 2014 ) . However , these complex modelling approaches are usually applied in the context where run - out distance is relatively short ( Frey et al . , 2018 ) , which is not the case in Bhutan , especially in the Punatsangchu basin . Here , most simulated floods ( 8 / 12 ) can reach up to 150\n\nkm downstream. The complex process chain modelling covering such a long flood reach distance is theoretically possible, it is highly challenging to model multiple scenarios using existing codes like r.avaflow (Mergili et al., 2017) due to high computation costs and numerical instabilities (Frey et al., 2018; Schneider et al., 2014). Therefore, since we modelled multiple scenarios over a very long run-out distance coupled with robust downstream risk assessment, the HEC-RAS 2D model is a reasonable alternative, especially for the first-order evaluation in this study. Nonetheless, through Google Earth high-resolution imagery, we observed that the Phochu river channel first starts with a relatively broad plain, especially in the Thanza area of Lunana, followed by a long stretch of deep valleys and gorges (20-30 km), and flood plains become wide as the river reaches the downstream site where settlements are most concentrated. This channel characteristic relates well to a typical feature that will allow erosion and entrainment of the enormous amount of debris from the upstream area that would multiply flood magnitude and thus damage caused to the lives and properties, as occurred during the 2016 GLOF event from Gongbatongsha Co in the central Himalaya (Sattar et al., 2022). Future studies addressing the critical challenges of GLOF modelling like long-runout distance, complex process chain, and high computational cost issues should be the priority.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.4. GLOF modelling approach and uncertainty", "section_headings": ["5. Discussion", "5.4. GLOF modelling approach and uncertainty"], "chunk_type": "text", "line_start": 349, "line_end": 359, "token_count_estimate": 533, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "257fb13a63867706", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.4. GLOF modelling approach and uncertainty\nType: text\n\nvalleys and gorges ( 20 - 30 km ) , and flood plains become wide as the river reaches the downstream site where settlements are most concentrated . This channel characteristic relates well to a typical feature that will allow erosion and entrainment of the enormous amount of debris from the upstream area that would multiply flood magnitude and thus damage caused to the lives and properties , as occurred during the 2016 GLOF event from Gongbatongsha Co in the central Himalaya ( Sattar et al . , 2022 ) . Future studies addressing the critical challenges of GLOF modelling like long - runout distance , complex process chain , and high computational cost issues should be the priority .\n\nBecause of their high discharge and entrainment capacity, GLOFs are typically known for carrying large boulders and mobilizing large amounts of sediment which can greatly modify channel morphology (Cook et al., 2018; Nativ et al., 2022)\\*\\*. For example, the 2016 GLOF from Gongbatongsha Co in central Himalaya, with a discharge volume about of  $1.1 \\times 10^5$  m<sup>3</sup>, changed channel morphology up to 40 km downstream and widened channel width up to 12 m at some cross sections (Cook et al., 2018, Chen et al. 2023). Likewise, the 1994 Lugge Tsho GLOF also changed channel morphology for up to 25 km downstream (Maurer et al., 2020), while debris deposits were evident as far as 200 km downstream (Watanbe and Daniel, 1996). Huber et al. (2020) suggest that numerous present-day boulders along the river channels in the Himalaya have likely been transported by historical GLOFs and estimate that GLOFs of discharge  $10^3$ – $10^5$  m<sup>3</sup> are capable of transporting boulders of ca. 10 m diameter or more. Here, the majority of the modelled GLOF scenarios (8/12) exceed peak discharges of 10<sup>3</sup> m<sup>3</sup>/s indicating their potential to transport sediments of different grades including boulders. This implies that if future GLOF are of the same magnitude as the modelled scenarios, the impact might be far more than what we projected with our clear water flood rheology. Therefore, future studies should focus on assessing the impact of GLOF through a complex debris flow model or those allowing phase changes.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.4. GLOF modelling approach and uncertainty", "section_headings": ["5. Discussion", "5.4. GLOF modelling approach and uncertainty"], "chunk_type": "text", "line_start": 349, "line_end": 359, "token_count_estimate": 616, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3a81878332e40f8", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 5. Discussion > 5.4. GLOF modelling approach and uncertainty\nType: text\n\ncapable of transporting boulders of ca . 10 m diameter or more . Here , the majority of the modelled GLOF scenarios ( 8 / 12 ) exceed peak discharges of 10 < sup > 3 < / sup > m < sup > 3 < / sup > / s indicating their potential to transport sediments of different grades including boulders . This implies that if future GLOF are of the same magnitude as the modelled scenarios , the impact might be far more than what we projected with our clear water flood rheology . Therefore , future studies should focus on assessing the impact of GLOF through a complex debris flow model or those allowing phase changes .\n\nPopulation and infrastructures are very dynamic in Bhutan as it is a rapidly developing country. According to ICIMOD's land cover and land use data, the built-up area within our 2D model domain has increased by ~194 % over the period from 2000 to 2020 with an annual rate of  $\\sim$ 0.36 km<sup>2</sup>/yr. The agricultural land has increased from  $\\sim$ 16.0 km<sup>2</sup> in 2000 to  $\\sim$ 19.5 km<sup>2</sup> in 2015, but has dropped to  $\\sim$ 17.5 km<sup>2</sup> in 2020, although the latter might be due to the conversion of farmland into builtup areas (Supplementary Fig. S4). Similarly, according to the Bhutan population and housing census 2017, the population in the Wangdue Phodrang and Punakha, the two districts most exposed to GLOF, have increased from 48,850 to 70,926 (+45 %) between 2005 and 2017 (NSB, 2018). This increasing downstream population and built-up area coupled with an increase in glacial lake area since the 1990s indicates that downstream GLOF risk would have also increased over the period. Most importantly, increasing GLOF risk in the downstream communities in the future means that continuous monitoring of GLOF in Punatsangchu basin is highly recommended.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "5. Discussion > 5.4. GLOF modelling approach and uncertainty", "section_headings": ["5. Discussion", "5.4. GLOF modelling approach and uncertainty"], "chunk_type": "text", "line_start": 349, "line_end": 359, "token_count_estimate": 507, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "9d550bcd67ba686b", "text": "Document: 1-s2.0-S0022169423002536-main\nSection: 6. Conclusion\nType: text\n\nThis study draws upon multiple data sources, including traditional declassified Corona KH-4 and Hexagon KH-9, Landsat (TM, ETM, and OLI), DEM, LULC, OpenStreetMap, population and housing census data.\n\nUsing these data and hydrodynamic modelling, we comprehensively assessed glacial lake outburst susceptibility, downstream exposure, hazard, vulnerability, and risk in the Bhutan Himalaya. The results showed that, although all the lakes in the Lunana glacier complex have one or more unfavourable GLOF susceptibility factors, Thorthormi Tsho and Lugge Tsho are relatively more susceptible to GLOF. A detailed hazard assessment for the potential GLOFs indicated that two hydropower projects and over 16,000 people dwelling along the Phochu and Punatsangchu banks in addition to numerous structures and agricultural land are exposed to these events. Our comprehensive GLOF risk assessment revealed 1 (Lunana) subdistrict block in the very high GLOF risk zone, 9 in the high-risk zone (mainly in Punakha and Wangdue Phodrang areas), 3 in medium, and 1 in a low-risk zone. The other 3 had zero GLOF risk under current conditions.\n\nWe recommend continuous monitoring of the glacial lakes in the Lunana glacier complex (through field investigation and high-resolution satellite imagery) with a particular focus on Thorthormi Tsho and Lugge Tsho. Second, as for the downstream GLOF disaster management, we recommend that Lunana should be given the highest priority, followed by subdistrict blocks concentrated in the Wangdue Phodrang and Punakha districts like Guma and Thedtsho. The study highlights the importance of multi-source data in improving the knowledge of downstream GLOF risk and serves as a base for improving the GLOF risk reduction strategies in the region.", "metadata": {"source_file": "data/('1-s2.0-S0022169423002536-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S0022169423002536-main", "section_path": "6. Conclusion", "section_headings": ["6. Conclusion"], "chunk_type": "text", "line_start": 361, "line_end": 366, "token_count_estimate": 444, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "06539b3b754caf51", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: ABSTRACT\nType: text\n\nThe Himalayas are home to many high-risk glacial lakes. Effective prevention of glacial lake outburst floods (GLOFs) in this region has recently become an urgent priority. As a major element of an integrated risk management strategy, the GLOF early warning system is a viable and promising tool for mitigating climate change-related risks. It prevents loss of life and reduces the economic and societal impacts of disasters. Within the framework of the Second Tibetan Plateau Scientific Expedition and Research program, we developed and implemented a monitoring and early warning system (EWS) for Cirenmaco, a transboundary high-risk glacial lake located in the central Himalayas. The EWS consists of monitoring lake-level change, end-moraine displacement, ice collapse, and downstream runoff. The monitoring data can be transmitted via the Beidou and Inmarsat satellites and a mobile network to the data center. The in-situ and real-time monitoring guarantee captures the precursors of ice collapse and glacial lake outburst and raises alarms in advance for downstream communities. In terms of data transmission, monitoring elements, and warning thresholds, the Cirenmaco EWS scheme is one of the most advanced among all similar GLOF early warning systems. Implementing an early warning system is the most practical strategy for mitigating potential threats to Himalayan high-risk glacial lakes. The EWS system is less expensive than lake dam immobilization and artificial drainage projects, and it provides more valuable environmental monitoring data in high mountain areas. The Cirenmaco EWS serves an effective demonstration for the construction of similar projects to prevent and mitigate GLOFs in the Himalayas.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 6, "token_count_estimate": 416, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "60aaba6b43e6a13f", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: text\n\nDue to climate warming and ongoing glacier recession, glacial lakes in the Tibetan Plateau and its surroundings (TPS) are in a state of rapid expansion [1–3], and numerous potential glacial lakes buried within the glacier beds are projected to be exposed in the future [4-7]. Over the past few decades, the number and area of glacial lakes on the plateau have increased by  $\\sim$ 10.7% and  $\\sim$ 15.2%, respectively [2]. The Himalayas have the highest concentration of contemporary glacial lakes among all subregions of the TPS [8]. Because of its longitudinal length, over 2000 km from east to west, and an altitude difference of up to 5000 m, great spatial heterogeneity is manifested across many aspects, such as climate composition, geographic resources, and ecological environment [9,10], prompting it to become one of the main study areas in cryospheric science, geology, and social humanities. To date, research on glacial\n\nhttps://doi.org/10.1016/j.ijdrr.2022.102914\n\n<sup>&</sup>lt;sup>b</sup> College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China\n\n<sup>\\*</sup> Corresponding author.\n\nCorresponding author. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China. E-mail addresses: weicaiwang@itpcas.ac.cn (W. Wang), zhangtg16@lzu.edu.cn (T. Zhang).\n\nlakes has focused on the distribution characteristics of contemporary or potential glacial lakes, and their accompanying hazards and/or risks [4,11–15], inventory of regional glacial lake outburst floods (GLOFs) [16–20], reconstruction of previous GLOFs and projection of GLOFs from high outburst potential lakes [21–26]. However, research on prevention and mitigation measures for GLOFs from high-hazard/risk glacial lakes remains lacking [27–29].\n\nPrevious studies on identifying potentially dangerous glacial lakes selected the central Himalayas as subjects, including a large number of high-hazard/risk glacial lakes, over the other subregions of the TPS [12,15,30,31]. Zheng et al. [7] conducted a risk assessment for glacial lakes in the TPS, and another study focused on future changes in glacial lakes, as well as the accompanying enhanced hazards and risks. The results revealed that approximately 3200 potential glacial lakes are buried in the Himalayan glacier beds, accounting for 23.4% of the total number across the TPS. Approximately 32.1% of the Himalayan potential glacial lakes will be exposed by 2050 and 54.4% by 2100, at RCP8.5 scenario. The results of the risk assessment indicated that future GLOF susceptibility in this region will be at least 10 times higher than the current level. There have been several studies on glacial lake hazard/risk assessment for the entire Himalayan area, and some typical regional areas [32–35]. It is noteworthy that, although the evaluation schemes are varied among different studies, assessments of high-hazard/risk glacial lakes have comparatively high consistency and consensus [15]. Therefore, an in-depth understanding of glacial lakes with high outburst potential in this region provides a reliable basis for further studies.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 26, "token_count_estimate": 844, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["100101", "102914", "730000"]}}
{"id": "774067a41ce03838", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: text\n\nat RCP8 . 5 scenario . The results of the risk assessment indicated that future GLOF susceptibility in this region will be at least 10 times higher than the current level . There have been several studies on glacial lake hazard / risk assessment for the entire Himalayan area , and some typical regional areas [ 32 – 35 ] . It is noteworthy that , although the evaluation schemes are varied among different studies , assessments of high - hazard / risk glacial lakes have comparatively high consistency and consensus [ 15 ] . Therefore , an in - depth understanding of glacial lakes with high outburst potential in this region provides a reliable basis for further studies .\n\nTPS regions have seen more than 100 GLOFs originating from glacial lakes (Fig. 1a) [18–20,36–38]. The Himalayas are a global hotspot of GLOFs in the TPS. Since the 1980s, the GLOF frequency has been  $\\sim$ 1.3 times per year [39], and there remains a distinctively steady trend of outbursts compared to other glaciered areas of the world where the GLOF frequency is decreasing or remain constant [40]. Recent studies have suggested that the frequency of future GLOFs in some areas will not increase. One such area is the Peruvian Andes. These tropical glaciers have already retreated quite close to steep headwalls [41,42], and numerous posterior bedrock-dammed lakes have become exposed. A decrease in GLOF susceptibility has been observed, the potential for future glacial lake development is not high, as lakes will become progressively distant from their mother glacier, which is the main source of triggers. Additionally, several remedial studies have been conducted to prevent GLOFs.\n\nUnexpected Himalayan glacial lake outburst floods have caused thousands of deaths and enormous socio-economic losses [16]. For", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 26, "token_count_estimate": 470, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "08bde3c88605a2d4", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: figure\nFigure\n\nImage /page/1/Figure/6 description: A figure composed of four panels, labeled a, b, c, and d, illustrating the location and characteristics of the Cirenmaco glacial lake.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 27, "line_end": 27, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "88c2e7933bb14f85", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: text\n\nPanel a is a map of a large mountainous region, showing the distribution of glacial lakes (blue), moraine-dammed GLOFs (yellow dots), and ice-dammed GLOFs (orange dots). Red stars indicate early warning systems. The location of Cirenmaco is marked. The map covers latitudes from 25°N to 45°N and longitudes from 70°E to 100°E.\n\nPanel b is a detailed topographic map of a specific area, showing the Poiqu River and its tributary, Zhangzangbo. Glaciers are highlighted in light blue with dashed outlines. The map's coordinates range from 28°2'N to 28°6'N and 86°0'E to 86°5'E.\n\nPanel c is a satellite image showing the Cirenmaco glacial lake, which is a distinct turquoise color, set within a rocky, mountainous landscape. The coordinates for this view are approximately 28°3'30\"N to 28°4'30\"N and 86°3'E to 86°5'E.\n\nPanel d consists of two bar charts showing changes in area over time from 1990 to 2020. The top chart, labeled 'Glacier', shows a general decrease in area from approximately 1.8 km² in 1990 to about 1.5 km² in 2020. The bottom chart, labeled 'Glacial lake', shows a general increase in area from about 0.12 km² in 1990 to a peak of over 0.3 km² in 2010, settling at around 0.28 km² in 2020.\n\nFig. 1. Location of Cirenmaco in the TPS. (a) Since the 1980s, there have been many GLOFs originating from moraine and ice-dammed lakes in the TPS. Meanwhile, several EWS have been established in this region. (b, c) Cirenmaco is located in the Zhangzangbo Valley, central Himalaya. (d) The area changes of the lake and its parent glacier from 1988 to 2020.\n\ninstance, a catastrophic GLOF originating from Luggye Tsho, Eastern Himalaya, on October 7, 1994, was triggered by the melting of buried ice beneath the moraine dam, and the resulting flood killed 21 people and destroyed local infrastructure [43,44]. The largest GLOF event recorded in the region occurred on June 16, 2013. Chorabari, a moraine-dammed lake in the central Himalayas, suddenly drained, and the outburst flood, accompanied by rainfall floods, washed away settlements downstream, causing more than 6000 deaths [45,46]. Therefore, determining how to prevent and mitigate these outburst flood disasters is urgent. It is necessary to assess potential GLOF hazards and risks and simulate possible outburst floods. Moreover, it is crucial to meaningfully implement a variety of remedial works, such as lake dam immobilization, artificial drainage, and construction of monitoring and early warning systems (EWS).", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 28, "line_end": 47, "token_count_estimate": 701, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4997b73fcd76e67d", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: text\n\nGLOF event recorded in the region occurred on June 16 , 2013 . Chorabari , a moraine - dammed lake in the central Himalayas , suddenly drained , and the outburst flood , accompanied by rainfall floods , washed away settlements downstream , causing more than 6000 deaths [ 45 , 46 ] . Therefore , determining how to prevent and mitigate these outburst flood disasters is urgent . It is necessary to assess potential GLOF hazards and risks and simulate possible outburst floods . Moreover , it is crucial to meaningfully implement a variety of remedial works , such as lake dam immobilization , artificial drainage , and construction of monitoring and early warning systems ( EWS ) .\n\nSuch engineering measures have been applied sporadically worldwide, even though numerous high-risk glacial lakes have been identified. The danger, high-altitude, and inaccessibility of high mountain areas make engineering measures several times more expensive than in densely populated plains. In the Himalayas, there are currently fewer than 10 engineering measures in place at present. For instance, Jialongco (Table 1), a moraine-dammed lake in the Himalayas, drained in 2002, resulting in severe downstream damage. Considering the serious threat posed, the local government artificially strengthened the moraine dam in 2020. In contrast, in the Peruvian Andes, at least 38 glacial lakes have been used for various remedial works, including 38 lakes that were artificially drained by open-cut excavation, tunnels, or siphon measures, 15 lake dams that were strengthened; and one lake that established an EWS [27,47]. These measures were taken mainly because many villages, towns, and cities are situated downstream of the high-risk glacial lakes in the surrounding region. An EWS generally consists of monitoring the glacial lake, its parent glaciers, and/or the downstream river, formulating a disaster mitigation plan, and training for evacuation. The latter two are non-engineering measures. However, they should be conducted based on an in-depth understanding of the original GLOF hazards. The difficulty lies in fostering strong support from local governments and residents [21,28].\n\nIt is vital for the implementation of remedial work that high-risk glacial lakes, adequate economic and engineering reserves, and long-term development plans are prepared in advance. Cirenmaco is located in the Poiqu River Basin, central Himalaya, close to the China–Nepal border (Fig. 1a and b). Three GLOFs from this lake have occurred in 1964, 1981, and 1983, respectively. In the past few decades, the high outburst susceptibility of Cirenmaco and the accompanied transboundary threat of its GLOF have plagued residents in both regions [7,12,48,49]. An EWS was installed near Zhangmu Port in 2001 unilaterally by the Nepalese government, to protect downstream hydropower projects [50]. However, the absence of a glacial lake monitoring system caused the EWS to lose a considerable part of its effective functions, as the upper Poiqu River Basin is home to many high-risk glacial lakes located in China. Therefore, supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program, we have establish an EWS for Cirenmaco to monitor dynamic changes in the lake and its parent glacier, lake and downstream river water-level, meteorology, etc., to provide a timely warning signal to the downstream area.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 28, "line_end": 47, "token_count_estimate": 865, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "fcaa799afaa0ec6d", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: text\n\nPort in 2001 unilaterally by the Nepalese government , to protect downstream hydropower projects [ 50 ] . However , the absence of a glacial lake monitoring system caused the EWS to lose a considerable part of its effective functions , as the upper Poiqu River Basin is home to many high - risk glacial lakes located in China . Therefore , supported by the Second Tibetan Plateau Scientific Expedition and Research ( STEP ) Program , we have establish an EWS for Cirenmaco to monitor dynamic changes in the lake and its parent glacier , lake and downstream river water - level , meteorology , etc . , to provide a timely warning signal to the downstream area .\n\nThe categories of remedial work for glacial lakes with high risk, and corresponding measures. A typical case is given for the specific measure.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 28, "line_end": 47, "token_count_estimate": 217, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "81371cd049aee6c3", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 1. Introduction\nType: table\nTable: Table 1\n\n| Remedial works                         | Measures                             | Typical cases                  |                                             |                                                                                                                                                                                                                                 | Reference     |\n|----------------------------------------|--------------------------------------|--------------------------------|---------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|\n|                                        |                                      | Location                       | Name                                        | Explanation                                                                                                                                                                                                                     |               |\n| Artificial drainage                    | Open cut                             | Cordillera Blanca, Peru     | Palcacocha                                  | This moraine-dammed lake failed in 1941, and then an open cut was excavated in 1974, lowering the lake to a level of 3 m.                                                                                                 | [27]          |\n|                                        | Tunnel                               | Cordillera Blanca, Peru     | Parón                                       | The tunnel became functional in 1984 and the lake level could be lowered by 52 m.                                                                                                                                            | [27]          |\n|                                        | Siphon                               | Cordillera Blanca, Peru     | Lake 513                                    | This bedrock-dammed lake failed in 1991 and 2010, but a siphon was already installed in 1989, lowering the lake level of 8 m. Subsequently, a combination of tunnels was used to lower the lake level by 20 m in 1993. | [51]          |\n| Lake dam immobilization             | Earth dam                            | Central Himalaya            | Jialongco                                   | This moraine-dammed lake had been drained in 2002 causing severe damage downstream, the lake is still a high risk. Thus, in 2020 the local government immobilized its dam.                                             | This study |\n|                                        | Arch dam                             | Alps, Swiss                    | Trift glacier lake                          | The dam, built primarily to generate electricity, was completed by 2007, but undoubtedly reduced the incidence of glacier-related hazards.                                                                                | [52]          |\n| Monitoring and Early warning system | Monitoring and/ or warning system | Central Caucasus, Russia | Bashkara Glacier lakes                   | The two sister lakes failed in 1958 and 1959. To protect the infrastructure and settlements downstream, an early warning and monitoring system was installed in 2008.                                                     | [53]          |\n|                                        | Disaster mitigation plan          | Cordillera Blanca, Peru     | Lake 513                                    | When information of the GLOF arrived, alarm will be issued to evacuate. At the same time, sirens were installed in all important communities and major transportation routes.                                          | [28]          |\n|                                        | Evacuation drills                    | Cordillera Blanca, Peru     | Palcacocha, Tullparaju, Cuchillacocha | Detailed evacuation plans and evacuation briefs were developed by relevant staff to protect the downstream city of Huaraz, and were given extensive publicity.                                                            | [21]          |", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "table", "table_caption": "Table 1", "columns": ["Remedial works", "Measures", "Typical cases", "", "", "Reference"], "table_row_start": 1, "table_row_end": 9, "line_start": 48, "line_end": 58, "token_count_estimate": 660, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "33a1bef02f1dc1ea", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\nCirenmaco ( $86^{\\circ}03'56''E$ ,  $28^{\\circ}04'02''N$ ) glacial lake is located at 4612 m above sea level. Since the 1990s, the region in which it is situated (south slope of the central Himalayas) has been designated as a region of intensively developed of glacial lakes [2,8]. Cirenmaco's perennial mean annual precipitation can be estimated at ~1000 mm and the relevant mean annual temperature at approximately 0 °C by interpolating the nearest meteorological stations [24]. The lake, which was drained in 1981, had reached a disastrous and alarming level. Based on a post-disaster investigation conducted by Xu [54], approximately  $19 \\times 10^6$  m³ of water was released, with a peak discharge of 15,920 m³/s at the breach and a total flood duration of 1 h. Because of the GLOF, substantial erosion was observed throughout the downstream valley, transporting approximately  $4 \\times 10^6$  m³ of solid material. There was a possibility of debris flow within the flood spreading in the Zhangzangbo Valley. The GLOF, which moved over 50 km along the downstream Poi-qu/Sun Koshi River, destroyed two bridges (at the mouth of Zhangzangbo Valley and Zhangmu Port), 6 km of China-Nepal roads and buildings on both sides, and one hydropower facility, resulting in 200 fatalities [48,54].", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 61, "line_end": 65, "token_count_estimate": 388, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "080180859f7cbb0b", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\ns at the breach and a total flood duration of 1 h . Because of the GLOF , substantial erosion was observed throughout the downstream valley , transporting approximately $ 4 \\ times 10 ^ 6 $ m³ of solid material . There was a possibility of debris flow within the flood spreading in the Zhangzangbo Valley . The GLOF , which moved over 50 km along the downstream Poi - qu / Sun Koshi River , destroyed two bridges ( at the mouth of Zhangzangbo Valley and Zhangmu Port ) , 6 km of China - Nepal roads and buildings on both sides , and one hydropower facility , resulting in 200 fatalities [ 48 , 54 ] .\n\nCirenmaco has been identified as a high-hazard/risk glacial lake in recent studies [12,15,55] due to its steep surroundings, buried ice in the moraine dam, and the high exposure of downstream communities and infrastructure. Although the area of Cirenmaco underwent a rapid increase from 0.11 to 0.33 km<sup>2</sup> beginning in the 1980s, a stable trend in lake area has been maintained since 2010 (Fig. 1c and d). On the surface, the development potential of this lake has reached its limits. However, the dead ice in the lake bottom may continuously melt to increase the lake volume, and the buried ice melting in the moraine dam may continuously affect dam stability and trigger outburst floods [24]. A bathymetric survey of Cirenmaco was conducted in 2012. The results showed that approximately  $18 \\times 10^6$  m<sup>3</sup> of water was held by this lake, with a maximum depth of 115 m [24]. Subsequently, Zhang et al. [25] simulated outburst floods at different drainage percentages and assessed the hazards and potential impacts of Cirenmaco GLOFs (Fig. 2a-c). Although their scheme for the outburst flood hydrograph as an input for the HEC-RAS model was proposed using an ideal drainage curve process, it has been demonstrated to be feasible in many other studies also [56,57]. In contrast, predicted GLOF propagation is transported entirely within a narrow valley. Because of DEM accuracy and simulation uncertainties, precisely identifying potentially damaged buildings, roads, etc., in such gullies is difficult. Therefore, prior simulation results for the Cirenmaco GLOF depth, velocity, and arrival time provide suitable reference points. The distance between Cirenmaco and the Zhangmu ports were found to be 23 km. The estimated travel time of the flood indicated that, if Cirenmaco released 100% of its volume, the GLOF would have at least 50 min to reach Zhangmu Port [25]. Moreover, studies of the 1981 GLOF reconstruction revealed that the flood time was more than an hour when it crossed this route [24]. However, we deduced that the actual flood migration time for this period may be", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 61, "line_end": 65, "token_count_estimate": 739, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "8f6866751e18a0e5", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: figure\nFigure\n\nImage /page/3/Figure/5 description: A multi-panel figure illustrating the simulation of a Glacial Lake Outburst Flood (GLOF). The figure is divided into four panels labeled a, b, c, and d.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "figure", "figure_caption": null, "line_start": 66, "line_end": 66, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa6ab677c295ed7b", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\nPanel (a) is a large topographic map showing a river valley in a mountainous region, spanning the border between China and Nepal. The Poiqu River flows from north to south through the valley. Several locations are marked with red dots, including Jialongco, Nyalam, Zhangzangbo Bridge, and Zhangmu Port. The map includes latitude and longitude grids, a scale bar from 0 to 4 km, and a north arrow. There are two circular insets with photographs. The top-left inset, pointing from Jialongco, shows a turquoise glacial lake surrounded by a rocky moraine. The bottom-right inset, pointing from an area near Zhangzangbo Bridge, shows a dry, rocky landscape.\n\nPanels (b), (c), and (d) are smaller maps showing the results of a flood simulation in a specific area downstream.\n- Panel (b) displays the 'Flood depth (m)', with a color scale ranging from 0.001 (blue) to 56.7 (magenta).\n- Panel (c) shows the 'Flood velocity (m/s)', with a color scale from 0.1 (light blue) to 22.3 (red).\n- Panel (d) presents a 'Hazard' map with a legend indicating four levels: Low (dark green), Medium (light green), High (yellow), and Very high (red).\n\nFig. 2. Simulation of the Circhmaco GLOF. (a) The outburst floods will cross the Zhangzangbo Bridge to Zhangmu Port and then enter Nepal at the largest drainage scenario (100%). (b) The Circhmaco GLOF depth and (c) velocity are used to composite the (d) hazard map. All data in this figure were provided by Zhang et al. [25]. The picture in the circle on the left shows the strengthened moraine dam of Jialongcuo, and on the right shows the melting of buried ice in the moraine dam of Circhmaco.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 67, "line_end": 76, "token_count_estimate": 497, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "569c6f09834c12a1", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A composite image of five photographs, labeled a, b, c, d, and e, each showing a different scientific monitoring station in a remote, natural environment. Image 'a' displays a complex weather station on a rocky mountainside with a person in a red jacket nearby and solar panels on the ground. Image 'b' shows a station with a large solar panel and colorful prayer flags, set against a backdrop of green hills. Image 'c' features a station on the shore of a turquoise lake with snow-capped mountains in the background and a person standing next to it. Image 'd' depicts a station with a solar panel next to a rushing river. Image 'e' shows a simpler station on a steep, rocky slope.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "figure", "figure_caption": null, "line_start": 77, "line_end": 77, "token_count_estimate": 215, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64f85fab4c7bcda1", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\nFig. 3. Early warning system installed at Cirenmaco. A total of nine different services are contained in this project. These include (a) monitoring of the lake and its parent glacier, (b) monitoring of the environment of the downstream river, (c) water level monitoring of the glacial lake, (d) water level monitoring of the downstream river and (e) moraine dam dynamic monitoring.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 78, "line_end": 80, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9cd91204b0b65c16", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: figure\nFigure\n\nImage /page/4/Figure/4 description: A multi-panel figure displaying real-time monitoring data from a glacial valley. Panel (a) is a map of the area, showing the locations of various sensors including GNSS, cameras, meteorological stations, and water level sensors along a river and near a lake. The map includes latitude and longitude coordinates and a scale bar. Panels (b) through (j) are a series of photographs taken over time. Panels (b-d) show a glacier terminating in a turquoise lake. Panels (e-g) show the surface of the turquoise lake. Panels (h-j) show a river flowing through a rocky channel. The bottom section contains four graphs labeled (k), (l), (m), and (n), all plotted against dates from 2021/10/3 to 2021/10/19. Graph (k) shows a water level decreasing from approximately 1.8 m to 0.5 m. Graph (l) shows a water level that is stable around 0.5 m before spiking to a peak of about 0.85 m around 2021/10/19. Graph (m) shows temperature fluctuating daily between approximately -1°C and 5°C, with precipitation events shown as blue bars, including a significant event around 2021/10/18. Graph (n) shows temperature fluctuating between approximately 4°C and 12°C, also with a significant precipitation event around 2021/10/18.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "figure", "figure_caption": null, "line_start": 81, "line_end": 81, "token_count_estimate": 358, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7fcee6fd3e8d4f6d", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\nFig. 4. Real-time monitoring data transmission via satellite and mobile networks. (a) Distribution of the main monitoring points in the Zhangzangbo Valley, and the monitoring data currently available, such as photographs taken by three visible light cameras, which are aligned (b–d) with the Cirenmaco inlet area and the snout of its parent glacier and (e–g) its outlet area, and (h–j) the terminus of the Zhangzangbo Valley. (k, l) Water level data and (m, n) meteorological data from the glacial lake and the mouth of the valley.\n\n~40 min, according to the observed average flow velocity (9.8 m/s) of the 1981 GLOF in Nepal [54]. These data suggest that approximately 40–60 min may be required to send a warning signal.\n\nIn addition, the hazard map was composited by flood depth and velocity and translated into four intensity levels: low, medium, high, and very high (Fig. 2d). In the future, more refined and accurate simulation results can be relied upon to establish this hazard map, which has implications for determining evacuation locations in downstream areas, constructing GLOF prevention and mitigation facilities, and implementation of economic development measures. These studies provide an understanding of the possible flood damage from Cirenmaco and are the basis of this EWS construction [21,28,58].", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 82, "line_end": 88, "token_count_estimate": 353, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "257136aefd733346", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A topographical map displaying water depth measurements of a lake situated in a mountainous region. The map is oriented with North at the top, as indicated by a north arrow. The y-axis shows latitude from 28°3'45\"N to 28°4'15\"N, and the x-axis shows longitude from 86°3'45\"E to 86°4'15\"E. A winding path of colored dots across the dark green lake surface represents the water depth. A legend in the bottom left corner, titled \"Water depth (m)\", explains the color scale. The depth ranges from 6.2 meters, represented by blue, to 115.3 meters, represented by red, with green, yellow, and orange indicating intermediate depths. The deepest area, shown in red, is located in the central part of the survey path. A scale bar indicates a length of 0.2 kilometers.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "figure", "figure_caption": null, "line_start": 89, "line_end": 89, "token_count_estimate": 257, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9cbea14766fc5a70", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\nFig. 5. In 2012. Wang et al. [24] measured the water depth and estimated the volume of Circumaco using an unmanned boat.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 90, "line_end": 92, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5cd2cd170ae0daa2", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: figure\nFigure\n\nImage /page/5/Figure/4 description: A schematic diagram illustrates the major components of an early warning system (EWS) for a glacial environment. The diagram shows a mountainous landscape with a glacier labeled \"Glacier ice\" on a slope of bedrock. The glacier feeds into a large glacial lake, which is held in place by a moraine dam. Below the dam, there is buried ice and a downstream river system. Various monitoring systems are in place, indicated by labels and icons. On the glacier, there is \"Glacier dynamic monitoring.\" Near the lake, there is \"Meteorological monitoring\" (with precipitation shown as snowflakes falling from clouds) and \"Topographic mapping\" (indicated by a drone). Within the lake, there is \"GLacial lake dynamic monitoring\" and a \"Bathymetric survey.\" At the lake's edge, \"Water level monitoring\" is shown. On the moraine dam and downstream area, there is \"GNSS displacement monitoring\" and \"Downstream river dynamic monitoring.\" Data from all these monitoring points is sent via blue lines to a central \"Data transmission\" tower. This tower transmits the data to a satellite, which then relays it to a \"Data center.\" The data center sends information to \"Observers,\" who can then issue an \"Early warning,\" represented by a red icon with sound waves.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "figure", "figure_caption": null, "line_start": 93, "line_end": 93, "token_count_estimate": 362, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf0981ab2b04aa40", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 2. Previous studies on Cirenmaco\nType: text\n\nFig. 6. Schematic diagram illustrating major components of the Cirenmaco EWS, including the lake and its parent glacier monitoring, downstream river monitoring, water-level monitoring, moraine dam dynamic monitoring, meteorological monitoring, bathymetric survey, topographic mapping, data transmission, and transferring the early warning signal.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "2. Previous studies on Cirenmaco", "section_headings": ["2. Previous studies on Cirenmaco"], "chunk_type": "text", "line_start": 94, "line_end": 96, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9bb0bc8e36386ec4", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 3. Characteristics of EWS > 3.1. A typical case: an EWS established for 513 Lake, Cordillera Blanca, Peru, in 2012\nType: text\n\nTwo drainage events occurred in the Peruvian glacial Lake 513, in 1994 and 2010. An ice avalanche triggered the 1994 GLOF. However, because of the prior artificial drainage of the lake, the GLOF magnitude was comparatively low, so no damage was caused to the downstream area [51,59]. The 2010 GLOF was a catastrophic event, also triggered by an ice avalanche, that severely damaged the downstream city of Carhuaz, destroying 22 buildings and 100 km of roads, and causing 100 casualties [59]. In response to the serious threat posed by Lake 513, the local government implemented an EWS in 2011–2012 to prevent potential disaster [28]. Notably, the\n\nEWS of Lake 513 is the first glacial lake early warning system worldwide with a fully functional service system. It includes risk knowledge (hazard and risk assessment), monitoring and warning services (monitoring and warning of hazard occurrence), dissemination and communication (dissemination of warning signals), and response capability (disaster mitigation plans and evacuation drills) [60].\n\nRisk knowledge requires field investigation and analytical work to make a hazard and risk assessment of glacial lakes to clarify lake outburst probability and downstream exposure within the GLOF path. Monitoring and warning services consist of five components [28, 61]. The first is a glacier and lake monitoring station at the lake shore, which consists of four geophones and two cameras that record ground shaking during ice avalanches and flooding, as well as glacier–glacial lake dynamics. The second component is a hydrometeorological monitoring station, which is installed downstream of the lake outlet. The third component is the repeater station, whose main function is to transmit the monitored data to the data center. The fourth component is the data center in the city of Carhuaz, which is responsible for data evaluation and storage. The fifth component is the warning station, which is primarily responsible for the warning signal. The functionality of the dissemination and communication components, as well as the response capability, largely depends on active coordination with local authorities and residents to maximize the full functionality of the EWS and reduce potential losses. The EWS of Lake 513 provides a good reference for the construction of an EWS for Cirenmaco.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "3. Characteristics of EWS > 3.1. A typical case: an EWS established for 513 Lake, Cordillera Blanca, Peru, in 2012", "section_headings": ["3. Characteristics of EWS", "3.1. A typical case: an EWS established for 513 Lake, Cordillera Blanca, Peru, in 2012"], "chunk_type": "text", "line_start": 100, "line_end": 106, "token_count_estimate": 588, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e35d33041e19734", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 3. Characteristics of EWS > 3.2. EWSs on the TPS\nType: text\n\nThe early warning system essentially differs in predicting GLOFs versus rainfall-type floods. The EWS for GLOFs focuses on monitoring various elements and issuing timely warnings rather than providing specific flood features. This is due to GLOFs' high magnitude, short duration, and destructive properties. Forecasting rainfall-type floods, on the other hand, concentrates primarily on real-time hydrological processes based on dynamic courses of flow regime, streambed roughness, fluid density, etc., to establish effective standard curves to predict and warn of floods downstream with metrics generally on peak discharge, maximum water level, and arrival time [62].\n\nFive EWSs have been established over the past few decades in the TPS region, to prevent GLOFs (Fig. 1a, Table 2). EWSs can be divided into two categories, based on their technical structures. The first is a semi-automatic system that relies on staff assistance. For example, a manual EWS in Bhutan was installed, with six water-level sensors and two automatic weather stations, and included a control room with two workers in a village downstream of the glacial lakes. The workers are equipped with satellite phones for information transmission [63,64]. The second was a fully automatic EWS. These services generally consist of real-time monitoring of the glacial lake and its parent glacier, hydrometeorological monitoring, data transmission and storage, and early warning [28,61]. For example, in 2014, an EWS was installed in Kyagar, an ice-dammed lake in Karakoram, China. It consists of three observation stations situated the lakeshore, estuary, and far downstream. The parameters, including the water-level of the lake and river, monitored by automatic sensors and photographs of the lake and riverbed taken by automatic time-lapse cameras, are all transferred through a satellite link to a data center [29]. If a GLOF was identified, warning signals were sent to authorities. In addition, fully automatic EWSs require an indispensable threshold to determine GLOFs, which may be real-time dynamic warning indicators such as lake and river water levels, deformation of the lake and glacier, or meteorological data [65]. The water-level threshold is frequently used in EWSs [28]. The main difference between a semi-automatic EWS and a fully automatic EWS is data transmission. Due to the remote and inaccessible environment in high-altitude mountainous regions, data transmission is very difficult and expensive. However, long-term observations using satellite for data transmission incur high costs. The frequency and cost of equipment maintenance are also higher than those of semi-automatic systems.\n\nOverall, the quality of the current EWS applied to high-risk glacial lakes varies greatly around the world. In theory, a simple prediction scheme would require only the installation of water-level sensors for real-time analysis. Some activities in the Hindu Kush-Karakorum range rely on primitive beaconing methods to provide early warning [66]. If the purpose is not to investigate the mechanism of GLOF, but rather to provide early warning, it is a success as long as the effective alarm signal can reduce downstream exposure damage. Due to the limited data transmission scheme, fully automated and multidimensional monitoring of EWS is rare. Under the\n\n The GLOF early warning systems widely established in the TPS region, including the Tian Shan Mountains, Karakoram, and Himalayas.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "3. Characteristics of EWS > 3.2. EWSs on the TPS", "section_headings": ["3. Characteristics of EWS", "3.2. EWSs on the TPS"], "chunk_type": "text", "line_start": 108, "line_end": 118, "token_count_estimate": 845, "basins": [], "subbasins": [], "countries": ["Bhutan", "China"], "lake_ids": []}}
{"id": "88b4a9f19f62b881", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 3. Characteristics of EWS > 3.2. EWSs on the TPS\nType: table\nTable: Table 2\n\n| ID | Lake name                                                          | Lake type          | Location                       | Longitude° | Latitude° | Explanation                                                                                                                         | References |\n|----|--------------------------------------------------------------------|--------------------|--------------------------------|------------|-----------|-------------------------------------------------------------------------------------------------------------------------------------|------------|\n| 1  | Teztor lake                                                        | Moraine- dammed | Tien Shan, Kyrgyzstan       | 42.54      | 74.43     | The EWS was installed in 2011, and successfully predicted the Teztor lake GLOF in 2012.                                             | [67]       |\n| 2  | Kyagar lake                                                        | Ice- dammed     | Karakoram, China            | 35.71      | 77.01     | The EWS combined three automatic terrestrial observation stations, and successfully predicted Kyagar GLOFs from 2015 to 2018.       | [29,38]    |\n| 3  | Ropal Tsho                                                         | Moraine- dammed | Central Himalaya, Nepal  | 27.86      | 86.47     | A manual EWS was set up in 1997, and evolved into a fully automatic system in 1998.                                                 | [50]       |\n| 4  | Imja Tsho                                                          | Moraine- dammed | Central Himalaya, Nepal  | 27.9       | 86.93     | The EWS combined two monitoring stations and two relay stations.                                                                    | [63]       |\n| 5  | Raphstreng Tsho; Luggye Tsho; Thorthormi Tsho; Besta lake | Moraine- dammed | Eastern Himalaya, Bhutan | 27.88      | 89.74     | The EWS was installed with six water level sensors and two weather stations, and successfully predicted Lemthang Tsho GLOF in 2015. | [63,64]    |", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "3. Characteristics of EWS > 3.2. EWSs on the TPS", "section_headings": ["3. Characteristics of EWS", "3.2. EWSs on the TPS"], "chunk_type": "table", "table_caption": "Table 2", "columns": ["ID", "Lake name", "Lake type", "Location", "Longitude°", "Latitude°", "Explanation", "References"], "table_row_start": 1, "table_row_end": 5, "line_start": 119, "line_end": 125, "token_count_estimate": 462, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "Nepal"], "lake_ids": []}}
{"id": "2ef6093a6b0e9f40", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 3. Characteristics of EWS > 3.2. EWSs on the TPS\nType: text\n\npremise of ensuring effective early warning efforts, a comprehensive monitoring system is crucial and valuable for understanding the GLOF generation mechanism, propagation process, and flow regime transformation. This is particularly true regarding the action of photo or video monitoring of glaciers and glacial lakes, which not only eliminates many false alarm signals but also motivates us to understand the potential glacier–glacial lake–moraine dam linkage process when GLOF occurs.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "3. Characteristics of EWS > 3.2. EWSs on the TPS", "section_headings": ["3. Characteristics of EWS", "3.2. EWSs on the TPS"], "chunk_type": "text", "line_start": 126, "line_end": 128, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "87c2020c26fa1d99", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS\nType: text\n\nThe Cirenmaco EWS project, which was established in 2020 and validated in 2021, is the first use of a comprehensive EWS application in a moraine-dammed lake in China. It is a fully automatic system that applies several new technologies and methods. The entire system complies with the current two basic mechanisms of moraine-dammed lake outbursts: overtopping and piping. Both mechanisms are likely to emerge in Cirenmaco drainage. Overtopping triggers mainly consist of the ice avalanche from its mother glacier, which has been detecting by photo and video monitoring and satellite remote sensing image analysis. A water-level sensor located at Cirenmaco Lake will record the potential wave displacement. This is the main surface dynamic process that we considered, because the Cirenmaco GLOF generated in 1981 was determined to have been caused by an ice avalanche based on field investigations. Piping is generally induced by the melting of buried ice; thus, surface displacement monitoring was employed to record the activity status of the Cirenmaco moraine dam. We also deployed three water-level sensors and two meteorological stations along the flood route in Zhangzangbo Valley to observe the response of the lake and river after the GLOF occurred, as well as the meteorological context of the disaster (see Table 3). We elaborates on this advanced EWS in terms of four major categories and 10 specific components.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS", "section_headings": ["4. Construction of the Cirenmaco EWS"], "chunk_type": "text", "line_start": 130, "line_end": 132, "token_count_estimate": 360, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0c9d5fc4f27808e4", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.1. Monitoring system\nType: text\n\n- (i). Glacial lake, related parent glacier, and downstream river monitoring. A 6 m observation tower was built on the right bank of the lake to equip various apparatuses (Fig. 3a). Three 360° rotatable high-definition dome cameras were mounted on the tower, to capture daytime video surveillance and high-frequency timing photographs (Fig. 4a). The first camera was pointed at the lake's inlet area and the snout of its parent glacier (filming every hour from 8 a.m. to 8 p.m.) (Fig. 4b–d). The second camera was pointed at the middle of Cirenmaco Lake (filming every 3 h from 8 a.m. to 8 p.m.), and the third was pointed at the outlet area of Cirenmaco (filming every 3 h from 8 a.m. to 8 p.m.) (Fig. 4e–g) and also monitors the status of the moraine dam. A thermal imaging dome camera (infrared night vision device) was installed at the same angle as the first camera to monitor the lake and glacier (filming every hour from 8 p.m. to 8 a.m.). The thermal camera has low power consumption and a fog-penetration function and can provide video and timed photos under nighttime or foggy conditions. We also installed a group of infrared and visible light cameras at the intersection of Zhangzangbo Valley and the Poiqu River to record the state of the downstream river environment (Figs. 3b and 4h–j). The filming frequency of both cameras was one frame per hour. - (ii). Water-level monitoring of glacial lakes and downstream rivers. This service consisted of three automatic water-level sensors that also monitored additional parameters, including water temperature and pressure. One was installed on the lake shore of Cirenmaco (Fig. 3c), recording the possible wave displacement induced by an ice avalanche. One was installed at the lake outlet, and the other was installed at the terminal of the Zhangzangbo Valley (Fig. 3d), near the location of the cameras. The recordings of these sensors were collected every 30 s (Fig. 4k and l). - (iii). Dynamic monitoring of moraine dam. This element includes thickness, displacement, and temperature monitoring of the moraine dam. The Cirenmaco moraine dam has accumulated an enormous quantity of rocks, debris, and buried dead ice (Fig. 2a). The stability of the moraine dam impacts the frequency and magnitude of outburst floods. Two global navigation satellite systems (GNSS) were used to detect the elements of the moraine dam (Fig. 3e). This technology was applied here for the first time to observe the moraine dam in a glacial lake. The main potential variations were the settlement of the moraine dam caused by the melting of buried ice, and the movement effect of the moraine dam when the GLOF occurred. Ground penetrating radar was used to detect the moraine dam and analyze its thickness. - (iv). Meteorological monitoring. Meteorological stations measured more than a dozen meteorological parameters, including wind speed, wind direction, precipitation, air temperature, air humidity, radiation, soil temperature, soil moisture, and atmospheric pressure (Figs. 3a and 4m–n). We selected three automatic meteorological stations at different altitudes in Zhangzangbo Valley.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.1. Monitoring system", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.1. Monitoring system"], "chunk_type": "text", "line_start": 134, "line_end": 143, "token_count_estimate": 866, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "781e8653d01df291", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.1. Monitoring system\nType: text\n\ndam in a glacial lake . The main potential variations were the settlement of the moraine dam caused by the melting of buried ice , and the movement effect of the moraine dam when the GLOF occurred . Ground penetrating radar was used to detect the moraine dam and analyze its thickness . - ( iv ) . Meteorological monitoring . Meteorological stations measured more than a dozen meteorological parameters , including wind speed , wind direction , precipitation , air temperature , air humidity , radiation , soil temperature , soil moisture , and atmospheric pressure ( Figs . 3a and 4m – n ) . We selected three automatic meteorological stations at different altitudes in Zhangzangbo Valley .\n\nThese were located at the lake shore of Cirenmaco, the middle altitude of the valley, and the intersection of the valley and the Poiqu River to monitor the three-dimensional and horizontal meteorological background of disasters within the study area. The total elevation difference between the three automatic meteorological stations was nearly 2500 m. - (v). Satellite Remote Sensing Image Monitoring. To trace the historical state of the entire sequence of Cirenmaco disasters, a series of satellite images, such as Landsat, Sentinel, and SPOT, were selected to illustrate the lake–glacier dynamics. Useful information such as glacier boundary, glacier velocity, and ice crevasse development should be extracted as much as possible. Regular updates and database maintenance are required to provide a broad contextual awareness for high-frequency water-level, meteorology, and displacement monitoring. - (vi). Bathymetric survey for the lake and topographic mapping for the lake and its surroundings. Lake volume is a critical metric in identifying potentially dangerous glacial lakes. Following the Cirenmaco bathymetric survey in 2012 (Fig. 5) [24], we conducted a more detailed bathymetric survey in 2020 to understand the lake's evolution. We also utilized drone images to create high-resolution topographic mapping around the lake.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.1. Monitoring system", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.1. Monitoring system"], "chunk_type": "text", "line_start": 134, "line_end": 143, "token_count_estimate": 526, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53bab34d07772087", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.1. Monitoring system\nType: table\nTable: Table 3 Primary equipment for the early warning system of Cirenmaco, as well as the involved monitoring elements, data acquisition intervals and accuracy.\n\n| Equipment                              | Number | Locations                                    | Primarily monitored elements                                                                                                                      | Accuracy                                                                 | Acquisition interval |\n|----------------------------------------|--------|----------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------|----------------------|\n| Pressure type water level sensor    | 3      | lake, downstream river                    | Water-level                                                                                                                                       | 0.1%FS                                                                   | 30 s                 |\n| Automatic meteorological station | 3      | lake, Zhangzangbo Valley                  | air temperature; precipitation; wind speed and direction; air humidity; radiation; soil temperature; soil moisture; atmospheric pressure | ±0.2 °C; ±0.4 mm; ±0.3 m/s; ±5°; ±3%; ±5 W/m2; ±0.5 °C; ±3%; ±0.3 hPa | 30 min               |\n| Camera                                 | 6      | glacier, glacial lake, moraine dam, river | dynamic processes                                                                                                                                 | /                                                                        | 1–3 h                |\n| GNSS                                   | 2      | moraine dam                                  | surface displacement                                                                                                                              | <1 m                                                                     | 0.25 s               |", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.1. Monitoring system", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.1. Monitoring system"], "chunk_type": "table", "table_caption": "Table 3 Primary equipment for the early warning system of Cirenmaco, as well as the involved monitoring elements, data acquisition intervals and accuracy.", "columns": ["Equipment", "Number", "Locations", "Primarily monitored elements", "Accuracy", "Acquisition interval"], "table_row_start": 1, "table_row_end": 4, "line_start": 144, "line_end": 149, "token_count_estimate": 353, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b75044434bd2cf6", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.2. Data-processing system\nType: text\n\n- (vii). Data transmission. The data measured in this ESW were transmitted via the Beidou and Inmarsat satellites and a mobile network to a data center (Fig. 6). The devices situated around Cirenmaco need to employ satellites to transmit data due to the lack of mobile signal coverage. The water-level and meteorological and imaging data at the mouth (middle) of Zhangzangbo Valley can be transmitted through the mobile network. Furthermore, all these devices are powered by solar panels.\n- (viii). Data Center. It is located at the Institute of Tibetan Plateau Research Chinese Academy of Sciences, China. The center is responsible mainly for data storage, real-time surveillance, and transferring early warning signals. All data were moved or directly transferred to the Tibet Autonomous Region Disaster Cloud Platform (http://holo-earth.cn:9022/), established by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences. There is a delay of approximately 2 min when data transmitted via satellite and mobile networks are loaded into the platform. Subsequently, the data can be immediately used for real-time alarm analysis. In addition, all the data measured from this EWS will be shared with Nepalese authorities, which is an important measure to increase cooperation and protect lives, livelihoods, and property on both sides downstream.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.2. Data-processing system", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.2. Data-processing system"], "chunk_type": "text", "line_start": 152, "line_end": 155, "token_count_estimate": 354, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "73489b2badb15a46", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.3. Early warning system\nType: text\n\n(ix). Warning system. In this study, a compound warning threshold was used. Those already successfully operated included the water-level and meteorological thresholds. In subsequent field investigations and indoor technical implementation studies, the deformation of the lake and glacier, moraine dam displacement threshold, and seismic threshold will be incorporated. It should be emphasized that we still lack experience with alarm techniques, and many novel strategies and thresholds are constantly being investigated and developed. The water-level threshold is a critical component of our early warning system, and the three water-level sensors scattered around Zhangzangbo Valley will be integrated. The warning level was separated into three grades, with the first level being the highest and the third being the lowest. The difference between the most recent measurement and the lowest value of the water-level over the past 10 min was used to calculate water-level variation. Eventually, three water levels with thresholds of 30, 25, and 20 cm were selected (Table 4), obtained based on the analysis of our monthly acquired monitoring data. The meteorological thresholds have an auxiliary role in this system and are simply intended to remind the observer to keep a closer eye on the Cirenmaco condition over time. A warning signal was automatically issued to the observer whenever the monitoring element exceeded a specified threshold. Subsequently, the observer checks the real time photos or video of the glacial lake and its parent glacier to determine the lake situation and informs the decision makers and authorities. Since the travel time of a GLOF from Circumaco to the cross-border is ~40 min, it was very important to make the warning process easy and efficient. Despite the EWS with a time delay of ~2 min for monitoring data transmitted to the data center, the data can be used for real-time warning analysis at the platform. This could benefit our valuable basic warning time.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.3. Early warning system", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.3. Early warning system"], "chunk_type": "text", "line_start": 157, "line_end": 159, "token_count_estimate": 489, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3388ba4afe9c8b11", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.4. Response capacity development\nType: text\n\nDisaster mitigation plans and evacuation drills for GLOFs are also critical in ensuring that potentially influenced people can be evacuated to a safe area and that their belongings are secured in a timely manner. This study examines the ability of authorities, who require a considerable degree of general awareness of the present and future challenges facing high mountain regions, specific knowledge of GLOF susceptibility, risks, and warning time, as well as considerable vigor and foresight. Prior to the establishment of the", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.4. Response capacity development", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.4. Response capacity development"], "chunk_type": "text", "line_start": 161, "line_end": 165, "token_count_estimate": 152, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71a5ebf23e2f1608", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.4. Response capacity development\nType: table\nTable: Table 4 Constructed and in-process designs of early warning indicators and their threshold settings.\n\n| Equipment                           | Thresholds                         | Status      |\n|-------------------------------------|------------------------------------|-------------|\n| Water level sensor at lake shore    | I:30 cm; II:25 cm; III: 20 cm      | Run         |\n| Water level sensor at lake outlet   | I:30 cm; II:25 cm; III: 20 cm      | Run         |\n| Water level sensor at valley mouth  | I:30 cm; II:25 cm; III: 20 cm      | Run         |\n| Precipitation at lake shore         | I:16 mm/h; II:10 mm/h; III: 8 mm/h | Run         |\n| Deformation of the lake and glacier |                                    | To be built |\n| Moraine dam displacement            |                                    | To be built |\n| Seismic                             |                                    | To be built |", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.4. Response capacity development", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.4. Response capacity development"], "chunk_type": "table", "table_caption": "Table 4 Constructed and in-process designs of early warning indicators and their threshold settings.", "columns": ["Equipment", "Thresholds", "Status"], "table_row_start": 1, "table_row_end": 7, "line_start": 166, "line_end": 174, "token_count_estimate": 262, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "831ce761456bc5ad", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 4. Construction of the Cirenmaco EWS > 4.4. Response capacity development\nType: text\n\nCirenmaco EWS, the relevant glacial lake research group, made up of researchers, had been working continuously in the area for five years and had played a key role in improving the knowledge of the local population and government regarding the glacial lake and its accompanying hazard/risk. Therefore, when the EWS was ready to be established, we gained strong support from the local government. This also facilitated many of our communication efforts for specific actions of GLOF prevention, and later maintenance of the EWS devices, and avoided some of the human damage caused by differences in perceptions, such as religious beliefs.\n\nFormulating disaster mitigation plans, implementing relief measures, and reserving relief supplies in advance are critical steps for minimizing casualties and losses [59]. Disseminating disaster prevention knowledge to residents, and allowing them to participate fully are also necessary. At present, relevant functionaries have been cooperating with the Disaster Emergency Management Department of the Tibet Autonomous Region and Nepal to fully utilize its function and help it become an effective, timeless, and representative EWS.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "4. Construction of the Cirenmaco EWS > 4.4. Response capacity development", "section_headings": ["4. Construction of the Cirenmaco EWS", "4.4. Response capacity development"], "chunk_type": "text", "line_start": 175, "line_end": 179, "token_count_estimate": 280, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "3fd0bc8e32672510", "text": "Document: 1-s2.0-S2212420922001339-main\nSection: 5. Summary and perspectives\nType: text\n\nIn the Himalayas, where numerous high-risk glacial lakes are distributed, the ability to prevent and mitigate GLOFs remains insufficient, especially for transboundary threats. There are no reports that mention methods to effectively solve this problem. The construction cost of the Cirenmaco EWS was kept under 0.5 million dollars, which is much less than the cost used to strengthen the moraine dam of Jialongco (>\\$10 million) in the same basin. Therefore, the EWS is relatively inexpensive to establish compared to other engineering measures on glacial lakes, and the availability of valuable field observations is important for furthering scientific research and development, making it suitable for large-scale replication in the Himalayas. The cost of the satellite transmission is also affordable. The pros and cons of each scenario must be weighed by the staff of each party, and any plan put in place must match the strength of regional social and economic development.\n\nThe Cirenmaco EWS is advanced and applies many new technologies such as real-time video transmission. One of the main objectives of applying this feature is to record the entire process of a glacial lake outburst, from the initial triggering event to the flood breaking through the dam and causing damage downstream; as the dynamics of a GLOF, as it occurs, are yet directly observed. We describe in detail the Cirenmaco EWS implemented in our project in four main categories: monitoring systems, data-processing systems, early warning systems, and response capacity development. All monitoring data have been successfully transmitted in real-time, and the EWS has been functional since October 2021. Both China and Nepal could benefit from the EWS project. Our study is of great significance in adapting to the increasing disaster risks in high-altitude mountainous areas and in providing a demonstration for the prevention and mitigation of GLOFs in the Himalayas and TPS.", "metadata": {"source_file": "data/('1-s2.0-S2212420922001339-main', '.pdf')_extraction.md", "document_title": "1-s2.0-S2212420922001339-main", "section_path": "5. Summary and perspectives", "section_headings": ["5. Summary and perspectives"], "chunk_type": "text", "line_start": 181, "line_end": 185, "token_count_estimate": 457, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "eb5746e65f46b0aa", "text": "Document: A glacial lake outburst flood risk assessment for the Phochhu river basin\nType: text\n\nAbstract. The melting of glaciers has led to an unprecedented increase in the number and size of glacial lakes, particularly in the Himalayan region. A glacial lake outburst flood (GLOF) is a natural hazard in which water from a glacial or glacier-fed lake is swiftly discharged. GLOFs can significantly harm life, infrastructure, and settlements located downstream and can have considerable ecological, economic, and social impacts. Based on a dam breach model, BREACH, and a hydrodynamic model, HEC-RAS (Hydrologic Engineering Centre's River Analysis System), we examined the potential consequences of a GLOF originating from the Thorthomi glacial lake, located within the Phochhu river basin, one of Bhutan's largest and rapidly expanding glacial lakes. Our analysis revealed that following a breach the Thorthomi glacial lake will likely discharge a peak flow of 16360 m<sup>3</sup> s<sup>-1</sup> within 4h. Such a discharge could potentially cause considerable damage, with an estimated 245 ha of agricultural land and over 1277 buildings at risk of inundation. To mitigate ecological, economic, and social impacts on downstream areas, our results emphasise an urgent need for understanding and preparing for the potential consequences of a GLOF from Thorthomi lake. Our findings provide valuable insights for policymakers and stakeholders involved in disaster management and preparedness.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "A glacial lake outburst flood risk assessment for the Phochhu river basin", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 4, "token_count_estimate": 365, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["16360"]}}
{"id": "8ce65b8e1eb7cd35", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.1 Glacial lakes and outburst floods\nType: text\n\nFloods are one of the most common natural disasters worldwide and can cause extensive socio-economic damage. Globally, over the last 2 decades, floods have affected approximately 2.3 billion people and have caused an estimated USD 622 billion in damage (UNISDR, 2015). Glacial lake outburst floods (GLOFs) are floods caused by sudden water release from glacial or glacier-fed lakes and cause a rapid rise in water level over a short time in downstream areas, resulting in devastating consequences (Gurung et al., 2017; Komori et al., 2012; Taylor et al., 2023). GLOFs are infrequent but highly destructive natural disasters that are difficult to predict. Prior to their occurrence, the extent of damage is also difficult to predict. Over the past few decades, the acceleration of glacier melt and recession, primarily driven by climate change, has led to a significant increase in the number of moraine-dammed (natural dams formed by glacial processes) glacial lakes (Sattar et al., 2021; Westoby et al., 2014; Worni et al., 2014). Taylor et al. (2023) estimated that approximately 15 million people are exposed to risks associated with potential GLOFs and that most of these populations are concentrated within High Mountain Asian (HMA) areas. Due to climate warming (Gardelle et al., 2011), the eastern Himalayan area, in particular, has seen a significant increase in the number and area of glacial lakes, thereby increasing the vulnerability of nearby communities to potential GLOF impacts (Hagg et al., 2021).\n\nAlthough GLOF research and studies have gained global momentum in recent years, only a few studies have been per-formed in Bhutan. Numerous studies conducted in Nepal and\n\n<sup>&</sup>lt;sup>1</sup>National Center for Hydrology and Meteorology, Thimphu, Bhutan\n\n<sup>&</sup>lt;sup>2</sup>Department of Civil and Environmental Engineering, Nagoya University, Nagoya 464-8603, Aichi, Japan\n\nChina have simulated and assessed GLOF risks, although detailed studies on Bhutan's exposure to GLOF-related hazards are scarce. Such scarcity can be attributed to a lack of required field data, as well as to Bhutan's limited exposure to the global scientific community.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.1 Glacial lakes and outburst floods", "section_headings": ["1 Introduction", "1.1 Glacial lakes and outburst floods"], "chunk_type": "text", "line_start": 8, "line_end": 18, "token_count_estimate": 584, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "Nepal"], "lake_ids": []}}
{"id": "d0479435967cd3a0", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.2 Past GLOF events in Bhutan\nType: text\n\nIn the past, Bhutan has faced several GLOF events; however, many of these events were either not reported or not documented. One of the most catastrophic GLOFs took place on 6 October 1994, when the moraine dam of Luggye lake partially collapsed, leading to the release of a massive amount of water and debris downstream, destruction to infrastructure and farmland, and the death of 21 people (Watanabe and Rothacher, 1996; Leber et al., 2000). Another significant GLOF occurred in 2009, when an outburst from Tshojo lake, located at the headwaters of the Phochhu river, caused downstream flooding. Based on satellite imagery and a sedimentological and geomorphological analysis, Komori et al. (2012) attributed an outburst from the supraglacial lake on the Tshojo glacier to the event. The most recent GLOF, the Lemthang Tsho outburst, took place on 28 July 2015. Gurung et al. (2017) reported that heavy rainfall triggered the event and that  $0.37 \\times 10^6 \\,\\mathrm{m}^3$  of water was discharged.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.2 Past GLOF events in Bhutan", "section_headings": ["1 Introduction", "1.2 Past GLOF events in Bhutan"], "chunk_type": "text", "line_start": 20, "line_end": 22, "token_count_estimate": 286, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "789f2d97bf58462b", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.3 Potentially dangerous glacial lakes in Bhutan\nType: text\n\nBased on the latest report from the National Center for Hydrology and Meteorology (NCHM) in Bhutan, 567 glacial lakes in the country span an area of 55.04 km<sup>2</sup>, accounting for 19.03% of total water bodies (NCHM, 2021). In 2001, the Department of Geology and Mines (DGM) in Bhutan and the International Centre for Integrated Mountain Development (ICIMOD) performed the first-ever inventory of glaciers, glacial lakes, and potentially dangerous glacial lakes (PDGLs), and they identified 24 glacial lakes that fit this category (Mool et al., 2001). However, in 2019, using field-verified data and the latest Sentinel-2 satellite images, NCHM reassessed the number of PDGLs and revised the number to 25, with eight lakes now considered to be safe based on lake morphology, surrounding features, bathymetry conditions, and associated feeding glaciers (NCHM, 2019). Figure 1 provides a map of rivers and the river basin system within Bhutan, together with the distribution of glaciers and glacial lakes. The Punatsangchhu river basin contains 11 PDGLs, which is the largest number of PDGLs per basin the in the country. The Phochhu sub-basin contains nine PDGLs, making it a hotspot for GLOFs and glacially related disasters.\n\nWarming climate exacerbates the hazards of GLOFs, so a comprehensive GLOF assessment is urgently needed since these risks will increase in the coming years. As such, a study assessing hazards associated with glacial lakes and GLOFs is crucial for understanding hazards, as well as their subsequent impacts on hydrological and socio-economic aspects within the Punatsangchhu river basin.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.3 Potentially dangerous glacial lakes in Bhutan", "section_headings": ["1 Introduction", "1.3 Potentially dangerous glacial lakes in Bhutan"], "chunk_type": "text", "line_start": 24, "line_end": 28, "token_count_estimate": 440, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "80522bd71be950f7", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.4 Increasing concern regarding a Thorthomi GLOF\nType: text\n\nThorthomi lake is the largest of nine PDGLs within the Phochhu basin. Due to the significant potential risk posed to downstream settlements resulting from a GLOF, the Thorthomi glacial lake has become a serious concern due to the following factors: (1) rapid expansion of the Thorthomi supraglacial lake, (2) the size of glaciers and probable future lake size, (3) the weakened left lateral moraine of the lake due to the 1994 Luggye GLOF, (4) active sliding on the moraine wall separating the Thorthomi and Rapstreng lakes, (5) seepage from the lake, and (6) rock and snow avalanches, as summarised by Karma (2013). To address these factors, the government of Bhutan initiated a highpriority project, referred to as the National Adaptation Plan of Action (NAPA), under the United Nations Framework Convention on Climate Change (UNFCCC) funding scheme in 2006. The project sought to reduce the GLOF risk potential from Thorthomi lake and involved lowering the lake's water level over 4 years, resulting in a reduction of 3.68 m. However, due to challenging working conditions and health issues, the project fell 1.32 m short of its target, although approximately  $17 \\times 10^6$  m<sup>3</sup> of lake water was artificially released. The project additionally included setting up a GLOF early warning system along the Punakha-Wangdue valley for alerting residents in the event of a GLOF.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.4 Increasing concern regarding a Thorthomi GLOF", "section_headings": ["1 Introduction", "1.4 Increasing concern regarding a Thorthomi GLOF"], "chunk_type": "text", "line_start": 30, "line_end": 32, "token_count_estimate": 363, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e02045512ea2a652", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.5 The focus of our study\nType: text\n\nTo contribute to risk management efforts, we evaluated the potential risk of a GLOF from Thorthomi lake. The physically based mathematical dam breach model, BREACH, was used to simulate a glacial lake dam breach and was coupled with the Hydrologic Engineering Centre's River Analysis System (HEC-RAS) to route the flood wave propagating downstream. We sought to simulate both the spatial extent and the lead time of flood wave arrival at several locations along the river. Prior to predicting Thorthomi GLOF hazards and potential risks, we reconstructed the 1994 Luggye GLOF event to validate the dam breach model and the flood wave routing model, which includes river topography and roughness\n\nOur study is one of a few studies that has simulated probable floods from Thorthomi lake and that has estimated inundation extent and flood arrival times within a scientific setting. Such studies form an essential basis for flood risk assessments, early warning system installation, economic planning, countermeasure planning, design, and stakeholder education and awareness programmes.\n\nThe main components of our study are as follows:", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.5 The focus of our study", "section_headings": ["1 Introduction", "1.5 The focus of our study"], "chunk_type": "text", "line_start": 34, "line_end": 40, "token_count_estimate": 274, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba72bef34f6586a4", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.5 The focus of our study\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A map of Bhutan showing its river and basin systems, along with the distribution of potentially dangerous glacial lakes (PDGLs). The map includes a north arrow, latitude and longitude lines from 27°0'0\"N to 28°0'0\"N and 89°0'0\"E to 92°0'0\"E, and a scale bar in kilometers. The legend details various features: Rivers (blue lines), Sub-basin boundaries (dashed lines), the Punatsangchhu basin (shaded gray), the Bhutan boundary (solid outline), Glaciers (light blue areas in the north), Main Roads (brown lines), and Urban Areas (yellow patches). The map also plots PDGLs as circles, categorized into two groups. The first group, 'PDGLs(sq.km)', is shown in purple with sizes corresponding to areas: 0.1 - 0.67, 0.67 - 1.46, and 1.46 - 2.9 sq.km. The second group, 'PDGLs(sq.km)-safe', is shown in green with sizes for areas: 0-0.06, 0.06-0.34, and 0.34-0.58 sq.km. Several lakes in the northern region are labeled, including Rapstreng Lake, Thorthomi Lake, and Luggye Lake.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.5 The focus of our study", "section_headings": ["1 Introduction", "1.5 The focus of our study"], "chunk_type": "figure", "figure_caption": null, "line_start": 41, "line_end": 41, "token_count_estimate": 343, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "72f63541852e852d", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.5 The focus of our study\nType: figure\nFigure: Figure 1. A map showing the rivers and basin system of Bhutan, as well as the distribution of potentially dangerous glacial lakes, the main road network, and major urban areas. Bubbles are scaled to total lake area and colour-coded for classification. PDGLs stand for potentially dangerous glacial lakes. Data source: National Center for Hydrology and Meteorology (NCHM).\n\n**Figure 1.** A map showing the rivers and basin system of Bhutan, as well as the distribution of potentially dangerous glacial lakes, the main road network, and major urban areas. Bubbles are scaled to total lake area and colour-coded for classification. PDGLs stand for potentially dangerous glacial lakes. Data source: National Center for Hydrology and Meteorology (NCHM).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.5 The focus of our study", "section_headings": ["1 Introduction", "1.5 The focus of our study"], "chunk_type": "figure", "figure_caption": "Figure 1. A map showing the rivers and basin system of Bhutan, as well as the distribution of potentially dangerous glacial lakes, the main road network, and major urban areas. Bubbles are scaled to total lake area and colour-coded for classification. PDGLs stand for potentially dangerous glacial lakes. Data source: National Center for Hydrology and Meteorology (NCHM).", "line_start": 43, "line_end": 43, "token_count_estimate": 204, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "446fd8949b2053cf", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.5 The focus of our study\nType: text\n\n- estimating the geometry and water volume of Thorthomi lake using available glacial lake geometry data;\n- estimating the potential outburst flood hydrograph using a dam breach model, BREACH, with available physical parameters; and\n- 3. assessing Thorthomi GLOF hazards and potential risks using a 2D hydraulic model.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.5 The focus of our study", "section_headings": ["1 Introduction", "1.5 The focus of our study"], "chunk_type": "text", "line_start": 44, "line_end": 48, "token_count_estimate": 97, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ad329e6ae5813c0", "text": "Document: 1 Introduction\nSection: 1 Introduction > 1.6 Structure of the paper\nType: text\n\nThe paper contains six sections. Section 1 introduces the overall concept of a GLOF and provides information obtained from previous studies and information related to GLOFs in the context of Bhutan. Section 2 describes the study area and the GLOF event used for model validation and calibration. We explain materials and methods in Sect. 3. Obtained results are reported in Sect. 4. Based on the results, we discuss the consequences of a Thorthomi GLOF in Sect. 5. We conclude our study in Sect. 6.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction > 1.6 Structure of the paper", "section_headings": ["1 Introduction", "1.6 Structure of the paper"], "chunk_type": "text", "line_start": 50, "line_end": 52, "token_count_estimate": 136, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "22625f49caca8b87", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: text\n\nWe assessed flood risk for a catchment of under-construction hydropower plants, specifically the Punatsangchhu Hydroelectric Project Authority (PHPA-I and II), caused by a GLOF from Thorthomi lake. The catchment is the upper part of the Punatsangchhu river basin (PRB), located within the central portion of Bhutan and indicated by the grey area in Fig. 2a. PRB is one of the largest basins in Bhutan, spanning an approximate area of 9760 km², which covers approximately 25 % of the total area of the country (38 394 km²), and is drained by the Phochhu and Mochhu rivers (Fig. 2a) to the Indian plains. The annual averaged discharge of the basin ranges from 194 to 374 m³ s<sup>-1</sup>, with the highest recorded discharge of 2654 m³ s<sup>-1</sup>, observed at the WangdiRapid station (location shown in Fig. 2c), occurring in 2009 during Cyclone Aila.\n\nThe Phochhu river, one of the main tributaries of the Punatsangchhu river, originates from the high mountains of Lunana, in northern Bhutan, and flows some 90 km downstream, where it joins Mochhu at Punakha Dzong (monastery) (Fig. 2c), and flows from this area as the Punatsangchhu river. The Thorthomi glacial lake, considered to be one of the most dynamic and dangerous glacial lakes within Bhutan, with an area of 4.3 km<sup>2</sup> (NCHM, 2019), is", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "text", "line_start": 54, "line_end": 58, "token_count_estimate": 398, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "6c411417175ec73e", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: The image displays three maps labeled (a), (b), and (c), detailing a study area in Bhutan. Map (a) is an inset showing the location of the Punatsangchu Basin (shaded gray) and a smaller study basin (outlined in red) within Bhutan, which borders China and India. Map (b) is a topographic map of the study basin, with an elevation scale ranging from 1153 m to 7207 m and a distance scale up to 40 kilometers. Map (c) is the main, detailed map of the area. It includes a legend defining symbols for River, Glacial lake, Study Basin, Punatsangchu\\_Basin, Settlement Points (circle), Gauging Station (green square), and Power Plant (green triangle). This map shows the Phochhu river and its tributaries, the Mochhu and Dangchhu. Key locations identified include the glacial lakes Thorthomi Lake and Luggye Lake, and settlement points such as Lhedi, Thanza, Dangsa, Samdingkha, Punakha Dzong, Khuruthang, Jagathang, and Bajo. It also marks the WangdiRapid Station (a gauging station), two hydropower plants under construction (PHPA-I & II), and the confluence points of the Phochhu with the Mochhu and Dangchhu rivers. The map indicates the flow direction of the Phochhu river and labels the administrative regions of GASA, PUNAKHA, and WANGDUE.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 59, "line_end": 59, "token_count_estimate": 385, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India"], "lake_ids": []}}
{"id": "6d5e48c440c0c1ad", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: figure\nFigure: Figure 2. Map of the study area. (a) A Bhutan map showing rivers and river basins (the grey area shows the Punatsangchhu river basin). (b) The elevation distribution of the study area (the catchment area of PHPA-I and II excludes the Mochhu river basin). (c) Major settlement points within the study area.\n\n**Figure 2.** Map of the study area. (a) A Bhutan map showing rivers and river basins (the grey area shows the Punatsangchhu river basin). (b) The elevation distribution of the study area (the catchment area of PHPA-I and II excludes the Mochhu river basin). (c) Major settlement points within the study area.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "figure", "figure_caption": "Figure 2. Map of the study area. (a) A Bhutan map showing rivers and river basins (the grey area shows the Punatsangchhu river basin). (b) The elevation distribution of the study area (the catchment area of PHPA-I and II excludes the Mochhu river basin). (c) Major settlement points within the study area.", "line_start": 61, "line_end": 61, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "4ef444175bdfedae", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: text\n\nlocated at the headwater of the Phochhu sub-basin at over 4440 m above sea level. The Thorthomi glacial lake is widely recognised as the likely consequence of climate warming and, since feeding glaciers terminating in the lake rapidly melt, is expanding each year. Based on a comprehensive analysis of cryospheric, geotechnical, and geomorphological factors, Rinzin et al. (2023) concluded that Thorthomi lake is highly susceptible to GLOF events.\n\nThe PRB consists of five administrative districts: Gasa, Punakha, Wangdue Phodrang, Dagana, and Tsirang. These districts constitute 16.6 % (735 533) of the total population of Bhutan (NSB, 2018). The Punakha and Wangdue Phodrang districts (Fig. 2) within the PRB are renowned as Bhutan's primary rice production regions, contributing 16 % and 11 %, respectively, to the nation's total rice output (NSB, 2021). The area is also rich in historical and cultural heritage, with notable landmarks such as Punakha Dzong, which served as the former capital of Bhutan. Fertile flood plains are located along the Phochhu and Punatsangchhu rivers, and the region encompasses settlements such as Samdingkha and Jagathang, together with major towns such as Khuruthang and Bajo. The floodplain of the Punatsangchhu river accommodates these settlements, while downstream, approximately 115 km away from Thorthomi lake, two significant", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "text", "line_start": 62, "line_end": 66, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "df6934e4c246ad7a", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: figure\nFigure\n\nImage /page/3/Figure/6 description: A topographic map showing four glacial lakes in a mountainous region. The lakes are labeled from left to right (west to east): Rapstreng Lake, Thorthomi Lake, Luggye Lake, and DrukChung Lake. A river, identified as 'Lake Outflow', flows along the southern side of the lakes. An arrow indicates the 'Flow Direction' is from east to west (right to left). A legend in the top right corner defines the symbols used: a blue shape for 'Glacial lake', a thin blue line for 'Lake Outflow', and a black dot for 'Lake outlet'. Each lake has a designated outlet marked on the map. In the bottom left, there is a scale bar indicating a length of 4 kilometers. A north arrow is located in the top left corner. The background of the map consists of gray contour lines representing the terrain.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 67, "line_end": 67, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "754308fca8a54308", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: figure\nFigure: Figure 3. Four glacial lakes within the Lunana region (the Lunana complex).\n\n**Figure 3.** Four glacial lakes within the Lunana region (the Lunana complex).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "figure", "figure_caption": "Figure 3. Four glacial lakes within the Lunana region (the Lunana complex).", "line_start": 69, "line_end": 69, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bcaf802fae712c35", "text": "Document: 1 Introduction\nSection: 2 Study area and GLOF events\nType: text\n\nhydropower plants, PHPA-I and II, are currently under construction (see the bottom of Fig. 2c for locations of the two hydropower plants). Given the exposure of critical infrastructure and settlements to potential GLOFs from PDGLs, especially the Thorthomi glacial lake, an assessment of hazards within this area is of paramount importance.\n\nThe Luggye glacial lake is one of the PDGLs in Lunana, Bhutan's northern region. The lake is one of four glacial lakes in an area that spans a few kilometres (Fig. 3) and had an outburst in 1994. Although there is no detailed official documentation on the GLOF at the Luggye glacial lake, reports and articles describing the event do exist (e.g. Koike and Takenaka, 2012; Meyer et al., 2006; Richardson and Reynolds, 2000; Watanabe and Rothacher, 1996). The event was also documented in a technical report (Leber et al., 2000), when the Royal Government of Bhutan launched a major investigative project in 2000 to study the cause of the event.\n\nThe 1994 Luggye GLOF was a cascading phenomenon, where sudden drainage of the upstream Druk Chung glacial lake (Fig. 3) into Luggye lake increased hydrostatic pressure on the moraine dam of Luggye lake, releasing  $18 \\times 10^6$  m<sup>3</sup> of flood water (Leber et al., 2000). The 1994 Luggye GLOF claimed the lives of 21 people and inflicted major damage to infrastructure and downstream settlements; notably, the Punakha Dzong (monastery) suffered significant damage, although it is located 93 km downstream from the lake (Richardson and Reynolds, 2000; Watanabe and Rothacher, 1996). During this period, a peak flow rate of  $2539 \\,\\mathrm{m}^3 \\,\\mathrm{s}^{-1}$ was observed at the WangdiRapid gauging station, located 15 km downstream of Punakha Dzong (Fig. 2) and approximately 108 km downstream of the flood source (data from NCHM). Contributions from the Mochhu (96  $\\mathrm{m}^3$  s<sup>-1</sup> on 7 October 1994), Dangchhu basins (no gauging station in the basin), and other small tributaries to the peak flow rate should have been minor due to limited base flow during the season.\n\nTo estimate the breach outflow hydrograph, several studies have attempted to reconstruct the 1994 Luggye GLOF event (e.g. JICA, 2001; Koike and Takenaka, 2012; Meyer et al., 2006). Koike and Takenaka (2012) estimated that peak discharge from the Luggye lake breach ranged from 1800 to\n\n $2500 \\,\\mathrm{m}^3 \\,\\mathrm{s}^{-1}$ , depending on inflow conditions measured by Yamada et al. (2004).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area and GLOF events", "section_headings": ["2 Study area and GLOF events"], "chunk_type": "text", "line_start": 70, "line_end": 80, "token_count_estimate": 724, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "6d8eaef7a7192857", "text": "Document: 1 Introduction\nSection: 3 Materials and methods\nType: text\n\nA schematic diagram showing input data, used models/methods, and outputs is provided in Fig. 4. We reconstructed the 1994 GLOF event to verify (1) a glacial lake geometry estimation model, (2) a dam breach model, (3) a digital elevation model and its error correction method, and (4) Manning's coefficient. Then, the same models were used to predict the potential risk caused by a Thorthomi GLOF.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods", "section_headings": ["3 Materials and methods"], "chunk_type": "text", "line_start": 82, "line_end": 84, "token_count_estimate": 114, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d7b3fd89ba4d8f8", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.1 Uncertainty in GLOF bathymetry\nType: text\n\nThe volume and geometry of a glacial lake are regulating factors of the glacial lake outburst process. Glacial lakes are generally formed from a depression left behind by retreating glaciers, which, in most cases, are produced when a moraine is filled with meltwater. Depending on geomorphology, the presence of sediment, and glacial over-deepening capacity, formed glacial lakes can manifest specific lake bathymetry and influence glacial hydrology (Cook and Swift, 2012). Due to remote locations and high elevations, accessing and conducting field surveys to map glacial lake bathymetry is challenging.\n\nDespite challenges, measurements of lake bathymetry are crucial for determining a lake's volume and surface area and are necessary for assessing potential flood volumes and the risk of GLOFs. In 2019, the National Centre for Hydrology and Meteorology (NCHM) conducted bathymetric surveys in 14 of the 25 identified potentially dangerous glacial lakes and mapped their maximum depth and volume. However, even though the Thorthomi glacial lake is considered to be a critical lake that could burst in the future, due to the difficulty associated with conducting a survey, the bathymetry of the Thorthomi glacial lake remains unknown. Since lake geometry is a crucial parameter for dam breach modelling and subsequent hydraulic routing, lake depth and volume needed to be estimated.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.1 Uncertainty in GLOF bathymetry", "section_headings": ["3 Materials and methods", "3.1 Uncertainty in GLOF bathymetry"], "chunk_type": "text", "line_start": 86, "line_end": 90, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "01b3ccb9314867bb", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.2 Estimating geometries of glacial lakes\nType: text\n\nEstimating the potential flood volume of a glacial lake is critical for determining the magnitude of a GLOF. Bathymetry data are necessary for calculating lake volume as well as potential flood volume, but bathymetry for glacial lakes is scarce due to the challenging and inaccessible environments in which glacial lakes are often located, including for Thorthomi lake. Although several reports have estimated Thorthomi lake volume (Karma, 2013; Singh, 2009), no details on how volumes were estimated have been documented.\n\nTo address data scarcity for glacial lake geometries, various studies have proposed methods for estimating glacial lake depth and volume based on other more accessible pa-\n\nrameters such as lake area (Cook and Quincey, 2015; Huggel et al., 2002; O'Connor et al., 2001; Sakai, 2012), as well as depression angle from the lakeshore (Fujita et al., 2013) and surrounding topography (Heathcote et al., 2015). Empirical relationships such as area–volume and area–depth are useful for estimating a lake's depth and potential flood volume. Cook and Quincey (2015) refined the area–volume relationship proposed by Huggel et al. (2002) by increasing sample size and removing duplicated samples. They also classified the predictability of lake volume and depth based on regions and lake types and determined that predictability is influenced by a lake's origin and evolution. The relationship proposed by Cook and Quincey (2015) takes the following form:\n\n$$D_{\\text{mean}} = 0.1697 A^{0.3778},\\tag{1}$$\n\nwhere  $D_{\\text{mean}}$  is the mean depth (in m) and A is the area (in m<sup>2</sup>). The volume–area relationship (V, volume in m<sup>3</sup>) can simply be derived by multiplying the area of both sides, as follows:\n\n$$V = 0.1697A^{1.3778}. (2)$$\n\nSakai (2012) used a similar approach and proposed a model for estimating maximum depth instead of mean depth. The bathymetric measurement data of 17 glacial lakes (15 moraine-dammed glacial lakes and 2 thermokarst lakes) from Bhutan, Nepal, and Tibet were used to derive an area—maximum depth—volume relationship, so estimations of depth and volume from the area of glacial lakes could be determined (Sakai, 2012). The regression equation took the following form:\n\n$$D_{\\text{max}} = 95.665 A^{0.489},\\tag{3}$$\n\nwhere  $D_{\\text{max}}$  is maximum depth (in m) and A is area (in km<sup>2</sup>). The volume–area relationship (V, volume in  $10^6 \\text{ m}^3$ ) takes the following form:\n\n$$V = 43.24A^{1.5307}. (4)$$\n\nFor predicting the moraine dam breach process explained in the following section, lake geometries, especially the maximum depth, are crucial, so we employed the equations proposed by Sakai (2012). The equations allow the independent calculation of maximum depth and volume. As conceptualised by Cook and Quincey (2015), bathymetry of the lake was estimated based on idealised geometric shape. The lake bottom was also assumed to follow an elliptical shape, as commonly observed in most moraine-dammed glacial lakes in Bhutan.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.2 Estimating geometries of glacial lakes", "section_headings": ["3 Materials and methods", "3.2 Estimating geometries of glacial lakes"], "chunk_type": "text", "line_start": 92, "line_end": 114, "token_count_estimate": 851, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": ["1697A"]}}
{"id": "34d382438df23339", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies\nType: text\n\nGLOFs are triggered by a breach of a moraine dam that holds the lake in place and are caused by an external triggering", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.1 Previous studies"], "chunk_type": "text", "line_start": 118, "line_end": 120, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09e130f9c2431158", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A flowchart illustrating a methodology for creating an inundation map and hazard zonation. The chart uses a legend to define its components: gray rounded rectangles for 'Input Data', blue parallelograms for 'Model', and green rounded rectangles for 'Result'. The process starts with input data including 'Digital Elevation Model', 'Lake bathymetry data', and 'Lake moraine data'. These inputs, along with processed data like 'Field bathymetry data for GLOF reconstruction' and 'Regression analysis data for GLOF prediction', are fed into a 'Dam Breach Model'. The output of this model is a 'Breach flow hydrograph'. This result, along with the 'Digital Elevation Model' and other inputs like 'Land Use Land Cover Data' and 'Satellite imagery', goes into a 'Hydrodynamic Model'. The 'Hydrodynamic Model' produces 'Simulated flow Hydraulics'. This result, combined with 'Land Use Land Cover Data', 'Satellite imagery', and 'Settlement data', is processed by a 'GIS' model. The final output of the entire process is an 'Inundation Map Hazard Zonation'.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.1 Previous studies"], "chunk_type": "figure", "figure_caption": null, "line_start": 121, "line_end": 121, "token_count_estimate": 334, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62fd692926aa4e52", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies\nType: figure\nFigure: Figure 4. A schematic diagram of the methodology employed in our study.\n\nFigure 4. A schematic diagram of the methodology employed in our study.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.1 Previous studies"], "chunk_type": "figure", "figure_caption": "Figure 4. A schematic diagram of the methodology employed in our study.", "line_start": 123, "line_end": 123, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b54c21cfb7cc4da4", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies\nType: text\n\nevent. While the structure of the dam itself is an important factor, destabilisation of a dam due to a trigger event is the primary cause of a breach. Since the overtopping of lake water is the major failure mode (Awal et al., 2010; Begam et al., 2018; Neupane et al., 2019), our study assumed that GLOFs are triggered by the overtopping of lake water.\n\nTo estimate flood flow and associated hazards resulting from a dam breach, several studies have simulated dam breach floods using dam breach models (Bajracharya et al., 2007; Hagg et al., 2021; Huggel et al., 2002; Koike and Takenaka, 2012; Maskey et al., 2020; Meyer et al., 2006; Shahrim and Ros, 2020; Wang et al., 2008; Worni et al., 2014). BREACH is a numerical model describing the dam breach process and the resulting outflow hydrograph. The model is based on fundamental principles of hydraulics, sediment transport, soil mechanics, and the physical properties of dam materials and the reservoir. The model is physically based and was designed to predict the size, shape, and time of dam breach development, as well as the resulting flow rate and the volume of water released. Unlike parametric models, physically based breach models, including BREACH, consider the geotechnical aspects of dam materials, as well as hydraulic and sediment transport (Fread, 1988; Maskey et al., 2020; Worni et al., 2014), which increases the predictive accuracy of future GLOF processes. Due to this, the BREACH model has been widely used in studies of dam breach flood hazards and risk assessments (Fread, 1988).\n\nKoike and Takenaka (2012) used the BREACH model coupled with the flood flow model, FLO-2D, to perform a sce-\n\nnario analysis on the risks of a GLOF on the Mangdechhu river basin, due to an outburst flood of the Metatshota glacial lake in Bhutan. The study concluded that although the breaching potential of the lake is low due to the wide crest and gentle slope of the moraine dam, a GLOF would affect several houses and farmland located on the flood plain (Koike and Takenaka, 2012). Hagg et al. (2021) performed a GLOF hazard assessment within the Mochhu basin in Bhutan using the HEC-RAS dam break module, simulating a dam breach of the Shintaphu glacial lake, and concluded that risk is comparably small.\n\nOur study used BREACH for describing the dam breach process for target lakes due to its better predictive accuracy for a future extraordinary Thorthomi GLOF event. The dam is assumed to breach due to overtopping flow resulting from a trigger event, such as an ice calving/avalanche or a rock avalanche. Most of the geotechnical properties of dam materials required as an input parameter are available in the report published by the National Center for Hydrology and Meteorology (NCHM, 2020). A few properties were published by Koike and Takenaka (2012). Some unavailable data were estimated by referring to previous studies.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.1 Previous studies"], "chunk_type": "text", "line_start": 124, "line_end": 134, "token_count_estimate": 767, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "48344022b32c4bf6", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach\nType: text\n\nFor reconstruction of the 1994 Luggye GLOF, the dam breach outflow hydrograph was estimated using BREACH (Fread, 1988). The bathymetry of Luggye lake (Fig. 5) and", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach"], "chunk_type": "text", "line_start": 136, "line_end": 138, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e820cce08dcd9455", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A bathymetric map showing the depth elevation of a lake. The lake is elongated and slightly curved. A north arrow is in the top left corner. The legend in the bottom left indicates the lake boundary with a thin black line and the lake outlet with a red-brown circle, which is located at the westernmost tip of the lake. A scale bar at the bottom shows a total length of 1.3 kilometers, with marks at 0, 0.325, and 0.65 kilometers. On the right, a vertical color scale labeled \"Bathymetry Depth elevation (m)\" ranges from 4353.5 (dark purple, deeper) to 4465.8 (dark green, shallower). The map shows the lake is shallowest around the edges and has its deepest point in the central-eastern area. The easternmost part of the lake is a white area labeled \"No Data\".", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach"], "chunk_type": "figure", "figure_caption": null, "line_start": 139, "line_end": 139, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f7171282c2528a5a", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach\nType: figure\nFigure: Figure 5. The bathymetry of Luggye lake (data from NCHM).\n\nFigure 5. The bathymetry of Luggye lake (data from NCHM).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach"], "chunk_type": "figure", "figure_caption": "Figure 5. The bathymetry of Luggye lake (data from NCHM).", "line_start": 141, "line_end": 141, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30ba1a325a28e745", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach\nType: text\n\nthe material properties of the moraine dam (the middle row of Table 1) required for the model were based on various reports (NCHM, 2019, 2020). Topographic data of the moraine dam were derived from the digital surface model (DSM). Since wave overtopping is a more common failure mode for moraine-dammed glacial lakes as compared to a piping failure (Neupane et al., 2019), to estimate breach outflow from Luggye lake, overtopping failure of the moraine dam was assumed. The properties of moraine dam material have a significant effect on the growth of a breach (Maskey et al., 2020; Westoby et al., 2014). The mechanism in which the formation of a breach largely occurs determines the shape of the breach outflow hydrograph (Westoby et al., 2014). Therefore, gathering accurate in situ data for reliable breach process reproduction is essential. Based on the estimation by Fujita et al. (2008), deduced from a combination of field measurements and remote-sensing observations, the level of lake water was reduced 19 m during the event.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach"], "chunk_type": "text", "line_start": 142, "line_end": 144, "token_count_estimate": 297, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e56059e18fd988a", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.3 The Thorthomi GLOF prediction\nType: text\n\nBreach initiation is assumed to occur due to an overtopping wave at the existing outlet (Fig. 3) induced by any probable triggering event. Moraine material properties and topographic data for the Thorthomi lake BREACH model were either estimated from available terrain data or adopted from available reports and research documents, and this is also listed in the right row of Table 1.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.3 The Thorthomi GLOF prediction", "section_headings": ["3 Materials and methods", "3.3 The moraine dam breach and its modelling", "3.3.3 The Thorthomi GLOF prediction"], "chunk_type": "text", "line_start": 146, "line_end": 148, "token_count_estimate": 122, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4475babcd2a0a450", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model\nType: text\n\nA hydrodynamic model is essential for understanding the characteristics of a flood wave caused by a GLOF propagating downstream, as well as for quantitatively evaluating the potential risks caused by a flood. To simulate the propagation of outflow from glacial lake breaches in Nepal, numerous studies, such as HEC-RAS used in Maskey et al. (2020), have employed various hydrodynamic models. Similar approaches that couple dam breach models to hydrodynamic models (e.g. Bajracharya et al., 2007; Koike and Takenaka, 2012; Westoby et al., 2015; Worni et al., 2014) have been\n\nperformed for modelling the GLOF process chain in various regions.\n\nWorni et al. (2014) provided a summary of various hydrodynamic models that have been used to model GLOFs. Discussed models include HEC-RAS, FLO-2D, BASEMENT, and Delft3D. The choice of a hydrodynamic model depends on factors such as the end objective, data availability, and the available budget. Each model has its own level of accuracy; however, the accuracy of results is primarily dependent on the precision of the elevation model, including channel geometry and floodplain topography. Errors in the elevation model can lead to inaccuracies in results (Casas et al., 2006; Xu et al., 2021).\n\nHEC-RAS is a commonly used hydrodynamic model that allows users to perform 1D and 2D steady/unsteady flow simulations (Brunner, 2016b). We used the HEC-RAS to perform a 2D unsteady flow simulation for floods caused by a glacial lake dam breach. Since they represent spatially varied flood hydraulics (Horritt and Bates, 2001), the twodimensional models employed are standard in flood modelling. In a 2D unsteady simulation, flow varies in time, along two spatial dimensions, and processes are predicted by the laws of conservation of mass (continuity) and the conservation of momentum for two horizontal directions. We used a full set of momentum equations (the shallow water equations) to simulate flooding as clear water flow. Although high-viscosity and hyper-concentrated (sediment entrained) flows are inherent to the GLOF phenomenon (Clague and Evans, 2000; Vuichard and Zimmermann, 1987), to simplify modelling complexity and data requirements, most studies (Hagg et al., 2021; Koike and Takenaka, 2012; Maskey et al., 2020; Rinzin et al., 2023) have simulated GLOFs as clear water flow.\n\nOther important considerations in hydrodynamic modelling are Manning's roughness coefficient and channel geometry. Both have significant impacts in predicting inundation extent and flow characteristics (Mosquera-Machado and Ahmad, 2007; Ye et al., 2018; Zhu et al., 2019). Hagg et al. (2021) demonstrated the influence of Manning's roughness coefficient for glacial lake outburst floods from the Shintaphu glacial lake in Mochhu basin, Bhutan, and concluded that channel roughness is not essential for inundation extent but exerts a significant effect on flood velocity and flood arrival time.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.1 The hydrodynamic model"], "chunk_type": "text", "line_start": 152, "line_end": 164, "token_count_estimate": 783, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "67c8bfd7ff519c13", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model\nType: text\n\nal . , 2023 ) have simulated GLOFs as clear water flow . Other important considerations in hydrodynamic modelling are Manning ' s roughness coefficient and channel geometry . Both have significant impacts in predicting inundation extent and flow characteristics ( Mosquera - Machado and Ahmad , 2007 ; Ye et al . , 2018 ; Zhu et al . , 2019 ) . Hagg et al . ( 2021 ) demonstrated the influence of Manning ' s roughness coefficient for glacial lake outburst floods from the Shintaphu glacial lake in Mochhu basin , Bhutan , and concluded that channel roughness is not essential for inundation extent but exerts a significant effect on flood velocity and flood arrival time .\n\nSince such information is needed to estimate the area needed for evacuation and the lead time for evacuation, flood travel time and peak flow are essential parameters for early warning purposes. In this study, flood travel time was calculated based on timing of the breach outflow hydrograph and the flow hydrograph at the point of interest, when there was significant inundation depth and extent. Peak flow is the maximum simulated flow resulting from a dam breach.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.1 The hydrodynamic model"], "chunk_type": "text", "line_start": 152, "line_end": 164, "token_count_estimate": 311, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "cb9860ab1d12e044", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model\nType: table\nTable\n\n| Moraine dam data                                 | 1994 Luggye GLOF | Thorthomi GLOF |\n|--------------------------------------------------|------------------|----------------|\n| Surface area of the lake (km2) (RSA)             | 1.46b            | 4.3b           |\n| Volume of water in the lake (106 m3), Eq. (4)    | 65.19b           | 400a           |\n| Maximum depth of the lake (m), Eq. (3)           | 96.93b           | 161a           |\n| Top elevation of the dam (m) (HU)                | 4465a            | 4446a          |\n| Toe elevation of the dam (m) (HL)                | 4370a            | 4345a          |\n| Slope of the upstream face of the dam (1 : ZU)   | 1 : 4.8a         | 1 : 6.2a       |\n| Slope of the downstream face of the dam (1 : ZU) | 1 : 6.5a         | 1 : 6.3a       |\n| Dam material properties                          |                  |                |\n| Grain size ( <i>D</i> 50) (mm)                   | 1.362b           | 2.01b          |\n| Porosity (%)                                     | 36.5c            | 36.5c          |\n| Cohesive strength (kN m-2)                       | 1.5b             | 1.5b           |\n| Internal friction (°)                            | 41b              | 39b            |\n| Unit weight (kN m-3)                             | 22.92b           | 22.43b         |\n| Manning's coefficient (s m-1/3)                  | 0.07b            | 0.07b          |", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.1 The hydrodynamic model"], "chunk_type": "table", "table_caption": null, "columns": ["Moraine dam data", "1994 Luggye GLOF", "Thorthomi GLOF"], "table_row_start": 1, "table_row_end": 14, "line_start": 165, "line_end": 180, "token_count_estimate": 413, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bfa13e7c6baf1a31", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model\nType: text\n\nTable 1. Input parameters for the 1994 Luggye GLOF reconstruction and the Thorthomi GLOF prediction.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.1 The hydrodynamic model"], "chunk_type": "text", "line_start": 181, "line_end": 183, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7a9aad1c2797ef15", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning\nType: text\n\nThe accuracy of hydrodynamic model results is heavily influenced by the quality of the elevation model used (Gyasi-Agyei et al., 1995; Yamazaki et al., 2014, 2017). Casas et al. (2006) demonstrated the effects of a topographic data source and resolution on flood peak discharge and the extent of inundation and then concluded that laser-based elevation data are a suitable source for hydraulic modelling. Similarly, the influence of grid size on inundation propagation and water depth under varied topographical settings in 2D modelling has been analysed (Tsubaki and Kawahara, 2013). Both fine grid size representing main topographic features of the floodplain and accurate elevations at each grid point are essential for simulating flood flow with less uncertainty. Therefore, using the best available elevation model for the hydrodynamic simulation of floods is essential.\n\nSince an accurate elevation model is essential for accurate hydrodynamic simulations, various methods for correcting generic noise errors and biases originating from topography measurements have been proposed and have been used in elevation models prior to running hydrodynamic/hydrological analyses.\n\nBelow, we compare three different elevation models covering the study area. The first model, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro DEM (Fig. 6a), was developed based on SRTM and AW3D DEM. Water layer data at a 3 arcsec resolution ( $\\sim 90\\,\\mathrm{m}$ ) were developed for river hydrology analyses at global, as well as at local, scales (Yamazaki et al., 2017, 2019). Other bias-corrected elevation data are the Forest And Buildings removed Copernicus DEM (FABDEM) (Fig. 6b), developed from Copernicus DEM (COPDEM), where the height of trees and buildings\n\nare removed using machine learning, and are also a preferable source for terrain data (Hawker et al., 2022). The AW3D digital surface model (DSM) (Fig. 6c) was jointly developed by the Remote Sensing Technology Centre (RESTEC) and the NTT DATA Corporation, utilising PRISM data acquired by the Advanced Land Observing Satellite (ALOS) of the Japan Aerospace Exploration Agency (JAXA). Roughresolution DSMs are distributed by several organisations free of charge; however, for our study, we used the finer commercial DSM product, AW3D 2.5 m, as the primary source of topography. The cell size of the DSM for the focus area was approximately  $2.2 \\,\\mathrm{m}$  for both the X and Y directions and was projected to WGS84 UTM zone 45N. The AW3D 2.5 m DSM represents details of topography, especially in the river valley and in the developed flood plain, much better than other models.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.2 Ground elevation models and pre-conditioning"], "chunk_type": "text", "line_start": 185, "line_end": 199, "token_count_estimate": 699, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1242641b23f1e6c0", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning\nType: text\n\nALOS ) of the Japan Aerospace Exploration Agency ( JAXA ) . Roughresolution DSMs are distributed by several organisations free of charge ; however , for our study , we used the finer commercial DSM product , AW3D 2 . 5 m , as the primary source of topography . The cell size of the DSM for the focus area was approximately $ 2 . 2 \\ , \\ mathrm { m } $ for both the X and Y directions and was projected to WGS84 UTM zone 45N . The AW3D 2 . 5 m DSM represents details of topography , especially in the river valley and in the developed flood plain , much better than other models .\n\nSince the AW3D DSM was obtained using satellite photogrammetry, representations of the river bottom, especially in forested and deep gorge areas, are sometimes inaccurate. If we directly used topography in hydrodynamic modelling, the DSM covered structures along the river, as well as bridges crossing the river, disturbing flood water flow. To avoid such anomalies in the elevation model and in the hydrodynamic simulation results, a river channel delineation was performed. The presence of spikes within the DSM, along the river's path, can obstruct flood water flow, resulting in the formation of non-existent deep pools. One way to improve topography surrounding a river is the use of bathymetric survey data. However, no such survey has been conducted within the study area. To improve representation for the river channel, our study utilised a rule-based correction method.\n\nThe Agriculture Conservation Planning Framework (ACPF) is a GIS-based tool developed by the United States\n\n<sup>&</sup>lt;sup>a</sup> Estimated in this study. <sup>b</sup> NCHM (2020). <sup>c</sup> Koike and Takenaka (2012).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.2 Ground elevation models and pre-conditioning"], "chunk_type": "text", "line_start": 185, "line_end": 199, "token_count_estimate": 482, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "002950bb44fca05f", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A figure with three panels labeled (a), (b), and (c), showing topographic maps of the same area at different resolutions. The area shown is around Punakha Dzong. Panel (a) is a low-resolution, pixelated map with a scale bar from 0 to 1.2 kilometers. Panel (b) shows the same area at a higher resolution, with less prominent pixelation. Panel (c) is a high-resolution, smooth map that includes a legend for Punakha Dzong and a north arrow in the upper right corner. All three maps use a color gradient from brown for higher elevations to blue-green for lower elevations and show the location of Punakha Dzong with a black outline.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.2 Ground elevation models and pre-conditioning"], "chunk_type": "figure", "figure_caption": null, "line_start": 200, "line_end": 200, "token_count_estimate": 204, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "03e2c80f7c757c3b", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning\nType: figure\nFigure: Figure 6. The Punakha Dzong region as represented by three different terrain models: (a) the MERIT HydroDEM 90 m, (b) the FABDEM-30 m, and (c) the AW3D 2.5 m DSM.\n\nFigure 6. The Punakha Dzong region as represented by three different terrain models: (a) the MERIT HydroDEM 90 m, (b) the FABDEM-30 m, and (c) the AW3D 2.5 m DSM.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.2 Ground elevation models and pre-conditioning"], "chunk_type": "figure", "figure_caption": "Figure 6. The Punakha Dzong region as represented by three different terrain models: (a) the MERIT HydroDEM 90 m, (b) the FABDEM-30 m, and (c) the AW3D 2.5 m DSM.", "line_start": 202, "line_end": 202, "token_count_estimate": 140, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a45fa20e5edd6771", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning\nType: text\n\nDepartment of Agriculture (USDA) to identify areas with impeded water flow and to improve hydrologic flow using flow direction and an accumulation analysis (Porter et al., 2016). While the ACPF is a valuable tool for hydrologic flow and watershed planning, it has limited applicability for terrain correction in hydrodynamic modelling because the ACPF does not allow users to define the bathymetry of a river channel.\n\nAnother widely used channel modification method is the inbuilt function of HEC-RAS. The Channel Design/Modification editor tool is a module used to modify an unrealistic cross-section or to introduce a user-defined channel crosssection (Brunner, 2016a). The tool effectively removes spikes in the elevation model along a river channel while maintaining the natural slope of the represented topography. The modified channel TIN (triangulated irregular network) can be overlain on the original DSM and exported as a single raster file with modified features. Rinzin et al. (2023) applied the method to modify terrain and to delineate river flow paths for a GLOF simulation. For our study, we used this tool to condition the DSM in the middle region of the model domain, a forested deep gorge, where huge spikes were included within the elevation model. The modification was only applied to the channel section, and the remaining portion was left as it was.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.2 Ground elevation models and pre-conditioning"], "chunk_type": "text", "line_start": 203, "line_end": 207, "token_count_estimate": 344, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58cca80baec8942d", "text": "Document: 1 Introduction\nSection: 3 Materials and methods > 3.4 Flood routing > 3.4.3 Implementation for GLOF reconstruction and prediction\nType: text\n\nDownstream propagation of the flood was simulated using the HEC-RAS model for the 1994 Luggye GLOF reconstruction. The calculation domain was defined by the 2D flow area. The overall size of the flow area was  $64 \\,\\mathrm{km^2}$ . The domain was modelled using a 20 m resolution computational grid, consisting of 157 188 computational cells, and solved with a time step of 1 s. The elevation of each grid cell was specified based on a 2.2 m, hydro-conditioned digital surface model, and Manning's n was set to 0.035, in the range provided by the HEC-RAS manual (Brunner, 2016a). The dam breach outflow hydrograph obtained from the BREACH\n\nmodel was used as the upstream boundary; and normal depth, calculated based on downstream slopes derived from the DSM, was used as the downstream boundary condition.\n\nFor the Thorthomi GLOF prediction, we used a hydrodynamic model similar to the model used for the 1994 Luggye GLOF reconstruction. The domain was a bit shortened because the Thorthomi glacial lake is located 2 km downstream of the Luggye glacial lake. The overall size of the flow area was 62 km<sup>2</sup>. The domain was modelled using a 20 m resolution computational grid consisting of 153 790 computational cells with a temporal resolution of 1 s.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Materials and methods > 3.4 Flood routing > 3.4.3 Implementation for GLOF reconstruction and prediction", "section_headings": ["3 Materials and methods", "3.4 Flood routing", "3.4.3 Implementation for GLOF reconstruction and prediction"], "chunk_type": "text", "line_start": 209, "line_end": 215, "token_count_estimate": 364, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb2a028dc15fd27b", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.1 Dam breach processes\nType: text\n\nSimulated peak flow  $(Q_p)$  of the dam breach outflow hydrograph was  $6030\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ , and  $1.9\\,\\mathrm{h}$  ( $\\sim 114\\,\\mathrm{min}$ ) was required to reach peak flow (the time to peak,  $T_p$ ) (Fig. 7a). The volume of the GLOF and the reduction of lake water level due to the event were  $21\\times10^6\\,\\mathrm{m}^3$  and  $19\\,\\mathrm{m}$ , respectively, in agreement with the findings of Fujita et al. (2008), which was based on a combination of in situ observations and remotesensing data. Dimensions for the estimated breach of the dam are provided in Fig. 7b.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.1 Dam breach processes", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.1 Dam breach processes"], "chunk_type": "text", "line_start": 221, "line_end": 223, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce69fbaa1ef6397a", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time\nType: text\n\nFlow hydrographs at various locations along the flow path are provided in Fig. 8. Conforming to the findings of Meyer et al. (2006), after approximately 6 h, the GLOF had a peak discharge of 2897 m<sup>3</sup> s<sup>-1</sup> as it reached Punakha Dzong, located 93 km downstream of the lake. Peak flow at the WangdiRapid station (shown in Fig. 2c), 15 km downstream of Punakha Dzong, was 2455 m<sup>3</sup> s<sup>-1</sup>, close to the recorded value of 2539 m<sup>3</sup> s<sup>-1</sup>. Here, the recorded flow rate included the contribution of normal flow from tributaries, which was not ac-", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.2 Downstream peak flow and flood travel time"], "chunk_type": "text", "line_start": 225, "line_end": 227, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0090ff7e411fc22e", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: The image contains two diagrams labeled (a) and (b). Diagram (a) is a line graph titled \"Flow vs. Time\". The x-axis is labeled \"Time (h)\" and ranges from 0 to 24. The y-axis is labeled \"Flow (m³ s⁻¹)\" and ranges from 0 to 6000. The graph shows a sharp peak in flow, reaching approximately 6000 m³ s⁻¹ at around 2 hours, and then gradually decreasing over the 24-hour period. Diagram (b) is a 3D representation of a trapezoidal channel cross-section. The top width of the channel is labeled as 102 m. There are two vertical height measurements indicated: one is 19 m from the bottom of the channel to an intermediate level, and the other is the total height of 22 m from the bottom to the top.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.2 Downstream peak flow and flood travel time"], "chunk_type": "figure", "figure_caption": null, "line_start": 228, "line_end": 228, "token_count_estimate": 250, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4438db745a96adf3", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time\nType: figure\nFigure: Figure 7. (a) A breach outflow hydrograph from the BREACH model and (b) an illustration of breach parameters, breach width $(\\leftrightarrow)$ , breach depth $(\\updownarrow)$ , and the change in water surface elevation $(\\updownarrow)$ .\n\nFigure 7. (a) A breach outflow hydrograph from the BREACH model and (b) an illustration of breach parameters, breach width  $(\\leftrightarrow)$ , breach depth  $(\\updownarrow)$ , and the change in water surface elevation  $(\\updownarrow)$ .", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.2 Downstream peak flow and flood travel time"], "chunk_type": "figure", "figure_caption": "Figure 7. (a) A breach outflow hydrograph from the BREACH model and (b) an illustration of breach parameters, breach width $(\\leftrightarrow)$ , breach depth $(\\updownarrow)$ , and the change in water surface elevation $(\\updownarrow)$ .", "line_start": 230, "line_end": 230, "token_count_estimate": 194, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "998297eae81518ba", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time\nType: figure\nFigure\n\nImage /page/9/Figure/4 description: A line graph showing Flow (m³ s⁻¹) versus Time (h). The y-axis, labeled \"Flow (m³ s⁻¹)\", ranges from 0 to 4000. The x-axis, labeled \"Time (h)\", ranges from 0 to 24. There are two curves on the graph. The black curve, labeled \"Punakha Dzong\", starts rising sharply around 5.5 hours, peaks at approximately 2900 m³/s at 6 hours, and then gradually decreases. The blue curve, labeled \"WangdiRapid Station\", starts rising around 7 hours, peaks at about 2500 m³/s around 8 hours, and then gradually decreases. A red horizontal arrow labeled \"~ 6 h\" and identified in the legend as \"Flood Travel Time\" extends from time 0 to the peak of the Punakha Dzong curve at 6 hours.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.2 Downstream peak flow and flood travel time"], "chunk_type": "figure", "figure_caption": null, "line_start": 232, "line_end": 232, "token_count_estimate": 250, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1de5a3b9b9cebcf1", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time\nType: figure\nFigure: Figure 8. A simulated GLOF hydrograph at different locations along the flow path.\n\n**Figure 8.** A simulated GLOF hydrograph at different locations along the flow path.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.2 Downstream peak flow and flood travel time"], "chunk_type": "figure", "figure_caption": "Figure 8. A simulated GLOF hydrograph at different locations along the flow path.", "line_start": 234, "line_end": 234, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d106ccd9f6c98bfd", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time\nType: text\n\ncounted for in our analysis. Good agreement of results for simulated flow and flood travel time with observed data, as well as previous studies, indicated that the performance of the employed models and the modelling approach were adequate and capable of yielding satisfactory results for predictive modelling of the target lake. The total inundated area along the basin was approximately 13.1 km<sup>2</sup>.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.1 The 1994 Luggye GLOF reconstruction", "4.1.2 Downstream peak flow and flood travel time"], "chunk_type": "text", "line_start": 235, "line_end": 237, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2843340baeda6d55", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry\nType: text\n\nThe estimated volume and maximum depth of Thorthomi lake based on Eqs. (3) and (4) were  $400 \\times 10^6$  m<sup>3</sup> and 161 m, respectively (the top right of Table 1). The estimated volume and maximum depth of Thorthomi lake falls within the predicted band, considering a 95% confidence level. The utilised equations showed a good relationship between area and volume and between and area and maximum depth, with the prediction of  $400 \\times 10^6$  m<sup>3</sup> for the mean and  $281 \\times 10^6$  m<sup>3</sup> and  $560 \\times 10^6$  m<sup>3</sup> for the lower bound and the upper bound, respectively. The prediction range for maximum depth was 161 m for the mean prediction (130 m and 270 m for the lower bound and the upper bound, respectively). Compared to other glacial lakes in Bhutan, the estimated parameters indicate that the Thorthomi glacial lake is one of the largest and deepest lakes. The bathymetry of Thorthomi lake, estimated based on the above parameters, is provided in Fig. 9.\n\nA recent study from Nepal proposed a glacial lake volume estimation equation by considering the width and length ratio", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.1 Lake bathymetry"], "chunk_type": "text", "line_start": 241, "line_end": 245, "token_count_estimate": 342, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "dc682d86afd0cb06", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry\nType: figure\nFigure\n\nImage /page/9/Figure/11 description: A bathymetric map of the Thorthomi Lake Area. The map includes a legend, a north arrow, a scale bar, and a color scale for elevation. The legend indicates symbols for Outlets (red circle), Outflow (wavy blue line), Thorthomi Lake Area (solid outline), PDGL area (dashed outline), and Glaciers (grey shaded area). The map shows the lake with a single outlet and outflow on its western side. Glaciers are located at the northern end of the lake, and a PDGL area is situated along the eastern side. The bathymetry, representing depth and elevation in meters, is shown with a color gradient from green (4446 m) to brown (4285 m), indicating the lake is deepest in its central and southern parts. A scale bar at the bottom is marked in increments up to 1.8 kilometers.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.1 Lake bathymetry"], "chunk_type": "figure", "figure_caption": null, "line_start": 246, "line_end": 246, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36fe90d3073253cf", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry\nType: figure\nFigure: Figure 9. The estimated bathymetry of Thorthomi lake.\n\n**Figure 9.** The estimated bathymetry of Thorthomi lake.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.1 Lake bathymetry"], "chunk_type": "figure", "figure_caption": "Figure 9. The estimated bathymetry of Thorthomi lake.", "line_start": 248, "line_end": 248, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd4ef544605f4900", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry\nType: text\n\nof the lake (Qi et al., 2022). Based on the equation provided in Qi et al. (2022), the water volume of Thorthomi lake can be estimated as  $227 \\times 10^6 \\, \\text{m}^3$ . This volume is substantially small compared to the volume estimated by Eq. (4). The discrepancy may be related to the dataset used for each study, namely Eq. (4) is based on lakes in Bhutan, Nepal, and Tibet; and Qi et al. (2022) is based on lakes in the Peruvian Andes and other areas, including non-moraine-dammed lakes.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.1 Lake bathymetry"], "chunk_type": "text", "line_start": 249, "line_end": 251, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "a3b80ecbe7f0d409", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes\nType: text\n\nDifferent dam breach scenarios for maximum breach and a partial breach for a half breach width and depth (50% of maximum width and depth) were simulated to ascertain the potential risk under various breaching possibilities, including a partial dam breach, which occurred in 1994 (e.g. the 1994 Luggye GLOF). Simulated peak flow  $(Q_p)$  resulting from the Thorthomi dam breach under different breach scenarios ranged from  $9700 \\,\\mathrm{m}^3 \\,\\mathrm{s}^{-1}$  (for a 50 % breach depth) to 16 360 m<sup>3</sup> s<sup>-1</sup> (for maximum breach width and depth), with a time to peak  $(T_p)$  of 3.4 to 4 h, respectively (Fig. 10a). The bathymetry of the lake and the topography of the moraine dam dictate the total lake drawdown depth and the volume of the outburst flood. In this study, we estimated that 100 m of lake water depth will be lowered before the breach outflow channel becomes sufficiently stable, after sending  $283 \\times 10^6 \\,\\mathrm{m}^3$  (approximately 70 % of estimated lake water) of flood water downstream (Fig. 10b). The breach outflow channel was assumed to be stable when its bottom elevation reached the natural bed level of the downstream channel and down-cutting ceased.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.2 Dam breach processes"], "chunk_type": "text", "line_start": 253, "line_end": 255, "token_count_estimate": 395, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3fd43d575025d122", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: The image contains two panels, labeled (a) and (b). Panel (a) is a line graph that plots Flow in cubic meters per second (m³ s⁻¹) against Time in hours (h). The x-axis ranges from 0 to 24 hours, and the y-axis ranges from 0 to 15,000 m³ s⁻¹. There are three curves on the graph, each representing a different scenario: 'Maximum Breach' (dark red line), 'Half Breach Width' (blue line), and 'Half Breach Depth' (black line). The 'Maximum Breach' curve peaks at the highest flow, over 15,000 m³ s⁻¹ at approximately 4 hours. The 'Half Breach Depth' curve peaks at around 13,500 m³ s⁻¹ at about 3.5 hours. The 'Half Breach Width' curve has the lowest peak, just under 10,000 m³ s⁻¹ at about 4.5 hours. Panel (b) is a 3D diagram of a trapezoidal channel, illustrating its dimensions. The top width is indicated as 225 m, and the depth is shown as 100 m.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.2 Dam breach processes"], "chunk_type": "figure", "figure_caption": null, "line_start": 256, "line_end": 256, "token_count_estimate": 311, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7554c4974d525823", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes\nType: figure\nFigure: Figure 10. (a) A dam breach outflow hydrograph obtained from the BREACH model for three different scenarios and (b) breach parameters, breach width $(\\leftarrow)$ , and breach depth $(\\cline{1})$ in metres for the maximum breach scenario.\n\nFigure 10. (a) A dam breach outflow hydrograph obtained from the BREACH model for three different scenarios and (b) breach parameters, breach width  $(\\leftarrow)$ , and breach depth  $(\\cline{1})$  in metres for the maximum breach scenario.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes", "section_headings": ["4 Results", "4.2 Future Thorthomi GLOF prediction", "4.2.2 Dam breach processes"], "chunk_type": "figure", "figure_caption": "Figure 10. (a) A dam breach outflow hydrograph obtained from the BREACH model for three different scenarios and (b) breach parameters, breach width $(\\leftarrow)$ , and breach depth $(\\cline{1})$ in metres for the maximum breach scenario.", "line_start": 258, "line_end": 258, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9583fb263aec451f", "text": "Document: 1 Introduction\nSection: 4 Results > 4.3 Downstream peak flow and flood travel time\nType: text\n\nThe simulated flow hydrographs for three different scenarios at eight major settlement areas are provided in Fig. 11. Peak flow of the GLOF gradually attenuated as it propagated downstream. Peak flow at Punakha Dzong ranged from 8900 to  $14\\,130\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$  and decreased from 8200 to  $11\\,500\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$  when it arrived at the hydropower plant (PHPA-I).\n\nA schematic representation of an approximate distance, peak flow, averaged channel slope, and the estimated flood travel time for a maximum breach condition is provided in Fig. 12 (refer to Sect. 3.4.1 for the definition of the peak flow and flood travel time in this study). The estimated peak flow at Punakha Dzong,  $14\\,130\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ , is expected to be over 5 times higher than the 1994 Luggye GLOF (the recorded value is  $2539\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ , and the value estimated in this study is  $2455\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ ).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.3 Downstream peak flow and flood travel time", "section_headings": ["4 Results", "4.3 Downstream peak flow and flood travel time"], "chunk_type": "text", "line_start": 261, "line_end": 265, "token_count_estimate": 360, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e1f96c764ff986a", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.1 Inundation hazard in five vulnerable areas under the maximum breach scenario\nType: text\n\nThe flood depth distribution, highlighting five vulnerable areas for a maximum breach scenario, is provided in Fig. 13. The villages of Thanza, Toncho, and Lhedi, located in the northernmost part of the study area (Fig. 2), are expected to be inundated under a Thorthomi GLOF scenario. The 1994 Luggye GLOF also caused major damage to these settlement areas, but a Thorthomi GLOF is expected to cause more severe damage due to larger flood volume and shorter lead time. Major settlements along the river basin lie in the lower valleys of the Punakha and Wangdue districts, where large areas are expected to be flooded. Major towns and settlements, such as Samdingkha, Khuruthang, and Bajo, are expected to be inundated. The Mochhu river converges with the Phochhu river at the top left of Fig. 13c. Substantial overflow surrounding the Mochhu river, around the confluence, has been predicted. This result is due to backwater flow from the Phochhu river. Water flow from the Mochhu river is not easy to accurately estimate in advance and was not accounted for in this study, so inundation surrounding Punakha Dzong may be underestimated. However, the contribution of water from the Mochhu river can be negligible because the base flow of the Mochhu river is approximately  $100\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ , which is substantially small compared to estimated peak flow in this area,  $14\\,130\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ . Total inundated area due to a Thorthomi GLOF, with a maximum breach, was estimated to be approximately  $22\\,\\mathrm{km}^2$ , which is almost twice the area inundated under the Luggye GLOF simulation ( $13.1\\,\\mathrm{km}^2$ , estimated in this study).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.1 Inundation hazard in five vulnerable areas under the maximum breach scenario", "section_headings": ["5 Discussion", "5.1 Inundation hazard in five vulnerable areas under the maximum breach scenario"], "chunk_type": "text", "line_start": 269, "line_end": 271, "token_count_estimate": 503, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c64af9170d42c55", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.2 A comparison between three scenarios\nType: figure\nFigure: Figure 14 compares the maximum inundation depth and extent for three different scenarios for the town of Khuruthang. Simulation results for the three scenarios considered in this study revealed that the overall inundation extent and flood depths were higher for the maximum breach scenario. However, the depth and flood extent for the two other scenarios were comparable to the maximum breach scenario. The results indicate that even for a partial breach of the moraine dam, substantial damage within the downstream settlement areas (Punakha and Wangdue) is expected. The results imply that the difference in glacial lake bathymetry may also affect the maximum inundation in downstream areas but is not very sensitive because of the nature of the GLOF event (consisting of a rapid dam breach process and flood routing in steep valleys).\n\nFigure 14 compares the maximum inundation depth and extent for three different scenarios for the town of Khuruthang. Simulation results for the three scenarios considered in this study revealed that the overall inundation extent and flood depths were higher for the maximum breach scenario. However, the depth and flood extent for the two other scenarios were comparable to the maximum breach scenario. The results indicate that even for a partial breach of the moraine dam, substantial damage within the downstream settlement areas (Punakha and Wangdue) is expected. The results imply that the difference in glacial lake bathymetry may also affect the maximum inundation in downstream areas but is not very sensitive because of the nature of the GLOF event (consisting of a rapid dam breach process and flood routing in steep valleys).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.2 A comparison between three scenarios", "section_headings": ["5 Discussion", "5.2 A comparison between three scenarios"], "chunk_type": "figure", "figure_caption": "Figure 14 compares the maximum inundation depth and extent for three different scenarios for the town of Khuruthang. Simulation results for the three scenarios considered in this study revealed that the overall inundation extent and flood depths were higher for the maximum breach scenario. However, the depth and flood extent for the two other scenarios were comparable to the maximum breach scenario. The results indicate that even for a partial breach of the moraine dam, substantial damage within the downstream settlement areas (Punakha and Wangdue) is expected. The results imply that the difference in glacial lake bathymetry may also affect the maximum inundation in downstream areas but is not very sensitive because of the nature of the GLOF event (consisting of a rapid dam breach process and flood routing in steep valleys).", "line_start": 274, "line_end": 274, "token_count_estimate": 394, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aaa5f9ef26a3eab4", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.3 Time series change of flood depth distribution surrounding Punakha Dzong\nType: text\n\nThe spatial distribution of flood depth for a maximum breach scenario, at different time steps, for Punakha Dzong and Khuruthang town are provided in Fig. 15. Due to higher peak flow and a longer flood duration, overall flood hazard potential for the inhabited area caused by the Thorthomi lake GLOF, as compared to damages during the 1994 Luggye GLOF, was significantly higher. Most of the flood path lies in the narrow V-shaped valley, where there are few to no settlements or infrastructure. We estimated that over 1277 houses, most in the lower region of the study area, will be inundated in a GLOF. Aside from this, infrastructure such as roads, bridges, and sand dredging equipment will be damaged.\n\nNotable damage during the 1994 GLOF occurred in Punakha Dzong. The area near the Punakha Dzong was completely inundated in the 1994 Luggye GLOF. The simulated future GLOF indicates that the Punakha Dzong area will be completely flooded, with a maximum depth of over 10 m (Figs. 13 and 15).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.3 Time series change of flood depth distribution surrounding Punakha Dzong", "section_headings": ["5 Discussion", "5.3 Time series change of flood depth distribution surrounding Punakha Dzong"], "chunk_type": "text", "line_start": 277, "line_end": 281, "token_count_estimate": 279, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5b5cb2e272da8076", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: text\n\nThe Punakha (the middle downstream of the domain; see Fig. 13) and the Wangdue districts (consisting of the Bajo and Jagathang settlements, as well as the downstream do-", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "text", "line_start": 283, "line_end": 285, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d0113eedae03ca6f", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: An image displaying eight line graphs in a 2x4 grid, each showing simulated flood hydrographs for different locations. The locations are Lhedi, Samdingkha, Punakha Dzong, and Khuruthang in the top row, and Jagathang, Bajo, WangdiRapid station, and Hydropower plant in the bottom row. Each graph plots Flow in cubic meters per second (m³ s⁻¹) on the y-axis against Time in hours (h) on the x-axis, which ranges from 0 to 24. A legend in the top-left graph indicates three scenarios: 'Max' (red line), 'BRD-50%' (black line), and 'BRW-50%' (blue line). In all graphs, the 'Max' scenario shows the highest peak flow, followed by 'BRD-50%', and then 'BRW-50%'. The peak flows generally decrease and occur later in time for locations further down the grid. For example, in the 'Lhedi' graph, the 'Max' flow peaks at approximately 16,000 m³/s around 5 hours, while in the 'Hydropower plant' graph, the 'Max' flow peaks at approximately 11,000 m³/s around 9 hours.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 286, "line_end": 286, "token_count_estimate": 321, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a00ac8d7654795bc", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: figure\nFigure: Figure 11. A simulated flow hydrograph at important locations, derived from the HEC-RAS; result for each scenario (max: maximum breach; BRD-50 %: half of maximum breach depth; BRW-50 %: half of maximum breach width).\n\n**Figure 11.** A simulated flow hydrograph at important locations, derived from the HEC-RAS; result for each scenario (max: maximum breach; BRD-50 %: half of maximum breach depth; BRW-50 %: half of maximum breach width).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "figure", "figure_caption": "Figure 11. A simulated flow hydrograph at important locations, derived from the HEC-RAS; result for each scenario (max: maximum breach; BRD-50 %: half of maximum breach depth; BRW-50 %: half of maximum breach width).", "line_start": 288, "line_end": 288, "token_count_estimate": 147, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ebc71aa66ed2dca", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: figure\nFigure\n\nImage /page/11/Figure/4 description: A schematic diagram illustrating the characteristics of a flood along a river path, starting from Thorthomi Lake. The top part of the diagram shows a profile view of the river's elevation, which decreases as the distance from the lake increases. Key locations along the path are marked with their distances from the lake: Lhedi (19 km), Samdingkha (83 km), Punakha Dzong (90 km), Khuruthang (94 km), Bajo (103 km), and a Hydropower Plant (115 km). Below this profile, three horizontal bars provide quantitative data for different segments of the river. The first bar, labeled \"Peak Flow (m³ s⁻¹)\", shows a decreasing flow rate from 16,300 at Lhedi to 11,550 at the Hydropower Plant. The second bar, \"Average Slope (dy/dx)\", shows the slope for each segment, with values ranging from 0.001 to 0.038. The third bar, \"Flood Travel Time (h)\", indicates the time taken for the flood to traverse each segment, with values such as 2.6 hours for the first segment and 7.1 hours for the last.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 290, "line_end": 290, "token_count_estimate": 292, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4000f6524ec6899b", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: figure\nFigure: Figure 12. A schematic representation of flood parameters at six important locations along the flow path for the maximum breach scenario.\n\nFigure 12. A schematic representation of flood parameters at six important locations along the flow path for the maximum breach scenario.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "figure", "figure_caption": "Figure 12. A schematic representation of flood parameters at six important locations along the flow path for the maximum breach scenario.", "line_start": 292, "line_end": 292, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21e3f17db4579aca", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: text\n\nmain; see Figs. 2 and 13) are leading producers of rice, an essential crop for the country's GDP and food security. Any damage to agricultural land would have a devastating impact on farmers and the nation. Aside from potential damage to buildings and infrastructure, such as roads and bridges, agricultural land would also become submerged and destroyed by a flood. We estimated that approximately 193 to 245 ha of agricultural land will be inundated under different scenarios\n\nin a Thorthomi GLOF event. Figure 16 shows the potential extent of floods for different land use classes and highlights probable damage to agricultural land, particularly in the areas of Samdingkha and Jagathang.\n\nThe overall hazard potential of a GLOF from Thorthomi lake under different scenarios is summarised in Table 2. Although the peak flow rate of each scenario is different (29 % to 37 % between the maximum and minimum for the result", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "text", "line_start": 293, "line_end": 299, "token_count_estimate": 225, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "79d0980b6acf350e", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: figure\nFigure\n\nImage /page/12/Figure/2 description: A figure displaying a series of maps illustrating flood inundation in a study area. The figure is composed of a central legend, a study area map, and five detailed maps labeled (a) through (e).", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 300, "line_end": 300, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4149936f37400fc1", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: text\n\nThe legend defines symbols for Bridges, Education Centres, Health Care Centres, Historical Places, Road Network, Settlements, River, and Thorthomi\\_Lake.\n\nThe study area map shows a river originating from Thorthomi\\_Lake with five locations marked along its path: (a), (b), (c), (d), and (e).\n\nEach of the five detailed maps shows a satellite view of one of these locations with a flood inundation overlay, a scale bar, and a legend for flood depth in meters (m).\n\n- Map (a) Thanza: Shows a wide flooded area. The flood depth legend is: 0 - 5 m, 5.1 - 10 m, and 10.1 - 15.6 m. The scale bar goes up to 50 kilometers.\n- Map (b) Samdingkha: Shows a flooded river channel. The flood depth legend is: 0 - 6 m, 6.1 - 12 m, and 12.1 - 18.7 m. The scale bar goes up to 1.6 kilometers.\n- Map (c) Punakha Dzong: Shows a flooded area at a river confluence. The flood depth legend is: 0 - 7 m, 7.1 - 14 m, and 14.1 - 23.2 m. The scale bar goes up to 2 kilometers.\n- Map (d) Khuruthang: Shows a flooded river bend. The flood depth legend is: 0 - 8 m, 8.1 - 15 m, and 15.1 - 25.8 m. The scale bar goes up to 3 kilometers.\n- Map (e) Jagathang & Bajo: Shows a flooded winding river. The flood depth legend is: 0 - 7 m, 7.1 - 15 m, and 15.1 - 22.6 m. The scale bar goes up to 3 kilometers.\n\nEach map uses shades of blue to represent flood depth, with darker shades indicating deeper water. Icons from the legend are placed on the maps to show the locations of infrastructure and settlements.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "text", "line_start": 301, "line_end": 315, "token_count_estimate": 466, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cba628a8dd6b4e75", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: figure\nFigure: Figure 13. A maximum GLOF inundation map of the study area under the maximum breach scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.\n\nFigure 13. A maximum GLOF inundation map of the study area under the maximum breach scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "figure", "figure_caption": "Figure 13. A maximum GLOF inundation map of the study area under the maximum breach scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.", "line_start": 316, "line_end": 316, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e929049575ee346", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.4 Socio-economic impact\nType: text\n\ndepicted in Fig. 11), the total inundation area, the number of submerged buildings, and the area of impacted cultivated land are not much different (12%, 22%, and 21%, respectively), implying that the estimated flood is significant even for the most minor flood scenario (BRW-50% scenario) for the Thorthomi GLOF. The scenarios indicate that most of the damage will occur for river properties and that farmland will be substantially damaged, even when a dam breach is not drastic. The soil in farmlands will also be eroded and covered by debris. Damage to irrigation is expected and may affect agriculture in farmland located behind flooded areas. Over the long term, damage to soil and irrigation would extensively reduce farmers' production. In advance, careful evacuation planning and business continuity planning (e.g. JICA,\n\n2015), including a plan for agriculture, are essential for mitigating damage caused by a future Thorthomi GLOF.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.4 Socio-economic impact", "section_headings": ["5 Discussion", "5.4 Socio-economic impact"], "chunk_type": "text", "line_start": 317, "line_end": 321, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f9d3682c136ae3d2", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: text\n\nDue to a lack of actual surveyed data, volume and maximum depth were estimated based on the statistical relationships established by past studies; an uncertainty for the estimated bathymetry of Thorthomi lake is a major limitation of our study. As compared to the bathymetry presented, the use of actual, surveyed bathymetric data may yield a more accurate prediction. An additional limitation of our study is the clear water assumption. Compared to clear water, hyperconcentrated water has different dynamic properties. Debris in flood water may cause substantial damage to farmland, in-", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "text", "line_start": 323, "line_end": 325, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f02b2e97e030fd0f", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: figure\nFigure\n\nImage /page/13/Figure/2 description: A figure composed of four panels, labeled (a), (b), (c), and (d), illustrating a flood model for a river system. Panel (a) is a map showing the overall area. It includes a north arrow and a scale bar from 0 to 40 kilometers. The legend in panel (a) identifies symbols for Khuruthang Town, a River, the Model Domain, Thorthomi Lake, and the Maximum breach inundation extent. The map displays Thorthomi Lake at the top, feeding a river that flows downwards. A small rectangular area on the map is magnified in the other panels. Panels (b), (c), and (d) are three similar close-up aerial views of a town next to a river, showing flood inundation. Each of these panels includes a legend for water depth in meters (m), with three color-coded ranges: light gray for 0 - 10 m, medium blue for 10.1 - 15 m, and dark blue for 15.1 - 25.9 m. The blue overlay on the aerial images shows the extent and depth of the floodwaters, with the deepest water in the main river channel.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "figure", "figure_caption": null, "line_start": 326, "line_end": 326, "token_count_estimate": 303, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7cdfa23e589f7323", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: figure\nFigure: Figure 14. A comparison of inundation depth and extent for three breach scenarios within the Khuruthang study area. (a) A model domain highlighting Khuruthang town. (b) The maximum breach scenario. (c) A 50 % breach depth scenario. (d) A 50 % breach width scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.\n\n**Figure 14.** A comparison of inundation depth and extent for three breach scenarios within the Khuruthang study area. (a) A model domain highlighting Khuruthang town. (b) The maximum breach scenario. (c) A 50 % breach depth scenario. (d) A 50 % breach width scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "figure", "figure_caption": "Figure 14. A comparison of inundation depth and extent for three breach scenarios within the Khuruthang study area. (a) A model domain highlighting Khuruthang town. (b) The maximum breach scenario. (c) A 50 % breach depth scenario. (d) A 50 % breach width scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.", "line_start": 328, "line_end": 328, "token_count_estimate": 210, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "702b1ae947c762f3", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: figure\nFigure\n\nImage /page/13/Figure/4 description: A multi-panel figure, labeled Figure 15, illustrating the temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. Panel (a) is a map showing the model domain, which includes Thorthomi Lake in the north and a river flowing south. Two locations are marked: Punakha Dzong and Khuruthang Town. The legend indicates symbols for the river, model domain, and Thorthomi Lake. A scale bar shows distances up to 20 kilometers. Panels (b) through (g) show a time-series of the flood's progression in a zoomed-in area around Punakha Dzong and Khuruthang Town. Each of these panels has a legend for water depth in meters, categorized into three levels: 0-10 m (light blue), 10.1-15 m (medium blue), and 15.1-25.9 m (dark blue). The panels show the increasing extent and depth of the flood at different time steps: (b) 05:54, (c) 06:06, (d) 06:12, (e) 06:24, (f) 06:30, and (g) 08:00. The sequence shows the flood wave arriving, peaking in depth and extent around time step 06:30, and then beginning to recede by 08:00, with significant inundation affecting Khuruthang Town.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "figure", "figure_caption": null, "line_start": 330, "line_end": 330, "token_count_estimate": 333, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e3dfcd007a698eb", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: figure\nFigure: Figure 15. The temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. (a) The hydrodynamic model domain. (b-g) The inundation depth at six time steps.\n\nFigure 15. The temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. (a) The hydrodynamic model domain. (b-g) The inundation depth at six time steps.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "figure", "figure_caption": "Figure 15. The temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. (a) The hydrodynamic model domain. (b-g) The inundation depth at six time steps.", "line_start": 332, "line_end": 332, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "928f07b8e767012b", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: text\n\nfrastructure, and human life. Research on glacial lakes and their outburst floods is an emerging field (e.g. Qi et al., 2022; Taylor et al., 2023). To obtain more accurate damage predictions, data and methods should be revised following research progress.\n\nThe close proximity of glacial lakes within the Lunana region, especially the Thorthomi and Rapstreng lakes (Fig. 3), poses an even greater potential risk due to a possible cascad-\n\ning GLOF event. Failure of the lateral moraine of Thorthomi lake would lead to lake water breaching into Rapstreng lake, which would consequently cause the failure of its moraine dam. Since our study considered failure of the terminal moraine in the direction of the existing outlet, it is highly unlikely for such an event to occur under the current scenario. Accordingly, a cascading GLOF was not assessed in our study, but such possibilities should also be explored to", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "text", "line_start": 333, "line_end": 339, "token_count_estimate": 231, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "520f0b6fac5217be", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: figure\nFigure\n\nImage /page/14/Figure/2 description: A figure displaying three land use maps of a mountainous region, with a comprehensive legend. The main map on the left shows a large area with a scale bar from 0 to 20 kilometers. It depicts a valley with various land use classes, including large areas of green (Alpine Scrubs; Forests; Meadows; Shrubs) and light blue (Moraines; Snow and Glacier). A white line delineates the 'Simulation Domain' along the valley. Two smaller, zoomed-in maps are on the right, labeled 'Samdingkha' and 'Jagathang', each with a scale bar from 0 to 1.2 kilometers. These maps show a more detailed, pixelated view of sections of the river, with a red line indicating the 'Inundation Boundary'. The legend provides a key for the 'Land use class' colors: Black for 'Built up', Blue for 'Water Bodies', Brown for 'Cultivated Agriculture', Green for 'Alpine Scrubs; Forests; Meadows; Shrubs', Light Blue for 'Moraines; Snow and Glacier', and Grey for 'Non Built up; Rocky Outcrops'. All maps include a north arrow.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "figure", "figure_caption": null, "line_start": 340, "line_end": 340, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f015c93a8c5bb009", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: figure\nFigure: Figure 16. The probable GLOF inundation extent on land use classes. (Land use data source: National Land Commission Secretariat, Bhutan.)\n\nFigure 16. The probable GLOF inundation extent on land use classes. (Land use data source: National Land Commission Secretariat, Bhutan.)", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "figure", "figure_caption": "Figure 16. The probable GLOF inundation extent on land use classes. (Land use data source: National Land Commission Secretariat, Bhutan.)", "line_start": 342, "line_end": 342, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "96cfce3d75d2daf3", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: table\nTable: Table 2. The damage potential of a GLOF from Thorthomi lake.\n\n| Hazards → Scenarios↓                 | Total inundation area (km2) | Number of buildings inundated | Total cultivated agricultural land impacted (ha) |\n|-----------------------------------------|-----------------------------|-------------------------------|--------------------------------------------------|\n| Maximum breach                          | 22.7                        | 1277                          | 245.6                                            |\n| Half of maximum breach depth (BRD-50 %) | 20.8                        | 1044                          | 206.4                                            |\n| Half of maximum breach width (BRW-50 %) | 19.9                        | 1000                          | 193.4                                            |", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "table", "table_caption": "Table 2. The damage potential of a GLOF from Thorthomi lake.", "columns": ["Hazards → Scenarios↓", "Total inundation area (km2)", "Number of buildings inundated", "Total cultivated agricultural land impacted (ha)"], "table_row_start": 1, "table_row_end": 3, "line_start": 346, "line_end": 350, "token_count_estimate": 177, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "650daca5f355c95d", "text": "Document: 1 Introduction\nSection: 5 Discussion > 5.5 Limitations of this study\nType: text\n\nbetter understand the potential risk of cascading events which may cause more severe damage to society.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion > 5.5 Limitations of this study", "section_headings": ["5 Discussion", "5.5 Limitations of this study"], "chunk_type": "text", "line_start": 351, "line_end": 353, "token_count_estimate": 38, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b229d11408d56c8", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: text\n\nWe explored future hazards and damages arising from a GLOF from Thorthomi lake, one of the potentially dangerous glacial lakes in Bhutan but not well investigated within the scientific literature to date. To validate the approach used in our study and to calibrate the model, we reconstructed the 1994 Luggye lake GLOF prior to assessing the hazards of a Thorthomi GLOF. The BREACH model was used to es-\n\ntimate the outflow hydrograph emanating from a failure of moraine dams due to overtopping flow. Moraine materials and soil parameters used to parameterise the model were obtained from a report published by the National Center for Hydrology and Meteorology (NCHM), Bhutan. Propagation of the GLOF was simulated using a 2D routing module in HEC-RAS for modelling unsteady flow, which is an inherent characteristic of a GLOF where there is a sharp rise in the flow hydrograph.\n\nThe bathymetry of Thorthomi lake was estimated based on a regression equation derived from the relationship between lake area—depth—volume found within moraine lakes. We estimated that the total volume of the lake is approximately  $400 \\times 10^6 \\,\\mathrm{m}^3$ , with a maximum depth of 161 m. According to the maximum breach scenario, the Thorthomi GLOF may release  $283 \\times 10^6 \\,\\mathrm{m}^3$  of water in under 12 h, with a peak flow rate of  $16\\,360 \\,\\mathrm{m}^3 \\,\\mathrm{s}^{-1}$ , occurring approximately 4 h following initiation of the breaching process. Outflow hydrographs estimated by the model were used as the upstream boundary condition in hydrodynamic modelling.\n\nFlood routing was performed to reach a length of approximately 115 km, and then peak discharge, flood travel time, and flood depths at major downstream settlements were estimated. According to the maximum breach scenario, Punakha Dzong, which lies 90 km downstream of Thorthomi lake and at the beginning of major settlements, would witness a peak discharge of  $14\\,128\\,\\mathrm{m}^3\\,\\mathrm{s}^{-1}$ , approximately 6 h following breach initiation. A potential GLOF from Thorthomi lake would cause extensive agricultural and infrastructural damage to 245 ha of agricultural lands, and, for a maximum breach scenario, 1277 buildings are expected to be inundated. Comparable damage is also expected for two minor flood scenarios, implying that such damage is inevitable for a future Thorthomi GLOF.\n\nA hazard assessment for a GLOF plays a crucial role for understanding and mitigating risks associated with these devastating natural events. Our study quantified the potential danger a GLOF from Thorthomi lake would pose to the downstream settlements and infrastructure. Such assessments will enable policymakers, local communities, and relevant stakeholders to make informed decisions regarding land use planning, disaster preparedness, and early warning systems.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "text", "line_start": 355, "line_end": 379, "token_count_estimate": 739, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "24fb2faa6208917a", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: text\n\nand infrastructural damage to 245 ha of agricultural lands , and , for a maximum breach scenario , 1277 buildings are expected to be inundated . Comparable damage is also expected for two minor flood scenarios , implying that such damage is inevitable for a future Thorthomi GLOF . A hazard assessment for a GLOF plays a crucial role for understanding and mitigating risks associated with these devastating natural events . Our study quantified the potential danger a GLOF from Thorthomi lake would pose to the downstream settlements and infrastructure . Such assessments will enable policymakers , local communities , and relevant stakeholders to make informed decisions regarding land use planning , disaster preparedness , and early warning systems .\n\nSince glacial environments are dynamic and subject to change due to climate variations, GLOF hazard assessments are not static. Continuous monitoring and regular reassessments of glacial lakes and associated hazards are essential to account for environmental shifts and to ensure the effectiveness of mitigation strategies. Furthermore, a multidisciplinary approach in GLOF hazard assessments is necessary. Collaborations between researchers, policymakers, local communities, and other stakeholders are essential for effective decision-making, disaster preparedness, and the implementation of mitigation measures. To essentially reduce GLOF risk, the development of methods to safely release dammed water to downstream areas is important.\n\n*Data availability.* Our study used open and commercial data (NSB, 2021, 2018; NCHM, 2021). Commercial data may be distributed under licence terms and conditions.\n\nAuthor contributions. TW: conceptualisation, data curation, method, and writing original draft; RT: conceptualisation, data curation, draft writing, and reviewing and editing.\n\nCompeting interests. The contact author has declared that neither of the authors has any competing interests.\n\n*Disclaimer.* Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.\n\nAcknowledgements. Tandin Wangchuk acknowledges a Human Resource Development Scholarship from the Japan International Cooperation Agency (JICA). We acknowledge Yuji Toda and Takashi Tashiro, as well as other lab members of the Hydraulic Research Laboratory at Nagoya University. We also express our gratitude to Shigeo Suizu, Tomoyuki Wada, and Toru Koike at Earth System Science Co., Ltd. We sincerely thank members of the National Center for Hydrology and Meteorology for their continued support of our study.\n\nReview statement. This paper was edited by Pascal Haegeli and reviewed by Stefan Ram and Rayees Ahmed.", "metadata": {"source_file": "data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "text", "line_start": 355, "line_end": 379, "token_count_estimate": 664, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e11799cea78a574", "text": "Document: An ontology for Emergency Managing and\nType: text\n\nAbstract—In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/.\n\nIndex Terms—Ontology, Vocabularies, Crisis Management, Hazard Domain, Emergency, Ontology Quality, Knowledge Reuse, Disaster Management.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "An ontology for Emergency Managing and", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 231, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b9bde992db48e4fd", "text": "Document: I. INTRODUCTION\nSection: I. INTRODUCTION\nType: text\n\nWe can not change the nature. However, we can promote our planning, preparation, and response strategies about crises happening in the three phases of hazards, i.e. (i) pre-hazard, (ii) in-hazard and (iii) post-hazard. Currently, a substantial body of situational information collected from sources such as satellite images, sensors, social media content generated by people who are involved in crisis reporting and response, etc. Indeed, harnessing and exploiting this hazard-related Big Data is an essential means towards situational awareness which helps to manage crises, threads and risks of hazards in each phase. Despite the availability of such data, still there is a significant deficiency in representing, annotating and more importantly integrating this heterogeneous hazardrelated big data. This deficiency can be relieved by providing an ontology which conceptualizes and organizes situational and environmental awareness data (events, activities) subjected to hazards of any kind. Our investigation in the state-of-the-art hazard-related conceptualization (i.e., taxonomy, vocabulary and ontology) revealed existing of a few works which mainly conceptualized either hazard domain or crisis management domain from a limited perspective or for a particular type. For example, Humanitarian eXchange Language (HXL) [1] and EDXL-RESCUER Ontology [2] are mainly concerned with help and rescue aspect of crisis management domain. However, the broader and more diverse nature of this domain requires a comprehensive and abstract modeling and representation. Another deficiency is related to lack of incorporating relations into the conceptualization. Thus, they indeed should be called vocabulary or taxonomy rather than ontology (e.g., Management of Crisis vocabulary (MOAC)). These shortages motivated us to introduce an ontology that takes into consideration the prior conceptualizations (taxonomies and vocabularies) while relying on a promoted representation. Also, it brings new concepts and relations which were ignored previously although playing an essential role in capturing situational awareness (e.g., surveillance information, human sensing report, humanitarian event: prayer, concepts synonyms and instances). This paper presents our contribution in providing an ontology for Emergency Managing and PlAnning abouT Hazard crIses (empathi).\n\nThe rest of the paper is organized as follows: Section II presents the relevant state-of-the-art hazard or crisis vocabularies and compare their main features. Section III lists the external vocabularies which are integrated empathi. Thereafter, Section IV introduces empathi along with its major top concepts. Section VII reviews the related work. We close with the conclusion and the future work.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "I. INTRODUCTION", "section_headings": ["I. INTRODUCTION"], "chunk_type": "text", "line_start": 8, "line_end": 12, "token_count_estimate": 645, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "16dd13b27d91c47f", "text": "Document: I. INTRODUCTION\nSection: II. STATE-OF-THE-ART VOCABULARIES\nType: text\n\nIn this section, we present an overview of the state-of-the-art vocabularies concerned with hazard domain as well as crisis management domain. With this respect, Table I represents a succinct comparative study of these vocabularies. The first column (i.e., Vocabulary) states the name of the state-of-theart vocabulary or ontology and empathi is included in the last row (based on recency). The second column (i.e., **Domain** Coverage) mentions the particular areas of the hazard-related domain or crisis management domain which it covers. The third column (i.e., URI) checks whether or not the URI of the vocabulary is dereferenceable. The fourth column (i.e., F for File) specifies the available format of the vocabulary (i.e., OWL, RDF, TTL). The fifth column (i.e., **D** for **Documenta**tion) shows whether the vocabulary has online documentation or not. Then, we list the significant publications utilizing this vocabulary within the sixth column (i.e., MC for Major Citations). The seventh and eight columns represent the number of classes #C) and relations #R) specified within the vocabulary. The last column represents the external resources (i.e., IV for Imported Vocabularies) imported by their respective vocabulary in the first column. In the following, we shortly describe each vocabulary.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. STATE-OF-THE-ART VOCABULARIES", "section_headings": ["II. STATE-OF-THE-ART VOCABULARIES"], "chunk_type": "text", "line_start": 14, "line_end": 16, "token_count_estimate": 379, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f200e26d1849ec8", "text": "Document: I. INTRODUCTION\nSection: II. STATE-OF-THE-ART VOCABULARIES\nType: table\nTable\n\n| Vocabulary | Domain Coverage                                                                          | U | F           | D | MC                                                | #C  | #R  | IV                                                                    |\n|------------|------------------------------------------------------------------------------------------|---|-------------|---|---------------------------------------------------|-----|-----|-----------------------------------------------------------------------|\n| HXL        | Disaster,Geography,Damage, Organization, Humanitarian Response                        | ✓ | TTL         | ✓ | [3], [1], [4], [5] [9], [10], [11], [12], [13] | 50  | 66  | [6], [7], [8]                                                         |\n| MOAC       | Impact of Crisis, Recovery and Response Activities, Geo-locations                  | ✗ | RDF         | ✓ | [3], [14] [10], [16] [17], [18]             | 70  | 30  | [7], [15]                                                             |\n| SMEM       | Social Media and Emergency Management                                                 | ✗ | ✗           | ✓ | -                                                 | -   | -   | [19], [20], [21], [22], [7], [8] [23], [24], [1], [25], [26], [27] |\n| DO         | Temporal and Spatial Concepts, Impact, Rehabilitation and Facilities Facilities | ✓ | Web- App | ✗ | -                                                 | 97  | -   | -                                                                     |\n| ERO        | Report Specification                                                                     | ✗ | ✗           | ✓ | [28], [29], [30], [31], [32]                      | -   | -   | [33], [34], [35]                                                      |\n| DoRES      | Events and Reports Specification                                                      | ✓ | RDF         | ✓ | -                                                 | 96  | 261 | [7], [8] [23], [36]                                                |\n| EF         | Fire Disaster Specification, Protocol Design and Planning                             | ✗ | ✗           | ✓ | -                                                 | 37  | 90  | -                                                                     |\n| empathi    | Hazard Situational Awareness, Crisis Management, Hazard Events                        | ✓ | OWL         | ✓ | -                                                 | 423 | 338 | [37], [19], [20], [8], [7] [36], [21], [23], [38], [39]            |", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. STATE-OF-THE-ART VOCABULARIES", "section_headings": ["II. STATE-OF-THE-ART VOCABULARIES"], "chunk_type": "table", "table_caption": null, "columns": ["Vocabulary", "Domain Coverage", "U", "F", "D", "MC", "#C", "#R", "IV"], "table_row_start": 1, "table_row_end": 8, "line_start": 17, "line_end": 26, "token_count_estimate": 562, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc8d3848c7909b1d", "text": "Document: I. INTRODUCTION\nSection: II. STATE-OF-THE-ART VOCABULARIES\nType: text\n\nTable I: Comparison of the state-of-the-art Hazard-related Vocabularies, Taxonomy, and Ontologies. U, F, D respectively stand for referenceability of URI, availability of File and Documentation. Furthermore, MC, C, R and IV respectively stand for Major Citations, number of classes, number of relations, and imported vocabularies. ERO: EDXL-RESCUER Ontology, DO: Disaster Ontology, EF: Emergency Fire.\n\n- a) HXL: HXL stands for Humanitarian eXchange Language. HXL<sup>1</sup> is a standard aiming at information sharing during humanitarian calamity by overcoming the burden of interoperability. HXL ontology has a total of 50 classes and 66 relations. Main concepts contained in HXL are Place, Survey and assessment, Operation, Cash and Finance, Crisis. Furthermore, HXL provides links to UN OCHA vocabularies such as Global Coordination Groups<sup>2</sup>, Disaster Types<sup>3</sup>, Organization Types<sup>4</sup>, Vulnerable groups and Humanitarian themes<sup>5</sup>. Furthermore, HXL provides a hashtag schema containing related social media tags such as #channel, #crisis, #impact, #event, etc. [1].\n- b) **MOAC**: MOAC<sup>6</sup> which is concerned with management of crisis is a vocabulary [40] providing concepts mainly related to crisis management. It was created by the Inter-Agency Standing Committee (IASC)<sup>7</sup>, Emergency Shelter Cluster in Haiti<sup>8</sup>, UN-OCHA 3W Who What Where Contact Database<sup>9</sup> and Ushahidi Platform<sup>10</sup>.\n- c) **DoRES**: DoRES stands for DOcument-Report-Event-Situation Ontology. DoRES<sup>11</sup> is an ontology sharing information between individuals and organizations using situational reports for describing the situation [13]. It helps humanitarian organizations to structure their plans.\n- d) EDXL-RESCUER Ontology: EDXL-RESCUER<sup>12</sup> stands for Emergency Data Exchange Language Reliable and Smart Crowdsourcing Solution for Emergency and Crisis", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. STATE-OF-THE-ART VOCABULARIES", "section_headings": ["II. STATE-OF-THE-ART VOCABULARIES"], "chunk_type": "text", "line_start": 27, "line_end": 34, "token_count_estimate": 603, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21a8f5ffba58b195", "text": "Document: I. INTRODUCTION\nSection: II. STATE-OF-THE-ART VOCABULARIES\nType: figure\nFigure\n\nImage /page/1/Figure/18 description: A diagram illustrating the components that feed into a central concept called \"empathi\". The central concept is enclosed in a yellow oval. There are two main categories of components, each with its own label and color coding. On the left, under a blue rectangular label \"Crisis/Hazard Related Taxonomy and Lexicon\", there are six blue ovals: FEMA, MA-Ont, HXL, MOAC, EM-DAT, and Geo-Names. On the right, under a green rectangular label \"Imported Vocabularies\", there are six green ovals: DC-Terms, SKOS, LODE, SIOC, iContact, and FOAF. Arrows point from each of these twelve ovals towards the central \"empathi\" oval, indicating that it integrates information from all these sources.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. STATE-OF-THE-ART VOCABULARIES", "section_headings": ["II. STATE-OF-THE-ART VOCABULARIES"], "chunk_type": "figure", "figure_caption": null, "line_start": 35, "line_end": 35, "token_count_estimate": 234, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c11bdfaa1169ae7d", "text": "Document: I. INTRODUCTION\nSection: II. STATE-OF-THE-ART VOCABULARIES\nType: figure\nFigure: Figure 1: Importing existing ontologies in empathi\n\nFigure 1: Importing existing ontologies in empathi", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. STATE-OF-THE-ART VOCABULARIES", "section_headings": ["II. STATE-OF-THE-ART VOCABULARIES"], "chunk_type": "figure", "figure_caption": "Figure 1: Importing existing ontologies in empathi", "line_start": 37, "line_end": 37, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b74d237c267995b", "text": "Document: I. INTRODUCTION\nSection: II. STATE-OF-THE-ART VOCABULARIES\nType: text\n\nManagement. An ontology-based on EDXL developed for coordinating and interchanging information with the legacy system [32], [2].\n\n- e) Emergency Fire (EF): It is an ontology explicitly designed for fires in the building. It comprises of 131 terms along with definitions created after subjective research. It serves as a protocol for information sharing, analysis, evaluation and comprehension by an organization in the situation of disaster caused by fire [41].\n- f) Social Media Emergency Management (SMEM): During an unprecedented onset of a natural hazard, social media overflows with textual content about situational information, prayers, weather information, the impact of crisis and events. Of all the available information on the social media,\n\n<sup>&</sup>lt;sup>1</sup>https://github.com/hxl-team/HXL-Vocab/blob/master/Tools/hxl.ttl\n\n<sup>&</sup>lt;sup>2</sup>https://goo.gl/CD6vHY\n\n<sup>&</sup>lt;sup>3</sup>https://reliefweb.int/taxonomy-descriptions#disastertype\n\n<sup>4</sup>https://goo.gl/Uzy9UA\n\n<sup>&</sup>lt;sup>5</sup>http://vocabulary.unocha.org\n\n<sup>&</sup>lt;sup>6</sup>http://observedchange.com/moac/ns\n\n<sup>&</sup>lt;sup>7</sup>https://goo.gl/ESn99F\n\n<sup>8</sup>https://goo.gl/nDoa9F\n\n<sup>9</sup>https://goo.gl/jQLnYh\n\n<sup>10</sup>https://goo.gl/XPSnyG\n\n<sup>11</sup>https://goo.gl/Sw4XGt\n\n<sup>12</sup>http://www.rescuer-project.org\n\nwhich portion is contextually relevant and action-oriented to personnel in charge of a relief-giver organization. SMEM ontology provides concepts in a hierarchical structure which transforms high-volume of messy content to low-volume of action-oriented information [42].\n\ng) Disaster Ontology: It is one of the ontologies listed in Finnish Ontology Library Service ONKI<sup>13</sup>. Disaster Ontology (DO)<sup>14</sup> is comprised of 97 concepts (classes) concerning man-made and natural hazard. This ontology is useful for managing disaster situations but disregards concepts related to social media (e.g., news reports, modality of data, surveillance, prayer and monitoring the status of the services provided by organizations).", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. STATE-OF-THE-ART VOCABULARIES", "section_headings": ["II. STATE-OF-THE-ART VOCABULARIES"], "chunk_type": "text", "line_start": 38, "line_end": 71, "token_count_estimate": 714, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64333ff58ca3f007", "text": "Document: I. INTRODUCTION\nSection: III. INTEGRATION OF EXTERNAL VOCABULARIES\nType: text\n\nIn this section, we list the external vocabularies which partially integrated into empathi. Not all of them are necessarily related to the domain of hazard or crisis management (we reuse generic concepts from well-known vocabularies, e.g., FOAF). Figure 1 concisely represents an integration aims at reusing the existing vocabularies following ontology design methodologies (Methontology [33] and NeOn [43]) or interlinking *empathi* to other vocabularies which enhance its visibility.\n\n- Federal Emergency Management Agency (FEMA)<sup>15</sup> provides a glossary of terms related to disaster preparation and management [37].\n- **Emergency Disasters Database** (**EM-DAT**)<sup>16</sup> provides a precise definition of concepts and furthermore a categorization of disturbance-related events [19].\n- MA-Ont<sup>17</sup> supports detailed properties describing media files and appropriate metadata mapping [20].\n- **iContact**<sup>18</sup> provides conceptual classes for defining international addresses. It is relevant for using GeoNames [36], [21] ontology for describing places.\n- **Friend Of A Friend (FOAF)**<sup>19</sup> was created for describing people, relations, and associated events. Coupled with SIOC [23] and disaster domain model [39], [38], it can describe social media communities formed during the disaster scenarios [7].\n- **GeoNames**<sup>20</sup> is a part of GeoNames Database providing information about 11 million places (toponyms) covering all the countries. Integration of GeoNames ontology to our ontology adds geospatial semantic information which is critical for actionable hazard response. The ontology contains 150 classes and two relations forming 758 axioms on location dereferencing. Mapping syntax provided by this ontology is compatible with schema.org, DBpedia\n\n- Linked Open Descriptions of Events (LODE)<sup>21</sup> defines event as an action which takes place at a certain time and has a specific location. It can be a historical action as well as a scheduled action. Thus, it provides the generic concept of Event along with locational (i.e., atPlace), temporal (i.e., atTime) aspects and people who play a role (i.e., involvedAgent).\n- **Simple Knowledge Organization System** (**SKOS**)<sup>22</sup>. We utilized this data model to describe the concepts of our domain. It provides a better organization of domain knowledge (i.e., Hazard Crisis) [8].\n- Semantically-Interlinked Online Communities (SIOC)<sup>23</sup> is a W3C ontological standard to describe information from online communities. It can support a volunteer or caregiver with actionable information in the realm of social media [23].", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "III. INTEGRATION OF EXTERNAL VOCABULARIES", "section_headings": ["III. INTEGRATION OF EXTERNAL VOCABULARIES"], "chunk_type": "text", "line_start": 73, "line_end": 86, "token_count_estimate": 708, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ffb2a2fdb5f8135f", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: figure\nFigure\n\nImage /page/2/Figure/15 description: A concept map or ontology diagram showing relationships between different entities. At the top, an oval labeled 'empathi' has a 'has subclass' relationship pointing to 'Impact'. 'Impact' in turn has a 'has subclass' relationship with 'Affected Population'. 'Affected Population' is a central node with several outgoing relationships: 'has subclass' to 'Trapped People' and 'Missing People'; 'requireHumanSupport' to 'Volunteer Support'; 'needHelp' to 'Service'; and 'contactInfo' to 'iContact Thing'. Both 'Trapped People' and 'Missing People' have an 'ageRange' relationship pointing to 'Age Group'. The 'Service' node has three relationships: 'isLocatedAt' pointing to 'Place', 'currentServiceStatus' pointing to 'Status', and 'contactInfo' pointing to 'iContact Thing'. The arrows representing relationships are color-coded and have different styles (solid or dashed).", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "figure", "figure_caption": null, "line_start": 89, "line_end": 89, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9cfc6c5ba7593848", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: figure\nFigure: Figure 2: Partial representation of the concept Affected Population in empathi\n\nFigure 2: Partial representation of the concept Affected Population in *empathi*", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "figure", "figure_caption": "Figure 2: Partial representation of the concept Affected Population in empathi", "line_start": 91, "line_end": 91, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bd9f0e8f1e963716", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: text\n\nAs Table I shows, *empathi* contains 423 classes and 338 relations. In figure 2, concepts linked to \"Affected Population\" via solid lines, forms structural concepts (\"is-a\"/ \"subclass\"), while the concepts linked via colored dotted line are semantically related concepts to \"Affected Population\". Furthermore, in figure 2, the concept \"iContactThing\" is taken from the vocabulary *iContact* shown in figure 1. In the following, we present only the super-classes which imply the generic coverage.\n\n- **Age Group:** this class groups people based on their age similarity by providing the following sub-classes (i) Adolescent, (ii) Adult, (iii) Child and (iv) Infant.\n- **Event:** this concept defines events along with spatial and temporal constraints happening in any phase of hazard. This concept embodies the following sub-classes\n\nontology, LinkedGeoData ontology and INSEE ontology [36].\n\n<sup>13</sup> https://onki.fi/en/\n\n<sup>14</sup>http://onki.fi/en/browser/overview/disaster\n\n<sup>15</sup>https://goo.gl/QtKzev\n\n<sup>&</sup>lt;sup>16</sup>http://www.emdat.be/Glossary.Itisadatabase\n\n<sup>&</sup>lt;sup>17</sup>https://www.w3.org/ns/ma-ont\n\n<sup>18</sup> http://ontology.eil.utoronto.ca/icontact.html\n\n<sup>19</sup>http://xmlns.com/foaf/spec/\n\n<sup>&</sup>lt;sup>20</sup>http://www.geonames.org/ontology/documentation.html\n\n<sup>&</sup>lt;sup>21</sup>http://linkedevents.org/ontology/\n\n<sup>&</sup>lt;sup>22</sup>https://www.w3.org/2004/02/skos/\n\n<sup>&</sup>lt;sup>23</sup>http://rdfs.org/sioc/spec/", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "text", "line_start": 92, "line_end": 121, "token_count_estimate": 570, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df097e2b2ace818c", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: table\nTable\n\n| Hazard Type | Tweet                                                                                                                      | Hazard Concept              |\n|-------------|----------------------------------------------------------------------------------------------------------------------------|-----------------------------|\n| Flood       | 1. 188 killed, Airport closed they say that the runway came peeling off. it may take time to resume.                       | Impact (AP, ID)             |\n|             | 2. @? Hope people get adequate relief and no one is left out.Nation stands with Chennai.                                   | Event (HP)                  |\n|             | 3. @? in who can offer places to stay, pls fill the form for volunteers collating info #chennairains                       | Event (VS)                  |\n|             | 4. @? Chennai has been declared disaster zone. Army has been deployed. Army Helpline - +XXX XXX XXXX #ChennaiRains         | Service (H)                 |\n| Hurricane   | 5. Surveillance video captures several people looting a Houston store #Harvey in the wake of Hurricane                     | Event (CA)                  |\n|             | 6. The aftermath. 10,000 people now homeless because of Hurricane Irma. #WednesdayWisdom #climatechange                    | Impact (AP), Event(CC)      |\n|             | 7. #HurricaneIrmaRecovery Drive for #Homestead & #FloridaKeys today! Drop off supplies at @? #DJLMS #dontgivebackjustgive. | Service (S)                 |\n|             | 8. At least 56 of Florida's 639 nursing homes still have *no* electricity this morning, five days after #HurricaneIrma     | Impact (ID)                 |\n| Blizzard    | 9. @? \"2-3 days, it could take before airlines begin to clear the backlog? @? at on #blizzard2016.                         | Impact (ID)                 |\n|             | 10. @?: I-75 in Kentucky closed due to large number of accidents, state patrol says #blizzard2016                          | Impact (AP,ID)              |\n|             | 11. @?: The baton is passed. Buoy 50 miles south of Wilmington #Jonas #blizzard2016                                        | Place (L)                   |\n|             | 12. @?: Blizzard with \"life and death implications\" hits Washington, Mid-Atlantic #blizzard2016                            | Place (L), Impact (AP)      |\n| Landslide   | 13. @?: Be sure to follow @BGSLandslides for lots of up to date information on landslides across the UK #StormFrank        | Report (ER)                 |\n|             | 14. #StormFrank landslide at Glasscarraig Norman Motte & Bailey in Co Wexford — @? #archaeology #floods                    | Place (L)                   |\n|             | 15. SRI LANKA: At least 73 dead after week of flooding, landslides; 243,000 in temp shelters — TorStar #ExtremeWeather     | Service (SH), Impact (AP)   |\n|             | 16. @?: #EcuadorEarthquake - landslides closing down roads & making it challenging for help to reach hardest hit towns     | Impact (ID), Hazard Type(*) |", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "table", "table_caption": null, "columns": ["Hazard Type", "Tweet", "Hazard Concept"], "table_row_start": 1, "table_row_end": 16, "line_start": 122, "line_end": 139, "token_count_estimate": 768, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4c3691488b8d3d10", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: text\n\nTable II: Sample of hazard-related tweets from different hazard types. AP: Affected Population, ID: Infrastructure Damage, HP: Human Prayer, VS: Volunteer Support, H: Helpline, CA: Criminal Activity, S: Supply, L: Location, ER: Expert Report, SH: Shelter, Hazard Type (\\*): One hazard (Earthquake) causing another hazard (Landslide).\n\n- (i) Climate Change (ii) Criminal Activity (iii) Emergency Exercises (iv) Evacuation Plan (v) Humanitarian Event (vi) Recovery Plan (vii) Rescue Operation (viii) State Mitigation Plan (ix) Volunteer Support and (x) Early Warning.\n- **Facility:** defines an amenity made accessible for a specific purpose. It attributes to following sub-classes (i) Communication (ii) Electricity (iii) Gas Facility (iv) Water Facility and (v) Education Resource.\n- Hazard Type: lists different types of hazards that can affect human community. It is an entity type that embodies sub-classes (i) Airburst (ii) Coastal erosion (iii) Drought (iv) Earthquake (v) Explosion (vi) Fire (vii) Flood (viii) Hurricane (ix) Landslide (x) Sandstorm (xi) Storm (xii) Tornado (xiii) Toxic Radioactivity (xiv) Tsunami (xv) Volcano and (xvi) Winterstorm.\n- **Hazard Phase:** categorizes different activities carried out by various organizations before, during and after a catastrophic event into three sub-classes (i) During Hazard (ii) Pre-Hazard and (iii) Post-Hazard.\n- **Impact:** a forceful negative affect on someone or something in an unprecedented manner. This concept embodies sub-classes (i) Affected Population (ii) Animal Loss (iii) Health Issues (iv) Food Shortage (v) Financial Crisis (vi) Contamination (vii) Infrastructure Damage (viii) Severity.\n- **Involved Actors:** People or Organisation associated (negative or positive) with any catastrophic event. Sub-classes of this concept are (i) Organisation (ii) People.\n- Modality of Data: Information (raw, structured or semi-", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "text", "line_start": 140, "line_end": 157, "token_count_estimate": 582, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed4356a3def205c9", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: text\n\nPost - Hazard . - * * Impact : * * a forceful negative affect on someone or something in an unprecedented manner . This concept embodies sub - classes ( i ) Affected Population ( ii ) Animal Loss ( iii ) Health Issues ( iv ) Food Shortage ( v ) Financial Crisis ( vi ) Contamination ( vii ) Infrastructure Damage ( viii ) Severity . - * * Involved Actors : * * People or Organisation associated ( negative or positive ) with any catastrophic event . Sub - classes of this concept are ( i ) Organisation ( ii ) People . - Modality of Data : Information ( raw , structured or semi -\n\n- structured) conveyed or represented by a particular arrangement or sequence of text, audio, video or photos. Sub-classes included by this concept are (i) Audio (ii) Photo (iii) Text and (iv) Video.\n- **Place:** a physical surrounding defined by longitude, latitude, and area, providing a relative position of the someone or something during a hazard situation. One sub-class of the Place is Location, described by longitude, latitude, and the area of the affected place.\n- Report: documents evidence of the destruction caused by the natural disaster. All the activities carried out by various governmental and non-governmental organizations (NGOs) are stated in the report. The report is a way to keep people vigilant. Sub-classes encompassed under the concept: Report is (i) Expert Report (ii) Human Sensing Report and (iii) Media Report.\n- **Service:** is an act of providing support to someone in a situation of distressing incidents. Core sub-classes of this concept are (i) Financial Care (ii) Healthcare Service (iii) Helpline (iv) Human Remains Management (v) Resource and Information Centre (vi) Supply (vii) Transportation and (viii) Prayer Location.\n- **Status:** defines the state of services that are planned during the pre/in/post hazard phases. Associated subclasses are (i) Available (ii) Offered (iii) Requested and (iv) Unavailable.\n- **Surveillance Information:** A systematic, ongoing collection, collation, and analysis of data and the timely dissemination of information to those who need to know", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "text", "line_start": 140, "line_end": 157, "token_count_estimate": 560, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4019140f176ceb3a", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: figure\nFigure\n\nImage /page/4/Figure/0 description: A diagram illustrating a hierarchical classification system, mapping concepts to a text example. At the top, a central oval labeled 'empathi' is the root of a tree structure. Solid arrows point downwards to create the hierarchy. From 'empathi', branches lead to 'Impact', 'Hazard phase', 'Place', and 'Hazard Type'. The 'Impact' node further branches into 'Population Affected' and 'Damage to Infrastructure'. 'Population Affected' leads to 'Dead and Toll', and 'Damage to Infrastructure' leads to 'Airport Damage'. The 'Hazard phase' node leads to 'Post Hazard', 'Place' leads to 'Location', and 'Hazard Type' leads to 'Flood'. At the bottom of the diagram, a sentence within a dashed box reads: 'Chennai Floods: 188 killed, Airport closed they say that the runway came peeling off. it may take time to resume'. Dashed arrows connect some of the lower-level ovals to highlighted phrases in the sentence: 'Location' points to 'Chennai' (highlighted in blue), 'Flood' points to 'Floods' (highlighted in orange), 'Dead and Toll' points to '188 killed' (highlighted in orange), and 'Airport Damage' points to 'Airport closed' (highlighted in green). An arrow from 'Post Hazard' also points towards the beginning of the sentence.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "figure", "figure_caption": null, "line_start": 158, "line_end": 158, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68eb0d5143870e43", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: figure\nFigure: Figure 3: Mapping tweet and its words to empathi concepts.\n\nFigure 3: Mapping tweet and its words to empathi concepts.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "figure", "figure_caption": "Figure 3: Mapping tweet and its words to empathi concepts.", "line_start": 160, "line_end": 160, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf65ab57e14470da", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: text\n\nso that action can be taken. The surveillance concept in the setting of natural disasters can help to identify the resulting health-related needs which in turn, will lead to the more rational and effective deployment of resources to affected populations.\n\nV. CASE STUDY\n\nA substantial impact of empathi ontology is empowering us to annotate text semantically (e.g., tweets posted during hazard). Figure 3 shows mapping of segments within a given tweet to empathi concepts. Thus, from an abstract level, this tweet is related to the flood occurred in Chennai and reports two associated impacts. With this respect, in a first experiment, we compiled 53 million tweets from the 30 significant hazards happened in the past. Such as Hurricane Sandy in 2012 and Irma in 2017, Oklahoma Wildfires in 2017, Chennai Flood in 2005, Alaska Earthquake in 2018, Florida Rains in 2000 and 2016, Houston Floods in 2017, New-Zealand Earthquake in 2016, Typhoon Haima in 2016, Winter Storm Kayla in 2016, and many more. After that, we identified the tweets related to the six central concepts of empathi i.e., (i) impact, (ii) modality of data, (iii) hazard type, (iv) place, (v) transportation and (vi) surveillance. Table III and IV show the statistics of identified tweets related to each of the chosen concepts and sub-concepts. Furthermore, Table II represents samples of these tweets (i.e., column two) along with the mapped *empathi* concepts (i.e., column three) from various hazard type (i.e., column one).", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "text", "line_start": 161, "line_end": 167, "token_count_estimate": 418, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4db0085a1b08171d", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: table\nTable\n\n| Sub-Concepts of empathi      | #Tweets |\n|------------------------------|---------|\n| Water Facility (Fac.)        | 218,968 |\n| Gas Facility (Fac.)          | 33,047  |\n| Involved Organization (Inv.) | 4,249   |\n| Severity (Imp.)              | 1,344   |", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "table", "table_caption": null, "columns": ["Sub-Concepts of empathi", "#Tweets"], "table_row_start": 1, "table_row_end": 4, "line_start": 168, "line_end": 173, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51801a2c5b87db55", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: text\n\nExtensive coverage by *empathi* provides the capability of extracting structured information from unstructured and sparse content (e.g., Twitter) [44]. For identifying relevant information from unstructured social media text, it is essential to map", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "text", "line_start": 174, "line_end": 178, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "da46cd1500ff9955", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: table\nTable: Table III: Mapping sub-concepts of empathi to tweets of hazards. Fac.: Facility, Inv.: Involved, and Imp.: Impact are concepts\n\n| Concepts of empathi | #Tweets   |\n|---------------------|-----------|\n| Hazard Type         | 3,034,257 |\n| Impact              | 618,446   |\n| Modality of Data    | 509,645   |\n| Facility            | 258,117   |\n| Place               | 16,397    |\n| Transportation      | 6,694     |\n| Surveillance        | 1,588     |", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "table", "table_caption": "Table III: Mapping sub-concepts of empathi to tweets of hazards. Fac.: Facility, Inv.: Involved, and Imp.: Impact are concepts", "columns": ["Concepts of empathi", "#Tweets"], "table_row_start": 1, "table_row_end": 7, "line_start": 179, "line_end": 187, "token_count_estimate": 175, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed237d7949bf7daf", "text": "Document: I. INTRODUCTION\nSection: IV. CORE CONCEPTS OF EMPATHI\nType: text\n\nTable IV: Mapping concepts of *empathi* to tweets of hazards.\n\nthe words to ontology classes enable efficient classification of tweets as relevant and irrelevant to crisis domain. For instance in figure 2, the tweet: Chennai Floods: 188 killed, Airport closed they say that the runway came peeling off. it may take time to resume is identified as a post-hazard tweet. Chennai links to concept Place, Floods links to concept Hazard type, 188 killed links to concept Affected population, and Airport closed links to concept Infrastructure Damage. Moreover, such a procedure is termed as semantic annotation using expanded concepts (a.k.a. hypernyms). It can improve understanding of social media messages which pose challenges like ill-formed sentences, ambiguous word senses, poor syntactic structure, and implicit referencing. Semantic features formed using empathi can enhance supervised and unsupervised learning in crisis domain [45].", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. CORE CONCEPTS OF EMPATHI", "section_headings": ["IV. CORE CONCEPTS OF EMPATHI"], "chunk_type": "text", "line_start": 188, "line_end": 192, "token_count_estimate": 241, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e2ece9d8a0740931", "text": "Document: I. INTRODUCTION\nSection: VI. EVALUATING QUALITY OF empathi\nType: text\n\nTo build up a quality ontology, we followed the principles of ontology methodologies such as NeON [43] and Methontology [33] which encourage the reuse of existing ontologies. However, to quantitatively measure the quality of *empathi*, we designed a user evaluation survey. This survey contained 17 questions concerning with hierarchical, relational and lexical aspects of *empathi* inspired by [46]. Precisely, the participants in the survey have to evaluate the following criteria: (1) the correctness of structure (hierarchy) (2) the correctness of relations between concepts, and (3) lexical evaluation, i.e., quality of annotations associated with both concepts and relations. In the following, we elaborate on these criteria.\n\n- a) Structural evaluation: In this evaluation, the hierarchical structure is assessed concerning the correctness of \"is-a\" relationship as of whether the given concept A is-a particular type of the given concept B. For instance, \"Animal Loss\" is-a subclass of \"Impact\" in *empathi*. Such evaluation is necessary to confirm the utility of ontology for classification task [47]. We presented parts of hierarchy in the survey, and asked the participants, how far this hierarchy makes sense to them. They have to rate in the range 1 (fully disagree) -5 (fully agree).\n- b) Semantic relational evaluation: Ontology is evaluated for holding semantically correct relations between concepts. For instance, there is a relation between the concept \"Affected Population\" and the concept \"Service\" referring \"need for help\". Thus, Affected Population is the domain and Service is the range. Concerning w.r.t. the prior survey [47], having quality relations, higher capability for summarization, subgraph extraction, and contextualization tasks. We represented a number of relations of empathi to the participants", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. EVALUATING QUALITY OF empathi", "section_headings": ["VI. EVALUATING QUALITY OF empathi"], "chunk_type": "text", "line_start": 194, "line_end": 199, "token_count_estimate": 463, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "311d233ff42923b4", "text": "Document: I. INTRODUCTION\nSection: VI. EVALUATING QUALITY OF empathi\nType: figure\nFigure\n\nImage /page/5/Figure/0 description: The image displays three bar charts stacked vertically. The top chart, labeled (a), is titled '7 Structure-related Questions (SQ)'. It has a y-axis for 'Agreement Percentage' from 0 to 100. The blue bars show the following percentages for each question: SQ1 is 92.3%, SQ2 is 92.3%, SQ3 is 100%, SQ4 is 92.3%, SQ5 is 53.8%, SQ6 is 84.6%, and SQ7 is 76.4%. The middle chart, labeled (b), is titled '7 Semantic Relations-related Questions (SRQ)'. It also has a y-axis for 'Agreement Percentage'. The purple bars show the following percentages: SRQ1 is 76.9%, SRQ2 is 92.3%, SRQ3 is 66.7%, SRQ4 is 84.6%, SRQ5 is 84.6%, SRQ6 is 84.6%, and SRQ7 is 53.8%. The bottom chart is a partially cropped grouped bar chart with a y-axis for 'Agreement Percentage'. It shows groups of three bars colored blue, orange, and green. The visible data points for the first group are 84.6%, 46.2%, and 76.9%. For the second group, they are 92.3%, 76.9%, and 92.3%. For the third and fourth groups, the values are identical: 84.6%, 69.2%, and 84.6%.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. EVALUATING QUALITY OF empathi", "section_headings": ["VI. EVALUATING QUALITY OF empathi"], "chunk_type": "figure", "figure_caption": null, "line_start": 200, "line_end": 200, "token_count_estimate": 396, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71e0c64de0f11039", "text": "Document: I. INTRODUCTION\nSection: VI. EVALUATING QUALITY OF empathi\nType: figure\nFigure: Figure 4: Agreement of 13 evaluators on (a). 7 Questions that evaluate Structure of empathi , (b). 7 Questions that evaluate Semantic Relations of empathi , and (c). 3 Questions that evaluate Lexical characteristic of empathi for 4 concepts: Animal Loss, Health Issues, Food Shortage, Human Prayer. Agreement percentage is calculated as the percentage of evaluators responded \"agree\" or \"yes\" in the ontology evaluation form.\n\nFigure 4: Agreement of 13 evaluators on (a). 7 Questions that evaluate Structure of *empathi*, (b). 7 Questions that evaluate Semantic Relations of *empathi*, and (c). 3 Questions that evaluate Lexical characteristic of *empathi* for 4 concepts: Animal Loss, Health Issues, Food Shortage, Human Prayer. Agreement percentage is calculated as the percentage of evaluators responded \"agree\" or \"yes\" in the ontology evaluation form.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. EVALUATING QUALITY OF empathi", "section_headings": ["VI. EVALUATING QUALITY OF empathi"], "chunk_type": "figure", "figure_caption": "Figure 4: Agreement of 13 evaluators on (a). 7 Questions that evaluate Structure of empathi , (b). 7 Questions that evaluate Semantic Relations of empathi , and (c). 3 Questions that evaluate Lexical characteristic of empathi for 4 concepts: Animal Loss, Health Issues, Food Shortage, Human Prayer. Agreement percentage is calculated as the percentage of evaluators responded \"agree\" or \"yes\" in the ontology evaluation form.", "line_start": 202, "line_end": 202, "token_count_estimate": 254, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c19f3de2100b1a0e", "text": "Document: I. INTRODUCTION\nSection: VI. EVALUATING QUALITY OF empathi\nType: text\n\nLQ1 LQ2 LQ3 Health Issues\n\nLQ1 LQ2 LQ3 Animal Loss\n\nLQ1 LQ2 LQ3 Food Shortage uestions(LQ)\n\nLQ1 LQ2 LQ3 Human Prayer\n\nand asked them whether or not they confirm having such a relation or not.\n\nc) Lexical evaluation: This part examines expressiveness, completeness, and clarity of annotations of a given ontology. Expressiveness states the efficacy of the ontology to identify relevant information using natural language processing techniques. Completeness [48] evaluates whether an illustration of a concept using definition and labels adequately define various scenarios in crisis domain. For instance, w.r.t. the given concept \"Human Prayer\" the definition \"prayer is a message to God from victim's relatives and family for protecting their lives and health\" is provided along with the labels \"send prayers\", \"heart prayers\", \"heart praying\", \"join pray-\n\ning\", \"love prayers\", \"prayers affected\", \"temple\", \"church\", \"prayers city\", \"prayers families\", \"prayers involved\", etc. Clarity evaluates whether or not the concept name in the ontology is meaningful and easily understandable to human and machine.\n\nd) **Results**: Our survey had 13 participants and the results have been illustrated in figure 4.\n\nThe structural evaluation section comprised of seven questions expressed as follows; (SQ1) Concerning a hazard situation, is \"Mental Stress\" and \"Physical Stress\" two important concepts under \"Health Issues\"? (SQ2) Does \"No Effect\", \"Minor\", \"Major\", \"Hazardous\", \"Catastrophic\" represents sub-classes of \"severity\"? (SQ3) Are \"Financial Crisis\", \"Food Shortage\", and \"Contamination\" probable impacts of a Hazard? (SQ4) Are following triples meaningful: \"Animal Loss isa Impact\", \"Communication Lines Failure is-a Infrastructure Damage\", \"Power Outage is-a Infrastructure Damage\", and \"Survived People is-a Affected Population\"? (SQ5) Do you consider non-government organization's (NGO) report is an expert report? (SQ6) Do health report, service feedback, and weather report define human sensing? (SO7) Can \"News Agencies Report\" and \"Social Media Report<sup>24</sup>\" be categorized under \"Media Report\", a sub-class of \"Report\"? Questions SQ1-SQ6 were Yes/No questions, and SQ7 follows Likert Scale. The detailed results are represented in Figure 4a. We observe the average agreement rate above 84.5%.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. EVALUATING QUALITY OF empathi", "section_headings": ["VI. EVALUATING QUALITY OF empathi"], "chunk_type": "text", "line_start": 203, "line_end": 225, "token_count_estimate": 651, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50cd0866e8ea12c4", "text": "Document: I. INTRODUCTION\nSection: VI. EVALUATING QUALITY OF empathi\nType: text\n\na Affected Population \" ? ( SQ5 ) Do you consider non - government organization ' s ( NGO ) report is an expert report ? ( SQ6 ) Do health report , service feedback , and weather report define human sensing ? ( SO7 ) Can \" News Agencies Report \" and \" Social Media Report < sup > 24 < / sup > \" be categorized under \" Media Report \" , a sub - class of \" Report \" ? Questions SQ1 - SQ6 were Yes / No questions , and SQ7 follows Likert Scale . The detailed results are represented in Figure 4a . We observe the average agreement rate above 84 . 5 % .\n\nRegarding evaluation of semantic relations, we designed seven questions as follows: SRQ1: Do you think the following triples make sense; \"Event occures in a Place\", \"Service is offered in a Place\", \"Each Hazard is associated to a couple (c) of Event\" and \"each Hazard leads to a couple of Services\"? SRQ2: Can concepts, Event, and Service be linked to the concept \"Place\" using \"isLocationAt\" relation? SRQ3: Do you think two different types of Hazard can be related concerning Event, Service, and Place? SRQ4: Is the \"currentStatus\" relation between Facility and Status semantically correct? SRQ5: Is available, offered, requested and unavailable suitable categories for Status? SRQ6: Can the concepts \"Service\" and \"Organization\" be concerning the concept \"Hazard Type\"? SRQ7: Is the relation \"needHelp\" correctly links \"Affected Population\" with \"Organization\" and \"Service\"? Questions SRQ2-SRQ7 are Yes/No, and SRQ1 follow Likert Scale. From figure 4b, an average agreement of 75.4% was concluded in confirming above facts.\n\nThe lexical evaluation of *empathi* was performed by representing definition and synonyms (or labels) describing each concept and asking participants to respond the following questions; (LQ1) Do labels appropriately represent the concept? (LQ2) Are labels complete? (LQ3) Are definitions and labels enough clear? Figure 4c shows the results with the total agreement rate 78.8%.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. EVALUATING QUALITY OF empathi", "section_headings": ["VI. EVALUATING QUALITY OF empathi"], "chunk_type": "text", "line_start": 203, "line_end": 225, "token_count_estimate": 541, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e87ed2e9d4393ddc", "text": "Document: I. INTRODUCTION\nSection: VII. RELATED WORK\nType: text\n\nThe aftermath of the disaster causes agencies/organization to structure a plan for the thoughtful recovery of the area. An\n\n<sup>&</sup>lt;sup>24</sup>http://www.aidforum.org/topics/disaster-relief/top-100-twitter-accounts\n\nopportunity to hasten this process is a need for a knowledge structure containing concepts, constraints, and links that provide before-hand information for disaster preparedness and act. In [49], the author designed a process model of the aftermath of the disasters using the Petri-Net containing inter-linked concepts for efficiency after-disaster execution and adaptation. Furthermore, the cost associated with the recovery of an area urges the need to have a structured source of events, concepts, and relations that define a distressing incident. In [50], the author defines the utility of an ontology providing seismic risk definition, prediction, and management to reduce damages. There is a recent study on crowdsourced emergency event detection in [51]. There, the authors propose the utilization of emerging knowledge from text using concepts and temporal information for events. In a recent work on crisis management [52], there has been a development of a suite of tools which can leverage our ontology for context-aware response generation. CrowdGeoKG [53] is a framework using the entities in the OpenStreetMap and is enriched by Wikidata.\n\nEvents mainly characterize a disaster scenario, and it is essential to understand what is happening in an emergency situation. In [54], a given user can utilize GPS data to create a simulation model for predicting human mobility. We assume that identification of core disaster domain-specific concepts can help in annotating the GPS data similar to in OpenStreetMap [55]. In 2012, Hurrican Sandy brought in towering traffic on social sensing sites urging the need of an information filtering mechanism for assisting crisis coordination. A psycholinguistic driven domain-dependent lexicon was created in [56] for assistive crisis response. Moreover, tweets on Hurricane Sandy 2012 identified \"power blackout\" as one of the implications of the disaster. To identify its associated repercussions, one need a structured domain-model. For instance, crashing out of power affected the medical facility. Hence, there is a need for an ontology to bridge the facilities in emergency situation [39]. In [57], the author created a twitter stream of deceptive and peripheral messages using a knowledge source assisting Public Information Officers (PIOs) to make conscious decisions in an emergency situation.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. RELATED WORK", "section_headings": ["VII. RELATED WORK"], "chunk_type": "text", "line_start": 227, "line_end": 237, "token_count_estimate": 622, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b5b43625165a8af", "text": "Document: I. INTRODUCTION\nSection: VII. RELATED WORK\nType: text\n\npsycholinguistic driven domain - dependent lexicon was created in [ 56 ] for assistive crisis response . Moreover , tweets on Hurricane Sandy 2012 identified \" power blackout \" as one of the implications of the disaster . To identify its associated repercussions , one need a structured domain - model . For instance , crashing out of power affected the medical facility . Hence , there is a need for an ontology to bridge the facilities in emergency situation [ 39 ] . In [ 57 ] , the author created a twitter stream of deceptive and peripheral messages using a knowledge source assisting Public Information Officers ( PIOs ) to make conscious decisions in an emergency situation .\n\nTable 1 motivates creating empathi. Prior ontologies related to crisis management and situational awareness fails to provide structural, lexical and relational benefits needed for extracting situation-specific information extraction from social media content [47]. It is either because these ontologies are diverse in their subject areas or are incomplete concerning referencing, documentation, and expressibility. On analyzing these state-of-the-art vocabularies, we identified subject areas, vocabularies concerning each subject area and used OWL to provide a formal representation. Since ontologies can be domain-specific (crisis domain) or generic (e.g., FOAF), we incorporate concepts relevant in addressing various issues in crisis management. For instance; concept Location was taken from Geonames and iContact, crisis-related concepts from HXL and MOAC. Hence, we addressed all the queries raised in [3]. Human social communication during an emergency event provides real-time insight into various domains such as facility, events, impact, report, surveillance, organization involved and activities carried out during and after the hazard. Extracting actionable information from active social channels is challenges because of 2 reasons: (1) absence of an ontology that map multiple concepts, (2) completeness and expressiveness of the ontology. We provide a utility based case-study (section V) where we used our ontology for mapping 53M tweets to the concepts and sub-concepts in *empathi*. Mapping of social media content to ontology concepts will improve classification and summarization task using state-of-the-art natural language processing and learning techniques.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. RELATED WORK", "section_headings": ["VII. RELATED WORK"], "chunk_type": "text", "line_start": 227, "line_end": 237, "token_count_estimate": 546, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3c57aee263a5eb9", "text": "Document: I. INTRODUCTION\nSection: VIII. CONCLUSION AND FUTURE WORK\nType: text\n\nWe propose *empathi* ontology as a crisis domain archetype that aids crisis management, hazard situational awareness, and hazard events during emergency scenarios. In our study, we demonstrated the prowess of ontology by its integration with relevant and state-of-the-art crisis vocabularies. Moreover, its efficacy was assessed through appropriate evaluation of its quality across three dominant criteria: structure, lexical and semantic relations using the human judge. Furthermore, we illustrated its effectiveness concerning social media domain by linking tweet content to ontology concepts. The ontology has been created using semantic web language (OWL) and links to FOAF, SIOC, DC-terms, and LODE. We aim to extend the ontology in following directions; the first direction is to introduce Internet of Things (IoT) for disaster management. A second possible direction is to improve the ontology with additional ontology quality tools as recommended by the PerfectO methodology<sup>25</sup>. For instance, use of LODE tool provides automatic documentation. WebVOWL tool provides an automated graph visualization. A third direction is to disseminate more the ontologies on ontology catalogs such as Linked Open Vocabularies (LOV) and LOV4IoT. LOV4IoT could be refined and extended to support the environment domain with various use cases such as flooding, fire, earthquake, tsunami.", "metadata": {"source_file": "data/('An ontology for Emergency Managing and', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VIII. CONCLUSION AND FUTURE WORK", "section_headings": ["VIII. CONCLUSION AND FUTURE WORK"], "chunk_type": "text", "line_start": 239, "line_end": 241, "token_count_estimate": 342, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ca5eaebbc70e345", "text": "Document: An Internet of Things ontology for earthquake emergency evaluation and response\nType: text\n\nAbstract—Recent years have seen the fast-diffusion of internetconnected devices and the rise of the Internet of Things (IoT) research and application area. Research works are dealing with technologies that enable the so-called things to communicate among them and with users in order to provide data and/or accomplish tasks. This scenario is posing several challenges so that more and more researchers are dealing with them. Our work deals with the definition of both machine and human understandable descriptions of things by means of ontologies in order to enable the collaboration among physical objects and IT systems. Indeed, recent works highlighted how the IoT technology may be profitably used in several scenarios in order to accompish complex tasks where physical objects are active participants. First, we extend the Semantic Sensor Network ontology defined by the W3C Semantic Sensor Networks Incubator Group with concepts and roles that describe actuators. This leads to the definition of a comprehensive Internet of Things ontology. Then, with the support from domain experts, we analyze the earthquake emergency scenario in order to define for it a domain ontology by means of adding domain-related concepts to the IoT ontology. Furthermore, we compare our work with others that use ontologies to formally describes things.\n\n*Keywords*— Internet of Things, Ontology, Earthquake Emergency.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "An Internet of Things ontology for earthquake emergency evaluation and response", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 327, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2395e0a0306dac67", "text": "Document: I. Introduction\nSection: I. Introduction\nType: text\n\nSince the term \"Internet of Things\" (IoT) was introduces by Kevin Ashton in 1999, the number of internet-connected devices had quickly increased. According to CISCO, the number of things connected to the internet exceeded the number of people on earth during 2008; furthermore, the company foresees that the number of internet-connected things will reach 50 billions in 2020. Nowadays, internet-connected devices are involved in many application domains [1]. Now, transportation and logistics, healthcare, smart environment (home, office, plant) can benefit from IoT technologies. Furthermore, the increasing number of new smart things and internet-connected devices developed by companies suggests that, in the near future, futuristic applications (such as robot taxi, city information model, and enhanced game room) will be available.\n\n1\"The Internet of Things\". http://share.cisco.com/internet-of-things.html. Accessed: 2014-3-4.\n\nBeside an exponential growing of the Internet of Things field, companies started to rethink their core activities including physical objects in their core processes [2], [3]. Indeed, whenever the Internet of Things enables the communication among physical objects and the business IT systems, such objects can be considered as active participants into the business processes of the companies [4]. For example, IoT-enabled objects such as sensors and/or actuators can provide data and/or accomplish operations may be used to execute business process tasks. This brings toward a collaboration among IT systems and physical objects.\n\nAlthough many efforts have been done in order to connect physical objects to IT systems and enable a collaboration among them, several core challenges arises [5], [6]: scalability, when millions of sensors and/or actuators can be reached to accomplish a given task the amount of information that a system has to manage could be unmanageable; deep heterogeneity, things are very heterogeneous, both regarding physical characteristics and data and/or operations that they provide and/or accomplish, this hinders the use of them in an IoT ecosystem; unknown topology, networks of things may be large and/or things may be unreachable; incomplete or innaccurate metadata, metadata may be used to enrich the description of things and their capabilities but they could be incomplete or inaccurate; and conflict resolution, regarding actuators, conflicts may arise, for instance, when multiple applications attempt to actuate the same device. Furthermore, it is noteworthy the fact that IoT end users are usually non-technical people [7], [3]. They are not experts of the IoT-related technologies, but they know very well the domain of application of the things. An IoT ecosystem should enable these users to use devices even if they do not know IoT implementation details. In order to deal with these challenges, several research works deal with the extension of things descriptions with semantic informations by means of ontologies. Indeed, an ontology is \"an explicit specification of a conceptualization\" [8] and it is suitable to formally describe a domain of interest, the Internet of Things domain in this case. Ontologies are key elements of the semantic web technologies [9] and were introduced to add semantic machine-understandable information to data on the web, enabling the so-called Web 3.0 [10]. Semantic enriched data can improve search and integration activities; furthermore, they also facilitate the machine capability to reason on data in order to achieve more knowledge of the domain of interest.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "I. Introduction", "section_headings": ["I. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 24, "token_count_estimate": 794, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "edd5280aa626fe5b", "text": "Document: I. Introduction\nSection: I. Introduction\nType: text\n\nwith semantic informations by means of ontologies . Indeed , an ontology is \" an explicit specification of a conceptualization \" [ 8 ] and it is suitable to formally describe a domain of interest , the Internet of Things domain in this case . Ontologies are key elements of the semantic web technologies [ 9 ] and were introduced to add semantic machine - understandable information to data on the web , enabling the so - called Web 3 . 0 [ 10 ] . Semantic enriched data can improve search and integration activities ; furthermore , they also facilitate the machine capability to reason on data in order to achieve more knowledge of the domain of interest .\n\nMotivations that enabled the use of ontologies on the web lead to utilization of them in the Internet of Things domain on which both systems (mainly, sensors and actuators) and data may be semantically described. In particular, using ontologies things and their capabilities may be modeled at an abstract level which enables non-technical people in using them.\n\nOur work proposes an ontology for Internet of Things in order to deal with the deep heterogeneity challenge of IoT introduced before. Indeed, in order to enable the collaboration among several physical obtects and IT systems, it is needed a common meaning of the involved resources and this can be obtained using semantics [11].\n\nWe designed an ontology whose goals are the description of an Internet of Things ecosystem. Starting from the work of the W3C Semantic Sensor Networks Incubator Group [11] on semantic sensor networks, we define an ontology that extends sensors description defined by the Incubator Group with a description suitable for actuators. A third part of the IoT ontology is domain related. In particular, we adopt the earthquake emergency evaluation and response domain as domain of interest of our ontology. The goal is to show how the developed ontology may be used to model the things that are involved in the earthquake emergencies.\n\nThe rest of the paper is organized as follows. Section II gives an overview of the works that combine Internet of Things with semantics. Section III describes the Internet of Things ontology we propose, focusing on its three-modules architecture and the modeling of sensors and actuators. Next, Section IV completes the description of the ontology depicting a domain-specific ontology suitable for the earthquake emergency evaluation and response. Section V briefly gives information about the implementation of ontologies. Finally, Section VI closes the paper with a discussion on our approach to the semantic Internet of Things and gives a prevision of the future work.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "I. Introduction", "section_headings": ["I. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 24, "token_count_estimate": 597, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4908efa88458fd90", "text": "Document: I. Introduction\nSection: II. RELATED WORK\nType: text\n\nIn the recent years, Internet of Things attracted many researchers. Indeed, due to its pervasiveness and the variety of involved technologies, Internet of Things interests several application areas, resulting in a great number of works in literature. Regarding collaboration, for example, researchers proposed standards that enable the syntactic interoperability among things [12], [13], [14]. Despite that, the formal modeling of things by means of semantics is still an emerging area and there not exists a de facto standard to do that. This obstructs the collaboration among things themselves and among things and IT systems [11]. This leads research groups\n\nto propose their own approaches to semantically describes the Internet of Things domain.\n\nDe et al. [15] suggest a service modeling of things using ontologies extendig the Entity, Resource, Service, and Device core elements [16] of a thing model. The W3C Semantic Sensor Network Incubator Group [11] defines an ontology for sensor networks. Such ontology goes deep in the description of devices that are capable to sense features of interest. This work represents a comprehensive description of sensors and the approach is domain independent. Hachem et al. [6] aim at solving the IoT challenges by means of three ontologies that describe in detail devices and physics of the domain of interest. Their work proposes a three-layer view of the IoT-based real world. In detail, the authors identify the following layers: a physical layer, an information layer, and a functional layer. For each of them they design a specific ontology in order to give a proper description of the IoT domain. In this work, some aspects of the information and the functional layers are noteworthy. Indeed, in the respective ontologies there are concepts that rely on reasoning about measurement units and physical concept relations, and they are defined from a mathematical viewpoint. This simplify the searching for and composing of data activities. Wang et al. [7] propose an ontology-based framework to support the discovery and invocation of actuators in home care systems. The goal of their work is to simplify the customization of the home care system policies by non-technical people. A care professional may declare system rules by means of human-understandable concepts defined in a care domain ontology. Then, software handlers are responsible for the translation between ontology-based and device-specific descriptions of sensors and actuators used by the home care system. Discovery and invocation of actuators are supported by querying the system knowledge base on which each device is registered. In a further work, Wang et al. [17] define an ontology for the IoT. Such an ontology aims at giving a comprehensive view on the IoT domain, from sensing and actuating capabilities to the quality of services and information. For this purpose, and in order to keep distinct each area of IoT domain, the description ontology is designed with a modular approach. Each module is responsible for one of the IoT-related areas (e.g., resources, services, QoS) and, in some cases, it is developed starting by and extending third party works on IoT. In this work, a particular attention is given to IoT service modeling and testing, but it overlooks how this ontology may be applied in real world scenarios.\n\nFinally, some considerations have to be done about the domain of application we consider and the related work which deals with it. Emergency management involves many organizations and actors, from national agencies to volunteers. Furthermore, recent works also highlights that citizens may contribute in generating useful data in such mass casualty incidents [18]. It emerges a complex scenario where a large amount of data has to be processed and combined in order to", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "II. RELATED WORK", "section_headings": ["II. RELATED WORK"], "chunk_type": "text", "line_start": 26, "line_end": 36, "token_count_estimate": 856, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "add1e4437afde2ae", "text": "Document: I. Introduction\nSection: II. RELATED WORK\nType: text\n\nby and extending third party works on IoT . In this work , a particular attention is given to IoT service modeling and testing , but it overlooks how this ontology may be applied in real world scenarios . Finally , some considerations have to be done about the domain of application we consider and the related work which deals with it . Emergency management involves many organizations and actors , from national agencies to volunteers . Furthermore , recent works also highlights that citizens may contribute in generating useful data in such mass casualty incidents [ 18 ] . It emerges a complex scenario where a large amount of data has to be processed and combined in order to\n\nbetter manage the emergency. For this purpose, several works propose the use of ontologies to add semantic information to data and processes involved in the emergency management activities [19], [20]. Despite that, the majority of them does not consider sensors and actuators in the domain ontologies. Thus, our work appear to consider another aspect of the emergency scenario: the things.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "II. RELATED WORK", "section_headings": ["II. RELATED WORK"], "chunk_type": "text", "line_start": 26, "line_end": 36, "token_count_estimate": 253, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "122d102eefa174f2", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY\nType: text\n\nThe following section describes the architecture of the Internet of Things ontology we propose. Such architecture is composed by three modules, namely Sensors Module, Actuators Module, and Domain Module, divided in two layers (Figure 1). On one hand, the first layer contains Sensors and Actuators modules and formally describes both sensors and actuators in an IoT ecosystem by means of two ontologies, one for sensors and one for actuators. On the other hand the second layer contains the Domain Module. It details the generic descriptions of things given by the first layer adding ontology elements that describe specific-domain devices and features of interest involved in the application scenario, in this case the earthquake emergency evaluation and response scenario. This modular two-layers architecture provides both flexibility and compatibility of the whole ontology: all the specific-domain concepts and roles can be added to the Domain Module without requiring the modification of other modules; furthermore, in order to describe the domain scenario, other ontologies may be used in the domain-related layer without violating the whole IoT ontology design principles.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY", "section_headings": ["III. THE IOT ONTOLOGY"], "chunk_type": "text", "line_start": 38, "line_end": 40, "token_count_estimate": 280, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f2b98f23aa501786", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY\nType: figure\nFigure\n\nImage /page/2/Picture/3 description: A block diagram illustrating the components of an \"IoT Ontology\". The diagram consists of a large, light gray, rounded rectangle labeled \"IoT Ontology\" at the top. Inside this main rectangle, there are three smaller, light blue, rounded rectangles. The top half of the main rectangle contains two of these smaller rectangles placed side-by-side. The one on the left is labeled \"Sensor\" and the one on the right is labeled \"Actuator\". Below these two, a single, wider, light blue rectangle spans the bottom half of the main container and is labeled \"Domain\".", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY", "section_headings": ["III. THE IOT ONTOLOGY"], "chunk_type": "figure", "figure_caption": null, "line_start": 41, "line_end": 41, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed7fe64a72243ccd", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY\nType: figure\nFigure: Figure 1. Internet of Things ontology architecture.\n\nFigure 1. Internet of Things ontology architecture.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY", "section_headings": ["III. THE IOT ONTOLOGY"], "chunk_type": "figure", "figure_caption": "Figure 1. Internet of Things ontology architecture.", "line_start": 43, "line_end": 43, "token_count_estimate": 45, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a91f014021cfe2fd", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY\nType: text\n\nIn the following, we illustrate the three modules of the ontology.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY", "section_headings": ["III. THE IOT ONTOLOGY"], "chunk_type": "text", "line_start": 44, "line_end": 46, "token_count_estimate": 36, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5913ad6c175528f9", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY > A. Sensor Module\nType: text\n\nThis module is responsible for the description of sensors. Since our work aims at the compatibility with other ontology implementations, we decided to use the ontology designed by the W3C Semantic Sensor Network Incubator Group *et al.* [11] to semantically describe the sensors. This ontology uses the *Stimulus-Sensor-Observation pattern* [21] as starting point to define the description of sensors. According to such pattern: a stimulus is a change or a state in an environment that a sensor can detect and use to measure a property; a sensor is anything that senses and, usually, it consists in an object that observes, transforming incoming stimuli into\n\nanother representation; then, an observation is the result of a measurement operation and connects a stimulus that a sensor measures and the sensor that observes such stimulus, it focuses on a single measurement that a sensor takes at a precise instant and in a precise location. By means of this pattern and the definition of the three related core concepts, the W3C Semantic Sensor Network Incubator Group designed the ontology for the semantic sensor networks. The above pattern may be analyzed through four different perspectives which are shown in the following.\n\nSensor Perspective. It describes the property the sensor measures (ssn:Property) and how (ssn:Sensing and ssn:Stimulus) measurement taken. Furthermore, this perspective deals with sensor capabilities (ssn:MeasurementProperty) in terms of accuracy (ssn:Accuracy), resolution (ssn:Resolution), (ssn:Precision), and other similar sensor characteristics. These characteristics are related to particular conditions (ssn:Condition), consequently several measurement capabilities (ssn:MeasurementCapability) can be associated with each sensor.\n\nSystem Perspective. It focuses on the platform (ssn:Platform) description. In the semantic sensor network, a system (ssn:System) may be composed by several subsystems (ssn:hasSubSystem). Furthermore, each sensing device must be considered a system as well (ssn:SensingDevice ⊑ ssn:Device ⊑ ssn:System). This allows the description of complex platforms on which several sensors are aggregated in a single device. Finally, each system has its own operating range (ssn:OperatingRange) and survival range (ssn:SurvivalRange).\n\nFeature and Property Perspective. This perspective focuses on the properties (ssn:Property) that may be sensed and the related observations that are related to them. Properties are grouped in features of interest (ssn:FeatureOfInterest) that represents homogeneous collections.\n\nObservation Perspective. Finally, the fourth perspective proposed by W3C SSN Incubator Group deals with observations and related metadata. Observations are taken by means of a sensor (ssn:Sensor) at a certain moment, measuring a stimulus (ssn:Stimulus), using a sensing method (ssn:Sensing), and observing a property (ssn:Property) of a particular feature of interest (ssn:FeatureOfInterest). The result of the observation corresponds to the output of the sensor (ssn:SensorOutput).", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY > A. Sensor Module", "section_headings": ["III. THE IOT ONTOLOGY", "A. Sensor Module"], "chunk_type": "text", "line_start": 48, "line_end": 60, "token_count_estimate": 810, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1cdc4de4cab3165a", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY > B. Actuator Module\nType: text\n\nAt the best of our knowledge, there not exist generic purpose ontologies for actuators networks; as a consequence, we propose a new ontology of which goal is the modeling of actuators networks at a generic description level. leaving apart the domain-related details. Our ontology is based on the work of the W3C Semantic Sensor Network Incubator Group described above and uses several concepts defined in the SSN ontology. Reasons for this choice rely on the compatibility, the the high level of flexibility, and the level of standardization of that ontology. Taking advantage of the SSN ontology and extending it, we propose the Semantic Actuator Network ontology (SAN ontology) (Figure 2) that formally describes concepts regarding actuators.\n\nIn the architecture of our Internet of Things ontology, the Semantic Actuator Network constitutes the actuator module. In conjunction with the SSN ontology it gives a comprehensive formalization for the representation of a thing network. Thus, by means of those two ontology, we can model any complex device composed by sensors and actuators.\n\nAs SSN proposes the Sensor-Stimulus-Observation pattern in order to describe how a sensing activity works, we propose the *Actuator-Stimulus-Operation pattern* in order to describe the actuators. According to this pattern, an Actuator is an object capable to modify a property in the environment where it operates. To do that, it produces a Stimulus that is able to modify a property. Finally, an Operation represents the activity that is accomplished by the actuator.\n\nIn order to better describe the proposed pattern, similarly to the the description of SSN ontology, we define two perspectives as follows.\n\nActuator Perspective. This perspective gives a static description of the actuator and highlights the properties that the actuator may manage and/or modify. Similarly to the sensor perspective, this perspective also defines actuator specifications. An actuator (san:Actuator) is able to modify a property (ssn:Property) by means of generating a stimulus (ssn:Stimulus). In this case, a stimulus is the change that an actuator may actuate (san:models) in order to modify (san:modify) a property. It is worthy of note that a stimulus does not necessarily refers to the property that the actuator modifies. For instance, let us consider an electric motor as actuator. The property that we want to manage is its rotational speed. In order to modify it, we vary the rotor current that, in this case, is the stimulus on which the actuator acts. Furthermore, the actuator implements (san:implements) an acting process (san:Acting  $\\square$  ssn:Process) that describes the process implemented by the actuator in order to manage the property of interest. As described for the sensors, in order to describe the actuator working specifications we define the change property concept (san:ChangePropery ≡ ssn:MeasurementProperty), related with properties (such as ssn:Accuracy, ssn:Resolution, etcetera) defined in the SSN ontology. Furthermore the concept san:ChangeCapability is defined. It links (ssn:inCondition) a set of working specifications with a specific condition (ssn:Condition).", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY > B. Actuator Module", "section_headings": ["III. THE IOT ONTOLOGY", "B. Actuator Module"], "chunk_type": "text", "line_start": 62, "line_end": 76, "token_count_estimate": 819, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cebd5a53bc8623aa", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY > B. Actuator Module\nType: text\n\n( san : implements ) an acting process ( san : Acting $ \\ square $ ssn : Process ) that describes the process implemented by the actuator in order to manage the property of interest . As described for the sensors , in order to describe the actuator working specifications we define the change property concept ( san : ChangePropery ≡ ssn : MeasurementProperty ) , related with properties ( such as ssn : Accuracy , ssn : Resolution , etcetera ) defined in the SSN ontology . Furthermore the concept san : ChangeCapability is defined . It links ( ssn : inCondition ) a set of working specifications with a specific condition ( ssn : Condition ) .\n\nOperation Perspective. It provides the dynamic description of actuators. Indeed, it focuses on the operation (san:Operation) that a given actuator accomplishes in a given instant (san:operationResultTime). The operation is accomplished (san:operatedBy) by an actuator in order to modify (san:controlledProperty) a property of a specific feature of interest (san:featureOfInterest). Then, this perspective defines both the stimulus that the actuators receives and the method it uses (san:actingMethodUsed). Obviously, the operation is related with the result we want to obtain. Such result is the actuator input (san:ActuatorInput) since it consists on the information on which depends the property modification that is desired. Returning to the above example of the electric motor, the new value of the rotational speed is the input of the actuator.\n\nBoth the *System perspective* and the *Feature and Property perspective* can be defined for the SAN ontology, as well. On the other hand, such perspectives have already been defined in the SSN ontology. Therefore, regarding the *System perspetive*, we simply introduced a concept to model an acting device (san:ActingDevice  $\\sqsubseteq$  san:Actuator, ssn:Device) and define its relations with ssn:Device. As a consequence, according to our IoT ontology (obtained without modifying the SSN ontology), a system is a platform that may include both sensors and actuators. Regarding the *Feature and Property perspective*, no extensions are required since features of interest and properties are the same described for the SSN ontology. The only difference is that features and properties must be changed by actuators instead of being observed by sensors.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY > B. Actuator Module", "section_headings": ["III. THE IOT ONTOLOGY", "B. Actuator Module"], "chunk_type": "text", "line_start": 62, "line_end": 76, "token_count_estimate": 623, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5127b6ee9984938d", "text": "Document: I. Introduction\nSection: III. THE IOT ONTOLOGY > C. Domain Module\nType: text\n\nThe domain module is responsible for the description of domain-specific things. Since it uses the Sensors and Actuators modules in order to define its own concepts and relations, we choosed to create a second layer for it in the our IoT ontology architecture. By means of it, the resulting architecture is flexible and ontology designers may focus on the specific application domain without taking care of the complex structures of the ontologies for sensors and actuators, but only using their concepts to define the new ones required by the domain of interest. The next section illustrates an example for the earthquake emergency evaluation and response domain.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "III. THE IOT ONTOLOGY > C. Domain Module", "section_headings": ["III. THE IOT ONTOLOGY", "C. Domain Module"], "chunk_type": "text", "line_start": 78, "line_end": 80, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "adb82651edda4524", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: text\n\nSeveral works deal with the use of semantics in the emergency management fields (e.g., see [19], [20]). Nevertheless, they usually do not focus on \"things\" that are involved in the emergency domain. Here, we propose the *Earthquake Emergency (EEM) ontology* that formally models sensors and actuators, the former used to evaluate the earthquake entity and the latter used to respond to the emergency. In order to design and develop this ontology, we", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "text", "line_start": 82, "line_end": 84, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36af17a86583619b", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: figure\nFigure\n\nImage /page/4/Figure/0 description: A concept map or ontology diagram illustrating relationships between various entities, primarily centered around an 'Actuator'. The diagram uses rounded rectangles for concepts and labeled arrows for relationships. Concepts in green include 'ActingDevice', 'Actuator', 'Acting', 'ActuatorInput', 'OperationValue', 'Operation', 'ChangeCapability', and 'ChangeProperty'. Concepts in blue include 'Device', 'Stimulus', 'Process', 'Property', 'FeatureOfInterest', 'Condition', 'Accuracy', 'Resolution', and a box with ellipses. Solid lines indicate 'is a' relationships: 'ActingDevice' is a 'Device', 'Acting' is a 'Process', and 'ChangeProperty' is a 'Property'. 'Accuracy', 'Resolution', and others are types of 'ChangeProperty'. Dashed lines represent other relationships. An 'Actuator' is connected to a 'Device', 'implements some' 'Acting', 'models only' a 'Stimulus', is 'operatedBy only' an 'Operation', and 'hasChangeCapability only' a 'ChangeCapability'. An 'Operation' is linked to 'Actuator' ('operatedBy'), 'Acting' ('actingMethodUsed'), 'Stimulus' ('includesEvent'), 'Property' ('controlledProperty'), 'FeatureOfInterest', and 'ActuatorInput' ('operationDesiredResult'). A 'Property' is modified by 'Acting', is a proxy for 'Stimulus', and has a 'FeatureOfInterest'.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "figure", "figure_caption": null, "line_start": 85, "line_end": 85, "token_count_estimate": 481, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d9410cc3c600a4dc", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: figure\nFigure: Figure 2. The Semantic Actuator Network ontology (the light blue concepts belong to SSN).\n\nFigure 2. The Semantic Actuator Network ontology (the light blue concepts belong to SSN).", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "figure", "figure_caption": "Figure 2. The Semantic Actuator Network ontology (the light blue concepts belong to SSN).", "line_start": 87, "line_end": 87, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a492b0b3fcceeeab", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: text\n\nbenefited from the help of earthquake domain experts: we consulted civil engineers that work in this kind of emergency and interviewed them to get a proper overview over the sensors and actuators involved in this scenario.\n\nIn our domain analysis, we first identify five features of interest and related properties<sup>2</sup> (Figure 3):\n\n- emergency site (eem:SiteFeature) that has ground acceleration and the displacement the properties (eem:SiteAccelerationProperty and as eem:SiteDisplacementProperty);\n- earthquake (eem:EarthquakeFeature) magnitudo, the epicenter, and the distance the has (eem:EarthquakeMagnitudoProperty, eem:EarthquakeDistanceProperty, eem:EarthquakeEpicenterProperty);\n- the evaluation of cracks (eem:CrackFeature) that has their widths as property (eem:CrackWidthProperty);\n- the localization (eem:LocalizationFeature) that latitude-longitude pair as property eem:GPSLocalizationProperty);\n- the alarm (eem:AlarmFeature) that aims at notifying people of the earthquake entity and of additional information in order to reach a safe place (denoted by properties such as eem:AlarmNotificationProperty, eem:AlarmLightProperty, and eem:AlarmSoundProperty).", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "text", "line_start": 88, "line_end": 98, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e7725bdaf289c20d", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: figure\nFigure\n\nImage /page/4/Figure/10 description: A line of black text on a white background, which appears to be a citation. The text reads: \"²See http://earthquake.usgs.gov/learn/glossary/. Accessed: 2014-3-3.\" The number '2' at the beginning is a superscript.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "figure", "figure_caption": null, "line_start": 99, "line_end": 99, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f7f3a1fcde20fae2", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: figure\nFigure\n\nImage /page/4/Figure/11 description: A diagram illustrating a conceptual model or ontology, organized into three main columns connected by arrows indicating relationships. The first column, on the left, is titled 'FeatureOf Interest' in a light green box. Below it, a vertical line with 'is a' connects to a list of light blue boxes: 'SiteFeature', 'Earthquake Feature', 'CrackFeature', 'Localization Feature', and 'AlarmFeature'. The middle column has a light green box labeled 'Property'. Below it, a list of light blue boxes are all connected to 'Property' with arrows labeled 'is a'. These boxes are: 'Site Acceleration Property', 'Site Displacement Property', 'Earthquake Distance Property', 'Epicenter Property', 'Magnitudo Property', 'Crack Width Property', 'GPS Localization Property', 'AlarmNotification Property', 'AlarmLight Property', and 'AlarmSound Property'. The third column, on the right, is titled 'Quantity Kind' in a yellow box. Below it, a vertical line with 'is a' connects to a list of yellow boxes: 'Acceleration', 'Distance', 'Geographic Coordinates', 'Magnitudo', and 'Dimensionless'. Dashed arrows labeled 'hasProperty' connect features from the first column to their corresponding properties in the second column. For example, 'SiteFeature' has properties 'Site Acceleration Property' and 'Site Displacement Property'. Solid arrows connect properties from the second column to their quantity kinds in the third column. For example, 'Site Acceleration Property' points to 'Acceleration', and 'Epicenter Property' points to 'Geographic Coordinates'.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "figure", "figure_caption": null, "line_start": 101, "line_end": 101, "token_count_estimate": 499, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5af4300bd6909129", "text": "Document: I. Introduction\nSection: IV. THE IOT EARTHQUAKE ONTOLOGY\nType: text\n\nThe Earthquake Emergency Management ontology: Features of Interest and Properties (green concepts belong to SSN, yellow concepts belong to QU).\n\nWe used the *Quantities Kinds and Units ontology* (QU)<sup>3</sup>\n\n<sup>&</sup>lt;sup>3</sup>\"Library for Quantity Kinds and Units: schema, based on QUDV model OMG SysML(TM), Version 1.2\", http://purl.org/NET/ssnx/qu/qu\\#. Accessed: 2013-11-23.\n\nto represent units of measurement of such properties. This ontology defines two main concepts: qu:QuantityKind denotes the kind of the measurement, and qu:Unit denotes the unit of measurement. The two concepts are linked through the property qu:unitKind. In EEM, the properties we defined extend (is-a) both ssn:Property and qu:QuantityKind. In this manner, we can specify the unit of measurement for each property. Furthermore, as some units of measurement we need are not defined in QU, we directly added them in EEM. For instance, we introduced the magnitudo (eem:Magnitudo) and the geographic coordinates (eem:GeographicCoordinates).\n\nAfter property identification, we analyzed the devices involved in the earthquake emergency scenario (see Figure 4).\n\nWe identify the following sensing devices: the fissurometer (eem:Fissurometer) that measures the width of cracks; the seismometer (eem:Seismometer), the accelerometer (eem:Accelerometer), and the GPS (eem:GPS), they all are needed to evaluate an earthquake; the seismograph station (eem:SeismographStation), that is composed by the previous three sensors; and the seismograph network (eem:SeismographNetwork), that is composed by three seismograph stations at least and is used to evaluate the earthquake epicenter. Then, we identify the following actuators: the alarm siren (eem:AlarmSiren) that informs people to evacuate a building; the signaling escape light (eem:SignalingEscapeLight) that highlights the escape path during a building evacuation; and, finally, the alarm message notifier (eem:AlarmMessageNotifier) that sends information about safe places after an earthquake to citizens' mobile devices.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "IV. THE IOT EARTHQUAKE ONTOLOGY", "section_headings": ["IV. THE IOT EARTHQUAKE ONTOLOGY"], "chunk_type": "text", "line_start": 102, "line_end": 114, "token_count_estimate": 615, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90700e3d3c3c23d0", "text": "Document: I. Introduction\nSection: V. IMPLEMENTATION\nType: text\n\nAs mentioned above, our IoT ontology is composed by three main ontologies: the Semantic Sensor Network (SSN) and the Semantic Actuator Network (SAN) ontologies describe the infrastructure of a network of things, whereas the Earthquake Emergency Management (EEM) ontology describes the domain of interest we evaluate in this work. We implemented the last two ontologies using the OWL 2 language [22]. It results in two files, san.owl and eem.owl, that define respectively the SAN ontology and the EEM ontology. These files are available at the following URL: https://bitbucket.org/gilbertotcc/iot-ontology.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "V. IMPLEMENTATION", "section_headings": ["V. IMPLEMENTATION"], "chunk_type": "text", "line_start": 116, "line_end": 118, "token_count_estimate": 175, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26e8d4431b09a6b5", "text": "Document: I. Introduction\nSection: VI. DISCUSSION AND CONCLUSION\nType: text\n\nThis work proposes an ontology for Internet of Things extending the ontology for semantic sensor networks developed by the W3C Semantic Sensor Network Incubator Group and applies it to the earthquake emergency domain.\n\nAlthough several works deal with semantic description of things, we focus on flexibility and compatibility proposing\n\na three modules ontology in two layers: one dealing with a generic model for the things and one dealing with specific-domain things. Indeed, De et al. [15] gave a detailed description of things but their approach is oriented to the users of those devices and do not focus on modeling such sensors and actuators as, for example, the Semantic Sensor Network ontology [11] does about sensors. Instead, our work extends the SSN ontology and proposes a detailed description model for the actuators too. The work by Hachem et al. [6] shows an interesting analysis of the IoT domain and proposes an ontology composed by three modules. Their work focuses on the combination of things, not only on their description, up to include the mathematical laws involved in the domain. This goes beyond the goals of our work which deals only with devices and physics of the domain of interest. In our opinions, our approach results more easy to learn for end users of the ontology. In comparison with the work of Wang et al. [7], our approach is more flexible. Due to its modularity, the ontology we propose is not domain specific but is suitable for each scenario on which things are involved. The work proposed by Wang et al. [17] shows a comprehensive description of the IoT domain which includes also services and testing areas and it goes beyond the goal of our work, as well. Furthermore, their work does not show any example and/or case study in order to evaluate differences with our approach.\n\nIn conclusion, our work represents a starting point to the development of an Internet of Things ontology also suitable for users, adding a semantic description for thing interfaces. By adding a semantic description to the physical objects, such ontology eases the collaboration among several physical objects such as sensors and actuators that use different communication protocols and data formats. At the moment, it is a comprehensive ontology to deeply describe both sensors and actuators. Furthermore, its architecture enables domain-specialists to build every domain ontology they need using the top layer of our IoT ontology offering a high level of flexibility.", "metadata": {"source_file": "data/('An_Internet_of_Things_ontology_for_earthquake_emergency_evaluation_and_response', '.pdf')_extraction.md", "document_title": "I. Introduction", "section_path": "VI. DISCUSSION AND CONCLUSION", "section_headings": ["VI. DISCUSSION AND CONCLUSION"], "chunk_type": "text", "line_start": 120, "line_end": 128, "token_count_estimate": 561, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "458a99eae26aba60", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial Lake Outburst Floods (GLOFs)\nType: text\n\nA **glacial lake outburst flood** (GLOF) is a release of meltwater from a moraine- or ice-dam glacial lake due to dam failure<sup>1,2</sup>. GLOFs often result in catastrophic flooding downstream, with major geomorphic and socioeconomic impacts<sup>3,4</sup>.\n\nGLOFs have three main features:\n\n- They involve **sudden** (and sometimes cyclic) releases of water.\n- They tend to be **rapid events**, lasting hours to days.\n- They result in **large downstream river discharges** (which often increase by an order of magnitude).", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial Lake Outburst Floods (GLOFs)", "section_headings": ["Glacial Lake Outburst Floods (GLOFs)"], "chunk_type": "text", "line_start": 3, "line_end": 11, "token_count_estimate": 189, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ddd04af3cfc07316", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial Lake Outburst Floods (GLOFs)\nType: figure\nFigure\n\nImage /page/0/Picture/11 description: An aerial photograph from 2002 captures a dramatic natural event at the terminus of the Hubbard Glacier in Alaska. The image shows a massive, textured glacier with white and light blue ice on the left, meeting a steep, dark, rocky mountainside on the right. A channel of turbulent, gray water with white foam flows between the glacier and the mountain, indicating a glacial lake drainage. The caption at the bottom reads, \"Glacial lake drainage at the terminus of the Hubbard Glacier, Alaska, 2002. Photo: USGS.\"", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial Lake Outburst Floods (GLOFs)", "section_headings": ["Glacial Lake Outburst Floods (GLOFs)"], "chunk_type": "figure", "figure_caption": null, "line_start": 12, "line_end": 12, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "59b2f293ec00979b", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial Lake Outburst Floods (GLOFs)\nType: text\n\nGlacial lake drainage at the margin of the Hubbard Glacier, Alaska, 2002. Photo: USGS, Wikimedia Commons.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial Lake Outburst Floods (GLOFs)", "section_headings": ["Glacial Lake Outburst Floods (GLOFs)"], "chunk_type": "text", "line_start": 13, "line_end": 15, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "578074f78574a5cf", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Why are GLOFs important?\nType: text\n\nSome of the largest floods in Earth's history have been GLOFs<sup>5,6</sup>. They have caused large-scale landscape change<sup>5</sup>, and even altered regional climate by releasing huge quantities of freshwater to the oceans<sup>7,8</sup>.\n\nToday, GLOFs pose a risk downstream communities and infrastructure. In Peru alone, GLOFs were responsible for  $\\sim$ 32,000 deaths in the 20<sup>th</sup> century<sup>3,4</sup>. They have killed hundreds to thousands of people in other mountain regions (e.g. the Himalayas), and destroyed roads, bridges, and hydroelectric developments<sup>3</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Why are GLOFs important?", "section_headings": ["Why are GLOFs important?"], "chunk_type": "text", "line_start": 17, "line_end": 21, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e1bbf23ae76d1cd", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: An increasing hazard\nType: text\n\nImportantly, the general global trend of glacier shrinkage through the 20<sup>th</sup> and 21<sup>st</sup> centuries has seen the number and size of glacial lakes increase<sup>9-11</sup>, at the same time as human activities have expanded further into glaciated catchments. The study of how GLOFs occur and their impacts is therefore important for future hazard mitigation.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "An increasing hazard", "section_headings": ["An increasing hazard"], "chunk_type": "text", "line_start": 23, "line_end": 25, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2efb715b6a6704cb", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: An increasing hazard\nType: figure\nFigure\n\nImage /page/1/Picture/6 description: An aerial photograph of a vast, mountainous landscape dominated by glaciers and snowfields. The mountains are rugged and brown, contrasting with the white snow and the light blue and white glaciers that flow down through the valleys. Several bodies of water, identified as \"glacial lakes\" by white text and lines, are visible. These lakes, varying in color from deep blue to turquoise, are situated at the terminus of the glaciers. The labels appear in three locations: the upper left, the upper center, and the lower right of the image.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "An increasing hazard", "section_headings": ["An increasing hazard"], "chunk_type": "figure", "figure_caption": null, "line_start": 26, "line_end": 26, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "807c35849fdeba0d", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: An increasing hazard\nType: text\n\nRecent rapid growth of glacial lakes in the Bhutan-Himalaya in response to retreating glacier termini. Photo: NASA/USGS, <u>Wikimedia Commons</u>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "An increasing hazard", "section_headings": ["An increasing hazard"], "chunk_type": "text", "line_start": 27, "line_end": 29, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "458120b3c49c52d6", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings\nType: text\n\nThere are two main settings in which glacial lakes form<sup>1,2</sup>: (1) behind **moraine dams**, and (2) behind **ice dams**.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings", "section_headings": ["Glacial lake settings"], "chunk_type": "text", "line_start": 31, "line_end": 33, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe96ac29377050c7", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings > Moraine-dammed lakes\nType: text\n\nMoraine-dammed lakes form during periods of glacier retreat from a moraine<sup>1,12</sup>. As a glacier margin retreats, water collects in the topographic low between the ice-front and the abandoned frontal and/or lateral moraine. Most existing moraine-dammed lakes (such as the Imja Tsho glacial lake in Nepal; see below) formed when mountain glaciers began to retreat from large moraine ridges constructed during the Little Ice Age<sup>12</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings > Moraine-dammed lakes", "section_headings": ["Glacial lake settings", "Moraine-dammed lakes"], "chunk_type": "text", "line_start": 35, "line_end": 37, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "1cb46cc1c2fe29c9", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings > Moraine-dammed lakes\nType: figure\nFigure\n\nImage /page/2/Picture/3 description: A photograph of a high-altitude mountain landscape illustrating various glacial features with labels. In the background, massive, jagged, snow-covered peaks rise against a clear blue sky. In the center of the image is a large, white, ice-covered body of water labeled 'lake'. To the upper left of the lake, a 'glacier' is shown descending from the mountains. To the right of the lake, a ridge of rock and debris is identified as a 'lateral moraine'. In the foreground, a vast, rugged area of rock, sediment, and small meltwater ponds is labeled as the 'terminal moraine complex'.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings > Moraine-dammed lakes", "section_headings": ["Glacial lake settings", "Moraine-dammed lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 38, "line_end": 38, "token_count_estimate": 204, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "29229596c41f573c", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings > Moraine-dammed lakes\nType: text\n\nImja Tsho (or Imja Lake) in eastern Nepal, dammed by a terminal moraine complex. The lake has been growing rapidly since the 1960s as the Imja Glacier has retreated. Photo: Sharad Joshi, Wikimedia Commons, Edited by J.Bendle.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings > Moraine-dammed lakes", "section_headings": ["Glacial lake settings", "Moraine-dammed lakes"], "chunk_type": "text", "line_start": 39, "line_end": 41, "token_count_estimate": 101, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "0ecc4dbe83b9be84", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings > Ice-dammed lakes\nType: text\n\nIn contrast to moraine-dammed lakes, ice-dammed lakes form when drainage is blocked by a glacier that advances or becomes thicker<sup>13,14</sup>. Consequently, ice-dammed lake growth is closely related to glacier mass balance and climate. Ice-dammed lakes form wherever a glacier blocks the drainage of meltwater.\n\nThe main settings for ice-dammed lakes are shown in the diagram below. These include: where a tributary valley is blocked by a trunk glacier; where a glacier from a tributary valley advances across the main trunk valley; in openings between the lateral glacier margin and ice-free valley sides; and at the point where two glaciers join.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings > Ice-dammed lakes", "section_headings": ["Glacial lake settings", "Ice-dammed lakes"], "chunk_type": "text", "line_start": 43, "line_end": 47, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5c79b08e8a03e118", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings > Ice-dammed lakes\nType: figure\nFigure\n\nImage /page/3/Picture/3 description: A diagram illustrating various features in a glacial environment. The image shows large, light-blue glaciers surrounded by gray land. Dotted arrows indicate the direction of 'ice flow'. Several features are labeled, including a 'tributary glacier' merging with a larger glacier at an 'ice confluence', and a 'retreating tributary glacier'. Different types of lakes, shown in a darker blue, are also labeled: a 'marginal lake' at the edge of the ice, a 'supraglacial lake' on top of the ice, and a 'proglacial lake' in front of the glacier. A 'river' is shown flowing between two large glacier masses. 'Streams' and 'drainage' are depicted as water flowing away from the glaciers onto the land. The bottom right corner includes the credit '© J.Bendle'.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings > Ice-dammed lakes", "section_headings": ["Glacial lake settings", "Ice-dammed lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 48, "line_end": 48, "token_count_estimate": 257, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4f29b116da6445a6", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Glacial lake settings > Ice-dammed lakes\nType: text\n\nSettings of ice-dammed lake growth. Potential routed of lake drainage, beneath or around glacier margins, are shown with dashed blue lines. © J.Bendle.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Glacial lake settings > Ice-dammed lakes", "section_headings": ["Glacial lake settings", "Ice-dammed lakes"], "chunk_type": "text", "line_start": 49, "line_end": 51, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a32629b98452cbb3", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams\nType: text\n\nThe failure of glacier and moraine dams depends on two main factors: (1) the **integrity of the dam**, and (2) the **nature of trigger mechanisms**<sup>1,2,12,13</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams", "section_headings": ["The failure of ice or moraine dams"], "chunk_type": "text", "line_start": 53, "line_end": 55, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a80d9bd1406da920", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures\nType: text\n\nMoraine dams tend to be narrow and sharp-crested<sup>12</sup>. As such, they are more likely to fail than broader dam types, such as ice-contact fans or landslides (see diagram below). Most moraines are made up of loose, poorly sorted, permeable sediment, and some contain ice cores<sup>12</sup>. Unconsolidated sediments are susceptible to failure, especially if saturated, while the melting of ice cores may cause moraines to subside over time. Despite these weaknesses, where a moraine is low, wide, and armoured by large rock material it may survive intact for hundreds or even thousands of years<sup>12</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures"], "chunk_type": "text", "line_start": 57, "line_end": 59, "token_count_estimate": 211, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "314db24cda18031f", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures\nType: figure\nFigure\n\nImage /page/4/Figure/3 description: A diagram comparing the cross-sectional profiles of various geological landforms and man-made structures. At the top, three brown shapes are labeled \"moraines\". Below that is a long, tapering, light-yellow shape labeled \"outwash fan\". Next are two long, low-profile brown landforms labeled \"Daguangbao rock avalanche\" and \"Yaoyingyan landslide\", with the former being longer than the latter. At the bottom left, two gray cross-sections of dams are labeled \"Man-made gravity dams\". A scale is provided at the bottom right, indicating a horizontal distance of 1000 m and a vertical distance of 200 m, illustrated with a generic shape labeled \"width\" and \"height\". The image is credited to \"© J.Bendle\" in the bottom left corner.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures"], "chunk_type": "figure", "figure_caption": null, "line_start": 60, "line_end": 60, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0224b26a7c169001", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures\nType: text\n\nApproximate width and height of typical moraine, outwash, landslide (see ref: 15), and man-made gravity dams. Compared to other common dam types, moraines are narrow, and contain a relatively small volume of material. © J.Bendle", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures"], "chunk_type": "text", "line_start": 61, "line_end": 63, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcf2a056efa5e33e", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Displacement waves\nType: text\n\nOutburst floods in moraine-dammed settings are often caused by the sudden input of material into a lake causing displacement of water and overtopping of the dam<sup>12,16</sup>. Displacement (or *seiche*) waves are commonly triggered by avalanches or rockfalls, or calving of a lake-terminating glacier as shown below.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Displacement waves", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Displacement waves"], "chunk_type": "text", "line_start": 65, "line_end": 67, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f32d6de7c2842b41", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Displacement waves\nType: figure\nFigure\n\nImage /page/4/Picture/7 description: A diagram illustrating how a seiche wave is formed in a moraine-dammed lake. On the left, a large light-blue glacier is shown on top of a brown landmass. The edge of the glacier is breaking off into a body of water in a process labeled \"calving\". This creates a large wave, labeled \"seiche wave\", in the \"moraine-dammed lake\". The wave travels to the right and crashes over a mound of earth labeled \"moraine dam\" in a process called \"overtopping\".", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Displacement waves", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Displacement waves"], "chunk_type": "figure", "figure_caption": null, "line_start": 68, "line_end": 68, "token_count_estimate": 184, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad1489a9ad588ac8", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Displacement waves\nType: text\n\nCalving of ice blocks at the glacier terminus is a common cause for lake water displacement, seiche wave development, and overtopping of moraine dams. © J.Bendle.\n\nOther triggers include, the rapid input of meltwater from an glacier upstream, heavy rainfall or snowmelt events that rapidly raise the lake level, or earthquakes that destabilise the moraine dam<sup>12,14</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Displacement waves", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Displacement waves"], "chunk_type": "text", "line_start": 69, "line_end": 73, "token_count_estimate": 149, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0e5db529a9e7bbe", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Breach incision\nType: text\n\nOnce overtopped, a process called **breach incision** can occur. This occurs as water flowing across the dam surface erodes a channel into the moraine, starting a positive feedback process where channel incision allows more water to escape, and the higher discharges leaving the outlet encourage greater rates of erosion (see diagram below). This process allows a moraine dammed lake to drain\n\nvery rapidly<sup>12,16</sup>. It also adds large volumes of unconsolidated sediment to the flood waters, which may then develop into highly destructive debris flows<sup>12</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Breach incision", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Breach incision"], "chunk_type": "text", "line_start": 75, "line_end": 79, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a7a1b87b84b06e78", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Breach incision\nType: figure\nFigure\n\nImage /page/5/Picture/4 description: A high-angle photograph of a mountainous, glacial landscape with several features labeled. In the center is a milky, light-green body of water labeled \"lake.\" This lake is dammed by a large ridge of rock and sediment, which is labeled \"moraine.\" A V-shaped gap in this ridge is identified as a \"moraine breach.\" Higher up the slope from the lake, a patch of snow and ice is labeled \"glacier.\" A narrow, winding trail crosses the steep, rocky terrain, and two hikers with large backpacks are visible on the path.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Breach incision", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Breach incision"], "chunk_type": "figure", "figure_caption": null, "line_start": 80, "line_end": 80, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85e6b62bfb6fc614", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Breach incision process\nType: figure\nFigure\n\nImage /page/5/Picture/6 description: A diagram illustrating the process of moraine incision over three time steps, labeled T1, T2, and T3. In T1, water is shown \"overtopping\" a moraine. In T2, this has led to a small \"moraine incision,\" with water flowing through it. In T3, the incision has become a much larger, V-shaped channel. Arrows indicate that this process leads to an \"increasing discharge\" of water as the channel deepens.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Breach incision process", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Breach incision process"], "chunk_type": "figure", "figure_caption": null, "line_start": 84, "line_end": 84, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bd31328ba7e508d1", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Breach incision process\nType: text\n\nExample of moraine breach at Schoolroom Glacier lake, Grand Teton National Park, USA, with schematic to show breach incision process © J.Bendle. Photo: National Park Service, Wikimedia Commons.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Breach incision process", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Breach incision process"], "chunk_type": "text", "line_start": 85, "line_end": 87, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f62baa0c1609c922", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Moraine-dam failures > Moraine degradation\nType: text\n\nPiping (the seepage of water and sediment through a moraine weakening its internal structure) or the melting of ice cores can trigger moraine collapse, or lower the moraine to the point where a smaller displacement wave will cause overtopping and breach incision<sup>12</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Moraine-dam failures > Moraine degradation", "section_headings": ["The failure of ice or moraine dams", "Moraine-dam failures", "Moraine degradation"], "chunk_type": "text", "line_start": 89, "line_end": 91, "token_count_estimate": 116, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c2699d519ae9e51", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures\nType: text\n\nUnlike moraine dam failures, the drainage of an ice-dammed lake does not necessarily end in dam destruction<sup>1,13,17</sup>. This is because water can drain through subglacial channels that close up when\n\nwater discharge drops, or because an ice-margin may temporarily float before regaining contact with the bed<sup>18,19</sup>. The time-lapse video below (provided by <u>Joe Mallalieu</u>) gives an example of glacial lake drainage at Russell Glacier, Greenland, due to temporary glacier flotation<sup>19</sup>.\n\nhttp://www.antarcticglaciers.org/wp-content/uploads/2018/10/RG-GLOF-2015 JoeMallalieu.mp4", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures"], "chunk_type": "text", "line_start": 93, "line_end": 99, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "324a002b8baf9e5d", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation\nType: text\n\nIce-dam floatation occurs due to the difference in density between ice ( $\\sim$ 0.9 g/cm³) and water ( $\\sim$ 1.0 g/cm³)<sup>13,18,19</sup>. As shown in the diagram below, when lake level reaches 90% of the glacier dam height the ice will lift from the bed, and water can escape through subglacial conduits. However, as a lake empties, and its level falls below 90% of dam height, the glacier drops to the bed and subglacial conduits close up. This stops lake drainage and allows a lake to refill.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures", "Ice dam flotation"], "chunk_type": "text", "line_start": 101, "line_end": 103, "token_count_estimate": 188, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e497e3469e98534", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation\nType: figure\nFigure\n\nImage /page/6/Picture/7 description: A scientific diagram, labeled T₁ in the upper right corner, illustrates a cross-section of a glacier or ice sheet interacting with a body of water. The diagram shows a large mass of light blue ice on the right and a body of darker blue water on the left, both resting on a sloping dark gray ground surface. A dashed black line labeled \"flotation level\" is shown above the water level. A section where the ice is grounded is marked with a bracket and the text \"no flotation\". An arrow points from this label to an inset box below, which provides a magnified view of the ice-ground interface. This inset is labeled \"sealed conduits\" and shows dashed, arch-shaped channels at the base of the ice.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures", "Ice dam flotation"], "chunk_type": "figure", "figure_caption": null, "line_start": 104, "line_end": 104, "token_count_estimate": 237, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "adfc1476d2f6d6e5", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation\nType: text\n\nhydrostatic pressure < ice overburden pressure", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures", "Ice dam flotation"], "chunk_type": "text", "line_start": 105, "line_end": 107, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62e69c9e18a43109", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation\nType: figure\nFigure\n\nImage /page/6/Picture/9 description: A scientific diagram illustrating the process of lake drainage under a glacier, labeled \"T₂\" in the upper right corner. The main diagram shows a cross-section of a body of water on the left, dammed by a large light-blue ice mass on the right, both resting on a downward-sloping grey ground. A section where the ice begins to lift off the ground is labeled \"ice-dam flotation.\" An arrow points from this area to a smaller, inset diagram below. The main diagram also shows water flowing from the lake under the glacier, indicated by an arrow and the label \"lake drainage.\" The channel under the glacier is labeled \"subglacial conduit.\" The inset diagram, connected by an arrow from \"ice-dam flotation,\" is a magnified view of the base of the ice, showing several rounded channels labeled \"open conduits.\"", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures", "Ice dam flotation"], "chunk_type": "figure", "figure_caption": null, "line_start": 108, "line_end": 108, "token_count_estimate": 265, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3cf60f5ce7da6f0", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation\nType: text\n\nhydrostatic pressure > ice overburden pressure\n\n© J.Bendle\n\nIce-dam flotation mechanism of glacial lake drainage. The ice dam lifts from the bed when hydrostatic (water) pressure is greater than ice overburden pressure. Lake water can then escape through subglacial conduits. © J.Bendle.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures", "Ice dam flotation"], "chunk_type": "text", "line_start": 109, "line_end": 115, "token_count_estimate": 116, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6474f6d2016d4e13", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: The failure of ice or moraine dams > Ice-dam failures > Overspilling\nType: text\n\nLake drainage can also occur by simple overspilling<sup>13,17</sup>, for example, during a large rainfall or snowmelt event that causes lake level to rise. This is most common at cold-based glaciers, which are frozen to the bed and less permeable than warm-based glaciers<sup>2</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "The failure of ice or moraine dams > Ice-dam failures > Overspilling", "section_headings": ["The failure of ice or moraine dams", "Ice-dam failures", "Overspilling"], "chunk_type": "text", "line_start": 117, "line_end": 119, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2510ddaae339429c", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Differences between moraine- and ice-dammed GLOFs\nType: text\n\nFor a given potential energy (think of this as lake volume) moraine dam outbursts typically produce higher magnitude floods (see diagram below)<sup>12</sup>. However, the threat of repeated GLOFs from a single lake is low because moraine dams are often destroyed during an outburst.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Differences between moraine- and ice-dammed GLOFs", "section_headings": ["Differences between moraine- and ice-dammed GLOFs"], "chunk_type": "text", "line_start": 121, "line_end": 123, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "766010f8744ff776", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Differences between moraine- and ice-dammed GLOFs\nType: figure\nFigure\n\nImage /page/7/Figure/5 description: A log-log plot titled 'Flood size vs. potential energy of dammed lake water', which is noted as being 'Redrawn and modified from Costa & Schuster (1988)'. The x-axis is 'Potential energy (joules) of dammed lake water', with a scale from 10^7 to 10^16. The y-axis is 'Maximum flood discharge (m/sec)', with a scale from 10^1 to 10^5. The graph shows two positively correlated trend lines. The upper, green dashed line is labeled 'Moraine-dam outbursts (higher discharges for same potential energy)'. The lower, blue dashed line is labeled 'Ice-dam outbursts (lower discharges for same potential energy)'. A vertical dashed line at a potential energy of 10^13 joules highlights two points, one on each trend line, which are collectively labeled 'GLOF'. At this potential energy, the moraine-dam outburst has a discharge of approximately 8x10^3 m/sec, while the ice-dam outburst has a discharge of approximately 2x10^2 m/sec.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Differences between moraine- and ice-dammed GLOFs", "section_headings": ["Differences between moraine- and ice-dammed GLOFs"], "chunk_type": "figure", "figure_caption": null, "line_start": 124, "line_end": 124, "token_count_estimate": 325, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "384e0d7d43b41756", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Differences between moraine- and ice-dammed GLOFs\nType: text\n\nFlood size vs. potential energy of lake water (plotted on logarithmic scale). Moraine-dam outbursts produce, on average, GLOFs with higher flood discharges, than ice-dam outbursts. For example, a moraine- and ice-dammed lake with the same potential energy of  $10^13$  joules (black dots), will lead to a peak flood discharge of  $10^4$  and  $10^2$  m/sec respectively. Diagram created based on data originally presented in refs 12 and 13. © J.Bendle.\n\nIce-dammed lake drainage, by contrast, does not often result in dam destruction, meaning that ice-dammed lakes can drain and refill many times. A good example is Lago Catchet Dos in Patagonia (see image below), dammed by the Colonia glacier<sup>20</sup>. This lake has drained in subglacial tunnels beneath the ice at least 21 times in the last 10 years<sup>20</sup>.\n\nSo, while in the modern-day ice-dammed lake outbursts typically produce lower discharge, and less destructive, floods<sup>12</sup>, they may have long-term impacts on downstream communities and infrastructure<sup>20</sup>.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Differences between moraine- and ice-dammed GLOFs", "section_headings": ["Differences between moraine- and ice-dammed GLOFs"], "chunk_type": "text", "line_start": 125, "line_end": 131, "token_count_estimate": 342, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31004cf48279d161", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Differences between moraine- and ice-dammed GLOFs\nType: figure\nFigure\n\nImage /page/8/Picture/3 description: A satellite image showing the Colonia Glacier and Lake Cachet Dos. The main image is in color and shows the large, light-blue glacier flowing through a valley. An area to the right of the glacier is labeled \"lake Cachet Dos\". In the lower left corner, there is a smaller, inset grayscale image showing a close-up of the glacier, which is labeled \"pre-GLOF\".", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Differences between moraine- and ice-dammed GLOFs", "section_headings": ["Differences between moraine- and ice-dammed GLOFs"], "chunk_type": "figure", "figure_caption": null, "line_start": 132, "line_end": 132, "token_count_estimate": 146, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d54aba48ec529723", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Differences between moraine- and ice-dammed GLOFs\nType: figure\nFigure\n\nImage /page/8/Picture/4 description: An annotated aerial photograph illustrating the aftermath of a glacial lake outburst flood (post-GLOF). The main image shows a large valley glacier flowing between dark, rocky mountains. Several features are labeled to explain the event. A label points to a small, dark body of water next to the glacier, identified as a \"lake drained.\" A blue dashed line with an arrow, labeled \"subglacial tunnel,\" traces the path of the water from the drained lake down the length of the glacier. In the top left, an inset image provides a magnified view of the glacier's terminus, showing numerous \"stranded icebergs\" in the water.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Differences between moraine- and ice-dammed GLOFs", "section_headings": ["Differences between moraine- and ice-dammed GLOFs"], "chunk_type": "figure", "figure_caption": null, "line_start": 134, "line_end": 134, "token_count_estimate": 212, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d4317a728fa71d91", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Differences between moraine- and ice-dammed GLOFs\nType: text\n\nLago Cachet Dos in Patagonia, southern South America, has repeatedly drained and refilled in recent times (ref: 20). During a GLOF event, lake water drains via subglacial conduits at the bed of the Colonia Glacier. Note the large icebergs left stranded on the former lake bed after drainage. Imagery: GoogleEarth, Edited by J.Bendle.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Differences between moraine- and ice-dammed GLOFs", "section_headings": ["Differences between moraine- and ice-dammed GLOFs"], "chunk_type": "text", "line_start": 135, "line_end": 137, "token_count_estimate": 130, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "28dd594497eee146", "text": "Document: Glacial Lake Outburst Floods (GLOFs)\nSection: Will GLOF hazards increase or decrease in the future?\nType: text\n\nAn important and interesting question! The threat from moraine-dammed GLOFs is typically greatest during periods of glacier retreat, whereas ice-dammed GLOFs are highest during periods of glacier growth.\n\nTherefore, we might expect the number of moraine-dammed GLOFs to increase as mountain glaciers continue to shrink worldwide. However, because moraine dams are normally destroyed in lake outbursts, the number of GLOFs will likely start to decrease over time, as the capacity for storing glacial meltwater is gradually lost.", "metadata": {"source_file": "data/('AntarcticGlaciers_Glacial_Lake_Outburst_Floods__GLOFs_', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Floods (GLOFs)", "section_path": "Will GLOF hazards increase or decrease in the future?", "section_headings": ["Will GLOF hazards increase or decrease in the future?"], "chunk_type": "text", "line_start": 139, "line_end": 143, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3eacea9a8aec0b9a", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nType: text\n\nIdentification of potentially dangerous glacial lakes and recognition of risks associated with them, including ranking of the critical lakes, has become a priority task. Once the critical lakes are identified, the planners, developers, and scientists involved need to develop and implement appropriate measures to reduce the potential risks from these lakes. Measures include: monitoring, to provide an early indication of changes; early warning systems, to provide downstream residents and owners of infrastructure time to take avoidance action; and mitigation measures, to physically change the situation and thus reduce the risk", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation"], "chunk_type": "text", "line_start": 2, "line_end": 4, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab22be449b1b1b07", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems\nType: text\n\nThe United Nations International Strategy for Disaster Reduction (UNISDR), in a 2006 report, defines early warning as \"the provision of timely and effective information, through identified institutions, that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response\" (United Nations 2006).\n\nEffective GLOF monitoring and early warning systems are an important part of disaster preparedness; they have the potential to greatly reduce loss of life and property. Any such system should involve application of remote sensing tools, such as the universally available earth observation satellite data, over-flight reconnaissance with small format cameras, telecommunication, and broadcasting systems.\n\nMuch progress has already been made in this area, particularly in Nepal and Bhutan. The National Action Plan for Adaptation (NAPA) to Climate Change prepared by the Royal Government of Bhutan has placed considerable emphasis on GLOF vulnerability reduction efforts. Similarly, the Government of India has brought out a 'National Communication on Climate Change Mitigation and Adaptation' which has also pinpointed GLOF vulnerability reduction efforts. However the results are not always without problems. Some examples of early warning systems are given in the following.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems"], "chunk_type": "text", "line_start": 6, "line_end": 12, "token_count_estimate": 311, "basins": [], "subbasins": [], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": []}}
{"id": "148a9806eda7e67a", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Sutlei river basin, India\nType: text\n\nSome measures have been put in place in the Sutlej River basin for monitoring, forecasting, and early warning to deal with flash floods, especially from cloudbursts. As GLOFs are one of the causative factors in propagating flash floods downstream, these measures also act as an early warning system for a GLOF. Telemetry stations set up by the Snow and Hydrology Division of the Central Water Commission in Sumdo, at the confluence of the Parechu and Spiti rivers, and Khaab, at the confluence of the Spiti and Sutlej rivers, and by the Naptha-Jhakri project at Dubling, are intended to monitor any increase in the water level and to relay information. They were introduced in response to the gap in early warning that was felt after the floods in 2000, and also for the protection of hydropower projects. Similarly, a wireless network at Reckong Peo, used by security personnel with connections to border outposts, and the Doordarshan Satellite Earth Station and All India Radio Relay Centre, have been very useful in generating warnings and in communicating during emergencies (UNDP 2008).", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Sutlei river basin, India", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "Early warning system in the Sutlei river basin, India"], "chunk_type": "text", "line_start": 14, "line_end": 16, "token_count_estimate": 323, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "7efb3bc73ca67417", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Tsho Rolpa and Tamakoshi valleys, Nepal\nType: text\n\nLake Tsho Rolpa in the Tamakoshi sub-basin in eastern Nepal is fed by Trakarding glacier and is one of a small number of glacial lakes that have been studied in detail, including field investigations of the lake itself and the downstream area. This investigation led to a realisation in the 1990s that the risk associated with the lake, whose level had risen to the crest of its containing end moraine dam, was potentially serious (WECS 1993, 1994). It was feared that Rolwaling valley downstream of the lake could be inundated due to catastrophic outflow and that widespread loss of life and serious damage\n\nto local infrastructure, including potential damage to the 60 MW Khimti Hydroelectric Project, was a high probability (Reynolds 1999).\n\nAttempts to significantly reduce the risk of a GLOF occurring from Tsho Rolpa have been extensively documented. Although the early warning system(s) was initially to some extent successful, a serious problem arose as a result of interference from local people. Details of the early warning systems are given below.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Tsho Rolpa and Tamakoshi valleys, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "Early warning system in the Tsho Rolpa and Tamakoshi valleys, Nepal"], "chunk_type": "text", "line_start": 18, "line_end": 24, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "5a7d49f07253671f", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: text\n\nAn intensive warning system is needed during short periods when construction projects are underway or for other reasons when a significant risk is identified. In June 1997, a manual early warning system was installed by the Nepal Government as an emergency measure for the local villagers and the Khimti Hydropower Project, under construction at that time. The emergency measure was needed in view of the rapid rate of deterioration of the moraine dam, and the construction activities of the Tsho Rolpa GLOF Risk Reduction Project (TRGRRP) which aimed to lower the lake level (see next section). Early warning systems were installed in the Rolwaling and Tamakoshi valleys. Army camps were established at the terminal moraine and at Naa village, the nearest village to the lake. Police posts were also established at Naa and Bedding villages. Each army camp and police post was provided with a high frequency radio transceiver; the army post at Naa also had a backup set. The police posts and the army camp in Naa were in regular radio contact with their respective headquarters in Kathmandu. The army posts were also provided with satellite telephones. The lakeside army post used one of the phones to contact the Disaster Prevention Cell at the Home Ministry twice a day to deliver status reports. In the event of a GLOF, Radio Nepal, the national broadcaster, would broadcast a warning (Reynolds, 1999; Bajracharya, et al., 2007).\n\nIn January 1998, MeteorComm and its partner, British Columbia Hydro International Ltd. (BCHIL) of Vancouver, signed a contract with the Government of Nepal, Department of Hydrology and Meteorology (DHM), to design, supply, install and commission an audible warning system downstream of Tsho Rolpa (www.MeteorComm – Wireless Communications Tsho Rolpa.mht). The project was financed by the World Bank (WB) at a total cost of US \\$ 1,032,000. This first early warning system installed at Tsho Rolpa and the villages of the Tamakoshi valley in May 1998 was intended to warn people living in downstream areas in the case of a GLOF event, and consisted of a GLOF sensing and warning system. The sensors would detect the occurrence of a GLOF and transmit relevant information to the transmitter station thus setting in motion the warning process. The warning would sound to alert the local people downstream. This system was fully automated and required no human intervention.\n\nThe GLOF sensing system consisted of six water level sensors installed along the right bank of the river channel immediately downstream of the lake outlet at Sangma Kharka; this was designed to detect the onset of a breach. Three sensors were connected by armoured and shielded cables to each of the two independently functioning transmitting stations (Figure 5a) located at a higher elevation and within 80 m of the sensors. Each sensor was located at a different elevation above the previous high water mark such that different sensors would be able to indicate any progressively rising river stages. Thus, the sensing system would detect the occurrence of a GLOF immediately and signal directly to all warning stations located downstream within two minutes of initiation of a flood. The remote station at Naa village had the dual function of forming part of the GLOF sensing system and providing local warning to the village residents.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "text", "line_start": 26, "line_end": 34, "token_count_estimate": 818, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "c6c1551b71b30480", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: text\n\nwere connected by armoured and shielded cables to each of the two independently functioning transmitting stations ( Figure 5a ) located at a higher elevation and within 80 m of the sensors . Each sensor was located at a different elevation above the previous high water mark such that different sensors would be able to indicate any progressively rising river stages . Thus , the sensing system would detect the occurrence of a GLOF immediately and signal directly to all warning stations located downstream within two minutes of initiation of a flood . The remote station at Naa village had the dual function of forming part of the GLOF sensing system and providing local warning to the village residents .\n\nThe warning system consisted of 19 warning and relay stations installed at the 17 villages of the Rolwaling and Tamakoshi valleys (Figure 5b). The warning stations located in the villages had Meteor Communication Corporation (MCC) 545-transceiver units mounted on 4.67m self-supporting standard galvanised iron power poles. The antennae were mounted on extensions to the poles approximately 5 m above the ground. Lightning rods and solar panels were mounted on the same poles. MCC 545 units, with battery and relay for the horns, were mounted inside sheet metal boxes with lockable shelters, also attached to the poles. All cables were protected by plastic conduits, covered by galvanised sheet metal and strapped to the poles. Air-powered horns, designed to operate off charged air cylinders for a period of two minutes, with a reserve for an additional one to two minutes, were also mounted on the poles. The air horns could provide a sound of 80 dB up to a minimum distance of 150 m under the most adverse conditions. They were backed up by electric horns which could operate for four minutes. The GLOF warning systems were based on 'extended line of sight' (ELOS) VHF radio technology. The warning signal would be transmitted via ELOS ground wave signals from remote station to remote station down the valley. Thus, an early warning signal would be triggered automatically if a GLOF was detected.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "text", "line_start": 26, "line_end": 34, "token_count_estimate": 566, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d0f107228d4a200", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: figure\nFigure: Figure 5: The Tsho Rolpa/Tamakoshi valley early warning system: a) Sangma Kharka transmitter station outside lake Tsho Rolpa receives signals from sensors and transmits to other remote warning stations; b) the warning and relay station Gongar village, comprising solar panel, battery, antenna and amplifier with siren; c) destroyed siren pole at Sigati village; d) damaged siren at Bhorle village\n\nFigure 5: The Tsho Rolpa/Tamakoshi valley early warning system: a) Sangma Kharka transmitter station outside lake Tsho Rolpa receives signals from sensors and transmits to other remote warning stations; b) the warning and relay station Gongar village, comprising solar panel, battery, antenna and amplifier with siren; c) destroyed siren pole at Sigati village; d) damaged siren at Bhorle village", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "figure", "figure_caption": "Figure 5: The Tsho Rolpa/Tamakoshi valley early warning system: a) Sangma Kharka transmitter station outside lake Tsho Rolpa receives signals from sensors and transmits to other remote warning stations; b) the warning and relay station Gongar village, comprising solar panel, battery, antenna and amplifier with siren; c) destroyed siren pole at Sigati village; d) damaged siren at Bhorle village", "line_start": 35, "line_end": 35, "token_count_estimate": 266, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a73a5c16710cb151", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: figure\nFigure\n\nImage /page/2/Picture/2 description: A photograph of a weather or communication station in a mountainous valley. A tall, grey metal pole is in the center, topped with a multi-element antenna. A small solar panel and a grey equipment box are attached to the side of the pole. The station is located on a rocky, grassy hillside, with steep, dark mountains in the background under a blue sky with white clouds. A partial metal fence is visible around the base of the pole. In the bottom left corner, there is a white circle with the letter 'a' inside.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "figure", "figure_caption": null, "line_start": 37, "line_end": 37, "token_count_estimate": 192, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3fc075545a8ba89f", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: figure\nFigure\n\nImage /page/2/Picture/3 description: A close-up photograph of a solar-powered communication and alarm system mounted on a grey pole against a backdrop of dense green foliage on a hillside. At the top of the pole is a Yagi-Uda antenna with several horizontal elements. Below the antenna, a red horn-style siren is attached. Further down, there is a small solar panel tilted upwards, and below that, a larger, light-colored utility box is fixed to the pole. In the bottom left corner of the image, there is a white circle containing the letter 'b'. The entire image is framed with a blue border.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "figure", "figure_caption": null, "line_start": 39, "line_end": 39, "token_count_estimate": 213, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93c746a016f17561", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: figure\nFigure\n\nImage /page/2/Picture/4 description: An outdoor, slightly blurry photograph of an overgrown, weedy area. A long, gray pipe lies on a grassy slope amidst dense green vegetation. In the background, there is a low, weathered stone or brick wall leading up to a building with a dark opening. The image is framed with a thick blue border, and there is a white circle with the letter 'c' in the bottom-left corner.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "figure", "figure_caption": null, "line_start": 41, "line_end": 41, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f9e804c3c466f456", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: figure\nFigure\n\nImage /page/2/Picture/5 description: A photograph of a rural, hilly landscape. In the foreground, a person wearing a white cap and a backpack walks through green vegetation. In the center of the image stands a tall, grey pole with a red siren mounted on top. Behind the person and near the pole is a small building with a corrugated metal roof. The background consists of a steep, rocky hillside with patches of green foliage, suggesting a landslide area. A small white circle containing the letter 'd' is in the bottom left corner.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "figure", "figure_caption": null, "line_start": 43, "line_end": 43, "token_count_estimate": 194, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "986e9d40f7b82343", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems\nType: text\n\nA second component of the system was the installation of an MCC meteor burst master station located in Dhangarhi, western Nepal. A meteor burst station uses the ionised trails of meteors to extend the range of transmitted radio signals to over 1,600 kilometres (1000 miles). Several of the warning stations, as well as a sensing station, were designed to transmit and receive signals from the master station, to provide further redundancy to the system. The master station also monitored the status of the entire warning system. Thus, the master station provided a communication link between remote stations located in the Rolwaling and Tamakoshi valleys and the monitoring station in Kathmandu.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The early warning systems"], "chunk_type": "text", "line_start": 44, "line_end": 46, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "20799008e3f6482f", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The outcome\nType: text\n\nBy 2002, four years after establishment, the early warning system was no longer operating (Figures 5c,d) despite the fact that it was a robust system commissioned with the latest technology. Lack of participation by the local communities and disruption of communications during Nepal's long period of political uncertainty, appears to have led to the system being ignored and then destroyed, with components being taken to use for other purposes locally. The people in the area thought that the lake had been reduced to a safe level and lost interest in the warning system. The tendency towards ignoring the importance of early warning was further intensified by the incidence of false alarms (Khanal et al. 2009).", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The outcome", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "The outcome"], "chunk_type": "text", "line_start": 48, "line_end": 50, "token_count_estimate": 210, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "b7172df2fa0708d1", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Upper Bhote Koshi, Nepal\nType: text\n\nAnother example of an early warning system, similar to that of Tsho Rolpa, is in place in the Upper Bhote Koshi valley of eastern Nepal. The system was installed in 2001, mainly for the Upper Bhote Koshi Hydroelectric Project. It consists of two remote sensing stations with data loggers near the Friendship Bridge at the Nepal-China border that are designed to receive, analyse, and transmit data from sensors. If the water level increases significantly, the system will transmit warning signals to stations located at the intake and the powerhouse site of the power station. In this system, there are seven GLOF detection sensors at the Friendship Bridge, one ultrasonic water level measuring device, and six float type water level switches. It operates on short-burst VHF radio signals using meteor burst technology. Warning sirens are set off from compressed air horns which transmit a sound of 127 dB at a minimum distance of 33 m. There are five such stations along the river (Bajracharya et al. 2007), but the early warning system is only installed up to the border between Nepal and China. From there, there is only 6 minutes of warning time down to the Bhote Koshi hydropower station. To be really useful, the early warning system would need to be extended further upstream into China (TAR). As of 2009, the system was still fully functioning – presumably because of the interest of the hydropower project.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Upper Bhote Koshi, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "Early warning system in the Upper Bhote Koshi, Nepal"], "chunk_type": "text", "line_start": 52, "line_end": 54, "token_count_estimate": 395, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "bd90a76a0cb24527", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Poiqu basin (Sunkoshi – Bhote Koshi basin)\nType: text\n\nThe inventory by Mool et al. (2005) indicated that the number of lakes in the Poiqu basin, TAR, China (Sunkoshi – Bhote Koshi basin), increased from 119 in the 1980s to 139 in 2000, with an increase of 22% in lake area. Nine potentially dangerous lakes were identified from analysis of multi-temporal satellite images and use of GIS tools, based on different criteria. It was recommended that the lakes be monitored regularly and detailed field studies undertaken. Ten sites were selected for installation of early warning systems, some located in Nepal. As of 2009, no systems have been installed.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Poiqu basin (Sunkoshi – Bhote Koshi basin)", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring and early warning systems", "Poiqu basin (Sunkoshi – Bhote Koshi basin)"], "chunk_type": "text", "line_start": 56, "line_end": 58, "token_count_estimate": 204, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "cb5bf018b1005aa9", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal\nType: text\n\nImja glacial lake has been one of the fastest growing lakes in the Himalayas and ICIMOD has been monitoring the lake as a basis for devising an early warning system. A remote sensing system (using geo-ICT tools and techniques) was developed in cooperation with the Department of National Parks and Wildlife Conservation (DNPWC) and Keio University of Japan (Bajracharya 2009). Two monitoring devices or field servers – the Internet field observation robot (Figures 6a,b) – were installed on the lake shore at 5,000 masl and close to the nearest big settlement, Namche Bazaar, in 2007 by a team led by Keio University, with other researchers from the National Agricultural Research Centre, Japan. It was connected to the Internet in collaboration with the Asia Pacific Advanced Network (APAN) and Nepal Research and Education Network (NREN). The field servers capture time lapse images of the lake and Namche Bazaar (Figures 6c, d) and meteorological data. These are transferred in real-time by Wi-Fi to a server located in Japan at http://fsds.dc.affrc.go.jp/data4/Himalayan/ (Asia Disaster Report 2007).\n\nThe World Wildlife Fund (WWF) – Nepal is also conducting a climate change impact assessment of the Everest region, especially Imja Tsho and downstream areas. In partnership with the Department of Hydrology and Meteorology, a simulation has been made of an Imja Tsho GLOF using a dam-break model. A detailed survey of Imja Tsho, and hazard mapping for", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal"], "chunk_type": "text", "line_start": 60, "line_end": 64, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "225de67f3dfb1d90", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal\nType: figure\nFigure: Figure 6: Monitoring of Imja glacial lake: a) field server the lake (2009); b) solar panel for field server (2009); c) part of image from field server at lake (26 June 2009); d) part of image from field server at Namche Bazar (1 Jan 2010)\n\nFigure 6: Monitoring of Imja glacial lake: a) field server the lake (2009); b) solar panel for field server (2009); c) part of image from field server at lake (26 June 2009); d) part of image from field server at Namche Bazar (1 Jan 2010)", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal"], "chunk_type": "figure", "figure_caption": "Figure 6: Monitoring of Imja glacial lake: a) field server the lake (2009); b) solar panel for field server (2009); c) part of image from field server at lake (26 June 2009); d) part of image from field server at Namche Bazar (1 Jan 2010)", "line_start": 65, "line_end": 65, "token_count_estimate": 175, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "548b02d827dce034", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal\nType: figure\nFigure\n\nImage /page/4/Picture/2 description: A side-by-side comparison of two photographs, labeled 'a' and 'b', set in a rocky, mountainous environment. In image 'a', various pieces of scientific monitoring equipment are shown amidst a field of grey rocks. This includes a tall white pole topped with a weather-proof housing for a sensor or camera, a thinner white antenna, and a smaller sensor on a tripod. In the background is a steep, rocky mountain slope. Image 'b' displays a large solar panel mounted on an angled aluminum frame, secured among large rocks. In the background of image 'b', a milky-colored glacial lake is visible at the base of a mountain with a glacier.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal"], "chunk_type": "figure", "figure_caption": null, "line_start": 67, "line_end": 67, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "a4e5f21ece32ae54", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal\nType: figure\nFigure\n\nImage /page/4/Picture/3 description: A photograph of a rugged mountain landscape under a cloudy sky, framed by a blue border. In the foreground, there is a body of murky, greenish-brown water, likely a glacial lake, with steep, rocky banks. Behind the lake, a large, dark mountain with patches of snow on its upper slopes rises into the clouds. A small white circle with the letter 'c' is visible in the upper left corner.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal"], "chunk_type": "figure", "figure_caption": null, "line_start": 69, "line_end": 69, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "d633a6f493066878", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal\nType: figure\nFigure\n\nImage /page/4/Picture/4 description: A high-angle photograph captures a village nestled in a steep, snowy mountain valley. The rugged, rocky mountainside in the background is partially covered with snow. The village consists of several buildings with colorful roofs, including green, blue, and red, all lightly dusted with snow. The ground is also covered in a layer of snow. In the foreground, colorful prayer flags are visible. A small white circle with the letter 'd' is in the top-left corner.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal"], "chunk_type": "figure", "figure_caption": null, "line_start": 71, "line_end": 71, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "535a9f5048807ac8", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal\nType: text\n\ncommunities at risk related to rockfalls and landslides, are also planned. The Information Centre established by WWF Nepal at Ghat, near Lukla provides relevant information.\n\nICIMOD carried out GLOF simulation studies for Dig Tsho and Imja Tsho in Nepal, and Lunana lake in Bhutan (Bajracharya et al. 2007), and GLOF modelling studies including socioeconomic vulnerability assessments for Zangzangbo, Imja, Tsho Rolpa, and Thulagi lakes in their downstream areas in 2009 (Khanal et al. 2009, unpublished report). The studies provide a basis for ascertaining the arrangements that would be needed for setting up early warning systems in the valleys. A good first approximation was made for land and settlement classification according to four defined levels of risk. This facilitated an estimate of potential losses that can be anticipated in the event of a worst case GLOF occurrence.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal"], "chunk_type": "text", "line_start": 72, "line_end": 76, "token_count_estimate": 259, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "ca418f8618a9a529", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal > Early warning system in the Lunana region, Bhutan\nType: text\n\nThe Lunana area at the head of the Pho Chhu River in west-central Bhutan has been of considerable concern to the Bhutanese authorities. The Luggye Tsho GLOF of 7 October 1994 caused heavy damage to the Dzong at Punakha and 23 deaths (Richardson and Reynolds 2000).\n\nA manually-operated early warning system has been placed in the Lunana region by the Flood Warning Section (FWS) of the Department of Energy (DoE). Two staff members from the FWS are stationed in the Lunana lake area. They are equipped with wireless sets and satellite telephones to report lake water levels on a regular basis and to issue warnings to downstream inhabitants. Several gauges have also been installed along the main river as well as at the lakes. These are monitored at various stations at different times depending on the distance from the station and base camp. The station is in regular contact with other wireless stations in the downstream areas along the Puna Tsang Chu, including the villages and towns of Punakha, Wangduephodrang, Sunkosh, Khalikhola, and Thimphu (Bajracharya et al. 2007).\n\nThe Japan International Cooperation Agency (JICA) and the Gross National Happiness Commission (GNHC) signed an agreement for the study of GLOF phenomena in Bhutan. This aims to build a network for sharing satellite data for research, to complete an inventory of historical glacial lake expansions, to conduct a detailed analysis of hazardous lakes, to assess risk factors and triggers of GLOFs, and to recommend effective countermeasures, such as deployment of early warning systems. The project will also be extended to cover the Mangde Chu basin, and to recommend mitigation measures (Kuensel Online, 11 June 2009).", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal > Early warning system in the Lunana region, Bhutan", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Monitoring of Imja lake in the Everest region, Nepal", "Early warning system in the Lunana region, Bhutan"], "chunk_type": "text", "line_start": 78, "line_end": 84, "token_count_estimate": 477, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "d9cd6e4339d4c442", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures\nType: text\n\nPotential outburst flood hazards can be alleviated by various techniques. The primary objective is to reduce the risk of a flood from the lake. However, coordinated measures to protect life and property in the downstream area must also be undertaken. It is imperative to have monitoring systems in place prior to, during, and after the construction of infrastructure so that settlements in the downstream area are protected against unintentional creation of hazards. Mitigation measures to effect risk reduction can be structural or non-structural. In the following, mainly structural measures are discussed.\n\nThe most common structural mitigation measures are aimed at reducing the volume of water in the lake. Reduction of the volume of water in the lake should reduce the potential peak surge discharge as well as the hydrostatic pressure exerted on the moraine dam, and is the most effective mitigation measure. There are different ways to achieve this that can be used alone or in combination:\n\n- 1. Controlled breaching of the moraine dam\n- 2. Construction of an outlet control structure\n- 3. Pumping or siphoning the water from the lake\n- 4. Tunnelling through the moraine barrier or under an ice dam\n\nMitigation measures must be brought into play in such a way that no unintentional increase in danger occurs. Since moraine dam stability is a major part of the problem, it follows that artificial disturbance of the dam itself during construction activity could actually increase the degree of danger while mitigation measures are being put into place. Thus, choice of an appropriate method for each individual lake is critical. This necessitates careful evaluation of the lake, glacier, damming\n\nmaterials, and surrounding landscape. Physical monitoring systems for the dam, lake, glacier, and surroundings are necessary at all stages of the mitigation process.\n\nIn addition to reducing the volume of lake water, there are other preventative measures around the area that can help reduce the likelihood, or impact of, a GLOF. These include removing masses of unstable rocks to guard against avalanches or rockfalls hitting the lake surface and causing a surge wave, and protecting infrastructure in the downstream area. Engineering work as a part of hazard reduction efforts, especially in such remote and high altitude areas, is very expensive, however.\n\nSome examples of programmes, projects, and interventions related to mitigation measures that have been applied to reduce the impact of GLOF risk in the Himalayan region are described below.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Mitigation measures"], "chunk_type": "text", "line_start": 86, "line_end": 103, "token_count_estimate": 594, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93316a71665dbbdc", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Bhutan\nType: text\n\nThe earliest awareness of glacial lakes in Bhutan dates from the 1960s (Gansser 1966). After the Luggye Tsho GLOF event of 7 October 1994 in Punakha Wangdue valley, emphasis was placed on GLOF risk mitigation. The Royal Government of Bhutan sent an Indo-Bhutan team of experts to the Lunana area in 1995 to investigate any residual risk. The team recommended immediate mitigation measures for Raphstreng Tsho, including an attempt to lower the lake's water level as soon as feasible. Funding was provided by the Government of India with consulting by Water and Power Consultancy Services (India) Ltd (WAPCOS). The original plan was to reduce the lake level by 20 m in the first phase. WAPCOS found that this would actually take seven to eight years, but that lowering of the lake level by only four metres would also significantly reduce the risk of overtopping. Controlled opening of the moraine dam was carried out by manually widening the outlet channel with crowbars, pickaxes, and spades. Although a method of pumping to reduce the lake level was attempted initially, it was discarded as both ineffective and too expensive for such a remote region. Despite numerous constraints, such as having to work with manual tools, and obstructions caused by huge boulders in the channel bed, the water level in the main lake was lowered by 0.95 m, and in the two subsidiary lakes by 0.94 m and 1.5 m, by October 1995 (Häusler and Leber 1998; ICIMOD 2001b). The lowering continued until the lake level was reduced by four metres in 1998 (UNDP-ECHO 2007a).\n\nPhase 2 of the Raphstreng Tsho Outburst Flood Mitigation Project began in 1999 supported by Austro-Bhutanese Cooperation. The main aim was to assess the geo-risks of Raphstreng Tsho and Thorthormi Tsho in the Lunana area. The activities involved fieldwork with an integrated multidisciplinary approach using remote sensing, geological, hydro-geological, and geophysical methods to interpret the subsurface characteristics of the moraine dam (Häusler et al. 2000; Mool et al. 2001b). The investigations indicated that the risk of an outburst from Raphstreng Tsho was low, but the risk from Thorthormi Tsho was high (Häusler et al. 2000).\n\nThus a project for 'Reducing climate change-induced risks and vulnerabilities from glacial lake outburst floods in the Punakha-Wangdue and Chamkhar valleys' was initiated by UNDP to run from 2008 to 2012 in conjunction with the Department of Geology and Mines, the Department of Energy (Ministry of Economic Affairs), and the Disaster Management Division (Ministry of Home and Cultural Affairs). The Global Environment Fund (GEF) has provided US\\$ 3.5 million; the project is mainly examining the effectiveness of structural risk reduction measures. Activities cover risk reduction measures for Thorthormi glacial lake, including artificial lowering; hazard zonation mapping in", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Bhutan", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Mitigation measures", "Bhutan"], "chunk_type": "text", "line_start": 105, "line_end": 119, "token_count_estimate": 763, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "2f80b4e43942988b", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Bhutan\nType: text\n\nThus a project for ' Reducing climate change - induced risks and vulnerabilities from glacial lake outburst floods in the Punakha - Wangdue and Chamkhar valleys ' was initiated by UNDP to run from 2008 to 2012 in conjunction with the Department of Geology and Mines , the Department of Energy ( Ministry of Economic Affairs ) , and the Disaster Management Division ( Ministry of Home and Cultural Affairs ) . The Global Environment Fund ( GEF ) has provided US \\ $ 3 . 5 million ; the project is mainly examining the effectiveness of structural risk reduction measures . Activities cover risk reduction measures for Thorthormi glacial lake , including artificial lowering ; hazard zonation mapping in\n\n\"Installing an automatic early warning system (EWS) to reduce the impact of a glacial lake outburst flood (GLOF) in the Punatshangchu basin has become urgent, according to participants of an inception workshop on the regional climate risk reduction project in Punakha. The plan to establish an automatic EWS has not taken off yet, although the government in early 2009 said that automatic sirens would be set up in the villages down from the Lunana area, which would warn people at the first hint of any impending floods in the area. The earlier plan was to install an automatic sensor a few kilometers upstream of Phochu to detect the first flood and five sirens along the area...... To make EWS comprehensive, there will now be two sensors at two locations: one at Lunana and the other 25-30 km upstream of the Phochu. United Nations Development Programme's (UNDP) Kinley Penjor said UNDP would develop a soft package to prepare or train people in the valleys to make EWS effective. He said 'after the machinery is in place, it's important for people to know what to do and how to respond to climate induced GLOF.' \" (Kuensel Online 18 Jan 2010)\n\nChamkar chu in Bumthang; and the expansion of an early warning system along the Punakha-Wangdue valley. In addition, there are plans for improvement of national, regional, and local capacities to avert climate change-induced disaster in the Punakha-Wangdue and Chamkhar valleys. The project should demonstrate practical measures to reduce the risks associated with the Thorthormi glacial lake and facilitate replication of the lessons learned in other high-risk areas, both within and outside Bhutan (Kuensel Online, 7 December 2009; www.managingclimaterisk.org).\n\nProposals have also been made for Samdingkha to have a siren tower, given its high vulnerability to GLOFs. Two possible locations were identified: Siren 1 below Lorina village on the right side of Pho Chu facing Samdingha and about 60 m from the river level; and Siren 2, about 200 m above the suspension bridge. Both locations provide a good view of the vulnerable areas.\n\nA GLOF hazard zonation plan for the Puna Tshang Chu valley from Khuruthang in Punakha to Kalikhola Lhamoyzinkha is among various risk management measures the Department of Geology and Mines (DGM) has prepared with the Netherlands Climate Assistance Programme (NCAP) (Karma et al. 2008; Kuensel Online 7 December 2009).", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Bhutan", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Mitigation measures", "Bhutan"], "chunk_type": "text", "line_start": 105, "line_end": 119, "token_count_estimate": 799, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "d02a9b673f98ece2", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Nepal\nType: text\n\nThe Tsho Rolpa Mitigation and Early Warning Programme was the first glacial lake outburst flood operation to include civil engineering structures in the entire HKH region. The early warning measures are described in some detail above. The preliminary investigation of Tsho Rolpa and its downstream section of the Rolwaling valley was undertaken by WECS in 1993 (WECS, 1993, 1994; Mool, 1995). Investigations in subsequent years led to a recommendation for immediate action, as well as for long-term measures. As an immediate measure, Wavin Overseas B.V., Holland, installed siphons over part of the terminal moraine in May 1995. This was primarily to test the use of siphons at high altitude. The project was undertaken in cooperation with the Netherlands-Nepal Friendship Association. The siphon system consisted of three inlet pipes submerged in the lake and connected to a single pipe with a discharge outlet located at a stable part of the outer flank of the moraine. Though the test siphons were installed successfully, the induced outflow was far below that required to ensure reducing the lake level by the targeted three metres, and it appeared that they might never exceed inflow of additional glacier and snow melt.\n\nIn the second phase of the mitigation measures, supported by a contribution of \\$2.9 million from the Dutch Government, an open channel was cut through the moraine dam and a four-metre deep artificial spillway succeeded in lowering the lake level by three metres. The spillway construction was completed by the Tsho Rolpa GLOF Risk Reduction Project of the Department of Hydrology and Meteorology in June 2000.", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Nepal", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Mitigation measures", "Nepal"], "chunk_type": "text", "line_start": 121, "line_end": 125, "token_count_estimate": 427, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "ab30ea973ae5082e", "text": "Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation\nSection: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Pakistan\nType: text\n\nThe high altitude, fragile environment, and isolated nature of Pakistan's Northern Areas poses special constraints and challenges for mitigating natural hazards such as glacial lake outburst floods.\n\nThe glacial lakes in this area are predominantly ice-dammed lakes resulting from local glaciers that have thickened and advanced in recent decades (Hewitt 2009). They must be differentiated from the supra-glacial and moraine-dammed lakes that are the principal concern of this report. Five GLOF events were reported during the first half of 2008 in the Gojal area of the Hunza valley and substantial damage to infrastructure and arable land was reported due to GLOF events in association with the Ghulkin and Passu glaciers. There is no regular early warning system in the Hunza river basin, and the local people use traditional methods, such as lighting torches and firing weapons, to warn the villagers of flash floods.\n\nRisk reduction measures taken in Gojal include excavation, channelling, and spillway development. (UNDP-ECHO 2007b). A local village community used a siphoning technique to drain the lake associated with the Ghulkin glacier and reduce the threat posed by a potential outburst flood. A lateral moraine was excavated to set up the siphon (Roohi et al. 2008).", "metadata": {"source_file": "data/('c_attachment_692_5891', '.pdf')_extraction.md", "document_title": "3 Early Warning Systems, Monitoring, and GLOF Mitigation", "section_path": "3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures > Pakistan", "section_headings": ["3 Early Warning Systems, Monitoring, and GLOF Mitigation", "Mitigation measures", "Pakistan"], "chunk_type": "text", "line_start": 127, "line_end": 132, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "40075cca8d5213a6", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nType: figure\nFigure\n\nImage /page/0/Figure/3 description: A user interface element showing an icon next to text that reads 'Check for updates'. The icon is a square containing a circle with a color gradient from yellow to blue to red, with a red shape in the center.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "", "section_headings": [], "chunk_type": "figure", "figure_caption": null, "line_start": 1, "line_end": 1, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "993c504c15f0bab2", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods\nType: text\n\nGuoqing Zhang <sup>1,14</sup> □, Jonathan L. Carrivick <sup>2,14</sup> □, Adam Emmer<sup>3,14</sup>, Dan H. Shugar <sup>4,14</sup>, Georg Veh<sup>5,14</sup>, Xue Wang<sup>1,6</sup>, Celeste Labedz<sup>4</sup>, Martin Mergili<sup>3</sup>, Nico Mölg<sup>7</sup>, Matthias Huss <sup>8,9,10</sup>, Simon Allen<sup>11,12</sup>, Shin Sugiyama <sup>13</sup> & Natalie Lützow <sup>5</sup>", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods"], "chunk_type": "text", "line_start": 4, "line_end": 6, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3dd6ff7bc0b9cdf5", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Abstract\nType: text\n\nGlobal glacier mass loss has accelerated, producing more and larger glacial lakes. Many of these glacial lakes are a source of glacial lake outburst floods (GLOFs), which pose threats to people and infrastructure. In this Review, we synthesize global changes in glacial lakes and GLOFs. More than 110,000 glacial lakes currently exist, covering a total area of ~15,000 km<sup>2</sup>, having increased in area by ~22% dec<sup>-1</sup> from 1990 to 2020. More than 10 million people are exposed to the impacts of GLOFs, commonly associated with dam failure or wave overtopping associated with mass movements. Although data limitations are substantial, more than 3,000 GLOFs have been recorded from 850 to 2022, particularly in Alaska (24%), High Mountain Asia (HMA; 18%) and Iceland (19%), the majority (64.8%) being from ice-dammed lakes. Recorded GLOFs have increased in most glaciated mountain regions of the world, with ongoing deglaciation and lake expansion expected to increase GLOF frequency further. In HMA, GLOF hazards are projected to triple by 2100, but changes in other regions will likely be lower given topographic constraints on lake evolution. Future research should prioritize acquiring field data on lake and dam properties, producing globally coordinated multitemporal lake mapping, and robust and efficient modelling of GLOFs for comprehensive hazard assessment and response planning.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Abstract", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Abstract"], "chunk_type": "text", "line_start": 8, "line_end": 10, "token_count_estimate": 386, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7a4acb6e0e96b82e", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Sections\nType: text\n\nIntroduction\n\nGlobal and regional changes of lake size and abundance\n\nHistorical glacial lake outburst floods\n\nFuture glacial lakes and outburst floods\n\nSummary and future perspectives\n\nA full list of affiliations appears at the end of the paper. ⊠e-mail: guoqing.zhang@itpcas.ac.cn; J.L.Carrivick@leeds.ac.uk", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Sections", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Sections"], "chunk_type": "text", "line_start": 12, "line_end": 24, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "742bff641f560f9d", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Introduction\nType: text\n\nGlaciers have been losing mass since the end of the Little Ice Age<sup>1-3</sup>, and at high – and accelerating – rates since the 1960s (refs. 4,5). Indeed, overall mass loss from glaciers and ice caps (excluding the Greenland and Antarctic ice sheets) totalled  $267 \\pm 16$  Gt year<sup>-1</sup> from 2000 to 2019 (ref. 4), much of which can be attributed to anthropogenic warming<sup>6-8</sup>. When the resultant meltwater is naturally dammed (by ice, a moraine or bedrock), glacial lakes can form on (supraglacial), within (englacial), under (subglacial), at the edge of (ice-marginal) or beyond the front of (proglacial) glaciers. There are an estimated 14,394 of these lakes (>0.05 km<sup>2</sup>) globally<sup>9</sup>, the majority of which are in Greenland, the Southern Andes, Antarctica and the Subantarctic, Alaska, Canada and High Mountain Asia (HMA) (Fig. 1a). The number and size of glacial lakes is generally increasing, especially for proglacial lakes, owing to close connections with contemporary glacier retreat and meltwater production at local<sup>10,11</sup>, regional<sup>12-15</sup> and global<sup>9</sup> scales. However, there is considerable regional variability<sup>9,16–19</sup>, including notable expansion in Alaska, Greenland periphery, Patagonia and HMA. Nevertheless, the overall growth pattern is expected to continue<sup>20-24</sup>, in line with a projected 26-41% decrease in glacier mass by 2100 under temperature increases of 1.5-4 °C25.\n\nThe stability and longevity of glacial lakes are highly variable. Glacial lakes can drain suddenly and very quickly (hours to days), releasing a glacial lake outburst flood (GLOF). More than 3,000 GLOFs have been reported worldwide from 850 to 2022 (ref. 26) (Fig. 1b), each with characteristic rapid rise to peak discharge and often very large flood volumes. For example, the 1996 volcanic eruption beneath the Vatnajökull ice cap, Iceland, produced an outburst flood with an estimated peak discharge of ~55,000 m³ s⁻¹, thereby temporarily becoming the second largest river in the world after the Amazon²7,28. Indeed, ice-dammed lakes have produced some of the largest peak discharges recorded in human history²9-3¹, including peak discharges of >10⁵ m³ s⁻¹ in HMA³² and 10° m³ s⁻¹ in the Andes and Alaska³0,33,3⁴.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Introduction", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Introduction"], "chunk_type": "text", "line_start": 26, "line_end": 34, "token_count_estimate": 728, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "957631292b98deae", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Introduction\nType: text\n\nFig . 1b ) , each with characteristic rapid rise to peak discharge and often very large flood volumes . For example , the 1996 volcanic eruption beneath the Vatnajökull ice cap , Iceland , produced an outburst flood with an estimated peak discharge of ~ 55 , 000 m³ s ⁻ ¹ , thereby temporarily becoming the second largest river in the world after the Amazon²7 , 28 . Indeed , ice - dammed lakes have produced some of the largest peak discharges recorded in human history²9 - 3¹ , including peak discharges of > 10⁵ m³ s ⁻ ¹ in HMA³² and 10 ° m³ s ⁻ ¹ in the Andes and Alaska³0 , 33 , 3⁴ .\n\nAccordingly, GLOFs rank among the most prominent glacier hazards, killing thousands of people over the past century<sup>35</sup> and causing extensive damage tens to hundreds of kilometres downstream. For instance, the 1941 GLOF in Huaraz, Peru, destroyed a third of the city and killed about 5,000 people<sup>36</sup>, and the 2013 GLOF in Kedarnath, India, killed more than 6,000 people<sup>37</sup>. Given that millions of people are potentially at risk from GLOFs worldwide<sup>38</sup>, especially in a warming climate, understanding the location, timing of formation, evolution and physical characteristics of glacial lakes is critical to mitigating the downstream impacts to communities and infrastructure.\n\nIn this Review, we synthesize glacial lake and GLOF hazard data sets from most glaciated regions of the world to assess their ongoing and projected changes. We begin by documenting the global and regional distribution of glacial lakes. We follow by assessing the characteristics and historical trends of GLOFs. We subsequently discuss future projections in glacial lakes and the corresponding GLOF hazard and risk, before ending with research priorities. Throughout, focus is on the physical aspects of glacial lakes and GLOFs in a warming world.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Introduction", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Introduction"], "chunk_type": "text", "line_start": 26, "line_end": 34, "token_count_estimate": 529, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0a161af4100da731", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance\nType: text\n\nUnderstanding glacial lakes is the first step in estimating the GLOF hazard and risk (Fig. 2). Methods used to map glacial lakes are now discussed, followed by quantification of their historical changes at global and regional scales.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance"], "chunk_type": "text", "line_start": 36, "line_end": 38, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "777595900dda21d8", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Mapping glacial lakes\nType: text\n\nA growing archive of satellite products and enhanced computing power<sup>39</sup> have improved mapping of glacial lakes (Fig. 2). In most cases, mapping relies on the use of the normalized difference water index (NDWI)<sup>40</sup> – a satellite image-derived calculation combining green and near-infrared wavelength bands to differentiate water bodies from all other landcover. Automated and semi-automated methods are used for lake mapping from an NDWI classification, although lake outlines have also been delineated manually with enhanced accuracy<sup>12,41</sup>. Landsat satellite imagery is often used as the basis for glacial lake mapping by the NDWI owing to its temporal coverage and a medium-resolution sensor 9,17,42. Nevertheless, Landsat imagery has limitations for NDWI-based lake mapping, including poor image contrast due to seasonal snow and  $ice\\,cover\\,on\\,lakes, as\\,well\\,as\\,confusion\\,introduced\\,by\\,mountain\\,shadows$ and cloud cover<sup>43</sup>. Cloud-free synthetic aperture radar (SAR) can overcome this limitation, but is of limited length and hence, generally, only suitable for validation of mapping algorithms or case studies  $^{44,45}\\!$  . Cloud computing, artificial intelligence and machine learning are rapidly improving the ability to efficiently manage and automatically analyse remote sensing data<sup>46,47</sup>. However, interactive visual inspection and manual editing combined with original imagery are still essential to ensure a consistent data quality with time.\n\nIn addition to mapping lake outlines, knowledge of glacial lake volume is important for quantifying hazards and for estimating downstream impacts of GLOFs<sup>48,49</sup> (Fig. 2). Empirical volume–area relationships for glacial lakes have been established in many instances (see Supplementary Table 1). Global compilations of lake depths, areas and volumes suggest that lake area and depths are moderately correlated, and lake area and volumes are well correlated<sup>50</sup>. These correlations allow for the estimation of lake depths and volumes from area, especially in regions where direct measurements are rare and difficult (see Supplementary Table 1). Bathymetric measurements of ice-marginal lakes have also been carried out in some locations, including the Southern Alps of New Zealand<sup>51,52</sup> and on the Tibetan Plateau<sup>53,54</sup>.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Mapping glacial lakes", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Mapping glacial lakes"], "chunk_type": "text", "line_start": 40, "line_end": 44, "token_count_estimate": 688, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b215c4a19f527ba3", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics\nType: text\n\nAfter mapping the outline of glacial lakes, it is possible to determine their overarching characteristics, including size, area and volume. Various such inventories exist (see Supplementary Table 2), each compiled from satellite imagery but differing in maximum distance between glaciers and lakes (buffers of 1, 3 or 5 km) and the smallest lake size included  $(0.0002, 0.005 \\, \\text{or} \\, 0.05 \\, \\text{km}^2)$ , introducing slight discrepancies at the regional and global scales. Here, several regional inventories  $^{9,12,18,19}$  are merged to produce a global contemporary glacial lake compilation; a size threshold of  $0.05 \\, \\text{km}^2$  is set for all Randolph Glacier Inventory (RGI) regions  $^{55}$  except Svalbard and Jan Mayen, Southern Andes and New Zealand (see Supplementary Table 2).\n\nThis global compilation provides much insight into glacial lake characteristics. A total of 22,133 glacial lakes were revealed in 2015–2019, collectively covering -14,438 km² (see Supplementary Table 2). These totals are more than other reports of 14,394 lakes (>0.05 km²) situated within 1 km of glacier margins, covering a total area of -8,950 km² in 2015–2018 (ref. 9). Regional differences in the number, area and volume of global glacial lakes are evident (Fig. 1a). Glacial lakes are predominantly distributed in Greenland (31% of the total area), the Southern Andes (16%), Alaska and western Canada (16%), the eastern Canadian Arctic (15%) and HMA (6%). Glacial lakes in HMA are widespread, but with a smaller signal size (average area of 0.22 km² and volume of 0.002 km³ per lake). The remaining water mass storage in", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Global glacial lake characteristics"], "chunk_type": "text", "line_start": 46, "line_end": 50, "token_count_estimate": 519, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6983f1e5c51e4090", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: This figure, titled \"Global distribution of glacial lakes and historical GLOFs,\" is divided into two parts, 'a' and 'b'.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Global glacial lake characteristics"], "chunk_type": "figure", "figure_caption": null, "line_start": 51, "line_end": 51, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a2e928aee495d27", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics\nType: text\n\nPart 'a', labeled \"Glacial lake characteristics,\" displays a world map with red dots indicating the distribution and size of glacial lakes. Superimposed on the map are several multi-ringed circular charts for different regions, showing the total volume of glacial lakes in Gigatons (Gt). The volumes for each region are as follows:\n- Alaska: 22.0 Gt\n- Western Canada and USA: 8.4 Gt\n- Arctic Canada, north: 1.9 Gt\n- Arctic Canada, south: 24.9 Gt\n- Greenland periphery: 43.6 Gt\n- Scandinavia: 5.6 Gt\n- Russian Arctic: 7.2 Gt\n- Iceland: 2.4 Gt\n- Central Europe: 0.2 Gt\n- Caucasus and Middle East: 0.03 Gt\n- Asia, North: 0.4 Gt\n- Asia, Central: 1.7 Gt\n- Asia, Southwest: 0.7 Gt\n- Asia, Southeast: 2.1 Gt\n- Low-latitude Andes: 0.7 Gt\n- Southern Andes: 38.3 Gt\n- New Zealand: 0.2 Gt\nA large chart at the bottom indicates a global total volume of 160.3 Gt. A legend explains that the concentric rings on these charts represent Lake size (km²), Volume ratio, Area ratio, and Number ratio.\n\nPart 'b', labeled \"GLOF characteristics,\" shows a similar world map illustrating the locations of historical Glacial Lake Outburst Floods (GLOFs). Pie charts for various regions depict the proportion of different lake (dam) types that caused these floods. The legend for the dam types includes: Bedrock, Combined, Ice, Moraine, Supraglacial, Englacial, and Others. The pie charts show that moraine-dammed lakes (orange) and bedrock-dammed lakes (light beige) are common causes of GLOFs in many regions. A legend also provides a scale for the number of GLOFs.\n\nFig. 1 | Global distribution of glacial lakes and historical GLOFs. a, Area and volume of present-day (-2018) glacial lakes within 1 km of glaciers, aggregated over 220 km hexagon grid cells (red shades), and regional statistics of lake volume, area and number according to Global Terrestrial Network for Glaciers (GTN-G) glacier regions of the Randolph Glacier Inventory (RGI) (doughnut plots); outer, middle and inner circles indicate the total volume, area and number percentage of glacial lakes in a given size interval, respectively, with the total lake area in each region in the centre of the circle. Two RGI subregions (Svalbard and Jan Mayen, Antarctic and Subantarctic) are not included as they are not part of\n\nthe underlying global glacial lake data set\\*. **b**, Global distribution of historical glacial lake outburst floods (GLOFs) aggregated over 220 km hexagon grid cells (red shading), and regional breakdown of the GLOF number by glacial lake (dam) types²6 (pie charts). Some RGI subregions are not presented as they are not covered by the global GLOF inventory²6. More than 80% of global glacial lakes are distributed predominantly in Greenland, the Southern Andes, Alaska and western Canada, the eastern Canadian Arctic and High Mountain Asia (HMA); >60% of historical GLOFs occurred in Alaska, HMA and Iceland.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Global glacial lake characteristics"], "chunk_type": "text", "line_start": 52, "line_end": 78, "token_count_estimate": 856, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af736ac3fee4e493", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics\nType: figure\nFigure\n\nImage /page/3/Figure/1 description: A diagram illustrating the components of assessing glacial lake outburst flood (GLOF) risk. The diagram features three main circular sections arranged in a triangle around a central concept of 'Risk'.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Global glacial lake characteristics"], "chunk_type": "figure", "figure_caption": null, "line_start": 79, "line_end": 79, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "65f670bf6efb567c", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics\nType: text\n\nThe top red section, 'Mapping of glacial lake', is divided into 'Manual' and 'Automatic or semi-automatic' methods. These methods utilize 'High-resolution satellite data', 'Cloud-free SAR imagery', 'Optical multispectral imagery', 'Cloud computing', 'Artificial intelligence', and 'Machine learning'.\n\nThe bottom-left yellow section is labeled 'Estimates of volume and discharge'. It shows a process flow from 'Area → volume' to 'Volume → discharge', based on an 'Empirical relationship'.\n\nThe bottom-right blue section is titled 'GLOF susceptibility and simulation'. The inner circle is split between 'GLOF susceptibility' and 'GLOF simulation'. The outer ring, labeled 'Hydrodynamic modelling', lists factors such as 'External triggers (landslide, ice/snow avalanche, extreme rainfall)', 'Lake expansion', 'Ice dam flotation', 'Dam stability', 'Downstream sediment transport and inundation', 'Moraine erosion and failure', 'Wave generation and overtopping', 'Hydrodynamics of lake', and 'Ice or rock avalanche into a lake'.\n\nArrows show that all three sections feed into the central concept of 'Risk', which is defined as 'Socio-economic exposure and vulnerability'. There is also an arrow from the 'GLOF susceptibility and simulation' section to the 'Estimates of volume and discharge' section.\n\nFig. 2 | Methodology for quantifying glacial lake changes and impacts. Methods to map glacial lakes (red), estimate lake volume and peak (yellow) and assess glacial lake outburst flood (GLOF) susceptibility and simulation (blue). Glacial lake mapping, estimates of GLOF susceptibility, volume and peak discharge, and hydrodynamic simulations provide important baseline integrated data for GLOF hazard assessment. SAR, synthetic aperture radar.\n\nthe world's glacial lakes in 2015–2018 was about 160 Gt, equivalent to about 0.43 mm of sea-level rise<sup>9</sup>.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Global glacial lake characteristics", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Global glacial lake characteristics"], "chunk_type": "text", "line_start": 80, "line_end": 92, "token_count_estimate": 589, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5342d70d0fb174d", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes\nType: text\n\nGlacial lakes are naturally transient features. Each tends to follow a common trajectory, as documented in many mountainous regions around the world<sup>16,56-59</sup>. Specifically, glacial lakes showed a relatively stable trend from 1850 to 1970, followed by a gradual expansion in most regions of the world from 1970 to 1990 and a robust expansion from 1990 to 2020 (see Supplementary Fig. 1). Various processes can lead to such changes in glacial lake evolution, including shifts in inflow or outflow; gradual infilling by sediment, particularly in deglaciating environments<sup>23,60,61</sup>; abrupt infilling following mass movements<sup>62,63</sup>; coalescence of several lakes; detachment of an ice-marginal lake from the parent glacier as it retreats; or partial or complete dam failure resulting in a GLOF<sup>23</sup>. Although global statistics are unavailable, evidence from the tropical Andes suggests that, on average, only one in ten GLOFs has resulted in complete lake drainage<sup>62</sup>. Indeed, most moraine-dammed and bedrock-dammed glacial lakes persist after a GLOF has occurred<sup>62</sup>. Accordingly, lakes can exist for vastly different timeframes: ice-dammed lakes can form and fail repeatedly as long as the ice dam is thick enough to impound a lake  $^{33,64}$ ; bedrock-dammed lakes within overdeepenings can persist for tens of thousands of years<sup>65-67</sup>; and moraine-dammed lakes can survive over centennial or millennial timescales<sup>68</sup>.\n\nThe aforementioned inventories provide an opportunity to track these glacial lakes over time (Fig. 3a; see Supplementary Fig. 1). Overall, the area of global glacial lakes expanded steadily from 1990 to 2018. Specifically, the glacial lake (>0.05 km²) number and area increased by -53% (from 9,414 to 14,379) and 51% (from  $5.93 \\times 10^3$  km² to\n\n$8.95 \\times 10^3$  km<sup>2</sup>) from 1990 to 2018 (ref. 9). However, this global increase is subject to uncertainties due to missing lakes when compared with regional multi-temporal lake inventories (see Supplementary Table 2).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Glacial lake changes"], "chunk_type": "text", "line_start": 94, "line_end": 114, "token_count_estimate": 663, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6a4fd29fea37181c", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes\nType: text\n\n, the area of global glacial lakes expanded steadily from 1990 to 2018 . Specifically , the glacial lake ( > 0 . 05 km² ) number and area increased by - 53 % ( from 9 , 414 to 14 , 379 ) and 51 % ( from $ 5 . 93 \\ times 10 ^ 3 $ km² to $ 8 . 95 \\ times 10 ^ 3 $ km < sup > 2 < / sup > ) from 1990 to 2018 ( ref . 9 ) . However , this global increase is subject to uncertainties due to missing lakes when compared with regional multi - temporal lake inventories ( see Supplementary Table 2 ) .\n\nRegional data sets, which undergo extensive quality control during compilation, also indicate a general increase in glacial lake area. Between ~1990 and ~2020, glacial lake area increased by an average of ~22% dec<sup>-1</sup> (Fig. 3a), higher than the 17% dec<sup>-1</sup> suggested by the global data set<sup>9</sup>. Of course, much regional and temporal variability is embedded. For instance, overarching changes of <10% dec<sup>-1</sup> from ~1990 to ~2020 were observed in the Southern Andes (from 4.463.9 km<sup>2</sup> to 4,789.7 km<sup>2</sup>), Central Europe (from 30.9 km<sup>2</sup> to 31.5 km<sup>2</sup>) and Svalbard (from 110.0 km<sup>2</sup> to 111.0 km<sup>2</sup>) (Fig. 3a), potentially highlighting the importance of short-term temporal variability; lake area temporarily decreased at the edge of the Northern Patagonian Icefield from 1945 to 1976 (ref. 69) and in Greenland from 1987 to 1992 and from 2005 to 2010 (ref. 16), perhaps contributing to the smaller, yet still positive, long-term trends<sup>16,69</sup>. In contrast, much larger changes of >40% dec<sup>-1</sup> were observed in Iceland (from 54.8 km<sup>2</sup> to 132.4 km<sup>2</sup>), Scandinavia (from 258.2 km<sup>2</sup> to 596.9 km<sup>2</sup>) and the Russian Arctic (from 189.9 km<sup>2</sup> to 478.4 km<sup>2</sup>) (Fig. 3a).\n\nIn HMA, local variability is observed, as evidenced by sub-regional records of glacial lake growth (see Supplementary Fig. 1). In the greater Himalaya, glacial lake area increased by ~3.6 km² year $^{-1}$  (0.6% year $^{-1}$ , from 666.4 km² to 774.7 km²) from 1990 to 2020. Yet changes in the Chinese Himalaya and Nepal Himalaya were ~1.28 km² year $^{-1}$  (0.8% year $^{-1}$ , from 166.5 km² to 215.3 km²) from 1970 to 2008 (ref. 70) and 0.6 km² year $^{-1}$  (1.1% year $^{-1}$ , from 55.5 km² to 81.0 km²) from 1977 to 2017 (ref. 71), respectively.\n\nIn addition to such substantial local and regional changes, there is also evidence of changes by glacial lake type. Ice-marginal proglacial", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Glacial lake changes"], "chunk_type": "text", "line_start": 94, "line_end": 114, "token_count_estimate": 879, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "56d57e99c0eb766d", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes\nType: text\n\n. 28 km² year $ ^ { - 1 } $ ( 0 . 8 % year $ ^ { - 1 } $ , from 166 . 5 km² to 215 . 3 km² ) from 1970 to 2008 ( ref . 70 ) and 0 . 6 km² year $ ^ { - 1 } $ ( 1 . 1 % year $ ^ { - 1 } $ , from 55 . 5 km² to 81 . 0 km² ) from 1977 to 2017 ( ref . 71 ) , respectively . In addition to such substantial local and regional changes , there is also evidence of changes by glacial lake type . Ice - marginal proglacial\n\nlakes are in direct contact with their parent glaciers and have strong interactions between them, which is mainly dammed by moraines, with more GLOFs recorded by lake type<sup>26,68</sup>. The imbalance between ice-marginal and glacier-detached proglacial lakes has been recorded worldwide (Fig. 3b-e). For example, in Southern Pamir, the number of proglacial lakes remained largely constant from 1968 to 1993 (the lower number in 1977 might be related to uncertainties due to poor image quality), increased to a peak of 44 in 2008 and then decreased to 26 by 2015 (ref. 72) (Fig. 3b).\n\nIn the Andes, the number and area of proglacial lakes have progressively increased. From 1948 to 2017, three to four new proglacial lakes formed each year, with an average expansion rate of ~0.1 km² year¹ (ref. 73). Of these total lakes, the number of proglacial lakes was stable from 1948 to 1990 (71–74 lakes, with some fluctuations in between) and then decreased to 35 by 2018 (ref. 73). For the Peruvian Andes, an almost constant gain of detached proglacial lake area was observed throughout the study period (Fig. 3c). This phenomenon, together with the increasing average elevation of proglacial lakes, is a strong indication that large lakes dammed by Little Ice Age terminal moraines have lost contact with their parent glaciers³3.\n\nIn the Southern Alps of New Zealand, an almost linear trend in proglacial lake area change has also been reported, with an average of -0.5 km² year⁻¹ in four time steps from 1990 to 2020 (ref. 74). Tasman Glacier, New Zealand's largest glacier, is retreating rapidly, and a proglacial lake formed in 1990 (ref. 52). The number of ice-marginal proglacial lakes in New Zealand was greatest in 2011, and thereafter has decreased slightly as lakes coalesced and as some became disconnected from the glaciers <sup>74</sup> (Fig. 3d).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Glacial lake changes"], "chunk_type": "text", "line_start": 94, "line_end": 114, "token_count_estimate": 699, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b768a988b3d9a10", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes\nType: text\n\nthe Southern Alps of New Zealand , an almost linear trend in proglacial lake area change has also been reported , with an average of - 0 . 5 km² year ⁻ ¹ in four time steps from 1990 to 2020 ( ref . 74 ) . Tasman Glacier , New Zealand ' s largest glacier , is retreating rapidly , and a proglacial lake formed in 1990 ( ref . 52 ) . The number of ice - marginal proglacial lakes in New Zealand was greatest in 2011 , and thereafter has decreased slightly as lakes coalesced and as some became disconnected from the glaciers < sup > 74 < / sup > ( Fig . 3d ) .\n\nIn the European Alps, particularly the Austrian Alps, the number and area of proglacial lakes increased from 1850 to 2015, with the mean change in area doubling from -0.038 km² year  $^{-1}$  during 1998–2006 to -0.079 km² year  $^{-1}$  during 2006–2015 (ref. 75). Similar changes have occurred in Switzerland. There, the number of proglacial lakes increased from 21 in 1900 to 47 in 2006, and from 47 in 2006 to 82 in 2016 (ref. 59) (Fig. 3e). The increase in the total area of proglacial lakes in the Swiss Alps has accelerated from -0.01 km² year  $^{-1}$  in 1850–1900 up to -0.15 km² year  $^{-1}$  in 2006–2016 (ref. 59). However, part of this variability is due to the artificial impoundment of some proglacial lakes, mainly for hydropower generation purposes.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Global and regional changes of lake size and abundance > Glacial lake changes", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Global and regional changes of lake size and abundance", "Glacial lake changes"], "chunk_type": "text", "line_start": 94, "line_end": 114, "token_count_estimate": 434, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c9dd75d4c4f4fdd2", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods\nType: text\n\nWith the presence of glacial lakes comes the risk of a GLOF. GLOFs arise when the lake dam is breached (by various triggers depending on the dam type) and meltwater is suddenly and rapidly released from the lake, subsequently flowing downstream where it can cause substantial societal and environmental impacts (Fig. 4). Together, data from river gauges, satellite images, sediment stratigraphy, and eyewitness and media reports suggest that at least 3,151 GLOFs have occurred globally since 850 (ref. 26) (Fig. 1b), with most recorded in Alaska (24%, n = 768), HMA (18%, n = 569) and Iceland (19%, n = 590).\n\nHowever, understanding of GLOFs is severely limited by inadequate data. Triggers are often unknown or speculated owing to absent field data, necessitating reliance on climatic or geological-geomorphological evidence<sup>35,76-78</sup>. Numerical modelling is similarly challenged by absent knowledge of parameter values such as lake water temperature or time-varying lake level, limiting insight<sup>58,79,80</sup>. Moreover, GLOF frequency calculations suffer from selective reporting and changes in instrumentation and satellite coverage<sup>33,81,82</sup>. Nevertheless, it is possible to glean evidence of GLOF characteristics, as now\n\ndiscussed for ice-dammed, moraine-dammed and bedrock-dammed GLOFs. Resulting hazards and changes are also assessed.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods"], "chunk_type": "text", "line_start": 116, "line_end": 122, "token_count_estimate": 421, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cfc6495f3b26b5cc", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Ice-dammed GLOFs\nType: text\n\nGLOFs from ice-dammed lakes are the most common type. Their activity is largely controlled by the activity of the damming glaciers and resulting fracture or flexure of the ice dam (Fig. 4a). Flotation of the ice dam once a critical lake level is reached also acts as a potential breach mechanism<sup>33,83,84</sup>. When lake water suddenly drains through an ice dam, thermal and mechanical erosion (usually progressively enlarging a subglacial tunnel) leads to an exponentially rising discharge<sup>54,85,86</sup>.\n\nGLOFs from ice-dammed lakes constitute roughly two-thirds (2,039 of 3,151) of all reported GLOFs worldwide<sup>26,81</sup>. Their frequent occurrence is linked to the fact that they can drain repeatedly as an ice dam will 're-heal' temporarily once energetic egress of lake water has subsided. At the regional scale, Alaska is a hot spot of GLOFs from ice-dammed lakes (n = 764; 99% within Alaska, 37% of the global total in this category)<sup>19,26,33</sup> (Fig. 1b). Indeed, 750 ice-dammed lakes have formed in Alaska over the twentieth century<sup>87,88</sup>, with some lakes having a volume >10<sup>9</sup> m<sup>3</sup> when impounded by a thick outlet glacier<sup>87</sup>. Yet their GLOFs rarely cause damage, owing to low exposure of assets along river channels. Ice-dammed lakes are also the dominant source of reported GLOFs in Greenland periphery (n = 153; 100% of all GLOFs within Greenland periphery, 7.5% of global ice-dammed GLOFs)<sup>89,90</sup> the Karakoram (n = 190; 33.4% within HMA, 9.3% of global ice-dammed GLOFs) $^{29,91}$ , Scandinavia (n = 183; 96.8% within Scandinavia, 9.0% of globalice-dammed GLOFs) $^{92}$ , Central Europe (n = 258; 58.2% within Central Europe, 12.7% of global ice-dammed GLOFs) and Iceland (n = 237; 40.2% within Iceland, 11.6% of global ice-dammed GLOFs)<sup>86</sup> (Fig. 1b). In Central Europe, fatalities and damage from these occasional ice-dam failures were particularly high before the twentieth century, possibly related to limited warning and flood control measures<sup>93</sup>. In Scandinavia, and particularly in Norway, although the percentage of ice-dammed GLOFs was high in the mid-twentieth century, the total number and size of outbursts are small compared with other regions<sup>26</sup>.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Ice-dammed GLOFs", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods", "Ice-dammed GLOFs"], "chunk_type": "text", "line_start": 124, "line_end": 128, "token_count_estimate": 786, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74c0f8291c6ebf2e", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs\nType: text\n\nGLOFs from moraine-dammed lakes are the second most common type globally after ice-dammed lakes (Fig. 1b). In these situations, dam breaching can be caused by seepage of lake water through unconsolidated material, degradation of an ice core, or overtopping due to calving, avalanches or landslides into the lake<sup>76,83</sup> (Fig. 4). Moraine-dammed lakes in the ice-marginal phase of evolution are the most likely to produce GLOFs due to calving processes and proximity to ice avalanche release zones. In contrast, glacial lakes detached from glaciers become disconnected from the most frequent GLOF-triggering processes with increasing distance from glaciers<sup>56</sup>. The almost instantaneous, and sometimes complete, failure of a moraine dam produces a linearly rising discharge<sup>94</sup>. Furthermore, complete failure of the (moraine) dam restricts any future ability to impound meltwater<sup>95,96</sup>. Hence, moraine-dammed lakes usually fail only once.\n\nGLOFs from moraine-dammed lakes make up at least -13% (398 of 3,151) of the global total number of recorded GLOFs from 850 to 2022 (Fig. 1b). However, their occurrence is highly regionally variable. The majority of those reported are found in the low-latitude Andes (n=100; 61.3% of GLOFs within the low-latitude Andes, 25.1% of global moraine-dammed GLOFs), Central Asia (n=110; 46.2% within Central Asia, 27.6% of global moraine-dammed GLOFs) and the Himalaya (n=71; 77.2% within the Himalaya, 17.8% of global moraine-dammed GLOFs) (Fig. 1b).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods", "Moraine-dammed GLOFs"], "chunk_type": "text", "line_start": 130, "line_end": 134, "token_count_estimate": 507, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7455d1b45dd2792", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs\nType: figure\nFigure\n\nImage /page/5/Figure/1 description: A multi-part figure from a scientific publication detailing changes in glacial lakes. The figure is divided into five sections labeled 'a' through 'e'.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods", "Moraine-dammed GLOFs"], "chunk_type": "figure", "figure_caption": null, "line_start": 135, "line_end": 135, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "370a804c233ad966", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs\nType: text\n\nPart 'a', titled 'Glacial lake area changes', displays a world map with bar charts showing the increase in glacial lake area (in km²) for various regions between two different years. Red dots on the map indicate the locations and the magnitude of area change per decade, with a legend showing dot sizes for percentage changes from 0-5% to 60-70%. The bar charts show the following approximate data:\n- Alaska: from ~650 km² in 1984 to ~1,300 km² in 2019.\n- Western Canada and USA: from ~250 km² in 1990 to ~400 km² in 2015.\n- Low-latitude Andes: from ~40 km² in 1990 to ~70 km² in 2015.\n- Arctic Canada, north: from ~110 km² in 1990 to ~230 km² in 2015.\n- Arctic Canada, south: from ~700 km² in 1990 to ~1,300 km² in 2015.\n- Greenland periphery: from ~1,800 km² in 1990 to ~3,000 km² in 2015.\n- Iceland: from ~40 km² in 1990 to ~140 km² in 2015.\n- Svalbard: from ~80 km² in 1990 to ~180 km² in 2020.\n- Scandinavia: from ~200 km² in 1990 to ~700 km² in 2015.\n- Russian Arctic: from ~150 km² in 1990 to ~550 km² in 2015.\n- Central Europe: from ~30 km² in 1990 to ~32 km² in 2015.\n- Caucasus and Middle East: from ~0.4 km² in 1990 to ~0.7 km² in 2015.\n- Asia, Southwest: from ~120 km² in 1990 to ~200 km² in 2018.\n- Asia, Southeast: from ~270 km² in 1990 to ~430 km² in 2018.\n- Asia, Central: from ~450 km² in 1990 to ~730 km² in 2018.\n- Asia, North: from ~10 km² in 1990 to ~19 km² in 2018.\n- Southern Andes: from ~3,500 km² in 1986 to ~5,100 km² in 2016.\n- New Zealand: from ~7 km² in 1990 to ~32 km² in 2020.\n- Global: from ~10,000 km² in 1990 to ~15,000 km² in 2020.\n\nParts 'b' through 'e' are titled 'Proglacial lake changes' and show detailed charts for specific regions. These charts plot the number of new lakes (blue, above the x-axis) and detached lakes (green, below the x-axis) per year, as well as the area of these lakes. The total area (A) is noted for each time period.\n\n- Part 'b' (Southern Pamir): Data for 1968 (A=0.68 km²), 1977 (A=0.83 km²), 1993 (A=1.09 km²), 2002 (A=1.45 km²), 2008 (A=1.46 km²), and 2015 (A=0.90 km²).\n- Part 'c' (Peruvian Andes): Data for 1948 (A=6.27 km²), 1962 (A=5.84 km²), 1970 (A=0.55 km²), 1990 (A=4.98 km²), 2000 (A=4.26 km²), 2010 (A=3.16 km²), and 2018 (A=1.65 km²).\n- Part 'd' (New Zealand): Data for 1990 (A=9.60 km²), 2000 (A=1.26 km²), 2011 (A=10.34 km²), and 2020 (A=20.55 km²).\n- Part 'e' (Switzerland): Data for 1900 (A=0.20 km²), 1946 (A=0.49 km²), 1973 (A=11.13 km²), 1982 (A=0.40 km²), 2006 (A=0.52 km²), and 2016 (A=0.93 km²).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods", "Moraine-dammed GLOFs"], "chunk_type": "text", "line_start": 136, "line_end": 170, "token_count_estimate": 897, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2f4189e5aba300e0", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs\nType: text\n\n( New Zealand ) : Data for 1990 ( A = 9 . 60 km² ) , 2000 ( A = 1 . 26 km² ) , 2011 ( A = 10 . 34 km² ) , and 2020 ( A = 20 . 55 km² ) . - Part ' e ' ( Switzerland ) : Data for 1900 ( A = 0 . 20 km² ) , 1946 ( A = 0 . 49 km² ) , 1973 ( A = 11 . 13 km² ) , 1982 ( A = 0 . 40 km² ) , 2006 ( A = 0 . 52 km² ) , and 2016 ( A = 0 . 93 km² ) .\n\n**Fig. 3** | **Global and regional changes in glacial lake area. a**, Absolute regional lake area in -1990 and -2020 (bars) and the percentage changes from 1990 to 2020 (red circles) (see Supplementary Table 2); owing to data availability, years were selected as close to 1990 and 2020 as possible. **b-e**, Changes in the number (dark blue line) and area (light blue bar) of new lakes, and the number (dark green line) and area (light green bar) of detached proglacial lakes in Southern\n\nPamir $^{72}$  (panel **b**), Peruvian Andes $^{73}$  (panel **c**), New Zealand $^{74}$  (panel **d**) and Switzerland $^{59}$  (panel **e**) (see panel **a** for locations); time blocks differ owing to contrasting data availability. Error bars indicate uncertainties and inaccuracies due to low image resolution or quality, or the use of images from different years for the same time step. Glacial lakes have expanded worldwide, especially for proglacial lakes.\n\nIndeed, 52% and 32% of glacial lakes in the Central Andes and Himalaya are moraine-dammed, respectively  $^{17,97}$ , and many others have associated geomorphological features suggesting possible past dam failures  $^{62,98,99}$ . The exact timing of these dam failures has yet to be determined, as the geomorphological evidence is largely limited to that visible in satellite images that were first available in the 1960s.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Moraine-dammed GLOFs", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods", "Moraine-dammed GLOFs"], "chunk_type": "text", "line_start": 136, "line_end": 170, "token_count_estimate": 588, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64907af188b93cb1", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Bedrock-dammed and subglacial GLOFs\nType: text\n\nBedrock dams are usually stable and therefore these glacial lakes produce fewer GLOFs than ice-dammed and moraine-dammed lakes, with dam overtopping being the only mechanism for GLOF occurrence 100,101. GLOFs from bedrock-dammed lakes make up only ~1% (30 of 3,151) of the total number of GLOFs from 850 to 2022 (Fig. 1b). Indeed, overtopping of bedrock barriers has only rarely been reported<sup>81</sup>, in contrast to the high regional abundance of bedrock-dammed lakes<sup>62</sup>, especially in the Himalaya and Andes 17,97. Their limited occurrence might arise from the fact that unfractured bedrock dams can withstand increases in hydrostatic pressure, and hence are only overtopped by displacement waves from a mass movement entering the lake<sup>62</sup>. GLOFs from bedrockdammed lakes are only reported after the 1960s and those occurred mainly in the low-latitude Andes (n = 27; 16.6% within the low-latitude Andes, 90% of global bedrock-dammed GLOFs) (Fig. 1b). Although evidence is limited, GLOF counts from bedrock-dammed lakes might be underestimated given the short preservation time of any geomorphic evidence, especially compared with moraine dam failures<sup>102</sup>.\n\nSimilarly, there are relatively few GLOFs attributed to englacial or subglacial lakes induced by geothermal or volcanic activity compared with other GLOF triggers and mechanisms<sup>103</sup>. GLOFs from englacial lakes produce poorly preserved geomorphological evidence and are difficult to detect with optical remote sensing. They are mainly reported in Central Europe (n = 112, 25.3% within Central Europe, 69.1% of global englacial GLOFs) (Fig. 1b). Floods caused by rapidly draining englacial lakes have also occasionally been reported in New Zealand, western Canada and the United States 104-106 (Fig. 1b). The majority of GLOFs from subglacial lakes are also found in Central Europe (n = 5; 1.13% within Central Europe, 71.4% of global subglacial GLOFs), but further evidence is found in Southeast Asia (n = 1; 1.18% within Southeast Asia, 14.3% of global subglacial GLOFs) and the low-latitude Andes (n = 1; 0.61%) within the low-latitude Andes, 14.3% of global subglacial GLOFs) (Fig. 1b). In Iceland, for example, at least 264 outbursts from a small number of subglacial lakes<sup>31</sup>, which repeatedly form and drain owing to geothermal activity beneath the ice caps, have been observed, with further evidence of glacier-volcano interactions both in historical and prehistoric times 107,108.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Historical glacial lake outburst floods > Bedrock-dammed and subglacial GLOFs", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Historical glacial lake outburst floods", "Bedrock-dammed and subglacial GLOFs"], "chunk_type": "text", "line_start": 172, "line_end": 176, "token_count_estimate": 771, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1dea1cad71a9db21", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment\nType: text\n\nUnderstanding the characteristics and changes of glacial lakes and GLOF triggers and mechanisms is the basis for GLOF hazard assessment, which is critical for disaster prevention and damage mitigation. Globally, more than 10 million people are potentially exposed to the impacts of GLOFs, mainly in HMA<sup>38,109</sup>. Potentially dangerous glacial lakes have\n\nbeen identified using a range of assessment approaches, ranging from large-scale automated methods considering key determinants such as lake size, catchment area, ice/rock avalanche potential and dam steepness<sup>110,111</sup>, through to detailed catchment or lake-specific assessments including field investigations<sup>112-114</sup>. Identifying the dam type of the glacial lake is a critical first step in GLOF hazard assessments, as it determines the stability of the lake, triggering processes, the potential magnitude of the GLOF and the mechanisms of drainage<sup>115</sup>.\n\nFor GLOF hazard assessment a distinction is typically made between those factors that are critical to the inherent stability of the lake dam (geometry and structure of the dam) and those that influence the potential for an external triggering event (a rock or ice avalanche)<sup>100</sup>. Additionally, the hydro-geomorphic characteristics of the lake catchment area, which can influence the susceptibility to precipitation or melt-triggered outburst events, have also been highlighted<sup>37,116</sup>. With high-resolution optical imagery (such as available from Google Earth) and corresponding high-quality digital terrain models, it has become possible to quantify various physical characteristics of the dam and catchment area remotely over large spatial scales<sup>111,117</sup>. However, precise geometric measurements (such as dam freeboard or dam height) and in situ characteristics (such as ice core, lithology) are lacking, and can only be confirmed through local site investigations.\n\nTypically, large glacial lakes are emphasized owing to the potential high-magnitude GLOFs that could originate from them, yet outbursts from small or even seasonal lakes have proven capable of eroding huge volumes of sediment and producing catastrophic downstream process chains 118. GLOFs can be both the trigger of a process chain or initiated as part of a larger process chain, as in the case of a mass movement causing a displacement wave, overtopping and a downstream flood 119. Several hydrodynamic numerical models 80,120-123 have been used to numerically propagate flood waves downstream, modelling not only a GLOF but also entire process chains (see Supplementary Table 3). However, in the absence of robust field data from past events, modelling is often undertaken with poorly constrained conditions and parameters. Some morphodynamic models that include sediment transport and bed elevation changes during a simulation have also been applied where independent pre-GLOF and post-GLOF topographic data are available 124,125. These models can estimate sediment entrainment and transport rates 126, and more generally this numerical modelling has the potential to interpolate between observations along a river reach, or to extend beyond them downstream.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "GLOF hazard assessment"], "chunk_type": "text", "line_start": 178, "line_end": 186, "token_count_estimate": 818, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f1ee0c47caf577d", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > Trends in GLOF frequency and magnitude\nType: text\n\nAlthough suffering from known biases and limitations <sup>62,102</sup>, remote sensing observations and geomorphological evidence offer an opportunity to roughly estimate whether GLOFs have changed in frequency. The number of reported GLOFs increased from 1 in 850 to 3,151 in 2022, peaking from 2000 to 2020 (refs. 26,35). However, poor instrumentation and limited available satellite imagery might be responsible for fewer records of historical GLOF events before -1980, suggesting a bias in the estimation of the historical GLOF trend<sup>26,81,127</sup>. At the regional", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > Trends in GLOF frequency and magnitude", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "GLOF hazard assessment", "Trends in GLOF frequency and magnitude"], "chunk_type": "text", "line_start": 188, "line_end": 190, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6783769412b99ca4", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > Trends in GLOF frequency and magnitude\nType: figure\nFigure\n\nImage /page/7/Figure/1 description: A flowchart titled 'a GLOF initiation' illustrates the causes, processes, and impacts of a Glacial Lake Outburst Flood (GLOF). The chart is color-coded with a legend indicating that blue boxes represent 'Trigger or mechanism', yellow boxes represent 'Flood modelling', and red boxes represent 'Downstream impact'.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > Trends in GLOF frequency and magnitude", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "GLOF hazard assessment", "Trends in GLOF frequency and magnitude"], "chunk_type": "figure", "figure_caption": null, "line_start": 191, "line_end": 191, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0bcd66e3a3d8988b", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > Trends in GLOF frequency and magnitude\nType: text\n\nThe 'Trigger or mechanism' section shows several pathways. A 'Moraine-dammed' lake leads to a 'Moraine', which can be breached by an 'Ice or rock avalanche, landslide, overtopping, piping and dam breach'. Separately, 'Ice-dammed' and 'Supraglacial' lakes lead to 'Ice', which can be breached by an 'Ice avalanche, subaerial breaching, terminal retreat and dam breach'. Other triggers like 'Earthquake', 'Temperature, snowfall or rainfall', and 'Ice avalanche or collapse' also contribute to the breach mechanisms.\n\nThese triggers lead to a 'GLOF', which is the start of the 'Flood modelling' section. The GLOF event influences its 'Discharge' and 'Extent'. The GLOF initiates the 'Process of flood flow downstream'.\n\nThis downstream flow leads to 'Erosion and hazard', which is the start of the 'Downstream impact' section. The impacts listed are on 'Road', 'Bridge', 'Hydropower station', 'Electrical facility', 'Building', 'Livelihood', and 'Culture heritage'.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > Trends in GLOF frequency and magnitude", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "GLOF hazard assessment", "Trends in GLOF frequency and magnitude"], "chunk_type": "text", "line_start": 192, "line_end": 198, "token_count_estimate": 361, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53d0ebd82461151b", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > b GLOF hazard chain\nType: figure\nFigure\n\nImage /page/7/Figure/3 description: An infographic titled \"Hazard chain\" illustrating the causes and effects of natural disasters in a mountainous, glacial environment. The top portion is a diagram showing a sequence of events. At the highest elevation, \"Extreme snowfall and rainfall\" occurs over snow-capped mountains. A \"Glacier\" flows down a valley, where an \"Ice avalanche\" (labeled 2) falls into a \"Glacial lake\" (1). A \"Landslide\" (3) is also shown falling into the lake. An \"Earthquake\" is indicated as another potential trigger. Below the lake, an \"Ice core\" (4) is depicted. The outflow from the lake creates a \"Debris fan\" (5), leading to \"Erosion\" (6) and eventually \"Downstream damage and deposition\" (7 and 8) in a village. An \"Ice-dammed lake\" is also shown in a separate valley. The bottom portion of the image consists of eight numbered photographs that correspond to the numbered locations in the diagram, providing real-world examples: 1. A muddy glacial lake. 2. An ice avalanche calving into a lake. 3. The scar of a landslide on a mountainside. 4. A glacier with a terminal lake. 5. A large debris fan in a valley. 6. A river valley showing significant erosion. 7. A damaged bridge and debris-filled riverbed. 8. A building damaged by large rocks and flood debris.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > b GLOF hazard chain", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "GLOF hazard assessment", "b GLOF hazard chain"], "chunk_type": "figure", "figure_caption": null, "line_start": 201, "line_end": 201, "token_count_estimate": 405, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "17510827df43d336", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > b GLOF hazard chain\nType: text\n\n**Fig. 4** | **The triggers and mechanisms of GLOFs. a**, The process of a glacial lake outburst flood (GLOF), from trigger of mechanisms (blue) and flood modelling (yellow) to downstream impact (red). **b**, The hazard chain, from triggering factors\n\nto glacial lake outburst, downstream impacts and damage, and corresponding photographs (1–8). Understanding the triggers and mechanisms of GLOFs and flood modelling practices can improve hazard mitigation.\n\nscale, GLOFs from ice-dammed lakes in Alaska revealed an unchanged frequency and decreasing magnitude from 1985 to 2020 (ref. 82). Furthermore, time-series analysis of satellite images in the Himalaya suggest an unchanged frequency of moraine-dammed GLOFs from 1988 to 2017 (ref. 128). Therefore, the increasing number of moraine-dammed and bedrock-dammed lakes in mountainous regions worldwide does not necessarily imply more GLOFs 912.\n\nLittle is also known about how average or extreme GLOF flood discharges and volumes have changed, but some general themes have emerged. For instance, flood volumes of the largest reported GLOFs from ice-dammed lakes worldwide have decreased by an order of magnitude since 1900 (ref. 33). In contrast, the average peak discharge and volume of ice-dammed GLOFs remain unchanged 33. These changes align with the thinning of ice dams with atmospheric warming and glacier ablation, such that maximum lake levels gradually fall and drainage of ice-dammed lakes occurs earlier in the melt season 33,83. Moreover, as glaciers retreat, many glacier dams can no longer impound lakes at their termini at lower elevations, or the glaciers are lost altogether 82. Where ice-dammed lakes can form at higher elevations they tend to be smaller because the topography is steeper, and thus the water storage capacity is limited.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > GLOF hazard assessment > b GLOF hazard chain", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "GLOF hazard assessment", "b GLOF hazard chain"], "chunk_type": "text", "line_start": 202, "line_end": 210, "token_count_estimate": 523, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7dce2680820b678e", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods\nType: text\n\nWhether the increasing number and size of glacial lakes will lead to higher-magnitude GLOF discharges and greater GLOF volumes in the future remains a controversial hypothesis. With such apparent historical changes in glacial lake number and corresponding GLOFs, predicting their changes in a warming climate is important for understanding future natural hazards <sup>129,130</sup>. The future of glacial lakes and potential hazard hot spots are now discussed.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods"], "chunk_type": "text", "line_start": 212, "line_end": 214, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34f873d4fb386a21", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution\nType: text\n\nThe life cycle of glacial lakes is known to shift in a warming climate (Fig. 5). Supraglacial lakes can grow, coalesce and, eventually, form proglacial lakes. Simultaneously, existing proglacial lakes can expand in area and in volume (Fig. 5). Formation of new lakes and the growth of existing proglacial lakes can accelerate glacier terminus retreat, via increased frontal ablation <sup>131,132</sup>. Future increases in the volume of proglacial lakes will generally increase the downstream damage of any GLOFs <sup>38</sup>. In addition, some ice-dammed lakes can form following glacier surges, and episodic failures of ice-dammed lakes are highly destructive <sup>133,134</sup> (Fig. 5). Ice-dammed lakes will continue to breach in the near future, even as glaciers thin and retreat <sup>33,64</sup>.\n\nBeyond these conceptual changes, estimating subglacial topography and identifying potential overdeepenings are critical to project the location and size of future glacial lakes. As glaciers retreat, depressions can be exposed that have the potential to fill partly or fully with meltwater to develop into glacial lakes<sup>129</sup>. Identification of these overdeepenings requires knowledge (or an estimate) of subglacial bedrock morphology, which can be modelled by subtracting ice thickness<sup>20,135</sup> from a digital elevation model<sup>136</sup>, and a hydrological filling algorithm applied to fill the overdeepenings<sup>137</sup>. The difference between the filled and the original subglacial bed topography provides a bathymetric grid suggesting the extent and volume of the overdeepenings and, hence, of potential future glacial lakes<sup>24,136</sup>. Glacier evolution models<sup>20,138</sup> can also predict future rates of glacier retreat so as to estimate when glacial lakes might begin to form<sup>21,24,136</sup>. However, these approaches cannot currently determine the dam type nor confidently suggest the height of the future lake outlet<sup>24</sup>. Nonetheless, despite the high and as yet unassessed uncertainty, these ice thickness–overdeepening approaches have been used to project regional changes in glacial lakes, including  $HMA^{21,24}$  (Fig. 6; see Supplementary Table 4).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future glacial lake evolution"], "chunk_type": "text", "line_start": 216, "line_end": 228, "token_count_estimate": 685, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85e6e74bba4279ec", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution\nType: text\n\n< sup > 20 , 138 < / sup > can also predict future rates of glacier retreat so as to estimate when glacial lakes might begin to form < sup > 21 , 24 , 136 < / sup > . However , these approaches cannot currently determine the dam type nor confidently suggest the height of the future lake outlet < sup > 24 < / sup > . Nonetheless , despite the high and as yet unassessed uncertainty , these ice thickness – overdeepening approaches have been used to project regional changes in glacial lakes , including $ HMA ^ { 21 , 24 } $ ( Fig . 6 ; see Supplementary Table 4 ) .\n\nIn HMA, glacial lake area and volume are generally expected to increase. These changes arise owing to high projected glacier volume losses (60–75% by 2100 under unabated  $\\rm CO_2$  emissions  $^{25,139}$ ), exposing subglacial overdeepenings and, thus, expanding existing lakes and developing new glacial lakes  $^{21,24}$ . For example, with complete glacier disappearance in HMA, -13,000 new glacial lakes (each larger than 0.01 km²) could potentially form, with a total area of -1,510 km² and volume of -52.3 km³ (ref. 24). Similar estimates of a maximum number of 10,068 [6,981, 13,538] new ice-dammed lakes are projected by 2100 under SSP585, with an area of 290 [204, 639] km² and volume of 2.9 [1.9, 4.9] km³ (ref. 64). However, these quantifications are uncertain. Indeed, a different ice thickness model and, hence, subglacial bed topography suggests -25,285 lakes (larger than 0.01 km²) could form by complete deglaciation, having an area of 2,683 ± 773.8 km² and volume of 99.1 ± 28.6 km³ (ref. 136).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future glacial lake evolution"], "chunk_type": "text", "line_start": 216, "line_end": 228, "token_count_estimate": 528, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55395320fe1e55fd", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution\nType: text\n\n, 13 , 538 ] new ice - dammed lakes are projected by 2100 under SSP585 , with an area of 290 [ 204 , 639 ] km² and volume of 2 . 9 [ 1 . 9 , 4 . 9 ] km³ ( ref . 64 ) . However , these quantifications are uncertain . Indeed , a different ice thickness model and , hence , subglacial bed topography suggests - 25 , 285 lakes ( larger than 0 . 01 km² ) could form by complete deglaciation , having an area of 2 , 683 ± 773 . 8 km² and volume of 99 . 1 ± 28 . 6 km³ ( ref . 136 ) .\n\nThe magnitude of the projected glacial lake changes in HMA is regionally variable as well as strongly dependent on the emission scenario (hence, temperature rise). Generally, greater changes are expected in western regions (Fig. 6a-c). The total area of glacial lakes, for example, is expected to increase by  $7.2 \\times 10^2$  km<sup>2</sup> in Central Asia compared with  $5.6 \\times 10^2$  km<sup>2</sup> in Southwest Asia and  $2.3 \\times 10^2$  km<sup>2</sup> in Southeast Asia (Fig. 6a,c). Glacial lake volume also reflects similar shifts (Fig. 6b,c). Future lake area and volume estimates show an increase of about 771.4 km<sup>2</sup> and 37.0 km<sup>3</sup>, respectively, from 2030 to 2100 under a high-emission scenario; generally, these have a continuous increase until about 2090 and a decrease thereafter (Fig. 6e,f). However, there are sub-regional differences, with larger increases in the Karakoram (82.5 km<sup>2</sup> and 4.2 km<sup>3</sup>) and Himalaya (187.0 km<sup>2</sup> and 10.4 km<sup>3</sup>) compared with smaller increases in the Hissar Alay (1.7 km<sup>2</sup>) and 0.03 km<sup>3</sup>) and the Hindu Kush (1.4 km<sup>2</sup> and 0.02 km<sup>3</sup>) over the same time period and emission scenario (Fig. 6e,f).", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future glacial lake evolution"], "chunk_type": "text", "line_start": 216, "line_end": 228, "token_count_estimate": 654, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8af07902b8fadd61", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution\nType: text\n\nsup > 3 < / sup > ) and Himalaya ( 187 . 0 km < sup > 2 < / sup > and 10 . 4 km < sup > 3 < / sup > ) compared with smaller increases in the Hissar Alay ( 1 . 7 km < sup > 2 < / sup > ) and 0 . 03 km < sup > 3 < / sup > ) and the Hindu Kush ( 1 . 4 km < sup > 2 < / sup > and 0 . 02 km < sup > 3 < / sup > ) over the same time period and emission scenario ( Fig . 6e , f ) .\n\nGlacial lake area and volume are also expected to increase somewhat in the European Alps. At the bigger regional scale under complete deglaciation, lake area and volume are expected to increase by about 477.0 km<sup>2</sup> and 1.7 km<sup>3</sup>, respectively (Fig. 6a,b). However, at a national level, different climate scenarios and glacier evolution models are often used that make comparisons difficult. In the Swiss Alps, the number and area of lakes are 987 and  $6.22 \\pm 0.25 \\,\\mathrm{km^2}$  in 2016, respectively (ref. 59). If all glaciers were to disappear, ~500 new glacial lakes (of at least 0.01 km<sup>2</sup>) could form, with a total area of about 50 km<sup>2</sup> and volume of 1.6 km<sup>3</sup> (ref. 129). These estimates are broadly in keeping with ice-free scenarios based on current ice thickness measurements; they suggest that 683 glacial lakes (larger than 0.005 km²) with a total area and volume of  $45.2 \\pm 9.3 \\text{ km}^2$  and  $1.16 [1.05, 1.32] \\text{ km}^3$  (mean with 95% confidence interval), respectively, could develop<sup>23</sup>. Looking at specific emission scenarios rather than total ice elimination, glacial lake volumes of 0.35 [0.12, 0.49] km<sup>3</sup> and 0.94 [0.51, 1.04] km<sup>3</sup> could be realized by 2100 for SSP1-2.6 and SSP5-8.5, respectively<sup>23</sup>. In the upper Rhône catchment of the south-western Swiss Alps alone, 100 sites are predicted to have a high potential for future glacial lake formation<sup>140</sup>.\n\nSmaller changes are anticipated in other regions of the European Alps. In Austria, 42 potential new glacial lakes with a total area of about 2 km² could form with complete deglaciation <sup>141</sup>, an increase from the current 1,410 lakes with a total area of 17.1 km² in 2015 (ref. 75). A similar number of new lakes are expected under complete ice loss", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future glacial lake evolution"], "chunk_type": "text", "line_start": 216, "line_end": 228, "token_count_estimate": 752, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a518897c13c493dd", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution\nType: figure\nFigure\n\nImage /page/9/Figure/1 description: A multi-panel figure illustrating the formation and growth of glacial lakes and the associated hazard of Glacial Lake Outburst Floods (GLOF). The figure is divided into four sections labeled a, b, c, and d. Sections a, b, and c each contain two diagrams comparing a 'Current climate' scenario with a 'Future climate' scenario. Section a, titled 'Supraglacial lake', shows small pools of water on a glacier's surface in the current climate, which increase in number and size in the future climate, a process labeled 'Supraglacial lake increase and proglacial lake formation'. Section b, 'Proglacial lake', depicts a lake at the glacier's terminus that grows larger as the glacier recedes in the future climate, labeled 'Proglacial lake growth'. Section c, 'Ice-dammed lake', shows the formation of a lake in a tributary valley dammed by the main glacier in the future climate, a process called 'Ice-dammed lake formation'. Section d, titled 'Increasing GLOF hazard potential', illustrates a GLOF event where a landslide falls into a glacial lake, causing a flood that damages a populated area downstream. This section also includes a Venn diagram where three overlapping circles for 'Hazard', 'Vulnerability', and 'Exposure' intersect to create 'Risk'. Arrows labeled 'Adaptation' and 'Countermeasure' are shown, linking the GLOF illustration to the risk concept.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future glacial lake evolution"], "chunk_type": "figure", "figure_caption": null, "line_start": 229, "line_end": 229, "token_count_estimate": 440, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a40385411f94a1bb", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution\nType: text\n\nFig. 5 | Future formation and growth of glacial lakes and GLOF hazard.  $\\mathbf{a}$ - $\\mathbf{c}$ , Evolution of supraglacial lakes (panel  $\\mathbf{a}$ ), proglacial lakes (panel  $\\mathbf{b}$ ) and ice-dammed lakes (panel  $\\mathbf{c}$ ) from the current climate (top panel) to a future climate (middle panel).  $\\mathbf{d}$ , The overarching change in glacial lake outburst flood\n\n(GLOF) hazard in a future climate ('+' and '-' indicate increases and decreases that influence the GLOF hazard), feeding into GLOF risk. The life cycle of different types of glacial lakes shifts in a warming climate and can contribute to increased GLOF risk, with more and larger lakes and greater exposure.\n\nin the Aosta Valley region, Italy; 46 potential new lakes larger than  $0.01\\,\\mathrm{km^2}$  might develop, with a total area of  $3.1\\pm0.9\\,\\mathrm{km^2}$  and a volume of  $0.06\\pm0.02\\,\\mathrm{km^3}$  (ref. 142), expanding the current 186 lakes with a total area of  $1.4\\,\\mathrm{km^2}$  (ref. 143). Likewise, 80 (48 with a high level of confidence) potential new lakes could form under full deglaciation in the Mont Blanc massif of the French Alps 144.\n\nSmaller changes are generally expected in glacial regions of the Southern Hemisphere, including the New Zealand Alps. Here, lake area is expected to increase by 42.3 km<sup>2</sup> by 2100 under RCP4.5 (Fig. 6a,b). More specifically, ice-marginal lakes across the Southern Alps could\n\nexpand from 20.9 km² in 2020 to 40.4 km² in 2050 (ref. 74). However, from 2050 onwards, the rate of increase in total area is projected to decrease from 0.65 km² year $^{-1}$  in 2020–2050 to 0.04 km² year $^{-1}$  in 2050–2100 as lakes detach from glaciers and/or fill with sediment $^{74}$ .\n\nIn the Peruvian Andes, glacial lake area is projected to increase by 65.5 km² and volume by 0.7 km³ by 2100 under RCP8.5 (refs. 73,145) (Fig. 6a,b). In the still glaciated Cordillera Blanca, 38 or 50 new lakes could emerge by 2100 under RCP2.6 or RCP8.5, respectively  $^{73}$ , increasing from the current 870 lakes; accordingly, the glacial lake area is expected to increase by -10% compared with 2018 values, from 35.2 km²", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future glacial lake evolution", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future glacial lake evolution"], "chunk_type": "text", "line_start": 230, "line_end": 242, "token_count_estimate": 717, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d662e5c7cddda176", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > a Projected glacial lake area\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A world map with several rectangular regions outlined. Some of these regions are color-coded according to a legend at the bottom. The legend is a color scale from dark blue to light blue, to pale yellow, to orange, corresponding to a numerical scale from 0 to 8, with the unit labeled as \"10² km²\". A large region in South America is colored dark blue. A region in Europe is colored pale yellow. A region in Central Asia is colored with orange, pale yellow, and light blue sections. An inset map of New Zealand in the bottom right is also colored dark blue. Other outlined regions in North America and northern Asia are not colored.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > a Projected glacial lake area", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "a Projected glacial lake area"], "chunk_type": "figure", "figure_caption": null, "line_start": 245, "line_end": 245, "token_count_estimate": 228, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14c7aba683725d3e", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > b Projected glacial lake volume\nType: figure\nFigure\n\nImage /page/10/Figure/4 description: A world map showing continents in light gray. Several rectangular regions are outlined across the globe. Some of these regions are color-coded based on a legend at the bottom. The legend is a color bar with a scale from 0 to 3 in units of 10 km³. The colors range from dark blue (0-1), to a lighter blue-gray (1-2), to a pale yellow (2-3). On the map, a large portion of South America is colored dark blue. The Mediterranean region is colored light blue-gray. A region in East Asia is colored pale yellow, with its southern coastal area colored dark blue.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > b Projected glacial lake volume", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "b Projected glacial lake volume"], "chunk_type": "figure", "figure_caption": null, "line_start": 249, "line_end": 249, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3bd65c52ae6fe0b6", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > C Projected glacial lake area and volume in HMA\nType: figure\nFigure\n\nImage /page/10/Figure/6 description: A map titled 'Projected glacial lake area and volume in HMA' shows a geographical region, likely High Mountain Asia, with data points represented by circles of varying sizes and colors. There are two legends. The first legend explains the size of the circles, which corresponds to the glacial lake area in square kilometers (km²). The categories are: a small dot for <3, and progressively larger circles for 3-10, 10-30, 30-60, and >60 km². The second legend is a color bar indicating the volume in 10⁻¹ km³, where colors range from blue (0.001) to red (1.5). The map shows a high concentration of large, red circles (indicating large area and high volume) in the central-western part of the region. Smaller circles, colored yellow and dark blue, are scattered more widely.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > C Projected glacial lake area and volume in HMA", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "C Projected glacial lake area and volume in HMA"], "chunk_type": "figure", "figure_caption": null, "line_start": 253, "line_end": 253, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "382a7ed95fcde5ea", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > d Projected GLOF hazard and risk in HMA\nType: figure\nFigure\n\nImage /page/10/Figure/8 description: A map showing projected GLOF (Glacial Lake Outburst Flood) hazard and risk in HMA (High Mountain Asia). The map displays a mountainous region divided into several sub-regions. Overlaid on these regions are groups of three vertical bar charts. A legend in the bottom-left corner explains that the bars represent 'Risk' (yellow), 'Exposure' (light orange), and 'Hazard' (darker orange). The legend also includes a scale bar labeled '600' corresponding to the height of the example 'Hazard' bar. The heights of the bars vary across the different regions on the map, indicating different levels of projected hazard, exposure, and risk.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > d Projected GLOF hazard and risk in HMA", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "d Projected GLOF hazard and risk in HMA"], "chunk_type": "figure", "figure_caption": null, "line_start": 257, "line_end": 257, "token_count_estimate": 243, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a092cfb2b8c7ce69", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > e Scenario dependence of HMA glacial lake area\nType: figure\nFigure\n\nImage /page/10/Figure/10 description: A set of four stacked area charts showing projections of glacial lake area from the year 2040 to 2100 under four different climate scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The vertical axis is labeled \"Glacial lake area (×10² km²)\" and ranges from 0 to 8. The horizontal axis shows the years 2040, 2060, 2080, and 2100. Each chart shows a progressive increase in glacial lake area over time, with the area represented by stacked layers of different shades of blue. The total area increases with the severity of the climate scenario. For SSP1-2.6, the area reaches approximately 4.5 ×10² km² by 2100. For SSP2-4.5, it reaches about 5.8 ×10² km². For SSP3-7.0, it reaches about 6.8 ×10² km². For SSP5-8.5, the area increases most sharply, exceeding 8 ×10² km² by 2100.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > e Scenario dependence of HMA glacial lake area", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "e Scenario dependence of HMA glacial lake area"], "chunk_type": "figure", "figure_caption": null, "line_start": 261, "line_end": 261, "token_count_estimate": 294, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21121c73eea1c743", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume\nType: figure\nFigure\n\nImage /page/10/Figure/12 description: A set of four stacked area charts illustrating the scenario dependence of HMA (High-Mountain Asia) glacial lake volume. The vertical axis represents \"Glacial lake volume (×10 km³)\" and ranges from 0 to 4. The horizontal axis for each chart shows the years from 2040 to 2100, with labels at 20-year intervals. Each chart depicts a different scenario, showing a projected increase in total glacial lake volume over time. The total volume is broken down by region, with each region represented by a different shade of blue or green in the stacked area chart. A legend at the bottom identifies the regions: Tibet, Himalaya, Karakoram, Kun Lun, Tien Shan, Qilian Shan, Pamir, Hissar Alay, Hindu Kush, and Hengduan Shan. In all four scenarios, the total volume increases from near zero in 2040. By 2100, the projected total volumes for the four scenarios are approximately 2.1, 2.8, 3.0, and 3.8 (×10 km³), respectively, from left to right.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "f Scenario dependence of HMA glacial lake volume"], "chunk_type": "figure", "figure_caption": null, "line_start": 265, "line_end": 265, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e92b9b78ba4c1430", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume\nType: text\n\n**Fig. 6 | Global potential future glacial lakes and GLOF hazard. a, b,** Published estimates of potential future glacial lake area (panel **a**) and potential future glacial lake volume (panel **b**) with complete deglaciation (see Supplementary Table 4). **c**, Potential future glacial lake area (circles) and volume (shading) in High Mountain Asia (HMA) by 2100 under RCP8.5 (ref. 24). Background shading depicts drainage basins using the colours in panels **e** and **f. d**, Future glacial lake outburst flood (GLOF) risk (light shading), exposure (medium shading) and\n\nhazard (dark shading) in HMA by 2100 under RCP8.5 (ref. 24). Physical or social vulnerability is not considered. **e,f**, Time series of potential future glacial lake area (panel **e**) and lake volume (panel **f**) in HMA under different SSP values, with shading representing different subregions<sup>21</sup>. Projected global glacial lakes will increase globally by 2100 under different emission scenarios, posing a potential hazard threat to downstream communities.\n\nto 37.0 km². In the deglaciating Vilcanota–Urubamba basin, the lake area in 2100 is projected to be 3.2% higher than 2016 values under RCP2.6 (from 26.9 km² to 27.8 km²) and 6.0% higher under RCP8.5 (from 26.9 km² to 28.5 km²)  $^{145}$ . The topographic setting means that the rate of increase is higher for 2016–2050 compared with 2050–2100 as glaciers are already retreating to very steep slopes by 2050 (ref. 145). In deglaciated areas of the Peruvian Andes, 201 sites (of at least 0.01 km²) with a total volume of 0.26 km³ have been compiled for a future glacial lake inventory  $^{146}$ .", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "f Scenario dependence of HMA glacial lake volume"], "chunk_type": "text", "line_start": 266, "line_end": 274, "token_count_estimate": 523, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "493408a91541c942", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume\nType: text\n\n26 . 9 km² to 27 . 8 km² ) and 6 . 0 % higher under RCP8 . 5 ( from 26 . 9 km² to 28 . 5 km² ) $ ^ { 145 } $ . The topographic setting means that the rate of increase is higher for 2016 – 2050 compared with 2050 – 2100 as glaciers are already retreating to very steep slopes by 2050 ( ref . 145 ) . In deglaciated areas of the Peruvian Andes , 201 sites ( of at least 0 . 01 km² ) with a total volume of 0 . 26 km³ have been compiled for a future glacial lake inventory $ ^ { 146 } $ .\n\nWhere lakes $ receive \\ , meltwater \\ , from \\ , GLOFs \\ , as \\ , well \\ , as \\ , from \\ , ablation \\ text { - } fed \\ , meltwater \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant $ flows , and where GLOF - induced bank erosion widens river channels hundreds of kilometres downstream < sup > 23 , 78 < / sup > , the sedimentation rates within future lakes will also be very high .", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "f Scenario dependence of HMA glacial lake volume"], "chunk_type": "text", "line_start": 266, "line_end": 274, "token_count_estimate": 1492, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1f8826f9864e04d1", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume\nType: text\n\n, constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant \\ , and \\ , constant $ flows , and where GLOF - induced bank erosion widens river channels hundreds of kilometres downstream < sup > 23 , 78 < / sup > , the sedimentation rates within future lakes will also be very high .\n\nOften not included in these assessments is sediment deposition within newly exposed overdeepenings, infilling potential future lakes and complicating specific projections. Such infilling is particularly likely in tectonically active, high-relief regions with abundant sediment supply such as HMA and the Southern Alps<sup>61,147</sup>. Indeed, erosion rates and sediment transport in glacierized basins are generally expected to increase owing to cryosphere degradation<sup>61,147</sup>, but there has been almost no quantitative analysis of this process. One exception is in the Swiss Alps, where approximately half of newly formed glacial lakes and 11-25% of the total overdeepening volume are expected to disappear by 2100 owing to sediment infilling<sup>23</sup>. If sediment delivery is not considered, the potential future glacial lakes are likely to be overestimated in number and size.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > f Scenario dependence of HMA glacial lake volume", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "f Scenario dependence of HMA glacial lake volume"], "chunk_type": "text", "line_start": 266, "line_end": 274, "token_count_estimate": 470, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7464c3e11d548c2b", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future GLOF hazard and risk\nType: text\n\nWith projected changes in glacial lakes come potential changes in GLOFs. Based on physical understanding and supported by models, increasing GLOFs are assumed for the future 24,64,136 (Fig. 5), albeit with much regional variability. In HMA, future GLOF hazards are expected to triple from current conditions due to lake development (Fig. 6d) owing to glacial and glacial lake changes, alongside a large and exposed downstream population. Even greater increases (greater than fivefold) are projected in the Karakoram and Pamir regions<sup>24</sup>, which are expected to become a hot spot of potential GLOF hazard<sup>136</sup>, although the timing of lake formation is uncertain 148. The characteristics of GLOFs in HMA are also expected to shift. Scenario modelling in the Poiqu River basin of China and Nepal, for example, suggests that permafrost degradation will enhance large mass movements (of tens of millions of cubic metres) into glacial lakes, resulting in larger flood magnitudes and, thereby, longer distances downstream of flood impact than historically observed149.\n\nFuture GLOF hazards in other regions of the world are unlikely to increase as in HMA. For example, there is relatively low potential future for GLOFs in the Mont Blanc massif (Western European Alps) owing to the flat glacier bed topography<sup>144</sup>. In the Cordillera Blanca (Peru), glacier retreat and lake formation are already at an advanced stage, and new lakes still to form will be predominantly dammed by stable bedrock<sup>73</sup>. However, in Iceland, mega GLOFs induced by volcanic activity can occur in the future, irrespective and independent of climate change<sup>89</sup>.\n\nOf course, although GLOF hazard might increase, overall risk also necessitates consideration of exposure and vulnerability. To date, comprehensive risk assessments considering the changing flood hazard and societal vulnerability and exposure have been lacking. However, the expansion of communities and infrastructure, including hydropower facilities, has been and will continue to be a major driver\n\nof GLOF risk<sup>150–152</sup>. Thus, infrastructure expansion in glacial valleys, coupled with expanded glacial lakes, will likely result in enhanced GLOF risk, threatening downstream populations and infrastructure<sup>130,153–155</sup>. Accordingly, there are new challenges for warning systems or other disaster risk reduction measures<sup>156</sup>.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Future glacial lakes and outburst floods > Future GLOF hazard and risk", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Future glacial lakes and outburst floods", "Future GLOF hazard and risk"], "chunk_type": "text", "line_start": 276, "line_end": 284, "token_count_estimate": 669, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "9fe87c3d05809cb4", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Summary and future perspectives\nType: text\n\nGlobal glacier melt has accelerated owing to atmospheric warming, feeding rapid growth of glacial lakes in most world regions from the 1960s onwards. Although uncertain, it is estimated that the glacial lake number and area have increased -50% from 1990 to 2018 (ref. 9), such that more than 110,000 glacial lakes with a total area of -15,000 km² in 2015–2019 have been identified. This trend in glacial lakes is associated with a potential increase in GLOF hazards and risks. More than 3,000 GLOFs have occurred worldwide from 850 to 2022, with ~88% recorded since 1900. Indeed, recorded GLOFs have increased in frequency and magnitude in most glaciated mountain regions, especially after 2000 (refs. 26,35), a trend that is likely to continue with ongoing deglaciation, increasing GLOF risk and hazard when coupled with enhanced development in downstream river valleys. In HMA, in particular, GLOF risk is projected to increase threefold with complete deglaciation<sup>24,127</sup>.\n\nAlthough GLOF knowledge has advanced considerably, key challenges remain, particularly surrounding data. As a starting point, homogeneous and standardized multi-temporal glacial lake inventories are needed, so far limited by mapping efficiency. Visual inspection and manual editing of lake outlines have been the main methods used to date — time-consuming approaches that rely heavily on individual expertise. The use of cloud computing, machine learning and artificial intelligence technologies should overcome manual limitations. However, access to regional high-quality lake inventories to train machine learning classifiers is vitally important. With any such inventories, consistent thresholds for minimum lake area and distance from glaciers are needed, as well as consistent classifications. An updated multi-period inventory of global glacial lakes with a single criterion is expected to fill the gaps of missing lakes and regional deficiencies.\n\nHydrodynamic modelling and reconstruction of GLOFs is also hindered by absent data. Field data of lake properties (including bathymetry, temperature and lake outflow peak discharge) and GLOF mechanisms are desperately needed in different glaciated environments and for different lake types to refine and test model parameters, as well as parametrize dam breach models. Yet such data are extremely difficult to acquire given accessibility and safety that make successful recovery of instrumentation and sensors challenging, especially in ice-proximal locations. Accordingly, local, regional and even global understanding of GLOF flow dynamics is limited, feeding into hazard assessment, early warning systems, infrastructure design and emergency response. In addition to the use of modern field observation instruments and data transmission, broader international cooperation and the establishment of data opening and sharing are important ways forward. Future research should focus on comparing model results with field observations, historical flood records and other independent data sets to improve model credibility.\n\nAssessments of future glacier lakes under-report the common problem of uncertainty about the location, size and timing of potential future lakes. The average error in measured ice thickness of  $10\\pm24\\%^{157}$  masks considerably higher uncertainties for specific glaciers, including an uncertainty range of  $\\pm30\\%$  suggested by the validation of ground-penetrating radar measurements in the Swiss Alps<sup>129</sup>. Thus, although", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Summary and future perspectives", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Summary and future perspectives"], "chunk_type": "text", "line_start": 286, "line_end": 300, "token_count_estimate": 828, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a02308182d74b9c9", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Summary and future perspectives\nType: text\n\nsharing are important ways forward . Future research should focus on comparing model results with field observations , historical flood records and other independent data sets to improve model credibility . Assessments of future glacier lakes under - report the common problem of uncertainty about the location , size and timing of potential future lakes . The average error in measured ice thickness of $ 10 \\ pm24 \\ % ^ { 157 } $ masks considerably higher uncertainties for specific glaciers , including an uncertainty range of $ \\ pm30 \\ % $ suggested by the validation of ground - penetrating radar measurements in the Swiss Alps < sup > 129 < / sup > . Thus , although\n\ncommon ice thickness models can estimate the total ice volume of all glaciers globally<sup>20,135</sup> or regionally<sup>158,159</sup>, differences between ice thickness models can lead to substantial local differences. Crucially, the potential future lake area is highly sensitive to small errors in, for example, the height of a subglacial rock barrier or the depth of an overdeepening<sup>23</sup>. Obtaining accurate glacier bed topography by ice thickness modelling remains a challenge, but it can be improved by increasing the accuracy of the underlying input data, adding measured thickness data and multi-model synthesis<sup>20,157,160,161</sup>. In the future, with the launch of P-band and L-band radar satellites, obtaining ice thickness using tomographic SAR technology will provide new opportunities and possibilities to improve the accuracy of future glacial lake prediction<sup>162,163</sup>.\n\nThese improved data and models are vital to enhance GLOF hazard assessment, particularly in a warming world associated with enhanced GLOF risk. However, GLOF hazard assessments suffer from inconsistent approaches (indicators and their thresholds) and the lack of a common assessment framework (filed survey, hazard and risk assessment and management action)<sup>110,117</sup>. Technical guidelines have been established at the international level, but have yet to be adapted or widely consolidated into regional or national approaches, as the level and development of guidelines vary widely across regions<sup>115</sup>. Where existing assessments provide conflicting results, efforts should be undertaken to consolidate findings to determine which lakes should be prioritized for monitoring and other response actions. In particular, the need to consider small lakes is emphasized, as such lakes have often been overlooked but can have devastating downstream impacts<sup>77,118</sup>. Importantly, this knowledge needs to be used to anticipate the hazards of future GLOF events, including worst-case scenarios. Given the tendency for GLOFs to be triggered by or associated with other mass movements and cascading processes, multi-hazard perspectives are encouraged.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Summary and future perspectives", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Summary and future perspectives"], "chunk_type": "text", "line_start": 286, "line_end": 300, "token_count_estimate": 752, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97037dbcc93e6293", "text": "Document: Characteristics and changes of glacial lakes and outburst floods\nSection: Characteristics and changes of glacial lakes and outburst floods > Summary and future perspectives\nType: text\n\n> 115 < / sup > . Where existing assessments provide conflicting results , efforts should be undertaken to consolidate findings to determine which lakes should be prioritized for monitoring and other response actions . In particular , the need to consider small lakes is emphasized , as such lakes have often been overlooked but can have devastating downstream impacts < sup > 77 , 118 < / sup > . Importantly , this knowledge needs to be used to anticipate the hazards of future GLOF events , including worst - case scenarios . Given the tendency for GLOFs to be triggered by or associated with other mass movements and cascading processes , multi - hazard perspectives are encouraged .\n\nFuture efforts to move beyond hazards to comprehensive GLOF risk assessment must also involve a wider network of local experts and broader disciplines. Failure to adequately consider local vulnerabilities, needs and perceptions can ultimately limit the success of disaster risk reduction measures, such as early warning systems <sup>164</sup>. As a transboundary phenomenon, GLOFs pose particular challenges for disaster risk reduction where cooperation and sharing across political boundaries are required, and where societal and institutional capacities to manage GLOF risks can vary widely between upstream and downstream regions. Scientists, policymakers and stakeholders should work together towards broader international and regional transboundary cooperation to reduce the risk of GLOFs.", "metadata": {"source_file": "data/('Characteristics and changes of glacial lakes and outburst floods', '.pdf')_extraction.md", "document_title": "Characteristics and changes of glacial lakes and outburst floods", "section_path": "Characteristics and changes of glacial lakes and outburst floods > Summary and future perspectives", "section_headings": ["Characteristics and changes of glacial lakes and outburst floods", "Summary and future perspectives"], "chunk_type": "text", "line_start": 286, "line_end": 300, "token_count_estimate": 383, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "724a7dcdcd7821a5", "text": "Document: Climate change and the global pattern of moraine-dammed\nType: text\n\nAbstract. Despite recent research identifying a clear anthropogenic impact on glacier recession, the effect of recent climate change on glacier-related hazards is at present unclear. Here we present the first global spatio-temporal assessment of glacial lake outburst floods (GLOFs) focusing explicitly on lake drainage following moraine dam failure. These floods occur as mountain glaciers recede and downwaste. GLOFs can have an enormous impact on downstream communities and infrastructure. Our assessment of GLOFs associated with the rapid drainage of moraine-dammed lakes provides insights into the historical trends of GLOFs and their distributions under current and future global climate change. We observe a clear global increase in GLOF frequency and their regularity around 1930, which likely represents a lagged response to post-Little Ice Age warming. Notably, we also\n\nshow that GLOF frequency and regularity – rather unexpectedly – have declined in recent decades even during a time of rapid glacier recession. Although previous studies have suggested that GLOFs will increase in response to climate warming and glacier recession, our global results demonstrate that this has not yet clearly happened. From an assessment of the timing of climate forcing, lag times in glacier recession, lake formation and moraine-dam failure, we predict increased GLOF frequencies during the next decades and into the 22nd century.\n\n<sup>&</sup>lt;sup>1</sup>College of Life and Environmental Sciences, Exeter University, Exeter, UK\n\n<sup>&</sup>lt;sup>2</sup>Planetary Science Institute, Tucson, AZ 85719, USA\n\n<sup>&</sup>lt;sup>3</sup>Department of Hydrology & Atmospheric Science, University of Arizona, Tucson, AZ 85742, USA\n\n<sup>&</sup>lt;sup>4</sup>Department of Geography, University of Zurich, 8057 Zurich, Switzerland\n\n<sup>&</sup>lt;sup>5</sup>Reynolds International Ltd, Suite 2, Broncoed House, Broncoed Business Park, Wrexham Road, Mold, Flintshire, UK\n\n<sup>&</sup>lt;sup>6</sup>Water, Sediment, Hazards, and Earth-surface Dynamics Laboratory, University of Washington Tacoma, WA, 98402, USA\n\n<sup>&</sup>lt;sup>7</sup>Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, UK\n\n<sup>&</sup>lt;sup>8</sup>Department of Physical Geography and Geoecology, Charles University in Prague, Faculty of Science, Albertov 6, 128 43 Prague, Czech Republic\n\n<sup>&</sup>lt;sup>9</sup>Centre for Glaciology, Department of Geography and Earth Sciences, Aberystwyth University, Wales, SY23 3DB, Aberystwyth, UK\n\n<sup>&</sup>lt;sup>10</sup>Department of Geology, University of Dayton, 300 College Park, Dayton, OH 45469-2364, USA\n\n<sup>&</sup>lt;sup>11</sup>Department of Human Dimensions of Global Change, Global Change Research Institute, Czech Academy of Sciences, Bělidla 986/4a, 60300 Brno, Czech Republic", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "Climate change and the global pattern of moraine-dammed", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 33, "token_count_estimate": 841, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["45469", "60300", "85719", "85742", "98402"]}}
{"id": "8a6b8253810322fb", "text": "Document: Climate change and the global pattern of moraine-dammed\nType: text\n\nsup > & < / sup > lt ; sup > 9 < / sup > Centre for Glaciology , Department of Geography and Earth Sciences , Aberystwyth University , Wales , SY23 3DB , Aberystwyth , UK < sup > & < / sup > lt ; sup > 10 < / sup > Department of Geology , University of Dayton , 300 College Park , Dayton , OH 45469 - 2364 , USA < sup > & < / sup > lt ; sup > 11 < / sup > Department of Human Dimensions of Global Change , Global Change Research Institute , Czech Academy of Sciences , Bělidla 986 / 4a , 60300 Brno , Czech Republic\n\n<sup>&</sup>lt;sup>12</sup>Department of Engineering Geology, Institute of Rock Structure and Mechanics, Czech Academy of Sciences,\n\nV Holešovičkách 41, 182 09 Prague 8, Czech Republic\n\n<sup>&</sup>lt;sup>13</sup>Himalayan Research Center, Lainchaur, Kathmandu, Nepal", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "Climate change and the global pattern of moraine-dammed", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 33, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": ["45469", "60300"]}}
{"id": "87443c6d8bcf7bb9", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nThere is increasing scientific and policy interest in detecting climate change impacts and assessing the extent to which these can be attributable to anthropogenic or natural causes. As a result, recent research demonstrating an anthropogenic fingerprint on a significant proportion of recent global glacier recession is an important step forward (Marzeion et al., 2014). The focus can now shift to glacier hazards but the complex nature of glacier–climate interactions (Roe et al., 2017) and their influence on hazards makes this a challenging task (Shugar et al., 2017).\n\nMountain glaciers have continued to recede (Kargel et al., 2014; Cramer et al., 2014) and thin from their late Holocene (Little Ice Age, LIA) positions and, in many cases, the rate of recession and thinning has increased over recent decades largely as a consequence of global warming (Marzeion et al., 2014). Thinning, flow stagnation and recession of glacier tongues have resulted in the formation of moraine-dammed lakes (Richardson and Reynolds, 2000). These moraines, some of which contain a melting ice core, are built from rock debris transported by glaciers. When they fail, large volumes of stored water can be released, producing glacial lake outburst floods (GLOFs). These floods have caused thousands of fatalities and severe impacts on downstream communities, infrastructure and long-term economic development (Mool et al., 2011; Riaz et al., 2014; Carrivick and Tweed, 2016).\n\nAlthough much research has been carried out on the nature and characteristics of GLOFs and hazardous lakes from many of the world's mountain regions (e.g. Lliboutry et al., 1977; Evans, 1987; O'Connor et al., 2001; Huggel et al., 2002; Bajracharya and Mool, 2009; Ives et al., 2010; Iribarren et al., 2014; Lamsal et al., 2014; Vilímek et al., 2014; Westoby et al., 2014; Perov et al., 2017), there are significant gaps in our knowledge of these phenomena at the global scale and concerning their relationship to anthropogenic climate change. Detecting changes in the magnitude, timing and frequency of glacier-related hazards over time and assessing whether changes can be related to climate forcing and glacier dynamical responses is also of considerable scientific and economic interest (Oerlemans, 2005; Stone et al., 2013). Multiple case studies are insufficient to achieve a better understanding of the mechanisms leading to GLOF initiation so a more comprehensive understanding of the global frequency and timing of GLOFs is necessary. Testing such relationships at a global scale is also an important step toward assessment of the sensitivity of geomorphological systems to climate change.\n\nDespite numerous inventories of GLOFs at regional scales (see Emmer et al., 2016), no global database has been created which focuses specifically on GLOFs relating to the failure of moraine dams. A global database is required to place GLOFs in their wider climatic context (Richardson and Reynolds, 2000; Mool et al., 2011). This means that we are unable to answer some important questions concerning", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 35, "line_end": 53, "token_count_estimate": 776, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "20d9359cf6dbff77", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nto GLOF initiation so a more comprehensive understanding of the global frequency and timing of GLOFs is necessary . Testing such relationships at a global scale is also an important step toward assessment of the sensitivity of geomorphological systems to climate change . Despite numerous inventories of GLOFs at regional scales ( see Emmer et al . , 2016 ) , no global database has been created which focuses specifically on GLOFs relating to the failure of moraine dams . A global database is required to place GLOFs in their wider climatic context ( Richardson and Reynolds , 2000 ; Mool et al . , 2011 ) . This means that we are unable to answer some important questions concerning\n\ntheir historic behaviour and therefore the changing magnitude and frequency of GLOFs globally through time, and their likely evolution under future global climate change. This latter point is made even more difficult by the lack of long-term climate data from many mountain regions. Given the size and impacts of GLOFs in many mountain regions, better understanding their links to present and future climate change is of great interest to national and regional governments, infrastructure developers and other stakeholders. We argue that glacier hazard research needs to be increasingly seen through the lens of change adaptation.\n\nThese issues and knowledge gaps can be addressed via a systematic, uniform database of GLOFs. Here we have compiled an unprecedented global GLOF inventory related to the failure of moraine dams. We discuss the problems involved in developing a robust attribution argument concerning GLOFs and climate change. This inventory covers only the subset of GLOFs that are linked to overtopping or failure of moraine dams. Our focus on moraine dams is motivated by (1) this type of event leaving clear diagnostic evidence of moraine-dam failures in the form of breached end moraines and lake basins, whereas ice-dammed lake failures commonly do not leave such clear and lasting geomorphological evidence and (2) the conventional hypothetical link between climate change, glacier response, moraine-dammed lake formation and GLOF production being more straightforward compared to the range of processes driving GLOFs from ice- and bedrock-dammed lakes.\n\nSuch GLOF events are often triggered by ice and rock-falls, rockslides or moraine failures into lakes, creating seiche or displacement waves, but also by heavy precipitation or ice melt/snowmelt events (Richardson and Reynolds, 2000). While climate change plays a dominant role in the recession of glaciers, downwasting glacier surfaces debuttress valley rock walls, leading to catastrophic failure in the form of rock avalanches or other types of landslides (Ballantyne, 2002; Shugar and Clague, 2011; Vilímek et al., 2014). Other climatically induced triggers of moraine dam failures include increased permafrost and glacier temperatures leading to failure of ice and rock masses into lakes and the melting of ice cores in moraine dams, which leads to moraine failure and lake drainage.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 35, "line_end": 53, "token_count_estimate": 733, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f880525f987aec2", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\ndisplacement waves , but also by heavy precipitation or ice melt / snowmelt events ( Richardson and Reynolds , 2000 ) . While climate change plays a dominant role in the recession of glaciers , downwasting glacier surfaces debuttress valley rock walls , leading to catastrophic failure in the form of rock avalanches or other types of landslides ( Ballantyne , 2002 ; Shugar and Clague , 2011 ; Vilímek et al . , 2014 ) . Other climatically induced triggers of moraine dam failures include increased permafrost and glacier temperatures leading to failure of ice and rock masses into lakes and the melting of ice cores in moraine dams , which leads to moraine failure and lake drainage .\n\nAttribution of climate change impacts is an emerging research field and no attribution studies on GLOFs are available so far. Even for glaciers only very few attribution studies have been published to date (Marzeion et al., 2014; Roe et al., 2017). Follow-up studies from the IPCC 5th Assessment Report (Cramer et al., 2014) proposed a methodological procedure to attribute impacts to climate change (Stone et al., 2013). Based on that, a methodologically sound detection and attribution study needs first to formulate a hypothesis of potential impacts of climate change. In our case physical process understanding supports the association between climate change and GLOFs associated with morainedam failure by climate warming, resulting in glacier reces-", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 35, "line_end": 53, "token_count_estimate": 371, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8915e092f6476f29", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nso far . Even for glaciers only very few attribution studies have been published to date ( Marzeion et al . , 2014 ; Roe et al . , 2017 ) . Follow - up studies from the IPCC 5th Assessment Report ( Cramer et al . , 2014 ) proposed a methodological procedure to attribute impacts to climate change ( Stone et al . , 2013 ) . Based on that , a methodologically sound detection and attribution study needs first to formulate a hypothesis of potential impacts of climate change . In our case physical process understanding supports the association between climate change and GLOFs associated with morainedam failure by climate warming , resulting in glacier reces -\n\nsion and glacial lake formation and evolution behind moraine dams which become unstable and fail catastrophically. The next step requires a climate trend to be detected, followed by the identification of the baseline behaviour of the system in the absence of climate change. The difficulty of identifying the baseline behaviour is related to several factors. The first is the existence of confounding factors, both natural and human related. For instance, the frequency of GLOFs from moraine dams also depends on factors such as the stability of the dam, including dam geometry and material or mitigation measures such as artificial lowering of the lake level (Portocarreo-Rodriguez and the Engility Corporation, 2014). Second, there are few long-term palaeo-GLOF records with which to assess baseline behaviour. Eventually, attribution includes the detection of an observed change that is consistent with the response to the climate trend, in our case a change in GLOF occurrence and the evaluation of the contribution of climate change to the observed change in relation to confounding factors. Our chief observational result is that there is an upsurge in GLOF frequency starting around 1930 and then a decline following roughly 1975 and persisting for decades (see also Carrivick and Tweed, 2014). At face value, when comparing this with the climate records, there seems to be no relationship between global GLOF frequency and concurrent climatic fluctuations, and a regional breakdown offers no solution; for example, strong climatic global (or Northern Hemisphere) warming during the period of declining GLOF frequency after 1975 appears to be counterintuitive. A simplistic inference would be that climate change does not influence GLOF incidence, but we reject this given our understanding of the physical drivers of glacier recession, lake development and drainage mechanisms. Although we know that GLOFs involve a complex set of dynamics, one of the important dynamical changes affecting GLOFs is the formation and growth of glacial lakes, and we know that there must be a relationship here to climatic warming. GLOF triggers also commonly involve extreme weather, such as extreme heat and extreme precipitation, which are intuitively linked to climate change as well, even if the attribution experiments have not yet been carried out. We thus have to dig deeper to see how GLOF frequency may be connected to climate change. The point arises that the conditions needed for a GLOF involve a long period of lake formation and growth, such that past climate changes are involved. In the Methods section we produce a model whereby the history of one climate variable and its time derivative – Northern Hemisphere mean temperature and warming rate – are linked to the GLOF record.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 35, "line_end": 53, "token_count_estimate": 792, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2fe8257c0a26cfb0", "text": "Document: 1 Introduction\nSection: 2 Methods\nType: text\n\nWe produced a database of GLOFs developed from a collation of regional inventories and reviews (e.g. GAPHAZ<sup>1</sup>, WGMS<sup>2</sup> and GLACIORISK<sup>3</sup> databases and the GLOF Database provided under ICL database of glacier and permafrost disasters from the University of Oslo, Reynolds and Richardson, 2000; RGSL, 1997, 2002), regional overviews and reviews (e.g. Clague et al., 1985; Xu, 1987; Costa and Schuster, 1988; Reynolds, 1992; Ding and Liu, 1992; Clague and Evans, 2000; O'Connor et al., 2001; Zapata, 2002; Raymond et al., 2003; Jiang et al., 2004; Carey, 2005; Osti and Egashira, 2009; Narama et al., 2010; Ives et al., 2010; Wang et al., 2011; Carey et al., 2012; Mergili and Schneider, 2011; Fujita et al., 2012; Iribarren et al., 2014, Emmer, 2017) and case studies of individual GLOFs (e.g. Kershaw et al., 2005; Harrison et al., 2006; Worni et al., 2012). A complete list is available in the Supplement. The GLOF database was developed from a collation of regional inventories and reviews (see Supplement). Only GLOFs that could be dated to the year and to moraine failure were included. Past temperature trends from the glacier regions of interest were extracted from three independent global temperature reconstructions (CRUTEM4.2, Jones et al., 2012, NOAA NCDC (National Oceanic and Atmospheric Administration - National Climatic Data Center), Smith et al., 2008, and NASA GISTEMP (Goddard's Institute Surface Temperature Analysis), Hansen et al., 2010). These data sets provided temperature anomaly data relative to a modern baseline beginning in 1850 for CRUTEM4.2 and 1880 for NOAA NCDC and NASA GIS-TEMP.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods", "section_headings": ["2 Methods"], "chunk_type": "text", "line_start": 55, "line_end": 57, "token_count_estimate": 488, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea6b9aec81b155ec", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.1 Test of direct linkage between GLOF rate and climate change\nType: text\n\nWe concentrate exclusively on the subset of GLOFs associated with the failure of moraine-dammed lakes as these are a major hazard in many mountain regions but also represent the best candidates of outburst floods for attribution to climate change. We differentiate these from other glacially sourced outburst floods, such as those resulting from the failure of an ice dam (Walder and Costa, 1996; Tweed and Russell, 1999; Roberts et al., 2003), dam overflow, volcanically triggered jökulhlaups (Carrivick et al., 2004; Russell et al., 2010; Dunning et al., 2013) or the sudden release of water from englacial or subglacial reservoirs (Korup and Tweed, 2007).\n\nThe period over which climate data are available is dependent on the region but starts in 1850 in CRUTEM4.2 and 1880 in NOAA NCDC and NASA GISTEMP. The resolution of the data is generally 5°; however, NASA GISTEMP is provided at 1° resolution but it should be noted this does\n\n<sup>&</sup>lt;sup>1</sup>http://www.gaphaz.org/\n\n<sup>&</sup>lt;sup>2</sup>http://www.wgms.ch/\n\n<sup>&</sup>lt;sup>3</sup>https://cordis.europa.eu/project/rcn/54168\\_en.html\n\nnot imply there are more observational data in this analysis. For each region, we extract all grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF). With the exception of the European Alps no data set contains a complete continuous record for the period 1900–2012. We therefore take all available data points to form time series for each data set and derive a mean linear trend for the 1990–2012 period. Given large uncertainties and data gaps no attempt is made to statistically test these trends. The trends presented here are therefore considered illustrative of past changes in temperature for these regions.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.1 Test of direct linkage between GLOF rate and climate change", "section_headings": ["2 Methods", "2.1 Test of direct linkage between GLOF rate and climate change"], "chunk_type": "text", "line_start": 59, "line_end": 71, "token_count_estimate": 512, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["54168"]}}
{"id": "db3b71f3cef90cfb", "text": "Document: 1 Introduction\nSection: 2 Methods > Wavelet analysis of GLOF incidence\nType: text\n\nWavelets are a commonly used tool for analysing non-stationary time series because they allow the signal to be decomposed into both time and frequency (e.g. Lane, 2007). Here, we follow the methodology of Shugar et al. (2010), although we use the Daubechies (db1) continuous wavelet. The wavelet power shown here has been tested for significance at 95 % confidence limits, and a cone of influence is applied to reduce edge effects. We follow Lane (2007) in choosing an appropriate number of scales (S = 28, see his Eq. 28), which are related to the shape of the cone of influence.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > Wavelet analysis of GLOF incidence", "section_headings": ["2 Methods", "Wavelet analysis of GLOF incidence"], "chunk_type": "text", "line_start": 73, "line_end": 75, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d5f3f837f32e711", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: text\n\nA potentially destructive GLOF may elapse after a glacial lake grows to a volume where a sudden release of glacial lake water can exceed a normal year's peak instantaneous discharge. There are timescales associated with the period between a climatic (or other) perturbation and the occurrence of a GLOF. The following thought experiment demonstrates the concept of the lagging responses of GLOF activity to climate change: an initialized stable condition allows glacierclimate equilibrium, where neither the climate nor the glacier has fluctuated much for some lengthy period and where no other strongly perturbing conditions exist; e.g. there are no significant supraglacial or ice-marginal or moraine-dammed lakes, and a steady state exists in the supply and removal of surface debris. We then impose a perturbation (climatic or other) which favours eventual lake development and growth and eventually a GLOF. We describe two successive time periods which must pass before a significant GLOF can occur, and then a third period before a GLOF actually occurs: lakeinception time  $(\\tau_i)$ , lake growth time  $(\\tau_g)$  and trigger time  $(\\tau_t)$ . The first two sum to the GLOF response time  $(\\tau_{GLOF})$ as we define it:  $\\tau_{GLOF} = \\tau_i + \\tau_g$ . The terms are for illustrative purposes: many supraglacial ponds initially go through a lengthy period where they fluctuate and drain annually and thus do not have a chance to grow beyond one season. Furthermore, lakes can grow to a point where limnological processes take over from climate; hence lake growth becomes\n\ndetached from climate change. Even so, our set of definitions can be used to explain the lagging responses of glacier lakes and GLOFs to climatic history.\n\nA GLOF does not necessarily occur upon climate step change date  $+\\tau_{GLOF}$ , which is the timescale over which the metastable system establishes a condition where a significant GLOF could occur. A trigger is needed (e.g. a large ice or rock avalanche into the lake or a moraine collapse as an ice core melts). After a sizeable glacial lake has developed, suitable GLOF triggers may occur with a typical random interval averaging  $\\tau_t$ , which depends on the topographic setting of the glacier lake, valley-side geology, steepness, moraine dam properties and climate. As a result,  $\\tau_t$  could range from years to centuries. Furthermore, as a lake usually continues to grow after  $\\tau_{GLOF}$  has elapsed,  $\\tau_{t}$  can in principle change, probably shortening as the lake lengthens and as the damming moraine degrades. The time elapsing between a climatic perturbation and a GLOF then is the sum of three characteristic sequential periods,  $\\tau_i + \\tau_g + \\tau_t$ .", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "text", "line_start": 77, "line_end": 89, "token_count_estimate": 759, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c5093dc851cb0306", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: text\n\n$ \\ tau_t $ , which depends on the topographic setting of the glacier lake , valley - side geology , steepness , moraine dam properties and climate . As a result , $ \\ tau_t $ could range from years to centuries . Furthermore , as a lake usually continues to grow after $ \\ tau_ { GLOF } $ has elapsed , $ \\ tau_ { t } $ can in principle change , probably shortening as the lake lengthens and as the damming moraine degrades . The time elapsing between a climatic perturbation and a GLOF then is the sum of three characteristic sequential periods , $ \\ tau_i + \\ tau_g + \\ tau_t $ .\n\nThe lake inception time  $\\tau_i$  might be approximated by the glacier response time, which has been defined parametrically (Jóhannesson et al., 1989; Bahr et al., 1998) but in general describes a period of adjustment toward a new equilibrium following a perturbation. We take a simple parameterization (Jóhannesson et al., 1989) and equate  $\\tau_1 = h/b$ , where h is the glacier thickness of the tongue near the terminus and b is the annual balance rate magnitude. The glacier response time approximating the lake inception time may be many decades for most temperate valley glaciers, but it can range between a few years and a few centuries. The glacier response time is a climate-change-forgetting timescale. After a few response times have elapsed, a glacier's state and dynamics no longer remember the climate change that induced the response to a new equilibrium. For illustration, we adopt  $\\tau_1 = 60$  years, a value typical of many temperate valley glaciers.\n\nA supraglacial pond may drain and redevelop annually (posing no significant GLOF risk), but at some point, if there is a sustained long-term negative mass balance, supraglacial ponds commonly grow, coalesce and form a water body big enough that rapid partial drainage can result in a significant GLOF. That lake growth period is defined here as  $\\tau_g$ , for which we adopt 20 years, a value typical of many temperate glacier lakes of the 20th century (e.g. Wilson et al., 2018; Emmer et al., 2015) Hence,  $\\tau_{GLOF} = \\tau_1 + \\tau_g \\approx 80$  years for the favoured values. Hence, a significant GLOF may occur at any time from 80 years following a large climatic perturbation: what the GLOF waits on is  $\\tau_t$ , which could be years or a century. This concept can be extended to the lagging response of a whole population of glaciers following a perturbation in regional climate (Fig. 1).\n\nWe distinguish between climate change, which may establish conditions needed for a GLOF to happen, and weather, which sometimes may be involved in a GLOF trigger. GLOF triggers are diverse; e.g. protracted warm summer weather may trigger an ice avalanche into the lake or moraine melt-", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "text", "line_start": 77, "line_end": 89, "token_count_estimate": 789, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "221437a70b8c154f", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A figure composed of six graphs, labeled (a) through (f), analyzing Northern Hemisphere temperature anomalies and warming rates over various time scales, and their connection to glacial lake outburst floods (GLOFs).", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "figure", "figure_caption": null, "line_start": 90, "line_end": 90, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa2c061e0e9c4c90", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: text\n\nGraphs (a), (b), and (c) plot the 'Northern Hemisphere temperature anomaly' on the y-axis against 'Year' on the x-axis. They show the temperature anomaly data (blue line) along with smoothed versions for tau = 20 years (red), 40 years (gray), and 80 years (black).\n- Graph (a) spans from year 600 to 2000, showing multi-proxy data until 1849 and instrumental data from 1850 onwards. The anomaly fluctuates around 0 until a sharp increase after 1850.\n- Graph (b) focuses on the period from 1850 to 2100, showing instrumental data up to 2014 and model projections from 2015 to 2100, depicting a continuous and steep rise in temperature anomaly.\n- Graph (c) is a zoomed-in view of the period from 1940 to 2100.\n\nGraphs (d) and (e) plot the 'Warming rate, degrees yr⁻¹' on the y-axis against 'Year' on the x-axis. They show the warming rate for tau = 20 years (red line) and smoothed versions for tau = 40 years (gray) and 80 years (black).\n- Graph (d) covers the years 500 to 2000, showing fluctuations in the warming rate that become more pronounced and positive in the later period.\n- Graph (e) covers the years 1850 to 2100, showing a significant increase in the warming rate, with the model projection area shaded in light red.\n\nGraph (f) is a dual-axis plot for the 'Northern Hemisphere land' from 1904 to 2097.\n- The left y-axis represents 'Warming rate, mK yr⁻¹' (red line and dots), ranging from -10 to 15.\n- The right y-axis represents 'GLOFs/year' (blue line), ranging from 0 to 40.\n- The plot is divided into a 'Data' section and a 'Model' section around the year 2008. It shows a correlation between the warming rate and the frequency of GLOFs, noting a '45 year glacial lake response time'.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "text", "line_start": 91, "line_end": 105, "token_count_estimate": 531, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46023fdc45966f4e", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: figure\nFigure: Figure 1. Reconciliation of GLOF and climate records. (a) Blue curve: composite record of Northern Hemisphere land surface temperature (merged from multi-proxy data and instrumental records, as described in the main text), plus a model of land surface temperature during the period 2015–2100. Red, grey and black curves: moving historical averages of the blue curve, as described in the text, using $\\tau_{GLOF} = 20$ , 40 and 80 years. (b, c) Close-up of the more recent periods covered in (a). (d) Warming rate extracted from the moving historical averages using $\\tau_{GLOF} = 20$ , 40 and 80 years. Periods of cooling and warming are shown with blue and red tints, respectively, using the $\\tau_{GLOF} = 80$ years curve. (e) Close-up of (d) to a more recent period. (f) Comparison of a smoothed GLOF frequency curve (red line, GLOFs/year historical moving average) with the moving historical average Northern Hemisphere temperature (black curve) using $\\tau_{GLOF} = 80$ years and shifted +45 years, where the 45-year shift is considered to be reflective of $\\tau$ , the GLOF trigger timescale. See supplement text for more description and explanation.\n\nFigure 1. Reconciliation of GLOF and climate records. (a) Blue curve: composite record of Northern Hemisphere land surface temperature (merged from multi-proxy data and instrumental records, as described in the main text), plus a model of land surface temperature during the period 2015–2100. Red, grey and black curves: moving historical averages of the blue curve, as described in the text, using  $\\tau_{GLOF} = 20$ , 40 and 80 years. (b, c) Close-up of the more recent periods covered in (a). (d) Warming rate extracted from the moving historical averages using  $\\tau_{GLOF} = 20$ , 40 and 80 years. Periods of cooling and warming are shown with blue and red tints, respectively, using the  $\\tau_{GLOF} = 80$  years curve. (e) Close-up of (d) to a more recent period. (f) Comparison of a smoothed GLOF frequency curve (red line, GLOFs/year historical moving average) with the moving historical average Northern Hemisphere temperature (black curve) using  $\\tau_{GLOF} = 80$  years and shifted +45 years, where the 45-year shift is considered to be reflective of  $\\tau$ , the GLOF trigger timescale. See supplement text for more description and explanation.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "figure", "figure_caption": "Figure 1. Reconciliation of GLOF and climate records. (a) Blue curve: composite record of Northern Hemisphere land surface temperature (merged from multi-proxy data and instrumental records, as described in the main text), plus a model of land surface temperature during the period 2015–2100. Red, grey and black curves: moving historical averages of the blue curve, as described in the text, using $\\tau_{GLOF} = 20$ , 40 and 80 years. (b, c) Close-up of the more recent periods covered in (a). (d) Warming rate extracted from the moving historical averages using $\\tau_{GLOF} = 20$ , 40 and 80 years. Periods of cooling and warming are shown with blue and red tints, respectively, using the $\\tau_{GLOF} = 80$ years curve. (e) Close-up of (d) to a more recent period. (f) Comparison of a smoothed GLOF frequency curve (red line, GLOFs/year historical moving average) with the moving historical average Northern Hemisphere temperature (black curve) using $\\tau_{GLOF} = 80$ years and shifted +45 years, where the 45-year shift is considered to be reflective of $\\tau$ , the GLOF trigger timescale. See supplement text for more description and explanation.", "line_start": 106, "line_end": 106, "token_count_estimate": 673, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe0848d1a2a3c49a", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: text\n\nthrough, or heavy winter snow may trigger an ice avalanche into the lake.\n\nHowever, the relevant controlling climate in this example is that of the prior climatic history and the conditioning period defined by  $\\tau_{GLOF}$  and the typical trigger interval  $\\tau_t$ . Hence,  $\\tau_{GLOF}$  is closely connected to climate, whereas  $\\tau_t$  can be connected to weather for certain types of triggers.\n\nThe assessment above is for a single step-function climate change. Considering that climate changes continuously and glacier characteristics vary, populations of glaciers must have full distributions of  $\\tau_i$ ,  $\\tau_g$  and  $\\tau_{GLOF}$ . Even while glaciers are still adjusting to any big recent historical climate change, more climate change accrues; glacier and lake dynamics take all that into account, either increasing the likelihood and perhaps size of a GLOF or decreasing or delaying it. Hence, the\n\noverall GLOF frequency record cannot be synchronous with climatic fluctuations, and it also should not simply trace past climate change with a time lag; rather, the GLOF frequency record for any large population of glaciers should be definitely but complexly related to the recent climatic history.\n\nThe functional dependence on climate history is not known for any glacier or population of glaciers, but to explore the concept of a lagged GLOF response to accrued climate changes, we assert that the integration function will tend to weight recent climatic shifts more strongly than progressively older climatic shifts, the memory of which is gradually lost as the glacier population adjusts. That is, because of glacier dynamics and the responses of a population of glaciers to climatic changes, the population eventually loses memory of sufficiently older climatic changes and adjusts\n\nasymptotically toward a new equilibrium. This should be true for any climate-sensitive glacier dynamics (Oerlemans, 2005). Though we do not know the functional form of the glacier responses (either for an individual glacier or a population), we nonetheless wish to illustrate our point while not driving fully quantitative conclusions. We propose that the integration of climate information into ongoing glacier dynamical adjustments occurs with exponentially declining weighting going backward in time from any given year. The exponential time-weighting constant may be similar to  $\\tau_{GLOF}$ . We have computed a moving time-average Northern Hemisphere temperature with the weighting of the average specified by an assumed  $\\tau_{GLOF} = 80$  years; the computed moving average pulls data, for any year, over the preceding period of  $\\tau_{GLOF}$ ; i.e. it includes temperature information up to 240 years prior to any given year. The weighting of earlier years' temperatures within that  $\\tau_{GLOF}$  is less than that of later years, according to the exponential. The cut-off at  $\\tau_{GLOF}$  is arbitrary and was done for computational expediency, seeing that any climate fluctuation occurring before  $\\tau_{GLOF}$  years earlier is inconsequential due to the exponential memory loss.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "text", "line_start": 107, "line_end": 129, "token_count_estimate": 785, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "724a09cb416085c1", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: text\n\n$ years ; the computed moving average pulls data , for any year , over the preceding period of $ \\ tau_ { GLOF } $ ; i . e . it includes temperature information up to 240 years prior to any given year . The weighting of earlier years ' temperatures within that $ \\ tau_ { GLOF } $ is less than that of later years , according to the exponential . The cut - off at $ \\ tau_ { GLOF } $ is arbitrary and was done for computational expediency , seeing that any climate fluctuation occurring before $ \\ tau_ { GLOF } $ years earlier is inconsequential due to the exponential memory loss .\n\nWe combined the Mann et al. (2008) multi-proxy Northern Hemisphere temperature anomaly from 501 AD to 1849, the Jones et al. (2012)<sup>4</sup> Northern Hemisphere land instrumental temperature record from 1850 to 2014 and a model of expected warming from 2015 to 2100. It is the recent climate history at each glacier lake or region that is strictly relevant, but lacking such records and needing here to only establish the concept, we settle for the treatment described above involving the Northern Hemisphere temperature anomaly.\n\nThe model is a constant  $2.7\\,^{\\circ}\\text{C}$  century<sup>-1</sup> warming; noise was added from a naturally noisy but overall non-trending instrumental record from 1850 to 1899, with some years repeated to append the 2015–2100 period (Fig. 1). The Mann et al. (2008) and Jones et al. (2012) data sets were brought into congruence in 1850. Then we smoothed the composite record and model results using the  $\\tau_{\\text{GLOF}}$  exponentially weighted filter, as described above, where the natural logarithmic \"forgetting\" timescale  $\\tau_{\\text{GLOF}} = 20$ , 40 or 80 years for three illustrative cases. Smoothing was computed for  $\\tau_{\\text{GLOF}}$ , i.e. 240 years if  $\\tau_{\\text{GLOF}} = 80$  years. Our favoured value  $\\tau_{\\text{GLOF}} = 80$  years is based on large Himalayan and other temperate glacier lakes. The shorter response times would likely apply to small glaciers or those occurring in steep valleys.\n\nRegardless of the functional form of the glacier response and lake dynamics, GLOF frequency in any given region or worldwide must lag the climate record. The historically filtered/smoothed temperature record and model incorporating  $\\tau_{\\rm GLOF} = 20$ , 40 and 80 years is shown in Fig. 1a–c together with the unsmoothed actual record and model temperature series. The temperature anomalies are plotted in panels (a), (b) and (c); and the warming rate in panels (d) and (e).\n\nThe historically averaged/smoothed temperature record lags fluctuations in the unsmoothed record. The lag is most easily seen where temperatures start to rise rapidly in the 20th and 21st centuries. The high-frequency temperature anomaly fluctuations also show concordantly but in damped form in the smoothed moving average curves because the curves are historical moving averages with the heaviest weighting toward the more recent years. The lagging responses are also seen at several times when the running average curves variously show warming and cooling for the same year depending on the value of  $\\tau_{\\rm GLOF}$ .", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "text", "line_start": 107, "line_end": 129, "token_count_estimate": 880, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a58f84755c8ec078", "text": "Document: 1 Introduction\nSection: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis\nType: text\n\nwarming rate in panels ( d ) and ( e ) . The historically averaged / smoothed temperature record lags fluctuations in the unsmoothed record . The lag is most easily seen where temperatures start to rise rapidly in the 20th and 21st centuries . The high - frequency temperature anomaly fluctuations also show concordantly but in damped form in the smoothed moving average curves because the curves are historical moving averages with the heaviest weighting toward the more recent years . The lagging responses are also seen at several times when the running average curves variously show warming and cooling for the same year depending on the value of $ \\ tau_ { \\ rm GLOF } $ .\n\nWe posit that the historically filtered warming rate (more than the temperature anomaly) drives GLOF frequency. In Fig. 1 we show GLOF frequency (smoothed over 10-year moving averages) together with the warming rate extracted from the historically filtered temperature and model temperature time series. To get a better match with the temperature treated as such, we applied a further 45-year shift. From a glacier and lake dynamics perspective, this shift might relate to the trigger timescale,  $\\tau_t$ . Singular values of  $\\tau_{GLOF}$ and  $\\tau_t$  should not pertain globally to all glaciers but should span wide ranges. The adopted values  $\\tau_{GLOF} = 80$  years and  $\\tau_t = 45$  years nonetheless make for a plausible match between the GLOF and climate records. These numbers make sense in terms of glacier and lake dynamics timescales, but we reiterate that our purpose with this climate-GLOF fitting exercise is illustrative. In sum, a notable shift in GLOF frequency does not connote a concordant shift in climate, though prior climate change may still underlie the cause.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis", "section_headings": ["2 Methods", "2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis"], "chunk_type": "text", "line_start": 107, "line_end": 129, "token_count_estimate": 484, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1bce16a9eb86e86c", "text": "Document: 1 Introduction\nSection: 3 Results\nType: text\n\nOur global analysis identifies 165 moraine-dam GLOFs, recorded since the beginning of the 19th century (Fig. 2a). The vast majority of these GLOFs (n = 160; 97 %) occurred since the beginning of the 20th century, at a time of climate warming and increasing glacier recession (Figs. 2 and 5). None of these GLOFs were associated with repeat events from the same lake. Around 65 % of GLOFs occurred between 1930 and 1990. Thirty-six GLOFs occurred in the mountains of western North America between 1929 and 2002 (Table S1 in the Supplement). Fifteen of these occurred in western Canada, 15 in the Cascade Range of the USA and four in Alaska. One occurred in Mexico and 1 in the Sierra Nevada. In the South American Andes we identified 40 GLOFs. Eleven occurred in Chile between 1913 and 2009 (including the large one in Patagonia at Laguna del Cerro Largo in 1989); one in Colombia in 1995 and 28 in Peru between 1702 and 1998. Fourteen GLOFs are listed from the European Alps. Three are from Austria between 1890 and 1940, five from Switzerland between 1958 and 1993 one from France in 1944 and five from Italy between 1870 and 1993. In the Pamir and Tien Shan mountains in central Asia, we identified 20 GLOFs, with most of these dating from the\n\n<sup>&</sup>lt;sup>4</sup>https://crudata.uea.ac.uk/cru/data/temperature/\\T1\\textbackslash#datdow", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "text", "line_start": 131, "line_end": 135, "token_count_estimate": 368, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f86063827f7c670c", "text": "Document: 1 Introduction\nSection: 3 Results\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A scientific figure composed of multiple panels, labeled (a) through (f), analyzing moraine-dammed glacial lake outburst floods (GLOFs) across different regions. Each row represents a region: (a) Global, (b) European Alps, (c) Pamir & Tien Shan, (d) Hindu Kush Himalayala, (e) Alaska & Canadian Rockies, and (f) South American Andes.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "figure", "figure_caption": null, "line_start": 136, "line_end": 136, "token_count_estimate": 126, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0c0e4a6ac32df83", "text": "Document: 1 Introduction\nSection: 3 Results\nType: text\n\nFor each region, the left panel plots data from 1860 to 2000. It shows the cumulative sum of events as a black line (left y-axis), the number of events each year as blue bars (inner left y-axis), and the discharge in 10^6 m^3 as red dots (right y-axis).\n\nFor regions (b) through (f), the right side contains three smaller plots showing regional temperature anomalies (°C) from 1850 to 2011 from three datasets: CRUTEM4.2 (blue), NOAA NCDC (green), and NASA GISTEMP (yellow). Each of these plots shows a linear trend line and its value in °C per 10 years. A grey shaded area marks the period from 1970 to 2011.\n\nKey data points for each region:\n- (a) Global: The left plot shows a cumulative sum of events approaching 200 by 2000, with a notable increase in event frequency after 1940. Discharge values reach up to 250 x 10^6 m^3.\n- (b) European Alps: Cumulative events reach about 17. Temperature trends are: CRUTEM4.2: 0.148 C/10 yr, NOAA NCDC: 0.108 C/10 yr, NASA GISTEMP: 0.125 C/10 yr.\n- (c) Pamir & Tien Shan: Cumulative events reach about 22. Temperature trends are: CRUTEM4.2: 0.149 C/10 yr, NOAA NCDC: 0.128 C/10 yr, NASA GISTEMP: 0.133 C/10 yr.\n- (d) Hindu Kush Himalayala: Cumulative events reach about 45. Temperature trends are: CRUTEM4.2: 0.056 C/10 yr, NOAA NCDC: 0.095 C/10 yr, NASA GISTEMP: 0.109 C/10 yr.\n- (e) Alaska & Canadian Rockies: Cumulative events reach about 70. Temperature trends are: CRUTEM4.2: 0.108 C/10 yr, NOAA NCDC: 0.101 C/10 yr, NASA GISTEMP: 0.099 C/10 yr.\n- (f) South American Andes: Cumulative events reach about 38. The discharge axis is logarithmic. Temperature trends are: CRUTEM4.2: 0.047 C/10 yr, NOAA NCDC: 0.122 C/10 yr, NASA GISTEMP: 0.158 C/10 yr.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "text", "line_start": 137, "line_end": 149, "token_count_estimate": 530, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26ffc816e72056bf", "text": "Document: 1 Introduction\nSection: 3 Results\nType: figure\nFigure: Figure 2. (a–f) Left: temporal distribution of regional GLOF frequency and magnitude. At all locations, the cumulative sum of events (black line) indicates an upsurge in the number of events per year. The timing of this upsurge differs by location and likely reflects an increase in reporting, especially in the early part of the record, rather than a change in GLOFs, at least until the 1970s–1990s after which the GLOF rate reduces. Right: global time series climate data from the five regions using CRUTEM 4.2, NOAA NCDC and NASA GISTEMP. Grey columns represent the baseline against which temperature is measured.\n\n**Figure 2.** (a–f) Left: temporal distribution of regional GLOF frequency and magnitude. At all locations, the cumulative sum of events (black line) indicates an upsurge in the number of events per year. The timing of this upsurge differs by location and likely reflects an increase in reporting, especially in the early part of the record, rather than a change in GLOFs, at least until the 1970s–1990s after which the GLOF rate reduces. Right: global time series climate data from the five regions using CRUTEM 4.2, NOAA NCDC and NASA GISTEMP. Grey columns represent the baseline against which temperature is measured.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "figure", "figure_caption": "Figure 2. (a–f) Left: temporal distribution of regional GLOF frequency and magnitude. At all locations, the cumulative sum of events (black line) indicates an upsurge in the number of events per year. The timing of this upsurge differs by location and likely reflects an increase in reporting, especially in the early part of the record, rather than a change in GLOFs, at least until the 1970s–1990s after which the GLOF rate reduces. Right: global time series climate data from the five regions using CRUTEM 4.2, NOAA NCDC and NASA GISTEMP. Grey columns represent the baseline against which temperature is measured.", "line_start": 150, "line_end": 150, "token_count_estimate": 328, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d772afca0e1a8bf6", "text": "Document: 1 Introduction\nSection: 3 Results\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: The image displays three vertically stacked plots, labeled (a), (b), and (c), all sharing a common horizontal axis representing the 'Year' from 1860 to 2010.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "figure", "figure_caption": null, "line_start": 152, "line_end": 152, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8a8233f0f48ffecc", "text": "Document: 1 Introduction\nSection: 3 Results\nType: text\n\nPlot (a) is a bar chart with the vertical axis labeled 'Count', ranging from 0 to 8. The chart shows the frequency of an event over the years. Before 1930, the counts are very low, mostly 0 or 1. After 1930, the frequency increases significantly, with numerous years having counts between 1 and 4. The highest count of 8 occurs around the year 1960.\n\nPlot (b) is a wavelet power spectrum. The vertical axis, labeled 'Period (y)', is on a logarithmic scale, primarily showing values between 2 and 10^1 (10). A color bar on the left indicates the 'Wavelet power (10^3 W m^-2)', ranging from 0 (dark blue) to 6 (dark red). The plot shows areas of high power (red and yellow) concentrated in two main periods. The first is from approximately 1920 to 1960 at periods of 2 to 5 years. The second area of high power is from roughly 1955 to 1980 at periods of 5 to 8 years. Other smaller, less intense (blue and green) areas are scattered after 1960.\n\nPlot (c) is a line graph. The vertical axis, labeled 'Wavelet power (Wm^-2)', is on a logarithmic scale, ranging from below 10^0 to 2. The line shows a low power level before 1920, followed by a sharp increase to a peak around 1935-1940. The power remains high with significant fluctuations until about 2000, with another notable peak around 1990, before declining towards 2010.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "text", "line_start": 153, "line_end": 159, "token_count_estimate": 364, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d45d246972dbdb4a", "text": "Document: 1 Introduction\nSection: 3 Results\nType: figure\nFigure: Figure 3. (a) Record of all precisely dated GLOFs from 1860 to 2011. (b) Wavelet power spectrum of global GLOF record, significant at 5 %. (c) Frequency-integrated wavelet power spectrum.\n\n**Figure 3.** (a) Record of all precisely dated GLOFs from 1860 to 2011. (b) Wavelet power spectrum of global GLOF record, significant at 5 %. (c) Frequency-integrated wavelet power spectrum.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "figure", "figure_caption": "Figure 3. (a) Record of all precisely dated GLOFs from 1860 to 2011. (b) Wavelet power spectrum of global GLOF record, significant at 5 %. (c) Frequency-integrated wavelet power spectrum.", "line_start": 160, "line_end": 160, "token_count_estimate": 122, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54a1c1e8ceaee2c2", "text": "Document: 1 Introduction\nSection: 3 Results\nType: text\n\nlate 1960s to the early 1980s. The largest number of GLOFs (55) is reported from the Hindu Kush Himalaya (HKH) including the mountains of Bhutan and Tibet, dated from the 20th and 21st century. Thirty are from Tibet (between 1902 and 2009), 12 from Nepal between 1964 and 2011(and one is reported to have occurred in 1543) and five in Pakistan between 1878 and 1974. There is uncertainty in reporting some of these GLOFs and we discuss this further in the Supplement.\n\nFrom around 1930 to about 1950, GLOFs occurred with regularity but a low frequency (Fig. 3). In other words, floods occurred with relatively long period variabil-\n\nity (50–60 years). Starting around 1960, the frequency of these events increased (period decreased to approximately 20 years), remaining relatively high until about 1975, after which the statistically significant periodicities end, though GLOFs continue to occur.\n\nWhile incomplete data restrict a full analysis of GLOF triggers, precise date, magnitude and initiation at a global scale, many GLOFS triggered by ice avalanches and rockfalls occur during summer (see Fig. 4). The characteristics of GLOFs that could be influenced by climate change include changes in magnitude, frequency, timing (either changes in seasonality or changes over longer timescales) and trigger", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "text", "line_start": 161, "line_end": 169, "token_count_estimate": 321, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "f369aa0fa6565f2b", "text": "Document: 1 Introduction\nSection: 3 Results\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A 3D bar chart showing the frequency of different types of failures across four seasons. The vertical axis is numbered from 0 to 35. The horizontal axis lists the categories: Total, Ice avalanche, Rock slope failure, Heavy rainfall, and Moraine failure. The legend indicates the seasons by color: blue for Spring, red for Summer, green for Autumn, and purple for Winter. The data presented is as follows: For the 'Total' category, Spring has a value of 2, Summer is 35, Autumn is 12, and Winter is 1. For 'Ice avalanche', Spring is 2, Summer is 27, Autumn is 5, and Winter is 1. For 'Rock slope failure', Spring is 0, Summer is 2, Autumn is 2, and Winter is 0. For 'Heavy rainfall', Spring is 0, Summer is 3, Autumn is 3, and Winter is 0. For 'Moraine failure', Spring is 0, Summer is 3, Autumn is 2, and Winter is 0.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "figure", "figure_caption": null, "line_start": 170, "line_end": 170, "token_count_estimate": 261, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "324ed1f5d6300602", "text": "Document: 1 Introduction\nSection: 3 Results\nType: figure\nFigure: Figure 4. Seasonal variation in the occurrence of GLOF associated with the failure of moraine dams. Only a proportion of the GLOFs have seasonal data on timings.\n\n**Figure 4.** Seasonal variation in the occurrence of GLOF associated with the failure of moraine dams. Only a proportion of the GLOFs have seasonal data on timings.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "figure", "figure_caption": "Figure 4. Seasonal variation in the occurrence of GLOF associated with the failure of moraine dams. Only a proportion of the GLOFs have seasonal data on timings.", "line_start": 172, "line_end": 172, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea2eb18a0d2d27ec", "text": "Document: 1 Introduction\nSection: 3 Results\nType: text\n\nmechanisms. In addition, many rock avalanches into lakes triggering a GLOF may represent a paraglacial response to deglaciation from the LIA or earlier times (Knight and Harrison, 2013; Schaub et al., 2013) and this delayed response demonstrates the need to account for lags between changes in forcing and responses in attribution studies.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Results", "section_headings": ["3 Results"], "chunk_type": "text", "line_start": 173, "line_end": 175, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6a146db408b55b96", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\nFrom this analysis, we highlight three key observations: (1) GLOFs became more common around 1930 but then their incidence was maintained at a quasi-steady level for a few decades thereafter; (2) since about 1975, GLOF periodicity has decreased globally; and (3) the periodicities of GLOF occurrence have changed throughout the 20th century. These observations are discussed below.\n\nOur first main observation is that GLOF frequency increased dramatically and significantly around 1930 globally and between 1930 and 1960 regionally (Figs. 1 and 2). We find no obvious reason for an abrupt improvement of GLOF reporting in 1930. While acknowledging that incompleteness of the record must be a pervasive factor throughout the early period covered by the database, we discount reporting variations as the cause of the abrupt shift. For instance, this pattern is observed in the European Alps, a region with a long history of mountaineering, glacier research and valley-floor habitation and infrastructure development. Given that we record individual GLOFs in the 19th and early 20th centuries we argue that the increase in GLOF frequency in the 1930s represents a real increase rather than an observational artefact. Following the increase around 1930, we observe a similar rate of GLOFs for the subsequent years, typically 1 per year in the following decade, increasing to 2-3 per year during the 1940s (e.g. Figs. 1a, 2a). Again, there is no evidence that incompleteness of data is a main cause of the observed pattern. We therefore conclude that the incidence of global GLOFs has remained generally constant between about 1940 and about 1960. In the 1960s and early 1970s, several years", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 177, "line_end": 181, "token_count_estimate": 393, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0fdb2e882c3c120c", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: figure\nFigure\n\nImage /page/8/Figure/8 description: A figure displaying five maps that show temperature anomalies in different mountain ranges, based on CRUTEM4.2 data for the period 1991–2012 relative to 1901–1920. A color scale at the bottom indicates the temperature anomaly in degrees Celsius, ranging from light orange (0 °C) to dark red (2.1 °C).", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 182, "line_end": 182, "token_count_estimate": 99, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "96954e4d5dbe992b", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\nThe five maps are:\n1. \\*\\*European Alps:\\*\\* Shows a high temperature anomaly, with the region colored in dark red (approximately 1.5 to 2.1 °C).\n2. \\*\\*Pamir and Tien Shan:\\*\\* Shows moderate to high anomalies, with colors ranging from orange to red (approximately 0.6 to 1.5 °C).\n3. \\*\\*Hindu Kush Himalaya:\\*\\* Shows lower temperature anomalies, with the region colored in light to medium orange (approximately 0.3 to 0.9 °C).\n4. \\*\\*Alaska and Canadian Rockies:\\*\\* Shows a high temperature anomaly, with the region colored in dark red (approximately 1.5 to 2.1 °C).\n5. \\*\\*South American Andes:\\*\\* Shows moderate anomalies, with the region colored in shades of orange (approximately 0.6 to 1.2 °C).\n\nEach map includes latitude and longitude lines, and the mountain ranges are indicated by clusters of black plus signs.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 183, "line_end": 192, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5f1b8d9f898d702b", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: figure\nFigure: Figure 5. Temperature anomalies in the CRUTEM4.2 data set for each mountain region. For each region we extract all the grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF) and these are shown as black crosses.\n\n**Figure 5.** Temperature anomalies in the CRUTEM4.2 data set for each mountain region. For each region we extract all the grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF) and these are shown as black crosses.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "figure", "figure_caption": "Figure 5. Temperature anomalies in the CRUTEM4.2 data set for each mountain region. For each region we extract all the grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF) and these are shown as black crosses.", "line_start": 193, "line_end": 193, "token_count_estimate": 155, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e642cda127920c6", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\nsaw more than five GLOFs. We argue below that the trend between 1940 and 1960 hides a more complex spatial and temporal pattern (Clague and Evans, 2000; Schneider et al., 2014).\n\nOur second main observation is that, while there is considerable variability between regions, GLOF incidence rates have decreased since about 1975 globally (Fig. 2). There are both more and larger GLOFs during the 1970s and early 1980s in the Pamir and Tien Shan, in the 1960s in the HKH and in the 1990s in Alaska, the Coast Mountains and Canadian Rockies; and then decreases in both magnitude and frequency follow these periods. In the Andes however, GLOF incidence decreased after the early 1950s. The latter observation may be at least partly attributable to considerable GLOF mitigation measures in Peru, such as engineeringbased lake drainage or dam stabilization (Carey et al., 2012; Portocarreo-Rodriguez, 2014). Carrivick and Tweed (2014) propose several reasons why \"glacial floods\" may have decreased in frequency in recent decades. These include successful efforts to stabilize moraine dams and changes in the ability of fluvial systems to transmit floods over time. We argue, conversely, that this reduction may represent a \"lagged\" response to glacier perturbations following climate change. More research is clearly needed on this question, and we believe that our analysis, along with that of Carrivick and Tweed's, will stimulate further work and discussion.\n\nOur third main observation is that for several decades in the 20th century, GLOF occurrence has been periodic, but that periodicity has varied. Since about 1975, and especially since 1990, the periodic nature of GLOF occurrence has diminished, even though GLOFs have continued. In other words, GLOFs since 1975 have become more irregular. We suspect that the switch to less periodic outburst floods in recent decades is related to an underlying mechanism such as topographic constraints and glacier hypsometries, with glaciers retreating into steeper slopes, implying a reduced rate of moraine-dammed lake formation – a phenomenon observed, for example, in the European Alps (Emmer et al., 2015).\n\nThe statistics of small numbers affect these regional, timeresolved records, but the overall validity of a similar mid-20th century increase and then decrease in the frequency of GLOFs can be further detected in the global record and is statistically significant (Fig. 3). We argue that the reduction in global GLOF frequency after the 1970s (especially in central Asia, HKH and North America) is real, because the contemporary reporting is likely to be nearly complete given the scientific and policy interest in glacier hazards from the late 20th century. Hence, our conclusion is that globally and regionally there have been interdecadal variations in the frequency of GLOFs, and in general the most recent couple of decades have seen fewer GLOFs than the early 1950s to early 1990s. The record's (in)completeness is not able to explain a decreasing incidence rate. This temporal variation in GLOF frequency and recent decrease is therefore a robust and surprising result and has occurred despite the clear trend of con-tinued glacier recession and glacier lake development in re-cent decades.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 194, "line_end": 234, "token_count_estimate": 798, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc2dfbfe3b77c6a3", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\nreporting is likely to be nearly complete given the scientific and policy interest in glacier hazards from the late 20th century . Hence , our conclusion is that globally and regionally there have been interdecadal variations in the frequency of GLOFs , and in general the most recent couple of decades have seen fewer GLOFs than the early 1950s to early 1990s . The record ' s ( in ) completeness is not able to explain a decreasing incidence rate . This temporal variation in GLOF frequency and recent decrease is therefore a robust and surprising result and has occurred despite the clear trend of con - tinued glacier recession and glacier lake development in re - cent decades .\n\nOur data allow us to test and refine the widespread assumption that GLOFs are a consequence of recent climate change (Bajracharya and Mool, 2009; Riaz et al., 2014). This is an important assumption because it implies that GLOF frequency will increase as the global climate continues to warm with potential major impacts for downstream regions.\n\nThe global increase in GLOF frequency after 1930 must be a response to a global forcing, considering global glacier retreat (Zemp et al., 2015), and physical process understanding suggests that this is a lagged response to the warming marking the end of the LIA (Clague and Evans, 2000). Although the global response appears sudden, in 1930 the region-by-region assessment shows that the response was asynchronous regionally and temporally over three decades (Fig. 2). This is consistent with the fact that the end of the LIA was not globally synchronous (Mann et al., 2009) and we also argue that this reflects regional variations in glacier response times.\n\nWe argue that as a climate shift occurs, after some period related to the glacier response time, previously stable or advancing glaciers start to thin and recede; after a further *limnological response time* proglacial ponds start to grow, coalesce and deepen into substantial moraine-dammed lakes. GLOFs typically occur after some additional period of time (the *GLOF response timescale*), but this time can be brief in glaciers with short response times, such as in the tropical Andes (Fig. 1).\n\nIn the HKH and central Asia the near-concordant formation of many Himalayan glacier lakes and the abrupt increase in GLOF rates in the 1950s and 1960s suggest that the GLOF response time is much smaller than the limnological response time. The moraine evidence here indicates that a shift from mainly glacier advance to recession and/or thinning occurred widely, though regionally asynchronously, between 1860 and 1910. The HKH underwent this shift by around 1860 (Owen, 2009; Solomina et al., 2015) in response to warming following the regional LIA. The limnological response time in the Himalayan–Karakoram region is thus around 100 years, i.e. substantially longer than in the tropical Andes.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 194, "line_end": 234, "token_count_estimate": 705, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6ad79bb3b90f6af", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\nformation of many Himalayan glacier lakes and the abrupt increase in GLOF rates in the 1950s and 1960s suggest that the GLOF response time is much smaller than the limnological response time . The moraine evidence here indicates that a shift from mainly glacier advance to recession and / or thinning occurred widely , though regionally asynchronously , between 1860 and 1910 . The HKH underwent this shift by around 1860 ( Owen , 2009 ; Solomina et al . , 2015 ) in response to warming following the regional LIA . The limnological response time in the Himalayan – Karakoram region is thus around 100 years , i . e . substantially longer than in the tropical Andes .\n\nWe have arrived at a plausible explanation for the post-1930 (1930–1960) increases in GLOF rates. They are most likely heterogeneous, lagging responses to the termination of the LIA, with limnological response times of the order of decades to 100 years, depending on the region (e.g. Emmer et al., 2015). The limnological response times may be of a similar order to the glacier dynamical response times (Jóhannesson et al., 1989; Raper and Braithwaite, 2009) but are appended to them. Thus, measured from a climatic shift to increased GLOFs, the combined glaciological and limnological response times (plus GLOF response times, which may be the shortest of the three response times) may sum to roughly 45–200 years (Fig. 1). It cannot be much more\n\nthan this, because then we would not see the multi-decadal oscillations in GLOF rates in some regions or globally.\n\nSome individual glaciers may have faster response times than estimated above (Roe et al., 2017), but taken on a broader statistical basis we infer that the most recent GLOFs are a delayed response to the end of the LIA. A fundamental implication is that anthropogenic climatic warming to date will likely manifest in increasing GLOFs in some regions of the world starting early this century and continuing into the 22nd century. In all the mountain regions considered here the available evidence indicates a warming trend over the last century around 0.1 °C decade<sup>-1</sup> (Figs. 2 and 5). The trend varies between data set and region, with the highest rates in the Pamir Tien Shan region and the lowest in the HKH. The most uncertain region is the Andes, where the sparseness of data prevents any meaningful assessment. The trends are consistent with the global mean land temperature trend  $0.95 \\pm 0.02$  to  $0.11 \\pm 0.02$  °C for 1901–2012, implying these regions have warmed at approximately the same rate as the global land surface.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 194, "line_end": 234, "token_count_estimate": 662, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0950f5325432e7d5", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\n1 ° C decade < sup > - 1 < / sup > ( Figs . 2 and 5 ) . The trend varies between data set and region , with the highest rates in the Pamir Tien Shan region and the lowest in the HKH . The most uncertain region is the Andes , where the sparseness of data prevents any meaningful assessment . The trends are consistent with the global mean land temperature trend $ 0 . 95 \\ pm 0 . 02 $ to $ 0 . 11 \\ pm 0 . 02 $ ° C for 1901 – 2012 , implying these regions have warmed at approximately the same rate as the global land surface .\n\nThe baseline behaviour of glacial lake systems in the absence of climate change is not known in detail, but the low rate of GLOFs prior to 1930 may indicate that without warming the frequency would be low. The difficulty of attributing individual GLOF behaviour to climate change relates to the presence of non-climatic factors affecting GLOF behaviour, such as moraine dam geometry and sedimentology, climate-independent GLOF triggers (e.g. earthquakes) and the timescales related to destabilization of mountain slopes, producing mass movements into lakes (Haeberli et al., 2017). This represents the period of paraglaciation (e.g. Ballantyne, 2002; Holm et al., 2004; Knight and Harrison, 2013). These system characteristics may vary regionally and temporally within the evolutionary stage of a receding mountain glacier, and non-climatic factors such as lake mitigation measures additionally influence GLOF frequency and magnitude (Clague and Evans, 2000; Portocarreo-Rodriguez, 2014). We argue that while the original driver of lake development is likely to involve climate change (resulting in glacier downwasting and slowed meltwater flux through glaciers systems as glacier surfaces reduce in gradient) other mechanical and thermodynamic processes likely assume more importance as the lakes evolve, and these includes small-scale calving and insolationinduced melting of ice cliffs (e.g. Watson et al., 2017).\n\nWe also recognize that contemporary mountain glaciers are dissimilar to those that existed in the LIA. They are, in the main, shorter, thinner and with prominent moraines. Assumptions that climate processes acted on similar glacial systems over time are therefore likely to be simplistic.\n\nBased on the analysis of our global GLOF database we have shown that a clear trend is detectable globally and regionally diversified in the 20th century with a sharp increase in GLOF occurrence around 1930. This trend is attributable to the observed climate trend, namely the warming since the end of the LIA. The delayed response of GLOF occurrence\n\nis an exemplar for the complexities of how natural systems respond to climate change, underlining the challenges of attribution of climate change impacts. We have shown here that attribution of GLOFs to climate change is possible, although the suite of factors influencing GLOF occurrence cannot be fully quantified.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 194, "line_end": 234, "token_count_estimate": 729, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3071c28836e9a2ac", "text": "Document: 1 Introduction\nSection: 4 Discussion\nType: text\n\ntherefore likely to be simplistic . Based on the analysis of our global GLOF database we have shown that a clear trend is detectable globally and regionally diversified in the 20th century with a sharp increase in GLOF occurrence around 1930 . This trend is attributable to the observed climate trend , namely the warming since the end of the LIA . The delayed response of GLOF occurrence is an exemplar for the complexities of how natural systems respond to climate change , underlining the challenges of attribution of climate change impacts . We have shown here that attribution of GLOFs to climate change is possible , although the suite of factors influencing GLOF occurrence cannot be fully quantified .\n\nIn addition, lake outbursts following moraine failures are likely to be quite different in different regions. This reflects differences in a number of factors including ground thermal conditions, presence or absence of ground ice and permafrost, influence of extreme weather and seismic processes, topography and glacial history. To assess these we would need to better understand the geomorphological timescales involved in lake evolution and failure to design a more robust statistical analysis and to understand each region's GLOF history. We thus recommend close attention by the Earth surface processes science community to various process timescales using field studies, satellite remote sensing and theoretical modelling.\n\nOur inventory and the global pattern of GLOFs that is derived from it lacks in many cases precise data on the processes responsible for GLOFs. This is a consequence of incomplete reporting of GLOFs in remote mountain regions, especially before the advent and wide use of remote sensing. In many cases the record is of a large flood being observed and then some time afterwards a collapsed moraine dam is seen and the flood is attributed to this collapse. Clearly the precise details of how the collapse occurred is not always available, and this uncertainty bedevils all similar detection and attribution studies, especially on those events associated with rapid geomorphological change. This intrinsic incompleteness in the record is problematic but should not prevent reasonable assertions on GLOF triggers to be made, especially if global-scale and consistent patterns in GLOF behaviour are observed.\n\nFuture research should therefore more systematically study the factors influencing GLOF frequency and magnitude and lake formation where a distinction between GLOF conditioning and triggering factors will be helpful (e.g. Gardelle et al., 2011).\n\nIf climate (such as temperature time series) influences GLOFs, as surely must be the case, long lag times are necessarily implied by the empirical data sets. With such lags as we have modelled, this brings the increase of GLOFs following 1930 into line with temperature increases at the end of the Little Ice Age. Subsequent changes in the GLOF rate (including a several-decades-long fall in GLOF rates) can similarly be attributed to fluctuations in global warming. If these conclusions are broadly correct, a further implication is that an acceleration in GLOF rates will probably occur in the 21st century, perhaps starting rather soon. Even though the actual global warming rate for the 21st century may be nearly constant, as modelled, the fitted warming rate as plotted in Fig. 1f accelerates because of the memory of a post-LIA, pre-anthropogenic quasi-stable climate. We are enter-\n\ning a stage where anthropogenic warming will increasingly dominate GLOF activity and attribution of GLOFs to anthropogenic global warming will be confirmed. For now, this remains a hypothetical projection or expectation and is not yet borne out in the GLOF record.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 194, "line_end": 234, "token_count_estimate": 862, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "49b391727f606264", "text": "Document: 1 Introduction\nSection: 5 Conclusions\nType: text\n\nWe conclude that the global record of GLOF following the failure of moraine dams shows a dramatic increase in GLOF occurrences from 1930 to 1970, then a decline. We also observe that the GLOF frequency has not fluctuated directly in response to global climate. A reasonable premise is that climate, glaciers, glacier lakes and GLOFs are closely connected, but the connections between climate and GLOFs are hidden in response time dynamics. We argue that response times do not necessarily reflect linear processes and that lake growth may result in none, single or multiple GLOFs from the same lake systems. Accordingly, the response times must vary widely from region to region and glacier to glacier. From this we infer that the 1930 to 1970 increase in global GLOF activity is likely a delayed response following warming that ended the LIA and decreased the rate of morainedammed lake formation. We also infer that the decrease in GLOF frequency after 1970 is likely related to a delayed response to the stabilization of climate following the LIA. In addition, a minor cause (though important locally, for instance in Peru and Switzerland in particular), GLOF mitigation engineering, may have circumvented a few GLOFs, thus contributing to the downward trend in recent decades. We can expect a substantial increase in GLOF incidence throughout the 21st century as glaciers and lakes respond more dynamically to anthropogenic climate warming. This is corroborated by recent modelling studies projecting the location, number and dimension of new lakes in areas where glacier will recede over the coming decades in the Alps, the Himalayas or the Andes (Linsbauer et al., 2016).\n\nAs a result, we argue that the sharply increased GLOF rates starting from 1930 followed by reduced GLOF frequency from high levels in the mid-20th century are both real and we speculate these trends may reflect the failure of sensitive glacial lake systems in a lagged response to initial glacier recession from LIA limits. The apparent robustness of contemporary lake systems suggests that only the most resilient moraine-dammed lakes have survived recent climate change. Predicting their future behaviour is of great importance for those living and working in mountain communities and those developing and planning infrastructure in such regions.\n\nData availability. The database of GLOFs analysed in this paper is attached as a supplement.\n\n*Supplement.* The supplement related to this article is available online at: https://doi.org/10.5194/tc-12-1195-2018-supplement.\n\nAuthor contributions. The project was designed by SH following discussions with JK, CH and JR. Climate model data were provided by AW and RAB. Data analysis was carried out by SH, JK, DHS, LR and UH. JR, VV and AE provided inventory data. All authors helped to write and review the text.\n\nCompeting interests. The authors declare that they have no conflict of interest.", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Conclusions", "section_headings": ["5 Conclusions"], "chunk_type": "text", "line_start": 236, "line_end": 254, "token_count_estimate": 683, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e6b9030bb97de82", "text": "Document: 1 Introduction\nSection: 5 Conclusions\nType: text\n\nis attached as a supplement . * Supplement . * The supplement related to this article is available online at : https : / / doi . org / 10 . 5194 / tc - 12 - 1195 - 2018 - supplement . Author contributions . The project was designed by SH following discussions with JK , CH and JR . Climate model data were provided by AW and RAB . Data analysis was carried out by SH , JK , DHS , LR and UH . JR , VV and AE provided inventory data . All authors helped to write and review the text . Competing interests . The authors declare that they have no conflict of interest .\n\nAcknowledgements. Stephan Harrison was funded by a Leverhulme Research Fellowship. Stephan Harrison, Richard A. Betts and Andy Wiltshire acknowledge funding under the HELIX (European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no. 603864). Andy Wiltshire and Richard A. Betts acknowledge funding from the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). John Kennedy of the Met Office Hadley Centre provided advice on handling the temperature observation data sets used in this project. Contributions by Jeffrey S. Kargel, Umesh K. Haritashya, Dan H. Shugar, and Dhananjay Regmi were supported by NASA's Understanding Changes in High Mountain Asia programme, the NASA/USAID SERVIR Applied Science Team programme, and by the United Nations Development Program. We thank C. Scott Watson and an anonymous reviewer for their detailed and incisive reviews of the paper. We also thank Georg Veh, Jonathan Carrivik and Sergey Chernomorets for further comments and clarifications of the inventory.\n\nEdited by: Chris R. Stokes\n\nReviewed by: C. Scott Watson and one anonymous referee", "metadata": {"source_file": "data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Conclusions", "section_headings": ["5 Conclusions"], "chunk_type": "text", "line_start": 236, "line_end": 254, "token_count_estimate": 427, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["603864"]}}
{"id": "431bd17a7a92b5fb", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ABSTRACT\nType: text\n\nAim/Purpose Vis-à-vis management of crisis and disaster situations, this paper focuses on\n\nimportant use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the\n\nuser request and provides a solution of unambiguous in nature.\n\nBackground Even though social media is information-rich, it has created a challenge for\n\nderiving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through\n\nemploying semantic web technologies.\n\nMethodology This paper evolves a disaster ontology-based system availing a framework\n\nmodel for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and\n\nAccepting Editor Iris A Humala | Received: June 23, 2019 | Revised: August 19, August 21, 2019 | Accepted: August 22, 2019.\n\nCite as: Narayanasamy, S., Muruganantham. D. & Elçi, A. (2019). Crisis and disaster situations on social media streams: An ontology-based knowledge harvesting approach. *Interdisciplinary Journal of Information, Knowledge, and Management*, 14, 343-366. https://doi.org/10.28945/4420\n\n(CC BY-NC 4.0) This article is licensed to you under a <u>Creative Commons Attribution-NonCommercial 4.0 International License</u>. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes.\n\n<sup>\\*</sup> Corresponding author\n\navailability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision.\n\nContribution\n\nThe model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models.\n\nFindings\n\nCrucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 60, "token_count_estimate": 865, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["28945"]}}
{"id": "1e6a7f41bf359880", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ABSTRACT\nType: text\n\nof this research lie in three aspects : ( 1 ) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event , but the rate of distribution among topics / categories differs . ( 2 ) On specific events such as disaster , crisis or any emergency situations , the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task . ( 3 ) Relational mapping / co - occurrence of terms has been well designed for conventional news media , but due to shortness and sparseness of tweets , there remains a bottleneck for researchers .\n\nRecommendations for Practitioners\n\nThough metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange.\n\nRecommendation for Researchers\n\nThe strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome.\n\nImpact on Society\n\nThe objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd's perspective.\n\nFuture Research\n\nIn the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.\n\nKeywords\n\ndisaster management, social media, ontological support, semantic search,\n\nSPARQL, RDF", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 60, "token_count_estimate": 560, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f46f4e5ef88fcb4", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: INTRODUCTION\nType: text\n\nFor long, there has been a huge demand to develop an efficient mechanism to effectively search and extract much-needed information from the social web. Manual annotation is effectively possible in information retrieval for a limited number of documents, but impractical for a large accumulation of content, particularly in social media. And moreover, automatic annotation processes are in an infant\n\nstage. As Ritter, Etzioni and Clark (2012) indicated, automatic annotation has not reached its complete stage. There would be deemed requirements to properly utilize ontologies to precisely govern the types of knowledge to harvest. With the support of ontology (Sakaki, Okazaki, & Matsuo, 2010), domain knowledge extraction is very relevant and relates all possible concepts. Ontology-based knowledge extraction is expected to provide a boost to the domain of this research, disaster management.\n\nHence, pervasive use of ontology has been highly suggested (Ha-Thuc, Mejova, Harris, & Srinivasan, 2010; Luo, Osborne, & Wang, 2012) that builds semantic oriented metadata for concept-based modeling, information flow searching and knowledge exchanging. Once the semantic aspect of metadata is built for the content available over the Web on a domain of interest, then it will provide common grounds for understanding and sharing the information, as well as increasing relevancy and reducing, perhaps even minimizing inherent ambiguities. In the past, there were projects aimed at scavenging Web content with exemplary results for semantically annotating the metadata for domain-specific semantic searches. Several noteworthy examples may be mentioned; for instance, systems like PlanetOnto, ArtEquAKT, sparse kernel learning continuous relevance model for image annotation (Moran & Lavrenko, 2014), and integration of linked data in Knowledge-Based Systems for process planning (Rehage, Joppen, & Gausemeier, 2016). However, when it comes to working with the fast transient contents of social networks, a different paradigm is needed where this research comes in to contribute.\n\nThe next potential task of the operation is accessing the news contents from social media platforms. Though the task seems simple, it is inherently complicated due to interoperability conundrum and accessibility disabilities. Most of the social media platforms have accorded privileges to the programmers accessing the content through its appropriate APIs, but they differ from one another in what they provide and are mostly resource-limited (i.e., in the sense of the number of allowed request for a unit time). The content in the social media can be accessed through two typical ways: permitting users to access and archive the past messages, it is called Search APIs; and allowing users to subscribe for the real-time data feeds, it is known as Streaming APIs. These two kinds of API processes can be used interchangeably and allow expressing information needs, such as filtering the news content by time, geographical region, keyword occurrence and availability ratio (Celik, Abel, & Houben, 2011; Raman, Kuppusamy, Dorasamy, & Nair, 2014). The harvested data however still requires further pre-processing.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 62, "line_end": 70, "token_count_estimate": 741, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bd313b5a686daae5", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: DATA PRE-PROCESSING AND NORMALIZATION\nType: text\n\nThough we have effective information extraction processes and well-established APIs to gather the news source content, there still is a pertinent need for preprocessing the extracted data. The news content extracted from social media can be pre-processed by natural language processing (NLP) toolkit. Common pre-processing operations are tokenization and labeling, part-of-speech tagging, semantic role labeling, dependency parsing and named entity linking (Kumar & Muruganantham, 2016; Lima, Espinasse, & Freitas, 2017). The next challenge on data pre-processing is to reduce the amount of data identifying and eliminating duplicate messages. It is not an easy task since every message posted in the social media can be valuable; de-duplicating the messages requires thorough clustering and then prioritizing them based on the event context. In order to prune this whole process, semantic-based technologies are used.\n\nApart from that, there are various issues associated with handling social media messages. The most prominent issues are scalability and content. The scalability issue concerns Twitter stream size, volume, and velocity. Particularly during any large crisis or severe havoc (Otegi, Arregi, Ansa, & Agirre, 2015; Sakaki et al., 2010), a huge volume of tweets and millions of messages pertaining to that event may be posted. In these critical situations, the tweet velocity would never be at a constant rate. Instead, it grows drastically and records a huge response from people over the event. If it is observed at various times, it would be discovered that same/similar tweets were repeated and reposted again\n\nand again for the same event. This is the foremost challenge for the scalability issue; redundancy avoidance is the core factor for decision making and enhances the level of understanding over the specified event. Next, the content issue deals with tweets that are very brief and canonical (Abel, Gao, Houben, & Tao, 2011); most of the tweets posted in the social media are akin to normal speech and they pose a seminal challenge for the computational methods to deliver the correct form.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "DATA PRE-PROCESSING AND NORMALIZATION", "section_headings": ["DATA PRE-PROCESSING AND NORMALIZATION"], "chunk_type": "text", "line_start": 72, "line_end": 78, "token_count_estimate": 508, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9db754c4f337e220", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: OBJECTIVES OF THIS RESEARCH\nType: text\n\nThe objective of this research is to provide informative summarization of social network content concerning happenings such as crisis, disaster, emergency and havoc based situations in the real world. A system is proposed that provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd's perspective (Samuel & Sharma 2018; Sheth, Thomas, & Mehra, 2010). Important events cannot be adjudged as such. Instead, the suggested events must have the root on the social media such as Twitter after the specified news inception, and it must take the serious impact on the social media through series of takes by the social media users (Lei, Rao, Li, Quan, & Wenyin, 2014). The system proposed and evolved in this research monitors a discussion thread whether it has reached its peak or decline after its root in the social forum like Twitter. Eventually, it would give us insight into the evolutionary trends of the specified events over time.\n\nThus this research aims to address the problems of entity ambiguity and its associated entity types for purposes of disaster management. We categorize the disaster based entity domains using ontology and enhance searching capability by incrementing the explicit connection which mutually exists between entity and ontology class. In order to achieve this task, we identify major issues to deal with and study them thoroughly for efficient processing of the following results: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms suits well conventional news media but due to shortness and sparseness of tweets there remains a bottleneck for researchers. Therefore, we cover the details of the above-stated problems at length and propose algorithms and methods to exercise the cases conservatively.\n\nIn the following sections, we present semantic filtering of entities from Twitter streams and identifying the potential meaning of tweets using domain ontology. Besides, we highlight a semantic model for disaster situations and detail at length different ontological tools available for effective filtering of semantic content. In that connection, we propose the model by which to analyze the semantic mapping of entities/terms over the concepts and disambiguate the potential named entities. Eventually, we detail the analysis and present the empirical results obtained from our proposed model that lead us to the conclusions.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "OBJECTIVES OF THIS RESEARCH", "section_headings": ["OBJECTIVES OF THIS RESEARCH"], "chunk_type": "text", "line_start": 80, "line_end": 86, "token_count_estimate": 650, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e2683a0e0923e19", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS\nType: text\n\nIn recent years, social media has gained momentum over collecting real-time events, and it has been proven that it has given the appropriate responses over the time of crisis when compared to other sources of decision-making systems. Celik et al. (2011) and Lei et al. (2014) did carry out timely searches over the crisis-related events even when the access to the online events was dropped consistently due to network latency and data traffic on the social media sites. Also, Abel et al. (2011) stated that extracting the relevant content over the crisis-related situations turned mostly ambiguous and many times redundant information. The key challenges that they addressed in the paper are how to overcome the difficulties in avoiding ambiguous results and removing redundancy. Sakaki et al. (2010) empirically discovered that during the course of extracting crisis-related information, compre-\n\nhending the phrases entered by social media users into the site is directly affecting the search results. Hence they used Wikipedia and other organized knowledge sources to map the canonical terms thus yielding better results through employing term dissimilarity.\n\nGrolinger, Capretz, Shypanski and Gill (2011) empirically showed event identification using statistical methods to observe an event's history and concluded on some proximity-based estimation of preciseness. They proposed a robust system that employs an algorithmic method based on 'Latent Dirichlet Allocation', which monitors the events and omits the content that is not relevant. They computed the average Euclidean distance between events, segregated abnormal changes present in the streams so that unknown distribution of data would be neglected, and got the Bag-of-Words. Another approach that they proposed in the algorithm is the log-likelihood rate by which the statistical ratio of data and TF-IDF difference present in the user-generated streams can be normalized based on term weighting and similarity score. In Wirtz, Kron, Löw and Steuer (2014), classification algorithms were used largely for event identification and classification. Many supervised learning algorithms were applied to divide the user-generated streams into the already-defined topic categories and carry out the detection process much easier than before. Approaches such as text classification and named entity recognition were extensively employed for exploring hidden events and disambiguating the selection process (using a vector space model to increase the viability of event detection).\n\nLiu, Brewster and Shaw (2013) explored the facts that covered the most common patterns on the disaster-related content and stored the details in a separate database. Weidong, Jidong, Jia, and Danni (2012) and Liu, Shaw, and Brewster, (2013) developed a system that detects online news events and searches related events on social media for the widespread collection of related event information. In contrast to our approach of ontology-based discrimination, all of the references mentioned above are statistical in nature.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS", "section_headings": ["RELATED WORKS"], "chunk_type": "text", "line_start": 88, "line_end": 98, "token_count_estimate": 693, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ee0f45844548d7c", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS\nType: text\n\nand disambiguating the selection process ( using a vector space model to increase the viability of event detection ) . Liu , Brewster and Shaw ( 2013 ) explored the facts that covered the most common patterns on the disaster - related content and stored the details in a separate database . Weidong , Jidong , Jia , and Danni ( 2012 ) and Liu , Shaw , and Brewster , ( 2013 ) developed a system that detects online news events and searches related events on social media for the widespread collection of related event information . In contrast to our approach of ontology - based discrimination , all of the references mentioned above are statistical in nature .\n\nLei Li and Tao Li (2013) developed a domain ontology for analyzing multi-document summarization pertaining to disaster or any crisis management. They provided many experimental models for developing an efficient ontological framework specifically on Hurricane Wilma in 2005. With that ontology, they precisely demonstrated the ontology-based multi-document summarization that performed well compared to other existing models. Jerman-Blažič, Matskanis and Bojanc (2017) discussed differences between man-made and natural calamities in detail and delineated the strategic measures, such as preparation, response, and recovery of any crisis or disaster-related situations. Their model was approved by the European Union and funded for the project REDIRNET. Later Selvam, Balakrishnan and Ramakrishnan (2018) constructed an ontology for Social Event Detection (SED) and applied it for Flickr (online photo management and sharing application) website by extracting the metadata features, such as geolocation, photoID, tags, description, title, timestamp, etc. On the other hand, they extensively utilized the Linked Open Data (LOD), such as Last.fm, Eventful, GeoNames, FourSquare, and so on, for productive discrimination of ontological properties. Although ontology/metadata-based, these works considered full documents or photo metadata in contrast to our study of short bursty messages, for example of Twitter.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS", "section_headings": ["RELATED WORKS"], "chunk_type": "text", "line_start": 88, "line_end": 98, "token_count_estimate": 496, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcfc60ec22ed18e4", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT\nType: text\n\nThe primary objective of Semantic Web technologies is to pave the way for users to easily find relevant information, navigate among diverse data sources and integrate heterogeneous information. For example, usage of semantic technologies will be highly important in getting relevant content and linking data elements to search concepts in the case of Twitter content during any heavy crisis or mass convergence event (Schulz, Ristoski, & Paulheim, 2013; Yates & Paquette, 2011). Nevertheless, the task is complicated because all such processes should be machine-readable and automated; this is where the semantic Web technologies come handy in affecting ontological enrichments (see Table 1).", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT", "section_headings": ["RELATED WORKS", "SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT"], "chunk_type": "text", "line_start": 100, "line_end": 102, "token_count_estimate": 186, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ff28b5836936f36", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT\nType: table\nTable\n\n| Component Name | Cached | Description                            |\n|-------------------|--------|----------------------------------------|\n| RDF, RDFS         | YES    | Vocabulary & Markup supported          |\n| MicroFormats      | YES    | Coupling Vocabulary & Markup Languages |\n| Data RSS Feeds    | YES    | Fetching Atom and Metadata             |\n| XSLT              | NO     | Define data prototyping                |\n| Web Services      | NO     | Interoperate with remote data objects  |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT", "section_headings": ["RELATED WORKS", "SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT"], "chunk_type": "table", "table_caption": null, "columns": ["Component Name", "Cached", "Description"], "table_row_start": 1, "table_row_end": 5, "line_start": 103, "line_end": 109, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "12c21b54130495df", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT\nType: text\n\nTable 1. Semantic technologies for unambiguous data handling\n\nIn the rest of this section, we expand on the need to semantically enrich social media content, survey semantic Web technologies to that end, and how to integrate data from various sources towards facilitating crisis management.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT", "section_headings": ["RELATED WORKS", "SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT"], "chunk_type": "text", "line_start": 110, "line_end": 114, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5d2c8b01e9c48c73", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: text\n\nAs the semantic Web technologies play a seminal role in extracting meaningful information from social media and in the context of any crisis management (Heath & Bizer, 2011; Liu, Shaw, & Brewster, 2013), robust methods are required in dealing with different expressions of text that all in turn point to the central concepts. To get out this discrimination existing in the social media messages, the sole ways of solving this complexity is through making the Web machine-readable. To deal with the above-stated problems, semantic technologies provide an efficient technique called \"named entity linking.\" Named entity linking is the process of detecting the entities prevailing in social media messages and associating the entities which are closely matched to the specified event (see Figure 1).", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "text", "line_start": 116, "line_end": 118, "token_count_estimate": 211, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d51917fa3a5563f8", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: figure\nFigure\n\nImage /page/5/Figure/6 description: A flowchart illustrating a data processing pipeline. At the top, a component labeled 'Online News Extractor' is depicted as a stack of documents. This component feeds into two parallel processing streams. The left stream consists of two steps: 'Key Event Detection and Extraction' followed by 'Add Semantic Annotation to the Events'. The right stream also has two steps: 'Twitter Streams Entity Recognition & Filtering' followed by 'Semantic Labelling and Disambiguation of Events'. The outputs from both streams converge into a central process labeled 'Semantic Modelling & Ontology Mapping'. The final output of this process is directed into a cylindrical database symbol labeled 'Knowledge Repository' at the bottom of the flowchart.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "figure", "figure_caption": null, "line_start": 119, "line_end": 119, "token_count_estimate": 234, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "80edaacc13e00526", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: figure\nFigure: Figure 1. Semantic enrichment of news contents\n\nFigure 1. Semantic enrichment of news contents", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "figure", "figure_caption": "Figure 1. Semantic enrichment of news contents", "line_start": 121, "line_end": 121, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5bc3728b9706799", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: text\n\nThe named entity linking does two things. Firstly, it crawls the social media messages related to the crisis event detecting the potentially emerging entities like names, places, locations, organizations, category, etc. Table 2 displays ontological relations instrumental in extracting potential named entities of various genres using semantic technologies.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "text", "line_start": 122, "line_end": 126, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d5b1e5797865758", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: table\nTable: Table 2. General domain-based ontological relations\n\n| Event                | General Relation                                                                                                              |\n|----------------------|-------------------------------------------------------------------------------------------------------------------------------|\n| Identify Relation    | is-a, isAbout, defines, occurs, exists, classifies, express, describes, isRelatedTo, sameAs.                                  |\n| Temporal Relation    | hasTime, timeInterval, time-span, timeStamp, during, eventDate, begin, end, since, nextTo.                                    |\n| Spatial Relation     | place, region, space, location, hasBoundary, nearTo, direction, overlap, placeName                                            |\n| Causal Relation      | cause, result, factor, agent, actor, action, activities, impact, consequence, result, participant, role, product, instrument. |\n| Exceptional Relation | isPartOf, hasSubEvent, hasComponent, hasMember, unifies, includes, involves, transitive, symmetric, negative, opposite        |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "table", "table_caption": "Table 2. General domain-based ontological relations", "columns": ["Event", "General Relation"], "table_row_start": 1, "table_row_end": 5, "line_start": 127, "line_end": 133, "token_count_estimate": 269, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d5b0ccac82de26a", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: text\n\nSecondly, for every candidate event found, the named entity links searches for events in proximity to the named entity in that context. Unlike conventional search engines, it never searches based on a keyword. Instead, it makes use of the ontologies to augment the search process rendering the system to automate the process through enabling techniques such as RDF, RDFS, and OWL (Celik et al., 2011; Gruhl, Nagarajan, Pieper, Robson, & Sheth, 2009). Table 3 provides a sample of ontologies relevant in crisis management domain.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "text", "line_start": 134, "line_end": 138, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fbc4507c3d9b09cf", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: table\nTable: Table 3. Domain specification of ontologies for crisis management\n\n| Domain Specification | Ontology Name                 | SW Representation  |\n|----------------------|-------------------------------|--------------------|\n| Resources            | SoKNOS                        | OWL-DL             |\n|                      | MOAC                          | RDF                |\n|                      | SIADEX                        | Unknown            |\n| People               | FOAF                          | RDF                |\n|                      | BIO                           | RDF                |\n| Organizations        | IntelLEO                      | RDF                |\n|                      | Organization OL               | RDF                |\n| Disaster             | EM-DAT                        | Online Query       |\n|                      | UNEP-DTIE                     | Online query       |\n|                      | Canadian Disaster Database | Application System |\n| Damage               | HXL                           | RDF                |\n| Infrastructure       | OTN                           | RDF                |\n|                      | EPANET                        | Development        |\n| Geography            | GeoNames                      | RDF                |\n| Meteorology          | NEW weather ontology       | OWL                |\n| Hydrology            | Ordnance Survey               | OWL                |\n|                      | Hydrology Ontology         |                    |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "table", "table_caption": "Table 3. Domain specification of ontologies for crisis management", "columns": ["Domain Specification", "Ontology Name", "SW Representation"], "table_row_start": 1, "table_row_end": 17, "line_start": 139, "line_end": 157, "token_count_estimate": 323, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2bd59e7f7c30650c", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT\nType: text\n\nOnce the named entity linking process is completed, thus having semantically enriched the messages present in the social media, then users can search for the information they want. This is called \"faceted search\". In faceted search, information is not crawled based on the keyword supplied but by associating the concepts for the term through proper ontological support (Grolinger et al., 2011; Malizia, Onorati, Díaz, Aedo, & Astorga-Paliza, 2010). For example, if we give the search word \"virus\", it will not search the information through using the keyword \"virus\"; instead, it finds relevant concepts for the keyword and associated terms in the ontology hierarchy and then related media messages based on the ontological concepts are returned.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT", "section_headings": ["RELATED WORKS", "SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT"], "chunk_type": "text", "line_start": 158, "line_end": 160, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "397ef5befbdf559e", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: RELATED WORKS > PERTAINING CHALLENGES\nType: text\n\nTweets extracted from Twitter entails a huge amount of challenges to overcome in order to put them to use for a particular purpose. Wirtz et al. (2014) pointed out precisely that there would be no metrics stipulated about how much of information need to be monitored, extracted and evaluated. The following are major problems that present various challenges in yielding useful results:\n\n- a) Tweets extracted cover different aspects of the event and fail to make the distinction between the choices of the events.\n- b) Many tweets tend to be very noisy and sometimes irrelevant to the event thus causing unnecessary computational problems.\n- c) No measure to counteract rumors germinated during the events and it has been feared that it would spread vehemently over the short course of a period.\n- d) Dealing with misspelled tweets is a big task since there had been no apt use of a dictionary to makeover.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "RELATED WORKS > PERTAINING CHALLENGES", "section_headings": ["RELATED WORKS", "PERTAINING CHALLENGES"], "chunk_type": "text", "line_start": 162, "line_end": 169, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d2b9d31723d7caf", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: EVENT EXTRACTION\nType: text\n\nEvent extraction from Twitter is carried out through the Twitter Streaming API, which is the standard application fulfilling the filtration process effectively (El-Halees, & Al-Asmar, 2017; Sakaki et al., 2010). It extracts the tweets for the event by crawling through the hashtags created for the events by various NEWS sources on Twitter and monitors the user-generated posts subsequently (Silva, Wuwongse, & Sharma, 2013). The crawler is started to fetch the tweets that have been monitored and stored in the data store. Each and every tweet consists of the original post, author, timestamp, geographic information and hashtag from which it was obtained. All these properties are very useful in deriving the patterns and identifying the purpose of the posts.\n\nThe formal definition for entity extraction of Twitter streams is expressed in the graph theory as G(E,V) where E and V represent some set of edges for the given set of vertices. To determine the potential social entities prevailed in the twitter streams and build the appropriate relationships between the entities, we link the set of edges E, in which  $v_i \\in V$ , i = 1...N denotes the extracted entities in twitter streams and  $v_iv_j \\in E$  denotes relationship between entities  $v_i, v_j \\in V$ . In this connection, to estimate the candidate entity for the query q, the search engine would normally generate most ambiguous entity sets about the given candidate entity, and it is termed as\n\n$$a_i = q \\leftarrow a_i \\tag{1}$$\n\nHowever, in our proposed approach, we have introduced a novel method to tackle this ambiguity prevailed over the search results by incurring the semantic web ontology for the domain at which the entity is dealt with through the appropriate level of ontological weight, and it can drastically reduce with the addition of ontology candidate keyword KW, i.e. consequent of\n\n$$aw_i = q \\leftarrow a_i, KW$$\n (2)\n\nwhich reduced the entity ambiguity with which  $|aw_i| \\le |a_i|$ ,  $|a_i| \\in a_i$  is a cardinality of  $a_i$  and  $|aw_i| \\in aw_i$  is a cardinality of  $a_i$ , KW. By utilizing a well-formed query for the candidate entity in the query, the named entity information would come as\n\n$$a_{\"i\"} = q \\leftarrow \"a_i\" \\tag{3}$$\n\nIn some cases,  $|a_{il}| \\le |a_i|$  and  $|a_{il}| \\in a_{il}$  is a cardinality of \"a;\". As well as with\n\n$$aw_{i''} = q \\leftarrow \\text{\"a}_i\\text{\"}, KW \\tag{4}$$\n\nis about one of the information concentrations of a named entity. Then after pruning the entity cardinality, in the next process, the relationship between two named entities is based on the concept of co-occurrence. Thus,\n\n$$a_i \\ a_i = q \\ \\leftarrow a_i, a_i \\tag{5}$$", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "EVENT EXTRACTION", "section_headings": ["EVENT EXTRACTION"], "chunk_type": "text", "line_start": 171, "line_end": 201, "token_count_estimate": 830, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb3d39061d6a12c7", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: EVENT EXTRACTION\nType: text\n\na_ { il } | \\ in a_ { il } $ is a cardinality of \" a ; \" . As well as with $ $ aw_ { i ' ' } = q \\ leftarrow \\ text { \" a } _i \\ text { \" } , KW \\ tag { 4 } $ $ is about one of the information concentrations of a named entity . Then after pruning the entity cardinality , in the next process , the relationship between two named entities is based on the concept of co - occurrence . Thus , $ $ a_i \\ a_i = q \\ \\ leftarrow a_i , a_i \\ tag { 5 } $ $\n\nwhich is a process to augment the semantic similarity between the two named entities and build the relationships between them, with which  $|a_i \\cap a_j| \\le |a_i|$  and  $|a_i \\cap a_j| \\le |a_j|$  and  $|a_i \\cap a_j| \\in a_i a_j$  is a cardinality of  $a_i a_j$ . Besides, with the supplement of a keyword towards the co-occurrence will usually subside the number of entities given, and that is\n\n$$aw_i aw_i = q \\leftarrow qa_i, KW$$\n (6)\n\nBut it should satisfy that  $|aw_i \\cap aw_j| \\le |a_i \\cap a_j|$ ,  $|aw_i \\cap aw_j| \\in aw_i aw_j$  is a cardinality of  $a_i a_j$ , KW. Similarly, effective utilization of the well-defined entity set for the query will yield the appropriate relationships between the two named entities.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "EVENT EXTRACTION", "section_headings": ["EVENT EXTRACTION"], "chunk_type": "text", "line_start": 171, "line_end": 201, "token_count_estimate": 466, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb19377d09676580", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT\nType: text\n\nSemantic technologies that support crisis events identification are often required for interacting between different developers and software applications operated by various agencies. In this context, affecting the semantically enabled system to communicate the information in a unified format is the critical challenge, and social media platforms behave differently to address the dimension of the problem to interrelate with one another. Interoperability shortcomings at the semantic level of concepts can be alleviated using common vocabularies as well as shared concepts in linking the whole processes (Liu, Brewster, & Shaw, 2013; Rajpathak & De, 2016). The best way of accomplishing this critical task is through the use of machine-understandable ontologies that can precisely define the concepts, categorize the events based on clustering approach and build an appropriate path between concepts and unified communication.\n\nThe next aspect of the challenge in retrieving the crisis-related information is in extracting related events or messages from blogs, forums, and referring wikis. These are the places where vibrant information is present at a high rate and shared opinions and suggestion made by several authors. Now the critical challenge is interlinking not only social media platforms but also these blogs and forums. It is very difficult in repurposing the content and tough to identify the common events among these sites. For instance, take Wikipedia – it is a huge repository of publicly accessible knowledge source but reusing the same knowledge for other applications presents new challenges and difficulties (Kumar & Muruganantham, 2016; Yates & Paquette, 2011). Furthermore, a user can create accounts on many sites like in blogs, forums, wikis, and other social media platforms, but it is very complicated to inter-relate the candidate entities among these different social sites. The major problem pertaining to these media sources is that the information items in such sites are entirely disconnected and completely separated from one another. There is an absolute lack of exchanging the semantics of entities and unable to derive the facts from such information silos. Every site holds the information posted by their registered users independently and, at times, it has turned into stagnant information silos which are untapped by others.\n\nTo meet the challenges rising from crisis or havoc situations, there would be a huge demand to decentralize the process and enable interactive processes to fetch hidden relevant facts from the content. Table 4 gives the details of handling the events from the time news originates to planning for future action taking against the crisis events (i.e., past to present).", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT"], "chunk_type": "text", "line_start": 203, "line_end": 209, "token_count_estimate": 608, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ea1029a705caee6", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT\nType: table\nTable\n\n|                         | News Inception                              | Present State                 | Future Plan                |\n|-------------------------|---------------------------------------------|-------------------------------|----------------------------|\n| Goal                    | Inform & Publish the news                   | Share & Collect the news      | Engage & Prune             |\n| Main Activity           | Gather relevant event- based information | Track & Monitor the events | Prepare the action         |\n| Content                 | Initially, discrete data                    | Clustered Data                | Find Relationships         |\n| Information Handling | Confidential                                | Privileged Access             | Absolute transpar- ency |\n| Software Tools          | In-house Software                           | Commercial Software           | Open Source Software    |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT"], "chunk_type": "table", "table_caption": null, "columns": ["", "News Inception", "Present State", "Future Plan"], "table_row_start": 1, "table_row_end": 5, "line_start": 210, "line_end": 216, "token_count_estimate": 203, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "467d4613a9ebef3e", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT\nType: text\n\nTable 4. Information handling for crisis situations\n\nSemantic Web recently provided the necessary tools for effective information linking and interoperability. Moreover, many semantic Web vocabularies have successfully been deployed at various social platforms facilitating machine-understandable message processing (Benali, & Rahal, 2017; Ritter et al., 2012). Some of the semantic Web vocabularies are RSS, FOAF (Friend of a Friend), and SIOC (Semantically Interlinked Online Communities). With the help of these and other more refined semantic vocabularies, interlinking communities and social sites became effective and helped curtail down information redundancy.\n\nFor example, let's consider the query for crisis-related content on Twitter such as \"Was there a storm near the city?\" In this query, the name of the city is not mentioned, but the tagging engine like DBPedia Spotlight (<a href=\"http://wiki.dbpedia.org/projects/dbpedia-spotlight\">http://wiki.dbpedia.org/projects/dbpedia-spotlight</a>) and OpenCalais (<a href=\"http://www.opencalais.com/\">http://www.opencalais.com/</a>) would annotate the given query and fix the appropriate entity for the annotated tokens based on the Agglomerative Clustering techniques applied on the collected tweets. Since there is a semantic link (i.e., rdf:type) between the query and DBPedia, it can be computed based on the similarity score and higher relevance of content. It is illustrated in the following:", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT"], "chunk_type": "text", "line_start": 217, "line_end": 223, "token_count_estimate": 405, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb43b7cd13a65a1c", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT\nType: figure\nFigure\n\nImage /page/9/Figure/6 description: A diagram illustrates a query and result relationship using DBpedia entities. The 'Query: Was there storm near the city?' points with a dashed arrow to a circle labeled 'Dbpedia-owl: City'. Below, the 'Result: There was heavy storm near New York City yesterday.' points with a dashed arrow to a circle labeled 'Dbpedia-city: New York City'. The two circles are connected by a double-headed arrow labeled 'rdf:type', indicating that 'Dbpedia-city: New York City' is a type of 'Dbpedia-owl: City'.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT"], "chunk_type": "figure", "figure_caption": null, "line_start": 224, "line_end": 224, "token_count_estimate": 195, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a57dee04699c95dd", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages\nType: text\n\nThe so-called 'semantic Web stack' comprising a number of semantic Web languages was suggested for effective utilization in information processing and that, by the way, leads to efficient semantic implementations on the retrieval systems. The first language that was suggested to use was the Resource Description Framework (RDF), by which the basic framework to represent the potential information on the Web content was availed. The basic structure of any RDF statements is just a triple (subject, predicate, and object), which further yields the hierarchical RDF graph to prune the data in a much effective way. In simpler terms, RDF statement is just denoting the relationship existing between the existing things called nodes and that node is interconnected with other nodes (i.e., semantically related nodes). RDF serialization uses XML syntax and terms such as element name, attributes, values, etc. The scope of using RDF is to make the system machine-readable and process the infor-\n\nmation semantically (see Table 5). Besides, it integrates the data without any serious glitches as it follows the well-formed logic which is universally acknowledged.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "Ontology Languages"], "chunk_type": "text", "line_start": 227, "line_end": 233, "token_count_estimate": 312, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55d5af80f6444a86", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages\nType: table\nTable: Table 5. Semantic Web languages and their structural contents\n\n| SW Languages | Structural Contents                              |\n|--------------|--------------------------------------------------|\n| OWL-S        | Services in a machine-understandable format      |\n| SWSL         |                                                  |\n| WSML         |                                                  |\n| OWL          | Equality                                         |\n|              | Relation between Classes                         |\n|              | Cardinality Restrictions for Properties          |\n|              | Relation of Properties                           |\n| RDF Schema   | Classes                                          |\n|              | Objects                                          |\n|              | Properties                                       |\n| RDF          | Prepositions as Triplets                         |\n| WSDL         | Services in an exact human understandable format |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "Ontology Languages"], "chunk_type": "table", "table_caption": "Table 5. Semantic Web languages and their structural contents", "columns": ["SW Languages", "Structural Contents"], "table_row_start": 1, "table_row_end": 12, "line_start": 234, "line_end": 247, "token_count_estimate": 216, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74e5e968f301398f", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages\nType: text\n\nA set of RDF statements form an RDF graph through interconnected nodes. As the RDF graph is conventionally followed in expressing the logical facts about the potential named entities, ontologies are used to give the domain and category of each thing (i.e., entity) and yield the appropriate relationship between them. Ontologies contain features to wholesomely express the rich relationship between entities and also set appropriate constraints on them (Grolinger et al., 2011; Wang et al., 2018). The language followed for effective creation of ontology is RDF Schema and OWL (see Tables 6 and 7). The RDF Schema (RDFS for short) employs the set of classes related to the entity and chooses the properties according to its domain.\n\nThe basic objective of RDFS is to provide a well-structured description of entities properties. Some ontologies used to set classes and properties are called RDF vocabularies, and examples include FOAF, SKOS, MOAC, Dublin Core, etc., whereas OWL is facilitating the information interoperability and providing additional vocabularies to enhance the formal semantics of RDF Schema.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "Ontology Languages"], "chunk_type": "text", "line_start": 248, "line_end": 254, "token_count_estimate": 303, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5537df1b3a1f28f1", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages\nType: table\nTable: Table 6. RDF and OWL Ontological Constructs\n\n| RDF/OWL  | Category   | Functions             |\n|----------|------------|-----------------------|\n| Class    | Definition | Class                 |\n|          |            | Enumerated Class      |\n|          |            | Restriction           |\n|          |            | IntersectionOf        |\n|          |            | UnionOf, ComplementOf |\n|          | Axiom      | subClassOf            |\n|          |            | Equality              |\n|          |            | disjointWith          |\n| Relation | Definition | Property              |\n|          |            | Domain, range         |\n|          |            | subPropertyOf         |\n|          | Axiom      | (Inverse)Functional   |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "Ontology Languages"], "chunk_type": "table", "table_caption": "Table 6. RDF and OWL Ontological Constructs", "columns": ["RDF/OWL", "Category", "Functions"], "table_row_start": 1, "table_row_end": 12, "line_start": 255, "line_end": 268, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b280f3a6851dfa36", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages\nType: table\nTable\n\n| RDF/OWL  | Category   | Functions                                    |\n|----------|------------|----------------------------------------------|\n|          |            | Equality, inverseOf Transitive, symmetric |\n| Instance | Definition | Type                                         |\n|          | Axiom      | (In)Equality                                 |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > Ontology Languages", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "Ontology Languages"], "chunk_type": "table", "table_caption": null, "columns": ["RDF/OWL", "Category", "Functions"], "table_row_start": 1, "table_row_end": 3, "line_start": 270, "line_end": 274, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9ddd8f327911076d", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > ONTOLOGY EDITORS\nType: text\n\nIn order to derive the meaning out of the collected information from Twitter streams and as we process the tweets into the respective semantic representations, there is a need to design an ontology that is well mapped to the information and facilitates fetching the content in the hierarchical format (Liu, Shaw, & Brewster, 2013; Malizia et al., 2010). In this connection, we are required to build ontology using standard text editors that are very simple in design and development. Besides, it can be formatted with the semantic Web languages called RDF-XML format. One such editor is called Protégé (<a href=\"https://protege.stanford.edu/\">https://protege.stanford.edu/</a>) which is a tool that permits designing OWL ontologies and further helps to connect to the data interrelated with one another in the overall ontological framework. With Protégé, querying and reasoning help to disambiguate the information and assist in making the filtration absolutely error-free. Other popular ontology editors are SWOOP, OntoStudio, NeOn, Altova, WebODE, and so on. Among all the editors, Protégé was the first, freely available, and open-source ontology editor and framework for building intelligent systems thus tops the list and widely deployed in much recent research.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > ONTOLOGY EDITORS", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "ONTOLOGY EDITORS"], "chunk_type": "text", "line_start": 277, "line_end": 281, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5795313812407775", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > ONTOLOGY EDITORS\nType: table\nTable: Table 7. A notional mapping between RDF/OWL and relational concepts\n\n| RDF/OWL Terms | Relational Concepts                        |\n|---------------|--------------------------------------------|\n| rdf:class     | Table                                      |\n| rdf:property  | Column                                     |\n| rdfs:domain   | Table that the rdf:property is a column of |\n| rdfs:range    | Data Type of the column                    |\n| rdf:type      | Values of the Primary Key column           |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > ONTOLOGY EDITORS", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "ONTOLOGY EDITORS"], "chunk_type": "table", "table_caption": "Table 7. A notional mapping between RDF/OWL and relational concepts", "columns": ["RDF/OWL Terms", "Relational Concepts"], "table_row_start": 1, "table_row_end": 5, "line_start": 282, "line_end": 288, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e97acaab8c7e8039", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > SEMANTIC MATCHING AND TRANSLATION\nType: text\n\nAs ontologies play a seminal role in semantic processing of information (Celik et al., 2011; Sheth et al., 2010), we should, therefore, try harnessing the potential meaning hidden in the collected information streams. Ontologies help process the meaning of different terms represented in the information and avail the system to understand the very basic structure of information in a precise way. The system processes the collected data automatically and does matching and translation with its rich vocabulary sets, which is fed as the dataset to the ontology while designing the complete framework of the domain ontology (Gruhl et al., 2009; Madani, Boussaid, & Zegour, 2015). Each term (i.e, a concept) in a tweet is mapped with corresponding vocabulary sets in the specific ontology domain, and sometimes it arches to other domains of vocabulary set to find the exact meaning of the concepts represented in the tweet. Precisely, the inclusion of mapping the terms over multiple ontologies is the biggest challenge in designing an application that is used to integrate the ontologies to disambiguate in parallel; that is a challenging research area to deal with. Further, to make the automation of semantic matching and translation effective, appropriate use of mapping rules over the information is necessary and should be defined using ontology matching tools. Ontology matching is variously called also as ontology aligning, mapping, and translation (for example, for Web services discovery: Fellah, Malki, & Elçi, 2016). Conventionally, ontology mapping tools come in two categories: element-based approach such as name similarity, entity similarity, concept similarity, etc., and structurebased approach such as sub/super -categories, -domains, -levels, etc. Besides, mapping requires infusing external knowledge such as Thesauri, WordNet, etc., to yield precision and high recall. Some of the ontology mapping tools available on the market are RiMOM, ASMOV, and AgreementMaker.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > SEMANTIC MATCHING AND TRANSLATION", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "SEMANTIC MATCHING AND TRANSLATION"], "chunk_type": "text", "line_start": 291, "line_end": 293, "token_count_estimate": 511, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "419ea087a2687d09", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > SEMANTIC SEARCH\nType: text\n\nThe next level in semantic utilization of social media harvested information is through semantic search, which should return results without any ambiguity and sparseness. As semantic mapping directly links information repositories, domain ontologies should facilitate the operation of effective semantic search (Kumar & Muruganantham, 2016) retrieving facts that are interconnected with one another in the ontological framework. In order to render the search process easier, appropriate use of indexing methods in the ontological inclusion over the concepts is deemed important. Ontologies follow the semantic indexing approach using its standard principle of \"indexing the RDF triplets\", thus smoothen the way of semantic search over the collected information. Several pieces of research have been carried out in this regard to fetch the precise and unambiguous results by semantically integrating information from diverse ontological frameworks in retrieving the results from multiple repositories.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > SEMANTIC SEARCH", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "SEMANTIC SEARCH"], "chunk_type": "text", "line_start": 295, "line_end": 297, "token_count_estimate": 249, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "974413020a46552c", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > INTEGRATION OF DATA\nType: text\n\nAnother critical issue faced in utilizing social media content is the integration of data, which may be considered from two different perspectives: data source (database/stream) reconciliation, and information integration. The basic objective of overcoming the problems of semantic heterogeneity between these two categories using the appropriate ontology framework is a challenging task to any research (Liu, Shaw, & Brewster, 2013). In considering database integration, the role of ontologies lies on the upper layer of the schema (i.e, semantic matching of information and table schema should be shared with the domain ontology and additionally, make use of the ontology to integrate the database schema rightly towards its order). While integrating information the core problem lies at integrating the terms from various sources and mapping the potential candidate terms relevant to its vocabulary sets and bring into the consolidated view called the new collection of a derived set. The challenge that lies here is to not change the original sense of the terms while mapping to the appropriate sense of terms in the vocabulary sets. Besides, in database integration, ontologies convert tables into respective classes in RDF triplet and columns in the table into data relation in the RDF Schema. The data models followed in the semantic conversion would always be 1:1 mapping cardinalities. In recent works (Kumar & Muruganantham, 2016; Kwak, Lee, Park, & Moon, 2010), the authors propose rules to dynamically map the data models into ontologies and consider mapping instances to class levels. Also, some language constructs are given to fetch data objects and, using the queries, they can be annotated dynamically representing in RDF.\n\nWhereas in information integration, accumulating the terms from various sources of datasets to bring them into a unified collection, several efforts were carried out in the recent past but failed to resolve it. Some early researchers have tried to apply the Description Logic (DL) as ontology language and observed few changes in the outcome. Later, the Prolog programming language is employed for expressing the information formally and integrating the terms using appropriate domain ontology.\n\nIn the next section, we introduce our proposed information-centric model for management of crisis and disaster based situations through integration of many of the technologies mentioned above combined by our innovative approach. Our proposed model is introduced, followed by the empirical tests and discussion of findings.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT > INTEGRATION OF DATA", "section_headings": ["ONTOLOGICAL INCLUSION FOR DISASTER MANAGEMENT", "INTEGRATION OF DATA"], "chunk_type": "text", "line_start": 299, "line_end": 305, "token_count_estimate": 575, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8de7b50db7a70546", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL\nType: text\n\nThe objective of this research is to potentially harness the information gathered from various social media platforms and render it relevantly interconnected with the selected news articles. In doing so,\n\nhere we introduce some of the notation and problems that we define formally before presenting the Semantic Search for Events Algorithm.\n\n**Problem 1 (News stream):** For every news article related to disaster or crisis situation, content must be analyzed and scrutinized for a further level of comprehension. Let  $N = \\{n_0, n_1, ..., n_i\\}$  be the news posted on various sites and gathered from various news agencies. For every news article posted say  $n_i$ , we find the actually published time t ( $n_i$ ). Since the origin of news story gives the real arrival of news, it brings in the proximity among related news articles.\n\n**Problem 2 (Tweet stream):** Upon the arrival of every news article related to disaster or crisis, the next task of the system is to identify the equivalent social media content such as Twitter where the relevant news item is discussed and promulgated. Let  $S = \\{s_0, s_1, ..., s_j\\}$  be the Twitter Streams for the taken news articles and load the inter-related Twitter messages posted by various potential social users. For every tweet  $s_i$ , we find the actually posted time t ( $s_i$ ) and responses for the message.\n\n**Problem 3 (News recommendation problem):** Once the news items N and its associated social media contents (Twitter Streams) S are mapped, then the real task is to find the top-k most relevant news for the topic. Let's take the set of users interacted on the particular news topic  $U = \\{u_0, u_1, u_2, ..., u_n\\}$  in the social media platforms and explicitly categorize the social messages and news streams of general interest (i.e., for any social user  $u \\in U$  at any point of time T, we recursively adopt the functional ranking which links the users interest among its neighbors).\n\n**Problem 4 (Social influence):** In order to find whether the news item  $n_i \\in N$  has influenced the social media users  $U = \\{u_0, u_1, u_2, ..., u_n\\}$  effectively, we give the social influence model  $S = |U| \\times |U|$  matrix where S (i, j) calculates the cumulative interest of the selected users  $u_i$  to the usergenerated content by  $u_j$ . This process states that each user in the context would pose an absolute interest to the user-generated content posted by the other user.\n\n**Problem 5 (Tweets-to-news model):** To merge the process, let N be the order of news collected and S be the streams of social media messages, we model the relationships between user-generated content and news items as  $M = |S| \\cdot |N|$  matrix Z where S(i,j) is the closest proximity of user-generated content  $s_i$  to news item  $n_i$ .\n\n```\nAlgorithm (Semantic Search for Events)\n\nInput: Seed words for each crisis event\n\nOutput: Generation of semantic classes\n\nBaseTerms ← set of seed words given;\n\nfor i: 1 to N (Number of Iterations) do\n\nBaseTerms ← ExpansionOf (Seed words, Corpus);\n\nBaseTerms ← Cluster (BaseTerms, Seed words, Corpus);\n\nend\n\nreturn BaseTerms\n```", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL", "section_headings": ["THE PROPOSED MODEL"], "chunk_type": "text", "line_start": 307, "line_end": 360, "token_count_estimate": 858, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2f3bfd0b3c46a7b9", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL\nType: text\n\nuser - generated content and news items as $ M = | S | \\ cdot | N | $ matrix Z where S ( i , j ) is the closest proximity of user - generated content $ s_i $ to news item $ n_i $ . ` ` ` Algorithm ( Semantic Search for Events ) Input : Seed words for each crisis event Output : Generation of semantic classes BaseTerms ← set of seed words given ; for i : 1 to N ( Number of Iterations ) do BaseTerms ← ExpansionOf ( Seed words , Corpus ) ; BaseTerms ← Cluster ( BaseTerms , Seed words , Corpus ) ; end return BaseTerms ` ` `\n\nThe algorithm carries two significant operations: (1) expand the seed words with the assistance of ontologies; and (2) cluster the events based on the similarities existing in the classification. For the given seed words for the event, it crawls for new terms that possess the similar distributional features to the seed word and assigned to the set of seed words (also called as candidate words). In fact, extracting the new terms for the seed word can be done based on the contextual features and top score similarity measure. For the clustering, the selection procedure would process the learned terms and\n\nseed terms based on the distributed similarity and set the minimum threshold value for estimating the exact precision of the terms.\n\nThe sole plan of this algorithm is to get the patterns which are interlinked with a semantic relation and bring in the semantic class for the search terms (Kumar & Muruganantham, 2016; Wang et al., 2018). As listed in the algorithm, it has three core operations for finding the patterns existing in the disaster based situations:\n\n- 1. using the semantic class expansion algorithm, extract the candidate terms for the disaster event tags;\n- 2. find the patterns for the candidate terms selection and fix the semantic category of the events; and\n- 3. choose the cluster events which hold similar action terms and evaluate the patterns for further classification.\n\nIt has been noted at several instances (Abel et al., 2011; Sheth et al., 2010; Wirtz et al., 2014) that news items and user-generated content at social media platforms (say, Twitter Streams) co-exist with one another with same news topic (see Figure 2). Sometimes, a published news story is pushed into social media platforms for further discussion and circulation. And, at many times, a news item first discussed vehemently on social networks then becomes a topic in news stories (Lei et al., 2014; Li, Liu, Li, Qin, 2016). In these two cases, the predominant factor is holding the current trending entities, which give the unflinching bond between social networks and news sites. There is an absolute relationship between user-generated content and news stories, which create an intermediary layer that paves the way to generalize the analysis. Hence, this would make our work equally applicable in deriving the ultimate decision for disaster management and assess the core patterns for decision making.\n\nDuring the analysis of the relationship between events and results, there would emerge a need to attain a similarity score for the ultimate decision process. The similarity score for the crisis/disaster management (Liu, Shaw, & Brewster, 2013; Malizia et al., 2010; Schulz et al., 2013) can be accepted and formulated based on the following assumptions:", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL", "section_headings": ["THE PROPOSED MODEL"], "chunk_type": "text", "line_start": 307, "line_end": 360, "token_count_estimate": 812, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "28678432672d4659", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL\nType: text\n\nbetween user - generated content and news stories , which create an intermediary layer that paves the way to generalize the analysis . Hence , this would make our work equally applicable in deriving the ultimate decision for disaster management and assess the core patterns for decision making . During the analysis of the relationship between events and results , there would emerge a need to attain a similarity score for the ultimate decision process . The similarity score for the crisis / disaster management ( Liu , Shaw , & Brewster , 2013 ; Malizia et al . , 2010 ; Schulz et al . , 2013 ) can be accepted and formulated based on the following assumptions :\n\n- 1. If there would be a high or low hazard during the disaster situations, then it requires the scientific or technical measure to be assured, and precautionary steps should be taken based on scientific or technical grounds.\n- 2. If there would be high or low outrage, then it is an emotive issue and should be tackled through proper negotiations or political balance.\n\nIn these two cases, the analysis of the events played a crucial role in disseminating the user-generated content posted on social networks and determining the effective decision-making process (Heath & Bizer, 2011).", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL", "section_headings": ["THE PROPOSED MODEL"], "chunk_type": "text", "line_start": 307, "line_end": 360, "token_count_estimate": 305, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6113ac3f2576425", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > DISASTER ONTOLOGY\nType: text\n\nTo substantiate our proposed model, we constructed an ontology for disaster datasets with a glossary consisting of more than 150 definitions (i.e. mostly of recurring terms) and further accumulated terms related to disaster from books, papers, survey on seismic risk and other relevant disaster web sites. We constructed the ontology with associated concepts, its attributes and proper relationships between concepts. In constructing this ontology, we followed many dictionary terms (i.e. entities related to disaster) with their associated meanings (axioms) and connected the terms with taxonomic relationships. Relationship mapping of terms can be done in many ways such as Taxonomic (IS-A relationship), Meronomic (PART-OF relationship) and Telic (PURPOSE-OF relationship). The relationship mapping of terms can be achieved through inference rules to augment better reasoning and increase the credibility ratio of knowledge representations. On this proposed system, we used Protégé, an ontology tool which is more of an object-oriented paradigm and well suited for term inher-\n\nitances. The relationship IS-A is a generalization/specialization between the candidate entities: superclass entities publically generalize the subclass entities and the sub-class entities particularly employ specialization of superclass entities. Likewise, Protégé permits to formulate the disaster ontology by considering different instances to insert and able to accommodate a huge set of information for a digital archive.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > DISASTER ONTOLOGY", "section_headings": ["THE PROPOSED MODEL", "DISASTER ONTOLOGY"], "chunk_type": "text", "line_start": 362, "line_end": 366, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f399acb951ce7d5c", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > DISASTER ONTOLOGY\nType: figure\nFigure\n\nImage /page/15/Figure/2 description: A screenshot of a software interface displaying a flowchart related to risk assessment. The window has a menu bar with \"File\", \"Tools\", and \"Help\", and a toolbar with buttons like \"Previous\", \"Next\", and \"Build graph\". The main area is divided into two panes. The left pane contains a list of terms, with \"Damage\" highlighted in bold. The right pane shows a flowchart. At the center is a grey box labeled \"Damage\". An arrow flows from \"Vulnerability\" to \"Damage\", and another from \"Damage\" to \"Exposure\". The \"Vulnerability\" box is influenced by \"Seismic Risk\" and \"Structural Vulnerability\". The \"Exposure\" box is influenced by \"Physics Elements Exposed\" and in turn influences \"Seismic Risk\", \"Functional Exposure\", and \"Strategic Exposure\". Several boxes are marked with small numbered circles.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > DISASTER ONTOLOGY", "section_headings": ["THE PROPOSED MODEL", "DISASTER ONTOLOGY"], "chunk_type": "figure", "figure_caption": null, "line_start": 367, "line_end": 367, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73921380f4887534", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > DISASTER ONTOLOGY\nType: figure\nFigure: Figure 2. Disaster Ontology using Protégé\n\nFigure 2. Disaster Ontology using Protégé", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > DISASTER ONTOLOGY", "section_headings": ["THE PROPOSED MODEL", "DISASTER ONTOLOGY"], "chunk_type": "figure", "figure_caption": "Figure 2. Disaster Ontology using Protégé", "line_start": 369, "line_end": 369, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3ff32c274daf830", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > DISASTER ONTOLOGY\nType: text\n\nOur proposed system concerns mostly about urban risk with specific governance on seismic risk management. The effective building of this ontology paves the way for common knowledge, makes the concepts understandable, and prompts information into unambiguous semantics. This ontology construction has been performed in three steps:\n\n- 1. Fetch the core concepts of the domain (Seismic Risk) and relevant terms in the glossary.\n- 2. Extract the Super-Classes and Sub-Classes of the concepts using the IS-A relationship.\n- 3. Find other related types of relationships using inference rules (properties, slots, and roles associated with each concept).\n\nRelation mapping for the collected tweets can be performed and filtered using the relational properties displayed in Table 8). Entity resolution and disambiguation have been effectively dealt with in Disaster Ontology constructed above and resolve the term ambiguity persisting over the collected documents (Twitter streams).", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > DISASTER ONTOLOGY", "section_headings": ["THE PROPOSED MODEL", "DISASTER ONTOLOGY"], "chunk_type": "text", "line_start": 370, "line_end": 378, "token_count_estimate": 244, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d16b00fec0f2300f", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > DISASTER ONTOLOGY\nType: table\nTable\n\n| Relation Name    | Source     | Target      | Description                                                                                                        |\n|------------------|------------|-------------|--------------------------------------------------------------------------------------------------------------------|\n| isResponsibleFor | Department | Process     | Identify which sector is responsible for the event and map the relationship between de- partment and process |\n| workIn           | Actor      | Department  | Map the relationship between the person and the department. Identify the actor responsible for the event.    |\n| isPartOf         | Task       | Process     | Find the task which is responsible for the pro- cess and filter out the concepts related to the event.       |\n| isA              | -          | -           | Relationship between super-class and sub-class                                                                     |\n| Perform          | Actor      | Task        | Group the actor performed the task on the event.                                                                |\n| Produce          | Task       | Information | Filter the information for the task on the event.                                                               |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > DISASTER ONTOLOGY", "section_headings": ["THE PROPOSED MODEL", "DISASTER ONTOLOGY"], "chunk_type": "table", "table_caption": null, "columns": ["Relation Name", "Source", "Target", "Description"], "table_row_start": 1, "table_row_end": 6, "line_start": 379, "line_end": 386, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1608db67563b2f2d", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > DISASTER ONTOLOGY\nType: text\n\nTable 8. Relationship mapping between concepts and classes", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > DISASTER ONTOLOGY", "section_headings": ["THE PROPOSED MODEL", "DISASTER ONTOLOGY"], "chunk_type": "text", "line_start": 387, "line_end": 389, "token_count_estimate": 42, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f4867c52ef6cb200", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE\nType: text\n\nThe disaster ontology has now become a knowledge source for our disambiguation effort. When we process each and every tweet, we find the exact match of those entities against the knowledge source such as DBpedia or YAGO. If it is not present, then it sends the NIL result. Now, by means of our proposed method, we can again cross-match with our own ontology created from news articles and find the exact match of those entities. In this method, the accuracy is relatively high because the created ontology is extracted from news articles related to the tweets and context of the news articles is highly relevant and appropriated match with the tweets. If we go for the entity-mention match with DBpedia, it lists out candidate mentions for the entity, and we need to probe for the context pertaining to the tweet. But if we match the same with our own ontology, it is exact and gives an appropriate match.\n\nHence, in our approach, we take the link probability (Kumar & Muruganantham, 2016; Yates & Paquette, 2011) for the entity with DBpedia mention, and it can be defined as follows:\n\n$$F_{(e,m)} = \\frac{Count(m,e)}{Count(m)} \\tag{7}$$\n\nHere, we utilized an outlined ontology to arrange the mentions for the given named entities and appropriately estimate the similarity distance between them. Now the task is to estimate the distance between the entity and the suggested set of mentions from DBpedia. In this connection, we have taken the Cosine Similarity measure to access the similarity difference existing between the entity and candidate mentions as follows:\n\n$$CosSim(e,m) = \\frac{Product(e,m)}{||e||*||m||}$$\n(8)\n\nBy this method, we categorically filter the exact match of mention for the given entity and appropriately reference with DBpedia URI as stated in (Liu, Brewster, & Shaw, 2013; Malizia et al., 2010; Schulz et al., 2013). We utilized the DBpedia Spotlight to get the URI match of each entity and return the JSON results for our implementation.\n\nThe result of the proposed approach would create a binary mapping of the entity and mentions, as seen in Table 9.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE", "section_headings": ["THE PROPOSED MODEL", "ENTITY RELATIONSHIP AND RANKING SCORE"], "chunk_type": "text", "line_start": 391, "line_end": 408, "token_count_estimate": 581, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f9fc86068d01cc0", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE\nType: table\nTable: Table 9. Identifying the relation between named entity and candidate mention\n\n| <i><b>Mention</b></i> | <i><b>NE Class</b></i> | <i><b>NE Link</b></i>          | <i><b>DBpedia Ontology Class</b></i> | <i><b>Score</b></i> |\n|-----------------------|----------------------------|--------------------------------|------------------------------------------|---------------------|\n| <i>Barack Obama</i>   | <i>Person</i>              | <i>Dbpedia: Obama, USA</i>     | <i>Dbpedia-owl: Person</i>               | <i>3</i>            |\n| <i>Chennai</i>        | <i>Location</i>            | <i>Dbpedia: Chennai, India</i> | <i>Dbpedia-owl: Place</i>                | <i>1</i>            |\n| <i>Cricket</i>        | <i>Sports</i>              | <i>Dbpedia: Cricket</i>        | <i>Dbpedia-owl: Sports</i>               | <i>2</i>            |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE", "section_headings": ["THE PROPOSED MODEL", "ENTITY RELATIONSHIP AND RANKING SCORE"], "chunk_type": "table", "table_caption": "Table 9. Identifying the relation between named entity and candidate mention", "columns": ["Mention", "NE Class", "NE Link", "DBpedia Ontology Class", "Score"], "table_row_start": 1, "table_row_end": 3, "line_start": 409, "line_end": 413, "token_count_estimate": 338, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b1f5fe937541f206", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE\nType: text\n\nGenerally, entities in DBpedia have its name, label, type, etc. and, to fetch the entity name given in the DBpedia for the specified URI, it can be queried through the SPARQL query as follows. For example, searching for 'Sachin Tendulkar':\n\n```\nSelect distinct *\nwhere {\n?URI rdf:label ?name\n?URI dbpprop:iupacname ?name\nfilter(str(?name) = \"Sachin Tendulkar\")\n}\n```\n\nIn order to get the category of a given entity from the DBpedia, we issue the following SPARQL query. For example, for 'Vehicle':\n\n```\nSelect *\nwhere {\n    <http://dbpedia.org/resource/Vehicle>\n    <http://purl.org/dc/terms/subject>\n?categories.\n}\n```", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "THE PROPOSED MODEL > ENTITY RELATIONSHIP AND RANKING SCORE", "section_headings": ["THE PROPOSED MODEL", "ENTITY RELATIONSHIP AND RANKING SCORE"], "chunk_type": "text", "line_start": 414, "line_end": 436, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bef28833cdb04f1b", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: EMPIRICAL TEST AND ANALYSIS\nType: text\n\nWe used Twitter4J API to gather disaster-related tweets from Twitter and utilized TextRazor API to effectively recognize the potential named entities present over the tweets and link them accordingly to its respective DBpedia URI. Additionally, we used the rich natural language processing tools of Stanford Core NLP Library to segregate tweet patterns and performed sentiment analysis for grasping the sense of the tweets. Tweets were collected on the month of August 2017 and, to witness the trust, we followed the leading news agencies on Twitter such as BBC World, CNN, New York Times, NDTV, and Breaking News. Tweets were crawled and stored only if they had at least one named entity that has its link on DBpedia URI. In our datasets, we were able to filter out 20 different topics and classified the tweets successively based on seismic risk by applying the classification rules. The algorithm proposed above is able to detect the factual information containing about 3 out of 5 tweets.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "EMPIRICAL TEST AND ANALYSIS", "section_headings": ["EMPIRICAL TEST AND ANALYSIS"], "chunk_type": "text", "line_start": 438, "line_end": 440, "token_count_estimate": 253, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "00792656f248a8fa", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: EMPIRICAL TEST AND ANALYSIS\nType: table\nTable\n\n| Event Category      | Total Events | Potential Sub-Events by Relevance |         |         |\n|---------------------|-----------------|-----------------------------------|---------|---------|\n|                     |                 | R3                                | R3+R2   | R3-R1   |\n| Earthquake          | 75              | 35(46%)                           | 51(68%) | 59(78%) |\n| Tsunami             | 120             | 46(38%)                           | 79(65%) | 88(73%) |\n| Cyberattack         | 114             | 51(44%)                           | 87(76%) | 95(83%) |\n| Unrest in a Country | 150             | 77(51%)                           | 90(60%) | 97(64%) |\n| Celebrity Death     | 115             | 43(37%)                           | 66(57%) | 81(70%) |\n| Terror Attack       | 120             | 68(56%)                           | 79(65%) | 85(70%) |", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "EMPIRICAL TEST AND ANALYSIS", "section_headings": ["EMPIRICAL TEST AND ANALYSIS"], "chunk_type": "table", "table_caption": null, "columns": ["Event Category", "Total Events", "Potential Sub-Events by Relevance", "", ""], "table_row_start": 1, "table_row_end": 7, "line_start": 441, "line_end": 449, "token_count_estimate": 261, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e203e43821fb35fb", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: EMPIRICAL TEST AND ANALYSIS\nType: text\n\nTable 10. Event relevance and categories\n\nWe tested the DBpedia corpus to identify potential events on seismic risk, which provided the six complex event categories listed in Table 10. The entities were extracted based on the recommendations stated above and identified their relationship types in corresponding DBpedia URI. Besides, we again queried the DBpedia Knowledge Source for the sub-events correlated with the events extracted from the tweets. We substantially ranked the sub-events on the basis of frequency of occurrence and chose the best-matched event category to a tweet. After evaluating the event categories against DBpedia, we determined whether the event is of positive instance or not. Sometimes, the retrieved events would pose a challenging task such as if it is partially relevant but not exactly appropriate to the categorized concepts. During these anomalies, we assigned the following three relevance scores in order to fit the events into their appropriate decks:\n\n- Relevance (R1): Events with fuzzy relationship to the concept/category.\n- Relevance (R2): Events with positive occurrences of sub-events or subject-object mapping.\n- Relevance (R3): Events are positive instances and fit into the category for the posted query.\n- Otherwise, the relevance zero indicates the events with absolutely NIL relationship.\n\nTable 10 displays detected event categories and potential sub-events or co-occurrence of events with relevance scores. As was witnessed, the precision values varied considerably among the categories. The Stanford NLP Library was deemed fit to extract the potentially relevant tweets, and type filtering of events was absolutely effective at identifying the appropriately named entities. We obtained an accuracy of 74.13% and computed the Precision (0.641), Recall (0.716) and F-Measure (0.691) respectively for the given datasets.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "EMPIRICAL TEST AND ANALYSIS", "section_headings": ["EMPIRICAL TEST AND ANALYSIS"], "chunk_type": "text", "line_start": 450, "line_end": 461, "token_count_estimate": 456, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "10e9e6ddcde95f54", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: DISCUSSION\nType: text\n\nThe dynamic change in the amount of information gathered at the various medium of platforms indicates the need for a rapid decision-making process in crisis events. It was observed that the information gotten from these sources rapidly varied. Statistics (Wirtz et al., 2014) showed that the frequency of report variation grows ten times greater than the previous day. Besides, to better account for the report variation of the information accumulation, the report dimensions were categorized into three crucial breakpoints, i.e., D+1, D+5, D+10. This elapsed gap fetches the detailed overview of the crisis or disaster based events and showed us the real potential of the event happenings (see Figure 3).", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "DISCUSSION", "section_headings": ["DISCUSSION"], "chunk_type": "text", "line_start": 463, "line_end": 465, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f331649876243e54", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: DISCUSSION\nType: figure\nFigure\n\nImage /page/19/Figure/1 description: A line chart titled 'DAY WISE TWEETS' that plots the 'TWEET COUNTS' for four different events over a period of 10 days. The y-axis, 'TWEET COUNTS', ranges from 100 to 350,100. The x-axis shows the days, from DAY 1 to DAY 10. There are four lines representing four events: 'Event 1' (blue line with diamonds), 'Event 4' (yellow line with 'x' markers), 'Event 2' (orange line with squares), and 'Event 3' (gray line with triangles). Generally, Event 3 has the highest tweet count, followed by Event 2, Event 4, and finally Event 1 with the lowest count. All events show a significant peak in tweet counts on Day 4, with Event 3 reaching approximately 300,000, Event 2 reaching about 240,000, Event 4 at 180,000, and Event 1 at 90,000. Events 2 and 3 show another high peak on Day 10, reaching approximately 200,000 and 280,000 respectively, before all four events show a sharp drop in tweet counts at the very end of the period.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "DISCUSSION", "section_headings": ["DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 466, "line_end": 466, "token_count_estimate": 293, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee6c41b3257016af", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: DISCUSSION\nType: figure\nFigure: Figure 3. Daily frequency of information on social media platforms\n\nFigure 3. Daily frequency of information on social media platforms", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "DISCUSSION", "section_headings": ["DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 3. Daily frequency of information on social media platforms", "line_start": 468, "line_end": 468, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dbbe64d90633364d", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: DISCUSSION\nType: text\n\nThrough the data obtained from various sources and on different days of report gathering, we can formulate deviance of patterns and get through the details of anomalies that exist in the report. By applying the pruning algorithm, we can sort the crisis events for the decision-making process and get to the core base of the events. In this research, the real task is to find the actual reason for the crisis event and get the substantiated evidence for its occurring. To augment this process, we classified the events into many chronological orders influenced by the usage of ontological background with semantic technologies. By mapping different day event reports, we scrutinize the process for discrimination (i.e., fetch the positive or negative or neutral feedback from the potential users on the social media) and allow filtering the facts based on cross-checking in tabulating the actual events of the situation.\n\nOur approach achieved the accuracy rate of 74.13% where other existing models succeeded getting 68.42% using Support Vector Machine (SVM), 67.93% using Maximum Entropy Model (MEM), and 64.71% using Conditional Random Fields (CRF) based on the analysis successfully performed with the help of Table 9. Since our proposed model extensively uses the dedicated ontology of Crisis and Disaster, instead of employing the Bag-of-Words (BoW) method, we employed Bag-of-Concepts (BoC) and Relevance of Concepts, as well as calculating the semantic similarity score between ambiguous terms. Deep proliferation of the ontological network paved the way to yield the subcategories of a topic and skimmed the words that are completely unambiguous. The relevance R of the concepts were derived with other three relevances R1, R2, and R3 as shown in Table 10, whereas the other existing methods mostly used only a single relevance score and restricted the research scope to Bag-of-Words model.\n\nThe major contribution of this research is in collecting crisis-related temporal data from multiple bursty short-message sources and decision making through semantic mapping of entities over concepts disambiguating potential named entities. The problems persisting over entity ambiguity and its associated entity types were addressed as well. We categorized the disaster-based entity domains using ontology and enhanced the searching capability of the system by incrementing the explicit connection mutually existing between entity and an ontology class.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "DISCUSSION", "section_headings": ["DISCUSSION"], "chunk_type": "text", "line_start": 469, "line_end": 475, "token_count_estimate": 583, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35db8082523251b9", "text": "Document: Crisis and Disaster Situations on Social Media Str\nSection: CONCLUSIONS\nType: text\n\nIn this paper, we proposed a novel solution to harvest and compare the content of Twitter streams and conventional news sources such as CNN, New York Times, BBC World, NDTV, and Breaking News in the cases of havoc situations. We developed a semantic filter that can map the concepts correlated between Twitter streams and traditional news sources, and can disambiguate the candidate entities based on the ontological framework particularly loaded with disaster/crisis events.\n\nThe major advantage of our work is that, instead of pruning a single news source, it paves the way for clustering the information from diverse sources and harnessing the potential information to derive the hidden facts in it. We also developed a disaster ontology for this research and used it to segregate the entities which pose ambiguity over other candidate sets.\n\nEmpirical results show that the approach based on our model outperforms other models available in the literature to solve this research gap by various other approaches. In the future, we shall strive to extend the model in order to help summarize and visualize the potential information ranked high by the model.", "metadata": {"source_file": "data/('Crisis_and_Disaster_Situations_on_Social_Media_Str', '.pdf')_extraction.md", "document_title": "Crisis and Disaster Situations on Social Media Str", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "text", "line_start": 477, "line_end": 483, "token_count_estimate": 269, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "308294c27bec2cd6", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: ABSTRACT\nType: text\n\nWe employ multi-modal data (i.e., unstructured text, gazetteers, and imagery) for location-centric demand/request matching in the context of disaster relief. After classifying the *Need* expressed in a tweet (the WHAT), we leverage OpenStreetMap to geolocate that *Need* on a computationally accessible map of the local terrain (the WHERE) populated with location features such as hospitals and housing. Further, our novel use of flood mapping based on satellite images of the affected area supports the elimination of candidate resources that are not accessible by road transportation. The resulting map-based visualization combines disaster-related tweets, imagery and pre-existing knowledge-base resources (gazetteers) to reduce decision-making latency and enhance resiliency by assisting individual decision-makers and first responders for relief effort coordination.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 5, "token_count_estimate": 232, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd3f4d566ffdb855", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: CCS CONCEPTS\nType: text\n\n• **Information systems** → *Decision support systems*;", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "CCS CONCEPTS", "section_headings": ["CCS CONCEPTS"], "chunk_type": "text", "line_start": 7, "line_end": 9, "token_count_estimate": 39, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c3dbe87821e829a", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: KEYWORDS\nType: text\n\ndisaster relief, location-centric processing, flood mapping, need matching", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "KEYWORDS", "section_headings": ["KEYWORDS"], "chunk_type": "text", "line_start": 11, "line_end": 13, "token_count_estimate": 43, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd66825e95763418", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: ACM Reference Format:\nType: text\n\nShruti Kar<sup>1\\*</sup>, Hussein S. Al-Olimat<sup>1\\*</sup>, Krishnaprasad Thirunarayan<sup>1</sup>, Valerie L. Shalin<sup>1</sup>, Amit Sheth<sup>1</sup>, Srinivasan Parthasarathy<sup>2</sup>. 2018. D-record: Disaster Response and Relief Coordination Pipeline. In *Proceedings of SIGSPATIAL Workshop (ARIC 2018)*, Jennifer B. Sartor, Theo D'Hondt, and Wolfgang De Meuter (Eds.). ACM, New York, NY, USA, Article 4, 4 pages. https://doi.org/10.475/123\\_4", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "ACM Reference Format:", "section_headings": ["ACM Reference Format:"], "chunk_type": "text", "line_start": 15, "line_end": 17, "token_count_estimate": 212, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "780fbd045a0225e6", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 1 INTRODUCTION\nType: text\n\nRecent catastrophic events, e.g., resulting from floods and hurricanes, combined with increased urbanization and interdependent infrastructure reveal an increasing vulnerability to natural and human-made hazards. A resilience framework must accommodate both precise information for action and for various levels of analysis, posing substantial challenges [4]. These include characterizing hazards with an integrated ontology across voluminous, multi-modal\n\nPermission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).\n\nARIC 2018, Nov. 2018, Seattle, Washington\n© 2018 Copyright held by the owner/author(s).\nACM ISBN 123-4567-24-567/08/06.\nhttps://doi.org/10.475/123\\_4\n\ndata sources, facilitating rescue/response actions, and assessing the effects of pre- and post-disaster actions on individual needs.\n\nThe dire real-time need for integrated, coherent multi-modal data from different sources has motivated substantial research [8]. Social media is a significant source of such data. Twitter users, for example, post short texts as updates. These messages (a.k.a. tweets) reveal *Needs* and requires matching with possible available help. These messages can provide critical information to first responders and individual decision-makers to contribute effectively to relief efforts [18]. Tweets authored about crisis events may contain crucial but unstructured location-centric information (such as incident reports) even when explicit geo-tagging is absent. The ability to extract specific location information from this unstructured terminology and the other attached information is critical for timely assistance by the first responders.\n\nCrowdsourced data (such as OpenStreetMap) that covers the geographical area of a disaster event can assist in the extraction of location content from tweets. To achieve this, we developed D-record: **D**isaster **re**sponse and **re**lief **coord**ination, a pipeline that utilizes multi-modal data (i.e., unstructured text, gazetteers, and satellite imagery). D-record allows first responders to visualize and match location-centric *Needs* and options for available help while taking into account transportation constraints and available options in the local area of a disaster.<sup>1</sup>", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "1 INTRODUCTION", "section_headings": ["1 INTRODUCTION"], "chunk_type": "text", "line_start": 19, "line_end": 34, "token_count_estimate": 623, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4a9a5ccc015bac1", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 2 RELATED WORK\nType: text\n\nThe following related work informs the D-record pipeline and clarifies its contributions\n\nDisaster-Centric Ontologies. Lightweight lexicons or vocabularies like the Management of Crisis Vocabulary (MOAC)<sup>2</sup>, CrisisNLP<sup>3</sup>, and CrisisLex [21] provide the different concepts for disaster management but lack the granularity required for describing response and miss crucial spatial and thematic details. Other efforts like UNOCHA's Humanitarian eXchange Language (HXL)<sup>4</sup> improve information sharing during disasters but lacks specific crisis-related concepts. The Social Media and Emergency Management (SMEM) ontology [13] combines emergency domain knowledge with social media information for Incident Classification. However, SMEM focuses on major coarse-grained categories of disasters such as meteorology or biology. As stated above, state-of-the-art techniques in event representation for disaster response is critically limited with respect to the usability and dissemination of recommended action and response (intentional intervention) required for relief\n\n<sup>\\*</sup>Authors contributed equally.\n\n<sup>&</sup>lt;sup>1</sup>The source code of the pipeline is available at https://github.com/shrutikar/d-record.\n\n<sup>&</sup>lt;sup>2</sup>http://observedchange.com/moac/ns/\n\n<sup>3</sup>http://crisisnlp.qcri.org/\n\n<sup>4</sup>http://hxlstandard.org/\n\nefforts coordination. Given the above limitations, D-record uses a location-centric ontology to assist in matching relevant needs with available help to support a disaster response use-case as discussed in Section 3.\n\n**Need Classification.** For extracting the type of *Needs* (e.g, food, shelter, or medical) from text data, state-of-the-art Need classification techniques such as [16] focus on the identification of information type. However, these techniques do not provide a comprehensive solution for matching classification output. [5] merely summarizes disaster information at a certain geographical level without providing any actionable decisions for response and relief. Instead, they provide summaries of topics to decision makers. Our previous classification technique [3] uses an ontology and location detection to enhance situational awareness through recognizing the interdependencies between disaster events, which helps in the early identification of Needs. Inspired by these techniques, D-record employs a coarser Need classifier to make its classifications compatible with the affordances of locations (e.g., a shelter can support a variety of Needs such as food and water whereas a hospital can provide medical and rescue support *Needs*). In other words, we resort to coarser needs (as opposed to fine-grained needs) for effective matching because available help centers satisfy multiple needs.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "2 RELATED WORK", "section_headings": ["2 RELATED WORK"], "chunk_type": "text", "line_start": 36, "line_end": 62, "token_count_estimate": 738, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d1f3e6ee37db372", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 2 RELATED WORK\nType: text\n\n. Our previous classification technique [ 3 ] uses an ontology and location detection to enhance situational awareness through recognizing the interdependencies between disaster events , which helps in the early identification of Needs . Inspired by these techniques , D - record employs a coarser Need classifier to make its classifications compatible with the affordances of locations ( e . g . , a shelter can support a variety of Needs such as food and water whereas a hospital can provide medical and rescue support * Needs * ) . In other words , we resort to coarser needs ( as opposed to fine - grained needs ) for effective matching because available help centers satisfy multiple needs .\n\n**Matching.** Our previous technique on Seekers and Suppliers matching [18] uses hand-crafted rules to match the *Need* requested with a possible provider, e.g., matching someone offering shelter with someone requesting shelter. Other approaches to *Need* matching such as [6] uses structured input from users and utilizes moderators in the loop to match those *Needs* with organizations presently available to help. The *Need*-capacity matching technique by [17] quantifies the degree of impact of a disaster and prioritizes the matching accordingly. Finally, [14] provides both online and offline solutions for requesting, providing and coordinating resources.\n\nLimitations of the above work include: (a) the inadequate assumption that all routes are available for matching by assuming the full accessibility of roads during a disaster [19], and (b) the matching problem being solved for a different level of granularity than for the required individual level of *Needs*.\n\n**Flood Mapping.** Some logistical matching methods assume the full accessibility of routes. Hu et al. [7], for example, provides an algorithm for optimal path selection in a logistics supply-chain management system. In contrast, [15] provides a portal for UN staff to upload spatial data including satellite images and crowdsourced data that can identify blocked routes. We use our customized state-of-the-art human-guided flood mapping technique [11] to prune the flooded routes to reflect the situation on the ground relevant for better-informed transportation.\n\n**Data Visualization.** The majority of the visualization techniques in the literature place geo-tagged tweets on a map [12] or display users' structured input such as in [20]. However, these techniques fail for social media-based systems where geo-tagged tweets are relatively infrequent, and the information is buried inside unstructured texts. [10] showed the effectiveness of layered visualization systems, where each layer can contain a different source of information. In D-Record, each layer can include a different *Need* class or OpenStreetMap location features. To visualize the kinds of", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "2 RELATED WORK", "section_headings": ["2 RELATED WORK"], "chunk_type": "text", "line_start": 36, "line_end": 62, "token_count_estimate": 690, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9095cdbee90ba362", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 2 RELATED WORK\nType: figure\nFigure\n\nImage /page/2/Figure/8 description: A diagram illustrating a system for disaster relief coordination and response, which processes data from multiple sources to create a layered map for matching seekers and providers. The process is shown in three main parts. The first part details the data sources on the left: 'Classify and Geoparse Tweets' using Twitter and LNEx; 'Satellite Image for Flood Mapping' using satellite imagery to identify flooded locations; and 'OpenStreetMap and Crowd-sourced Location Features'. The outputs from these sources are processed to 'Prune Flooded Locations' and are then combined into a stack of map layers shown in the center. The layers, from top to bottom, are: Flood Areas, Twitter Medical/Rescue Needs, CS Medical/Rescue Available, OSM Medical/Rescue Available, Twitter Shelter/Food/Supplies Needs, CS Shelter/Food/Supplies Available, OSM Shelter/Food/Supplies Available, and a base layer from MapBox. Each layer is associated with a specific icon. The final part, at the bottom, is labeled 'Disaster Relief Coordination and Response' and contains a box for 'Map Directions & Seekers/Providers Matching', illustrated with icons of a person, a map, and another person, signifying the matching of those in need with those providing aid.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "2 RELATED WORK", "section_headings": ["2 RELATED WORK"], "chunk_type": "figure", "figure_caption": null, "line_start": 63, "line_end": 63, "token_count_estimate": 360, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cdeb417dbf6289cd", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 2 RELATED WORK\nType: figure\nFigure: Figure 1: D-record map layers and pipeline functions\n\nFigure 1: D-record map layers and pipeline functions", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "2 RELATED WORK", "section_headings": ["2 RELATED WORK"], "chunk_type": "figure", "figure_caption": "Figure 1: D-record map layers and pipeline functions", "line_start": 65, "line_end": 65, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a093b0aa45775302", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 2 RELATED WORK\nType: text\n\n*Needs* effectively they are bound to their spatial footprints extracted using our tool LNEx [2].", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "2 RELATED WORK", "section_headings": ["2 RELATED WORK"], "chunk_type": "text", "line_start": 66, "line_end": 68, "token_count_estimate": 52, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc3d38d8c97d9436", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 3 METHOD\nType: text\n\nThe following section describes the different data sources, the functionality of the pipeline, and the map layers we employ that support the visualization of information. Figure 1 contains a summary of map layers and their purpose.\n\n**Data Sources.** D-Record utilizes three data sources: Twitter, OpenStreetMap, and Satellite Images. Twitter data consists of two groups. The first group contains a set of two targeted streams from two disaster events, namely, the Chennai Flood in 2015 and Houston Flood in 2016 [2]. The second group of crisis tweets is from CrisisNLP [9] and CrisisLexT26[16] datasets labeled for their information type (e.g., affected individuals, donations, and volunteering). The first group drives the pipeline. The second is used to train a binary classifier to recognize the expression of *Need* in tweets and expand the concepts in the underlying ontology (see Section 3). Crowd-sourced data from volunteers (contributed as Excel sheets) contain information about shelters and help requests/offers (e.g., Chennai Flood - List of Corporation and Relief Centres<sup>5</sup>).\n\nThe OpenStreetMap gazetteer provides location names and metadata (such as geo-coordinates) that produce the affordances of each location with respect to the needed help (e.g., a hospital can provide rescue and medical support) allowing for matching a help offer with a *Need* request from tweets. The Output of our flood mapping tool [11], which uses satellite images, determines the flooded geo-points and overlays this information on top of the map.\n\n**Location-Centric Ontological Modeling.** Determining spatially and temporally specific instances for coordinating resources *Needs* benefit from a domain-specific location-centric event ontology for social media data. Event location is key to both aggregation and analysis of related event instances. Therefore, we created an event ontology that uses information from past disaster and risk reports<sup>6</sup> to enrich existing disaster event vocabularies.\n\nThe D-Record ontology<sup>7</sup> represents the temporal distinction between the response and relief phases of a disaster. The class \"Concepts\" includes two kinds of concepts: *Needs* and Availability. The class OSMFeatures covers the affordances of various location types (i.e., OSM map features<sup>8</sup>) in relation to the *Need* classes.\n\n<sup>&</sup>lt;sup>5</sup>https://rebrand.ly/reliefspreadsheet\n\n<sup>6</sup> such as by https://www.acaps.org/\n\n<sup>&</sup>lt;sup>7</sup>The ontology can be found at https://github.com/shrutikar/d-record.\n\n<sup>&</sup>lt;sup>8</sup>http://wiki.openstreetmap.org/wiki/Map\\_Features", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "3 METHOD", "section_headings": ["3 METHOD"], "chunk_type": "text", "line_start": 70, "line_end": 88, "token_count_estimate": 760, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b990062eacad5db4", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 3 METHOD\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A flowchart detailing a data processing pipeline for disaster response and scenario dissemination. The process begins on the left with a 'Data' section, which includes three sources: 'Stream and Crowd Sourced Data' (with icons for Twitter and Excel sheets), 'Pre-Disaster Data' (with the OpenStreetMap logo), and 'Satellite Imagery' (with the NOAA logo). The data flows through a series of processing steps. Stream and crowd-sourced data undergo 'Semantic Filtering' and then 'Information Retrieval/Extraction'. This extraction process, also fed by pre-disaster data, branches into 'Geoparsing' (using LNEx), 'Seekers / Providers need classification', and 'OSM location classification with map feature types'. The 'Seekers / Providers' data is further processed by 'Event Ontology & Situational Data' using 'Lexicon NLP'. Satellite imagery is used for 'Flood Mapping'. The processed data streams converge: 'Geoparsing' and 'Event Ontology' data become 'Typed geolocated data', while 'OSM location classification' and 'Flood Mapping' data are used to 'Prune/Filter Locations'. Both of these outputs are then stored via 'Caching in ElasticSearch'. From the cache, 'map layers' are created using 'mapbox'. These layers are used for 'Seekers / Providers Matching'. This matching process leads to 'Provide turn-by-turn directions with contact information', which is also informed by 'Prune non-available routes' from the flood mapping. There is a feedback loop from providing directions back to the matching process. The final step is 'Scenario Dissemination', illustrated by a map with colored risk zones and a route between points X and A.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "3 METHOD", "section_headings": ["3 METHOD"], "chunk_type": "figure", "figure_caption": null, "line_start": 89, "line_end": 89, "token_count_estimate": 482, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50d0743c81da3044", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 3 METHOD\nType: figure\nFigure: Figure 2: D-record system architecture\n\nFigure 2: D-record system architecture", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "3 METHOD", "section_headings": ["3 METHOD"], "chunk_type": "figure", "figure_caption": "Figure 2: D-record system architecture", "line_start": 91, "line_end": 91, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a475c10af187e582", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 3 METHOD\nType: table\nTable\n\n| <b><i>Need</i> Class</b>              | <b>CrisisNLP and CrisisLexT26 Classes</b>                                                     |\n|---------------------------------------|-----------------------------------------------------------------------------------------------|\n| Shelter/Food/ Supplies <i>Need</i> | donation_needs_or_offers_or_volunteering_services, displaced_people_and_evacuation         |\n| Medical/Rescue Help <i>Need</i>    | missing_trapped_or_found_people, deaths_reports, in- jured_or_dead_people, affected_people |", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "3 METHOD", "section_headings": ["3 METHOD"], "chunk_type": "table", "table_caption": null, "columns": ["Need Class", "CrisisNLP and CrisisLexT26 Classes"], "table_row_start": 1, "table_row_end": 2, "line_start": 93, "line_end": 96, "token_count_estimate": 186, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c0ba9debb80ec0a8", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 3 METHOD\nType: text\n\nTable 1: Our mapping of CrisisNLP and CrisisLexT26 classes\n\nThe set of keywords for a \"Needs\" concept was expanded using topic modeling learned from the labeled data for the two classes: Shelter/Food/Supplies and Medical/Rescue Help. The relevance probability of each word to the Need class/topic functioned as a feature while training our Need classifier as shown in Section 3. Finally, the sub-classes of the Needs concept are mapped to the OSMFeatures sub-classes representing the options for available help at each location type (i.e., the affordances). For example, hospital maps to the Medical/Rescue Help class and pharmacy maps to the Shelter/Food/Supplies Need class.\n\n**Text Classification.** To leverage the existing labeled data from CrisisNLP and CrisisLexT26, we combined the overlapping classes to form the two *Need* classes (see Table 1). We used tweets from these datasets to train an SVM-based classifier which allows Drecord to categorize any given crisis related text into one of the two classes: Shelter/Food/Supplies *Need* or Medical/Rescue Help *Need*. The *Need* class Shelter/Food/Supplies includes donations, volunteer services, and *Needs* for food, water, shelter, or clothes. The *Need* class Medical/Rescue Help comprises affected people, death reports, injured or dead people, missing or trapped people, and other medical and rescue-related information. The classified streaming text appears on the map for matching.\n\nThe classifier had to capture tweet semantics. A simple Bag-of-Words model lacks context. Instead, feature engineering [22] vectorizes the text sentences to capture their semantics adequately. Before featurizing, the text was preprocessed by stemming, case folding and removing \"noisy\" lexical elements (such as URLs, non-ASCII characters, mentions, punctuations, dataset-specific stopwords, and hashtags). Finally, we designed an SVM classifier with lexicon-based features, TF-IDF vectors, and gensim's word2vec embeddings<sup>9</sup>.\n\nUsing the relevance probabilities from topic modeling described earlier, we form lexicon-based two-tuple feature vectors (i.e., Shelter/Food/Supplies and Medical/Rescue Help). Each element in the vector represents the word frequency multiplied by its relevance score. This vector was concatenated with the other features from TF-IDF and gensim's word2vec embeddings, which captures the semantics of a word by looking at the context where it was mentioned. To address the class imbalance, SMOTE oversampled the minority class synthetically.\n\n**System Architecture.** D-record uses three major forms of data (see Figure 2): Streaming and Crowdsourced, pre-disaster, and satellite imagery data. The knowledge extracted from text streams (filtered using hashtags of the disaster event) and crowdsourced excel sheets provides situational awareness and the various kinds of help available at each location. The pre-disaster information available from OpenStreetMap (sliced using a bounding box of the disaster event) represented the available help confirmed or pruned using satellite images. The output from our flood mapping method [11] helped prune out routes that were unavailable during the matching process for the location seeking help.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "3 METHOD", "section_headings": ["3 METHOD"], "chunk_type": "text", "line_start": 97, "line_end": 111, "token_count_estimate": 871, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "baa55a7a7c819be6", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 3 METHOD\nType: text\n\n* * D - record uses three major forms of data ( see Figure 2 ) : Streaming and Crowdsourced , pre - disaster , and satellite imagery data . The knowledge extracted from text streams ( filtered using hashtags of the disaster event ) and crowdsourced excel sheets provides situational awareness and the various kinds of help available at each location . The pre - disaster information available from OpenStreetMap ( sliced using a bounding box of the disaster event ) represented the available help confirmed or pruned using satellite images . The output from our flood mapping method [ 11 ] helped prune out routes that were unavailable during the matching process for the location seeking help .\n\nOur tool LNEx extracts and attaches each location to their latlong information which locates tweet information on the map, following *Need* classification. Since OpenStreetMap provides the metadata for locations (including names, types, and geo-coordinates), the flood map information is used to prune out the locations that are flooded and not accessible for help. D-record caches all of this information in Elasticsearch. The *Need* information and location of a tweet indicate where the help is needed while OSM location features identify available help. We create the map layers on top of MapBox<sup>10</sup>. The system then matches a *Need* in the ontology with the locations that can fulfill that *Need*. D-record ultimately prunes all routes containing a flooded section according to the flood map.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "3 METHOD", "section_headings": ["3 METHOD"], "chunk_type": "text", "line_start": 97, "line_end": 111, "token_count_estimate": 384, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6981a4d3e9feb95", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 4 RESULTS\nType: text\n\nSVM with the SMOTE (to partially overcome class imbalance) and the Gradient Boosting Algorithm performed the best and achieved a 0.8 F-Score in the *Need* classification task. We tested it using the leave-one-out technique (i.e., testing on one dataset and training using the rest). D-record extracted locations from tweets to support plotting these tweets on the map. The experiments used our Chennai and Houston datasets, containing 169,838 and 415,057 tweets, respectively. From these tweets, LNEx extracted 85,564 locations from the Chennai dataset and 241,684 locations from Houston dataset. As for the number of OpenStreetMap location features, 1,103 and 2,826 locations were retrieved for the affected areas of Chennai and Houston, respectively. Other crowdsource data received from the excel sheets were relatively few, around 41 locations.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "4 RESULTS", "section_headings": ["4 RESULTS"], "chunk_type": "text", "line_start": 113, "line_end": 115, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f19ccd2b0e93f618", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 5 DEMONSTRATION\nType: figure\nFigure: Figure 3 shows D-record where users can choose the time range to filter the data using ⓐ, the dataset using ⓑ, and select the map layers using ⓒ. To match a Need , the user clicks on the orange icons as in ① which shows a textbox with the tweet text and the extracted location. Users can click on a \"Match Need\" button to get the closest and non-flooded nearby location which can provide help for the given Need , as in ②. Users obtain the matched location information by hovering the mouse over the green icons as in ③. The tool will provide the full information of the matched location and the contact number if available with the turn-by-turn directions obtained using MapBox directions API11 as shown in ④.\n\nFigure 3 shows D-record where users can choose the time range to filter the data using ⓐ, the dataset using ⓑ, and select the map layers using ⓒ. To match a *Need*, the user clicks on the orange icons as in ① which shows a textbox with the tweet text and the extracted location. Users can click on a \"Match Need\" button to get the closest and non-flooded nearby location which can provide help for the given *Need*, as in ②. Users obtain the matched location information by hovering the mouse over the green icons as in ③. The tool will provide the full information of the matched location and the contact number if available with the turn-by-turn directions obtained using MapBox directions API<sup>11</sup> as shown in ④.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "5 DEMONSTRATION", "section_headings": ["5 DEMONSTRATION"], "chunk_type": "figure", "figure_caption": "Figure 3 shows D-record where users can choose the time range to filter the data using ⓐ, the dataset using ⓑ, and select the map layers using ⓒ. To match a Need , the user clicks on the orange icons as in ① which shows a textbox with the tweet text and the extracted location. Users can click on a \"Match Need\" button to get the closest and non-flooded nearby location which can provide help for the given Need , as in ②. Users obtain the matched location information by hovering the mouse over the green icons as in ③. The tool will provide the full information of the matched location and the contact number if available with the turn-by-turn directions obtained using MapBox directions API11 as shown in ④.", "line_start": 118, "line_end": 118, "token_count_estimate": 386, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d91debdcd1483bda", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 5 DEMONSTRATION\nType: text\n\n<sup>9</sup>https://radimrehurek.com/gensim/\n\n<sup>10</sup> https://www.mapbox.com/\n\n<sup>11</sup>https://www.mapbox.com/help/define-directions-api/", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "5 DEMONSTRATION", "section_headings": ["5 DEMONSTRATION"], "chunk_type": "text", "line_start": 119, "line_end": 125, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3816c238cf99b719", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 5 DEMONSTRATION\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A screenshot of a disaster management user interface called \"HazardsSEES\". The interface is split into a left sidebar and a main map view. The left sidebar has a logo, a time range selector set to \"12-01-2015 1:30 PM - 01-30-2016 2:00 PM\", a location selector with \"Chennai\" selected, and a legend for map icons such as \"Flooded Areas\", \"Medical/Rescue Help Available\", and \"Shelter/Food/Supplies Needed\". The main map area displays a map of Chennai with purple patches indicating flooded areas. On the map, there is a pop-up window showing a message: \"@Uber\\_Chennai - Need help in rescuing a group of girls from Perumalagaram Salai Chennai! #ChennaiMicro #ChennaiFloods\" with a \"Match Need\" button. A blue line indicates a route on the map. Another pop-up identifies the \"ACS Medical College And Hospital\". In the bottom right, a box shows \"Matched Location Info\" for the hospital, including its address and turn-by-turn directions.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "5 DEMONSTRATION", "section_headings": ["5 DEMONSTRATION"], "chunk_type": "figure", "figure_caption": null, "line_start": 126, "line_end": 126, "token_count_estimate": 298, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7867b4654e660216", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 5 DEMONSTRATION\nType: figure\nFigure: Figure 3: Example screenshot of the D-record tool.\n\nFigure 3: Example screenshot of the D-record tool.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "5 DEMONSTRATION", "section_headings": ["5 DEMONSTRATION"], "chunk_type": "figure", "figure_caption": "Figure 3: Example screenshot of the D-record tool.", "line_start": 128, "line_end": 128, "token_count_estimate": 56, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7fc3ab4ea526f78a", "text": "Document: D record Disaster Response and Relief Coordination Pipeline\nSection: 6 CONCLUSIONS AND FUTURE WORK\nType: text\n\nA domain-specific location-centric event ontology is crucial for situation awareness and disaster response. We demonstrated our D-record pipeline for *Need*-offer matching and discussed the functionality of the system and the multi-modal data used to fire its engine. The pipeline can be used to match coarse-grained *Needs* with possible suppliers meaningfully using location information available on the map. In the future, a finer-grained classifier can be designed to do more flexible or specific matching. Additionally, we plan to develop a custom entity extractor building on our prior work [1], to extract emerging entities during the onset of a disaster for more advanced spatiotemporal reasoning. We also plan to use weather data and background knowledge to mark flood-prone areas to help in preparedness in addition to response. To empower first and local responders, we intend to bring this pipeline for broader use by the disaster response community and port it to smartphones.", "metadata": {"source_file": "data/('D_record__Disaster_Response_and_Relief_Coordination_Pipeline', '.pdf')_extraction.md", "document_title": "D record Disaster Response and Relief Coordination Pipeline", "section_path": "6 CONCLUSIONS AND FUTURE WORK", "section_headings": ["6 CONCLUSIONS AND FUTURE WORK"], "chunk_type": "text", "line_start": 131, "line_end": 133, "token_count_estimate": 252, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6b966a2cfe5b281c", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: ABSTRACT\nType: text\n\nGlacial Lake Outburst Flood (GLOF) has become a crucial aspect as the increase in the meltdown of glaciers results in the breach of unstable debris dams. Hence, it is essential to understand the nature of the glacial lakes for proper planning and development of the region in the long term. In this paper, a deep learning network is developed for GLOF hazard and risk assessment. The Shepard Convolutional Neural Network Fused Deep Maxout Network (ShCNNFDMN) is developed by fusing the Shepard Convolutional Neural Networks (ShCNN) and the Deep Maxout Network (DMN) based on regression analysis. Here, various data and feature attributes, like geometric properties, location properties, lake-based properties, and global properties are determined from the glacial lake data. Afterthat, hazard assessment is carried out based on these parameters by the ShCNNFDMN. Then, risk assessment is performed based on the hazard levels and the feature attributes. The ShCNNFDMN is analyzed based on metrics, such as Hazard modelling error, Risk prediction error, Mean Average Error (MAE), and R-Squared are found to produce values of 0.462, 0.423, 0.358, and 0.288, respectively. The proposed method is useful in applications, like infrastructure planning, taking preventive and mitigative actions in downstream areas of glacier lakes.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 6, "token_count_estimate": 344, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb899df26d8c6389", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: ABSTRACT > ARTICLE HISTORY\nType: text\n\nReceived 30 October 2023 Accepted 10 June 2024", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "ABSTRACT > ARTICLE HISTORY", "section_headings": ["ABSTRACT", "ARTICLE HISTORY"], "chunk_type": "text", "line_start": 8, "line_end": 10, "token_count_estimate": 42, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f82cc2bd0dbf998a", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: KEYWORDS\nType: text\n\nGLOF; risk assessment; hazard assessment; Shepard Convolutional Neural Networks; Deep Maxout Network", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "KEYWORDS", "section_headings": ["KEYWORDS"], "chunk_type": "text", "line_start": 12, "line_end": 14, "token_count_estimate": 50, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cde4b8737e3cafc2", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 1. Introduction\nType: text\n\nGlobal warming and increased human activities have caused major changes in the drylands, and terrestrial water resources all over the world (Zhang et al. 2023). The high voluminous melting glacier water runoff, and accumulates in front of receding glaciers or depressions over sagging and thinning glacier surfaces creating moraine-dammed lakes (Sattar et al. 2021). Generally, glacier shrinking results in the expansion and creation of a huge number of glacial lakes on/in front of the glaciers (Wang et al. 2018). A huge volume of water is released because of certain failures in the moraine or sudden emptying of these lakes due to the overflow of dams, where the destructive events caused by released water sediment are termed Glacial Lake Outburst Floods (GLOF) (Liu et al. 2020; Worni, Huggel, and Stoffel 2013). The GLOF is characterised by its potential for erosion and high transport (Breien et al. 2008) and it can be effectively converted into debris\n\nflows of about 1.5 tm<sup>-3</sup> densities. It is also due to dynamic slope movements of the glaciers over the lakes, namely slopes (Wang and Goh 2021), landslides, rock falls, or icefalls (Awal et al. 2010; Emmer and Vilímek 2013). The incidence of GLOF has been high, especially in the Himalayan area, and has posed a tremendous risk to the local inhabitants (Li et al. 2018). Further, the quantity of water flow in the river affects various activities, such as the allocation of water for hydropower projects, irrigation systems, ecosystems, etc. (Ali et al. 2020).\n\nAn in-targeted framework based on the adaption of climate change as well as glacier hazard management is performed from the lessons of the devastating GLOF events (Wang et al. 2018). Over the past decades, the GLOF hazard has increased the risk level due to the significant growth of the existing glacial lakes (Sattar, Goswami, and Kulkarni 2019). The impact of GLOF is transboundary, where the floods destroy most of the downstream infrastructure and kill hundreds of people\n\nthus causing huge damage (Khanal et al. 2015; Liu et al. 2020). Based on a regional scale, the GLOF hazard evaluation is often divided into three parts. The first stage involves gathering the fundamental data about glacial lakes, including their size, position, and surrounding terrain. In the second stage, glacial lakes that are potentially dangerous are identified using the variables that have been chosen. Finally, by using model simulation, field investigation, and high-resolution remote sensing images the study of dangerous glacial lakes is performed (Huggel et al. 2004; McKillop and Clague 2007; Osti, Egashira, and Adikari 2013; Wang et al. 2018). The implementation of different GLOF-related hazard remediations is carried out to develop a significant hazard management system (Sattar, Goswami, and Kulkarni 2019). The GLOF hazard assessment models are developed by considering the glacial lake outburst debris flow scenario (Worni et al. 2012) after earthquakes and small glacial lakes in the areas, which are familiar for landslides and collapses along the channels (Liu et al. 2020). Numerous approaches have been proposed in the past for performing GLOF risk assessment and these techniques vary from each other depending on the subjectivity, input data, choice of examined characteristics, quantity, structure, and so on (Washakh et al. 2019).", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 16, "line_end": 26, "token_count_estimate": 865, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e2de6f88a574a96b", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 1. Introduction\nType: text\n\nremediations is carried out to develop a significant hazard management system ( Sattar , Goswami , and Kulkarni 2019 ) . The GLOF hazard assessment models are developed by considering the glacial lake outburst debris flow scenario ( Worni et al . 2012 ) after earthquakes and small glacial lakes in the areas , which are familiar for landslides and collapses along the channels ( Liu et al . 2020 ) . Numerous approaches have been proposed in the past for performing GLOF risk assessment and these techniques vary from each other depending on the subjectivity , input data , choice of examined characteristics , quantity , structure , and so on ( Washakh et al . 2019 ) .\n\nIn general, because of the potentially hazardous conditions in high mountains, the assessment of glacier hazards cannot be based solely on historical records and past events. Hence, it is necessary to implement various modelling approaches for the determination of present and future GLOF risks and hazards (Allen et al. 2016; Frey et al. 2018; Schaub et al. 2013; Schneider et al. 2014). Generally speaking, identifying exposure, vulnerability, and convergence of hazards are the fundamental elements utilised in risk assessment and management. The subset of machine learning techniques based on representation learning and artificial neural networks is known as deep learning (Wang et al. 2023). Images are processed using a convolutional neural network (Wu et al. 2023). for object detection and classification. The relative GLOF risk towards the downstream region is caused by the cumulative and peak discharge of the hazard components. This leads to overtopping, potential GLOF triggers, damming moraine conditions, and drainable volume functions of the lake. It is also helpful for the determination of hazards based on the vulnerability and exposure of elements downstream of the lake (Emmer 2018; Frey et al. 2018). Moreover, for responsive disaster preparedness and mitigation implementing detailed models and assessments are essential to address extreme-magnitude scenarios (Sattar et al. 2021).", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 16, "line_end": 26, "token_count_estimate": 511, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21390a337b87d905", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 1. Introduction > 1.1. Motivation\nType: text\n\nIn the Himalayan region, glacial lakes have emerged and expanded quickly as a result of glacier recession brought on by climate change. The region is now more vulnerable to Glacial Lake Outburst Floods (GLOFs) as a result of the increased melting. The infrastructure and way of life in the nearby low-lying communities could suffer if potentially hazardous glacier lakes collapsed catastrophically. GLOF has become a crucial aspect in the economic and social stability of the downstream areas as the increase in the meltdown of glaciers results in the breach of unstable debris dams. Hence, it is essential to understand the nature of the glacial lakes for proper planning and development of the region in the long term. Also, the prevailing works carried out with a focus on GLOF risk assessment with their merits and problems confronted that motivated the development of the ShCNNFDMN.\n\nThe major intention of this research is to implement a GLOF risk assessment model using the proposed ShCNNFDMN. Here, the GLOF risk assessment is carried out by considering the various properties of the glacial lake. Initially, properties, like geometric, location, lake-based, and global properties are excerpted from the input data. Thereafter, these features are applied to the ShCNNFDMN for assessing the hazard and risk. Here, the ShCNNFDMN is modelled by fusing the ShCNN with the DMN based on regression modelling. At first, the hazard assessment is carried out by the ShCNNFDMN based on the extracted features, and thereafter, the ShCNNFDMN carries out the risk assessment using the features as well as the hazard levels.\n\nThe key contribution of this work is as follows:\n\n• Proposed ShCNNFDMN for risk/hazard assessment: In this work, GLOF risk and hazard assessment is carried out by using the ShCNNFDMN, which is formulated by combining ShCNN with DMN, based on regression modelling to improve the efficiency of assessment. Here, fusion is accomplished by applying the concept of Fractional Calculus (FC) to produce a regression model.\n\nThe remaining part of the work is arranged as given: section 2 depicts the related works, section 3 portrays the ShCNNFDMN proposed in this work for GLOF risk assessment. The experimental results are shown in Section 4, and Section 5 concludes the study with recommendations for enhancements.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "1. Introduction > 1.1. Motivation", "section_headings": ["1. Introduction", "1.1. Motivation"], "chunk_type": "text", "line_start": 28, "line_end": 38, "token_count_estimate": 580, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc4b202ae5226e43", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 2. Literature review\nType: text\n\nSattar et al. (2021) proposed a physical hydrodynamic model for modelling the lake outburst and hazard\n\nassessment of the glacial lakes. This method was developed for assessing the GLOF risks across the Barun-Arun river valley in Nepal. Here, the hydraulic flow of the river at six downriver encampments was analyzed, and the possible impact at every location was estimated by evaluating two low-level, two moderate-level, two high-level, and two extreme-level magnitudes were investigated for the current lake size and using this, the future dimension of the lake was modelled. Liu et al. (2020) developed a GLOF hazard assessment model for analyzing the risk of GLOF in Bhote Koshi Basin (BKB). This model assessed the GLOF hazard levels by regarding the situation that the numerous landslides caused by earthquakes evolved into debris in the outburst flood thereby enlarging the volume and discharge of the debris flow. This approach was effective in comprehending the data required for GLOF hazard analysis. However, the method was not appropriate for very high-hazard glacial lakes. Saifullah et al. (2020) devised a Remote sensing technique for GLOF risk assessment along the China-Pakistan Economic Corridor (CPEC). This research was aimed at investigating the formation and impacts of barrier lakes, supra-glacial, and end moraine based onsitu and remote sensing approaches in the Hunza River basin along CPEC considering the peak discharges and volume of the lakes. Emmer and Vilímek (2013) studied the Lake and breach hazard assessment model for assessing the risks of GLOF of moraine-dammed lakes. In this work, various methods for assessing the lake and breach hazards were outlined and the approaches were applied for assessing the hazard levels of the moraine-dammed lakes in Cordillera Blanca (Peru).\n\nWang et al. (2018) introduced a Hydrologic Engineering Center-River Analysis System (HEC-RAS) for carrying out the integrated hazard assessment of the Cirenmaco glacial lake. This method combined various approaches, like 2D hydraulic modelling, bathymetric survey, and remote sensing for assessing the hazards presented by the Cirenmaco glacial lake. The results produced by the approach were shown to be highly effective in mitigating the risks, but specific care was needed when examining the outcomes of the model because of the complicated nature of GLOFs. Sattar, Goswami, and Kulkarni (2019) developed a two-dimensional hydrodynamic modelling approach for assessing the GLOF hazards of South LhonakLake. Here, a realistic bathymetric framework was constructed for depicting the various GLOF scenarios, and the hazard probability was assessed by utilising one and two-dimensional hydrodynamic modelling techniques. This method made it easier to build structures in the middle of the flow channel and may help manage the risk that extreme flow occurrences bring to areas downstream. Unfortunately, the approach did not provide in-", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "2. Literature review", "section_headings": ["2. Literature review"], "chunk_type": "text", "line_start": 40, "line_end": 48, "token_count_estimate": 719, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "e9aa3c9ff77cabf2", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 2. Literature review\nType: text\n\nthe risks , but specific care was needed when examining the outcomes of the model because of the complicated nature of GLOFs . Sattar , Goswami , and Kulkarni ( 2019 ) developed a two - dimensional hydrodynamic modelling approach for assessing the GLOF hazards of South LhonakLake . Here , a realistic bathymetric framework was constructed for depicting the various GLOF scenarios , and the hazard probability was assessed by utilising one and two - dimensional hydrodynamic modelling techniques . This method made it easier to build structures in the middle of the flow channel and may help manage the risk that extreme flow occurrences bring to areas downstream . Unfortunately , the approach did not provide in -\n\nsitu measurements of the geomorphic characteristics over the flow channel or an analysis of engineering properties. Frey et al. (2018) proposed a Scenario-based multisource GLOF hazard mapping technique for assessing the risks of multisource GLOFs. Here, the cascaded mass movement of debris was simulated by utilising a chain of interrelating numerical models. Later, vulnerability evaluations for breach formation and rock-ice avalanches were employed for defining the events of related probability and varying magnitude. However, it failed to enhance the hazard mapping in urban areas that includes a detailed computation of the impacts of urban infrastructure on the flow behaviour of GLOFs. Li et al. (2018) used an Unmanned Surface Vessel (USV) and remote sensing to assess the hazards of GLOF in the Jialong Co glacial lake. Geomorphological analysis, volume assessment, and area dynamics were used for estimating the hazard potential of Jialong Co. The evolution of the Jialong Co in the last years was analyzed using Sentinel 2 satellite and Landsat series image data. Then, the volume, and underwater topography of the lake were detected utilising USVs for bathymetric measurements. Later, the hazard potential was determined by combining the geomorphic conditions, volume data, and area dynamics obtained from in-situ measurements. This plan assisted in determining the volume and underwater topography of the lake, but it did not improve accuracy or carry out longterm geographic monitoring of the moraine dam. Ahmed et al. (2022). uses a combination of remote sensing, GIS, and dam break modelling to evaluate the GLOF danger of Gangabal Lake, which is situated in the Upper Jhelum basin of the Kashmir Himalaya. Glab-Top-2, multitemporal satellite data, and the Cosi - Corr model were also used to evaluate the parameters of the Harmukh glacier, which feeds Gangabal Lake. Wangchuk and Tsubaki (2024) studied the possible effects of a GLOF coming from one of Bhutan's biggest and fastest-growing glacial lakes, Thorthomi, which is a glacial lake in the Phochhu River Basin. The findings here highlight the critical necessity to comprehend and get ready for the possible aftermath of a GLOF from Thorthomi Lake in order to lessen the effects on downstream ecosystems, economies, and societies.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "2. Literature review", "section_headings": ["2. Literature review"], "chunk_type": "text", "line_start": 40, "line_end": 48, "token_count_estimate": 747, "basins": [], "subbasins": ["Jhelum"], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "0da3fbf1c16d3cd7", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management\nType: text\n\nIn the current era, the dramatic rise in temperature worldwide due to global warming has resulted in an increase in the number of glacier lakes as well as the dimension of the existing ones. These phenomena have led to a potential risk of GLOF that can wreak havoc on man-made infrastructure and the natural environment. Hence, it is necessary to assess the risk of GLOF to avoid and mitigate any undue casualties. This paper proposes a hybrid ShCNNFDMN-based GLOF risk assessment technique, which is implemented as follows. Gathering information from the dataset is the primary step in the risk assessment process. After that, a number of features, including geometric, locationbased, lake-based, and global properties, are ascertained by applying the input data to the data and feature attribute extraction phase. The location properties that are taken into consideration are indicated by the latitude, longitude, and altitude, with the geometric property being the shape of the lake. Additionally, global characteristics like the Global Terrestrial Network for Glaciers (GTN-G) region and the Universal Transverse Mercator (UTM) grid zone, as well as lake-based parameters like lake area, perimeter, water volume, type, and uncertainty, are also extracted from the input data. Following the mining of the data and feature properties, the suggested hybrid deep learning method ShCNNFDMN is used to assess the hazards. The ShCNN (Ren et al. 2015) and DMN (Sun, Su, and Wang 2018) are combined to create the suggested ShCNNFDMN. The GLOF hazard is evaluated in this instance and assigned to one of the following five categories: Very High (VH), High (H), Medium (M), Low (L), and Very Low (VL). Lastly, the suggested ShCNNFDMN is used to execute the risk assessment based on the data, feature properties, and hazard levels. The GLOF risk is also divided into five categories, which include VH, H, M, L, and VL. The suggested ShCNNFDMN's structural view for the GLOF risk assessment is displayed in Figure 1.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management"], "chunk_type": "text", "line_start": 50, "line_end": 52, "token_count_estimate": 538, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c4d583fd25eb748a", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.1. Data acquisition\nType: text\n\nThe initial step in GLOF assessment is to aggregate the data from the GLOF inventory dataset (https://zenodo. org/record/4477945#.ZGXf7nZBxPZ), which can be formulated as.\n\n$$C = \\{C_1, C_2, \\dots, C_i, \\dots, C_c\\}$$\n (1)\n\nwhere,  $C_i$  refers to the *i*th record contained in the dataset  $C_i$ which is considered for GLOF risk prediction, and c specifies the total number of records contained in the dataset.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.1. Data acquisition", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.1. Data acquisition"], "chunk_type": "text", "line_start": 54, "line_end": 61, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["4477945"]}}
{"id": "a4d4697403001041", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes\nType: text\n\nOnce the data  $C_i$  is retrieved, it is subjected to the extraction of various feature attributes, such as geometric properties, location properties, lake-based properties, and global\n\nproperties are determined from the input data. The main intention of this task is to identify the key features of the lake, thereby enabling efficient hazard and risk assessment.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes"], "chunk_type": "text", "line_start": 63, "line_end": 67, "token_count_estimate": 126, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dd4b56401cd6e843", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.1. Geometric properties\nType: text\n\nThe geometric property of the glacial lake is considered an important aspect as it determines the stability of the lake. Here, the shape of the lake is taken into account and is found from the input lake image. At first, the boundary values of the lake are computed and then the image is split up into multiple grids. The grids that are contained inside the boundary of the lake are assigned a value of \"1\", and those falling outside the boundary are allocated a value of \"0\". Thus, a shape feature matrix comprising of the binary value is obtained from the input lake image, and the shape index matrix thus generated is represented as N.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.1. Geometric properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.1. Geometric properties"], "chunk_type": "text", "line_start": 69, "line_end": 71, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2af4a4b8befb8459", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.2. Location properties\nType: text\n\nLocation is another significant feature that influences the severity of calamities caused by GLOF. The glacial lakes are generally located in uninhabited and remote mountain valleys, however, wide-ranging GLOFs can cause significant damage to properties as well as multiple casualties downstream up to tens of kilometres. Here, various location attributes, such as longitude, latitude, and altitude are computed from the input data, as the location of the glacial lake can be accurately found using these attributes.\n\n- (i) *Longitude*: Longitude is a reference with respect to the prime meridian and it refers to the angular distance (decimal degree) to the west or east of the prime meridian. The longitude feature is represented as  $J_1$ .\n- (ii) Latitude: Latitude gives the location of any place with respect to the equator, and is given as a measure of angular distance (decimal degree) to the south or north of the equator. Most of the glaciers are located in higher-latitude regions, such as the Arctic and Antarctic. But the low-latitude regions with high mountain ranges, like the Himalayas and Andes also contain glaciers. The latitude of the glacial lake measured is indicated as  $J_2$ .\n- (iii) Altitude: This attribute refers to the elevation of the glacier lake above sea level and is measured in metres/feet. Glacial lakes are more commonly found in high-altitude regions, and their numbers have increased with the retreat of glaciers. Let  $J_3$  designate the altitude feature.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.2. Location properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.2. Location properties"], "chunk_type": "text", "line_start": 73, "line_end": 79, "token_count_estimate": 431, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3c96c3bcb7dde4c5", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties\nType: text\n\nThe devastating power of GLOF depends on the characteristics of the glacial lake and hence, these parameters", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.3. Lake-based properties"], "chunk_type": "text", "line_start": 81, "line_end": 83, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85b6cfd325908061", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties\nType: figure\nFigure\n\nImage /page/5/Figure/3 description: A flowchart titled 'Figure 1. Structural view of the proposed ShCNNFDM N for GLOF risk assessment.' The diagram illustrates a process for risk assessment. At the top, under 'Data and feature attributes', four categories of properties are listed with their corresponding data points: 'Geometric properties' lead to 'Shape of the lake'; 'Location properties' lead to 'Latitude, longitude, and altitude'; 'Lake-based properties' lead to 'Lake area, perimeter, water volume, lake type, and uncertainty'; and 'Global properties' lead to 'UTM grid zone and GTN-G region'. An arrow points down from 'Data and feature attributes' to two parallel processes: 'Risk assessment' on the left and 'Hazard assessment' on the right. The 'Hazard assessment' box contains a 'Proposed ShCNNFDM N' model, which is composed of 'Shepard Convolutional Neural Networks (ShCNN)' and 'Deep Maxout Network (DMN)'. The output of this assessment is a 'Hazard class'. The 'Risk assessment' box also contains a 'Proposed ShCNNFDM N' model, which receives inputs from 'ShCNN' and 'DMN'. This risk assessment process also takes the 'Hazard class' as an input. The final output from the 'Risk assessment' box is the 'Risk class'.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.3. Lake-based properties"], "chunk_type": "figure", "figure_caption": null, "line_start": 84, "line_end": 84, "token_count_estimate": 406, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d5519c1db10f0c7", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties\nType: figure\nFigure: Figure 1. Structural view of the proposed ShCNNFDMN for GLOF risk assessment.\n\nFigure 1. Structural view of the proposed ShCNNFDMN for GLOF risk assessment.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.3. Lake-based properties"], "chunk_type": "figure", "figure_caption": "Figure 1. Structural view of the proposed ShCNNFDMN for GLOF risk assessment.", "line_start": 86, "line_end": 86, "token_count_estimate": 101, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "547e116ad42c00b8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties\nType: text\n\nhave to be considered while evaluating the risk assessment. The lake-based properties, such as lake area, perimeter, water volume, lake type, and uncertainty (Zheng et al. 2021) are considered here.\n\n- (i) Lake area: Lake area indicates the surface area of the lake and is obtained by multiplying the length and width of the lake and is expressed as  $J_4$ . Lake area is measured in square metres and is measured based on the UTM.\n- (ii) *Perimeter*: The total length of the shoreline line of the lake gives the perimeter value and is indicated\n- (iii) Water volume: The volume of the lake is measured by finding the product of the surface area of the\n\n- lake, and is expressed in cubic metres. The water volume feature of the glacial lake is characterised as  $J_6$ . The destructive potential of GLOF depends on the volume of water contained in the lake.\n- (iv) Lake type: The impact of GLOF depends on the type of the glacial lakes, which can be either Ice (I), Moraine (M), or others (O). Moraine-dammed glacial lakes are found to be a more prominent cause of GLOF than other glacial lakes. The laketype feature is symbolised as  $J_7$ .\n- (v) Uncertainty: This parameter deals with the uncertainty of the measurements of the glacial topology obtained using remote sensing techniques, and is considered so that the values can be analyzed as per their relevance. Uncertainty of the lake area is\n\njan\n\nexpressed as,\n\n$$J_8 = J_6/A \\times A^2/2 \\times 0.6872 \\tag{2}$$\n\nwhere A refers to the image's spatial resolution, I<sub>6</sub> designates the perimeter, and  $J_8$  represents the uncertainty feature.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.3. Lake-based properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.3. Lake-based properties"], "chunk_type": "text", "line_start": 87, "line_end": 105, "token_count_estimate": 508, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fcd516b0bc61aae0", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.4. Global properties\nType: text\n\nIn addition to the above features, the global properties, such as the UTM grid zone, and GTN-G region of the glacial lake are considered to improve the risk assessment efficiency.\n\n- (i) UTM grid zone: This refers to the UTM zone in which the glacial lake is present. The UTM coordinate system partitions the globe into 60 zones, and in all zones, the coordinates are measured in northings and eastings in metres, and each zone has a width of 6° longitude. The UTM grid zone is designated as  $J_9$ .\n- (ii) *GTN-G region*: The GTN-G region in which the glacial lake is located is termed the GTN-G region feature, and is characterised by  $J_{10}$ . These regions are essential in analyzing the variations in glaciers and other attributes in a region. Once the various features are excerpted from the database, they are combined to obtain a feature vector as given below,\n\n$$J = \\{J_1, J_2, \\dots, J_{10}\\}$$\n (3)\n\nwhere,  $J_1$  is the latitude,  $J_2$  specifies the longitude,  $J_3$ symbolises the altitude,  $J_4$  refers to the lake area,  $J_5$ characterises the perimeter of the lake, J<sub>6</sub> denotes the water volume,  $J_7$  represents the lake type,  $J_8$  is the uncertainty feature,  $J_9$  exemplifies the UTM grid zone, and  $J_{10}$  terms the GTN-G region. The feature vector is generated J and the shape index matrix N is applied to the proposed ShCNNFDMN for hazard assessment.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.2. Feature attributes > 3.2.4. Global properties", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.2. Feature attributes", "3.2.4. Global properties"], "chunk_type": "text", "line_start": 107, "line_end": 117, "token_count_estimate": 475, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be45df1366269eb8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment\nType: text\n\nA hazard assessment is carried out to find the probability that the water will be released from a glacial lake. This process is carried out by identifying the potentially hazardous lake first and then assessing the probability of water release based on the hazard index of the lake from the features extracted J. Here, hazard assessment is carried out by using the proposed ShCNNFDMN, which is created by combining the ShCNN (Ren et al. 2015) with DMN (Sun, Su, and Wang 2018) based on regression analysis. A statistical\n\nmethod for determining the relationships between variables in a given set of data is regression analysis. It can evaluate the statistical significance of the association, or the probability that the correlation is the product of chance, as well as show how strong the relationship is. One of the two main purposes of regression analysis is to either predict the value of the dependent variable for those for whom some information about the explanatory factors is available, or to assess the effect of an explanatory variable on the dependent variable. The ShCNNFDMN is fed with the shape index matrix N and a feature is generated J for assessing the GLOF hazard. The ShCNNFDMN comprises three parts, such as the ShCNN model, the DMN model, and the ShCNNFDMN layer. Initially, the shape index matrix N is subjected to the ShCNN model, which classifies the hazard into either of the five categories, such as VH, H, M, L, and VL. Thereafter, the output of the ShCNN is forwarded to the ShCNNFDMN layer, which fuses the output of the ShCNN with the feature vector *J* based on regression modelling, which is applied to determining the relationship between the two inputs, thereby enhancing the effectiveness of hazard assessment. Later, the fused output generated by the ShCNNFDMN layer is subjected to the DMN model for hazard assessments along with the shape index matrix N. Here, regression modelling is accomplished by applying the concept of Fractional Calculus (FC) (Bhaladhare and Jinwala 2014). Figure 2 demonstrates the architectural view of the proposed ShCNNFDMN for hazard assessment, and the various processes undertaken are expounded on in the ensuing subsections.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment"], "chunk_type": "text", "line_start": 119, "line_end": 123, "token_count_estimate": 574, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "04aeb4e6c49d61b8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel\nType: text\n\nInitially, the hazard assessment is accomplished by using the ShCNN (Ren et al. 2015) by applying the shape index matrix N. The ShCNN is a type of CNN that is developed by incorporating the Shepard technique for altering the basic CNN to attain Translation Variant Interpolation (TVI). The key advantage of using the ShCNN is its low computational cost and its ability in realising end-to-end TVI operations for intermittently spaced data. The network attains superior results by adding a smaller number of feature maps and optimisation of the TVI process. The Shepard structure computes the weights of the familiar pixels based on the spatial distance between them and the target pixels differently, and its convolution form is formulated\n\n$$H_1 k = \\begin{cases} (Z*N)_k / (Z*L)_k & \\text{if } L_k = 0\\\\ N_k & \\text{if } L_k = 1 \\end{cases}$$\n (4)", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.1. ShCNNmodel"], "chunk_type": "text", "line_start": 125, "line_end": 130, "token_count_estimate": 297, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ef3d5e1b17045564", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel\nType: figure\nFigure\n\nImage /page/7/Figure/3 description: An architectural diagram illustrating the proposed ShCNNFDMN model for hazard assessment. The diagram shows a multi-stream process. In the top stream, a 'Shape index matrix N' is fed into an 'ShCNN model', which is represented by a linear sequence of seven blocks, producing 'Output H1'. In the middle stream, a 'Feature vector J' is input to the 'ShCNNFDMN layer'. This layer, which also receives 'Output H1', contains two components: 'Regression Modeling' and 'Fusion'. The output of this layer is 'Output H2'. In the bottom stream, the 'Shape index matrix N' is also input into a 'DMN model'. This model is depicted as a more complex structure with three rows of interconnected blocks. The final output of the entire system, originating from the DMN model, is the 'Risk level R'.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.1. ShCNNmodel"], "chunk_type": "figure", "figure_caption": null, "line_start": 131, "line_end": 131, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "277da148919c09e0", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel\nType: figure\nFigure: Figure 2. Architectural view of the proposed ShCNNFDMN for hazard assessment.\n\nFigure 2. Architectural view of the proposed ShCNNFDMN for hazard assessment.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.1. ShCNNmodel"], "chunk_type": "figure", "figure_caption": "Figure 2. Architectural view of the proposed ShCNNFDMN for hazard assessment.", "line_start": 133, "line_end": 133, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fdac77e07208048d", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel\nType: text\n\nwherein, N represents the shape index matrix to the ShCNN, k signifies the image coordinate,  $H_{1k}$  refers to the output, \\* implies the convolution function, L characterises the binary operator, which has a value  $L_k=0$  in case the pixel values are not known, and Z refers to the kernel function that has a weight inversely proportional to the separation among the pixel under consideration and  $Z_k=1$ .\n\nThe characterisation of the convolutional kernel is the major factor that affects the interpolation result in the Shepard framework, and so a Shepard interpolation layer is employed to allow a kernel design that has good flexibility and is data-driven.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.1. ShCNNmodel"], "chunk_type": "text", "line_start": 134, "line_end": 138, "token_count_estimate": 224, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cd8cb1842e2dd4f3", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel > Shepard interpolation layer:\nType: text\n\nThe expression given below describes the Shepard interpolation layer's feed-forward pass,\n\n$$G_{j}^{d}(G^{d-1}, L^{d}) = \\alpha \\left( \\sum_{l} \\frac{Z_{jl}^{d} * G_{l}^{d-1}}{Z_{jl}^{d} * L_{l}^{d}} + p^{d} \\right), \\quad d = 1, 2, 3, \\dots \\tag{5}$$\n\nHere, d represents the index of layer,  $G^{d-1}$  indicates the input of the present layer, whose mask is characterised as  $L^d$ , the term j in  $G_j^{d-1}$  and  $G_j^d$  denotes the feature map index in the (d-1) th and dth layer, respectively,  $Z_{il}$  signifies the trainable parameters and convolution of\n\n[Icon of a ship in a circle]\n\n $Z_{il}$  is performed with the mask of the present layer  $L^d$  in the denominator and activations of the final layer in the numerator.  $G^{d-1}$  can signify the feature maps of the convolution or pooling layers of the CNN, and this can also be considered as the preceding Shepard interpolation layer. The term  $\\alpha$  is used to impose non-linearity in the network and *p* symbolises bias.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel > Shepard interpolation layer:", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.1. ShCNNmodel", "Shepard interpolation layer:"], "chunk_type": "text", "line_start": 140, "line_end": 150, "token_count_estimate": 428, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba8bf332e7194e0f", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel > Shepard interpolation layer:\nType: figure\nFigure: Figure 3 explicates the structure of the ShCNN, where it is applied with the shape index matrix N. The various inputs, such as feature maps/images, and masks representing the position where interpolation must happen are fed as input to the Shepard interpolation layer. Any complex interpolation operator can be constructed by applying the interpolation layer in a repeated manner with several non-linear layers. The mask refers to the binary map which has a value \"0\" for missing areas and \"1\" for familiar areas, and the same kernel is used with the mask as well as the image. The insignificant values of the preceding convolved mask $Z^d L^d$ are zeroed and a threshold is applied to generate the mask of the (d+1)th layer. Shepard interpolation layer with multiple stages is required to learn the sophisticated manner of propagation in processes where relatively bigger missing regions, like inpainting, occur. The output generated by the ShCNN is represented as $H_1$ .\n\nFigure 3 explicates the structure of the ShCNN, where it is applied with the shape index matrix N. The various inputs, such as feature maps/images, and masks representing the position where interpolation must happen are fed as input to the Shepard interpolation layer. Any complex interpolation operator can be constructed by applying the interpolation layer in a repeated manner with several non-linear layers. The mask refers to the binary map which has a value \"0\" for missing areas and \"1\" for familiar areas, and the same kernel is used with the mask as well as the image. The insignificant values of the preceding convolved mask  $Z^d*L^d$  are zeroed and a threshold is applied to generate the mask of the (d+1)th layer. Shepard interpolation layer with multiple stages is required to learn the sophisticated manner of propagation in processes where relatively bigger missing regions, like inpainting, occur. The output generated by the ShCNN is represented as  $H_1$ .", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.1. ShCNNmodel > Shepard interpolation layer:", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.1. ShCNNmodel", "Shepard interpolation layer:"], "chunk_type": "figure", "figure_caption": "Figure 3 explicates the structure of the ShCNN, where it is applied with the shape index matrix N. The various inputs, such as feature maps/images, and masks representing the position where interpolation must happen are fed as input to the Shepard interpolation layer. Any complex interpolation operator can be constructed by applying the interpolation layer in a repeated manner with several non-linear layers. The mask refers to the binary map which has a value \"0\" for missing areas and \"1\" for familiar areas, and the same kernel is used with the mask as well as the image. The insignificant values of the preceding convolved mask $Z^d L^d$ are zeroed and a threshold is applied to generate the mask of the (d+1)th layer. Shepard interpolation layer with multiple stages is required to learn the sophisticated manner of propagation in processes where relatively bigger missing regions, like inpainting, occur. The output generated by the ShCNN is represented as $H_1$ .", "line_start": 151, "line_end": 151, "token_count_estimate": 558, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5fba02023289cdb", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer\nType: text\n\nOnce the output of the ShCNNH<sub>1</sub> is obtained, it is fed to the ShCNNFDMN layer together with the location properties, lake-based properties, and global properties of the lake. One of the main advantage of ShCNNFDMN layer is simple and robust. The location properties are given by  $P_1 = \\{J_1, J_2, J_3\\}$  which include the latitude  $J_1$ , longitude  $J_2$ , and altitude  $J_3$ . Further, the lake-based properties are given by  $P_2 = \\{J_4, J_5, J_6, J_7, J_8\\}$  that comprise the lake area  $J_4$ , perimeter  $J_5$ , water volume  $J_6$ , lake type  $J_7$ , and uncertainty  $J_8$ . The global properties of the lake  $P_3 = \\{J_9, J_{10}\\}$  incorporate the UTM grid zone  $J_9$  and GTN-G region  $J_{10}$ . Here, the features are fused with the classified output of the ShCNN by using the concept of regression modelling. The relationship between the features and the classified output can be determined by using regression modelling, which is carried out here by utilising FC (Bhaladhare and Jinwala 2014), which is a subdivision of applied mathematics that solves the integral and derivative equations by using the Laplace transform. Here, the problem is first solved by converting it into the Laplace domain and later, the actual solution is determined by applying inverse transform. Here, the features excerpted as considered to be taken at various time intervals, and the\n\nweighted features are combined with the classified output of the ShCNN. This process is formulated as follows,\n\nAt the interval t, the output of the ShCNNFDMN layer is expressed as,\n\n$$g = \\sum_{q=1}^{n_1} P_{1q} \\omega_q \\tag{6}$$\n\nHere,  $P_1$  refers to the location properties of the lake comprising latitude, longitude, and altitude,  $n_1$  refers to the number of location properties, and  $\\omega$  refers to the weight coefficients.\n\nWhen the time interval t-1 is considered, then the output of the ShCNNFDMN layer is obtained by considering the lake-based properties, and this is mathematically modelled as,\n\n$$g_1 = \\sum_{q=1}^{n_2} P_{2q} \\omega_q \\tag{7}$$\n\nwhere,  $P_2$  symbolises the lake-based properties and  $n_2$ represents the number of lake-based properties. Further, the output of the ShCNNFDMN layer at time instance t-2 is formulated based on the global properties of the glacial lake, and is modelled as,\n\n$$g_2 = \\sum_{q=1}^{n_3} P_{3q} \\omega_q \\tag{8}$$\n\nHere,  $P_3$  represents the global properties of the lake, such as the UTM grid zone, and GTN-G region, and  $n_3$  characterises the number of global properties.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.2. ShCNNFDMN layer"], "chunk_type": "text", "line_start": 154, "line_end": 185, "token_count_estimate": 849, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cbea4e03c7f808af", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer\nType: text\n\nwhere , $ P_2 $ symbolises the lake - based properties and $ n_2 $ represents the number of lake - based properties . Further , the output of the ShCNNFDMN layer at time instance t - 2 is formulated based on the global properties of the glacial lake , and is modelled as , $ $ g_2 = \\ sum_ { q = 1 } ^ { n_3 } P_ { 3q } \\ omega_q \\ tag { 8 } $ $ Here , $ P_3 $ represents the global properties of the lake , such as the UTM grid zone , and GTN - G region , and $ n_3 $ characterises the number of global properties .\n\nMoreover, the output of the ShCNNFDMN at  $(t-3)^{th}$  intervals is considered to be the classified layer of the  $ShCNNH_1$ , and these are combined by using FC. Applying the concept of FC (Bhaladhare and Jinwala 2014),\n\n$$\\begin{aligned} y(t+1) &= u \\cdot y(t) + \\frac{1}{2}u \\cdot y(t-1) \\\\ &\\qquad + \\frac{1}{6}(1-u)y(t-2) \\\\ &\\qquad + \\frac{1}{24}u(1-u)(2-u) \\cdot y(t-3) \\end{aligned}\\tag{9}$$\n\nSubstituting the respective values ShCNNFDMN layer, the equation depicted above can be written as,\n\n$$H_2 = u \\cdot g + \\frac{1}{2}u \\cdot g_1 + \\frac{1}{6}(1 - u)g_2 + \\frac{1}{24}u(1 - u)(2 - u) \\cdot H_1$$\n (10)\n\nShape index matrix N", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.2. ShCNNFDMN layer"], "chunk_type": "text", "line_start": 154, "line_end": 185, "token_count_estimate": 511, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1c5ddfac9e085451", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer\nType: figure\nFigure\n\nImage /page/9/Figure/4 description: A diagram illustrating the architecture of a neural network. The data flows from left to right through a series of layers. The sequence of layers and the dimensions of the data after each layer are as follows: two light blue 'Conv layers' resulting in dimensions of 1x26x32 and 1x26x64 respectively; a light yellow 'Leaky ReLU layer' with an output of 1x26x64; a light blue 'Maxpooling layer' resulting in 1x13x64; an olive green 'Flatten layer' with an output of 1x832; and two light yellow 'Dense layers' with outputs of 1x32 and 1x5. The final output of the network is labeled 'Output H₁'.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.2. ShCNNFDMN layer"], "chunk_type": "figure", "figure_caption": null, "line_start": 186, "line_end": 186, "token_count_estimate": 241, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7000d3afdc7a7060", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer\nType: figure\nFigure: Figure 3. Structure of the ShCNN.\n\nFigure 3. Structure of the ShCNN.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.2. ShCNNFDMN layer"], "chunk_type": "figure", "figure_caption": "Figure 3. Structure of the ShCNN.", "line_start": 188, "line_end": 188, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3815593321a17f5", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer\nType: text\n\nApplying the values of g,  $g_1$ , and  $g_2$  from equations (6-8),\n\n$$\\begin{aligned}H_{2} ={}& u \\cdot \\sum_{q=1}^{n_{1}} P_{1q} \\omega_{q} + \\frac{1}{2} u \\cdot \\sum_{q=1}^{n_{2}} P_{2q} \\omega_{q} \\\\ & + \\frac{1}{6} (1 - u) \\sum_{q=1}^{n_{3}} P_{3q} \\omega_{q} \\\\ & + \\frac{1}{24} u (1 - u) (2 - u) \\cdot H_{1}\\end{aligned}\\qquad (11)$$\n\nHere,  $H_1$  characterises the output of the ShCNN and is found using equation (4),  $H_2$  which signifies the output generated by the ShCNNFDMN layer, and u symbolises a constant that signifies the derivative order in FC.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.2. ShCNNFDMN layer", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.2. ShCNNFDMN layer"], "chunk_type": "text", "line_start": 189, "line_end": 195, "token_count_estimate": 327, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1478aa8ac0242b3f", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.3. DMN model\nType: text\n\nThe DMN is applied with the shape index matrix N, and the output  $H_2$  produced by the ShCNNFDMN layer for performing hazard assessment. The output produced by the ShCNN is also a hazard assessment level, and the output produced by the ShCNN is combined with the features and given to the DMN, thereby boosting the efficiency of hazard assessment. The ShCNN and DMN perform classification at various stages, and thus in the DMN, more refined features are used along with the shape-indexed matrix, which minimises the classification cost as well. Here, the DMN (Sun, Su, and Wang 2018) is mainly applied for hazard assessment due to its ability to produce superior outcomes in resource-constrained scenarios. DMN predict more consistently and accurately. The DMN is applied with an inputS;  $(S = \\{N, H_2\\})$  created by considering the shape indexed matrix N and the output of the\n\nShCNNFDMN layer  $H_2$ , and the various processes taking place in the DMN are explicated as given below,\n\n$$b_{x,y}^{1} = \\max_{y \\in [1,v_1]} S^T V_{\\dots xy} + w_{xy}$$\n (12)\n\n$$b_{x,y}^{2}T = \\max_{y \\in [1,v_{2}]} b_{x,y}^{1}TV_{...xy} + w_{xy}$$\n (13)\n\n$$b_{x,y}^{e}T = \\max_{v \\in [1,v_e]} b_{x,y}^{e-1}TV_{...xy} + w_{xy}$$\n (14)\n\n$$b_{x,y}^{f}T = \\max_{y \\in [1,y_f]} b_{x,y}^{f-1}TV_{\\dots xy} + w_{xy}$$\n (15)\n\n$$R_{x} = \\max_{y \\in [1, y_{f}]} b_{x, y}^{f} \\tag{16}$$\n\nHere, x refers to the total count of layers in DMN,  $A_x$  refers to the hazard assessed by the DMN,  $v_e$  designates the overall count of units in the  $e^{th}$  layer,  $w_{xy}$  characterises the bias,  $V_{xy}$  symbolises the weight, and  $b_{x,y}^e$  designates the output produced by the eth layer. As depicted in the above equation, a max-pooling function is used by the DMN, and the maximal value of the output generated in each layer is fed to the consecutive layers. When v > 2 the DMN can estimate any conventional nonlinear activation functions. The architectural view of the DMN is depicted in Figure 4 and the output produced is indicated as R.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.3. Hazard assessment > 3.3.3. DMN model", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.3. Hazard assessment", "3.3.3. DMN model"], "chunk_type": "text", "line_start": 197, "line_end": 217, "token_count_estimate": 775, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "61ed67bdc7372ca8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN\nType: text\n\nAfter the hazard level *R* is identified by the ShCNNFDMN, risk assessment is carried out based on the hazard levels *R*", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.4. Risk management using the proposed ShCNNFDMN"], "chunk_type": "text", "line_start": 219, "line_end": 221, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8af62904fa8fd2fd", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN\nType: figure\nFigure\n\nImage /page/10/Figure/1 description: The number 106 is shown in black text against a white background.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.4. Risk management using the proposed ShCNNFDMN"], "chunk_type": "figure", "figure_caption": null, "line_start": 222, "line_end": 222, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f00ceba49faed2d", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A diagram illustrating the architecture of a convolutional neural network. The model processes an input labeled 'S = {N, H₂}' through a series of layers to output a 'Risk level R'. The architecture is laid out in three rows. The first row consists of an Input layer (64x64x3), a Convolution layer (62x62x64), a Lamda layer (62x62x32), a Batch Normalization layer (62x62x32), and a Max pooling layer (31x31x32). The second row continues with a Convolution layer (29x29x128), a Lamda layer (29x29x64), a Batch Normalization layer (29x29x64), a Max pooling layer (14x14x64), a Dropout layer (14x14x64), and another Convolution layer (12x12x256). The third row includes a Lamda layer (12x12x64), a Batch Normalization layer (12x12x64), a Dropout layer (6x6x64), a Flatten layer (1x2304), and a Dense layer (1x1) which produces the final output. A legend at the bottom clarifies the layer types corresponding to different colors: Input layer, Lamda, Max pooling layer, Flatten, Convolution layer, Batch Normalization layer, Dropout layer, and Dense.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.4. Risk management using the proposed ShCNNFDMN"], "chunk_type": "figure", "figure_caption": null, "line_start": 224, "line_end": 224, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3e12ba28dc4eb410", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN\nType: figure\nFigure: Figure 4. Architectural view of the DMN.\n\nFigure 4. Architectural view of the DMN.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.4. Risk management using the proposed ShCNNFDMN"], "chunk_type": "figure", "figure_caption": "Figure 4. Architectural view of the DMN.", "line_start": 226, "line_end": 226, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64897157225eeaca", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN\nType: text\n\nand various feature attributes N, and J. Risk assessment is extremely essential as it gives a clear picture of the aftereffects of a GLOF, thereby helping in taking effective risk reduction strategies. Glacial lakes that are assessed to be under HVH hazard levels pose a greater risk to the communities residing downstream (Zheng et al. 2021). Here, risk assessment is performed using the ShCNN based on the risk index, which can be obtained considering the exposure index and the hazard index. The exposure index refers to the potency of GLOF causing devastation to the infrastructures as well as life and hazard index terms the combined magnitude and likelihood of the GLOF. The ShCNNFDMN is already elucidated in section 3.3., and combines the ShCNN (Ren et al. 2015) and the DMN (Sun, Su, and Wang 2018) using regression analysis.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management > 3.4. Risk management using the proposed ShCNNFDMN", "section_headings": ["3. Proposed ShCNNFDMN-based glof modelling for hazard assessment and risk management", "3.4. Risk management using the proposed ShCNNFDMN"], "chunk_type": "text", "line_start": 227, "line_end": 229, "token_count_estimate": 254, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62327d712a5695ce", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion\nType: text\n\nThe results obtained during the experimentation of the ShCNNFDMN for GLOF risk assessment are detailed in this section. Further, the experimental set-up, evaluation measures, dataset, and analysis of the approach are also demonstrated.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion", "section_headings": ["4. Results and Discussion"], "chunk_type": "text", "line_start": 231, "line_end": 233, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "743cff502021d4ae", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.1. Experimental set-up\nType: text\n\nThe ShCNNFDMN for GLOF risk assessment proposed in this work is implemented on a system with the Python language using the glacial lake inventories dataset (https://zenodo.org/record/4477945#. ZGXf7nZBxPZ). Table 1 shows the experimental parameters of the proposed method.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.1. Experimental set-up", "section_headings": ["4. Results and Discussion", "4.1. Experimental set-up"], "chunk_type": "text", "line_start": 235, "line_end": 239, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["4477945"]}}
{"id": "96db23f8c4a9e0a8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.1. Experimental set-up\nType: table\nTable: Table 1. Experimental parameters of the proposed method.\n\n| Methods          | Physical hydrodynamic model | GLOF Hazard assessment | Lake and breach Hazard assessment | HEC-RAS | Two-dimensional hydrodynamic model | Scenario-based multi-source GLOF | Proposed ShCNNFDMN |\n|------------------|-----------------------------------|---------------------------|--------------------------------------|---------|---------------------------------------|-------------------------------------|-----------------------|\n| Epochs           | 20                                | 20                        | 25                                   | 20      | 20                                    | 20                                  | 30                    |\n| Batch size       | 32                                | 32                        | 32                                   | 32      | 32                                    | 32                                  | 64                    |\n| Learning rate | 0.001                             | 0.001                     | 0.001                                | 0.001   | 0.001                                 | 0.001                               | 0.001                 |", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.1. Experimental set-up", "section_headings": ["4. Results and Discussion", "4.1. Experimental set-up"], "chunk_type": "table", "table_caption": "Table 1. Experimental parameters of the proposed method.", "columns": ["Methods", "Physical hydrodynamic model", "GLOF Hazard assessment", "Lake and breach Hazard assessment", "HEC-RAS", "Two-dimensional hydrodynamic model", "Scenario-based multi-source GLOF", "Proposed ShCNNFDMN"], "table_row_start": 1, "table_row_end": 3, "line_start": 240, "line_end": 244, "token_count_estimate": 234, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a58b8fc9e1ac1acc", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.1. Experimental set-up\nType: text\n\nThe bold values represent the best performance.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.1. Experimental set-up", "section_headings": ["4. Results and Discussion", "4.1. Experimental set-up"], "chunk_type": "text", "line_start": 245, "line_end": 247, "token_count_estimate": 43, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "40316311e69a3ef6", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.1. Experimental set-up\nType: figure\nFigure\n\nImage /page/11/Figure/3 description: A geographical map showing the distribution of glacial lakes over a Digital Elevation Model (DEM) of a mountainous region. The map is bounded by coordinates from 70°0'0\"E to 100°0'0\"E longitude and from 20°0'0\"N to 40°0'0\"N latitude. A compass rose is present in the upper right corner. The legend indicates that the blue areas represent 'Glacial Lakes'. The DEM values, shown in grayscale, range from a 'Low' of 232 to a 'High' of 8233. The map includes a scale bar marked up to 1,240 Kilometers and a textual scale of '1 cm = 210 km'.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.1. Experimental set-up", "section_headings": ["4. Results and Discussion", "4.1. Experimental set-up"], "chunk_type": "figure", "figure_caption": null, "line_start": 248, "line_end": 248, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97f4d6bdf31c7540", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.1. Experimental set-up\nType: figure\nFigure: Figure 5. Glacial Lake Dataset.\n\nFigure 5. Glacial Lake Dataset.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.1. Experimental set-up", "section_headings": ["4. Results and Discussion", "4.1. Experimental set-up"], "chunk_type": "figure", "figure_caption": "Figure 5. Glacial Lake Dataset.", "line_start": 250, "line_end": 250, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9b6dbf13ad54dab8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.2. Dataset description\nType: text\n\nThe data used for the experimentation of the ShCNNFDMN is acquired from the glacial lake inventories dataset and has been mapped in Figure 5 (https://zenodo.org/record/4477945#.ZGXf7nZBxPZ). This dataset comprises the data of the glacial lakes present in the Third Pole region. The data encompassed are acquired over a period of 15 years from 1990 to 2015, Further, the data is modelled for considering future conditions for 2050 and 2100 on the Third Pole under an ice-free scenario, Representative Concentration Pathway (RCP) 8.5, RCP 4.5, and RCP 2.6. It contains various data regarding the glacial lake shape, location, uncertainty, lake water volume, hazard value, risk value, exposure value, etc.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.2. Dataset description", "section_headings": ["4. Results and Discussion", "4.2. Dataset description"], "chunk_type": "text", "line_start": 253, "line_end": 255, "token_count_estimate": 220, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["4477945"]}}
{"id": "9e4e436601f2c51e", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.3. Evaluation measures\nType: text\n\nTwo parameters, such as Hazard modelling error, and Risk prediction error are considered to evaluate the supremacy of the ShCNNFDMN.\n\n(i) *Hazard modelling error*: The hazard modelling error is measured by finding the deviation of the predicted hazard value by the ShCNNFDMN from the expected values.\n\n$$HME = \\frac{\\sum_{i=1}^{n} |y_i - x_i|}{n}.$$\n (17)\n\nwhere, HME refers to the Hazard Modelling Error,\n\n $y_i$  refers to the prediction, $x_i$  refers to the true value, and n designates the total number of data.\n\n(ii) *Risk prediction error:* The risk prediction error is also found similar to the hazard modelling error by determining the difference between the predicted risk value from the anticipated risk value.\n\n$$RPE = \\frac{\\sum_{i=1}^{n} |y_i - x_i|}{n}.$$\n (18)\n\nwhere, *RPE* refers to the Risk Prediction Error.\n\n(iii) *Mean Average Error (MAE):* The MAE, is a statistical measure of inaccuracies between paired observations that represent the same occurrence.\n\n$$MAE = \\frac{\\sum_{i=1}^{n} |y_i - x_i|}{n}.$$\n (19)\n\nwhere, MAE refers to the Mean Average Error.\n\n(iv) *R-Squared:* The percentage of a dependent variable's variance that can be accounted for by an independent variable is expressed statistically as R-squared.\n\n$$R^2 = 1 - \\frac{RSS}{TSS}. (20)$$\n\nwhere, RSS refers to the sum of squares of residuals, and TSS refers to the total sum of squares.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.3. Evaluation measures", "section_headings": ["4. Results and Discussion", "4.3. Evaluation measures"], "chunk_type": "text", "line_start": 257, "line_end": 288, "token_count_estimate": 480, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a180ff12dcbb4ac3", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis\nType: text\n\nThe performance of the ShCNNFDMN is examined by considering the values of Hazard modelling error, and Risk\n\nprediction error determined when different k-fold values and training data percentages are used for various iterations.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis"], "chunk_type": "text", "line_start": 290, "line_end": 294, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc68eeee5ee4cfda", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.1. Based on k-fold\nType: figure\nFigure: Figure 6 demonstrates the performance assessment of the ShCNNFDMN while varying the k-fold values. In Figure 6 (a), the analysis of the ShCNNFDMN based on hazard modelling error is exhibited. With a k-fold of 9, the value of hazard prediction error measured by the ShCNNFDMN is 0.558, 0.526, 0.518, 0.505, and 0.487, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of risk prediction error is illustrated in Figure 6(b). The ShCNNFDMN is observed to have attained a risk prediction error of 0.495 for 20 iterations, 0.507 for 40 iterations, 0.497 for 60 iterations, 0.484 for 80 iterations, and 0.428 for 100 iterations with k-fold of 9. In Figure 6(c), the analysis of the ShCNNFDMN based on MAE is showed. With a k-fold of 9, the value of MAE measured by the ShCNNFDMN is 0.450, 0.414, 0.410, 0.395, and 0.370,\n\nFigure 6 demonstrates the performance assessment of the ShCNNFDMN while varying the k-fold values. In Figure 6 (a), the analysis of the ShCNNFDMN based on hazard modelling error is exhibited. With a k-fold of 9, the value of hazard prediction error measured by the ShCNNFDMN is 0.558, 0.526, 0.518, 0.505, and 0.487, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of risk prediction error is illustrated in Figure 6(b). The ShCNNFDMN is observed to have attained a risk prediction error of 0.495 for 20 iterations, 0.507 for 40 iterations, 0.497 for 60 iterations, 0.484 for 80 iterations, and 0.428 for 100 iterations with k-fold of 9. In Figure 6(c), the analysis of the ShCNNFDMN based on MAE is showed. With a k-fold of 9, the value of MAE measured by the ShCNNFDMN is 0.450, 0.414, 0.410, 0.395, and 0.370,", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.1. Based on k-fold", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.1. Based on k-fold"], "chunk_type": "figure", "figure_caption": "Figure 6 demonstrates the performance assessment of the ShCNNFDMN while varying the k-fold values. In Figure 6 (a), the analysis of the ShCNNFDMN based on hazard modelling error is exhibited. With a k-fold of 9, the value of hazard prediction error measured by the ShCNNFDMN is 0.558, 0.526, 0.518, 0.505, and 0.487, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of risk prediction error is illustrated in Figure 6(b). The ShCNNFDMN is observed to have attained a risk prediction error of 0.495 for 20 iterations, 0.507 for 40 iterations, 0.497 for 60 iterations, 0.484 for 80 iterations, and 0.428 for 100 iterations with k-fold of 9. In Figure 6(c), the analysis of the ShCNNFDMN based on MAE is showed. With a k-fold of 9, the value of MAE measured by the ShCNNFDMN is 0.450, 0.414, 0.410, 0.395, and 0.370,", "line_start": 297, "line_end": 297, "token_count_estimate": 587, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fce26513bebfe428", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.1. Based on k-fold\nType: text\n\ncorresponding to 20, 40, 60, 80, and 100 iterations. Figure 6 (d) explicates the assessment of the ShCNNFDMN on the basis of R-Squared. For k-fold of 9, the ShCNNFDMN recorded R-Squared of 0.346 for 20 iterations, 0.331 for 40 iterations, 0.324 for 60 iterations, 0.312 for 80 iterations, and 0.292 for 100 iterations.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.1. Based on k-fold", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.1. Based on k-fold"], "chunk_type": "text", "line_start": 298, "line_end": 300, "token_count_estimate": 146, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "044fbff4251dc14b", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: text\n\nThe evaluation of the ShCNNFDMN considers the hazard modelling error and risk prediction error while taking into account various training data percentages. Figure 7(a) explicates the assessment of the ShCNNFDMN on the basis of hazard modelling error. For 90% of training data, the ShCNNFDMN recorded hazard modelling error of 0.644 for 20 iterations, 0.582 for 40 iterations, 0.561 for 60 iterations, 0.540 for 80 iterations, and 0.484 for 100 iterations. Further, the assessment of the ShCNNFDMN concerning the risk prediction error is exhibited in Figure 7(b). The risk prediction error measured by the ShCNNFDMN", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "text", "line_start": 302, "line_end": 304, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa621ef30d51c546", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: figure\nFigure\n\nImage /page/12/Figure/9 description: The image displays four line graphs, labeled (a), (b), (c), and (d), showing the performance assessment of a model named ShCNNFDM. Each graph plots a different performance metric against 'K-Fold' on the x-axis, which ranges from 5 to 9. Each graph includes five lines representing the model run with a different number of iterations: 20 (blue with 'x' markers), 40 (pink with '\\*' markers), 60 (green with right-pointing triangle markers), 80 (teal with left-pointing triangle markers), and 100 (red with circle markers).", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "figure", "figure_caption": null, "line_start": 305, "line_end": 305, "token_count_estimate": 200, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "974eb7f9dd50cc51", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: text\n\n(a) The top-left graph shows 'Hazard modelling error' on the y-axis, from 0.50 to 0.70. For all lines, the error generally decreases as K-Fold increases. The error is lowest for the model with 100 iterations (red line, from ~0.53 to ~0.48) and highest for the model with 20 iterations (blue line, from ~0.71 to ~0.56).\n\n(b) The top-right graph shows 'Risk prediction error' on the y-axis, from 0.45 to 0.70. Similar to the first graph, the error decreases as K-Fold increases and as the number of iterations increases. The 100-iteration model has the lowest error (red line, from ~0.54 to ~0.43), while the 20-iteration model has the highest (blue line, from ~0.72 to ~0.51).\n\n(c) The bottom-left graph shows 'MAE' (Mean Absolute Error) on the y-axis, from 0.38 to 0.52. The MAE decreases as K-Fold increases and as the number of iterations increases. The 100-iteration model performs best with the lowest MAE (red line, from ~0.45 to ~0.37), and the 20-iteration model performs worst with the highest MAE (blue line, from ~0.52 to ~0.45).\n\n(d) The bottom-right graph shows 'R-squared' on the y-axis, from 0.30 to 0.44. In this graph, the R-squared value decreases as K-Fold increases for all models. Contrary to the error metrics, a higher R-squared value is better, and the model with fewer iterations performs better. The 20-iteration model has the highest R-squared (blue line, from ~0.44 to ~0.34), while the 100-iteration model has the lowest (red line, from ~0.36 to ~0.29).", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "text", "line_start": 306, "line_end": 314, "token_count_estimate": 494, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "040f15f86c03ae5d", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: figure\nFigure: Figure 6. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared.\n\nFigure 6. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "figure", "figure_caption": "Figure 6. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared.", "line_start": 315, "line_end": 315, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8837d424bc611c69", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: figure\nFigure\n\nImage /page/13/Figure/3 description: The image contains four line graphs, labeled (a), (b), (c), and (d), which show the performance assessment of a model called ShCNNFDMN. Each graph plots a different performance metric against the percentage of training data, which ranges from 50% to 90% on the x-axis. All graphs compare five versions of the model based on the number of iterations: 20, 40, 60, 80, and 100.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "figure", "figure_caption": null, "line_start": 317, "line_end": 317, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d9a5417b13c21af7", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: text\n\n(a) The first graph plots 'Hazard modelling error' on the y-axis from 0.50 to 0.75. For all lines, the error decreases as the training data increases. The error is lowest for 100 iterations (red line, from ~0.56 to ~0.48) and highest for 20 iterations (blue line, from ~0.75 to ~0.64).\n\n(b) The second graph plots 'Risk prediction error' on the y-axis from 0.45 to 0.75. The trend is similar to the first graph, with the error decreasing with more training data and more iterations. The 100-iteration line drops from ~0.57 to ~0.44, while the 20-iteration line drops from ~0.74 to ~0.62.\n\n(c) The third graph plots 'MAE' (Mean Absolute Error) on the y-axis from 0.375 to 0.525. Again, the error decreases as training data and the number of iterations increase. The 100-iteration line goes from ~0.43 to ~0.36, and the 20-iteration line goes from ~0.53 to ~0.42.\n\n(d) The fourth graph plots 'R-squared' on the y-axis from 0.30 to 0.42. In this case, the R-squared value decreases as the training data percentage increases. The model with 20 iterations has the highest R-squared values (from ~0.425 to ~0.335), while the model with 100 iterations has the lowest (from ~0.39 to ~0.295).\n\nThe caption below reads: 'Figure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-squared.'", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "text", "line_start": 318, "line_end": 328, "token_count_estimate": 451, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3329c2dbe20861cc", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: figure\nFigure: Figure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.\n\nFigure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "figure", "figure_caption": "Figure 7. Performance assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.", "line_start": 329, "line_end": 329, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2453c53742464906", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data\nType: text\n\nwith 20, 40, 60, 80, and 100 iterations is 0.620, 0.569, 0.524, 0.463, and 0.436, respectively, for 90% of training data. In Figure 7(c), the analysis of the ShCNNFDMN based on MAE is exhibited. With 90% of training data, the value of MAE measured by the ShCNNFDMN is 0.423, 0.422, 0.417, 0.387, and 0.364, corresponding to 20, 40, 60, 80, and 100 iterations. Likewise, the assessment of the ShCNNFDMN in terms of R-Squared is illustrated in Figure 7(d). The ShCNNFDMN is observed to have attained a R-Squared of 0.338 for 20 iterations, 0.318 for 40 iterations, 0.318 for 60 iterations, 0.311 for 80 iterations, and 0.296 for 100 iterations with 90% of training data.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.4. Performance analysis > 4.4.2. Considering training data", "section_headings": ["4. Results and Discussion", "4.4. Performance analysis", "4.4.2. Considering training data"], "chunk_type": "text", "line_start": 330, "line_end": 332, "token_count_estimate": 259, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "92e1253a44938218", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.5. Comparative techniques\nType: text\n\nThe efficiency of the ShCNNFDMN is investigated by comparing the proposed technique with other prevailing\n\nGLOF risk assessment methods, such as the Physical hydrodynamic model (Sattar et al. 2021), GLOF Hazard assessment (Liu et al. 2020), Lake and breach Hazard assessment (Emmer and Vilímek 2013), HEC-RAS (Wang et al. 2018), Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.5. Comparative techniques", "section_headings": ["4. Results and Discussion", "4.5. Comparative techniques"], "chunk_type": "text", "line_start": 334, "line_end": 338, "token_count_estimate": 136, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ab6733ca401d8e3", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis\nType: text\n\nThe analysis of the ShCNNFDMN is carried out to examine its superiority in performing GLOF risk/hazard assessment by taking into consideration different values of training data and k-fold.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis"], "chunk_type": "text", "line_start": 340, "line_end": 342, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ed7b651b825d8ec", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold\nType: text\n\nThe examination of the ShCNNFDMN based on k-fold is with respect to hazard modelling error and risk prediction error is displayed in Figure 8. The hazard", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.1. Using K-fold"], "chunk_type": "text", "line_start": 344, "line_end": 346, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "edad612e875a9d16", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold\nType: figure\nFigure\n\nImage /page/14/Figure/1 description: Two line graphs, labeled (a) and (b), compare the performance of seven different models. Both graphs plot an error metric on the y-axis against 'K-Fold' on the x-axis, which ranges from 5 to 9. Graph (a) shows 'Hazard modelling error' on the y-axis, ranging from 0.50 to 0.75. Graph (b) shows 'Risk prediction error' on the y-axis, ranging from 0.45 to 0.75. The seven models compared are: 'Physical hydrodynamic model' (blue), 'GLOF Hazard assessment' (pink), 'Lake and breach Hazard assessment' (green), 'HEC-RAS' (cyan), 'Two-dimensional hydrodynamic model' (red), 'Scenario-based multi-source GLOF' (yellow), and 'Proposed ShCNNFDMN' (dark gray). In both graphs, the 'Proposed ShCNNFDMN' model consistently has the lowest error across all K-Fold values. In graph (a), its error decreases from approximately 0.52 at K-Fold 5 to 0.47 at K-Fold 9. In graph (b), its error decreases from approximately 0.56 at K-Fold 5 to 0.43 at K-Fold 9. The other models generally show higher and more variable error rates.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.1. Using K-fold"], "chunk_type": "figure", "figure_caption": null, "line_start": 347, "line_end": 347, "token_count_estimate": 375, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "82ef2db8638b4635", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold\nType: figure\nFigure\n\nImage /page/14/Figure/2 description: A line graph, labeled (c), plots the Mean Absolute Error (MAE) on the y-axis against the K-Fold value on the x-axis. The x-axis ranges from 5 to 9, while the y-axis ranges from 0.375 to 0.525. The graph compares the performance of seven different models, each represented by a colored line with unique markers. The legend identifies six of the models: 'GLOF Hazard assessment' (dark blue line with 'x' markers), 'Lake and breach Hazard assessment' (pink line with star markers), 'HEC-RAS' (cyan line with triangle markers), 'Two-dimensional hydrodynamic model' (red line with circle markers), 'Scenario-based multi-source GLOF' (yellow line with diamond markers), and 'Proposed ShCNNFDMN' (gray line with pentagon markers). A seventh line, green with circular markers, is also present but not identified in the legend. The 'Proposed ShCNNFDMN' model consistently shows the lowest MAE across all K-Fold values, decreasing from approximately 0.445 at K-Fold 5 to 0.362 at K-Fold 9. Most other models also show a general downward trend in MAE as the K-Fold value increases.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.1. Using K-fold"], "chunk_type": "figure", "figure_caption": null, "line_start": 349, "line_end": 349, "token_count_estimate": 368, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b78a22e91a6385d8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold\nType: figure\nFigure\n\nImage /page/14/Figure/3 description: A line graph, labeled (d), showing the relationship between R-squared values and K-Fold for seven different models. The x-axis is labeled 'K-Fold' and ranges from 5 to 9. The y-axis is labeled 'R-squared' and ranges from 0.30 to 0.44. The graph displays seven lines, each representing a different model, and all lines show a downward trend, indicating that the R-squared value decreases as the K-Fold value increases. The models and their approximate data points are as follows: 'Physical hydrodynamic model' (blue line with x markers) starts at approximately R-squared=0.445 at K-Fold=5 and ends at 0.348 at K-Fold=9. 'GLOF Hazard assessment' (pink line with star markers) starts at 0.443 and ends at 0.345. 'Lake and breach Hazard assessment' (green line with circle markers) starts at 0.435 and ends at 0.33. 'HEC-RAS' (cyan line with triangle markers) starts at 0.42 and ends at 0.33. 'Two-dimensional hydrodynamic model' (red line with circle markers) starts at 0.422 and ends at 0.325. 'Scenario-based multi-source GLOF' (yellow line with diamond markers) starts at 0.412 and ends at 0.312. 'Proposed ShCNNFDMN' (gray line with pentagon markers) consistently has the lowest R-squared values, starting at 0.398 and ending at 0.30.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.1. Using K-fold"], "chunk_type": "figure", "figure_caption": null, "line_start": 351, "line_end": 351, "token_count_estimate": 435, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b0ec44b8da418f5", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold\nType: figure\nFigure: Figure 8. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared considering k-fold.\n\nFigure 8. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared considering k-fold.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.1. Using K-fold"], "chunk_type": "figure", "figure_caption": "Figure 8. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared considering k-fold.", "line_start": 353, "line_end": 353, "token_count_estimate": 148, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f892abb62a4b6a67", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold\nType: text\n\nmodelling error-based assessment of the ShCNNFDMN is demonstrated in Figure 8(a). The ShCNNFDMN is found to have produced a hazard modelling error of 0.483with k-fold of 8, while the prevailing risk assessment approaches, such as the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach assessment, HEC-RAS, Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF, measured hazard modelling error of 0.620, 0.594, 0.619, 0.612, 0.583, and 0.516, correspondingly. In Figure 8(b), the investigation of the ShCNNFDMN with respect to risk prediction error is presented, For a k-fold value of 8, the risk prediction error recorded by the Physical hydrodynamic model is 0.639, GLOF Hazard assessment is 0.554, Lake and breach Hazard assessment is 0.526, HEC-RAS is 0.544, Two-dimensional hydrodynamic model is 0.527, Scenario-based multi-source GLOF is 0.496, and ShCNNFDMN is 0.454. The MAE-based assessment ShCNNFDMN is demonstrated in Figure 8(c). The ShCNNFDMN is found to have produced a MAE of 0.379 with k-fold of 8, while the prevailing risk assessment approaches, such as the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF, measured hazard modelling error of 0.470, 0.450, 0.448, 0.425, 0.416, and 0.415, correspondingly. In Figure 8(d), the investigation of the ShCNNFDMN with respect to R-Squared is presented, For a k-fold value of 8, the R-Squared recorded by the Physical hydrodynamic model is 0.366, GLOF Hazard assessment is 0.356, Lake and breach Hazard assessment is 0.355, HEC-RAS is 0.350, Two-dimensional hydrodynamic model is 0.344, Scenario-based multi-source GLOF is 0.330, and ShCNNFDMN is 0.313.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.1. Using K-fold", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.1. Using K-fold"], "chunk_type": "text", "line_start": 354, "line_end": 356, "token_count_estimate": 545, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d72828ccc404ab94", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data\nType: figure\nFigure: Figure 9 represents the analysis of the ShCNNFDMN for GLOF risk/hazard assessment considering different percentages of training data. The ShCNNFDMN is examined for its efficacy on the basis of hazard\n\nFigure 9 represents the analysis of the ShCNNFDMN for GLOF risk/hazard assessment considering different percentages of training data. The ShCNNFDMN is examined for its efficacy on the basis of hazard", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.2. Based on training data"], "chunk_type": "figure", "figure_caption": "Figure 9 represents the analysis of the ShCNNFDMN for GLOF risk/hazard assessment considering different percentages of training data. The ShCNNFDMN is examined for its efficacy on the basis of hazard", "line_start": 359, "line_end": 359, "token_count_estimate": 144, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f1a911e6eaac1dc", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data\nType: figure\nFigure\n\nImage /page/15/Figure/3 description: A set of four line graphs, labeled (a), (b), (c), and (d), comparing the performance of seven different models against varying amounts of training data. The x-axis for all graphs is 'Training data (%)', ranging from 50 to 90. The seven models compared are: 'Physical hydrodynamic model', 'GLOF Hazard assessment', 'Lake and breach Hazard assessment', 'HEC-RAS', 'Two-dimensional hydrodynamic model', 'Scenario-based multi-source GLOF', and 'Proposed ShCNNFDMN'.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.2. Based on training data"], "chunk_type": "figure", "figure_caption": null, "line_start": 361, "line_end": 361, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3f8890a317b9ed1", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data\nType: text\n\nGraph (a) plots 'Hazard modelling error' on the y-axis (from 0.45 to 0.75). Generally, the error for all models decreases as training data increases. The 'Proposed ShCNNFDMN' model consistently has the lowest error, decreasing from approximately 0.54 to 0.48. The 'Physical hydrodynamic model' starts with the highest error at about 0.74.\n\nGraph (b) plots 'Risk prediction error' on the y-axis (from 0.45 to 0.75). The error trends downwards for most models with more training data. The 'Proposed ShCNNFDMN' model again shows the best performance with the lowest error, dropping from about 0.57 to 0.42.\n\nGraph (c) plots 'MAE' (Mean Absolute Error) on the y-axis (from 0.350 to 0.550). All models show a decrease in MAE as training data increases. The 'Proposed ShCNNFDMN' model has the lowest MAE, decreasing from around 0.435 to 0.36. The 'Physical hydrodynamic model' has the highest MAE across the range.\n\nGraph (d) plots 'R-squared' on the y-axis (from 0.30 to 0.44). In this graph, the R-squared values for all models generally decrease as the percentage of training data increases. The 'Physical hydrodynamic model' has the highest R-squared values, while the 'Proposed ShCNNFDMN' has the lowest.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.2. Based on training data"], "chunk_type": "text", "line_start": 362, "line_end": 370, "token_count_estimate": 404, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca80f2bb0c6fd109", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data\nType: figure\nFigure: Figure 9. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.\n\nFigure 9. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.2. Based on training data"], "chunk_type": "figure", "figure_caption": "Figure 9. Comparative assessment of the ShCNNFDMN based on (a) Hazard modelling error, (b) Risk prediction error, (c) MAE, and (d) R-Squared based on training data.", "line_start": 371, "line_end": 371, "token_count_estimate": 148, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6dc249370ccf7cae", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data\nType: text\n\nmodelling error and this is portrayed in Figure 9(a). For training data of 80%, the hazard modelling error produced by the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, Scenario-based multi-source GLOF, and ShCNNFDMN is 0.601, 0.613, 0.627, 0.669, 0.592, 0.542, and 0.484, respectively. Likewise, Figure 9(b) illustrates the examination of the ShCNNFDMN on the basis of risk prediction error. The risk prediction error measured by ShCNNFDMNis 0.463, for 80% of training data, whereas the other techniques produced risk prediction error of 0.563 for the Physical hydrodynamic model, 0.553 for GLOF Hazard assessment, 0.517 for Lake and breach Hazard assessment, 0.541 for HEC-RAS, 0.516 for Two-dimensional hydrodynamic model, 0.495 for Scenario-based multi-source GLOF. The ShCNNFDMN is examined for its efficacy on the basis of MAE and this is portrayed in Figure 9(c). For training data of 80%, the MAE produced by the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, Scenario-based multisource GLOF, and ShCNNFDMN is 0.473, 0.452, 0.446, 0.443, 0.440, 0.423, and 0.380, respectively. The R-Squared-based assessment of the ShCNNFDMN is demonstrated in Figure 9(d). The ShCNNFDMN is found to have produced a R-Squared of 0.307 with training data 80%, while the prevailing risk assessment approaches, such as the Physical hydrodynamic model, GLOF Hazard assessment, Lake and breach Hazard assessment, HEC-RAS, Two-dimensional hydrodynamic model, and Scenario-based multi-source GLOF, measured hazard modelling error of 0.370, 0.350, 0.349, 0.339, 0.320, and 0.318, correspondingly.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.6. Comparative analyis > 4.6.2. Based on training data", "section_headings": ["4. Results and Discussion", "4.6. Comparative analyis", "4.6.2. Based on training data"], "chunk_type": "text", "line_start": 372, "line_end": 374, "token_count_estimate": 532, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3792c07f1ed918d8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.7. Comparative discussion\nType: text\n\nThe GLOF risk/hazard assessment technique proposed in this work using ShCNNFDMN is examined for its", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.7. Comparative discussion", "section_headings": ["4. Results and Discussion", "4.7. Comparative discussion"], "chunk_type": "text", "line_start": 376, "line_end": 380, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "703851cedf29dfc6", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.7. Comparative discussion\nType: table\nTable: Table 2. Comparative discussion of the ShCNNFDMN.\n\n| Variations       | Metrics                      | Physical hydrodynamic model | GLOF Hazard assessment | Lake and breach Hazard assessment | HEC- RAS | Two-dimensional hydrodynamic model | Scenario-based multi-source GLOF | Proposed ShCNNFDMM |\n|------------------|------------------------------|-----------------------------------|---------------------------|-----------------------------------------|-------------|------------------------------------------|----------------------------------------|-----------------------|\n| K-fold           | Hazard modelling error | 0.613                             | 0.634                     | 0.603                                   | 0.563       | 0.557                                    | 0.507                                  | 0.473                 |\n|                  | Risk prediction error  | 0.554                             | 0.604                     | 0.551                                   | 0.517       | 0.483                                    | 0.467                                  | 0.427                 |\n|                  | MAE                          | 0.450                             | 0.442                     | 0.437                                   | 0.436       | 0.415                                    | 0.398                                  | 0.363                 |\n|                  | R-squared                    | 0.348                             | 0.343                     | 0.330                                   | 0.328       | 0.325                                    | 0.313                                  | 0.297                 |\n| Training data | Hazard modelling error | 0.565                             | 0.601                     | 0.578                                   | 0.638       | 0.573                                    | 0.527                                  | <b>0.462</b>          |\n|                  | Risk prediction error  | 0.592                             | 0.545                     | 0.592                                   | 0.466       | 0.453                                    | 0.435                                  | <b>0.423</b>          |\n|                  | MAE                          | 0.439                             | 0.427                     | 0.416                                   | 0.390       | 0.382                                    | 0.371                                  | <b>0.358</b>          |\n|                  | R-squared                    | 0.353                             | 0.340                     | 0.328                                   | 0.313       | 0.305                                    | 0.303                                  | <b>0.288</b>          |", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.7. Comparative discussion", "section_headings": ["4. Results and Discussion", "4.7. Comparative discussion"], "chunk_type": "table", "table_caption": "Table 2. Comparative discussion of the ShCNNFDMN.", "columns": ["Variations", "Metrics", "Physical hydrodynamic model", "GLOF Hazard assessment", "Lake and breach Hazard assessment", "HEC- RAS", "Two-dimensional hydrodynamic model", "Scenario-based multi-source GLOF", "Proposed ShCNNFDMM"], "table_row_start": 1, "table_row_end": 8, "line_start": 381, "line_end": 390, "token_count_estimate": 504, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9cfb1284adfb13f8", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.7. Comparative discussion\nType: text\n\nefficiency considering hazard modelling error and risk prediction error with respect to other related works and this is explicated in Table 2. The values of hazard modelling error, risk prediction error, MAE, and Rsquared portrayed are recorded when k-fold value of 9 and training data of 90% are considered. The superior values of 0.462, 0.423, 0.358, and 0.288 are obtained by the ShCNNFDMN assessment based on hazard modelling error, risk prediction error, MAE, and R-Squared for 90% training data. The usage of multistage classifier developed with ShCNN and DMN based on regression analysis effectively modelled the hazard and risk values, thus minimising the risk prediction error.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.7. Comparative discussion", "section_headings": ["4. Results and Discussion", "4.7. Comparative discussion"], "chunk_type": "text", "line_start": 391, "line_end": 393, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1586517a263b6ec", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:\nType: text\n\nThe proposed ShCNNFDMN is formulated by combining the ShCNN and DMN. The key advantage of using the ShCNN is its low computational cost and its ability in realising end-to-end TVI operations for intermittently spaced data. The network attains superior results by adding a smaller number of feature maps and optimisation of the TVI process. DMN perform classification at various stages, and thus in the DMN, more refined features are used along with the shapeindexed matrix, which minimises the classification cost. Here, the DMN is mainly applied for hazard assessment due to its ability to produce superior outcomes in resource-constrained scenarios. DMN predict", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:", "section_headings": ["4. Results and Discussion", "4.7. Comparative discussion", "Below are some of the factors that contribute to the proposed approach's superior performance:"], "chunk_type": "text", "line_start": 395, "line_end": 399, "token_count_estimate": 214, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7ee11bfaf61ff227", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:\nType: table\nTable: Table 3. ANOVA test for the proposed method.\n\n|             | Sum of squares | Degrees of freedom | Mean squares | F    | P value   |\n|-------------|----------------|-----------------------|--------------|------|-----------|\n| Factor 1 | 14.35          | 3                     | 7.845        | 18.2 | 7.823e-05 |\n| Error       | 6.15           | 15                    | 0.571254     | -    | -         |\n| Total       | 19             | 17                    | -            | -    | -         |", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:", "section_headings": ["4. Results and Discussion", "4.7. Comparative discussion", "Below are some of the factors that contribute to the proposed approach's superior performance:"], "chunk_type": "table", "table_caption": "Table 3. ANOVA test for the proposed method.", "columns": ["", "Sum of squares", "Degrees of freedom", "Mean squares", "F", "P value"], "table_row_start": 1, "table_row_end": 3, "line_start": 400, "line_end": 404, "token_count_estimate": 189, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["571254"]}}
{"id": "73d222acfa534d98", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:\nType: text\n\nmore consistently and accurately. Thus, the proposed method ShCNNFDMN produce better performance.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.7. Comparative discussion > Below are some of the factors that contribute to the proposed approach's superior performance:", "section_headings": ["4. Results and Discussion", "4.7. Comparative discussion", "Below are some of the factors that contribute to the proposed approach's superior performance:"], "chunk_type": "text", "line_start": 405, "line_end": 407, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee47a0126cba5382", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 4. Results and Discussion > 4.8. Analysis of ANOVA Test\nType: text\n\nThe ANOVA test analysis is shown in Table 3. It's known as the statistical approach. It is the procedure for breaking down the data variables into distinct parts in order to conduct further tests. To get information about the independent and dependent variables, the data are grouped using ANOVA. The F test is the another name for the ANOVA test. The ANOVA formula is written as follows:\n\n$$F = \\frac{MST}{MSE}$$\n\nWhere, F = ANOVA coefficient, MST = Mean sum of squares due to treatment, MSE = Mean sum of squares due to error.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "4. Results and Discussion > 4.8. Analysis of ANOVA Test", "section_headings": ["4. Results and Discussion", "4.8. Analysis of ANOVA Test"], "chunk_type": "text", "line_start": 409, "line_end": 415, "token_count_estimate": 175, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "825466812edcea94", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: 5. Conclusion\nType: text\n\nThis paper uses a unique deep-learning network called ShCNNFDMN to propose a fresh framework for assessing GLOF risks and hazards. In this case, evaluation is done based on a number of glacial lake attributes, including geometric, geographical, lake-based, and global attributes. First, the features are taken from the input data and evaluated by the ShCNNFDMN. Regression analysis is performed using the FC idea, and the resulting ShCNNFDMN is the result of the fusion of ShCNN and DMN. Initially, the ShCNN is trained with the geometric feature of the lake that was retrieved in the form of a shape-indexed matrix for classification. The final output and features are combined using the ShCNNFDMN layer, and for fine risk assessment, the DMN and shape-indexed matrix are applied. Superior values of 0.462, 0.423, 0.358, and 0.288 are noted in the ShCNNFDMN assessment based on hazard modelling error, risk prediction error, MAE, and R-Squared. The proposed method helps to prevent major hazards and risks. The proposed method is useful in various applications, like proper infrastructure planning, and taking preventive and mitigative actions in downstream areas of glacier lakes. The limitation of the proposed method is that it does not consider more evalution metrics to evaluate the model's performance. In future work, parameters, like the geomorphic parameters of the river channel, such as slope, and moraine can be considered to improve the risk assessment efficiency. Also, some more evaluation metrics will be considered to evaluate the model's performance.", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "5. Conclusion", "section_headings": ["5. Conclusion"], "chunk_type": "text", "line_start": 417, "line_end": 419, "token_count_estimate": 403, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5cd6b856cc96bc6f", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: Disclosure statement\nType: text\n\nNo potential conflict of interest was reported by the author(s).", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "Disclosure statement", "section_headings": ["Disclosure statement"], "chunk_type": "text", "line_start": 421, "line_end": 423, "token_count_estimate": 37, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd672f54d9033846", "text": "Document: Deep learning-based GLOF modelling for hazard assessment and risk management\nSection: Funding\nType: text\n\nThis work was supported by The International Talent Program of the Chinese Academy of Sciences [grant number 2021PC0045].", "metadata": {"source_file": "data/('Deep learning-based GLOF modelling for hazard assessment and risk management', '.pdf')_extraction.md", "document_title": "Deep learning-based GLOF modelling for hazard assessment and risk management", "section_path": "Funding", "section_headings": ["Funding"], "chunk_type": "text", "line_start": 425, "line_end": 427, "token_count_estimate": 50, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["2021PC0045"]}}
{"id": "b69019027d2ae3aa", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nType: text\n\nAbstract: Glacial lakes are not only the important refresh water resources in alpine region, but also act as a trigger of many glacial hazards such as glacial lake outburst flood (GLOF) and debris flow. Therefore, glacial lakes play an important role on the cryosphere, climate change and alpine hazards. In this paper, the issues of glacial lake were systematically discussed, then from the view of glacial lake inventory and glacial lake hazards study, the glacial lake was defined as natural water mainly supplied by modern glacial meltwater or formed in glacier moraine's depression. Furthermore, a complete classification system of glacial lake was proposed based on its formation mechanism, topographic feature and geographical position. Glacial lakes were classified as 6 classes and 8 subclasses, i.e., glacial erosion lake (including cirque lake, glacial valley lake and other glacial erosion lake), moraine-dammed lake (including end moraine-dammed lake, lateral moraine-dammed lake and moraine thaw lake), ice-blocked lake (including advancing glacier-blocked lake and other glacier-blocked lake), supraglacial lake, subglacial lake and other glacial lake. Meanwhile, some corresponding features exhibiting on remote sensing image and quantitative indices for identifying different glacial lake types were proposed in order to build a universal and operational classification system of glacial lake.\n\nKeywords: glacial lake; glacier; definition; classification; inventory; hazard", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 377, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aab0bcf26b74cd82", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: Introduction\nType: text\n\nLakes refer to water bodies which are formed in land surface basin or waterlogged depression, having certain water area and water exchange being relatively slow (Ma et al., 2011). Lakes are not only the important component of the Earth's hydrosphere, but also can faithfully record the regional climate change in different time scales and human activities around\n\n**Received:** 2017-04-23 **Accepted:** 2017-06-05\n\nFoundation: National Natural Science Foundation of China, No.41261016, No.41561016; Opening Foundation Projection of State Key Laboratory of Cryosphere Sciences, CAS, No.SKLCS-OP-2016-10; Youth Scholar Scientific Capability Promoting Project of Northwest Normal University, No.NWNU-LKQN-14-4; Geological Survey Project of China Geological Survey, No.DD2016034206\n\nAuthor: Yao Xiaojun (1980-), PhD and Associate Professor, specialized in the research of GIS and cryospheric change. E-mail: xj\\_yao@nwnu.edu.cn\n\nlake. So lake is often considered as an important information carrier to reveal global climate change and the regional response (Cenderelli and Wohl, 2001; Ding *et al.*, 2006; Yao *et al.*, 2014; Liu *et al.*, 2014). Glacial lakes, as one of the lake types, are precious fresh water resources and the natural landscape in alpine region, and yet act as a trigger of many glacial hazards (Richardson and Reynolds, 2000; Wang *et al.*, 2010). Under the background of global warming, glacier-related hazards such as glacial lake outburst flood (GLOF) and debris flow presented an increase tendency in amount and harmful intensity (Cui *et al.*, 2014). In the Tibetan Plateau, for example, there had been at least 28 GLOF events since the 1930s (Sun *et al.*, 2014; Yao *et al.*, 2014), and had characteristics of frequency increase and temporal extension after 2000. Therefore, glacial lakes have attracted widespread attention from the academia and local government (Cui *et al.*, 2014).", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 20, "token_count_estimate": 528, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["41261016", "41561016"]}}
{"id": "c5a78d1ff6a8c46f", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: Introduction\nType: text\n\n. Under the background of global warming , glacier - related hazards such as glacial lake outburst flood ( GLOF ) and debris flow presented an increase tendency in amount and harmful intensity ( Cui * et al . * , 2014 ) . In the Tibetan Plateau , for example , there had been at least 28 GLOF events since the 1930s ( Sun * et al . * , 2014 ; Yao * et al . * , 2014 ) , and had characteristics of frequency increase and temporal extension after 2000 . Therefore , glacial lakes have attracted widespread attention from the academia and local government ( Cui * et al . * , 2014 ) .\n\nGlobally, glacial lakes are mainly located in North America (e.g. Rocky Mountains and Coastal Mountains of Alaska, USA) (O'Connor and Costa, 1993; Clague and Evans, 2000), South America (e.g. Andes Mountains) (Carey, 2005), Europe (e.g. Iceland, the Alps and Caucasus) (Huggel et al., 2002; Björnsson, 2003; Stokes et al., 2007; Emmer et al., 2015), and Asia (e.g. Altay Mountains, Tianshan Mountains, Karakoram Range, the Himalayas) (Fujita et al., 2008; Chen et al., 2010; Janský et al., 2010; Li et al., 2011; Engel et al., 2012; Wang et al., 2012; Wang et al., 2013; Wang et al., 2016; Song et al., 2016; Song et al., 2017). The interest area of glacial lakes in China was mainly concentrated in the Himalayas (Gao et al., 2015; Liao et al., 2015), Tianshan Mountains (Wang et al., 2013), Karakoram Range (Wortmann et al., 2014), Altay Mountains (Chen et al., 2015) and southeastern Tibet (Wang et al., 2012; Song et al., 2016). And these studies focused on the change of glacial lakes (Liu et al., 2011; Wang et al., 2013; Gao et al., 2015; Song et al., 2016; Wang et al., 2016), the identification of potential dangerous glacial lakes (Cao et al., 2016; Liu et al., 2016), and the simulation of GLOFs (Le et al., 2014). In recent years, the inventory of glacial lakes in some mountainous regions had been carried out based on multi-source remote sensing images by scholars and institutions. For instance, Wang et al. (2010) and Pradeep et al. (2001) digitalized the glacial lakes in the Hindu Kush-Himalaya Range, respectively. These datasets had been an important basis for recognizing the spatial-temporal characteristics of glacial lakes change and understanding the response of glacial lakes to the climate change in this region. However, there is still great controversy about the definition and classification system of glacial lakes, which will directly lead to some difficulties for comparing the result from different researches. Some conclusions of glacial lakes change were even opposite. For example, Gao et al. (2015) and Wang et al. (2014) analyzed the glacial lakes change in the Koshi River basin from 2000 to 2010, the former believed that both number and area of glacial lakes were increasing, but the latter thought that the number was decreasing and yet the area was increasing. Even more puzzling was the fact that, the remote sensing images in 2000 used in the two studies above were the same, with the number of glacial lakes being 1228 and 1680, respectively. Therefore, it is necessary and urgent to accurately define glacial lakes, to build a complete classification system of glacial lakes and to provide corresponding features for remote sensing identification.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 20, "token_count_estimate": 924, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "75f961d159975e0f", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 2 Glacial lake\nType: text\n\nIn the Glossary of Cryospheric Science (Qin et al., 2016), glacial lake is defined as lake\n\nformed by glaciation. In the Wikipedia Encyclopedia (https://en.wikipedia.org), a glacial lake is a lake with origins in a melted glacier. They are formed when a glacier erodes the land, and then melts, filling the hole or space that it has created. Lü et al. (1999) thought that a glacial lake was a natural water body similar to artificial reservoir formed by ancient or modern glaciers. Additionally, there are many similar definitions of glacial lake: (1) glacial lake is one kind of lake formed by glaciation or supplied by glacial meltwater (Cao et al., 2016); (2) glacial lake is plateau lake located at the terminus or the lateral part of one glacier and glacier provides water resource when it retreats or melts (Tang et al., 2014); (3) glacial lakes were located in a basin formed by alpine glacier movement since the Last Glaciation Maximum (LGM), and their water were mainly from modern glacial meltwater or atmospheric precipitation (Chen et al., 2015). In these definitions of glacial lake above, it is emphasized that glacial lakes are formed by glaciation. The main differences are whether the time information being given or not and the material source of glacial lakes. The discrepancy of accepted concept of glacial lake caused that it was difficult to separate glacial lakes from natural lakes, which was particularly obvious in the inventory of glacial lakes and the change researches afterwards. Instead, the lakes in a specified distance of glaciers were treated as glacial lakes by scholars. For instance, Wang et al. (2013) and Zhang et al. (2015) selected lakes in a 10 km buffer of glaciers as glacial lakes in the Tianshan Mountains and Tibetan Plateau. However, it is debatable that these lakes selected are whether supplied by glacial meltwater or there are other lakes formed by glaciation beyond the specified extent. Undoubtedly, glacial meltwater is the main material source of glacial lakes as these definitions above mentioned. But it is very difficult to quantify the glacial meltwater and calculate its proportion in lake water volume in reality. If one lake receives glacial meltwater but is distant from the glacier, or the glacier supplied lake disappears, it is also one problem to judge whether these lakes are glacial lakes or not. These challenges had been causing that some research results of glacial lakes were not comparable; meanwhile, it was also difficult to share dataset of glacial lakes among different organizations.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "2 Glacial lake", "section_headings": ["2 Glacial lake"], "chunk_type": "text", "line_start": 22, "line_end": 30, "token_count_estimate": 652, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "482474c414c0d253", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 2 Glacial lake\nType: text\n\nor there are other lakes formed by glaciation beyond the specified extent . Undoubtedly , glacial meltwater is the main material source of glacial lakes as these definitions above mentioned . But it is very difficult to quantify the glacial meltwater and calculate its proportion in lake water volume in reality . If one lake receives glacial meltwater but is distant from the glacier , or the glacier supplied lake disappears , it is also one problem to judge whether these lakes are glacial lakes or not . These challenges had been causing that some research results of glacial lakes were not comparable ; meanwhile , it was also difficult to share dataset of glacial lakes among different organizations .\n\nFrom the mechanism of glacial lake formation, glacial lakes are generated in erosion depressions caused by glaciers advance and retreat, and then receive glacial meltwater and precipitation. That is to say, glaciation is the dominant factor in the formation of glacial lakes. The LGM is the latest glaciation period when glacier coverage was the largest on the Earth. Theoretically, a glacial lake can be defined as the natural water body formed in the depression by glaciation since the LGM. However, there are still many problems and controversies in this definition of glacial lake. One thorny issue is the determination of the coverage extent of glaciers in the LGM. The other is whether the great lakes over the Tibetan Plateau such as Namco, Yamzho Yumco Lake are glacial lakes or not. Recently, the studies of glacial lake were mainly focused on the following fields: the inventory of glacial lakes based on remote sensing images, the response of glacial lakes to modern climate change, the coupling relationship between glacier variation and glacial lake evolution, the identification of dangerous glacial lakes and glacial lake outburst flood/debris flow disasters, etc. On the one hand, the focus of these studies is modern evolution processes of glacial lakes in the temporal dimension, and on the other hand they all emphasized the role of modern glaciers on the formation and evolution of glacial lakes. Therefore, in view of the modern process and practical application, glacial lakes can be defined as natural water mainly supplied by\n\nmodern glacial meltwater or formed in glacier moraine's depression. In the latter definition of glacial lake, it is emphasized on the role of modern glaciers in the formation and change of glacial lakes. For the inventory or digitalization of glacial lakes in one region, it is suggested that the glacier dataset including the WGI, the First and Second Chinese Glacier Inventory can be selected as the foundation of modern glaciers. To be sure, the classification system of glacial lakes in the next section is based on the latter definition of glacial lake.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "2 Glacial lake", "section_headings": ["2 Glacial lake"], "chunk_type": "text", "line_start": 22, "line_end": 30, "token_count_estimate": 684, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d93b86d8e221e8d7", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake\nType: text\n\nInternationally, there has been so far no accepted standard for the classification system of glacial lakes. Some organizations and scholars proposed the different classification systems of glacial lakes according to their own research purposes. In the inventory of glacial lakes in the Hindu Kush-Himalayan region, ICIMOD (Pradeep et al., 2001) divided the glacial lakes into 5 classes: glacial erosion lake, moraine-dammed lake, ice-blocked lake, supraglacial lake and subglacial lake. Based on this classification schema, Wu et al. (2011) proposed a detailed classification system including 3 classes and 10 subclasses. In the study of glacial lakes in the Altay Mountains, Yi and Cui (1994) suggested a multiple classification schema. According to the mechanical and thermal differences in the formation of glacial lakes, they were classified as glacial erosion lake, ice-blocked lake, moraine-dammed lake, glacial thaw lake and glacial composite lake. According to the water supply of glacial lakes, they were divided into ice-water lake and non-ice-water lake; the former were mainly supplied by glacial meltwater and the latter were dominated by surface runoff from atmospheric precipitation. In the inventory of glacial lakes in the Chinese Himalayas, Wang et al. (2010) presented a classification system including moraine-dammed lake, ice-blocked lake, cirque lake, glacial erosion lake, landslide-dammed lake, supraglacial lake and glacial valley lake. Cao et al. (2016) adopted a classification system including 3 classes and 6 subclasses: glacial erosion lake (including cirque lake and other glacial erosion lake), moraine-dammed lake (including end moraine-dammed lake, lateral moraine-dammed lake and other moraine-dammed lake) and supraglacial lake. Additionally, Wang et al. (2016) proposed an integrated classification schema of glacial lakes according to the formation age of glacial lakes, the properties of dam, the shape of lake basin, the water supply, the area change, the spatial relation between glacial lake and its supply glacier, the risk level, etc. But there was a defect of disagreement between the actual glacial lakes classification and the theoretical classification system. On the basis of previous researches and the principles of systematic, normalization, operability and scalability, we proposed a complete classification schema of glacial lakes including 6 classes: glacial erosion lake, moraine-dammed lake, ice-blocked lake, supraglacial lake, subglacial lake and other glacial lake (Table 1). According to the position or geomorphologic characteristics of glacial lakes, the first 3 classes can be divided into 8 subclasses; cirque lake, glacial valley lake, other glacial erosion lake, end moraine-dammed lake, lateral moraine-dammed lake, moraine thaw lake, advancing glacier-blocked lake and other glacier-blocked lake.\n\nDue to the wide application of satellite remote sensing images in the inventory of glacial lakes and studies of their changes, the interpretation characteristics of different types of glacial lakes are given in the following. Before the interpretation of glacial lake types, it is noted that researchers should firstly determine the approximate distribution of glacial lakes based on the inventory of glaciers and digital elevation model (DEM) data; that is to say,", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake", "section_headings": ["3 Classification system of glacial lake"], "chunk_type": "text", "line_start": 32, "line_end": 38, "token_count_estimate": 841, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "867215d87064f481", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake\nType: text\n\n; cirque lake , glacial valley lake , other glacial erosion lake , end moraine - dammed lake , lateral moraine - dammed lake , moraine thaw lake , advancing glacier - blocked lake and other glacier - blocked lake . Due to the wide application of satellite remote sensing images in the inventory of glacial lakes and studies of their changes , the interpretation characteristics of different types of glacial lakes are given in the following . Before the interpretation of glacial lake types , it is noted that researchers should firstly determine the approximate distribution of glacial lakes based on the inventory of glaciers and digital elevation model ( DEM ) data ; that is to say ,\n\nlakes are only considered in the basin of modern glaciers. Meanwhile, although the lake is easily identified because of the obvious differences in the spectral characteristics of water body and its surrounding features in remote sensing images, it is very difficult to distinguish the types of glacial lakes scarcely by a single sensor or mono-phase remote sensing image. So other data sources and technologies should be needed, too. For instance, DEM data can be used to produce the mountain shadow so as to remove the wrong interpretation of glacial lakes; 3D visualization technology such as the Google Earth and high-resolution remote sensing images can be integrated to distinguish the type of glacial lakes.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake", "section_headings": ["3 Classification system of glacial lake"], "chunk_type": "text", "line_start": 32, "line_end": 38, "token_count_estimate": 354, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e65959b7fad2c0ed", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake\nType: table\nTable\n\n| Table 1 | The classification | system of | glacial lakes |\n|---------|--------------------|-----------|---------------|\n|---------|--------------------|-----------|---------------|", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake", "section_headings": ["3 Classification system of glacial lake"], "chunk_type": "table", "table_caption": null, "columns": ["Table 1", "The classification", "system of", "glacial lakes"], "table_row_start": 1, "table_row_end": 1, "line_start": 39, "line_end": 41, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68e26ea1d004f320", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake\nType: table\nTable\n\n| Class                   | Subclass                       | Description                                                                          |\n|-------------------------|--------------------------------|--------------------------------------------------------------------------------------|\n| Glacial erosion lake | Cirque lake                    | The lake in one cirque                                                               |\n|                         | Glacial valley lake            | The lake in U-shaped valley by glaciation                                            |\n|                         | Other glacial erosion lake     | The lake formed by glacier erosion but not belonged to other classes of glacial lake |\n| Moraine-dammed lake  | End moraine-dammed lake        | The lake between the end moraine ridge and glacier terminus                          |\n|                         | Lateral moraine-dammed lake    | The lake beside the lateral moraine ridge                                            |\n|                         | Moraine thaw lake              | The lake on the moraine ridge                                                        |\n| Ice-blocked lake        | Advancing glacier-blocked lake | The lake blocked by advancing glacier                                                |\n|                         | Other glacier-blocked lake     | The lake with the dam being glacier ice                                              |\n| Supraglacial lake       |                                | The lake on the surface of glacier                                                   |\n| Subglacial lake         |                                | The lake within the glacier or over the glacier bed                                  |\n| Other glacial lake      |                                | The lake blocked by landslide, avalanche, debris flow, etc.                          |", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake", "section_headings": ["3 Classification system of glacial lake"], "chunk_type": "table", "table_caption": null, "columns": ["Class", "Subclass", "Description"], "table_row_start": 1, "table_row_end": 11, "line_start": 43, "line_end": 55, "token_count_estimate": 357, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83273ef0144aebcd", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake\nType: text\n\nGlacial erosion lake refers to the water body in the depression formed by erosion and abrasion of glacier in the process of glacier movement. At present, the common glacial erosion lakes were mostly formed by erosion of Quaternary glaciers. These glacial lakes were mainly supplied by atmospheric precipitation and had few modern glaciers in their upper part. As mentioned above, if no modern glaciers exist in the upper lakes, these lakes will not belong to glacial lakes. That is to say, ancient glacial erosion lakes should not be considered. According to the location and geomorphologic characteristics of glacial erosion lakes, they can be divided into the following 3 subclasses.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake"], "chunk_type": "text", "line_start": 58, "line_end": 60, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "467f4a861614ef90", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.1 Cirque lake\nType: text\n\nThe outstanding feature of the cirque lake is that it is located in the cirque. The typical cirque is like an arm-chair surrounded by three steep rock walls and a high reverse rock sill (see Figure 1, the centroid coordinates of the lake are 29°42'18\"N, 96°18'32\"E). Cirque lakes are usually small and near the equilibrium-line altitude (ELA) of glaciers. In the identification of cirque lake based on remote sensing images, it is necessary to use slope data derived from DEM data or Google Earth software to check the consistency between terrain features of glacial lakes and the cirque. If its terrain features satisfy the shape of the cirque, glacial lakes can be classified into cirque lake.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.1 Cirque lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.1 Cirque lake"], "chunk_type": "text", "line_start": 62, "line_end": 64, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a4606d4e6beedf1b", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake\nType: text\n\nLarge glaciers usually formed a U-shaped valley being steep and straight on both sides and\n\nflat at the bottom by laterally eroding in tributary and downward eroding on the glacier bed. Glacial meltwater and atmospheric precipitation flowed into the valley and then constituted a lake, namely glacial valley lake. This kind of glacial lakes were usually larger and far from modern glaciers. In order to expediently distinguish with moraine-dammed lakes since the Little Ice Age (LIA) and consider their potential danger on the downstream areas, some lakes in alpine region could been classified into glacial valley lake. According to the distance from the Akkol Lake to its", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.2 Glacial valley lake"], "chunk_type": "text", "line_start": 66, "line_end": 70, "token_count_estimate": 209, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e68f80ca5476f0a8", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake\nType: figure\nFigure\n\nImage /page/5/Picture/3 description: A Google Earth satellite image displays a rugged, snow-covered mountain range. In the center of the image, a dark blue body of water is outlined in yellow and identified by a label in the bottom-left corner as a \"Cirque lake.\" The lake is situated in a large, bowl-shaped valley, or cirque, surrounded by steep, icy mountain peaks and glaciers.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.2 Glacial valley lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 71, "line_end": 71, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bbbbb432a6d21580", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake\nType: figure\nFigure: Figure 1 Cirque lake\n\nFigure 1 Cirque lake", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.2 Glacial valley lake"], "chunk_type": "figure", "figure_caption": "Figure 1 Cirque lake", "line_start": 73, "line_end": 73, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ee9af834b829e51", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake\nType: text\n\nsupply glacier (Kanas Glacier) in the Altay Mountains and the area of Kanas Glacier (Figure 2), it is suggested that the identification of glacial valley lake can be based on the following criteria: (1) the morphology of lake should be U shaped; (2) there should be large glacier with an area of above 20 km<sup>2</sup> in the upper lake; (3) the distance from lake to its supply glacier should be less than 15 km so as to distinguish with tectonic lakes.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.2 Glacial valley lake"], "chunk_type": "text", "line_start": 74, "line_end": 76, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bcc23117ff951f39", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake\nType: figure\nFigure\n\nImage /page/5/Figure/6 description: A Landsat TM, 5-4-3 band combination satellite image of a mountainous, glaciated region. The image is framed by latitude and longitude coordinates, with latitude ranging from 49°0'N to 49°10'N and longitude from 87°30'E to 87°50'E. The landscape is dominated by light blue glaciers and snow, with exposed mountain ridges appearing in reddish-brown and green hues. Two key features are labeled: 'Akkol Lake', a dark blue body of water on the left, and 'Kanas Glacier', a large glacier on the right. A scale bar in the bottom right corner indicates a length of 4 km.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.2 Glacial valley lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 77, "line_end": 77, "token_count_estimate": 219, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8417d874daa66b38", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake\nType: figure\nFigure: Figure 2 Glacial valley lake\n\nFigure 2 Glacial valley lake", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.2 Glacial valley lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.2 Glacial valley lake"], "chunk_type": "figure", "figure_caption": "Figure 2 Glacial valley lake", "line_start": 79, "line_end": 79, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "064e1e3c121ddace", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.3 Other glacial erosion lake\nType: text\n\nGlacial lakes formed by glacier erosion, which are difficult to be identified by morphological features and do not belong to the other types describing in the flowing, can be classified as other glacial erosion lake.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.1 Glacial erosion lake > 3.1.3 Other glacial erosion lake", "section_headings": ["3 Classification system of glacial lake", "3.1 Glacial erosion lake", "3.1.3 Other glacial erosion lake"], "chunk_type": "text", "line_start": 82, "line_end": 84, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36b2bd74685e8d28", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake\nType: text\n\nMoraine-dammed lake is a water body between moraine ridge and glacier due to the ob-\n\nstruction of moraine ridge. According to the location, moraine-dammed lakes can be divided into 3 subclasses: end moraine-dammed lake, lateral moraine-dammed lake and moraine thaw lake", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake"], "chunk_type": "text", "line_start": 86, "line_end": 90, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50288feac84944b8", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake\nType: text\n\nWhen glacier retreats, glacial meltwater accumulated in the space between glacier terminus and end moraine ridge, then formed a lake, namely end moraine-dammed lake. This kind of moraine-dammed lake is the main type causing glacial lake outburst flood or debris flow in the Himalayas and Nyainqentanglha Mountains. End moraine-dammed lakes are usually easy to distinguish in the remote sensing image because they are mostly linked to glacier terminus or the distance between them is very small (Figure 3a). In addition, the end moraine ridge uplifting is clear in the Google Earth software (Figure 3b), which can help to identify the end moraine-dammed lake, too. In the downstream of larger glacier terminus, there are usually several end moraine ridges and some lakes are formed among them. These lakes are mainly supplied by glacial meltwater and are formed by glaciation, so they can be classified as end moraine-dammed lake. Based on the measurement among these lakes in the Himalayas, the distance between the upper and the lower end moraine-dammed lakes is less than 2.0 km. In other words, the lakes with a distance being far from the upper end moraine-dammed lake than 2.0 km should be not identified as end moraine-dammed lake.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.1 End moraine-dammed lake"], "chunk_type": "text", "line_start": 92, "line_end": 94, "token_count_estimate": 364, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "edd00dc071edef0f", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake\nType: figure\nFigure\n\nImage /page/6/Figure/5 description: A side-by-side comparison of two satellite images of a glacial region, labeled (a) and (b). Both images show the same area, with latitude coordinates from 27°54'N to 27°58'N and longitude from approximately 88°3'E to 88°6'E. Panel (a) is a 'Landsat OLI, 5-4-3 band combination' image. It displays a mountainous landscape with white glaciers and two blue lakes outlined and identified by a legend as 'End moraine-dammed lake'. A scale bar indicates a length of 1 km. Panel (b) is a 'Google Earth' image, providing a more detailed, photographic view. In this image, the lakes appear icy and light blue. Arrows point to the 'End moraine', a ridge of glacial debris that dams the lower lake.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.1 End moraine-dammed lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 95, "line_end": 95, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4cc4dadd41fe2c40", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake\nType: figure\nFigure: Figure 3 End moraine-dammed lake\n\nFigure 3 End moraine-dammed lake", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.1 End moraine-dammed lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.1 End moraine-dammed lake"], "chunk_type": "figure", "figure_caption": "Figure 3 End moraine-dammed lake", "line_start": 97, "line_end": 97, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2fa2baac8c9166f4", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.2 Lateral moraine-dammed lake\nType: text\n\nLateral moraine-dammed lake refers to the water body beside the lateral moraine ridge of glacier. These glacial lakes usually appear around the larger valley glacier and are formed by main valley glacier blocking the meltwater of tributary valley glacier. For instance, there are 5 lateral moraine-dammed lakes beside the Ngozumpa Glacier which is the largest glacier in the southern Mount Everest (Figure 4), and the third Gokyo Lake with an area of 0.59 km<sup>2</sup> is the biggest one.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.2 Lateral moraine-dammed lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.2 Lateral moraine-dammed lake"], "chunk_type": "text", "line_start": 100, "line_end": 102, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1e58f0d0eff988b", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake\nType: text\n\nIn the end/lateral moraine ridge of glacier, there are usually many but small ponds (Figure 5),\n\nwhich are named as glacial thaw lake or moraine-dammed lake (Yi and Cui. 1994; Wang et al., 2016). The principal agent controlling these lakes is freeze-thaw process. For example, dead ice within the moraine ridge melts due to the temperature rising, and then the surface sinks and forms the depression retaining water. Although these ponds do not receive glacial meltwater but are formed by the dead ice meltwater of glacier, they can be classified as glacial lakes, namely moraine thaw lake. It should be pointed out that thaw lakes widely distributed in rock glacier and frozen ground region do not belong to moraine thaw lake, and they should be", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.3 Moraine thaw lake"], "chunk_type": "text", "line_start": 104, "line_end": 108, "token_count_estimate": 244, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ce85d6fdf2a7a42", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake\nType: figure\nFigure\n\nImage /page/7/Picture/3 description: A Landsat TM satellite image, using a 4-3-2 band combination, shows the Ngozumpa Glacier flowing through a rugged, snow-covered mountain range. The glacier is a large, light-colored river of ice running vertically through the center of the image. Several dark blue lakes are visible in the surrounding brown and white terrain. Some of these lakes, located alongside the glacier, are outlined in purple to indicate they are \"Lateral moraine-dammed lakes,\" as explained by a legend in the bottom left corner.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.3 Moraine thaw lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 109, "line_end": 109, "token_count_estimate": 186, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0282ba34835c2da6", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake\nType: figure\nFigure: Figure 4 Lateral moraine-dammed lake\n\nFigure 4 Lateral moraine-dammed lake", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.3 Moraine thaw lake"], "chunk_type": "figure", "figure_caption": "Figure 4 Lateral moraine-dammed lake", "line_start": 111, "line_end": 111, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55cc4a321100f13c", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake\nType: text\n\neliminated in the inventory of glacial lakes.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.2 Moraine-dammed lake > 3.2.3 Moraine thaw lake", "section_headings": ["3 Classification system of glacial lake", "3.2 Moraine-dammed lake", "3.2.3 Moraine thaw lake"], "chunk_type": "text", "line_start": 112, "line_end": 114, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4b0b00eabc179b4", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.3 Ice-blocked lake\nType: text\n\nWhether the glacier advanced and blocked the valley or the branch glacier rapidly retreated and was separated from the main glacier, they all resulted in the water accumulation, and then formed a lake. The common feature of these lakes is that their dams are composed of glacier ice, so they can be named as ice-blocked lake. According to their forming mechanism, ice-blocked lakes can be divided into advancing glacier-blocked lake and other glacier-blocked lake. The representation of the former is Kyagar Thso Lake located in the Yarkant River basin in China (Zhang et al., 1989; Wang et al., 2009). An example of the latter is Merzbacher Lake near the South Inylchek Glacier in Kyrgyzstan (Liu et al., 1998; Shen et al., 2009). Other glacier-blocked lakes also exist in the interior of the Tibetan", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.3 Ice-blocked lake", "section_headings": ["3 Classification system of glacial lake", "3.3 Ice-blocked lake"], "chunk_type": "text", "line_start": 116, "line_end": 118, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "a4453b010bf1a9cd", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.3 Ice-blocked lake\nType: figure\nFigure\n\nImage /page/7/Figure/8 description: A figure with two panels, (a) and (b), showing satellite imagery of glacial lakes. Panel (a) is a Landsat OLI image displaying a wide view of a mountainous, glaciated area with latitude and longitude markings from 28°10'N to 28°14'N and 86°36'E to 86°40'E. A scale bar indicates a length of 2 km. A red box highlights a specific region containing several lakes. Panel (b) is a zoomed-in Google Earth image of the highlighted area from panel (a). It provides a detailed view of different types of lakes, which are labeled with arrows. These include a large 'end moraine-dammed lake', a smaller 'lateral moraine-dammed lake', and a cluster of small lakes within a yellow dashed outline identified as 'moraine thaw lake'.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.3 Ice-blocked lake", "section_headings": ["3 Classification system of glacial lake", "3.3 Ice-blocked lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 119, "line_end": 119, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "98cf2f9d9ede1866", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.3 Ice-blocked lake\nType: figure\nFigure: Figure 5 Moraine thaw lake (water body in blue-green color within the yellow-color polygon in (b)\n\nFigure 5 Moraine thaw lake (water body in blue-green color within the yellow-color polygon in (b)", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.3 Ice-blocked lake", "section_headings": ["3 Classification system of glacial lake", "3.3 Ice-blocked lake"], "chunk_type": "figure", "figure_caption": "Figure 5 Moraine thaw lake (water body in blue-green color within the yellow-color polygon in (b)", "line_start": 121, "line_end": 121, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21db1edaa13616c1", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.3 Ice-blocked lake\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A satellite image from Landsat OLI, using a 5-4-3 band combination, displays a mountainous region with glaciers. The map is marked with longitude lines from 80°48'E to 80°54'E and latitude lines from 34°18'N to 34°20'N. A specific body of water is outlined in yellow and identified in the legend as an 'Ice-blocked lake'. A scale bar in the lower-left corner indicates a distance of 2 kilometers.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.3 Ice-blocked lake", "section_headings": ["3 Classification system of glacial lake", "3.3 Ice-blocked lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 123, "line_end": 123, "token_count_estimate": 162, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e26708c13aad2af", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.3 Ice-blocked lake\nType: figure\nFigure: Figure 6 Ice-blocked lake over the Tibetan Plateau\n\nFigure 6 Ice-blocked lake over the Tibetan Plateau", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.3 Ice-blocked lake", "section_headings": ["3 Classification system of glacial lake", "3.3 Ice-blocked lake"], "chunk_type": "figure", "figure_caption": "Figure 6 Ice-blocked lake over the Tibetan Plateau", "line_start": 125, "line_end": 125, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "179fa13119247c86", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.3 Ice-blocked lake\nType: text\n\nPlateau (Figure 6), which has not been previously reported. For advancing glacier-blocked lakes, they usually survived for a few months to one year affected by temperature increase in low altitude and lake water erosion (Wang *et al.*, 2009). For other glacier-blocked lakes, their evolution presented different characteristics. For example, Merzbacher Lake outburst floods mostly occurred in July – September and caused 50 GLOF events from 1932 to 1997 (Liu *et al.*, 1998), showing a life cycle of glacier-blocked lake to some extent. But for glacier-blocked lakes in the interior of the Tibetan Plateau, they are usually stable.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.3 Ice-blocked lake", "section_headings": ["3 Classification system of glacial lake", "3.3 Ice-blocked lake"], "chunk_type": "text", "line_start": 126, "line_end": 128, "token_count_estimate": 200, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "88e6fac38401fedd", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.4 Supraglacial lake\nType: text\n\nSupraglacial lake refers to the water body on the surface of glacier due to different ablation. These lakes usually appear on the surface of ablation zone of debris-covered glaciers. As seen in Figure 7, there are many supraglacial lakes in Rongbuk Glacier on the northern Mount Everest, the largest of which had an area of 0.47 km² in 2016. In Tomur region of Tianshan Mountains, there are also many supraglacial lakes in some debris-covered glaciers, such as Tomur Glacier, Tugaibieliqi Glacier, Qong Terang Glacier, Koxkar Baxi Glacier, etc. When supraglacial lakes are connected with the drainage system inside the glacier, the lake water can be quickly exhausted. So supraglacial lakes change rapidly in annual and inter-annual scales. In view of the spatial resolution of Landsat TM/ETM+/OLI remote sensing images widely used, the quick variation of supraglacial lake, and being the origin of moraine-dammed lake (Richardson and Reynolds, 2000), it is suggested that the supraglacial lake with an area of above 0.02 km² can be collected in the inventory of glacial lakes. The main difference between supraglacial lake and moraine thaw lake is their locations: the former is located on the surface of glacier; the latter is located on the moraine ridge of glacier.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.4 Supraglacial lake", "section_headings": ["3 Classification system of glacial lake", "3.4 Supraglacial lake"], "chunk_type": "text", "line_start": 130, "line_end": 132, "token_count_estimate": 372, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d8ad21d5488ed15d", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.5 Subglacial lake\nType: text\n\nThe water body within the glacier can be named as subglacial lake. It had been found that there were more than 140 subglacial lakes under the Antartic ice sheet, the largest one was the Vostok Lake (Wingham *et al.*, 2006). For alpine glaciers, lakes like subglacial lake had not been reported until now. However, small supraglacial lakes possibly exist in the distribution region of maritime glaciers such as southeastern Tibetan Plateau, because the drainage", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.5 Subglacial lake", "section_headings": ["3 Classification system of glacial lake", "3.5 Subglacial lake"], "chunk_type": "text", "line_start": 134, "line_end": 136, "token_count_estimate": 152, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73ca331c25186bed", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.5 Subglacial lake\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A two-panel figure displaying satellite imagery of the Rongbuk Glacier. The left panel is a wide view from a Landsat OLI, 5-4-3 band combination, showing a vast area of snow-covered mountains and glaciers. The coordinates range from 86°45'E to 87°0'E longitude and 28°0'N to 28°10'N latitude. A scale bar at the bottom indicates a length of 4 km. A red box highlights a specific area on the glacier. The right panel is a magnified view of the highlighted area, showing a close-up of a supraglacial lake. The lake is colored light blue and outlined in magenta. A legend identifies the outlined shape as a 'Supraglacial lake'. The latitude for this panel ranges from 28°5'N to 28°10'N, and a scale bar at the bottom is labeled from 0 to 500 km.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.5 Subglacial lake", "section_headings": ["3 Classification system of glacial lake", "3.5 Subglacial lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 137, "line_end": 137, "token_count_estimate": 264, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb89ff9d8abb0ea1", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.5 Subglacial lake\nType: figure\nFigure: Figure 7 Supraglacial lake\n\nFigure 7 Supraglacial lake", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.5 Subglacial lake", "section_headings": ["3 Classification system of glacial lake", "3.5 Subglacial lake"], "chunk_type": "figure", "figure_caption": "Figure 7 Supraglacial lake", "line_start": 139, "line_end": 139, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ebeb7787311eb314", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.5 Subglacial lake\nType: text\n\nchannels under the glacier are well developed. It is impossible to identify subglacial lake only relying on satellite remote sensing image, other instrument like ground penetrating radar (GPR) is needed. For building a complete classification system of glacial lake, subglacial lake is listed, but it can be removed in the inventory of glacial lakes based on satellite remote sensing images.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.5 Subglacial lake", "section_headings": ["3 Classification system of glacial lake", "3.5 Subglacial lake"], "chunk_type": "text", "line_start": 140, "line_end": 142, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "38471861b5349f88", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 3 Classification system of glacial lake > 3.6 Other glacial lake\nType: text\n\nExcept for 5 classes of glacial lake mentioned above, the water body formed by landslide, rock collapse, avalanche and debris flow blocking the valley in the glaciation area is named as other glacial lake. This kind of lake belongs to dammed lake and has a great potential harm to the downstream settlements, roads, hydropower stations and other infrastructures. Therefore, it should be taken into account in the inventory of glacial lakes. The first basis for identifying the other glacial lake is its dam which can be examined from the high-resolution remote sensing images. The second is that the appearance of the other glacial lake is usually related to the earthquake, debris flow and other geologic activities, and there is an obvious change of lake size around the time. For instance, the debris flow caused by Ranzeria Co Lake outburst formed two glacial lakes in Jiali County (Sun *et al.*, 2014). The quantitative index of identifying the other glacial lake is suggested that the distance from the glacier terminus is below 10 km (Wang *et al.*, 2013; Zhang *et al.*, 2015).", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "3 Classification system of glacial lake > 3.6 Other glacial lake", "section_headings": ["3 Classification system of glacial lake", "3.6 Other glacial lake"], "chunk_type": "text", "line_start": 144, "line_end": 146, "token_count_estimate": 304, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d957e3384ee9810", "text": "Document: Definition and classification system of glacial lake for inventory and hazards study\nSection: 4 Conclusions\nType: text\n\nAs an important object of the cryospheric science, glacial lakes are not only closely related to the climate change and glacier movement, but also play a role on mountain disaster chain. Therefore, glacial lakes had been paid more and more attentions by many scientists and governments. In this paper, the definition of glacial lake was comprehensively discussed. It was noted that the definition was based on two perspectives of glaciation and glacial melt-\n\nwater. But the fuzzy of spatial and temporal information made the poor operability in the inventory of glacial lakes. In short, it is difficult to determine whether one lake is a glacial lake or not. Theoretically, a glacial lake can be defined as the natural water body formed in the depression by glaciation since the LGM. However, the controversy of glacial coverage area in the LGM and researches' knowledge deficiency of Quaternary glaciology also caused difficulties of identifying glacial lakes based on theoretical concept of glacial lake. On the consideration of the interest of glacial lakes' studies, an alternative definition of glacial lake was proposed, i.e. glacial lake could be defined as the natural water mainly supplied by modern glacial meltwater or formed in glacier moraine's depression. Meanwhile, a complete classification system of glacial lake was proposed based on its formation mechanism, topographic feature and geographical position. Glacial lakes were classified as 6 classes and 8 subclasses: glacial erosion lake (including cirque lake, glacial valley lake and other glacial erosion lake), moraine-dammed lake (including end moraine-dammed lake, lateral moraine-dammed lake and moraine thaw lake), ice-blocked lake (including advancing glacier-blocked lake and other glacier-blocked lake), supraglacial lake, subglacial lake and other glacial lake.\n\nAlthough this paper attempted to provide the features or quantitative indices for identifying different types of glacial lakes using satellite remote sensing images, and emphasized that the existence of modern glaciers was the primary basis for distinguishing a glacial lake, there were still some problems in the inventory of glacial lakes. When glaciers previously existed in the First Chinese Glacier Inventory and there were glacial lakes in the downstream, if these glaciers disappeared in the Second Chinese Glacier Inventory, did these lakes belong to glacial lakes? So it is very important to ensure the consistency of subject. In addition, the quantitative indices proposed for identifying some types of glacial lakes were derived from the typical glacial lakes, and their representativeness needs to be further studied.", "metadata": {"source_file": "data/('Definition and classification system of glacial lake for inventory and hazards study', '.pdf')_extraction.md", "document_title": "Definition and classification system of glacial lake for inventory and hazards study", "section_path": "4 Conclusions", "section_headings": ["4 Conclusions"], "chunk_type": "text", "line_start": 148, "line_end": 154, "token_count_estimate": 647, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "225532f8182e5fe2", "text": "Document: Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices\nType: text\n\nAbstract\n\nThis paper describes the creation and deployment of a Disaster Early Warning System (DEWS) designed to detect Glacial Lake Outburst Floods (GLOFs). This system is a Comprehensive Integrated dIsaster pRevention Configuration (CIIRC)that has been built to withstand sub-zero temperatures and harsh weather conditions while displaying durability and resilience. GLOFs pose a significant risk to communities living near glacial lakes, necessitating the use of advanced monitoring equipment. The proposed system may transfer crucial data in real time from remote glacier lake monitoring stations to a central hub via radio frequency/ long-range communication, or satellite communication technology. Key properties such as lake water level, temperature, hydro pressure, and seismic activity are continuously recorded and transmitted from remote areas to a central monitoring station (CMS). The CMS examines incoming data using risk assessment algorithms (thresholds), which provide warnings and notifications as soon as potential GLOF issues are detected. The integration of this DEWS with local emergency response plans allows for more prompt and informed decisionmaking, which helps to mitigate GLOF-related tragedies. This comprehensive system intends to improve community resilience by delivering early warnings and facilitating proactive reaction measures.\n\nKeywords: GLOF, EWS, Disaster, Pressure sensor, LoRa.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 7, "token_count_estimate": 356, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "25d5c5391b7fbb9a", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nA GLOF is defined as a rapid and catastrophic overflow due to the breakage of the moraine dam which dams a glacial lake. Normally, the moraine dams of a glacial lake comprise unconsolidated rocks and sediments, which are sensitive to climatic changes and inputs generated by seismic activity as well as anthropogenic factors [1]. However, GLOF disasters may lead to destruction in the lower areas. This results in severe destruction and loss [2], [3] Climate change is resulting in increased incidence and intensity of GLOFs. Hence, the current need to increase early monitoring and risk mitigation of GLOF. Effective early prediction of GLOF It will update you on dangers of GLOF in advance. This can be done with various methods like\n\n- Remote Sensing: Besides the satellite image, there is other remote sensing-based data that could be used in monitoring the changes in the size of glacial lakes, water level, among other indicators of GLOF risk.\n- Ground-based monitoring: Ground-based stations are also used in gathering data on several parameters, including water levels, turbidity, and many more.\n  - •Numerical modeling: Numerical models can simulate how glacial lakes and moraine dams behave under different conditions.\n\n- GLOF mitigation aims at reducing the chances of a GLOF happening or its impacts if one happens. Some of the possible strategies adopted include:\n- Water level in the glacial lake: It can be drained with a controlled release or even by developing tunnels or spillways.\n- The moraine dams may be reinforced by either adding more material within the dam or constructing support structures.\n- •Relocation of communities and infrastructure outside hazard zones of GLOFs is considered the most effective measure of mitigation taken in the long term.\n  - Early Warning Systems (EWS): EWS may give the communities an opportunity to evacuate before the occurrence of a GLOF event. Most EWS depend on the combination of several early detection means with a communication system that issues warnings to affected communities.\n\nThe work on GLOF early detection and mitigation has been ongoing, and the new methods and technologies are in the pipeline very continuously. The most important thrust in such work is on making more appropriate and reliable techniques for early detection. The other key areas of research include developing cost-effective sustainable mitigation. In the current focus, this research relates to the design of DEWS for GLOF with real-time measurement and communication of data, analysis, and action.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 24, "token_count_estimate": 587, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30188454484206a4", "text": "Document: 1 Introduction\nSection: 2 Literature review\nType: text\n\nGLOFs are one of the foremost natural hazards occurring in mountainous regions, especially in the Himalayas and Andes. This is following the breach of the glacial lake dam, releasing a massive amount of water and debris that travels downstream. GLOFs also trigger incredible damage to structures, properties, and human life. Of late, there has been more frequency and intensity along with climate and glacier retreat. This requires GLOF detection with adequate EWS in place to mitigate the risks associated with such floods.\n\nThere are several studies that have been conducted on GLOF detection and EWS. For example, Wang W (2022) et.al [4] designed an early warning system for the Cirenmaco glacial lake located in the central Himalayas. The system is more interested in monitoring and providing various features with timely warnings rather than specific flood features. Another study about the computational challenges and solutions involved in the development of GLOF early warning system by Kumar B (2022) et.al [5]. It believes that an early warning system is required to monitor vulnerable glacier\n\nlakes and provide robust tools to the disaster managers to plan mitigation, thus saving human life and property. International Centre for Integrated Mountain Development (ICIMOD) has also made some studies on GLOF detection and EWS. In the report of Mitigation (2008) [6], ICIMOD speaks about EWS, monitoring, and GLOF mitigation measures. The GLOF warning systems were based on 'extended line of sight' (ELOS) VHF radio technology. The warning signal would be transmitted via ELOS ground wave\n\nAn early warning system on glacial lake outbursts to save lives and livelihoods of Nepal Himalaya communities was developed through a joint initiative of the Government of Nepal, UNDP in Nepal, and the Global Environment Facility [7]. The system encompasses hydro-met stations; GLOF sensors automated early warning sirens and a linked dynamic mass SMS alert system polygon. The system benefits more than 71,000 vulnerable people; both the local and the tourists visiting the Everest Region of Nepal. The system has the potential for replication and upscaling, with 21 critical lakes in Nepal and 25 in Tibet, China. Integration of climate actions in the routine planning and implementation holds the key for resilient and sustainable development. Among these, Kumar B (2020) et.al [5] proposed an ultrasonic sensor-based design architecture of a GLOF early warning system. The study majorly depicts the need to have an early warning system, especially one that detects GLOFs in real-time with timely warnings to the communities at risk.\n\nThe monitoring of GLOF and EWS is crucial for risk management associated with GLOF. This includes the deployment of remote sensing, ground-based sensors, and machine learning algorithms that incorporate data collection, processing, and analysis methods in the detection and prediction of GLOFs. These systems have been evaluated based on real-world data compared with existing GLOF detection and EWS. Besides this, new insight coupled with enhanced process understanding facilitated from reconstructing past GLOFs has contributed significantly to effective detection of GLOFs and EWS [8].", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Literature review", "section_headings": ["2 Literature review"], "chunk_type": "text", "line_start": 26, "line_end": 36, "token_count_estimate": 745, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "500dd84a5fd7bd79", "text": "Document: 1 Introduction\nSection: 3 Insights for CIIRC design > • Measuring Water Pressure:\nType: text\n\nMeasured at the installation point, the system measures the pressure of water within streams or rivers and in glacial lakes. Sudden changes in pressure may indicate a rising level of water-a warning sign for possible GLOF. A rise in hydro pressure could initiate subglacial conduits or tunnels, which would further weaken the glacier dam. By monitoring hydro pressure, the likelihood and triggering of a GLOF can be reflected and a prognosis drawn on the potential for dam failure.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Insights for CIIRC design > • Measuring Water Pressure:", "section_headings": ["3 Insights for CIIRC design", "• Measuring Water Pressure:"], "chunk_type": "text", "line_start": 40, "line_end": 48, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6235d90f363322e3", "text": "Document: 1 Introduction\nSection: 3 Insights for CIIRC design > Measuring Water Level:\nType: text\n\nThe system also monitors the fluctuations of the water level in the lake, which will trigger alarms with quick increases. Rapid rises in water level might be indicative of\n\nsudden water inflow, perhaps due to a breach in a glacier dam or icefall collapse. Ongoing monitoring of water level conditions can hence provide advance warnings to communities downstream.\n\n127 128", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Insights for CIIRC design > Measuring Water Level:", "section_headings": ["3 Insights for CIIRC design", "Measuring Water Level:"], "chunk_type": "text", "line_start": 50, "line_end": 56, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be63fcac982e4a31", "text": "Document: 1 Introduction\nSection: 3 Insights for CIIRC design > Measuring Temperature:\nType: text\n\nContinuous monitoring of temperature offers valuable information relating melt rates with changes in lake, river, and stream dynamics. Changes in lake water temperatures may indicate changes in the amount of glacial meltwater input, which can be due to increased ice melting or changes in the environment of glacial regions. Monitoring this change identifies the actual stability of the glacier lake. Statistical anomalies in lake water temperature, among other monitoring parameters, can generate alarms and warnings. A quick and steady rise in temperature may be an initial sign of the changes in the glacial environment and may give an early prelude to a GLOF.\n\n137 138", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Insights for CIIRC design > Measuring Temperature:", "section_headings": ["3 Insights for CIIRC design", "Measuring Temperature:"], "chunk_type": "text", "line_start": 58, "line_end": 62, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fdb4631a6f9e6c38", "text": "Document: 1 Introduction\nSection: 3 Insights for CIIRC design > • Measuring Seismic activities:\nType: text\n\nIt can capture sudden movements or changes in the glacier structure that may be a precursor to a GLOF event, providing more detail about the evolving dynamics. Recording acceleration or changes in velocity can add valuable data in many ways. Abrupt changes in seismic activity near the glacier dam may indicate structural weaknesses or failure points. Continuous monitoring will be able to deliver an estimation of probable loss due to dam failure and subsequent water overflow. Seismic signals that occur with a GLOF can be studied to determine more about the dynamics of the events, including the time of occurrence and the magnitude. This information will be used in understanding the characteristics of the flood and increasing the ability to analyze future risks. Pressure sensors combined with seismic sensors will enable subglacial processes to be monitored. For instance, even the process of creating subglacial conduits or tunnels may affect pressure dynamics and cause seismic signals. Integration of pressure and seismic data could thus offer a more defined view of subglacial activity.\n\n4 The DEWS Design flow\n\n- The proposed PSA based DEWS has been presented in a block diagram in figure 1. The proposed DEWS, therefore, caters to a specific requirement such as glacial lake monitoring and assessing the risk of GLOFs. The system, thus, is provided with Pressure Sensing Apparatus (PSA) along with a communication system, well suited to provide real-time data, such as hydro pressure and water level measurements and temperature measurements. The system can also incorporate a seismic sensor for\n- instance accelerometers for monitoring the seismic activity.\n\nIt has advanced communication interfaces such as RF/long range (LoRa)/ SATCOM modules (transmitter module at GLOF site and receiver module at central monitoring station) for early warning capabilities. Integrated data loggers of the pressure sensor (DCX-22) can be programmed to record and transmit the data to a central monitoring\n\n188 189\n\n195 196\n\nstation at regular intervals (minimum interval of 1min). Real-time data transmission can be derived from satellite communication, with suitable uplink and downlink facilities or through any other form of wireless RF communication means thus ensuring real-time monitoring at the location. The moment remote-sensing-based data from the DEWS is available, a DAS comes into play as a fundamental part of the early warning process.\n\nThe DAS is under constant surveillance, checking the real-time data against set thresholds and patterns. Such thresholds are mostly set by topographical data and are carefully fine-tuned by the insights gained from historical and researched contexts. The data acquisition system instantly sends an alarm signal whenever deviations cross a set threshold. The risk is thus passed to the concerned authorities, usually present at site in the locality; in this case, the local community in-charge / National Disaster Management Authority, NDMA through the signal and hence causes an urgency call for action. Advanced communication technology hastens the alarm signal and disaster management becomes urgent with a current emphasis. With the NDMA being the custodian authority, it thus coordinates quick and prompt disaster response to manage or lessen the impending disasters. Such responses by the NDMA will include various emergency measures that involve the inclusion of immediate alerts to the communities at risk. Such alerts will include such actions as emergency evacuation messages to people living in the vicinity of the disaster, thus reducing possible risks and saving lives and livelihoods.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Insights for CIIRC design > • Measuring Seismic activities:", "section_headings": ["3 Insights for CIIRC design", "• Measuring Seismic activities:"], "chunk_type": "text", "line_start": 64, "line_end": 85, "token_count_estimate": 826, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "559b9814e50ca106", "text": "Document: 1 Introduction\nSection: 3 Insights for CIIRC design > • Measuring Seismic activities:\nType: figure\nFigure\n\nImage /page/5/Figure/4 description: A block diagram illustrating a system for monitoring Glacial Lake Outburst Floods (GLOF). The diagram is divided into two main parts. The left part, labeled \"Glacial Lake at a Remote Place,\" shows a process flow starting from a \"GLOF Site\" to a \"Pressure Sensor,\" then to \"PSA,\" then to \"Glacial lake parameters,\" and finally to an \"RF / SATCOM / LoRa Transmitter\" which sends a signal via an antenna. The right part, labeled \"Central Monitoring Station,\" shows a signal being received by an antenna connected to an \"RF / SATCOM / LoRa Receiver.\" The data then goes to a \"Data Acquisition System (DAS).\" A decision block checks for the \"Threshold for GLOF.\" If the condition is not met (\"No\"), the process loops back to the DAS. If the condition is met (\"YES\"), an \"Alarm to Local community / NDMA\" is issued.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Insights for CIIRC design > • Measuring Seismic activities:", "section_headings": ["3 Insights for CIIRC design", "• Measuring Seismic activities:"], "chunk_type": "figure", "figure_caption": null, "line_start": 86, "line_end": 86, "token_count_estimate": 266, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7eeb6489d408fa79", "text": "Document: 1 Introduction\nSection: 3 Insights for CIIRC design > • Measuring Seismic activities:\nType: text\n\nF", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Insights for CIIRC design > • Measuring Seismic activities:", "section_headings": ["3 Insights for CIIRC design", "• Measuring Seismic activities:"], "chunk_type": "text", "line_start": 87, "line_end": 89, "token_count_estimate": 29, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b7e94e12ad7a6d7a", "text": "Document: 1 Introduction\nSection: 5 Proposed Pressure Sensing Apparatus (PSA)\nType: figure\nFigure: Figure 2 Shows the graphic layout of the PSA. A high strength hose 10m long and 26mm in diameter is used, where accurate measurements of the pressure are ensured by the pressure sensor contained in this hose. This sensor is fixed on one end of an iron rod larger than the internal diameter of the hose at about 2cm in length. The rod is inserted in the hose using silicone sealant, but the lower end is held inside with hose clamps. It served the purpose by giving a waterproof seal and provides weight to stabilize the sensor's position. At the top of the hose, a T-junction is fitted so that a logger cable can be connected to connect up a pressure sensor to the data logger. The second leg of this T-junction feeds out to a 2-liter rubber expansion bladder. This bladder is normally half full; this way, volume changes in the antifreeze solution, such as those due to solar heating, build only enough pressure in the assembly that it can easily be sealed. The hose is filled with an appropriate quantity of antifreeze and water; the illustration here is 50:50 mix of ethylene glycol with water. It serves as the antifreeze solution. In establishing the installation of the PSA, a pole with a length of 5 meters is installed in the central region/ shallow side/ shallow bank of the stream/river. The length of the pole depends on the demand. The guides attached to the Teflon pulley hold the PSA on the pole. The pulley system makes sure that water level fluctuations do not pull the pressure sensor off of the river or streambed [9]. In case the PSA is deployed on an ice floor of a frozen pond/lake, this pulley arrangement allows the pressure sensor to dip as the ice melts, thereby ensuring that it remains in contact with the underlying surface of the pond or lake where the meltwater collects. The installation process involves using drilling equipment with extensions to a depth of 3 meters in the riverbed or streambed or frozen pond/lake.\n\nFigure 2 Shows the graphic layout of the PSA. A high strength hose 10m long and 26mm in diameter is used, where accurate measurements of the pressure are ensured by the pressure sensor contained in this hose. This sensor is fixed on one end of an iron rod larger than the internal diameter of the hose at about 2cm in length. The rod is inserted in the hose using silicone sealant, but the lower end is held inside with hose clamps. It served the purpose by giving a waterproof seal and provides weight to stabilize the sensor's position. At the top of the hose, a T-junction is fitted so that a logger cable can be connected to connect up a pressure sensor to the data logger. The second leg of this T-junction feeds out to a 2-liter rubber expansion bladder. This bladder is normally half full; this way, volume changes in the antifreeze solution, such as those due to solar heating, build only enough pressure in the assembly that it can easily be sealed. The hose is filled with an appropriate quantity of antifreeze and water; the illustration here is 50:50 mix of ethylene glycol with water. It serves as the antifreeze solution. In establishing the installation of the PSA, a pole with a length of 5 meters is installed in the central region/ shallow side/ shallow bank of the stream/river. The length of the pole depends on the demand. The guides attached to the Teflon pulley hold the PSA on the pole. The pulley system makes sure that water level fluctuations do not pull the pressure sensor off of the river or streambed [9]. In case the PSA is deployed on an ice floor of a frozen pond/lake, this pulley arrangement allows the pressure sensor to dip as the ice melts, thereby ensuring that it remains in contact with the underlying surface of the pond or lake where the meltwater collects. The installation process involves using drilling equipment with extensions to a depth of 3 meters in the riverbed or streambed or frozen pond/lake.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Proposed Pressure Sensing Apparatus (PSA)", "section_headings": ["5 Proposed Pressure Sensing Apparatus (PSA)"], "chunk_type": "figure", "figure_caption": "Figure 2 Shows the graphic layout of the PSA. A high strength hose 10m long and 26mm in diameter is used, where accurate measurements of the pressure are ensured by the pressure sensor contained in this hose. This sensor is fixed on one end of an iron rod larger than the internal diameter of the hose at about 2cm in length. The rod is inserted in the hose using silicone sealant, but the lower end is held inside with hose clamps. It served the purpose by giving a waterproof seal and provides weight to stabilize the sensor's position. At the top of the hose, a T-junction is fitted so that a logger cable can be connected to connect up a pressure sensor to the data logger. The second leg of this T-junction feeds out to a 2-liter rubber expansion bladder. This bladder is normally half full; this way, volume changes in the antifreeze solution, such as those due to solar heating, build only enough pressure in the assembly that it can easily be sealed. The hose is filled with an appropriate quantity of antifreeze and water; the illustration here is 50:50 mix of ethylene glycol with water. It serves as the antifreeze solution. In establishing the installation of the PSA, a pole with a length of 5 meters is installed in the central region/ shallow side/ shallow bank of the stream/river. The length of the pole depends on the demand. The guides attached to the Teflon pulley hold the PSA on the pole. The pulley system makes sure that water level fluctuations do not pull the pressure sensor off of the river or streambed [9]. In case the PSA is deployed on an ice floor of a frozen pond/lake, this pulley arrangement allows the pressure sensor to dip as the ice melts, thereby ensuring that it remains in contact with the underlying surface of the pond or lake where the meltwater collects. The installation process involves using drilling equipment with extensions to a depth of 3 meters in the riverbed or streambed or frozen pond/lake.", "line_start": 92, "line_end": 92, "token_count_estimate": 1005, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac523777cdcc2d98", "text": "Document: 1 Introduction\nSection: 5 Proposed Pressure Sensing Apparatus (PSA)\nType: figure\nFigure\n\nImage /page/6/Picture/4 description: A diagram illustrates a monitoring system set up in a glacial lake. The main part of the diagram shows a cross-section of a body of water labeled \"Glacial Lake,\" with a device submerged in it. This device is connected by a hose to equipment on the shore. Two inset diagrams provide detailed views of the components. A red arrow points from the submerged device to a close-up diagram showing its internal structure. This structure is a long tube containing a \"Metal sinker\" at the bottom, a \"Pressure sensor\" above it, a \"Cable,\" and \"Antifreeze liquid,\" with a \"Hose\" connected at the top. Another red arrow points from the equipment on the shore to a second close-up diagram. This diagram shows a green, spherical \"Bladder\" connected via a \"Hose\" and \"Fittings\" to a \"Cable to logger.\"", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Proposed Pressure Sensing Apparatus (PSA)", "section_headings": ["5 Proposed Pressure Sensing Apparatus (PSA)"], "chunk_type": "figure", "figure_caption": null, "line_start": 94, "line_end": 94, "token_count_estimate": 246, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f754cef5880b1c3", "text": "Document: 1 Introduction\nSection: 5 Proposed Pressure Sensing Apparatus (PSA)\nType: text\n\nFig. 2. Design flow", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Proposed Pressure Sensing Apparatus (PSA)", "section_headings": ["5 Proposed Pressure Sensing Apparatus (PSA)"], "chunk_type": "text", "line_start": 95, "line_end": 97, "token_count_estimate": 29, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d0629a9b0ba2aa4", "text": "Document: 1 Introduction\nSection: 5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device\nType: text\n\nThe DEWS can usually contain several sensors and equipment to measure water level, temperature, pressure, or a single pressure sensor like DCX-22 (which can work up to -10-degree celcius without the PSA setup / with PSA setup can work up to -80 celcius). The KELLER Druckmesstechnik AG, Switzerland sensor DCX-22 of figure 3 has a logger for data and can measure the water level, hydro pressure, and temperature. The pressure sensor used in the PSA is DCX-22 and is flexible to measure the water level or pressure of water and temperature. The sensor is also able to give accurate readings for submerged applications. With the data logger, which is equipped with a long-lasting battery, measurements can be recorded for long periods of time, from minutes to hourly intervals. Also, the pressure sensors are able to be calibrated by the users using the calibration program provided by the vendor. Sensors must be calibrated in an environment exactly the same as that in which they will be used. Sensors are often calibrated upright on a level surface. Sensor recalibration may be needed in several cases, such as when the sensor is serving under new or changed maintenance conditions, its measuring setup is altered, or its station has been in service for many years.\n\nThe pressure sensor, in combination with the data logger, can be programmed to acquire data on hydro pressure and temperatures for set time intervals. The result ensures that the collection of measurements during the monitoring period will be accurate. Nevertheless, it could be such that there will be cases wherein data gaps may have occurred through the exposure of the sensor to either freezing or low water levels, mostly. The densities of the surrounding environment should be considered when determining the height of a water level using the measurements of hydrostatic pressure acquired with the help of the pressure sensing device. Equation 1 supports the conversion of pressure readings in N/m2 to the corresponding height of the water column in meters. Another important consideration in the calculations is that the length of an iron rod needs to be added to the depth/water level for appropriate estimates.\n\n$$d = \\frac{P}{(0*9)} \\tag{1}$$\n\nwhere P = hydrostatic pressure (Nm-2),  $\\rho = water$  density (kgm-3), g = gravitational acceleration (ms-2), d = height of water column (m).", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device", "section_headings": ["5 Proposed Pressure Sensing Apparatus (PSA)", "Pressure Sensing device"], "chunk_type": "text", "line_start": 99, "line_end": 107, "token_count_estimate": 582, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0207832a1761f415", "text": "Document: 1 Introduction\nSection: 5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device\nType: figure\nFigure\n\nImage /page/7/Picture/7 description: A studio photograph of an industrial sensor on a white background. The device consists of a long, cylindrical stainless steel probe connected to a long, coiled black cable. The other end of the cable terminates in a multi-part stainless steel connector. The tip of the probe is a black cap with small holes, and the connector also has a visible sensor element at its end.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device", "section_headings": ["5 Proposed Pressure Sensing Apparatus (PSA)", "Pressure Sensing device"], "chunk_type": "figure", "figure_caption": null, "line_start": 108, "line_end": 108, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c635a94d8318fb6f", "text": "Document: 1 Introduction\nSection: 5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device\nType: text\n\nFig. 3. DCX-22 KELLER Pressure Transducer", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Proposed Pressure Sensing Apparatus (PSA) > Pressure Sensing device", "section_headings": ["5 Proposed Pressure Sensing Apparatus (PSA)", "Pressure Sensing device"], "chunk_type": "text", "line_start": 109, "line_end": 111, "token_count_estimate": 44, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5526c986878d3e3b", "text": "Document: 1 Introduction\nSection: 7 Installed PSA at field\nType: text\n\nThe configuration in figures 4 and 5 here constitutes the working scheme of the PSA: the RF transmitter is located at a remote position, while the RF receiver mounted with a Data Acquisition System (DAS) is located at the central monitoring station. This design, primarily aimed at the purpose of observing depth and water levels in lakes in Antarctica, promises much towards the detection of GLOFs. In fact, by serving as a DEWS, it can be fundamentally important in the premature observation and alerting authorities about possible GLOF catastrophes. As a result of unfavorable climatic conditions and inaccessibility of regular transportation for frequent visits, the system has been engineered to possess wireless communication capabilities. This allows real-time data transmission from the installation site to a central monitoring station about 2 kilometers off the lake site. Long Range (LoRa) transmitter and receiver modules are chosen in the wireless communications technology that will be made use of for this system since this will allow the transcription of critical lake parameters, like hydro pressure, temperature, and water level.\n\nThe system comprises reliable backup mechanisms in terms of power to continuously and uninterruptedly transmit and receive data. It employs the use of solar panels and insulation for the proper protection of the parts constituting the transmitter and receiver from probable disruptions through power and ensures a smooth, steady flow of data. The pressure sensor is programmed to become operational to serially measure the lake parameters at regular intervals. The acquired data is then sent to the central monitoring station, as exemplified in figure 5. This systematic approach, of course, allows comprehensive data to be collected but also ensures timely updates at the monitoring station-thus making the whole system of monitoring effective in the challenging polar environment.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "7 Installed PSA at field", "section_headings": ["7 Installed PSA at field"], "chunk_type": "text", "line_start": 113, "line_end": 117, "token_count_estimate": 417, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a17c4845c33619c", "text": "Document: 1 Introduction\nSection: 7 Installed PSA at field\nType: figure\nFigure\n\nImage /page/8/Picture/5 description: A scientific instrument is set up on the edge of a body of water in a snowy, icy landscape. The instrument consists of a metal pole with a grey box labeled \"CRANE\" in red letters at the top, and a reel with tubing attached to the side. The pole is mounted on a wooden plank that extends from a rocky bank over the partially frozen water, with the bottom of the pole submerged. The background is a vast expanse of snow and ice under a blue sky.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "7 Installed PSA at field", "section_headings": ["7 Installed PSA at field"], "chunk_type": "figure", "figure_caption": null, "line_start": 118, "line_end": 118, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74548c8a1ebeecad", "text": "Document: 1 Introduction\nSection: 7 Installed PSA at field\nType: text\n\nFig. 4. Working model of PSA setup installed in a lake.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "7 Installed PSA at field", "section_headings": ["7 Installed PSA at field"], "chunk_type": "text", "line_start": 119, "line_end": 125, "token_count_estimate": 33, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b0471c13cd2e0b1", "text": "Document: 1 Introduction\nSection: 7 Installed PSA at field\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A composite image, labeled Fig. 5, containing five panels labeled (a) through (e), illustrating a data acquisition system and its output.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "7 Installed PSA at field", "section_headings": ["7 Installed PSA at field"], "chunk_type": "figure", "figure_caption": null, "line_start": 126, "line_end": 126, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86d63d6f26449fbd", "text": "Document: 1 Introduction\nSection: 7 Installed PSA at field\nType: text\n\nPanel (a) shows a small solar panel and an electronic device in a blue case placed on rocks outdoors.\n\nPanel (b) is a close-up of the electronics inside a box, including a yellow battery pack labeled \"LEMON 14.8V\", a blue circuit board, and various wires.\n\nPanel (c) depicts a person sitting at a desk, working on a laptop with other computer equipment nearby.\n\nPanel (d) is a line graph plotting Temperature (°C) versus Time. The y-axis ranges from -6 to 12. The x-axis shows time over a 24-hour period. There are two lines: a blue line for \"Water Temperature\" and an orange line for \"Surface Temperature\". The water temperature fluctuates more widely, peaking around 11°C, while the surface temperature remains lower, peaking around 2°C.\n\nPanel (e) is a screenshot of a spreadsheet with data columns for Time, Timestamp, Date, CH1 Pressure(mbar), and CH2 Temperature(°C). The data shown is for 16-Jan-23, with timestamps starting at 12:44:29. Sample data includes a pressure of 984.68 mbar and a temperature of 5.6°C.\n\nThe caption below reads: \"Fig. 5. (a) & (b) Transmitter module with solar panel and power backup (c) Sample...\" with the rest of the text cut off.\n\n**Fig. 5.** (a) & (b) Transmitter module with solar panel and power backup (c) Sample data measured at 15min interval by PSA (d) Central monitoring station with receiver and (e) Sample of real time data received from PSA at 1 minute interval using LoRa receiver", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "7 Installed PSA at field", "section_headings": ["7 Installed PSA at field"], "chunk_type": "text", "line_start": 127, "line_end": 141, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8686eeceeadfc8c8", "text": "Document: 1 Introduction\nSection: Features of proposed DEWS\nType: text\n\n- The proposed Pressure Sensing Assembly can come out as an important monitoring device for Glacial Lake Outburst Floods and therefore can help in the prevention or reduction of its impact. With an interfacing of an appropriate wireless communication\n- system with the PSA, it is likely to provide real-time data to the NDMA via early warnings through PSAs in the following ways:\n- 1. An Early Warning System: Place the PSA strategically around/at the glacial lakes. It will constantly observe the change in water pressure and lake levels. If the water pressure increases rapidly, along with an increase in the water level, that might depict a GLOF event. The sooner an event can be observed, the better it can alert downstream\n- 2. Real-time Data: The PSA can also forward in real time data concerning pressure levels and the temperature of the water. These data then forwarded to a central monitoring station for close scrutiny by experts to determine emerging trends that could characterise an impending GLOF.\n- 3. Trend analysis: So, with this data for very long periods, the scientists and researchers get to understand how this glacial lake acts historically, thus they will be in a better position to identify some trends and patterns which may have been happening before these GLOFs. It means better predictions are made, and pro-active mitigations can be developed.\n- 400 4. Risk Assessment: By these continuous updates of the lake conditions, PSA facilitates risk assessment towards GLOF. Information collected in this context may help policymakers decide about evacuations, infrastructure development, and so on.\n- 5. Remote Access: The PSAs that are equipped with remote data transmission, can prove very useful in remote and inaccessible glacial regions. This will enable experts to monitor conditions and collect data even from challenging environments.\n\nRegular maintenance and calibration of the PSA is essential to ensure that accurate data would be collected. Proper maintenance also prevents one from false alarms hence ensuring that one's PSA would still be reliable for GLOF monitoring. To minimize data loss from a low battery, a solar panel can be interfaced to charge the system. In a nutshell, PSA has become an essential tool in early warnings, assessments, and monitoring of glacial lakes, which contribute to preventing and mitigating the impacts of GLOFs, finally serving to safeguard communities downstream from ice melts that trigger outburst floods.\n\nThe development and deployment of a dedicated Disasters Early Warning System for Glacial Lake Outburst Floods are hence among the very important advances into mitigating impact involvement with this kind of catastrophic happening. Integration of satellite communication technology makes it possible to monitor remote glacial lake conditions in real time, thus enabling the early detection of potential GLOF risks. Combining data from sensors measuring the levels of water, temperature, and hydro-pressure activity, the system uses complex algorithms to riskassess and generate accurate alerts. The addition of a seismic sensor in this system will enable monitoring of seismic activity. Successful integration of this DEWS with local emergency response plans helps communities get warnings in time, or even permit an efficient evacuation and response strategy. Because climate change is increasingly influencing glacial dynamics, this system would be proactive in protecting the most vulnerable populations against the rising threat of GLOFs, thus supporting sustainable disaster risk reduction efforts. In general, DEWS the CIIRC structure is very robust and likely to be with us for a long time. This assurance originates from its launching and rigid testing under the extreme conditions of Antarctica's polar region for nearly three months.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "Features of proposed DEWS", "section_headings": ["Features of proposed DEWS"], "chunk_type": "text", "line_start": 143, "line_end": 156, "token_count_estimate": 854, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "41ece922e4326bf0", "text": "Document: 1 Introduction\nSection: 438 Acknowledgment\nType: text\n\n- The authors gratefully acknowledge the support rendered by Centre for Incubation,\n- 440 Innovation, Research and Consultancy (CIIRC), Jyothy Institute of Technology,\n- 441 Bengaluru and Sri Sringeri Sharada Peetham.", "metadata": {"source_file": "data/('Design and Implementation of an Advanced Glacier2Lake Outburst Flood Early Warning System(GLOF 3EWS) Using LoRa IoT devices', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "438 Acknowledgment", "section_headings": ["438 Acknowledgment"], "chunk_type": "text", "line_start": 158, "line_end": 162, "token_count_estimate": 71, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6a9bc7d8986bed3a", "text": "Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information\nType: text\n\nAbstract: Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.\n\n**Keywords:** disaster prediction knowledge graph; spatio-temporal; disaster dynamic prediction; multi-source data fusion; forest fire risk prediction; geological landslide risk prediction", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 304, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eaa9a93e8cb943d7", "text": "Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information\nType: figure\nFigure\n\nImage /page/0/Picture/14 description: A graphic on a white background features a yellow circular icon with a white checkmark inside. To the right of the icon, the text 'check for updates' is displayed in a dark sans-serif font. The words 'check for' are on the top line, and the word 'updates' is on the bottom line in a bolded font.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information", "section_path": "", "section_headings": [], "chunk_type": "figure", "figure_caption": null, "line_start": 7, "line_end": 7, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e81c757c9d7190d", "text": "Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information\nType: text\n\nCitation: Ge, X.; Yang, Y.; Chen, J.; Li, W.; Huang, Z.; Zhang, W.; Peng, L. Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information. *Remote Sens.* 2022, 14, 1214. https://doi.org/10.3390/rs14051214\n\nAcademic Editors: Hideomi Gokon, Yudai Honma and Shunichi Koshimura\n\nReceived: 31 January 2022 Accepted: 26 February 2022 Published: 1 March 2022\n\n**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 8, "line_end": 16, "token_count_estimate": 187, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea7079b58567c693", "text": "Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information\nType: figure\nFigure\n\nImage /page/0/Picture/19 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle divided into two horizontal sections. The top section is grey and contains two white circles with black outlines. The left circle has the letters 'CC' in black, and the right circle has a black stick figure icon. The bottom section is black with the letters 'BY' in white.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information", "section_path": "", "section_headings": [], "chunk_type": "figure", "figure_caption": null, "line_start": 17, "line_end": 17, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf46006e26a56a0b", "text": "Document: Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information\nType: text\n\nCopyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 18, "line_end": 20, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34765e12e74ce079", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nDisaster is a general term for events that can have a destructive impact on humans and the environments on which they depend. Although natural disasters are inevitable, it must take efforts to greatly reduce losses. However, due to the wide distribution of natural disasters, time, location, and scale of natural disasters have great uncertainty. It greatly increases the difficulty to resist natural disasters. Therefore, the critical issue is the prediction of disasters with multi-source data of disaster scenarios for formulating emergency plans.\n\nRemote sensing technology can obtain a large amount of strongly dynamic information in large observation ranges with high speed in real-time [1]. It can identify the fine features of ground objects, and extract information such as buildings, roads, and cultivated land by combining remote sensing technology and artificial intelligence technology. It plays an important role in the fields of disaster monitoring, early warning, and emergency decision-making. The critical problem is the fusion of different data types, different data structures, and different types of expert knowledge when the remote sensing information extraction for the multi-scale, multi-temporal, multi-type, multi-precision remote sensing\n\nRemote Sens. 2022, 14, 1214 2 of 21\n\nextraction information, other geographic information, and other spatio-temporal data in disaster scenes. There are studies for addressing the problem of spatio-temporal disaster information fusion, including knowledge representation of spatio-temporal disaster data, attribute analysis based on data spatio-temporal relations, and spatio-temporal dynamic geographic information of and expert knowledge.\n\nIn the real-world disaster scene, there exists large-scale multi-source heterogeneous data. There are complex associations between the data, as well as between the data and the disaster knowledge. It can effectively represent and model the data as well as the in-between relations using a knowledge graph. This is an artificial intelligence technology proposed by Google in 2012. Knowledge graph is a data modeling method that represents knowledge as concept, entity, and semantic relations with each other in the form of a graph [2]. Knowledge graphs can connect multi-source spatio-temporal disaster data and expert knowledge by a graph in order to support the analysis of the multi-source heterogeneous data under dynamic and static situations in natural disaster scenarios.\n\nThe spatio-temporal knowledge graph is a knowledge graph that can realize the semantic association of spatio-temporal data, which is the basis of disaster prediction. Sun et al. proposed a spatio-temporal knowledge graph ontology model which can manage and query spatio-temporal data in specific fields by adding spatio-temporal attribute information to entities [3]. Geographic information is a type of spatio-temporal data with spatial and temporal characteristics. Therefore, the construction technology of a geographic knowledge graph is very meaningful for this study. Zhang et al. proposed a method for constructing a geographic knowledge graph that takes the spatio-temporal characteristics into account. They studied the formal representation of geographic knowledge and laid a foundation for the acquisition, fusion, reasoning, and application of the spatio-temporal data [4]. Based on ontology, Liu et al. proposed a fast retrieval method that takes the semantic knowledge of geospatial data into account for reducing users' dependence on data storage methods and database grammar rules [5]. Jiang et al. proposed the construction process of a geographic knowledge graph, which helps to realize the knowledge of geographic information by reviewing the geographic knowledge graph [6].", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 22, "line_end": 46, "token_count_estimate": 830, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d86aac8e979fa87d", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nconstructing a geographic knowledge graph that takes the spatio - temporal characteristics into account . They studied the formal representation of geographic knowledge and laid a foundation for the acquisition , fusion , reasoning , and application of the spatio - temporal data [ 4 ] . Based on ontology , Liu et al . proposed a fast retrieval method that takes the semantic knowledge of geospatial data into account for reducing users ' dependence on data storage methods and database grammar rules [ 5 ] . Jiang et al . proposed the construction process of a geographic knowledge graph , which helps to realize the knowledge of geographic information by reviewing the geographic knowledge graph [ 6 ] .\n\nAt present, there exists research work regarding the construction of ontology and knowledge graph for specific disasters. Qiu et al. constructed model ontology and data ontology for flood management based on the four phases of disaster management [7]. Xu et al. constructed a conceptual model for earthquake disasters based on geo-ontology and relevant rules of earthquake emergency decision-making [8]. Scheuer et al. constructed flood risk assessment ontology by integrating SWEET ontology and MONITOR ontology [9]. Tao et al. summarized the knowledge graph construction process and key methods for the integrated comprehensive disaster reduction, and took the Jiuzhaigou earthquake as an example to demonstrate the knowledge graph construction process and construction results for Jiuzhaigou earthquake disaster reduction [10]. However, the above studies only focused on the knowledge representation and ontology construction for a single disaster type when building a knowledge model without considering the unified knowledge graph for different disaster types.\n\nFurthermore, the existing knowledge graph models usually do not focus on strategies and rules based on common semantics so that it is hard to provide specific disaster-handling strategies in disaster emergency scenarios for supporting decision-making. The disaster prediction knowledge graph developed by Qiu et al. [7] visualizes the disaster situation. It can more autonomously determine the environmental models and disaster-related data used in different disaster phases using the semantic constraints defined in the ontology [11]. Li et al. constructed a knowledge graph for flow disasters with the personalized visualization of different scenes for multilevel users [12]. Xie et al. combined the complex semantic relationship between earthquake prevention and control entities by extracting various earthquake disaster information, and established a semantic network by constructing a knowledge graph of earthquake disaster prevention and mitigation as the core [13]. Du et al. proposed a top-down and bottom-up method for constructing a\n\nRemote Sens. 2022, 14, 1214 3 of 21\n\nnatural disaster emergency knowledge graph [14]. They realized the transformation from multi-source data to interconnected knowledge by taking the flood disaster emergency knowledge graph as an example for experimental verification. At present, the most related research on the disaster knowledge graph respectively works on the perspectives of disaster ontology, disaster knowledge, and disaster events, particularly paying attention to construction methods. In applications, there are only domain knowledge-driven analyses. This rarely integrates dynamic disaster data and model methods deeply for the analysis based on the fused multi-feature fusion. It leads to the problem that the emergency plan cannot be flexibly adapted to the actual situations.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 22, "line_end": 46, "token_count_estimate": 791, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "037775ff22d2c79b", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nknowledge graph [ 14 ] . They realized the transformation from multi - source data to interconnected knowledge by taking the flood disaster emergency knowledge graph as an example for experimental verification . At present , the most related research on the disaster knowledge graph respectively works on the perspectives of disaster ontology , disaster knowledge , and disaster events , particularly paying attention to construction methods . In applications , there are only domain knowledge - driven analyses . This rarely integrates dynamic disaster data and model methods deeply for the analysis based on the fused multi - feature fusion . It leads to the problem that the emergency plan cannot be flexibly adapted to the actual situations .\n\nAffected by the attribute characteristics of surface elements, the development of disasters will show certain patterns when the disasters spatially migrate and change over time. This study selects forest fires and geological landslides as typical research objects. This paper proposes the Disaster Prediction Knowledge Graph (DPKG) that facilitates multi-source spatio-temporal data management and knowledge association. The proposed method supports semantic association of spatio-temporal data, efficient query, and quantitative analysis in order to implement information analysis with multiple types of disasters. The proposed method can be used for intelligent deduction of disaster situation and intelligent identification of disaster risk. This paper demonstrates experiments for verifying the benefits of the proposed method with respect to forest fire prediction and geological landslide risk prediction.\n\nThe main contributions of this paper are as follows: (1) This paper proposes a disaster prediction method which realizes dynamic data-driven disaster prediction based on the DPKG. (2) This paper proposes a fusion method for the dynamically changing spatio-temporal data and knowledge model. The proposed method is beneficial to improve the ability of natural disaster monitoring, early warning, and emergency response. It can provide a quantitative reference for disaster prediction and play a practical role to a certain extent.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 22, "line_end": 46, "token_count_estimate": 469, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d76984a1e9d85fe1", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture\nType: text\n\nThis section describes the theoretical way for mapping disaster prediction scenarios to knowledge graphs, mainly including the hierarchical semantic model of disaster prediction, and the mapping relationship between disaster prediction scenarios and DPKG architecture.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture"], "chunk_type": "text", "line_start": 50, "line_end": 52, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "84dd5c0f4bb96ba6", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios\nType: text\n\nThe occurrence of disasters is related to a variety of factors. It facilities disaster prediction and decision-making by the analysis of disaster-inducing factors and their interrelationships.\n\nThe affect factors of occurrence of natural disasters are composed of common factors and characteristic factors. The common factors are closely related to different types of disasters. The characteristic factors are closely related to the occurrence of a certain natural disaster and weakly related to other types of natural disasters. This study establishes a hierarchical semantic model of disaster prediction considering the affect factors, which provided a theoretical basis for the construction of a conceptual layer for forest fire risk prediction and geological landslide risk prediction, as shown in Figure 1. Aiming at the prediction of forest fires and geological landslides, this paper extracts the common factors and characteristic factors of disaster occurrence by considering remote sensing information (vegetation, farmland, roads, buildings, and water bodies), terrain, meteorology, and human factors closely related to the occurrence of disasters.\n\nRemote Sens. 2022, 14, 1214 4 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios"], "chunk_type": "text", "line_start": 54, "line_end": 60, "token_count_estimate": 303, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "17631f1727bfba05", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios\nType: figure\nFigure\n\nImage /page/3/Figure/1 description: A flowchart illustrating the factors used for Forest Fire Risk Prediction and Geological Landslide Risk Prediction. The chart is divided into three color-coded sections. On the left, in a pink section, are \"Common factors in disasters,\" which include Terrain (Slope, Aspect, Height), Meteorological (Wind speed, Wind direction, Precipitation, Temperature, Humidity), and Ground cover (Arable land, Woodland, Water, ......). These common factors feed into a central box labeled \"Disaster Occurrence Characteristics.\" From this central box, the chart branches into two specific disaster types. The top right, in a blue section, details factors for \"Forest Fire Risk Prediction,\" specifically the \"Combustibles factor,\" which includes Vegetation types, Vegetation Coverage, Species, and Tree age. The bottom right, in a green section, details factors for \"Geological Landslide Risk Prediction,\" specifically \"Geological factors,\" which include Stratigraphic lithology, Fault structure, Surface roughness, Surface relief, Plane curvature, and Section curvature. Brackets indicate that the common factors combined with the combustibles factor lead to Forest Fire Risk Prediction, and the common factors combined with geological factors lead to Geological Landslide Risk Prediction.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios"], "chunk_type": "figure", "figure_caption": null, "line_start": 61, "line_end": 61, "token_count_estimate": 359, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d1c9d5bc58e24bf", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios\nType: figure\nFigure: Figure 1. A hierarchical semantic model for disaster prediction scenarios regarding forest fires and geological landslides.\n\n**Figure 1.** A hierarchical semantic model for disaster prediction scenarios regarding forest fires and geological landslides.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.1. Hierarchical Semantic Model of Disaster Prediction Scenarios"], "chunk_type": "figure", "figure_caption": "Figure 1. A hierarchical semantic model for disaster prediction scenarios regarding forest fires and geological landslides.", "line_start": 63, "line_end": 63, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21cc08de56cb736d", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture\nType: text\n\nFor modeling disaster prediction using a knowledge graph, it is necessary to represent the disaster prediction scenario as the hierarchical semantic model. It maps the data with spatio-temporal characteristics in the disaster prediction scenario into the DPKG according to the structure of the semantic model. The disaster analysis model can be transformed into knowledge. The DPKG also needs to integrate the transformed disaster analysis model and expert knowledge for disaster prediction.\n\nIn addition, the architecture design of the DPKG for disaster prediction needs to consider the fusion of multi-source heterogeneous spatio-temporal data including remote sensing extraction information, terrain, meteorology, vegetation, and human factors, as well as the fusion of spatio-temporal data with disaster analysis models and expert knowledge for linkage analysis. This study designs an architecture of DPKG for disaster prediction, as shown in Figure 2, in order to support the modeling of disaster prediction and the collaborative analysis with fused data.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture"], "chunk_type": "text", "line_start": 66, "line_end": 70, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7879ebce6f047f4d", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture\nType: figure\nFigure\n\nImage /page/3/Figure/6 description: A flowchart diagram illustrating the 'DPKG architecture'. The architecture is divided into several components within nested boxes. The main section is split into two parts: 'Set of rules (TBox)' and 'Set of facts (ATox)'. The 'Set of rules (TBox)' contains a 'Conceptual layer' (with 'Space ontology', 'Time ontology', and 'Common semantic ontology of disaster prediction') and 'Inference rules' (with 'First-order logical reasoning rules', 'Production inference rules', and 'Spatio-temporal semantic reasoning rules'). The 'Set of facts (ATox)' shows a flow where 'Unstructured data', 'Semi-structured data', 'Structured data', and 'Disaster prediction reasoning criterion' feed into a 'Knowledge extraction' process, which results in a 'Set of facts (ABox)'. An arrow points from this main section to two modules at the bottom: 'Disaster fusion data spatio-temporal semantic query module' and 'Disaster prediction rule reasoning module'.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture"], "chunk_type": "figure", "figure_caption": null, "line_start": 71, "line_end": 71, "token_count_estimate": 335, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e5a7a56f5aeae956", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture\nType: figure\nFigure: Figure 2. DPKG architecture.\n\nFigure 2. DPKG architecture.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture"], "chunk_type": "figure", "figure_caption": "Figure 2. DPKG architecture.", "line_start": 73, "line_end": 73, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2de23f9cc12f2ecd", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture\nType: text\n\nRemote Sens. 2022, 14, 1214 5 of 21\n\nThe DPKG for disaster prediction consists of two parts, namely the rule set (TBox) and the fact set (ABox). The TBox consists of the conceptual layer and the inference rules. The conceptual layer is the common semantic basis for describing the hierarchical relationship of various factors in disaster prediction scenarios. The inference rules are the common logic basis for multi-source spatio-temporal data that supports semantic reasoning. The ABox constitutes the instance layer of the knowledge graph, which contains the time, space, status, and other attribute information of various types of ground objects related to disaster emergency extracted from multi-source heterogeneous data. The ABox and the TBox constitute the reasoning basis of the DPKG. Through the rapid retrieval of spatio-temporal data in disaster areas, multi-source spatio-temporal data analysis can be realized. The architecture diagram will be described in detail later.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.1. DPKG Architecture > 2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture", "section_headings": ["2. Materials and Methods", "2.1. DPKG Architecture", "2.1.2. Mapping between Disaster Prediction Scenarios and DPKG Architecture"], "chunk_type": "text", "line_start": 74, "line_end": 78, "token_count_estimate": 269, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68630bb431592003", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph\nType: text\n\nThis section introduces the knowledge representation language, the design of the conceptual layer and instance layer, and the knowledge representation of the disaster prediction model.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph"], "chunk_type": "text", "line_start": 80, "line_end": 82, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d68f4d78000c56a", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.1. Knowledge Representation Language\nType: text\n\nKnowledge graph representation refers to the use of computer symbols to scientifically mark the objectively existing knowledge for facilitating the semantic reasoning [15].\n\nOWL (Web Ontology Language) is a semantic description standard including three sub-languages: OWL-Lite, OWL-DL, and OWL-Full. At present, OWL is the most standardized, rigorous, and expressive language for a knowledge graph. The commonly used knowledge representation form is RDF (Resource description framework) triples. Each piece of knowledge can be represented as the following triple format: (subject, predicate, object), such as (vegetation, yes, combustible). This study chooses OWL as the knowledge representation language.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.1. Knowledge Representation Language", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.1. Knowledge Representation Language"], "chunk_type": "text", "line_start": 84, "line_end": 88, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abc34baa3750d342", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer\nType: text\n\nThe conceptual layer of the DPKG is a logical structure for multi-source spatiotemporal data. It contains semantic concepts and their interrelationships. Based on the semantic associations between different concepts, such as the subordinate relationship, attribute subject-object relationship, a tree-like hierarchical concept triple interconnection network is constructed for ensuring the consistence of the intrinsic semantic concepts of multi-source spatio-temporal data. The conceptual layer of disaster prediction DPKG is composed of time ontology, space ontology, and disaster prediction common semantic ontology.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer"], "chunk_type": "text", "line_start": 90, "line_end": 92, "token_count_estimate": 178, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc260baa5272ba73", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction\nType: text\n\nThe dynamic prediction of forest fires and geological landslide disasters needs the semantic reasoning and collaborative computing of multi-source spatio-temporal data.\n\nFor this goal, this paper proposes a tree-like taxonomy of multi-source concepts of geographic entity associated with multi-source spatio-temporal data. According to the taxonomy, concepts of geographic entity are divided into five fields. This paper defines the domain attribute predicates related to the concepts of each geographic entity.\n\nThe concepts of geographic entity constitute a common semantic ontology for the prediction of forest fire and geological landslide disaster.\n\nThe storage model of common semantic ontology is hierarchical and expandable. It builds the foundation for the predicate logic reasoning based on the common hierarchical semantic relationship of disaster prediction, as shown in Figure 3.\n\nRemote Sens. 2022, 14, 1214 6 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "1. Common Semantic Ontology of Disaster Prediction"], "chunk_type": "text", "line_start": 94, "line_end": 104, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad711636e8b234e4", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction\nType: figure\nFigure\n\nImage /page/5/Figure/1 description: A black and white flowchart titled 'Conceptual Framework of Disaster Prediction KG'. The main concept branches into three categories: 'Time object', 'Space object', and 'Role object'. 'Time object' further divides into 'Effective time' and 'Time granularity'. 'Space object' divides into 'Geographic description' and 'Features'. 'Role object' divides into 'Mechanism' and 'Post'. The 'Features' category from 'Space object' is connected to a lower level of five boxes: 'Ground cover', 'Meteorological', 'Topography', 'Geology and lithology', and 'Historical disaster'.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "1. Common Semantic Ontology of Disaster Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 105, "line_end": 105, "token_count_estimate": 234, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3560a7bc733ccb67", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction\nType: figure\nFigure: Figure 3. Common semantic ontology of disaster prediction.\n\nFigure 3. Common semantic ontology of disaster prediction.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 1. Common Semantic Ontology of Disaster Prediction", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "1. Common Semantic Ontology of Disaster Prediction"], "chunk_type": "figure", "figure_caption": "Figure 3. Common semantic ontology of disaster prediction.", "line_start": 107, "line_end": 107, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "de454cc9056104de", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology\nType: text\n\nTime ontology provides a specification of unified time semantic representation in order to ensure that the time information of entities is comparable and computable. This paper leverages the Semantic Web Rule Language (SWRL) time ontology proposed by Stanford University for representing the common time concepts of the DPKG [16]. The logical structure of the SWRL time ontology is shown in Figure 4.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "2. Time Ontology"], "chunk_type": "text", "line_start": 110, "line_end": 112, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e7c3d790f6c8b34a", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology\nType: figure\nFigure\n\nImage /page/5/Figure/5 description: A diagram illustrating a data model or ontology for time-related concepts. It shows several colored rectangular boxes connected by labeled arrows. A pink box labeled \"ExtendProposition\" connects to a central blue box \"ValidTime\" via two arrows: a solid one labeled \"hasValidTime\" and a dotted one labeled \"hasPredictTime\". The \"ValidTime\" box connects to a yellow box \"Granularity\" with an arrow labeled \"hasGranularity\". Below \"ValidTime\", a light green box \"ValidInstant\" and a yellow box \"ValidPeriod\" are shown as subclasses, indicated by arrows labeled \"SubClassOf\" pointing to \"ValidTime\". At the bottom, a large red box is labeled \"xsd::DateTime\". \"ValidInstant\" has an arrow labeled \"hasTime\" pointing to \"xsd::DateTime\". \"ValidPeriod\" has two arrows pointing to \"xsd::DateTime\", one labeled \"hasStartTime\" and the other \"hasFinishTime\".", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "2. Time Ontology"], "chunk_type": "figure", "figure_caption": null, "line_start": 113, "line_end": 113, "token_count_estimate": 324, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1354e70a591dbf33", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology\nType: figure\nFigure: Figure 4. SWRL temporal ontology.\n\nFigure 4. SWRL temporal ontology.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > 2. Time Ontology", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "2. Time Ontology"], "chunk_type": "figure", "figure_caption": "Figure 4. SWRL temporal ontology.", "line_start": 115, "line_end": 115, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5da30d30fb9f844c", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Space Ontology\nType: text\n\nThe expression of spatial ontology applies A Geographic Query Language for RDF Data (GeoSPARQL) [17] that is a geographic semantic query specification proposed by OGC (Open Geospatial Consortium) [18]. The geographic semantic query mainly includes the following three common contents.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Space Ontology", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "Space Ontology"], "chunk_type": "text", "line_start": 118, "line_end": 120, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6c4ba73432b594fb", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary\nType: text\n\nIt represents all feature entities as subclasses of Spatial Objects. All geometric objects can be subdivided into points, linestrings, and polygons. Geometric objects are represented using features and geometric objects (Geometry) and their defined relationships, as shown in Figure 5.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "OWL Ontology Vocabulary"], "chunk_type": "text", "line_start": 122, "line_end": 124, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf51074af09f2c65", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary\nType: figure\nFigure\n\nImage /page/5/Figure/11 description: A class diagram illustrates the relationships between different spatial object types, prefixed with 'ogc:'. At the top is the superclass 'ogc:SpatialObject'. Two classes, 'ogc:Feature' and 'ogc:Geometry', inherit from 'ogc:SpatialObject'. There is a directed association from 'ogc:Feature' to 'ogc:Geometry' labeled 'ogc:hasGeometry'. Three other classes, 'ogc:Point', 'ogc:LineString', and 'ogc:Polygon', all have arrows pointing to the 'ogc:Geometry' class. The 'ogc:Geometry' class is detailed in a larger box with two sections: 'Metadata' and 'Serialization'. Under 'Metadata', the properties are listed as: 'ogc:dimension : xsd:int', 'ogc:coordinateDimension : xsd:int', 'ogc:spatialDimension : xsd:int', 'ogc:isEmpty : xsd:boolean', 'ogc:isSimple : xsd:boolean', and 'ogc:is3D : xsd:boolean'. Under 'Serialization', the properties are 'ogc:asWKT' and 'ogc:WKTLiteral'.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "OWL Ontology Vocabulary"], "chunk_type": "figure", "figure_caption": null, "line_start": 125, "line_end": 125, "token_count_estimate": 398, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "82f63b9b3ca03f4b", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary\nType: figure\nFigure: Figure 5. GeoSPARQL part of the ontology table.\n\nFigure 5. GeoSPARQL part of the ontology table.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "OWL Ontology Vocabulary"], "chunk_type": "figure", "figure_caption": "Figure 5. GeoSPARQL part of the ontology table.", "line_start": 127, "line_end": 127, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5c4ecaf550e2423d", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary\nType: text\n\nRemote Sens. 2022, 14, 1214 7 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > OWL Ontology Vocabulary", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "OWL Ontology Vocabulary"], "chunk_type": "text", "line_start": 128, "line_end": 130, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21f6c27de023ed12", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Geometry extension expression\nType: text\n\nThe GeoSPARQL specification follows the OGC standard for the expression format of point, line, and surface. For example, the coordinate information of a surface data will be recorded in the attribute of *ogc:asWKT*, where *WKT* stands for Well Known Text, which is the ASCII code representation method of spatial objects.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Geometry extension expression", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "Geometry extension expression"], "chunk_type": "text", "line_start": 132, "line_end": 134, "token_count_estimate": 129, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4bfbf776e788b6cc", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Topology extension query\nType: text\n\nGeoSPARQL specifies eight query relationships for all spatial objects, as shown in Table 1. In addition, it provides many commonly used distance query functions (*geof:distance*), buffer query functions (*geof:buffer*), and convex Package constructor (*geof:convexHull*), to enhance spatial query.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Topology extension query", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "Topology extension query"], "chunk_type": "text", "line_start": 136, "line_end": 140, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d818f2bf047a8c7c", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Topology extension query\nType: table\nTable: Table 1. GeoSPARQL spatial topological relationship.\n\n| geof:sfEquals  | geof:sfDisjoint | geof:sfIntersects | geof:sfTouches  |\n|----------------|-----------------|-------------------|-----------------|\n| geof:sfCrosses | geof:sfWithin   | geof:sfContains   | geof:sfOverlaps |", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.2. Design of Conceptual Layer > Topology extension query", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.2. Design of Conceptual Layer", "Topology extension query"], "chunk_type": "table", "table_caption": "Table 1. GeoSPARQL spatial topological relationship.", "columns": ["geof:sfEquals", "geof:sfDisjoint", "geof:sfIntersects", "geof:sfTouches"], "table_row_start": 1, "table_row_end": 1, "line_start": 141, "line_end": 143, "token_count_estimate": 155, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "415dc55d18c04f4c", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > Construction of First-Order Logical Inference rules\nType: text\n\nFirst-order logic (FOL) is a formal system used in mathematics, philosophy, linguistics, and computer science. Specifically, hierarchical relationships and object attribute relationships can be described as first-order predicate logic, as shown in Figure 6. The common ontology, time ontology, and space ontology of disaster prediction are formally modeled with OWL language. The hierarchical relationship and attribute relationship implies a series of first-order logical reasoning.\n\n```\n```", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > Construction of First-Order Logical Inference rules", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "Construction of First-Order Logical Inference rules"], "chunk_type": "text", "line_start": 148, "line_end": 154, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fed6f9280ec1d783", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > Construction of First-Order Logical Inference rules\nType: figure\nFigure: Figure 6. Hierarchical relationship and object attribute relationship.\n\nFigure 6. Hierarchical relationship and object attribute relationship.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > Construction of First-Order Logical Inference rules", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "Construction of First-Order Logical Inference rules"], "chunk_type": "figure", "figure_caption": "Figure 6. Hierarchical relationship and object attribute relationship.", "line_start": 155, "line_end": 155, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "857b1864b2e8ac7d", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 2. Construction of Production Inference rule\nType: text\n\nDue to the limitation of the knowledge representation of first-order logic, this paper applies SWRL (Semantic Web Rule Language). It extends the set of OWL axioms to include Horn-like rules. In this study, SWRL is used to further enhance the expression of rules for DPKG. A rule has the form, where both antecedent and subsequent are conjunctions of atoms written. Using this syntax, a rule asserting that the combination of the tree species' properties and the tree species' forest fire risk-level properties implies the tree's forest fire risk-level properties, as shown in Figure 7.\n\n```\n\\langle species(?x,?y) \\land forestFireRiskLevel(?y,?z) \\Rightarrow forestFireRiskLevel(?x,?z) \\rangle\n```", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 2. Construction of Production Inference rule", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "2. Construction of Production Inference rule"], "chunk_type": "text", "line_start": 158, "line_end": 164, "token_count_estimate": 252, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6fa1587b1c921d1", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 2. Construction of Production Inference rule\nType: figure\nFigure: Figure 7. SWRL inference rule example.\n\n**Figure 7.** SWRL inference rule example.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 2. Construction of Production Inference rule", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "2. Construction of Production Inference rule"], "chunk_type": "figure", "figure_caption": "Figure 7. SWRL inference rule example.", "line_start": 165, "line_end": 165, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9bd057dea42d8521", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule\nType: text\n\nAlthough SWRL can support basic quantitative calculations, it cannot support quantitative analysis of space and time including spatio-temporal semantics. This study leverages the construction method of production inference rules to build the spatio-temporal semantic inference rules. The spatio-temporal semantic inference rule *RuleObject* is a set of rules for the automatic execution of reasoning programs by the DPKG. Each rule is composed\n\nRemote Sens. 2022, 14, 1214 8 of 21\n\nof an event object TriggerObject and an action object ActionObject, which are expressed as RuleObject = (Tr, Ac), where Tr represents the event contained in the RuleObject, Ac represents the action object ActionObject contained in the RuleObject, and R is the reasoning result. The event TriggerObject in this article is defined as a triple that is represented as TriggerObject = (O, T, S), where O represents the geographic entity set contained in the event object, T and S respectively represent the intersection of the geographic entity set in the time dimension and the space dimension. A spatio-temporal co-occurrence scene with a set of geographic entities can be described as an event object that is a definition of the applicable conditions of an inference rule.\n\nThe event (or action) object concept is further subdivided into independent events (or actions) and event (or action) combinations, which together form the concept of knowledge inference rules. The logical structure is shown in Figure 8.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "3. Construction of Spatio-Temporal Semantic Inference Rule"], "chunk_type": "text", "line_start": 168, "line_end": 176, "token_count_estimate": 407, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ffa89a50b67a0cab", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule\nType: figure\nFigure\n\nImage /page/7/Figure/3 description: A hierarchical diagram illustrating the structure of a 'RuleObject'. The 'RuleObject' is at the top and branches into two main components: 'TriggerObject' on the left and 'ActionObject' on the right. The 'TriggerObject' branch is further subdivided. It points to 'IndependentTrigger' and 'TriggerCombination'. 'IndependentTrigger' then points to 'NormalTrigger' and 'AbnormalTrigger'. 'TriggerCombination' points to 'AndTrigger Combination' and 'OrTrigger Combination'. The 'ActionObject' branch is similarly subdivided. It points to 'IndependentAction' and 'ActionCombination'. 'ActionCombination' then points to 'AndAction Combination' and 'OrAction Combination'. There are also arrows pointing upwards, indicating relationships back to parent objects. For instance, on the right side, arrows point from 'IndependentAction' and 'ActionCombination' back to 'ActionObject', and from 'AndAction Combination' and 'OrAction Combination' back to 'ActionCombination'. A similar pattern of upward-pointing arrows exists on the 'TriggerObject' side of the diagram.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "3. Construction of Spatio-Temporal Semantic Inference Rule"], "chunk_type": "figure", "figure_caption": null, "line_start": 177, "line_end": 177, "token_count_estimate": 387, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "61c7a561fe63fc70", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule\nType: figure\nFigure: Figure 8. Schematic diagram of the conceptual hierarchy of knowledge inference rules.\n\nFigure 8. Schematic diagram of the conceptual hierarchy of knowledge inference rules.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.3. Construction of Disaster Prediction Inference Rules > 3. Construction of Spatio-Temporal Semantic Inference Rule", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.3. Construction of Disaster Prediction Inference Rules", "3. Construction of Spatio-Temporal Semantic Inference Rule"], "chunk_type": "figure", "figure_caption": "Figure 8. Schematic diagram of the conceptual hierarchy of knowledge inference rules.", "line_start": 179, "line_end": 179, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "021a8f5ea5329508", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > Knowledge Extraction from Unstructured Data\nType: text\n\nThe dynamic disaster prediction of forest fires and geological landslides requires high spatio-temporal resolution of land cover data. This paper uses Gaofen-2 satellite remote sensing images with the resolution of 0.8 m as the data source. The spatial distribution of disaster-bearing bodies such as buildings and roads is extracted by deep learning methods. The spatial distribution of surface vegetation is obtained by NDVI numerical calculation method [1,19]. It converts the spatial information into triples according to the specification of spatio-temporal and professional attribute representation defined by the concept layer. The triples are stored in the graph database *GraphDB*.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > Knowledge Extraction from Unstructured Data", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.4. Design of Instance Layer", "Knowledge Extraction from Unstructured Data"], "chunk_type": "text", "line_start": 184, "line_end": 186, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c7ffd6555077c24", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 2. Knowledge Extraction from Semi-Structured Data\nType: text\n\nThe terrain data for dynamic disaster prediction of forest fires and geological landslide is the raster geographic data with GeoTIFF format. It converts all types of raster data into vector data of surface elements. It converts geological and lithological data including stratigraphic age, fault, and lithology distribution data into vector geographic information with SHP format. For all types of vector geographic information, it converts the spatial information and feature attributes into triple that are stored in *GraphDB*.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 2. Knowledge Extraction from Semi-Structured Data", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.4. Design of Instance Layer", "2. Knowledge Extraction from Semi-Structured Data"], "chunk_type": "text", "line_start": 188, "line_end": 190, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e5025689c0bfbea1", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 3. Knowledge Extraction from Structured Data\nType: text\n\nThe meteorological data for the dynamic disaster prediction of forest fires and geological landslide is multi-field structured data, which has a direct mapping relationship with the spatio-temporal and professional attributes. Since the vast majority of meteorological data is normal non-hazardous data, this paper only converts the spatial information element attributes of potential disaster-causing meteorological indicators into a triple, so as to avoid the low reasoning speed caused by a large amount of irrelevant data to the reasoning. The triple is stored in *GraphDB*.\n\nRemote Sens. 2022, 14, 1214 9 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 3. Knowledge Extraction from Structured Data", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.4. Design of Instance Layer", "3. Knowledge Extraction from Structured Data"], "chunk_type": "text", "line_start": 192, "line_end": 196, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "76ad95148a96c94c", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 4. Knowledge Extraction from Disaster Prediction Reasoning Criterion\nType: text\n\nThis paper introduces the knowledge extraction method by taking the geological landslide prediction model as an example. It is hard to build a general prediction model for a geological landslide because a single model with idiosyncratic conditions cannot deal with the rich types of geological landslide prediction models and the involved data. This paper defines the inference rules for the geological landslide disaster prediction according to the geological landslide risk probability calculation using machine learning and the effective precipitation models. Based on the general law of geological landslide risk analysis [20], this paper selects the H-index shown in Formula (1) as the metric for the quantitative evaluation of the geological landslide risk:\n\n$$H = P_{(T)} \\times P_{(S)} \\times P_{(I)}, \\tag{1}$$\n\nwhere H is the risk probability of geological landslide disasters within a specific time and space range;  $P_{(T)}$  is the time probability, i.e., the probability of geological landslide disasters occurring within a specific time range;  $P_{(s)}$  is spatial probability, i.e., the probability of geological landslide disasters occurring within a specific spatial range;  $P_{(I)}$  is the intensity probability, i.e., the intensity of possible geological landslide disasters.\n\nThis paper introduces a time probability model based on dynamic precipitation data. The model is mainly composed of two parts. One is the effective precipitation; another is the fitting relationship between the effective precipitation and the frequency of geological landslides. The effective precipitation R in the region considers the respective precipitation contributions of the previous days to the cumulative precipitation that causes the geological landslide. The later the time, the greater the contribution rate to the geological landslide.\n\n$$R = R_0 + \\alpha_1 R_1 + \\ldots + \\alpha_n R_n, \\tag{2}$$\n\nwhere  $R_0$  is the daily precipitation;  $\\alpha$  is the contribution rate; n is the number of precipitation days, and  $R_n$  is the precipitation on the n-th day before. A linear fitting relationship between the effective precipitation and the frequency of geological landslides is defined for the time probability of geological landslides associated with dynamic precipitation data.\n\nThe geological landslide strength is considered as a parameter representing the destructive force of the geological landslide. It includes the volume of the geological landslide, and the sliding velocity. At present, there is no unified set of indicators to describe them. In this paper, the geological landslide volume parameter is used to obtain the geological landslide strength, and the formula is as follows:\n\n$$m_L = log V_{L}, \\tag{3}$$\n\nwhere  $V_L$  is the volume of a single geological landslide with the unit of m<sup>3</sup>; which is calculated based on DEM, and  $m_L$  represents the strength of a single geological landslide. By calculating the strength of each geological landslide in the study area by the above formula, it can obtain the statistics of geological landslide strength according to the frequency.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 4. Knowledge Extraction from Disaster Prediction Reasoning Criterion", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.4. Design of Instance Layer", "4. Knowledge Extraction from Disaster Prediction Reasoning Criterion"], "chunk_type": "text", "line_start": 198, "line_end": 220, "token_count_estimate": 850, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c80cf380f7d45c58", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 4. Knowledge Extraction from Disaster Prediction Reasoning Criterion\nType: text\n\n. In this paper , the geological landslide volume parameter is used to obtain the geological landslide strength , and the formula is as follows : $ $ m_L = log V_ { L } , \\ tag { 3 } $ $ where $ V_L $ is the volume of a single geological landslide with the unit of m < sup > 3 < / sup > ; which is calculated based on DEM , and $ m_L $ represents the strength of a single geological landslide . By calculating the strength of each geological landslide in the study area by the above formula , it can obtain the statistics of geological landslide strength according to the frequency .\n\nThere are many common factors that lead to forest fires and geological landslides. Certain triggering factors can be monitored when forest fires and geological landslides occur. The development of the two types of disasters will develop and spread within a certain time and space range. Affected by the attribute characteristics of surface elements, both disasters will show certain change patterns when they dynamically migrate and change in space over time. Therefore, this paper extends the geological landslide risk calculation formula to forest fires by considering the above-mentioned commonalities of the two disasters. It builds the unified prediction model for forest fire according to Formula (1). Based on Analytic Hierarchy Process, it builds the forest fire-driven comprehensive index for forest fire risk prediction model.\n\nRemote Sens. 2022, 14, 1214 10 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.2. Construction of Disaster Prediction Knowledge Graph > 2.2.4. Design of Instance Layer > 4. Knowledge Extraction from Disaster Prediction Reasoning Criterion", "section_headings": ["2. Materials and Methods", "2.2. Construction of Disaster Prediction Knowledge Graph", "2.2.4. Design of Instance Layer", "4. Knowledge Extraction from Disaster Prediction Reasoning Criterion"], "chunk_type": "text", "line_start": 198, "line_end": 220, "token_count_estimate": 413, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fead52c2ff48ce84", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data\nType: text\n\nWhen a specific attribute of the external dynamic data satisfies the numerical conditions defined by the rule, it is regarded as an abnormal event candidate. Its time range and space range are treated as the basic condition of semantic query. It queries with SPARQL Protocol and RDF Query Language (SPARQL) [21] statements for a geographic entity that has an intersection with the abnormal event candidate and satisfies the reasoning conditions. When querying the spatio-temporal relationship in the graph database, it can quickly locate the affected area of the abnormal event candidate because the graph database is good at efficient depth-first query, as shown in Figure 9. It plays an important role in the emergency field with high response speed requirements.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data"], "chunk_type": "text", "line_start": 224, "line_end": 226, "token_count_estimate": 225, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f8720206eaa7588", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart\nType: figure\nFigure\n\nImage /page/9/Figure/5 description: A flowchart illustrating a process for handling dynamic data to identify and query abnormal events. The process begins at a 'Start' node and proceeds to 'Real-time input of dynamic data (such as meteorological data)'. Next, a decision is made in an oval labeled 'Judging the dynamics Whether the data attribute satisfies the condition'. If the condition is not met ('No'), the process goes directly to the 'End' node. If the condition is met ('Yes'), the flow continues to 'Generate abnormal events to be judged based on the spatiotemporal scope of external dynamic data'. This is followed by 'Based on the abnormal events to be determined and the geographic location affected by the abnormal events Entity Type Generate Query Statement'. The next step is 'With the help of efficient in-depth retrieval of graph database, the area affected by abnormal events can be quickly locked'. This leads to the final process step, 'Query to get the affected geographic entities within the scope of the abnormal event', which then connects to the 'End' node.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "Disaster prediction reasoning flow chart"], "chunk_type": "figure", "figure_caption": null, "line_start": 229, "line_end": 229, "token_count_estimate": 333, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ad823fbfc20534f", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart\nType: figure\nFigure: Figure 9. Disaster prediction query flow chart.\n\nFigure 9. Disaster prediction query flow chart.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "Disaster prediction reasoning flow chart"], "chunk_type": "figure", "figure_caption": "Figure 9. Disaster prediction query flow chart.", "line_start": 231, "line_end": 231, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2610802617ca3aac", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart\nType: text\n\nWith the help of the spatio-temporal semantic query of the disaster fusion data, it can quickly find geographic entities located within the temporal and spatial scope of the AbnormalTrigger based on the temporal-spatial intersection relationship in order to filter the buildings and roads affected by the AbnormalTrigger, as shown in Figure 10.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "Disaster prediction reasoning flow chart"], "chunk_type": "text", "line_start": 232, "line_end": 234, "token_count_estimate": 139, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a216d0d9c90eb45c", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart\nType: figure\nFigure\n\nImage /page/9/Figure/8 description: A conceptual diagram on a 2D coordinate system. The horizontal axis is labeled \"spatial dimension\" and the vertical axis is labeled \"time dimension\". The diagram features two large, overlapping ovals. The oval on the left is light green and is associated with the green text \"Spatio-temporal distribution of buildings and roads\", with a green arrow pointing up from the oval to the text. The oval on the right is light orange and is associated with the orange text \"Spatial and temporal distribution of AbnormalTrigger\", with an orange arrow pointing up from the oval to the text. The overlapping area of the two ovals is colored reddish-pink. A pink arrow points from this intersection to the text below, which reads \"Buildings and roads affected by AbnormalTrigger\" in pink.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "Disaster prediction reasoning flow chart"], "chunk_type": "figure", "figure_caption": null, "line_start": 235, "line_end": 235, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "98ad0df5abb7eb01", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart\nType: figure\nFigure: Figure 10. Schematic diagram of the spatio-temporal intersection of anomalous events and geographic entities.\n\n**Figure 10.** Schematic diagram of the spatio-temporal intersection of anomalous events and geographic entities.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "Disaster prediction reasoning flow chart"], "chunk_type": "figure", "figure_caption": "Figure 10. Schematic diagram of the spatio-temporal intersection of anomalous events and geographic entities.", "line_start": 237, "line_end": 237, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42e3f60d19f4faaa", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart\nType: text\n\nIn order to ensure the efficiency of spatial information query in disaster emergency scenarios, this paper builds multi-scale geocoding indexes for multi-source geographic entities in GeoSPARQL. It significantly improves the depth-first query speed of random geospatial information in comparison with the frequent joint query of multiple tables in the relational database.\n\nRemote Sens. 2022, 14, 1214 11 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data > Disaster prediction reasoning flow chart", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.1. Spatio-Temporal Semantic Query for Disaster Fusion Data", "Disaster prediction reasoning flow chart"], "chunk_type": "text", "line_start": 238, "line_end": 242, "token_count_estimate": 157, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90d6f6a261324846", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 1. First-order logical reasoning\nType: text\n\nAs defined in the Common Semantic Ontology of Disaster Prediction, surface combustibles include Woodland, Farmland, Building, and Grassland, and Coniferous Forest belongs to Woodland. Based on first-order logical reasoning, it concludes that Coniferous Forest belongs to Surface Combustibles. With the prefix of *hazard*, the above process can be formally defined as shown in Figure 11. Based on these expressions, *(hazard : Coniferous Forest rdf : subClassOf hazard : Surface Combustibles)* can be inferred.\n\n```\n```", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 1. First-order logical reasoning", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "1. First-order logical reasoning"], "chunk_type": "text", "line_start": 246, "line_end": 252, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb4c1c1a9ccf73b3", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 1. First-order logical reasoning\nType: figure\nFigure: Figure 11. First-order logical reasoning process.\n\nFigure 11. First-order logical reasoning process.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 1. First-order logical reasoning", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "1. First-order logical reasoning"], "chunk_type": "figure", "figure_caption": "Figure 11. First-order logical reasoning process.", "line_start": 253, "line_end": 253, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7aa992436be5e66f", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning\nType: text\n\nThis paper takes SWRL rule reasoning as an example. The feasible reasoning on the DPKG includes reasoning of geographic entity from one attribute to other attributes; reasoning from one geographic entity and its attributes to geographic entities and their attributes.\n\nFor example, given the forest fire risk entity and its attribute, i.e., the comprehensive forest fire-driven index *fireDangerValue*, the goal is to build an inference rule for the forest fire risk-level prediction. For this task, the domain analysis rule \"when fireDangerValue > 3.8, fireDangerLevel is level 3\" is formalized as shown as line 1 in Figure 12.\n\n```\n```", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "2. Production reasoning"], "chunk_type": "text", "line_start": 256, "line_end": 264, "token_count_estimate": 213, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46178452c4e3b925", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning\nType: figure\nFigure: Figure 12. Production reasoning process.\n\nFigure 12. Production reasoning process.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "2. Production reasoning"], "chunk_type": "figure", "figure_caption": "Figure 12. Production reasoning process.", "line_start": 265, "line_end": 265, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e41a8664ffa5a6fb", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning\nType: text\n\nOne more example, knowing the value of aspect entity and aspect attribute, the goal is to construct an inference rule to infer the upslope aspect value. For this task, the domain analysis rule \"When aspect is  $<180^{\\circ}$ , upAspect = aspect +  $180^{\\circ}$ \" is formalized as shown as line 2 in Figure 12.\n\nThe prediction can be carried out using the SWRL reasoning engine with the inference rules.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 2. Production reasoning", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "2. Production reasoning"], "chunk_type": "text", "line_start": 266, "line_end": 270, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ca5fcd5ab092b39", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 3. Spatio-temporal semantic reasoning\nType: text\n\nThis paper builds an automatic real-time monitoring mechanism for emergency information for disaster prediction. It extracts the dynamic information related to the occurrence and development of disasters, i.e., attributes such as space, time, and status of target objects, from structured, semi-structured, and unstructured data sources. It encapsulates the extracted information into real-time event message objects with GeoJSON format. The update of dynamic data will trigger the automatic judgment by the DPKG as shown in Figure 13. The results drive the chained reasoning workflow defined by the disaster prediction inference rules.\n\nRemote Sens. 2022, 14, 1214 12 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > 3. Spatio-temporal semantic reasoning", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "3. Spatio-temporal semantic reasoning"], "chunk_type": "text", "line_start": 272, "line_end": 276, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d81dd629174dd95a", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: A flowchart titled 'Calculation process' illustrates a system for predicting geological and forest fire risks. The process begins with two inputs: 'Weather forecast data for the next 7 days (updated at 8:00 and 20:00 daily)' and 'Weather monitoring data (lag about 10-20 minutes, updated every 5 minutes)'. Both are fed as 'input' into a process called 'Instance layer dynamic update'. The output of this stage is then used as 'input' for the central calculation step, 'The triplet disaster prediction model starts the calculation'. This model also utilizes 'Spatio-temporal knowledge graph data'. The model produces two 'output' streams: 'Prediction results of geological landslide risk in the next 7 days' and 'Forecast results of forest fire risk in the next 7 days'. The flowchart also includes two dashed lines indicating dependencies. The top line states, 'Forecast days depend on the availability of weather forecast data', connecting the forecast data to the final predictions. The bottom line states, 'Actual real-time performance depends on the lag of meteorological monitoring data', connecting the monitoring data to the final predictions.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "Calculation process"], "chunk_type": "figure", "figure_caption": null, "line_start": 279, "line_end": 279, "token_count_estimate": 341, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "979101b0056460a3", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process\nType: figure\nFigure: Figure 13. Workflow of disaster dynamic prediction.\n\nFigure 13. Workflow of disaster dynamic prediction.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "Calculation process"], "chunk_type": "figure", "figure_caption": "Figure 13. Workflow of disaster dynamic prediction.", "line_start": 281, "line_end": 281, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "43a0660b5ae480fb", "text": "Document: 1. Introduction\nSection: 2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process\nType: text\n\nThis paper designs a chain-type automatic reasoning method based on the reasoning criterion. The method divides the calculation logic of the geological landslide prediction model into a series of antecedents and consequences. The antecedents are execution conditions that contain a series of geographic entities. When there exists an intersection of the geographic entities and the conditions set in the antecedent are met, the subsequent associated with the antecedent, i.e., the action object, will be triggered.\n\nIt establishes the automatic flow logic between independent criteria such as time probability, space probability, intensity probability, vulnerability of hazard-affected body, and value amount by defining for RuleObject. In this way, it can analyze the relations between time information and space information, which is not feasible using the production reasoning with SWRL.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Materials and Methods > 2.3. Query and Reasoning of Disaster Prediction DPKG > 2.3.2. Disaster Prediction Rule Reasoning > Calculation process", "section_headings": ["2. Materials and Methods", "2.3. Query and Reasoning of Disaster Prediction DPKG", "2.3.2. Disaster Prediction Rule Reasoning", "Calculation process"], "chunk_type": "text", "line_start": 282, "line_end": 286, "token_count_estimate": 239, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f9d05513539ab917", "text": "Document: 1. Introduction\nSection: 3. Results\nType: text\n\nThis section demonstrates experiments and cases studies of the forest fire and geological landslide risk based on DPKG. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster predictions.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results", "section_headings": ["3. Results"], "chunk_type": "text", "line_start": 288, "line_end": 290, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cd64681605aed870", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: text\n\nIn this case study, Yanyuan County, in the Liangshan Yi Autonomous Prefecture of China's Sichuan Province, was selected to carry out a dynamic prediction experiment of forest fire disasters. Yanyuan County is located between  $100^{\\circ}42'09''-102^{\\circ}03'44''$  east longitude and  $27^{\\circ}06'31-28^{\\circ}16'31''$  north latitude, with a total area of 8398.6 square kilometers. There are rich vegetation and many miscellaneous irrigation and thatch grass in the area. The forest fire disasters are easily caused by the inducing factors such as low rainfall. Therefore, Yanyuan County strongly needs to forecast the situation in advance.\n\nIn this case, multi-source heterogeneous data related to forest fire risk prediction are collected, such as vegetation data, terrain data, meteorological data, and land cover data. In order to fully represent the spatio-temporal features of the above data in the DPKG knowledge graph, this paper uses Protégé to construct a time ontology and a space ontology. The concepts related to forest fire prediction form the conceptual layer of DPKG. For the above-mentioned multi-source heterogeneous data, we built a diversified knowledge extraction method for transferring the data into triples according to the semantics of the conceptual layer.\n\nThe triples form an instance layer of the DPKG of disaster prediction. An example of the extraction process of the slope in the terrain data is shown in Figure 14.\n\nRemote Sens. 2022, 14, 1214 13 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "text", "line_start": 292, "line_end": 300, "token_count_estimate": 395, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "a45b7ba8f5fef407", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure\n\nImage /page/12/Figure/1 description: A diagram illustrates a data processing workflow for geographic information. The flow starts with a TIFF file, a raster image of a geographical area. An arrow labeled 'Raster to Vector' points to a Shapefile, which is a colored vector map of the same area. From the Shapefile, an arrow labeled 'Vector to GeoJSON' points to a code snippet of a GeoJSON file. The GeoJSON code shows a 'FeatureCollection' with properties for a 'Slope'. An arrow labeled 'GeoJSON to Triple' points from the GeoJSON to a knowledge graph representation. This graph has a central node 'SlopeEntity' connected to four other nodes via labeled edges: 'hasSlope' points to '21', 'hasTileCode' points to 'z20\\_x322\\_y786', 'hasTime' points to '2021-10-27 01:17', and 'hasCenterLonLat' points to '102.98438°E, 26.502315°N'.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 301, "line_end": 301, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["502315", "98438"]}}
{"id": "37d2c48f71bce47d", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure: Figure 14. Knowledge extraction process using slope as an example.\n\nFigure 14. Knowledge extraction process using slope as an example.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 14. Knowledge extraction process using slope as an example.", "line_start": 303, "line_end": 303, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1df42932e72c1e6e", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: text\n\nForest fire risk prediction needs the theoretical support because the above spatiotemporal data is only a prerequisite for forest fire risk prediction. In this case, the AHP (Analytic Hierarchy Process) is used to evaluate forest fire risk indicators. This paper builds an indicator system for four types of forest fire risk factors: meteorology, terrain, vegetation, and man-made. The weights are shown in Figure 15.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "text", "line_start": 304, "line_end": 306, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0c7ccb1f53e0ea9", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure\n\nImage /page/12/Figure/4 description: A flowchart diagram illustrating the 'Comprehensive Index of Forest Fire Drivers'. This main index branches into four primary factors, each with a numerical weight. The 'Vegetation factor' has a weight of 0.24 and is subdivided into 'Vegetation Types' (0.18) and 'Canopy' (0.06). The 'Meteorological factor' has the highest weight of 0.40 and is broken down into four sub-factors: 'Wind speed' (0.10), 'Precipitation' (0.10), 'Temperature' (0.10), and 'Humidity' (0.10). The 'Terrain factor' has a weight of 0.21 and includes 'Slope' (0.10), 'Aspect' (0.06), and 'Height' (0.05). Finally, the 'Human factor' has a weight of 0.15 and is represented by a single sub-factor, 'Festival' (0.15).", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 307, "line_end": 307, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a52d36d95013d3ad", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure: Figure 15. Forest fire risk factor indicator system.\n\nFigure 15. Forest fire risk factor indicator system.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 15. Forest fire risk factor indicator system.", "line_start": 309, "line_end": 309, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "427af2d0f4f55e5c", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: text\n\nThe comprehensive indicator of forest fire driving factors is defined as Formula (4):\n\n$$CIFFD = \\sum v_i \\times w_i \\ i \\in [1, 10], \\tag{4}$$\n\nwhere *CIFFD* represents a composite index of forest fire drivers;  $v_i$  indicates the level corresponding to the original value of the factor, and  $w_i$  indicates the weight corresponding to the factor. The larger the CIFFD, the greater the risk of forest fires.\n\nIn this case, 10 factors in four categories of terrain, vegetation, meteorology, and human factors in the experimental area are used as the query input for the DPKG instance layer. The definition in Formula (4) is transformed into spatio-temporal semantic reasoning rules. The reference output is a comprehensive index of forest fire driving factors.\n\nDefine spatio-temporal semantic inference rule 1: If the update of meteorological data is detected, it divides the spatial range of the meteorological data update into several rectangular areas. The rectangular areas automatically triggers the query of 10 factors of\n\nRemote Sens. 2022, 14, 1214 14 of 21\n\nterrain, vegetation, meteorology, and human factors in the rectangular areas for obtaining the original value of the factor.\n\nDefine spatio-temporal semantic inference rule 2: It obtains the corresponding level value based on the original value of the factor. Based on Formula (4), the comprehensive index of forest fire driving factors is obtained.\n\nDefine spatio-temporal semantic inference rule 3: Based on the natural discontinuity method, the calculated comprehensive index of forest fire driving factors is divided into 7 intervals for representing different degrees from low to high. The forest fire risk warning information of the experimental area is obtained, as shown in Figure 16.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "text", "line_start": 310, "line_end": 328, "token_count_estimate": 446, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4dfe4cdadb2129e7", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure\n\nImage /page/13/Figure/4 description: A flowchart diagram titled \"Semantic inference of forest fire disaster\". The diagram is divided into three vertical sections by dashed lines, labeled \"Rule one\", \"Rule two\", and \"Rule three\".", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 329, "line_end": 329, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "763150ac1683e6bb", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: text\n\nIn \"Rule one\", the process starts with \"Meteorological data update\", which leads to \"Divide rectangular area\". An arrow points from this to a box labeled \"Query regional forest fire-related factors\".\n\nAn arrow from \"Rule one\" points to \"Rule two\". In this section, there are two icons: a square with a diagonal line and a circle inside, and a stacked triangle. Below them, the text reads \"Gets the corresponding level value\". This leads to a box labeled \"Forest fire driver composite index\".\n\nAn arrow from \"Rule two\" points to \"Rule three\". In this final section, there is a bar chart with bars of increasing height, labeled \"Forest fire driving factor composite index divided into intervals\". The process concludes in a box labeled \"Get rank\".", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "text", "line_start": 330, "line_end": 336, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7b242e23b6d1ea99", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure: Figure 16. Forest fire risk factor index system.\n\nFigure 16. Forest fire risk factor index system.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 16. Forest fire risk factor index system.", "line_start": 337, "line_end": 337, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6fc84d8b8a110b3c", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: text\n\nThe antecedent rules in the above spatio-temporal semantic inference rules can trigger the subsequent rules when satisfying the conditions. Therefore, the dynamic changes of meteorological data can automatically trigger the forest fire risk calculation. It realizes the automatic completion of the chain reasoning of the whole process of DPKG without manual effort.\n\nIn this case, the actual meteorological record data of Yanyuan County Meteorological Observatory on 3 January 2022 is used as the query input of the instance layer of the DPKG. The output is calculated based on the reasoning of the DPKG.\n\nFor the forest fire risk index of the whole county of Yanyuan, the larger the index value, the higher the forest fire risk. The experimental results are shown in Figure 17.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "text", "line_start": 338, "line_end": 344, "token_count_estimate": 187, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "caad9b393ebcc59b", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure\n\nImage /page/13/Figure/9 description: A map displaying the Forest Fire Risk Index for an irregularly shaped geographical area. The map uses a color scale to represent different levels of risk. A legend on the left side details the index values corresponding to each color: dark green for 0.85-2.90, medium green for 2.91-3.60, light green for 3.61-4.00, yellow for 4.01-4.10, light orange for 4.11-4.20, orange for 4.21-4.60, and red for 4.61-5.29. Scattered across the map are several purple dots, which the legend identifies as 'January 3, 2022 06:35 fire point'. In the bottom right corner, there is a scale bar marked in miles, with increments shown at 0, 3, 6, 12, 18, and 24 miles.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 345, "line_end": 345, "token_count_estimate": 215, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "235fd6afe2f3f454", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: figure\nFigure: Figure 17. Map of landslide hazards in Yanyuan County based on multi-Graded Cascade Random Forest.\n\n**Figure 17.** Map of landslide hazards in Yanyuan County based on multi-Graded Cascade Random Forest.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 17. Map of landslide hazards in Yanyuan County based on multi-Graded Cascade Random Forest.", "line_start": 347, "line_end": 347, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1ccad098dd4e939", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Case Study: Forest Fire Risk Prediction\nType: text\n\nRemote Sens. 2022, 14, 1214 15 of 21\n\nNASA's Fire Information for Resource Management System shows the actual fire point in Yanyuan County at 6:35 on 3 January 2022, as shown in Figure 17. The results show that the proposed method successfully hits the fire point of the forest fire species within the time and space range of the predicted forest fire risk index greater than 4.21.\n\nThe case uses the traditional spatial analysis tool ArcGIS as a baseline method to calculate the forest fire risk in Yanyuan County for the comparative experiments. It is necessary to load and query Yanyuan County data from 14 types of raster datasets. Steps such as uniform coordinate system, uniform pixel size, grid cropping, and stitching are performed on each raster data, and then the prediction calculation can be performed. There are many steps in the baseline operation, and the whole process takes more than 190 min. When using the grid calculator, the performance is low because it needs complex numerical computations in multiple steps.\n\nIn addition, the boundaries of various data are not completely coincident, which will lead to missing or abnormal results of the boundary area analysis, and wrong predictions. Although it is possible to forcibly align the pixel positions by means of translation, it will cause errors by an operation that lacks a realistic basis, and changes the spatial distribution of the original data. In addition, it has a low prediction efficiency when using the traditional spatial analysis tools because they cannot process other target regions in parallel.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Case Study: Forest Fire Risk Prediction", "section_headings": ["3. Results", "3.1. Case Study: Forest Fire Risk Prediction"], "chunk_type": "text", "line_start": 348, "line_end": 356, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "094c1fb2bfa30355", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: text\n\nIn another case study, Xiji County in Guyuan City of China's Ningxia Hui Autonomous Region is selected to carry out an experiment regarding the dynamic prediction of geological landslide hazards. Xiji County locates between  $105^{\\circ}20'-106^{\\circ}04'$  east longitude and  $35^{\\circ}35'-36^{\\circ}14'$  north latitude, which has a total area of 3130 square kilometers. In this area, the loess landslide disaster caused by the Haiyuan earthquake is particularly serious because of the special geological environmental conditions. It has formed 765 geological landslides [22]. It is very dangerous that new geological landslide disasters are easily generated by the inducing factors, e.g., rainfall. Therefore, it is urgent to take an effective method to predict disaster in advance.\n\nIn this case, the basic geological data, basic geographic data, hydrological data, human activities, and land is collected for the prediction and calculation of geological landslide risk. In order to fully represent the spatio-temporal features of the above data in the knowledge graph, this paper uses Protégé [23] to construct a time ontology and a space ontology, and concepts related to geological landslide prediction form the conceptual layer of spatial-temporal knowledge graph for disaster prediction. For the above-mentioned multi-source heterogeneous data, a diversified knowledge extraction method is adopted to convert the data into triples according to the semantics of the conceptual layer. It forms the instance layer of the DPKG for disaster prediction using the triples.\n\nThe above spatio-temporal data is only a prerequisite for geological landslide risk prediction. It needs the theoretical support of the geological landslide risk prediction. The landslide risk assessment is defined as Formula (2). Geological landslide risk prediction involves time, spatial, and intensity probability. In this case, there are 13 factors belonging to four categories of topography, geology, lithology, meteorology and hydrology, land cover, and human activities in the experimental area. The information of the factors is queried from the knowledge graph. It takes the DEM factors in the topographic data as the query input of the instance layer of the knowledge graph. The linear fitting model between historical landslides and DEM data is transformed into spatio-temporal semantic reasoning rules, as shown in Figure 18. In this way, the spatial probability of landslide disasters in the experimental area can be calculated.\n\nRemote Sens. 2022, 14, 1214 16 of 21", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "text", "line_start": 358, "line_end": 366, "token_count_estimate": 646, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "3633a1c8e76dd75e", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure\n\nImage /page/15/Figure/1 description: A flowchart illustrating a model for calculating Geological Landslide Risk Probability. At the top, a computer monitor icon is labeled \"Geological Landslide Risk Probability,\" which points to a formula: H = P(T) × P(S) × P(I). The flowchart has two main input branches. The first branch combines three inputs: \"The local precipitation data for the past 10 days is an empirical function of the independent variable,\" \"Deep Random Forest Model,\" and \"Linear fit model between historical landslides and DEM data.\" These are processed through a step called \"Knowledge Representation\" to generate \"Spatiotemporal Semantic Reasoning Rules.\" The second branch combines three other inputs: \"Precipitation rate,\" \"Topography, Geology and lithology, Meteorology and hydrology, Land cover and human activities,\" and \"DEM.\" These are processed through a step called \"Instantiate\" to create a \"Disaster prediction spatiotemporal knowledge graph instance layer.\" This instance layer provides \"Data Support\" to the \"Spatiotemporal Semantic Reasoning Rules.\" Finally, the \"Spatiotemporal Semantic Reasoning Rules\" are used to derive the three probabilities P(T), P(S), and P(I) for the final risk calculation.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 367, "line_end": 367, "token_count_estimate": 346, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc59982648707b78", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure: Figure 18. Geological landslide risk prediction calculation formula.\n\nFigure 18. Geological landslide risk prediction calculation formula.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 18. Geological landslide risk prediction calculation formula.", "line_start": 369, "line_end": 369, "token_count_estimate": 60, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7c6bafc3f807da20", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: text\n\nThe time probability is a dynamic element. In this case, the time probability is defined as the empirical function with the independent variable of the local precipitation data in the past 10 days, as shown in Formula (3). It transforms Formula (3) into spatio-temporal semantic inference rules, and the time probability of landslide disaster occurrence in the experimental area.\n\nDefine spatio-temporal semantic inference rule 1: If the time probability satisfies specific conditions, it is defined as a dangerous time probability.\n\nDefine spatio-temporal semantic reasoning rule 2: For a new dangerous time probability, the calculation of the spatial probability and the intensity probability will be automatically triggered. The spatial distribution of the geological landslide risk value in the test area will be calculated according to Formula (1).\n\nDefine spatio-temporal semantic inference rule 3: Based on the natural discontinuity method, the geological landslide risk probability value calculated by the previous rule is divided into 10 intervals in ascending order, representing different degrees, every two intervals are combined into a grade for a total of five grades, as shown in Figure 19.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "text", "line_start": 370, "line_end": 378, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c38eb7575f24e3d0", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure\n\nImage /page/15/Figure/7 description: A flowchart diagram titled \"Semantic reasoning of landslide disaster\" is divided into three sections labeled \"Rule one,\" \"Rule two,\" and \"Rule three.\" In \"Rule one,\" two inputs, \"Day time probability\" and \"Nearest time interval time probability,\" feed into a box labeled \"Time probability of dangerous geological landslide.\" An arrow from this box leads to \"Rule two.\" In \"Rule two,\" the process is described as \"Geographic grid computing,\" represented by an icon of a map with a grid overlay. This process results in the \"Spatial distribution of geological landslide risk values.\" An arrow from this result leads to \"Rule three.\" In \"Rule three,\" the \"Natural discontinuity method\" is applied to the risk values. The outcome is visualized as five vertical bars of increasing height, representing different risk levels labeled below as \"VL, L, M, H, VH,\" which likely stand for Very Low, Low, Medium, High, and Very High.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 379, "line_end": 379, "token_count_estimate": 293, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3f7022f81465c36", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure: Figure 19. Space-time semantic reasoning logic with criteria (VL: very low, L: low, M: moderate, H: high, VH: very high).\n\n**Figure 19.** Space-time semantic reasoning logic with criteria (VL: very low, L: low, M: moderate, H: high, VH: very high).", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 19. Space-time semantic reasoning logic with criteria (VL: very low, L: low, M: moderate, H: high, VH: very high).", "line_start": 381, "line_end": 381, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a49b818b4a97f31b", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: text\n\nThe antecedent rules in the above spatio-temporal semantic inference rules can trigger the subsequent rules when the conditions are satisfied. Therefore, the dynamic change of precipitation data can automatically trigger the geological landslide risk calculation for\n\nRemote Sens. 2022, 14, 1214 17 of 21\n\nrealizing the automatic completion of the whole process of DPKG chain reasoning without manual effort.\n\nIn this case, the actual precipitation record data of Xiji County Meteorological Observatory on 18 September 2018 is used as the query input of the instance layer of the DPKG. Based on the reasoning of spatial-temporal knowledge graph, the geological landslide time probability was daily calculated for the whole county of Xiji from 8 to 28 September 2018. From 18 to 19 September 2018, the geological landslide time probability in the county met the above geological landslide risk time probability conditions for the two consecutive days. The experimental results are shown in Figure 20. Due to the setting of reasoning rule 2, the calculation of geological landslide risk will be triggered automatically. The calculation results of geological landslide risk probability on 18 September 2018 are shown in Figure 21.\n\nThe graph of the time probability of landslide", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "text", "line_start": 382, "line_end": 392, "token_count_estimate": 303, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "224909c9add4c1ba", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure\n\nImage /page/16/Figure/4 description: A bar chart titled 'The graph of the time probability of landslide'. The vertical axis is labeled '%0' and ranges from 0 to 20 in increments of 2. The horizontal axis displays dates from 9/8 to 9/28. The chart consists of blue vertical bars for each day, showing the time probability. There are notable peaks in probability on 9/15, with a value of approximately 17.8, and on 9/19, with a value of approximately 18.5. Another smaller peak occurs on 9/18 with a value of about 16. For most other days, the probability fluctuates between approximately 6 and 10. A dashed blue line is overlaid on the chart, starting at a value of about 14, dipping to around 13, and then leveling off at approximately 12.5 for the remainder of the dates.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 393, "line_end": 393, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a059d6cf582f094", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: text\n\nBar: The graph of the time probability of landslide in Xiji County from September 8 to 28, 2018\n\nBroken line: The graph of dangerous time probability of landslide in Xiji Countyfrom September 8 to 28,2018", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "text", "line_start": 394, "line_end": 398, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09e2dd5723f8109b", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure: Figure 20. The graph of the time probability of landslide. The bar is the graph of the time probability of landslide in Xiji County from 8–28 September 2018. The broken line is the graph of dangerous time probability of landslide in Xiji County from 8–28 September 2018.\n\n**Figure 20.** The graph of the time probability of landslide. The bar is the graph of the time probability of landslide in Xiji County from 8–28 September 2018. The broken line is the graph of dangerous time probability of landslide in Xiji County from 8–28 September 2018.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 20. The graph of the time probability of landslide. The bar is the graph of the time probability of landslide in Xiji County from 8–28 September 2018. The broken line is the graph of dangerous time probability of landslide in Xiji County from 8–28 September 2018.", "line_start": 399, "line_end": 399, "token_count_estimate": 167, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44de86f33f0f7bba", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure\n\nImage /page/16/Figure/8 description: A map displaying the geological landslide risk probability for a specific region. The map is color-coded, with a legend indicating the probability levels. A north arrow is in the top left corner. The legend, titled 'Geological Landslide Risk Probability', shows a scale from low risk (dark green) to high risk (red). The probability ranges are as follows: 0-0.024 (dark green), 0.025-0.078 (green), 0.079-0.149 (light green), 0.15-0.231 (yellow-green), 0.232-0.329 (yellow), 0.33-0.439 (light orange), 0.44-0.553 (orange), 0.554-0.675 (dark orange), 0.676-0.812 (orange-red), and 0.813-1 (red). The map is predominantly green, with patches of yellow, orange, and red indicating higher-risk areas. A single yellow dot on the map is labeled 'September 18, 2018 Landslide section'. A scale bar at the bottom right is marked in meters, with intervals at 0, 6250, 12500, and 25000.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 401, "line_end": 401, "token_count_estimate": 303, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["12500", "25000"]}}
{"id": "7627bc3970240f0d", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure: Figure 21. Map of landslide hazards in Xiji County based on multi-Graded Cascade Random Forest.\n\nFigure 21. Map of landslide hazards in Xiji County based on multi-Graded Cascade Random Forest.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 21. Map of landslide hazards in Xiji County based on multi-Graded Cascade Random Forest.", "line_start": 403, "line_end": 403, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72f3fd0392d63df6", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: text\n\nRemote Sens. 2022, 14, 1214 18 of 21\n\nIn this case, since the distribution data of buildings and roads in Xiji County is included in the DPKG, it further filtered the buildings and roads in the high-risk areas of geological landslides on 18 September 2018, as shown in Figure 22. From Internet news, we learned that a landslide occurred on National Highway 309 on 18 September 2018, which caused road blockage. The actual geological landslide section is located in the dangerous road area that has been correctly predicted by our method, as shown in Figure 22. Experiments show that it successfully hit the geological landslide event within the time and space range of the warning.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "text", "line_start": 404, "line_end": 408, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2dd5c943f55acfee", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure\n\nImage /page/17/Figure/2 description: A diagram illustrating the identification and data representation of geological hazards. On the left is a map of a region with a legend indicating different features: grey for Road, pink for Building, red for Risk road, orange for Risk Building, and purple for the September 18, 2018 Landslide section. The map includes a north arrow and a scale bar from 0 to 24000 meters. A small section of the map is magnified and shown in the center as a satellite image of a winding road in a mountainous area. This image has colored overlays corresponding to the legend. To the right, two entity relationship diagrams show how these features are structured as data. An arrow from the purple landslide section points to a 'Landslide Entity' diagram with the following attributes: 'hasCenterLonLat' is '102.98438°E, 26.502315°N', 'hasTime' is '2018-09-18', and 'hasTileCode' is 'z20\\_x321\\_y765'. An arrow from the red road section points to a 'RiskRoad Entity' diagram with attributes: 'hasCenterLonLat' is '102.98438°E, 26.502315°N', 'hasTime' is '2018-09-18', and 'hasTileCode' is 'z20\\_x321\\_y766'.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 409, "line_end": 409, "token_count_estimate": 359, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["24000", "502315", "98438"]}}
{"id": "bdefb178ab610a96", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure: Figure 22. Road distribution map of Xiji County included in the landslide disaster warning area on 18 September 2018.\n\n**Figure 22.** Road distribution map of Xiji County included in the landslide disaster warning area on 18 September 2018.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 22. Road distribution map of Xiji County included in the landslide disaster warning area on 18 September 2018.", "line_start": 411, "line_end": 411, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bdbea91c35e7e2a6", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: text\n\nPrediction was carried out in Xiji Country respectively for the area of 3130.00 km<sup>2</sup>, the area of 1509.54 km<sup>2</sup>, and the area of 800.88 km<sup>2</sup>. The experimental results are shown in Figure 23. With the increase of the experimental range, the time of experimental calculation also increases. The performance is basically in a satisfied performance range. Therefore, our method is applicable for large-scale disaster prediction.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "text", "line_start": 412, "line_end": 414, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "581a84efc6f32cad", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure\n\nImage /page/17/Figure/5 description: A horizontal bar chart with three bars. The vertical axis has three labels: 3130.00, 1509.54, and 800.88. The horizontal axis is labeled in minutes (min) and ranges from 0.00 to 10.00, with major gridlines at intervals of 2.00. The top bar, corresponding to 3130.00, extends to a value of 7.81. The middle bar, for 1509.54, has a value of 3.09. The bottom bar, for 800.88, has a value of 2.70. The bars are blue, with the top and bottom bars having a gradient from light to dark blue.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": null, "line_start": 415, "line_end": 415, "token_count_estimate": 178, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09ce30e5b391939b", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Case Study: Geological Landslide Risk Prediction\nType: figure\nFigure: Figure 23. Comparison of forecast time for different areas in Xiji Country.\n\nFigure 23. Comparison of forecast time for different areas in Xiji Country.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Case Study: Geological Landslide Risk Prediction", "section_headings": ["3. Results", "3.2. Case Study: Geological Landslide Risk Prediction"], "chunk_type": "figure", "figure_caption": "Figure 23. Comparison of forecast time for different areas in Xiji Country.", "line_start": 417, "line_end": 417, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "889756be3192189d", "text": "Document: 1. Introduction\nSection: 4. Discussion\nType: text\n\nCompared with the traditional prediction methods, the disaster prediction method based on the DPKG has the advantages of more accurate spatial overlay analysis, high degree of automation, and fast calculation speed, as shown in Table 2. However, it cannot be ignored that the proposed method in this paper still has certain limitations: (1) Before forecasting the target area, the data required for disaster prediction needs to be preprocessed and stored in advance, and data storage requires a certain amount of time. The larger the area, the more data dimensions required for prediction, and the longer the time. (2) The data used in this case is not accurate enough. For example, the spatial resolution of terrain\n\nRemote Sens. 2022, 14, 1214 19 of 21\n\ndata is 30 m, and the spatial resolution of surface coverage data for disaster prediction is 10 m. The accuracy still needs to be improved. If higher-resolution data is obtained, and more detailed tree species, tree age, and other data can be involved in disaster prediction, the accuracy of disaster prediction can be further improved. (3) The comparison with traditional disaster forecasting methods is not sufficient in the experiment that only considers the most well-known tool ArcGIS.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 420, "line_end": 428, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac6c78ffaac15be3", "text": "Document: 1. Introduction\nSection: 5. Conclusions\nType: text\n\nCompared with plain text data and structured databases, DPKG has the advantages of richer semantic representation, more accurate data content, and better query performance. The knowledge graph provides a new means for the organization, management, fusion, and analysis of multi-source heterogeneous data in complex disaster environments. It facilitates prediction, prevention, and mitigation of the natural disasters. This paper proposes the DPKG that integrates multi-source remote sensing information in disaster scenes with other multi-source heterogeneous information. It considers not only dynamic data describing spatio-temporal facts but also domain analysis models. Based on the analysis of disaster influencing factors, this paper proposes a common disaster prediction method based on the DPKG for the dynamic data-driven disaster prediction. This paper introduces new ideas by taking forest fires and geological landslides as examples. The proposed method can serve the research and application of disaster emergency response with the strong help of artificial intelligence technology.\n\n- (1) From the perspective of cross-domain knowledge integration, knowledge graph integrates remote sensing knowledge and expert knowledge through semantic technology. It effectively connects multi-source heterogeneous data (GIS, meteorology, terrain, ground sensors, etc.) with expert knowledge in the field of disasters;\n- (2) The DPKG contains dynamically updated spatio-temporal facts that reflect changes in the real world. Through knowledge graph query, the query performance in disaster emergency scenarios can be improved, and dynamic data updates can automatically drive prediction;\n- (3) It provides efficient data storage and management methods for practitioners in the fields of remote sensing and geo-information, which helps to improve the efficiency\n\nRemote Sens. 2022, 14, 1214 20 of 21\n\nof spatio-temporal data query. It reduces the manual effort by using reasoning of the knowledge graph.\n\nThe DPKG proposed in this paper aims at the integration of remote sensing information, geographic information, and the correlation between dynamic data and static knowledge in complex disaster environments. It lays the foundation for future in-depth research, particularly for knowledge extraction and knowledge discovery for disaster prediction. In future work, indexing and query performance need to be further verified in the case of more types and larger amounts of data. The depth of knowledge reasoning needs to be further improved. In future research, we will enhance the DPKG by leveraging graph neural networks and deep learning models, so as to integrate a large number of spatio-temporal facts, and realize disaster emergency decision-making, historical data verification, and new model construction; it needs to build a knowledge service system for natural disaster monitoring and prediction as well as the emergency decision-making. In addition, this article basically focuses on verifying the availability and usability of our model through case studies. We will compare with more traditional prediction methods in the future\n\n**Author Contributions:** Conceptualization, X.G. and Y.Y.; methodology, X.G. and W.L.; validation, X.G., W.L. and W.Z.; resources, W.L.; data curation, X.G. and W.L.; writing—original draft preparation, X.G. and J.C.; writing—review and editing, X.G., Y.Y., L.P. and Z.H.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.\n\n**Funding:** This work was supported by the Beijing Municipal Science and Technology Project (Z191100001419002), and Ningxia Key R&D Program (2020BFG02013).", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Conclusions", "section_headings": ["5. Conclusions"], "chunk_type": "text", "line_start": 430, "line_end": 454, "token_count_estimate": 856, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["2020BFG02013"]}}
{"id": "d55db02e660a172a", "text": "Document: 1. Introduction\nSection: 5. Conclusions\nType: text\n\n. Z . ; resources , W . L . ; data curation , X . G . and W . L . ; writing — original draft preparation , X . G . and J . C . ; writing — review and editing , X . G . , Y . Y . , L . P . and Z . H . ; funding acquisition , L . P . All authors have read and agreed to the published version of the manuscript . * * Funding : * * This work was supported by the Beijing Municipal Science and Technology Project ( Z191100001419002 ) , and Ningxia Key R & D Program ( 2020BFG02013 ) .\n\n**Institutional Review Board Statement:** Not applicable.\n\nInformed Consent Statement: Not applicable.\n\nData Availability Statement: Data sharing not applicable.\n\nConflicts of Interest: The authors declare no conflict of interest.", "metadata": {"source_file": "data/('Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Conclusions", "section_headings": ["5. Conclusions"], "chunk_type": "text", "line_start": 430, "line_end": 454, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["2020BFG02013"]}}
{"id": "14dc0ede11a89a9c", "text": "Document: Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)\nType: text\n\nAbstract: \"Glacial Lake Outburst Floods (GLOFs)\" are sudden, high-magnitude floods resulting from the breach of natural dams containing glacial lakes. With climate change accelerating glacial lake formation, GLOFs present growing threats to downstream communities and infrastructure. This study introduces an AIpowered chatbot system designed for real-time GLOF risk prediction and assessment. By leveraging predictive models such as ARIMA, GRU, and LSTM, the system analyses user-provided environmental data, including temperature and water levels, to generate actionable insights. A simple and intuitive chatbot interface allows users to input key parameters, which are processed using predefined thresholds to categorize risk levels as high, moderate, or low. The system enhances disaster preparedness and management through accessible and timely decision support. Future work aims to integrate IoT-enabled realtime data streams to improve prediction accuracy further, making this solution a scalable and effective tool for mitigating GLOF risks in vulnerable regions.\n\nKeywords: GLOFs, AI-Powered chat bot, Predictive models.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 300, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8342975b3e46eff7", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nIn recent years, advancements in artificial intelligence (AI) and predictive analytics have significantly transformed disaster risk management. Among these, the prediction of Glacial Lake Outburst Floods (GLOFs) has garnered attention due to their catastrophic impact on downstream communities and infrastructure. With climate change accelerating glacial lake formation, GLOF prediction has become increasingly vital. However, the task is challenging due to the dynamic nature of glacial systems, limited real-time data, and the complex interplay of environmental factors.\n\nTo address these challenges, AI-powered chatbot systems have emerged as an innovative solution. By leveraging pretrained predictive models like ARIMA, GRU, and LSTM, these systems analyze environmental data such as temperature and water levels to predict GLOF risks. This approach reduces computational complexity, ensures real-time responsiveness, and provides accurate risk assessments. Additionally, the chatbot interface enhances accessibility, allowing non-experts to receive actionable insights for disaster preparedness and management.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 12, "token_count_estimate": 255, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e584e2018901898c", "text": "Document: 1. Introduction\nSection: 2. Literature Review > A. Traditional Methods\nType: text\n\nTraditional methods for GLOF prediction primarily relied on field surveys, remote sensing, and hydrological modelling. While effective for historical assessments, these methods lacked real-time responsiveness and required significant expertise and resources. Additionally, they were unable to account for rapidly changing environmental conditions, limiting their predictive accuracy in dynamic glacial systems.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Literature Review > A. Traditional Methods", "section_headings": ["2. Literature Review", "A. Traditional Methods"], "chunk_type": "text", "line_start": 16, "line_end": 18, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46e47da3bede4a1f", "text": "Document: 1. Introduction\nSection: 2. Literature Review > B. Environmental Data Analysis\nType: text\n\nEnvironmental data analysis plays a crucial role in GLOF prediction. Early approaches focused on analyzing individual factors, such as glacial lake volumes or temperature trends, but failed to integrate multiple parameters effectively. Recent advancements in machine learning models like ARIMA and GRU have enabled the analysis of complex, multivariate datasets. These models can identify temporal patterns and interactions between variables, significantly improving prediction accuracy and reliability.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Literature Review > B. Environmental Data Analysis", "section_headings": ["2. Literature Review", "B. Environmental Data Analysis"], "chunk_type": "text", "line_start": 20, "line_end": 22, "token_count_estimate": 122, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "668c456599576350", "text": "Document: 1. Introduction\nSection: 2. Literature Review > C. AI and Chatbot Systems\nType: text\n\nThe integration of AI-powered chatbots has revolutionized the accessibility and usability of early-warning systems. Predictive models, including LSTM, have been incorporated to analyze real-time data and provide accurate risk categorizations. Chatbots offer a user-friendly interface, allowing stakeholders to input environmental parameters and receive actionable insights instantly. This eliminates the need for specialized knowledge and reduces reliance on computationally expensive systems. Future advancements include integrating IoT sensors for continuous data collection and enhancing prediction accuracy through real-time feedback loops.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Literature Review > C. AI and Chatbot Systems", "section_headings": ["2. Literature Review", "C. AI and Chatbot Systems"], "chunk_type": "text", "line_start": 24, "line_end": 26, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b4f0158e58448e26", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing\nType: text\n\nThe dataset utilized for this project includes historical and real-time environmental data sourced from public repositories, including satellite imagery, hydrological datasets, and meteorological records. Key parameters such as temperature, precipitation, and water levels were analyzed to ensure\n\n<sup>\\*</sup>Corresponding author: harrini.d.s@gmail.com\n\naccuracy and consistency. Preprocessing steps involved normalizing data to handle varying units and filling missing values using interpolation techniques. Data augmentation, such as synthetic generation of extreme scenarios, was employed to improve the robustness of predictive models. The dataset was split into training, validation, and testing sets in a 70:20:10 ratio for optimal model evaluation.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing", "section_headings": ["3. Methodology Data Collection and Preprocessing"], "chunk_type": "text", "line_start": 28, "line_end": 34, "token_count_estimate": 192, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e6a5c029507f55b", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing > A. Model Development\nType: text\n\nPredictive models, including ARIMA, GRU, and LSTM, were employed for analyzing temporal and multivariate data. These models were selected for their ability to capture timeseries patterns and long-term dependencies. The final predictive layers were tailored to classify GLOF risk into high, moderate, or low categories. A threshold-based logic system was integrated to interpret model outputs into actionable risk levels.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing > A. Model Development", "section_headings": ["3. Methodology Data Collection and Preprocessing", "A. Model Development"], "chunk_type": "text", "line_start": 36, "line_end": 38, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8615a508e164c9f5", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing > B. Training and Validation\nType: text\n\nThe models were trained using the Adam optimizer and mean squared error (MSE) loss function. Early stopping was implemented to avoid overfitting, and learning rate scheduling was applied to enhance convergence. The training process was monitored using validation metrics, including root mean square error (RMSE) and accuracy. Augmented scenarios in the training data ensured better generalization for unseen conditions.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing > B. Training and Validation", "section_headings": ["3. Methodology Data Collection and Preprocessing", "B. Training and Validation"], "chunk_type": "text", "line_start": 40, "line_end": 42, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "115acda0c6cbe19c", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing > C. Testing and Evaluation\nType: text\n\nThe trained models were tested on a holdout test set comprising unseen environmental data. Evaluation metrics such as RMSE, accuracy, precision, and recall were computed to assess the system's predictive performance. Misclassified or ambiguous cases were analyzed to refine model thresholds and improve overall reliability.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing > C. Testing and Evaluation", "section_headings": ["3. Methodology Data Collection and Preprocessing", "C. Testing and Evaluation"], "chunk_type": "text", "line_start": 44, "line_end": 46, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "15596889afcd18ee", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing > D. Deployment\nType: text\n\nThe predictive system was deployed as an AI-powered chatbot application. The chatbot interface, built using Python's Flask or FastAPI frameworks, allows users to input environmental data such as temperature and water levels. Based on these inputs, the chatbot provides real-time GLOF risk categorization and actionable recommendations. The frontend offers an intuitive chat container to ensure accessibility for users without technical expertise.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing > D. Deployment", "section_headings": ["3. Methodology Data Collection and Preprocessing", "D. Deployment"], "chunk_type": "text", "line_start": 48, "line_end": 50, "token_count_estimate": 121, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "45d71c9314a5f3e0", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing > D. Deployment\nType: figure\nFigure\n\nImage /page/1/Figure/12 description: A flowchart, labeled \"Fig. 1. Flow diagram,\" illustrating a machine learning workflow. The process flows downwards through several stages, each represented in a separate horizontal section on a light blue background. The stages are: 1. Data Collection & Preprocessing, 2. Model Development, 3. Training & Validation, 4. Testing & Evaluation, and 5. Deployment. The final stage, Deployment, branches out to two components: \"User Input (Temperature, Water Level)\" and \"Backend Prediction (Flood Risk).\"", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing > D. Deployment", "section_headings": ["3. Methodology Data Collection and Preprocessing", "D. Deployment"], "chunk_type": "figure", "figure_caption": null, "line_start": 51, "line_end": 51, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74ddbb79771f689f", "text": "Document: 1. Introduction\nSection: 3. Methodology Data Collection and Preprocessing > D. Deployment\nType: text\n\nFig. 1. Flow diagram", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methodology Data Collection and Preprocessing > D. Deployment", "section_headings": ["3. Methodology Data Collection and Preprocessing", "D. Deployment"], "chunk_type": "text", "line_start": 52, "line_end": 54, "token_count_estimate": 32, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf752bffef5aed1f", "text": "Document: 1. Introduction\nSection: 4. Results\nType: text\n\nThe early prediction system for Glacial Lake Outburst Floods (GLOFs), utilizing advanced machine learning models like LSTM and GRU, demonstrated significant predictive accuracy, achieving an RMSE of approximately 0.85 and an accuracy of 88% on the test dataset. The models effectively captured temporal patterns in environmental data, including temperature, precipitation, and water levels, generalizing well to unseen scenarios. Training metrics, including validation loss and accuracy, showed consistent improvement, indicating robust model learning.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results", "section_headings": ["4. Results"], "chunk_type": "text", "line_start": 56, "line_end": 58, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e855d7d1f5f43dd0", "text": "Document: 1. Introduction\nSection: 5. Conclusion\nType: text\n\nIn this project, we successfully developed an early prediction system for Glacial Lake Outburst Floods (GLOFs) using advanced machine learning models integrated into a chatbot interface. The predictive models, including LSTM and GRU, were trained on environmental datasets comprising parameters like temperature, precipitation, and water levels. By leveraging pre-trained models and fine-tuning them for GLOF prediction, the system achieved high accuracy and reduced computational overhead compared to traditional approaches.\n\nThe chatbot provided a user-friendly platform for real-time risk assessment, allowing non-expert users to input data and receive actionable insights. While the system demonstrated strong performance in categorizing risk levels, slight inaccuracies were observed in cases with overlapping environmental thresholds, reflecting the inherent complexity of GLOF prediction. Future enhancements, such as incorporating IoT-based real-time data, are planned to further improve prediction accuracy and system reliability.", "metadata": {"source_file": "data/('Early Prediction System for Glacier Lake Outburst Floods (using ARIMA GRU LSTM)', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Conclusion", "section_headings": ["5. Conclusion"], "chunk_type": "text", "line_start": 60, "line_end": 64, "token_count_estimate": 241, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0db7de63dbbcf816", "text": "Document: Early Warning System for Glacial Lake Outburst Using Multi Source Data\nType: text\n\nAbstract—Glacial Lake Outburst Floods (GLOFs) pose serious risks to ecosystems, infrastructure, and communities downstream. By combining several data sources, such as satellite imaging, remote sensing, hydrometeorological data, and real-time sensor networks, this study presents a unique early warning system (EWS) intended to mitigate these hazards. The EWS can monitor and forecast GLOF incidents more accurately and quickly by using machine learning algorithms and predictive models, which will improve preparedness and mitigation activities in areas that are at risk. The system continuously monitors glacial lakes using satellite imaging and remote sensing, gathering vital information on the dynamics of the lakes and the surrounding ecosystem. This data is paired with hydrometeorological data gathered from real-time sensor networks and ground-based stations, including temperature variations and precipitation levels.\n\nIndex Terms—Glacial Lake Outburst, Early Warning System, climate change, Multi source data", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "Early Warning System for Glacial Lake Outburst Using Multi Source Data", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 257, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe704762a0d078d3", "text": "Document: I. INTRODUCTION\nSection: I. INTRODUCTION\nType: text\n\nGlobal glaciers are rapidly retreating as a result of the accelerating speed of climate change. As a result of this process, glacial lakes have formed and grown, frequently being blocked by loosely consolidated moraines or unstable ice. Glacial lake outburst floods, or GLOFs, are caused by abrupt breaches of these natural dams. GLOFs can release enormous amounts of water in a matter of minutes and are distinguished\n\nby their great size and abrupt onset. These floods can have disastrous aftereffects, resulting in a large number of fatalities, extensive property damage, and extensive harm to downstream communities' infrastructure and natural environments.\n\nThe current techniques for tracking glacial lakes and fore-casting GLOFs are frequently constrained by their need for recurring field surveys. The world's glaciers are melting at previously unheard-of speeds as a result of climate change. The creation and growth of glacial lakes as a result of this melting presents a serious risk of glacial lake outburst floods (GLOFs). When the water in a glacial lake is released, usually as a result of the lake's natural dam collapsing or other precipitating events like landslides or ice avalanches, GLOFs—sudden, high-magnitude floods—occur. These floods can have disastrous effects, including downstream agricultural and natural landscape degradation, infrastructure damage, and fatalities.\n\nDue to traditional monitoring methods not being able to provide real-time data or adequate warning signals, anticipating GLOFs in glacial lakes has proven to be incredibly difficult.\n\nThe retreat and melt of glaciers across the globe is at its peak, and all this is mainly due to an increase in global temperature owing to climate change. Unstable glacial lakes have started to appear in various mountainous regions due to this change. These lakes are often obstructed by loose rocks\n\nor ice moraines, making them incredibly prone to surprising breaches. Such breaches can lead to glacial lake outburst floods (GLOFs) which unleash tremendous amounts of water and debris in a short period of time. Such phenomena put a lot of downstream communities at risk along with causing a lot of destruction to several ecosystems, infrastructural buildings, and even agricultural lands. These GLOFs are expected to occur more frequently than they already do, putting the atrisk populations in danger.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "I. INTRODUCTION", "section_headings": ["I. INTRODUCTION"], "chunk_type": "text", "line_start": 8, "line_end": 20, "token_count_estimate": 596, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55e9655bbbe0fe34", "text": "Document: I. INTRODUCTION\nSection: II. BACKGROUND\nType: text\n\nGlacial lake outburst floods (GLOFs) are characterized by the rapid and sudden release of moisture from glacial lakes, formed as glaciers melt and retreat. Usually, ice or moraines, which are naturally unstable and prone to fracturing, act as barriers to keep these lakes back. including breaches can be caused by a wide range of events such as landslides, seismic activity, extreme rainfall, or the gradual warming of ice dams brought on by warming global temperatures. Particularly in high-altitude areas like the Himalayas, the Andes, and the Alps, the number of glacial lakes has increased as a result of the glaciers' accelerated retreat due to ongoing climate change.\n\nGLOF prediction and prevention has always been a major challenge due to the flexible and changing complexity of glacial lake systems at a multi-level throughout history. While infrequent field surveys and remote sensing are some of the lake monitoring techniques, they are not capable of collecting data in real time and capturing the fast changes in the environment rendering them ineffective. The alarming systems set up to signal GLOF has been mostly late or ineffective which catastrophically impacts the communities located downstream. The advancements in machine learning, remote sensing, sensor networks and satellite technology have provided an opportunity to develop more efficient real time GLOF monitoring systems. By combining data from different sources such as satellite images, hydrometeorological data and groundbased sensors, an integrated system enables efficient monitoring of glacial lakes. This approach is enhanced even further through the use of machine learning algorithms which are able to analyze the data and detect early indicators, making the alarms timelier.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "II. BACKGROUND", "section_headings": ["II. BACKGROUND"], "chunk_type": "text", "line_start": 22, "line_end": 26, "token_count_estimate": 417, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2dc6fdc1ab8de68", "text": "Document: I. INTRODUCTION\nSection: III. LITERATURE SURVEY > A. Glacial Lake Formation and GLOF Events\nType: text\n\nThe impact and frequency of Glacial Lake Outburst Floods (GLOFs) has been a concerning factor on a global scale especially in the higher altitudinal belts which are witnessing accelerated global warming which in turn is causing drastic changes in glaciers. Places like the Himalayas, Andes, Alps and Rockies are particularly vulnerable to such events. Various researchers have studied the processes involved such as J.S. Kargel, U.K. Haritashya [1] highlighted the reasons behind the formation of glacial lakes and the various reasons that act as triggers for GLOFs. Their investigations\n\nreveal the glacier boundary destabilization factors such water in the form of ice avalanches, precipitation, earthquakes and other volcanic activities. Such factors would create a certain level of chaos which would lead to terrifying bursts endangering the regions prone downstream.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "III. LITERATURE SURVEY > A. Glacial Lake Formation and GLOF Events", "section_headings": ["III. LITERATURE SURVEY", "A. Glacial Lake Formation and GLOF Events"], "chunk_type": "text", "line_start": 30, "line_end": 34, "token_count_estimate": 242, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b5cf98acb902bf88", "text": "Document: I. INTRODUCTION\nSection: III. LITERATURE SURVEY > B. Traditional Monitoring Methods and Limitations\nType: text\n\nOver the last few years, satellite monitoring of glacial lakes has increased due to the ability to produce higher resolution data and cover wider areas compared to traditional groundbased methods. For instance, X. Wang, X. Guo, C. Yang [2] examined the accuracy of monitoring glacier lakes with satellite imagery. One major disadvantage of satellite monitoring is that it is not as frequent as the events it is trying to capture, thus it may not capture the rapid and highly dynamic changes in the lake glacier's conditions at a time, despite the advantages that come with it. These weaknesses show that there is a requirement for an ongoing, more holistic monitoring system that can offer real time information and early warnings. A more sophisticated early warning system would significantly enhance the identification and forecasting of GLOF events with the help of advancements in satellite technology, remote sensing, and sensor networks, as well as the analytical capabilities of machine learning algorithms.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "III. LITERATURE SURVEY > B. Traditional Monitoring Methods and Limitations", "section_headings": ["III. LITERATURE SURVEY", "B. Traditional Monitoring Methods and Limitations"], "chunk_type": "text", "line_start": 36, "line_end": 38, "token_count_estimate": 254, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e47a3044c0aea573", "text": "Document: I. INTRODUCTION\nSection: III. LITERATURE SURVEY > C. Advances in Remote sensing for Glacial Monitoring\nType: text\n\nBetter and continuous monitoring of glacier lakes is now possible due to recent improvements in satellite technology and remote sensing capabilities. In their study, for instance, G. Zhang, T. Yao, H. Xie [3] employed high- resolution satellite data from MODIS, Sentinel-2, and Landsat to inform their work. Glacial lakes' growth and volume changes have been effectively monitored and charted in satellite data studies, proving the effectiveness of remote sensing technology in pattern and trend identification. This paper highlights how data from these cutting-edge satellite images can be used by Glacial Lake Outburst Flood (GLOF) Early Warning Systems (EWS). Through the collection of wide-ranging, high-quality lake images, these platforms deliver valuable information on the shifting processes and ecological status of glacier lakes.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "III. LITERATURE SURVEY > C. Advances in Remote sensing for Glacial Monitoring", "section_headings": ["III. LITERATURE SURVEY", "C. Advances in Remote sensing for Glacial Monitoring"], "chunk_type": "text", "line_start": 40, "line_end": 42, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1928624d6f980ec", "text": "Document: I. INTRODUCTION\nSection: III. LITERATURE SURVEY > D. Multisource Data Integration for Real-time Monitoring\nType: text\n\nGround-based sensors provide timely measurements of physical attributes such as temperature, precipitation and seis mic activity, while high resolution satellite imagery allows for continuous monitoring of glacial lake dynamics [4]. This multifaceted approach, in addition to enhancing the capacity to detect early signs of potential GLOF events improves the overall reliability of forecasts. However, these benefits do not come without a number of logistical and technical challenges. The two major obstacles in this regard are merging numerous data sources, and it demands clever data processing", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "III. LITERATURE SURVEY > D. Multisource Data Integration for Real-time Monitoring", "section_headings": ["III. LITERATURE SURVEY", "D. Multisource Data Integration for Real-time Monitoring"], "chunk_type": "text", "line_start": 44, "line_end": 46, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0d910e952074a11c", "text": "Document: I. INTRODUCTION\nSection: knowledge to ensure seamless workflow [5]. > E. Case studies and Early warning system Implementations\nType: text\n\nSeveral case studies have successfully implemented earlywarning systems for Glacial Lake Outburst Floods (GLOFs), which have shown the potential benefits of GLOFs early warning systems. The International Centre for Integrated Mountain Development (ICIMOD) has piloted early warning systems that leverage satellite data, remote sensing and community- based monitoring systems to issue timely alarms, in the Hi- malayas. This integrated approach not only improves the accurate prediction but also ensures active participation of the community in monitoring the system, which makes people more prepared and provides a sense of ownership. Work like this has been done in the Fan, X., Xu, Q., van Westen, C.J., Huang. (2008) state.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "knowledge to ensure seamless workflow [5]. > E. Case studies and Early warning system Implementations", "section_headings": ["knowledge to ensure seamless workflow [5].", "E. Case studies and Early warning system Implementations"], "chunk_type": "text", "line_start": 50, "line_end": 52, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b9cb7a9f5ebae14", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > A. Data Processing\nType: text\n\nMake the satellite images into shapes that help objects and Track changes. This may include the use of techniques such as twitching images specific to highlight Hydrometeorological data: Mix data from weather stations and Censorship in the usual time slot to create a clear time Shows patterns over time. You can stick together and sort data from ground -based sensor to create a complete overview of the environmental conditions, Similar to the hydrometeorological data.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > A. Data Processing", "section_headings": ["IV. METHODOLOGY", "A. Data Processing"], "chunk_type": "text", "line_start": 56, "line_end": 58, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5df5668d3a3c8db7", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > B. Feature Extraction and Analysis > Ground -based full sensor data recovery:\nType: text\n\nTo detect the symptoms of potential volatility in the lake, the sensors gathered on the ground to extract the environment and physical symptoms from the records. Earthquake hobbies, temperature races and downs are important variables required to maintain a look. Initiative warnings of instability inside Glacial Lake -Gadget may be located using journal aggregation and evaluation methods, including non -conformity detection and time collection analysis.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > B. Feature Extraction and Analysis > Ground -based full sensor data recovery:", "section_headings": ["IV. METHODOLOGY", "B. Feature Extraction and Analysis", "Ground -based full sensor data recovery:"], "chunk_type": "text", "line_start": 62, "line_end": 64, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "902e61f0d81e4acf", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > B. Feature Extraction and Analysis > Image processing:\nType: text\n\nSatellite analysis for PC images for important features for the use of refined photo processing techniques. This involves monitoring the shift within the boundaries of the lake and detecting the variation inside the floor space of the lake, and comparing ice and ice conditions on site. To draw attention to significant differences and patterns in SNAP shots, techniques can be used with image partitions, part detection and alternative identity algorithms.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > B. Feature Extraction and Analysis > Image processing:", "section_headings": ["IV. METHODOLOGY", "B. Feature Extraction and Analysis", "Image processing:"], "chunk_type": "text", "line_start": 66, "line_end": 68, "token_count_estimate": 128, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d199283032fb7cb0", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > C. Machine Learning Model Development > Exercise and testing:\nType: text\n\nTo create a reliable model, data must be divided into training and test kits. The model can learn and recognize high -risk signals by practicing historical GLOF events. The model is fed data during the training phase so that it can identify and absorb patterns related to previous GLOF events. To confirm the accuracy of the model after training, unseen data must be used for testing. The model's ability to extract the prophecies of the prophecies, the ability to extract for real world conditions is guaranteed by this stage. To maximize accuracy and performance, the hyperparameter may need to be replaced in this phase.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > C. Machine Learning Model Development > Exercise and testing:", "section_headings": ["IV. METHODOLOGY", "C. Machine Learning Model Development", "Exercise and testing:"], "chunk_type": "text", "line_start": 72, "line_end": 74, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54c9ee09f05abb56", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > C. Machine Learning Model Development > Future modeling:\nType: text\n\nThe model can evaluate the current data in real time so that it is trained and verified to find a gloff risk pattern. Unusual temperature spikes, sudden changes in the surface of the lake, or closer to seismic activity to the lake, can be all important signs. The sustained monitoring of these parameters is performed by the model, which immediately releases the alarm, when possible, dangers are identified.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > C. Machine Learning Model Development > Future modeling:", "section_headings": ["IV. METHODOLOGY", "C. Machine Learning Model Development", "Future modeling:"], "chunk_type": "text", "line_start": 76, "line_end": 78, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "02645804b722a6e6", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > D. Early Warning System Implementations\nType: text\n\nReal -time warning: Install an exact intersection points for each risk category, when the alarm is immediately triggered to warn of the importance of a potential gloff cross the significant values. For high -risk conditions, the first warning system (EWS) creates automatically alerts, which later expanded the right authorities and population nearby. This guarantees that interested parties are properly informed so that they can take the right protection measures.\n\nData Virtue and Dashboard: Create an interactive dashboard showing model conditions and real -time data. For each lake during observation, this gives the dashboard a variety of visualization, including time series charts, maps and risk level indicators. Straits can quickly evaluate GLOFF risk for these visual aids and evaluate them, who inform and inform decisions to reduce any effect.\n\nRisk assessment: Determine the risk level of each monitored lake using the production from the approximate model. The model uses a mixture of historical trends and uses variable power variables to classify lakes in low, medium, or high-risk groups. The greatest risk of lakes that pose the greatest risk, it helps to determine priorities for classification resources and monitoring of activities.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > D. Early Warning System Implementations", "section_headings": ["IV. METHODOLOGY", "D. Early Warning System Implementations"], "chunk_type": "text", "line_start": 80, "line_end": 86, "token_count_estimate": 292, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5f5714ac43f0f8cb", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > E. Testing and Calibration\nType: text\n\nField Test: Conduct field experiments of the initial warning system (EWS) by modeling multiple risk scenarios for glacier flooding (GLOF). These tests are required to confirm that\n\nthe system can predict GLoft events and provide timely alerts. Real-time simulation exercises can assess the accuracy and reaction of the system; simulation exercises can include controlled liberation, false stress tests at lake boundaries, or artificially elevated water levels.\n\nAdjustment and calibration of the model: model: Change the parameters of the model to improve its accuracy and dependence in light of this field test results. Some important steps, including calibration, are\n\nRefining sensor box: The sensor is sufficient to identify gloves without installing the false alarm to adjust the threshold. Change the Parameters to Machine Learning Models: Adjust the parameters of machine learning algorithms in EWS. This can change the complexity of the model, learning speed, or decision limit to increase the performance of prediction.\n\nChanging data preparation methods: In order to guarantee the best possible processing of input, you can visit the data to pre -process processes again. To better prepare the data, they can adjust the generalization area, upgrade the noise filtering strategies, or refine the data change processes.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > E. Testing and Calibration", "section_headings": ["IV. METHODOLOGY", "E. Testing and Calibration"], "chunk_type": "text", "line_start": 88, "line_end": 98, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c53fea15312ca2f4", "text": "Document: I. INTRODUCTION\nSection: IV. METHODOLOGY > F. Continuous Monitoring and Improvement\nType: text\n\nFeedback loop: Establish a method of ongoing monitoring to evaluate the first warning method (EWS) efficiency. To determine the user inputs to get the improvement needed. The accuracy and effect of the system is maintained by adjusting the future model in response to fresh data and user response. EWS can be adjusted under changing conditions and may be better over time due to this relapse method.\n\nSystem upgrade: To use the latest development in technology, update regular data sources, machine learning algorithms and alarm systems. EWS skills can be improved by integrating new techniques and approaches when they become available. To guarantee reliable function, inspect and maintain regular sensors and data collection equipment. Reorganization of the sensor, updates software and confirms that each part is involved in this process is all included in this process.\n\nEWS can be effective and adapted to the changing conditions of the glacial lake isos system by implementing a strong feedback loop and system improvement.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IV. METHODOLOGY > F. Continuous Monitoring and Improvement", "section_headings": ["IV. METHODOLOGY", "F. Continuous Monitoring and Improvement"], "chunk_type": "text", "line_start": 100, "line_end": 106, "token_count_estimate": 237, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "adb964580b1c25b0", "text": "Document: I. INTRODUCTION\nSection: V. CONSTRUCTION OF CIRENMACO EWS\nType: text\n\nTo take advantage of the latest technology developments, update data sources, machine learning algorithms and\n\nalarm systems regularly. EWS capabilities can be improved by integrating new technologies and approaches as they are available. To ensure reliable operation, inspect and maintain data collection sensors and equipment regularly. Recalibration sensors, updating the software and confirming that all parties are operating as intended are all included in this process.\n\nEWS can remain efficient and adaptable to the conditions of change of lake's glacial ecosystems, implementing a strong feedback cycle and system improvements. This strategy helps to reduce hazards related to Glacial Lake Outburst Floods (GLOFs) by guaranteeing that the system will continue to issue precise and timely warnings. After an assessment of the vulnerability of the lake, the EWS design phase focuses on building the required infrastructure. This involves setting up a communication network around high-risk regions and strategically placing sensors. Among the key sensors used are: water level sensors: These provide vital information on growing or declining water volumes by tracking variations in the lake's water levels. seismic sensors: They identify soil movement that can indicate potential triggers such as earthquakes or landslides.\n\nMeteorological stations: By monitoring changes in temperature and rainfall, these stations help determine environmental factors that can cause gloff.\n\nFlow Sensor: These downstream sensors keep an eye on the discharge of unusual water that may indicate a adjacent explosion. To ensure real-time data transfer in a central control center, all these sensors are associated with a reliable communication network, which often uses satellite technologies or GSM. Continuous monitoring and quick notifications are possible by this configuration, which is necessary to reduce the risks associated with the glof and improve the security of downstream communities. Data processing and integrations come after the infrastructure setup. Important information about lake levels, seismic activity and weather is shown on a real -time dashboard that tracks the sensor data in a centralized database. While the risk thresholds are set for critical matrix, machine learning algorithms examine the data to find the pattern to find the risk to explore that gloff events. If sensor data exceeds specified levels, EWS automatically triggers alerts and notifications. These signs enable emergency services, local communities, and relevant officers to prepare and react quickly. This method helps communities to be informed and flexible against potential flood hazards in the Cirenmaco region.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "V. CONSTRUCTION OF CIRENMACO EWS", "section_headings": ["V. CONSTRUCTION OF CIRENMACO EWS"], "chunk_type": "text", "line_start": 108, "line_end": 118, "token_count_estimate": 571, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "880861d03e0c5bd4", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > A. Software Implementation\nType: text\n\nThe main goal of real -time monitoring, data processing, predictive modeling, and alert generation is the main goal of a reliable, scalable and user -friendly platform software", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > A. Software Implementation", "section_headings": ["VI. IMPLEMENTATION", "A. Software Implementation"], "chunk_type": "text", "line_start": 122, "line_end": 124, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "41de395431b88fbd", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > A. Software Implementation\nType: figure\nFigure\n\nImage /page/4/Figure/1 description: A figure containing a map labeled 'A' and an inset bar chart labeled 'B'. The map shows the GLOF (Glacial Lake Outburst Flood) susceptibility of a mountainous region. A legend in the bottom left defines the susceptibility levels with colored dots: Very low (white), Low (light green), Medium (yellow), High (blue), and Very high (red). These dots are plotted across the topographical map, which displays snow-covered peaks in light blue and lower elevations in brown and green. Several specific locations are labeled, such as M20, M24, M47, and M51. The map includes a 10 km scale bar and coordinate markings. The inset bar chart 'B' shows the count of lakes versus their size-interval in square kilometers. The x-axis shows size intervals from 0.05-0.1 to >0.5 km². The y-axis shows the count, up to 30. The bars are stacked and colored according to the susceptibility legend, indicating that the most numerous lakes are in the smallest size category (0.05-0.1 km²) and have low susceptibility, while a large portion of the biggest lakes (>0.5 km²) have very high susceptibility.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > A. Software Implementation", "section_headings": ["VI. IMPLEMENTATION", "A. Software Implementation"], "chunk_type": "figure", "figure_caption": null, "line_start": 125, "line_end": 125, "token_count_estimate": 331, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42d7ef40bcdb49f9", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > A. Software Implementation\nType: text\n\nFig. 1. —(A) spatial distribution of 64 glacial lakes assessed with their GLOF susceptibility classes across the Mahalangur Himalaya. (B) The bar graph shows the GLOF susceptibility of glacial lakes in various size interval [13].\n\nproject that applies CIRENMACO Early Warning System (EWS) for Glascable Lake Outburst Flood (Glofs). Use a trusted cloud-based solution, such as Google Cloud platform or AWS (Amazon web services), to effectively manage the processing and storage of large-scale real-time data. For managing sensors data and historical records, cloud solution provides scalability, safety and reliable performance in Fig 1. Set the risk threshold for important matrix. The system should automatically sound alarm and inform the proper authorities, nearby communities and emergency services when the threshold is crossed. For automated alert delivery, use services such as AWS SNS (simple notification service) or comparable solution.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > A. Software Implementation", "section_headings": ["VI. IMPLEMENTATION", "A. Software Implementation"], "chunk_type": "text", "line_start": 126, "line_end": 130, "token_count_estimate": 244, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "95d81f6405e9238c", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > B. Frontend Development\nType: text\n\nDesign of user interface (UI): Provide a simple, easy-to- use interface that shows major data visualizations, such as historical trends, danger conditions and changes in the level of lake. Users should be able to interact with various charts and map-based visualizations, view real-time data and track the level of risk through user interfaces.\n\nDashboard features:\n\n- Create a dashboard that allows users to see real -time sensor data classified by seismic activity and water level.\n- Get historical data to analyze trends.\n- Check the color-coded risk map of the item lake area.\n- When the thresholds cross, get information and alert.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > B. Frontend Development", "section_headings": ["VI. IMPLEMENTATION", "B. Frontend Development"], "chunk_type": "text", "line_start": 132, "line_end": 141, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e6eb2e70afa31f0", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > C. Data Processing and Preprocessing\nType: text\n\nData normalization and cleaning: As the system comes into the system, put the data in procedures to validate, clean and standardize. This involves managing outlier or missing", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > C. Data Processing and Preprocessing", "section_headings": ["VI. IMPLEMENTATION", "C. Data Processing and Preprocessing"], "chunk_type": "text", "line_start": 143, "line_end": 145, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0f34af260133501", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > C. Data Processing and Preprocessing\nType: figure\nFigure\n\nImage /page/4/Figure/13 description: The image displays two stacked bar charts, labeled A and B, analyzing different types of lakes based on their size. The legend identifies four types of lakes by color: green for Supraglacial lakes, orange for Proglacial lakes, purple for Unconnected glacier-fed lakes, and yellow for Non-glacier-fed lakes. The x-axis for both charts is labeled \"Size-interval (km²)\" and has six categories: 0.001-0.002, 0.002-0.01, 0.01-0.02, 0.02-0.1, 0.1-0.5, and >0.5.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > C. Data Processing and Preprocessing", "section_headings": ["VI. IMPLEMENTATION", "C. Data Processing and Preprocessing"], "chunk_type": "figure", "figure_caption": null, "line_start": 146, "line_end": 146, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fae05c3257370844", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > C. Data Processing and Preprocessing\nType: text\n\nChart A, on the left, shows the \"Number\" of lakes on its y-axis, which ranges from 0 to 140. The highest number of lakes, approximately 125, is found in the 0.002-0.01 km² size interval. The number of lakes generally decreases for larger size intervals, with the >0.5 km² category having fewer than 10 lakes.\n\nChart B, on the right, shows the total \"Area (km²)\" of the lakes on its y-axis, which ranges from 0 to 8. In contrast to the number of lakes, the total area generally increases with the size interval. The largest total area, approximately 7.5 km², is contributed by lakes in the >0.5 km² size category, which are predominantly proglacial and unconnected glacier-fed lakes. Proglacial lakes contribute the most to the total area in the larger size intervals (0.1-0.5 and >0.5 km²).\n\nFig. 2. The frequency and area of glacial lakes according to different size interval and types [14].\n\nvalues, ensuring that the measurement units are consistent, and convert data into a format that is compatible.\n\nAdapt the storage and recovery by Fig.2 structuring the database with effective sequencing to provide quick data recovery, which is necessary for real -time, mass dataset analysis.\n\nTo install the necessary equipment in high -risk areas, the process begins with the initial fieldwork. During this phase, Cirenmaco Lake is surrounded with carefully downstream flow sensors, meteorological stations, seismic sensors and water level sensors. It asks for reliable equipment that can avoid challenging environmental conditions, as well as trained workers to guarantee ideal sensor placements and accuracy. Each sensor is connected to a reliable communication network and is set to collect data continuously. Typically, satellite or GSM technology is employed, providing a reliable link to send data to the Central Monitoring Center in real time.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > C. Data Processing and Preprocessing", "section_headings": ["VI. IMPLEMENTATION", "C. Data Processing and Preprocessing"], "chunk_type": "text", "line_start": 147, "line_end": 159, "token_count_estimate": 459, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be63e6f30bf1dd12", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > D. Co-ordination and Integration of data\nType: text\n\nA centralized data receives the sections of repository data that collect from the sensor. To maintain stability between sources, data is processed, cleaned and normalized here. After that, the dashboard has been configured for real -time monitoring, showing important data metrics and visualizations that help analysts track signs of lake stability, such as earthquake activity and variations in the water level. At this point, the system is calibrated to identify abnormalities that can point to potential glof events, and baseline data is collected to understand specific trends.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > D. Co-ordination and Integration of data", "section_headings": ["VI. IMPLEMENTATION", "D. Co-ordination and Integration of data"], "chunk_type": "text", "line_start": 161, "line_end": 163, "token_count_estimate": 157, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc247224444b4d80", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > E. Using Machine Learning\nType: text\n\nA comprehensive strategy that mixes machine learning for future stating analysis with real -time environmental data collection is required to apply Cirenmaco Early Warning System (EWS) for Glassel Lake Outbst Floods (Glofs) as a software project. Designing system architecture is the first stage, and data processing and storage are controlled by cloud-based services such as AWS or Google Cloud. A streaming pipeline (such as Apache Kafka) is used to manage the import of real -time data. This pipeline collects data from the sensor that monitors weather, seismic activity and water\n\nlevel. After that, this data is pre -evacted using procedures to clean, normally and format it to guarantee accuracy. Other time-series features are developed to catch temporary patterns, and major features-as temperature fluctuations and water level trends-are chosen based on their importance for glof risk.\n\nAlgorithm Selection: Classify risk levels using past GLOF data by using supervised learning techniques like Random Forest or Support Vector Machine (SVM). Potential trends in water levels or other indicators can be predicted using timeseries analysis (e.g., LSTM neural networks).\n\nTraining and Testing Models: Create and train a machine learning model to identify risk indicators using historical data, then assess the model's accuracy using a testing dataset. On the basis of incoming data, the trained model may then forecast future GLOF hazards.\n\nBackend integration: Put the learned model into practice in the backend, where it may process sensor data in real time and produce a prediction or danger level.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > E. Using Machine Learning", "section_headings": ["VI. IMPLEMENTATION", "E. Using Machine Learning"], "chunk_type": "text", "line_start": 165, "line_end": 175, "token_count_estimate": 392, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2a923a32f8aa6d2f", "text": "Document: I. INTRODUCTION\nSection: VI. IMPLEMENTATION > F. Alert and Notification System\nType: text\n\nThe Cirenmaco Early Warning System's (EWS) warning and notification system is made to give stakeholders quick and accurate alerts so they can respond to the hazards of glacial lake outburst floods (GLOFs). When crucial thresholds are crossed, this system sends out alarms. It does this by continuously monitoring environmental data, including temperature, water levels, and seismic activity. In order to provide a progressive reaction that matches the intensity of the detected risk, the alert system is set up with risk levels—typically low, moderate, high, and emergency—that correlate to various alert circumstances.\n\nThe system automatically sends out messages across a variety of channels to important stakeholders when sensor data shows that risk thresholds are reached. Local authorities, emergency responders, and community leaders receive notifications through push notifications on mobile apps, email, and SMS. The solution guarantees prompt and effective notification distribution by utilizing APIs such as Firebase Cloud Messaging for mobile app alerts, SendGrid for emails, and Twilio for SMS. In the event of a severe emergency, the warning system also enables automatic voice calls to high-priority receivers, guaranteeing that vital information reaches them right away.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VI. IMPLEMENTATION > F. Alert and Notification System", "section_headings": ["VI. IMPLEMENTATION", "F. Alert and Notification System"], "chunk_type": "text", "line_start": 177, "line_end": 181, "token_count_estimate": 308, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a7b53e4d542b2ef7", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE\nType: text\n\nThe Early Warning System (EWS) for Glacial Lake Outburst Floods (GLOFs) combines multi-channel alerting mechanisms, machine learning-based risk prediction, and real-time data collection. By utilizing cloud computing, IoT devices, and powerful analytics, the design guarantees dependable and scalable operations. The architecture is\n\nbroken down in detail below:", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE", "section_headings": ["VII. SYSTEM ARCHITECTURE"], "chunk_type": "text", "line_start": 183, "line_end": 187, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a316ee2977f0a4cc", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > A. Data Collection Layer:\nType: text\n\nIoT devices and sensors:\n\n- Real-time data is gathered by sensors positioned throughout the glacial lake, including\n- Water levels and flow rates are examples of hydrological data\n- Weather information: humidity, precipitation, and temperature.\n- Seismic Information: Possible landslide activity and ground vibrations.\n- Wireless communication technologies, such as LoRa, Zigbee, or GSM, are used to transfer data.\n- Remote sensing and satellite data: lake surface area, changes over time, and snow/ice melt patterns are all provided by satellite imagery (e.g., from Landsat, Sentinel).", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > A. Data Collection Layer:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "A. Data Collection Layer:"], "chunk_type": "text", "line_start": 189, "line_end": 198, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb81aa6f25ba8695", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > B. Data Transmission and Integration Layer: > Edge Technology:\nType: text\n\nSensor data preprocessing and short-term storage are managed by IoT gateways at the data collection location. This guarantees that there is less data loss when being transmitted.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > B. Data Transmission and Integration Layer: > Edge Technology:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "B. Data Transmission and Integration Layer:", "Edge Technology:"], "chunk_type": "text", "line_start": 202, "line_end": 204, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c4f2146aea64cc3", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > B. Data Transmission and Integration Layer: > Cloud-Based Communication:\nType: text\n\nProtocols like HTTPS and MQTT are used to safely transfer data to a central cloud-based system. Redundancy measures are put in place to guarantee dependability and uninterrupted functioning. This configuration increases the early warning system's overall efficacy by enabling safe and effective real-time data transmission.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > B. Data Transmission and Integration Layer: > Cloud-Based Communication:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "B. Data Transmission and Integration Layer:", "Cloud-Based Communication:"], "chunk_type": "text", "line_start": 206, "line_end": 208, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f2b81ed5f1fd15fa", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > C. Data Storage Layer:\nType: text\n\nTo ensure minimal loss during transmission, edge devices—like IoT gateways at the data collection site—preprocess and temporarily store sensor data. Protocols like HTTPS and MQTT are used to securely transfer the data to a central cloud-based system, with redundancy mechanisms in place for dependability. Real-time sensor data and historical datasets for analysis and machine learning model training are stored on centralized storage platforms such as AWS, Azure, or Google Cloud. Structured data is managed by SQL databases (PostgreSQL), whereas unstructured or semi-structured data is handled by NoSQL databases (MongoDB). In order to prevent data loss and guarantee data durability and availability for long-term storage, periodic backups are kept in cold storage systems such as Amazon S3 Glacier. This configuration facilitates secure, scalable, and effective data handling.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > C. Data Storage Layer:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "C. Data Storage Layer:"], "chunk_type": "text", "line_start": 210, "line_end": 212, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7fe9f7b994bebc2b", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > D. Data Processing and Analytical Layer:\nType: text\n\nThe preprocessing engine handles missing values, eliminates outliers, and arranges data for analysis by cleaning, validating, and normalizing raw sensor data to guarantee accuracy and consistency. In order to identify anomalous patterns in water levels or seismic activity and forecast the probability of a Glacial Lake Outburst Flood (GLOF) catastrophe, machine learning models such as LSTM and Random Forest examine both historical and current data. The risk assessment module uses time-series analysis and dynamic updates to classify risk levels—low, medium, high, and critical—based on machine learning outputs and predetermined thresholds. Vulnerable communities' safety and readiness are improved by this configuration, which guarantees effective and precise detection and prediction of GLOF events, facilitating prompt risk assessment and response.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > D. Data Processing and Analytical Layer:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "D. Data Processing and Analytical Layer:"], "chunk_type": "text", "line_start": 214, "line_end": 216, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "75a6ed1d79d6b8fa", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > E. User Interface and Visualization Layer:\nType: text\n\nThe dashboard interface provides real-time data visualizations to stakeholders, including academics and authorities. This comprises geographic maps showing the locations of lakes and the corresponding danger ratings, as well as trends from sensor data, machine learning forecasts, and alarms. Easy access is ensured by the interface's compatibility with mobile applications and online browsers.\n\nAn end-user-friendly interface for seeing alerts and recommendations is offered by the mobile app and notifications. For important alerts, users receive push notifications, guaranteeing prompt information and action. With this configuration, end users and stakeholders are always informed and able to take prompt action to reduce risks.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > E. User Interface and Visualization Layer:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "E. User Interface and Visualization Layer:"], "chunk_type": "text", "line_start": 218, "line_end": 222, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "080d41c0b72a0116", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > F. Alert and Notification Layer:\nType: text\n\nNotifications are sent by the automatic alarm system when risk thresholds are surpassed. It makes use of many notification channels, such as push notifications (using Firebase Cloud Messaging), emails (using SendGrid or AWS SES), and SMS (using Twilio or comparable APIs). Alerts give information about the location, level of risk, and suggested courses of action. The system initiates extra actions, such as automated voice calls or direct integration with disaster management systems, in response to high-priority alarms, including impending GLOFs. This all-inclusive alarm system guarantees prompt and efficient communication to the appropriate authorities and local communities, improving their capacity to react quickly and reduce hazards.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > F. Alert and Notification Layer:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "F. Alert and Notification Layer:"], "chunk_type": "text", "line_start": 224, "line_end": 226, "token_count_estimate": 194, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6836e873ebd56736", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > G. Monitoring and Maintenance Layer:\nType: text\n\nThe system tracks health, uptime, and error rates using cloud monitoring technologies such as Google Cloud Monitoring and AWS CloudWatch. To maintain openness and facilitate postevent study, it records all information, forecasts, and warnings. There is a feedback loop in place, whereby new data is frequently added to machine learning models to increase their accuracy. Additionally, user input is incorporated to improve system usability and alert thresholds. In order to successfully enable real-time monitoring, data processing, predictive modeling, and warning generation for glacial lake outburst floods (GLOFs), this all-encompassing methodology guarantees the system's dependability, transparency, and continual improvement.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > G. Monitoring and Maintenance Layer:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "G. Monitoring and Maintenance Layer:"], "chunk_type": "text", "line_start": 228, "line_end": 230, "token_count_estimate": 199, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d0c693b1f8895ea2", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:\nType: text\n\nAs more lakes and sensors are added, the system's scalability is built to manage growing data volumes, guaranteeing futureproofing as the project expands. Redundant systems that provide minimal downtime and continuous operation help to preserve their resilience. Access control, data encryption (SSL/TLS), and frequent security audits are used to safeguard private data from breaches and unwanted access. Because of its interoperability, the system may easily interact with other systems, such as disaster response platforms and GIS tools, to offer complete risk management. Glacial lake outburst flood (GLOF) risk monitoring and mitigation are successfully supported by the system's strong, effective, and flexible modular design in Fig 3.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "H. Key Features of the Architecture:"], "chunk_type": "text", "line_start": 232, "line_end": 234, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5efc5eadbad61877", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:\nType: figure\nFigure\n\nImage /page/6/Figure/12 description: A flowchart illustrating the methodology for assessing Glacial Lake Outburst Flood (GLOF) susceptibility. The process begins with 'Satellite imagery', which is used for 'Digitization' to create a 'Glacial Lake Inventory'. A decision diamond then checks if the 'Lake area' is greater than or equal to 0.05 km². If 'No', the lake is 'Excluded'. If 'Yes', it is 'Included for further analysis'. This analysis involves 'Factors computation & setting criteria (Index value)', which uses 'Lake Glacier Watershed DEM' as an input. Parallel to this, factors are identified, selected, and defined (F1, F2, F3, F4, F5, F6). Another input, 'AHP (determination of factor weights)', provides factor weights. The next step is the 'Multiplication of index value with factor weight', followed by the 'Sum of Scores'. This sum leads to the final 'GLOF Susceptibility' assessment. The susceptibility is categorized into five levels, each with a corresponding score range and color: 'Very Low (0.25-0.40)' in green, 'Low (0.40-0.55)' in light green, 'Medium (0.55-0.70)' in yellow, 'High (0.70-0.85)' in blue, and 'Very High (≥0.85)' in red.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "H. Key Features of the Architecture:"], "chunk_type": "figure", "figure_caption": null, "line_start": 235, "line_end": 235, "token_count_estimate": 389, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3334c52b12b2a698", "text": "Document: I. INTRODUCTION\nSection: VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:\nType: text\n\nFig. 3. Flow chart for GLOF [15]", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VII. SYSTEM ARCHITECTURE > H. Key Features of the Architecture:", "section_headings": ["VII. SYSTEM ARCHITECTURE", "H. Key Features of the Architecture:"], "chunk_type": "text", "line_start": 236, "line_end": 238, "token_count_estimate": 44, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b4c029bb216fb5a7", "text": "Document: I. INTRODUCTION\nSection: VIII. EXPLANATION OF FLOW CHART\nType: text\n\nSatellite photos: The first step in the procedure is gathering high-resolution satellite photos of glacier lakes and the areas around them.\n\nDigitization: A Glacial Lake Inventory is produced by digitizing these satellite photos. In this process, the visual data is transformed into an analyze-able digital format.\n\nGlacial Lake Inventory: Using the digitized data, a list of all the glacial lakes found in the satellite photos is created.\n\nLake Area 0.05 km 2: The surface area of the lakes is used to filter them out of the inventory. For additional analysis, only lakes having an area of at least 0.05 km<sup>2</sup> are taken into account.\n\nFactor Identification: Choose pertinent geological and environmental elements.\n\nFactor Computation: To calculate factor values and establish criteria, use data from Digital Elevation Models (DEMs).\n\nAHP Analysis: Apply the Analytic Hierarchy Process (AHP) to ascertain factor weights. Index values are multiplied by weights, and scores are added up.\n\nSort lakes according to their GLOF susceptibility levels: very low, moderate, medium, high, or very high.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "VIII. EXPLANATION OF FLOW CHART", "section_headings": ["VIII. EXPLANATION OF FLOW CHART"], "chunk_type": "text", "line_start": 240, "line_end": 256, "token_count_estimate": 291, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c8f761e98ee9712", "text": "Document: I. INTRODUCTION\nSection: IX. DISCUSSION\nType: text\n\nThe deployment of an Early Warning System (EWS) for Glacial Lake Outburst Floods (GLOFs) powered by machine learning greatly improves catastrophe risk management in glacial areas impacted by climate change. By combining automated alarms, predictive analytics, and real-time data collection, the system enhances outburst forecasts by analyzing hydrological and environmental data using models like LSTM networks. Scalability and dependability for huge sensor and satellite image collections are guaranteed by cloud-based storage. Generalizing models to different terrains and ensuring data quality in remote places are challenges. In spite of this, the EWS has enormous potential to help sustainable development, safeguard vulnerable people, and reduce GLOF risks. With further improvements, it might serve as a template for other dangerous regions around the world.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "IX. DISCUSSION", "section_headings": ["IX. DISCUSSION"], "chunk_type": "text", "line_start": 258, "line_end": 260, "token_count_estimate": 219, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "78d0a7e10ac8d7ce", "text": "Document: I. INTRODUCTION\nSection: X. CONCLUSION\nType: text\n\nDisaster risk management in glacial regions is revolutionized by the deployment of an Early Warning System (EWS) for Glacial Lake Outburst Floods (GLOFs) based on machine learning. The system offers a proactive way to lessen the effects of GLOF by combining automatic alerting systems, real-time data collection, and sophisticated predictive analytics. For a thorough risk assessment, it examines a variety of datasets, such as hydrological, meteorological, and seismic inputs, enabling authorities and communities to take preventative measures.\n\nThe project's approach is flexible and scalable for different glacier situations, even in the face of obstacles like data availability, model generality, and infrastructure constraints.\n\nMulti-channel notifications and cloud computing improve reach and dependability. In addition to providing a foundation for upcoming developments in environmental monitoring and early warning systems, this initiative emphasizes the role that technology may play in mitigating climate threats.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "X. CONCLUSION", "section_headings": ["X. CONCLUSION"], "chunk_type": "text", "line_start": 262, "line_end": 268, "token_count_estimate": 253, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "915ae4a1db5fb6cd", "text": "Document: I. INTRODUCTION\nSection: XI. ACKNOWLEDGEMENT\nType: text\n\nWith great appreciation, we would like to thank everyone who helped us finish this project on the Cirenmaco Early Warning System (EWS) for glacial lake outburst floods (GLOFs). We are really appreciative of the direction and assistance given by our mentors and advisors, whose knowledge and experience were crucial throughout this project.\n\nWe sincerely thank the research institutions and organizations that gave us access to vital data and necessary resources. We are particularly appreciative of the meteorological and environmental organizations' technical assistance, whose data allowed us to create a more dependable and strong early warning system. We also want to express our gratitude to our team members for their commitment, diligence, and cooperation.", "metadata": {"source_file": "data/('Early_Warning_System_for_Glacial_Lake_Outburst_Using_Multi_Source_Data', '.pdf')_extraction.md", "document_title": "I. INTRODUCTION", "section_path": "XI. ACKNOWLEDGEMENT", "section_headings": ["XI. ACKNOWLEDGEMENT"], "chunk_type": "text", "line_start": 270, "line_end": 274, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14434e981a125ceb", "text": "Document: early warning systems for glacier lake outburst floods and debris flows\nType: text\n\nAbstract: Climate change affects the cryosphere in a strong way. In 2012 the IPCC (IPCC, 2012) concluded that there is high confidence that changes in heat waves, glacial retreat and/or permafrost degradation will have a strong on high mountain phenomena such as slope instabilities, mass movements and glacial lake outburst floods (GLOF). The overall frequency of debris flows may decrease in absolute terms, but the magnitude of events may increase. This was concluded from an analysis of debris flow events in the past 150 years and using this information for future projections on climate change. Two case studies from China and Georgia demonstrate the evidence of ongoing climate change. In both situation early warning systems and glacier monitoring are the key for a) detailed hazard assessment, b) a longterm observation of the system development and c) the warning and alarming of a major damage potential (settlements, main touristic roads, gas pipline). While the early warning system in the Yarkant River at Kyagar glacier (Chinese Karakoram Mountains) has already been tested in a real GLOF event in 2015, the system in Georgia for huge debris flows induced by glacier collapses in the on top of Mt. Kazbeg will be installed in spring 2016. In both project GEOTEST AG and GEOPREAVENT AG build a successful joint venture; while the experienced geologists and glaciologists of Geotest assess the hazard potential and evaluate mitigation measures, Geopraevent's engineers develop, install and run the early warning system. Both systems are based on modern technologies and devices.\n\n**Keywords:** early warning system, risk management, glacial lake outburst flood (GLOF), debris flow", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "early warning systems for glacier lake outburst floods and debris flows", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 416, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0d151d2f661882d6", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nEarly warning is the provision of timely and effective information, through identified institutions, that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response. In other words, early warning can be defined as the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss (UNISDR, 2010). The definition does not include a reference to the time scale on which a warning is given. Early Warning Systems (EWS) include a chain of concerns, namely: understanding and mapping the hazard; monitoring and forecasting impending events; processing and disseminating understandable warnings to political authorities and the population, and undertaking appropriate and timely actions in response to the warnings.\n\nA complete and effective EWS comprises four elements, spanning knowledge of the risks faced through to preparedness to act on early warning. Failure in any one part can mean failure of the whole system. The \"four elements of effective early warning systems\", the early warning chain, include the development and operation of early warning systems in regard to: (a) knowledge of risks; (b) monitoring and warning services; (c) warning dissemination and communication; and (d) emergency response (CCES 2013; Sättele & Bründl, 2015). These four elements imply that early warning is based on the assessment of risk and vulnerability. Moreover, early warning should be communicated appropriately and ensure response capability of the people at risk, taking into account short and long-term measures.\n\nMountain systems are particularly sensitive to climate change. It is important to stress that climate change will not only adversely affect the resilience of communities, environment, urban infrastructures, transportation, energy suppliers and other sectors but also EWS. It is a common opinion that beyond the need for mitigation, adaptation to climate change is an important task. It should be highlighted here that in this context not only the exemplary listed sector above will have to adapt to climate change but also existing and planned early warning concepts need to be climate checked. The need for adapting EWS may reveal for all parts of the early warning chain.\n\nThe following two case studies from China and Georgia demonstrate the evidence of ongoing climate change. In both situation early warning systems and glacier monitoring are the key for a) detailed hazard assessment, b) a longterm observation of the system development and c) the warning and alarming of a major damage potential (settlements, main touristic roads, gas pipline).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 16, "token_count_estimate": 611, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "6a10c92db7165d1c", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction\nType: text\n\nThe hazard assessment of glacier lakes at remote and high-elevation sites of the Himalaya, Karakoram and Tien Shan is difficult due to restricted field site accessibility. Yarkant river in the Chinese Karakoram (Fig. 2.1) drains an area of 50'248 km2 and ranks as number one in terms of flood frequency and damage in Xinjiang. Its glacial outburst floods, with peak discharges of up to 6'000 m3 s-1, originate from a remote ice-dammed glacier lake at 4'750 m a.s.l. in the Shaksgam valley, approx. 560 km upstream of the floodplains. The hazardous lake is impounded behind the snout of the Kyagar Glacier which blocks the riverbed of Shaksgam valley. In the past the maximum lake volume was 230 million m<sup>3</sup>. Based on the hazard assessment of Kyagar Lake and newly gained knowledge about the glacier dynamics, a sophisticated monitoring and EWS for GLOFs was successfully implemented (Fig. 2.2). The system is operational since 2012.\n\nThe project is designed to significantly reduce human and material losses through adaptation in vulnerable communities in the floodplains of glacier-fed river system, by considering long-term development of the flood hazard situation in the catchment of Yarkant River. Practical approaches to climate change adaptation have been developed: (1) establishment of an early warning system (EWS) for GLOFs, (2) risk management in the floodplain, (3) glacier and climate change monitoring.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.1 Intruduction"], "chunk_type": "text", "line_start": 20, "line_end": 24, "token_count_estimate": 390, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "1d5cf3b1be371ef5", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction\nType: figure\nFigure\n\nImage /page/1/Figure/7 description: A figure with three maps labeled a, b, and c, illustrating the location of the Shaksgam Valley and Kyagar Glacier. Map 'a' is a large-scale map of a region in China, showing the Tarim River, Taklamakan Desert, and mountain ranges like Tien Shan, Karakoram, and Kunlun Shan. It includes an inset map of China and highlights the Shaksgam Valley. Map 'b' is a more detailed view of the Shaksgam Valley, showing the Yarkant River, Keleqin River, and several glaciers numbered 1 through 6. A legend at the bottom identifies them as: (1) North Gasherbrum Glacier, (2) Urdok Glacier, (3) Staghar Glacier, (4) Singhi Glacier, (5) Kyagar Glacier, and (6) Shaksgam Glacier. This map also marks the Kyagar Glacier Lake and an Observation Station. Map 'c' is a close-up satellite image of the Upper Shaksgam Valley and the Kyagar Glacier, showing the location of the observation station and the fields of view for two cameras, 'cam a' and 'cam b'.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.1 Intruduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 25, "line_end": 25, "token_count_estimate": 305, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "a8e12162fb771bf7", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction\nType: text\n\nFig. 2.1: Situation of (a) Yarkant river in the Tarim basin, (b) Shaksgam valley and (c) Kyagar Glacier tongue with the location of the observation station and the indicated camera view directions of the automatic camera.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.1 Intruduction", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.1 Intruduction"], "chunk_type": "text", "line_start": 26, "line_end": 28, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "8d35a8bbd8d0fc7b", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System\nType: text\n\nBecause the lake is situated in a remote area, the EWS utilized a combination of satellite remote-sensing and automatic terrestrial measurements (Fig. 2.3). Based on the DEM of the empty lake basin and periodically acquired SAR images, the evolving morphology of both lake and glacier can be observed within a time interval of 11 days. The lake volume was then calculated, and the hazard level determined and transmitted to the Chinese decision-makers. In addition to remote-sensing techniques, terrestrial solar-powered, fully automatic observation stations were installed directly at the lake and further downstream along Keleqin and Yarkant rivers at Cha Hekou and Kuluklangan (Fig. 2.1 and 2.3). The observation station close to the ice dam (Fig. 2.2) was able to take daily photographic images from different viewing angles (Fig. 2.1) and to record the local meteorological conditions (air temperature, solid and liquid precipitation, humidity, global radiation). At the two stations downstream radar sensors continuously log the water level of the rivers, and pictures of the riverbed are taken. Several times a day, all data are automatically transmitted via satellite link to a specific data portal.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.2 The Early Warning System"], "chunk_type": "text", "line_start": 30, "line_end": 32, "token_count_estimate": 310, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "475e4e46bf352ecb", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System\nType: figure\nFigure\n\nImage /page/2/Picture/4 description: An outdoor photograph, labeled as Fig. 2.2, shows two people performing installation work on an observation station in a mountainous, glacial environment. In the foreground, one person in a blue jacket stands on top of a grey equipment box to adjust sensors on a tall pole, which also has a solar panel attached. A second person in a bright orange jacket crouches below, working on the wiring inside the open equipment box. The station is set on a rocky, barren slope next to a small, muddy stream. In the background, the large, white Koxkar Glacier stretches across the landscape, with tall, rugged, snow-capped mountains behind it under a cloudy sky. The caption reads: \"Fig. 2.2 Installation work on the observation station. The Koxkar Glacier is visible in the background.\"", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.2 The Early Warning System"], "chunk_type": "figure", "figure_caption": null, "line_start": 33, "line_end": 33, "token_count_estimate": 235, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "a4a6bfd43a729e0b", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System\nType: text\n\nFig. 2.2: Installation work on the observation station. The Kyagar Glacier is visible in the background.\n\nWith the glacier observation station a possible outburst can be recognized at an early stage. The lead time of an outburst observation till the impact of the flood wave at the floodplain is from the glacier station appox. 34 - 36 hours, the one from Cha Hekou 22 h and from Kuluklangan 7.5 h. GLOFs typically show an obvious peak with rapidly rising and declining discharge. If a GLOF is detected, an automatically generated alarm-signal will immediately be sent to mobile phones of the Chinese authorities. Thus, emergency actions can be initiated. The station at Kuluklangan covers a large catchment area and is therefore suitable to monitor GLOFs, meltwater and rainfall floods. In case of a GLOF it serves as a redundant alarm station of the one at Cha Hekou.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.2 The Early Warning System"], "chunk_type": "text", "line_start": 34, "line_end": 38, "token_count_estimate": 243, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "84647ffb031f4ad0", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System\nType: figure\nFigure\n\nImage /page/3/Picture/2 description: A diagram illustrating a monitoring and alarm system for a river basin, likely for Glacial Lake Outburst Floods (GLOFs). The diagram features a map of the Yarkant River and Keleqin River with several key locations marked. Data is collected by three observation stations, shown in inset photographs: one at Kyagar Glacier Lake, one at Cha Hekou, and one at Kuluklangan. A Satellite using SAR remote sensing also collects data. Information on 'hazard potential' is sent from the satellite and the Kyagar Glacier Lake station to a central server. 'Monitoring' data flows from the observation stations along the rivers to the server. The server then disseminates information to 'End-users' depicted as people working on computers in the towns of Markit, Shache, and Zepu. A separate 'alarm' pathway, indicated by a red arrow, travels from the observation stations along the river to two smartphones displaying 'GLOF' alerts. An alarm clock icon indicates a time of '22 h' along this path. The map includes a scale from 0 to 60 km and a north arrow.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.2 The Early Warning System"], "chunk_type": "figure", "figure_caption": null, "line_start": 39, "line_end": 39, "token_count_estimate": 309, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "1633e715e86a9c6f", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System\nType: text\n\nFig. 2.3: Concept of the early warning system, combining satellite remote sensing and automatic terrestrial observation stations.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.2 The Early Warning System"], "chunk_type": "text", "line_start": 40, "line_end": 42, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "3f2f60116b86d68c", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge\nType: text\n\nThe daily images from the automatic camera installed at Kyagar Lake in September 2012 provide an exceptional possibility to assess geometrical changes close to the glacier terminus in a qualitative way at high temporal resolution.\n\nThe repeated observations of glacier flow speed show that ice flow was very slow (10-30 m yr-1) on the glacier tongue until 2010. Over the last years, however, a considerable speed-up of the glacier was observed, first in the upper reaches of Kyagar Glacier, later extending towards the glacier terminus. In some places the speed has nearly doubled between 2011 and 2012 (Haemmig et al. 2014). The changes in surface velocity have been interpreted as the result of a glacier surge. In fact, the surge activity announced by a DEM comparison and by the analysis of surface velocities has arrived in the region of the ice dam in 2014/2015. It has been leading to a dramatic acceleration of flow speed and a considerable thickening of the glacier (Fig. 2.4).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.3 Observation of Glacier Surge"], "chunk_type": "text", "line_start": 44, "line_end": 48, "token_count_estimate": 254, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "9e4f2b1c84a66461", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge\nType: figure\nFigure\n\nImage /page/3/Figure/7 description: A photograph of a glacier in front of a snow-covered mountain range, with a glacial lake in the foreground. The image is annotated with five colored lines that trace the surface level of the glacier at different points in time, showing its thickening. A legend in the top right corner indicates the date for each colored line: the red line represents February 2014, the yellow line represents June 2014, the pink line represents October 2014, the orange line represents February 2015, and the green line represents June 2015. The lines show a progressive increase in the glacier's height from the earliest date to the latest.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.3 Observation of Glacier Surge"], "chunk_type": "figure", "figure_caption": null, "line_start": 49, "line_end": 49, "token_count_estimate": 184, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0d9807a403ac5c34", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge\nType: text\n\nFig. 2.4: Direct comparison of the glacier surface between February 2014 and June 2015. The thickening is most pronounced in the upstream region and propagates towards the glacier terminus. The ice-dam at the lake thickened by about 1/3 within one year, which corresponds to an elevation increase of the ice surface of approx. 30 - 50 meters (Haemmig et al. 2014).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.3 Observation of Glacier Surge"], "chunk_type": "text", "line_start": 50, "line_end": 52, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "cd17d027a9c2a7b9", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015\nType: text\n\nSince 2014 a dangerous lake has been impounding behind the ice-dam (Fig. 2.5). Based on camera images acquired by the Kyagar observation station, the lake volume has been estimated using terrestrial markers on the mountain flanks. Hence, it was possible to directly identify the hazard potential on photographs taken by the automatic camera.\n\nAn accurate forecast of an outburst event is extremely complex due to the continuous processes at the ice-dam (i.e. surge-activity, ablation, availability of subglacial discharge channels) and the highly variable water-influx into the lake basin. Sophisticated monitoring technologies, such as terrestrial observation stations and satellite remote sensing are the base of a robust GLOF hazard assessment. The GLOF hazard level was periodically estimated based on terrestrial and satellite images.\n\nIn summer 2015, due to the thickening of the ice-dam, the onset of the flotation equilibrium was estimated at a lake level of approx. 4'840 m a.s.l. with a corresponding lake volume of approx. 40 million m<sup>3</sup> (see Fig. 2.6). On June 29<sup>th</sup> 2015, the lake reached a volume of approx. 17.2 million m<sup>3</sup>.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.4 GLOF Event July 2015"], "chunk_type": "text", "line_start": 54, "line_end": 60, "token_count_estimate": 336, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "c972dad428b242df", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015\nType: figure\nFigure\n\nImage /page/4/Picture/6 description: A side-by-side comparison of two photographs, labeled 'a' and 'b', showing a glacial lake from different perspectives. Image 'a' is a wide shot of the lake with a large glacier and snow-capped mountains in the background under a cloudy sky. The glacier's icy front meets the dark, rippling water. Image 'b' is a fisheye lens view from a low angle, showing the lake with a brown, rocky mountain and a portion of the glacier in the distance. In the immediate foreground of image 'b', a solar panel and some chunks of ice are visible at the water's edge.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.4 GLOF Event July 2015"], "chunk_type": "figure", "figure_caption": null, "line_start": 61, "line_end": 61, "token_count_estimate": 194, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "d6029df735df08d5", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015\nType: text\n\nFig. 2.5: Glacial lake, observed by automatic observation station at Kyagar Glacier (June 29<sup>th</sup>, 2015).\n\nSince June 30<sup>th</sup>, 2015, after almost three years of successful monitoring, the automatic observation at Kyagar Glacier Lake has been out of operation. The station was submerged due to the rapid impoundment of the glacial lake, triggered by the surge-activity of the glacier tongue.\n\nAfterwards the ongoing rise of the lake level and the hazard potential have been estimated based on very high-resolution SAR data of the TerraSAR-X satellites. Based on SAR backscatter, lake surfaces for every image could be delineated. Lake volumes were calculated using lake shorelines and the detailed reference DEM of the lake basin established in 2011. Hazard levels were estimated as a result of the lake size.\n\nThe glacial lake with an estimated volume of approx. 50 million m<sup>3</sup> drained completely at the end of July 2015. Based on satellite remote sensing, the timing and volume of the outburst could be reliably predicted. The implemented fully automatic GLOF early warning system successfully warned the Chinese decision-makers.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.4 GLOF Event July 2015"], "chunk_type": "text", "line_start": 62, "line_end": 70, "token_count_estimate": 316, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "45c2c793958fb658", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A line graph showing the change in lake volume and elevation over time during the year 2015. The x-axis represents the months from January to October. The left y-axis shows 'Lake volume [million m³]' from 0 to 60, and the right y-axis shows 'Lake elevation [m a.s.l.]' from 4'765 to 4'848. There are two data lines on the graph. A black line, labeled 'terrestrial monitoring', shows a slow increase in lake volume from near 0 in January to about 18 million m³ in late May. A horizontal line at this level is labeled 'terrestrial observation station submerged'. A green line, labeled 'satellite remote sensing', starts in late May and shows a rapid, steep increase in lake volume, peaking at approximately 43 million m³. A vertical red line marks the date of a 'Spontaneous Glacial Lake Outburst, July 28 2015', which corresponds to the peak of the green line. A light blue shaded area at the top of the graph, between approximately 40 and 60 million m³, is labeled 'level of flotation equilibrium'.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.4 GLOF Event July 2015"], "chunk_type": "figure", "figure_caption": null, "line_start": 71, "line_end": 71, "token_count_estimate": 313, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "2c13ba54c771ced6", "text": "Document: 1. Introduction\nSection: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015\nType: text\n\nFig. 2.6: Based on terrestrial and satellite based monitoring techniques an accurate forecast of the GLOF was possible. Terrestrial monitoring was based on the original DEM 2011, whereas volume estimates by satellite remote sensing (sentinel-1) is based on a modified DEM 2011.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015", "section_headings": ["2. Case Study China – Glacier Lake Outburst Flood", "2.4 GLOF Event July 2015"], "chunk_type": "text", "line_start": 72, "line_end": 74, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "68fe6b8139f883f9", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.1 Intruduction\nType: text\n\nThe Greater Caucasus is a large mountain range between Georgia and Russia with Mt. Elbrus as its highest peak. The main road connecting Georgia with Russia runs along Mt. Kazbek, a 5'048 m high volcano and one of the highest summits of the Caucasus range (Fig. 3.1).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.1 Intruduction", "section_headings": ["3. Case study Georgia", "3.1 Intruduction"], "chunk_type": "text", "line_start": 78, "line_end": 80, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc45f27010ce4a6c", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.1 Intruduction\nType: figure\nFigure\n\nImage /page/5/Figure/7 description: The image displays three maps related to the Kazbegi area in Georgia. On the left, there are two maps for location context. The top-left is a physical map of the Caucasus region, showing Georgia and its neighboring countries (Russia, Turkey, Armenia, Azerbaijan), with the Kazbegi Area circled. The bottom-left map is a globe showing the location of Georgia in the world. The main, larger map on the right is a detailed topographic map of the Kazbegi area. This map shows the border between Russia and Georgia, the Amali River, the Tergi river, and the Georgian military road. It specifically highlights a \"Glacier collapse area; May 17th 2014\" near Mt. Kazbeg (5'033 m a.s.l.). A dashed purple line indicates the \"Debris flow path\" originating from the collapse and following the Devdoraki-glacier and Amali River. Other labeled features include a gas-pipeline, a \"Border Control to Russia\" point, and elevation points such as Pt. 3'284 and Pt. 2'967. The map includes a scale in meters and coordinate grids.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.1 Intruduction", "section_headings": ["3. Case study Georgia", "3.1 Intruduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 81, "line_end": 81, "token_count_estimate": 301, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe168ea65f28cc88", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.1 Intruduction\nType: text\n\nFig. 3.1: Situation of Devdoraki area within the Mt.Kazbeg region in Georgia.\n\nIn May 2014 a huge debris flow consisting of rock and ice blocked the main road just south of the Russian border (Fig. 3.2). The source of the debris flow was high up at the headwall of Mt. Kazbeg, where part of the glacier had collapsed (Fig. 3.3). The debris slid down the Devdoraki glacier, covering the road as well as damaging an\n\nimportant gas pipeline in the main valley. Further events in August 2014 affected the infrastructure of the border control, the customs service and a hydro power plant.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.1 Intruduction", "section_headings": ["3. Case study Georgia", "3.1 Intruduction"], "chunk_type": "text", "line_start": 82, "line_end": 88, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e95957bf459a397", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.1 Intruduction\nType: figure\nFigure\n\nImage /page/6/Picture/3 description: A figure labeled \"Fig. 2.2\" displays two images side-by-side. The left image is a topographic map overlaid on an aerial photograph of a mountainous region. The map includes contour lines with elevations like 3000 and 4000, and grid lines with coordinates such as 4'730'000 and 462'500. A large, reddish-brown area, possibly a landslide or debris flow, is outlined with a red dotted line. The terrain shows snow-covered peaks and some cloud cover. The right image is an aerial photograph of a steep valley, showing the aftermath of what appears to be the same event. A large, dark debris field has flowed down the mountainside, covering a significant portion of the valley floor and reaching a body of water at the bottom. Some industrial-looking buildings are visible at the top of the debris field.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.1 Intruduction", "section_headings": ["3. Case study Georgia", "3.1 Intruduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 89, "line_end": 89, "token_count_estimate": 236, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21f5aa663c3d0708", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.1 Intruduction\nType: text\n\nFig. 3.2: Pleiades satellite image; acquired on May 18<sup>th</sup> 2014 with outlined process area of the May 17<sup>th</sup> 2014 event (red dotted line, left) and debris flow deposition in the main valley (right).\n\nBased on a detailed hazard assessment local authorities decided to implement an early warning system for a) monitoring the glacier activities in the upper part of Mt. Kazbeg and b) alarming affected people and infrastructures in the valley from devastating debris flow processes. The concept of the EWS is identical with the one implemented in China (monitoring and alarm stations, data transfer and processing). It is foreseen to install the whole system in April 2016.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.1 Intruduction", "section_headings": ["3. Case study Georgia", "3.1 Intruduction"], "chunk_type": "text", "line_start": 90, "line_end": 94, "token_count_estimate": 189, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "f0a6a61b8d20d65a", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: text\n\nThe reason of the event in May 2014 was a combination of a gravitational slope failure along pre-existing discontinuities, longterm degradation of permafrost and glacier melting and probably also volcanic activity expressed in low seismicity and thermal activities. The seismic institute of Illia Stat University, Tbilisi, registered a small tremor prior to the May  $17^{th}$  2014 event. Volcanic activity (tremor / temperature) may eventually also have a negative impact on the slope stability situation on a very short term and trigger catastrophic events depending on the intensity of the activity.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "text", "line_start": 96, "line_end": 98, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e6c57a53ff010dd", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: figure\nFigure\n\nImage /page/6/Picture/8 description: An aerial, eye-level shot of a majestic, snow-covered mountain peak against a clear, deep blue sky. A large, reddish-brown landslide or rockfall path cuts through the snow on the mountain's face, extending from near the summit down the slope. The surrounding terrain is also rugged and covered in snow and ice.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "figure", "figure_caption": null, "line_start": 99, "line_end": 99, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d254aebcba517e0f", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: figure\nFigure\n\nImage /page/6/Picture/9 description: An aerial photograph of a vast, rugged mountain range under a partly cloudy sky. In the background, a large, snow-covered peak is partially obscured by clouds. In the foreground, a rocky ridge with patches of snow and sparse green vegetation slopes down. To the right, a deep valley features a prominent, steep slope with a distinct reddish-brown color, contrasting with the surrounding gray and brown rock faces.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "figure", "figure_caption": null, "line_start": 101, "line_end": 101, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1fa594dd475c4ffb", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: text\n\nFig. 3.3: Main scarp of the glacier collapse in the summit area of Mt. Kazbeg (left) and silent witnesses of the debris flow event in the Devdoraki valley (right).\n\nRising mean annual air temperatures are causing enduring progressive permafrost degradation (Noetzli and Gruber, 2009) while percolation of melted water can advectively penetrate into bedrock along joints and therefore lead to thermal perturbation and fast modification of the mechanical conditions at depth. Such observations give evidence that the observed increase in frequency and magnitude of mass movements in glacial environments have a relation to climate change (Huggel et al., 2010). The global permafrost zonation index map shows that the glaciated part of Mt. Kazbeg lies within a zone of highly probable permafrost occurrence.\n\nIn the lower part of the eastern slope of Mt. Kazbeg the main Devdoraki glacier divides into several glacier tongues. One of the tributary glaciers just ends on a steep slope several dozens of meters above the main glacier. The tongue is mainly detached from the main glacier by huge cracks. Signs of strain and deformation along the limits of the tongue (lose rock material, silent witnesses of actual icefall events, flowing water) have been detected in autumn 2015.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "text", "line_start": 102, "line_end": 108, "token_count_estimate": 340, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee809c3c4b7e440a", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: figure\nFigure\n\nImage /page/7/Figure/4 description: A side-by-side comparison of two images of a mountainous region. The image on the left is a topographical map with a color overlay indicating \"Permafrost Zonation\". A legend in the top left corner explains the color coding: blue for \"Permafrost in nearly all conditions (cold, deep)\" and a pink-to-yellow gradient for \"Permafrost only in very favorable conditions (warm, shallow)\". The map is credited to the University of Zurich and has a red oval circling an area on the upper mountain. The image on the right is a photograph of a snow-covered mountain peak and a large glacier, which is labeled \"Devdoraki glacier\".", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "figure", "figure_caption": null, "line_start": 109, "line_end": 109, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f47d37ee27e1d0c6", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: text\n\nFig. 3.4: Global permafrost zonation index map (Noetzli and Gruber, 2009) on Google Earth showing the detachment area of the ice collapse within the main permafrost area (left). View of Mt. Kazbeg from the east with active tributary glacier tongue in the lower part of the picture.\n\nAccording to the hazard assessment in the detachment zone and along the Devdoraki valley, the general structural setting as well as the rough glacier stability analysis the scenarios for future events have been defined:\n\n- Rock-ice slope failures similar to the May 17<sup>th</sup> 2014 event, with volumes of several millions m<sup>3</sup>, can happen again in the next 10 30 years. Such big slope failures are often preceded by smaller events in the detachment area possibly announcing an imminent bigger event.\n- At the tongue of the tributary glacier, signs of strong stress have been detected in the ice. Experiences from hanging glacier collapses in the Alps indicate that a complete detachment of this part of the tributary glacier cannot be excluded. The instable glacier mass has a volume of approx. 1 Mio. m<sup>3</sup>\n\nEvens and Claque (1988) stated that glacial environments can significantly enhance the runout distance of rapid mass movements by travelling on low-friction surfaces such as on glaciers, by melting of ice and snow due to frictional heating that causes pore pressure effects at the base of the moving mass or fluidizes the entire flow body, and by channeling or air-launching the debris by moraines. Schneider et al. (2011) as well as Bottino et al. (2002) quantified by means of numerical simulations the observed runout distances of rock-ice avalanches in glacial environments to exceed predicted ones for pure rock avalanches by up to 30%. The event of May 2014 confirms these conclusions. Thus we know that both scenarios end in huge debris flows reaching down to the main valley endangering the main road, a hydropower plant, a gas pipeline, border police as well as all people using the road. In order to predict the flow velocity of the different events as well as flow heights numerical simulations for the event itself and the defined scenarios have been carried out with the model RAMMS (Christen et al., 2012, Fig. 3.5). According to the simulations the warning time for a debris flow detection system installed at location A (Fig. 3.1 and Fig. 3.6) will be around 5 minutes, which is an absolute minimum to evacuate the whole traffic on the critical road section.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "text", "line_start": 110, "line_end": 119, "token_count_estimate": 636, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a719e85358a2bda1", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A topographical map displaying the maximal velocity of a flow, likely a debris flow, in a mountainous region. The map features a color-coded overlay along a river valley, with a legend in the top-left corner that correlates colors to velocity ranges in meters per second (m/s). The legend is as follows: white for 'no value', light purple for '< 7 m/s', darker purple for '7-14 m/s', blue for '14-21 m/s', cyan for '21-28 m/s', light green for '28-35 m/s', yellow-green for '35-42 m/s', yellow for '42-49 m/s', orange for '49-56 m/s', and dark red for '56-74 m/s'. The flow path originates in the southwest, where it is widest and shows the highest velocities (dark red, orange, yellow), and proceeds northeast, narrowing as it follows the course of the 'Devdoraki' and 'Amali River' towards the 'Tergi river' on the eastern side of the map. The velocity generally decreases downstream, with lower speeds indicated by greens, blues, and purples. The map includes coordinate markings, such as 44°32'0\"E and 470'000.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "figure", "figure_caption": null, "line_start": 120, "line_end": 120, "token_count_estimate": 349, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8eb31089dc5e42a3", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios\nType: text\n\nFig. 3.5: Debris flow simulation with RAMMS to define flow velocities of the main event in 2014. The maximum warning time interval with an early warning system positioned at location A (fig. 3.1 and fig 3.7) can be deviated from the velocities.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.2 Hazard Assessment – Scenarios", "section_headings": ["3. Case study Georgia", "3.2 Hazard Assessment – Scenarios"], "chunk_type": "text", "line_start": 121, "line_end": 123, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "130b9eba4ae07565", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System\nType: text\n\nThe early warning system consists of a glacier observation station and a debris flow alarm system. While the glacier observation point is situated on the eastern ridge of Mt. Kazbeg at 3000 m a. s. l., the debris flow detection is located along the main channel down in the valley (Fig. 3.1).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System"], "chunk_type": "text", "line_start": 125, "line_end": 127, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4245312091774ff2", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras\nType: text\n\nGlaciers terminating in steep terrain are potentially hazardous. This has been shown in different historical events, where devastating rock-ice avalanches have buried towns or entire cities (e.g. Mattmark, Switzerland 1965 (McCall et al. 1992) or Huascaran, Peru 1962 and 1970 (Evans et al., 2009)). In order to detect such an ice collapse in the topmost area of Mt. Kazbek as well as on the tongue of the tributary glacier a monitoring station will be implemented (Fig. 3.6).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Glacier monitoring cameras"], "chunk_type": "text", "line_start": 129, "line_end": 131, "token_count_estimate": 159, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4f4a97e38c79ef24", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A figure displaying a topographic map and corresponding photographs from webcams. On the left is a \"Map of northeastern slope of Mt. Kazbeg headwall\" with a scale of 1:35000. The map shows contour lines, latitude and longitude coordinates, and a scale bar in meters. A point labeled \"EWS location C\" is marked, from which two fields of view are projected. A purple cone represents the \"view of webcam no. 1,\" and a green dashed cone represents the \"view of webcam no. 2.\" Several areas are labeled on the map, including the \"tongue of tributary glacier,\" the \"tongue of Devdoraki glacier,\" and the \"detachment area May 2014 event.\" On the right, two photographs are shown. The top photo, labeled \"view webcam no. 1,\" shows a snowy mountain peak circled in purple. The bottom photo, labeled \"view webcam no. 2,\" shows a wider view of a glacier and mountainside, outlined with a green dashed line. A legend at the top right explains that the purple cone is the \"view of webcam no. 1 from EWS location C to Mt. Kazbeg\" and the green dashed cone is the \"view of webcam no. 2 from EWS location C to tongue of tributary glacier and tongue of Devdoraki glacier.\"", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Glacier monitoring cameras"], "chunk_type": "figure", "figure_caption": null, "line_start": 132, "line_end": 132, "token_count_estimate": 365, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["35000"]}}
{"id": "1d7d5eea7ff9a2e3", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras\nType: text\n\nFig. 3.6: Situation of the glacier monitoring station at location C on 3000 m a.s.l. The critical ice masses will be monitored by two automatic cameras transmitting the taken pictures directly on a password protected data portal.\n\nTwo webcams will shoot images of the critical glacier areas at regular intervals, allowing local authorities to determine the glacier movement based on feature tracking within the images (example see fig. 3.7). All webcam images can be accessed on a password protected data portal, providing additional information of all implemented devices.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Glacier monitoring cameras"], "chunk_type": "text", "line_start": 133, "line_end": 137, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "33416cce0e0d73de", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras\nType: figure\nFigure\n\nImage /page/9/Figure/5 description: A scientific figure, labeled \"Figure 3.7: Displacement monitoring based on image analysis (example from Trift...)\", displays a photograph of a rocky and icy mountainside overlaid with a dense field of colorful vectors. The vectors, represented by arrows of varying colors and lengths, illustrate surface displacement. A prominent feature, likely a glacier or rockslide, runs diagonally from the upper left to the lower right, characterized by a high concentration of arrows colored green, yellow, orange, and red, indicating significant movement downwards along the slope. The surrounding stable rock areas are marked with smaller, predominantly dark blue arrows, signifying minimal displacement. In the upper left corner, a snow-covered area is marked with numerous magenta 'x's and some arrows.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Glacier monitoring cameras"], "chunk_type": "figure", "figure_caption": null, "line_start": 138, "line_end": 138, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ecff3bda5574bd7a", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras\nType: figure\nFigure: Figure 3.7: Displacement monitoring based on image analysis (example from Trift glacier, Switzerland). Green: small displacement, red: large displacement, purple crosses: stable\n\nFigure 3.7: Displacement monitoring based on image analysis (example from Trift glacier, Switzerland). Green: small displacement, red: large displacement, purple crosses: stable", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Glacier monitoring cameras"], "chunk_type": "figure", "figure_caption": "Figure 3.7: Displacement monitoring based on image analysis (example from Trift glacier, Switzerland). Green: small displacement, red: large displacement, purple crosses: stable", "line_start": 140, "line_end": 140, "token_count_estimate": 123, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4517509947aa5328", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: text\n\nFor the automated closure of the road and the alerting of local authorities, a large scale monitoring system will be installed in the lower part of the debris channel approximately 3 km away from the damage potential (Fig. 3.8). The alarm system is equipped with the following components:\n\n- three gauge radars hanging on lines spanned across the channel to estimate flow height and debris flow magnitude (Fig. 3.8, right).\n- Two webcams with live-access and infrared floodlights allow day and night alarm verification.\n- One geophone positioned at the channel border to record seismic data for a better understanding the physical processes of the debris flows", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "text", "line_start": 143, "line_end": 149, "token_count_estimate": 179, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ce694b7222d0a3c", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A two-part figure displaying a topographical map and a corresponding elevation profile graph. The left panel is a topographical map showing a cross-section marked by a green line between points A1 and A2 across the Amali River. The right panel is a graph plotting the elevation profile from East to West. The vertical axis is labeled \"Elevation [m.a.s.l.]\" and ranges from 1750 to 1850. The horizontal axis is unlabeled but has markers from 0 to 240. A green line labeled \"topography channel\" shows the ground profile, dipping into a valley. Two red squares, \"fundament A1\" and \"fundament A2\", are shown at the ends of the profile. A red line connects these two fundaments, passing over the valley. Along this red line, three symbols indicate the position of \"level radars and webcams\". A vertical bracket shows the maximum height difference between the red line and the bottom of the channel, labeled \"ΔH\\_max: 35 m - 40 m\".", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "figure", "figure_caption": null, "line_start": 150, "line_end": 150, "token_count_estimate": 288, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5312848877158803", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: figure\nFigure: Figure 3.8: Debris flow detection installation at position A with level radars and cameras. The channel width of 250 to 270 m is a challenge for system.\n\nFigure 3.8: Debris flow detection installation at position A with level radars and cameras. The channel width of 250 to 270 m is a challenge for system.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "figure", "figure_caption": "Figure 3.8: Debris flow detection installation at position A with level radars and cameras. The channel width of 250 to 270 m is a challenge for system.", "line_start": 152, "line_end": 152, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3b633454dcb1465", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: text\n\nThe dimensions of the channel as well as the rough climate during winter time will be the major challenges for the system. At the installation point the channel is about 40 m deep and 250 m wide. Influence of wind and icebuilding on the cables can hardly be calculated in a proper way.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "text", "line_start": 153, "line_end": 155, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fbf812b44c7665c9", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: figure\nFigure\n\nImage /page/10/Picture/5 description: A two-panel image. The left panel shows a wide, high-angle view of a mountain valley with a river running through it. The slopes are a mix of green and brown vegetation and exposed reddish and grey rock. A black line is drawn across the valley, labeled \"270 m\". The right panel shows a close-up of a piece of scientific equipment, consisting of a metal frame with electronic boxes, a yellow sensor, and four flat panels, suspended by a network of cables over a steep, rocky slope.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "figure", "figure_caption": null, "line_start": 156, "line_end": 156, "token_count_estimate": 165, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e746b4d79a63dee", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: figure\nFigure: Figure 3.9: Location A (left) where gauge radars for the alarm system will be positioned on wire ropes (right).\n\nFigure 3.9: Location A (left) where gauge radars for the alarm system will be positioned on wire ropes (right).", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "figure", "figure_caption": "Figure 3.9: Location A (left) where gauge radars for the alarm system will be positioned on wire ropes (right).", "line_start": 158, "line_end": 158, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4fbc1d9f631e6d84", "text": "Document: 1. Introduction\nSection: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow\nType: text\n\nLocal authorities developed an action plan in case of an alarm (debris flow) as well as an upcoming glacier collapse. Today this document is still under construction and strictly confidential.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow", "section_headings": ["3. Case study Georgia", "3.3 The Early Warning System", "Alarm installations for debris flow"], "chunk_type": "text", "line_start": 159, "line_end": 161, "token_count_estimate": 71, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "316cb8f8440098df", "text": "Document: 1. Introduction\nSection: 4. Conclusions\nType: text\n\nAs mountainous countries China, Georgia and Switzerland are affected by more frequent extreme flood events and increased glacier melt due to climate change. Switzerland has a lot of experience and knowledge regarding natural hazards and risks. In both project the main target is to improve international knowledge in the assessment of climate impacts and risks, and to develop practical approaches to climate change adaptation and emergency response.\n\nIn remote areas, a continuous glacier monitoring proved to be important for the hazard assessment. Early warning system based on satellite data and terrestrial stations is a reliable and efficient tool for local communities / authorities. Expert know-how and human resources on local level are limited. Pragmatic planning and proactive support is necessary. Cooperation and know-how exchange on local level with a strong governmental backing/support is helpful.\n\nThe strategy to avoid the unmanageable (mitigation – land-use planning) and to manage the unavoidable (adaptation to climate change – early warning system) is valid around the globe. The implemented methodology in the Karakoram Mountains as well as in the Caukasus (monitoring, early warning system, risk management) can be transferred to other regions.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Conclusions", "section_headings": ["4. Conclusions"], "chunk_type": "text", "line_start": 163, "line_end": 169, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "75937cfc8e85182e", "text": "Document: 1. Introduction\nSection: 4. Conclusions\nType: figure\nFigure\n\nImage /page/11/Picture/1 description: The image displays the logos and names of several organizations, separated into two columns by a vertical dotted line. On the left is the UNESCO logo, which resembles a classical temple, with the text \"United Nations Educational, Scientific and Cultural Organization\" below it. On the right, at the top, are the logos for \"uniTwin\" and \"EPFL ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE\". Below these logos is a bulleted list stating \"UNESCO Chair in technologies for development\" and \"Lausanne (Switzerland)\".", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Conclusions", "section_headings": ["4. Conclusions"], "chunk_type": "figure", "figure_caption": null, "line_start": 170, "line_end": 170, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ae9c7c1ad982fff", "text": "Document: 1. Introduction\nSection: 4. Conclusions\nType: text\n\nThe working partnership between specialized private companies, authorities and universities is the backbone of a sustainable project management. Adaptation strategies in those projects mean anticipating the effects of climate change and taking appropriate actions to reduce the damage they can cause.", "metadata": {"source_file": "data/('early_warning_systems_for_glacier_lake_outburst_floods_and_debris_flows', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Conclusions", "section_headings": ["4. Conclusions"], "chunk_type": "text", "line_start": 171, "line_end": 173, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce9e2bffd28792dd", "text": "Document: Extreme Precipitation in a Warming Climate Climate Central\nType: text\n\nClimate Matters • May 1, 2024", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate Climate Central", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 2, "token_count_estimate": 29, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf10d921727e9640", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate\nType: figure\nFigure\n\nImage /page/0/Picture/4 description: An infographic on a dark blue background titled \"WARMER AIR HOLDS MORE MOISTURE\". A text box states, \"1°F increase = 4% more water vapor\". A graph shows \"Temperature\" on the x-axis and \"Moisture\" on the y-axis, with a blue line curving upwards to illustrate that as temperature rises, the air holds more moisture. To the right, a cloud is shown above a temperature bar that is blue on the left and transitions to yellow on the right, with \"32°F\" marked at the transition point. The cloud is shown producing \"Heavier snow\" on the colder side and \"Heavier rain\" on the warmer side. The Climate Central logo is in the bottom right corner.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate", "section_headings": ["Extreme Precipitation in a Warming Climate"], "chunk_type": "figure", "figure_caption": null, "line_start": 5, "line_end": 5, "token_count_estimate": 215, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ea5fab5881e4cd1", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate\nType: text\n\nClick the downloadable graphic: Warmer air holds more moisture", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate", "section_headings": ["Extreme Precipitation in a Warming Climate"], "chunk_type": "text", "line_start": 6, "line_end": 8, "token_count_estimate": 44, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff2ef27b52fb1877", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate\nType: figure\nFigure\n\nImage /page/0/Picture/6 description: A map of the United States titled 'HEAVIER DOWNPOURS' with the subtitle 'Change in precipitation on heaviest 1% of days'. The map is color-coded to show the percentage change in precipitation from 1958 to 2021. A color key on the right shows a scale from 0% to 40%, with colors ranging from light green to dark blue. The map displays the following regional percentage changes: Northeast +60%, Midwest +45%, Southeast +37%, a southern region +21%, a northern plains region +24%, Southwest +17%, Northwest +1%, Alaska +21%, Hawaii -19%, and the Caribbean -24%. The source is cited at the bottom as 'USGCRP, 2023: Fifth National Climate Assessment'. The Climate Central logo is in the bottom right corner.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate", "section_headings": ["Extreme Precipitation in a Warming Climate"], "chunk_type": "figure", "figure_caption": null, "line_start": 9, "line_end": 9, "token_count_estimate": 228, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0160158f09cdb2d8", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate\nType: text\n\nClick the downloadable graphic: Extreme Precipitation Change 1958 to 2021", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate", "section_headings": ["Extreme Precipitation in a Warming Climate"], "chunk_type": "text", "line_start": 10, "line_end": 12, "token_count_estimate": 45, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e91efe0c7bc1d4a9", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate\nType: figure\nFigure\n\nImage /page/0/Picture/8 description: A map of the contiguous United States titled \"WARMER FUTURE, MORE EXTREME PRECIPITATION,\" which shows the \"Projected change in precipitation on heaviest 1% of days.\" The map is color-coded by county to represent the \"% change at 2°C (3.6°F) of global warming.\" A vertical color bar legend on the right indicates the percentage change, ranging from 0% (light beige) to 40% (dark green) in increments of 10%. Most of the country is shaded in green, indicating an increase in extreme precipitation, with the darkest shades concentrated in the Northeast, Great Lakes region, and parts of the Southeast. A small area in the Southwest, including parts of Texas and New Mexico, is shaded in light beige, indicating little to no change. The source is cited at the bottom as \"USGCRP, 2023: Fifth National Climate Assessment,\" and the map is branded with the \"CLIMATE CENTRAL\" logo.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate", "section_headings": ["Extreme Precipitation in a Warming Climate"], "chunk_type": "figure", "figure_caption": null, "line_start": 13, "line_end": 13, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "119a490ac5ecbc9b", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate\nType: text\n\nClick the downloadable graphic: Future Extreme Precipitation 2024", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate", "section_headings": ["Extreme Precipitation in a Warming Climate"], "chunk_type": "text", "line_start": 14, "line_end": 16, "token_count_estimate": 44, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ddcb44dd61a37a51", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > KFY CONCEPTS\nType: text\n\nClimate change is supercharging the water cycle, bringing heavier precipitation extremes — and related flood risks — across the U.S.\n\nAs the climate has warmed from 1958 to 2021, the most extreme precipitation days have intensified in every major U.S. region, led by the Northeast (+60%) and Midwest (+45%).\n\nThis hazardous intensification is expected to continue with future warming.\n\nWith 2°C (3.6°F) of warming, 85% of 3,111 total U.S. counties are likely to experience a 10% or higher increase in precipitation falling on the heaviest 1% of days.\n\nHigh future levels of extreme precipitation intensification are concentrated in: Alaska, Hawaii, Tennessee, Alabama, Georgia, Mississippi, Maine, North Carolina, and Kentucky.\n\nPeople, ecosystems, and infrastructure in both wet and dry locations are facing the risks that come with short bursts of extreme rainfall.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > KFY CONCEPTS", "section_headings": ["Extreme Precipitation in a Warming Climate", "KFY CONCEPTS"], "chunk_type": "text", "line_start": 18, "line_end": 30, "token_count_estimate": 241, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7451806084467d9b", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Download KML map and XLSX\nType: text\n\n**data file:** projected change in precipitation extremes under 2°C of warming for over 3,000 U.S. counties", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Download KML map and XLSX", "section_headings": ["Extreme Precipitation in a Warming Climate", "Download KML map and XLSX"], "chunk_type": "text", "line_start": 32, "line_end": 34, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6cd99c07c6af725c", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle\nType: text\n\nClimate change is bringing heavier rainfall extremes and increased, inequitable flood risk to many parts of the U.S.\n\nFor every 1°F of warming the air can hold an extra 4% of moisture, increasing the chances of heavier downpours that contribute to the risk of flash floods.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle"], "chunk_type": "text", "line_start": 36, "line_end": 40, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5b03a4ffebdf03b8", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle\nType: figure\nFigure\n\nImage /page/2/Picture/7 description: An infographic on a dark blue background titled \"WARMER AIR HOLDS MORE MOISTURE\". The infographic explains the relationship between air temperature and moisture content. A graph shows \"Temperature\" on the x-axis, increasing from left to right, and \"Moisture\" on the y-axis. A blue line curves upward, indicating that as temperature rises, the air's capacity to hold moisture increases. A text box states, \"1°F increase = 4% more water vapor\". An arrow points from the upward-curving line to a large grey cloud. Below the cloud, precipitation is shown. On the left, under colder conditions, snowflakes fall, labeled \"Heavier snow\". On the right, under warmer conditions, raindrops fall, labeled \"Heavier rain\". A temperature bar below the precipitation is blue on the left and transitions to yellow on the right, with a marker at \"32°F\" indicating the freezing point. The logo for Climate Central is in the bottom right corner.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle"], "chunk_type": "figure", "figure_caption": null, "line_start": 41, "line_end": 41, "token_count_estimate": 292, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e88a962bff226610", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle\nType: text\n\nClick the downloadable graphic: Warmer air holds more moisture\n\nHeavy downpours bring more rain, faster — causing flash flooding and landslides that can displace families, drown crops, damage infrastructure, and expose people to hazardous debris, contaminants, and waterborne disease.\n\nThe rapid onset of flash floods limits time to get people out of harm's way. In the U.S., racial minorities and those living in mobile homes are disproportionately exposed to flood risk, especially in the South and in rural areas.\n\nIn the U.S., extreme daily rainfall has become more frequent since the 1980s. Hourly rainfall intensity has also increased since 1970 — by 13% on average across 150 U.S. locations analyzed by Climate Central.\n\nRisky rainfall extremes are the focus of analysis of past and projected future change in extreme rainfall across the U.S. featured in the Fifth National Climate Assessment.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle"], "chunk_type": "text", "line_start": 42, "line_end": 52, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e7ca937dc4181dda", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier\nType: text\n\nAs the climate has warmed over recent decades, the most extreme precipitation days have become more intense across the U.S.\n\nAs precipitation extremes intensify, the wettest days each year bring increasing flood hazards. And this intensification trend has been widespread.\n\nIn the Northeast and Midwest, the amount of precipitation falling on the heaviest 1% of days has increased 60% and 45%, respectively, from 1958 to 2021 according to the Fifth National Climate Assessment.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle", "Heaviest precipitation days have become heavier"], "chunk_type": "text", "line_start": 54, "line_end": 60, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "63d5fc67c7bdb569", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier\nType: figure\nFigure\n\nImage /page/4/Picture/2 description: A map of the United States titled \"HEAVIER DOWNPOURS\" shows the \"Change in precipitation on heaviest 1% of days\" from 1958 to 2021. The map is color-coded to represent the percentage change, with a legend on the right showing a scale from 0% (light yellow-green) to 40% (dark blue). The eastern half of the country is shaded in shades of blue, indicating a significant increase in precipitation, while the western half is shaded in greens, indicating a smaller increase. Specific percentage changes are noted for different regions: Northeast +60%, Midwest +45%, Southeast +37%, Northern Plains +24%, Southern Plains +21%, Southwest +17%, and Northwest +1%. Hawaii shows a decrease of -19%, and a shape representing Puerto Rico/U.S. Virgin Islands shows a decrease of -24%. The source is cited as \"USGCRP, 2023: Fifth National Climate Assessment,\" and the Climate Central logo is in the bottom right corner.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle", "Heaviest precipitation days have become heavier"], "chunk_type": "figure", "figure_caption": null, "line_start": 61, "line_end": 61, "token_count_estimate": 298, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b2504c24f645c5e8", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier\nType: text\n\nClick the downloadable graphic: Extreme Precipitation Change 1958 to 2021\n\nThe heaviest rainfall events have become wetter across all other major regions of the continental U.S. from 1958 to 2021 as well, led by: the Southeast (+37%); the Northern Rockies and Plains (+24%); and the South (+21%).", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle", "Heaviest precipitation days have become heavier"], "chunk_type": "text", "line_start": 62, "line_end": 66, "token_count_estimate": 128, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "84669a4cd5ca3d84", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes\nType: text\n\nWith continued warming, precipitation extremes are likely to increase globally — even in regions with decreasing average precipitation, according to the latest reports from the Intergovernmental Panel on Climate Change.\n\nMost of the U.S. is projected to see increases in precipitation extremes with 2°C (3.6°F) of global warming, according to analysis in the Fifth National Climate Assessment. The planet has already warmed more than 1.1°C (2°F). Only by rapidly expanding and accelerating efforts to reduce heat-trapping emissions can we ensure that clobal warming levels remain below 2°C.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle", "Warmer future, heavier precipitation extremes"], "chunk_type": "text", "line_start": 68, "line_end": 72, "token_count_estimate": 190, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bcce42f1d041bfea", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes\nType: figure\nFigure\n\nImage /page/5/Picture/2 description: A choropleth map of the contiguous United States titled \"WARMER FUTURE, MORE EXTREME PRECIPITATION,\" which shows the \"Projected change in precipitation on heaviest 1% of days.\" The map is color-coded by county to indicate the percentage change at 2°C (3.6°F) of global warming. A legend on the right shows a color scale from light tan to dark green, representing a range from 0% to 40% change. The darkest green, indicating the highest increase in precipitation (30-40%), is concentrated in the Northeast, the Upper Midwest, and parts of the Pacific Northwest. Most of the rest of the country is shaded in lighter greens, indicating increases of 10-30%. The Southwest, particularly Arizona and parts of California and New Mexico, shows the lowest change, with colors in the light tan and light green range (0-10%). The source is cited as \"USGCRP, 2023 Fifth National Climate Assessment,\" and the logo for \"CLIMATE CENTRAL\" is in the bottom right corner.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle", "Warmer future, heavier precipitation extremes"], "chunk_type": "figure", "figure_caption": null, "line_start": 73, "line_end": 73, "token_count_estimate": 307, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6dcdbc947be03925", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes\nType: text\n\nClick the downloadable graphic: Future Extreme Precipitation 2024\n\nWith 2°C (3.6°F) of warming, the majority (85% or 2,645) of the 3,111 total U.S. counties are likely to experience a 10% or higher increase in precipitation falling on the heaviest 1% of days.\n\nOn average, U.S. counties are likely to experience a 17% increase in precipitation falling on the heaviest 1% of days.\n\nCounties likely to experience at least a 30% increase in extreme precipitation are concentrated in: Tennessee, Alabama, Georgia, Mississippi, Maine, North Carolina, and Kentucky.\n\nAlaska and Hawaii are likely to experience some of the highest levels of extreme precipitation intensification (43% and 30%, respectively) at 2°C (3.6°F) of global warming.\n\nA recent study suggests that, with high future levels of heat-trapping emissions (RCP8 5), U.S. flash floods could also intensify — especially in\n\nthe Southwest — underscoring that the risks posed by short bursts of extreme rainfall affect both wet and dry regions.\n\nThese rising rainfall intensity risks are unlikely to be equally shared. A recent study suggests that the burdens of an estimated 26% increase in overall U.S. flood risk by 2050 could disproportionately impact Black communities along the Atlantic and Gulf coasts.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes", "section_headings": ["Extreme Precipitation in a Warming Climate", "Climate change is supercharging the water cycle", "Warmer future, heavier precipitation extremes"], "chunk_type": "text", "line_start": 74, "line_end": 90, "token_count_estimate": 357, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3e143c74cda0bff1", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate\nType: text\n\n1. For every 1°F of warming, the air can hold an extra 4% of moisture.\n\nThis relationship between air temperature and water vapor pressure is governed by the laws of thermodynamics and represented in the Clausius-Clapeyron equation.\n\nThe U.S. has already warmed 2.6°F since 1970 — meaning our atmosphere can already hold about 10% more moisture.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate", "section_headings": ["Extreme Precipitation in a Warming Climate", "Three key points: extreme precipitation in a warming climate"], "chunk_type": "text", "line_start": 92, "line_end": 98, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72514a76e7f5d700", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate > 2. More moisture in warmer air increases heavier downpours.\nType: text\n\nTheory suggests that rainfall becomes more intense with warming. Both rain gauge data and modeling experiments support this theory.\n\nGlobally, the frequency and intensity of heavy rainfall events have increased since the 1950s, largely due to humancaused climate change, according to the latest reports from the Intergovernmental Panel on Climate Change.\n\nIn the U.S., extreme daily rainfall events have been on the rise since the 1980s.\n\n3. Intensifying rainfall due to climate change has cost the U.S. billions in inland flood damages over the last three decades.\n\nInland flooding in the U.S. caused \\$230 billion in damages from 1988 to 2021. Over one-third (37%) of those damages are attributed to precipitation changes due to climate warming.\n\nThat's \\$84 billion in past flood damage due to warming-induced rainfall intensification. And the most intense downpours have caused the largest damages.\n\nLearn more: Extreme Weather\nToolkit: Heavy Rain and Flooding\n\nLOCAL STORY ANGLES", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate > 2. More moisture in warmer air increases heavier downpours.", "section_headings": ["Extreme Precipitation in a Warming Climate", "Three key points: extreme precipitation in a warming climate", "2. More moisture in warmer air increases heavier downpours."], "chunk_type": "text", "line_start": 100, "line_end": 117, "token_count_estimate": 298, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8860ec406a95e5f3", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate > How vulnerable is your area to flooding?\nType: text\n\nFactors like local topography, age of infrastructure, watershed health, and\n\nprecipitation trends influence flood vulnerability. See current flood risks in your neighborhood with FloodFactor's ZIP code-level risk identifier. A 2021 report found that one-fourth of critical infrastructure is put at risk of failure by flooding. The American Society of Civil Engineers grades the integrity of infrastructures by state and category.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate > How vulnerable is your area to flooding?", "section_headings": ["Extreme Precipitation in a Warming Climate", "Three key points: extreme precipitation in a warming climate", "How vulnerable is your area to flooding?"], "chunk_type": "text", "line_start": 119, "line_end": 123, "token_count_estimate": 144, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a155eea56e6d7ca", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Who is most vulnerable to heavy rain-related hazards?\nType: text\n\nRacial minorities and mobile homes are disproportionately exposed to flooding, especially in rural areas and in the southern U.S. The American Flood Coalition has resources on adaptation, equitable disaster recovery, and funding opportunities for flood-resilient communities.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Who is most vulnerable to heavy rain-related hazards?", "section_headings": ["Extreme Precipitation in a Warming Climate", "Who is most vulnerable to heavy rain-related hazards?"], "chunk_type": "text", "line_start": 125, "line_end": 127, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "451405f981a0d0a4", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > What are the health and safety risks from flooding?\nType: text\n\nClimate Central's report, *After the Storm: Health risks from damp, moldy homes*, summarizes the potential health risks from poor indoor air quality following heavy precipitation and flooding. Mold is just one of many potential hazards in a storm-damaged home. Other safety hazards include debris, structural damage, or downed electrical wires. The CDC provides tips to protect health and safety following a storm or flooding.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > What are the health and safety risks from flooding?", "section_headings": ["Extreme Precipitation in a Warming Climate", "What are the health and safety risks from flooding?"], "chunk_type": "text", "line_start": 129, "line_end": 131, "token_count_estimate": 144, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c530c6c50f58fe13", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > What can you do to protect against flooding and extreme precipitation?\nType: text\n\nFirstly, know your own risk using the Federal Emergency Management Agency's flood maps. Individual homeowners can insure their homes, maintain rainwater systems, reduce impervious surfaces on their property, elevate important utilities and equipment, and take on low-cost indoor projects recommended by FEMA. Communities can invest in flood management systems, green infrastructure and watershed restoration.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > What can you do to protect against flooding and extreme precipitation?", "section_headings": ["Extreme Precipitation in a Warming Climate", "What can you do to protect against flooding and extreme precipitation?"], "chunk_type": "text", "line_start": 133, "line_end": 135, "token_count_estimate": 130, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4c7ddc030dac79d3", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > CONTACT EXPERTS > Greg Carbin\nType: text\n\nBranch Chief, Forecast Operations National Weather Service\n\n**Contact:** gregory.carbin@noaa.gov **Related expertise:** short-term forecasting of extreme rainfall", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > CONTACT EXPERTS > Greg Carbin", "section_headings": ["Extreme Precipitation in a Warming Climate", "CONTACT EXPERTS", "Greg Carbin"], "chunk_type": "text", "line_start": 139, "line_end": 143, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "adc9dea2d0873c4b", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > FIND EXPERTS\nType: text\n\nSubmit a request to SciLine from the American Association for the Advancement of Science or to the Climate Data Concierge from Columbia University. These free services rapidly connect journalists to relevant scientific experts.\n\n**Browse maps** of climate experts and services at regional NOAA, USDA,\n\nand Department of the Interior offices.\n\n**Explore databases** such as 500 Women Scientists, BIPOC Climate and Energy Justice PhDs, and Diverse Sources to find and amplify diverse expert voices.\n\n**Reach out** to your State Climate\nOffice or the nearest Land-Grant\nUniversity to connect with scientists,\neducators, and extension staff in your\nlocal area.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > FIND EXPERTS", "section_headings": ["Extreme Precipitation in a Warming Climate", "FIND EXPERTS"], "chunk_type": "text", "line_start": 145, "line_end": 159, "token_count_estimate": 184, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c7026028d073028", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > METHODOLOGY\nType: text\n\nData on past (1958–2021) and projected future (under 2°C of global warming) change in total precipitation (including rain and snow) falling on the heaviest 1% of days across the U.S. are from: USGCRP, 2023: *Fifth National Climate Assessment*. Crimmins, A.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, B.C. Stewart, and T.K. Maycock, Eds. U.S. Global Change Research Program, Washington, DC, USA.\n\nhttps://doi.org/10.7930/NCA5.2023. Data were accessed via Figure 2.8a and Figure 2.12a metadata and the Fifth National Climate Assessment Interactive Atlas.\n\n(", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > METHODOLOGY", "section_headings": ["Extreme Precipitation in a Warming Climate", "METHODOLOGY"], "chunk_type": "text", "line_start": 161, "line_end": 167, "token_count_estimate": 216, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d48823666d0276b4", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Custom email signup\nType: text\n\nGet updates, media alerts, and climate reporting resources\n\nSign up  $\\rightarrow$", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Custom email signup", "section_headings": ["Extreme Precipitation in a Warming Climate", "Custom email signup"], "chunk_type": "text", "line_start": 169, "line_end": 173, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69d2c7d34bb8ae64", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Custom email signup > Support our work\nType: text\n\nThere are lots of ways to help fund Climate Central today\n\nLearn more\n\nContact Donate Financials\nUs Funders\n\nCareers\n\nInstagram Facebook X\n\nYouTube\n\n(", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Custom email signup > Support our work", "section_headings": ["Extreme Precipitation in a Warming Climate", "Custom email signup", "Support our work"], "chunk_type": "text", "line_start": 175, "line_end": 190, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7ca4bd537773085e", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Custom email signup > Support our work > Extreme Precipitation in a Warming Climate | Climate Central\nType: text\n\nTerms of Privacy Editorial use policy independence", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Custom email signup > Support our work > Extreme Precipitation in a Warming Climate | Climate Central", "section_headings": ["Extreme Precipitation in a Warming Climate", "Custom email signup", "Support our work", "Extreme Precipitation in a Warming Climate | Climate Central"], "chunk_type": "text", "line_start": 192, "line_end": 194, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "463885ca77a167d5", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Custom email signup > Support our work > Extreme Precipitation in a Warming Climate | Climate Central\nType: figure\nFigure\n\nImage /page/12/Picture/3 description: A logo on a white background. At the top, the words 'POWERED BY' are written in a light gray, sans-serif font, with a short horizontal line on either side. Below and to the left is a stylized letter 'C' logo, composed of three colored segments: blue at the top, yellow on the left, and red at the bottom.", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Custom email signup > Support our work > Extreme Precipitation in a Warming Climate | Climate Central", "section_headings": ["Extreme Precipitation in a Warming Climate", "Custom email signup", "Support our work", "Extreme Precipitation in a Warming Climate | Climate Central"], "chunk_type": "figure", "figure_caption": null, "line_start": 195, "line_end": 195, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "391602c9d84e687f", "text": "Document: Extreme Precipitation in a Warming Climate\nSection: Extreme Precipitation in a Warming Climate > Custom email signup > Support our work > Extreme Precipitation in a Warming Climate | Climate Central\nType: text\n\nCopyright © 2025 Climate Central Registered 501(c)(3). EIN: 26-1797336\n\nEN ES", "metadata": {"source_file": "data/('Extreme Precipitation in a Warming Climate _ Climate Central', '.pdf')_extraction.md", "document_title": "Extreme Precipitation in a Warming Climate", "section_path": "Extreme Precipitation in a Warming Climate > Custom email signup > Support our work > Extreme Precipitation in a Warming Climate | Climate Central", "section_headings": ["Extreme Precipitation in a Warming Climate", "Custom email signup", "Support our work", "Extreme Precipitation in a Warming Climate | Climate Central"], "chunk_type": "text", "line_start": 196, "line_end": 199, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1797336"]}}
{"id": "bc239459b5c1c47e", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: ABSTRACT\nType: text\n\nFloods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three stateof-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.\n\n© 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 8, "token_count_estimate": 513, "basins": [], "subbasins": ["Teesta"], "countries": ["China"], "lake_ids": []}}
{"id": "2c2aa948757effd3", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 1. Introduction\nType: text\n\nAmong various hydro-meteorological hazards, floods are identified as the most devastating hazards in the world (Prăvălie and Costache, 2013; Mishra and Sinha, 2020; Sarkar and Mondal, 2020). Flood often causes severe loss of human lives and socio-economic damage (Hirabayashi et al., 2013; Costache, 2019). As per the United Nations Office for Disaster Risk Reduction (UNISDR) statistical data, about 150,061 flood events happened worldwide, and around 157,000 people died by\n\nE-mail address: nguyentthuylinh58@duytan.edu.vn (N.T.T. Linh).\n\nfloods from 1995 to 2015, which were liable for 11.1% of the global disaster casualties (Wahlstrom and Guha-Sapir, 2015; Hong et al., 2018a). Several investigations report that floods affected around 200 million people each year on a global scale (Tien Bui et al., 2019a). Moreover, considering climate change predictions, land use pattern changes and the increase of population also, it is anticipated that the occurrence rates and intensity of floods are likely to be exacerbated by 2050 which may cause huge loss ( $\\approx$ US\\$ 1 trillion) (Bubeck and Thieken, 2018; Ali et al., 2019; Alexander et al., 2019; Huang et al., 2019; Jadandideh-Tehrani et al., 2019; Xu et al., 2019; Serraj and Pingali, 2019). Thus, studies on flood modelling spatially occurring at basin-scale at the regional level are urgently needed to alleviate or take adaptation and mitigation measures against the devastating flood's effects (Cao et al., 2019; Su et al., 2019; Costache et al., 2020).\n\n$<sup>^{\\</sup>ast}\\,$  Corresponding author at: Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam.\n\nGenerally, based on their occurrences, floods are of four types, i.e., flash floods, riverine floods, coastal floods, and urban floods (Tien Bui et al., 2019b; Costache et al., 2020). Flash floods are most devastating among these types of floods, which may cause huge losses of life and properties because of its rapid onset attributes along with grate velocity of flow (Bui et al., 2020a). Moreover, a massive amount of debris has moved along the river basin; thus, floods bring severe destruction for the infrastructure and fatalities (Siegel, 2020; Brinkmann, 2020; Luino et al., 2020; Kuriqi et al., 2016). In the under-developing and developed countries, also the severe destruction by the effect of flash floods are reported (Kuriqi and Ardiçlioğlu, 2018; Bui et al., 2020b; Munir et al., 2020; Okaka, 2020). On the other side, the developing countries are more affected by flash floods compared to developed countries due to the lack of proper infrastructures, essential financial assets, and technological advancement to predict the flood events or to mitigate the consequences of the floods. It is, thus, of immense importance to build up a high-performance prediction model for flood events likelihood and the susceptibility mapping of inundation regions.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 32, "token_count_estimate": 804, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["550000"]}}
{"id": "ae8921e1b88166c4", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 1. Introduction\nType: text\n\n- developing and developed countries , also the severe destruction by the effect of flash floods are reported ( Kuriqi and Ardiçlioğlu , 2018 ; Bui et al . , 2020b ; Munir et al . , 2020 ; Okaka , 2020 ) . On the other side , the developing countries are more affected by flash floods compared to developed countries due to the lack of proper infrastructures , essential financial assets , and technological advancement to predict the flood events or to mitigate the consequences of the floods . It is , thus , of immense importance to build up a high - performance prediction model for flood events likelihood and the susceptibility mapping of inundation regions .\n\nBangladesh, a highly flood-prone country, is positioned in the junction of three mighty Ganges-Brahmaputra-Meghna river systems in the Southeast Asian region (Rahman, 2019). Because of its unique hydro-geographic setting and low-lying flood plain area, Bangladesh experiences floods every year from June to September during the rainy season. Flash floods cause tremendous economic loss, which may be exceeded by US\\$20 million and a significant number of fatalities, damage to housing, and infrastructure, including roads and bridges (Fao and Unicef, 2017). Flash floods are frequent events in northern Bangladesh that often occur in the downstream regions, especially in the lower Teesta River basin. For example, in August 2017, a recent flash flood occurred in the lower Teesta River basin, especially in the districts of Kurigram, Lalmanirhat, and Nilphamari, resulting in a significant landmass being submerged and five individuals swept away. According to the Network for Information, Response and Preparedness Activities on disaster (NIRAPAD), a flash flood adversely affected about 6.8 million people and more than 560,000 hectares of cropland in August 2017; the total damage is up to US\\$ 10 million (Fao and Unicef, 2017). Flood Susceptibility Modeling (FSM) should be undertaken in the flood danger areas to support decision makers and emergency management in mitigating floods and maintaining risk-prone natural resources. Therefore, FSM is a valuable method to classify at-risk regions and to secure these high-risk regions and natural resources (Masks et al., 2019).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 32, "token_count_estimate": 568, "basins": ["Brahmaputra"], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "4d772f70c91a36b2", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 1. Introduction\nType: text\n\nInformation , Response and Preparedness Activities on disaster ( NIRAPAD ) , a flash flood adversely affected about 6 . 8 million people and more than 560 , 000 hectares of cropland in August 2017 ; the total damage is up to US \\ $ 10 million ( Fao and Unicef , 2017 ) . Flood Susceptibility Modeling ( FSM ) should be undertaken in the flood danger areas to support decision makers and emergency management in mitigating floods and maintaining risk - prone natural resources . Therefore , FSM is a valuable method to classify at - risk regions and to secure these high - risk regions and natural resources ( Masks et al . , 2019 ) .\n\nPrediction and development of flood's risk maps are challenging due to several factors, such as roughness heterogeneity (Ardıclıoğlu and Kuriqi, 2019) and the complexity of the river geomorphology (Kuriqi et al., 2019). Nevertheless, in recent decades, regional data were obtained from the satellite by using Remote Sensing (RS) methods coupled with Synthetic Aperture Radar (SAR) data (Li et al., 2019; Nikolaos et al., 2019; Pourghasemi et al., 2020a). Also, optical sensor images (Arora et al., 2019; Talha et al., 2019; Bui et al., 2020a) have been applied to predict flood-prone susceptible regions. The RS data, along with GIS technology, are utilized in a combined fashion to build the list of flood occurrence and several affecting parameters. Remote sensing databases combined with several statistical and mathematical techniques have been used in different research works on basin-scale FSM and flood hazard appraisals (Pradhan, 2010; Youssef et al., 2011; Pourghasemi et al., 2020b). These state-of-art techniques can handle spatial datasets and produce high resolution and prediction performances (Ma et al., 2020; Abba et al., 2020; Uthayakumar et al., 2020).\n\nMany scholars have applied several kinds of methods for developing flood susceptible models (Sahana et al., 2020; Costache et al., 2020; Talukdar et al., 2020). Therefore, many forms of flood-susceptible models can be (Chen et al., 2019a; Bui et al., 2019a; Mahmood et al., 2019; Hong et al. 2018a; Termeh et al., 2018; Siahkamari et al., 2018), like (1) analytical hierarchy process (expert based model) (Souissi et al., 2019; Dano et al., 2020; Nachappa et al., 2020), (2) statistical and bi-variate based models like frequency ratio (FR) (Chen et al.,", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 32, "token_count_estimate": 679, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c0d282f605a21fa6", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 1. Introduction\nType: text\n\n; Talukdar et al . , 2020 ) . Therefore , many forms of flood - susceptible models can be ( Chen et al . , 2019a ; Bui et al . , 2019a ; Mahmood et al . , 2019 ; Hong et al . 2018a ; Termeh et al . , 2018 ; Siahkamari et al . , 2018 ) , like ( 1 ) analytical hierarchy process ( expert based model ) ( Souissi et al . , 2019 ; Dano et al . , 2020 ; Nachappa et al . , 2020 ) , ( 2 ) statistical and bi - variate based models like frequency ratio ( FR ) ( Chen et al . ,\n\n2020a; Moghaddam et al., 2019; Khosravi et al., 2019a, Sahana et al., 2020), information value (IV model) (Xu, 2013; Chen et al., 2014), certainty factor, logistic regression (LR) (Tien Bui et al., 2019a; Shafapour Tehrany et al., 2019; Pham et al., 2020a; Ali et al., 2020), weights-of-evidence (WoE model) (Chen et al., 2019b; Paul et al., 2019), fuzzy logic (FL) (Wang et al., 2019a; Sahana and Patel, 2019), neuro fuzzy logic (ANFIS) (Termeh et al., 2018; Hong et al., 2018b) (3) machine learning algorithms (MLAs) (Bui et al., 2019b; Wang et al., 2019b; Wang et al., 2020; Shahabi et al., 2020; Dodangeh et al., 2020), and (4) hydrological models like Soil Water Assessment Tool (SWAT) (Oeurng et al., 2011) and Hydraulic Engineering Centre-River Analysis System among others (Getahun and Gebre, 2015). Machine learning methods, which many researchers use in FSMs, have recently gained more and more interest (Bui et al., 2020a; Chen et al., 2019a; Hong et al., 2018a; Wang et al., 2020). The most popular machine learning techniques are random forest (RF) (Avand et al., 2019; Paul et al., 2019; Chen et al., 2020b; Vafakhah et al., 2020; Nhu et al., 2020), decision trees (DT) (Choubin et al., 2019; Moghaddam et al., 2019; Chen et al., 2020c), support vector machine (SVM) (Termeh et al., 2018; Khosravi et al., 2019a), artificial neural networks (ANN) (Falah et al., 2019; Moghaddam et al., 2019; Pham et al., 2020b; Bui et al., 2020b), and that can forecast flash flooding in the areas at risk, FSM, however, faces numerous obstacles, such as choosing the best modeling methods from a large range of methods, and each approach yields distinct outcomes (Shafizadeh-Moghadam et al., 2018). Also, there are several drawbacks to modeling or forecasting the FSMs for any of these approaches. Consequently, these drawbacks were solved by hybrid ensemble machine learning algorithms, which demonstrated greater results than standard and single models (Pham et al., 2016, 2017a). Of late, researchers have implemented algorithms of ensemble machine learning and techniques of data mining quite extensively, such as Reptree (Chen et al., 2019a; Ghasemain et al., 2020), naive Bayes (Ali et al., 2020; Pham et al., 2020c; Tang et al., 2020), bagging (Shahabi et al., 2020; Chen et al., 2019b; Yariyan et al., 2020), random subspace (Pham et al., 2020b; Chen et al., 2019a) for high precision flood susceptibility models. The excellent results of hybrid machine learning algorithms inspire the implementation and creation of hybrid machine learning algorithms for many natural hazard models. There was, however, no universal consensus on the selection of the best tool for various forms of simulation of natural hazards, such as landslides or flood susceptibility (Chen et al., 2019b).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 32, "token_count_estimate": 1027, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1006f49e6e2aaadc", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 1. Introduction\nType: text\n\n; Tang et al . , 2020 ) , bagging ( Shahabi et al . , 2020 ; Chen et al . , 2019b ; Yariyan et al . , 2020 ) , random subspace ( Pham et al . , 2020b ; Chen et al . , 2019a ) for high precision flood susceptibility models . The excellent results of hybrid machine learning algorithms inspire the implementation and creation of hybrid machine learning algorithms for many natural hazard models . There was , however , no universal consensus on the selection of the best tool for various forms of simulation of natural hazards , such as landslides or flood susceptibility ( Chen et al . , 2019b ) .\n\nResearchers advocate designing and evaluating new methods for mapping flood susceptibility and other forms of simulation of natural hazards (Chen et al., 2019a). Therefore, in the present study, we also developed the ensemble machine learning algorithms like alternating decision tree-based random subspace and Dagging models for flood susceptibility modelling in the Teesta river basin. However, Dagging ensemble machine learning algorithms have been applied very rarely in different branches of environmental science and disaster management. Thus, we attempted to compare the performance of conventional machine learning and ensemble machine learning algorithms for building flood susceptibility models. The hybrid ensembles of the Dagging, and the RS, coupled with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) models have been applied scarcely for natural hazards modelling.\n\nThus based on concerns mentioned above, the main aim of the present study is (i) to investigate and compare the ANN, SVM, RF, RS, and Dagging model's ability to predict flood susceptibility maps, and (ii) to assess flood influencing factors for generating flood susceptibility maps in the Teesta River basin, Bangladesh. In this study, for generating the flash flood susceptibility map, the Teesta River basin, which is prone to regular flash floods was chosen as a study case. The novelty of this study lies on (i) for the first time, the mapping of flood susceptibility in the Teesta River basin of northern Bangladesh was generated more rigorously; and (ii) for the first time a new hybrid ensemble Dagging model that to best of the author's knowledge has never been utilized before was employed in modelling flood susceptibility maps. The goal of\n\nthis contribution is to apply four state-of-art models and ensemble models (RF, ANN, SVM, and RS) along with the proposed Dagging hybrid model to map flood susceptibility and distinguish flood hazard regions in the studied basin. The outcome of the study will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 32, "token_count_estimate": 706, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "6dac4e410eaf0b8e", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.1. Study area description and materials\nType: text\n\nThe basin considered in this study is positioned in the Teesta subcatchment of the northern region of Bangladesh, occupying about 2284 km² (Fig. 1), spanning over the five major districts, including Nilphamary, Lalmanirhat, Kurigram, Rangpur, and Gaibandha districts. In Bangladesh part, this basin is situated between the geographical locations of latitudes of 25°30′02′′N–26°18′37′′N, and 88°52′58′′E–89°45′ 34′′E longitudes. From the geomorphologic point of view, the floodplain region is the highest geomorphic unit in Bangladesh, and the drainage system comprises several small rivers, which are spread over elevation varying from 5 m to 110 m. For the occurrence of floods, the general slope of the river ranges of 0.47–0.55 m/km (Rahman et al., 2011), which indicates a quite flat terrain. Morphologically, the depression of the basin is shallow and in the moribund river valley, which generates changes of long and morphologically in the pathways of the river basin. This basin is susceptible to flooding. Every year, the flash floods\n\nare a common occurrence; the situation of the basin in the rainy season is worst concerning flood conditions. The hydrological characteristics of the study area are relatively complex, where the distribution of the river network is very dense in association with six rivers, including Buri-Teesta, Naotora, Ghagot, Dharla, Jamuna, Old-Brahmaputra.\n\nIn terms of geology, the Teesta basin is situated under the Bengal basin, on the Rangpur saddle of the Indian platform. The new floodplain deposits, including silt, clay, fine to medium sand, are associated with this region (Islam et al., 2014).\n\nThe subtropical monsoonal climate in association with two predominant seasons, namely, monsoon (June to September) and dry season (October to May), are the characteristics of this basin. The average annual rainfall of this basin is higher than in 1900 mm. (Akter et al., 2019), and accounts for more than 80% of the total annual rainfall mostly occurred in the monsoon season. In August 2017, the basin area was recorded to have heavy and unusual rainfall. The tropical depression occurred from 6 to 10 August 2017, which was considered as the most destructive phenomenon that happened in the study area.\n\nConsequently, the flash flood occurred, which leads to destroying a tremendous amount of farmland and more than 20,000 houses (Akter et al., 2019). The average temperature in this basin during monsoon and dry seasons is around 15°C and 35°C, respectively. Evapotranspiration has reached its maxima in April, when other climatic parameters including sunshine, wind speed are close to their maximum as well (Zaharia et al., 2020; Hampf et al., 2020). As per the population census 2011 (BBS, 2016), approximately 10.42 million inhabitants are living in this study area. The study area comprised 14% of the", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.1. Study area description and materials", "section_headings": ["2. Material and methods", "2.1. Study area description and materials"], "chunk_type": "text", "line_start": 36, "line_end": 46, "token_count_estimate": 770, "basins": ["Brahmaputra"], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "85c7a11159f5cc24", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.1. Study area description and materials\nType: figure\nFigure\n\nImage /page/2/Figure/10 description: A map of the Teesta River Basin, showing its elevation and the location of training and validation points. The map is titled \"Teesta River Basin\" and includes a scale bar from 0 to 20 km and a compass rose. The main map displays the river basin with elevations ranging from a low of 18 meters (light green) to a high of 69 meters (dark brown), as indicated by the legend. A river, represented by a light green line, meanders through the basin. Scattered across the map are numerous black triangles representing \"Training Points\" and white pentagons representing \"Validation Points\". The map is gridded with longitude lines from 88°53'E to 89°44'E and latitude lines from 25°36'N to 26°10'N. In the bottom left corner, an inset map shows the location of the study area, highlighted by a red box, within the context of surrounding countries including India, Bangladesh, Nepal, Bhutan, China, and Myanmar, with the Bay of Bengal to the south. The caption below the map reads: \"Fig. 1. The location of the study area having the training and validation flood points.\"", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.1. Study area description and materials", "section_headings": ["2. Material and methods", "2.1. Study area description and materials"], "chunk_type": "figure", "figure_caption": null, "line_start": 47, "line_end": 47, "token_count_estimate": 315, "basins": [], "subbasins": ["Teesta"], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "dce3ff4a6470c3fc", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.1. Study area description and materials\nType: text\n\nFig. 1. The location of the study area having the training and validation flood points.\n\nagricultural land to the total agricultural land of the Northern part of Bangladesh. Thus, this basin has become a lifetime for Bangladesh. The basin area has been covered with sub-urban types of settlements which are dominated by poor people. Thus, the present study area has been exposed to frequent flooding because of inadequate resources and lack of awareness.\n\nAs the research region has undergone regular flooding each year, the historical flooding inventories were also prepared based on the field survey and understanding of local residents. For flood susceptibility modeling, we used multiple data forms in the current analysis. To prepare land use land cover maps, we used Landsat and Operational Land Imager (OLI) (Path/Row: 138/42, Spatial resolution: 30 m., date:19/03/2019), obtained from the website of the United States Geological Survey (USGS). We used ASTER GDEM (Version 2) to extract topographical variables and hydrological variables (Spatial Resolution: 30 m). We collected rainfall from Bangladesh Meteorological Department (BMC), Dhaka, Bangladesh. We utilized a soil taxonomy map from the Natural Resources Conservation Service (NRCS) of the Department of Agriculture of the United States (USDA). The drainage map was generated using topographic maps (scale-1:250,00) collected from the Bangladesh Water Development Board.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.1. Study area description and materials", "section_headings": ["2. Material and methods", "2.1. Study area description and materials"], "chunk_type": "text", "line_start": 48, "line_end": 54, "token_count_estimate": 364, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1c731d31da55bd08", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology\nType: text\n\nThe methodological flow chart was prepared to show the summary of the whole work that includes flood inventory map, generation of flood conditioning factors, evaluation of the flood conditioning factors using information gain ratio and multi-collinearity test and flood susceptibility models using machine learning methods (ANN, SVM, RF, RS, and Dagging) were utilized for the present study (Fig. 2).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology", "section_headings": ["2. Material and methods", "2.2. Methodology"], "chunk_type": "text", "line_start": 56, "line_end": 58, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dfcefb63f3e7688a", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map\nType: text\n\nThe preparation of flood inventory map of the research area is the preliminary stage for creating the flood susceptibility map (Bui et al.,\n\n2020a; Sarkar and Mondal, 2020). The accessible technologies of GIS was used to achieve and investigate the normal state at the flooded sites. Since flood prediction relies on predictive analysis, where the near-future flood likelihood would be influenced by the recent past factor affecting flood, and that is why this is a critical step. 2.283 km<sup>2</sup> of the Teesta River Basin for the preparation of the flood inventory map, where 167 flood points (Fig. 1) of the Teesta River Basin were collected in this report (Plate 1). Hence, the flood points were checked from historical data sources, fieldwork, perception of local residents, and Google Earth ®. The obtained 167 flood points were then randomly divided into 80 percent (134 points) and 20 percent (33 points) categories to train and test flood-susceptible models. Flood vulnerability mapping, however, is known to be a binary classification in which the flood inventory has been categorized into two groups, such as flood points and nonflood points. Therefore, binary values such as 1 as flood points and 0 as non-flood points are needed to prepare the training flood inventory, which is considered to be the dependent variable for prediction. The flood points were the exact points where recurrent floods have been observed, while the non-flood points were the points where floods have not been registered in the last 12 years. We are expected to obtain negative samples or non-flood points, close to flood points. Several researchers suggested choosing the same number of non-flood points as the positive or flood samples to circumvent bias (Tang et al., 2020). Therefore, based on the topographic map, historical flood info, field survey and NDWI 12-year maps, we randomly selected 167 non-flood points. Non-flood points have been allocated to 0. Subsequently, we split the non-flood points arbitrarily into 80% (134 points) and 20% (33 points) categories. Thus, as training data sets, we prepared a dependent factor that included 134 flood points as 1 and 134 non-flood points as 0.\n\nSimilarly, validation datasets were also designed to test the final models, including both 33 flood and non-flood points as 1 and 0. Both the datasets for training and validation are seen in Fig. 1. Depend on the training datasets, by using the 'extract values to point' tools in", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.1. Flood inventory map"], "chunk_type": "text", "line_start": 60, "line_end": 66, "token_count_estimate": 629, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "4b33aa0b7e10723a", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map\nType: figure\nFigure\n\nImage /page/3/Figure/10 description: A flowchart illustrating a methodological approach for flood susceptibility analysis. The process is divided into three main sections: Data collection and analysis, Statistical test, and Flood susceptibility modeling.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.1. Flood inventory map"], "chunk_type": "figure", "figure_caption": null, "line_start": 67, "line_end": 67, "token_count_estimate": 99, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc2ea34e366daec8", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map\nType: text\n\n1. \\*\\*Data collection and analysis\\*\\*: This section starts with identifying 'Flood influencing factors'. Data is collected from four sources: DEM, Satellite image, Meteorological data, and River. These sources are used to derive several factors:\n \\* From DEM: Slope, Aspect, Soil type, TRI, Curvature, TWI, SPI, STI.\n \\* From Satellite image: LULC.\n \\* From Meteorological data: Rainfall.\n \\* From River: Distance to road.\n\n2. \\*\\*Statistical test\\*\\*: This section, labeled 'Selection of Flood conditioning factors', involves the 'Selection of influencing factors' using three methods: Information gain ratio, Multicollinearity diagnostics tests, and Pearson's correlation.\n\nBoth the data from the analysis and the selected factors from the statistical tests feed into a central 'Training and validation data set'.\n\n3. \\*\\*Flood susceptibility modeling\\*\\*: This section uses the training and validation data set for two parallel processes:\n \\* \\*\\*Building of flood models\\*\\*: The models listed are Artificial Neural Network, Support Vector Machine, Random forest, Random subspace, and Dagging.\n \\* \\*\\*Validation of flood susceptibility models\\*\\*: The validation metrics are ROC curves, RMSE, MAE, and R-square.\n\nThe outputs from both the model building and validation processes contribute to the final step, which is 'Flood Susceptibility Mapping'.\n\nFig. 2. Methodological approach applied for flood susceptibility analysis.\n\nArcGIS 10.5 software, we derived data from twelve flood conditioning parameters (spatial datasets). We then imported these datasets into the WEKA package (version 3.9.3), and all the modeling was performed over there.\n\nIn plate 1. field photographs of the flooding situation in the Teesta river basin representing (a,c,d) flooded road, (b) damaged houses due to flooding, (e) flooded village, (f, h) destroyed road due to devastating flooding, (g) camping on the national road by the people affected by flooding, and (i) over flow on the culvert.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.1. Flood inventory map"], "chunk_type": "text", "line_start": 68, "line_end": 90, "token_count_estimate": 583, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "f9cbf87a21a7ebdc", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\nThe construction of the susceptible spatial flood model is typically very complex and precise, as many geospatial topographical and hydrological variables are needed. Recognition of variables driving the flood is thus a critical activity, and the scientifically selected parameters will validate the accuracy of the flood susceptibility maps. Based on the available literature on flood susceptibility, in the current study area, the twelve flood influencing parameters were selected, such as elevation, curvature, aspect, slope, topographic roughness index (TRI), topographic wetness index (TWI), stream power index (SPI), sediment\n\ntransport index (STI), land use/land cover (LULC), distance to the river, soil type, and rainfall (Janizadeh et al., 2019; Paul et al., 2019; Bui et al., 2019b; Chen et al., 2019b; Sturzenegger et al., 2019; Moghaddam et al., 2019; Arabameri et al., 2019). The all influencing factors were transformed into the format of raster with 30m spatial resolution.\n\nTopographic factors are of paramount importance for modelling flood studies, which directly and indirectly influence the hydrological characteristics of the study area (Bui et al., 2020b; Arabameri et al., 2020; Lei et al., 2020). At first, a Digital Elevation Model (DEM) was prepared from the ASTER GDEM (Version 2) with 30m spatial resolution for the study basin in ArcGIS 10.2 environment. We have derived topographic factors from DEM, such as slope, curvature, aspect, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), and topographic roughness index (TRI) in ArcGIS environment.\n\n*2.2.2.1. Elevation.* The elevation is the most vital factor affecting modelling floods (Dodangeh et al., 2020; Bui et al., 2020b). The flooding is inversely related to the elevation, Higher the elevation, lower the chances", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 92, "line_end": 100, "token_count_estimate": 518, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "33d16f340743fd16", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: figure\nFigure\n\nImage /page/4/Figure/9 description: A figure, labeled Fig. 3, displays four maps of the same geographical region, illustrating data layers for flood susceptibility mapping. The maps are labeled a, b, c, and d.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 101, "line_end": 101, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f722aa2b392f7584", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\nMap (a) shows Elevation in meters (m). The color scale ranges from red for high elevation (69 m) to green for low elevation (18 m). A legend also indicates that a black dot represents a flooding point.\n\nMap (b) shows Curvature. The color scale ranges from green for high curvature (0.824) to red for low curvature (0.327).\n\nMap (c) shows Aspect (Direction). A detailed legend provides color codes for different directions: Flat (-1) is grey, North (0-22.5 and 337.5-360) is red, Northeast (22.5-67.5) is orange, East (67.5-112.5) is yellow, Southeast (112.5-157.5) is light green, South (157.5-202.5) is light blue, Southwest (202.5-247.5) is medium blue, West (247.5-292.5) is purple, and Northwest (292.5-337.5) is dark purple.\n\nMap (d) shows Slope in degrees (°). The color scale ranges from blue for high slope (5.75°) to red for low slope (0°).\n\nA scale bar at the bottom of the figure indicates a length of 80 kilometers.\n\nFig. 3. Data layers for flood susceptibility mapping (a) elevation, (b) curvature, (c) aspect and (d) slope\n\nof occurrences of flooding, and vice versa (Chen et al., 2020d). Hence, the Teesta river basin is situated in a flat and lower elevated region. Consequently, the chances of flooding are frequent in the entire region (Basin, 2017) (Fig. 3a).\n\n2.2.2.2. Curvature. As the curvature affects the flooding water budget, it is thus, used for the modelling of flood susceptibility (Ahmadlou et al., 2019). Consequently, The curvature distinguishes the divergent and convergent runoff regions (Torcivia and López, 2020). The runoff convergence operation is related to the negative value regions (Costache and Bui, 2020; Rau et al., 2019). These areas are highly vulnerable to flash-flooding (Fig. 3b).\n\n2.2.2.3. Aspect. Aspect, another factor, affects the directions of flooded water flows, along with which maintains the soil humidity (Chu et al., 2020). The aspect is, thus, indirectly affecting the flooding. For example, the shaded slope area, where the humidity of soil is relatively high and with extreme runoff (Fig. 3c).\n\n2.2.2.4. Slope. The other important factor for influencing the flood is the slope, which controls the flowing water's (flooded) speed (Stevaux et al., 2020). The higher the slope angle, the lower the chances of water stagnation, a lower rate of infiltration, and the higher the flow\n\nvelocity. Therefore, the lower and flat regions, like the Teesta river basin, will have more chances of flooding (Fig. 3d).\n\n2.2.2.5. Topographic roughness index. Topographic roughness index (TRI) is one of the major influencing factors for flood events. It depends on the local topographic of the study basin. The occurrence of higher flood probability is associated with the lower TRI value. In the present study, the TRI map was kept in stretch format, and the value ranges between 0-27 (Fig. 4a).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 102, "line_end": 132, "token_count_estimate": 830, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "6c3a871a9137e95a", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\nlower rate of infiltration , and the higher the flow velocity . Therefore , the lower and flat regions , like the Teesta river basin , will have more chances of flooding ( Fig . 3d ) . 2 . 2 . 2 . 5 . Topographic roughness index . Topographic roughness index ( TRI ) is one of the major influencing factors for flood events . It depends on the local topographic of the study basin . The occurrence of higher flood probability is associated with the lower TRI value . In the present study , the TRI map was kept in stretch format , and the value ranges between 0 - 27 ( Fig . 4a ) .\n\n2.2.2.6. Topographic wetness index. For the occurrence of flood, Topographic Wetness Index (TWI), is an effective factor (Abdel Hamid et al., 2020), by which the difference in the wetness of a basin was spatially expressed (Meles et al., 2020). This index shows the amount of water contained in every pixel size of the region. (Zhang et al., 2020), and that is computed using the Eq. (1):\n\n$$TWI = \\frac{\\ln{(A_s)}}{\\tan{\\beta}} \\tag{1}$$\n\nThe specific catchment area  $(m^2m^{-1})$  and the slope gradient (in degrees) are indicated by  $A_s$  and  $\\beta$ , respectively. In general, the high TWI values and flooding are strongly correlated with each other (Shit et al.,", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 102, "line_end": 132, "token_count_estimate": 421, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "362b5fb429e499c5", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: figure\nFigure\n\nImage /page/5/Figure/11 description: A figure displaying four maps of the same geographical region, labeled a, b, c, and d, each illustrating a different topographical index. A scale bar at the bottom indicates distances from 0 to 100 km.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 133, "line_end": 133, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6171bf6f1dee3f4d", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\nMap (a) shows the Topographic Roughness Index (TRI). It is a pixelated map with colors ranging from green for low values to red for high values. The legend indicates a range from a low of 0 to a high of 27.\n\nMap (b) shows the Topographic Wetness Index (TWI). This is a higher resolution map with colors from green (low) to red (high). The legend shows a range from a low of -1.54 to a high of 7.72.\n\nMap (c) shows the Stream Power Index (SPI). The entire region is colored a uniform brownish-yellow. The legend has a color gradient from green to yellow, labeled 'Low' and 'High' respectively, without numerical values.\n\nMap (d) shows the Stream Transport Index (STI). This map highlights a network of streams in shades of yellow and red against a dark green background. The legend indicates a range from a low of 0 to a high of 140.64.\n\nFig. 4. Data layers for flood susceptibility mapping (e) topographic roughness index, (f) topographic wetness index, (g) Stream power index, (h) stream transport index.\n\n2020). The TWI value in the study area ranges from -1.54 to 7.72 (Fig. 4b).\n\n2.2.2.7. Stream power index. Stream power index (SPI) has an essential influence on the fluvial system (Knighton, 1999). The SPI is computed using Eq. (2).\n\n$$SPI = A_s tan\\beta \\tag{2}$$\n\nWhere,  $A_s$  and the slope gradient represent the specific catchment area is indicated by  $\\beta$  (radians) (Wu et al., 2020a). The sediment transport capacity and the erodibility of its bed is referred to as total SPI (Chen et al., 2020c).\n\n2.2.2.8. Sediment transport index. Sediment transport index (STI), another factor for causing flooding, can increase the frequency of flood, which upshots in the damage of foundation (Wang et al., 2019c). The STI is derived from DEM using Eq. (3).\n\n$$STI = \\left(\\frac{A_s}{22.13}\\right)^{0.6} \\left(\\frac{sin\\beta}{0.0896}\\right)^{1.3} \\tag{3}$$\n\nwhere, each pixel of the slope is represented by  $\\beta$ , and the area of upstream is indicated as  $A_s$  (Wang et al., 2019c). Based on hydro-climatic and geomorphologic attributes of the basin area, the STI is calculated\n\n(Shukla and Gedam, 2019). The channel's bed changes with the deposition of sediment, which reduced the capability of storing water within the channel and caused flooding (Antoniazza et al., 2019). In the present study, the STI value ranges from 0 to 140.64 (Fig. 4d).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 134, "line_end": 164, "token_count_estimate": 741, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f4598af22fa4b09e", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\n{ 3 } $ $ where , each pixel of the slope is represented by $ \\ beta $ , and the area of upstream is indicated as $ A_s $ ( Wang et al . , 2019c ) . Based on hydro - climatic and geomorphologic attributes of the basin area , the STI is calculated ( Shukla and Gedam , 2019 ) . The channel ' s bed changes with the deposition of sediment , which reduced the capability of storing water within the channel and caused flooding ( Antoniazza et al . , 2019 ) . In the present study , the STI value ranges from 0 to 140 . 64 ( Fig . 4d ) .\n\n2.2.2.9. Land use/Land cover. LULC influences the runoff of the surface and sediment transportation and, consequently, directly affects the frequency of floods (Benito et al., 2010). Because the LULC directly controls the surface runoff generation and infiltration. The flooding is more frequent in the built-up area as these regions do not allow to infiltrate water and generate surface water. In contrast, the forest area allows infiltrating water that causes less flooding (Abdel Hamid et al., 2020). The relationship between flood events and the density of vegetation is inverse when it compares hydrological responses at various temporal scales (García-Ruiz et al., 2008; Dodangeh et al., 2020). In the present study, we have prepared a LULC map from Landsat and OLI image (path/row: 138/43; spatial resolution: 30 m) using an Artificial neural network in ENVI software (version 5.3). We classified six classes in the LULC map, such as vegetation, bare land, built-up area, sand bar, agricultural land, and water body (Fig. 5a).\n\n*2.2.2.10. Distance to the river.* Most affected flood-inundation areas are usually located adjacent to the river. The distance from the river is an essential conditioning factor for identifying the regions which are", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 134, "line_end": 164, "token_count_estimate": 513, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b6d77ec90f5e581", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: figure\nFigure\n\nImage /page/6/Figure/12 description: The image displays four maps of the same geographical region, labeled a, b, c, and d, each illustrating a different environmental factor. A scale bar at the bottom indicates a range from 0 to 80 km.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 165, "line_end": 165, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24d0d28eaa88a9ad", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\nMap (a) is titled \"Land use land cover\" and shows the distribution of different land types. The legend includes: Vegetation (dark green), Bare land (light blue), Built up (red), Sand bar (gray), Agricultural land (orange), and Water body (dark blue). The map shows a river system running through the region, surrounded by agricultural land, sand bars, and built-up areas, with large expanses of vegetation further away.\n\nMap (b) is titled \"Distance to river (m)\" and uses a grayscale gradient to show proximity to the river. Black represents a low distance of 0 m, while white represents a high distance of 1503 m. The river channels are clearly delineated in black.\n\nMap (c) is titled \"Soil types (USDA)\" and uses a color-coded system to display different soil classifications. The legend includes: Water (dark green), Usterts (medium green), Aquults (light green), Humults (lighter green), Udults (even lighter green), Ustults (yellow-green), Aqualfs (yellow), Ustalfs (light orange), Ochrepts (medium orange), Aquepts (darker orange), and Psamments (darkest orange).\n\nMap (d) is titled \"Rainfall (mm)\" and shows rainfall distribution using a color gradient. The legend indicates that red represents high rainfall (550.411 mm) and green represents low rainfall (361.066 mm). The map shows higher rainfall in the northwest, gradually decreasing towards the southeast.\n\nFig. 5. Data layers for flood susceptibility mapping (a) land use, land cover, (b) distance to river, (c) soil types, and (d) rainfall.\n\nvulnerable to flooding of a basin (Tehrany et al., 2015) because the distance controls the incident of flooding and the river flow to river factor (Gupta, 2020). The higher the distance to the river is, the lower the likelihood of flood events will be (Binh et al., 2020). At the basin scale, the regional level flooding is related to the storage of terrestrial water (Deng et al., 2020; Frappart et al., 2019). In the present study, we have prepared the distance to the river map from the topographic map having a scale of 1: 50,000 and Google Earth (Fig. 5b).\n\n2.2.2.11. Soil types. One of the vital influencing factors which control the mechanism of rainfall-runoff is soil types (Xie et al., 2019). The soil properties directly control the water infiltration that influences the rainfall-runoff generation, although the other factors like local weather conditions, erosion process control rainfall-runoff (Flügel, 1995; Phillips et al., 2019). The higher the rate of infiltration, the lower the chances of flooding occurrences. In the present study, we have used the United States Geological Survey (USDA) soil map and classified it into 12 classes based on USDA soil taxonomy (Fig. 5c).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 166, "line_end": 182, "token_count_estimate": 768, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "59fd3328d4fb44f5", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors\nType: text\n\nOne of the vital influencing factors which control the mechanism of rainfall - runoff is soil types ( Xie et al . , 2019 ) . The soil properties directly control the water infiltration that influences the rainfall - runoff generation , although the other factors like local weather conditions , erosion process control rainfall - runoff ( Flügel , 1995 ; Phillips et al . , 2019 ) . The higher the rate of infiltration , the lower the chances of flooding occurrences . In the present study , we have used the United States Geological Survey ( USDA ) soil map and classified it into 12 classes based on USDA soil taxonomy ( Fig . 5c ) .\n\n2.2.2.12. Rainfall. Rainfall has been chosen as one of the major influencing factors for the occurrence of flooding (Pourghasemi et al., 2020a). Because the high-intensity rainfall within a short period can cause flooding (Aristizábal et al., 2020; Lu et al., 2020; Peptenatu et al., 2020). We have obtained rainfall data from four meteorological stations of Bangladesh and employed the kriging interpolation method for preparing rainfall maps in ArcGIS 10.3 environment. We used the kriging method for interpolation because we have data only for four points, and this method is highly suggested when the amount of data is very less (Kourgialas and Karatzas, 2011). However, the annual rainfall ranges from 361 to 550mm in the study area (Fig. 5d).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.2. Methodology > 2.2.2. Methods for preparing flood conditioning factors", "section_headings": ["2. Material and methods", "2.2. Methodology", "2.2.2. Methods for preparing flood conditioning factors"], "chunk_type": "text", "line_start": 166, "line_end": 182, "token_count_estimate": 401, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71c8d9d31d793e11", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.3. Method for flood influencing factors using Information gain ratio and multicollinearity test\nType: text\n\nPrimarily, the evaluation of the flood influencing parameter's importance or the ability for flooding is critical before the training and validation checking of the model (Sameen et al., 2020). The quantification of the importance of each collected parameter has been derived based on their statistical properties and correlation with the flooding. To identify the influential parameters for the prediction of FSMs, the Information Gain Ratio (InGR) technique (Bui et al., 2020a) has been utilized. For measuring the importance of every influencing factor, an InGR value is allocated. The higher InGR values are associated with, the more significant influencing factor. The InGR model is chosen to be employed in the present study due to its simplicity and efficacy and calculated using Eq. (4).\n\n$$\\begin{aligned} \\text{Gain ratio}(x,Z) = & \\frac{\\text{Entropy }(Z) - \\sum_{1}^{n} \\sum_{\\substack{i=1 \\ |Z|}}^{n} \\frac{|Z_i|}{|Z|} \\text{ Entropy }(Z_i)}{-\\sum_{i=1}^{n} \\frac{|Z_i|}{|Z|} \\log \\frac{|Z_i|}{|Z|}} \\end{aligned} \\tag{4}$$\n\nwhere from the training point Z with  $Z_i 1 = 1, 2, 3, .... n$  subsets, the attribute x is belonging.\n\nSeveral multi-collinearity tests, such as correlation coefficients of Pearson (Xu and Li, 2020), variance decomposition proportions (Zhang et al., 2019), conditional index (Al-Quraishi et al., 2020), and Variance Inflation Factors (VIF) and tolerances (Javidan et al., 2020) have been used to quantify influencing factors for all probability models. In this study, we have utilized the Pearson's correlation coefficient and VIF for quantifying the importance of twelve flood conditioning factors. The VIF >9 and very low correlation shows the problem of multicollinearity in the factors (Duque and Aquino, 2020). It is, thus, highly recommended to exclude the conditioning factor for modelling, which has the VIF >9.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.3. Method for flood influencing factors using Information gain ratio and multicollinearity test", "section_headings": ["2. Material and methods", "2.3. Method for flood influencing factors using Information gain ratio and multicollinearity test"], "chunk_type": "text", "line_start": 184, "line_end": 192, "token_count_estimate": 625, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "370c85f0dd1eb5f8", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.1. Artificial Neural Networks (ANN)\nType: text\n\nIn the present study, with a Back-Propagation (BP) and errorcorrection learning algorithm, the ANN model having three layers (input, hidden and output layers) has been used and successfully employed in the flood susceptibility modelling (Bui et al., 2019b; Zhao et al., 2020). In this study, for the input layers and ten hidden nodes, which are identical with the numbers of the critical parameters were set (Table 1). On the other hand, one node has been employed for the output layer, which is coded as 1 for flood events and 0 for non-flood. Many algorithms are available for the training of an ANN model; however, the most popular one is BP. Thus, for estimating the nonlinear relationship between the critical parameters and the events of the flood, the BP based ANN model has been applied (Dodangeh et al., 2020). Firstly, the initial weights are chosen randomly by BP. The comparison between computed and observed values have taken place. The differences between computed and observed value can be termed as an error. It has been evaluated by several error measures techniques like mean squared error (MSE), root mean square error (RMSE) (Hou et al., 2020). Based on a rule of generalized delta (Rumelhart et al., 1986), the adaptation of initial weights occurs to allocate the total error among the neurons in the network (Pijanowski et al., 2002). The iteration of this process occurs, while the level of error is at a lower level.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.1. Artificial Neural Networks (ANN)", "section_headings": ["2. Material and methods", "2.4. Methods for flood susceptibility modelling", "2.4.1. Artificial Neural Networks (ANN)"], "chunk_type": "text", "line_start": 196, "line_end": 198, "token_count_estimate": 418, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "170d6df391fb49f1", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.2. Support Vector Machine (SVM)\nType: text\n\nA widely popular machine learning model is the SVM, along with a set of linear indicator functions, which has been employed for the issues of function determination, is introduced by Vapnik (2013). For the data transformation in the SVM model, the function of the kernel mathematical system has been used. By using the training datasets, a hyper-plane has been generated, when the conversion from actual SVM datasets to high dimensional feature space has occurred (Choi et al. 2020). For the differentiation of the actual output space, the best linear hyper-plane has been applied. It also used to categorize the data into two classes, such as the susceptibility of non-flood and flood {0, 1}. The capability of the SVM model mainly depends on the suitable kernel functions, e.g., polynomial kernel (PL), sigmoid kernel (SIG), radial basis function (RBF) and linear kernel (LN). Several studies (Tien Bui et al., 2012; Tehrany et al., 2015) reported that the RBF outweighs among different kernel functions for flood susceptibility modelling, which is selected as a benchmark kernel function. Due to the flexibility of radial basis kernel to deal with various dimensionalities of the dataset and its better generalization ability, it has been applied mainly for modelling the flood susceptibility (Chen et al., 2020a; Yang and Cervone, 2019). The significant limitations of modelling with SVM are commonly related to its difficulty in capturing vital parameters (Choubin et al., 2019).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.2. Support Vector Machine (SVM)", "section_headings": ["2. Material and methods", "2.4. Methods for flood susceptibility modelling", "2.4.2. Support Vector Machine (SVM)"], "chunk_type": "text", "line_start": 200, "line_end": 204, "token_count_estimate": 425, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36851ae3c24ac67e", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.2. Support Vector Machine (SVM)\nType: table\nTable: Table 1 The parameters of the machine learning algorithm used for flood susceptibility modelling\n\n| Model name                   | Description of parameters                                                                                                                                                        |\n|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Artificial neural network | Hidden layer-6, learning rate-0.28, momentum-0.17, seed-4, training time-500, validation threshold-20, Normal to binary filter-TRUE                                              |\n| Random forest                | Batch size-100, seed-5, number of iteration-150, max depth-2, calc out of bag-TRUE, Compute attribute importance-TRUE                                                            |\n| Random subspace              | Classifier-J48, max depth- -1, minimum number-2, minimum proportion of the variance-0.002, seed-5, numbers of executions slots-1, number of iteration-20, subspace size-0.45     |\n| Support vector machine    | Complexity parameter-1, epsilon-1.0E-12, kernel- radial basis function, random seed-5, number of fold- -1, tolerance parameter-0.001, check turned off-True, Calibrator-Logistic |\n| Dagging                      | Classifier-M5P, max depth- -1, minimum number-2, a minimum proportion of the variance-0.001, seed-3, numbers of fold-10, verbose-TRUE                                            |", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.2. Support Vector Machine (SVM)", "section_headings": ["2. Material and methods", "2.4. Methods for flood susceptibility modelling", "2.4.2. Support Vector Machine (SVM)"], "chunk_type": "table", "table_caption": "Table 1 The parameters of the machine learning algorithm used for flood susceptibility modelling", "columns": ["Model name", "Description of parameters"], "table_row_start": 1, "table_row_end": 5, "line_start": 205, "line_end": 211, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3f968c28772f792", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.3. Random forest\nType: text\n\nThe Random forest (RF) approach, is the aggregate of decision trees for classification or regression to predict, was pioneered by Breiman (2001). It is a popular ensemble-learning algorithm (Rahman et al., 2019). The RF is divided into two subsets; Breiman's \"random subspace random subspace random subspace random subspace\" idea is the first one, and the second is Ho's \"random selection features. The Random subspace is an ensemble machine learning process, which utilizes and improves the accurate prediction of a weak classifier by the creation of a set of classifiers. The RF generates various sets of samples by sampling with replacement and creates multiple matching regression tree training phases and then estimates the data classification based on the voting outcomes of multiple classifiers. In the end, a classification with a considerable number of votes over all the trees in the forest has been found and selected by RF.\n\nIn the training phase of the regression tree, the rules based on the response parameters were developed to categorize the observation datasets until the prediction has the lowest possible node deviation. One of the significant problems with regression trees is that it tends to outfit the training detests and, thus, performs inadequately when given an uncertain dataset (Criminisi and Shotton, 2013). RF is a feasible way to overcome this weakness. When each regression tree has been trained in the RF algorithm, a portion of the input records and predictor parameters have been randomly chosen as input to the training phase. A set of regression trees has been built after multiple sampling practices, and each set of regression trees is only a training outcome for a randomly chosen subset. It is not advisable to apply a full sample to train the decision trees, because the full sample training neglects the laws of local samples. In the case of flood susceptibility to make the comparison with the outcomes of a new hybrid model, the model which is applied as one of the benchmark models is the RF model.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.3. Random forest", "section_headings": ["2. Material and methods", "2.4. Methods for flood susceptibility modelling", "2.4.3. Random forest"], "chunk_type": "text", "line_start": 214, "line_end": 218, "token_count_estimate": 528, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "984579127b709215", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.4. Random subspace\nType: text\n\nHo (1989) introduced the Random subspace model as a new ensemble machine learning algorithm for solving environmental problems. On modified feature space, the several classifiers of this model are integrated and trained. For the classifiers, which are training base, this model creates several training subsets (Ho, 1998). Unlike other ensemble models, it uses several samples on feature space, rather than the instance space (Havlíček et al., 2019). This method gets the benefit from the bootstrapping and the aggregation. The training set x, the base classifier w, the number of subspaces L, and the number of subspaces is the four inputs of RS (Luo et al., 2019). The L subsets of ds features have been produced randomly and gathered in subspace, in RS. The base classifier has been trained for generating L different classifiers in subspace on each of the subsets. For producing an ensemble classifier E, these classifiers are combined. In Kuncheva et al. (2010), a detailed description of the algorithm is given. For avoiding the problems regarding over-fitting and to deal with the datasets with the most unnecessary feature, it is highly recommended to use this method (Pham et al., 2017b).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.4. Random subspace", "section_headings": ["2. Material and methods", "2.4. Methods for flood susceptibility modelling", "2.4.4. Random subspace"], "chunk_type": "text", "line_start": 220, "line_end": 222, "token_count_estimate": 335, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "37379d76dd4e8f91", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.5. Dagging\nType: text\n\nDagging was first pioneered by Ting and Witten (1977), which is a renowned re-sampling ensemble model applying most votes for the integration of different classifiers to get better precision in prediction for the base classifiers (Zhao et al., 2013; Walia and Kumar, 2019). The application of Dagging ensemble technique in the field of flood susceptibility is rare. However, this model has been broadly employed in the fields of medicine (Hosni et al., 2019), banking (Sawant and Bhurchandi, 2019), and recently, in landslide susceptibility modelling (Pham et al., 2017a). Instead of creating the bootstrap samples to obtain the base classifier, it creates several separate samples. It is considered a\n\npromising machine learning algorithm for classification in recent decades. The Dagging ensemble model has been applied for solving numerous problems regarding classification in the case of the real world. For a particular training dataset including N samples, the creation of an M dataset can occur and come from the actual training datasets. Every dataset has several n (n<N) samples that are unique from each other. In Dagging, the technique of sampling which is utilized in this. The samples are not replaced in the actual training datasets and can be selected as a part of a specific dataset where the number of sample datasets is increased. Accordingly, on every sample dataset, a base classifier was built. In the end, several classifiers will be gained based on the training dataset.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.4. Methods for flood susceptibility modelling > 2.4.5. Dagging", "section_headings": ["2. Material and methods", "2.4. Methods for flood susceptibility modelling", "2.4.5. Dagging"], "chunk_type": "text", "line_start": 224, "line_end": 228, "token_count_estimate": 400, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f314ae349b3e5b45", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.5. Precision appraisal\nType: text\n\nEither a positive or negative pixel has been included in each class of flood susceptibility mapping classification. The real positives and true negatives are referred to as those positive (flood) and negative (nonflood) pixels, which are divided accurately into positive or negative classes. In contrast to this, false positives and false negatives are referring to those positive and negative points which have been mistakenly divided (Schumann et al., 2014). The area has explained the precision of two classes in a receiver operating curve (ROC) graph under the ROC curve (AUC) value. This method has been employed for validating the five flood susceptible models. Hence, by presenting the 1-specificity on X-axis against sensitivity on Y-axis, the ROC curve has been plotted. The sensitivity indicates the sum of the pixels accurately divided as the pixels of the flood. Also, the 1-specificity represents the several non-flood pixels accurately divided, by utilizing the following Eq. (5) the value of AUC can be calculated (Tien Bui et al., 2018).\n\n$$AUROC = \\frac{\\dot{a} TP + \\dot{a} TN}{P + N}$$\n (5)\n\nWhere the AUC has been calculated to appraise the model's overall performance when categorizing indefinite data, in general, AUC varying from 0.5 to 0.6 donates an incompetent model. In contrast, the AUC value between 0.6 and 0.7 indicates a poor performance model. Along with it, a model with 0.7 to 0.8 AUC value demonstrates moderate performance. When the AUC value greater than 0.8 indicates a high fitness model with the dataset (Sajedi-Hosseini et al., 2018). In addition to the AUC value, the root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) was applied for evaluation and comparison of model outcomes in this study.\n\nTo appraise the accuracy of flood susceptibility modelling, several non-parametric statistics have been applied in this study. Any statistical inference is not required for non-parametric models (Derrac et al., 2011). The non-parametric tests, such as the Friedman test and paired t-test (Friedman, 1937) were adopted when the distribution of the data was normal (Martínez-Álvarez et al., 2013). These tests have been employed to compare the significant differences between two or more models (Beasley and Zumbo, 2003). At the 5% (p<0.5) (null hypothesis) significance level, the flood model's performances are similar, and this is the first inference for running this test. The outcomes were not applied, in the case of true p-values for all the models in the Friedman test were >0.5 (Bui et al., 2018).\n\nThe Wilcoxon signed-rank test was conducted to verify the flood models by evaluating the statistical significance of systematic pairwise differences among the models. In the case of the five susceptibility models, the p-value and the z-value were utilized to evaluate the significance of differences among them to make sure of the statistical significance. The null hypothesis will be rejected if the value of p is < 5%, and the value of z is more than the critical values of z (+1.96 and -1.96).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.5. Precision appraisal", "section_headings": ["2. Material and methods", "2.5. Precision appraisal"], "chunk_type": "text", "line_start": 230, "line_end": 241, "token_count_estimate": 827, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a12ceaeb31f7b9db", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.5. Precision appraisal\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A vertical bar chart showing the 'Average Merit' for different 'Flood conditioning parameters'. The y-axis, labeled 'Average Merit', ranges from -0.1 to 0.6. The x-axis, labeled 'Flood conditioning parameters', lists ten categories: LULC, Distance to road, Elevation, Slope, TRI, SPI, STI, Curvature, TWI, and Aspect. Each bar is blue and includes a black error bar. The bar for LULC is the tallest, with an average merit of approximately 0.52. The next three bars, 'Distance to road', 'Elevation', and 'Slope', have similar heights, with average merits of about 0.12, 0.11, and 0.11, respectively. The remaining parameters have much lower average merits, all below 0.05: TRI (approx. 0.03), SPI (approx. 0.03), STI (approx. 0.015), Curvature (approx. 0.01), TWI (approx. 0.01), and Aspect (approx. 0.005).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.5. Precision appraisal", "section_headings": ["2. Material and methods", "2.5. Precision appraisal"], "chunk_type": "figure", "figure_caption": null, "line_start": 242, "line_end": 242, "token_count_estimate": 299, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cce9540176753c4a", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 2. Material and methods > 2.5. Precision appraisal\nType: text\n\nFig. 6. The importance of flood conditioning factors using the information gain ratio, the standard error bars show the accuracy of the results.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "2. Material and methods > 2.5. Precision appraisal", "section_headings": ["2. Material and methods", "2.5. Precision appraisal"], "chunk_type": "text", "line_start": 243, "line_end": 245, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b9db113c1d17e630", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.1. The influential factors in modelling flood susceptibility by InGR and multicollinearity tests\nType: text\n\nThe analysis result is presented in Fig. 6, where the values of the InGR of each parameter are calculated using a 10-fold cross-validation technique. The findings of InGR showed that the LULC (0.52), DR (0.12), elevation (0.11), and slope (0.495) are the more significant with the high InGR values as compared to other factors of influence. Total roughness index TRI (0.03), stream power index SPI (0.03), and sediment transport index STI (0.02) demonstrate moderate relevancy. Similarly, the total wetness index, TWI (0.01), and curvature (0.01) show low relevance. It is noteworthy; the aspect factor exhibits the value of InGR= 0.00; therefore, it is a less influencing factor for predicting FSMs.\n\nThe studied river basin is predominated by agricultural land near the main river. In contrast, the vegetation occupies 55% of the total area features. For the flash floods incident, these features are most favourable (lcyimpaye, 2018). Also, for the occurrence of floods, the most recognized significant features are the elevation and slope. This is much found and explained in the works of Tien Bui et al. (2019b). The uneven topography with lower elevation and curvatures in convergent pattern and low slope angle causes a higher chance of flooding in the study area. However, for the occurrence of flood, there is no predictive value in case of an aspect due to the flooding point distribution in the directions of the slope is flat and smooth.\n\nThe outcomes of multicollinearity diagnostics tests (Table 2) reveal that elevation with the largest VIF (2.71), followed by rainfall, STI, SPI, slope, soil type, distance to the river, TRI, TWI, LULC, curvature, and aspect respectively. The results also indicate that there is no collinearity problem among 12 potential factors for involving flood susceptible models. Additionally, Pearson's correlation between flooding and its\n\npotential influencing factors are presented in Table 3. According to the prior research (Douglas et al., 2000), the probability of flood occurrence is increasing with higher correlation values. The critical potential factors for the occurrence of the flood were distance to the river, followed by elevation, soil type, rainfall, aspect, curvature, TWI, LULC, slope, SPI, STI, TWI. In this immediate area of study, the majority of floods occurred where the distance to the river is low (0-100) m, the soil type is aquafssol, the elevation is 18–69 m, the curvature is >0.327, land cover class have bare land, SPI is high, STI is high, rainfall ranges from 361 to 550 mm, the slope belonging from 0–5°, TWI belonging from – 1.54 to 7.72, TRI is high, and the aspect is flat.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.1. The influential factors in modelling flood susceptibility by InGR and multicollinearity tests", "section_headings": ["3. Results", "3.1. The influential factors in modelling flood susceptibility by InGR and multicollinearity tests"], "chunk_type": "text", "line_start": 249, "line_end": 257, "token_count_estimate": 724, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "103568c278ecbf27", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.2. The analysis of flood susceptible models\nType: text\n\nThe five machine learning models, such as ANN, SVM, RF, subspace random, and Dagging, were employed to compute the maps of flood susceptibility for each pixel in the basin. The Dagging model has been proved as the high-performance model of prediction among all the benchmark models for the geospatial datasets obtained based on the above results of the experiment. Several techniques, such as natural break, equal interval, quantile, regular interval, standard deviation, and manual technique, are available for reclassifying the flood susceptible models in the ArcGIS 10.3. Among these techniques, quantile and natural break methods have been widely reported in the available literature of flood susceptibility studies (Tehrany et al., 2015; Tien Bui et al., 2019b). The quantile is one of the famous reclassifying techniques which is able to provide better outcomes in comparison to other reclassifying techniques, and it has been mostly used for reclassifying the flood susceptible maps (Tehrany et al., 2015). For this reason, in this present study, the quantile reclassifying approach was selected. The flood susceptibility models were reclassified into five classes such as very low, low, moderate, high and very high (Fig. 7).", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.2. The analysis of flood susceptible models", "section_headings": ["3. Results", "3.2. The analysis of flood susceptible models"], "chunk_type": "text", "line_start": 259, "line_end": 263, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a2cbc050eae65c24", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.2. The analysis of flood susceptible models\nType: table\nTable: Table 2 The evaluation of the flood conditioning factors using a multi-collinearity test (VIF).\n\n| Parameters                      | Elevation | Aspect | Curvature | Slope   | Topographic roughness index | Topographic wetness index | Stream power index | Sediment transport index | Soil types | Distance to river | Rainfall | Land use land cover |\n|---------------------------------|-----------|--------|-----------|---------|-----------------------------------|---------------------------------|--------------------------|--------------------------------|------------|----------------------|----------|---------------------------|\n| Multicollinearity test (VIF) | 2.7128    | 1.0786 | 1.08072   | 1.33623 | 1.196                             | 1.1935                          | 1.56453                  | 1.6207                         | 1.31539    | 1.29923              | 2.67087  | 1.10026                   |", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.2. The analysis of flood susceptible models", "section_headings": ["3. Results", "3.2. The analysis of flood susceptible models"], "chunk_type": "table", "table_caption": "Table 2 The evaluation of the flood conditioning factors using a multi-collinearity test (VIF).", "columns": ["Parameters", "Elevation", "Aspect", "Curvature", "Slope", "Topographic roughness index", "Topographic wetness index", "Stream power index", "Sediment transport index", "Soil types", "Distance to river", "Rainfall", "Land use land cover"], "table_row_start": 1, "table_row_end": 1, "line_start": 264, "line_end": 266, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["08072", "10026", "29923", "31539", "33623", "56453", "67087"]}}
{"id": "73e3000a5faa570f", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.2. The analysis of flood susceptible models\nType: table\nTable: Table 3 The Pearson's correlation coefficient between flooding and its potential factors.\n\n| Influential factors | Distance to river | Elevation | Soil types | Rainfall | Aspect | Curvature | Topographic wetness index | LULC   | Slope  | Stream power index | Sediment transport index | Topographic roughness index |\n|------------------------|----------------------|-----------|---------------|----------|--------|-----------|------------------------------|--------|--------|-----------------------|-----------------------------|--------------------------------|\n| Correlation (Pearson)  | 0.6024               | 0.4519    | 0.4429        | 0.3463   | 0.2325 | 0.1417    | 0.0947                       | 0.0662 | 0.0435 | 0.0359                | 0.0289                      | 0.0169                         |", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.2. The analysis of flood susceptible models", "section_headings": ["3. Results", "3.2. The analysis of flood susceptible models"], "chunk_type": "table", "table_caption": "Table 3 The Pearson's correlation coefficient between flooding and its potential factors.", "columns": ["Influential factors", "Distance to river", "Elevation", "Soil types", "Rainfall", "Aspect", "Curvature", "Topographic wetness index", "LULC", "Slope", "Stream power index", "Sediment transport index", "Topographic roughness index"], "table_row_start": 1, "table_row_end": 1, "line_start": 270, "line_end": 272, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c6d516681478d2f", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.2. The analysis of flood susceptible models\nType: text\n\nThe five flood susceptible models having five classes have been represented in Fig. 7. The findings showed that, in the case of Dagging model, the smallest area percentage (14.03%) comes under the very high class, followed by low (14.89%), high (15.61%), moderate (23.78%), and very low (31.70.42%) classes. The percentages of area are 30.20%, 16.22%, 13.32%, 16.88%, and 23.39 % for the very low, low, moderate, high, and very high classes, respectively, in the case of the SVM model. In the case of RS, RF, and ANN models, the area percentages for very high flood susceptible classes are 21.70%, 29.93%, and 33% (Fig. 8). The very high flood susceptibility class has occupied the highest area (1160 km<sup>2</sup>; indicated by red colour), and the very low flood susceptible class has occupied the smallest area (510 km<sup>2</sup>; indicated by dark green) in case of Dagging hybrid model. In the case of the SVM model, the maximum area (1120 km<sup>2</sup>) has come under the very high flood susceptible class, while the moderate flood susceptible class has covered the minimum area (500 km<sup>2</sup>). Out of the total area 1020, 1090, and 90 km<sup>2</sup> areas have come under the very high flood susceptibility class of the RS, RF, and ANN models respectively. In the class of low flood susceptibility, the areas are 780, 1130 and 1230 km<sup>2</sup>.\n\nIt is obtained in all maps, in the areas of a flat plain which is close to the main river, which has very high and high flood susceptible areas. The identical results were found in the works of Tehrany et al. (2015). Also, in the eastern and south-eastern parts of the river basin, the very high and high susceptible classes were mainly located. Almost the same spatial distribution pattern of flood susceptible areas has been found in the case of all five models in terms of area under high and very high class, though in the case of the Dagging model, the percentages (14.03%) is comparatively smaller than the other models. This\n\nindicates a more practical outcome related to the Dagging hybrid model than other models, with greater accuracy and dependability. This outcome may be time decreasing and costing of a capable mollification planning in the Teesta river basin targeted for land-use plan and policy implication.\n\nThe region surrounding the river basin and the main channel of the river were majorly susceptible to flood. They were in the class of very high susceptibility as depicted by the map of flood susceptibility. This outcome was also according to the results of Yang et al. (2015) in Hainan Province, China. The settlement area of the study areas, indicated by red colour (Fig. 5a), was found in the same region (eastern part of the study area) where very high and high flood susceptible areas were located (Fig. 7). Yang et al. (2015) also reported that the identical spatial pattern of flood susceptible zones and urban areas were found in their working area. Yang et al. (2015) stated that the flooding has mostly harmed the world's approximately 0.18 million people at annual scale in those regions, which are nearer the rivers and mostly dominated by urban and semi-urban areas.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.2. The analysis of flood susceptible models", "section_headings": ["3. Results", "3.2. The analysis of flood susceptible models"], "chunk_type": "text", "line_start": 273, "line_end": 281, "token_count_estimate": 824, "basins": [], "subbasins": ["Teesta"], "countries": ["China"], "lake_ids": []}}
{"id": "632beb0c567d4e7d", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: text\n\nWe have validated the five flood susceptible models using RoC based on the validation and training points (Fig. 9). The validation for the flood susceptible maps has analysed, which were generated utilizing the models through the datasets of training point (for flood existing the success rate curve) and validation point (for anticipated flood event the prediction rate curve). That is why, on the maps of flood susceptibility, the points of flood validation and training were positioned. The Area", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "text", "line_start": 283, "line_end": 285, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3f914a652be62e1", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: figure\nFigure\n\nImage /page/10/Figure/10 description: A figure displaying five maps, labeled (a), (b), (c), (d), and (e), each illustrating 'Flood Susceptibility' for the same geographical region. The maps use a color gradient to indicate the level of risk. A legend on the right side of the figure defines the color scheme: dark green for 'Very low', light green for 'Low', yellow for 'Moderate', orange for 'High', and dark red for 'Very high'. All five maps show a similar pattern with a central, elongated area of high to very high susceptibility (orange and red), surrounded by areas of low and very low susceptibility (green) along the irregular borders. The extent and intensity of the high-risk areas vary slightly across the five maps. Below the legend, a scale bar indicates distances from 0 to 80 km, with markings at 0, 10, 20, 40, 60, and 80 km.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "figure", "figure_caption": null, "line_start": 286, "line_end": 286, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5775fae9aa2a005", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: text\n\nFig. 7. Flood susceptibility models using (a) Artificial neural network, (b) Support Vector machine, (c) Random forest, (d) Random subspace, and (e) Dagging,", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "text", "line_start": 287, "line_end": 289, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ff5e866ed2cf3bc", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: A clustered bar chart compares the area distribution across different flood susceptible zones as predicted by five different models. The y-axis is labeled \"Area (km²)\" and ranges from 0 to 1600. The x-axis is labeled \"Flood susceptible zones\" and has five categories: Very Low, Low, Moderate, High, and Very high. The legend indicates the five models: ANN (blue), RF (red), Random subspace (green), SMO (purple), and Dagging (teal). The approximate data for each zone is as follows: For the 'Very Low' zone, the areas are: ANN ~80, RF ~1100, Random subspace ~1020, SMO ~1120, and Dagging ~1160 km². For the 'Low' zone, the areas are: ANN ~1350, RF ~580, Random subspace ~540, SMO ~600, and Dagging ~550 km². For the 'Moderate' zone, the areas are: ANN ~520, RF ~360, Random subspace ~600, SMO ~490, and Dagging ~880 km². For the 'High' zone, the areas are: ANN ~500, RF ~510, Random subspace ~720, SMO ~620, and Dagging ~580 km². For the 'Very high' zone, the areas are: ANN ~1220, RF ~1100, Random subspace ~800, SMO ~860, and Dagging ~510 km².", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "figure", "figure_caption": null, "line_start": 290, "line_end": 290, "token_count_estimate": 389, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a2fdb58cb12530f5", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: text\n\nFig. 8. The flood susceptible area under different classes of five machine learning models.\n\ncan explain the accuracy of the models Under Curve (AUC). The higher the AUC, the higher the model's performance to predict. According to the success rate curve's (training dataset) outcome, the Dagging model (AUC = 0.87) outperformed the RF (AUC = 0.86), RS (AUC = 0.85), SVM (AUC = 0.84), and ANN (AUC = 0.83) models (Fig. 9a). Similarly, the outcomes of the curve of prediction rate by utilizing validation dataset (Fig. 7b) the AUC values for the Dagging model is 0.873, which outweighs the SVM (AUC = 0.86), RS (AUC = 0.83), ANN (AUC = 0.83), and RF (AUC=0.81). The results suggest that though the five FSMs had high prediction accuracy (AUC >0.80), the Dagging model is the best for the flood susceptibility modelling in the case of this study area, as the accuracy of prediction from 0.86 to 0.87 was enhanced by this model", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "text", "line_start": 291, "line_end": 303, "token_count_estimate": 313, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "364134519d14f9b3", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: text\n\nprediction rate by utilizing validation dataset ( Fig . 7b ) the AUC values for the Dagging model is 0 . 873 , which outweighs the SVM ( AUC = 0 . 86 ) , RS ( AUC = 0 . 83 ) , ANN ( AUC = 0 . 83 ) , and RF ( AUC = 0 . 81 ) . The results suggest that though the five FSMs had high prediction accuracy ( AUC > 0 . 80 ) , the Dagging model is the best for the flood susceptibility modelling in the case of this study area , as the accuracy of prediction from 0 . 86 to 0 . 87 was enhanced by this model\n\nThe analytical performance of five flood susceptible models based on the training and testing data using several error measures like root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination  $(R^2)$  are presented in Table 4. The finding clearly showed that the proposed Dagging model had the lowest RMSE= 0.19, lowest MAE=0.08, and very high  $R^2 = 0.84$  compared to other benchmarked models for the training stages (Table 4). In the training stage, the result of statistical performance for the models (five) follows the decreasing order: Dagging>ANN>FR>SVM>RS. Likewise, in the training stage, in the validation phase, the Dagging ensemble model showed the lowest RMSE = 0.18, lowest MAE = 0.08. Very high  $R^2$  = 0.85, which represents a very high level of satisfaction in case of FSMs along with fewer errors. The performance analysis of the five models in the validation stage follows the descending order: Dagging> RF>ANN>SVM> RS. Overall, the Dagging hybrid model had the best performance, while the least achievement is in the case of random subspace for the mapping of flood susceptibility regarding the spatial prediction by the use of both the datasets of training and validation. In fact, for this study, the Dagging model has been found as the most dominant ensemble hybrid model that performed with the higher accuracy of prediction among all models. For the evaluation of the flood susceptibility mapping, this outcome is substantially agreed, and the method of the hybrid ensemble model is appropriate, logical, and reliable, which was confirmed by Tehrany and Jones (2017). By using the Friedman test (Ipe, 1987) and Wilcoxon signed-rank tests (Hong et al., 2019a) at the level of 95% confidence (Tables 5 and 6), the considerable differences among the FSMs of five models were computed. In Tables 5 and 6, the outcomes of the Freidman test and the Wilcoxon signed-rank test are revealed. The findings of Friedman's test showed that the confidence level (or p-value) was < 0.05, which suggested to accept the primary assumption and reject the null hypothesis because, at the level of 95% confidence, the considerable difference among the benchmark models was absent. The values of chi-square ( $\\chi^2$ ) and p-value of five", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "text", "line_start": 291, "line_end": 303, "token_count_estimate": 772, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6cdfc8a870fecc59", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: text\n\n95 % confidence ( Tables 5 and 6 ) , the considerable differences among the FSMs of five models were computed . In Tables 5 and 6 , the outcomes of the Freidman test and the Wilcoxon signed - rank test are revealed . The findings of Friedman ' s test showed that the confidence level ( or p - value ) was < 0 . 05 , which suggested to accept the primary assumption and reject the null hypothesis because , at the level of 95 % confidence , the considerable difference among the benchmark models was absent . The values of chi - square ( $ \\ chi ^ 2 $ ) and p - value of five\n\nFSMs are far-away from 0.05 and 3.841, respectively, which are the standard values (Table 5). Whether any significant differences are present or not among the five FSMs, cannot be detected by the Friedman test. To overcome this disadvantage, many statistical tests are available. However, we have employed the Wilcoxon signed-rank test to investigate whether statistical differences are present or not between any pairs of FSMs. If the p-value and z value were lesser than the level of significance (<0.05) and higher than the critical values of z (+1.96 and -1.96) respectively, the null hypothesis must be discarded. There were significant differences in the performance of the FSMs. Also, the results of the Wilcoxon signed-rank test (Table 5) indicates that p-value and z value are both distant from the standard values of 0.05 and (from -1.96 to +1.96), respectively for all pairs except for the pairs of Dagging vs. ANN, Dagging vs. SVM, digging vs. RF, Dagging vs. RS, and RF vs. SVM. This implies statistically significant differences among all FSMs. On the other hand, the p values were > 0.05, and the z values exceeded the critical values: therefore, the outcome undoubtedly stated that there are no statistically significant differences in the case of the Dagging model's performance as compared to the other FSMs except for the ANN.\n\nAdditionally, to confirm the superiority of the Dagging model over other models, the paired t-test (Altman, 1990) has been performed in this study. It is noted that a significant level of  $\\alpha$  is chosen to be p<0.05. If the estimated p-value is less than the significant level  $\\alpha$ , the null hypothesis will be rejected that there is no statistical difference between the two paired models. The Dagging model has notably outperformed ANN (p-value =0.0001) and RS (p-value =0.017), except for RF (p-value =0.107) and SVM (p-value=0.263). Thus, based on the statistical t-test, the Dagging model is considered the best fitted for modelling of FSMs in the Teesta River basin.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "text", "line_start": 291, "line_end": 303, "token_count_estimate": 723, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "ae64b2daf8ed651a", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 3. Results > 3.3. Validation and comparison\nType: text\n\n< 0 . 05 . If the estimated p - value is less than the significant level $ \\ alpha $ , the null hypothesis will be rejected that there is no statistical difference between the two paired models . The Dagging model has notably outperformed ANN ( p - value = 0 . 0001 ) and RS ( p - value = 0 . 017 ) , except for RF ( p - value = 0 . 107 ) and SVM ( p - value = 0 . 263 ) . Thus , based on the statistical t - test , the Dagging model is considered the best fitted for modelling of FSMs in the Teesta River basin .\n\nFor investigating the Dagging ensemble model's performance, the ANN, RF, RS, and SVM were regarded in this study. To increase the best performance, the accurate measurement of the parameters of these state-of-arts models should be set carefully. In Table 1, the parameters for these models are shown. By the utilization of the best flood influencing factor, SVM, ANN, RS, RF, Dagging hybrid models were used for modelling flood susceptibility prediction. The criteria of appraisal (RMSE, MAE,  $R^2$ - and AUC) were computed for the comparison among the Dagging model and other benchmarked methods based on training and validation datasets.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "3. Results > 3.3. Validation and comparison", "section_headings": ["3. Results", "3.3. Validation and comparison"], "chunk_type": "text", "line_start": 291, "line_end": 303, "token_count_estimate": 352, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "429264d87c555242", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: text\n\nThe accurate estimation of flood susceptibility is an essential process for safeguarding people 's and developing feasible and effective mitigation measures (Sahana et al., 2020). Therefore, flooding management to", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 305, "line_end": 307, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "742bab4c16ea8991", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: figure\nFigure\n\nImage /page/12/Figure/2 description: The image displays two ROC (Receiver Operating Characteristic) curve plots, labeled A and B. Both plots graph Sensitivity on the y-axis against 1 - Specificity on the x-axis, with scales from 0.0 to 1.0. Each plot compares the performance of five different models and includes a diagonal red reference line.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 308, "line_end": 308, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f45405e1e16923fd", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: text\n\nPlot A shows five curves that are closely grouped, indicating similar performance. The legend, titled \"Source of the Curve\", provides the Area Under the Curve (AUC) values for each model:\n- ANN (blue line): AUC = 0.834\n- RF (green line): AUC = 0.863\n- Random subspace (light green line): AUC = 0.859\n- Dagging (purple line): AUC = 0.871\n- SVM (yellow line): AUC = 0.845\n\nPlot B also shows five curves, but they are more spread out than in plot A. The legend provides the following AUC values:\n- ANN (blue line): AUC = 0.835\n- RF (green line): AUC = 0.811\n- Random Subspace (light green line): AUC = 0.839\n- SVM (purple line): AUC = 0.865\n- Dagging (yellow line): AUC = 0.873\n\nFig. 9. The validation of five flood susceptible models using RoC based on training point (a) and validation point (b).\n\nprevent and decrease the damages caused by flooding is the primary requirement. To do so, the FSM has become the foundation of management across the world (Sahana et al., 2020; Wang et al., 2020). Therefore, researchers always try to use new and robust techniques to obtain very high precision and accurate results, which will be very\n\nhelpful to propose flood management plans (Wang et al., 2019a, 2020; Bui et al., 2020a; Nachappa et al., 2020). This is the reason; we also attempted to apply some advanced models, like ANN, SVM, RF, random subspace and newly proposed Dagging algorithms to prepare flood susceptibility maps for Teesta river basin of Bangladesh. It is indispensable", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 309, "line_end": 331, "token_count_estimate": 431, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "3c430e2a881492c4", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: table\nTable: Table 4 The accuracy assessment of five flood susceptible models for training and testing data using different error measures.\n\n| Methods | ANN      |            | RF       |            | Random subspace |            | SMO      |            | Dagging  |            |\n|---------|----------|------------|----------|------------|-----------------|------------|----------|------------|----------|------------|\n|         | Training | Validation | Training | Validation | Training        | Validation | Training | Validation | Training | Validation |\n| RMSE    | 0.22     | 0.27       | 0.252    | 0.254      | 0.3             | 0.31       | 0.253    | 0.28       | 0.19     | 0.189      |\n| MAE     | 0.121    | 0.16       | 0.172    | 0.173      | 0.2             | 0.203      | 0.152    | 0.17       | 0.081    | 0.084      |\n| R2      | 0.774    | 0.718      | 0.712    | 0.738      | 0.67            | 0.599      | 0.696    | 0.677      | 0.839    | 0.852      |", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "table", "table_caption": "Table 4 The accuracy assessment of five flood susceptible models for training and testing data using different error measures.", "columns": ["Methods", "ANN", "", "RF", "", "Random subspace", "", "SMO", "", "Dagging", ""], "table_row_start": 1, "table_row_end": 4, "line_start": 332, "line_end": 337, "token_count_estimate": 311, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3e4e3c26a92e4518", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: table\nTable: Table 5 The results of the Friedman test for five flood susceptible models with the p value = 0.05.\n\n| Models  | Mean ranks | Chi-square | Significance |\n|---------|------------|------------|--------------|\n| ANN     | 1.91       | 218.82     | 0            |\n| RF      | 3.2        |            |              |\n| Rsp     | 3.58       |            |              |\n| SMO     | 3.03       |            |              |\n| Dagging | 3.28       |            |              |", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "table", "table_caption": "Table 5 The results of the Friedman test for five flood susceptible models with the p value = 0.05.", "columns": ["Models", "Mean ranks", "Chi-square", "Significance"], "table_row_start": 1, "table_row_end": 5, "line_start": 341, "line_end": 347, "token_count_estimate": 157, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91bfb6e9cfa5c7ad", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: text\n\nto model the flood susceptibility in this region as the whole country has experienced frequent flooding for a very long time.\n\nAmong the applied approaches, ANN, SVM, and RF have been widely used for landslide susceptibility modelling (Hong et al., 2019b; Fang et al., 2020; Wu et al., 2020b), flood susceptibility modelling (Chen et al., 2019b; Chen et al., 2020c; Ali et al., 2020; Khosravi et al., 2019b), gully erosion (Amiri et al., 2019; Tien Bui et al., 2019a; Amiri et al., 2019) and forest fire modelling (Gigović et al., 2019; Tehrany et al., 2019; Pourghasemi et al., 2020b), but scarce studies applied random subspace and Dagging model for different natural hazards prediction including flood susceptibility. Therefore, the performance of the conventional three models and two new models was evaluated and compared by using the AUC value of the ROC curve at training and testing scale and a series of non-parametric statistics. Also, the influences of the flood conditioning parameters were assessed by using the information gain ratio.\n\nThe findings showed that the proposed Dagging models outperformed (AUC for training datasets: 0.87, and AUC for testing data sets: 0.873) the other four models (Pham et al., 2017a; Hong, 2018; Yariyan et al., 2020). However, the other four models also performed better as the AUC value achieved more than 0.8 at both training and testing datasets. Therefore, it is reasonable to conclude that Dagging performed better than other models because it is an ensemble machine learning technique which has been efficiently used for obtaining accurate results for predicting any natural hazards or other environmental components (Pham et al., 2017b). Several scholars already applied Dagging models along with other models for predicting the landslide susceptibility and found highly accurate findings (Pham et al., 2017a, 2020a; Nguyen et al., 2019; Kalantar et al., 2020; Nhu et al., 2020). Nguyen et al. (2020) reported that the Dagging model outperformed the other four ensemble machine learning models like random subspace, random subspace, and cascade generalization for groundwater potentiality modelling. Nguyen et al. (2020) accounted that Dagging was more capable than other algorithms for reducing the variance, bias, and noise of the groundwater potential modelling. In the present study, Dagging performed better for training and testing datasets; therefore, it can be stated that the Dagging model is also highly capable of modelling the flood susceptibility. Therefore, it", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 348, "line_end": 356, "token_count_estimate": 662, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae7b3c17e69ffc82", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: table\nTable: Table 6 The comparison of the performances of the ensemble machine learning models for flood susceptibility using the Wilcoxon signed-rank test.\n\n| Pairwise Comparison            | Positive | Negative | Z value | P-value | Significance |\n|--------------------------------|----------|----------|---------|---------|--------------|\n| ANN vs. RF                     | 296      | 37       | -12.27  | 0.05    | Yes          |\n| ANN vs. Ransdom Subspace       | 269      | 64       | -9.53   | 0.05    | Yes          |\n| ANN vs. SMO                    | 225      | 108      | -7.75   | 0.05    | Yes          |\n| ANN vs. Dagging                | 239      | 94       | -6.64   | 0.05    | Yes          |\n| RF vs. Random subspace         | 247      | 86       | -5.93   | 0.05    | Yes          |\n| RF vs. SMO                     | 152      | 181      | 1.83    | 0.01    | No           |\n| RF vs. Dagging                 | 198      | 135      | -0.63   | 0.01    | No           |\n| Random subspace vs. SMO        | 145      | 188      | 2.73    | 0.05    | Yes          |\n| Random subspace vs. Dagging | 143      | 190      | 1.91    | 0.05    | No           |\n| SMO vs. Dagging                | 180      | 153      | -1.67   | 0.05    | No           |", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "table", "table_caption": "Table 6 The comparison of the performances of the ensemble machine learning models for flood susceptibility using the Wilcoxon signed-rank test.", "columns": ["Pairwise Comparison", "Positive", "Negative", "Z value", "P-value", "Significance"], "table_row_start": 1, "table_row_end": 10, "line_start": 357, "line_end": 368, "token_count_estimate": 392, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "206bae0e10cb6b46", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 4. Discussion\nType: text\n\nis highly recommended to apply the Dagging model for other natural hazards prediction for obtaining high precision results.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 369, "line_end": 371, "token_count_estimate": 48, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d3dc1f2396aac2eb", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: 5. Conclusion\nType: text\n\nTwo new hybrid ensemble models, namely Dagging and the RS, were used for the first time in the present analysis, along with three machine learning models, e.g., the ANN, the RF, and the SVM, to model the flood susceptibility mapping in the Teesta River basin, Northern Bangladesh. This analysis primarily selected a total of 413 flooding points with twelve variables such as elevation, slope, curvature, aspect, SPI, TWI, STI, LULC, rainfall, river width, TWI, and soil types affecting the flooding. Multi-collinearity diagnostic tests (VIF), IGR, and the correlation matrix of Pearson were utilized to measure the relevance of flood conditioning parameters. The effect of the parameters of flood conditioning was measured by the approaches of feature selection, such as the information gain ratio. To verify the flood prone models based on the training and validation datasets, the Friedman test, Wilcoxon signed-rank test, t-paired test, and the RoC curve have been used. In the case of the Dagging model, the highest versatility and predictive capabilities were found, followed by the RF, the ANN, the SVM and the RS model. In the result of validation step, the implementation of the ROC Curve shows that, relative to other methods, the Dagging model has the maximum performance (AUC = 0.863-training stage; AUC = 0.873-validating stage). The standard of all models for the mapping of flood susceptibility, however, was acceptable and reliable. Dagging is one of the most capable methods for flood-susceptible modeling, the results of the study showed. A total area of 29.62 percent was described as extremely vulnerable to flooding as an ideal model. The biggest downside, however, is that for certain variables, like SPI and land use, the implementation of these models does not take into consideration the modifications over time, since these variables are very complex. Future analysis on the temporal scale will be done depending on the availability of temporal databases of these variables. In addition, by conducting sensitivity analysis on different influential variables, these models can be upgraded. Compared to the other models, the dagging model has advantages, including less candidate parameters, high optimization power, and rapid convergence to prepare susceptibility maps for flush\n\nFlood management strategies have recently been considered a top priority, particularly in Bangladesh, where flash floods occur every year. Other basins and areas, however, have not been tested for flood control measures yet. The present research, therefore, was conducted in the Teesta River basin using some sophisticated machine learning algorithms to provide useful knowledge on the methods to be implemented to help the implementation of successful flash flood mitigation strategies and land-use policy planning by local authorities and other parties.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "5. Conclusion", "section_headings": ["5. Conclusion"], "chunk_type": "text", "line_start": 373, "line_end": 377, "token_count_estimate": 679, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "9f1b1139b55ece43", "text": "Document: Flood susceptibility modelling using advanced ensemble machine learning models\nSection: Appendix A. Supplementary data\nType: text\n\nSupplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2020.09.006.", "metadata": {"source_file": "data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md", "document_title": "Flood susceptibility modelling using advanced ensemble machine learning models", "section_path": "Appendix A. Supplementary data", "section_headings": ["Appendix A. Supplementary data"], "chunk_type": "text", "line_start": 389, "line_end": 391, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ce52075aba35217", "text": "Document: flood kg\nSection: ABSTRACT\nType: text\n\nIncreasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage. Researchers have been dedicating to urban flood risk assessments in recent years. However, current research is still facing the challenges of multi-modal data fusion and knowledge representation of urban flood events. Therefore, in this paper, we propose an Urban Flood Knowledge Graph (UrbanFloodKG) system that enables KG to support urban flood risk assessment. The system consists of data layer, graph layer, algorithm layer, and application layer, which implements knowledge extraction and storage functions, integrates knowledge representation learning models and graph neural network models to support link prediction and node classification tasks. We conduct model comparison experiments on link prediction and node classification tasks based on urban flood event data from Guangzhou, and demonstrate the effectiveness of the models used. Our experiments prove that the accuracy of risk assessment can reach 91% when using GEN, which provides a a promising research direction for urban flood risk assessment.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 5, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7fbb5e9759e8efea", "text": "Document: flood kg\nSection: CCS CONCEPTS\nType: text\n\n• **Information systems**  $\\rightarrow$  *Clustering and classification.*", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "CCS CONCEPTS", "section_headings": ["CCS CONCEPTS"], "chunk_type": "text", "line_start": 7, "line_end": 9, "token_count_estimate": 33, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcdd573db0bd7054", "text": "Document: flood kg\nSection: KEYWORDS\nType: text\n\nurban flood, knowledge graph, link prediction, graph neural network", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "KEYWORDS", "section_headings": ["KEYWORDS"], "chunk_type": "text", "line_start": 11, "line_end": 13, "token_count_estimate": 30, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1207ab77cc1eb561", "text": "Document: flood kg\nSection: ACM Reference Format:\nType: text\n\nYu Wang, Feng Ye, Binquan Li, Gaoyang Jin, Dong Xu, and Fengsheng Li. 2023. UrbanFloodKG: An Urban Flood Knowledge Graph System for Risk Assessment. In *Proceedings of the 32nd ACM International Conference on*\n\nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.\n\nCIKM '23, October 21-25, 2023, Birmingham, United Kingdom\n\n© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0124-5/23/10...\\$15.00 https://doi.org/10.1145/3583780.3615105\n\nInformation and Knowledge Management (CIKM '23), October 21–25, 2023, Birmingham, United Kingdom. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3583780.3615105", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "ACM Reference Format:", "section_headings": ["ACM Reference Format:"], "chunk_type": "text", "line_start": 15, "line_end": 25, "token_count_estimate": 335, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["3583780", "3615105"]}}
{"id": "7f9a78e2b259a64c", "text": "Document: flood kg\nSection: 1 INTRODUCTION\nType: text\n\nIncreasing numbers of people live in flood-prone areas worldwide. With continued development, urban flood will become more frequent, which has caused casualties and property damage [58]. With the rapid development of machine learning, deep learning methods have been increasingly explored for their potential application in urban flood risk assessment. Existing research has demonstrated that Long Short Term Memory (LSTM) [56], Recurrent Neural Network (RNN) [3] and Convolutional Neural Network (CNN) [30] are commonly used in flood forecasting. However, the structures of the above models are complex and the explainable ability is poor. Therefore, it is worth exploring how to use knowledge related to urban flood events to help human capture the relationships between time and space, overcome the limitations of above models, and further improve the accuracy of risk assessment.\n\nIn addition, recent studies emphasize the importance of data fusion in specific applications, such as data fusion [16] is proposed to improve the accuracy of hydraulic simulation of urban flood, and a remote sensing and text bimodal data fusion model based on UFCLI [49] is proposed to improve accuracy of urban flood damage. However, no researchers try to fuse multi-modal data for urban flood risk assessment. Meanwhile, some studies try to extract explicit knowledge from data for performance enhancement and semantic reasoning [6, 54, 55]. But in the field of flood assessment, there are mainly data-driven researches [30, 38], and remains a lack of effective representation of knowledge to aid in flood risk assessment. Considering existing research has not explored how to integrate multi-modal data into knowledge from urban flood to support downstream tasks, we summarize the key challenges for further research on urban flood risk assessment from two aspects:\n\n• Multi-modal Data Fusion. Due to the diverse sources and tools of data collection, the collected data has different structures and forms. On the one hand, urban flood related data is usually stored in different structures, such as tables, texts, and images. On the other hand, there are many relationships between data, such as the similar duration of event A\n\n- and event B. Therefore, how to conduct effective fusion of multi-modal data has not been fully explored.\n- Knowledge Representation. Urban flood events involve data from multiple aspects, including flood table data organized by monitoring departments, textual data reported in news broadcasts, on-site image data collected by sensors, etc. For this multi-modal data, how to embed multi-modal data related to urban flood events to downstream task-driven algorithm models is a challenge.\n\nKnowledge graph (KG) [34] is a technology that represents and stores entities and relationships in a graph form and has been successfully applied in fields such as KG-based recommendation systems [13] and KG-based question answering systems [17]. Specifically, KG uses triple facts (head entity, relationship, tail entity) to store and represent knowledge in the real world, where entities can be objects, events, locations, or abstract concepts, and relationships describe their connections [39]. Meanwhile, Liu [23] combines urban computing with KG system, exploring new modes of urban data management and analysis. Therefore, we believe that this provides a reasonable and feasible way for risk assessment of urban flood events.\n\nTo unlock the full potential of urban flood data for efficient risk assessment, we present a system called Urban Flood Knowledge Graph (UrbanFloodKG). The system consists of data layer, graph layer, algorithm layer and application layer to meet various needs in urban flood risk assessment research. Finally, we conduct link prediction and node classification experiments by using different models to demonstrate the feasibility of the proposed UrbanFloodKG system.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "1 INTRODUCTION", "section_headings": ["1 INTRODUCTION"], "chunk_type": "text", "line_start": 27, "line_end": 48, "token_count_estimate": 862, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9cfd12009c2a6490", "text": "Document: flood kg\nSection: 1 INTRODUCTION\nType: text\n\nMeanwhile , Liu [ 23 ] combines urban computing with KG system , exploring new modes of urban data management and analysis . Therefore , we believe that this provides a reasonable and feasible way for risk assessment of urban flood events . To unlock the full potential of urban flood data for efficient risk assessment , we present a system called Urban Flood Knowledge Graph ( UrbanFloodKG ) . The system consists of data layer , graph layer , algorithm layer and application layer to meet various needs in urban flood risk assessment research . Finally , we conduct link prediction and node classification experiments by using different models to demonstrate the feasibility of the proposed UrbanFloodKG system .\n\nThe main contributions of our work are as follows:\n\n- We propose a system that retrieves urban flood-related data from various data sources, construct a KG of urban flood events, and further integrate various KG representation algorithms and graph neural network models. This system is the first KG-based urban flood risk assessment system, providing a new perspective for assessing urban flood events.\n- We present a systematic scheme for UrbanFloodKG construction, which identifies the key elements in urban flood environment as entities, and describes their semantic connections as relations. The proposed construction scheme provides a general framework to fuse the urban flood-related data into the KG, and potentially benefits various downstream tasks.\n- We construct a dataset for link prediction and node classification tasks using urban flood-related data collected from Guangzhou over the years. After using different models, we compare their performance and demonstrate the effectiveness and feasibility of our proposed system.\n\nIn the following sections, we present the design and implementation of our UrbanFloodKG system. Section 2 introduces the background of KG and the requirements for our system. Section 3 provides an overview of the system architecture and details each layer. In Section 4, we present the application scenarios and compare various models for knowledge representation and graph neural networks. Section 5 discusses related work, and we conclude the paper in Section 6.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "1 INTRODUCTION", "section_headings": ["1 INTRODUCTION"], "chunk_type": "text", "line_start": 27, "line_end": 48, "token_count_estimate": 475, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0c09652c63e9fdf", "text": "Document: flood kg\nSection: 2 PRELIMINARY & REQUIREMENTS > 2.1 Preliminary\nType: text\n\nHere we formally define the KG as follows [11, 23, 57].\n\nDefinition 2.1 (Knowledge Graph). A knowledge graph (KG) is represented as a graph structure called  $\\mathcal{G} = \\{\\mathcal{E}, \\mathcal{R}, \\mathcal{F}\\}$ , where  $\\mathcal{E}, \\mathcal{R}$ , and  $\\mathcal{F}$  denote the sets of entities, relations, and facts, respectively. Specifically, the fact set  $\\mathcal{F} = (h, r, t) \\mid h, t \\in \\mathcal{E}, r \\in \\mathcal{R}$  stores the triples in the KG. Each triple  $(h, r, t) \\in \\mathcal{F}$  represents a directed edge from entity h to entity t with a relation type r.\n\nDefinition 2.2 (Knowledge Graph Construction). KG construction is facilitated through the utilization of a procedure denoted as f, which maps a data source to a KG:  $f:D\\times f_k(D)\\to \\mathcal{G}$ . In this context, D represents the set of data sources, and  $f_k(D)$  corresponds to the background knowledge associated with the data target, which can include domain-specific knowledge. It is crucial to highlight that the availability of background knowledge plays a significant role in KG construction. This background knowledge can be provided through pre-designed rules or a language model that generates representations. Without such background knowledge, the process of KG construction is often hindered or unable to proceed.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "2 PRELIMINARY & REQUIREMENTS > 2.1 Preliminary", "section_headings": ["2 PRELIMINARY & REQUIREMENTS", "2.1 Preliminary"], "chunk_type": "text", "line_start": 52, "line_end": 58, "token_count_estimate": 421, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09f2ff18dd63c25c", "text": "Document: flood kg\nSection: 2 PRELIMINARY & REQUIREMENTS > 2.2 Requirements\nType: text\n\nTo guide our system design for a comprehensive and reproducible platform, we summarize the requirements as following three as-pects:\n\n- Data and storage compatibility. When integrating data of different types, formats, and sources into the same storage system, the integrity, consistency, and accessibility of the data can be maintained.\n- Algorithm universality. Knowledge representation algorithms can be applied universally to downstream subtasks.\n- Knowledge maintainability. The system must effectively manage and update urban flood event data to ensure its accuracy and relevance.\n\nTherefore, according to the above definitions and requirements, we build the UrbanFloodKG system, which is introduced in the following.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "2 PRELIMINARY & REQUIREMENTS > 2.2 Requirements", "section_headings": ["2 PRELIMINARY & REQUIREMENTS", "2.2 Requirements"], "chunk_type": "text", "line_start": 60, "line_end": 68, "token_count_estimate": 176, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "418d611dc76a03dc", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM\nType: text\n\nIn this section, we present an overview of our designed Urban-FloodKG system. Subsequently, we delve into the specific details from a layered perspective.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM"], "chunk_type": "text", "line_start": 70, "line_end": 72, "token_count_estimate": 56, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "12fa2c267e55ca10", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview\nType: text\n\nThe high-level system architecture of the UrbanFloodKG system is shown in Fig. 1. The different layers are described as follows:\n\n- Data. The data layer is responsible for collecting data from multiple sources and cleaning the collected data.\n- Graph. This graph layer constructs UrbanFloodKG by defining its schema, extracting entities and relationships from urban data of different structures and forms, and enriching them with additional attributes. By integrating urban flood data in this way, the construct UrbanFloodKG provides a comprehensive and effective platform for urban flood risk", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.1 System Overview"], "chunk_type": "text", "line_start": 74, "line_end": 79, "token_count_estimate": 148, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a1f3737d6f0bdb2", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A diagram illustrating the high-level architecture of the UrbanFloodKG system, organized into four distinct layers: Application, Algorithm, Graph, and Data. The bottom layer, Data, involves 'Data Collection & Cleaning' of 'Structured data', 'Semi-structured data', and 'Unstructured data'. The next layer up is Graph, which is divided into 'Knowledge Extraction' (including Schema, Entity, Relation, and Attribute) and 'Knowledge Storage' (using OrientDB). Above that is the Algorithm layer, which has two parts: 'Knowledge Representation' (using 'Translation-based models & GNN-based models') and 'Operation' (including Query, Embedding, Link Prediction, and Node Classification). The top layer is Application, which handles Tasks such as 'Knowledge Reasoning', 'Event Classification', 'Urban Decision-making', and others indicated by an ellipsis.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.1 System Overview"], "chunk_type": "figure", "figure_caption": null, "line_start": 80, "line_end": 80, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58c6addbda240b15", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview\nType: figure\nFigure: Figure 1: The high-level architecture of UrbanFloodKG system.\n\nFigure 1: The high-level architecture of UrbanFloodKG system.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.1 System Overview"], "chunk_type": "figure", "figure_caption": "Figure 1: The high-level architecture of UrbanFloodKG system.", "line_start": 82, "line_end": 82, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "664fe7961810e990", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview\nType: text\n\nassessment. Next, all constructed triplets are transformed into graph data structures and input into OrientDB [36].\n\n- Algorithm. The algorithm layer utilizes Translation-based models and GNN-based models to transform the triplets in the KG into vector embeddings, while also offering fundamental operations.\n- Application. In the application layer, the application scenarios of this system are introduced. This layer provides services for knowledge reasoning, event classification, and urban decision-making tasks.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.1 System Overview", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.1 System Overview"], "chunk_type": "text", "line_start": 83, "line_end": 88, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb8c240e91adc51f", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer\nType: text\n\nThe data layer provides data collection and data cleaning functions.\n\n*Data Collection.* The data types of urban flood events can be divided into three categories: structured data, semi-structured data, and unstructured data, as shown in Fig. 2.\n\n- **Structured data.** Most structured data are mainly tables, and we extract key information manually.\n- Semi-structured data. Most semi-structured data are texts. Named entity recognition [19, 26] and relation extraction tasks are of great help in extracting knowledge from unstructured text. We use the UIE [24] framework to extract\n\n- knowledge from text automatically, extract key attributes as event attribute information.\n- Unstructured data. The unstructured data mainly consist of manually collected images of water-logging scenes.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.2 Data Layer"], "chunk_type": "text", "line_start": 90, "line_end": 100, "token_count_estimate": 206, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fed67c6e5aaa592c", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer\nType: figure\nFigure\n\nImage /page/2/Picture/14 description: A diagram illustrating three types of information, labeled \"Table,\" \"Image,\" and \"Text,\" arranged horizontally. On the left, under the label \"Table,\" is a black and white icon of a grid with four columns and three rows. In the center, under the label \"Image,\" is a photograph of a flooded urban street with reflections of buildings in the water and a car in the distance. On the right, under the label \"Text,\" is a text box containing the sentence: \"The Shinan Avenue experienced urbanflood, lasting for one hour.\"", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.2 Data Layer"], "chunk_type": "figure", "figure_caption": null, "line_start": 101, "line_end": 101, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b238628ce937210", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer\nType: figure\nFigure: Figure 2: Multi-modal data related to urban flood events.\n\nFigure 2: Multi-modal data related to urban flood events.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.2 Data Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.2 Data Layer"], "chunk_type": "figure", "figure_caption": "Figure 2: Multi-modal data related to urban flood events.", "line_start": 103, "line_end": 103, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "358dbd23acc92aba", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer\nType: text\n\nThe graph layer is responsible for the management of KG data. In the knowledge extraction stage, we summarize the following four key aspects: schema definition, entity identification, relation extraction and attribute enrichment.\n\n*Schema Definition.* To ensure entities in the KG have a consistent structure and semantics, thereby enhancing the reliability of the KG, a schema or ontology is used to describe the high-level\n\nstructure of the KG. This includes defining the types of entities and relations present within the graph [15]. Fig. 3 illustrates the schema of UrbanFloodKG, wherein the rectangles represent various entity types and the edges depict their relationships within the UrbanFloodKG.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.3 Graph Layer"], "chunk_type": "text", "line_start": 106, "line_end": 112, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d7db5ace78fa483", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer\nType: figure\nFigure\n\nImage /page/3/Figure/3 description: A concept map or entity-relationship diagram showing the relationships between various entities. The entities are represented by light blue rounded rectangles, and the relationships are shown as labeled arrows. The diagram shows that an 'Image' is an 'on-siteImageOf' a 'Waterlogging Area'. The 'Waterlogging Area' is 'locateAt' a 'Region', and a 'Department' is also 'locateAt' a 'Region'. A 'Reason' can 'leadTo' an 'Event', which can 'occurIn' a 'Waterlogging Area'. An 'Event' can 'leadTo' an 'Influence' and is a 'cateOf' a 'Risk-level'. There is a recursive relationship on 'Event' labeled 'samePosition, closeTime'. A 'Leader' can 'resolve' an 'Event', and the 'Leader' 'belongTo' a 'Department'.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.3 Graph Layer"], "chunk_type": "figure", "figure_caption": null, "line_start": 113, "line_end": 113, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6559cd1e86b23367", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer\nType: figure\nFigure: Figure 3: The schema of UrbanFloodKG. Each rectangle represents a schema of an entity.\n\nFigure 3: The schema of UrbanFloodKG. Each rectangle represents a schema of an entity.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.3 Graph Layer"], "chunk_type": "figure", "figure_caption": "Figure 3: The schema of UrbanFloodKG. Each rectangle represents a schema of an entity.", "line_start": 115, "line_end": 115, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51a98fd9fbd0e7ba", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer\nType: text\n\n**Entity Identification.** To ensure the standardization and consistency of the content in the KG, it is necessary to establish conventions for its representation. Therefore, the following entities can be identified:\n\n- Event. Event represents each specific urban flood event, which is a key entity in the research of UrbanFloodKG.\n- Influence. Influence represents the impact caused by urban flood events.\n- Reason. Reason represents the causes of urban flood events summarized by professionals.\n- Leader. Leader represents the personnel responsible for handling urban flood events.\n- Department. Department represents the department to which personnel responsible for handling urban flood events belong.\n- Region. The Region denotes the administrative area of the city to which the urban flood event is attributed.\n- Water-logging Area. Water-logging Area represents the specific location where the urban flood events occurred.\n- Image. Image stores on-site photographs depicting the urban flood events.\n- **Risk-level**. Risk-level represents the the assessment of the risk level of urban flood events conducted by experts during the later stages.\n\n**Relation Extraction.** After analyzing the identified entity types within UrbanFloodKG, we extract representative relations to capture the semantic connections between entities. These relations are categorized as follows:\n\n• **Spatial Relations.** The spatial relationship model characterizes the spatial association between entities. The relationships \"locatAt\" and \"occurIn\" simulate the spatial connections between event entities and location entities, as well as region entities, respectively.\n\n- Personal Relations. Personal relationships emphasize individual knowledge. The \"belongTo\" relationship links the department entity to the leader's workplace, while the \"resolve\" relationship connects the leader entity to the event entity that they are responsible for handling.\n- Causal Relations. Causal relationships aid in comprehending and predicting causal connections among entities. The relationship \"leadTo\" establishes a link between the event entity and the reason entity or the influence entity, forming a comprehensive chain of events.\n- Affiliated Relations. The association relationship signifies\n  the linkage between entities. \"on-siteImageOf\" connects the\n  on-site image entity with the water-logging area entity, while\n  \"cateOf\" establishes a connection between the event entity\n  and the risk-level entity. \"samePosition\" and \"closeTime\" link\n  event entities with the same location or close occurrence\n  time, effectively capturing the association between entities.\n\nAttribute Enrichment. To increase the amount of information and expressiveness of entities in the KG. By adding more attributes to entities, a more detailed and comprehensive description can be provided, making the KG richer and more useful. Therefore the system further enriches the entities with attribute provided, which are described as follows:\n\nEvent Attributes. The event attributes are displayed in Table 1\n\nAttribute\n Classified Group\n\nStartTime (ms)\n timestamp\n\nDepth (m)\n 0-0.3,0.3-0.5,0.5-0.8,>0.8\n\nDuration (h)\n 0-1,1-3,3-5,>5\n\nLanes (number)\n\nTable 1: Event Attributes.\n\n• Influence Attributes. An event's influence is described through relevant text, which can be extracted as feature maps using the Word2Vec[27] model, allowing identification of the important features of the impact text for better understanding of the event's influences.\n\n0-3,3-6,6-9,>9", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.3 Graph Layer"], "chunk_type": "text", "line_start": 116, "line_end": 181, "token_count_estimate": 816, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "16648a69799f9c19", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer\nType: text\n\nm ) 0 - 0 . 3 , 0 . 3 - 0 . 5 , 0 . 5 - 0 . 8 , > 0 . 8 Duration ( h ) 0 - 1 , 1 - 3 , 3 - 5 , > 5 Lanes ( number ) Table 1 : Event Attributes . • Influence Attributes . An event ' s influence is described through relevant text , which can be extracted as feature maps using the Word2Vec [ 27 ] model , allowing identification of the important features of the impact text for better understanding of the event ' s influences . 0 - 3 , 3 - 6 , 6 - 9 , > 9\n\n- Reason Attributes. The cause of an event is also conveyed through text, with its characteristic attribute being the relevant textual description of what triggered the event. This text-based information can also be extracted as feature maps using the Word2Vec model, allowing for a better understanding of the cause of the event.\n- Leader Attributes. The leader attribute includes the name and, contact phone number.\n- Department Attributes. The department attribute includes the name and location information.\n- Region Attributes. The region attribute includes the boundary, area, and number of buildings of the region.\n- Water-logging Area Attributes. The attribute of the water-logging area comprises specific location information and area information pertaining to the water-logging area.\n\n- Image Attributes. The on-site image attribute of the event is extracted as feature maps using the ResNet[14] model.\n- Risk-level Attributes. The event's risk-level attribute comprises three categories: mild, moderate, and severe.\n\nKnowledge Storage. Currently, there are several popular graph databases available, including Neo4j [47], JanusGraph [29], Arango-DB [53], and TigerGraph [8]. It is worth mentioning that we have discovered that OrientDB [36], as a multi-model database, is also capable of effectively storing graph data. OrientDB is a full-function, NoSQL MMDMS, addressing the big data variety problem with one single, multi-model store and a SQL-based, multi-model query language.\n\nThe UrbanFloodKG system uses an object-oriented approach to store triples as node and edge classes. It employs the OrientDB database, which supports graph-based processing, SQL queries, and parallel query execution. OrientDB is not only a graph database but also a multi-model database that can handle both graphs and documents simultaneously. Table 2 shows three types of node and two types of edge in OrientDB and Fig. 4 shows a portion of the KG", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.3 Graph Layer"], "chunk_type": "text", "line_start": 116, "line_end": 181, "token_count_estimate": 644, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b5afd1d95e39a62", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer\nType: table\nTable: Table 2: In OrientDB, there are three categories of node classes - Schema Node, Entity Node and Attribute Node. Additionally, there are two types of edge classes - Schema Edge and Entity Edge. Each node and edge possesses its unique \"Name\" attribute.\n\n| Class      | Type                    | Name                             |\n|------------|-------------------------|----------------------------------|\n| Schema     | Schema Node             | Schema name                      |\n| Entity     | Entity Node             | SchemaName_Rid                   |\n| Attribute  | Attribute Node          | Attribute value                  |\n| SchemaEdge | Schema Relation Edge | hasInstance, Relation name    |\n| EntityEdge | Entity Relation Edge | Attribute name, Relation name |", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.3 Graph Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.3 Graph Layer"], "chunk_type": "table", "table_caption": "Table 2: In OrientDB, there are three categories of node classes - Schema Node, Entity Node and Attribute Node. Additionally, there are two types of edge classes - Schema Edge and Entity Edge. Each node and edge possesses its unique \"Name\" attribute.", "columns": ["Class", "Type", "Name"], "table_row_start": 1, "table_row_end": 5, "line_start": 182, "line_end": 188, "token_count_estimate": 217, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b63ff7b70cd53818", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: text\n\n*Translation-based models*. The urban flood event information is stored in OrientDB in the form of nodes and edges. It is difficult to directly analyze them, and existing research has proposed KG representation learning [11, 45]. It learns low-dimensional continuous representation vectors (also called embeddings) of entities and relationships while preserving the inherent structure and semantics of the KG.\n\nThe process of KG representation learning is described below. Given a knowledge graph  $\\mathcal{G}=(\\mathcal{E},\\mathcal{R},\\mathcal{F})$ , which includes embedding vectors  $\\mathcal{E}$  and  $\\mathcal{R}$  representing entities and relationships, along with a set of triples  $\\mathcal{F}$ , the KG representation learning algorithm employs various scoring functions  $\\phi$  to calculate scores for entity and relationship embeddings. The goal is to assign higher scores to valid triples compared to invalid ones. By utilizing a predefined loss\n\nfunction  $\\mathcal{L}$ , such as cross-entropy loss or hinge loss [37], the KG representation learning algorithm updates the embedding parameters iteratively until convergence is achieved.\n\nTypical scoring functions utilized for KG representation encompass translation-based models [2]. The system incorporates two translation-based models, namely TransE [2] and HolE [32]. However, to address concerns regarding scalability, two decomposition-based models, namely ComplEx [43] and DisMult [52], are also supported.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "text", "line_start": 191, "line_end": 199, "token_count_estimate": 407, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c20a14bb16ab8a5", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: figure\nFigure\n\nImage /page/4/Figure/13 description: A diagram illustrates a neural network architecture for graph processing, flowing from left to right. The process begins with an 'Input' block containing a graph 'G' with four green nodes. This input is fed into a block labeled 'GEN', which contains four variations of the input graph. The output of the 'GEN' block passes through a 'ReLU' activation function. This is followed by a second 'GEN' block and another 'ReLU' function. The next stage is a fully connected layer labeled 'FC', depicted with four input nodes and four output nodes, all interconnected. Finally, the 'Output' block shows the resulting graph where the nodes are colored differently (orange, yellow, and green) and labeled C1, C2, and C3, suggesting a node classification or clustering task.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "figure", "figure_caption": null, "line_start": 200, "line_end": 200, "token_count_estimate": 239, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0dd6d429d6e72bf6", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: figure\nFigure: Figure 5: The GEN Network model for urban flood event classification generates a corresponding classification (C1, C2, C3) for each node.\n\nFigure 5: The GEN Network model for urban flood event classification generates a corresponding classification (C1, C2, C3) for each node.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "figure", "figure_caption": "Figure 5: The GEN Network model for urban flood event classification generates a corresponding classification (C1, C2, C3) for each node.", "line_start": 202, "line_end": 202, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d1a551cdc304c919", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: text\n\n*GNN-based models*. Some graph neural network models can also be used for knowledge representation, the network structure used in our system is based on the GENeralized Graph Convolution Network (GEN) [21]. In the forward propagation process of the GEN network, the feature vector update equation for each urban flood event node is:\n\n$$\\mathbf{x}_{i}' = MLP(\\mathbf{x}_{i} + AGG(ReLU(\\mathbf{x}_{j} + \\mathbf{e}_{ji}) + \\epsilon : j \\in \\mathcal{N}(i)))$$\n(1)\n\nThe mechanism of information collation and updating in the GEN network is delineated by Equation (1). This procedure involves the renewal of a node's feature vector via a multi-layer perceptron (MLP) and an aggregation function (AGG). In this context,  $\\mathbf{x}_i$  signifies the feature vector of node i, while  $\\mathbf{x}_j$  is a symbol for the feature vector of node j. Additionally,  $\\mathbf{e}_{ji}$  denotes the edge feature vector, encapsulating the attribute information of the edge that connects node j to node i. Besides,  $\\epsilon$  is a learnable parameter vector employed to modify the scale and shift of the node feature vector. The model's AGG function makes use of a summation aggregation function.\n\nThe structure of the event classification model is shown in Fig. 5. The urban flood event classification model defines two layers of GEN networks and a fully connected layer. The input is a graph G, which includes the feature vectors of each node (node features), the edge indices of adjacent nodes (edge indices), and the feature vectors of each edge (edge attributes). The forward propagation process of the GEN model involves propagating and integrating information on graph-structured data. Nodes update their feature vectors based on information from neighboring nodes, capturing both local and global information within the graph structure.\n\n*Operations*. Typical functions in traditional graph systems [50] are supported by the UrbanFloodKG system, such as Cypher and embedding access. In addition, in order to adapt the system to urban flood risk assessment applications, we have developed three types of functions, which are abstracted as basic operations in Table 3.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "text", "line_start": 203, "line_end": 214, "token_count_estimate": 609, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5dd1a8d799541a96", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A diagram illustrates a knowledge graph with a schema level and an entity level. A legend explains the components: white circles are Schema Nodes, light blue are Entity Nodes, light green are Attribute Nodes, black arrows are Schema Relation Edges, and blue arrows are Entity Relation Edges. The schema level shows nodes for 'Event', 'Department', and 'Leader'. The relationships are: 'Leader' resolves 'Event', and 'Event' belongs to 'Department'. The entity level shows instances: 'Departmnet\\_#32:1' is an instance of 'Department' with the name 'Drainage Company'. 'Event\\_#30:1' is an instance of 'Event' with a duration of '0-1' and a startTime of '1621900800'. 'Leader\\_#31:1' is an instance of 'Leader' with the name 'Zhang' and phone '1391111111'. An entity relation edge connects 'Event\\_#30:1' to 'Leader\\_#31:1'.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "figure", "figure_caption": null, "line_start": 215, "line_end": 215, "token_count_estimate": 293, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1391111111", "1621900800"]}}
{"id": "985bd64de4037dde", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: figure\nFigure: Figure 4: The portion of UrbanFloodKG. Each Entity Node is linked to multiple Attribute Nodes via an Entity Relation Edge, where the edge name corresponds to the attribute name.\n\nFigure 4: The portion of UrbanFloodKG. Each Entity Node is linked to multiple Attribute Nodes via an Entity Relation Edge, where the edge name corresponds to the attribute name.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "figure", "figure_caption": "Figure 4: The portion of UrbanFloodKG. Each Entity Node is linked to multiple Attribute Nodes via an Entity Relation Edge, where the edge name corresponds to the attribute name.", "line_start": 217, "line_end": 217, "token_count_estimate": 116, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "351f85e441389377", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: table\nTable: Table 3: Operations.\n\n| Operation                               | Return             | Description                                              |\n|-----------------------------------------|--------------------|----------------------------------------------------------|\n| query(Sql_Command)                      | return result      | Executes Cypher query based on OrientDB on UrbanFloodKG. |\n| get_emb(entities, embedding_type,model) | return embedding   | Get the embeddings of entities or relations.             |\n| node_cla(feature_Vector,model)          | return level       | Return risk level of event.                              |\n| link_pred(src_ent, tar_ent, rel, model) | return probability | Calculates plausibility of an input triple.              |", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "table", "table_caption": "Table 3: Operations.", "columns": ["Operation", "Return", "Description"], "table_row_start": 1, "table_row_end": 4, "line_start": 221, "line_end": 226, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cdc61e37270f6a12", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer\nType: text\n\nWe use the interfaces provided by ampligraph [7] and PyG [12] to implement the latter three proposed operations.\n\n query. This operation accepts the Cypher query from the user, which returns the corresponding results on the Urban-FloodKG. For example, users can obtain information about urban flood events that occurred on Shi Nan Road via below query command.\n\n```\nMATCH {class:Entity,\nWHERE:(name='ShiNanRoad')}.\ninE('EntityEdge'){as:m,\nWHERE:(name='occurIn')}\nRETURN m.out\n```\n\n• get\\_emb. This operation offers a direct interface for accessing the embeddings of entities or relations in UrbanFloodKG. It particularly provides embeddings for models depending on the input model.\n\n- node\\_cla. This operation accomplishes the node classification task within the KG. By supplying the entity's vector feature, the UrbanFloodKG system invokes this operation to perform classification using the embeddings learned by a GNN-based model.\n- link\\_pred. This operation enables relational link prediction between two entities, src\\_ent and tar\\_ent, by calculating a score using the input model. And users can obtain the likelihood of a triple fact.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.4 Algorithm Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.4 Algorithm Layer"], "chunk_type": "text", "line_start": 227, "line_end": 244, "token_count_estimate": 337, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "76c4e9ed0dac2e81", "text": "Document: flood kg\nSection: 3 THE URBAN FLOOD KG SYSTEM > 3.5 Application Layer\nType: text\n\nThe proposed framework in this paper can be applied to various aspects of urban flood control, which can be summarized as follows:\n\n**Knowledge reasoning**. Based on the KG representation learning model, the possibility of a triplet can be predicted. After training with a large amount of data, users can enter a triplet by themselves,\n\nFile Name **Basic Sample Format** Records Event\\_ID | StartTime | Duration | Lanes | Depth | Area\\_ID | Influence\\_ID event.txt 10,000 | RiskLevel 1|1621900800 | 0-3| 1-3 | 0.3-0.5| 4 | 2 | mild Area\\_ID | Name 422 waterlogging-area.txt 1|Shinan Avenue Reason\\_ID | Name |Event\\_ID reason.txt 873 1|large drainage area|1 Influence\\_ID | Name influence txt 653 1|Power and communication system interruption Event\\_ID | Event\\_ID same\\_position.txt 2155 1|2 Event ID | Event ID close\\_time.txt 1415 1|2Image\\_ID | Embedding |Area\\_ID image.txt 10000 1 | [0.164,0.135,0.223,...] | 1\n\nTable 4: The files of UrbanFlood Dataset. Each entity has its own attributes and associated ID values with other related entities.\n\nfor example (?, leadTo, a certain event), users can input some possible causes of urban flood in the ?, and the model will return a possibility for reasoning the cause of urban flood for a certain event.\n\n*Event classification.* Users can enter various information about urban flood events, such as rainfall, depth, duration, etc. The model predicts the risk level of the event, assigning it a classification grade includes: mild, moderate, or severe. Based on the input information, the model analyzes the event's characteristics, conducts quantitative analysis, calculates the risk score, and maps it to the corresponding risk level.\n\n*Urban decision making*. The model accurately assesses and predicts the risks of urban flood events, aiding city decision-makers in understanding the risk characteristics and impact scope of such events and taking more effective countermeasures. KG-based risk assessment and prediction of urban flood events provides important data and knowledge support for city decision making, contributing to the sustainable development and safe operation of cities.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "3 THE URBAN FLOOD KG SYSTEM > 3.5 Application Layer", "section_headings": ["3 THE URBAN FLOOD KG SYSTEM", "3.5 Application Layer"], "chunk_type": "text", "line_start": 246, "line_end": 260, "token_count_estimate": 584, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["10000", "1621900800"]}}
{"id": "806dfe194aa54fd1", "text": "Document: flood kg\nSection: 4 EXPERIMENT\nType: text\n\nIn this section, firstly, the construction of the urban flood event dataset is introduced. Then, combined with specific cases, model comparison experiments of link prediction and node classification tasks are carried out to evaluate the effectiveness and applicability of the UrbanFloodKG system we designed.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT", "section_headings": ["4 EXPERIMENT"], "chunk_type": "text", "line_start": 262, "line_end": 264, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0bc04080e8e7f997", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.1 DataSet Construction & Environment\nType: text\n\nWe gather 10,000 urban flood event data from 2010 to 2018 in Guangzhou and make the dataset. The files included in Table 4 provide details about the dataset. Based on this dataset, we conduct subsequent experiments of link prediction and node classification. The experiments are conducted using PyTorch 2.0.0 on a\n\nhost equipped with an AMD Ryzen7 5800 CPU, 32GB RAM, and one NVIDIA GeForce RTX 4080 GPU.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.1 DataSet Construction & Environment", "section_headings": ["4 EXPERIMENT", "4.1 DataSet Construction & Environment"], "chunk_type": "text", "line_start": 266, "line_end": 270, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0219c1c1c6fc3748", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: text\n\nIn this part, we explore representative applications in the urban flood scenario and summarize the challenges in urban flood research that can be regarded as link prediction problems.\n\nFor performance comparison, we adopt the commonly used metrics in the respective task. By conducting performance comparison experiments with various models, we demonstrate the effectiveness of the UrbanFloodKG system.\n\n**Causal Prediction.** The causal prediction use case formulates the traditional causal prediction problem [31] into the link prediction problem on the UrbanFloodKG, which is stated as follows:\n\nProblem 1. UrbanFloodKG-based Reason Prediction Problem. Given the UrbanFloodKG  $\\mathcal{G} = \\{\\mathcal{E}, \\mathcal{R}, \\mathcal{F}\\}$ , a recorded information of urban flood event like reason  $e_r$  lead to the event  $e_e$  can be expressed as  $(e_r, r_{leadTo}, e_e)$  with  $e_r$ , and  $e_e$  as entities and  $r_{leadTo}$  as relation therein.\n\nHence, the reason prediction problem of potential reasons of an urban flood event  $e_e$ , can be formulated as the link prediction problem of  $(?, r_{leadTo}, e_e)$  in UrbanFloodKG.\n\nThe overall framework is depicted in Fig. 6, where the application layer utilizes the link\\_pred operation to predict if there exist leadTo links between reason entities and event entities.\n\nTo evaluate the proposed framework, we extract a subset of data from our self-constructed UrbanFlood dataset. The dataset is split into train/validation/test sets, following a ratio of 7:1:2.\n\nBy using four KG representation learning models: TransE, ComplEx, DistMult, and HolE, we evaluate the effectiveness of our system for reason prediction using the MRR, MR, and Hits@n metrics. Table 5 presents results and illustrates the successful performance of our system in reason prediction.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "text", "line_start": 272, "line_end": 288, "token_count_estimate": 505, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "185ce003f3a5b86c", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A flowchart illustrating a data processing pipeline. An initial block labeled \"UrbanFlood System\" has two arrows pointing to two separate databases, represented by cylinders. The top database is labeled \"Reason\" and the bottom one is labeled \"Event\". From the \"Reason\" database, an arrow labeled \"get\\_emb\" points to a parallelogram labeled \"Reason embedding\". Similarly, from the \"Event\" database, an arrow labeled \"get\\_emb\" points to a parallelogram labeled \"Event embedding\". Both the \"Reason embedding\" and \"Event embedding\" blocks have arrows that converge into a single line. This line is labeled \"link\\_pred\" and points to a final rounded rectangle labeled \"leadTo\".", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 289, "line_end": 289, "token_count_estimate": 229, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85de51e723a38d2b", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: figure\nFigure: Figure 6: The illustration of leveraging UrbanFloodKG system for reason prediction problem.\n\nFigure 6: The illustration of leveraging UrbanFloodKG system for reason prediction problem.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "figure", "figure_caption": "Figure 6: The illustration of leveraging UrbanFloodKG system for reason prediction problem.", "line_start": 291, "line_end": 291, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73ce155c0fadad11", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: table\nTable: Table 5: The result comparison of reason prediction task.\n\n| Model    | MRR  | MR      | Hits@10 | Hits@3 | Hits@1 |\n|----------|------|---------|---------|--------|--------|\n| ComplEx  | 0.36 | 1166.96 | 0.69    | 0.54   | 0.16   |\n| DistMult | 0.39 | 1010.31 | 0.72    | 0.56   | 0.19   |\n| HolE     | 0.49 | 884.47  | 0.85    | 0.71   | 0.27   |\n| TransE   | 0.38 | 457.92  | 0.66    | 0.49   | 0.13   |", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "table", "table_caption": "Table 5: The result comparison of reason prediction task.", "columns": ["Model", "MRR", "MR", "Hits@10", "Hits@3", "Hits@1"], "table_row_start": 1, "table_row_end": 4, "line_start": 295, "line_end": 300, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e98df2ebebb2647", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: text\n\nProblem 2. UrbanFloodKG-based Influence Prediction Problem. Given the UrbanFloodKG  $\\mathcal{G} = \\{\\mathcal{E}, \\mathcal{R}, \\mathcal{F}\\}$ , a recorded information of urban flood event like event  $e_e$  led to the influence  $e_i$ , can be expressed as  $(e_e, r_{leadTo}, e_i)$  with  $e_e$ , and  $e_i$  as entities and  $r_{leadTo}$  as relation therein.\n\nHence, the influence prediction problem of potential influences caused by an urban flood event  $e_e$ , can be formulated as the link prediction problem of  $(e_e, r_{leadTo}, ?)$  in UrbanFloodKG.\n\nThe overall framework is illustrated in Fig. 7. Especially, the application layer calls the operation link\\_pred to predict if there exist leadTo links between event entities and influence entities.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "text", "line_start": 301, "line_end": 307, "token_count_estimate": 264, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f2356c0d68d8c4bc", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: figure\nFigure\n\nImage /page/7/Figure/9 description: A flowchart illustrating a system for predicting relationships. The process starts with an \"UrbanFlood System\" box, which branches into two parallel paths. The top path leads to a database labeled \"Event,\" which is then processed by a \"get\\_emb\" function to create an \"Event embedding.\" The bottom path leads to a database labeled \"Influence,\" which is also processed by a \"get\\_emb\" function to create an \"Influence embedding.\" Both the \"Event embedding\" and \"Influence embedding\" are then combined and fed into a function labeled \"link\\_pred,\" which results in a final output labeled \"leadTo.\"", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 308, "line_end": 308, "token_count_estimate": 198, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "16f6b7a146690216", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: figure\nFigure: Figure 7: The illustration of leveraging UrbanFloodKG system for influence prediction problem.\n\nFigure 7: The illustration of leveraging UrbanFloodKG system for influence prediction problem.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "figure", "figure_caption": "Figure 7: The illustration of leveraging UrbanFloodKG system for influence prediction problem.", "line_start": 310, "line_end": 310, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f276cc3bb2446958", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: text\n\nTo evaluate the proposed framework, we additionally extract a subset of data from our self-constructed UrbanFlood dataset. The dataset is split into train/validation/test sets, following a ratio of 7:1:2. We evaluate the effectiveness of our system for influence prediction by employing four KG representation learning models. Table 6 presents results and illustrates the successful performance of our system in influence prediction.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "text", "line_start": 311, "line_end": 315, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58b3971c2c764f71", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.2 Link Prediction Analysis\nType: table\nTable: Table 6: The result comparison of influence prediction task.\n\n| Model    | MRR  | MR     | Hits@10 | Hits@3 | Hits@1 |\n|----------|------|--------|---------|--------|--------|\n| ComplEx  | 0.55 | 158.83 | 0.67    | 0.60   | 0.49   |\n| DistMult | 0.63 | 135.06 | 0.74    | 0.67   | 0.56   |\n| HolE     | 0.70 | 165.20 | 0.82    | 0.75   | 0.64   |\n| TransE   | 0.43 | 62.15  | 0.57    | 0.47   | 0.35   |", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.2 Link Prediction Analysis", "section_headings": ["4 EXPERIMENT", "4.2 Link Prediction Analysis"], "chunk_type": "table", "table_caption": "Table 6: The result comparison of influence prediction task.", "columns": ["Model", "MRR", "MR", "Hits@10", "Hits@3", "Hits@1"], "table_row_start": 1, "table_row_end": 4, "line_start": 316, "line_end": 321, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b5bd90f981093c38", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: text\n\nIn this part, we summary the issues about risk assessment in ur-banFlood research that can be considered as node classification problems.\n\n**Risk-level classification.** For the risk assessment of urban flood events, we believe that two hypotheses can be proposed:\n\n- The risk of individual events increases as the number of urban flood events linked to time and location rises. For example, when multiple urban flood events transpire in a particular area within a short period, it suggests that the area possesses inadequate flood prevention measures, thereby escalating the risk of subsequent events.\n- The risk increases with the degree of the event node. For instance, when an urban flood event is connected to numerous surrounding urban flood entities, it suggests that the event is located in a high-risk event aggregation area and consequently carries a higher level of risk itself.\n\nIn summary, the risk assessment of events should consider both time and spatial elements, not just the events themselves. In our constructed UrbanFlood dataset, the expert evaluation level of the risk of each event, represented by the Risk-level entity, has already been included. Starting from the events' own attributes and considering the related events, we can discover the classification rules between events and effectively assess future urban flood events.\n\nThe overall framework is illustrated in Fig. 8. Especially, the application layer calls the operation node\\_cla to classify each event node.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "text", "line_start": 324, "line_end": 335, "token_count_estimate": 336, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ef9e4fc1c163fdf", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: figure\nFigure\n\nImage /page/7/Figure/21 description: A block diagram illustrates a data processing workflow. The process starts with a rectangle labeled \"UrbanFlood System\" which sends a \"query\" to a database labeled \"Event-related entity\". Data from this database and another database labeled \"Event\" are then processed by a \"Feature Engineering\" block. The output is a parallelogram labeled \"Feature Vector\". This vector is then used in a process labeled \"node\\_cla\" to produce the final output, which is a rounded rectangle labeled \"Risk-level\".", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 336, "line_end": 336, "token_count_estimate": 165, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e875e12dd5cd7b3", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: figure\nFigure: Figure 8: The illustration of leveraging UrbanFloodKG system for Risk-level classification problem.\n\nFigure 8: The illustration of leveraging UrbanFloodKG system for Risk-level classification problem.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "figure", "figure_caption": "Figure 8: The illustration of leveraging UrbanFloodKG system for Risk-level classification problem.", "line_start": 338, "line_end": 338, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69d5518edb2dafe3", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: text\n\nTo evaluate the proposed framework, we sample a subset of data from our self-constructed UrbanFlood dataset. The basic statistics of the dataset are summarized in Table 7. The dataset is split into train/validation/test sets, following a ratio of 7:1:2.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "text", "line_start": 339, "line_end": 343, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "949c3d587ca10c05", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: table\nTable: Table 7: Statistics of UrbanFlood dataset for risk-level classification.\n\n| #Nodes | #Edges | #Attrs | Train | Valid | Test |\n|--------|--------|--------|-------|-------|------|\n| 10,000 | 3570   | 6      | 7000  | 1000  | 2000 |", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "table", "table_caption": "Table 7: Statistics of UrbanFlood dataset for risk-level classification.", "columns": ["#Nodes", "#Edges", "#Attrs", "Train", "Valid", "Test"], "table_row_start": 1, "table_row_end": 1, "line_start": 344, "line_end": 346, "token_count_estimate": 112, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b56850e08660a6f3", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: text\n\nThe experiment compares multiple models and evaluates them using accuracy, precision, recall, and F1-score metrics. The experimental results are shown in Table 8.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "text", "line_start": 347, "line_end": 351, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eebf3807aa4464c8", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: table\nTable: Table 8: The result comparison of risk-level classification prediction task.\n\n| Model     | Accuracy | Precision | Recall | F1-score |\n|-----------|----------|-----------|--------|----------|\n| GATv2 [4] | 0.3564   | 0.5883    | 0.3564 | 0.2669   |\n| SG [48]   | 0.3832   | 0.7636    | 0.3832 | 0.2123   |\n| AGNN [42] | 0.6741   | 0.6640    | 0.6741 | 0.6558   |\n| TAG [10]  | 0.3815   | 0.7640    | 0.3815 | 0.2107   |\n| GIN [51]  | 0.3914   | 0.7618    | 0.3914 | 0.2202   |\n| GEN       | 0.9136   | 0.9129    | 0.9136 | 0.9126   |", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "table", "table_caption": "Table 8: The result comparison of risk-level classification prediction task.", "columns": ["Model", "Accuracy", "Precision", "Recall", "F1-score"], "table_row_start": 1, "table_row_end": 6, "line_start": 352, "line_end": 359, "token_count_estimate": 243, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d61eb4af756597e8", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: text\n\nThe classification network model based on the GEN model achieves an accuracy of 91%, surpassing other graph neural network models. Additionally, it reveals that by considering the features and properties of urban flood event nodes, it is possible to represent nodes of different urban flood events as vectors for accurate risk assessment.\n\nThe classification results are visualized in Fig. 9. After the multidimensional feature vectors of each urban flood event node are reduced by PCA, they are divided into three categories based on the predicted level: mild, moderate, and severe. This approach can effectively assess and predict the risk level of urban flood events.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "text", "line_start": 360, "line_end": 364, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a19858bc6d6b5dc", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: figure\nFigure\n\nImage /page/8/Figure/9 description: A scatter plot with an x-axis ranging from -15 to 15 and a y-axis ranging from -10 to approximately 12. The plot displays three categories of data points, indicated by a legend in the top right corner. Blue dots represent 'mild', orange dots represent 'moderate', and green dots represent 'severe'. The green 'severe' points are clustered on the left side of the plot, primarily for x-values less than 0. The orange 'moderate' points form a vertical band in the center of the plot. The blue 'mild' points are clustered on the right side of the plot, primarily for x-values greater than 5.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 365, "line_end": 365, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a52f40ee9509e195", "text": "Document: flood kg\nSection: 4 EXPERIMENT > 4.3 Node Classification Analysis\nType: figure\nFigure: Figure 9: The Visualization of the Classification Results.\n\nFigure 9: The Visualization of the Classification Results.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "4 EXPERIMENT > 4.3 Node Classification Analysis", "section_headings": ["4 EXPERIMENT", "4.3 Node Classification Analysis"], "chunk_type": "figure", "figure_caption": "Figure 9: The Visualization of the Classification Results.", "line_start": 367, "line_end": 367, "token_count_estimate": 50, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50725cb1f7150bab", "text": "Document: flood kg\nSection: 5 RELATED WORK\nType: text\n\nWe summarize the related work into two aspects: KG-based systems and flood risk assessment-based systems.\n\nKnowledge graph-based systems. Traditional KG-based systems encompass Freebase [1], DBpedia [20], WordNet [28], Wikidata [44], YAGO [41], and others. These systems primarily concentrate on general or encyclopedic knowledge, which is constructed from vast amounts of unstructured text data and structured semantic network data, such as Wikipedia. For instance, the first three systems gather structured knowledge from user contributions on Wikipedia, WordNet provides formal linguistic knowledge about words, and YAGO integrates factual information extracted from Wikipedia using rule-based and heuristic methods, along with WordNet.\n\nFlood risk assessment-based systems. With the development of flood risk assessment in recent years [9, 33], the latest research has designed systems for terrain data management [5, 18, 40]. Wang [46] leverages multiple information sources to determine the parameters of the flood model. They also investigate the influence of different approaches to handling terrain datasets on the outcomes of flood modeling. Notably, their study emphasizes the significance of capturing the micro-features of cities to improve the accuracy of modeling results. Lyu [25] employs the FAHP-FCA method to incorporate various factors, including regional and subway longitudinal subsidence, in order to assess the flood risk of the Shanghai subway system within a subsidence environment. Rahadianto [35] uses analytical hierarchy process to help the system to assess how much impact and damage that will be hit the risky area and give the recommendation to government and people how to increase the preparedness so it can reduce the damage from flood. Li [22] proposes a hydraulic model and flood calculation program, demonstrating the feasibility of the proposed method and identifying areas with inadequate flood control capacity in the Xiushui River.", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "5 RELATED WORK", "section_headings": ["5 RELATED WORK"], "chunk_type": "text", "line_start": 370, "line_end": 376, "token_count_estimate": 448, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a48c8623325da13", "text": "Document: flood kg\nSection: 6 CONCLUSION\nType: text\n\nIn this paper, we propose the UrbanFloodKG system, a KG-based urban flood system. We propose a solution to construct KG from urban flood data with different structures and patterns and implements multi-modal data fusion. Based on the constructed UrbanFloodKG, the system seamlessly combines knowledge representation models and graph neural network models to offer comprehensive relevant operations. Finally, we classify the practical application problems of flood risk assessment into link prediction and node classification tasks and evaluate the effectiveness and feasibility of the models using multiple models, providing a new perspective for flood risk assessment", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "6 CONCLUSION", "section_headings": ["6 CONCLUSION"], "chunk_type": "text", "line_start": 378, "line_end": 380, "token_count_estimate": 149, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b5197113a1b3b88", "text": "Document: flood kg\nSection: 7 ACKNOWLEDGEMENTS\nType: text\n\nThe paper is supported by the Research on Key Technologies for Improving Flood Control Safety System of Nansha District, Guangzhou (823005916); the Jiangsu Province Water Conservancy Science and Technology Project (2022003); the Major Science and Technology Project of the Ministry of Water Resources (SKS-2022139).", "metadata": {"source_file": "data/('flood_kg', '.pdf')_extraction.md", "document_title": "flood kg", "section_path": "7 ACKNOWLEDGEMENTS", "section_headings": ["7 ACKNOWLEDGEMENTS"], "chunk_type": "text", "line_start": 382, "line_end": 384, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["2022003", "2022139", "823005916"]}}
{"id": "dac70fe1d3f1071c", "text": "Document: geosciences-15-00211-v2\nType: text\n\nAbstract: Glacial Lake Outburst Floods (GLOFs) have emerged as a critical threat to highmountain communities and ecosystems, driven by accelerated glacier retreat and lake expansion under climate change. This review synthesizes advancements in remote sensing technologies and methodologies for GLOF monitoring, risk assessment, and mitigation. Through a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)guided systematic literature review and bibliometric analysis of studies from 2010 to 2025, we evaluate the transformative role of remote sensing in overcoming traditional fieldbased limitations. Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. This review underscores the importance of these technologies in improving GLOF risk assessments and supporting early warning systems, which are crucial for safeguarding vulnerable high-mountain communities. It addresses existing challenges, such as data integration and model calibration, and advocates for collaborative efforts between scientists, policymakers, and local stakeholders to translate technological advancements into effective mitigation strategies, ensuring the sustainability of these at-risk regions.\n\n**Keywords:** GLOFs; monitoring; risk assessment; remote sensing; climate change", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "geosciences-15-00211-v2", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 327, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00211"]}}
{"id": "b469d9ed45436ac1", "text": "Document: geosciences-15-00211-v2\nType: figure\nFigure\n\nImage /page/0/Picture/10 description: An icon with a white checkmark inside a yellow circle is positioned to the left of the text 'check for updates'. The words 'check for' are in a standard black font, while 'updates' is in a bold black font, all set against a white background.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "geosciences-15-00211-v2", "section_path": "", "section_headings": [], "chunk_type": "figure", "figure_caption": null, "line_start": 7, "line_end": 7, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00211"]}}
{"id": "041f68c97a815599", "text": "Document: geosciences-15-00211-v2\nType: text\n\nAcademic Editor: Sabina Porfido\n\nReceived: 1 April 2025 Revised: 27 May 2025 Accepted: 1 June 2025 Published: 5 June 2025\n\nCitation: Nurakynov, S.; Sydyk, N.; Baygurin, Z.; Balakay, L. Advancements in Remote Sensing for Monitoring and Risk Assessment of Glacial Lake Outburst Floods. Geosciences 2025, 15, 211. https:// doi.org/10.3390/geosciences15060211\n\nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "geosciences-15-00211-v2", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 8, "line_end": 16, "token_count_estimate": 188, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00211"]}}
{"id": "f318901b0abdbf2d", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nGLOFs have emerged as one of the most critical natural hazards in high mountain regions worldwide. These catastrophic events, characterized by the sudden release of water from glacial lakes, pose a severe threat to downstream communities, infrastructure, and ecosystems [1]. Driven by accelerated glacier retreat and the rapid expansion of glacial lakes under changing climatic conditions, GLOFs have become increasingly frequent and destructive [2,3]. The growing number, size, and volume of glacial lakes, coupled with the instability of their natural dams, have heightened the risk of dam failure, leading to devastating floods with far-reaching impacts [4]. GLOFs have been documented for centuries, yet their frequency and magnitude have increased in recent decades as a direct consequence of accelerating glacier retreat driven by climate change [5].\n\nThe significance of GLOFs lies in their potential to cause widespread devastation. Downstream ecosystems are particularly vulnerable to the sudden influx of water and sediment, which can alter aquatic habitats, disrupt riparian vegetation, and change water chemistry [6]. For human communities, the consequences of GLOFs can be dire. Floodwaters can destroy roads, bridges, agricultural fields, and hydroelectric infrastructure, leading to substantial economic losses. In some cases, entire villages have been swept\n\naway, resulting in significant loss of life [7]. Historical records and recent assessments indicate that GLOFs have caused substantial damage in regions such as High Mountain Asia (HMA), the Andes, and the European Alps [8–10].\n\nThe socio-economic impacts of GLOFs are particularly pronounced in densely populated regions like HMA, where millions of people live in close proximity to glacial lakes. Recent events, such as the 2023 South Lhonak GLOF in Sikkim, India, and the 2016 Gongbatongsha GLOF in the Tibetan Himalayas, underscore these risks [11,12]. The South Lhonak outburst destroyed critical infrastructure, including roads and hydropower projects, displacing a large number of people and causing great economic losses, while the Gongbatongsha flood inundated villages and agricultural land, disrupting livelihoods for thousands. Even modest flood events can have dramatic consequences in these areas, where infrastructure is often inadequate to withstand the force of a GLOF [13]. Moreover, the long-term impacts of GLOFs on water resources, agriculture, and energy production can exacerbate existing vulnerabilities, particularly in developing countries where adaptive capacity is limited [14]. As the number and size of glacial lakes continue to grow under the influence of climate change, the potential hazard posed by GLOFs is expected to increase, making them a critical subject of study for disaster risk reduction and management [15].\n\nClimate change is the primary driver behind the dramatic changes observed in glacial environments over recent decades [16]. Global warming has led to increased glacier melting, resulting in the retreat of glacier fronts and the consequent formation and expansion of glacial lakes [17]. The sensitivity of glaciers to temperature variations means that even small increases in average temperatures can lead to substantial ice loss [18]. As glaciers retreat, the over-deepened bedrock is exposed, creating ideal conditions for the accumulation of meltwater in the form of glacial lakes [2].", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 18, "line_end": 50, "token_count_estimate": 803, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "760d0f54ade1f258", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\na critical subject of study for disaster risk reduction and management [ 15 ] . Climate change is the primary driver behind the dramatic changes observed in glacial environments over recent decades [ 16 ] . Global warming has led to increased glacier melting , resulting in the retreat of glacier fronts and the consequent formation and expansion of glacial lakes [ 17 ] . The sensitivity of glaciers to temperature variations means that even small increases in average temperatures can lead to substantial ice loss [ 18 ] . As glaciers retreat , the over - deepened bedrock is exposed , creating ideal conditions for the accumulation of meltwater in the form of glacial lakes [ 2 ] .\n\nThis dynamic has profound implications for GLOF hazards. With more glacial lakes forming and existing lakes expanding in size, the potential for dam failure increases significantly. Moreover, the feedback mechanisms associated with lake expansion—such as reduced albedo and enhanced meltwater production—can further accelerate glacier retreat [18,19]. These changes are not uniform across regions; some areas, such as High Mountain Asia (HMA) and the Karakoram, exhibit unique behaviors. In some parts of the Karakoram, for instance, certain glaciers have even shown a temporary mass gain due to complex climatic and topographic interactions [1,18]. However, the overall trend is one of increasing instability and heightened flood risk.\n\nThe interplay between climate change and glacial dynamics is a critical component of GLOF research. Not only does it influence the physical parameters of glacial lakes—such as their surface area and volume—but it also affects the frequency and intensity of extreme weather events that can trigger dam failure [14,20]. Heavy rainfall, rapid snowmelt, and changes in precipitation patterns all contribute to the stress on glacial dams, making the study of climate change impacts an essential part of understanding GLOF hazards [21]. Furthermore, the cascading effects of climate change—ranging from altered hydrological regimes to shifts in ecosystem composition—add additional layers of complexity to the risk posed by GLOFs [22,23].\n\nHistorically, the identification and monitoring of glacial lakes and GLOFs relied heavily on field-based investigations. Early studies utilized GPS, laser rangefinders, theodolites, and total stations for direct measurements of glacial lake characteristics [20,24]. Field surveys provide detailed insights into the geomorphology, lake bathymetry, and the hydrodynamic behavior of these lakes [25,26]. These in situ methods are essential for understanding the processes leading to lake formation and dam failure, as well as for calibrating empirical models that estimate water volumes and potential flood discharges [14,27]. However, field-based assessments are inherently challenging in high mountain environments. The\n\nGeosciences 2025, 15, 211 3 of 36\n\nremote and often inaccessible terrain, coupled with severe weather conditions, limits the frequency and spatial coverage of ground surveys [6,18]. Moreover, the dynamic nature of glacial environments means that conditions can change rapidly, rendering periodic field campaigns insufficient to capture the continuous evolution of glacial lakes [28]. These limitations underscore the need for methodologies that can offer extensive temporal and spatial monitoring, particularly in regions where the risk of GLOFs is high.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 18, "line_end": 50, "token_count_estimate": 817, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "01735a1f2e2b7d24", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nand potential flood discharges [ 14 , 27 ] . However , field - based assessments are inherently challenging in high mountain environments . The Geosciences 2025 , 15 , 211 3 of 36 remote and often inaccessible terrain , coupled with severe weather conditions , limits the frequency and spatial coverage of ground surveys [ 6 , 18 ] . Moreover , the dynamic nature of glacial environments means that conditions can change rapidly , rendering periodic field campaigns insufficient to capture the continuous evolution of glacial lakes [ 28 ] . These limitations underscore the need for methodologies that can offer extensive temporal and spatial monitoring , particularly in regions where the risk of GLOFs is high .\n\nIn recent decades, remote sensing has emerged as a transformative tool for the detection, monitoring, and assessment of glacial lakes and GLOFs [14,28]. Unlike traditional field-based methods, remote sensing offers a synoptic and continuous view of vast and remote areas [29,30]. Satellite platforms such as Landsat, Sentinel-1, Sentinel-2, and MODIS, along with high-resolution commercial satellites, have revolutionized our ability to monitor glacial lakes on a global scale [31,32]. These technologies provide multi-temporal and multi-spectral data, enabling researchers to track changes in lake size, volume, and surrounding glacier dynamics with unprecedented accuracy. Various studies have demonstrated accuracy in satellite-derived lake boundary delineation and volume estimation when cross-verified with field measurements and previously available data, underscoring the reliability of remote sensing for GLOF risk assessment [9,33]. Remote sensing allows for continuous monitoring, even in regions that are difficult or dangerous to access [32]. This capability is particularly critical for GLOF risk assessment, as it enables the detection of early warning signs, such as sudden changes in lake area or dam stability, which can precede an outburst event [34,35].\n\nMoreover, the integration of optical and radar data, along with advancements in machine learning and deep learning, has further enhanced the precision and reliability of remote sensing applications [19]. These innovations have not only improved the accuracy of glacial lake mapping but have also paved the way for automated detection systems and real-time monitoring. By combining remote sensing data with field observations and hydrological models, researchers can now estimate key parameters such as lake volume, potential peak discharge, and dam failure dynamics, which are essential for developing effective early warning systems [34]. The growing availability of remote sensing data and the rapid evolution of analytical techniques have made it possible to assess GLOF risks at both regional and global scales. This capability is particularly important in the context of climate change, which is driving the rapid expansion of glacial lakes and increasing the likelihood of GLOFs. As such, remote sensing has become an indispensable tool for disaster risk reduction, providing critical insights that inform mitigation strategies and safeguard vulnerable communities [35].", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 18, "line_end": 50, "token_count_estimate": 740, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "759942f751aadf46", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nresearchers can now estimate key parameters such as lake volume , potential peak discharge , and dam failure dynamics , which are essential for developing effective early warning systems [ 34 ] . The growing availability of remote sensing data and the rapid evolution of analytical techniques have made it possible to assess GLOF risks at both regional and global scales . This capability is particularly important in the context of climate change , which is driving the rapid expansion of glacial lakes and increasing the likelihood of GLOFs . As such , remote sensing has become an indispensable tool for disaster risk reduction , providing critical insights that inform mitigation strategies and safeguard vulnerable communities [ 35 ] .\n\nConsidering the increasing threat posed by GLOFs, there is an urgent need to consolidate current knowledge on glacial lake monitoring, risk assessment, and mitigation strategies. Traditional methods of field observation, while invaluable, are no longer sufficient to capture the rapid changes occurring in glacial environments [29,36]. The integration of advanced remote sensing techniques has significantly enhanced our monitoring capabilities, enabling more precise, timely, and comprehensive assessments of glacial lakes and their associated hazards [37]. This review paper aims to provide a comprehensive synthesis of the state-of-the-art remote sensing techniques for monitoring and managing glacial lakes, with a specific focus on GLOF hazards. As the scientific community continues to unravel the complex interplay between glacier dynamics, climate change, and flood hazards, this review will serve as a comprehensive reference that outlines current achievements, identifies existing challenges, and suggests future directions for research in the monitoring and management of glacial lake outburst floods.\n\nThe following sections of this review will build on this introduction by exploring in detail the technological advancements in remote sensing, the evolution of monitoring\n\nGeosciences 2025, 15, 211 4 of 36\n\nmethodologies, and the integrated approaches used in risk assessment. Through an interdisciplinary lens that encompasses physical processes, technological innovations, and socio-economic considerations, the review aims to offer a robust framework for understanding and mitigating the risks associated with GLOFs in a rapidly changing world.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 18, "line_end": 50, "token_count_estimate": 522, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6776a79444e4375d", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis\nType: text\n\nThis study aims to provide a comprehensive review of the current state of knowledge regarding remote sensing applications for GLOF monitoring and risk assessment. We adopted a rigorous and transparent approach to systematically identify, screen, and analyze the literature for this review. A systematic approach was adopted to ensure the objectivity and reproducibility of the research, encompassing both a PRISMA-based literature search (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and a subsequent bibliometric analysis. This dual approach allowed for a robust synthesis of current knowledge while also providing quantitative insights into the trends and patterns within the field.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis"], "chunk_type": "text", "line_start": 52, "line_end": 54, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5c66d5c67fb40516", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.1. Database Search and Identification\nType: text\n\nThe literature search was conducted using the Scopus database, one of the most comprehensive and widely used sources for peer-reviewed scientific literature. The search was limited to the period from 2010 to 2025 to capture the most recent advancements in remote sensing technologies and their applications in GLOF monitoring and risk assessment. The search strategy was designed to identify studies that explicitly address the use of remote sensing for GLOF-related research. The search string employed was (GLOF\\* OR glaci\\* lake\\* outburst flood\\*) AND (monitor\\* OR risk\\* OR assess\\* OR detect\\* OR \"hazard assessment\" OR \"risk management\") AND (remote sensing OR \"satellite imagery\" OR \"aerial photography\" OR \"SAR\" OR \"InSAR\"). This search string ensured that the retrieved studies were relevant to the core themes of the review, including GLOF monitoring, risk assessment, and the application of remote sensing technologies such as satellite imagery, aerial photography, Synthetic Aperture Radar (SAR), and Interferometric SAR (InSAR). The initial search yielded a total of 246 records (X = 246).", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.1. Database Search and Identification", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.1. Database Search and Identification"], "chunk_type": "text", "line_start": 56, "line_end": 58, "token_count_estimate": 298, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2a511e93feafeb1", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process\nType: text\n\nThe selection of articles for inclusion in this review followed PRISMA guidelines, ensuring a transparent and rigorous selection process. Figure 1 illustrates the PRISMA flow diagram, which outlines the step-by-step process of identifying, screening, and finalizing the studies. A total of 246 records were initially identified through a comprehensive search of the Scopus database, covering journal articles, conference papers, and book chapters published between 2010 and 2025. After removing duplicates and records that were irrelevant based on language (non-English studies) and scope, 68 records were excluded, leaving 178 records for initial screening. The titles, abstracts, and keywords of these records were then screened to assess their relevance to the review's focus on remote sensing for GLOF monitoring and risk assessment. During this stage, 75 records were excluded due to irrelevance, such as studies focusing on non-glacial floods or unrelated remote sensing applications, resulting in 103 records for full-text review. The full texts of these records were carefully reviewed to determine their suitability for inclusion, with 18 records excluded for not explicitly addressing remote sensing applications for GLOFs or lacking sufficient methodological detail. Consequently, 85 records were retained for inclusion in the review. To ensure comprehensiveness, the reference lists of the included studies were examined, leading to the identification of seven additional records that met the inclusion\n\nGeosciences **2025**, 15, 211 5 of 36\n\ncriteria. After completing the screening process, a total of 92 studies were included in the review, forming the basis for the qualitative synthesis and bibliometric analysis presented in subsequent sections.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.2. Screening and Selection Process"], "chunk_type": "text", "line_start": 60, "line_end": 66, "token_count_estimate": 413, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62f645098377be9f", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A flowchart illustrating a literature search and screening process. The process starts with 'Database Search and Identification' from Scopus for the period 2010-2025, identifying a total of 246 records (X = 246). The next step is 'Initial Screening', where 68 articles were removed due to irrelevance by language and duplicates (Z = 68). This leaves 178 records to be screened (Y = X - Z = 178). A 'Second Screening' follows, where 75 unavailable or irrelevant articles were removed based on title, abstract, and keyword (W = 75), resulting in 103 records screened (Z = Y - W = 103). From this point, the flow splits. On one side, 'Final Screening' excludes 18 articles after a full-text reading (M = 18). On the other side, 7 additional articles were reviewed after checking references (P = 7). The process concludes with the 'Total number of included studies', which is 92 (N = Z - M + P = 92).", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.2. Screening and Selection Process"], "chunk_type": "figure", "figure_caption": null, "line_start": 67, "line_end": 67, "token_count_estimate": 283, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42b3c8f0979e9fb2", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process\nType: figure\nFigure: Figure 1. PRISMA Workflow for Literature Search Strategy.\n\nFigure 1. PRISMA Workflow for Literature Search Strategy.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.2. Screening and Selection Process"], "chunk_type": "figure", "figure_caption": "Figure 1. PRISMA Workflow for Literature Search Strategy.", "line_start": 69, "line_end": 69, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e89bc7f614ed33ff", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: text\n\nTo complement the systematic literature review, a bibliometric analysis of 92 studies was conducted to identify key themes, map the intellectual structure of the field, and visualize the geographical distribution of research on GLOFs and remote sensing. The bibliometric analysis was carried out using a combination of tools and software to ensure a comprehensive and multi-dimensional evaluation. An open-source Geographic Information System (GIS) software, QGIS (version Desktop 3.28.2), was employed to prepare maps highlighting the geographical distribution of study areas, giving a clear view of where GLOF-related research has been focused. This spatial analysis helps identify regions that have received significant attention and those that remain understudied, offering insights into potential gaps in the literature. For the analysis of literature metrics, Python Version 3.10 was utilized, leveraging libraries such as nltk, wordcloud, bibtexparser, pandas, tabulate, and matplotlib. These libraries enabled the extraction, processing, and visualization of key bibliometric data, including publication trends, citation patterns, keyword co-occurrence, and collaboration networks. The nltk library was used for natural language processing tasks, such as keyword extraction and text analysis, while wordcloud facilitated the visualization of frequently occurring terms and themes. The bibtexparser library was employed to parse bibliographic data, and pandas was used for data manipulation and analysis. Finally, matplotlib and tabulate were utilized to generate visualizations and tables summarizing the findings. By combining spatial analysis with advanced bibliometric techniques, this approach offers a holistic understanding of the field's evolution, intelGeosciences **2025**, *15*, 211 6 of 36\n\nlectual structure, and geographical focus, while also identifying areas for future research and collaboration.\n\nThe geographical distribution of study areas (Figure 2) provides critical insights into the regions where research on GLOFs and remote sensing has been concentrated. We employed natural Jenks symbology and classified it into five groups: 0, 0-2, 2-13, 13-26, 26-32. Based on the analysis of the studies included in this review, the research landscape is heavily skewed toward specific high-mountain regions, particularly in Asia, which has emerged as the focal point for GLOF-related studies. The highest concentration of research is observed in the Hindu Kush, Karakoram, and Himalayan regions, with significant contributions from countries such as India, China, Nepal, and Pakistan. These regions are particularly vulnerable to GLOFs due to their extensive glacial coverage, rapid glacier retreat under climate change, and high population density in downstream areas. Central Asian countries, including Kazakhstan, Kyrgyzstan, and Tajikistan, have also been the focus of research, though to a lesser extent compared to the Himalayas. Bhutan, despite its small size, has been a notable focus due to its high vulnerability to GLOFs. In Europe, research has primarily centered on the Alpine region, with studies conducted in Switzerland, Austria, France, and Norway. In the Americas, research has been limited, with a few studies focusing on Canada, the United States, and Peru, the latter highlighting the risks posed by glacial lakes in the Andes. This geographical distribution highlights a clear concentration of research in High Mountain Asia (HMA), particularly in the Himalayas and surrounding regions, which are among the most vulnerable to GLOFs.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "text", "line_start": 72, "line_end": 78, "token_count_estimate": 824, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "717c35fd7c6f5351", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/5/Figure/3 description: A world map displaying 'The number of study areas' per country using a color-coded system. The map is projected with longitude values from -180.00 to 180.00 on the x-axis and latitude values from -100.00 to 140.00 on the y-axis. A legend in the bottom-left corner indicates the color scale for the number of study areas: 0 is light pink, 1-2 is light blue, 3-13 is a medium blue, 14-26 is teal, and 27-32 is dark green. The map shows the highest concentration of study areas in South and Central Asia. India is colored dark green, indicating the highest number of study areas (27-32). China, Pakistan, Nepal, and Bhutan are colored teal (14-26). Russia and some Central Asian countries are medium blue (3-13). The United States, Canada, Greenland, and Peru are light blue (1-2). Most of Africa, South America, Australia, and the Middle East are colored light pink, indicating zero study areas. The map includes a north arrow in the top-right corner and a scale bar in the bottom-right.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 79, "line_end": 79, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "8c4cd2706ac069ac", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 2. Geographic distribution of study areas.\n\nFigure 2. Geographic distribution of study areas.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 2. Geographic distribution of study areas.", "line_start": 81, "line_end": 81, "token_count_estimate": 56, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "873ed9557843b96b", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: text\n\nFor this review, publications from 2010 to 2025 were selected, a period characterized by significant advancements in remote sensing technology and an intensified emphasis on climate-related phenomena. This period coincides with the rapid evolution of remote sensing applications for GLOF monitoring and mapping, driven by improvements in sensor accuracy, data processing capabilities, and the availability of high-resolution satellite imagery. A bar graph (Figure 3) depicting the annual number of publications reveals a clear upward trend, reflecting the increasing research activity in this field. This growth highlights the expanding reliance on remote sensing technologies for effective risk assessment and a deeper understanding of glacial lake dynamics under changing climatic conditions. To further analyze the evolution of the research focus, the frequency of key terms used in publications over the years was examined using Python for data extraction, processing, and visualization. The results, summarized in Figure 4, demonstrate notable trends and shifts in research priorities. Terms such as \"lake\", \"glacier\", and \"flood\" consistently dominate the literature, underscoring the central focus on glacial lakes and their outburst mechanisms. Over time, the increasing prominence of terms like \"remote\", \"sensing\", and \"data\" reflects the growing reliance on technological advancements and data-driven methodologies in GLOF research. These trends not only illustrate the evolution of research priorities but also emphasize the critical role of remote sensing in advancing our understanding of GLOFs and mitigating their risks.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "text", "line_start": 82, "line_end": 84, "token_count_estimate": 401, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ce5bd5f12d2c455", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A vertical bar chart showing the number of publications per year from 2010 to 2024. The x-axis is labeled \"Year\" and the y-axis is labeled \"Number of Publications,\" with a scale from 0.0 to 20.0. The data shows a fluctuating but generally increasing trend over the years. The approximate number of publications for each year is as follows: 2010: 1, 2011: 2, 2012: 5, 2013: 2, 2014: 3, 2015: 3, 2016: 1, 2017: 5, 2018: 8, 2019: 5, 2020: 11, 2021: 7, 2022: 5, 2023: 7. The year 2024 shows a significant spike, with the bar extending beyond the 20.0 mark to approximately 21 publications.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 85, "line_end": 85, "token_count_estimate": 213, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c65053a81b97141d", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 3. Number of publications per year.\n\nFigure 3. Number of publications per year.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 3. Number of publications per year.", "line_start": 87, "line_end": 87, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90cc069f04e29525", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/6/Figure/4 description: A stacked bar chart showing the frequency of various terms over time. The x-axis represents the year, with labels for 2010, 2012, 2014, 2016, 2018, 2020, 2022, and 2024. The y-axis represents frequency, ranging from 0 to over 600. The legend on the right, titled 'Term', lists the terms represented by different colors in the bars: lake (blue), glacier (orange), flood (green), outburst (red), area (purple), glof (brown), study (pink), change (gray), region (olive), and hazard (turquoise). The chart shows a general upward trend in the frequency of these terms over the years, with a very large spike in 2024, where the total frequency exceeds 600. In most years, 'lake' and 'glacier' are the most frequent terms. The approximate total frequencies for the years are: 2010 (~25), 2012 (~160), 2014 (~85), 2016 (~40), 2018 (~260), 2020 (~275), 2022 (~215), and 2024 (~620).", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 89, "line_end": 89, "token_count_estimate": 308, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e79aa77bf0f242bc", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 4. Annual frequency distribution of top 10 terms (2010–2024).\n\nFigure 4. Annual frequency distribution of top 10 terms (2010–2024).", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 4. Annual frequency distribution of top 10 terms (2010–2024).", "line_start": 91, "line_end": 91, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "81049da734fb10ba", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: text\n\nAlongside highlighting the evolution of the literature, the co-authorship network (Figure 5) provides valuable insight into the collaborative landscape of GLOF research.\n\nGeosciences 2025, 15, 211 8 of 36\n\nThe co-authorship network, visualized in the largest connected component, highlights the collaborative relationships among researchers in the field of GLOFs and remote sensing. The network reveals a dense web of connections, indicating strong collaboration patterns among key authors. Each node in the network represents an individual author, and edges between nodes indicate co-authored publications—where thicker edges signify stronger collaboration. This visualization underscores the interdisciplinary nature of GLOF research, where expertise in remote sensing, glaciology, and risk assessment converges. The presence of well-connected authors and collaborative clusters highlights the importance of teamwork in addressing the complex challenges posed by GLOFs, particularly in the context of climate change.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "text", "line_start": 92, "line_end": 98, "token_count_estimate": 257, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d77be9eeff04e829", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A network graph on a white background, showing connections between individuals. The nodes are represented by light blue circles, each labeled with a name. The edges are thin gray lines connecting the nodes. The graph has a central hub, 'Weicai Wang', who is connected to a large number of other individuals, including 'Guoqing Zhang', 'Jinyuan Yu', 'Fenglin Xu', 'Menger Peng', 'Sonam Bindal', 'Ashutosh Ballab Kattel', 'Binod Dawadi', 'Zheenbek Kulenbekov', 'Mirlan Daryrov', 'Manoranjan Mishra', 'Daniel Joswiak', 'Meilin Zhu', 'Tandong Yao', 'Wei Yang', 'Xiaoxin Yang', 'Shichang Kang', 'Abhishek Banerjee', 'Michael EDMadhavanengupta', and 'Taigang Zhang'. From this central cluster, the network extends downwards and to the right, with 'Wanqin Guo' acting as a bridge to another cluster. This second cluster includes 'Yongjian Ding', 'Jun Liu', 'Da Li', and others. Further to the right, another cluster is centered around 'Xiaojun Yao', who is connected to individuals like 'Lei Han', 'Limin Zhao', 'Yu Wang', and 'Jiayu Hu'.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 99, "line_end": 99, "token_count_estimate": 383, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4b2fcb8043cec92f", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 5. Co-authorship network.\n\nFigure 5. Co-authorship network.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 5. Co-authorship network.", "line_start": 101, "line_end": 101, "token_count_estimate": 60, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7437551128c3f588", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: text\n\nThe keyword co-occurrence network (Figure 6) provides a visual representation of the most frequently occurring and interconnected terms in the literature on GLOFs and remote sensing. Central to the network are terms such as \"glacial lakes\", \"climate change\", and \"glacial retreat\", which highlight the primary focus of research on the formation and dynamics of glacial lakes in the context of global warming. The strong connections between these terms and \"hazard assessment\" and \"risk assessment\" underscore the emphasis on evaluating the risks posed by GLOFs to downstream communities and infrastructure. The network also reveals the integration of advanced methodologies, as evidenced by terms like \"multisource remote sensing\" and \"hydrodynamic model\", which reflect the growing reliance on diverse remote sensing technologies and modeling approaches to study GLOFs. Additionally, terms such as \"geomorphology\" and \"glacial lake outburst flood\" indicate a focus on understanding the physical processes and geomorphic impacts of these events. This visualization not only maps the key themes in GLOF research but also highlights the interdisciplinary nature of the field, where climate science, remote sensing, hydrology, and risk management converge. The interconnectedness of these terms illustrates the comprehensive approach required to address the complex challenges posed by GLOFs in a rapidly changing climate.\n\nGeosciences 2025, 15, 211 9 of 36", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "text", "line_start": 102, "line_end": 106, "token_count_estimate": 357, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e2433f1772a83f3", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/8/Figure/1 description: A network graph or concept map on a white background, illustrating the relationships between various terms related to glaciology and environmental science. The graph consists of light blue circular nodes with black text labels, connected by gray lines (edges) of varying thickness. Thicker lines suggest stronger connections between concepts. The nodes are organized into several interconnected clusters. A central hub includes terms like 'remote sensing', 'glacial lake', 'glof', and 'glacier'. A dense cluster in the upper right includes 'risk assessment', 'western himalaya', 'gangabal lake', 'jhelum basin', and 'hecras'. Other connected terms include 'climate change', 'glacier retreat', 'hazard assessment', 'himalaya', and 'karakoram'. Smaller clusters at the bottom include terms like 'glacial lake outburst flood', 'geomorphology', 'disastrous', 'anthropogenic', 'hydrodynamic model', and 'multisource remote sensing'.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 107, "line_end": 107, "token_count_estimate": 304, "basins": [], "subbasins": ["jhelum"], "countries": [], "lake_ids": []}}
{"id": "997f3740e9153dc3", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 6. Keyword co-occurrence network.\n\nFigure 6. Keyword co-occurrence network.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 6. Keyword co-occurrence network.", "line_start": 109, "line_end": 109, "token_count_estimate": 60, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "078777f3148f6b00", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: text\n\nIn addition to co-authorship analysis, we further analyzed the textual data from the \"Title, Abstract, and Keyword\" from the literature using N-grams (Figure 7) and word cloud (Figure 8) visualizations to get additional insights into the most frequently occurring phrases and terms in GLOF research. These tools are particularly valuable in bibliometric studies because they distill large textual datasets into interpretable visual or quantitative outputs, allowing us to gauge prevalent research topics and shifts in thematic focus. Ngrams, generated using the Natural Language Toolkit (NLTK) in Python (Version 3.13.0), reveal the most common multi-word sequences, such as \"glacial lake outburst flood\", \"remote sensing data\", and \"digital elevation model.\" These phrases highlight the central themes of the research, emphasizing the focus on GLOF dynamics, the use of remote sensing technologies, and the application of elevation models for hazard assessment. The word cloud, created using the WordCloud library in Python, visually represents the frequency of individual terms, with larger words indicating higher occurrence. Prominent terms like \"lake\", \"glacial\", \"outburst\", and \"flood\" dominate the visualization, reinforcing the primary focus of the literature. The presence of terms like \"remote sensing\", \"climate change\", \"hazard\", and \"risk assessment\" further underscores the integration of advanced technologies and the emphasis on understanding GLOFs in the context of global warming. These analyses are crucial in bibliometric studies as they provide a quantitative and visual summary of the key themes and trends in the literature. The N-grams and word cloud not only validate the findings from the keyword co-occurrence network but also offer a more granular understanding of the research priorities. While the word cloud identifies highly frequent individual concepts, N-grams reveal the critical contextual relationships between these concepts, illustrating the evolving methodological and thematic frameworks. Together, they illustrate the interdisciplinary nature of GLOF studies, where remote sensing, climate science, and risk management converge to address the challenges posed by these hazardous events. Thus, future glacial-lake research is likely to delve deeper into the interplay of these advanced technologies for proactive hazard mitigation and adaptation strategies.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "text", "line_start": 110, "line_end": 112, "token_count_estimate": 574, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14b732e4f0427153", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/9/Figure/1 description: A horizontal bar chart displaying the frequency of various N-grams. The vertical axis is labeled \"N-grams\" and lists ten phrases. The horizontal axis is labeled \"Frequency\" with a scale from 0 to 120. The bars are a solid light blue color. The N-grams and their approximate frequencies are as follows: \"lake outburst flood\" at 116, \"glacial lake outburst\" at 112, \"outburst flood glofs\" at 46, \"outburst flood glof\" at 26, \"potentially dangerous glacial\" at 18, \"dangerous glacial lake\" at 18, \"remote sensing data\" at 16, \"glacier lake outburst\" at 14, \"using remote sensing\" at 12, and \"digital elevation model\" at 12.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 113, "line_end": 113, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "074382e122f8b0fb", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 7. N-grams showing the frequency of a \"sequence of words\" utilized in literature.\n\nFigure 7. N-grams showing the frequency of a \"sequence of words\" utilized in literature.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 7. N-grams showing the frequency of a \"sequence of words\" utilized in literature.", "line_start": 115, "line_end": 115, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11ebb80da3da5a9d", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure\n\nImage /page/9/Figure/3 description: A word cloud on a white background with words in various sizes and colors, including shades of purple, maroon, orange, and blue. The most prominent words, indicating their high frequency in the source text, are \"lake\", \"outburst\", \"glacial\", \"glacier\", and \"flood\". Other significant, slightly smaller words include \"risk\", \"area\", \"change\", \"region\", \"himalaya\", \"glof\", and \"hazard\". Numerous smaller words are scattered throughout, such as \"monitoring\", \"assessment\", \"climate change\", \"retreat\", \"sensing\", \"water\", \"Karakoram\", and \"event\". The collection of words suggests the topic is related to glacial lake outburst floods (GLOFs), their risks, and the impact of climate change, particularly in regions like the Himalaya.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 117, "line_end": 117, "token_count_estimate": 247, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "40882fdadff19f3e", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: figure\nFigure: Figure 8. Word cloud illustrating the frequency of terms in reviewed articles.\n\nFigure 8. Word cloud illustrating the frequency of terms in reviewed articles.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "figure", "figure_caption": "Figure 8. Word cloud illustrating the frequency of terms in reviewed articles.", "line_start": 119, "line_end": 119, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2f0ae63a7c539110", "text": "Document: 1. Introduction\nSection: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis\nType: text\n\nThis review employs a narrative synthesis methodology, systematically organizing and integrating findings according to the specific remote sensing technologies utilized and their application in understanding GLOF dynamics and monitoring. This approach facilitates a detailed discussion on the efficacy of various remote sensing tools and the technological advancements within GLOF research. By adopting this structured framework, this review seeks to provide a comprehensive overview of how remote sensing has been implemented to enhance our understanding and monitoring of GLOFs. It focuses on summarizing technological advancements, evaluating the effectiveness of diverse remote sensing techniques, and identifying key areas for future research within this critical domain of hazard assessment.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis", "section_headings": ["2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis", "2.3. Bibliometric Analysis"], "chunk_type": "text", "line_start": 120, "line_end": 122, "token_count_estimate": 186, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "45d601431c33f01e", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies\nType: text\n\nGLOFs represent a critical natural hazard that has drawn increasing scholarly attention over recent decades. The integration of remote sensing into glaciological research has evolved from early aerial photography to sophisticated multi-sensor data fusion techniques, fundamentally transforming the way researchers monitor glacier dynamics and assess flood hazards [38]. This section provides a comprehensive review of the role of remote sensing in\n\nGeosciences 2025, 15, 211 11 of 36\n\nGLOF studies by discussing its historical evolution, current technological advancements, and data analysis methodologies used to quantify and predict glacial lake dynamics.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies", "section_headings": ["3. Role of Remote Sensing in GLOF Studies"], "chunk_type": "text", "line_start": 124, "line_end": 130, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "037fa58e9e37da29", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology\nType: text\n\nRemote sensing techniques have long been recognized as indispensable tools in the field of glaciology. The evolution of these techniques (Figure 9) over the decades has significantly enhanced our ability to monitor glaciers and glacial lakes, providing critical insights into their dynamics and the associated hazards, such as GLOFs [39,40].", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology"], "chunk_type": "text", "line_start": 132, "line_end": 134, "token_count_estimate": 115, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35a6034bd0d0cf14", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology\nType: figure\nFigure\n\nImage /page/10/Figure/4 description: A timeline infographic illustrating the evolution of glacier monitoring technologies from the 1950s to the 2020s. The timeline is represented by a horizontal orange line with an arrow at the right end. There are five key periods marked along the timeline with colored location pin icons.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology"], "chunk_type": "figure", "figure_caption": null, "line_start": 135, "line_end": 135, "token_count_estimate": 115, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "755f49058f00f43f", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology\nType: text\n\n- 1950s-60s (Orange pin): Technology is Aerial Photography, used for baseline glacier mapping and initial surveys of glacier extent and terminus positions.\n\n- 1970s-80s (Yellow pin): Technology is Landsat 1-3 (Multispectral Scanner System), used for multispectral monitoring of ice cover, initial detection of glacier changes, and mapping of glacier facies.\n\n- 1990s (Light green pin): Technology is ERS-1/2 SAR, used for all-weather ice velocity and lake monitoring, detection of glacier flow dynamics, and monitoring of glacial lake expansion.\n\n- 2000s (Dark green pin): Technologies are Terra/Aqua MODIS & ASTER, UAVs. MODIS was for large-scale ice sheet monitoring, ASTER for high-resolution thermal and elevation data, and UAVs for high-resolution 3D modeling of glacial lakes and detailed glacier surface mapping.\n\n- 2010s-20s (Dark green pin): Technologies are Sentinel-1/2, ICESat-2, Planet Labs, and AI integration. These allow for daily revisits and high-resolution monitoring, precise elevation change measurements, automated analysis and hazard prediction, and the integration of artificial intelligence.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology"], "chunk_type": "text", "line_start": 136, "line_end": 146, "token_count_estimate": 330, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a37e851dd8abcae3", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology\nType: figure\nFigure: Figure 9. Historical milestones in remote sensing for glaciology.\n\nFigure 9. Historical milestones in remote sensing for glaciology.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology"], "chunk_type": "figure", "figure_caption": "Figure 9. Historical milestones in remote sensing for glaciology.", "line_start": 147, "line_end": 147, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53c35ebac0e96bb2", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology\nType: text\n\nIn the early stages of glacial research, aerial photography was the primary means of capturing images of remote glacierized areas [41]. These early studies, conducted in the 1950s and 1960s, provided the first systematic documentation of glacier extents and the formation of glacial lakes behind terminal moraines. Aerial surveys were particularly useful for creating baseline maps of glacier coverage and identifying the formation of glacial lakes behind terminal moraines [42,43]. Despite the labor-intensive nature of manual image interpretation and field validation, these pioneering efforts laid the groundwork for the systematic study of glacial processes and GLOF hazards.\n\nThe advent of satellite remote sensing in the 1970s marked a significant milestone. The launch of the Landsat program, particularly Landsat 1–3 with their Multispectral Scanner System (MSS), introduced the capability to acquire multispectral imagery at moderate spatial resolutions. This enabled researchers to monitor extensive glaciated terrains over time, detect changes in glacier facies, and map glacier extents more efficiently [37,44]. Landsat Thematic Mapper (TM) sensor, with a spatial resolution of 30 m, was particularly instrumental in identifying and mapping glacial lakes and assessing changes in glacier extents over large areas [45]. Early applications predominantly relied on optical data; however, these methods were hampered by issues such as cloud cover and seasonal snow, which limited temporal consistency in the data [46].\n\nDespite these challenges, early satellite-based studies established the potential of remote sensing for detecting changes in glacier morphology and glacial lake dynamics. The initial inventories derived from Landsat imagery not only provided spatially extensive data but also introduced quantitative methods for assessing glacial retreat and lake expansion [46,47]. These pioneering studies highlighted that even modest changes in glacier mass balance could have significant implications for downstream flood hazards, thereby emphasizing the need for continuous monitoring and advanced analytical techniques [48].\n\nThroughout the 1990s, advancements in Synthetic Aperture Radar (SAR) technology, particularly through satellites like ERS-1/2, complemented optical remote sensing. SAR allowed for all-weather, day-and-night capabilities, thus revolutionizing glacial monitoring under frequently cloudy or snowy conditions [49]. This technology enabled the detection of glacier flow dynamics, measurement of ice velocities, and effective monitoring of the expansion of glacial lakes, providing new insights into glacier stability and lake-induced hazards under any weather conditions [50,51]. These advancements in technology helped\n\nGeosciences 2025, 15, 211 12 of 36\n\nin highlighting the importance of monitoring lake expansion rates and the potential for cascading GLOF events, which could have devastating impacts on downstream communities and infrastructure [52]. Moreover, the ability to monitor these lakes year-round, regardless of weather conditions, was a significant advancement in GLOF hazard assessment [53]. However, early SAR data had coarser resolution compared to contemporary optical sensors and could be complex to process.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology"], "chunk_type": "text", "line_start": 148, "line_end": 166, "token_count_estimate": 772, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "23f3a35954fb4da6", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology\nType: text\n\nexpansion of glacial lakes , providing new insights into glacier stability and lake - induced hazards under any weather conditions [ 50 , 51 ] . These advancements in technology helped Geosciences 2025 , 15 , 211 12 of 36 in highlighting the importance of monitoring lake expansion rates and the potential for cascading GLOF events , which could have devastating impacts on downstream communities and infrastructure [ 52 ] . Moreover , the ability to monitor these lakes year - round , regardless of weather conditions , was a significant advancement in GLOF hazard assessment [ 53 ] . However , early SAR data had coarser resolution compared to contemporary optical sensors and could be complex to process .\n\nThe 2000s brought further advancements with the launch of Terra/Aqua MODIS and ASTER sensors. MODIS provided large-scale ice sheet monitoring, while ASTER offered high-resolution thermal and elevation data, enabling more precise mapping of glacier surfaces and glacial lakes [47,54]. Additionally, the use of unmanned aerial vehicles (UAVs) became more prevalent, allowing for high-resolution 3D modeling of glacial lakes and detailed glacier surface mapping [55]. However, their utilization could be limited by battery life, payload capacity, flight regulations, and reliance on clear weather.\n\nIn the 2010s and 2020s, the integration of advanced sensing technologies and artificial intelligence marked a new era in glaciology. Sentinel-1/2, ICESat-2, and Planet Labs, equipped with capabilities for daily revisits and high-resolution monitoring, significantly improved the frequency and detail of observations [56,57]. Precise elevation change measurements from ICESat-2 and automated analysis through AI integration transformed data processing, enhancing the predictive capabilities for identifying potential GLOF events [58,59]. These advancements not only facilitated continuous and precise monitoring but also enabled timely warnings and effective mitigation strategies.\n\nThe historical evolution of remote sensing in glaciology has been marked by significant advancements in technology and methodology. From the early days of aerial photography to the current use of high-resolution satellite imagery and AI, remote sensing has provided invaluable insights into glacier retreat, glacial lake formation, and GLOF hazards. The integration of multiple data sources and methodologies has enabled researchers to develop more accurate and comprehensive assessments of GLOF susceptibility, particularly in understudied regions like the Canadian Cordillera and the Himalayas. As climate change continues to drive glacier retreat and lake expansion, the role of remote sensing in monitoring and mitigating GLOF risks will remain critical.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology"], "chunk_type": "text", "line_start": 148, "line_end": 166, "token_count_estimate": 667, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3de1a75e740fd1ae", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring\nType: text\n\nIn recent years, remote sensing technologies have undergone rapid advancements, providing a suite of tools that greatly enhance the monitoring and analysis of glacial lake dynamics and GLOF hazards. These technologies, ranging from optical and radar satellites to unmanned aerial vehicles (UAVs), have revolutionized our ability to assess glacier retreat, lake expansion, and the stability of glacial lake dams.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring"], "chunk_type": "text", "line_start": 168, "line_end": 170, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "39a6d8b14998f835", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring\nType: text\n\nTo understand the complex dynamics of glacial lakes and assessing the risks of outburst floods, satellites carrying both optical and radar sensors (Table 1) are essential. Their ability to provide detailed, consistent data regardless of weather conditions allows for comprehensive monitoring of lake changes, glacier movement, and surrounding terrain, all crucial for effective GLOF hazard assessment.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring"], "chunk_type": "text", "line_start": 172, "line_end": 176, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b14c9d4b2c59d524", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring\nType: table\nTable: Table 1. Overview of satellite datasets for GLOF studies.\n\n| Satellite        | Sensor                                                                    | Resolution (Spatial/Temporal)                                   | Type            | Key Features                                                                                              | Application                                                                                                                              |\n|------------------|---------------------------------------------------------------------------|--------------------------------------------------------------------|-----------------|-----------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|\n| Sentinel-1       | Synthetic Aperture Radar (SAR) (C-band)                             | 5–20 m/6 days                                                      | Radar           | All-weather, day and night imaging; interferometric capabilities                                 | Monitoring glacier movement, detecting ground deformation, assessing dam stability, and tracking lake expansion.          |\n| TerraSAR-X       | SAR (X-band)                                                              | 1–40 m/11 days                                                     | Radar           | High-resolution, all-weather imaging                                                                | Detailed mapping of glacier surfaces, monitoring ice flow, and detecting small-scale changes in glacial lakes.               |\n| RADARSAT-2       | SAR (C-band)                                                              | 3–100 m/24 days                                                    | Radar           | Flexible imaging options, fine resolution capabilities; all-weather, day and night imaging | Monitoring glacier dynamics, detecting surface deformation, and assessing GLOF risks.                                        |\n| ALOS-PALSAR      | SAR (L-band)                                                              | 10–100 m/46 days                                                   | Radar           | Penetrates vegetation, wide-area mapping                                                         | Monitoring glacier movement, detecting subsurface changes, and assessing glacial lake expansion in forested regions.      |\n| RISAT            | SAR (C-band)                                                              | 1–50 m/25 days                                                     | Radar           | All-weather, day and night imaging; high-resolution capabilities                                 | Monitoring glacier movement, detecting surface deformation, and assessing GLOF risks in cloud-prone regions.              |\n| COSMO- SkyMed | SAR (X-band)                                                              | 1–40 m/1–4 days                                                    | Radar           | High-resolution, all-weather imaging                                                                | Monitoring glacier dynamics, detecting surface deformation, and assessing GLOF risks in high-mountain regions.            |\n| Envisat          | SAR                                                                       | 30–50 m/35 days                                                    | Radar           | Wide-swath imaging, all-weather capabilities                                                     | Historical monitoring of glacier retreat and glacial lake expansion, especially in remote regions.                           |\n| ALOS World 3D    | SAR (L-band) derived DEM                                               | 5 m/static DEM                                                     | Radar           | 3D terrain model                                                                                          | Creating high-resolution 3D models of glacial lakes and surrounding terrain for GLOF risk assessment.                        |\n| Sentinel-2       | Multispectral Imager (MSI)                                             | 10–60 m/5 days                                                     | Optical         | Multispectral, frequent revisits, wide area coverage                                                | Mapping glacial lake boundaries, monitoring lake area changes, and assessing water quality and turbidity.                    |\n| Satellite        | Sensor                                                                    | Resolution (Spatial/Temporal)                                   | Type            | Key Features                                                                                              | Application                                                                                                                              |\n| Landsat-8        | Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) | 30 m/16 days                                                       | Optical/Thermal | Long-term record of Earth's surface, thermal infrared data                                       | Historical analysis of glacial lake expansion, monitoring glacier retreat, and assessing thermal changes in glacial lakes.               |\n| SPOT-6           | High-Resolution Visible (HRV)                                          | 1.5 m/1–4 days                                                     | Optical         | High-resolution, fast revisit                                                                          | Detailed mapping of glacial lake boundaries and monitoring small-scale changes in lake morphology.                                       |\n| WorldView        | HRV                                                                       | 0.31 m panchromatic, 1.24 m multispec- tral/Daily Revisit | Optical         | Very high spatial resolution, high Accuracy                                                         | High-resolution mapping of glacial lakes, monitoring dam stability, and assessing small-scale changes in glacier termini. |\n| Pleides          | HRV                                                                       | 0.5 m panchromatic, 2 m multispec- tral/Daily Revisit     | Optical         | High-resolution imagery, fast revisit                                                                     | Detailed monitoring of glacial lake boundaries and dam structures, especially in remote and inaccessible regions.                        |\n| Planet           | HRV                                                                       | 3–5 m/Daily Revisit                                             | Optical         | Daily revisit, global coverage                                                                         | Frequent monitoring of glacial lake changes, tracking seasonal variations, and assessing GLOF risks.                                     |\n| ASTER GDEM       | VNIR, TIR                                                                 | 30 m/Static Dem                                                    | Optical         | Digital elevation model (DEM)                                                                          | Generating topographic maps of glacial lakes, assessing lake volume changes, and modelling GLOF scenarios.                   |\n| SuperView-1      | HRV                                                                       | 0.5 m panchromatic, 2 m multispectral/ 2 days             | Optical         | High-resolution, short revisit time                                                                       | Detailed mapping of glacial lake boundaries and monitoring rapid changes in lake morphology.                                 |\n| Rapid Eye        | HRV                                                                       | 5 m panchromatic, 15 m multispec- tral/Daily Revisit         | Optical         | Large-area monitoring, daily revisit                                                                | Monitoring large glacial lakes, tracking seasonal changes, and assessing GLOF risks over wide areas.                                     |\n| LISS             | Linear Imaging Self-Scanning Sensor                                 | 5.8–70 m/5 days                                                    | Optical         | Multi-spectral imaging, wide coverage                                                               | Monitoring glacial lake boundaries, assessing lake area changes, and tracking glacier retreat over time.                     |\n| Gaofen-1         | HRV                                                                       | 2 m panchromatic, 8 m multispectral/ 4 days                  | Optical         | High-resolution, wide coverage                                                                         | Monitoring glacial lake expansion, assessing water quality, and mapping glacier retreat.                                        |\n| Satellite        | Sensor                                                                    | Resolution (Spatial/Temporal)                                   | Type            | Key Features                                                                                              | Application                                                                                                                              |\n| CARTOSAT         | Panchromatic and Multispectral Sensors                              | 1–2.5 m/4–5 days                                                   | Optical         | High-resolution stereo mapping                                                                         | Detailed topographic mapping of glacial lakes, monitoring dam stability, and assessing GLOF risks in high-mountain areas. |\n| Super Dove       | HRV                                                                       | 3 m panchromatic, 12 m multispec- tral/Daily Revisit         | Optical         | Daily global coverage, high revisit                                                                 | Frequent monitoring of glacial lake dynamics, tracking seasonal variations, and assessing GLOF risks.                        |\n| GRACE            | Gravity Recovery and Climate Experiment                             | NA                                                                 | Gravity         | Measures changes in Earth's gravity field                                                           | Monitoring changes in glacial mass balance and lake water storage, which are critical for GLOF risk assessment.              |\n| ICESat-2         | Advanced Topographic Laser Altimeter System (ATLAS)              | 0.7 m (along-track)/ 91 days                                 | LiDAR           | High-precision elevation measurements                                                               | Measuring glacier thickness changes, monitoring lake volume, and assessing GLOF risks.                                       |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring"], "chunk_type": "table", "table_caption": "Table 1. Overview of satellite datasets for GLOF studies.", "columns": ["Satellite", "Sensor", "Resolution (Spatial/Temporal)", "Type", "Key Features", "Application"], "table_row_start": 1, "table_row_end": 25, "line_start": 177, "line_end": 203, "token_count_estimate": 1853, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8994940b6319cdeb", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring\nType: text\n\nTable 1. Cont.\n\nGeosciences **2025**, 15, 211 15 of 36\n\nTable 1. Cont.\n\nContemporary optical sensors, such as those on Landsat 8 and Sentinel-2, offer moderate to high spatial and spectral resolution data that are critical for mapping glacial features [60,61]. Landsat 8, with its 30-m multispectral bands and 15-m panchromatic band, allows for detailed delineation of glacier and lake boundaries. Similarly, Sentinel-2, with a resolution of 10 m in several spectral bands, facilitates the detection of subtle changes in water bodies and land cover. These platforms enable time series analysis of glacial lakes, making it possible to quantify annual changes in lake area and volume. For instance, multi-temporal analyses have been used to document the rapid expansion of glacial lakes in High Mountain Asia, where increasing lake size often correlates with heightened GLOF risk [62]. Furthermore, high-resolution optical satellites like WorldView (0.3 m panchromatic) and Pleiades (0.5 m panchromatic) deliver detailed imagery to monitor small-scale morphological changes in lake boundaries and dam structures, particularly in rugged terrains [14,28]. Gaofen-1 (2 m panchromatic) and SPOT-6 (1.5 m resolution) further enhance this capability by combining wide coverage with rapid revisit times, making them ideal for large-scale glacial lake inventories [3,63]. Sensors such as LISS (IRS series) and RapidEye (5 m panchromatic) support historical trend analysis, while SuperDove (3 m panchromatic) and Planet (3-5 m resolution) enable daily global monitoring to detect abrupt changes during monsoon seasons [46,61,64]. Thermal bands on Landsat-8 also support identifying ice-melt patterns and thermal anomalies near glacial lakes [65].\n\nHowever, optical systems face significant challenges that limit their effectiveness in glacial environments. Cloud cover frequently obstructs optical imagery in mountainous regions, leading to data gaps during critical monitoring periods [53]. Additionally, optical sensors are dependent on daylight, rendering them ineffective at night. They also cannot penetrate ice, snow, or vegetation to detect subsurface changes or subtle ground deformation, which are critical for assessing GLOF risks [66]. Furthermore, spectral misclassification in shadowed or turbid water areas may lead to errors in lake boundary extraction [67]. These limitations highlight the need for complementary technologies, such as radar and\n\nGeosciences 2025, 15, 211 16 of 36\n\nInSAR systems, which overcome environmental constraints and provide critical subsurface and deformation data.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring"], "chunk_type": "text", "line_start": 204, "line_end": 222, "token_count_estimate": 698, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be9a449c69bfc986", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring\nType: text\n\nin mountainous regions , leading to data gaps during critical monitoring periods [ 53 ] . Additionally , optical sensors are dependent on daylight , rendering them ineffective at night . They also cannot penetrate ice , snow , or vegetation to detect subsurface changes or subtle ground deformation , which are critical for assessing GLOF risks [ 66 ] . Furthermore , spectral misclassification in shadowed or turbid water areas may lead to errors in lake boundary extraction [ 67 ] . These limitations highlight the need for complementary technologies , such as radar and Geosciences 2025 , 15 , 211 16 of 36 InSAR systems , which overcome environmental constraints and provide critical subsurface and deformation data .\n\nRadar systems address the shortcomings of optical sensors by offering all-weather, day-and-night imaging capabilities, making them indispensable in cloud-prone glacial environments. Synthetic Aperture Radar (SAR) satellites, such as Sentinel-1 (C-band, 5–20 m resolution) and RISAT (C-band, 1–50 m resolution), utilize interferometric techniques (InSAR) to measure millimeter-scale ground deformation, glacier velocities, and dam stability [49,51,68]. For example, Sentinel-1's repeat-pass InSAR detects subsidence or bulging in moraine dams, providing early warnings of potential failure [69]. High-resolution radar platforms like TerraSAR-X (X-band, 1–40 m resolution) and COSMO-SkyMed (X-band, 1–40 m resolution) map ice flow dynamics and crevasses with unparalleled detail, even in darkness or storm conditions [70,71]. L-band SAR sensors, such as ALOS-PALSAR (10–100 m resolution), penetrate vegetation and snow cover to monitor subsurface glacier movement and buried ice in moraines, which is critical for assessing hidden GLOF triggers [72]. RADARSAT-2 (C-band, 3–100 m resolution) supports flexible imaging modes for regional-scale glacier dynamics, while historical missions like Envisat (30–150 m resolution) and ERS-1/2 provide long-term datasets to analyze glacier retreat trends [73,74].\n\nThe integration of radar and optical data further enhances GLOF monitoring. For instance, combining Sentinel-1 (radar) with Sentinel-2 (optical) improves the detection of lake area changes and dam deformation, while multi-sensor fusion with LiDAR and gravity data (e.g., GRACE) enables comprehensive assessments of glacial mass balance and lake volume changes [57,75,76]. Together, these technologies provide a robust framework for real-time monitoring, historical trend analysis, and predictive modelling of GLOF risks, empowering stakeholders to implement timely mitigation strategies in vulnerable regions.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.1. Satellite Imagery: Overview of Satellite Technologies Used in GLOF Monitoring"], "chunk_type": "text", "line_start": 204, "line_end": 222, "token_count_estimate": 750, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "39fdc2bc5074db51", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.2. Unmanned Aerial Vehicles (UAVs) and Aerial Photography\nType: text\n\nIn addition to satellite-based remote sensing, UAVs and high-resolution aerial photography have emerged as critical tools for localized glaciological studies. UAVs are capable of capturing imagery with resolutions of less than one meter, which is invaluable for validating satellite-derived measurements and for detailed studies of dam morphology and topographic changes [77,78]. UAV-based surveys are particularly useful in areas where high-resolution satellite imagery is unavailable or when rapid, localized assessments are required following a GLOF event [55,79]. For instance, UAVs can quickly deploy to capture post-event imagery, enabling rapid damage assessment and identifying newly formed ice-dammed lakes [80].\n\nAerial photography has played a fundamental role in glaciology since its inception. Historically, it provided the first means to systematically document glacier extents and the formation of glacial lakes, setting the stage for modern remote sensing techniques [41]. Today, integrating historical aerial photographs with contemporary UAV imagery offers a time-extended view of glacial changes, aiding in the study of long-term glacier retreat and lake evolution [43].\n\nThe integration of UAV imagery with satellite data further refines the accuracy of mapping and modelling efforts. UAV-derived DEMs capture fine-scale topographic variations around glacial lakes, such as cracks in moraine dams or subtle changes in glacier termini, which are often missed by coarser satellite data [80]. These high-resolution DEMs enhance the precision of lake volume estimations and flood modelling efforts, providing critical inputs for GLOF risk assessments [81]. Furthermore, UAVs can be deployed quickly and provide data in real-time, which is vital for emergency response and disaster readiness in the event of a GLOF. The agility of UAVs makes them an invaluable asset in dynamic and rapidly changing environments typical of glacial landscapes.\n\nGeosciences 2025, 15, 211 17 of 36\n\nIn summary, the application of UAVs and aerial photography in glacial studies not only supports traditional remote sensing data but also extends the capabilities of glacial monitoring systems. These tools enable more detailed and frequent observations that are crucial for the effective management and mitigation of risks associated with glacial lake outbursts.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.2. Unmanned Aerial Vehicles (UAVs) and Aerial Photography", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.2. Unmanned Aerial Vehicles (UAVs) and Aerial Photography"], "chunk_type": "text", "line_start": 224, "line_end": 234, "token_count_estimate": 622, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee7e7658219a1d97", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion\nType: text\n\nIn the realm of glacial monitoring, the integration of data from various remote sensing technologies marks a significant evolution, enhancing the depth and accuracy of environmental analysis. Multi-sensor data fusion stands out as one of the most notable advancements in recent remote sensing research, particularly for its application in glaciology [82]. This approach synergizes the strengths of diverse sensing technologies, including optical, radar (SAR), and digital elevation models (DEMs), to forge a comprehensive understanding of glacial dynamics and GLOF risks [57].\n\nThe core of multi-sensor data fusion involves blending the high spatial resolution and spectral capabilities of optical sensors with the all-weather, day-and-night imaging capabilities of SAR systems, supplemented by the topographic and volumetric data provided by DEMs [75,83]. The fusion of these data types enables the development of sophisticated models that can accurately estimate glacial lake parameters and assess dam stability. For example, combining Sentinel-2 (optical) with Sentinel-1 (radar) allows for the simultaneous detection of surface water changes and subtle dam movements, providing early warnings of potential GLOF triggers [84]. Integrating ICESat-2 (LiDAR) with ALOS World 3D (SAR) refines volume estimations and flood modelling by capturing both surface and subsurface changes, while UAV-derived DEMs enhance the precision of satellite-based topographic models, enabling detailed assessments of moraine dam stability and glacier retreat patterns [85]. A typical workflow for multi-sensor data fusion involves data acquisition from optical, radar, LiDAR, and UAV platforms, followed by pre-processing to correct atmospheric effects, geometric distortions, and radiometric calibration [64,86]. Advanced algorithms are then used to extract key parameters such as lake boundaries, glacier velocities, and ground deformation, which are integrated using machine learning or statistical models to create a unified representation of glacial lake dynamics. This integrated approach supports the development of predictive models for GLOF risk assessment, improving accuracy and reliability by incorporating inputs from multiple sensors.\n\nThe practical implementation of multi-sensor data fusion involves several steps, beginning with the collection and preprocessing of data from various sources. Figure 10 outlines the steps in multi-sensor data fusion—from data acquisition through pre-processing to parameter extraction and model integration. The data are then aligned and integrated using algorithms that handle the different resolutions and data types to produce a cohesive dataset. Advanced analytical techniques, such as interferometric synthetic aperture radar (InSAR), are then applied to this integrated dataset. InSAR is particularly useful for detecting subtle deformations in the glacier surface or the moraine dams that might not be visible with optical images alone.\n\nIn conclusion, multi-sensor data fusion represents a transformative approach to glacial lake monitoring, enabling comprehensive assessments of GLOF hazards by leveraging the complementary strengths of optical, radar, LiDAR, and UAV technologies. This integrated framework not only improves the accuracy of risk assessments but also supports the development of early warning systems and mitigation strategies for vulnerable communities.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.3. Multi-Sensor Data Fusion"], "chunk_type": "text", "line_start": 236, "line_end": 244, "token_count_estimate": 807, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6819851ef50a07e7", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion\nType: figure\nFigure\n\nImage /page/17/Figure/1 description: A flowchart titled 'Figure 10. Multi-sensor data fusion process workflow'. The chart illustrates a top-down process with color-coded stages. The flow starts with 'Data Acquisition' (dark blue), leading to 'Pre-processing' (teal), which includes 'Time Sync', 'Cleaning', and 'Calibration'. This is followed by 'Integration' (green), with sub-steps 'Alignment' and 'Association'. The next stage is 'Feature Engineering' (yellow), which breaks down into 'Parameter Extraction' and 'Dimensionality Reduction'. This leads to 'Fusion' (orange), with sub-steps 'Method Selection' and 'Execution'. Following this is 'Modeling' (red), which includes 'Development', 'Integration', and 'Refinement'. The final stage is 'Output' (purple), which results in 'Decisions' and 'Visualization'.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.3. Multi-Sensor Data Fusion"], "chunk_type": "figure", "figure_caption": null, "line_start": 245, "line_end": 245, "token_count_estimate": 294, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19c5e757ba67b382", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion\nType: figure\nFigure: Figure 10. Multi-sensor data fusion process workflow.\n\nFigure 10. Multi-sensor data fusion process workflow.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.2. Current Technologies in GLOF Monitoring", "3.2.3. Multi-Sensor Data Fusion"], "chunk_type": "figure", "figure_caption": "Figure 10. Multi-sensor data fusion process workflow.", "line_start": 247, "line_end": 247, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d4903c45ae9ebefc", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods\nType: text\n\nThe effective application of remote sensing in GLOF studies depends on the availability of advanced sensors and the development of robust data analysis methodologies. These methods span change detection, physical parameter estimation, and glacier dynamics monitoring, each contributing uniquely to understanding glacial lake evolution and GLOF hazards. The following subsections detail key techniques employed in analyzing remote sensing data for monitoring glacial lakes and assessing GLOF hazards.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods"], "chunk_type": "text", "line_start": 250, "line_end": 252, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "20c1e50147c5dc5b", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection\nType: text\n\nChange detection is a foundational analytical technique for monitoring temporal variations in glacial lakes and their associated glaciers, playing a vital role in GLOF risk assessment [87,88]. It is a cornerstone of glacial lake monitoring, enabling researchers to quantify temporal variations in lake area, ice/snowmelt, and glacier dynamics. The ability to identify and quantify changes in lake area, ice extent, and terrain stability is critical for assessing GLOF risks, particularly in remote and inaccessible alpine regions [46]. These analyses are critical for identifying emerging GLOF risks, such as rapid lake expansion or moraine dam instability. Over time, methodologies have evolved from the manual interpretation of multi-temporal images to advanced automated workflows that can integrate multi-sensor data and advanced techniques [2]. Several methods are employed in GLOF studies, each with its strengths and limitations. Table 2 provides a comprehensive overview of key change detection techniques, detailing their specific applications in GLOF research and analyzing their strengths and weaknesses.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.1. Change Detection"], "chunk_type": "text", "line_start": 254, "line_end": 258, "token_count_estimate": 286, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b5e5ef5ae8b840ea", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection\nType: table\nTable: Table 2. Change detection techniques for GLOF studies.\n\n| Technique                                                     | Applicability in GLOF Studies                                                                                                                  | Strengths                                                                                                | Limitations                                                                                                                     | Data Requirements                                                                     |\n|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|\n| Spectral Index Differencing (NDWI, MNDWI, etc.)         | Detecting changes in water surface area and ice/snow melt, lake boundary delineation                                                     | Simple, computationally efficient, ideal for large-scale monitoring.                            | Sensitive to atmospheric/cloud conditions, struggles with turbid/shadowed water, may not detect subtle changes.  | Multi-temporal optical satellite imagery (e.g., Landsat, Sentinel-2).           |\n| Image Differencing (Band Ratioing, Simple Differencing) | Identifying changes in pixel values between images (e.g., ice collapse, lake expansion)                                               | Easy to implement, highlights areas of change.                                                     | Sensitive to radiometric differences, requires precise image co-registration, can generate many false positives. | Multi-temporal optical or radar satellite imagery.                              |\n| Change Vector Analysis (CVA)                               | Analyzing both magnitude and direction of change in spectral space. (e.g., ice-to-water transitions)                                  | Provides more detailed change information, robust to some radiometric differences.              | Requires accurate atmospheric correction and co-registration, more complex than simple differencing.                | Multi-temporal optical or radar satellite imagery.                              |\n| Principal Component Analysis (PCA) Change Detection     | Compressing multi-band data and highlighting significant changes. (e.g., glacier thinning)                                            | Reduces data dimensionality, emphasizes major changes.                                          | The interpretation of principal components can be challenging and sensitive to noise.                                  | Multi-temporal multi-spectral satellite imagery.                                |\n| Post-Classification Comparison                             | Comparing classified images from different dates.                                                                                           | Provides clear land-cover transition maps                                                          | Accuracy depends on classification accuracy and can propagate classification errors.                                   | Multi-temporal classified satellite imagery.                                    |\n| Object-Based Image Analysis (OBIA) Change Detection     | Analyzing changes in image objects (segments) rather than individual pixels. (e.g., dam morphology, debris-covered ice mapping) | More robust to noise and radiometric variations, can incorporate contextual information.     |                                                                                                                                 |                                                                                       |\n| SAR Coherence Change Detection                             | Detecting changes in surface roughness and dielectric properties. (e.g., all-weather lake surface monitoring, dam stability)       | Sensitive to water surface changes, can penetrate clouds and provide all-weather monitoring. | Affected by temporal decorrelation, requires precise co-registration, interpretation can be complex.                | Multi-temporal SAR imagery (e.g., Sentinel-1).                                  |\n| Digital Elevation Model (DEM) Differencing              | Monitoring changes in glacier elevation and volume.                                                                                         | Provides direct measurement of elevation changes and can detect subtle changes.              | Requires accurate DEMs, affected by DEM errors and co-registration issues.                                             | Multi-temporal DEMs (e.g., from LiDAR, stereo- photogrammetry, or InSAR). |\n| Thermal Infrared (TIR) Change Detection                 | Monitoring changes in surface temperature, which can indicate melting or water presence.                                              | Sensitive to temperature variations, can detect changes in thermal properties.                  | Affected by atmospheric conditions, requires accurate atmospheric correction, spatial resolution may be limited. | Multi-temporal TIR satellite imagery (e.g., Landsat thermal bands).          |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.1. Change Detection"], "chunk_type": "table", "table_caption": "Table 2. Change detection techniques for GLOF studies.", "columns": ["Technique", "Applicability in GLOF Studies", "Strengths", "Limitations", "Data Requirements"], "table_row_start": 1, "table_row_end": 9, "line_start": 259, "line_end": 269, "token_count_estimate": 1005, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "94f2138f49ddebad", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection\nType: text\n\nSpectral index differencing, such as the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI), remains a widely adopted method for delineating lake boundaries and tracking surface area changes [72]. NDWI, calculated using green and\n\nGeosciences 2025, 15, 211 20 of 36\n\nnear-infrared bands, is effective for mapping clear-water lakes but struggles in turbid or shadowed conditions. MNDWI, which substitutes shortwave infrared (SWIR) for near-infrared, improves performance in debris-rich environments. Despite their simplicity, these indices are sensitive to atmospheric interference and cloud cover, limiting their utility in monsoon-prone regions [37]. Building on spectral indices, image differencing, another optical technique, identifies abrupt changes such as ice collapse or lake expansion by subtracting pixel values between two dates [89]. While straightforward, this method requires precise radiometric calibration and co-registration to avoid false positives caused by seasonal illumination variations.\n\nBuilding upon the concept of differencing but offering greater dimensionality, Change Vector Analysis (CVA) extends traditional differencing by analyzing both the magnitude and direction of spectral changes in multi-dimensional space [2]. This approach is particularly useful for mapping gradual transitions, such as ice-to-water conversion or progressive glacier thinning. However, CVA demands rigorous atmospheric correction and is sensitive to misregistration errors. Similarly, Principal Component Analysis (PCA) reduces the dimensionality of multi-temporal datasets, emphasizing dominant change patterns like glacier retreat or lake growth. While effective for noise reduction, interpreting principal components requires domain expertise, as they often represent composite environmental changes [90]. Post-classification comparison, which involves classifying images from different epochs and quantifying land-cover transitions, provides intuitive results but risks propagating classification errors into change maps [91,92].\n\nAddressing the inherent limitations of optical methods, particularly concerning weather and daylight dependency, SAR coherence change detection addresses the limitations of optical methods by operating independently of weather and daylight. By analyzing phase differences in SAR data, this technique detects surface changes such as water encroachment or dam subsidence [39]. However, temporal decorrelation caused by snowmelt or vegetation growth can degrade coherence, complicating interpretation [93]. Moving from pixel-based to object-oriented analysis, Object-Based Image Analysis (OBIA) segments images into homogeneous objects (e.g., lake patches, moraine dams) using spectral, spatial, and contextual features [94]. This approach reduces salt-and-pepper noise, which is common in pixel-based methods, and enhances dam morphology mapping. Nevertheless, OBIA requires careful parameter tuning and high-resolution data, making it computationally intensive.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.1. Change Detection"], "chunk_type": "text", "line_start": 270, "line_end": 284, "token_count_estimate": 749, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "01245072e39f7b8f", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection\nType: text\n\nwater encroachment or dam subsidence [ 39 ] . However , temporal decorrelation caused by snowmelt or vegetation growth can degrade coherence , complicating interpretation [ 93 ] . Moving from pixel - based to object - oriented analysis , Object - Based Image Analysis ( OBIA ) segments images into homogeneous objects ( e . g . , lake patches , moraine dams ) using spectral , spatial , and contextual features [ 94 ] . This approach reduces salt - and - pepper noise , which is common in pixel - based methods , and enhances dam morphology mapping . Nevertheless , OBIA requires careful parameter tuning and high - resolution data , making it computationally intensive .\n\nShifting focus to elevation metrics, Digital Elevation Model (DEM) differencing quantifies glacier thinning and lake volume changes by comparing multi-temporal elevation data [95]. LiDAR and UAV-derived DEMs offer sub-meter accuracy, while satellite-based DEMs (e.g., ASTER, TanDEM-X) provide broader coverage. This technique is indispensable for estimating potential flood volumes but requires precise co-registration to minimize errors. Thermal infrared (TIR) change detection identifies temperature anomalies linked to ice melt or subglacial water activity. For instance, Landsat-8's thermal bands have been used to detect warming trends near glacier termini, signaling increased meltwater input to lakes [96]. However, TIR's coarse spatial resolution (~100 m) limits its application in small-scale glacial basins.\n\nTo handle the complexity and volume of these diverse datasets, advances in machine learning have improved the accuracy of change detection. These methods are proficient in handling the complex spectral variability inherent in alpine environments, where factors such as cloud cover, shadows, and mixed pixels can complicate analysis. Automated change detection techniques not only expedite the mapping process but also enable the identification of abrupt changes in lake area that may signal a potential outburst event.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.1. Change Detection"], "chunk_type": "text", "line_start": 270, "line_end": 284, "token_count_estimate": 544, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcebc16d187bbe53", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters\nType: text\n\nIn the field of remote sensing applied to GLOF risk assessment, the estimation of physical parameters of glacial lakes and their surrounding environments is a critical component [61,97]. These parameters, which include glacier extent, lake volume, ice thickness, and topographic characteristics, provide the foundational data needed to assess hazard potential, model flood scenarios, and design mitigation strategies [64,98,99]. Remote sensing technologies have revolutionized the ability to measure these parameters over large and often inaccessible regions, offering high-resolution, multi-temporal datasets that were previously unattainable through ground-based methods alone [87,100,101]. However, the accuracy of these measurements varies depending on sensor capabilities, environmental conditions, and data processing techniques (Table 3). For instance, glacier extent mapping using optical imagery achieves high accuracy (>90%) under clear-sky conditions but suffers from errors (~20–30%) in debris-covered or shadowed areas [102,103]. Similarly, lake volume estimates derived from DEM differencing exhibit great uncertainties due to co-registration errors and terrain complexity [104], while LiDAR and UAV photogrammetry reduce these errors in localized studies [78,81]. Despite these variability-driven challenges, advancements in multi-sensor integration and error-correction algorithms enable researchers to derive robust estimates of critical parameters. This accurate knowledge of these parameters not only deepens our understanding of the physical processes leading to GLOFs but also enhances our ability to predict and mitigate these potentially catastrophic events, enabling more reliable risk assessments and early warning systems.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.2. Estimation of Physical Parameters"], "chunk_type": "text", "line_start": 286, "line_end": 290, "token_count_estimate": 445, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab3b55b64fa834e4", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters\nType: table\nTable: Table 3. Comprehensive physical parameters estimated using remote sensing.\n\n| Parameter                           | Measurement Technique                                                                            | Remote Sensing Tools                                                       | Application in GLOF Studies                                                                     | Accuracy/Reliability                                                                                                                            |\n|-------------------------------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|\n| Glacier Extent/Area              | Optical imagery classification, feature extraction                                            | Landsat, Sentinel-2, high-resolution satellites (e.g., WorldView) | Monitoring glacier retreat, lake expansion, and overall changes in glacial environments.           | High accuracy for clear imagery; accuracy affected by cloud cover and debris.                                                                   |\n| Glacier Surface Elevation/Volume | DEM differencing, InSAR, LiDAR                                                                   | SRTM, TanDEM-X, ICESat-2, airborne LiDAR                             | Assessing glacier mass balance, detecting ice thinning, and estimating potential outburst volumes. | Accuracy varies with DEM source and terrain complexity; LiDAR provides highest accuracy.                                                        |\n| Glacial Lake Area/Volume         | Spectral indices (NDWI, MNDWI), optical imagery, DEM analysis, Area-Volume Relationship | Landsat, Sentinel-2, high-resolution satellites, DEMs                | Tracking lake expansion, identifying unstable lakes, and estimating potential flood volumes.       | Accuracy dependent on water clarity and image resolution; DEMs improve volume estimation. Area-Volume relationships provide useful estimations. |\n| Lake Water Level                    | Radar altimetry, optical imagery, DEM analysis                                                      | Sentinel-3, ICESat-2, high-resolution time series.                   | Monitoring lake level fluctuations, identifying rapid changes that may indicate instability.       | Radar altimetry provides good accuracy; optical and DEM methods are less precise.                                                      |\n| Ice/Snow Cover                      | Spectral indices (NDSI), optical imagery                                                         | MODIS, Landsat, Sentinel-2                                              | Monitoring snow/ice melt rates, identifying potential triggers for GLOFs.                          | Accuracy affected by atmospheric conditions and debris cover.                                                                                   |\n| Surface Temperature              | Thermal infrared (TIR) imagery                                                                      | Landsat-8 TIRS, ASTER                                                      | Detecting changes in ice/snow temperature, identifying areas of rapid melt.                        | Accuracy affected by atmospheric correction and emissivity.                                                                                     |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.2. Estimation of Physical Parameters"], "chunk_type": "table", "table_caption": "Table 3. Comprehensive physical parameters estimated using remote sensing.", "columns": ["Parameter", "Measurement Technique", "Remote Sensing Tools", "Application in GLOF Studies", "Accuracy/Reliability"], "table_row_start": 1, "table_row_end": 6, "line_start": 291, "line_end": 298, "token_count_estimate": 673, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7435ca4c829e2d34", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters\nType: table\nTable: Table 3. Cont.\n\n| Parameter                          | Measurement Technique                | Remote Sensing Tools | Application in GLOF Studies                                                           | Accuracy/Reliability                                                 |\n|------------------------------------|-----------------------------------------|----------------------|------------------------------------------------------------------------------------------|----------------------------------------------------------------------|\n| Water Turbid- ity/Sediment Load | Spectral analysis of optical imagery | Sentinel-2, Landsat. | Indicating sediment transport, potential dam weakening, and downstream hazards. | Accuracy varies with water clarity and sediment concentration. |\n| Ice Thickness and Mass Balance  | Radar Penetration, Altimetry         | SAR, LiDAR, ICESat-2 | Understanding glacier health and melt dynamics                                     | Reliable in ice thickness, varied in mass balance                 |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.2. Estimation of Physical Parameters"], "chunk_type": "table", "table_caption": "Table 3. Cont.", "columns": ["Parameter", "Measurement Technique", "Remote Sensing Tools", "Application in GLOF Studies", "Accuracy/Reliability"], "table_row_start": 1, "table_row_end": 2, "line_start": 302, "line_end": 305, "token_count_estimate": 242, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c7fdf68ab701276", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters\nType: text\n\nTable 3 highlights various physical parameters that can be quantified using remote sensing technologies, detailing their measurement techniques, the specific remote sensing tools utilized, and their relevance in GLOF studies. The estimation of lake area and volume is foundational in assessing the potential impact of GLOF events [6,25]. Satellite optical imagery, particularly from Landsat and Sentinel-2, is typically used to delineate lake boundaries [32,98]. These measurements are critical for applying empirical relationships that estimate lake volume based on observed surface area [102,103]. Further refinement is achieved through the integration of Digital Elevation Models (DEMs), which provide a three-dimensional perspective of the lake basin and aid in more precise volume calculations [104]. This data is essential for modelling how lakes might respond under different flood scenarios and for understanding the dynamics of water release during GLOF events.\n\nQuantifying glacier thinning and volume loss is essential for understanding mass balance and predicting future lake formation [27]. Digital Elevation Model (DEM) differencing, which compares multi-temporal elevation datasets, is the primary method for measuring surface elevation changes [25]. Satellite-derived DEMs, such as ASTER (30 m) and TanDEM-X (12 m), offer broad coverage, while airborne LiDAR and UAV photogrammetry provide sub-meter vertical accuracy for localized studies [104,105]. ICESat-2's laser altimetry, for instance, measures ice sheet elevation changes with millimeter precision, offering critical insights into glacier health. However, DEM accuracy varies with terrain complexity, and co-registration errors can inflate uncertainty in volume calculations [6,9]. In short, DEM differencing delivers unrivalled quantitative insight into glacier mass balance, yet its reliability is contingent on meticulous co-registration and terrain-specific error modelling.\n\nThe delineation of glacial lake boundaries and estimation of lake volume are central to GLOF risk assessment. Spectral indices like NDWI and Modified NDWI (MNDWI) are widely used to map lake boundaries in optical imagery [54,106]. UAV-derived bathymetry and LiDAR improve volume estimates by incorporating depth measurements and reducing reliance on area-based approximations [107]. Monitoring lake water levels and ice/snow cover provides insights into seasonal variability and potential triggers for GLOFs [108]. Radar altimetry (e.g., Sentinel-3, ICESat-2) tracks water level fluctuations with centimeter-scale precision, detecting rapid changes indicative of instability [58]. Optical sensors like MODIS and Sentinel-2 monitor ice and snow cover using indices such as NDSI, which identify meltwater sources [30,106]. However, atmospheric interference and debris cover can degrade accuracy, necessitating complementary thermal or SAR data for validation. Thus, fusing optical, radar, and UAV observations yields robust lake-volume and stability metrics, although persistent atmospheric noise and surface debris continue to challenge classification accuracy.\n\nSurface temperature, measured using thermal infrared (TIR) bands on Landsat-8 and MODIS, helps identify melt hotspots and subglacial water pathways [96,109]. Water turbidity and sediment load, estimated through spectral analysis of red and near-infrared\n\nGeosciences 2025, 15, 211 23 of 36", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.2. Estimation of Physical Parameters"], "chunk_type": "text", "line_start": 306, "line_end": 322, "token_count_estimate": 873, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21939f55743158db", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters\nType: text\n\n, which identify meltwater sources [ 30 , 106 ] . However , atmospheric interference and debris cover can degrade accuracy , necessitating complementary thermal or SAR data for validation . Thus , fusing optical , radar , and UAV observations yields robust lake - volume and stability metrics , although persistent atmospheric noise and surface debris continue to challenge classification accuracy . Surface temperature , measured using thermal infrared ( TIR ) bands on Landsat - 8 and MODIS , helps identify melt hotspots and subglacial water pathways [ 96 , 109 ] . Water turbidity and sediment load , estimated through spectral analysis of red and near - infrared Geosciences 2025 , 15 , 211 23 of 36\n\nbands, signal erosion processes that weaken moraine dams [44]. For instance, PlanetScope's high revisit frequency enables near-real-time monitoring of sediment plumes in HMA lakes, though accuracy depends on water clarity and sun-glint effects [110]. Collectively, TIR and high-revisit optical sensors allow near-real-time tracking of thermal and sediment anomalies, yet their performance is moderated by water clarity and solar-reflection artefacts.\n\nIce thickness, a critical factor in glacier stability, is measured indirectly using ground-penetrating radar (GPR) or inferred from surface velocity models [107]. Satellite altimetry (e.g., ICESat-2) and SAR penetration (L-band) enhance large-scale assessments, though field validation remains essential [8,111]. Understanding the topography of glaciers and lakes involves measuring changes in surface elevation and overall glacier volume [112]. Techniques such as DEM differencing and Interferometric Synthetic Aperture Radar (InSAR) are crucial in these assessments [24]. Data from platforms like the Shuttle Radar Topography Mission (SRTM) and TanDEM-X provide detailed elevation information that is vital for tracking glacier retreat and detecting thinning ice areas, which can indicate potential vulnerabilities in glacier dam stability [104]. These methods provide indispensable data for understanding glacier stability and potential dam vulnerabilities, though they often require indirect measurements and field validation.\n\nEach of these parameters benefits significantly from the integration of remote sensing data, which provides a continuous, detailed, and accessible means of monitoring changes in the glacial environment. The capabilities of modern remote sensing technologies to deliver timely data across large, often inaccessible areas make them indispensable tools in the ongoing effort to mitigate the risks associated with GLOFs.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.2. Estimation of Physical Parameters"], "chunk_type": "text", "line_start": 306, "line_end": 322, "token_count_estimate": 679, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e4991d05d3a7c8d", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics\nType: text\n\nAn important aspect of GLOF risk assessment is the continuous monitoring of glacier dynamics, as changes in glacier behavior directly influence the stability of ice- and moraine-dammed lakes. Understanding the temporal evolution of glaciers, particularly their flow, deformation, and mass balance, provides essential insights into the stability of glacier-fed lakes and the potential for dam failure [113]. Glacier dynamics encompass processes such as ice flow velocity, surface deformation, terminus retreat, and mass balance, all of which provide critical insights into the likelihood of dam failure and subsequent flooding [40,114,115]. Remote sensing technologies, including Synthetic Aperture Radar (SAR), optical imagery, and UAV photogrammetry, have emerged as powerful tools for tracking these dynamic processes across vast and inaccessible mountain regions.\n\nThe key parameters and monitoring methods for glacier dynamics are summarized in Table 4. Glacier flow velocity, a key indicator of ice dynamics, is measured using Interferometric SAR (InSAR) and optical feature tracking. It is crucial for predicting ice calving events and glacier surges, both of which can destabilize glacial lakes [110]. Surface deformation, including subsidence or bulging of moraine dams, can be monitored using Differential InSAR (DInSAR) and Persistent Scatterer InSAR (PSInSAR), as well as high-resolution UAV photogrammetry [64]. These techniques quantify vertical and horizontal displacements with sub-centimeter precision, identifying areas of stress accumulation.\n\nTime series analyses using satellite imagery have also proven invaluable. By constructing multi-temporal datasets, researchers can track the progression of glacier retreat and correlate these changes with corresponding variations in lake area. Such analyses not only reveal trends in glacier mass loss but also help establish thresholds beyond which the risk of GLOF increases significantly [98,102].", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.3. Monitoring Glacier Dynamics"], "chunk_type": "text", "line_start": 324, "line_end": 330, "token_count_estimate": 505, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e413c0f3eda02ce", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics\nType: table\nTable\n\n| <b>Table 4.</b> Glacier dynamics p | parameters and | monitoring methods. |\n|------------------------------------|----------------|---------------------|\n|------------------------------------|----------------|---------------------|", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.3. Monitoring Glacier Dynamics"], "chunk_type": "table", "table_caption": null, "columns": ["Table 4. Glacier dynamics p", "parameters and", "monitoring methods."], "table_row_start": 1, "table_row_end": 1, "line_start": 331, "line_end": 333, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34967008b7297796", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics\nType: table\nTable\n\n| Parameter             | Monitoring Method                                  | Remote Sensing Tools                   | Importance in GLOF Studies                                                   | Data Requirements                                                                  |\n|-----------------------|----------------------------------------------------|----------------------------------------|---------------------------------------------------------------------------------|------------------------------------------------------------------------------------|\n| Glacier Flow Velocity | InSAR, optical feature tracking                 | Sentinel-1, Landsat-8, PlanetScope  | Predicts ice calving events and glacier surges that destabilize lakes. | Multi-temporal SAR data (6–12-day intervals), cloud-free optical imagery. |\n| Surface Deformation   | InSAR (DInSAR, PSInSAR), UAV photogrammetry  | Sentinel-1, TerraSAR-X, UAVs        | Detects subsidence or bulging in moraine dams, signaling instability.  | High-frequency SAR acquisitions, UAV campaigns during stable weather.     |\n| Terminus Retreat      | Optical time series analysis                    | Landsat-8, Sentinel-2                  | Tracks glacier retreat linked to lake expansion and ice-dam formation. | Multi-decadal optical imagery (16–30 m resolution).                          |\n| Mass Balance          | DEM differencing, gravimetry                    | ICESat-2, GRACE-FO                     | Quantifies ice loss/gain to predict lake volume changes.                  | High-accuracy DEMs (LiDAR/InSAR), GRACE-FO gravity data.                     |\n| Crevasse Formation    | UAV photogrammetry, high-res optical imagery | UAVs, WorldView                        | Identifies stress zones on glaciers prone to collapse.                    | Sub-meter resolution imagery, repeat UAV surveys.                            |\n| Subglacial Hydrology  | Ground-penetrating radar (GPR)                  | UAV-mounted GPR                        | Maps subglacial drainage systems that may trigger outbursts.              | High-resolution radar data, ice-penetrating frequencies.                     |\n| Terrain Stability     | InSAR (DInSAR), optical feature tracking        | Sentinel-1, TerraSAR-X, PlanetScope | Monitors slope instability and moraine dam movement.                      | Regular SAR acquisitions (6–12 days), optical time series.                   |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.3. Monitoring Glacier Dynamics"], "chunk_type": "table", "table_caption": null, "columns": ["Parameter", "Monitoring Method", "Remote Sensing Tools", "Importance in GLOF Studies", "Data Requirements"], "table_row_start": 1, "table_row_end": 7, "line_start": 335, "line_end": 343, "token_count_estimate": 598, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e13f2a66104d0a15", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics\nType: text\n\nAnother important parameter, glacier mass balance, which is the difference between accumulation and ablation, is estimated using DEM differencing and gravimetry. It provides a comprehensive assessment of ice loss or gain [6,66]. DEM differencing compares elevation changes over time, while gravimetry measures changes in the Earth's gravitational field due to ice mass variations. These measurements are essential for predicting changes in lake volume and understanding the overall health of the glacier.\n\nCrevasse formation, a marker of glacier stress, is mapped using UAV photogrammetry and high-resolution optical imagery (e.g., PlanetScope or WorldView). Sub-meter resolution datasets capture crack density and orientation, identifying zones prone to ice collapse [110]. Subglacial hydrology, mapped using ground-penetrating radar, reveals subglacial drainage systems that may trigger outbursts. Finally, InSAR and optical feature tracking monitor terrain stability, which assesses slope instability and moraine dam movement.\n\nMonitoring glacier dynamics through remote sensing provides a comprehensive understanding of the processes driving GLOF hazards. Techniques like InSAR, optical tracking, and UAV photogrammetry offer high-precision, multi-scale insights into ice flow, deformation, and dam stability. When integrated with hydrodynamic models, these data empower stakeholders to predict outburst scenarios, prioritize mitigation efforts, and implement timely interventions. As climate change accelerates glacier retreat, advancing these monitoring frameworks will be vital for safeguarding vulnerable communities in high-mountain regions.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.3. Monitoring Glacier Dynamics", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.3. Monitoring Glacier Dynamics"], "chunk_type": "text", "line_start": 344, "line_end": 350, "token_count_estimate": 422, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6aabbc025b510e09", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research\nType: text\n\nThe complexities inherent in GLOF research, characterized by dynamic glacial environments and intricate spatial–temporal interactions, necessitate the adoption of advanced\n\nGeosciences **2025**, 15, 211 25 of 36\n\nanalytical techniques. Machine learning (ML) has emerged as a transformative tool in this domain, offering unprecedented capabilities for data processing, pattern recognition, and predictive modelling [4,36]. By leveraging the power of algorithms and computational resources, ML techniques are enhancing our understanding of GLOF mechanisms and improving the accuracy of risk assessments.\n\nTable 5 details the specific applications, strengths, limitations, and data requirements of each machine learning technique employed in GLOF studies. This table serves as a critical resource for understanding how different machine learning approaches are applied to remote sensing data to address various challenges in GLOF risk assessment.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research"], "chunk_type": "text", "line_start": 352, "line_end": 362, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "768ca2dc9660d911", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research\nType: table\nTable: Table 5. Machine learning techniques in GLOF research.\n\n| Technique                                                       | Application                                                                                | Strengths                                                                         | Limitations                                                                          | Data Requirements                                                                                |\n|-----------------------------------------------------------------|--------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|\n| Supervised Classification (e.g., Random Forest, SVM)   | Glacier/lake mapping, landslide detection, land cover classification                 | High accuracy with labeled data; handles multi-source inputs (spectral, DEMs)     | Requires large labeled datasets; performance depends on feature engineering | Labeled optical/SAR imagery, DEMs, slope/aspect layers                                     |\n| Unsupervised Clustering (e.g., K-means, ISODATA)                | Identifying melt patterns, lake expansion trends, terrain deformation clusters | No labels needed; discovers hidden patterns in data                         | Clusters may lack physical interpretability; sensitive to initialization | Multi-temporal Sentinel-1/2 data, InSAR coherence maps                                     |\n| Deep Learning (e.g., CNNs, U-Net, Transformers)           | Automated lake/ice segmentation, change detection, glacier flow modeling                   | Learns hierarchical features; excels in complex spatial-temporal tasks   | Computationally intensive; prone to overfitting without large datasets               | High-resolution optical/SAR imagery, DEMs, annotated labels                                |\n| Time Series Analysis (e.g., LSTM, ARIMA)                     | Predicting lake level fluctuations, glacier melt rates, anomaly detection         | Captures temporal dependencies; robust for forecasting                      | Requires long, continuous time series data; sensitive to missing values     | Historical Landsat/MODIS data, climate variables (temperature, precipitation)                    |\n| Object-Based Image Analysis (OBIA) with ML                | Mapping debris-covered ice, unstable slopes, moraine dam cracks                   | Combines spectral, spatial, and contextual features; reduces noise          | Parameter tuning is complex; computationally demanding                      | High-resolution imagery (e.g., WorldView), DEMs, ancillary data (e.g., geology maps) |\n| Anomaly Detection (e.g., Autoencoders, Isolation Forests) | Detecting sudden lake drainage, abnormal glacier velocity, seismic triggers       | Identifies outliers without prior knowledge; adaptable to rare events    | High false-positive rate; thresholds require calibration                       | Multi-temporal SAR/optical data, InSAR deformation maps, seismic records                |\n| Ensemble Learning (e.g., XGBoost, Stacking)               | Improving GLOF risk prediction, integrating multi-sensor data                              | Reduces overfitting; combines model strengths for higher accuracy        | Computationally expensive; requires diverse base models                              | Multi-source data (optical, SAR, DEMs, climate)                                            |\n| Geospatial ML (e.g., Graph Neural Networks)               | Modeling spatial interactions (e.g., lake-glacier-terrain dynamics)               | Captures large-scale spatial dependencies; integrates heterogeneous data | Demands domain expertise; resource-intensive                                   | Spatially referenced data (imagery, DEMs, hydrological models)                             |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research"], "chunk_type": "table", "table_caption": "Table 5. Machine learning techniques in GLOF research.", "columns": ["Technique", "Application", "Strengths", "Limitations", "Data Requirements"], "table_row_start": 1, "table_row_end": 8, "line_start": 363, "line_end": 372, "token_count_estimate": 924, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3014b0d987f8a47f", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research\nType: text\n\nSupervised classification techniques like Random Forest and Support Vector Machines (SVM) are employed extensively in glacier and lake mapping, landslide detection, and land cover classification [4,116]. These models are particularly valued for their ability to handle multi-source inputs and provide high accuracy when trained with adequately labeled data. They rely on a range of data, including optical and SAR imagery, digital elevation models (DEMs), and additional geographical layers, such as slope and aspect,\n\nGeosciences **2025**, 15, 211 26 of 36\n\nwhich help in accurately delineating and classifying different surface features relevant to GLOF dynamics. However, their performance hinges on extensive labeled training data, which can be labor-intensive to acquire in remote glacial environments.\n\nUnsupervised clustering methods such as K-means and ISODATA play a crucial role in identifying melt patterns, lake expansion trends, and clusters of terrain deformation without the need for labeled datasets [117]. These techniques analyze multi-temporal satellite data to discover hidden patterns and changes over time, providing insights into the gradual changes that might not be immediately apparent. A key limitation is their sensitivity to initialization parameters, and resulting clusters may lack direct physical interpretability, requiring domain expertise to contextualize outputs.\n\nDeep learning algorithms, including convolutional neural networks (CNNs), U-Net, and transformers, are at the cutting edge of automating complex tasks such as lake and ice segmentation, change detection, and glacier flow modelling [59,118]. These algorithms excel at learning hierarchical features from large datasets, making them exceptionally good at handling spatial-temporal tasks that are common in high-resolution remote sensing data analysis. However, these models are computationally intensive and prone to overfitting without large annotated datasets—a challenge in data-scarce regions.\n\nTime series analysis using models like long short-term memory networks (LSTM), artificial neural network (ANN), or autoregressive integrated moving average (ARIMA) models are employed to predict lake level fluctuations, glacier melt rates, and detect anomalies. These models are adept at capturing temporal dependencies, making them particularly useful for forecasting based on historical data [119]. Their effectiveness diminishes with fragmented or incomplete time series data, underscoring the need for continuous monitoring systems [50].\n\nObject-based image analysis (OBIA) integrated with machine learning combines spectral, spatial, and contextual information to map complex phenomena such as debris-covered ice or moraine dam cracks [120]. This approach helps in reducing noise and improving the precision of feature extraction in cluttered or heterogeneous environments. However, its computational demands and complex parameter tuning limit scalability [121]. Anomaly detection techniques such as autoencoders and isolation forests are crucial for detecting unusual events like sudden lake drainage, abnormal glacier velocities, or seismic activities that could potentially trigger a GLOF. These techniques are designed to identify outliers and anomalies in datasets without needing prior labeling of what constitutes a normal or an anomalous event [122]. Ensemble learning methods like XGBoost and model stacking are used to integrate findings from multiple models, thereby reducing overfitting and enhancing the reliability of predictions [123]. These methods combine the strengths of various models to improve overall prediction accuracy for GLOF risk assessment.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research"], "chunk_type": "text", "line_start": 373, "line_end": 391, "token_count_estimate": 884, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7df432c77ee6b60f", "text": "Document: 1. Introduction\nSection: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research\nType: text\n\nand complex parameter tuning limit scalability [ 121 ] . Anomaly detection techniques such as autoencoders and isolation forests are crucial for detecting unusual events like sudden lake drainage , abnormal glacier velocities , or seismic activities that could potentially trigger a GLOF . These techniques are designed to identify outliers and anomalies in datasets without needing prior labeling of what constitutes a normal or an anomalous event [ 122 ] . Ensemble learning methods like XGBoost and model stacking are used to integrate findings from multiple models , thereby reducing overfitting and enhancing the reliability of predictions [ 123 ] . These methods combine the strengths of various models to improve overall prediction accuracy for GLOF risk assessment .\n\nGeospatial machine learning approaches, including graph neural networks, are leveraged to model complex spatial interactions among various elements like lakes, glaciers, and terrain. These models are capable of processing large-scale spatial data and integrating diverse datasets, providing a comprehensive view of the environmental factors influencing GLOF risks [124].\n\nThe integration of these techniques offers a holistic framework for GLOF risk assessment. For example, combining supervised classification for lake mapping with LSTM-based forecasts and anomaly detection creates a multi-layered early-warning system", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research", "section_headings": ["3. Role of Remote Sensing in GLOF Studies", "3.3. Data Analysis Methods", "3.3.4. Advanced Analytical Techniques: Integration of Machine Learning in GLOF Research"], "chunk_type": "text", "line_start": 373, "line_end": 391, "token_count_estimate": 366, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "218b340969b159e8", "text": "Document: 1. Introduction\nSection: 4. Risk Assessment Models for GLOFs\nType: text\n\nGLOFs pose significant threats to downstream communities, infrastructure, and ecosystems in high-mountain regions. Risk assessment models are critical tools for quantifying these hazards, predicting potential impacts, and guiding mitigation strategies [125]. Mod-\n\nGeosciences **2025**, 15, 211 27 of 36\n\nern GLOF risk assessment methodologies have evolved to incorporate detailed physical parameters and glacier dynamics data obtained through remote sensing, enhancing the precision and reliability of hazard evaluations. These models can be categorized into three broad approaches: quantitative (e.g., hydrodynamic simulations), semi-quantitative (e.g., statistical frameworks), and qualitative (e.g., multi-criteria decision systems), each addressing distinct aspects of GLOF risk. Traditional approaches often relied on empirical observations, but contemporary models utilize sophisticated techniques to quantify and predict GLOF hazards [72]. Table 6 provides a comparative analysis of key model types employed in GLOF risk assessment, detailing their specific input parameters, the role of remote sensing data in parameter acquisition, and models' respective strengths and limitations.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Risk Assessment Models for GLOFs", "section_headings": ["4. Risk Assessment Models for GLOFs"], "chunk_type": "text", "line_start": 393, "line_end": 401, "token_count_estimate": 289, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9b56c8f9f544be8d", "text": "Document: 1. Introduction\nSection: 4. Risk Assessment Models for GLOFs\nType: table\nTable: Table 6. Comparative framework for GLOF risk assessment.\n\n| Model Type                                | Input Parameters                                                                                                                | Remote Sensing Data for Parameter Acquisition                                                                                                                                                                                  | Strengths                                                                                                                | Limitations                                                                                                                     |\n|-------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|\n| Hydrodynamic Models (e.g., HEC-RAS) | - Lake volume - Channel geometry - Topography/DEM - Flow roughness - Upstream discharge                             | - Satellite altimetry, optical imagery, and DEMs for lake volume - DEMs and high-resolution imagery for channel geometry - DEMs for topography - Land cover classification (optical/SAR) for roughness | - Detailed flood wave propagation - Accurate inundation extent mapping - Supports infrastructure planning | - Highly sensitive to DEM accuracy - Requires calibration with field data - Limited in steep/vegetated terrain   |\n| Statistical Models                        | - Historical GLOF frequency - Lake area changes - Glacier retreat rates - Climate variables                         | - Historical optical/SAR imagery and GLOF databases - Time series satellite data (e.g., Landsat, Sentinel) for lake/glacier dynamics - Reanalysis climate data                                                  | - Quantifies probabilistic risk - Low computational cost - Identifies historical trends                   | - Relies on past events (fails for novel triggers) - Limited in data-scarce regions - Ignores physical processes |\n| Multi-Criteria Decision Models         | - Lake expansion rate - Dam type - Proximity to infrastructure - Avalanche/snowpack risk - Population density | - Time series optical/SAR for lake monitoring - DEMs and settlement maps (e.g., nighttime lights) - Snow cover maps (optical/radar) for avalanche risk                                                       | - Holistic risk prioritization - Flexible integration of socio-environmental factors                         | - Subjective weight allocation - Requires expert validation - Qualitative outputs                                   |", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Risk Assessment Models for GLOFs", "section_headings": ["4. Risk Assessment Models for GLOFs"], "chunk_type": "table", "table_caption": "Table 6. Comparative framework for GLOF risk assessment.", "columns": ["Model Type", "Input Parameters", "Remote Sensing Data for Parameter Acquisition", "Strengths", "Limitations"], "table_row_start": 1, "table_row_end": 3, "line_start": 402, "line_end": 406, "token_count_estimate": 542, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24cf59c02e85cb54", "text": "Document: 1. Introduction\nSection: 4. Risk Assessment Models for GLOFs\nType: text\n\nAs a quantitative approach, hydrodynamic models, such as HEC-RAS, are designed to simulate water flow dynamics and predict flood propagation pathways [88]. They are widely used to map inundation extents and assess downstream impacts by integrating parameters such as lake volume, channel geometry, topography (derived from DEMs), flow roughness, and upstream discharge [119]. Satellite altimetry and optical imagery provide critical inputs for estimating lake volume, while DEMs and land cover classification (using optical/SAR data) define channel geometry and flow resistance. Though these models offer high accuracy in infrastructure planning, their reliability hinges on DEM precision and field calibration, with reduced efficacy in steep or densely vegetated terrain [57].\n\nStatistical models represent a semi-quantitative approach, prioritizing probabilistic risk assessment by leveraging historical GLOF events, lake area changes, glacier retreat rates, and climate variables (e.g., temperature, precipitation) [126]. These models identify trends and recurrence intervals using time series optical/SAR imagery (e.g., Landsat,\n\nGeosciences 2025, 15, 211 28 of 36\n\nSentinel) and reanalysis climate data. While computationally efficient and valuable for identifying historical patterns, they are inherently limited by their reliance on past events, failing to account for novel triggers like unprecedented rainfall or ice avalanches in a warming climate.\n\nMulti-criteria decision models fall under a qualitative framework, adopting a holistic approach to prioritize risk by synthesizing diverse factors such as lake expansion rates, dam type, proximity to infrastructure, avalanche/snowpack risk, and population density [91,127,128]. Remote sensing supports these models through time series optical/SAR imagery for lake monitoring, DEMs for avalanche-prone slopes, and night-time lights data for mapping settlements. Although they excel in integrating socioenvironmental factors, their subjective criteria weighting and qualitative outputs necessitate expert validation [129].\n\nContemporary GLOF risk frameworks increasingly combine these models to offset individual limitations. For instance, statistical models identify high-probability lakes while, hydrodynamic models map downstream impacts [125,126]. Integrating remote sensing data into these models improves input accuracy and enhances their predictive capabilities, making them essential tools for addressing the hazards posed by GLOFs [127,130]. This dynamic interplay between advanced modelling techniques and cutting-edge remote sensing technologies underpins modern strategies for disaster risk reduction in vulnerable mountainous areas.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Risk Assessment Models for GLOFs", "section_headings": ["4. Risk Assessment Models for GLOFs"], "chunk_type": "text", "line_start": 407, "line_end": 419, "token_count_estimate": 644, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86fa9ce7062f95d6", "text": "Document: 1. Introduction\nSection: 5. Challenges and Limitations\nType: text\n\nThe integration of remote sensing technologies and advanced modelling frameworks has significantly advanced GLOF research. However, several challenges and limitations persist, hindering the accuracy, reliability, and practical implementation of these tools in risk assessment and mitigation.\n\nThe accuracy and resolution of remote sensing data play a crucial role in the reliability of GLOF risk assessments [62,130]. High-resolution data, such as those from high-resolution commercial satellites or UAVs, can provide detailed information on lake morphology and dam conditions. However, these data sources may be limited in terms of temporal coverage or spatial extent, making it challenging to monitor changes over time or across large regions. On the other hand, moderate-resolution data from satellites like Landsat or Sentinel may offer broader coverage and longer temporal records but may lack the level of detail needed for detailed assessments [131]. Additionally, atmospheric conditions, such as cloud cover or haze, can affect the quality. Moreover, integrating data from multiple sources and sensors involves significant challenges in terms of data compatibility, calibration, and processing [132]. This integration is critical for creating accurate models, but can be technically complex and resource-intensive for optical remote sensing data, limiting their effectiveness in certain regions or time periods.\n\nPredicting GLOF events and their impacts is subject to various uncertainties, including uncertainties in model parameters, input data, and model structure. For example, hydrodynamic models used to simulate flood propagation may be sensitive to uncertainties in lake bathymetry, channel roughness, or precipitation patterns [37,88]. Statistical models, while useful for probabilistic risk assessment, fail to account for novel triggers such as unprecedented rainfall or cascading hazards (e.g., ice avalanches triggered by seismic activity), limiting their predictive power in a warming climate [126]. Furthermore, machine learning frameworks face challenges in generalizing across regions due to data scarcity and variability in glacier dynamics, often overfitting to localized conditions [124]. These uncertainties can lead to discrepancies between model predictions and observed events, highlighting the need for careful validation and calibration of models.\n\nGeosciences 2025, 15, 211 29 of 36\n\nThe impacts of climate change are altering glacier behaviors and lake formations at unpredictable rates, which complicates the ability to use past data to predict future events [133]. This rapid environmental change challenges the ability of current models to forecast GLOFs accurately. The geological settings of glacial lakes vary widely, and the hydrological processes involved in lake formation and drainage are complex and not fully understood [134]. These uncertainties make it difficult to generalize findings from one region to another or to extrapolate results from small-scale studies to larger regions.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Challenges and Limitations", "section_headings": ["5. Challenges and Limitations"], "chunk_type": "text", "line_start": 421, "line_end": 433, "token_count_estimate": 682, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "786e119540b4f71d", "text": "Document: 1. Introduction\nSection: 5. Challenges and Limitations\nType: text\n\nneed for careful validation and calibration of models . Geosciences 2025 , 15 , 211 29 of 36 The impacts of climate change are altering glacier behaviors and lake formations at unpredictable rates , which complicates the ability to use past data to predict future events [ 133 ] . This rapid environmental change challenges the ability of current models to forecast GLOFs accurately . The geological settings of glacial lakes vary widely , and the hydrological processes involved in lake formation and drainage are complex and not fully understood [ 134 ] . These uncertainties make it difficult to generalize findings from one region to another or to extrapolate results from small - scale studies to larger regions .\n\nThe implementation of advanced remote sensing technologies and models remains constrained by logistical, financial, and institutional barriers. High-mountain regions, particularly in developing countries like Nepal or Pakistan, lack the infrastructure for real-time data transmission and processing, delaying early warnings [29,30,135]. High-resolution satellite imagery (e.g., WorldView, Pleiades) and UAV campaigns are costly, limiting their adoption in resource-strapped regions. Even when data are available, translating them into actionable policies requires interdisciplinary collaboration between glaciologists, policy-makers, and local communities—a coordination often hampered by communication gaps or competing priorities. Additionally, field validation efforts are hindered by the remoteness and harsh conditions of glacial environments, risking incomplete or outdated ground-truth data. These challenges underscore the need for continued innovation in remote sensing technologies, robust validation protocols, and inclusive governance frameworks to bridge the gap between scientific advancements and on-the-ground risk reduction.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Challenges and Limitations", "section_headings": ["5. Challenges and Limitations"], "chunk_type": "text", "line_start": 421, "line_end": 433, "token_count_estimate": 430, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "8eb05c4fe3675e27", "text": "Document: 1. Introduction\nSection: 6. Future Directions\nType: text\n\nAs GLOF hazards intensify under climate change, advancing remote sensing technologies and interdisciplinary frameworks will be critical to enhancing risk assessment and mitigation. Therefore, it is important to focus on innovative research and technological advancements that can improve predictions, enhance monitoring, and streamline mitigation efforts.\n\nThe field of remote sensing is continually evolving, with new technologies and techniques emerging regularly. For GLOF risk assessment, future research could explore the use of higher resolution imaging systems, such as CubeSats or advanced UAV platforms, to capture more detailed information on lake morphology and dam conditions. Advances in hyperspectral imaging and quantum sensors could improve spectral discrimination of turbid water and debris-covered ice, reducing classification errors. The incorporation of emerging technologies such as unmanned aerial vehicles (UAVs), ground-penetrating radar (GPR), and more sophisticated LiDAR systems can fill gaps left by traditional remote sensing methods, especially in inaccessible or cloud-prone areas. Crucially, future efforts should converge on establishing an integrated air-ground-space GLOF early warning and prediction system. This system would leverage the real-time data transmission capabilities enabled by Internet of Things (IoT) sensors, particularly through edge computing, to bypass traditional latency issues. Furthermore, the integration of real-time data processing and analysis capabilities could enable more timely detection of potential GLOF events, allowing for faster response and mitigation efforts. Specifically, Artificial Intelligence (AI), particularly transformer-based models and federated learning systems, holds potential for automating early warning systems by synthesizing multi-source data (optical, SAR, DEMs) to predict outburst triggers like rapid lake expansion or moraine subsidence. These AI and IoT technologies are paramount for enhancing the accuracy and timeliness of risk prevention.\n\nTo enhance the accuracy and reliability of GLOF risk assessments, future research could focus on improving the integration of remote sensing data with dynamic risk models. This could involve the development of advanced machine learning algorithms to better capture\n\nGeosciences 2025, 15, 211 30 of 36\n\nthe complex relationships between environmental factors and GLOF events. Additionally, the use of enhanced simulation techniques, such as coupled hydrological-geotechnical models, could provide more realistic representations of GLOF processes and their impacts.\n\nAs climate change remains a critical driver of glacial changes, establishing long-term environmental monitoring programs will provide essential data to understand and anticipate future changes in glacial behavior and lake dynamics. Research should also focus on developing sustainable development strategies that minimize environmental impacts and enhance the adaptive capacity of communities living in high-risk regions.\n\nAdvancements in technology and modelling have the potential to significantly influence policy-making and practical hazard management strategies for GLOFs. For example, improved monitoring capabilities could lead to the development of more effective early warning systems, while enhanced risk assessment models could inform the design of targeted mitigation measures. Future research could explore how these advancements can be translated into actionable policies and management practices, particularly in the context of climate change adaptation and sustainable development.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "6. Future Directions", "section_headings": ["6. Future Directions"], "chunk_type": "text", "line_start": 435, "line_end": 449, "token_count_estimate": 747, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9dcc91fb414989d7", "text": "Document: 1. Introduction\nSection: 7. Conclusions\nType: text\n\nThis review has systematically explored the evolving landscape of remote sensing technologies and their applications in monitoring and risk assessment of GLOFs, a growing threat in high-mountain regions worldwide. We have highlighted the progression from traditional methods to the current era of advanced satellite and UAV-based monitoring, emphasizing the critical role of multi-sensor data fusion and machine learning in enhancing the accuracy and efficiency of GLOF studies. The integration of these technologies not only improves the precision of risk assessments but also supports the development of early warning systems that are crucial for safeguarding vulnerable communities.\n\nDespite technological advancements, challenges such as data integration, model calibration, and the need for high-resolution data continue to constrain the full potential of remote sensing applications. Moreover, environmental and climatic variability introduces additional complexities to modeling efforts.\n\nFuture efforts should focus on refining remote sensing techniques, enhancing predictive modeling through machine learning, and translating these advancements into actionable policies and management practices. The scientific community, policymakers, and local stakeholders must work together to build resilience and mitigate the impacts of GLOFs, ensuring the safety and sustainability of high-mountain regions.\n\nAs climate change continues to drive glacier retreat and increase the frequency and intensity of GLOFs, the insights from this review call for sustained research, innovation, and collaboration. Collaboration among the scientific community, policymakers, and local stakeholders is imperative to enhance the resilience of mountainous regions against GLOF hazards. By integrating scientific advancements with policy and community-based actions, we can more effectively mitigate the impacts of GLOFs and ensure the sustainability of these high-risk regions.\n\n**Author Contributions:** Conceptualization, S.N. and N.S.; methodology, S.N.; software, Z.B.; validation, Z.B. and L.B.; formal analysis, L.B.; investigation, S.N.; resources, Z.B.; data curation, L.B.; writing—original draft preparation, N.S.; writing—review and editing, S.N.; visualization, L.B.; supervision, S.N.; project administration, S.N.; funding acquisition, S.N. All authors have read and agreed to the published version of the manuscript.\n\n**Funding:** This research was funded by Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number BR21882365.\n\nData Availability Statement: Data are contained within this article.\n\nGeosciences 2025, 15, 211 31 of 36\n\n**Acknowledgments:** We are grateful to all the authors of the articles that were discussed in this review.\n\nConflicts of Interest: The authors declare no conflicts of interest.", "metadata": {"source_file": "data/('geosciences-15-00211-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "7. Conclusions", "section_headings": ["7. Conclusions"], "chunk_type": "text", "line_start": 451, "line_end": 471, "token_count_estimate": 671, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1da7d14354ac3890", "text": "Document: Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment\nType: text\n\nAbstract: Glacial lake outburst floods (GLOF) evolve into debris flows by erosion and sediment entrainment while propagating down a valley, which highly increases peak discharge and volume and causes destructive damage downstream. This study focuses on GLOF hazard assessment in the Bhote Koshi Basin (BKB), where was highly developed glacial lakes and was intensely affected by the Gorkha earthquake. A new 2016 glacial lake inventory was established, and six unreported GLOF events were identified with geomorphic outburst evidence from GaoFen-1 satellite images and Google Earth. A new method was proposed to assess GLOF hazard, in which large numbers of landslides triggered by earthquake were considered to enter into outburst floods enlarge the discharge and volume of debris flow in the downstream. Four GLOF hazard classes were derived according to glacial lake outburst potential and a flow magnitude assessment matrix, in which 11 glacial lakes were identified to have very high hazard and 24 to have high hazard. The GLOF hazard in BKB increased after the earthquake due to landslide deposits, which increased by  $216.03 \\times 10^6$  m<sup>3</sup>, and provides abundant deposits for outburst floods to evolve into debris flows. We suggest that in regional GLOF hazard assessment, small glacial lakes should not be overlooked for landslide deposit entrainment along a flood route that would increase the peak discharge, especially in earthquake-affected areas where large numbers of landslides were triggered.\n\n**Keywords:** glacial lake outburst flood (GLOF); debris flow; Bhote Koshi; landslides; Gorkha earthquake; hazard assessment", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 432, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "76d6f3cb5227deba", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nMany studies have demonstrated that most glaciers are retreating because of global warming and that the meltwater makes an important contribution to the development of glacial lakes in the Himalayas [1–5]. The sudden emptying of these lakes due to dam overflow and moraine or ice dam failure releases large volumes of water and sediment in destructive events called glacial lake outburst floods (GLOF) [6,7]. In the Himalaya region, at least 62 GLOF have been reported, which caused catastrophic destruction and fatalities in downstream regions [8–12]. The peak flood discharge can easily attain tens of thousands of m³/s and travel more than 100 km away [8,13]. Given their high magnitude discharge and long runout distance characteristics, the GLOF impact is sometimes transboundary, especially in the Himalayas. More than 10 GLOF events originated in Tibet, and the catastrophic floods killed hundreds of people and destroyed much infrastructure downstream, causing enormous damage in Nepal and India [14,15]. As a result, GLOF hazard assessment is receiving increased attention from researchers and governments.\n\nWater 2020, 12, 464 2 of 20\n\nIn previous GLOF hazard assessment studies, only glacial lakes with an area  $>10^5$  m<sup>2</sup> or volume  $>10^6$  m<sup>3</sup> were considered to be risky of outburst [15,16]. However, some small outbursts occurred in the high mountain regions, but are often ignored due to the limited scale of the events or difficult access [17]. Veh [18] detected 10 previously unreported GLOF from Landsat time series in a study area covering only 10% of the Hindu Kush Himalayan region. In addition, glacial lake outburst floods are highly unsteady flows characterized by pronounced changes as they propagate down to the valley [13]. The outburst flood can change from a normal flood to a hyperconcentrated flow or debris flow [19,20], and the volumes and peak discharges can increase several to ten times owing to erosion, slides from lateral slopes, sediment entrainment and bulking process along the flow path [21,22]. As an example, in Norway, a glacial lake outburst flood developed into a debris flow due to erosion and blockage, and the volume increased nearly ten times from 25,000 to 240,000 m<sup>3</sup> [23]. Sediment can be entrained by scouring unconsolidated deposits of the channel bed, or eroding landslide and collapse of lateral slopes [24,25]. In the seismic belts, large numbers of weak structures and broken rocks are developed along the active fracture zone, and the soils become looser after an earthquake [26]. Studies showed that large earthquakes, such as the Chi-chi earthquake and Wenchuan earthquake, trigger many collapses and landslides, resulting in an increase in loose deposits [27,28]. Although rare reported GLOF events in the Himalaya are directly triggered by earthquakes [29], the loose deposits and landslides induced by earthquakes may affect the magnitude and impact of GLOF. Therefore, it is necessary to build a GLOF hazard assessment model, considering small glacial lakes and the scenario of glacial lake outburst debris flows after earthquakes, especially in areas where many collapses and landslides have developed along the channels.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 20, "token_count_estimate": 823, "basins": [], "subbasins": [], "countries": ["India", "Nepal"], "lake_ids": []}}
{"id": "6c7d23cfb2283b59", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nafter an earthquake [ 26 ] . Studies showed that large earthquakes , such as the Chi - chi earthquake and Wenchuan earthquake , trigger many collapses and landslides , resulting in an increase in loose deposits [ 27 , 28 ] . Although rare reported GLOF events in the Himalaya are directly triggered by earthquakes [ 29 ] , the loose deposits and landslides induced by earthquakes may affect the magnitude and impact of GLOF . Therefore , it is necessary to build a GLOF hazard assessment model , considering small glacial lakes and the scenario of glacial lake outburst debris flows after earthquakes , especially in areas where many collapses and landslides have developed along the channels .\n\nThe Bhote Koshi Basin across China and Nepal, is a highspot area of glacial lakes and GLOF events (Wang and Jiao, 2015). Four glacial lakes have experienced six GLOF events since 1935 (Figure 1). Taraco Lake failed on 28 August 1935, and the GLOF damaged more than 10 hm<sup>2</sup> of wheat fields (Lv, 1999); The Jialongco GLOF occurred on 23 May and 29 June 2002, which caused 7.5 million yuanin economic losses in Nyalam County (Chen et al., 2007). The Cinrenmaco Lake experienced two GLOF events, first in 1964 and second on 10 July 1981. The GLOF in 1981 had the most destructive effects, in which more than 200 people were killed in Nepal, and the total losses were estimated at approximately three million dollars [14,30]. The latest GLOF event occurred on the night of 5 July 2016, which was caused by Gongbatongshaco (GBTSC) Lake in the Zhangzangbo Valley. GBTSC is a small moraine-dammed lake, with a surface area of  $1.7 \\times 10^4$  m<sup>2</sup> and it was almost empty after the outburst. Although it only released  $1.1 \\times 10^5$  m<sup>3</sup> of water, the peak discharge reached 2400 m<sup>3</sup>/s at Khukundol, 30 km downstream from the lake, due to severe erosion and sediment entrainment [31]. The GLOF caused severe damage downstream of Bhote Koshi, damaging 77 houses, 3 bridges and the Araniko Highway, and destroying the intake dam of the Upper Bhote Koshi Hydropower Project in Nepal (Figure 1). The 2016 GLOF damage sits within the area affected by the Gorkha earthquake (magnitude M 7.8 in 2015), where extensive landslides and rockfalls were triggered on the slopes, and some landslide deposits even blocked the river [32,33]. Therefore, the small glacial lake GBTSC GLOF caused a serious disaster, which caused us to reconsider the small-lake induced GLOF hazard after\n\nThe aims of this study are: (1) to establish a detailed glacial lake inventory of BKB after the Gorkha earthquake, based on high resolution remote sensing satellite images; (2) to evaluate the GLOF hazard of BKB considering the scenario that outburst floods evolve into debris flows due to erosion and entrainment of loose solids.\n\nWater 2020, 12, 464 3 of 20", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 20, "token_count_estimate": 790, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "8a39c79f5b04bf28", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: A figure composed of four photographs labeled a, b, c, and d, illustrating the effects of a glacial lake outburst flood. Panel 'a' shows a rocky basin with a small lake, with a blue dashed line indicating the much higher 'Lake surface before outburst' and a person circled and labeled 'Man' for scale. Panel 'b' displays a wide mountain landscape with snow-capped peaks, showing the 'Breach' in the moraine from which the lake burst and the resulting large 'Debris fan' of rocks and sediment below. Panel 'c' depicts a river valley downstream, where a village is situated next to the river, with signs of landslides and erosion on the steep hillsides. Panel 'd' shows an aerial view of a concrete dam structure being overwhelmed and damaged by a powerful, muddy flood carrying significant debris.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 21, "line_end": 21, "token_count_estimate": 237, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0cbfe501f191a7a2", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: figure\nFigure: Figure 1. Photographs of Gongbatongshaco (GBTSC) Lake and the glacial lake outburst floods (GLOF) damage caused downstream: (a) GBTSC Lake after outburst, (b) the breach and debris fan in front of the lake, (c) landslides and river bank collapse triggered by GLOF near Friendship Bridge and (d) the destroyed dam of Upper Bhote Koshi Hydroelectric Project in Nepal.\n\n**Figure 1.** Photographs of Gongbatongshaco (GBTSC) Lake and the glacial lake outburst floods (GLOF) damage caused downstream: (a) GBTSC Lake after outburst, (b) the breach and debris fan in front of the lake, (c) landslides and river bank collapse triggered by GLOF near Friendship Bridge and (d) the destroyed dam of Upper Bhote Koshi Hydroelectric Project in Nepal.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "figure", "figure_caption": "Figure 1. Photographs of Gongbatongshaco (GBTSC) Lake and the glacial lake outburst floods (GLOF) damage caused downstream: (a) GBTSC Lake after outburst, (b) the breach and debris fan in front of the lake, (c) landslides and river bank collapse triggered by GLOF near Friendship Bridge and (d) the destroyed dam of Upper Bhote Koshi Hydroelectric Project in Nepal.", "line_start": 23, "line_end": 23, "token_count_estimate": 231, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "1722d27c2985ec06", "text": "Document: 1. Introduction\nSection: 2. Study Area\nType: text\n\nThe study site is located in the central Himalayas and covers latitudes  $27^{\\circ}37'-28^{\\circ}31'$  N and longitudes  $85^{\\circ}40'-86^{\\circ}20'$  E with an area of 3406 km² (Figure 2). Bhote Koshi, which is also called Poiqu in China, is a transboundary river with a length of 143 km. It originates in the Bangbulei Mountains in northern Nyalam County, China, flows into Nepal, and at last feeds into the Ganges River. The Araniko Highway, built along the Bhote Koshi Valley, is a key trade and transport route between China and Nepal. Approximately 200,000 people live in the watershed, among which only 2.7% of them live in China, and the infrastructure in this region is particularly vulnerable [34].\n\nThe Bhote Koshi Basin stretches across the Higher Himalaya and Lower Himalaya, and the South Tibetan Detachment System (STDS) and the Main Central Thrust (MCT) pass through it. The basin is strongly affected by seismic activity. According to the statistics of the United States Geological Survey (USGS) earthquake records (http://earthquake.usgs.gov/earthquakes), there were 213 earthquakes (magnitude larger than M 4.5) in the area of 150 km² around BKB from 1983 to 2016, including a M 8.3 earthquake, three earthquakes larger than M 7.0 and 79 earthquakes equal or larger than M 5.0. The latest large earthquake, M 7.8, on 25 April 2015 and its largest aftershock (M 7.3) on 12 May 2015, produced severe impact in the study area. The epicenter of the major aftershock was only 19 km southeast of Kodari. The lower part from Zhangzangbo Valley to Dolalghat, which is approximately half of the region, was located in seismic intensity zones VIII and VII, and the upper part was in the VI zone according to the seismic intensity of the Gorkha earthquake provided by the USGS National Earthquake Information Center (Figure 2).\n\nThe elevation ranges from the highest peak of Mt. Shishapangma at 8012 m to the lowest point of 591 m in Dolalghat, Nepal. Given the large relief, the landforms are different from north to south. In the north and central parts of the basin are alpine regions and gorges, while the valley becomes broader in the south. The climate also varies considerably from south to north. The Himalayan southern slope region of the basin is affected by the Indian monsoon and experiences high precipitation levels. Meanwhile, due to blockage by the Himalayan range, the warm, moist air from the Indian monsoon\n\nWater 2020, 12, 464 4 of 20\n\ncan hardly reach the northern part of the basin. According to monthly data obtained from the Nyalam meteorological station (3810 m a.s.l.) and the Zhangmu meteorological station (2250 m a.s.l.), the mean annual temperature ranges from 3.8  $^{\\circ}$ C to 10.1  $^{\\circ}$ C, and the mean annual precipitation ranges from 643.4 mm in the north to 2820.6 mm in the south.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Study Area", "section_headings": ["2. Study Area"], "chunk_type": "text", "line_start": 26, "line_end": 36, "token_count_estimate": 766, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "cfe68cf27fbb9c88", "text": "Document: 1. Introduction\nSection: 2. Study Area\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A detailed topographical and geological map titled \"Figure 2. Study area and GLOF events in Bhotekoshi Basin.\" The map covers a region from 85°40'E to 86°20'E longitude and 27°40'N to 28°30'N latitude. It displays elevation using a color scale, ranging from 591 to 8012 meters above sea level (m a.s.l.). Key features are identified in a comprehensive legend. These include an earthquake epicenter marked with a yellow star (M7.3), GLOF (Glacial Lake Outburst Flood) events marked with orange circles at locations like Jialongco and Taraco, settlements like Kodari and Barhabise, rivers such as Bhotekoshi and Poiqu, faults shown as red lines, and the national boundary. The map also delineates glaciers, glacial lakes, the Araniko Highway, and zones of seismic intensity labeled with Roman numerals V, VI, VII, and VIII. A north arrow and a scale bar for 0 to 20 km are included. An inset map in the bottom right corner places the study area within the Central Himalaya, showing its relation to India, Nepal, and China, and marking additional earthquake epicenters.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Study Area", "section_headings": ["2. Study Area"], "chunk_type": "figure", "figure_caption": null, "line_start": 37, "line_end": 37, "token_count_estimate": 312, "basins": [], "subbasins": [], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "1c981af08e73e5dd", "text": "Document: 1. Introduction\nSection: 2. Study Area\nType: figure\nFigure: Figure 2. Study area and GLOF events in Bhote Koshi Basin.\n\nFigure 2. Study area and GLOF events in Bhote Koshi Basin.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Study Area", "section_headings": ["2. Study Area"], "chunk_type": "figure", "figure_caption": "Figure 2. Study area and GLOF events in Bhote Koshi Basin.", "line_start": 39, "line_end": 39, "token_count_estimate": 50, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0d498d4267b41125", "text": "Document: 1. Introduction\nSection: 2. Study Area\nType: text\n\nWater 2020, 12, 464 5 of 20", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Study Area", "section_headings": ["2. Study Area"], "chunk_type": "text", "line_start": 40, "line_end": 42, "token_count_estimate": 23, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d787482b2dd19530", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping\nType: text\n\nGlacial lake and landslide identification were based on GaoFen-1 (GF-1) satellite images. Twenty-two GF-1 images obtained from the China Center for Resources Satellite Data and Application (http://www.cresda.com/CN/) were Level-1A products, with cloud coverage less than 20% (Table 1). Seven images were used to map glacial lakes and fifteen images were used to map landslides preand post-earthquake. Geometric correction and image sharpening were conducted in ENVI 5.2 before mapping in ArcGIS 10.2. The resolution of pan sharpened images was 2 m. This high-quality imagery available allowed us to recognize glacial lakes as small as 0.01 km<sup>2</sup>. Manual visual interpretation was used to identify glacial lakes and landslides.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping", "section_headings": ["3. Methods", "3.1. Glacial Lake and Landslide Inventory Mapping"], "chunk_type": "text", "line_start": 46, "line_end": 48, "token_count_estimate": 221, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "b4591d2b209e53d0", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping\nType: table\nTable\n\n| Data Usage                           | Sensor | Product ID | Date              | Cloud (%) |\n|--------------------------------------|--------|------------|-------------------|-----------|\n| Glacial lake mapping              | PMS1   | 2056413    | 20 December 2016  | 13        |\n|                                      | PMS1   | 2056415    | 20 December 2016  | 4         |\n|                                      | PMS1   | 2056414    | 20 December 2016  | 3         |\n|                                      | PMS1   | 1929663    | 1 November 2016   | 13        |\n|                                      | PMS1   | 1929664    | 1 November 2016   | 16        |\n|                                      | PMS1   | 1929665    | 1 November 2016   | 6         |\n|                                      | PMS2   | 1524197    | 14 April 2016     | 15        |\n| Landslide mapping post-earthquake | PMS2   | 1242505    | 13 December 2015  | 11        |\n|                                      | PMS2   | 1242506    | 13 December 2015  | 1         |\n|                                      | PMS2   | 820531     | 22 May 2015       | 1         |\n|                                      | PMS1   | 827786     | 23 May 2015       | 3         |\n|                                      | PMS2   | 1062062    | 26 May 2015       | 14        |\n|                                      | PMS2   | 1062061    | 26 September 2015 | 0         |\n|                                      | PMS2   | 1242505    | 13 December 2015  | 11        |\n|                                      | PMS1   | 1251892    | 17 December 2015  | 3         |\n| Landslide mapping pre-earthquake  | PMS2   | 751296     | 11 April 2015     | 0         |\n|                                      | PMS2   | 598009     | 19 January 2015   | 12        |\n|                                      | PMS2   | 507470     | 9 December 2014   | 1         |\n|                                      | PMS3   | 507469     | 9 December 2014   | 19        |\n|                                      | PMS1   | 646048     | 22 September 2014 | 16        |\n|                                      | PMS1   | 232717     | 22 May 2014       | 2         |\n|                                      | PMS1   | 142225     | 30 December 2013  | 9         |", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping", "section_headings": ["3. Methods", "3.1. Glacial Lake and Landslide Inventory Mapping"], "chunk_type": "table", "table_caption": null, "columns": ["Data Usage", "Sensor", "Product ID", "Date", "Cloud (%)"], "table_row_start": 1, "table_row_end": 22, "line_start": 49, "line_end": 72, "token_count_estimate": 581, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1062061", "1062062", "1242505", "1242506", "1251892", "142225", "1524197", "1929663", "1929664", "1929665", "2056413", "2056414", "2056415", "232717", "507469", "507470", "598009", "646048", "751296", "820531", "827786"]}}
{"id": "df3a71460647a015", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping\nType: text\n\nTable 1. GaoFen-1 images used in this study.\n\nAll glacial lakes were verified and modified against Google Earth to see if there are some misinterpretations of the results due to the effect of terrain shadow. The characteristics and surrounding information of all lakes (larger than 0.01 km²) were measured or estimated, aided by Topography Mission digital elevation model (SRTM DEM) (30 m) and Google Earth. These data compose a complete inventory and provide a basis for identifying dangerous glacial lakes. The inventory of the database consisted of 17 parameters, and some important attributes are explained as follows:\n\n- (a) Name: some glacial lakes were annotated according to the topographical map of 1978.\n- (b) Longitude and latitude: the central location of a glacial lake was calculated automatically in ArcGIS based on WGS84 coordinates.\n- (c) Elevation (m a.s.l.): the central elevation of a glacial lake was derived from the DEM.\n- (d) Dam type: moraine dam, ice dam and bedrock dam, which was specified based on remote sensing images and the topography map (1:100,000; produced in 1978).\n- (e) Area (km<sup>2</sup>): the glacial lake surface area was calculated automatically in ArcGIS 10.2, based on UTM projection zone 48 on a WGS84 ellipsoid.\n- (f) Dam width (m): these values were estimated using Google Earth.\n\nWater 2020, 12, 464 6 of 20\n\n(g) Volume (m³): each glacial lake's volume was estimated using Equation (1), which was established between lake areas and volumes of lake water based on data from 33 Himalayan glacial lakes measured in the field [34],\n\n$$V_{\\rm gl} = 0.0578 A_{\\rm gl}^{1.4683} \\tag{1}$$\n\nwhere,  $A_{gl}$  is glacial lake area.\n\n- (h) Estimated freeboard values (1, or 0): the height of the freeboard is difficult to measure by remote sensing but is a crucial parameter that influences dam failure. Here, we estimated whether the height was larger than only a few meters (the value was 1) or indeed close to zero (the value was 0) [35], so it is a semiquantitative parameter.\n- (i) Potential triggering impacts: whether the mass movement around a glacial lake can enter into the lake, such as rockfalls (R), landslides (L), ice and glacier avalanches (IGA), debris flows (DF) or flood from a lake situated upstream (ULF). If there is no mass movement, the value was null. This was identified based on Google Earth and the slope maps derived from the DEM, so it is also a semiquantitative parameter.\n- (j) Distance to mother glacier (m): the distance between the back edge of a glacial lake to the mother glacier. If they are in contact, the value was 0; if there is no glacier around the lake, the value was set to null.\n- (k) Distance to the nearest settlement (m): the drainage distance from the glacial lake dam to the nearest major settlement was measured using ArcGIS 10.2.\n- (l) Drainage gradient (°): the average drainage gradient was estimated by a DEM-derived drainage map.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping", "section_headings": ["3. Methods", "3.1. Glacial Lake and Landslide Inventory Mapping"], "chunk_type": "text", "line_start": 73, "line_end": 104, "token_count_estimate": 818, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "88878c2077e5b3d0", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping\nType: text\n\nso it is also a semiquantitative parameter . - ( j ) Distance to mother glacier ( m ) : the distance between the back edge of a glacial lake to the mother glacier . If they are in contact , the value was 0 ; if there is no glacier around the lake , the value was set to null . - ( k ) Distance to the nearest settlement ( m ) : the drainage distance from the glacial lake dam to the nearest major settlement was measured using ArcGIS 10 . 2 . - ( l ) Drainage gradient ( ° ) : the average drainage gradient was estimated by a DEM - derived drainage map .\n\nIn this study, the term landslide refers to mass movement of a slope, including rockfalls, slope failure and soil slides. Most landslides can be easily identified by visual inspection for vegetation loss or deposits. If there is no vegetation in some areas, the morphology needs careful attention. Landslides are classified as pre-earthquake and post-earthquake landslides. The landslide volume (V) was estimated using a power-law landslide area–volume empirical formula (Equation (2)):\n\n$$V_{\\rm s} = \\alpha A_{\\rm s}^{\\gamma} \\tag{2}$$\n\nwhere  $A_s$  is landslide area,  $\\alpha$  and  $\\gamma$  are empirically calibrated scaling parameters derived from mixed soil and bedrock landslides in the Himalayas;  $\\alpha$  is 0.257, and  $\\gamma$  is 1.36 [36].", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.1. Glacial Lake and Landslide Inventory Mapping", "section_headings": ["3. Methods", "3.1. Glacial Lake and Landslide Inventory Mapping"], "chunk_type": "text", "line_start": 73, "line_end": 104, "token_count_estimate": 423, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aeb081a00a7940f5", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment\nType: text\n\nGlacial lake outburst hazard assessment includes two steps, glacial lake outburst potential assessment and flow magnitude assessment. First, a qualitative method was used to identify glacial lake outburst potential; then, the outburst flow characteristics were determined, flood or debris flow according to loose matter along the flow path and the channel gradient, and then the magnitude at the nearest settlement was calculated. Finally, the GLOF hazard was derived by the glacial lake outburst probability and flow magnitude based on a matrix diagram, which has been widely used in flood, landslide and rock fall hazard assessments [7,37]. GLOF hazards in BKB were divided into four classes: \"Very High\", \"High\", \"Medium\" and \"Low\". The process of GLOF hazard assessment is summarized in Figure 3.\n\nWater 2020, 12, 464 7 of 20", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment", "section_headings": ["3. Methods", "3.2. Glacial Lake Outburst Hazard Assessment"], "chunk_type": "text", "line_start": 106, "line_end": 110, "token_count_estimate": 229, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b2355e8ce26b4035", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment\nType: figure\nFigure\n\nImage /page/6/Figure/1 description: A flowchart illustrating the process for a glacier lake outburst hazard assessment. The process starts with two main inputs: 'GF-1 images Google Earth' and 'SRTM DEM'. The flowchart is divided into two main parallel paths that converge at the end. The left path determines the 'Qualitative outburst potential assessment'. It starts with 'Mapping glacial lakes' from the GF-1 images, leading to a 'Glacial lake inventory' and then 'Identifying glacial lake outburst potential'. This identification is based on five criteria: 'Dam type' (Bedrock dam vs. Moraine/ice-dam), 'Distance to glacial lake' (>1000, 500-1000, <500), 'Dam width crest' (>60, <60), 'Freeboard' (>1, ~0), and 'Rockfalls/landslide/debris flow/flood from upstream' (No, Yes). Based on these, a 'Qualitative outburst potential assessment' is made, categorized as Low, Medium, or High. The right path determines the 'Flow magnitude assessment'. It uses 'Mapping landslides' from GF-1 images to create a 'Landslides inventory'. It also uses the 'SRTM DEM' to determine 'Slope' and a 'Stream map', which are combined to find the 'Drainage gradient'. Two decision points follow: 'Landslide in downstream?' and 'Gradient > 8°?'. If the answer to both is 'No', it results in 'Flood discharge'. If the answer to either is 'Yes', it results in 'Debris flow discharge'. Both discharge types lead to a 'Flow magnitude assessment', categorized as <200, 200-500, or >500. Finally, the 'Qualitative outburst potential assessment' (relabeled as 'Outburst probability' with levels Low, Medium, High) and the 'Flow magnitude assessment' (with levels Low, Medium, High) are combined in a hazard matrix to produce the final 'Glacier lake outburst hazard assessment'. The matrix shows the resulting hazard level: Low, Medium, High, or Very High. For example, a High outburst probability and a High flow magnitude result in a Very High hazard. The entire flowchart uses a color code: blue for low risk, yellow for medium risk, and red/dark red for high/very high risk.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment", "section_headings": ["3. Methods", "3.2. Glacial Lake Outburst Hazard Assessment"], "chunk_type": "figure", "figure_caption": null, "line_start": 111, "line_end": 111, "token_count_estimate": 619, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56bbc710b756e581", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment\nType: figure\nFigure: Figure 3. Flow chart illustrating the process used for creating the glacial lake inventory of Bhote Koshi Basin in 2016, classifying glacial lake outburst potential and outburst risk assessment.\n\n**Figure 3.** Flow chart illustrating the process used for creating the glacial lake inventory of Bhote Koshi Basin in 2016, classifying glacial lake outburst potential and outburst risk assessment.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment", "section_headings": ["3. Methods", "3.2. Glacial Lake Outburst Hazard Assessment"], "chunk_type": "figure", "figure_caption": "Figure 3. Flow chart illustrating the process used for creating the glacial lake inventory of Bhote Koshi Basin in 2016, classifying glacial lake outburst potential and outburst risk assessment.", "line_start": 113, "line_end": 113, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6cc2b806627c1e9a", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.1. Glacial Lake Outburst Potential Assessment\nType: text\n\nMany criteria and schemes, derived from GLOF experiences all around the world, have been proposed to assess and identify potentially dangerous glacial lakes based on GLOF [38–42]. Here, a qualitative assessment method is proposed to identify the potentially dangerous glacial lakes from three criteria. The first one is potential triggering impacts, such as rockfalls, landslides, snow and ice avalanches, debris flows and flood from a lake situated upstream [43]. Such mass movement entering the lakes trigger displacement waves that subsequently overtop and erode the dams is the most common cause of dam failure in the Himalaya [8,17,44–46]. Steep glacier surfaces that are in contact with or close to a lake are prone to ice avalanches [47–49]. In addition, steep topography is also likely to cause rockfalls and landslides, and as a glacier retreats, much glacial debris remains, which may start a debris flow under heavy rainfall or intense glacier melting [8,38,50]. The second one is dam stability. Studies show most of the GLOF events in the Himalayas are caused by moraine-or ice dam failures, and the bedrock dams are the more stable with low outburst probability [8,51]. The dam width crest is an indicator for the susceptibility of a dam to fail [52]. The third one is freeboard, which is considered a crucial parameter that influences whether a potential impact wave overtops the dam [7,49].\n\nWater 2020, 12, 464 8 of 20\n\nFive key indicators were selected to identify glacial lake outburst potential according to the three criteria. These parameters of each glacial lake were easily obtained from the 2016 glacial lake inventory database. The critical values for assessment are given in Figure 3. The key indicator was defined with qualitative probabilities high, medium and low, and considered independently. The overall potential is not the mean of the individual indicators. A high-potential outburst glacial lake must satisfy three criteria that are high, a low-potential lake has two or three low criteria and no high criteria and the rest are medium-potential lakes. Finally, three potential degrees were classified as high, medium and low.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.1. Glacial Lake Outburst Potential Assessment", "section_headings": ["3. Methods", "3.2. Glacial Lake Outburst Hazard Assessment", "3.2.1. Glacial Lake Outburst Potential Assessment"], "chunk_type": "text", "line_start": 116, "line_end": 122, "token_count_estimate": 561, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a60246da3d4cf608", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.2. Flow Magnitude Assessment\nType: text\n\nThe flow magnitude is highly dependent on the peak discharge at the breach and the channel condition [53]. The peak discharge depends on the lake volume and the breach geometry [38]. For a rapid hazard assessment, complex breach processes and flow behavior are beyond the scope. In this paper, the worst breach scenario was assumed, i.e., a full breach that empties the glacial lake water completely. The maximum discharge ( $Q_p$ ) was estimated using the empirical formula (Equation (3)):\n\n$$Q_p = 2V_{ql}/t \\tag{3}$$\n\nwhere  $V_{\\rm gl}$  is the glacial lake volume, and t is the drainage duration in seconds, which is assumed to be 1000 s [54].\n\nThe outburst flood peak discharge increases due to erosion and entrained sediments. Thus, we first needed to judge whether an outburst flood would develop into a debris flow. The average channel gradient and unconsolidated deposits along the channel are key factors that affect whether an outburst flood evolves into a debris flow [13,55]. Erosion is found to occur where the channel gradients exceed 8° [38] and abundant unconsolidated deposits are distributed in the channel and on the slopes [56]. Channel gradients were calculated based on drainage maps derived from the DEM. The unconsolidated deposits include moraine deposits, fluvial and glaciofluvial sediments and landslide deposits. The maximum eroded sediment volume per unit channel length varies from ten to hundreds of cubic meters due to local and regional differences in geology, topography and hydrology of torrent catchments [38,54]. Therefore, it was hard to set a value certain of sediment depth or volume eroded by flood in different channels or basins. A rough assessment was used to estimate the flow magnitude to the nearest settlement. Flood peak discharge was estimated using an empirical equation (Equation (4)) [57,58]:\n\n$$Q_{\\rm pl} = \\frac{W}{\\frac{W}{Q_p} + \\frac{L}{VK}} \\tag{4}$$\n\nwhere  $Q_p$  is the flood peak discharge m<sup>3</sup>/s; W is the capacity of the lake, m<sup>3</sup>;  $Q_p$  is the peak discharge at the breach, m<sup>3</sup>/s; L is the distance from the glacial lake dam, m; and VK is an empirical coefficient equal to 3.13 for rivers on plains, 7.15 for mountain rivers and 4.76 for rivers flowing through terrain with intermediate relief [59], which here we set the value as 7.15.\n\nFor an outburst debris flow, the water source is the outburst flood. Therefore, the peak discharge of the debris flow consists of outburst flood discharge and soil particle flow. Blocking was not considered here, so the debris flow peak discharge ( $Q_{df}$ ) can be calculated [60]:\n\n$$Q_{\\rm df} = (1 + \\varphi)Q_{vl} \\tag{5}$$\n\nwhere  $\\varphi$  is the increase coefficient of debris flow peak discharge, which can be calculated by:", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.2. Flow Magnitude Assessment", "section_headings": ["3. Methods", "3.2. Glacial Lake Outburst Hazard Assessment", "3.2.2. Flow Magnitude Assessment"], "chunk_type": "text", "line_start": 124, "line_end": 152, "token_count_estimate": 851, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55a4ae3bc69b714f", "text": "Document: 1. Introduction\nSection: 3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.2. Flow Magnitude Assessment\nType: text\n\n] , which here we set the value as 7 . 15 . For an outburst debris flow , the water source is the outburst flood . Therefore , the peak discharge of the debris flow consists of outburst flood discharge and soil particle flow . Blocking was not considered here , so the debris flow peak discharge ( $ Q_ { df } $ ) can be calculated [ 60 ] : $ $ Q_ { \\ rm df } = ( 1 + \\ varphi ) Q_ { vl } \\ tag { 5 } $ $ where $ \\ varphi $ is the increase coefficient of debris flow peak discharge , which can be calculated by :\n\n$$\\varphi = (\\gamma_s - \\gamma_w)/(\\gamma_s - \\gamma_c) \\tag{6}$$\n\nwhere  $\\gamma_s$  is the specific gravity of the solid material, g/cm<sup>3</sup>, and usually determined as 2.65 g/cm<sup>3</sup>;  $\\gamma_w$  is the unit weight of water,  $\\gamma_w = 1$  g/cm<sup>3</sup>;  $\\gamma_c$  is the unit weight of the debris flow, g/cm<sup>3</sup>. Studies show\n\nWater 2020, 12, 464 9 of 20\n\nglacial lake outburst debris flow in Tibet is usually diluted flow [52], and the density is 1.3–1.8 g/cm<sup>3</sup>. For the convenience of calculation, here we set the average value of  $\\gamma_c$  as the density of GBTSC outburst debris flow, 1.55 g/cm<sup>3</sup>.\n\nAccording to Chinese debris flow prevention and control standards (DZT-0220-2006), a peak flow discharge of more than 200 m $^3$ /s is defined as a large hazard. However, the scale of a glacial outburst flood/debris flow is usually larger than that of a rainfall-triggered debris flow [16]. Therefore, in this paper, three flow magnitude classes were established: flow discharge <200 m $^3$ /s (low), 200–500 m $^3$ /s (medium) and >500 m $^3$ /s (high). Finally, the GLOF hazard was derived by the glacial lake outburst probability and flow magnitude based on a matrix diagram, which has been widely used in flood, landslide and rock falls hazard assessments [7,37]. GLOF hazards in BKB are divided into four classes: \"Very High\", \"High\", \"Medium\" and \"Low\".", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Methods > 3.2. Glacial Lake Outburst Hazard Assessment > 3.2.2. Flow Magnitude Assessment", "section_headings": ["3. Methods", "3.2. Glacial Lake Outburst Hazard Assessment", "3.2.2. Flow Magnitude Assessment"], "chunk_type": "text", "line_start": 124, "line_end": 152, "token_count_estimate": 691, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0397c881226c9e37", "text": "Document: 1. Introduction\nSection: 4. Results > 4.1. Glacial Lake Inventory\nType: text\n\nA total of 122 glacial lakes larger than 0.01 km² with an area of 20.38 km² were identified based on the GF-1 images from 2016 (see Supplementary Materials). According to the dam type, 84 moraine-dammed lakes with a total area of 16.87 km² accounts for the largest number and area of all lakes. These moraine-dammed lakes are mainly distributed at 5100–5400 m a.s.l. There are 25 bedrock-dammed lakes that account for 15.3% of the area of all lakes. The average area of a bedrock-dammed lake is 0.12 km², and are mainly distributed at 4100–4700 m a.s.l. The ice-dammed lakes are the least and smallest, occupying 10.7% and 1.9% of the total number and area. The ice-dammed lakes consist of tiny and small lakes, with a mean area of 0.03 km², mainly distributed at 5000–5200 m a.s.l. (Figure 4a).", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.1. Glacial Lake Inventory", "section_headings": ["4. Results", "4.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 156, "line_end": 158, "token_count_estimate": 263, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "446c85427aedcdfb", "text": "Document: 1. Introduction\nSection: 4. Results > 4.1. Glacial Lake Inventory\nType: figure\nFigure\n\nImage /page/8/Figure/6 description: The image displays characteristics of a glacial lake basin in 2016 through three panels labeled a, b, and c.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.1. Glacial Lake Inventory", "section_headings": ["4. Results", "4.1. Glacial Lake Inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 159, "line_end": 159, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "98d31684721c07cd", "text": "Document: 1. Introduction\nSection: 4. Results > 4.1. Glacial Lake Inventory\nType: text\n\nPanel a is a topographical map showing the distribution of glacial lakes. A legend indicates the dam type and size class of the lakes. Dam types are bedrock dam (light green circles), ice dam (yellow circles), and moraine dam (dark green circles). The size classes, in square kilometers, are represented by circles of increasing size: ≤ 0.02, 0.02 - 0.1, 0.1 - 0.5, 0.5 - 1, and > 1. The map includes rivers and place names such as Tajilin, Nyalam, and Zhangmu.\n\nPanel b is a dual-axis bar chart showing the number and area of glacial lakes by size class. The x-axis shows size classes: <0.02, 0.02 - 0.1, 0.1 - 0.5, 0.5 - 1, and >1 km². The left y-axis represents the number of lakes (grey bars), and the right y-axis represents the area in km² (teal bars). The data shows that the number of lakes is highest in the smallest size class (<0.02 km²) with approximately 56 lakes, and decreases as size increases. Conversely, the total area is greatest for the largest size class (>1 km²), at about 12 km², despite having the fewest lakes (approximately 2).\n\nPanel c is a bar chart showing the percentage of glacial lakes by area (teal bars) and by number (grey bars) at different elevations. The x-axis shows elevation in meters, from 4000 to 5800. The y-axis shows the percentage. The distribution is concentrated at higher elevations, with the largest percentages found between 5100 m and 5400 m. The peak percentage by area is at 5200 m (around 31%), while the peak percentage by number is at 5300 m (around 14%).", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.1. Glacial Lake Inventory", "section_headings": ["4. Results", "4.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 160, "line_end": 166, "token_count_estimate": 438, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca5c39903e6dc7c2", "text": "Document: 1. Introduction\nSection: 4. Results > 4.1. Glacial Lake Inventory\nType: figure\nFigure: Figure 4. Characteristics of glacial lakes in 2016. (a) Glacial lakes distribution of different dam type and size class, (b) the number and area variation in size classes and (c) the percentage of number and area at different elevations.\n\n**Figure 4.** Characteristics of glacial lakes in 2016. (a) Glacial lakes distribution of different dam type and size class, (b) the number and area variation in size classes and (c) the percentage of number and area at different elevations.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.1. Glacial Lake Inventory", "section_headings": ["4. Results", "4.1. Glacial Lake Inventory"], "chunk_type": "figure", "figure_caption": "Figure 4. Characteristics of glacial lakes in 2016. (a) Glacial lakes distribution of different dam type and size class, (b) the number and area variation in size classes and (c) the percentage of number and area at different elevations.", "line_start": 167, "line_end": 167, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8907eb638026e8f2", "text": "Document: 1. Introduction\nSection: 4. Results > 4.1. Glacial Lake Inventory\nType: text\n\nAs the area of glacial lakes vary greatly, from 0.01 to  $5.29 \\, \\text{km}^2$ , we classify them into five size classes: tiny (A  $\\leq 0.02 \\, \\text{km}^2$ ), small ( $0.02 < A \\leq 0.1 \\, \\text{km}^2$ ), medium ( $0.1 < A \\leq 0.5 \\, \\text{km}^2$ ), large ( $0.5 < A \\leq 1 \\, \\text{km}^2$ ) and giant (A  $> 1 \\, \\text{km}^2$ ). The percentage of numbers and areas for each size class are shown in Figure 4b.\n\nWater 2020, 12, 464 10 of 20\n\nThe main size class of a glacial lake is tiny, accounting for 45.9% (n = 56) of the total number, and small glacial lakes account for approximately 36.1% (n = 44). The total area of tiny and small lakes is 12.4%. Four glacial lakes are large, and three are giant lakes, occupying 69.7% of the total area. Tha mean value of lake area is  $0.17 \\text{ km}^2$ , and the largest glacial lake is Galongco, with a surface area of  $5.29 \\text{ km}^2$ .\n\nThe glacial lakes are distributed at elevations ranging from 4100 to 5750 m a.s.l. and are separated into different elevation classes every 100 m (Figure 4c). Most glacial lakes are located at elevations of 5000–5600 m a.s.l., accounting for 66.4% and 86.6% of the total number and area, respectively. Approximately 27.9% of glacial lakes are located below 5000 m a.s.l. and are evenly distributed in each elevation class with an average 3.5% by number. Approximately 13.9% of glacial lakes are distributed from 5200–5300 m a.s.l. and account for 28.1% of the area of all lakes. It is noticeable that the largest percentage by area is distributed at 5000–5100, which accounts for 30.55%.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.1. Glacial Lake Inventory", "section_headings": ["4. Results", "4.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 168, "line_end": 176, "token_count_estimate": 547, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e98ce293b59a68be", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: text\n\nThe glacial lake outburst potential assessment results show that 19 glacial lakes have high outburst potential, in which all of these lakes are moraine-dammed and ice/glacier avalanche is the main potential triggering impact; 51 are medium risk, in which two are ice-dammed and nine are bedrock-dammed, and 42 lakes with an area less than 0.1 km²; 52 glacial lakes are low, in which 11 are ice-dammed and 16 are bedrock-dammed (Figure 5a). It is noticeable that 11 out of 19 high outburst potential lakes have an area less than 0.1 km², and the one bedrock-dammed lake, Gongco, with an area of 2.9 km², has low outburst potential.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "text", "line_start": 178, "line_end": 180, "token_count_estimate": 203, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9a7ff07a500963e", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: figure\nFigure\n\nImage /page/9/Figure/5 description: The image displays two maps, labeled 'a' and 'b', of the same mountainous river basin. Both maps show elevation, rivers, and settlements, with coordinates ranging from 85°40'E to 86°20'E and 27°40'N to 28°30'N. A scale bar indicates a length of 20 km. The elevation ranges from 591 m to 8012 m. Map 'a' shows the 'Outburst potential' categorized as Low (small blue dots), Medium (yellow dots), and High (large red dots). Map 'b' shows the 'Hazard rank' categorized as Low (blue dots), Medium (orange dots), High (red dots), and Very High (large red dots). Map 'b' also includes an overlay of landslides, distinguishing between 'pre-earthquake' (yellow areas) and 'post-earthquake' (pink areas), with the latter being more extensive. Several settlements are named, including Dolalghat, Barhabise, Gumthang, Zhangmu, Quxiang, Nyalam, Gangga, and Tajilin.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "figure", "figure_caption": null, "line_start": 181, "line_end": 181, "token_count_estimate": 313, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abb8c2c907bef159", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: figure\nFigure: Figure 5. (a) Glacial lake outburst potential and (b) glacial lake outburst flow/debris flow hazard assessed in this study, considering landslide deposit distribution.\n\n**Figure 5.** (a) Glacial lake outburst potential and (b) glacial lake outburst flow/debris flow hazard assessed in this study, considering landslide deposit distribution.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "figure", "figure_caption": "Figure 5. (a) Glacial lake outburst potential and (b) glacial lake outburst flow/debris flow hazard assessed in this study, considering landslide deposit distribution.", "line_start": 183, "line_end": 183, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9ad4c29092d00313", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: text\n\nIn this study, 1670 landslides with a total area of 18.70 km<sup>2</sup> were identified, in which 1183, with an area of 12.18 km<sup>2</sup>, were triggered by the Gorkha earthquake (Figure 5b). These post-earthquake landslides vary in size ranging from 230 m<sup>2</sup> to 254,474 m<sup>2</sup>. Most of the landslides are distributed in the middle and southern parts of the basin, and a large number of Gorkha earthquake-triggered landslides are concentrated in the VII region of seismic intensity. A lot of landslides were distributed in the sub-basins such as Gumthang, Deqingdang, Chongduipu, Zhangzangbo and Dianchangchanggou. Large landslides reach channels, and some small landslides are mostly located on steep slopes\n\ndisconnected from a river channel. The other 487 landslides occurred before the Gorkha earthquake and have an area of 6.51 km<sup>2</sup>. The largest mapped landslide that occurred before the earthquake is  $0.81 \\text{ km}^2$ . The total landslide deposit volume is estimated at  $91.67 \\times 10^6 \\text{ m}^3$  before the earthquake, and the volume increased to  $216.03 \\times 10^6 \\text{ m}^3$  after the earthquake. Considering landslide distribution, 73 glacial lake outburst floods are highly prone to debris flow, which will increase the magnitude.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "text", "line_start": 184, "line_end": 192, "token_count_estimate": 422, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c4befc71d43550f3", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: text\n\nGorkha earthquake and have an area of 6 . 51 km < sup > 2 < / sup > . The largest mapped landslide that occurred before the earthquake is $ 0 . 81 \\ text { km } ^ 2 $ . The total landslide deposit volume is estimated at $ 91 . 67 \\ times 10 ^ 6 \\ text { m } ^ 3 $ before the earthquake , and the volume increased to $ 216 . 03 \\ times 10 ^ 6 \\ text { m } ^ 3 $ after the earthquake . Considering landslide distribution , 73 glacial lake outburst floods are highly prone to debris flow , which will increase the magnitude .\n\nAccording to the glacial lake outburst potential and flow magnitude, GLOF hazard assessment results are shown in Table 2 and Figure 5. Eleven glacial lakes are identified with very high hazard, among which seven could evolve into debris flows. Twenty-four glacial lakes are high hazard, among which 11 could evolve into debris flows. Thirty-two glacial lakes are identified with medium hazard; the other 55 glacial lakes are considered to have low to no hazard to downstream areas. Four very high-hazard glacial lakes are located in the Chongduipu gully, which presents a large threat to Nyalam County, especially the giant glacial lake Galongcuo that could generate a peak flow discharge of about 224,449 m<sup>3</sup>/s. Both Jialongcuo and Cirenmacuo have burst out twice before, and are also identified as very high hazard due to high freeboards and hanging glaciers behind the lakes. Eight glacial lakes with low-outburst probability but high-magnitude flow are considered to have high hazard. Among these lakes, Gongcuo and Darecuo are bedrock-dammed lakes and have no potential triggering impacts around the lakes, so they are considered to have a low probability of outburst. However, because of their large volumes, the outburst flows are assumed to be high. 63% (n = 22) of the very high and high glacial lakes' areas are larger than 0.1 km<sup>2</sup>, and their peak flow discharges were larger than 1000 m<sup>3</sup>/s. Ten small glacial lakes (area <0.1 km<sup>2</sup>) identified as high hazard. Three small glacial lakes, No. 16 (area 0.09 km<sup>2</sup>), No. 18 (area 0.05 km<sup>2</sup>) and No. 81 (Nongjue, area 0.07 km<sup>2</sup>), are considered very high hazard for they may cause peak debris flow discharges of 1118 m<sup>3</sup>/s, 597 m<sup>3</sup>/s and 994 m<sup>3</sup>/s at the nearest settlement, respectively.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "text", "line_start": 184, "line_end": 192, "token_count_estimate": 736, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e7a03b5766e35b65", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: table\nTable: Table 2. Very high and high hazard glacial lakes.\n\n| Id  | Name          | Longitude (°) | Latitude (°) | Elevation (m) | Dam Type | Area (km²) | Qpl (m3) | Qdf (m3) | Probability of Outburst | Flow Magnitude | Hazard    |\n|-----|---------------|------------------|-----------------|------------------|----------|------------|----------|----------|----------------------------|-------------------|-----------|\n| 1   | Qiezelaco     | 86.26            | 28.37           | 5532             | moraine  | 0.26       | 1967     |          | High                       | High              | Very High |\n| 3   | Cawuqudenco   | 86.19            | 28.34           | 5423             | moraine  | 0.55       | 6666     |          | High                       | High              | Very High |\n| 7   | Paquco        | 86.16            | 28.30           | 5307             | moraine  | 0.58       | 7950     |          | High                       | High              | Very High |\n| 16  |               | 86.09            | 28.22           | 5178             | moraine  | 0.09       | 726      | 1814     | High                       | High              | Very High |\n| 18  |               | 86.06            | 28.17           | 5194             | moraine  | 0.05       | 239      | 597      | High                       | High              | Very High |\n| 22  | Cirenmaco     | 86.07            | 28.07           | 4633             | moraine  | 0.34       | 5087     | 12,717   | High                       | High              | Very High |\n| 34  | Gangxico      | 85.87            | 28.36           | 5212             | moraine  | 4.52       | 172,879  |          | High                       | High              | Very High |\n| 61  | Galongco      | 85.84            | 28.32           | 5077             | moraine  | 5.29       | 145,746  | 364,365  | High                       | High              | Very High |\n| 62  |               | 85.82            | 28.30           | 5093             | moraine  | 0.27       | 1832     | 4580     | High                       | High              | Very High |\n| 80  | Jialongco     | 85.85            | 28.21           | 4380             | moraine  | 0.63       | 8336     | 20,840   | High                       | High              | Very High |\n| 81  | Nongjue       | 85.87            | 28.19           | 4628             | moraine  | 0.07       | 398      | 994      | High                       | High              | Very High |\n| 2   | Youmojiaco    | 86.23            | 28.35           | 5337             | moraine  | 0.55       | 4881     |          | Medium                     | High              | High      |\n| 6   | Gangpuco      | 86.16            | 28.32           | 5543             | moraine  | 0.22       | 2355     |          | Medium                     | High              | High      |\n| 8   | Southhu       | 86.15            | 28.30           | 5343             | moraine  | 0.17       | 1227     |          | Medium                     | High              | High      |\n| 9   | Taracuo       | 86.13            | 28.29           | 5257             | moraine  | 0.23       | 2186     |          | Medium                     | High              | High      |\n| 10  | Tuzhuocuo     | 86.10            | 28.25           | 5201             | moraine  | 0.15       | 1309     | 3272     | Low                        | High              | High      |\n| 23  |               | 86.03            | 28.07           | 4486             | bedrock  | 0.03       | 257      | 642      | Medium                     | High              | High      |\n| 32  | Yinreco       | 85.89            | 28.37           | 5245             | moraine  | 0.28       | 2878     |          | Low                        | High              | High      |\n| 40  | Mabiya        | 85.91            | 28.32           | 5384             | moraine  | 0.14       | 931      |          | Medium                     | High              | High      |\n| 42  |               | 85.92            | 28.32           | 5345             | moraine  | 0.08       | 504      |          | Medium                     | High              | High      |\n| 43  | Mulaco        | 85.93            | 28.32           | 5306             | moraine  | 0.11       | 760      |          | Medium                     | High              | High      |\n| 44  | Xiahu         | 85.95            | 28.31           | 5232             | moraine  | 0.31       | 3352     |          | Medium                     | High              | High      |\n| 51  | Cuonongjue    | 85.92            | 28.26           | 5095             | moraine  | 0.23       | 2353     |          | Low                        | High              | High      |\n| 63  |               | 85.83            | 28.29           | 5013             | moraine  | 0.26       | 1863     | 4658     | Medium                     | High              | High      |\n| 64  |               | 85.83            | 28.29           | 5050             | moraine  | 0.06       | 204      | 511      | Medium                     | High              | High      |\n| 70  |               | 85.78            | 28.29           | 5418             | moraine  | 0.05       | 130      | 324      | High                       | Medium            | High      |\n| 72  |               | 85.78            | 28.27           | 5309             | moraine  | 0.07       | 184      | 459      | High                       | Medium            | High      |\n| 83  |               | 85.87            | 28.17           | 4712             | moraine  | 0.04       | 125      | 312      | High                       | Medium            | High      |\n| 84  | Daroco        | 85.92            | 28.18           | 4366             | bedrock  | 0.48       | 10,966   | 27,414   | Low                        | High              | High      |\n| 85  |               | 85.91            | 28.15           | 4486             | ice      | 0.20       | 2468     |          | Medium                     | High              | High      |\n| 86  |               | 85.92            | 28.14           | 4871             | moraine  | 0.09       | 597      |          | Medium                     | High              | High      |\n| 88  |               | 85.94            | 28.07           | 4524             | bedrock  | 0.06       | 391      | 977      | Low                        | High              | High      |\n| 89  | Bhairab Kunda | 85.88            | 27.99           | 4102             | bedrock  | 0.06       | 304      | 760      | Low                        | High              | High      |\n| 102 |               | 85.83            | 28.05           | 4250             | bedrock  | 0.07       | 210      | 524      | Low                        | High              | High      |\n| 103 | Congco        | 85.87            | 28.33           | 5113             | bedrock  | 2.09       | 28936    |          | Low                        | High              | High      |", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "table", "table_caption": "Table 2. Very high and high hazard glacial lakes.", "columns": ["Id", "Name", "Longitude (°)", "Latitude (°)", "Elevation (m)", "Dam Type", "Area (km²)", "Qpl (m3)", "Qdf (m3)", "Probability of Outburst", "Flow Magnitude", "Hazard"], "table_row_start": 1, "table_row_end": 35, "line_start": 193, "line_end": 229, "token_count_estimate": 1781, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["28936"]}}
{"id": "715a549eae7b87fc", "text": "Document: 1. Introduction\nSection: 4. Results > 4.2. Glacial Lake Outburst Flood Hazard\nType: text\n\nNote:  $Q_{\\rm pl}$  is the flood peak discharge at the nearest settlement and  $Q_{\\rm df}$  is the debris flow peak discharge at the nearest settlement.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Results > 4.2. Glacial Lake Outburst Flood Hazard", "section_headings": ["4. Results", "4.2. Glacial Lake Outburst Flood Hazard"], "chunk_type": "text", "line_start": 230, "line_end": 232, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae54d90c4e060e55", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: text\n\nThe new 2016 glacial lake inventory indicates that BKB is highly developed glacial lakes. Glacial lake inventory studies have also been conducted in other regions along the Himalayas [61–64]. Glacial lake studies in the Himalayas show that the greatest numbers and areas of glacial lakes are distributed in the central Himalaya [2,7,9,10,62,65]. To compare the glacial lakes and GLOF of BKB with other regions throughout the central Himalayas, the Gyirong River Basin (GRB), which is next to BKB with a similar area was selected and the glacial lake density (glacial lake number/basin area) and lake area per basin area (total glacial lake area/basin area) were calculated (Table 3). The results show that the glacial lake density of BKB is four times that of the central Himalayas and the lake area per basin area is four times that of GRB. The basin area of GRB is larger than BKB, and the glacial lakes density is similar, while the lake per basin area varies greatly. This is due to more large and giant lakes in BKB. According to the statistics, the largest lake is less than 0.5 km² in GRB, while there are seven lakes larger than 0.5 km² with the largest being 5.29 km² in BKB. Studies show the glacial lake expansion rate reaching 0.26 km²/year in Poiqu [66], while the rate of GRB is 0.09 km²/year [63]. Glacial lake expansion is the result of glacier retreating response to climate change. That means BKB is more sensitive to climate change than GRB.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 236, "line_end": 240, "token_count_estimate": 409, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74fb85063a1b9ec6", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: table\nTable: Table 3. Comparison of glacial lake and glacial lake outburst flood among Bhote Koshi Basin, Gyirong River Basin and Central Himalaya.\n\n| Region                   | Basin Area (km2) | Number of Glacial Lakes | Glacial Lake Area (km2) | Glacial Lake Density | Lake per Basin Area |\n|--------------------------|---------------------|----------------------------|----------------------------|-------------------------|------------------------|\n| Bhote Koshi Basin        | 3406                | 122                        | 20.38                      | 0.04                    | 0.0060                 |\n| Gyirong River Basin [63] | 4640                | 148                        | 7.12                       | 0.03                    | 0.0015                 |\n| Central Himalaya [2]     | 280,000             | 1943                       | 203.7                      | 0.01                    | 0.0007                 |", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "table", "table_caption": "Table 3. Comparison of glacial lake and glacial lake outburst flood among Bhote Koshi Basin, Gyirong River Basin and Central Himalaya.", "columns": ["Region", "Basin Area (km2)", "Number of Glacial Lakes", "Glacial Lake Area (km2)", "Glacial Lake Density", "Lake per Basin Area"], "table_row_start": 1, "table_row_end": 3, "line_start": 241, "line_end": 245, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0702a67223e2b25b", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: text\n\nThe analysis of multitemporal and high-resolution remote sensing images during the compilation of the glacial lake inventories provided a good opportunity to identify previously unreported GLOF events [67,68]. Six unreported glacial lake outburst events were found when we mapped glacial lakes from GF-1 and Google Earth. These glacial lakes have retained typical outburst geomorphic and sedimentological features, such as V-shaped breaches, debris fans and subsequent devastated channels (Figure 6). All of them were moraine-dammed lakes, and their surface areas are 0.01–0.11 km<sup>2</sup>. Two glacial lakes (Figure 6a,b) are located in Keyapu Valley and the other four (Figure 6c–f) are in Chongduipu Valley. All glacial lakes except No. 31 are fed by glaciers, and the distances to the glaciers are less than 500 m. The V-shaped breach and debris fan of glacial lake No. 86 is the largest, and its mother glacier is thick and hangs behind the lake. The surface area of the glacial lake is 0.09 km<sup>2</sup> and the freeboard is much more than one. The rest of the outburst events were small scale and seemed to cause no downstream damage since no erosion was observed in downstream channels. The outburst flood formed a deposition fan at the intersection with the main channel, such as glacial lake No. 31, where the outburst deposition blocked the channel and formed a small lake. Some vegetation has covered the debris fan (glacial lake No. 86) and deposition fan (glacial lake No. 81). It shows that the glacial lake outburst occurred a long time ago. However, the outburst time (year) cannot be determined because of the lack of long-term and high-quality (low cloud cover or high-resolution) data. We traced theses lakes on Google Earth images, and it shows that outburst signs have existed since 1984. As we documented in the literature, we found there was a GLOF event in 1955 in Nyalam, but the record did not mention which lake burst out [30]. Four glacial lakes (Figure 6c-f) are located in the Chongduipu Basin upstream of Nyalam County. As we cannot be sure of the exact outburst time, the outburst magnitudes can be estimated only through debris fans. Glacial lake No. 86 had the largest outburst magnitude with a debris fan area of approximately 252,808 m<sup>2</sup>, and the gully downstream is highly eroded. This lake may have caused damage in downstream areas. The other three glacial lake deposits\n\nare found at the intersections with the main channel (Chongduipu), which means their outburst floods did not propagate downstream. Therefore, we conclude that the GLOF event in 1955 was caused by glacial lake No. 86.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 246, "line_end": 250, "token_count_estimate": 721, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a7d84c3d46aa74f7", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: figure\nFigure\n\nImage /page/13/Figure/2 description: A composite image of three satellite photographs, labeled a, b, and c, showing different glacial landscapes with annotations. Each panel includes coordinates and a date. Panel a, dated June 08, 2015, at 85.88°E, 28.39°N, shows a light-colored glacial lake with a breach leading to a deposition area that has formed a smaller, green 'deposition blocked lake'. Panel b, dated November 01, 2016, at 85.93°E, 28.32°N, displays a large, green glacial lake fed by a glacier, with a breach at its outlet and a 'debrirs fan' (likely debris fan) below it. Panel c, dated October 10, 2017, at 85.78°E, 28.27°N, shows two dark glacial lakes near a glacier, with a 'V-shaped breach' and a prominent 'eroded channel' downstream.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 251, "line_end": 251, "token_count_estimate": 246, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93c14ed3dc7b5707", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: figure\nFigure\n\nImage /page/13/Figure/3 description: A composite image of three satellite views, labeled d, e, and f, showing different glacial landscapes, all dated November 01, 2016. Each panel includes a north arrow. Panel d, on the left, shows a glacier feeding into a dark glacial lake. Below the lake, labels point to a 'breach', a 'debris fan', and a 'deposition fan'. The location is given as No.81: 85.87°E, 28.19°N. Panel e, in the middle, displays a glacier, a brownish-yellow glacial lake, a 'debris fan', and a 'V-shaped breach'. The location is No.83: 85.87°E, 28.17°N. Panel f, on the right, shows a glacier, a green glacial lake, a 'V-shaped breach', and a 'debris fan'. The location is No.86: 85.92°E, 28.14°N.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 253, "line_end": 253, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4cf97468bdf47bed", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: figure\nFigure: Figure 6. Unreported glacial lake outburst flood events were identified based on Google Earth images ( a , c ) and GaoFen-1 (GF-1) images ( b , d - f ) in the Bhote Koshi Basin.\n\n**Figure 6.** Unreported glacial lake outburst flood events were identified based on Google Earth images (**a**,**c**) and GaoFen-1 (GF-1) images (**b**,**d**-**f**) in the Bhote Koshi Basin.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "figure", "figure_caption": "Figure 6. Unreported glacial lake outburst flood events were identified based on Google Earth images ( a , c ) and GaoFen-1 (GF-1) images ( b , d - f ) in the Bhote Koshi Basin.", "line_start": 255, "line_end": 255, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6404420f0844b41d", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.1. Glacial Lake Inventory\nType: text\n\nThe six undocumented outburst events found show that high-resolution remote sensing images make it possible to trace minor GLOF events that were unreported because of difficult access or few people living in high mountain regions. It also proves that the GLOF frequency is high in BKB. Other unknown and unpublished GLOF have also been found in Bhutan, Nepal and other parts of the Himalayas, based on long-time-series remote sensing data that show glacial lake changes (disappearance or abrupt shrinkage) and typical topographic features, such as exposed debris fans and sediment tails in downstream river channels [9,17,18]. A database of past GLOF events as complete as possible is essential for robust and reliable GLOF hazard assessment [69]. The gradually improving GLOF inventory helps us better understand the mechanism of GLOF and to do hazard assessment.\n\nWater 2020, 12, 464 15 of 20", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.1. Glacial Lake Inventory", "section_headings": ["5. Discussion", "5.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 256, "line_end": 260, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "90c27a94813c17d1", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.2. Glacial Lake Outburst Hazard\nType: text\n\nA GLOF is a complex process, and the hazard magnitude is determined by the outburst water volume and flood routing [8,22,38]. The outburst water volume is mainly related to the glacial lake volume [54,70]. Since the depth of a glacial lake is hard to acquire by remote sensing data and investigation, the surface area of the lake becomes an important indicator for assessing lake volume and hazard. In previous GLOF hazard assessment studies, glacial lakes smaller than 0.1 km² were assumed not to pose a hazard potential relevant to downstream locations [47]. Khanal et al. [34] identified 10 critical lakes in the BKB, and all of them are larger than 0.2 km². Indeed, small glacial lake outbursts can cause damage to downstream locations. According to the inventory of historical glacial lake outburst floods in the Himalayas, small GLOF, such as Zanaco, Geiqu and Choradari Lakes with areas smaller than 0.1 km², damaged downstream roads and villages [9]. On one hand, a small outburst can create a much larger outburst from another lake located downstream, for examples, the reach of GLOF from lakes Artesoncocha and Chacrucocha [43,67]. On the other hand, a small glacial lake outburst flood can transform into debris flows due to downstream sediment entrainment. For example, a small ice-dammed lake outburst in 2009 (area of 34,000 m²) at Keara in the Andes caused damage 10 km downstream [41].\n\nDuring the flood routing, landslides on the slopes enter the flood, transforming the flood into debris flows and greatly increasing the discharge, volume and impact. Ignoring the earthquake-induced landslides would underestimate the basin's GLOF hazard. If we do not consider landslides triggered by earthquakes transforming the glacial lake outburst floods into debris flows in the BKB, only nine glacial lakes are identified as having very high hazard, 16 are at high hazard, 12 are at medium hazard and 85 are at low hazard. The hazards rank of two very high hazard small glacial lakes, eight high hazard lakes and 20 medium hazard lakes, accounting for 24.6% of the total lakes, would be decreased. It leads to the GLOF hazard of BKB greatly underestimated.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.2. Glacial Lake Outburst Hazard", "section_headings": ["5. Discussion", "5.2. Glacial Lake Outburst Hazard"], "chunk_type": "text", "line_start": 262, "line_end": 268, "token_count_estimate": 576, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee304d551a4985c2", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.2. Glacial Lake Outburst Hazard\nType: text\n\n, volume and impact . Ignoring the earthquake - induced landslides would underestimate the basin ' s GLOF hazard . If we do not consider landslides triggered by earthquakes transforming the glacial lake outburst floods into debris flows in the BKB , only nine glacial lakes are identified as having very high hazard , 16 are at high hazard , 12 are at medium hazard and 85 are at low hazard . The hazards rank of two very high hazard small glacial lakes , eight high hazard lakes and 20 medium hazard lakes , accounting for 24 . 6 % of the total lakes , would be decreased . It leads to the GLOF hazard of BKB greatly underestimated .\n\nIn this study, the GLOF caused by GBTSC on 25 July 2016 is a good example. GBTSC is located in the Zhangzangbo Valley on the right bank of the Poiqu River, and the average gradient is 182‰ (Figure 7b). This lake was tiny; the surface area before the outburst was 0.01 km<sup>2</sup>. After the outburst, the lake was almost empty as shown in Figure 7b. The width of the breach was 27 m, and the depth was 9 m. The peak discharge was 618 m<sup>3</sup>/s, and increased to 4019 m<sup>3</sup>/s at the section of the Zhangzangbo Valley mouth (approximately 7.2 km from the breach), according to the investigation and assessment report written by the Institute of Mountain Hazards and Environment (http://www.imde.ac.cn). The discharge increased by almost eight times because the outburst flood changed to a debris flow. The Zhangzangbo was in the seismic intensity zone VII and intensely impacted by the Gorkha earthquake. Many landslides were triggered along the river, and some landslide deposits blocked the river (Figure 7c,d). The loose mass volume increased to  $8.74 \\times 10^6$  m<sup>3</sup> in Zhangzangbo according to the landslide distribution. These landslide deposits provided rich masses for the debris flow. Once the GLOF occurred, these deposits were easily eroded and entrained, leading the flood to change to a debris flow and amplifying the discharge. Tens of thousands of landslides were triggered by the M 7.8 (Gorkha) and M 7.3(Dolakha) earthquakes [71,72]. It will take some time to transport these landslide deposits, which accumulated on the slope or in the channel. Thus, in the region affected by strong earthquakes, we must strengthen the monitoring of high-hazard glacial lakes and pay special attention to glacial lake outburst debris flows after an earthquake.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.2. Glacial Lake Outburst Hazard", "section_headings": ["5. Discussion", "5.2. Glacial Lake Outburst Hazard"], "chunk_type": "text", "line_start": 262, "line_end": 268, "token_count_estimate": 705, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e85ad6d2eeb1276a", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.2. Glacial Lake Outburst Hazard\nType: figure\nFigure\n\nImage /page/15/Figure/1 description: A composite figure with four panels labeled a, b, c, and d, showing satellite images that compare channel morphology in a mountainous region before and after a geological event.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.2. Glacial Lake Outburst Hazard", "section_headings": ["5. Discussion", "5.2. Glacial Lake Outburst Hazard"], "chunk_type": "figure", "figure_caption": null, "line_start": 269, "line_end": 269, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "22e0c1a6f9689f86", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.2. Glacial Lake Outburst Hazard\nType: text\n\nPanel a is a large overview image dated Oct. 24, 2016. It shows the Zhangzangbo and Poiqu rivers, and two lakes named Gongbatongshaco and Cirenmaco. Three areas along the Zhangzangbo river are highlighted with red dashed boxes and labeled B, C, and D. The image includes a scale bar from 0 to 2,200 meters and a north arrow.\n\nPanel b shows two images of area B. The top image, from Nov. 10, 2015, shows a river channel. The bottom image, from Oct. 24, 2016, shows significant changes, with labels pointing to \"Moraine terrace erosion\" and a newly formed \"debris fan\" in the widened river channel.\n\nPanel c displays two images of area C. The left image, from Jun. 01, 2015, shows a river bend. The right image, from Oct. 24, 2016, shows the same bend with significant widening, labeled to indicate \"Lateral erosion\" and \"Bank collapse,\" which is highlighted with a yellow dashed line.\n\nPanel d presents two images of area D. The left image, from Jun. 01, 2015, shows a \"Blockage\" and two areas of \"Landslide deposits\" circled in yellow. The right image, from Oct. 24, 2016, shows that the previous landslide deposits have been eroded, and new features are labeled, including a \"Landslide\" and a \"Boulder\" in the debris-filled channel.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.2. Glacial Lake Outburst Hazard", "section_headings": ["5. Discussion", "5.2. Glacial Lake Outburst Hazard"], "chunk_type": "text", "line_start": 270, "line_end": 278, "token_count_estimate": 363, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7cf235fa7a653cf7", "text": "Document: 1. Introduction\nSection: 5. Discussion > 5.2. Glacial Lake Outburst Hazard\nType: figure\nFigure: Figure 7. Comparison of channel changes before and after GBTSC GLOF along the Zhangzangbo Valley. (a) The flow path of GBTSC GLOF; (b) GF-1 images showing that the flood left a large debris fan in the front of the lake, and eroded the moraine terrace; (c) lateral erosion and bank collapse in the moraine terrace and the width of channel increased; (d) landslide deposits distributed along the channel and blocked the channel before bursting, while GLOF eroded landslide deposits and triggered bank slump and landslide afterward, causing an increase in channel width. (Note: the 1 June 2015 images of c and d are from Google Earth, others are GF-1 images).\n\n**Figure 7.** Comparison of channel changes before and after GBTSC GLOF along the Zhangzangbo Valley. (a) The flow path of GBTSC GLOF; (b) GF-1 images showing that the flood left a large debris fan in the front of the lake, and eroded the moraine terrace; (c) lateral erosion and bank collapse in the moraine terrace and the width of channel increased; (d) landslide deposits distributed along the channel and blocked the channel before bursting, while GLOF eroded landslide deposits and triggered bank slump and landslide afterward, causing an increase in channel width. (Note: the 1 June 2015 images of c and d are from Google Earth, others are GF-1 images).", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Discussion > 5.2. Glacial Lake Outburst Hazard", "section_headings": ["5. Discussion", "5.2. Glacial Lake Outburst Hazard"], "chunk_type": "figure", "figure_caption": "Figure 7. Comparison of channel changes before and after GBTSC GLOF along the Zhangzangbo Valley. (a) The flow path of GBTSC GLOF; (b) GF-1 images showing that the flood left a large debris fan in the front of the lake, and eroded the moraine terrace; (c) lateral erosion and bank collapse in the moraine terrace and the width of channel increased; (d) landslide deposits distributed along the channel and blocked the channel before bursting, while GLOF eroded landslide deposits and triggered bank slump and landslide afterward, causing an increase in channel width. (Note: the 1 June 2015 images of c and d are from Google Earth, others are GF-1 images).", "line_start": 279, "line_end": 279, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b55c540d3d59a199", "text": "Document: 1. Introduction\nSection: 6. Conclusions\nType: text\n\nIn this study a new detailed 2016 inventory of glacial lakes in the BKB was established and six unreported GLOF that occurred before 1984 have been detected with geomorphic outburst evidence based on high-resolution remote sensing images. The BKB is one of the most hotspot small river basins for glacial lakes and GLOF in the central Himalayas. High-resolution remote sensing images are useful for detecting unreported GLOF events in high mountainous regions and sparsely populated regions, which is conducive to improving the GLOF inventory and better assessing GLOF hazard. A rough but more comprehensive method was proposed to assess GLOF hazard, which considers the probability for a flood to develop into a debris flow in the downstream, where large numbers of landslides triggered by earthquake are distributed. The GLOF hazard in BKB increases due to landslide deposits volume, which increased approximately  $124.36 \\times 10^6$  m<sup>3</sup> after the Gorkha earthquake, and 11 glacial lakes are identified as very high hazard, nine are high hazard, 32 are medium hazard and 55 are low hazard. However, about 24.6% of the all lakes' hazards would be underestimated without earthquake-induced landslides, in which most of them are small glacial lakes. Therefore, for regional GLOF hazard assessment, small glacial lakes should not be overlooked for landslide deposit entrainment along a flood route and flood eroding channel bed would increase the peak discharge, especially in earthquake affected areas where large numbers of landslides were triggered. We suggest\n\nWater 2020, 12, 464 17 of 20\n\nthat more attention should be paid to the very high and high-hazard glacial lakes and to improving the engineering security standard for defending against flood hazards downstream of BKB.\n\n**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4441/12/2/464/s1. Table S1: glacial lake inventory of Bhote Koshi.\n\n**Author Contributions:** N.C. and M.L. conceived the original ideas and drafted the original manuscript. Y.Z. and M.D. carried out field investigation and data collection. N.C. and M.L. revised the original manuscript. All authors have read and agreed to the published version of the manuscript.\n\n**Funding:** This research was funded by National Natural Science Foundation of China (Grant NOs. 41671112 and 41861134008) and the 135 Strategic Program of the IMHE, CAS (Grant NO. SDS-135-1705).\n\nConflicts of Interest: The authors declare there no conflict of interest.", "metadata": {"source_file": "data/('Glacial Lake Inventory and Lake Outburst Flood Debris Flow Hazard Assessment', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "6. Conclusions", "section_headings": ["6. Conclusions"], "chunk_type": "text", "line_start": 282, "line_end": 296, "token_count_estimate": 638, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["41671112", "41861134008"]}}
{"id": "10d14e8ffae29ef5", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: Abstract\nType: text\n\nGLOFs, driven by climate change-induced glacier melt, present a serious threat to downstream communities in mountainous regions. The present study addresses this pressing issue by employing a systematic methodology to analyze scientific literature on GLOF risk management and hazard mitigation. The research synthesizes key insights through thematic analysis, focusing on GLOF hazard mitigation, hazard and risk mapping, and the development of a methodological framework for managing GLOF hazards and risks. It evaluates structural measures such as spillways and diversion canals, and non-structural measures, including strategic land-use planning and community-based strategies. The study highlights the importance of Early Warning Systems (EWS), GLOF risk management skills, and knowledge transfer, emphasizing integration with climate change adaptation. It also discusses facilitating factors like policy legislation, institutional support, and addressing knowledge disparities. While the physical aspects of GLOFs have been widely explored, research on their social impacts is relatively limited, leaving a crucial gap in understanding the full extent of these disasters. The findings of the study offer a comprehensive resource for policymakers, planners, and disaster management professionals, providing a holistic approach to mitigating GLOF risks and enhancing resilience in vulnerable regions. This research is poised to significantly contribute to effective GLOF hazard mitigation and climate change adaptation strategies.\n\n**Keywords** GLOFs; Hazard and Risk; Management · Methodological Framework; EWS · Knowledge disparity · Policy legislation", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "text", "line_start": 3, "line_end": 7, "token_count_estimate": 378, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69059d357b1baed4", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 1 Introduction\nType: text\n\nGlacial lakes, a prominent feature of the glacial landscape, form as a result of the dynamic interaction between the glacial processes and surrounding topography (Steffen et al. 2022; Peng et al. 2023). These glacial lakes originate from several processes and mechanisms, primarily involving the accumulation of glacial meltwater behind the ice dams, moraines\n\nDepartment of Geography and Disaster Management, School of Earth and Environmental Sciences, University of Kashmir, Srinagar 190 006, India", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 13, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "ea70e5370ecac6bb", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 1 Introduction\nType: figure\nFigure\n\nImage /page/0/Picture/12 description: The Springer logo, featuring a black outline of a chess knight piece on the left and the word 'Springer' in a black serif font on the right, all on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 14, "line_end": 14, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "60b951ca6cf48c9f", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 1 Introduction\nType: text\n\nRayees Ahmed\nRayeesrashid84@gmail.com\n\nor bedrock barriers that obstruct the normal flow of the water (Zhang et al. 2022; Hu et al. 2022). The formation of the glacial lakes is closely linked to the glacier retreat during the deglaciation period (Molg et al. 2021; Rather et al. 2024a, b; Knight and Harrison 2018). These lakes vary in size, volume, depth, and morphology and can be broadly classified into four main types, such as moraine-dammed lake (formed when glacial debris creates a natural dam obstructing the meltwater flow), ice-dammed lake (forms when a glacier temporarily blocks a river impounding meltwater), glacier erosion lake (found in depressions on mountain slopes that have been glacially sculpted out by erosion) and other glacial lakes (Lakes formed within glaciated valleys primarily fed by the direct glacial melt) (Gupta et al. 2023; Ahmed et al. 2021). The glacial lake expansion is considered as one of the important indicators of the climate change (Nie et al. 2013; Zheng et al. 2021). The ongoing lake formation and expansion of different types of glacial lakes is directly linked to the warming climate and rapid melting of the glaciers (Pandey et al. 2021; Ahmed et al. 2022; Mir et al. 2022, 2024).\n\nGLOFs are serious natural hazards that could have devastating impacts on communities that inhabit downstream of glacial lakes. The chance of GLOF incidence is rising as a result of climate change (Harrison et al. 2018). The melting of glaciers leads to the formation of new lakes and increasing water levels, which raises the possibility of severe flooding (Benn et al. 2012; Zheng et al. 2021; Munoz et al. 2024). As such, the drafting of GLOF hazard and disaster management strategies has become a key issue for planners, policymakers and disaster management professionals. A multidisciplinary approach is necessary to design efficient GLOF hazard and disaster management strategies, drawing on knowledge from a variety of domains, such as hydrology, geography, disaster management, meteorology, glaciology, and engineering. These approaches need to take into account the different needs and vulnerabilities of different communities or social groups, as well as the complex interactions between physical, social, and economic factors.\n\nInstalling EWS in glacial lake regions requires robust planning, accurate data transfer, and careful assessment of site-specific variables. The Laguna 513 EWS in the Peruvian Andes serves as a successful model (Huggel et al. 2020), highlighting the need for location-specific adaptation. Wireless sensor networks (WSNs) offer cost-effective, real-time monitoring for GLOF risk reduction (Singhal et al. 2022). In Sikkim, India, the GLOF EWS integrates GIS and real-time sensors for effective flood simulation and evacuation planning (Kumar et al. 2022). Effective risk management also demands community engagement and addressing institutional gaps to enhance preparedness and mitigation efforts in vulnerable regions. Recent studies emphasize the importance of risk communication, capacity building, and community involvement in GLOF disaster management. For instance, Pandey et al. (2021) identified key challenges in developing community-centered strategies in Nepal. Effective GLOF management requires cross-disciplinary collaboration and careful execution to protect vulnerable communities. Internationally, GLOFs are prioritized in the Sendai Framework for Disaster Risk Reduction, urging the development of regional and national mitigation plans (UNDRR 2015)", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 26, "token_count_estimate": 869, "basins": [], "subbasins": [], "countries": ["India", "Nepal"], "lake_ids": []}}
{"id": "d2a47c985cf7ae0d", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 1 Introduction\nType: text\n\neffective flood simulation and evacuation planning ( Kumar et al . 2022 ) . Effective risk management also demands community engagement and addressing institutional gaps to enhance preparedness and mitigation efforts in vulnerable regions . Recent studies emphasize the importance of risk communication , capacity building , and community involvement in GLOF disaster management . For instance , Pandey et al . ( 2021 ) identified key challenges in developing community - centered strategies in Nepal . Effective GLOF management requires cross - disciplinary collaboration and careful execution to protect vulnerable communities . Internationally , GLOFs are prioritized in the Sendai Framework for Disaster Risk Reduction , urging the development of regional and national mitigation plans ( UNDRR 2015 )\n\nAwareness of GLOFs has grown, leading to efforts like the NDMA's collaboration with Swiss agencies to create management guidelines and a global risk reduction program. These initiatives focus on hazard mapping, early warning systems, and community-based strategies. Effective GLOF management must consider the political and social challenges of remote, marginalized regions, incorporating local knowledge for sustainability. Hazard and disaster management plans need to take into account the distinct social, economic, and environmental conditions of the impacted communities in order to reduce the hazards associated with GLOFs. Comprehensive risk assessments, early warning systems,", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 26, "token_count_estimate": 329, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "91a73234a475cb6d", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 1 Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/5 description: The logo for Springer, featuring a black and white stylized chess knight icon to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 27, "line_end": 27, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19e0f66a5c1d929c", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 1 Introduction\nType: text\n\nand emergency preparedness are all essential components of successful plans. Since local communities are frequently the first to respond to GLOF events, the solutions also need to empower and involve them. The efficacy and sustainability of GLOF hazard and disaster management systems can be improved by incorporating traditional knowledge and practices.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 28, "line_end": 30, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8aa3c35227233ea7", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 2 Methodology\nType: text\n\nThis review-based study employed a methodical approach to thoroughly analyse the scientific literature on risk management, hazard mitigation, and GLOFs, with an emphasis on the consequences of glacial retreat brought on by climate change. The evaluation process was guided by well-crafted research questions that focused on important issues such as risk management techniques, GLOF hazard mitigation strategies, and the relationship between these factors and the effects of climate change in high-mountain areas.\n\nExplicit inclusion and exclusion criteria were developed in order to guarantee a strict selection procedure. Systematic search strings using keywords like \"Glacial Lake Outburst Floods,\" \"Hazard Mitigation,\" \"Hazard Assessment,\" \"GLOF modeling,\" \"Risk Management,\" \"Structural and Non-Structural measures,\" and \"Climate Change\" were conducted to find pertinent literature on reliable scholarly databases such as Scopus, google scholar, PubMed and Web of Science (Figure S1). Titles and abstracts were used in the first screening process to find research that met the predetermined criteria. This process eventually resulted in the selection of peer-reviewed articles, academic papers, and reports that precisely addressed the themes that were being investigated (Fig. 1).\n\nA comprehensive full-text analysis of the articles selected was carried out, with a focus on rigorous methodology and relevance assessment (Gao et al. 2024). During this procedure, important", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "2 Methodology", "section_headings": ["2 Methodology"], "chunk_type": "text", "line_start": 32, "line_end": 38, "token_count_estimate": 365, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e359c9a35e7f1340", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 2 Methodology\nType: figure\nFigure\n\nImage /page/2/Figure/7 description: A flowchart illustrating a six-step methodology for a literature review. The process starts with a box labeled \"Literature Review\" for the \"Extraction of relevant research articles for thematic analysis\". Step 1 is the \"Selection of Keywords,\" which are categorized into GLOFs, Hazard and Risk Assessment, GLOF mitigation measures, and GLOF management and mitigation. Step 2 is the \"Selection of relevant database,\" with a word cloud of terms like \"glacial lake(s)\" pointing to four databases: Web of Science, Scopus, Google Scholar, and PubMed. Step 3, \"Extraction of research items,\" shows the number of articles found in each database: WOS (n=612), Scopus (n=590), Google Scholar (n=557), and PubMed (n=334). Step 4 is the \"Selection of time frame,\" which is 1990-2023. Step 5 shows the result after filtering, with \"Records after removing duplicate items n=823\". Finally, Step 6 shows the final count after further filtering: \"Records after removing conference papers, books, reports and book chapters n=685\".", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "2 Methodology", "section_headings": ["2 Methodology"], "chunk_type": "figure", "figure_caption": null, "line_start": 39, "line_end": 39, "token_count_estimate": 310, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cfead773087c22c4", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 2 Methodology\nType: text\n\n**Fig. 1** Methodology flow chart for the extraction of research articles from 1990 to 2023. The red boxes show steps for generating the database for the Review analysis", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "2 Methodology", "section_headings": ["2 Methodology"], "chunk_type": "text", "line_start": 40, "line_end": 42, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9a1981073f6f47b", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 2 Methodology\nType: figure\nFigure\n\nImage /page/2/Picture/9 description: The logo for Springer, featuring a black line drawing of a chess knight piece on the left, followed by the word \"Springer\" in a black serif font on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "2 Methodology", "section_headings": ["2 Methodology"], "chunk_type": "figure", "figure_caption": null, "line_start": 43, "line_end": 43, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b9a92227849aeb6", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 2 Methodology\nType: text\n\ndata was extracted, such as the approaches used in each study, the primary conclusions, and any limitations noted by the authors. After the data were retrieved, a thematic analysis was conducted on the data to classify the studies according to common themes such as risk mapping, early warning systems, structural and non-structural strategies, and climate change adaptation. The synthesis of results was a critical step in developing a thorough understanding of GLOF hazard reduction and risk management measures. Based on the collective insights gleaned from the scholarly literature, the synthesis of knowledge from many investigations allowed for the formulation of clear conclusions and suggestions. The methodology relied heavily on critical reflection, which involved an analysis of the strengths and limitations inherent in the evaluated literature. This critical viewpoint aided in the construction of a well-structured review study by allowing for a sophisticated assessment of the state of knowledge on GLOF hazard reduction. The documentation of the whole review process, including search strategies, inclusion/exclusion decisions, and data synthesis methodologies, guaranteed the study's findings were transparent and reproducible.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "2 Methodology", "section_headings": ["2 Methodology"], "chunk_type": "text", "line_start": 44, "line_end": 46, "token_count_estimate": 294, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "119110cec1a76a24", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 3 GLOF Hazard Mitigation\nType: text\n\nGLOFs are a significant natural hazard in regions where glaciers are retreating due to climate change. Glacial lakes are formed when glaciers melt and are contained by unstable moraine dams composed of loose rock and debris (see Fig. 2c). A GLOF could happen if the dam's foundational stability is compromised by the pressure of the water behind it, causing a sudden and disastrous release of water and debris downstream. GLOFs have the potential to result in significant financial losses, infrastructure destruction, and fatalities. As a result, populations residing downstream of glacial lakes must implement efficient hazard mitigation measures. Effective GLOF hazard mitigation consists of a number of elements, such as hazard mapping, early warning systems, and structural and non-structural mitigation. The schematic representation of the GLOF triggering mechanism and downstream impacts is illustrated in Fig. 3.\n\nA crucial part of GLOF hazard mitigation plans is early warning systems. EWS enables the population downstream to warn in advance of potential GLOF events so they can take the necessary precautions and safety measures to reduce damage and casualties. Monitoring systems, communication networks, and dissemination systems are the parts of a typical early warning system. Physical measurements like lake level, water temperature, and seismic activity can be included in monitoring systems. The state of the moraine dam and the extent and volume of the glacial lake can both be usefully inferred from remote sensing techniques like satellite imagery and aerial photography. To ensure that warnings are efficiently transmitted to populations downstream, communication networks are essential. Radios, TVs, cell phones, and sirens are examples of communication networks. All community members must be able to access and rely on the communication networks, regardless of their socioeconomic level or place of residence. Traditional media, like radio and newspapers, as well as new media, like social media and smartphone apps, can be used as dissemination platforms. Reaching a wider audience through the usage of new media is especially beneficial for younger generations who are more inclined to use social media and mobile apps. A number of countries have successfully put in place EWS for GLOFs. The Department of Hydrology and Meteorology in Nepal, for instance, has reduced the effects of GLOFs in the country by implementing the EWS for GLOFs (Pandey et al. 2021). To offer early warning of possible GLOF incidents, the system combines monitoring systems, communication networks, and dissemination systems.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "3 GLOF Hazard Mitigation", "section_headings": ["3 GLOF Hazard Mitigation"], "chunk_type": "text", "line_start": 48, "line_end": 52, "token_count_estimate": 617, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "2ac5d62830719a9f", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 3 GLOF Hazard Mitigation\nType: figure\nFigure\n\nImage /page/3/Picture/5 description: The image displays the logo for the publisher Springer. The logo consists of a black icon of a knight chess piece on the left, followed by the word \"Springer\" in a black serif font on the right, all set against a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "3 GLOF Hazard Mitigation", "section_headings": ["3 GLOF Hazard Mitigation"], "chunk_type": "figure", "figure_caption": null, "line_start": 53, "line_end": 53, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "406419f97f6f378e", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 3 GLOF Hazard Mitigation\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A figure titled 'Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management...' is presented in three panels labeled a, b, and c. Panel (a) is a schematic diagram illustrating a GLOF monitoring and warning system. It shows a satellite using remote sensing to monitor a glacial lake, with data on 'Hazard Potential' sent to a server. Downstream, gauge and warning stations along a river send 'Alarm' signals to the server. The server then alerts 'End Users'. The system also includes regular monitoring via remote sensing and GIS to assess risk, ultimately leading to a 'GLOF Warning' for communities. Panel (b) is a circular diagram detailing the 'GLOF hazard and Risk Management' cycle. The cycle includes 'Risk Analysis', 'Disaster Risk Reduction' (with measures like lowering lake water level and engineering solutions), 'Response' (including Early Warning Systems and emergency measures), and 'Recovery' (including relief, rehabilitation, and reconstruction). Panel (c) is a visual depiction of a GLOF event, showing a 'Moraine dammed Lake' at a 'Breaching point'. The resulting 'Flood water' and 'Debris flow' travel down through forests and valleys, impacting 'Agriculture fields', a 'Bridge', and a 'Settlement' downstream.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "3 GLOF Hazard Mitigation", "section_headings": ["3 GLOF Hazard Mitigation"], "chunk_type": "figure", "figure_caption": null, "line_start": 55, "line_end": 55, "token_count_estimate": 374, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "008b7b732b0f8e64", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 3 GLOF Hazard Mitigation\nType: text\n\nFig. 2 a) A Schematic diagram illustrating the location of monitoring stations and the monitoring/warning processes, b) Structural and Non-structural measures for the GLOF Hazard and Risk management, c) Illustration of GLOF event and downstream impacts", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "3 GLOF Hazard Mitigation", "section_headings": ["3 GLOF Hazard Mitigation"], "chunk_type": "text", "line_start": 56, "line_end": 58, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5c4aed368231098d", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 4 Hazard and Risk Mapping\nType: text\n\nHazard mapping is another key element for GLOF hazard mitigation. The mapping of various parameters related to GLOF hazard and Risk in the downstream region such as location of the glacial lake and its feeding glacier, moraine dams, ice cores, mass movements, evolution of the lake, downstream area, population, infrastructure, potential flood propagation", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "4 Hazard and Risk Mapping", "section_headings": ["4 Hazard and Risk Mapping"], "chunk_type": "text", "line_start": 60, "line_end": 62, "token_count_estimate": 112, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1a5d30bb954659d", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 4 Hazard and Risk Mapping\nType: figure\nFigure\n\nImage /page/4/Picture/6 description: The logo for Springer, featuring a black and white icon of a chess knight's head facing left, positioned above two horizontal lines. To the right of the icon, the word \"Springer\" is written in a black serif font. The background is white.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "4 Hazard and Risk Mapping", "section_headings": ["4 Hazard and Risk Mapping"], "chunk_type": "figure", "figure_caption": null, "line_start": 63, "line_end": 63, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "63130f21f80cac41", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 4 Hazard and Risk Mapping\nType: figure\nFigure\n\nImage /page/5/Picture/1 description: A diagram illustrating the causes and effects of a Glacial Lake Outburst Flood (GLOF). The diagram is split into two main sections: 'GLOF Triggering Mechanism' at the top and 'Downstream Impacts' at the bottom. The top section shows a mountainous, glacial environment with a large blue glacial lake contained by a moraine. Several triggering mechanisms are shown with red arrows pointing into the lake, including a rock fall, a snow avalanche, ice calving from a glacier, and a cloud burst. The bottom section, labeled 'Downstream Impacts,' shows the floodwater rushing from the breached lake into a valley below. This flood impacts a community, showing houses, agricultural fields, forests, a bridge, and a more developed area with a factory and a road. People are depicted in an orange raft on the floodwaters. A legend on the right side of the diagram explains the various symbols used: Agricultural field, Forest, Bridge, House, Glacial lake, Moraine, Glacier Snout, Snow avalanche, Rock fall, Potential flood from lake in the upstream, Ice calving, Ice cored moraine, and Cloud burst.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "4 Hazard and Risk Mapping", "section_headings": ["4 Hazard and Risk Mapping"], "chunk_type": "figure", "figure_caption": null, "line_start": 65, "line_end": 65, "token_count_estimate": 330, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9d1490dc1893cc7e", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 4 Hazard and Risk Mapping\nType: text\n\nFig. 3 GLOF triggering mechanism and downstream impacts\n\nparameters etc is called as Hazard and Risk mapping. The mapping can be used to create risk evaluations and provide guidance for creating plans to mitigate hazards. The process of GLOF hazard and risk can be carried out using a combination of Remote sensing techniques and hydrodynamic models such as satellite imagery, aerial photographs, field investigations, etc. Besides physical parameters, the mapping process should also take into consideration the social and economic factors that increase the vulnerability of the downstream population. For instance, the National Centre for Hydrology and Meteorology in Bhutan has created an inventory of glacier lakes that contains details about the lakes' locations, sizes, and potential hazards (Bajracharya et al. 2020). The hazard maps created from the inventory have influenced the creation of GLOF hazard mitigation plans in Bhutan. Several studies have been carried out in different parts of the world on the GLOF hazard and Risk assessment. For example, Ives et al., 2010 analysed the evolution of glacial lakes and risk assessment in the Hindu Kush Himalayas, Wang et al. 2022 have done an integrated assessment of GLOF disaster in Qinghai Tibetan Plateau. Similarly, in Nepal GLOF risk assessment was carried out by various researchers (Shrestha et al. 2010; Khanal et al. 2015; Rounce et al. 2016; Hu et al. 2022; Khadka et al. 2019; Sattar et al. 2021; Khanal et al. 2015), Bhutan (Ives et al. 2010; Rinzin et al. 2023; Gurung and Khanal 2017; Hagg et al. 2021; Scapozza et al. 2019; Rinzin et al. 2021; Rawat et al. 2023b; Negi et al. 2021; Ahmed et al. 2023; Wangchuk et al. 2023). A boom of studies on the GLOF risk assessment and Inventorization of glacial lakes have been conducted in several catchments of the Indian Himalayan Region (Sattar et al. 2021; Ahmed et al. 2021, 2022; Mir et al. 2018; Majeed et al. 2021; Rawat et al. 2023; Aggarwal et al. 2016; Mal et al. 2021; Dubey and Goyal 2020; Deswal et al. 2021; Gupta et al. 2022; Rinzin et al. 2023; Thakur et al. 2016). In the Peruvian Andes GLOF hazard and risk have been studied extensively for instance Frey et al. (2018) have proposed a robust GLOF management strategy for a data-scarce region like Santa Teresa in Peru. Emmer and Vilmek (2014) have provided a new method to evaluate the potential danger of glacial lakes in Peru. Similarly, a huge number of studies have been carried out in the region which have focused on the preparation of lake and", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "4 Hazard and Risk Mapping", "section_headings": ["4 Hazard and Risk Mapping"], "chunk_type": "text", "line_start": 66, "line_end": 70, "token_count_estimate": 672, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "3d825fac5e2e5296", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 4 Hazard and Risk Mapping\nType: figure\nFigure\n\nImage /page/5/Picture/4 description: The black and white logo for Springer, featuring a stylized chess knight icon to the left of the word 'Springer' in a serif font.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "4 Hazard and Risk Mapping", "section_headings": ["4 Hazard and Risk Mapping"], "chunk_type": "figure", "figure_caption": null, "line_start": 71, "line_end": 71, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af3f17bc689d98e4", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 4 Hazard and Risk Mapping\nType: text\n\nGLOF inventories, hazard level, risk assessment as well as structural and non-structural measures for the GLOF hazard and risk management (Drenkhan et al. 2019; Ahmed et al. 2022; Frey et al. 2018; Huggel et al. 2020; Emmer and Vilmek 2014; Munoz et al. 2024; Hagen et al. 2023; Batka et al. 2020; Huggel et al. 2020; Carey et al. 2012; Wood et al. 2021). From the above-mentioned studies, it is quite clear that researchers are long been working on the glacial lake hazard and risk mapping, however, only findings from a few studies have been implemented for the planning on the ground. These risk maps along with other useful findings should be taken into consideration while planning and developmental activity in the ecologically fragile areas to lessen the impacts of the potential GLOFs.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "4 Hazard and Risk Mapping", "section_headings": ["4 Hazard and Risk Mapping"], "chunk_type": "text", "line_start": 72, "line_end": 74, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55978953db387bcb", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 5 Methodological Framework for GLOF hazard and Risk Mapping\nType: text\n\nThe development of a comprehensive methodological framework is one of the key elements for evaluating and reducing the possible risks associated with GLOFs. An extensive literature survey was conducted to build this framework, ensuring a strong foundation based on current knowledge and techniques. The initial phase includes the detection and Inventorization of glacial lakes at different scales, employing field surveys and data from remote sensing to create an extensive database. The dynamics of glacial lakes, such as changes in volume, size, and possible triggers for outburst events, are then evaluated using modeling approaches and remote sensing technology. The comprehensive methodological framework for GLOF hazard and risk mapping is illustrated in Fig. 4. Framework for the evaluation of hazard level, physical vulnerability and social vulnerability of GLOFs is illustrated in Supplementary Figure S3.\n\nThe following stage is dedicated to hazard assessment and includes identifying susceptible downstream locations, estimating the volume of the flood, and analysing possible flood routes. Simulating possible GLOF scenarios requires the use of sophisticated hydrological and hydraulic modelling techniques in conjunction with GIS. After that, risk mapping combines vulnerability and exposure analyses with hazard data, taking into account downstream areas' infrastructure, population density, and land use. This stage helps to prioritise risk reduction efforts by giving a spatial representation of the potential impact zones.\n\nStakeholder participation is crucial to this process; local communities, governmental organisations, and scientific specialists should all be included. Their contributions improve data quality, evaluate models, and add useful local knowledge. The developed methodological framework serves as a crucial resource for planners, policymakers and disaster management authorities. Targeted policy interventions, such as land-use planning, early warning systems, and infrastructure development in high-risk zones, are made possible by the spatial visualisation of prospective risk areas. Decision-makers can use the framework's results to help create and carry out policies that lessen communities' susceptibility to GLOF disasters. Additionally, the methodological framework provides a systematic and standardized approach to GLOF hazard and risk assessment, ensuring consistency and comparability across different regions and contexts. Adaptive management strategies can be improved by regular updates that are based on continuous observation and investigation. In conclusion, strengthening policy implementation and protecting vulnerable communities from the potentially disastrous effects of GLOFs depends on the development of a strong GLOF hazard and risk mapping framework that is backed by scientific data and stakeholder cooperation.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "5 Methodological Framework for GLOF hazard and Risk Mapping", "section_headings": ["5 Methodological Framework for GLOF hazard and Risk Mapping"], "chunk_type": "text", "line_start": 76, "line_end": 82, "token_count_estimate": 613, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d3186ed09a34287c", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 5 Methodological Framework for GLOF hazard and Risk Mapping\nType: figure\nFigure\n\nImage /page/6/Picture/7 description: The logo for the publisher Springer, featuring a black outline of a chess knight's head facing left, positioned above two horizontal lines. To the right of the icon is the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "5 Methodological Framework for GLOF hazard and Risk Mapping", "section_headings": ["5 Methodological Framework for GLOF hazard and Risk Mapping"], "chunk_type": "figure", "figure_caption": null, "line_start": 83, "line_end": 83, "token_count_estimate": 112, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "63e51dbc675fcc5d", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 5 Methodological Framework for GLOF hazard and Risk Mapping\nType: figure\nFigure\n\nImage /page/7/Figure/1 description: A flowchart illustrating a framework for the hazard and risk mapping of glacial lakes. The process begins with inputs like Satellite imagery, Maps, Reports, Past inventories, and Toposheets, which are used for 'Mapping glacial lakes at different time points' with the help of 'Geospatial tools (RS and GIS)'. This leads to the creation of a 'Glacial lake data base'. The next step is the 'Identification of PDGLs' (Potentially Dangerous Glacial Lakes) using 'Integrated criteria' and 'Geospatial tools'. This generates a 'List of PDGLs'. These are then ranked for hazard level using various 'Ranking Criteria' such as 'Lake area, volume and Peak discharge', 'Moraine condition', 'Past GLOF history', and others. The output is a 'Hazard Level' categorized as High, Medium, or Low, determined using the 'Natural Jenks Classification method in ArcGIS'. Following this, 'high priority lakes' are selected for further assessment. This involves an 'In-depth analysis through field investigation' covering eight 'Thematic areas of study' including 'Moraine dam condition' and 'Analysis of the downstream agriculture and infrastructure'. This detailed study, including field investigation and modeling, results in 'Inundation maps, risk maps for the planners and policy makers'. After 'Analysis and interpretation of data', the process culminates in a 'Final Report'. The flowchart includes small map images as examples of inundation maps.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "5 Methodological Framework for GLOF hazard and Risk Mapping", "section_headings": ["5 Methodological Framework for GLOF hazard and Risk Mapping"], "chunk_type": "figure", "figure_caption": null, "line_start": 85, "line_end": 85, "token_count_estimate": 429, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e63b169583cbd02e", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 5 Methodological Framework for GLOF hazard and Risk Mapping\nType: text\n\nFig. 4 Framework for the hazard and risk mapping of glacial lakes", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "5 Methodological Framework for GLOF hazard and Risk Mapping", "section_headings": ["5 Methodological Framework for GLOF hazard and Risk Mapping"], "chunk_type": "text", "line_start": 86, "line_end": 88, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1713464b662f3af3", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 6 Structural Measures\nType: text\n\nThe structural measures involve the construction and development of physical infrastructure to minimise the risk associated with the potentially dangerous glacial lakes. Structural measures comprise of the construction of spillways, diversion channels, siphoning as well as other engineering approaches to manage the rapid flow of water and debris material in the event of a GLOF. The effective implementation of the structural measures depends on the location and characteristics of the moraine dam. The effectiveness is also determined by the availability of funds and technical expertise. The following steps outline the key components of this framework.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "6 Structural Measures", "section_headings": ["6 Structural Measures"], "chunk_type": "text", "line_start": 90, "line_end": 92, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5e1a3c467632b4a", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 6 Structural Measures\nType: figure\nFigure\n\nImage /page/7/Picture/5 description: The black and white logo for Springer. On the left is an icon of a knight chess piece, a horse's head facing left, with two horizontal lines underneath. To the right of the icon is the word \"Springer\" in a serif font.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "6 Structural Measures", "section_headings": ["6 Structural Measures"], "chunk_type": "figure", "figure_caption": null, "line_start": 93, "line_end": 93, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "680e5018f63e3ab6", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 6 Structural Measures > 6.1 Site-Specific Vulnerability Assessment\nType: text\n\nConducting a comprehensive site-specific vulnerability assessment is the first step towards identifying regions that are vulnerable to GLOF events. This entails identifying the most vulnerable areas by carefully examining glacial lakes, possible flood routes, and downstream areas. Essential information is gathered by field surveys and data from remote sensing.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "6 Structural Measures > 6.1 Site-Specific Vulnerability Assessment", "section_headings": ["6 Structural Measures", "6.1 Site-Specific Vulnerability Assessment"], "chunk_type": "text", "line_start": 96, "line_end": 98, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58c9c5f85cb715cd", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 6 Structural Measures > 6.2 Infrastructure Development and Engineering Solutions\nType: text\n\nThe framework presents engineering solutions that are customised to the specific characteristics of every risk zone that has been identified, taking into account the results of the vulnerability assessment. To prevent flooding, structural measures could involve the construction of retaining walls, debris dams, and other barriers. Floodwaters can also be redirected away from inhabited areas by channelizing rivers and creating diversion channels.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "6 Structural Measures > 6.2 Infrastructure Development and Engineering Solutions", "section_headings": ["6 Structural Measures", "6.2 Infrastructure Development and Engineering Solutions"], "chunk_type": "text", "line_start": 100, "line_end": 102, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2f86f73ff5a8a6b3", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 6 Structural Measures > 6.3 Early Warning Systems and Monitoring Infrastructure\nType: text\n\nTo give at-risk communities timely warnings, the framework places a strong emphasis on the integration of early warning systems and monitoring infrastructure. Real-time data collection is facilitated by the installation of sensors, weather monitoring equipment, and gauging stations close to glacial lakes. When combined with communication networks, these methods allow authorities to alert people in good time, which facilitates preparation and efficient evacuation (Fig. 2a).", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "6 Structural Measures > 6.3 Early Warning Systems and Monitoring Infrastructure", "section_headings": ["6 Structural Measures", "6.3 Early Warning Systems and Monitoring Infrastructure"], "chunk_type": "text", "line_start": 104, "line_end": 106, "token_count_estimate": 140, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ae37822881aac75", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 6 Structural Measures > 6.4 Community Involvement and Capacity Building\nType: text\n\nLocal communities must be actively involved for structural measures to be implemented successfully. The framework emphasises how crucial community involvement and capacity-building initiatives are. These programmes inform locals about possible dangers, how to evacuate, and how crucial it is to keep an eye on and maintain the structural safeguards put in place.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "6 Structural Measures > 6.4 Community Involvement and Capacity Building", "section_headings": ["6 Structural Measures", "6.4 Community Involvement and Capacity Building"], "chunk_type": "text", "line_start": 108, "line_end": 110, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1cf24d234dc00f2f", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 7 Non-Structural Measures\nType: text\n\nNon-structural measures emphasise on community-based strategies for hazard mitigation rather than involving physical infrastructure. Land-use planning, public awareness campaigns, and community readiness and response planning are examples of non-structural measures. Planning for community readiness and response entails collaborating with nearby communities to create strategies for tackling GLOF events. Plans for evacuation routes, emergency shelters, and protocols for community member collaboration and communication can all be included in these plans. Campaigns to raise public awareness are essential to ensuring that communities are aware of the dangers posed by GLOFs and are equipped to respond to emergencies. Media campaigns, instructional materials, and community gatherings are a few examples of public awareness initiatives. By making sure that future development considers the possibility of GLOF risks land-use planning may", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "7 Non-Structural Measures", "section_headings": ["7 Non-Structural Measures"], "chunk_type": "text", "line_start": 112, "line_end": 114, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3bcdc0723a265d6", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 7 Non-Structural Measures\nType: figure\nFigure\n\nImage /page/8/Picture/12 description: The image displays the logo for Springer, a publishing company. The logo consists of a black icon and black text on a white background. On the left, there is an icon of a chess knight's head facing left, positioned above two short horizontal lines. To the right of the icon, the word \"Springer\" is written in a serif font.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "7 Non-Structural Measures", "section_headings": ["7 Non-Structural Measures"], "chunk_type": "figure", "figure_caption": null, "line_start": 115, "line_end": 115, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6c3e88ebad17b841", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 7 Non-Structural Measures\nType: text\n\ncontribute to reducing the risk of GLOFs. Zoning regulations, construction restrictions, and other measures to lessen a community's susceptibility to GLOFs are examples of landuse planning (Fig. 2b).\n\nAlthough GLOF hazard mitigation approaches are important, there are a number of obstacles that prevent them from being implemented effectively. The isolation and political marginalisation of numerous communities at danger from GLOFs is one of the primary obstacles. The lack of finances, technical know-how, and political clout in these communities can make it challenging to put into practice sensible hazard mitigation plans. The lack of funds for GLOF hazard mitigation measures is another issue. Low-income nations with little funding for hazard mitigation and infrastructure development make up a large number of those at risk from GLOFs. Notwithstanding these obstacles, there exist prospects for enhancing GLOF hazard reduction tactics.\n\nGLOF risk reduction involves several strategies to mitigate the catastrophic impacts of GLOFs, which are becoming more frequent due to climate change and glacier retreat (Shijin et al. 2022; Taylor et al. 2023). Key measures include infrastructure development (e.g., spillways, dams), hazard mapping, early warning systems, and land use planning (Ahmed et al. 2024). Effective risk reduction also requires educating and involving local communities, as well as addressing the underlying causes of GLOFs, such as rising temperatures and glacial retreat. International cooperation is essential, particularly in transboundary regions, with organizations like ICIMOD facilitating collaborative efforts to develop and implement regional risk reduction strategies.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "7 Non-Structural Measures", "section_headings": ["7 Non-Structural Measures"], "chunk_type": "text", "line_start": 116, "line_end": 122, "token_count_estimate": 423, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b57778a33f671a69", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 8 Early Warning System (EWS)\nType: text\n\nAn EWS is a vital component of GLOF risk reduction. It is intended to identify and monitor changes in glacial lakes and to send alerts to people downstream in the event of a potential GLOF. EWS can give fast and accurate information to decision-makers and local populations, allowing them to take necessary action to mitigate the impact of a GLOF. EWS usually involves a range of monitoring devices, like water level gauges, weather stations, and seismometers, that are installed in and around glacial lakes to track changes in water level, temperature, and seismic activity (Fig. 2a). This monitoring data is sent to a central control center, where it undergoes analysis and processing to determine the likelihood of a GLOF. When a GLOF is detected, the EWS sends an alarm to downstream populations through different means of communication such as sirens, text messaging, and social media. The alert informs individuals about the location and timing of the anticipated flood and urges them to escape to safe areas. In addition to alarms, EWS can give real-time information regarding the extent and magnitude of a GLOF, allowing emergency personnel to better organize their response and deploy resources (Fig. 2a). The efficacy of an EWS, on the other hand, is dependent upon aspects, including the reliability and quality of monitoring data, the timeliness and accuracy of alerts, and the ability of local people to respond to alerts and escape to safe locations. To reduce the impact of GLOFs, EWS should be supplemented with other risk reduction measures such as risk mapping, land use planning, and infrastructure development.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "8 Early Warning System (EWS)", "section_headings": ["8 Early Warning System (EWS)"], "chunk_type": "text", "line_start": 124, "line_end": 126, "token_count_estimate": 412, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db64280c298cc88c", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 8 Early Warning System (EWS)\nType: figure\nFigure\n\nImage /page/9/Picture/6 description: The logo for the publisher Springer, featuring a black line drawing of a chess knight icon to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "8 Early Warning System (EWS)", "section_headings": ["8 Early Warning System (EWS)"], "chunk_type": "figure", "figure_caption": null, "line_start": 127, "line_end": 127, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3308c3cc2c51ba6", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 9 GLOF Risk Management Skills and Knowledge Transfer\nType: text\n\nEffective GLOF risk reduction hinges on knowledge transfer and skill development. This involves sharing expertise and best practices among stakeholders—scholars, policymakers, emergency responders, and local communities—to enhance local capabilities in recognizing GLOF indicators, responding to alerts, and evacuating safely. Knowledge transfer improves the accuracy of risk assessments through data sharing on glacial dynamics and climate effects, and by incorporating lessons from past GLOF events. It also fosters global collaboration, crucial for managing risks in transboundary regions. By focusing on capacity building, data exchange, and international cooperation, communities can build resilience, refine risk assessments, and implement effective mitigation strategies.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "9 GLOF Risk Management Skills and Knowledge Transfer", "section_headings": ["9 GLOF Risk Management Skills and Knowledge Transfer"], "chunk_type": "text", "line_start": 130, "line_end": 132, "token_count_estimate": 199, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "76630bacb75bf3ac", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 10 GLOF Hazard Mitigation and Climate Change Adaptation\nType: text\n\nClimate change impacts, such as rising temperatures and changing patterns of precipitation, are increasingly being attributed to GLOFs, which may lead to the development and instability of glacial lakes (Zheng et al. 2021). As a result, measures to lower the danger of future GLOFs should be closely integrated with climate change adaptation initiatives. One important strategy for GLOF hazard prevention and climate change adaptation is to design and build infrastructure, such as dams and spillways. These engineering structures can aid in regulating the flow of water from glacial lakes, lowering the risk of massive flooding. However, such infrastructural solutions must be carefully developed and planned to ensure long-term effectiveness and sustainability.\n\nAnother strategy is to encourage ecosystem-based adaptation (EbA) initiatives, which can assist in lowering the risk of GLOFs and increase community resilience to the effects of climate change. Restoration of deteriorated habitats, such as wetlands, marshes, bogs, and forests, may assist with managing the flow of water and lessen the risk of floods as part of EbA efforts. Furthermore, EbA strategies can help to strengthen local populations' capacity to adapt to climate change impacts and resilience to GLOFs. Furthermore, successful GLOF hazard reduction and climate change adaptation necessitate the development of integrated risk management strategies that address the root causes and drivers of GLOFs, such as climate change impacts, glacier retreat, and human activities. To develop and implement appropriate risk reduction strategies, different stakeholders, including researchers, policymakers, emergency responders, and local communities, must work together.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "10 GLOF Hazard Mitigation and Climate Change Adaptation", "section_headings": ["10 GLOF Hazard Mitigation and Climate Change Adaptation"], "chunk_type": "text", "line_start": 134, "line_end": 138, "token_count_estimate": 421, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c3d4ef00b8d215e", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation\nType: text\n\nGLOFs are a severe threat throughout mountainous areas, particularly where glacial lakes exist. Glaciers are melting as a result of climate change, increasing the likelihood of GLOFs and posing a severe threat to people, infrastructure, and the environment. Several supportive factors can promote GLOF hazard mitigation and climate change adaptation activities to decrease the risks associated with GLOFs and adapt to the impacts of climate change. Several", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation", "section_headings": ["11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation"], "chunk_type": "text", "line_start": 140, "line_end": 142, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca2b122812bf5027", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation\nType: figure\nFigure\n\nImage /page/10/Picture/9 description: The logo for Springer, featuring a black line drawing of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation", "section_headings": ["11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation"], "chunk_type": "figure", "figure_caption": null, "line_start": 143, "line_end": 143, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cddbd5cba869e169", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation\nType: text\n\nactions can help to mitigate GLOF hazards and adapt to climate change. A few examples are given below:\n\n- Strong political will\n- Collaboration and partnerships\n- Engagement and the empowerment of local people\n- Adequate funding\n- Integration with other development goals\n- Access to technology and data\n- Awareness and education", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation", "section_headings": ["11 Facilitating Factors for GLOF Hazard Mitigation and Climate Change Adaptation"], "chunk_type": "text", "line_start": 144, "line_end": 154, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e197dc5167739c44", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 12 Policy Legislation and Institutional Support\nType: text\n\nEffective GLOF hazard mitigation and climate change adaptation require strong policy, law, and institutional support. Governments and other stakeholders have to develop and enforce laws and regulations to support risk management plans, provide needed resources, and encourage transparency and accountability. Furthermore, organizations must be built or enhanced to allow policy implementation and risk reduction measures. Climate litigation is being informed by interdisciplinary research on GLOF hazards, particularly concerning anthropogenic climate change. Notably, a Peruvian resident is suing a German energy firm for possible GLOF dangers; the case is presently pending before a German court. Research relates GLOF dangers and glacier changes to greenhouse gas emissions. Even though these incidents have an impact, it's important to take into account larger viewpoints on accountability and justice. An analysis of the Palcacocha case (Figure S2) highlights the different duties at different levels and highlights the impact of governance, socioeconomic conditions, cultural features, and climatic and non-climatic elements in creating exposure and vulnerability to GLOF threats.\n\n- Policy and legislation\n- Institutional support\n- Financial support\n- Capacity building\n- Monitoring and assessment\n\nEffective GLOF and climate change management relies on robust policies, institutional support, financial resources, capacity building, and monitoring. Policies should reduce emissions and promote sustainable practices, while institutions coordinate efforts and provide technical support. Financial backing is essential for infrastructure and early warning systems, sourced from various channels. Capacity building enhances stakeholder skills through targeted training. Comprehensive monitoring ensures progress and informs improvements in risk management strategies.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "12 Policy Legislation and Institutional Support", "section_headings": ["12 Policy Legislation and Institutional Support"], "chunk_type": "text", "line_start": 156, "line_end": 166, "token_count_estimate": 411, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83a10cbfb7b2590a", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 13 Knowledge Disparity\nType: text\n\nKnowledge disparity, the uneven distribution of information and expertise, significantly impacts GLOF hazard mitigation and climate change adaptation. Communities lacking access to scientific data, technical skills, and early warning systems face challenges", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "13 Knowledge Disparity", "section_headings": ["13 Knowledge Disparity"], "chunk_type": "text", "line_start": 168, "line_end": 170, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6cae18003b1321ed", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 13 Knowledge Disparity\nType: figure\nFigure\n\nImage /page/11/Picture/19 description: The Springer logo, featuring a black and white icon of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "13 Knowledge Disparity", "section_headings": ["13 Knowledge Disparity"], "chunk_type": "figure", "figure_caption": null, "line_start": 171, "line_end": 171, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c0beafd028a40be3", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 13 Knowledge Disparity\nType: text\n\nin addressing GLOF risks. Financial constraints further exacerbate this issue, limiting the ability to implement protective measures and adapt strategies. Institutional capacity gaps, such as inadequate specialized agencies or poor coordination, also hinder effective responses. Additional factors like language barriers, traditional knowledge, and gender roles can contribute to knowledge imbalances, affecting the success of risk reduction efforts. Addressing these disparities requires a collaborative approach, including information sharing, institutional strengthening, and social inclusivity, to enhance community understanding and resilience against GLOFs and climate change.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "13 Knowledge Disparity", "section_headings": ["13 Knowledge Disparity"], "chunk_type": "text", "line_start": 172, "line_end": 174, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af380d0165ce6ed0", "text": "Document: Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies\nSection: 14 Conclusion\nType: text\n\nThis study underscores the critical need for a comprehensive framework to mitigate GLOF hazards, particularly in the face of accelerating glacier retreat driven by climate change. By integrating both structural and non-structural measures, the research highlights the importance of proactive land-use planning, community-based strategies, and early warning systems (EWS) in managing GLOF risks effectively. The study's emphasis on detailed risk mapping and spatial analysis equips decision-makers with the tools necessary for targeted interventions, ensuring resources are allocated efficiently in highrisk areas. The implications of this study extend to policymakers, practitioners, and researchers, providing a roadmap for developing sustainable and adaptable solutions to the complex challenges posed by climate change in high-mountain regions. Legislative support, institutional capacity, and knowledge transfer are identified as crucial elements in enhancing the effectiveness of GLOF risk mitigation efforts. Addressing knowledge disparities and fostering interdisciplinary collaboration are essential for building resilient communities. Looking forward, the study advocates for ongoing research, particularly in understanding the socio-economic impacts of GLOFs, to ensure a holistic approach to disaster risk reduction. By integrating climate change adaptation into GLOF mitigation strategies, the findings offer a pathway for safeguarding vulnerable communities and preserving the stability of high-mountain environments.\n\n**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1007/s11269-024-03958-x.\n\n**Funding** No funding was received to conduct this study.\n\nData Availability Not applicable.", "metadata": {"source_file": "data/('Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies', '.pdf')_extraction.md", "document_title": "Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies", "section_path": "14 Conclusion", "section_headings": ["14 Conclusion"], "chunk_type": "text", "line_start": 176, "line_end": 184, "token_count_estimate": 404, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["03958"]}}
{"id": "dff3e43e720754a1", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Abstract\nType: text\n\nGlacier-associated hazards are becoming a common and serious challenge to the high mountainous regions of the world. Glacial lake outburst floods (GLOFs) are one of the most serious unanticipated glacier hazards, with the potential to release a huge amount of water and debris in a short span of time, resulting in the loss of lives, property, and severe damage to downstream valleys. The present study used multi-temporal Landsat and Google earth imageries to analyze the spatio-temporal dynamism of the selected glacial lake (moraine-dammed) in the Satluj basin of Western Himalayas. Furthermore, GLOF susceptibility of the lake was assessed using a multi-criteria decision-based method. The results show that the lake area has increased from 0.11 to 0.26 km<sup>2</sup> over the past 28 years from 1990 to 2018. The susceptibility index value for the lake was calculated as 0.75, which indicates that the lake is highly susceptible to the GLOF. The depth and volume of the lake were estimated to be 16 m and 57×10<sup>5</sup> m<sup>3</sup>, respectively, using an empirical formula. HEC-RAS, HECGeo-RAS, and Arc-GIS software were utilized in this study to perform unsteady flow analysis and to determine the GLOF impact on the downstream area. The worst-case GLOF scenario (breach width of 75 m) was revealed during an overtopping failure of the moraine dam, resulted in a peak discharge of  $4060 \\text{ m}^3$ /s and releasing a total water volume of  $57 \\times 10^5 \\text{ m}^3$ . The breach hydrograph has been routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river channel. The analysis further reveals that the routed flood waves reach the nearest settlement, i.e., Rajpur town, situated at a distance of 102 km in the downstream valley of the lake at 6 h after the beginning of the lake breach event with a peak discharge/flood of 1757 m<sup>3</sup>/s and maximum flow velocity of 1.5 m/s. With the ongoing climate warming and glacier retreat, moraine-dammed lakes are becoming more hazardous and thus increasing the total threat. Therefore, it is mandatory to monitor and assess such lakes at regular intervals of time to lessen the disastrous impacts of GLOFs on the livelihood and infrastructure in the downstream valleys. The findings of this study will aid in the creation of risk management plans, preparatory tactics, and risk reduction techniques for GLOF hazards in the region.\n\n $\\textbf{Keywords} \\ \\ Glacier \\ recession \\cdot Glacial \\ lake \\ expansion \\cdot GLOF \\ susceptibility \\cdot Glacial \\ lake \\ outburst \\ flooding \\ (GLOFs) \\cdot HEC-RAS \\cdot Western \\ Himalaya-India$", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "text", "line_start": 4, "line_end": 8, "token_count_estimate": 788, "basins": [], "subbasins": ["Satluj"], "countries": ["India"], "lake_ids": []}}
{"id": "ca830dae7f68408b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Introduction\nType: text\n\nThe glacierized basins of the Himalayan region have witnessed a serious effect of the changing climate, and even little changes in climate have a wide range of consequences/implications for the Himalayas and its ecology (Ahmad\n\nResponsible Editor: Philippe Garrigues\n\nRayees Ahmed rayeesrashid84@gmail.com\n\nExtended author information available on the last page of the article\n\net al. 2022; Gurung et al. 2021; Wani et al. 2022). The ongoing climate change/variability has resulted in the significant mass loss of the glaciers throughout the high terrains of the world and several among them are under threat (Kumar et al. 2020; Ahmed et al. 2021a; Sarkar et al. 2020); consequent to that, a large number of glacial lakes have shown a discernible increase in terms of number and area (Bolch et al. 2012). The meltwater from these retreating glaciers not only increases the water level of the existing lakes, but it has also led to the formation of thousands of new lakes (Mir et al. 2018; Ahmad et al. 2022). For example, over 2070 lakes were formed in the Nepal Himalayas alone during the second", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 20, "token_count_estimate": 289, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "e07ce7dfa9265b02", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Introduction\nType: figure\nFigure\n\nImage /page/0/Picture/15 description: The logo for Springer, featuring a black line drawing of a chess knight piece facing left, positioned on two horizontal lines. To the right of the icon, the word \"Springer\" is written in a black serif font. The background is white.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 21, "line_end": 21, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a0eb74337a094e4", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Introduction\nType: text\n\nhalf of the twentieth century as a result of changing climatic regimes (Maharjan et al. 2018). Some of these lakes are potentially dangerous/disastrous and pose a serious threat to the life and property in the downstream region.\n\nSnow and glacier melt in the Himalayas is caused by a local weather system driven by mountain-induced dynamic and thermodynamic processes that affect precipitation pattern, cloudiness, and other factors, forming glacial lakes near the terminus (proglacial lakes), on the surface (supraglacial lakes), or within the glaciers (subglacial lakes). Due to the fast accumulation of glacier meltwater in these lakes, the effects of rapid flash flooding in riparian areas downstream have been disastrous (Richardson and Reynolds 2000; Jain et al. 2012). The melting of mountain glaciers and the growth of massive glacial lakes are two of the most visible and dramatic effects of climate change in this environment (Bolch et al. 2012). The inventory of glacial lakes has shown a total of around 2000 glaciers (Prasad et al. 2019) and 769 glacial lakes in the Satluj basin (Randhawa and Sharma 2013). Similar to other parts of the Himalayas, glaciers in the Satluj basin are retreating and glacial lakes are expanding at a greater pace (Gaddam et al. 2016; Mandal and Sharma 2020; Singh et al. 2021) and are expected to lose 53% and 81% of the area at the end of the century under the RCP 8.5 scenarios of CNRMCM5 and GDFDL-CM3 models (Prasad et al. 2019). As a result, the size and number of supraglacial and proglacial lakes that merge to form larger glacier lakes behind the loose moraine may grow faster (IPCC 2012; Worni et al. 2013; Emmer 2017; Harrison et al. 2018). Moraine-dammed glacial lakes have a higher risk of breaching, making them potentially dangerous due to the massive volume of water accumulated. Large outburst flood (GLOF) and risks can be produced in downstream areas during any sort of breaching and rapid discharge of water and debris from such lakes (Richardson and Reynolds 2000; Westoby et al. 2014; Worni et al. 2014). GLOF represents a catastrophic event in the Himalayas and is common in the glacierized areas of the region, thus posing a potential risk to the downstream hydro-power plant projects due to the extreme flow of a river to cause great damage. GLOF phenomenon is related to complex flow characteristics in both spatial and temporal scales (Westoby et al. 2014).\n\nFrom the last few decades, the Indian Himalayas have witnessed a large number of glaciers associated events, for example, Chog Kumdan GLOF event (1929) in Shyok River basin (Gunn 1930), Chorabari GLOF event (2013) in Mandakini basin (Allen et al. 2016), and recent being Chamoli disaster (2021) in Uttarakhand (Shugar et al. 2021) are some of the events well documented in the scholarly literature. These disastrous GLOF incidents are an example of calamitous GLOF events that have caused havoc on people's lives and livelihoods (Carrivick and Tweed 2016). According to the study carried out by Dubey and Goyal", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 22, "line_end": 28, "token_count_estimate": 810, "basins": [], "subbasins": ["Satluj", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "df5720829a001664", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/5 description: A block of black text on a white background. The text reads: \"2020, in Indian Himalayas, there are around 23 glacial lakes that are at very high risk and 56 in the high-risk category. Nonetheless, the risk posed by multiple triggering mechanisms should be estimated, particularly in the Himalayan region, where 68 percent of hydropower projects are located on probable GLOF tracks (Schwanghart et al. 2016).\" The numbers \"2020\" and \"2016\" are in blue text.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 29, "line_end": 29, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "75bc344a0371ccb7", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Introduction\nType: text\n\nIn the year 2000, the Satluj valley witnessed a devastating flood caused by cloud bursts and the resultant GLOF events, causing extensive damage in downstream areas. Thus, the region needs continuous monitoring and assessment of lakes that have the potential to cause a GLOF event (Randhawa et al. 2021). Preparation of an inventory and identification of lakes as potentially dangerous is the initial step for the hazard assessment of the lakes, followed by the GLOF modeling. To prevent and monitor the GLOF hazard and analyze the damage that may occur in the future, the integration of remote sensing and GIS could play an important role in identifying dangerous lakes and monitoring GLOF events in real-time. Geographic information system (GIS) and remote sensing (RS) technology have been successfully used globally for the monitoring and assessment of glaciers, glacial lakes, and associated hazards. The GIS and RS-based tools and techniques allow rapid investigation of huge glaciated areas in a very inexpensive way and are therefore considered very suitable for glacial lake and GLOF studies (Imdad et al. 2022; Ahmed et al. 2021b). Keeping in view the above discussion on how climate change is inducing glacier retreat and thereby glacial lake expansion and increasing hazard potentiality of the GLOFs, the aim of the present study is to achieve the following objectives.\n\n- To analyze the spatio-temporal changes in a selected lake for the GLOFs study using multi-temporal satellite imageries from 1990 to 2018.\n- To assess the GLOF susceptibility of the lake using a multi-criteria decision-based method.\n- To perform flood routing assessment of glacial lake hazard through hydrodynamic model (HEC-RAS), the study incorporates the following: (i) estimate the volume of a lake using empirical equation, (ii) evaluate multiple dam breach scenarios on the basis of varying breach width and breach formation time, producing breach outflow hydrograph, (iii) one dimensional (1-D) hydrodynamic mathematical modeling to evaluate the hazard potential of the selected lake, and (iv) a detailed study on flow hydraulic of potential GLOF and its downstream impact at different sites along the river.\n- The main objective of this research is to create a flood inundation map by employing the HEC-RAS model, RS and GIS, including predictions of water spread, maximum flood depth, travel duration, and peak discharge owing to the GLOF hazard.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 30, "line_end": 37, "token_count_estimate": 595, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "fcc61092c2ccf88c", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/11 description: The Springer logo, featuring a black outline of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 38, "line_end": 38, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6a7ea2cf1ede02a", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Study area\nType: text\n\nThe present study has selected a potentially dangerous glacial lake from the Satluj basin of the Indian Himalayas to carry out a GLOF modeling using HEC-RAS. The lake is located between 31° 39′ 36.47″ N Latitude and 78° 09′ 59.85″ E longitude at an elevation of 4267 m above sea level (Fig. 1). The lake is dammed by an unconsolidated moraine and connected to the snout of the feeding glacier (compound basin). The nearest settlement which is vulnerable to the probable GLOF event is located at a distance of approximately 8.5 km in the downstream region. The HPPCL Kashang hydropower project is also located in the downstream region of the lake at a distance of 39.06 km from the outlet. The snout of the glacier (feeding lake)\n\nhas shown a retreat of 153 m at the rate of 7.6 m/year from 2000 to 2020. The current area of the lake and its feeding glacier is estimated to be 0.26 km<sup>2</sup> and 5.72 km<sup>2</sup>, respectively. The glacier meltwater from this lake is supplied to the Satluj River. Satluj River is one of the key rivers in the Indus system. The Satluj River rises in Tibet at Manasarovar and ends in India near Namigia and it runs through the two states of India, i.e., Punjab and Himachal Pradesh. Satluj reaches the Anandpur Dun, a valley area between Sivaliks and Himalayan outer range near Nangal Town. The earlier studies have focused on the mapping, evolution, identification, and hazard assessment of glacial lakes. We have tried to evaluate the impacts of GLOF in the downstream valley and GLOF was routed from the lake outlet to the last selected location (Rajpur town), covering a distance of 102 km (Fig. 1).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Study area", "section_headings": ["Study area"], "chunk_type": "text", "line_start": 41, "line_end": 45, "token_count_estimate": 471, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": ["India"], "lake_ids": []}}
{"id": "1802611c75bc475b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Study area\nType: figure\nFigure\n\nImage /page/2/Figure/5 description: A figure composed of four maps, labeled a, b, c, and d, showing the location of a selected lake for GLOF (Glacial Lake Outburst Flood) hazard assessment.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Study area", "section_headings": ["Study area"], "chunk_type": "figure", "figure_caption": null, "line_start": 46, "line_end": 46, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f71132ecabd1a4a", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Study area\nType: text\n\nMap 'a' displays a map of India with the state of Himachal Pradesh highlighted in red. The map includes latitude and longitude lines and a scale bar in kilometers.\n\nMap 'b' is an elevation map of Himachal Pradesh, showing the Sutlej basin outlined in black. The legend indicates elevation ranges from a low of 243 meters to a high of 6619 meters. Major rivers like the Chenab, Sutlej, Yamuna, and Ganges are labeled.\n\nMap 'c' provides a more detailed elevation map of the Sutlej basin, outlined in red. The elevation ranges from a low of 454 meters to a high of 6446 meters. It shows the location of a specific lake, Rajpur town, and HPPCL Karcham. Rivers such as the Sutlej, Chenab, and Indus are also shown.\n\nMap 'd' is a close-up satellite image of the lake. The lake is outlined in red and is fed by a glacier, shown in cyan. Key features are labeled, including the 'Glacier', its 'Snout', the 'Lake' itself, the 'Moraine-dam' that contains the lake, the 'Lake outlet', and the resulting 'Stream'.\n\nFig. 1 Location map of the selected lake for GLOF hazard assessment, (a) Himachal Pradesh on map of India, (b) Satluj basin on the map of Himachal Pradesh, (c) Lake location on the Satluj basin map and (d) Glacio-geomorphological features around the lake", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Study area", "section_headings": ["Study area"], "chunk_type": "text", "line_start": 47, "line_end": 57, "token_count_estimate": 390, "basins": ["Indus"], "subbasins": ["Chenab", "Satluj", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "8f24059bb9100aee", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Study area\nType: figure\nFigure\n\nImage /page/2/Picture/7 description: The logo for Springer, featuring a black and white line drawing of a chess knight icon to the left of the word \"Springer\" in a black serif font, all on a white background. The knight icon is facing left and has two horizontal lines underneath it.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Study area", "section_headings": ["Study area"], "chunk_type": "figure", "figure_caption": null, "line_start": 58, "line_end": 58, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e80d0cf6a205b402", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology\nType: text\n\nSatellite data have been widely used for the change detection purpose of various parameters of glaciers and glacial lakes (Paul and Andreassen 2009). Traditional field-based surveys, which are highly recommended for measuring and monitoring changes in glaciers and glacial lakes, are often limited by the ruggedness and harshness of Himalayan terrain (Pratap et al. 2016). To overcome this problem multi-temporal satellite data offers a wide range of opportunities to monitor such glaciers and glacial lakes. In this study, we used satellite data of four decades to analyze the spatio-temporal dynamism of the selected lake. Landsat imagery with its free availability and fine spatial resolution of 30 m hosted by the Global Land Survey (GLS) data system of the United States Geological Survey (USGS) was downloaded for the years, namely 1990 (4–5 Thematic Mapper (TM)), 2000 (4–5 Thematic Mapper (TM)), 2010 (Enhanced Thematic Mapper (ETM)), and 2018 (Operational Land Imager (OLI)). Change detection of the glacial lake on the multi-temporal Landsat images is depicted in Fig. 2. The other data set which is required to\n\ncarry out GLOF modeling is digital elevation model (DEM) (Table 1).\n\nIn this study, we used Satellite Radar Topography Mission (SRTM) DEM with a spatial resolution of 30 m downloaded from the web portal www.earthexplorer.usgs.gov. The DEM was used to extract the catchment/watershed of the glacial lake and its feeding glacier. It was also used to generate a cross-section at 1 km intervals as well as elevation data. The cross-sections were created downstream from the lake to the final selected location. The cross-sections from", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology", "section_headings": ["Datasets and methodology"], "chunk_type": "text", "line_start": 61, "line_end": 69, "token_count_estimate": 450, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6471803b818a6262", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology\nType: table\nTable: Table 1 Satellite data used in the study\n\n| Date of acquisition | Sensor        | Spatial resolution (m) |\n|---------------------|---------------|------------------------|\n| 3 December 1990     | Landsat 4-5TM | 30                     |\n| 19 December 2000    | Landsat 4-5TM | 30                     |\n| 22 August 2010      | Landsat 4-5TM | 30                     |\n| 6 October 2018      | Landsat 8OLI  | 30                     |\n| 9 October 2018      | Google earth  | 1.5–6                  |\n| 12 November 2014    | SRTM-DEM      | 30                     |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology", "section_headings": ["Datasets and methodology"], "chunk_type": "table", "table_caption": "Table 1 Satellite data used in the study", "columns": ["Date of acquisition", "Sensor", "Spatial resolution (m)"], "table_row_start": 1, "table_row_end": 6, "line_start": 70, "line_end": 77, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d335bf5237cd6ced", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology\nType: figure\nFigure\n\nImage /page/3/Figure/8 description: A figure displaying four satellite images in a 2x2 grid, illustrating the growth of Satluj lake over time. Each panel is labeled with a letter and includes a title, a north arrow, coordinate grids, and a scale bar.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology", "section_headings": ["Datasets and methodology"], "chunk_type": "figure", "figure_caption": null, "line_start": 79, "line_end": 79, "token_count_estimate": 101, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "acaea54fe2f586c9", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology\nType: text\n\n- Panel a, titled \"Satluj lake - 1990\", shows a small, light-blue lake outlined in green.\n- Panel b, titled \"Satluj lake - 2000\", shows the lake has grown in size and is a darker blue, outlined in magenta.\n- Panel c, titled \"Satluj lake - 2010\", depicts a further enlarged, dark blue lake outlined in red.\n- Panel d, titled \"Satluj lake - 2018\", shows the lake at its largest extent in the series, appearing dark teal and outlined in yellow.\n\nAll images show the surrounding terrain in reddish-brown hues. The longitude ranges from approximately 78°9'45\"E to 78°10'15\"E, and the latitude ranges from 31°39'30\"N to 31°39'45\"N. A scale bar in each image indicates distances up to 0.3 km.\n\nFig. 2 Time series Landsat images showing growth of glacial lake area from 1990 to 2018, **a** lake area 1990 **b** lake area 2000, **c** lake area 2010, and **d** lake area 2018", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology", "section_headings": ["Datasets and methodology"], "chunk_type": "text", "line_start": 80, "line_end": 89, "token_count_estimate": 286, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "6be0173ec70aee5d", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology\nType: figure\nFigure\n\nImage /page/3/Picture/10 description: The Springer logo, featuring a black and white icon of a knight chess piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology", "section_headings": ["Datasets and methodology"], "chunk_type": "figure", "figure_caption": null, "line_start": 90, "line_end": 90, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0d86c2bf6961727b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology\nType: text\n\nthe DEM were generated because the field-measured crosssections were not available for this area due to the rugged terrain and harsh weather conditions. Besides that, highresolution Google Earth imagery was used for the crosschecking and validation of the lake extents. Chow, 1959, has suggested a Manning coefficient range between 0.03 to 0.07 for the hilly terrain with a steep slope and no vegetation, gravel, cobbles, boulders, and bushes on the banks. Considering this, we have used a Manning coefficient range of 0.04 to 0.06 as an input parameter for the GLOF routing of the selected lake of Satluj basin in HEC-RAS software. The overall methodology is given in Fig. 3.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology", "section_headings": ["Datasets and methodology"], "chunk_type": "text", "line_start": 91, "line_end": 93, "token_count_estimate": 209, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "0f4b463420a3cb48", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > GLOF susceptibility and AHP\nType: text\n\nIn order to assess the GLOF susceptibility, different parameters have been employed in the past studies pertaining to various parts of the Himalayan region (Che et al. 2014; Jain et al. 2012; Aggarwal et al. 2014; Kougkoulos et al. 2018;\n\nAllen et al. 2016). However, as of now, there is no particular set of parameters that can be used to analyze the GLOF susceptibility. In this study, we have used 5 parameters or potential factors that govern the mechanism of potential GLOF to assess the GLOF susceptibility of the selected lake. The parameters were identified and compiled from the literature survey which include lake area, lake expansion, distance between lake and feeding glacier, type of dam, and potential of ice or rock avalanche (Khadka et al. 2018; Gurung et al. 2021). These parameters have been calculated using the satellite imagery (Landsat TM/ETM+/OLI) and digital elevation model. The parameters and their source are given in Table 2.\n\nAfter the identification and calculation of parameters weights were allocated to each factor using analytical hierarchy process (AHP). AHP model propounded by Satty, 1980, has been widely used for the hazard and risk assessment such as land degradation, landslides, snow avalanche, debris flow (Himan et al. 2014), and GLOF hazard assessment (Khadka", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > GLOF susceptibility and AHP", "section_headings": ["Datasets and methodology", "GLOF susceptibility and AHP"], "chunk_type": "text", "line_start": 95, "line_end": 101, "token_count_estimate": 365, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c832e34b6a8d9983", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > GLOF susceptibility and AHP\nType: figure\nFigure\n\nImage /page/4/Figure/7 description: A methodology flowchart illustrating the steps for a GLOF (Glacial Lake Outburst Flood) analysis using the HEC-RAS model. The process begins with two primary inputs in yellow boxes: 'SRTM 30m DEM' and 'Landsat data series 1990-2018'. The SRTM data is used to 'Delineate river reach' and then 'Creation of geometry database along the river in Hec-GeoRAS'. This involves creating 'Bank lines', 'Flow path centerlines of river', and 'cross-section generation across the river', which leads to 'Topology creation' and then 'Export RAS data into HECRAS'. This data is then adjusted for 'cross section', 'bank station', and 'Manning coefficient'. The Landsat data is used for 'Mapping of glacial lake', which informs both the geometry creation and the 'Lake area and volume estimation from Huggle's equation'. This leads to generating a 'Lake breach hydrograph' using Froehlich's equation. The core of the model is the '1 D hydrodynamic modeling' which uses 'Boundary condition' inputs of 'Breach Hydrograph' and 'Friction slope'. The adjusted RAS data and the modeling results feed into 'Unsteady flow estimation'. This is followed by 'Flood routing of breach hydrograph along the river channel'. The final outputs of the analysis are 'GLOF inundation mapping' and 'Downstream impact assessment'.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > GLOF susceptibility and AHP", "section_headings": ["Datasets and methodology", "GLOF susceptibility and AHP"], "chunk_type": "figure", "figure_caption": null, "line_start": 102, "line_end": 102, "token_count_estimate": 418, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "23f9ae65def6eed5", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > GLOF susceptibility and AHP\nType: text\n\nFig. 3 Methodology flow chart depicting the steps followed in HEC-RAS model for the GLOF analysis", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > GLOF susceptibility and AHP", "section_headings": ["Datasets and methodology", "GLOF susceptibility and AHP"], "chunk_type": "text", "line_start": 103, "line_end": 105, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3164cd1a340ef95", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > GLOF susceptibility and AHP\nType: figure\nFigure\n\nImage /page/4/Picture/9 description: The image displays the logo for the publisher Springer. On the left is a black outline of a chess knight piece, specifically the horse's head, facing left, with two horizontal lines beneath it. To the right of the icon, the word \"Springer\" is written in a black serif font. The background is white.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > GLOF susceptibility and AHP", "section_headings": ["Datasets and methodology", "GLOF susceptibility and AHP"], "chunk_type": "figure", "figure_caption": null, "line_start": 106, "line_end": 106, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35aeb6dc61ee7def", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > GLOF susceptibility and AHP\nType: table\nTable: Table 2 Index values assigned to factors/parameters\n\n| Factors (f) | Factor/parameter                           | Class                                           | Rank                  | Index value (Ci) | Rank | Selected lake | Factor weight (Wi) | Data source                            | Reference                                                                  |\n|-------------|--------------------------------------------|-------------------------------------------------|-----------------------|---------------------|------|---------------|--------------------------|----------------------------------------|----------------------------------------------------------------------------|\n| f1          | Lake area (km2)                            | > 0.5 0.5–0.1 < 0.1                       | High Medium Low | 1 0.50 0.25   | 5    | 0.262         | 0.04                     | Landsat data (2020)                 | Campbell (2005); Aggarwal et al. (2014); Washakh et al. (2019) |\n| f2          | Lake expansion (%)                         | < 50% 50–20% < 20%                        | High Medium Low | 1 0.50 0.25   | 3    | 56.42         | 0.16                     | Multi-temporal satellite data       | Bolch et al. (2012); Che et al. (2014); Jain et al. (2012)     |\n| f3          | Distance between lake and glacier    | At snout > 500 < 500                      | High Medium Low | 1 0.50 0.25   | 4    | At the snout  | 0.09                     | Landsat and Google Earth imagery | Che et al. (2014); ICIMOD (2011)                                     |\n| f4          | Type of dam                                | Ice dam Moraine dam Other types of dam | High Medium Low | 1 0.50 0.25   | 2    | Moraine dam   | 0.24                     | Google Earth imagery and DEM     | Huggel et al. (2002)                                                    |\n| f5          | Potential of ice or rock ava- lanche | Susceptible Not susceptible                  | High Low           | 1 0.25           | 1    | High          | 0.22                     | Google Earth imagery                | Allen et al. (2016); Islam and Patel (2020)                       |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > GLOF susceptibility and AHP", "section_headings": ["Datasets and methodology", "GLOF susceptibility and AHP"], "chunk_type": "table", "table_caption": "Table 2 Index values assigned to factors/parameters", "columns": ["Factors (f)", "Factor/parameter", "Class", "Rank", "Index value (Ci)", "Rank", "Selected lake", "Factor weight (Wi)", "Data source", "Reference"], "table_row_start": 1, "table_row_end": 5, "line_start": 110, "line_end": 116, "token_count_estimate": 506, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6409696455c454b9", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > GLOF susceptibility and AHP\nType: text\n\net al. 2018; Gurung et al. 2021). Each parameter/factor were ranked into three classes, viz., high, medium, and low with a corresponding index value of 1, 0.25, and 0.5 (Prakash and Nagrajan 2017; Gurung et al. 2021). The final weights for each of the parameters/factors were determined by multiplying the factor weight (Wi) by the class index values (Ci) based on the measurement of lake factors. At the last stage, final weights of each factor were added to assess the GLOF susceptibility of the lake (Eq. (1)).\n\nGLOF Susceptibility =\n$$\\sum_{i=0}^{n} (Ci) * (Wi)$$\n (1)\n\nThe GLOF susceptibility index value ranges from 0.25 to 1. Using an equal interval class system, the values are divided into five categories: very low (0.25-0.40), low (0.40-0.55), medium (0.55-0.70), high (0.70-0.85), and very high (>8.5).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > GLOF susceptibility and AHP", "section_headings": ["Datasets and methodology", "GLOF susceptibility and AHP"], "chunk_type": "text", "line_start": 117, "line_end": 125, "token_count_estimate": 282, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f83e8ec79633b9d", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > Hydrodynamic modeling using HEC-RAS\nType: text\n\nModeling of GLOF hazard and risk has been carried out in several locations throughout the world, using a wide range of approaches and methodologies. In this study, we have used HEC-RAS hydrodynamic model to perform GLOF modeling and to evaluate impacts of flood caused due to the potential GLOF in the downstream region of the selected lake located in the Satluj basin. HEC-RAS model has been widely used for this purpose across the world (Dortch et al. 2011; Klimeš et al. 2014b; Watson et al. 2015; Hussain", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > Hydrodynamic modeling using HEC-RAS", "section_headings": ["Datasets and methodology", "Hydrodynamic modeling using HEC-RAS"], "chunk_type": "text", "line_start": 127, "line_end": 129, "token_count_estimate": 168, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "0b7e9df9ab77350b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > Hydrodynamic modeling using HEC-RAS\nType: figure\nFigure\n\nImage /page/5/Picture/9 description: A close-up of a paragraph of text from a document, printed in a black serif font on a white background. The text reads: et al. 2020; Kougkoulos et al. 2018; Wang et al. 2018). It is an integrated system of software that is designed to use in a multi-tasking environment (Hussain et al. 2020) and is most significantly used for glacial hazard studies (Klimeš et al. 2016). It is a user-friendly reliable model that has the dynamic capability of performing complex flow simulations in design, management, and operation of river systems. The model is based on one-dimensional St. Venant equations that are used to simulate flood scenarios generated by a glacial lake outburst. In hydraulic model, two key input data sets (geometry data and flow data) are required to compute Unsteady Flow Analysis (UFA). Cross-section data derived from the field survey is recommended, but due to the rugged terrain and harsh weather conditions, it is not easy to carry out the field survey.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > Hydrodynamic modeling using HEC-RAS", "section_headings": ["Datasets and methodology", "Hydrodynamic modeling using HEC-RAS"], "chunk_type": "figure", "figure_caption": null, "line_start": 130, "line_end": 130, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "632474a6396f9496", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > Hydrodynamic modeling using HEC-RAS\nType: text\n\nGeometry data (river cross-sections, center lines, bank lines, flow lines, and elevation data) was derived from the satellite imagery in a plugin of HEC-Geo-RAS in ArcGIS environment. Subsequently, the geometric data was imported into HEC-RAS software for the unsteady flow simulation. The flow data requires upstream and downstream boundary conditions. The dam breach outflow hydrograph is commonly used for upstream and downstream boundary conditions as normal depth, which is calculated using the channel bed slope. The present study considered a glacial lake as a dam failure structure with a specific crest level and crest length. Various parameters such as simulation time series, elevation above the invert level and its breach dimensions were specified to the corresponding lake. An", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > Hydrodynamic modeling using HEC-RAS", "section_headings": ["Datasets and methodology", "Hydrodynamic modeling using HEC-RAS"], "chunk_type": "text", "line_start": 131, "line_end": 133, "token_count_estimate": 230, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1944d8df37c51e8e", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > Hydrodynamic modeling using HEC-RAS\nType: figure\nFigure\n\nImage /page/5/Picture/11 description: The logo for the publisher Springer, presented in black on a white background. On the left is an icon of a chess knight's head facing left, positioned above two short, parallel horizontal lines. To the right of the icon, the word \"Springer\" is written in a serif font.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > Hydrodynamic modeling using HEC-RAS", "section_headings": ["Datasets and methodology", "Hydrodynamic modeling using HEC-RAS"], "chunk_type": "figure", "figure_caption": null, "line_start": 134, "line_end": 134, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8b018fc70529844", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > Hydrodynamic modeling using HEC-RAS\nType: text\n\ninline structure that characterizes the lateral-frontal moraine is entered at the lower elevation of the lake, and the failure catastrophe is modeled and evaluated by breaching the inline structure before performing a dam break analysis. For an upper boundary condition, glacial lake acts as a storage area by its elevation storage relationship. The total volume and maximum depth acquired from the empirical equations and DEM were used to generate the elevation-storage volume. DEM was also used to get the Triangular Irregular Network (TIN) terrain to set up the 1-D (hydrodynamic) modeling and subsequently minimum elevation of the lake was derived from the TIN network. The storage capacity at the minimum elevation of the lake (last elected location) is zero, whereas it was obtained for the maximum elevation (topmost elevation of lake). On the basis of these two parameters, elevation storage capacity for the lake was generated. Such parameters are required as an input to the empirical equations that estimate the potential peak discharge and the failure time of a GLOF event (moraine dam breach event). For a dam break analysis, the Froehlich model uses fewer input parameters to estimate peak breach outflow hydrograph. At the final stage, unsteady flow analysis was computed to derive important results like maximum flood depth, maximum flood velocity, peak flood hydrographs, and water surface elevation at different crosssections in the downstream region.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > Hydrodynamic modeling using HEC-RAS", "section_headings": ["Datasets and methodology", "Hydrodynamic modeling using HEC-RAS"], "chunk_type": "text", "line_start": 135, "line_end": 137, "token_count_estimate": 385, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d2348a1e4529cee", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Datasets and methodology > Moraine dam breach modeling\nType: text\n\nGlacial lake area, maximum depth, and volume of water in the lake are the important parameters required to perform GLOF modeling. As discussed above, field-based observation was not possible for the current studied lake due to the rugged terrain and harsh weather conditions; thus, we relied on the area-based empirical relationship to obtain the depth and volume of the glacial lake. There are several empirical formulas available in the scholarly literature but the empirical relation developed by Huggel is most extensively employed in past GLOF hazard studies, where bathymetric surveying data are not available. However, this equation was developed for the Swiss Alps region, but due to the unavailability of bathymetry data, we used this equation to derive the volume of the glacial lake. The equation given by Huggel et al. (2002) is given as:\n\n$$V = 0.104A^{1.42} \\tag{2}$$\n\nV (m<sup>3</sup>) and A (m<sup>2</sup>) are the volume and area of the lake, respectively. By employing Eq. (2), we get a total volume of  $5749 \\times 10^3$  m<sup>3</sup>.\n\nImportant parameters such as failure time of dam, extent of overtopping prior to the failure, shape, size, and breach formation time should be considered for the evaluation of the dam failure, because the dam-break flood estimation is dependent upon these parameters. Factors such as initial and final breach width and level, breach shape, breach development formation time, and reservoir level at the time of initial breach are all important breach features that need to be included into existing dam break models (Jain et al. 2012).\n\nWe have estimated these essential breach characteristics (breach width and breach formation time) that are required as inputs to carry out dam break analysis have been approximated using Froehlich's empirical relations:\n\n$$B_{w} = 0.1803K_{o}(V_{w})^{0.32}(h_{b})^{0.19}$$\n(3)\n\n$$T_{f} = 0.00254 (V_{w})^{0.53} (h_{b})^{0.9}$$\n(4)\n\nIn the above equations,  $B_w$  and  $V_w$  are the breach width (m) and volume of lake (m³), whereas  $h_b$  and  $T_f$  are the breach height (m) and breach formation time (hr). Equation 1 mentioned in the above section was used to compute the volume of the lake.  $B_w$  is the function of  $V_w$  and  $h_b$ . In the present study, a total of nine dam breach scenarios based on varied breach width and breach formation time are designed for a mechanism of a glacial lake outburst flood. The failure mechanism as an overtopping is considered in breach scenarios to evaluate a potential worst-case GLOF event of a lake. These scenarios produce peak flood hydrographs at the downstream cross-sections generated at various locations.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Datasets and methodology > Moraine dam breach modeling", "section_headings": ["Datasets and methodology", "Moraine dam breach modeling"], "chunk_type": "text", "line_start": 139, "line_end": 157, "token_count_estimate": 780, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00254", "1803K_"]}}
{"id": "945fbf6afbd3d029", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Change in glacial lake area\nType: text\n\nUsing Landsat imagery in combination with high-resolution google earth imagery, we have identified and mapped the selected glacial lake, its feeding glacier, and other geomorphological features such as moraines, crevasses, snout, supraglacial channel, and streams. The lake extents derived from the multi-temporal satellite images have shown a considerable increase of the lake area from 0.112 km<sup>2</sup> in 1990 to 0.26 km<sup>2</sup> in 2018. Thus, showing an overall increase of 0.12 km<sup>2</sup> (56.4%) over the period of 28 years indicates a rapid expansion at the rate of 0.01 km<sup>2</sup>/year. The snout of the glacier (feeding lake) has shown a retreat of 153 m at the rate of 7.56 m/year from 2000 to 2020. The lake has been identified as potentially dangerous by employing the various parameters derived from the scholarly literature. Previously, it has been identified as potentially dangerous glacial lake by Prakash et al. (2013). The present glacial lake is situated at the retreating glacier snout dammed by loose and unconsolidated material. Furthermore, it has been identified that there are probable rockfall/avalanche zones in the", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Change in glacial lake area", "section_headings": ["Results and discussion", "Change in glacial lake area"], "chunk_type": "text", "line_start": 161, "line_end": 163, "token_count_estimate": 339, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a40b552d0cc7dee7", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Change in glacial lake area\nType: figure\nFigure\n\nImage /page/6/Picture/16 description: The logo for Springer, featuring a black and white line drawing of a chess knight piece to the left of the word \"Springer\" in a black serif font. The background is white.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Change in glacial lake area", "section_headings": ["Results and discussion", "Change in glacial lake area"], "chunk_type": "figure", "figure_caption": null, "line_start": 164, "line_end": 164, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "39a7f1ba58a284d3", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Change in glacial lake area\nType: text\n\nupstream region of the lake that can be disastrous at some point of time when they hit the lake. The nearest settlement and Kashnag project power house are located just 17 km from the lake outlet. Keeping in view these factors, we have carried out the GLOF modeling of the lake for a distance of 102 km in the downstream region. In this study, we have calculated the breach width and breach formation time by using Eqs. (2) and (3). Breach width is a function of the total volume of water in upstream and the breach height. A breach width of 75 m was obtained by using Eq. (2) to calculate the initial dam-breach hydrograph and subsequently to perform an unsteady flow analysis.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Change in glacial lake area", "section_headings": ["Results and discussion", "Change in glacial lake area"], "chunk_type": "text", "line_start": 165, "line_end": 167, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c075e5699d026171", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > GLOF susceptibility\nType: text\n\nIn the present study, utilizing satellite data such as Landsat (TM, ETM + and OLI), Google Earth imagery, and DEM, the characteristics of the glacial lake and its feeding glacier were investigated in detail. The major conclusions drawn from the GLOF susceptibility index based on the essential parameters mentioned in Table 2 are as follows. The investigation reveals that the lake has an area of 0.26 km<sup>2</sup> which is greater than the threshold area mentioned for the lake to be potentially dangerous (Campbell 2005; Aggarwal et al. 2014; Washakh et al. 2019). The lake area has shown a significant increase in area over a period of 28 years from 1990 to 2018, indicating a growth of 56.42% with a mean increase of 0.003 km<sup>2</sup>, thus depicting the high index value in terms of GLOF susceptibility. Lakes that are in direct contact with the glacier (at the snout) have a high potential to cause a GLOF event due the sudden calving of the retreating snout (Che et al. 2014; ICIMOD 2011). In our case, the selected lake is located at the snout of the glacier with little glacier calving visible on the Google Earth imagery (Fig. 1). The type of dam plays an important role in determining the hazard level of a particular lake. Lake dammed by ice or moraines are considered potentially most dangerous as compared to the landslide or bedrock dammed lakes. The selected lake has been classified as a moraine dammed lake through the visual interpretation technique (Huggel et al. 2002). It is also crucial to note that certain triggering factors, including as heavy rain, landslides, rockfall, mass movements, avalanches, and cloud bursts, may cause these glacial lakes to suddenly burst. From the Google Earth imagery, potential ice and rock avalanche zones are clearly visible. In case if a lake is hit by such triggering factors, it may cause sudden breaching of the moraine dam and the resultant flood may wash away the agricultural land and infrastructure in the downstream region.\n\nIt is quite clear from the Table 2 that all of the selected parameters have shown high index value and their corresponding rank value is also high. The lake is area is greater than 0.2 km<sup>2</sup>, with an expansion rate of more than 56% and it\n\nis present at the retreating snout of the glacier. Furthermore, lake is dammed by moraine and susceptible to potential rock and ice avalanche. In terms of susceptibility index, the total value of the lake was calculated as 0.75, which indicates that the lake is highly susceptible to GLOF and therefore needs continuous monitoring and assessment with the help of remote sensing GIS and hydrodynamic modeling.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > GLOF susceptibility", "section_headings": ["Results and discussion", "GLOF susceptibility"], "chunk_type": "text", "line_start": 169, "line_end": 175, "token_count_estimate": 687, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d767f7f7918ce48a", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF\nType: text\n\nThe GLOF simulation has been carried out for the selected lake dammed by an unconsolidated moraine as visible on the Google Earth imagery. The lake is present at the snout of the glacier which is retreating rapidly from the past few decades and adding more and more influx of the glacier melt into the lake. With the increasing temperatures and resultant glacier melting, more expansion is expected from this lake in the coming years. Considering all these factors, the flood routing from a glacial lake up to a distance of 102 km to the basin outlet has been characterized in the model by adding various river cross-sections derived from SRTM DEM. Each cross-section is identified with a different river station and subsequently given as an input in the HEC-RAS model as depicted in Fig. 4.\n\nThe downstream effects of the GLOF caused by the dam failure are modelled using the unsteady flow analysis in HEC-RAS model. The one-dimensional moraine breach simulations were used to evaluate the unsteady flow hydrodynamic models understanding and sensitivity to the input dam-breach parameter (breach width and breach formation time). Therefore, to estimate and evaluate its effect on initial breach hydrograph, possible varied breach width  $(B_w)$  and breach failure time  $(T_f)$  of the lake were considered for different worst-case scenarios, in which breach width has been taken as 40, 60, and 75 m and breach formation time has been taken as 40, 30, and 20 min, respectively (Fig. 5).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF"], "chunk_type": "text", "line_start": 177, "line_end": 181, "token_count_estimate": 399, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4f5a3702621a82df", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF\nType: figure\nFigure: Figure 5a to f shows the one-dimensional breach hydrographs scenarios, executed with varied breach width (Bw) and breach formation time (Tf). We have considered three scenarios with varied Bw = 75 m, Bw = 60 m, and Bw = 40 mwith a constant breach formation time (Tf). Similarly, keeping breach width (Bw) constant, model sensitivity to breach formation time was also analyzed by considering three scenarios with varied Tf (20, 30, and 40 min). Based on the various breach parameters, overtopping failure mechanism have been used to analyze the various scenarios. The results derived from these scenarios are given in Table 3. It is inferred from this study that, among the nine different breach scenarios, GLOF breach hydrograph for 75 m breach width (Bw) and 30 min breach formation time (Tf), which produced peak discharge of 4507 m3/s, has been considered an input for upstream boundary condition and generates flood routing at different sites along the river flow channel,\n\nFigure 5a to f shows the one-dimensional breach hydrographs scenarios, executed with varied breach width (Bw) and breach formation time (Tf). We have considered three scenarios with varied Bw = 75 m, Bw = 60 m, and Bw = 40 mwith a constant breach formation time (Tf). Similarly, keeping breach width (Bw) constant, model sensitivity to breach formation time was also analyzed by considering three scenarios with varied Tf (20, 30, and 40 min). Based on the various breach parameters, overtopping failure mechanism have been used to analyze the various scenarios. The results derived from these scenarios are given in Table 3. It is inferred from this study that, among the nine different breach scenarios, GLOF breach hydrograph for 75 m breach width (Bw) and 30 min breach formation time (Tf), which produced peak discharge of 4507 m<sup>3</sup>/s, has been considered an input for upstream boundary condition and generates flood routing at different sites along the river flow channel,", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF"], "chunk_type": "figure", "figure_caption": "Figure 5a to f shows the one-dimensional breach hydrographs scenarios, executed with varied breach width (Bw) and breach formation time (Tf). We have considered three scenarios with varied Bw = 75 m, Bw = 60 m, and Bw = 40 mwith a constant breach formation time (Tf). Similarly, keeping breach width (Bw) constant, model sensitivity to breach formation time was also analyzed by considering three scenarios with varied Tf (20, 30, and 40 min). Based on the various breach parameters, overtopping failure mechanism have been used to analyze the various scenarios. The results derived from these scenarios are given in Table 3. It is inferred from this study that, among the nine different breach scenarios, GLOF breach hydrograph for 75 m breach width (Bw) and 30 min breach formation time (Tf), which produced peak discharge of 4507 m3/s, has been considered an input for upstream boundary condition and generates flood routing at different sites along the river flow channel,", "line_start": 182, "line_end": 182, "token_count_estimate": 547, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ad73a9c8005c300", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF\nType: figure\nFigure\n\nImage /page/7/Picture/11 description: The logo for the publisher Springer, featuring a black outline of a knight chess piece to the left of the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF"], "chunk_type": "figure", "figure_caption": null, "line_start": 184, "line_end": 184, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e750375dbfc4996a", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF\nType: text\n\n**Fig. 4** Cross-section network generated along the river channel from the lake outlet", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF"], "chunk_type": "text", "line_start": 185, "line_end": 187, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36d732705e5443dc", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF\nType: figure\nFigure\n\nImage /page/8/Figure/3 description: A map showing a river system with its source at a glacial lake. The map is gridded with longitude and latitude lines, ranging from 77°45'0\"E to 78°15'0\"E and 31°25'0\"N to 31°40'30\"N. The river flows from the 'Glacial lake' in the northeast to an 'Outlet point (Rajpur town)' in the southwest. The river's path is marked with numerous red cross-sections, which are numbered sequentially from 1000 near the outlet to 104000 near the lake. A legend in the bottom right corner defines the symbols used: a light blue area for the glacial lake, a blue line for the river, a purple line for flowpaths, a red line for a cross section, and a green dot for the outlet point. A scale bar at the bottom indicates distances in kilometers, up to 24 km. A north arrow is present in the top right corner.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF"], "chunk_type": "figure", "figure_caption": null, "line_start": 188, "line_end": 188, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["104000"]}}
{"id": "84642dd92ede7198", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF\nType: text\n\nwhich gives different peak flood hydrograph and other significant flood wave parameters like water depth, flow velocity, flood peak arrival time, and the corresponding water surface elevations in the downstream area and so this was used for detailed studies on downstream impact.\n\nThe assessment of the different magnitude of flood peak hydrographs at just downstream of the lake and various locations across the river is shown in Fig. 6. In this study, attenuated flood peak hydrographs have been evaluated at five different locations at a distance of 29.3 km, 39.1 km, 44.4 km, 47.7 km, and 102 km (Rajpur town) downstream from the lake. The estimated flood peak 4507 m<sup>3</sup>/s at GLOF site and the same get mitigated to 464 m<sup>3</sup>/s at the downstream outlet site, respectively. The flood peak arrival time from lake to the outlet at a distance of 102 km is about 6 h and 5 min. This reflects that the downstream river reach is relatively wide with a gentle slope compared to that narrow valley of upstream glacial area; as a result, flood water spreads across a flood plain resulting in gradually decreasing of peak discharge as it proceeds downstream from the lake as shown in Fig. 6 and Table 4. In addition to this, flood routing along the river reach allowed calculation of other flood hydraulic parameters such as flow depth, flow velocity, and flood peak arrival time which are given in Table 4 for each of the affected locations.\n\nThe resultant flood at the nearest site (bridge) located 29.3 km from the lake site will arrive at 01:40 with a maximum discharge of 3229  $\\rm m^3~s^{-1}$  and maximum flood depth and velocity of 8.13 m and 8.3 m/s, respectively. At Karcham barrage located 39 km from the lake outlet, a peak flood with\n\n2692 m³ s<sup>-1</sup> will arrive at 2:20 with a maximum depth of 3.8 m and a maximum flood velocity of 4.4 m/s. At the settlement 1 situated at a distance of 44 km from the lake site, the maximum discharge was estimated as 2672.12 m³ s<sup>-1</sup> with maximum flood depth and velocity of 4.1 m and 4.4, respectively. Similarly, at settlement 2 located at a distance of 48 km, the maximum peak discharge was estimated as 2510 m³ s<sup>-1</sup>. The maximum flood depth and velocity for the same was estimated by the model as 4.4 m and 5.4 m/s, respectively. The peak flood hydrograph and other flood wave parameters were also calculated for the last selected site, namely, Rajpur town located at a distance of 102 km from the lake site. The results show a maximum peak discharge of 481 m³ s<sup>-1</sup> with a maximum flood depth and velocity of 3.3 m and 1.5 m/s (Fig. 6).\n\nAfter the analysis of Table 4 and Fig. 6, it was observed that the intensity of peak flood hydrographs and other significant flood wave parameters like water depth, flow velocity, and water surface elevation are decreasing from the lake site to the last selected location, i.e., Rajpur town. This is because of the elevation differences that decrease from the lake site to the downstream areas. The water spread area increase with the decrease in elevation and thereby decreasing the peak discharge and other associated parameters such as maximum depth, velocity, and water surface elevation.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF"], "chunk_type": "text", "line_start": 189, "line_end": 199, "token_count_estimate": 880, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d257dbb51c86e3d", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nIn the present study, unsteady flow routing of simulated GLOF hydrograph was computed from the selected lake up", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 201, "line_end": 203, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7cc50acc4ba2d09f", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/8/Picture/11 description: The image displays the logo for Springer, a publishing company. The logo consists of a black-and-white graphic and text on a white background. On the left, there is a stylized icon of a chess knight piece, which is a horse's head facing left, positioned above two short, parallel horizontal lines. To the right of the icon, the word \"Springer\" is written in a black serif font.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 204, "line_end": 204, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4e4a506dfc9f6037", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: The image displays six line graphs, labeled a through f, illustrating GLOF (Glacial Lake Outburst Flood) hydrographs under different scenarios. All graphs plot discharge, Q (m³/s), on the y-axis against Time (HH:MM) on the x-axis, which ranges from 00:00 to 14:24. Each graph contains three curves representing different conditions, with their peak discharge values labeled.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 206, "line_end": 206, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce8aeadc2d2c2f47", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nGraphs a, b, and c show hydrographs for breach formation time scenarios:\n- Graph a (constant breach width = 75m): Shows peak discharges of 5174.62, 4507.01, and 4068.17 m³/s for breach formation times of 20, 30, and 40 minutes, respectively.\n- Graph b (constant breach width = 60m): Shows peak discharges of 4817.09, 4151.72, and 3761.88 m³/s for breach formation times of 20, 30, and 40 minutes, respectively.\n- Graph c (constant breach width = 40m): Shows peak discharges of 4159.71, 3609.08, and 3362.44 m³/s for breach formation times of 20, 30, and 40 minutes, respectively.\n\nGraphs d, e, and f show hydrographs for breach width scenarios:\n- Graph d (Constant Tf = 30 min): Shows peak discharges of 4507.01, 4151.73, and 3609.07 m³/s for breach widths of 75m, 60m, and 40m, respectively.\n- Graph e (Constant Tf = 20 min): Shows peak discharges of 5174.62, 4817.09, and 4159.71 m³/s for breach widths of 75m, 60m, and 40m, respectively.\n- Graph f (Constant Tf = 40 min): Shows peak discharges of 4068.17, 3761.88, and 3362.44 m³/s for breach widths of 75m, 60m, and 40m, respectively.\n\nFig. 5 Lake breach outflow hydrographs showing peak discharge obtained for varied (a-c) breach formation time (Tf) keeping breach width constant, similarly (d-e) varied breach width (Bw) keeping the breach formation time constant", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 207, "line_end": 221, "token_count_estimate": 500, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "894659a486930256", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: table\nTable: Table 3 Breach scenarios with varied breach width and breach formation time\n\n| Failure mode- overtopping   |            |            |            |            |            |            |            |            |            |\n|-----------------------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|\n|                             | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | Scenario 7 | Scenario 8 | Scenario 9 |\n| Breach width (m)            | 75         | 60         | 40         | 75         | 60         | 40         | 75         | 60         | 40         |\n| Breach formation time (min) | 20         | 20         | 20         | 30         | 30         | 30         | 40         | 40         | 40         |\n| Peak discharge (m3/s)       | 5174.62    | 4817.09    | 4159.71    | 4507.01    | 4151.72    | 3609.08    | 4068.17    | 3761.88    | 3362.44    |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "table", "table_caption": "Table 3 Breach scenarios with varied breach width and breach formation time", "columns": ["Failure mode- overtopping", "", "", "", "", "", "", "", "", ""], "table_row_start": 1, "table_row_end": 4, "line_start": 222, "line_end": 227, "token_count_estimate": 326, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "82a1e5b0ff6679dc", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/9/Picture/6 description: The Springer logo, featuring a black and white line drawing of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 229, "line_end": 229, "token_count_estimate": 102, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad4255f480515c63", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\n**Fig. 6** Routed flood hydrographs at selected downstream locations along the flow path", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 230, "line_end": 232, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "02bf6ad502709ffc", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/10/Figure/3 description: A line graph plots Discharge in cubic meters per second (m³/s) on the y-axis versus Time in hours and minutes (HH:MM) on the x-axis. The y-axis ranges from 0 to 5000, and the x-axis ranges from 0:00 to 14:24. The graph shows several colored lines representing discharge at different locations and distances. A legend in the top right corner identifies five data series: GLOF at 0 Km (dark blue), Bridge at 29.3 Km (red), Karcham barrage at 39.06 Km (light blue), Settlement 1 at 44.39 Km (purple), and Settlement 2 at 47.69 Km (red). The GLOF line shows a sharp peak at 4500 m³/s shortly after 0:00. The subsequent lines show peaks that are progressively later in time and lower in magnitude: the Bridge line peaks at about 3250 m³/s around 2:00; the Karcham barrage and Settlement 1 lines peak around 2700 m³/s between 2:24 and 2:45; and the Settlement 2 line peaks at 2500 m³/s around 3:00. There is also a green line on the graph, not listed in the legend, which shows a later, smaller peak of about 1800 m³/s around 6:00.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 233, "line_end": 233, "token_count_estimate": 346, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "950f92fa1c3a198b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: table\nTable: Table 4 Peak flood and time of peak at different sites along the flow routing\n\n| Sites            | Distance from lake (km) | Peak flood (m3/s) | Maximum depth (m) | Maximum velocity (m/s) | Flood peak arrival time (HH:MM) |\n|------------------|-------------------------|-------------------|-------------------|------------------------|---------------------------------------|\n| Just d/s of lake | 0                       | 4507              | 8.1               | 8.3                    | 00:20                                 |\n| Bridge           | 29.3                    | 3229              | 9.7               | 4.3                    | 01:40                                 |\n| Karcham barrage  | 39.1                    | 2692              | 3.8               | 4.4                    | 02:20                                 |\n| Settlement 1     | 44.3                    | 2672              | 4.2               | 4.4                    | 02:30                                 |\n| Settlement 2     | 47.6                    | 2510              | 3.9               | 5.5                    | 02:40                                 |\n| Rajpur town      | 102                     | 481               | 3.3               | 1.5                    | 06:05                                 |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "table", "table_caption": "Table 4 Peak flood and time of peak at different sites along the flow routing", "columns": ["Sites", "Distance from lake (km)", "Peak flood (m3/s)", "Maximum depth (m)", "Maximum velocity (m/s)", "Flood peak arrival time (HH:MM)"], "table_row_start": 1, "table_row_end": 6, "line_start": 237, "line_end": 244, "token_count_estimate": 305, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bdc6e44f5c6d5a79", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nto the Rajpur town, which is located at a distance of 102 km downstream from lake. The flood wave routed the Rajpur town at 6 h and 5 min after the beginning of the GLOF event. Maximum water depth and maximum flow velocity along the flow channel were estimated as 27.4 m and 13.3 m/s, respectively. Figures 7 and 10 depict the spatial plots of inundation depth and flow velocity along the main flow channel from the Satluj lake to the Rajpur town and also provide a visualization of the three main inundated areas downstream of the lake.\n\nThe resultant flooding may cause damage to the infrastructure such as roads and bridges along the river's flow path downstream of the GLOF lake. The flood wave inundates a bridge located at 30 km downstream of the lake shown in Fig. 8a. Within 8 min of the flood wave arriving, flow velocities reach a high of 13.6 m/s (Fig. 9) and a water depth of 10.2 m (Fig. 7) with an arrival timing of 8 min is recorded, potentially causing significant inundation of the bridge and roads. GLOF wave reaches the first bridge at location 27°39′22.79″ N, and 88°36′16.26″ E located at 29.3 km downstream of lake shown in Fig. 8a, where flow depth is estimated up to 9 m and flow velocity of 4.28 m/s.\n\nThe GLOF potentially inundates three settlements, one barrage along the river flow path. The flood wave encounters the first settlement at locations 27°36′22.58″ N, and 88°38′31.76″ E along the bank of the channel at 44.4 km downstream, with a maximum flow velocity of 4.5 m/s and maximum flow depth of 4.2 m (Figs. 7 and 10). Figure 8d shows that the maximum flow depth and velocity recorded are 3.932 m and 5.498 m/s in the second settlement.\n\nThe analysis shows that the flood routing varies and gradually decreases along with the downstream of lakes. In this flood routing, peak flood decreases gradually from the lake up to 5 km then sharply declined as it proceeds downstream as shown in Fig. 10. We have observed that flood height have shown rise and fall continuously along the downstream of the river reach covering all the various sites located at few kilometers distance. The maximum flood height would be around 16 m at 20 km downstream of the lake and the peak flood at this location was found to be 3126 m³/s. The food peak reached Karcham barrage at 39 km downstream would be around 2692 m³/s.\n\nThe estimated flood depth in the downstream area varies depending upon the morphology of the river channel. The", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 245, "line_end": 255, "token_count_estimate": 692, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "70343c43812ce987", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/10/Picture/11 description: The image displays the logo for Springer, a publishing company. The logo consists of a black-and-white graphic and text on a white background. On the left, there is a stylized icon of a chess knight piece, which is a horse's head facing left, positioned above two short, parallel horizontal lines. To the right of the icon, the word \"Springer\" is written in a black serif font.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 256, "line_end": 256, "token_count_estimate": 155, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0812dbf34df02fcf", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: A figure from a scientific paper showing a map of a mountainous region with a river valley, illustrating maximum flood depth. The map is bounded by longitudes 77°40'0\"E to 78°10'0\"E and latitudes 31°27'0\"N to 31°37'30\"N. A main legend in the bottom right indicates that the maximum flood depth ranges from a low of 0.001 m (blue) to a high of 27.444 m (magenta). There are three inset maps that provide detailed views of specific locations along the river. Each inset includes a line graph plotting flood depth in meters against flood wave arrival time in hours. The top-left inset shows a graph with a peak flood depth of about 8 m. The top-middle inset shows a peak flood depth of about 10 m. The bottom-middle inset shows a peak flood depth of about 4.5 m. Each inset has its own legend for flood depth, showing different ranges. A scale bar at the bottom left of the main map indicates distances from 0 to 32 km.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 258, "line_end": 258, "token_count_estimate": 310, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd830d75ba1ddc8e", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nFig. 7 Spatial distribution of Maximum Flood inundation", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 259, "line_end": 261, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11382f360957c818", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/11/Figure/4 description: A composite image of four maps, labeled a, b, c, and d, each showing a satellite view of a river with a color overlay indicating the maximum flood depth. Each map includes a legend, a scale bar in kilometers, and geographic coordinates. Map (a) highlights a bridge crossing the river, with a legend for Max Flood Depth (m) showing ranges: 0.02-2.26, 2.26-4.55, 4.55-6.95, 6.95-9.3, and 9.30-13.61. Map (b) shows an area of settlements along a river bend, with a legend showing flood depth ranges: 0.02-2.48, 2.48-4.47, 4.47-6.23, 6.23-8.36, and 8.36-11.64. Map (c) shows a settlement along a wider section of the river, with a legend showing flood depth ranges: 0.04-2.16, 2.16-3.93, 3.93-5.46, 5.46-7.19, and 7.19-10.83. Map (d) shows a dense settlement on a sharp river bend, with a legend showing flood depth ranges: 0.12-2.41, 2.41-4.95, 4.95-7.81, 7.81-11.31, and 11.31-16.32. In all maps, the colors range from light blue for shallower depths to dark purple for the deepest floodwaters.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 262, "line_end": 262, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d93eaa895904ea10", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nFig. 8 Flood inundation maps: a focused view at inundate bridge, b zoomed view of inundated settlement 1, c zoomed view at inundate settlement 1, and d zoomed view of Rajpur town", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 263, "line_end": 265, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6b389d63b56be344", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/11/Picture/6 description: The logo for Springer, featuring a black and white line drawing of a chess knight piece on the left, followed by the word \"Springer\" in a black serif font. The knight is facing left and is positioned above two horizontal lines. The background is white.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 266, "line_end": 266, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8e701178c5301b99", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/12/Figure/2 description: A figure from 'Environmental Science and Pollution Research' showing a map of a mountainous region, illustrating flood data. The main map, with coordinates from 77°40'0\"E to 78°10'0\"E and 31°27'0\"N to 31°37'30\"N, displays a yellow outline of a flood path. A legend in the bottom right indicates the 'Max Flow Velocity (m/s)' with a color scale from green (Low: 0.480) to red (High: 13.280). There are three inset maps providing detailed views of specific locations. Each inset includes a color-coded representation of flow velocity and a line graph plotting 'Flow velocity (m/s)' against 'Flood wave arrival time, hr' from 00:00 to 14:24. The top-left inset shows a settlement with flow velocities up to 8.66 m/s and a graph peaking around 1.2 m/s. The top-middle inset shows a bridge with flow velocities up to 9.08 m/s and a graph peaking just below 4 m/s. The bottom-middle inset shows settlements with flow velocities up to 13.28 m/s and a graph also peaking just below 4 m/s. A scale bar at the bottom left of the main map indicates a distance of 32 km.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 268, "line_end": 268, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "823471af758f5e64", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nFig. 9 Spatial distribution of maximum velocity inundation\n\n**Fig. 10** Peak flood and flood height impact on the downstream of glacial lake", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 269, "line_end": 273, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "674b060818ea29ec", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/12/Figure/5 description: A line graph plots two variables, Peak Flood and Flood Height, against the Distance from Lake outlet. The x-axis represents the 'Distance from Lake outlet (km)' and ranges from 0 to 95. There are two y-axes. The left y-axis represents 'Peak Flood (m³/s)' and ranges from 0 to 5000. The right y-axis represents 'Flood Height (m)' and ranges from 0 to 18. The legend indicates that the blue line with circular markers represents 'Peak flood', and the red line with triangular markers represents 'Flood Height'. The 'Peak flood' line shows a general downward trend, starting at approximately 4500 m³/s near the outlet and decreasing to about 1750 m³/s at 95 km. The 'Flood Height' line fluctuates significantly. It starts around 8 m, peaks at nearly 12 m, drops, then spikes to a maximum of about 14.5 m at a distance of 20 km. It then drops to its lowest point of about 5 m at 35 km, followed by several other peaks and troughs, including a double peak of about 11 m between 75 and 80 km, and ends with a sharp rise to about 12.5 m at 95 km.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 274, "line_end": 274, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf4c89718cd42c0e", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nflood heights and peak flood discharges were estimated using a one-dimensional hydrodynamic model and to analyze the potential impact of GLOFs on downstream areas. The obtained results of flood simulation were used to generate a flood inundation map. Regular monitoring is required to analyze the behavior of the glacial lake and to prevent and monitor the GLOF hazard and assess the damage that may occur in the future. Over the past few decades, GLOFs are", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 275, "line_end": 277, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe6f51a04c29003e", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: figure\nFigure\n\nImage /page/12/Picture/8 description: The logo for the publisher Springer, shown in black on a white background. On the left is an icon of a knight chess piece facing left, and to the right is the word \"Springer\" in a serif font.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "figure", "figure_caption": null, "line_start": 278, "line_end": 278, "token_count_estimate": 110, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f542b80a55e4a9b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation\nType: text\n\nbecoming common in the glacierized basins of the world, resulting in thousands of deaths and severe damage to the property in the downstream regions. Given the frequency of earthquakes and ongoing warming, the likelihood of GLOFs and related disasters is significant. It is not only the likelihood of a glacial lake outburst that comes under the category of risk but vulnerability and adaptability of various elements such as population, roads, and infrastructure are also part of it.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Results and discussion > Assessment and simulation of GLOF > Flood inundation and simulation", "section_headings": ["Results and discussion", "Assessment and simulation of GLOF", "Flood inundation and simulation"], "chunk_type": "text", "line_start": 279, "line_end": 281, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19d8bab03f01d89b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Discussions\nType: text\n\nIn the current study, we investigated the dynamics of the highly susceptible glacial lake (dammed by moraine) in the Satluj basin of Western Himalayas for a period of 28 years from 1980 to 2018. The results show a clear growth in the glacial lake area from 0.112 km<sup>2</sup> in 1990 to 0.262 km<sup>2</sup> in 2018 derived from the multi-temporal satellite (Landsat TM, ETM and OLI) with a spatial resolution of 30 m. The glacial lake studies using multi-temporal satellite data, especially Landsat series, have been successfully employed by various researchers in different parts of the Himalayan region. The investigations reveal that glacial lakes are increasing in both number and area; for example, in Jhelum basin of Kashmir Himalayas, the number has increased from 253 in 1990 to 324 in 2018, with a growth rate of 21.4% and the area on the other hand has increased from  $18.84 \\pm 0.1 \\text{ km}^2$  in 1990 to  $22.13 \\pm 0.12 \\text{ km}^2$  in 2018 with a growth rate of 14.7% (Ahmed et al. 2021a, 2021b). Similarly in Koshi basin, the glacial lakes have shown an increase of 31.5% between 1975 and 2010 (Shresth et al. 2017), Central Himalayas by 17 and 24% (Nie et al. 2013; Shukla et al. 2018), Nepal Himalayas by 25% during 1977 to 2015 (Khadka et al. 2018), Nepal and Bhutan Himalayas by 20–60% during 1990–2009 (Gardelle et al. 2011), Sikkim Himalayas by 8.3% during 1988–2014 (Debnath et al. 2018), Entire third pole by 23% during 1990-2010 (Zhang et al. 2019), and Eastern Himalayas by (Begam and Sen 2019; Mal et al. 2021). Rapid glacier recession and glacial lake expansion in the high terrains of the Himalayan region are a clear indicator of the changing climate and global warming. Temperature plays a key and dominant role in influencing the sensitivity of the glaciers. Glaciers in the Himalayan region are under the influence of the western disturbances, and Indian and Asian monsoons. Over the last few decades, glaciers in the Himalayan region are retreating and losing mass at a faster rate under the influence of the climate change (increasing temperatures and decreasing precipitation) resulting in the formation and development of various types of glacial lakes especially supraglacial lakes, moraine-dammed lakes, icedammed lakes, etc. Some of these lakes are potentially dangerous, which can burst and may cause huge damage to the life and property in the downstream valleys as in case of the Chorabari lake outburst (Allen et al. 2016) which results in the number of deaths and destruction to the property in the downstream regions. Therefore, monitoring and assessment of the potentially dangerous glacial lakes and evaluating their probable GLOF impacts are a key step to reduce the damage and loss associated with such events.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Discussions", "section_headings": ["Discussions"], "chunk_type": "text", "line_start": 283, "line_end": 291, "token_count_estimate": 734, "basins": [], "subbasins": ["Jhelum", "Satluj"], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "23336dfc981b2807", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Discussions\nType: text\n\ntemperatures and decreasing precipitation ) resulting in the formation and development of various types of glacial lakes especially supraglacial lakes , moraine - dammed lakes , icedammed lakes , etc . Some of these lakes are potentially dangerous , which can burst and may cause huge damage to the life and property in the downstream valleys as in case of the Chorabari lake outburst ( Allen et al . 2016 ) which results in the number of deaths and destruction to the property in the downstream regions . Therefore , monitoring and assessment of the potentially dangerous glacial lakes and evaluating their probable GLOF impacts are a key step to reduce the damage and loss associated with such events .\n\nApart from the glacial lake dynamics, we have also evaluated the probable GLOF impacts from the high susceptible glacial lake on various important locations in the downstream region using combined approaches of remote sensing GIS and one-dimensional hydrodynamic model (HEC-RAS). GLOF event with a breach width of 75 m was revealed during the overtopping failure of the moraine dam, which resulted a peak discharge of 4063 m<sup>3</sup>/s and releasing a total volume of  $57 \\times 10^5$  m<sup>3</sup> water. The GLOF hydrograph has been routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river. The findings of our modeling in Satluj basin revealed that channel roughness is not necessary for the extent of the inundation, but it is critical for the flood's velocity and the flood arrival time. Our results are in line with the findings from other studies pertaining to the HEC-RAS modeling (Sattar et al. 2020; Hagg et al. 2021).\n\nHEC-RAS has been widely used in the past by various researchers for the GLOF hazard assessment and it is considered an important tool to analyze the likelihood of GLOFs resulted due to the breaching of moraine dams. The model output can be used for better planning and preventive measures. In the Himalayan region, a number of hydropower projects are situated on the various rivers and tributaries and a large number are being planned to be constructed on these rivers. There is a considerable potential that these dams will be breached, putting the hydropower plants and projects safety in danger (AHEC 2011). As a result, taking into account GLOF scenarios and estimating hydrologic parameters are now required for effective planning and design of hydroelectric power projects viz a viz their safety. Since field survey was not possible for the current studied lake due to the rough terrain, harsh weather conditions, and lack of instrumentations and funding, therefore, we relied on the satellite data and hydrodynamic modeling (HEC-RAS). But we suggest an in-depth analysis of the selected susceptible glacial lake through field-based investigations.\n\nHEC-RAS has been effectively applied to model GLOF simulations in different parts of the Himalayan region. For example, Sattar et al. (2020) have used the HEC-RAS model for the hazard assessment of the potentially dangerous glacial lake (Safed Lake) in central Himalayas. Thakur et al. (2016) have used the one-dimensional hydrodynamic model (HEC-RS) to evaluate the impacts of GLOFs on the hydropower projects in Dhauliganga River basin. Similarly,", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Discussions", "section_headings": ["Discussions"], "chunk_type": "text", "line_start": 283, "line_end": 291, "token_count_estimate": 833, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "e0dcf4864459087b", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Discussions\nType: figure\nFigure\n\nImage /page/13/Picture/9 description: The logo for Springer, shown in black on a white background. On the left is an icon of a knight chess piece, and to the right is the word \"Springer\" in a serif font.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Discussions", "section_headings": ["Discussions"], "chunk_type": "figure", "figure_caption": null, "line_start": 292, "line_end": 292, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ec0652176f02cc39", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Discussions\nType: text\n\nKhanal et al. (2015) have applied the HEC-RAS model for the GLOF risk assessment in Nepal transboundary region. Maskey et al. (2020) have also used HEC-RAS for the GLOF modeling of Thulagi and Barun glacial lakes in Nepal Himalayas. Besides that, there are various studies that have successfully this model to evaluate the GLOF impacts on the downstream region in the Himalayas (Bajrachraya et al. 2007; Gilany and Iqbal 2020; Zaidi et al. 2013; Hussain et al. 2020; Amin et al. 2020; Majeed et al. 2021; Sattar et al. 2019) and outside the Himalayan region (Klimeš et al. 2014a; Kougkoulos et al. 2018; Anacona et al. 2015; Somos-Valenzuela and McKinney 2011; Klimes et al. 2015; Ahmed et al. 2022a, b).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Discussions", "section_headings": ["Discussions"], "chunk_type": "text", "line_start": 293, "line_end": 295, "token_count_estimate": 232, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "bd3c651e99fce19f", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Discussions > Limitations and recommendations\nType: text\n\nWith the ongoing climate change-induced continuous glacier retreat, new glacial lakes have formed and enlarged the existing ones; therefore, the frequency of glacier lake outburst floods (GLOFs) is anticipated to grow. We have investigated the potentially dangerous glacial lake in the Satluj basin by employing the 1-D hydrodynamic modeling. Glacial lake outburst flow modeling is nothing more than an approximation of a physical phenomenon that can be used to study the physical phenomenon and its impacts on water resource structure design and flood management. In GLOF modeling, assumptions are mostly related to breach parameters, particularly breach breadth and depth, which affect flood peak and flood arrival times. Due to the lack of fieldbased bathymetry data and several other related parameters associated with the dam breach as well as routing of the flood hydrograph are the rough estimates/assumptions that cause errors and uncertainties in the results. The use of DEM with a coarse spatial resolution (30 m) also overestimates the inundation and the magnitude of the flood; therefore, we suggest the use of fine or high-resolution terrain data (DEM) to reduce the uncertainties in the results. Generally, in case if the volume of the storage area (lake) is unknown, empirical equations developed in different parts of the world are used to calculate the volume of the lake. But different equations yield different volumes, thus showing a huge variation in the results. Thus, in this backdrop, we suggest that an average of all these empirical equations should be taken into account while computing the volume of the lake also suggested by Emmer et al. (2021) and Ahmed et al. (2021a). In general, the mechanism of glacial lake bursting and the genesis of glacial lake breaches are not well understood. Irrespective of the limitations and assumptions discussed above, hydrodynamic modeling serves a valuable purpose as it provides a reasonable estimate of glacial lake outburst flood and allows for an effective estimation of the design flood.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Discussions > Limitations and recommendations", "section_headings": ["Discussions", "Limitations and recommendations"], "chunk_type": "text", "line_start": 297, "line_end": 299, "token_count_estimate": 521, "basins": [], "subbasins": ["Satluj"], "countries": [], "lake_ids": []}}
{"id": "bd8f2fdcb650371d", "text": "Document: Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling\nSection: Conclusions\nType: text\n\nGlacial lake outburst floods are one of the major concerns in the Himalayan region. With the expansion and formation of new glacial lakes, the risk of GLOF is further increasing in the region. This study used multi-temporal satellite data, SRTM DEM, and HEC-RAS software to analyze the changes in the selected GLOF-susceptible lake and to evaluate the potential impacts of GLOF in the downstream area. Using a multi-criteria decision-based method, the selected lake was found to be highly susceptible to GLOF. The lake area has shown a discernible increase from 0.12 km<sup>2</sup> in 1990 to 0.26 km<sup>2</sup> in 2018 with an overall increase of 0.11 km<sup>2</sup> (56.4%) over the period of 28 years, indicating a rapid expansion at the rate of 0.01 km<sup>2</sup>/year. Several hydrodynamic simulations were performed for different scenario models with varied breach width, breach formation time and breach depth. The worst case GLOF scenario (breach width=75 m) is revealed during an overtopping failure of the moraine dam, which resulted a peak discharge of 4063 m<sup>3</sup>/s and releasing a total water volume of  $5749 \\times 10^3$  m<sup>3</sup>. The GLOF hydrograph was routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river. The findings of this study show that the routed flood waves reach the nearest town, i.e., Rajpur located at a distance of 102 km downstream of a lake at 6 h after the initiation of the breach event, with a peak flood of 1756 m<sup>3</sup>/s and maximum flow velocity of 1.5 m/s. If a GLOF occurs, it can have severe consequences for downstream livelihood and infrastructure. As a result, such lakes must be examined regularly, and mitigating efforts to reduce GLOF vulnerability should be prioritized. With the increasing and apparent GLOF risk across the Himalayan region, the findings of this study highlight the urgent need of forward-looking, collaborative, and long-term initiatives to prevent future impacts of GLOFs as well as to boost the sustainable development in the region.\n\n**Acknowledgements** The authors are thankful to the United States Geological Survey (USGS) for freely providing the satellite data used in this study.\n\nAuthor contribution Every author has contributed to the successful compilation of this study. MR and RA: conceptualization, methodology, software, writing—original draft, formal analysis. MR, RA, SKJ and AKL: data curation, formal analysis, writing—review and editing. SKJ and AKL: writing—review, editing, supervision. All authors read and approved the final manuscript.\n\nData availability Not applicable.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling", "section_path": "Conclusions", "section_headings": ["Conclusions"], "chunk_type": "text", "line_start": 301, "line_end": 309, "token_count_estimate": 733, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ae5bd91bb5fe9b8", "text": "Document: Glacial lake outburst flood risk assessment\nSection: ARSTRACT\nType: text\n\nThe ongoing trend of warming climate has made Glacial Lake Outburst Floods (GLOFs) a major cryospheric hazard worldwide, especially in the Himalayas. GLOFs in the Himalayan region are mostly caused by moraine-dammed proglacial lakes and icedammed lakes. These sporadic disasters have resulted in significant loss of life and property. This study offers a comprehensive analysis of the GLOF hazard potential of a potentially dangerous proglacial lake (PDGL) in the Ladakh region. This research explores the GLOF threat from the lake using multi-criteria analysis and advanced 2D hydrodynamic modeling approaches. The mass balance response of the mother glacier, its flow dynamics, and glacier-lake interactions were examined for the past 22 years. The findings show that over this period, the PDGL has had a notable expansion of 78.7%, accompanied by a significant recession of 13.2% in its feeding glacier. The glacier has witnessed an average thickness loss of  $\\sim$ 7 m at the rate of 0.32 m  $a^{-1}$  during this period. The average, lowest, and maximum depth of the glacier were found to be 30.95, 14.30, and 50.57 m, respectively and the average velocity of the glacier was estimated as 3.38 m a<sup>-1</sup>. Because of the lake's rapid expansion and steep surrounding slopes, it was classified as a high-hazard lake. The risk to the downstream community was assessed through 2D hydrodynamic modeling using the HEC-RAS tool. The maximum discharge under the worst-case scenario for the piping and overtopping failures was estimated as 3890.99 m<sup>3</sup> s<sup>-1</sup> and 5111.39 m<sup>3</sup> s<sup>-1</sup>, respectively. The area potentially under the threat of inundation was calculated to be 4.74 and 5.38 km<sup>2</sup> for the moderate and worst-case scenarios respectively. The expected maximum flood velocities range from 18.26 to 23.78 meters, respectively for the moderate and worst-case scenarios. At several locations in the downstream area, routed hydrographs representing the GLOF propagation were generated. The findings show that the flood wave in the worst-case scenario", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "ARSTRACT", "section_headings": ["ARSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 5, "token_count_estimate": 582, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "448f82cebec494f7", "text": "Document: Glacial lake outburst flood risk assessment\nSection: ARTICLE HISTORY\nType: text\n\nReceived 5 June 2024 Accepted 3 October 2024", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "ARTICLE HISTORY", "section_headings": ["ARTICLE HISTORY"], "chunk_type": "text", "line_start": 7, "line_end": 9, "token_count_estimate": 35, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e31f9b2a02bfd3bd", "text": "Document: Glacial lake outburst flood risk assessment\nSection: KEYWORDS\nType: text\n\nGLOF; glacial lake expansion; risk assessment; HECRAS, Ladakh region; Western Himalayas; Panikhar, Kargil would arrive at the first settlement in  $50\\,\\text{min}$ , with a peak velocity of  $12.36\\,\\text{m s}^{-1}$ . The potentially inundated area includes critical infrastructure such as bridges, residential houses, and roads. To mitigate the potential risk associated with this lake, a more detailed and on-site study is highly recommended.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "KEYWORDS", "section_headings": ["KEYWORDS"], "chunk_type": "text", "line_start": 11, "line_end": 13, "token_count_estimate": 147, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "29dc21e29b6c91d7", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 1. Introduction\nType: text\n\nIn most glaciated parts of the world, the effects of climate change have resulted in ongoing glacier recession and the formation of glacial lakes behind recently exposed unstable moraines (Zemp et al. 2009; Bolch et al. 2012; Nie et al. 2017). Mountain settlements in the Himalayas, where numerous such glacial lakes are created, are seriously at risk from glacial lake outburst floods (Clague and Mathews 1973; Ding and Liu 1992; Ahmed 2024). Glacial lake outburst floods (GLOFs) are catastrophic occurrences that take place when a glacial lake, impounded by a glacier or moraine, breaks, releasing a significant amount of water and debris downstream causing flash floods in the downstream area, ruining crops, damaging infrastructure like bridges and hydropower plants, and resulting in fatalities. The majority of these flood episodes are linked to processes like dam breach creation and subsequent damming moraine failure (Richardson and Reynolds 2000; Emmer and Vilímek 2013; Worni et al. 2014). The possible triggers of a GLOF event may be glacial calving activity at the lake terminus (Zhang et al. 2020), snow or ice avalanches (Schaub et al. 2016), landslides, extreme weather events (Schmidt et al. 2020) like cloudbursts (Bhambri et al. 2016) or seismic activity (Kargel et al. 2016). These events are potent threats whose effects can reach distances of more than 100 km in the downstream region (Richardson and Reynolds 2000).\n\nGLOFs pose a threat to about 15 million people globally and the most prominent high-vulnerability zone is the Himalayan region followed by the Andes (Taylor et al. 2023). Due to the Himalayan terrain's steep slopes and constrained flow pathways, GLOF episodes have typically been described as being particularly catastrophic (Mool 1995; Reynolds 1998; Richardson and Reynolds 2000; Worni et al. 2013). The recent Kedarnath disaster in the Central Himalayas in June 2013, which was brought on by unrelenting rain and subsequent eruption of the Chorabari glacial lake, serves as an illustration of the degree of fatality these occurrences can bring about (Das et al. 2015; Allen et al. 2016). The increased GLOF hazard in the region requires rigorous research initiatives for the identification and monitoring of hazardous glacial lakes in this region. Given that most of the glacial lakes in the Himalayas are inaccessible because of its tough terrain and harsh climate, remote sensing methods are used to monitor these lakes through time and space. Numerous studies have been conducted in the past to evaluate the distribution and GLOF hazard of glacial lakes throughout the world, especially in the Himalaya, which includes countries like Nepal, Tibet, Bhutan, and India (Bajracharya et al. 2007; Bolch et al. 2008; Fujita et al. 2009; Ives et al. 2010; Shrestha et al. 2010; Raj et al. 2013; Rounce et al. 2017; Emmer et al. 2018; Jiang et al. 2018; Nie et al. 2018; Byers et al. 2018; Emmer et al. 2020; Ashraf et al. 2021; Ahmed et al. 2022a, 2022b; Banerjee and Bhuiyan 2023).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 29, "token_count_estimate": 800, "basins": [], "subbasins": [], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": []}}
{"id": "a35c3d59dce2842d", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 1. Introduction\nType: text\n\nand GLOF hazard of glacial lakes throughout the world , especially in the Himalaya , which includes countries like Nepal , Tibet , Bhutan , and India ( Bajracharya et al . 2007 ; Bolch et al . 2008 ; Fujita et al . 2009 ; Ives et al . 2010 ; Shrestha et al . 2010 ; Raj et al . 2013 ; Rounce et al . 2017 ; Emmer et al . 2018 ; Jiang et al . 2018 ; Nie et al . 2018 ; Byers et al . 2018 ; Emmer et al . 2020 ; Ashraf et al . 2021 ; Ahmed et al . 2022a , 2022b ; Banerjee and Bhuiyan 2023 ) .\n\nThe Himalayan region, hence, has been the most well-known case for GLOF disaster risk studies. In the central Himalayas, a GLOF hazard vulnerability study by the International Centre for Integrated Mountain Development (ICIMOD), highlighted some priority sites for GLOF risk mitigation. The study mapped 1,947 glacial lakes using data from the Global Land Ice Measurement from Space (GLIMS) program, of which 835 were deemed dangerous because of their locations upstream of populated areas and major roadways. The study showed that Nepal urgently needs to reduce GLOF danger, especially in the regions downstream of the 835 little glacial lakes (Bajracharya et al. 2007). Rounce et al. (2017) evaluated the risk presented by GLOFs in Nepal using satellite data from 2000 to 2015 and the previous GLOF data. The risk connected to each lake was estimated by combining the hazard and projected downstream effects. The results reported 31 high-risk lakes and 11 very high-risk lakes.\n\nFor the Upper Indus River basin, Gupta et al. (2021) conducted an assessment of potentially hazardous glacial lakes identifying 20 and 48 high-risk lakes for two different GLOF scenarios respectively. The evolving status and potential consequences of glacier lakes and the GLOF threat in the Hindu Kush Himalaya region were studied by Ashraf et al. (2021). An overall increase of 26% in the number of glacial lakes was found from 2001 to 2013 and 36 lakes were characterized as potentially dangerous. The increased risk posed by GLOFs in the context of a warming climate is highlighted, along with the necessity for proactive monitoring, risk assessment, and adaptation measures. From an Indian perspective, Jammu and Kashmir has the highest cumulative degree of GLOF hazard, followed by Uttarakhand, Arunachal Pradesh, Sikkim, Himachal Pradesh, and Arunachal Pradesh (Mal et al. 2021). Dubey and Goyal (2020) also placed J&K in second place, only after Sikkim, concerning the GLOF threat. Ahmed et al. (2022) analyzed the GLOF hazard scenario in the Jhelum basin using remote sensing and GIS and identified 21 potentially dangerous and 7 highly hazardous lakes.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 29, "token_count_estimate": 697, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": []}}
{"id": "7e0d3f55447e1985", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 1. Introduction\nType: text\n\nthe context of a warming climate is highlighted , along with the necessity for proactive monitoring , risk assessment , and adaptation measures . From an Indian perspective , Jammu and Kashmir has the highest cumulative degree of GLOF hazard , followed by Uttarakhand , Arunachal Pradesh , Sikkim , Himachal Pradesh , and Arunachal Pradesh ( Mal et al . 2021 ) . Dubey and Goyal ( 2020 ) also placed J & K in second place , only after Sikkim , concerning the GLOF threat . Ahmed et al . ( 2022 ) analyzed the GLOF hazard scenario in the Jhelum basin using remote sensing and GIS and identified 21 potentially dangerous and 7 highly hazardous lakes .\n\nStudies focusing on individual glacial lakes have also been conducted in all parts of the Himalayan region. Majeed et al. (2021) reconstructed the 2014 Gya glacial lake outburst flood in Ladakh using the hydrodynamic model HEC-RAS and the results revealed the possible cause of the GLOF to be the piping failure. The study also predicted the possibility of 5 times more severe GLOF in the future from the lake. Mir et al. (2018) evaluated the GLOF risk of Dalung and Padam lakes in the Zanskar Valley using the Mike 11 model. The results declared both the lakes as potentially dangerous and susceptible to GLOFs of significant intensities. Sattar et al. (2021) analyzed the GLOF potential of Lower Barun glacial lake in Sikkim Himalayas and the lake was found to be susceptible to GLOF in case of a strong triggering event. Ahmed et al. (2023) estimated the risk associated with the Gangabal lake in Kashmir Himalayas using the HEC-RAS model. The results indicate that the lake has the potential to generate a peak discharge of 16,601.03 m<sup>3</sup>/s in the worst-case scenario at the time of a GLOF event. Rawat et al. (2023a) assessed a PDGL in the Satluj River basin in India and reported an areal expansion from 0.11 to 0.26 km<sup>2</sup> over the past 28 years from 1990 to 2018 and high GLOF risk. Sattar et al. (2019) conducted the moraine breach modeling of the South Lhonak lake in Sikkim and declared the lake to be highly vulnerable to GLOF, owing to its alarming rate of expansion over the\n\npast few decades and a loosely consolidated moraine dam. This very lake witnessed a severe GLOF disaster on October 4, 2023, killing at least 40 people and destroying public infrastructure in its downstream area. Similar studies have been conducted far and wide in the Himalayas that focus on predictive GLOF modeling of hazardous glacial lakes (Emmer and Vilímek 2013; Haemmig et al. 2014; Klimeš et al. 2014; Round et al. 2017; Wang et al. 2018, Sattar et al. 2019; Hussain et al. 2020; Majeed et al. 2021; Sattar et al. 2021; Ahmed et al. 2022; Wang et al. 2022; Gouli et al. 2023; Das et al. 2024; Schmidt et al., 2020) etc.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 29, "token_count_estimate": 764, "basins": [], "subbasins": ["Jhelum", "Satluj"], "countries": ["India"], "lake_ids": []}}
{"id": "0c2258f66a7cf5b3", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 1. Introduction\nType: text\n\nleast 40 people and destroying public infrastructure in its downstream area . Similar studies have been conducted far and wide in the Himalayas that focus on predictive GLOF modeling of hazardous glacial lakes ( Emmer and Vilímek 2013 ; Haemmig et al . 2014 ; Klimeš et al . 2014 ; Round et al . 2017 ; Wang et al . 2018 , Sattar et al . 2019 ; Hussain et al . 2020 ; Majeed et al . 2021 ; Sattar et al . 2021 ; Ahmed et al . 2022 ; Wang et al . 2022 ; Gouli et al . 2023 ; Das et al . 2024 ; Schmidt et al . , 2020 ) etc .\n\nThe present study is one such attempt at understanding the GLOF hazard risk associated with a rapidly expanding moraine-dammed glacial lake in the Ladakh region of the Western Himalayas using multi-criteria decision analysis and 2D GLOF modeling. The identified PDGL is situated above the Panikhar and Pranti villages in the Kargil district of Ladakh. This study focused on the following objectives. (1) Studying the glacier-lake interactions of the Panikhar glacier and glacial lake for the past 22 years (2) Estimating the mass balance, ice thickness, and surface velocity of the Panikhar glacier, (3) Assessing the GLOF hazard level of the lake and associated risk to the downstream community using geospatial analysis and hydrodynamic modeling.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 29, "token_count_estimate": 352, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "637cf823f3c529c8", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 2. Study area\nType: text\n\nThe glacial lake under investigation in this study is situated above the Panikhar and Pranti villages of the Kargil district in the Suru River catchment of the Western Himalayas. This lake is located between 34°2′2.154″N latitude and 75°46′59.172″E longitude, 4062 meters above sea level while its mother glacier lies, in close proximity, to its West (Figure 1). The Suru catchment is drained by the Suru River which flows through Kargil district and subsequently joins the Indus as its left bank tributary. The total catchment area is 4408 km² and the elevation range is from 2588 masl to 7047 masl. The mother glacier of the lake (GLIMS ID: G075817E34034N) is situated in the southwest of the Suru River catchment at a distance of about 15 kilometers from the well-known Parkachik glacier. The glacial lake or its mother glacier has not been identified with any specific names in previous literature. Because of this, we have given them the names of the village that is closest to them: Panikhar Glacier and Glacial Lake. The Panikhar village is situated about 13 kilometers downstream in the North-East of the glacier-lake complex and is vulnerable to potential GLOF events from the lake.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "2. Study area", "section_headings": ["2. Study area"], "chunk_type": "text", "line_start": 31, "line_end": 33, "token_count_estimate": 324, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58b32ebaca0f65fe", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 2. Study area > 2.1. Glacial geomorphology\nType: text\n\nPanikhar glacier is oriented in the West-East direction and the lake is proglacially located close to its snout at its eastern side. The ablation zone of the glacier exhibits a bend towards the northeast according to the local geomorphology. The elevation of the glacial valley ranges from 5500 to 4100 meters above sea level (masl) and the glacial lake is situated at a height of 4100 masl. The contour lines clearly show that the steep slope to the northwest of the glacial lake is prone to landslides and snow", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "2. Study area > 2.1. Glacial geomorphology", "section_headings": ["2. Study area", "2.1. Glacial geomorphology"], "chunk_type": "text", "line_start": 35, "line_end": 37, "token_count_estimate": 162, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "005d72794f9c7dce", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 2. Study area > 2.1. Glacial geomorphology\nType: figure\nFigure\n\nImage /page/5/Figure/3 description: A figure titled \"Figure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village.\" The figure is composed of several maps and photographs. In the top left, two maps show the location of the study area within India and the region of Jammu & Kashmir. The main map in the top center is a topographic map of the Suru Catchment, with elevations ranging from a low of 2588m to a high of 7047m. This map includes a legend for the Glacial lake, Panikhar Glacier, Suru Catchment, and River Streams. To the right of this map are three photographs: one showing icebergs floating on the lake's surface, a full view of the lake, and a view of the Moraine Dam. The bottom half of the figure is a large Landsat 8 satellite image from October 2022. This image details the Panikhar Glacier, the glacial lake, the downstream flowpath, and Panikhar Village, which are outlined and labeled. A legend for this satellite image indicates that the RGB channels correspond to Band\\_5 (Red), Band\\_4 (Green), and Band\\_3 (Blue).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "2. Study area > 2.1. Glacial geomorphology", "section_headings": ["2. Study area", "2.1. Glacial geomorphology"], "chunk_type": "figure", "figure_caption": null, "line_start": 38, "line_end": 38, "token_count_estimate": 312, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b0e10ddf7ef66761", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 2. Study area > 2.1. Glacial geomorphology\nType: figure\nFigure: Figure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village.\n\nFigure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "2. Study area > 2.1. Glacial geomorphology", "section_headings": ["2. Study area", "2.1. Glacial geomorphology"], "chunk_type": "figure", "figure_caption": "Figure 1. Location map of the Panikhar glacier, its proglacial lake and Panikhar village.", "line_start": 40, "line_end": 40, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4808e8f840b4ce23", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 2. Study area > 2.1. Glacial geomorphology\nType: text\n\navalanches, which might catalyze a GLOF. The glacial lake is impounded by the glacier's terminal moraine and the Panikhar settlement lies 13 km northeast of the glacial lake (Figure 2).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "2. Study area > 2.1. Glacial geomorphology", "section_headings": ["2. Study area", "2.1. Glacial geomorphology"], "chunk_type": "text", "line_start": 41, "line_end": 43, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df562c021872c75d", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 3. Datasets\nType: text\n\nThis study used Sentinel-2A (10 m), PlanetScope (3 m), Landsat TM (30 m), Landsat ETM+ (30 m), and Landsat OLI (30 m) for the mapping of glacial lake and its mother glacier and estimating area changes in the glacial lake and its feeding glacier from 2000 to 2022. Moreover, high-resolution Google imagery was also used for verification and validation purposes. Advanced Land Observing Satellite (ALOS) Phased Array Synthetic-Aperture Radar (PALSAR) radiometrically terrain corrected (RTC) Digital Elevation Model (DEM) was downloaded online from https://asf.alaska.edu (accessed on 23rd May 2023), from which topographic information such as elevation, slope, aspect, hill shade, etc. was obtained. Furthermore, ASTER DEM (30 m) was", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "3. Datasets", "section_headings": ["3. Datasets"], "chunk_type": "text", "line_start": 45, "line_end": 47, "token_count_estimate": 219, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba3a4ccc9a5e4806", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 3. Datasets\nType: figure\nFigure\n\nImage /page/6/Figure/1 description: A topographical map showing the glacial geomorphology of the Panikhar glacier. The map includes latitude and longitude lines, with longitude ranging from 75°47'0\"E to 75°51'0\"E and latitude from 34°1'0\"N to 34°3'0\"N. A scale bar indicates a range up to 2.25 km. The map displays contour lines with elevations from 4100 to 5800 meters. A legend defines the symbols used on the map: a black outline for Glacial Valley, a thin line for Contour line, light blue shading for Panikhar glacier, darker blue for Glacial lake, a red triangle for U shaped valley, a green asterisk for Cirque, a purple diamond for Roche moutainee, a red dashed line for Lateral moraines, and a thick black-and-white dashed line for Terminal moraine. The map illustrates the glacier, a glacial lake, and the locations of these various geomorphological features. An inset map in the bottom right corner shows a satellite image of the same area.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "3. Datasets", "section_headings": ["3. Datasets"], "chunk_type": "figure", "figure_caption": null, "line_start": 48, "line_end": 48, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "845256e5159a7b68", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 3. Datasets\nType: figure\nFigure: Figure 2. Glacial geomorphology of the study area.\n\nFigure 2. Glacial geomorphology of the study area.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "3. Datasets", "section_headings": ["3. Datasets"], "chunk_type": "figure", "figure_caption": "Figure 2. Glacial geomorphology of the study area.", "line_start": 50, "line_end": 50, "token_count_estimate": 52, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f1283ba48aa69a7", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 3. Datasets\nType: table\nTable: Table 1. Datasets used in the study.\n\n| S. No | Data/Tool               | Resolution | Year                   | Source                                                    |\n|-------|-------------------------|------------|------------------------|-----------------------------------------------------------|\n| 1.    | Planet CubeSat          | 3m         | 2016, 2018, 2020, 2022 | Planet https://www.planet.com/                         |\n| 2.    | Landsat 7 ETM+          | 30 m       | 2000, 2010             | USGS Earth Explorer http://www.earthexplorer.usgs.gov  |\n| 3.    | Landsat 8 OLI           | 30 m       | 2022                   | USGS Earth Explorer http://www.earthexplorer.usgs.gov  |\n| 4.    | Sentinel 2 A            | 10 m       | 2016, 2018, 2020, 2022 | USGS Earth Explorer http://www.earthexplorer.usgs.gov/ |\n| 5.    | ALOS-PALSAR DEM      | 12.5 m     | 2015                   | https://asf.alaska.edu/                                   |\n| 6.    | SRTM DEM                | 90 m       | 2000                   | USGS Earth Explorer http://www.earthexplorer.usgs.gov/ |\n| 7.    | ASTER DEM               | 30 m       | 2022                   | https://www.earthdata.nasa.gov/                           |\n| 8.    | Copernicus (GLO 30) DEM | 30 m       | 2023                   | https://spacedata.copernicus.eu/collection                |\n| 9.    | Google Earth Imagery    | <1 m       | 2000–2022              | Google Earth Pro Tool                                     |\n| 10.   | CRU gridded data        | 0.5°       | 1990–2002              | https://crudata.uea.ac.uk/cru/data/hrg                    |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "3. Datasets", "section_headings": ["3. Datasets"], "chunk_type": "table", "table_caption": "Table 1. Datasets used in the study.", "columns": ["S. No", "Data/Tool", "Resolution", "Year", "Source"], "table_row_start": 1, "table_row_end": 10, "line_start": 54, "line_end": 65, "token_count_estimate": 449, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3afffc9735b52a7", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 3. Datasets\nType: text\n\nalso used for glacier mass balance estimation through the geodetic method. PlanetScope imagery (3 m resolution) was acquired from the Planet Labs website www.planet.com. Landsat data was downloaded *via* the USGS Earth Explorer site, https://earthexplorer.usgs.gov, and Sentinel-2A images were obtained from the \"Copernicus internet hub of European Space Agency\" (ESA). Table 1 gives a summary of the datasets utilized in this study. These datasets have been extensively used in previous studies to analyze changes occurring on the Earth's surface (Ahmad et al. 2022a; Imdad et al. 2023; Malik et al. 2024; Mushtaq et al. 2024; Saleem et al. 2024).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "3. Datasets", "section_headings": ["3. Datasets"], "chunk_type": "text", "line_start": 66, "line_end": 68, "token_count_estimate": 197, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a8f90e9bac95df93", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 3. Datasets\nType: figure\nFigure\n\nImage /page/7/Picture/2 description: A black and white circular icon showing a stylized boat on water. The boat is a simple silhouette with a small mast, floating on a wavy line, all enclosed within a circle.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "3. Datasets", "section_headings": ["3. Datasets"], "chunk_type": "figure", "figure_caption": null, "line_start": 69, "line_end": 69, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3840e1b74e69f499", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.1. Glacial lake mapping and change detection\nType: text\n\nWe have employed Planet CubeSat RGB images to delineate the glacial lake outlines for the years 2016, 2018, 2020, and 2022. However, the Planet CubeSat data is available from the year 2016 onwards. Hence glacial lake outline delineation for 2000 and 2010 was carried out from Landsat 7 ETM+ images. Out of several techniques and approaches previously used for glacial lake mapping, we have employed an integrated approach using the normalized difference water index (NDWI) method coupled with manual digitization for the removal of any possible errors. NDWI is a widely employed method for the extraction of water pixels from a satellite image, using the difference in the reflectance values of Blue and Green bands (McFeeters 1996; Ahmed et al. 2022; Zhang et al. 2022; Rather et al. 2024a). However, the NDWI method faces certain issues while classifying pixels containing muddy patches, shadows, cloud cover, or ice (Kaplan and Avdan 2017; Wang et al. 2020; Ahmad et al. 2022b, Mir et al. 2022; Rather et al. 2024b). These issues were tackled through the generation of relief, slope, aspect, and hill shade maps from the ALOS Pulsar DEM accompanied by manual correction using high-resolution Google Earth imagery. Cloud cover acts as a major impediment in the process of mapping different features from satellite imagery, therefore, images were selected from the post-monsoon months. Wherever a cloud-free image was not available, images from the adjacent months were taken to address this problem. The high resolution of the Planet CubeSat images (3 m) proved helpful in the minimization of any errors in the lake boundary delineation.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.1. Glacial lake mapping and change detection", "section_headings": ["4. Methods", "4.1. Glacial lake mapping and change detection"], "chunk_type": "text", "line_start": 74, "line_end": 76, "token_count_estimate": 438, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "433fe930d64dec43", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge\nType: text\n\nA glacier lake's depth, volume, and peak flow are crucial factors in determining the GLOF threat connected to that specific glacial lake. For calculating the glacial lake depth and volume from satellite datasets, we have to rely upon certain empirical equations which are based on the glacial lake area, developed by researchers across the world. Field surveys of glacial lakes require enormous effort and resources owing to the harsh terrain of the Himalayan cryosphere; therefore, empirical equations are widely employed for this purpose (Evans 1986; Wang et al. 2011; Emmer and Vilímek 2013; Fujita et al. 2013). Several equations for the calculation of glacial lake depth, volume, and potential peak discharge have been developed which include those by Huggel et al. (2002), Wang et al. (2011), Qi et al. (2022), Patel et al. (2017), Fujita et al. (2013), etc. A summary of these equations is given in Table 2. To reduce bias, we computed the lake volume using these empirical equations and took the average of all the equations into account for the final result. For lake volume estimation the following equations: (Equations (1) and (2)), given by Qi et al. (2022), showed results closest to the average values. These equations use the geometry of the lake beside the lake area to calculate the lake volume.\n\nFor lakes larger than 0.1 km<sup>2</sup>\n\n$$V = 40.67^{1.184} - 3.218 \\frac{mxw}{mxl} \\tag{1}$$", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge", "section_headings": ["4. Methods", "4.1. Glacial lake mapping and change detection", "4.1.1. Estimation of glacial lake depth, volume and peak discharge"], "chunk_type": "text", "line_start": 78, "line_end": 86, "token_count_estimate": 431, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "910ec86d1e45420e", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge\nType: table\nTable: Table 2. Empirical equations used for depth and volume calculations.\n\n| Empirical Equation                           | Parameter Calculated | Reference            |\n|----------------------------------------------|----------------------|----------------------|\n| $D = 0.104 \\times A0.42$                     | Lake Depth           | Huggel et al. (2002) |\n| $D_{m} = 55 \\times A0.25$                    | Lake Depth           | Fujita et al. (2013) |\n| $Dm = 4 \\times 10^{-5} \\times A + 5.0564$    | Lake Depth           | Patel et al. (2017)  |\n| $V = 0.104 \\times A1.42$                     | Lake Volume          | Huggel et al. (2002) |\n| $V = 0.035 \\times A1.5$                      | Lake Volume          | Evans (1986)         |\n| $V = 0.0578 \\times A1.4683$                  | Lake Volume          | (Liu et al., 2020)   |\n| $V = 40.67^{1.184} - 3.218 \\text{ mxw/mxl}$  | Lake Volume          | Qi et al. (2022)     |\n| $V = 557.4^{2.455} + 0.2005 \\text{ mxw/mxl}$ | Lake Volume          | Qi et al. (2022)     |\n| $V = A \\times D_{m}$                         | Lake Volume          | Patel et al. (2017)  |\n| $Q_{max} = 0.00077 \\times V^{1.017}$         | Peak discharge       | Huggel et al. (2002) |\n| $Q_{max} = 0.72 \\times V0.53$                | Peak discharge       | Evans (1986)         |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge", "section_headings": ["4. Methods", "4.1. Glacial lake mapping and change detection", "4.1.1. Estimation of glacial lake depth, volume and peak discharge"], "chunk_type": "table", "table_caption": "Table 2. Empirical equations used for depth and volume calculations.", "columns": ["Empirical Equation", "Parameter Calculated", "Reference"], "table_row_start": 1, "table_row_end": 11, "line_start": 87, "line_end": 99, "token_count_estimate": 468, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00077"]}}
{"id": "e37f94c06e845ce0", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge\nType: text\n\nFor lakes smaller than 0.1 km<sup>2</sup>\n\n$$V = 557.4^{2.455} + 0.2005 \\frac{mxw}{mxl}$$\n (2)", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.1. Glacial lake mapping and change detection > 4.1.1. Estimation of glacial lake depth, volume and peak discharge", "section_headings": ["4. Methods", "4.1. Glacial lake mapping and change detection", "4.1.1. Estimation of glacial lake depth, volume and peak discharge"], "chunk_type": "text", "line_start": 100, "line_end": 105, "token_count_estimate": 101, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7ed9cc1889b72746", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.2. Mapping the feeding glacier\nType: text\n\nWe employed Planet CubeSat images along with Landsat 7 ETM+ and Sentinel 2 A imagery for mapping the feeding glacier of the lake. The Normalized difference Snow Index (NDSI) combined with visual interpretation was used for glacier area change detection analysis. NDSI method has been extensively used in the Himalayan cryosphere by several studies and is considered to be a reliable method for glacier mapping and change detection (Bolch et al. 2012; Kulkarni et al. 2012; (Paul et al. 2015)).\n\nThe error encountered in the estimation of glacier and lake areas is directly proportional to the resolution of the satellite images and it was evaluated using Equation (3).\n\n$$A_{er} = 100 (n^{0.5} \\times m) / A_{gl}$$\n (3)\n\nWhere n is the ratio of the glacial lake's perimeter to the spatial resolution, m is the unit pixel area of the image (m<sup>2</sup>), and  $A_{ol}$  is the glacial lake area (m<sup>2</sup>).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.2. Mapping the feeding glacier", "section_headings": ["4. Methods", "4.2. Mapping the feeding glacier"], "chunk_type": "text", "line_start": 107, "line_end": 116, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3f63eb0e4f49c93", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.2. Mapping the feeding glacier > 4.2.1. Mass balance estimation of the glacier\nType: text\n\nEstimating the mass balance of glaciers can reveal important information about how glaciers behave in response to climate change and how glaciers interact with glacial lakes over a certain period. Various studies have employed different techniques to estimate the mass balance of glaciers globally (Soruco et al. (2009), Zemp et al. (2009), Barrand et al. (2010), Pratap et al. (2016) and Kumar et al. (2019) such as the glaciological method ((Kulkarni, 1992)), geodetic method (Kumar et al. 2017; Majeed et al. 2021), Hydrological method (Bhutiyani 1999; Pratap et al. 2016), etc. However, in this study, we conducted the mass balance estimation of the Panikhar glacier using the geodetic method as this is a convenient method to estimate mass balance keeping in view the harsh terrain of the region ((Rashid et al. 2020)). The basis of the geodetic approach is the calculation of the glacier's surface elevation difference over\n\ntime. This method computes the surface elevation difference or thickness change of the glacier using digital elevation models from different time periods. The glacier area and ice density assumptions are then used to translate the elevation change into mass loss. (Braithwaite 2002; Muhammad et al. 2019; Romshoo et al. 2023). SRTM DEM and ASTER DEM (30 m resolution), acquired from https://www.earthdata.nasa.gov/ for the years 2000 and 2022 respectively, were used to calculate the elevation change of the glacier during this period using the DEM differencing technique. The DEMs were pre-processed in ArcMap 10.3 and the elevation difference was computed by comparing the elevation values of corresponding pixels from the two DEMs, using the raster calculator tool. The elevation difference was then translated into volume change using the cell size of the DEM. Assuming the ice density to be constant at 850 kg/m<sup>3</sup>, the mass loss from the glacier was calculated in kg/m<sup>3</sup> which was subsequently converted into meter water equivalent (m.w.e.).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.2. Mapping the feeding glacier > 4.2.1. Mass balance estimation of the glacier", "section_headings": ["4. Methods", "4.2. Mapping the feeding glacier", "4.2.1. Mass balance estimation of the glacier"], "chunk_type": "text", "line_start": 118, "line_end": 122, "token_count_estimate": 549, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24454cee0b3d8eb1", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.2. Mapping the feeding glacier > 4.2.2. Glacier surface velocity and ice thickness estimation\nType: text\n\nSurface velocities for the Panikhar Glacier were determined using the Glacier Image Velocimetry (GIV), a feature-tracking-based technique. GIV is a software package specifically designed for computing glacier velocity fields at high spatial resolution, utilizing unique points or characteristics on the glacier surface visible in consecutive satellite images. This meticulous process involves filtering individual velocity maps based on user-specified criteria, detecting and eliminating anomalous values using outlier detection functions, and computing statistical metrics for each grid cell over time. Sentinel-2 and Landsat true color composites were analyzed using GIV, with 20 multitemporal images with 30-meter spatial resolution from 2017 to 2023. Ice-thickness modeling during the same period utilized Copernicus DEM data at the same spatial resolution. Sentinel-2 images were used as input data for estimating glacier velocity, spanning from 2017 to 2023, with GIV configured to derive surface velocity considering a minimum temporal baseline gap of approximately 11 d and a maximum of three years. The displacement for each pair of images was computed using a multi-pass frequency domain cross-correlation algorithm, with subsequent filtering to remove pixels exceeding maximum velocity thresholds and enhancing result accuracy through spatial smoothing and rectification of systematic georeferencing errors using stable grounds.\n\nThe estimation of glacier thickness was conducted in MATLAB. For ice-thickness modeling, we employed the Copernicus DEM at the same spatial resolution. Our study adapted codes originally given by van Wyk de Vries et al. to suit our specific research requirements in our investigation of the Panikhar Glacier. We estimated ice thickness using an ice-velocity-based method. The equation follows the methodology proposed by (Gantayat et al., 2014), with further refinements by van Wyk de Vries et al., particularly focusing on the mean slope and surface velocity computations.\n\n$$H = \\frac{4\\sqrt{1 \\cdot 5U_s}}{Af^3 \\ (\\rho f sin\\alpha)3} \\tag{4}$$\n\nIn this equation, H depicts the ice thickness, Us denotes the surface velocity, f is the slope factor (often assumed as 0.8, especially suitable for Himalayan glaciers as noted by (Paterson, 1991)), g stands for the acceleration due to gravity (9.8 m/s<sup>-2</sup>),  $\\alpha$  signifies the glacier slope, and  $\\rho$  indicates the ice density (typically taken as 900 kg/m<sup>3</sup> according to (Farinotti et al., 2009)).\n\nGlacier volume estimation adopted a piece-wise method, involving the summation of the product of the squared ground pixel size and its respective ice thickness. In Equation (5),  $H_{j,k}$  represents the mean value of each pixel in the ice-thickness map, while r depicts the ground resolution of the pixel, assuming square pixels applicable to optical remote sensing data.\n\n$$Vp = \\sum_{i=1}^{n_j} \\sum_{k=1}^{n_k} H_{j,k} \\times r^2$$\n (5)", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.2. Mapping the feeding glacier > 4.2.2. Glacier surface velocity and ice thickness estimation", "section_headings": ["4. Methods", "4.2. Mapping the feeding glacier", "4.2.2. Glacier surface velocity and ice thickness estimation"], "chunk_type": "text", "line_start": 124, "line_end": 137, "token_count_estimate": 870, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6bc11390e9099955", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.3. GLOF hazard analysis\nType: text\n\nIn the Himalayan region, a widely used method for determining GLOF vulnerability is predictive analysis utilizing a multicriteria framework (Bolch et al. 2008; Wang et al. 2011; Worni et al. 2013; Rounce et al. 2017; Wang et al. 2018; Dubey and Goyal 2020; Khadka et al. 2021; Ahmed et al. 2022). Despite the extensive use of this method in GLOF investigations, this method involves some amount of subjective expert judgment. This is because the GLOF susceptibility analysis is a complex process, and local variations in the influencing criteria make it nearly difficult to develop a uniform framework for this purpose globally (Allen et al. 2019; Taylor et al. 2023). In this study, we adopted the framework given by Che et al. (2014) as found to be suitable in our study area. This framework includes 10 relevant criteria pertaining to the glacial lake and its surrounding conditions that affect GLOF susceptibility and classifies the lakes into 5 levels of hazard from level 1 to level 5 using the weighted sum method (Table 3).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.3. GLOF hazard analysis", "section_headings": ["4. Methods", "4.3. GLOF hazard analysis"], "chunk_type": "text", "line_start": 139, "line_end": 141, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dba614c9b04a4677", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling\nType: text\n\nThe two-dimensional (2D) GLOF modeling of the lake was done using HEC-RAS 2D (Version 6.3.1) to simulate two distinct scenarios: piping and overtopping. The aim was to assess the potential flood impacts from the glacial lake on the nearby downstream settlements. This approach has been extensively used to model the GLOF events globally as well as in the Himalayan region (Klimeš et al. 2014; Worni et al.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling", "section_headings": ["4. Methods", "4.4. GLOF modeling"], "chunk_type": "text", "line_start": 143, "line_end": 147, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "308a159af68d2862", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling\nType: table\nTable: Table 3. Integrated criteria used for assessing GLOF hazard, adopted from Che et al. (2014).\n\n| Index                                        | Criteria                     | Weight | Criteria Presence |\n|----------------------------------------------|------------------------------|--------|-------------------|\n| Type of glacial lake                         | Terminal moraine dammed lake | 0.15   | Yes               |\n| Area of lake                                 | >0.2 km2                     | 0.15   | Yes               |\n| Distance between lake and its mother glacier | <500 m                       | 0.15   | Yes               |\n| Average slope of the glacier                 | >7°                          | 0.10   | Yes               |\n| Slope of the downstream                      | >20°                         | 0.10   | No                |\n| Top width of the dam                         | <60 m                        | 0.10   | No                |\n| Area of the glacier                          | >2 km2                       | 0.09   | Yes               |\n| Slope between lake and its mother glacier    | >8°                          | 0.07   | No                |\n| Change of lake area                          | >10% in a decade             | 0.06   | Yes               |\n| Elevation of the lake                        | >5000 m                      | 0.03   | No                |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling", "section_headings": ["4. Methods", "4.4. GLOF modeling"], "chunk_type": "table", "table_caption": "Table 3. Integrated criteria used for assessing GLOF hazard, adopted from Che et al. (2014).", "columns": ["Index", "Criteria", "Weight", "Criteria Presence"], "table_row_start": 1, "table_row_end": 10, "line_start": 148, "line_end": 159, "token_count_estimate": 316, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "182885f3a61ddee6", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling\nType: figure\nFigure\n\nImage /page/11/Picture/2 description: A close-up, black and white line drawing of a circle. Inside the circle, there is a horizontal line with a small, dark rectangle resting on top of it. Below the horizontal line is a dark, filled, semi-circular shape. There are faint markings that appear to be letters 'd' and 'f' above the rectangle.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling", "section_headings": ["4. Methods", "4.4. GLOF modeling"], "chunk_type": "figure", "figure_caption": null, "line_start": 161, "line_end": 161, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f23b7cc26be11036", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling\nType: text\n\n2014; Sattar et al. 2019; Sattar et al. 2021; Majeed et al. 2021; Ahmed et al. 2022; Rawat et al. 2023b).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling", "section_headings": ["4. Methods", "4.4. GLOF modeling"], "chunk_type": "text", "line_start": 162, "line_end": 164, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd11b978790ea16f", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling > 4.4.1. Model input and setup\nType: text\n\nHEC-RAS requires a digital elevation model (DEM), for providing detailed terrain data for the study area. This raster-based DEM serves as the foundation for defining the flow area of interest, spanning from the glacial lake to the vulnerable settlements downstream. Within this defined flow area, a 2D computational mesh is constructed using cells of uniform dimensions. Each cell in the mesh is characterized by: Manning's roughness coefficient (N value), representing the flow resistance and the elevation data extracted from the DEM, specifying the terrain height at each cell location. ALOS PALSAR DEM was employed in this study given its better spatial resolution of 12.5 meters.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling > 4.4.1. Model input and setup", "section_headings": ["4. Methods", "4.4. GLOF modeling", "4.4.1. Model input and setup"], "chunk_type": "text", "line_start": 166, "line_end": 168, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5939fb16d57d472b", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling > 4.4.2. Simulating two different GLOF failures\nType: text\n\n- Piping Failure: This scenario simulates the failure of the lake dam or natural barrier due to internal erosion (piping). The modeling process involves steps like defining an initial breach hydrograph corresponding to the piping failure, setting the boundary conditions to initiate breach formation, and simulating flood propagation downstream. The 2D hydrodynamic modeling approach captures the breach evolution and flood wave propagation characteristics along the flow path. The characteristics of the potential flood wave were simulated for two different case scenarios presuming 50% and 100% drainage of the lake respectively.\n- b. Overtopping Failure: In overtopping failure, flood generation occurs when water levels exceed the capacity of the dam or barrier, causing water to spill over the top. The modeling procedure includes defining an appropriate initial breach hydrograph corresponding to the overtopping event and configuring the boundary conditions to simulate breach initiation and the subsequent flood wave propagation downstream. This is followed by employing the 2D hydraulic model to capture the breach dynamics and the flood wave behavior, considering terrain elevations and flow resistances. This was also done keeping in view the moderate and worst-case scenarios with 50% and 100% volume of the lake.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling > 4.4.2. Simulating two different GLOF failures", "section_headings": ["4. Methods", "4.4. GLOF modeling", "4.4.2. Simulating two different GLOF failures"], "chunk_type": "text", "line_start": 170, "line_end": 173, "token_count_estimate": 323, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24b809e916efdffe", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 4. Methods > 4.4. GLOF modeling > 4.4.3. Model outputs and analysis\nType: text\n\nThe 2D modeling process generates spatially distributed outputs including water depth distribution, flow velocity profiles indicating flood wave dynamics, and inundation extents depicting areas affected by the flood waters. These outputs enable detailed analysis of flood impacts along the route from the Lake to the settlements for both piping and overtopping scenarios. By integrating terrain data, hydraulic properties, and the breach conditions, the modeling study provides critical insights into potential flood hazards and helps in the development of mitigation frameworks for disaster preparedness and response. This comprehensive approach leverages advanced hydraulic modeling techniques to simulate complex flood scenarios, facilitating informed decision-making and risk assessment in vulnerable regions prone to GLOF events.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "4. Methods > 4.4. GLOF modeling > 4.4.3. Model outputs and analysis", "section_headings": ["4. Methods", "4.4. GLOF modeling", "4.4.3. Model outputs and analysis"], "chunk_type": "text", "line_start": 175, "line_end": 177, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ccc4dc8f267ef6fe", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.1. Depth, volume, and peak discharge of the lake\nType: text\n\nThe glacial lake depth and volume estimations were carried out using the various regression equations stated in Table 4. The results of these equations vary considerably and hence an average value of all of these equations was considered as the final estimate of lake depth and lake volume to minimize the bias in the final estimates as suggested by Emmer (2018). Using this approach, the average depth of the lake was estimated as  $31.50\\,\\mathrm{m}$  while the average volume was estimated as  $14.04\\times10^6\\,\\mathrm{m}^3$ . The equation developed by Fujita showed exaggerated estimates of lake depth in comparison to the other equations and in calculating the lake volume, the maximum value was obtained through Liu's equation while Qi's equation showed results closest to the average value of all equations.\n\nThe estimation of the Peak discharge of the lake was done using the equations given by Huggel et al. (2002) and Evans (1986), and the values obtained from these equations are recorded in Table 4. To minimize the bias, the average value of these two equations, which came out to be 7954.19 m<sup>3</sup>s<sup>-</sup>1, was taken as the final estimate of the peak discharge of the lake as suggested by Emmer et al. (2018).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.1. Depth, volume, and peak discharge of the lake", "section_headings": ["5. Results", "5.1. Depth, volume, and peak discharge of the lake"], "chunk_type": "text", "line_start": 181, "line_end": 185, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d7fa99949011e5e9", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.2. Glacial lake area change\nType: text\n\nThe change detection analysis of the lake, using multi-temporal datasets, revealed an overall increase of 78.72% in the lake's area at a rate of  $0.017\\,\\mathrm{km^2}$  per annum. The lake area has gone up from  $0.10\\,\\mathrm{km^2}\\,\\pm\\,75.9~\\mathrm{m^2}$  in 2000 to  $0.22\\,\\mathrm{km^2}\\,\\pm\\,0.82~\\mathrm{m^2}$  in 2010,  $0.33\\,\\mathrm{km^2}\\,\\pm\\,0.82~\\mathrm{m^2}$  in 2016,  $0.37\\,\\mathrm{km^2}\\,\\pm\\,0.72\\,\\mathrm{m^2}$  in 2018,  $0.41\\,\\mathrm{km^2}\\,\\pm\\,0.67\\,\\mathrm{m^2}$  in 2020 and  $0.47\\,\\mathrm{km^2}\\,\\pm\\,0.62~\\mathrm{m^2}$  in 2022. The lake area has witnessed an overall increase of  $\\sim\\!0.37\\,\\mathrm{km^2}$  in the past 22 years. It has almost doubled in each decade from 2000 to 2020 which depicts a very rapid expansion scenario, much likely to accelerate further in the future decade of 2020–2030 keeping in view the increasing temperature trends in the region. Figure 3 depicts the expansion of the lake through lake outline maps.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.2. Glacial lake area change", "section_headings": ["5. Results", "5.2. Glacial lake area change"], "chunk_type": "text", "line_start": 187, "line_end": 189, "token_count_estimate": 415, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e15d9bbd95f1402", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: text\n\nThe feeding glacier of Panikhar Lake was also subjected to spatio-temporal change detection analysis. The results revealed a significant decrease of  $\\sim 0.61 \\, \\mathrm{km}^2$  in the glacier area accounting for a 13.2% overall decrease in the glacier area for the past", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "text", "line_start": 193, "line_end": 195, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cfcd4da8625f6b3d", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: table\nTable\n\n|  | Table 4. A summary of | f various equations used | d for depth and vo | lume measurements. |\n|--|-----------------------|--------------------------|--------------------|--------------------|\n|--|-----------------------|--------------------------|--------------------|--------------------|", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "table", "table_caption": null, "columns": ["", "Table 4. A summary of", "f various equations used", "d for depth and vo", "lume measurements."], "table_row_start": 1, "table_row_end": 2, "line_start": 196, "line_end": 198, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3d86c0697bd7a05", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: table\nTable\n\n| Emp. Equation | Depth (m) | Volume (106 m3) | Peak Discharge (m3/s) |\n|---------------|-----------|-----------------|-----------------------|\n| Huggel        | 25.08     | 11.78           | 11,963.14             |\n| Fujita        | 45.58     | –               | –                     |\n| Evans         | –         | 11.33           | 3945.24               |\n| Liu           | –         | 19.17           | –                     |\n| Qi            | –         | 14.43           | –                     |\n| Patel         | 23.86     | 11.21           | –                     |\n| Average Value | 31.50     | 14.04           | 7954.19               |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "table", "table_caption": null, "columns": ["Emp. Equation", "Depth (m)", "Volume (106 m3)", "Peak Discharge (m3/s)"], "table_row_start": 1, "table_row_end": 7, "line_start": 200, "line_end": 208, "token_count_estimate": 213, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d6fa0e61c63f870", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: figure\nFigure\n\nImage /page/13/Figure/3 description: A figure displaying six satellite images in a 2x3 grid, illustrating the expansion of a lake from the year 2000 to 2022. The images are for the years 2000, 2010, and 2016 in the top row, and 2018, 2020, and 2022 in the bottom row. Each image shows the lake outlined and colored in dark blue against a terrestrial background. Each panel includes a scale bar from 0 to 0.4 kilometers, a north arrow, and coordinate axes for latitude (from 4°2'0\"N to 34°2'20\"N) and longitude (from 75°50'20\"E to 75°51'0\"E). The lake appears as two small, separate bodies of water in 2000, which merge and grow significantly in size through 2010, 2016, 2018, 2020, and 2022. The images for 2016, 2018, and 2022 show small white patches on the lake's surface. The caption below the images reads: 'Figure 3. Expansion of Pari lake from 2000 to 2022 depicted on Planet Cube Sat (RGB)'.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "figure", "figure_caption": null, "line_start": 210, "line_end": 210, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "940b494d7f814d2b", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: figure\nFigure: Figure 3. Expansion of Panikhar glacial lake from 2000 to 2022 depicted on Planet CubeSat (RGB) and Landsat 7 ETM+ images.\n\nFigure 3. Expansion of Panikhar glacial lake from 2000 to 2022 depicted on Planet CubeSat (RGB) and Landsat 7 ETM+ images.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "figure", "figure_caption": "Figure 3. Expansion of Panikhar glacial lake from 2000 to 2022 depicted on Planet CubeSat (RGB) and Landsat 7 ETM+ images.", "line_start": 212, "line_end": 212, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bddebf018f9415f7", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: figure\nFigure\n\nImage /page/13/Figure/5 description: A line graph showing the relationship between Lake Area and Glacier Area from the year 2000 to 2022. The x-axis represents the Year, from 1998 to 2024. There are two y-axes: the left y-axis represents Lake Area in square kilometers (km²), ranging from 0 to 0.5, and the right y-axis represents Glacier Area in square kilometers (km²), ranging from 3.8 to 4.8. The graph contains four lines: a solid green line for Lake Area, a solid red line for Glacier Area, a dashed green line for the linear trend of Lake Area, and a dashed red line for the linear trend of Glacier Area. The Lake Area shows a steady increase from 0.1 km² in 2000 to 0.47 km² in 2022. Specific data points for Lake Area are (2000, 0.1), (2010, 0.22), (2016, 0.33), (2018, 0.37), (2020, 0.41), and (2022, 0.47). The Glacier Area shows a general decrease from 4.63 km² in 2000 to 4.02 km² in 2022, with a sharp drop between 2018 and 2020. Specific data points for Glacier Area are (2000, 4.63), (2010, 4.58), (2016, 4.4), (2018, 4.39), (2020, 4.04), and (2022, 4.02). The trend lines confirm the inverse relationship: as the glacier area decreases, the lake area increases.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "figure", "figure_caption": null, "line_start": 214, "line_end": 214, "token_count_estimate": 375, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1e9138177fbcf51", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: figure\nFigure: Figure 4. Glacier and glacial lake area trends.\n\nFigure 4. Glacier and glacial lake area trends.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "figure", "figure_caption": "Figure 4. Glacier and glacial lake area trends.", "line_start": 216, "line_end": 216, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "560ff4b3f37f54ff", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes\nType: text\n\n22 years. The overall area of the glacier has decreased from  $4.63\\,\\mathrm{km}^2\\pm139~\\mathrm{m}^2$  in 2000 to  $4.58\\,\\mathrm{km}^2\\pm138.4~\\mathrm{m}^2$  in 2010,  $4.50\\,\\mathrm{km}^2\\pm0.01\\,\\mathrm{m}^2$  in 2016,  $4.39\\,\\mathrm{km}^2\\pm0.01\\,\\mathrm{m}^2$  in 2018,  $4.04\\,\\mathrm{km}^2\\pm0.02\\,\\mathrm{m}^2$  in 2020 and  $4.02\\,\\mathrm{km}^2\\pm0.02\\,\\mathrm{m}^2$  in 2022 with an annual decrease rate of  $\\sim\\!0.03\\,\\mathrm{km}^2$  (Figures 4 and 5). The glacier has witnessed a snout retreat of 348 m in this period. The glacier and the glacial lake areas behold a reciprocal relationship with each other which means that the glacial lake is expanding at the expense of its feeding glacier (Figure 4).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.1. Area changes", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.1. Area changes"], "chunk_type": "text", "line_start": 217, "line_end": 219, "token_count_estimate": 340, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4eb67649f5bdec86", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier\nType: text\n\nThe glacier mass balance was estimated through the geodetic method and the results reveal that the glacier has, on average, undergone a thickness loss of 7.04 m from 2000", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.2. Mass balance of the glacier"], "chunk_type": "text", "line_start": 221, "line_end": 223, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b9d7e45e2c5722ca", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier\nType: figure\nFigure\n\nImage /page/14/Figure/1 description: A map titled \"Figure 5. Spatio-temporal evolution of the Penikhar glacier for the last 22 years.\" The main image is a satellite view of a glacier, shown in light blue, surrounded by mountainous terrain. The map includes latitude and longitude coordinates, a scale bar from 0 to 1.7 Kms, and a north arrow. Overlaid on the glacier are several colored outlines indicating its extent in different years. A legend titled \"Glacier Outlines\" shows the following: a light purple line for 2022, a light green line for 2020, a blue line for 2018, a black line for 2016, a tan line for 2010, and a red line for 2000. The satellite data is from Sentinel 2A (2022), using Band 11 (red), Band 8 (green), and Band 3 (blue). In the bottom right corner, there is an inset map titled \"Glacier Snout,\" which is a close-up of the glacier's terminus. This inset has its own scale bar from 0 to 0.4 Kms and a legend showing the glacier snout outlines for 2016 (dark green), 2010 (light blue), 2000 (black), 2022 (red), 2020 (pink), and 2018 (light green). The outlines on both maps generally show the glacier receding over time.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.2. Mass balance of the glacier"], "chunk_type": "figure", "figure_caption": null, "line_start": 224, "line_end": 224, "token_count_estimate": 353, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b65ed2df904a3a24", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier\nType: figure\nFigure: Figure 5. Spatio-temporal evolution of the Panikhar glacier for the last 22 years.\n\nFigure 5. Spatio-temporal evolution of the Panikhar glacier for the last 22 years.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.2. Mass balance of the glacier"], "chunk_type": "figure", "figure_caption": "Figure 5. Spatio-temporal evolution of the Panikhar glacier for the last 22 years.", "line_start": 226, "line_end": 226, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "29f790c4f7c19e53", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier\nType: text\n\nto 2022 at the rate of  $0.32\\,\\mathrm{m~a}^{-1}$ . The total mass balance of the glacier was estimated to be  $-5.922\\,\\mathrm{m}$  w.e. which amounts to a total mass loss of  $\\sim 5.92$  metric tons per square metre during this period at the rate of 0.27 metric tons per square metre per year (Figure 6). The mass balance values across the glacier fall from  $-0.05\\,\\mathrm{m}$  w.e. to  $0.06\\,\\mathrm{m}$  w.e. with a mean negative value. This depicts that despite certain accumulation regions on the glacier, the glacier has an overall negative mean mass balance. These results are in proximity to other studies in the region. For instance, Kumar et al. (2017) reported an annual mass balance of  $-0.53\\pm0.16\\,\\mathrm{m}$  we. per year for the glaciers in the Western Himalayas. Majeed et al. (2021) reported a mass balance of  $-0.24\\,\\mathrm{m}$  w.e. per year for clean glaciers and  $-0.37\\,\\mathrm{m}$  w.e. per year for debris-covered ones in the Pangong region.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.2. Mass balance of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.2. Mass balance of the glacier"], "chunk_type": "text", "line_start": 227, "line_end": 229, "token_count_estimate": 356, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6c973525ec59d95", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: text\n\nAccording to the velocity estimations, the glacier's mean velocity was estimated to be  $3.38 \\,\\mathrm{m}$  a-1, while its surface velocity varied from  $0.87 \\,\\mathrm{m/y}$  to  $9.76 \\,\\mathrm{m}$  a<sup>-1</sup>. (Figure 7a). The glacier is characterized by a slowly moving snout with slightly increasing velocities in the upper accumulation zone. Previous studies in the region also report the existence of an increasing trend in the surface velocity of the glaciers with increasing elevation until a maximum is reached towards the transient snow lines (Bhushan et al. 2018; Ahmed et al. 2022).\n\nThe ice thickness measurement of the glacier, conducted in MATLAB, reveals that the thickness of the glacier varies from 14.39 m to 50.87 m (Figure 7b) while the mean thickness was estimated to be 30.95 m. The thickness of the glacier increases", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "text", "line_start": 231, "line_end": 235, "token_count_estimate": 299, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a5b2fee8f68b7eb", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: figure\nFigure\n\nImage /page/15/Figure/3 description: A map illustrating the spatial variation of mass balance on a glacier. The map is oriented with a north arrow pointing up. The glacier is outlined with a blue 'Glacier Boundary' line and is overlaid with a color gradient representing the 'Mass Balance (m.w.e)'. The color scale in the legend ranges from red (0.06) to yellow (-0.01) to green (-0.05), indicating areas of mass gain and loss. The western, higher-elevation part of the glacier shows a small red area of positive mass balance, while the majority of the glacier is yellow and green, indicating negative mass balance. Brown 'Contour (100m)' lines show the topography, with elevations ranging from 4100 meters at the eastern tip to over 5100 meters in the west. The map includes a scale bar from 0 to 2 kilometers. The area is located between approximately 75°47'0\"E and 75°50'0\"E longitude and 34°1'0\"N and 34°3'0\"N latitude.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "figure", "figure_caption": null, "line_start": 236, "line_end": 236, "token_count_estimate": 310, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f4de73d64b695eb", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: figure\nFigure: Figure 6. Spatial variation of geodetic mass balance across Panikhar glacier in meter water equivalent (m.w.e).\n\nFigure 6. Spatial variation of geodetic mass balance across Panikhar glacier in meter water equivalent (m.w.e).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "figure", "figure_caption": "Figure 6. Spatial variation of geodetic mass balance across Panikhar glacier in meter water equivalent (m.w.e).", "line_start": 238, "line_end": 238, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b34502570c602665", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: figure\nFigure\n\nImage /page/15/Figure/5 description: The image displays two maps, labeled A and B, showing characteristics of a glacier. Both maps are georeferenced with latitude and longitude coordinates and include a 1 km scale bar and a north arrow. Map A, titled \"Glacier Velocity (m/y)\", uses a color scale from blue (Low: 0.87 m/y) to red (High: 9.76 m/y) to show glacier surface velocity. The highest velocities are concentrated in the central, wider part of the glacier, indicated by yellow and red patches. Map B, titled \"Glacier Thickness (m)\", uses a color scale from blue (Low: 14.39 m) to magenta (High: 50.87 m). This map shows the glacier is thickest in the central area, depicted in magenta, and thins out towards the edges, shown in blue.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "figure", "figure_caption": null, "line_start": 240, "line_end": 240, "token_count_estimate": 276, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0c6a4ab68f6b3c5", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: figure\nFigure: Figure 7. Figure depicting (A) glacier surface velocity and (B) Ice thickness distribution.\n\nFigure 7. Figure depicting (A) glacier surface velocity and (B) Ice thickness distribution.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "figure", "figure_caption": "Figure 7. Figure depicting (A) glacier surface velocity and (B) Ice thickness distribution.", "line_start": 242, "line_end": 242, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90a60b92c6c31b47", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: figure\nFigure\n\nImage /page/16/Figure/1 description: A line graph displays Mean Temperature and Total annual precipitation from 1990 to 2020. The x-axis represents the Year, from 1990 to 2020. The left y-axis represents Temperature in degrees Celsius, ranging from -1 to 8. The right y-axis represents Precipitation in millimeters (mm), ranging from 50 to 400. There are two data series plotted. The first, in red with square markers, shows the Mean Temperature (T\\_mean). It exhibits a clear upward trend over the period, starting near 0 degrees Celsius in 1990 and rising to about 1 degree Celsius by 2020. A red dashed trendline with a shaded confidence interval is shown, with an R-squared value of R²=0.275. The second data series, in green with square markers, shows the Total annual precipitation. This series shows significant year-to-year fluctuation, with values ranging from approximately 150 mm to over 300 mm. A green dashed trendline with a shaded confidence interval is nearly horizontal, indicating no significant trend, which is supported by a very low R-squared value of R²=0.0138.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "figure", "figure_caption": null, "line_start": 244, "line_end": 244, "token_count_estimate": 323, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a638b385b509c9b6", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: figure\nFigure: Figure 8. Temperature and precipitation trends in Ladakh.\n\nFigure 8. Temperature and precipitation trends in Ladakh.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "figure", "figure_caption": "Figure 8. Temperature and precipitation trends in Ladakh.", "line_start": 246, "line_end": 246, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e166d14fbfa5f6c6", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier\nType: text\n\ntowards the center and decreases as we move towards the edges. The total glacier volume as estimated from the thickness estimates and the glacier pixel areas was found to be 45,783 m<sup>3</sup>.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.3. Changes in the feeding glacier of the lake > 5.3.3. Surface velocity, ice thickness, and volume of the glacier", "section_headings": ["5. Results", "5.3. Changes in the feeding glacier of the lake", "5.3.3. Surface velocity, ice thickness, and volume of the glacier"], "chunk_type": "text", "line_start": 247, "line_end": 249, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b9e53a7c9ca4764e", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 5. Results > 5.4. Temperature and precipitation variability over the region\nType: text\n\nThe Ladakh region is overall a data-scarce region with only a few observatories for the generation of observed temperature and precipitation data. Keeping this in mind, we projected the trends in the mean annual temperature (T mean) and Total annual precipitation in the region using a gridded climatic dataset given by the Climate Research Unit (CRU) to peek into the climatic controls of the glacial lake expansion. It was found that the mean annual temperature has witnessed a slight positive trend ( $R^2 = 0.275$ ) during this period while the total annual precipitation in the region has witnessed a slight negative trend with an  $R^2$  value of 0.0138 (Figure 8).\n\nThe warming temperature trends in this region and declining precipitation trends are also confirmed by previous studies that use various gridded climate data products to analyze the climatic variability in Ladakh (Shafiq et al. 2016; Ahmad et al. 2019; Mann et al. 2022). This may lead to further glacier recession and subsequent glacial lake expansion in the near future making the local populations vulnerable to cryospheric disasters such as the GLOF.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "5. Results > 5.4. Temperature and precipitation variability over the region", "section_headings": ["5. Results", "5.4. Temperature and precipitation variability over the region"], "chunk_type": "text", "line_start": 251, "line_end": 255, "token_count_estimate": 298, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a34459c6a8a68df0", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.1. GLOF susceptibility analysis\nType: text\n\nWe carried out the GLOF susceptibility analysis of the glacial lake based on the approach used by Che et al. (2014) which includes criteria pertaining to both the lake and the surrounding conditions that affect GLOF susceptibility (Table 3) and classifies the lakes into 5 levels of hazard from level 1 to level 5. The lake was found to be a High-hazard lake associated with a level 4 hazard. Some of the major reasons that account for the high hazard index of the lake are its large size, unstable moraine dam, direct connection to its mother glacier, marked rate of expansion, and a large-", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.1. GLOF susceptibility analysis", "section_headings": ["6. GLOF risk", "6.1. GLOF susceptibility analysis"], "chunk_type": "text", "line_start": 259, "line_end": 261, "token_count_estimate": 167, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7a7df71ab29e84d8", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.1. GLOF susceptibility analysis\nType: figure\nFigure\n\nImage /page/17/Figure/3 description: A geographical map illustrating potential zones of mass movement in a mountainous region. The map is set against a satellite image background and includes longitude and latitude markings, a scale bar, and a compass rose. An inset map in the top-left corner provides a closer satellite view of the 'Glacier Snout' and 'Panikhar Lake', with a steep slope outlined in red. Red lines connect this outlined area to the main map, which highlights the 'Potential Zones of Mass Movement'. These zones are color-coded based on slope steepness. A legend in the bottom-right corner defines the symbols and colors used. 'Glacial Lake' is shown in solid light blue, and 'Glacier' is a white area with blue hatching. The 'Slope (degrees)' is categorized by color: green for 0-8, light green for 8-19, yellow for 19-31, orange for 31-45, and red for >45. The map shows that the areas with the highest potential for mass movement (red and orange) are on the steep slopes adjacent to the glacial lake and glacier.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.1. GLOF susceptibility analysis", "section_headings": ["6. GLOF risk", "6.1. GLOF susceptibility analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 262, "line_end": 262, "token_count_estimate": 310, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4420e56153b1f245", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.1. GLOF susceptibility analysis\nType: figure\nFigure: Figure 9. The local topographic setting of the Panikhar glacier-lake complex and zones of possible mass movement.\n\n**Figure 9.** The local topographic setting of the Panikhar glacier-lake complex and zones of possible mass movement.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.1. GLOF susceptibility analysis", "section_headings": ["6. GLOF risk", "6.1. GLOF susceptibility analysis"], "chunk_type": "figure", "figure_caption": "Figure 9. The local topographic setting of the Panikhar glacier-lake complex and zones of possible mass movement.", "line_start": 264, "line_end": 264, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ab371762d844f8b", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.1. GLOF susceptibility analysis\nType: text\n\nsized mother glacier (4.02 km<sup>2</sup>) with a slope of 16.5°. Moreover, the lake is prone to rock fall/debris fall from its adjoining steep slopes as evident from the slope profile of the lake surroundings (Figure 9).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.1. GLOF susceptibility analysis", "section_headings": ["6. GLOF risk", "6.1. GLOF susceptibility analysis"], "chunk_type": "text", "line_start": 265, "line_end": 267, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "33e27ae0fa71d9c8", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling\nType: text\n\nWe analyzed the potential impacts of an outburst event originating from the Panikhar Lake by analyzing the temporal and spatial properties of the flood wave using two-dimensional hydraulic modeling for two different case scenarios. In scenario 1, we assumed that 50% of the total volume of the lake is drained whereas scenario 2 was the worst-case scenario assuming a 100% drainage of the lake.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling"], "chunk_type": "text", "line_start": 269, "line_end": 271, "token_count_estimate": 121, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "03b4e43fe2906512", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released\nType: text\n\nGiven the dynamic nature of high-magnitude flood events, we focused on understanding the changing hydraulic properties over time, particularly examining discharge, flow velocity, and depths across the flow channel. Assuming drainage of 50% of the lake's total water volume, we found that the maximum discharge at the point of the breach was 1908.25 m³/s and 3271.50 m³/s for the piping and overtopping failures respectively (Figure 10a). The routed hydrographs depict that the flood wave in this scenario will reach the first settlement in 1 h and 4 min after the dam breach in both the piping and the overtopping failures with a peak discharge of 2701.1 m³/s and 2714.7 m³/s respectively (Figure 11a). The flood depths along the flow path are", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.1. Scenario 1: assuming 50% water volume of the lake is released"], "chunk_type": "text", "line_start": 273, "line_end": 275, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9fb6e22d1c62d741", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released\nType: figure\nFigure\n\nImage /page/18/Figure/1 description: A close-up, black and white image showing the number 18 followed by a plus sign enclosed in a circle.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.1. Scenario 1: assuming 50% water volume of the lake is released"], "chunk_type": "figure", "figure_caption": null, "line_start": 276, "line_end": 276, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14a5dede9eecda6f", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released\nType: figure\nFigure\n\nImage /page/18/Figure/2 description: The image contains two line graphs, labeled A and B, which are breach hydrographs comparing 'Piping Failure' and 'Overtopping failure' scenarios. Both graphs plot Discharge (m³/s) on the y-axis against Time (hh:mm:ss) on the x-axis.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.1. Scenario 1: assuming 50% water volume of the lake is released"], "chunk_type": "figure", "figure_caption": null, "line_start": 278, "line_end": 278, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42119175e5e57f56", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released\nType: text\n\nGraph A has a y-axis ranging from 0 to 3500 m³/s. The blue line represents 'Piping Failure' and shows a peak discharge of 1908.25 m³/s. The orange line represents 'Overtopping failure' and shows a higher peak discharge of 3271.50 m³/s. The x-axis ranges from 1:00:00 to 4:22:55.\n\nGraph B has a y-axis ranging from 0 to 5000 m³/s. The green line represents 'Piping Failure' with a peak discharge of 3890.99 m³/s. The red line represents 'Overtopping Failure' with a higher peak discharge of 5111.39 m³/s. The x-axis ranges from 1:00:00 to 3:25:42.\n\nThe caption below the graphs reads: 'Figure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping'.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.1. Scenario 1: assuming 50% water volume of the lake is released"], "chunk_type": "text", "line_start": 279, "line_end": 285, "token_count_estimate": 264, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1b0f56b372f451e", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released\nType: figure\nFigure: Figure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping failures (A) 50% volume of the lake (B) 100% volume of the lake (worst case scenario).\n\nFigure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping failures (A) 50% volume of the lake (B) 100% volume of the lake (worst case scenario).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.1. Scenario 1: assuming 50% water volume of the lake is released"], "chunk_type": "figure", "figure_caption": "Figure 10. Breach hydrographs depicting peak discharge at the breach in piping and overtopping failures (A) 50% volume of the lake (B) 100% volume of the lake (worst case scenario).", "line_start": 286, "line_end": 286, "token_count_estimate": 148, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e21dbca6a7ff7444", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released\nType: text\n\ndepicted in Figure 12. The maximum flood velocity was estimated to be 18.26 m/s while the maximum depth of the flood wave was found to be 14.87 m.\n\nThe maximum area under the threat of inundation along the valley was calculated as 4.74 km<sup>2</sup> for both the overtopping and pipping failure situations. The mean flood depth across the flow channel was estimated to be 3.41 meters and the mean flood velocity was found to be 3.84 m/s. We found that the potential flood wave in this scenario inundates bridges, roads, and a few residential houses along the channel in settlement 2 and near the Suru Valley bridge (Figure 13).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.1. Scenario 1: assuming 50% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.1. Scenario 1: assuming 50% water volume of the lake is released"], "chunk_type": "text", "line_start": 287, "line_end": 291, "token_count_estimate": 210, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f743860cc22747d3", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released\nType: text\n\nThe maximum discharge, in the worst-case scenario with 100% water release, at the point of breach was calculated to be 3890.99 m<sup>3</sup>/s and 5111.39 m<sup>3</sup>/s for the piping and overtopping scenarios respectively (Figure 10b). A routed flood wave in the simulation reached the closest settlement (settlement 1) of the Panikhar village in 50 min after the lake-breach event with a peak discharge of 6345.06 m<sup>3</sup>/s and 6453.9 m<sup>3</sup>/s for the piping and overtopping scenarios respectively (Figure 11b).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.2. Scenario 2: assuming 100% water volume of the lake is released"], "chunk_type": "text", "line_start": 293, "line_end": 295, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cfd1bb506603035a", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released\nType: figure\nFigure\n\nImage /page/19/Figure/3 description: The image displays four line graphs arranged in a 2x2 grid, labeled A and B on the right side for each row. The graphs show discharge over time for different locations under two failure scenarios: \"Piping Failure\" (left column) and \"Overtopping Failure\" (right column).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.2. Scenario 2: assuming 100% water volume of the lake is released"], "chunk_type": "figure", "figure_caption": null, "line_start": 296, "line_end": 296, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06d45d95dcda0e71", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released\nType: text\n\nRow A contains two graphs with the y-axis representing Discharge (m³/s) from 0 to 3000. The x-axis represents Time in hh:mm:ss format.\n- The top-left graph, \"Piping Failure,\" shows five hydrographs for different locations: Settlement 1, Surrur Valley Bridge, Settlement 2, Bridge 2, and Picnic Spot Damasna. The peak discharge for Settlement 1 is approximately 2600 m³/s at around 2:10:36. The other locations have lower peaks that occur later in time.\n- The top-right graph, \"Overtopping Failure,\" shows similar hydrographs. The peak discharge for Settlement 1 is also around 2600 m³/s but occurs earlier, at approximately 2:00:04.\n\nRow B contains two graphs with the y-axis representing Discharge (m³/s) from 0 to 8000. The x-axis represents Time in hh:mm format.\n- The bottom-left graph, \"Piping Failure,\" shows five hydrographs for: Settlement 1, Suru Valley Bridge, Settlement 2, Bridge 2, and Picnic Spot Damasna. The peak discharge for Settlement 1 and Suru Valley Bridge is approximately 6400 m³/s at around 02:00. The other locations have lower peaks that occur later.\n- The bottom-right graph, \"Overtopping Failure,\" shows similar hydrographs. The peak discharge for Settlement 1 and Suru Valley Bridge is also around 6400 m³/s but occurs slightly earlier, at approximately 01:55.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.2. Scenario 2: assuming 100% water volume of the lake is released"], "chunk_type": "text", "line_start": 297, "line_end": 305, "token_count_estimate": 407, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cdc6ada75e91cff8", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released\nType: figure\nFigure: Figure 11. Routed hydrographs at various locations along the downstream flow channel in piping and overtopping failures (A) Assuming release of 50% of the water volume of the lake (B) Assuming release of 100% of the water volumeof the lake.\n\n**Figure 11.** Routed hydrographs at various locations along the downstream flow channel in piping and overtopping failures (A) Assuming release of 50% of the water volume of the lake (B) Assuming release of 100% of the water volumeof the lake.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.2. Scenario 2: assuming 100% water volume of the lake is released"], "chunk_type": "figure", "figure_caption": "Figure 11. Routed hydrographs at various locations along the downstream flow channel in piping and overtopping failures (A) Assuming release of 50% of the water volume of the lake (B) Assuming release of 100% of the water volumeof the lake.", "line_start": 306, "line_end": 306, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bcc03a059cb8b4b5", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released\nType: text\n\nThe two-dimensional routing of the breach hydrograph is simulated over a distance of 25.6 km along the flow channel, from the lake to the Damasna picnic spot in Panikhar village. The maximum area under the threat of inundation along the valley was calculated as 5.38 km² for both the overtopping and piping failures (Figure 16). Across the entire channel, the mean flow discharge and mean flow depth are recorded at 588 m³/s and 5.31 m for pipping failure 755 m³/s, and 5.33 m for overtopping failure with a mean flow velocity of 5.37 m/s. The flood depth and velocity vary along the flow channel as per the terrain profile of the flow channel (Figure 13). The flood depth and velocity values decline towards the lower reaches of the flow channel, as a result of the increase in the channel width and decrease in the slope.\n\nThe potential flood wave inundates infrastructure such as roads, bridges, and settlements along the channel with varying depths and velocities for both the piping and overtopping failures (Figures 14 and 15, Table 5). We evaluated the flood depths and flood velocities along the flow channel at settlements 1 and 2 to determine the maximum flood depths, peak flood velocities, and the time of the peak. Settlements 1 and 2 witness the highest potential flood depths of 7.9 and 7.65 m in the overtopping scenario whereas the lowest flood depths are witnessed at the Damasna picnic spot. Settlement 1 witnesses the highest peak velocities of 12.36 m/s and 11.35 m/s in the Piping and the overtopping scenarios (Figure 15).", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "6. GLOF risk > 6.2. Hydrodynamic modelling > 6.2.2. Scenario 2: assuming 100% water volume of the lake is released", "section_headings": ["6. GLOF risk", "6.2. Hydrodynamic modelling", "6.2.2. Scenario 2: assuming 100% water volume of the lake is released"], "chunk_type": "text", "line_start": 307, "line_end": 311, "token_count_estimate": 444, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5f336ec0e66793c4", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: text\n\nThe Himalayan region is a hotspot of GLOF risk (Taylor et al. 2023) and this risk is projected to increase three times by the year 2100 (Zhang et al. 2024). 18% of all the", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "text", "line_start": 313, "line_end": 315, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ec955bbfc73919ff", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure\n\nImage /page/20/Figure/1 description: A close-up, black and white image showing the number 20 next to a circular icon. The icon contains a simple line drawing of an oil lamp with a flame, and a short line extends downwards from the bottom of the circle.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 316, "line_end": 316, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8743b5e647b78de8", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure\n\nImage /page/20/Figure/2 description: This image, labeled Figure 12, displays two sets of maps illustrating flood depth and velocity profiles in a downstream flow channel. The top set of three maps, labeled (A), (B), and (C), shows the 'Flood Depth (m)'. A color scale indicates that the depth ranges from a low of 0.001 m (blue/green) to a high of 14.87 m (red). The maps depict a river channel winding through a mountainous terrain, with varying depths indicated by the color coding. The bottom set of three maps, also labeled (A), (B), and (C) for the same geographical areas, shows the 'Flood Velocity (m)'. The corresponding color scale ranges from a low of 0.001 m (green) to a high of 18.26 m (red). Each map includes latitude and longitude coordinates and a 2 km scale bar.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 318, "line_end": 318, "token_count_estimate": 249, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b6e40ac48b07a03", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure: Figure 12. Flood depth and velocity profiles in the downstream flow channel in the first case scenario (50% volume). Parts A, B and C represent the 1st, 2nd and 3rd sections along the flow channel.\n\nFigure 12. Flood depth and velocity profiles in the downstream flow channel in the first case scenario (50% volume). Parts A, B and C represent the 1st, 2nd and 3rd sections along the flow channel.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 12. Flood depth and velocity profiles in the downstream flow channel in the first case scenario (50% volume). Parts A, B and C represent the 1st, 2nd and 3rd sections along the flow channel.", "line_start": 320, "line_end": 320, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc65c86f93481535", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure\n\nImage /page/21/Figure/3 description: A composite image showing several annotated satellite views of a river valley. The river and its floodplain are highlighted in a translucent green. The main panel on the left shows a wide view of the river with a white arrow indicating the \"Flow Direction.\" Several points are marked with red pins and labels along the river: \"Picnic Spot Damasna,\" \"Bridge 2,\" \"Settlement 2,\" \"Settlement 1,\" and \"Suru Valley Bridge.\" Red boxes highlight the areas of Bridge 2 and Settlement 2. The middle panel is a closer view of the \"Suru Valley Bridge\" area, with white arrows pointing to structures labeled \"Houses,\" one of which is circled. The right side of the image contains two smaller, zoomed-in panels. The top right panel shows a close-up of \"Settlement 2,\" with a red box around it and an arrow pointing to \"Houses.\" The bottom right panel is a close-up view of \"Bridge 2\" spanning the river.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 322, "line_end": 322, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1cc5559d85fcc7e1", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure: Figure 13. Buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation in scenario (1) in the downstream flow channel of Panikhar lake.\n\nFigure 13. Buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation in scenario (1) in the downstream flow channel of Panikhar lake.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 13. Buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation in scenario (1) in the downstream flow channel of Panikhar lake.", "line_start": 324, "line_end": 324, "token_count_estimate": 99, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c53cf831e5f8e0d5", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: text\n\nprevious GLOF events have occurred in the High Mountain Asia (HMA) which includes the Himalayan region. Most of the GLOF events in Himalayan region have been reported from the moraine and ice-dammed glacial lakes (Shrestha et al. 2010). The key triggers of GLOF in the region are extreme precipitation events and mass movements such as ice avalanches (Shrestha et al. 2010; Zhang et al. 2024). The rapid expansion of mountainous populations into high-risk zones is one of the major contributors to increasing GLOF risk in this region (Taylor et al. 2023). A large body of literature has been produced by researchers to generate baseline data about the GLOF phenomenon in the data-scarce Himalayan region (Emmer et al. 2022). These studies have focused on the preparation of glacial lake inventories, identification of potentially dangerous glacial lakes, and estimation of GLOF risk associated with hazardous lakes.\n\nCategorizing a lake as potentially dangerous is very difficult given the multitude of factors influencing a GLOF event such as lake size, lake dam, lake surroundings, landslide, avalanche susceptibility, or extreme weather event. Therefore, under particular circumstances, an otherwise stable lake can also produce a GLOF event. Extreme precipitation events have caused notable GLOF events in the past through dam overtopping or collapse. This adds to the complexity of understanding and predicting the GLOF disaster. The same problem arises with the categorization of glacial lakes as potentially dangerous or otherwise based on size. Generally, some area threshold (e.g.  $A > 0.1 \\text{ km}^2$ ) is defined to exclude smaller lakes from the list of potentially dangerous lakes. For instance, Worni et al. (2013) did not classify the Gya Lake in Ladakh as potentially hazardous, maybe because it was ice-covered or because its size was below the mapping threshold of the study (0.01 km<sup>2</sup>), leading to the assumption that it did not pose any significant threat to the settlements downstream. It can be challenging to leave out lakes that may be dangerous, as the same was demonstrated by the 2013 Chorabari lake outburst event (Das et al. 2015; Bhambri et al.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "text", "line_start": 325, "line_end": 329, "token_count_estimate": 553, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11b020bfb2b951a1", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure\n\nImage /page/22/Figure/1 description: This image is Figure 14, which displays flood depth and velocity profiles for a downstream flow channel. The figure is divided into two main horizontal sections. The top section shows flood depth, and the bottom section shows flood velocity. Each section contains three maps labeled (A), (B), and (C), which depict different segments of a river flowing through a mountainous terrain. For the top section, a color scale represents 'Flood Depth (m)', ranging from a low of 0.001 (blue) to a high of 17.60 (red). For the bottom section, a color scale represents 'Flood Velocity (m/s)', ranging from a low of 0.001 (green) to a high of 23.78 (red). The maps include geographical coordinates, scale bars in kilometers, and a north arrow on the 'C' panels.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 330, "line_end": 330, "token_count_estimate": 237, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4428c7c3dcd12f1", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure: Figure 14. Flood depth and velocity profiles in the downstream flow channel in the worst case scenario (100% volume). Parts A, B and C represent 1st, 2nd and 3rd sections along the flow channel.\n\nFigure 14. Flood depth and velocity profiles in the downstream flow channel in the worst case scenario (100% volume). Parts A, B and C represent 1st, 2nd and 3rd sections along the flow channel.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 14. Flood depth and velocity profiles in the downstream flow channel in the worst case scenario (100% volume). Parts A, B and C represent 1st, 2nd and 3rd sections along the flow channel.", "line_start": 332, "line_end": 332, "token_count_estimate": 123, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d01e2ca8c8deeab", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: text\n\n2016). According to several studies, small glacial lakes have the potential to seriously harm local livelihoods and cause major problems, as demonstrated by the Domkhar food in Ladakh (Ikeda et al. 2016) and other minor foods in Hunza region (Ashraf et al. 2021). The triggering factors of the GLOF are often dynamic and may change from time to time both quantitatively and qualitatively and hence require regular monitoring. e.g. Lakes that are stable as such, may get exposed to unstable", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "text", "line_start": 333, "line_end": 335, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2a4dea0c7bf7d59", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure\n\nImage /page/23/Figure/3 description: A figure with two side-by-side panels, each showing a map with a flood depth overlay and an inset graph of water velocity over time. The left panel is located at 75°57'0\"E and shows a map with a settlement labeled \"Settlement 1\". The flood depth is represented by a color scale from blue (Low: 0.001 m) to red (High: 17.60 m). The inset graph shows velocity in m/s on the y-axis and time in hh:mm on the x-axis, from 01:26 to 03:50. The velocity peaks at over 10 m/s around 02:00. The right panel is located at 75°58'0\"E and 34°8'0\"N and shows a map with a settlement labeled \"Settlement 2\". It uses the same depth color scale. The inset graph shows velocity peaking at over 5 m/s around 02:00 over the same time period. Both maps include a north arrow.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 336, "line_end": 336, "token_count_estimate": 262, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e5c0d85560aa347f", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure: Figure 15. Flood depth and flood velocity curves at settlements 1 and 2 in Panikhar village.\n\nFigure 15. Flood depth and flood velocity curves at settlements 1 and 2 in Panikhar village.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 15. Flood depth and flood velocity curves at settlements 1 and 2 in Panikhar village.", "line_start": 338, "line_end": 338, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9635ed4bdef928d5", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: table\nTable: Table 5. Flood parameters at various locations of the flow path downstream of Panikhar lake for the worst case scenario i.e.100% water release.\n\n| Location               | Distance from the Lake (km) | Overtopping Failure |                  |                        | Pipping Failure   |                  |                        |\n|------------------------|-----------------------------|---------------------|------------------|------------------------|-------------------|------------------|------------------------|\n|                        |                             | Mean Depth (m)   | Max Depth (m) | Peak Velocity (m/s) | Mean Depth (m) | Max Depth (m) | Peak Velocity (m/s) |\n| Settlement 1           | 15.6                        | 4.35                | 7.97             | 11.35                  | 3.05              | 6.61             | 12.36                  |\n| Suru valley Bridge  | 16.11                       | 2.25                | 4.16             | 9.05                   | 1.96              | 4.29             | 8.66                   |\n| Settlement 2           | 18.01                       | 4.63                | 7.65             | 5.27                   | 4.71              | 7.67             | 3.70                   |\n| Bridge 2               | 19.08                       | 2.73                | 5.33             | 5.82                   | 1.97              | 3.99             | 5.36                   |\n| Picnic Spot Damasna | 22.50                       | 1.70                | 6.96             | 6.615                  | 5.13              | 6.70             | 7.187                  |", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "table", "table_caption": "Table 5. Flood parameters at various locations of the flow path downstream of Panikhar lake for the worst case scenario i.e.100% water release.", "columns": ["Location", "Distance from the Lake (km)", "Overtopping Failure", "", "", "Pipping Failure", "", ""], "table_row_start": 1, "table_row_end": 6, "line_start": 342, "line_end": 349, "token_count_estimate": 371, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "87f75c4d6c7aaa81", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure\n\nImage /page/23/Picture/7 description: A composite figure displaying satellite maps of a river valley to highlight specific locations and features. The main panel on the left shows a winding river in a mountainous region, with a green shaded area along its banks. The river's flow direction is indicated by a white arrow pointing upwards. Several locations are marked with red pins and labeled, including \"Settlement 1\", \"Suru Valley Bridge\", \"Settlement 2\", \"Bridge 2\", and \"Picnic Spot Damasna\". On the right, there are three zoomed-in panels. The top panel shows a close-up of \"Settlement 2\", with red arrows pointing to \"Houses\". The middle panel is a close-up of \"Bridge 2\", with red arrows indicating \"Bridge 2 and buildings\". The bottom panel provides another view of the area, with a red oval highlighting \"Roads and houses in Settlement 2\".", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 351, "line_end": 351, "token_count_estimate": 252, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c8a53e18d8843010", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: figure\nFigure: Figure 16. Inundation area in scenario 2 (100% water release) with some important areas with buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation.\n\nFigure 16. Inundation area in scenario 2 (100% water release) with some important areas with buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 16. Inundation area in scenario 2 (100% water release) with some important areas with buildings, roads, bridges and other infrastructure under potential threat of GLOF inundation.", "line_start": 353, "line_end": 353, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3fe318115449cedb", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 7. Discussion\nType: text\n\nsurrounding slopes or ice avalanche zones after expanding into the glacial valleys thus enhancing their susceptibility (Haeberli et al., 2017).\n\nAs moraine and ice-dammed lakes are more vulnerable to GLOF, they should receive greater attention when it comes to GLOF prioritization. Also, lakes that are in close proximity to the parent glacier are more likely to produce a GLOF as these lakes are more at threat of Icefalls and glacial calving activity. The likelihood of piping activity leading to a dam collapse is higher in lakes with underground drainage (Narama et al. 2018). Additionally, because thermokarst processes may block the intra-moraine channels, which can result in overflow and subsequent lake outburst through breaching, glacial lakes with subterranean drainage are more prone to GLOFs unlike the ones with stable surface drainage (Narama et al. 2018; Petrakov et al. 2020). Lakes with smaller dam width-to-height ratios and lower freeboard levels should be the focus of the GLOF study as they are important indicators of the dam's stability and can be estimated using high-resolution satellite imagery.\n\nStudies similar to the present study had been previously conducted for the South-Lhonak Lake in Sikkim, which, later on, witnessed a severe GLOF event this year killing at least 40 people. However, effective measures were not taken to prevent this disaster which could have been easily done through certain practical measures taken on time. To avoid such tragedies in the future, a closer examination of the GLOF hazard is necessary, aided by more precise tools and dependable datasets. However, scientific knowledge of the environmental processes at play in the Himalayan region is often hampered by the sparse network of monitoring sites required to generate an ample amount of reliable meteorological and glaciological data. Regularly updated weather data coupled with high-resolution imagery, DEMs and detailed inventories can help in understanding the processes of the Himalayan cryosphere much more closely to prevent any future disasters. In addition to fine satellite datasets, bathymetric surveys are needed to estimate lake volumes more precisely because, even in similar geographic locations, lakes' sizes are not always equally predictable (Cook and Quincey 2015). Although there are numerous empirical equations devised for the estimation of the depth and volume of the lakes, there is always a chance of over or underestimation of these attributes. Hence bathymetric datasets can be extremely helpful in the estimation of the lake volume and the potential maximum discharge from a glacial lake in case of a GLOF event.\n\nMapping the societal elements of risk associated with the GLOF disaster is a largely ignored domain in the Himalayan region. At the national and state levels, GLOF risk profiles, that map the exposure and vulnerability of human life and property to this specific cryospheric danger must be produced and regularly updated. India lacks an early warning system infrastructure for the GLOF disaster even when the northern and the northeastern states of the country are under serious GLOF danger. Hence effective early warning systems need to be installed at critically hazardous glacial lakes that will help in evacuating the vulnerable population at the time of a future GLOF disaster and evading major risks associated with human life and key infrastructure such as the hydroelectric projects.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "7. Discussion", "section_headings": ["7. Discussion"], "chunk_type": "text", "line_start": 354, "line_end": 362, "token_count_estimate": 808, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "36deb3be0c631468", "text": "Document: Glacial lake outburst flood risk assessment\nSection: 8. Conclusion\nType: text\n\nThe glacier-lake interactions were studied for the Panikhar glacial lake and its feeding glacier for the past 22 years. We found that the glacial lake has undergone a remarkable expansion during this period. The area of the lake has increased by 0.37 km<sup>2</sup>, from\n\n~0.10 km<sup>2</sup> in 2000 to ~0.47 km<sup>2</sup> in 2022, showing a net increase of 78.7%, at the rate of 0.017 km<sup>2</sup> per year while as its mother glacier has retreated significantly from ~4.63 km<sup>2</sup> in 2000 to ~4.02 km<sup>2</sup> in 2022 showing a total decrease of 13.2% in its total area i.e. 0.03 km<sup>2</sup> per annum. The glacier and glacial lake area plots show an inverse relationship which shows that the glacial lake is rapidly expanding at the expense of its mother glacier. The mass balance estimate of the Panikhar glacier also reveals a mass loss of 5.92 metric tons per square metre and an average thickness loss of 7.04 m over the past 22 years. The glacial lake is impounded by the terminal moraine of the glacier, rendering it hazardous, due to its swift rate of expansion and various other contextual factors. Notably, the presence of zones susceptible to potential mass movement in the surrounding areas also contributes to the heightened risk associated with this lake. The mean surface velocity and thickness of the glacier were estimated to be 3.38 m s<sup>-1</sup> and 30.95 m respectively. The potential outburst flood was modeled for two different GLOF scenarios using the HEC-RAS tool to generate flood depth and velocity profiles along the flow path for a distance of 25 km. The results reveal that the glacial lake is a potential threat to the downstream villages, especially the nearby Panikhar village. A total area of 5.38 km<sup>2</sup> which includes key infrastructure such as settlements, bridges, and roads is vulnerable to a potential GLOF event in the worst-case scenario. The routed hydrographs indicate that the flood wave generated at the time of an overtopping failure will arrive at the nearest settlement in 50 min with a peak velocity of 12.36 m/s. This study suggests regular monitoring of the lake and the installation of an early warning system (EWS) to mitigate and manage the risk from the lake in the future, especially at the time of an extreme weather event.", "metadata": {"source_file": "data/('Glacial lake outburst flood risk assessment', '.pdf')_extraction.md", "document_title": "Glacial lake outburst flood risk assessment", "section_path": "8. Conclusion", "section_headings": ["8. Conclusion"], "chunk_type": "text", "line_start": 364, "line_end": 368, "token_count_estimate": 631, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ba6a3c24d4f2b44", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nGlaciers are formed by accumulation of snow at higher altitudes where the average temperature lies in the freezing zone. As the accumulation increases, deeply buried snowflakes get tightly packed and crystallized, resulting in glacial ice formation. As the average global temperature has increased due to heightened anthropogenic activities, the glaciers have receded significantly, resulting in formation of a greater number of moraine-impounded glacier lakes [1-4]. The surface area and the depth of these glacial lakes are growing as a result of glacial retreat, raising the possibility of breaching of moraine dams. Breaching of these dams can further be triggered by extreme precipitation, permafrost deterioration, and dead-ice melting [5]. This will result in instant release of large quantities of sediment-laden glacial water that can ignite vast damage to the downstream parts of the dams. These events, known as Glacial Lake Outburst Floods (GLOFs), can get aggravated if erodible materials are present [6]. Due to its high velocity, continued entrainment of sediment in its flow path, and tendency to flow along existing river channels for very long runout distances, these debris floods/flow events pose significant threat to the lives and property located along its flow channel [6].\n\nIn India, the Kedarnath disaster in 2013 [7], and the Chamoli disaster in 2021 [6], caused by cloud outbursts and ice/rock avalanches, respectively, claimed thousands of lives and caused substantial socio-economic losses. Two series of occurrences struck Kedarnath in 2013: the first resulted in landslides, breaching, blockades, flooding, and failures of river banks; the second was outburst flooding in Chorabari Tal Lake in conjunction with erosion of the terrain and concomitant landslides. In the Chamoli Disaster in 2021, a significant amount of rubble blocks fell into the glacial pocket,\n\nDepartment of Civil Engineering, Indian Institute of Technology Jammu, Jagti 181221, India e-mail: 2021pce1008@iitjammu.ac.in\n\nP. Mahajan ( $\\boxtimes$ ) · R. Kumar · R. Bhowmik\n\nforcing the lake to burst. This event had endangered lives and inflicted significant financial losses. Nie et al. [8] inventoried and studied over 60 GLOF events in the Himalayan region alone and reported the estimated losses to be over hundreds of billion dollars. Thus, implementation of adequate mitigation measures and land-use and development plans is vital while developing infrastructure in the mountainous regions.\n\nThis paper aims to inventory the reported GLOF events, regarding their causes, the methodologies employed for their study, and predictive dam-breach models reported in the literature. The outcome of this study will aid in consolidating the knowledge developed regarding the effect of climate change, especially on the young and fragile Himalayan region.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 4, "line_end": 16, "token_count_estimate": 699, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["181221"]}}
{"id": "137263a51d7d774a", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs\nType: text\n\nClassification of Glacial lakes into different categories plays a vital role in understanding their origin and evolution. However, there is presently no globally accepted classification system for glacial lakes. Numerous researchers assigned classifications to the glacial lakes in accordance with their knowledge of the region's morphology. In 2018, ICIMOD [9] classified glacial lakes into four groups and seven sub-groups, as shown in Table 1.\n\nAn attempt has also been made to summarize the reported cases of moraine-impounded glacial lakes with the objective of understanding the causes of their formation and to study the areas likely to be affected if failure occurs. This summary is given in Table 2. Table 2 further summarizes the volume, depth, and peak discharge of the lakes, probable triggering factors, and methodologies employed to analyze the probability of the GLOF events.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs", "section_headings": ["2 Glacial Lakes and GLOFs"], "chunk_type": "text", "line_start": 18, "line_end": 22, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "776fe4ec3c292fcd", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools\nType: text\n\nIn conventional times, data was collected from the topographic maps available by reputed agencies to identify the aerial extent of the glacial lake. With the increase in advancement of technology, various remote sensing (RS) techniques such as multispectral scanner (MSS) [4], linear imaging self-scanning (LISS) [12] satellite data, and thematic mapper (TM) [4] tools are used, provided a minimum cover of snow and cloud is present in the sites. Data elevation models (DEMs) such as SRTM [1] and ASTER [5] are also used to recreate the digital elevation models of the sites and conduct in-depth studies of the concerned areas.\n\nIn Zanskar Basin [4], the conventional methodology of using topographic maps is utilized in conjunction with remote sensing methodologies like multi-spectral", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.1 Remote Sensing Tools"], "chunk_type": "text", "line_start": 26, "line_end": 32, "token_count_estimate": 220, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ab2a77185271760", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools\nType: table\nTable: Table 1 ICIMOD classification of Glacial lakes (ICIMOD [9])\n\n| Sl. No | Glacial lak                   | e type                                   | Description                                                                                                                                                                                                         |\n|-----------|-------------------------------|------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| 1         | Moraine dammed lake (M) | End moraine dammed lake                  | The lake's water usually touches the walls of the side moraines, but the water is held back by the end moraine (dam), but not necessarily, in contact with the glacier, and may have glacier ice at the lake bottom |\n| 2         |                               | Lateral moraine dammed lake              | Lake formed in the tributary valley, trunk valley, or between the lateral moraine and the valley wall, or at the junction of two moraines. Lake is held back by the outside wall of a lateral moraine      |\n| 3         |                               | Other moraine dammed lake                | Lake dammed by other moraines (including kettle lakes and thermokarst lakes)                                                                                                                                        |\n| 4         | Ice dammed                 | Supra-glacial lake                       | Body of water (ponds or lakes) on the surface of a glacier                                                                                                                                                          |\n| 5         | lake (I)                      | Dammed by tributary valley glacier | Lake dammed by glacier ice with no lateral moraines. Can be at the side of a glacier between the glacier's margin and valley wall                                                                                   |\n| 6         | Bedrock                       | Cirque lake                              | A small pond occupying a cirque                                                                                                                                                                                     |\n| 7         | dammed lake (B)            | Other glacier erosion lake               | Body of water occupying depressions formed by glacial erosion. These are usually located on the mid-slope of hills, but not necessarily in a cirque                                                                 |\n| 8         | Other dam                     | med lakes                                | Lakes formed in a glaciated valley and fed by glacier melt, but the damming material is not directly part of the glacial process, for example, debris flow, alluvial, or landslide-blocked lakes           |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.1 Remote Sensing Tools"], "chunk_type": "table", "table_caption": "Table 1 ICIMOD classification of Glacial lakes (ICIMOD [9])", "columns": ["Sl. No", "Glacial lak", "e type", "Description"], "table_row_start": 1, "table_row_end": 8, "line_start": 33, "line_end": 42, "token_count_estimate": 536, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c9fd4e8617b7d8ca", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools\nType: text\n\nscanner, linear imaging self-scanning, advanced space-borne thermal emission and reflection radiometer to determine the characteristics of the glacial lakes. In Chamlang Lake Tsho [5], images obtained from RS tools and photogrammetric techniques, and field-based methodologies, like sonar-sounding machine, GPS, and fishing line method were utilized to study the area. Figure 1 depicts the various steps involved in processing the data to recreate the profile of the glacial lakes.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.1 Remote Sensing Tools", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.1 Remote Sensing Tools"], "chunk_type": "text", "line_start": 43, "line_end": 45, "token_count_estimate": 149, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b9fc81f0a1669335", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: text\n\nFor the study of glacial lakes, empirical formulations and numerical modeling are often used. One of the widely used numerical tools is Hydrologic Engineering Centre's River Analysis System (HEC-RAS) [6]. This tool simulates GLOF phenomenon from the source to the downstream flow path locations. Using the model, cross-sectional measurements of the main river section at regular intervals can be determined, and the same can be utilized for routing floods. Hydrographs can be generated along different sections of the flow channel which can be further used to estimate the peak discharge intensity [1, 6]. However, there are some limitations of\n\n(continued)", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "text", "line_start": 47, "line_end": 51, "token_count_estimate": 189, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46360664de8d3dc0", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: table\nTable\n\n| e         |\n|-----------|\n| ă         |\n| 급         |\n| .2        |\n| gla       |\n| Į.        |\n| 0.        |\n| Sis       |\n| Ly.       |\n| Ωï        |\n| 9         |\n| Ŧ         |\n| ö         |\n| ψ         |\n| ior       |\n| at        |\n| Ę         |\n| sic       |\n| cons      |\n| <u>ت</u>  |\n| Ö         |\n| n T       |\n| <u>s</u>  |\n| ta        |\n| C         |\n| ž         |\n| Ĕ         |\n| Гa        |\n| pa        |\n| ō         |\n| а         |\n| g         |\n| <u>S</u>  |\n| 9         |\n| ğ         |\n| et;       |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "table", "table_caption": null, "columns": ["e"], "table_row_start": 1, "table_row_end": 35, "line_start": 52, "line_end": 107, "token_count_estimate": 255, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42667c8e0fda6b3b", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: table\nTable\n\n| e         |\n|-----------|\n| me        |\n| ŝ         |\n| se        |\n| an        |\n| дc        |\n| Ę         |\n| ō         |\n| də.       |\n| Į.        |\n| 0 /       |\n| ar,       |\n| Ξ         |\n| шш        |\n| Su        |\n|           |\n| e 2       |\n| Table 2   |\n| <u>Ta</u> |\n| F .       |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "table", "table_caption": null, "columns": ["e"], "table_row_start": 36, "table_row_end": 54, "line_start": 52, "line_end": 107, "token_count_estimate": 155, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c0dfdf8638c2591a", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: table\nTable\n\n| Table 2 Summary of reported causes, methodology and parameters taken for consideration for the analysis of glacial lake |                                                                  |                                                                               |                                                                                 |                                                                                                             |                                                                                     |                                      |                                      |                               |                            |\n|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------|-------------------------------------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|--------------------------------------|--------------------------------------|-------------------------------|----------------------------|\n| Sl. No                                                                                                                  | Events                                                           | Area                                                                          | Triggering factors                                                           | Tools                                                                                                       | Methodology                                                                         | Volume of lake (million m³) | Lake average depth (meters) | Peak discharge (cumecs) | References                 |\n| 1                                                                                                                       | Glacial lake (33° 09′ 32.56″ N and 76° 59′ 05.38″ E) | Zanskar Basin, J&K, India                                                  | Collapse of unstable natural dams                                         | Topographical maps in early days. Replaced by remote sensing using MSS, TM, LISS III, ASTER DEM | Monitoring of extent of glacial lake is observed                              | 4.2                                  | 10                                   | 1.7–196                       | Babu Govinda Raj [4] |\n| 2                                                                                                                       | Glacial Lake L2                                               | Dhauliganga River Basin, Pithoragarh Distt, Uttarakhand           | Lake area expansion, dead ice melting, seepage, seismic activity | HEC-RAS (hydrologic engineering centre’s river analysis system)                                       | Analysis of the glacial lake using numerical modeling                      | 2                                    | 30                                   | 4272                          | Jha and Khare [6]       |\n| 3                                                                                                                       | Lumi Chimi Lake                                               | Sun-Koshi Basin, Transboundary between Tibet (China) and Nepal | Retreat of glaciers                                                          | Dambreak and hydrodynamic modeling                                                                       | Risk assessment of the event before it occurs                                 | 307.2                                | 80                                   | 5040–8380                     | Shrestha et al. [1]     |\n| 4                                                                                                                       | Chamlang South Tsho                                           | Hongu valley, eastern Nepal Himalaya                                    | Lake area expansion, ice avalanches, rock falls, landslides         | Observational data, photographs, ground-based surveys, field and remote sensing data            | GLOF hazards, bathymetry, surrounding material decide the vulnerability | 34.9–35.6                            | 40.2                                 | Not available                 | Lamsal et al. [5]       |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "table", "table_caption": null, "columns": ["Table 2 Summary of reported causes, methodology and parameters taken for consideration for the analysis of glacial lake", "", "", "", "", "", "", "", "", ""], "table_row_start": 1, "table_row_end": 5, "line_start": 109, "line_end": 115, "token_count_estimate": 612, "basins": [], "subbasins": [], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "ecc7bfa73f704245", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: text\n\nReferences Gurung et al. [11] Aggarwal et al. [12] Liu et al. [3] et al. [10] Goodsell Not available discharge (cumecs) 2611.136 Peak 1270 1198 average (meters) depth Lake 6.29 14.1 25 37 Volume of available (million 42.93 0.37 0.97  $m^3$ Not position due to ice 51 houses swept away, 18 bridges channel shifted to 0.976 Nu. Million Five people died, washed away and outburst flooding using MIKE-11 Dam Break agricultural land more than CNY Analysis of the dangerous lake economic loss estimated loss Methodology Four bridges washed away, supra-glacial collapse and affected and 100 million Sub-glacial potential Model from Landsat and Indian Remote Sensing, MIKE-11 Dam Break Model observation and remote Satellite images, field Satellite images, field investigation and past studies Visual representation Satellite data, images data was utilized sensing data Tools composition of dam expansion, ice avalanches blackhole due to collapse of ice Formation of supra-glacial observations Earthquake, draining of temperature and rainfall, Triggering factors Lake area rainfall, Climatic material puod Westland Tai Poutini National Park North-western Bhutan Bomê County, South-eastern Tibet (China) New Zealand, Gasa District, Sikkim Area\n Table 2\n (continued)\n Guangxieco Lake Teesta basin Lemthang Events Joseph Glacier Franz Tsho SI. No S 9 \\_  $\\infty$\n\n(continued)\n\nReferences\n\nKhan et al. [13]\n\nSchneider et al. [2]\n\n9000-40,000 Peak discharge (cumecs) 4500 Volume of lakeLakelakeaverage(million depthdepthm³)(meters) Not available 21 0.2 - 0.4 $\\infty$ Santa Valley highway affected, people are prone to flood, Karakoram Highway Damaged several bridges swept away and agricultural land affected More than 2000 Methodology Images from Landsat and Sentinel, Global Digital Elevation Model (GDEM), ArcMap 10.5 Remote sensing images, digital elevation data, RAMMS and IBER model—numerical and physically based avalanche and debris Tools flow Blockage of melt water originating from Mochuwar Triggering factors Rock-ice avalanche glacier Gilgit-Baltistan, Pakistan Carhuaz, Peru Area Table 2 (continued) Shishper Glacier lake Lake 513 Sl. Events\nNo 10 6", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "text", "line_start": 116, "line_end": 132, "token_count_estimate": 589, "basins": [], "subbasins": ["Gilgit", "Teesta"], "countries": ["Bhutan", "China"], "lake_ids": []}}
{"id": "b7e5a21763bafad9", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A flowchart illustrating a three-step process, with each step represented by a blue, right-pointing chevron arrow. The first step is labeled \"Image Processing to Delineate Glacial Lakes.\" The second step is \"Stereo-data Processing for 3D Topographic Maps.\" The third and final step is \"Lake Bathymetry.\" The text within each chevron is white.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "figure", "figure_caption": null, "line_start": 133, "line_end": 133, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ece33cfeeb84fbd", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling\nType: text\n\nFig. 1 Steps involved for the interpretation for the data\n\nthis model, one of which is its inability to consider debris and sediment-laden water in the simulations. In the study on the Dhauliganga Basin [6], HEC-RAS was used to create the glacial lake representation. HEC-RAS was also used to generate the hydrological model for the Mandakini River [7].", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.1 Methodologies > 2.1.2 Numerical Modeling", "section_headings": ["2 Glacial Lakes and GLOFs", "2.1 Methodologies", "2.1.2 Numerical Modeling"], "chunk_type": "text", "line_start": 134, "line_end": 138, "token_count_estimate": 116, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9403d0ebcca63b37", "text": "Document: 1 Introduction\nSection: 2 Glacial Lakes and GLOFs > 2.2 Empirical Equations for Discharge Estimation and Stability of Moraine Dams\nType: text\n\nThe simplest and fastest methods for determining the various parameters required for vulnerability assessment of moraine dams are empirical relationships. Empirical relationships, on the other hand, do not incorporate fundamental rules of fluid mechanics and hydraulics as they are derived through statistical study of historical events. Various researchers have proposed empirical equations to determine peak discharge values based on estimated average breach width and time of failure in moraine dams [3].\n\nPeak discharge is an important consideration for the assessment because it helps to visualize the size of the flood downstream. The findings of the multiple regression analysis showed that compared to other factors like embankment width and embankment length  $(E_1)$ , empirical relationships with parameters like volume of water  $(V_w)$  and height of water  $(H_w)$  gave more precise forecasts of maximum flow discharge  $(Q_{\\text{max}})$  [14]. The models that predict peak outflow using only  $H_w$  and  $V_w$  do not considerably benefit from the addition of  $E_1$  and  $Q_{\\text{max}}$  to the relationship, making  $H_w$  and  $V_w$  more significant features to define peak discharge compared to other parameters. Table 3 shows the summary of the empirical relationships developed by various researchers.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Glacial Lakes and GLOFs > 2.2 Empirical Equations for Discharge Estimation and Stability of Moraine Dams", "section_headings": ["2 Glacial Lakes and GLOFs", "2.2 Empirical Equations for Discharge Estimation and Stability of Moraine Dams"], "chunk_type": "text", "line_start": 140, "line_end": 144, "token_count_estimate": 349, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3564c33d761f1c05", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: text\n\nUsing the empirical relationships mentioned in Table 3, the average breach width B, time of failure  $t_f$ , and peak discharge values for 15 different cases of moraine dam are estimated and tabulated in Table 4. The results showed that when the glacial lake's volumetric capacity increases, the peak discharge of the lake also consistently rises.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "text", "line_start": 146, "line_end": 150, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0545afac780be606", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: table\nTable: Table 3 Summary of empirical relationships\n\n| <b>Table 3</b> Summary of empirical relationships |                             |                                                                                                   |                                                      |\n|---------------------------------------------------|-----------------------------|---------------------------------------------------------------------------------------------------|------------------------------------------------------|\n| Sl. No                                            | Parameter                   | Lake volume (m3)**                                                                                | References                                           |\n| 1                                                 | Lake volume (m3)         | $V = 0.104A^{1.42}$                                                                               | Huggel et al. [15]                                   |\n| 2                                                 |                             | $V = 0.035A^{1.5}$                                                                                |                                                      |\n| 3                                                 |                             | $V = 0.104A^{1.42}$                                                                               | Evans [16]                                           |\n| 4                                                 |                             | $V = A * MD$                                                                                      | Wang et al. [17]                                     |\n| 5                                                 | Lake depth (m)              | $D = 0.104A^{0.42}$                                                                               | Huggel et al. [15]                                   |\n| 6                                                 |                             | $MD = 4A * 10^{-5} + 5.0564$                                                                      | Wang et al. [17]                                     |\n| 7                                                 |                             | $D = 0.087A^{0.434}$                                                                              | Patel et al. [18]                                    |\n| 8                                                 | Average breach width (m) | $B = 0.1803V_{W}^{0.32}h_{b}^{0.19}$                                                              | Froehlich [19]                                       |\n| 9                                                 | Time of failure (h)      | $t_{f} = 0.00254V_{W}^{0.53}h_{b}^{-0.90}$                                                        |                                                      |\n| 10                                                | Peak discharge (m3s-1)   | $Q_{max} = 75V^{0.67}$                                                                            | Clague and Mathews [20]                              |\n| 11                                                |                             | $Q_{max} = 0.72V^{0.53}$                                                                          | Evans [16]                                           |\n| 12                                                |                             | $Q_{max} = 0.0048V^{0.896}$                                                                       | Popov [21]                                           |\n| 13                                                |                             | $Q_{max} = 0.00077V^{1.017}$                                                                      | Huggel et al. [15]                                   |\n| 14                                                |                             | $Q_{max} =$ $1.165(\\frac{L}{B})^{\\frac{1}{10}}(\\frac{B}{b})^{\\frac{1}{3}}b(H-h)^{\\frac{3}{2}}$ | Lue et al. [22]                                      |\n| 15                                                |                             | $Q_{max} = 0.00013(PE)^{0.60}$                                                                    | Huggel et al. [15]                                   |\n| 16                                                |                             | $Q_{max} = \\frac{2V}{T_{w}}$                                                                      | Huggel et al. [15]; Popov [21], and Haeberli [23] |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "table", "table_caption": "Table 3 Summary of empirical relationships", "columns": ["Table 3 Summary of empirical relationships", "", "", ""], "table_row_start": 1, "table_row_end": 17, "line_start": 151, "line_end": 169, "token_count_estimate": 725, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00013", "00077V", "00254V_", "0048V", "1803V_"]}}
{"id": "e8ddc26ca508d6c7", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: text\n\n<sup>\\*\\*</sup>Where V is the volume of glacier lake; A is an area of glacier lake in  $m^2$ ; MD is the average depth of glacial lake in m;  $V_w =$  reservoir volume in  $m^3$ ;  $h_b$  is the height of water above breach invert level; L is the lake's length (m), B is the breach's maximum width (m), b is its avg width (m), H is the lake's maximum depth (m), and h is height of remaining dam (m)\n\nHowever, it has been observed that the average breach width, B, and time of failure,  $t_f$ , change in proportion to the mean bathymetric depth, as shown in Table 5.\n\nFor the Safed glacial lake located in Goriganga Basin, Uttrakhand, India [34], Table 5 shows the parametric effect of the breach depth and volume released on the average breach width and time of the failure. When the breach depth is reduced to half, as in Scenario 2, it is shown that the breach breadth decreases by 30%. The average breach width further decreases by 30% if the drop continues to 15 m, as shown in Scenario 2. When the time of failure is taken into account, it can be seen that the moraine dam's failure time increases by 29 and 30% in comparison to scenarios 1 and 2, respectively, based on the equations proposed by Froehlich, 1995 [12]. This shows that the time of failure is directly connected to volume released but indirectly related to breach width, whereas the breach width is directly related to volume released but indirectly related to breach depth. Due to the inverse relationship between average breach width and time of failure, the breach depth has a significantly\n\n(continued)\n\n Summary of physical information about glacial lakes including average breach width and time of failure", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "text", "line_start": 170, "line_end": 181, "token_count_estimate": 471, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5af4fbad4ab1dd07", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: table\nTable: Table 4\n\n| <b>Table 4</b> Summary of physical information about glacial lakes including average breach width and time of failure |                  |                  |                    |                |                |                                                                                    |                                                                                |\n|-----------------------------------------------------------------------------------------------------------------------|------------------|------------------|--------------------|----------------|----------------|------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|\n| Sl. No                                                                                                                | Lake             | Area (m2, × 106) | Volume (m3, × 106) | Mean depth (m) | Type of lake   | Average breach width <i>B</i> = 0.1803*1.4* <i>V</i> w0.32 <i>h</i> b0.19 | Time of failure <i>t</i> f = 0.00254 <i>V</i> w0.53 <i>h</i> b(−0.90) |\n| 1                                                                                                                     | Ice Cave Lake    | 0.0035           | 0.01               | 2.9            | Ice-dammed     | 5.9                                                                                | 0.1                                                                            |\n| 2                                                                                                                     | Gruben Lake 5    | 0.01             | 0.05               | 5              | Thermokarst    | 10.9                                                                               | 0.2                                                                            |\n| 3                                                                                                                     | Crusoe-Baby Lake | 0.017            | 0.08               | 4.7            | Ice-dammed     | 12.6                                                                               | 0.3                                                                            |\n| 4                                                                                                                     | Gruben Lake 3    | 0.021            | 0.15               | 7.1            | Ice-dammed     | 16.6                                                                               | 0.2                                                                            |\n| 5                                                                                                                     | Gruben Lake 1    | 0.023            | 0.24               | 10.4           | Moraine-dammed | 20.8                                                                               | 0.2                                                                            |\n| 6                                                                                                                     | MT' Lake         | 0.0416           | 0.5                | 12             | Ice-dammed     | 27                                                                                 | 0.3                                                                            |\n| 7                                                                                                                     | Lac d'Arsine     | 0.059            | 0.8                | 13.6           | Moraine-dammed | 32.1                                                                               | 0.3                                                                            |\n| 8                                                                                                                     | Nostetuko lake   | 0.2622           | 7.5                | 28.6           | Moraine-dammed | 75.7                                                                               | 0.5                                                                            |\n| 9                                                                                                                     | Between Lake     | 0.4              | 7.5                | 18.8           | Ice-dammed     | 69.9                                                                               | 0.8                                                                            |\n| 10                                                                                                                    | Abmachimai Co    | 0.565            | 19.4               | 34.3           | Moraine-dammed | 106.2                                                                              | 0.8                                                                            |\n| 11                                                                                                                    | Gjanupsvatn      | 0.6              | 20                 | 33.3           | Ice-dammed     | 106.6                                                                              | 0.8                                                                            |\n| 12                                                                                                                    | Quongzonk Co     | 0.753            | 21                 | 27.9           | Moraine-dammed | 104.7                                                                              | 1                                                                              |\n| 13                                                                                                                    | Laguna Parón     | 1.6              | 75                 | 46.9           | Moraine-dammed | 173.6                                                                              | 1.2                                                                            |\n| 14                                                                                                                    | Summit Lake      | 5                | 250                | 50             | Ice-dammed     | 258.4                                                                              | 2.1                                                                            |\n| 15                                                                                                                    | Phantom Lake     | 6                | 500                | 83.3           | Ice-dammed     | 355.4                                                                              | 1.9                                                                            |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "table", "table_caption": "Table 4", "columns": ["Table 4 Summary of physical information about glacial lakes including average breach width and time of failure", "", "", "", "", "", "", ""], "table_row_start": 1, "table_row_end": 16, "line_start": 182, "line_end": 199, "token_count_estimate": 780, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00254"]}}
{"id": "a2c3191adf99e88e", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: table\nTable\n\n| 5 |\n|---|\n|---|", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "table", "table_caption": null, "columns": ["5"], "table_row_start": 1, "table_row_end": 1, "line_start": 201, "line_end": 203, "token_count_estimate": 37, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "970c5c8ce1b99505", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: table\nTable\n\n| Discharge                  |                          |                                    |                                   |                          |                         | Reference                     |                               |\n|----------------------------|--------------------------|------------------------------------|-----------------------------------|--------------------------|-------------------------|-------------------------------|-------------------------------|\n| Glacier, tunnel events     |                          | Glacier, non-tunnel events         |                                   | Moraines                 | Earth- and rock-fill |                               |                               |\n| Clague and Mathews [20] | Walder and Costa [24] | Haeberli [23]                      | Walder and Costa [24]          | Popov [21]               | Evans [16]              |                               |                               |\n| $Qmax = 75(V/106)0.67$     | $Qmax = 46(V/106)0.66$   | $Qmax = 2 V/tw$ where $tw =1000 s$ | $Qmax = 1100(V/106)0.44$          | $Qmax = 0.0048 V0.896$   | $Qmax = 0.72 V0.53$     |                               |                               |\n| 3.43                       | 2.2                      | 20                                 | 145.01                            | 18.42                    | 94.91                   | Maag and Janischowsky [25] |                               |\n| 10.08                      | 6.37                     | 100                                | 294.4                             | 77.9                     | 222.73                  | Clague and Mathews [20]    |                               |\n| 13.81                      | 8.69                     | 160                                | 362.04                            | 118.69                   | 285.74                  | Maag and Janischowsky [25] |                               |\n| 21.04                      | 13.15                    | 300                                | 477.39                            | 208.46                   | 398.71                  | Kääb and Haeberli [26]     |                               |\n| 28.83                      | 17.93                    | 480                                | 587.06                            | 317.62                   | 511.5                   | Kääb and Haeberli [26]     |                               |\n| 47.14                      | 29.11                    | 1000                               | 810.85                            | 613.08                   | 754.72                  | Blown and Church [27]      |                               |\n| 64.58                      | 39.7                     | 1600                               | 997.13                            | 934.14                   | 968.21                  | Vallon [28]                   |                               |\n| 289.3                      | 173.9                    | 15,000                             | 2669.44                           | 6938.99                  | 3170.41                 | Evans and Clague [29]      |                               |\n| 289.3                      | 173.9                    | 15,000                             | 2669.44                           | 6938.99                  | 3170.41                 | Maag and Janischowsky [25] |                               |\n| 546.88                     | 325.62                   | 38,800                             | 4055.32                           | 16,259.66                | 5246.47                 | Meon and Schwarz [30]      |                               |\n| Table 4 (continued)        |                          |                                    |                                   |                          |                         |                               |                               |\n| Sl. No                     | Discharge                |                                    |                                   |                          |                         |                               | Reference                     |\n|                            | Glacier, tunnel events   |                                    | Glacier, non-tunnel events        |                          | Moraines                | Earth- and rock-fill          |                               |\n|                            | Clague and Mathews [20]  | Walder and Costa [24]              | Haeberli [23]                     | Walder and Costa [24]    | Popov [21]              | Evans [16]                    |                               |\n|                            | $Qmax = 75(V/106)0.67$   | $Qmax = 46(V/106)0.66$             | $Qmax = 2 V/tw where tw = 1000 s$ | $Qmax = 1100(V/106)0.44$ | $Qmax = 0.0048 V0.896$  | $Qmax = 0.72 V0.53$           |                               |\n| 11                         | 558.15                   | 332.23                             | 40,000                            | 4110.04                  | 16,709.53               | 5331.86                       | Costa and Schuster [31]       |\n| 12                         | 576.7                    | 343.1                              | 42,000                            | 4199.23                  | 17,456.2                | 5471.53                       | Meon and Schwarz [30]         |\n| 13                         | 1353.17                  | 794.87                             | 150,000                           | 7352.24                  | 54,613.01               | 10,742.74                     | Lliboutry et al. [32]         |\n| 14                         | 3031.66                  | 1759.54                            | 500,000                           | 12,487.81                | 160,618.47              | 20,334.84                     | Mathews and Clague [33]       |\n| 15                         | 4823.6                   | 2780.21                            | 1,000,000                         | 16,941.02                | 298,894.8               | 29,362.07                     | Maag and Janischowsky [25] |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "table", "table_caption": null, "columns": ["Discharge", "", "", "", "", "", "Reference", ""], "table_row_start": 1, "table_row_end": 23, "line_start": 205, "line_end": 229, "token_count_estimate": 1159, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "75ba124b20a66d58", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: table\nTable\n\n| Sl. No | GLOF scenarios | Breach depth (hb) | Volume released (106 × m3) | Average breach width B = 0.1803*1.4*Vw0.32 hb0.19 | Time of failure tf = 0.00254Vw0.53 hb(-0.90) |\n|-----------|----------------|----------------------|-------------------------------|---------------------------------------------------------|----------------------------------------------------|\n| 1         | Scenario 1     | 60                   | 4.34                          | 73.10                                                   | 0.21                                               |\n| 2         | Scenario 2     | 30                   | 2.17                          | 51.30                                                   | 0.27                                               |\n| 3         | Scenario 3     | 15                   | 1.08                          | 36.00                                                   | 0.35                                               |", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No", "GLOF scenarios", "Breach depth (hb)", "Volume released (106 × m3)", "Average breach width B = 0.1803 1.4 Vw0.32 hb0.19", "Time of failure tf = 0.00254Vw0.53 hb(-0.90)"], "table_row_start": 1, "table_row_end": 3, "line_start": 231, "line_end": 235, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "538b88a0baba4bea", "text": "Document: 1 Introduction\nSection: 3 Estimation of Peak Discharge of Reported Cases\nType: text\n\nTable 5 Parametric effect of the breach depth and volume released\n\nmore noticeable effect even when the effect of volume released is assumed to be the same in all circumstances.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Estimation of Peak Discharge of Reported Cases", "section_headings": ["3 Estimation of Peak Discharge of Reported Cases"], "chunk_type": "text", "line_start": 236, "line_end": 240, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4cc0518e9d6a3d82", "text": "Document: 1 Introduction\nSection: 4 Concluding Remarks\nType: text\n\nThe high-altitude glaciers have suffered greatly as a result of extensive anthropogenic activities that led to climate change. In areas where glacial lakes are growing in lateral extent and bathymetry, increasing possibility of higher peak discharges and GLOFs are risking the lives of the local inhabitants, local flora, fauna, and other infrastructure.\n\nIn this paper, reported studies on glacial lakes are examined in order to ascertain their formational behavior, its after-effects, and potential corrective actions. The approaches that are utilized globally to anticipate the size of these glaciers include remote sensing and numerical simulation utilizing HEC-RAS. These lakes have been categorized by ICIMOD based on the failure behavior that is anticipated to occur in the future. In order to predict the peak discharge, breach width, and time of failure of various structures that result in the abrupt discharge of enormous amounts of water in the downstream region, numerous empirical equations have been developed and reviewed in this paper. The established empirical relationships are then used to estimate the average breach width, B, time of failure,  $t_f$ , and peak discharge for 15 identified cases of glacial lakes.\n\nThe peak discharge values aid in identifying the glacial lake's susceptibility in the downstream sections of the impacted area. The results show that as the depth of the breach grows, so does the likelihood of failure. If the breach depth is reduced by 50%, the time of failure increases by 28–29%, and the average breach width drops by about 30%.\n\nReviewing all the various glacial lakes enables us to spot certain significant problems that, if resolved, can reduce the impact of GLOF to some level: (a) Quantification of the climatic conditions in space—time and further determination of projections for the future, (b) establishment of a database that compiles data on glacier surface areas and their pace of retreat across time, (c) development of an algorithm that aids in projecting the size of lakes based on present-day spatial features, (d) development of hydrological models that account for potential changes in the regional availability of water.", "metadata": {"source_file": "data/('GlacialLakeOutburstFloods-AReviewofEventsCausesandImpact', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Concluding Remarks", "section_headings": ["4 Concluding Remarks"], "chunk_type": "text", "line_start": 242, "line_end": 250, "token_count_estimate": 530, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1315ce523b06067", "text": "Document: Author(s):\nSection: Author(s):\nType: text\n\nBy Christian Huggel, Alejo Cochachin, Fabian Drenkhan, Javier Fluixá-Sanmartín, Holger Frey, Javier García Hernández, Christine Jurt, Randy Muñoz, Karen Price, Luis Vicuña", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Author(s):", "section_headings": ["Author(s):"], "chunk_type": "text", "line_start": 4, "line_end": 6, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "49516ba7ce1e25f4", "text": "Document: Author(s):\nSection: Author(s):\nType: figure\nFigure\n\nImage /page/0/Picture/16 description: A wide-angle photograph of a majestic mountain landscape. In the background, a large, snow-covered mountain peak with glaciers reaches up into a blue sky with white clouds. Below the mountain, a vibrant turquoise alpine lake sits in a rocky basin, surrounded by steep, rugged cliffs. The foreground is a rocky slope with numerous large boulders and some sparse vegetation, offering a high vantage point looking down towards the lake.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Author(s):", "section_headings": ["Author(s):"], "chunk_type": "figure", "figure_caption": null, "line_start": 7, "line_end": 7, "token_count_estimate": 140, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4e45e9ceab1d69f8", "text": "Document: Author(s):\nSection: Author(s):\nType: figure\nFigure\n\nImage /page/0/Picture/17 description: An icon of an image file, depicting a green landscape, is shown to the left of the text 'Site location' in a dark sans-serif font on a white background.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Author(s):", "section_headings": ["Author(s):"], "chunk_type": "figure", "figure_caption": null, "line_start": 9, "line_end": 9, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b7081e7ab9299904", "text": "Document: Author(s):\nSection: Author(s):\nType: text\n\nSite location: a) Peru, b) Cordillera Blanca and c) the Hualcán-Carhuaz area (in dashed line).\n\nGlacier shrinkage – accelerated over the last decades due to climate change – is exposing large areas in mountain regions worldwide. But an even dire consequence of the melting ice is the\n\nforming of more glacier lakes, which are increasing in size. Glacial lakes have caused some of the world's most devastating floods, for example, in the Andes, Himalayas and Alps, where thousands of human lives were lost and huge infrastructure damages reported (Carrivick and Tweed, 2013; Bajracharya et al., 2007; Carey 2005). Climate change is rapidly reshaping living conditions in high mountains – altering flood patterns and creating new flood hazards – leaving populations at imminent risk in several regions (Cook et al., 2016; Emmer et al., 2015; Frey et al., 2016; Drenkhan e al., 2019).\n\nClimatic, glaciological and hydrological information and services can play an essential role for early detection of potential hazards and risks, and for effectively reducing risks. However, infrastructure for climate and associated services are poorly developed in many high mountain areas and need to be substantially strengthened. This report on the design, implementation, operation and circumstances around the setting up of an early warning system for glacier lake outburst floods (GLOFs) in the Peruvian Andes highlights the challenges related to accessing and installing equipment in many high mountains regions.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Author(s):", "section_headings": ["Author(s):"], "chunk_type": "text", "line_start": 10, "line_end": 18, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4f999e1618387f34", "text": "Document: Author(s):\nSection: Laguna 513 disaster\nType: figure\nFigure\n\nImage /page/1/Picture/5 description: An overview map displaying a mountainous region with a red line tracing the path of an event. The map features a large aerial view and a smaller, inset close-up view. The inset, outlined in a blue box, shows a snow-covered mountain labeled \"Mt. Hualcán\" and a turquoise lake labeled \"Laguna 513\". A red oval on the mountain peak is marked as the \"Detachment area (11 Apr 2010)\". The red line on the main map originates from this detachment area, flows down the mountain, and follows a valley path towards a town labeled \"Carhuaz\" and the \"Santa River\". A scale bar at the bottom right indicates a distance of 2 kilometers, and a north arrow is present for orientation.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Laguna 513 disaster", "section_headings": ["Laguna 513 disaster"], "chunk_type": "figure", "figure_caption": null, "line_start": 21, "line_end": 21, "token_count_estimate": 210, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d953cda9f5cbb91f", "text": "Document: Author(s):\nSection: Laguna 513 disaster\nType: text\n\nOverview map of Lake 513 and Carhuaz, indicating the 2010 ice avalanche source zone producing the GLOF that reached down to Carhuaz.\n\nThe Cordillera Blanca in the tropical Andes of Peru is a glaciated mountain range with a long history of disastrous GLOF events (Carey 2005; 2010). GLOF risks result from considerable physical hazard levels and the high levels of vulnerability and exposure of downstream populations (Frey et al., 2018). The Laguna 513 glacier lake (9°12'45\"S, 77°33'00\"W) is located at 4 428 metres (m) above sea level at the foot of Mount Hualcán (6 104 m) in the Santa River basin (Figs. 1, 2). The lake, which formed in the late 1960s as a result of glacier shrinkage, was declared highly dangerous in 1988 and subjected to exhaustive security works to artificially lower its level by some 20 m until 1994. This did not reduce GLOFs risk to zero, though the probability of occurrence and magnitude of GLOFs was substantially lowered. In 2004, authorities and specialists produced a report indicating that the lake could be considered safe due to the infrastructure in place (INDECI 2004; Muñoz et al. 2016).\n\nHowever, Laguna 513 was heavily impacted when a 450 000 cubic metres (m3) rock-ice avalanche detached from the southwest slope of Mt. Hualcán (Carey et al., 2012) (Fig. 2) on 11 April 2010, at about 8 a.m. local time. The avalanche caused a tsunami-like push-wave on the lake, resulting in a dam spillover despite the over 20 m freeboard. Traces of the wave indicate an overtopping of the dam by about 5 m – corresponding to a wave height of about 24 to 25 m – with a peak discharge of rate of several tens of thousands m3 per second (Schneider et al., 2014). The resulting GLOF damaged several bridges, water service infrastructure along its trajectory and eventually reached the debris fan of the city of Carhuaz (about 20 000 inhabitants), where the coarse material of the GLOF was deposited. A total of 0.7 km2 of agricultural land was buried and the Santa Valley highway was affected, but no lives were lost.\n\nLocal and national authorities, as well as Peruvian and international experts, met in the weeks following the disaster to discuss ways to better protect people and their assets in future incidents. As a result, plans for a GLOF Early Warning System (EWS) were initiated in 2011 and implemented within three years. The GLOF EWS, the first in the Andean region, was established in the framework of the Glacier Project (<a href=\"www.proyectoglaciares.pe\">www.proyectoglaciares.pe</a>) with financial support from the Swiss Agency for Development and Cooperation (SDC). CARE Peru and the University of Zurich jointly implemented the EWS in close collaboration with the municipality of Carhuaz and the National Water Authority of Peru (ANA) and its Office for Glacier and Lake Evaluations (former Glaciology and Water Resources Unit - UGRH) in Huaraz.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Laguna 513 disaster", "section_headings": ["Laguna 513 disaster"], "chunk_type": "text", "line_start": 22, "line_end": 30, "token_count_estimate": 754, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "140894e948a8106c", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System\nType: text\n\nThe design, organization and operation of the GLOF was structured to adhere to internationally recognized EWS components (cf. Fluixá-Sanmartín et al., 2018):\n\n- risk knowledge\n- monitoring and warning\n- · dissemination and communication\n- response capability.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System", "section_headings": ["Development and implementation of a GLOF Early Warning System"], "chunk_type": "text", "line_start": 32, "line_end": 39, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf737191f54efaef", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Risk knowledge\nType: text\n\nUnderstanding the risks encountered at a particular location is fundamental for the design of an EWS. Risks can be assessed using established methods that analyze physical hazards by means of critical indicators and thresholds (in this case related to the different components involved in the GLOF process), exposure of people and assets (e.g. infrastructure) and the vulnerability (e.g. social, economic) of the elements at risk. Comprehensive risk assessments for GLOFs are rare (Allen et al., 2016) and complex because GLOFs are typically the results of a cascade of triggering and propagating mass flow processes (Schneider et al., 2014; Westoby et al., 2014).\n\nThe 2010 GLOF served as a reference to analyze the physical hazards by simulating the process cascade with an iterative approach of coupled, physically-based numerical mass movement and hydrodynamic models (RAMMS and IBER). This model chain was then used to simulate three potential future scenarios of different magnitudes (small, medium, large) and corresponding probabilities of occurrence (high, medium, low, respectively). This hazard assessment procedure followed international standards and was in line with the recently established guidelines of the International Commission on Glacier and Permafrost Hazards in Mountains (www.gaphaz.org), a joint commission of the International Association of Cryopsheric Sciences (IACS) and the International Permafrost Association (IPA). The modelling, together with field surveys, resulted in a GLOF hazard map for local communities and city of Carhuaz (Schneider et al., 2014) (Fig. 3). Exposure and vulnerability were assessed using publicly available data sources (such as census data) and additional surveys in the catchments.\n\nHowever, risks are perceived in very different ways by different actors. It is a challenge to understand and take these differences in perception into consideration but this is essential for wide user acceptance and the long term success of risk reduction measures. Repeated workshops were conducted in the different communities of the catchment to learn about the risk perceptions and priorities of local leaders and people. At a later stage, ethnographic studies, which included longer (several months) research visits with the local communities, were also conducted in the catchment. These were important to gain more in-depth understanding of how local people perceive their natural environment, and the relationships they maintain with mountains, glaciers and lakes that surround them. This led to a deeper understanding of how they perceived the diverse types of risks and how they understood the connections between those risks and those involved in the GLOF EWS project. Thereby cultural and political aspects were highlighted and people's concerns about water came to the fore, that is water availability, mostly in terms of access to water (e.g. water rights, allocation).", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Risk knowledge", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Risk knowledge"], "chunk_type": "text", "line_start": 41, "line_end": 47, "token_count_estimate": 678, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53b183008d981cc0", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Monitoring and warning\nType: figure\nFigure\n\nImage /page/3/Picture/5 description: A generic image file icon, which depicts a landscape with a green hill and a cloud, is shown next to the text \"Glacier Lake\" in a dark gray sans-serif font on a white background.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Monitoring and warning", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Monitoring and warning"], "chunk_type": "figure", "figure_caption": null, "line_start": 50, "line_end": 50, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "affc6b4c26fcc04f", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Monitoring and warning\nType: text\n\nMonitoring and warning are central elements of an EWS. Monitoring instruments and technical measuring must be set up to detect hazards so that timely warnings can be issued. The challenge is to identify the environmental processes and variables that are critical to the early detection of an extreme event (such as a GLOF) – and that are measurable by sensors. The insights and improved understanding of the processes gained from the reconstruction of the 2010 outburst and the modelling of potential future scenarios (risk knowledge element) served to identify where and what to measure and monitor. Knowledge about GLOF travel times – from triggering to reaching population centres – are, for instance, critical for the design of an EWS and for later visualization and planning with local authorities and communities (see \"Dissemination and communication\").\n\nReference projects for GLOF EWS were rare at the time of the design for Laguna 513 (2011/2012), and completely non-existent for the Latin American region. The \"art\" in such a design is in taking account of all possible flood trigger processes while measuring the point that still allows for timely warning. The different types of flood trigger processes – ice avalanches, moraine instabilities, rock slope failures – strongly depend on local conditions. It is critical to adequately understand the physical environment and interplay of processes that can result in different GLOF scenarios.\n\nThe harsh, extreme physical environment in which glacier lakes (as origin of GLOFs) form are often the biggest challenge. At high altitudes, such as that of Laguna 513, there are large daily temperature fluctuations, long periods of cloudiness, heavy precipitation and high solar radiation as well as a steep topography in what is a remote environment. All of these factors need to be considered in the design and implementation. The GLOF EWS adventurer/scientists had to make\n\nprovisions for reduced energy, for complications with data transmission from the sensors and the limited access for sensor installation and frequent following-up and maintenance.\n\nAnother crucial element for monitoring and warning, in particular for an EWS in extreme environments such as in this case, is the redundancy in the system. Even in a well-calibrated and tested EWS, a sensor or data transmission failure is likely to occur at some point, sufficient redundancy is indispensable to avoid that sensor failure results in EWS failure as a whole.\n\nAn addition problem is funding the long term maintenance of the EWS. Small municipalities with limited budget have other priorities like investing in health and education services.\n\nWith the knowledge and information gathered, the local and international team worked together, each contributing their expertise, to design an EWS that would overcome the challenges of Laguna 513. The design comprised two stations – a main station at the Laguna 513 dam and a station in the Pampa Shonquil, which would include meteorological measuring instruments – a data centre in the municipality of Carhuaz, a warning station in the community of Pariacaca, and a repeater station for transferring the signal from the lake to the data centre (Fig. 3).\n\nThe stations were equipped with the following instruments:", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Monitoring and warning", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Monitoring and warning"], "chunk_type": "text", "line_start": 51, "line_end": 83, "token_count_estimate": 739, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ffca52ad68b92b69", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Monitoring and warning\nType: text\n\nother priorities like investing in health and education services . With the knowledge and information gathered , the local and international team worked together , each contributing their expertise , to design an EWS that would overcome the challenges of Laguna 513 . The design comprised two stations – a main station at the Laguna 513 dam and a station in the Pampa Shonquil , which would include meteorological measuring instruments – a data centre in the municipality of Carhuaz , a warning station in the community of Pariacaca , and a repeater station for transferring the signal from the lake to the data centre ( Fig . 3 ) . The stations were equipped with the following instruments :\n\n- Data centre Carhuaz (2 640 m a.s.l.): receiving antenna, screen with real-time data access, server for data storage, infrastructure for launching alarms.\n- Repeater station (3 189 m a.s.l.): receiving and sending antenna.\n- Station Laguna 513 (4 491 m a.s.l.): 2 cameras taking photos every 5 seconds during daylight times, one looking at the face of Mt. Hualcán, one observing the dam. 4 geophones located close to the station, continuously measuring and sending data in 5 second intervals, in order to detect potential mass movements (e.g. ice avalanche) impacting the lake. Receiving and sending antenna and data logger.\n- Station Pampa Shonquil (3 600 m a.s.l.): river discharge station (using pressure sensor), meteorological station with sensors for measuring air temperature and humidity, precipitation, wind speed, and solar radiation. Sending antenna and data logger.\n- Information receiving and warning station at Pariacaca (3 138 m a.s.l.): the monitoring system informs locals about events at Laguna 513 and sirens activated from the Data Centre Carhuaz to facilitate evacuation.\n\nAll stations were equipped with solar panels and batteries for energy generation and storage, however energy availability remained a limiting factor, in particular at the glacier lake station as the peaks of the Cordillera Blanca experience a high frequency of cloud coverage. Each station had a mast where most of the instruments were fixed, a concrete lockable box for the electronic equipment, and a protective fence. Emergency power aggregates were available in the municipal building to prevent data losses and interrupted access during blackouts.\n\nThe geophones (devices recording ground movements and converting them into voltage) were the principle instruments used for registering a potential GLOF trigger. The back-up cameras could be used to get an overview of the current situation and, particularly during the test phase of the system, for relating geophone measurements to the magnitude of (avalanche) events. The pressure sensor in the riverbed at the Pampa Shonquil station added redundancy to the system and, if calibration measurements were taken, could be used to continuously record runoff. Later, it was planned to install wire sensors in the river channel bed below Laguna 513 that would detect unusually high and dangerous river flow discharge, which could be applied in debris flow warning systems.\n\nA permanently manned hut with wardens next to the station at Pampa Shonquil was an important element of the EWS, especially for consideration of redundancy. Its main purpose was to control the freshwater intake for the municipality of Carhuaz but the location gave the wardens a perfect view towards Laguna 513, they could radio warnings to the authorities in case of an event (as was the case in the 2010 event).", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Monitoring and warning", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Monitoring and warning"], "chunk_type": "text", "line_start": 51, "line_end": 83, "token_count_estimate": 860, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db0d20c4c239d5dc", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Monitoring and warning\nType: text\n\nto continuously record runoff . Later , it was planned to install wire sensors in the river channel bed below Laguna 513 that would detect unusually high and dangerous river flow discharge , which could be applied in debris flow warning systems . A permanently manned hut with wardens next to the station at Pampa Shonquil was an important element of the EWS , especially for consideration of redundancy . Its main purpose was to control the freshwater intake for the municipality of Carhuaz but the location gave the wardens a perfect view towards Laguna 513 , they could radio warnings to the authorities in case of an event ( as was the case in the 2010 event ) .\n\nFor security, all data is first stored in the data logger at each respective station, then transmitted at 5-second intervals to the data centre server, which has a cloud back-up. All data are directly transferred to a website that permits real-time remote access. In the data centre itself – a separate office in the municipality of Carhuaz – a screen continuously displays the data from this webpage.\n\nWarning protocols represent essential elements of an EWS. The protocol documents and defines warning procedures, typically differentiating a number of warning levels and associated actions as well as the responsible institutions, organizations or committees and people. Local, regional and national laws, rules and guidelines had to be taken into consideration in the Laguna 513 warning protocol. The members of the Local Emergency Operation Centre, civil defense, selected government officials and the mayor, whose authority it is to launch the evacuation alarm, had to be involved. Accordingly, the protocol was accompanied by a list of responsible persons and their phone numbers. It defined three warning levels – yellow, orange and red – plus a normal green baseline level and how these warning levels are reached and what type of actions need to be taken. To this purpose, thresholds of physical variables and processes had to be determined based on sensor measurements. Definition of these thresholds is critical and involves an extended period of calibration and testing, typically of many months, especially if no prior measurements are available as in the case of Laguna 513.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Monitoring and warning", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Monitoring and warning"], "chunk_type": "text", "line_start": 51, "line_end": 83, "token_count_estimate": 527, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad36909b52c69563", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Dissemination and communication\nType: text\n\nIf measurements on a geophone surpasses a defined threshold, a short message to immediately check EWS data and information is automatically sent to the mobile phones of all the responsible personnel identified in the warning protocol. The subsequent steps to be taken are based on the action plan and on the available data. Alarms cannot be automatically launched by the EWS because, under Peruvian law, only the mayor can authorize an evacuation.\n\nCarhuaz's alarm module has two long-range acoustic sirens and the capacity to send predefined text messages to community and district leaders and stakeholders such as school principals, hospital chiefs, the police and firefighters. Communities upstream of Carhuaz receive warnings and associated information through the Local Emergency Operation Centre and the central authorities of Carhuaz. Pariacaca, which is on the flood pathway, has a warning station with sirens. Furthermore, the EWS protocols were adapted to fit with the Peruvian protocols for risk assessment, allowing communication with the National Emergency Operations Centre at Lima to ask for help (Muñoz et al. 2016).", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Dissemination and communication", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Dissemination and communication"], "chunk_type": "text", "line_start": 85, "line_end": 89, "token_count_estimate": 282, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "382a54dbad504499", "text": "Document: Author(s):\nSection: Development and implementation of a GLOF Early Warning System > Response capability\nType: text\n\nThe ability of people at risk to appropriately respond to the levels of warning issued is possibly the most critical element of an EWS – it is also the most susceptible to failure as the last element of the EWS chain. Failures or errors along the monitoring and warning chain have to be accommodated such that this last element is not adversely affected or threatened.\n\nFor the Laguna 513 EWS, information sessions were held with the population at risk. During these, the concept and functionality of the EWS, as well as its potentials and limitations, and clear instructions on actions that have to be taken in case of an alarm were explained and discussed. The instructions include the directive to immediately escape the endangered zones, and a clear indication of the evacuation routes and safety zones. A detailed map with all evacuation routes\n\nwas prepared by the civil defense of Carhuaz on the basis of the hazard map developed in the risk knowledge phase of the EWS GLOF design. Emergency simulations are scheduled several times a year for the entire country as Peru's seismic risk is very high. Such simulations, some of them taking place at nighttime, have been used to expose both the population as well as the responsible authorities to a test evacuation und near-realistic conditions, and to familiarize them with the EWS of Laguna 513.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Development and implementation of a GLOF Early Warning System > Response capability", "section_headings": ["Development and implementation of a GLOF Early Warning System", "Response capability"], "chunk_type": "text", "line_start": 91, "line_end": 97, "token_count_estimate": 321, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56574dc76a5f8a1c", "text": "Document: Author(s):\nSection: Operational aspects and lessons learned\nType: text\n\nIn 2010, when discussions and activities related to the GLOF EWS started, Carhuaz was the main local actor and the centre for data and information reception. However, the technical, operational and social dimensions of the EWS were beyond the capacity of such a small city. Long-standing national and international expertise – necessitating regular presence on site and permanent joint capacity building and exchange with local people and authorities – was indispensable to address the challenges. In July 2015, full responsibility of the EWS was handed over to the local authorities in a ceremony attended by representatives from the local, provincial and national governments of Peru, from the Swiss government, and from local communities and schools as well as national and international experts. By that time, the EWS had made headlines in the Peruvian, Swiss and international media.\n\nIn 2016, much of the central tropical Andes, including the Cordillera Blanca region, was affected by a strong drought. In normal years, after a long dry Austral winter season, farmers count on the start of the rainfall season in October. In 2016, no rainfall was recorded in October and November.\n\nAfter first requests to remove the EWS by some local inhabitants (cf. Fraser, 2017), farmers got desperate and rumors started to spread that the rain gauges and antennas of the EWS at Laguna 513 were responsible for the lack of rainfall. In a rather dramatic turn of events – driven by community-level power policy factors and weak communications from authorities on the extraordinary meteorological events – a large number of locals gathered at Laguna 513 on 24 November and decided to dismantle the EWS station at the lake. Reactions at the local, national and international levels were vigorous. There was animated disorderly mix of supporters of the EWS on social media. Others expressed incomprehension, disgust, shame and critiqued the voluntarily exposure of lives at risk by this destructive action.\n\nThe destruction of the station affected the monitoring and warning components of the EWS from a technical and operational point of view. But service could be maintained thanks to the wardens located at the intermediate site (Pampa Shonquil). Response capabilities and institutional mechanisms were not affected. However, it was crucial to understand the root causes of this action.\n\nThe results of intensive research into the incident, which centred on social sciences, are summarized below. The lessons learned are relevant for the development of climate and warning services beyond Peru.\n\nThe dismantlement of meteorological and EWS stations by local people is not unique to this site, nor to Peru. Similar incidents have occurred in other regions, such as Himalayas, Andes and Alps of Europe, though those experiences were poorly documented.\n\nLocal intra and inter-community conflicts, as well as distrust and biases against the participation of, and installations from, external institutions, can have a strong, but invisible, impact on acceptance.\n\nThe relation of local (risk exposed) people to their natural environment and their perceptions of different risks strongly determines their attitude towards risk reduction efforts. Local perspectives\n\nmay differ substantially from government or technical and scientific perspectives. For instance, local people can have intimate relations with mountains, glaciers and lakes as places of spirituality and the origin of life. Hence, a GLOF may be understood as a reaction of, for example, a glacier (as a mountain spirit) and a lake (as a being) to human disturbance or inappropriate human behavior. Traditional knowledge and narratives have to be recognized and acknowledged as part of a constructive dialogue and in finding acceptable solutions.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Operational aspects and lessons learned", "section_headings": ["Operational aspects and lessons learned"], "chunk_type": "text", "line_start": 99, "line_end": 121, "token_count_estimate": 820, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8d7374da6fea16b", "text": "Document: Author(s):\nSection: Operational aspects and lessons learned\nType: text\n\n) people to their natural environment and their perceptions of different risks strongly determines their attitude towards risk reduction efforts . Local perspectives may differ substantially from government or technical and scientific perspectives . For instance , local people can have intimate relations with mountains , glaciers and lakes as places of spirituality and the origin of life . Hence , a GLOF may be understood as a reaction of , for example , a glacier ( as a mountain spirit ) and a lake ( as a being ) to human disturbance or inappropriate human behavior . Traditional knowledge and narratives have to be recognized and acknowledged as part of a constructive dialogue and in finding acceptable solutions .\n\nAs a consequence, the acquisition of a profound understanding of the social, political and cultural conditions, particularly in terms of power dynamics, is a prerequisite for early warning as well as more generally for climate adaptation service development. It is necessary for collaboration among diverse people, actors and experts, including local populations, physical and social scientists, engineers, local governments, technical governmental institutions, and nongovernmental organization (NGOs). It is encouraged to give the social sciences a more prominent role.\n\nAuthorities often believe that an EWS is primarily a technical measuring and data transmission system. The recognition that an EWS also consists of institutional, social, cultural and political components is fundamental because an EWS can only be operational if all components fulfill their function. Furthermore, it is critical that the local authorities and people understand that an EWS cannot reduce risks to zero – its main objective is to avoid harm to human lives. Therefore, it needs to be accompanied by other risk reduction measures, in particular appropriate land-use planning.", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Operational aspects and lessons learned", "section_headings": ["Operational aspects and lessons learned"], "chunk_type": "text", "line_start": 99, "line_end": 121, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a420d7475a092844", "text": "Document: Author(s):\nSection: Conclusion\nType: text\n\nEWS in extreme environments, such as glacier lakes, bring many challenges. The system needs to be carefully designed to achieve robust energy provision, smooth and reliable data transmission, measurement of critical physical variables and the required degree of redundancy. Many months of system calibration are indispensable. Local authorities must clearly understand this. In addition, maintenance of the EWS needs to be budgeted annually by the local authorities to guarantee the sustainability of the system.\n\nThe Laguna 513 EWS has become the model for several other EWS in the Peruvian Andes (e.g. Huaraz-Palcacocha, Urubamba-Chicón) and beyond. While the experience and capacity development can be replicated, it is also crucial to acknowledge that every location is an individual case with special characteristics that need appropriate attention.\n\n#", "metadata": {"source_file": "data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md", "document_title": "Author(s):", "section_path": "Conclusion", "section_headings": ["Conclusion"], "chunk_type": "text", "line_start": 123, "line_end": 128, "token_count_estimate": 192, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3314f1c29ccdbc78", "text": "Document: Glacier Lakes & Floods\nSection: Glacier Lakes & Floods\nType: text\n\nGlacier lakes are indirectly a result of climatic changes expressed by glacier fluctuations. During the earth history glacier lakes have survived only a short time period, but glacier lake outbursts had a profound impact in shaping the landscape. The dominant type of glacial lakes may shift over time from glacier-dammed lakes to moraine-dammed lakes in different glaciation situations in dependence of the topographic setting. In the course of deglaciation, the types of glacier will change and therefore the type of glacial lakes. Glacier-dammed lakes occur mainly in times of glacier advance, but there are also some constellations in which they are the result of glacier retreat. Moraine-dammed lakes are mainly linked to glacier retreat regimes.\n\nGlacier lake outbursts from these lakes can attain extremely high peak discharges by sudden dam failure. As a consequence of topographical and climatic conditions, a characteristic distribution pattern of glacier lake types can be recognized in individual mountain areas. In terms of natural hazards, sudden outbursts from small lakes with high peak discharges may have a more severe impact on human settlement than the gradual drainage of large ice-dammed lakes. Not all glacier lake outbursts have to be necessarily released as water floods. They may also occur as debris flow with a high hazard potential.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "Glacier Lakes & Floods", "section_headings": ["Glacier Lakes & Floods"], "chunk_type": "text", "line_start": 4, "line_end": 8, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0d29090907364da8", "text": "Document: Glacier Lakes & Floods\nSection: Glacial Lake Outburst Flood (GLOF)\nType: text\n\nThe acronym GLOF is used for glacier floods caused by the drainage of naturally dammed lakes in the glacier, on or at the margin of glaciers. \"Glacier floods represent in general the highest and most far reaching glacial risk with the highest potential of disaster and damages\". A lake outburst can be triggered by several factors: ice or rock avalanches, the collapse of the moraine dams due to the melting of ice buried within, the washing out of fine material by springs flowing through the dam (piping), earthquakes or sudden inputs of water into the lake e.g. through heavy rains or drainage from lakes further up-glacier. The glacier lake may breach at the end-moraine or at the lateral moraine.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "Glacial Lake Outburst Flood (GLOF)", "section_headings": ["Glacial Lake Outburst Flood (GLOF)"], "chunk_type": "text", "line_start": 10, "line_end": 12, "token_count_estimate": 216, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7acbc9d19a2dd5ba", "text": "Document: Glacier Lakes & Floods\nSection: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State\nType: text\n\nThrough extensive research, CDAC has deployed India's first GLOF Early Warning System for Sikkim to predict GLOFs, which will help the Government authorities in case of an impending GLOF event. The GLOF Early Warning System has the following capabilities:", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State", "section_headings": ["GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State"], "chunk_type": "text", "line_start": 14, "line_end": 16, "token_count_estimate": 112, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "55d12aa1436af23e", "text": "Document: Glacier Lakes & Floods\nSection: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State\nType: figure\nFigure\n\nImage /page/1/Picture/10 description: A screenshot of a software application titled \"GLOF Information System\". The main window features a scenic background image of snow-capped mountains rising above a layer of clouds under a clear blue sky, with a dark mountain silhouette on the right. Overlaid on this image in large white text is the title \"Glacial Lake Outburst Flood Management System\". The application has a menu bar with the options: Home, GLOF Analysis, Settings, Help, and About Us. At the bottom, it displays \"Powered by ICIMOD 2012\", \"Version 1.3\", and a red button for \"Licensing Policy\".", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State", "section_headings": ["GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State"], "chunk_type": "figure", "figure_caption": null, "line_start": 17, "line_end": 17, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44e3ca07341a066b", "text": "Document: Glacier Lakes & Floods\nSection: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State\nType: figure\nFigure\n\nImage /page/1/Picture/11 description: A simple, green icon of a person standing with their arms raised and spread out to the sides, and their legs apart, against a white background. The figure is slightly blurry.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State", "section_headings": ["GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State"], "chunk_type": "figure", "figure_caption": null, "line_start": 19, "line_end": 19, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8da162b20263c595", "text": "Document: Glacier Lakes & Floods\nSection: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State\nType: text\n\nWater Level Sensors developed indigenously by C-DAC, has been deployed at Shakho Chho Glacial Lake, North Sikkim.\n\nThe near-real time sensors transmit data through INSAT satellite to control centre at Gangtok (SSCST).\n\nThe flood simulation model runs the simulation for various GLOF scenarios. The model is capable of presenting Flood Simulation, Inundation information and Flood Arrival Time, in the event of a glacier lake outburst flood.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State", "section_headings": ["GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State"], "chunk_type": "text", "line_start": 20, "line_end": 26, "token_count_estimate": 157, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ae18a503d140281", "text": "Document: Glacier Lakes & Floods\nSection: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State\nType: figure\nFigure\n\nImage /page/2/Picture/4 description: A screenshot of a software application titled 'GLOF Information System'. The 'Lake Information' tab is selected, displaying data for the year 2010. The selected lake is '78A013A1084027 (South Lhonak Chhu)'. The 'Lake Details' section provides the following information: Lake ID: 78A013A1084027, Lake Name: South Lhonak Chhu, alternate Name: South Bonak chhu, Lake Type: C, Lake Class: M, Lake Condition: Overtopping, Past GLOF occurrence: Yes, Dam Type: --NA--, Dam Condition: --NA--, Perimeter [mtr.]: 4957.20, Area [sq. mtr.]: 1009353.73, and Sensor Installed: --NA--. To the right, there is a small satellite image of the lake and a green box indicating 'Total Lakes in Sikkim: 203'.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State", "section_headings": ["GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State"], "chunk_type": "figure", "figure_caption": null, "line_start": 27, "line_end": 27, "token_count_estimate": 274, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1009353", "78A013A1084027"]}}
{"id": "2e16fc7ea954e7e7", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: text\n\nAnalysis, Glacial Lake Management & Flood Simulation Statistics.\n\nThe GLOF Analysis Tool has three sub-tools: Lake Information, Simulation & the Sensor Information. The \"Lake Information\" tool gives basic information about the glacial lake selected by the user from the drop-down menu, such as, Lake Name, Lake Condition, etc. The satellite image of the glacial lake is shown on the right hand side.\n\nThe Simulate tool gives information regarding the GLOF simulation progress and the outputs of the simulation. The user will be able to see the time at which the GLOF at a particular glacial lake occurred and the period of GLOF simulation.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "text", "line_start": 30, "line_end": 36, "token_count_estimate": 198, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "16b1d379c6321e3c", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: figure\nFigure\n\nImage /page/2/Picture/9 description: A screenshot of a software application titled \"GLOF Information System\". The window displays the \"Sensor Information\" tab. On the left, there are buttons for \"View\", \"Add\", \"Edit\", and \"Delete\". The main area shows details for a sensor. Under \"View Sensor Information\", a dropdown for \"Sensor Installed on Lake\" shows \"70A0934184040 [ ]\". Under \"Sensor Details\", the \"Sensor ID\" is \"SM516\", the \"Sensor Location\" Easting is \"27.8709000000\" and Northing is \"86.6103400000\". The fields for \"Sensor Model\", \"Sensor Type\", and \"Sensor Accuracy\" are blank. A green button indicates \"Number of Lakes with sensor: 1\".", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "figure", "figure_caption": null, "line_start": 37, "line_end": 37, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["6103400000", "70A0934184040", "8709000000"]}}
{"id": "9a0ebcd87cc2ba87", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: text\n\nThe user can choose to see the simulation results such as Simulation video, Inundation map, Inundation report & Hydrographs.\n\nThe Sensor Information tool gives basic information about the sensors deployed at the glacial lakes for monitoring water-levels. Each sensor has a Sensor ID linked to the Lake ID. The user may add information of new sensors when sensors are deployed on new glacial lakes in future or delete existing ones, in case if they are decommissioned.\n\nThe Settings tool gives the user the flexibility to load the input data from any location on the computer. The data will be copied to an in-built database system for computations.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "text", "line_start": 38, "line_end": 44, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f11314c0dceece2a", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: figure\nFigure\n\nImage /page/3/Picture/3 description: A screenshot of a software application titled \"GLOF Information System\". The window has a menu bar with options for \"Home\", \"GLOF Analysis\", \"Settings\", \"Help\", and \"About Us\". The main content area features buttons to \"Connect to Database\" and \"Activate the Model\". Below these are several sections, including \"Input Settings\", \"Path Settings\", \"Database Settings\", and \"Simulation Settings\". The \"Input Settings\" section is expanded, showing a field for \"Lake Shapefile\" with the \"Year\" set to \"1990\" and an \"Upload\" text box. A lower section, \"Summary of Settings Made\", displays a list of configurations:\n1. GIS Path: C:/Program Files/Quantum GIS Wroclaw/bin/qgis.bat\n2. Lakes in Year 1962: F:/All\\_Glacier\\_Data\\_for\\_QC/Lake Layers/Other Lakes/TA\\_29Oct1962.shp", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "figure", "figure_caption": null, "line_start": 45, "line_end": 47, "token_count_estimate": 301, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "418d084fe19b8312", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: text\n\n3. DB Host: localhost\n4. Lakes in Year 1990: F:/All\\_Glacier\\_Data\\_for\\_QC/Lake Layers/Other Lakes/TA\\_12Oct1990.shp\nAt the bottom of the window are buttons for \"Save current Settings\", \"Restore last saved Settings\", and \"Delete\".", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "text", "line_start": 48, "line_end": 51, "token_count_estimate": 136, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1b65d60eaf9ec5a", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: figure\nFigure\n\nImage /page/3/Picture/4 description: A low-resolution screenshot of a mapping or GIS software interface. The main window displays a map with a large, irregularly shaped yellow polygon in the center. Inside the polygon, the text \"Kanchipuram Taluk\" is visible. On the left side of the screen, there is a control panel with various options and a data table. The top part of the panel is titled \"Route and Locate\" and has fields for \"View\" and \"Layer,\" with \"Taluk Name\" selected in the Layer field. Below this, a table shows attribute data with columns such as \"OBJECTID\" and \"Taluk Name\".", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "figure", "figure_caption": null, "line_start": 52, "line_end": 52, "token_count_estimate": 211, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee2a324915fc4363", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF\nType: text\n\nThe Query tool gives the user three options based on his expertise or authority in using the system. The \"Ready-made Queries\" option is for user who wishes to know basic information about the glacial lake of interest on the GIS interface. The queries are tailor-made for users who do not have expertise in GIS. In this mode, only the basic functionalities such as zoom in/out, info tool, select feature, etc. are only available on the GIS interface.\n\nThe Custom Query option invokes the \"Advanced Query\" tool for users who are familiar with the GIS query system. The needs to select the query of interest from the list of attributes displayed on the left panel. The output will be displayed as a map.\n\nThe Advanced Mode option invokes all the tools and features available on the GIS interface for the advanced users familiar with GIS.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF"], "chunk_type": "text", "line_start": 53, "line_end": 59, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "04acfc1addd1701a", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),\nType: text\n\nEmerging Solutions & e-Governance Group (ES&EG)\n\n4th Floor, C-DAC Innovation Park,\n\nPanchawati,\n\nPune - 411008, Maharashtra, India\n\nPhone No. +91-20-2550 3233.\n\nFax: +91-20-2550 3231\n\nEmail: geomatics[at]cdac[dot]in / sandeepk[at]cdac[dot]in", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Contact Us", "Centre for Development of Advanced Computing (C-DAC),"], "chunk_type": "text", "line_start": 63, "line_end": 77, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["411008"]}}
{"id": "1ea83857af300f9a", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),\nType: figure\nFigure\n\nImage /page/3/Picture/17 description: A low-contrast image showing a white logo on a white background. The logo consists of the word \"MEITY\" in a large, bold, sans-serif font. Below it, in a smaller font, are the words \"MINISTRY OF ELECTRONICS &\" on the first line and \"INFORMATION TECHNOLOGY\" on the second line.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Contact Us", "Centre for Development of Advanced Computing (C-DAC),"], "chunk_type": "figure", "figure_caption": null, "line_start": 78, "line_end": 78, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "363be00fc5512134", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),\nType: figure\nFigure\n\nImage /page/3/Picture/18 description: The image displays the logo for 'Azadi Ka Amrit Mahotsav' on a white background. The logo features a stylized, waving Indian flag with saffron on top and green on the bottom, separated by a white space. Below the flag, the word 'Azadi' is written in saffron, and directly underneath it, the word 'Mahotsav' is written in green.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Contact Us", "Centre for Development of Advanced Computing (C-DAC),"], "chunk_type": "figure", "figure_caption": null, "line_start": 80, "line_end": 80, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5d9bc9764db408cf", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),\nType: figure\nFigure\n\nImage /page/3/Picture/19 description: A low-resolution digital graphic on a white background, featuring a green stick figure of a person with their arms raised to the sides. To the right of the figure is a small, vertical, stylized shape that is green on the top and red on the bottom.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Contact Us", "Centre for Development of Advanced Computing (C-DAC),"], "chunk_type": "figure", "figure_caption": null, "line_start": 82, "line_end": 82, "token_count_estimate": 137, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "376cb26e2aa8cc44", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > CENTERS\nType: text\n\n- Bengaluru\n- Chennai\n- Delhi\n- Hyderabad\n- Kolkata\n- Mohali\n- Mumbai\n- Noida\n- North East\n- Patna\n- Pune\n- Thiruvananthapuram", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > CENTERS", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "CENTERS"], "chunk_type": "text", "line_start": 85, "line_end": 98, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db031e4b1ed17002", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > LINKS\nType: text\n\n- About Us\n- Products & Services\n- R&D\n- Careers\n- Tenders\n- Press Kit\n- Video Gallery\n- Events\n- Awards\n- Downloads\n- Achievements\n- Alliance", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > LINKS", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "LINKS"], "chunk_type": "text", "line_start": 100, "line_end": 113, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d9534570a4bc937f", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > CONTACT US\nType: figure\nFigure\n\nImage /page/4/Picture/29 description: A cropped screenshot of a digital map. The map displays an area labeled \"MANSAROVAR\" in all caps, with \"मानसरोवर\" written in Hindi script directly below it. Other visible labels include \"Overhead Water Tank\" and \"माराडा कालना\" in Hindi script at the top right. A partially visible label at the top left reads \"ite of ology\". The map shows roads as thin gray lines and green areas as light green patches.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > CONTACT US", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "CONTACT US"], "chunk_type": "figure", "figure_caption": null, "line_start": 116, "line_end": 116, "token_count_estimate": 176, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "385f891ab0160ec7", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,\nType: text\n\nPanchavati, Pashan, Pune - 411 008, Maharashtra (India)\n\nPhone: +91-20-25503100 Fax: +91-20-25503131", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Centre for Development of Advanced Computing C-DAC Innovation Park,"], "chunk_type": "text", "line_start": 119, "line_end": 123, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["25503100", "25503131"]}}
{"id": "6798a4f827f4e6a1", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,\nType: figure\nFigure\n\nImage /page/4/Picture/33 description: A simple, stylized icon of a person in a vibrant green color against a white background. The figure is depicted standing with its arms and legs spread out, resembling a star shape or someone doing a jumping jack. The image is slightly blurry or pixelated.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Centre for Development of Advanced Computing C-DAC Innovation Park,"], "chunk_type": "figure", "figure_caption": null, "line_start": 124, "line_end": 124, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6ec323f8be77272", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,\nType: text\n\nHelp | Website Policies | Copyright Policy | Terms & Conditions | Reach Us | Sitemap\n\nWebsite owned & maintained by: Centre for Development of Advanced Computing (C-DAC) © 2025 C-DAC. All rights reserved.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Centre for Development of Advanced Computing C-DAC Innovation Park,"], "chunk_type": "text", "line_start": 125, "line_end": 129, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb0909ec4e78bd67", "text": "Document: Glacier Lakes & Floods\nSection: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,\nType: figure\nFigure\n\nImage /page/5/Picture/4 description: A simple, green stick figure icon is shown against a white background. The figure has its arms raised and spread out to the sides, and its legs are also spread apart, resembling the pose of a jumping jack.", "metadata": {"source_file": "data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md", "document_title": "Glacier Lakes & Floods", "section_path": "The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,", "section_headings": ["The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF", "Centre for Development of Advanced Computing C-DAC Innovation Park,"], "chunk_type": "figure", "figure_caption": null, "line_start": 130, "line_end": 130, "token_count_estimate": 126, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5adf2f4f8958d7b2", "text": "Document: Glacier Retreat and Glacial Lake Outburst Floods (GLOFs)\nSection: Summary\nType: text\n\nGlacier retreat is considered to be one of the most obvious manifestations of recent and ongoing climate change in the majority of glacierized alpine and high-latitude regions throughout the world. Glacier retreat itself is both directly and indirectly connected to the various interrelated geomorphological/hydrological processes and changes in hydrological regimes. Various types of slope movements and the formation and evolution of lakes are observed in recently deglaciated areas. These are most commonly glacial lakes (ice-dammed, bedrock-dammed, or moraine-dammed lakes).\n\n\"Glacial lake outburst flood\" (GLOF) is a phrase used to describe a sudden release of a significant amount of water retained in a glacial lake, irrespective of the cause. GLOFs are characterized by extreme peak discharges, often several times in excess of the maximum discharges of hydrometeorologically induced floods, with an exceptional erosion/transport potential; therefore, they can turn into flow-type movements (e.g., GLOF-induced debris flows). Some of the Late Pleistocene lake outburst floods are ranked among the largest reconstructed floods, with peak discharges of up to 107 m3/s and significant continental-scale geomorphic impacts. They are also considered capable of influencing global climate by releasing extremely high amounts of cold freshwater into the ocean. Lake outburst floods associated with recent (i.e., post-Little Ice Age) glacier retreat have become a widely studied topic from the perspective of the hazards and risks they pose to human society, and the possibility that they are driven by anthropogenic climate change.\n\nDespite apparent regional differences in triggers (causes) and subsequent mechanisms of lake outburst floods, rapid slope movement into lakes, producing displacement waves leading to dam overtopping and eventually dam failure, is documented most frequently, being directly (ice avalanche) and indirectly (slope movement in recently deglaciated areas) related to glacial activity and glacier retreat. Glacier retreat and the occurrence of GLOFs are, therefore, closely tied, because glacier retreat is connected to: (a) the formation of new, and the evolution of existing, lakes; and (b) triggers of lake outburst floods (slope movements).\n\n**Keywords:** glacier retreat, GLOFs, natural hazards, slope movement, climate change\n\n**Subjects:** Floods, Glacial Lake Outburst (GLOFs)", "metadata": {"source_file": "data/('Glacier Retreat and Glacial Lake Outburst Floods (GLOFs)', '.pdf')_extraction.md", "document_title": "Glacier Retreat and Glacial Lake Outburst Floods (GLOFs)", "section_path": "Summary", "section_headings": ["Summary"], "chunk_type": "text", "line_start": 4, "line_end": 14, "token_count_estimate": 642, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "61f7dad20afcb36e", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nType: text\n\nThe GLOF Hazard Landscape: Formation and Dam Mechanics", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems"], "chunk_type": "text", "line_start": 2, "line_end": 4, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ccdbc8e68e691ea6", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Defining the GLOF Phenomenon\nType: text\n\nA Glacial Lake Outburst Flood (GLOF) is the sudden and catastrophic release of a significant volume of water from a lake fed by glacier melt. These lakes can form at the side, in front, within, beneath, or on the surface of a glacier, impounded by natural dams composed of moraine complexes, glacial ice, or bedrock. GLOFs are distinguished from more common hydrometeorological floods by their defining characteristics: extreme peak discharges that can be several times greater than those of rainfall-induced floods, and an exceptional capacity for erosion and sediment transport. This high energy allows them to mobilize vast quantities of rock, ice, and mud, often transforming into highly destructive debris flows as they travel downstream. These events represent one of the most severe and unpredictable hazards in high-mountain regions, posing a significant threat to human lives, livelihoods, and critical infrastructure such as roads, bridges, and hydropower facilities. The rapid onset and immense destructive power of GLOFs necessitate the development of specialized and highly reliable Early Warning Systems (EWS) to mitigate their devastating impacts.\n\nGlacial Lake Genesis in a Warming Climate\n\nThe formation and proliferation of glacial lakes are a direct and highly visible consequence of global climate change.<sup>5</sup> As atmospheric temperatures rise, glaciers worldwide are retreating at an accelerated rate.<sup>7</sup> This process of deglaciation uncovers over-deepened basins and depressions in the landscape, often located between the retreating glacier terminus and a proglacial moraine—a ridge of unconsolidated debris deposited by the glacier at its furthest extent.<sup>1</sup> These depressions subsequently fill with meltwater, giving birth to new glacial lakes.<sup>1</sup>\n\nThis process is not static; it is a dynamic feedback loop. The formation of a glacial lake can itself accelerate further ice loss from the parent glacier through calving (the breaking off of ice chunks from the glacier front) and subaqueous melting at the ice-water interface. Consequently, as the climate continues to warm, existing lakes expand and new lakes form, leading to a rapid global increase in the number, total area, and stored volume of glacial water. This escalates the potential energy available for release, inherently increasing the potential magnitude and frequency of future GLOF events. In High Mountain Asia, for instance, a warming rate of 0.32 °C per decade is driving this widespread expansion of glacial lakes, amplifying the regional GLOF hazard.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Defining the GLOF Phenomenon", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Defining the GLOF Phenomenon"], "chunk_type": "text", "line_start": 6, "line_end": 14, "token_count_estimate": 724, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dee9684616c80fe3", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics\nType: text\n\nThe nature of a GLOF event—its likelihood, trigger mechanism, and recurrence potential—is fundamentally dictated by the type of dam impounding the glacial lake. Understanding the distinct mechanical properties and failure modes of the two primary dam types, moraine and ice, is therefore a prerequisite for designing an effective and appropriately targeted EWS.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dam Typology and Associated Failure Characteristics"], "chunk_type": "text", "line_start": 16, "line_end": 18, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d8a644a37c3ad10", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics > Moraine-Dammed Lakes\nType: text\n\nMoraine-dammed lakes are the most common type in many of the world's most vulnerable mountain ranges, including the Hindu Kush Himalaya. The dams are composed of moraine: a heterogeneous and unconsolidated mixture of boulders, gravel, sand, and fine sediment left behind by a retreating glacier. These structures are inherently weak, poorly sorted, and often highly permeable, making them susceptible to various failure mechanisms. Furthermore, many moraine dams contain a core of buried glacial ice, which can melt over time, compromising the dam's structural integrity from within.\n\nThe failure of a moraine dam is typically a singular, catastrophic event. Mechanisms such as overtopping by a large wave or excessive inflow can initiate a process of \"breach incision,\" where flowing water rapidly erodes a channel through the loose dam material.<sup>4</sup> This process creates a positive feedback loop—a deeper channel allows a higher discharge, which in turn accelerates erosion—leading to a complete and often explosive failure of the dam structure.<sup>4</sup> Because the dam is often destroyed in the process, the threat of repeated GLOFs from the same lake is generally low.<sup>4</sup> The primary hazard is therefore a single, high-magnitude flood.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics > Moraine-Dammed Lakes", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dam Typology and Associated Failure Characteristics", "Moraine-Dammed Lakes"], "chunk_type": "text", "line_start": 20, "line_end": 24, "token_count_estimate": 415, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5caf773b22a8c7d5", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics > Ice-Dammed Lakes\nType: text\n\nIce-dammed lakes form when a glacier itself blocks the natural drainage of a valley.<sup>17</sup> These lakes are responsible for a large number of GLOF events globally, particularly in regions like Alaska and Patagonia.<sup>4</sup> Unlike moraine dams, ice dams do not typically fail through erosional breaching. Instead, they are subject to unique failure mechanisms driven by the physical properties of ice. The most common mechanism is flotation, which occurs when the water level in the lake reaches approximately 90% of the ice dam's height. Due to the density difference between water () and ice (), the buoyant force of the water can physically lift the glacier off its bed, allowing water to escape through subglacial tunnels.<sup>4</sup>\n\nA critical characteristic of ice-dammed GLOFs is their potential for recurrence. As the lake drains and the water level drops below the 90% threshold, the glacier settles back onto its bed, and the subglacial conduits can close up, resealing the dam.<sup>4</sup> This allows the lake to refill, setting the stage for future outburst events.<sup>17</sup> Ice-dammed lakes can therefore produce cyclic GLOFs, posing a persistent, long-term hazard that may recur annually or over several years.<sup>4</sup>\n\nThe fundamental distinction between the failure modes of moraine and ice dams necessitates tailored EWS philosophies. An EWS designed for a moraine-dammed lake, which is prone to a single, catastrophic failure, must prioritize the detection of immediate, high-energy triggers like landslides or extreme rainfall that can initiate a breach. The focus is on capturing the precursors to a singular, high-consequence event. Conversely, an EWS for an ice-dammed lake, which can fail and reform cyclically, requires a design focused on long-term, continuous monitoring of the recurring hazard cycle. This system must track the slower buildup of hydrostatic pressure and lake level over multiple seasons to predict the next drainage event. This critical difference impacts the required sensor longevity, power management strategies, data analysis algorithms, and the nature of community preparedness and response planning.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics > Ice-Dammed Lakes", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dam Typology and Associated Failure Characteristics", "Ice-Dammed Lakes"], "chunk_type": "text", "line_start": 26, "line_end": 32, "token_count_estimate": 651, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cafa65358190716b", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers\nType: text\n\nMeteorological conditions are a fundamental driver of GLOF activity, acting as both long-term conditioning factors that increase overall hazard levels and as acute, short-term triggers that can initiate a dam failure. An effective EWS must be capable of monitoring and interpreting these atmospheric influences across different timescales.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Meteorological Drivers as Primary GLOF Triggers"], "chunk_type": "text", "line_start": 34, "line_end": 36, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "17508d01ed1a3f56", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > The Role of Temperature\nType: text\n\nRising atmospheric temperature is the primary climate-driven force increasing GLOF risk globally. Its influence is multifaceted, affecting the entire glacial environment from the ice mass itself to the stability of the surrounding landscape.\n\nLong-Term Conditioning: Sustained warming trends are the principal cause of glacier mass loss, which directly leads to the formation of new glacial lakes and the expansion of existing ones. This relentless increase in the number and volume of lakes inherently elevates the potential GLOF hazard across a region. Beyond the lakes themselves, rising temperatures have a profound impact on the geotechnical stability of high-mountain terrain. As temperatures increase, permafrost—perennially frozen ground that acts as a cement binding rock and soil together—begins to thaw and degrade. This process significantly reduces the stability of rockwalls and moraine slopes surrounding glacial lakes, \"pre-conditioning\" the landscape and increasing the probability of rockfalls, avalanches, and landslides. These mass movements are a primary GLOF trigger, as detailed in Section 4.\n\n**Short-Term Triggering:** Superimposed on the long-term warming trend are short-term extreme temperature events or heatwaves. These periods of intense warmth can cause a rapid and dramatic acceleration of snow and ice melt.<sup>7</sup> The resulting surge in meltwater runoff can overwhelm a glacial lake's natural outflow capacity, causing the water level to rise rapidly over a period of hours to days.<sup>2</sup> This rapid increase in lake volume can directly trigger an outburst flood through the mechanism of dam overtopping, particularly in moraine-dammed lakes.<sup>7</sup> Analysis of historical GLOF events indicates a strong correlation, with outbursts being significantly more likely to occur during anomalously warm periods or during abrupt climatic transitions from cold to warm conditions.<sup>18</sup>", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > The Role of Temperature", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Meteorological Drivers as Primary GLOF Triggers", "The Role of Temperature"], "chunk_type": "text", "line_start": 38, "line_end": 44, "token_count_estimate": 563, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1380536e06f30bd1", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > Intense Precipitation and Snowmelt Dynamics\nType: text\n\nWhile temperature sets the long-term stage, intense precipitation events are a common and powerful proximate trigger for GLOFs. The link between climate change and such events is well-established; a warmer atmosphere can hold more moisture (approximately 7% more for every 1°C of warming), which increases the potential for heavier downpours and more extreme rainfall events.<sup>20</sup>\n\n**Mechanism of Failure:** An episode of intense, heavy rainfall or an event combining rain with rapid snowmelt can deliver a massive volume of water into a glacial lake's catchment area in a very short time.<sup>2</sup> This sudden influx causes the lake level to rise swiftly, potentially leading to the overtopping of the dam crest.<sup>4</sup> This mechanism is particularly critical for moraine dams due to their unconsolidated nature.\n\nThe Positive Feedback Loop of Breach Incision: The process does not stop at simple overtopping. Once water begins to flow over the crest of a moraine dam, its erosive power starts to cut a channel into the loose material.<sup>4</sup> This initial incision creates a pathway for a greater volume of water to be discharged. This increased flow possesses even more erosive energy, which deepens and widens the breach channel further. This self-reinforcing, or \"positive feedback,\" cycle can cause the dam to fail catastrophically in a matter of hours, releasing the entire volume of the lake in a devastating flood wave.<sup>4</sup>\n\n**Link to Weather Systems:** In many high-mountain regions, these extreme precipitation events are associated with large-scale meteorological patterns. In the Himalayas, for example, the summer monsoon can bring vigorous, moisture-laden systems that result in orographic lifting—where air is forced upward by mountain slopes, causing it to cool and release its moisture as extremely heavy rainfall. The 2013 Kedarnath disaster in the Indian Himalaya was a tragic example of a GLOF from Lake Chorabari triggered by exceptionally heavy rainfall.\n\nThe interplay between these meteorological factors creates a classic cascading hazard chain that a sophisticated EWS must be designed to monitor and interrupt. The causal sequence is not a simple linear progression from rain to flood. It begins with anthropogenic climate change, which leads to a warmer atmosphere capable of holding and delivering more moisture. This increases the frequency and intensity of extreme precipitation events. When such an event occurs over a glacial catchment, it causes a rapid rise in the lake level, leading to dam overtopping. This, in turn, initiates the critical positive feedback loop of breach incision, culminating in a GLOF. Simultaneously, the same long-term warming that fuels these storms is also degrading permafrost on adjacent slopes, increasing the likelihood of a landslide trigger. Therefore, an effective EWS cannot rely on a single parameter like a rainfall gauge. It must adopt a holistic approach, integrating meteorological forecasts to predict intense precipitation, temperature sensors to monitor melt rates and long-term warming trends, and geotechnical sensors to assess slope stability, recognizing that these factors are deeply interconnected and can amplify one another's hazardous effects.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > Intense Precipitation and Snowmelt Dynamics", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Meteorological Drivers as Primary GLOF Triggers", "Intense Precipitation and Snowmelt Dynamics"], "chunk_type": "text", "line_start": 46, "line_end": 56, "token_count_estimate": 882, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db564bc68d78f38c", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > Atmospheric Pressure: An Assessment of Influence\nType: text\n\nThe user query specifically requested information on the role of atmospheric pressure as a GLOF trigger. A thorough review of the scientific literature indicates that while atmospheric pressure is an integral component of weather systems, it is not considered a direct physical trigger for the failure of a glacial dam.<sup>1</sup>\n\nThe hazardous weather conditions associated with GLOFs, such as intense storms, are indeed characterized by low-pressure systems.<sup>19</sup> However, the dam failure is caused by the physical impacts of the associated phenomena—namely, the mass of water from heavy precipitation or the rapid influx from temperature-driven melt—rather than the change in barometric pressure itself. The direct force exerted by atmospheric pressure changes on the lake surface or dam structure is negligible compared to the immense forces of hydrostatic pressure from the water column or the kinetic energy of an avalanche.\n\nFor the purpose of designing an EWS, monitoring atmospheric pressure is highly valuable, but its role is predictive rather than causative. As a key parameter measured by any standard Automatic Weather Station (AWS), barometric pressure trends are crucial for forecasting the approach of storm systems that could bring triggering conditions (i.e., heavy precipitation). Therefore, it serves as an important input for meteorological forecasting models that can provide advance warning of hazardous weather, but it should not be mistaken for a direct physical trigger of dam failure. The critical pressure that must be monitored for direct risk assessment is the **hydrostatic pressure** exerted by the lake's water, which is a function of water depth.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > Atmospheric Pressure: An Assessment of Influence", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Meteorological Drivers as Primary GLOF Triggers", "Atmospheric Pressure: An Assessment of Influence"], "chunk_type": "text", "line_start": 58, "line_end": 64, "token_count_estimate": 474, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "905b3b00a9670d32", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity\nType: text\n\nWhile external meteorological and geotechnical events often act as the final trigger, the inherent state of the glacial lake and its dam determines the system's underlying susceptibility to an outburst. Factors such as the water level, the resulting hydrostatic pressure, and the long-term structural integrity of the dam are critical parameters that an EWS must continuously monitor.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity"], "chunk_type": "text", "line_start": 66, "line_end": 68, "token_count_estimate": 180, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ced45c29ebb7a77", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity > Hydrostatic Pressure and Lake Level Thresholds\nType: text\n\nThe most direct and quantifiable threat posed by a growing glacial lake is the increase in hydrostatic pressure. As a lake fills with meltwater, its volume and depth increase, exerting a progressively greater force on the inner face of the impounding dam.<sup>1</sup> A GLOF can occur through \"dam self-destruction\" when this hydrostatic pressure—the force exerted by the weight of the water column—exceeds the lithostatic pressure and shear strength of the dam material, causing a structural failure without a dynamic external trigger.<sup>27</sup> This risk is amplified in deep lakes and where the dam has been weakened by other long-term degradation processes.<sup>27</sup>\n\nCrucially for the design of a quantitative EWS, empirical research has identified specific, monitorable thresholds that correlate with a high probability of an outburst flood, particularly for ice-dammed lakes. Studies of recurring GLOFs have shown that an outburst becomes highly likely when the lake's **normalized depth (n')**—the ratio of the water depth to the dam height—exceeds a value of **0.60**.<sup>28</sup> This critical depth corresponds to a typical water pressure on the dam face of approximately **510 kPa**.<sup>28</sup>\n\nThese empirically derived values are of paramount importance for EWS development. They provide a concrete, physics-based foundation for moving beyond qualitative hazard assessment to a quantitative, automated alarm system. A monitoring system equipped with water level and pressure sensors can be programmed with these thresholds to automatically escalate alert levels. For example, a normalized depth approaching 0.60 or a pressure nearing 510 kPa could trigger a \"Yellow Alert\" or \"Warning\" status, prompting heightened surveillance and preparedness activities. A continued rise beyond these thresholds could trigger a \"Red Alert\" or \"Evacuation\" order. These quantifiable parameters represent the most direct and reliable indicators of imminent risk from pressure-induced dam failure.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity > Hydrostatic Pressure and Lake Level Thresholds", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity", "Hydrostatic Pressure and Lake Level Thresholds"], "chunk_type": "text", "line_start": 70, "line_end": 76, "token_count_estimate": 611, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc9b5a3e170f9d87", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity > Long-Term Dam Degradation Processes\nType: text\n\nThe structural integrity of a moraine dam is not static; it can degrade over time through slow, often invisible processes. These long-term mechanisms weaken the dam, reducing its ability to withstand hydrostatic pressure and making it susceptible to failure from a much smaller or less intense trigger event.\n\n**Melting of Buried Ice:** Many moraine dams, particularly those formed during the Little Ice Age, contain significant cores of buried glacial ice. This ice, insulated by the overlying debris, can persist for centuries. However, as regional atmospheric temperatures rise due to climate\n\nchange, this buried ice begins to melt.<sup>2</sup> The melting of the ice core causes a loss of volume and structural support within the dam, leading to subsidence, the formation of internal voids, and a general weakening of the entire structure.<sup>4</sup> This internal degradation is a critical long-term destabilization mechanism that can ultimately lead to the dam's collapse.<sup>12</sup>\n\n**Piping and Seepage:** Moraine dams are composed of unconsolidated materials with varying permeability. This allows water from the lake to seep into and through the dam structure. This seepage can create preferential flow paths within the dam. If the velocity of this internal flow is sufficient, it can begin to erode and transport fine-grained sediment from the dam's core, a process known as \"piping\". Over time, this internal erosion can create large conduits or \"pipes\" through the dam, progressively weakening its structure until it can no longer support the pressure of the impounded water, leading to a sudden and catastrophic failure.\n\nThe existence of these quantifiable pressure and depth thresholds provides a powerful tool for building an automated EWS. However, the \"hidden\" nature of long-term dam degradation processes like ice core melt and internal piping reveals a potential vulnerability in a monitoring strategy that relies solely on surface-level measurements. A dam that has been significantly weakened from within might fail at a hydrostatic pressure *below* the established 510 kPa threshold, meaning an alarm based on that value alone might come too late or not at all. This implies that a truly comprehensive EWS for a high-risk, moraine-dammed lake must be informed by a deeper understanding of the dam's internal composition and integrity. This requires a preliminary geotechnical assessment, potentially using techniques like Ground Penetrating Radar (GPR) to map the presence and extent of any buried ice core.<sup>32</sup> The results of such a survey can provide a more accurate picture of the true risk level and allow for the calibration of alarm thresholds that are tailored to the specific structural condition of the dam, closing a critical blind spot in the monitoring strategy.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity > Long-Term Dam Degradation Processes", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity", "Long-Term Dam Degradation Processes"], "chunk_type": "text", "line_start": 78, "line_end": 88, "token_count_estimate": 774, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6b4089bdb5122346", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity\nType: text\n\nWhile meteorological and lake-centric factors create and condition the GLOF hazard, the most common and often most violent triggers are dynamic geotechnical events. These triggers are characterized by their rapid onset, typically occurring over seconds to minutes, and their immense energy, which can overwhelm even a relatively stable dam.\n\nMass Movement Impacts: Avalanches, Landslides, and Displacement", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dynamic and Geotechnical Triggers: Mass Movements and Seismicity"], "chunk_type": "text", "line_start": 90, "line_end": 94, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7451e84416bb3051", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Waves\nType: text\n\nThe most frequently documented trigger for GLOFs, particularly from moraine-dammed lakes, is the sudden impact of a large mass movement into the lake basin.<sup>15</sup> These events include ice avalanches from hanging glaciers, rockfalls from destabilized valley walls, or debris-laden landslides.<sup>4</sup>\n\n**Mechanism of Displacement Wave Generation:** When a substantial volume of ice, rock, or debris rapidly enters a lake, it displaces a massive volume of water, generating a high-energy impulse wave, also referred to as a seiche or a tsunami-like wave. The size of this wave is a function of the volume, velocity, and geometry of the incoming mass. These waves can be exceptionally large, with observed or modeled heights reaching tens of meters. <sup>35</sup>\n\nFrequency and Dam Failure: This trigger mechanism is remarkably common. In the Himalayas, displacement waves from ice or rock avalanches are responsible for nearly half of all recorded moraine-dam failures. <sup>12</sup> Globally, it is the most common dynamic cause of GLOFs. <sup>34</sup> The resulting displacement wave can easily overtop the freeboard (the height of the dam crest above the water level). This initial overtopping can be powerful enough to cause immediate dam rupture, but more commonly, it initiates the catastrophic breach incision process. The initial surge of water over the dam crest begins to erode the unconsolidated moraine, and the subsequent reflected waves continue to overtop the dam, rapidly carving a breach that leads to the complete and violent drainage of the lake. <sup>4</sup>\n\nThe prevalence of mass movements as the primary GLOF trigger has a profound implication for EWS design: a GLOF EWS cannot be merely lake-focused; it must be *catchment-focused*. A monitoring system that only includes sensors at the lake itself, such as water level gauges, would only detect the *consequence* of the trigger—the arrival of the displacement wave at the dam. This would provide, at best, only seconds to minutes of warning time, which is insufficient for any meaningful downstream evacuation. To provide a true *early* warning, the system must be capable of detecting the precursor to the trigger: the instability of the slope *before* it fails. This fundamentally expands the spatial scope and technological complexity of the required sensor network, necessitating the monitoring of the stability of surrounding slopes, hanging glaciers, and permafrost zones with instruments like geophones, extensometers, or remote sensing techniques like InSAR.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Waves", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dynamic and Geotechnical Triggers: Mass Movements and Seismicity", "Waves"], "chunk_type": "text", "line_start": 96, "line_end": 104, "token_count_estimate": 766, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1eb078cac2a5dcb3", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Seismic Activity: Direct and Indirect Triggering Mechanisms\nType: text\n\nIn tectonically active mountain ranges like the Himalayas and the Andes, earthquakes are\n\nanother significant GLOF trigger. Seismic events can initiate dam failure through both direct and indirect pathways.\n\n**Direct Impact:** Intense ground shaking during an earthquake can directly compromise the structural integrity of a moraine dam. The seismic waves can cause liquefaction of saturated sediments within the dam or induce cracking and slumping, leading to a rapid structural failure.<sup>2</sup>\n\n**Indirect Impact (More Common):** More frequently, earthquakes act as an indirect trigger by initiating the very mass movements described in the preceding section.<sup>4</sup> The seismic energy can be the final catalyst that destabilizes an already precarious slope, causing a massive ice or rock avalanche to plunge into the lake, thereby generating a displacement wave.<sup>39</sup>\n\nPost-Earthquake Hazard Amplification: The GLOF hazard in a region can be dramatically amplified for months or even years following a major earthquake. A large seismic event can trigger thousands of landslides throughout a mountain range, depositing vast quantities of loose, unconsolidated sediment onto valley floors and slopes. This material then becomes readily available for entrainment by any subsequent flood. A GLOF that might have otherwise been a relatively clean water flood can scour and incorporate this loose debris, transforming into a much larger, denser, and more destructive debris flow. This means that even a small glacial lake, previously considered low-risk, could produce a catastrophic debris flow if its outburst path is now loaded with earthquake-induced landslide deposits. Hazard assessments must therefore be re-evaluated in the aftermath of any significant regional earthquake.\n\nA second, paradigm-shifting realization for EWS design comes from the analysis of the seismic signals generated by GLOFs themselves. Detailed studies of past events, such as the 1994 GLOF from Lugge Tsho in Bhutan, have revealed that the flood's highly turbulent flow and the transport of boulders and sediment along the riverbed generate a continuous, high-frequency seismic signal. This GLOF-induced ground vibration is distinct from the short, sharp signal of an earthquake and can be detected by seismometers located up to 100 kilometers away. By using arrays of seismometers and applying signal processing techniques like cross-correlation, it is possible to not only detect the onset of a GLOF but also to track the location of the flood front as it propagates down the valley in real-time. This transforms the role of seismometers from merely being detectors of a potential trigger (an earthquake) to being a powerful, remote tool for detecting and tracking the hazard itself. This provides a robust, wide-area monitoring capability that is less vulnerable to being destroyed by the flood and can provide hours of warning time to downstream communities.\n\nThe following table synthesizes the primary GLOF triggers, their physical mechanisms, and the key parameters that can be monitored by an EWS.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Seismic Activity: Direct and Indirect Triggering Mechanisms", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dynamic and Geotechnical Triggers: Mass Movements and Seismicity", "Seismic Activity: Direct and Indirect Triggering Mechanisms"], "chunk_type": "text", "line_start": 106, "line_end": 120, "token_count_estimate": 841, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "f51350e4c139b81f", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Seismic Activity: Direct and Indirect Triggering Mechanisms\nType: table\nTable\n\n| Trigger Category           | Specific Trigger                                                                                                   | Physical Mechanism                                                                                                               | Primary Monitorable Parameters                                                         | Secondary/ Proxy Parameters                                                  | Typical Onset Time                                                       |\n|-------------------------------|-----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------|\n| Meteorological                | Intense Rainfall / Rapid Snowmelt                                                                            | Dam overtoppin g leading to positive feedback of breach incision.4                                                   | Precipitatio n intensity (); Rate of lake level rise ()                          | Air temperatur e; Snow line elevation; Barometric pressure trend | Hours to Days                                                            |\n|                               | Sustained High Temperatures                                                                                     | Accelerated glacier and snow melt increasing lake volume; Permafrost degradatio n weakening slopes.10 | Air temperatur e anomalies; Cumulative positive degree days             | Solar radiation; Glacier surface velocity                              | Days to Weeks (trigger); Years to Decades (conditionin g) |\n| Lake-Centric                  | Hydrostatic Pressure                                                                                               | Water pressure exceeds the structural strength (lithostatic pressure) of the dam, causing failure.27  | Lake water level/depth; Sub-surfac e water pressure (hydrostatic pressure) | Lake volume; Normalized depth ()                                          | Weeks to Months                                                          |\n|                               | Dam Degradation                                                                                                    | Melting of buried ice core or internal erosion                                                                          | Dam surface temperatur e; Seepage rate/turbidit y                             | Ground-pe netrating radar (GPR) survey results (for                    | Years to Decades                                                         |\n|                               |                                                                                                                       | (piping) weakens the dam structure, lowering its failure threshold. 4                                                               | y; Surface subsidence (via InSAR)                                                      | ice core presence)                                                              |                                                                             |\n| Dynamic / Geotechni cal | Ice/Rock Avalanche or Landslide                                                                              | Rapid mass entry into lake generates a large displaceme nt (seiche) wave, causing dam overtoppin g and breach. 1                    | Ground vibration (geophone/ seismomete r); Acoustic signal                                   | Slope deformatio n rate (InSAR, extensomet ers); Rock/ice temperatur e             | Seconds to Minutes                                                       |\n|                               | Earthquake  Ground shaking directly compromis es dam stability or indirectly triggers mass movements into the lake. 7 |                                                                                                                                     | Seismic P- and S-wave arrival; Peak Ground Acceleratio n (PGA)                | Regional seismic hazard maps                                              | Seconds                                                                     |", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Seismic Activity: Direct and Indirect Triggering Mechanisms", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Dynamic and Geotechnical Triggers: Mass Movements and Seismicity", "Seismic Activity: Direct and Indirect Triggering Mechanisms"], "chunk_type": "table", "table_caption": null, "columns": ["Trigger Category", "Specific Trigger", "Physical Mechanism", "Primary Monitorable Parameters", "Secondary/ Proxy Parameters", "Typical Onset Time"], "table_row_start": 1, "table_row_end": 7, "line_start": 121, "line_end": 129, "token_count_estimate": 731, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c8ec5c439de687e", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS)\nType: text\n\nThe complex, multi-causal nature of GLOFs dictates that a reliable EWS cannot be based on a single sensor or parameter. Instead, a robust framework must be built upon an integrated,\n\nmulti-sensor approach that provides detection, verification, and redundancy.<sup>36</sup> This framework should be structured around the four internationally recognized components of a modern EWS: Risk Knowledge, Monitoring and Warning, Dissemination and Communication, and Response Capability.<sup>36</sup> The following sections focus on the technological aspects of the Monitoring and Warning component.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS)", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Framework for a GLOF Early Warning System (EWS)"], "chunk_type": "text", "line_start": 132, "line_end": 136, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6766e0758e19036a", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > A Multi-Sensor, Integrated Approach\nType: text\n\nThe optimal EWS design is a hierarchical, multi-scale system that intelligently allocates monitoring resources based on risk. This approach avoids the financially and logistically prohibitive task of installing dense in-situ sensor networks at every one of the thousands of glacial lakes in a region.<sup>1</sup>\n\nThe framework begins with wide-area **surveillance** using freely available, moderate-resolution satellite data (e.g., Landsat, Sentinel) to create and regularly update comprehensive glacial lake inventories. This regional-scale analysis identifies \"hotspots\"—lakes that are expanding rapidly, are situated beneath potentially unstable slopes, or exhibit other characteristics associated with high risk. These identified hotspots are then prioritized for more focused and resource-intensive **intensive care** monitoring. This may involve tasking high-resolution commercial satellites for more detailed analysis or, for the most critical and potentially dangerous glacial lakes, deploying a ground-based network of in-situ sensors. This tiered strategy ensures that the most sophisticated monitoring efforts are directed where the risk is greatest, creating a scalable and economically viable national or regional EWS.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > A Multi-Sensor, Integrated Approach", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Framework for a GLOF Early Warning System (EWS)", "A Multi-Sensor, Integrated Approach"], "chunk_type": "text", "line_start": 138, "line_end": 142, "token_count_estimate": 387, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69a097760779b302", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > In-Situ Monitoring Technologies and Parameters\nType: text\n\nIn-situ, or ground-based, sensors are the cornerstone of a real-time GLOF alarm system. They provide high-frequency, continuous data directly from the hazard source, enabling the detection of rapid-onset triggers and the tracking of critical parameters against predefined alarm thresholds.\n\n- Automatic Weather Stations (AWS): An AWS is fundamental for monitoring meteorological triggers. It should be equipped to measure air temperature, precipitation (type and intensity), solar radiation (a key input for melt models), wind speed, and barometric pressure (for weather system forecasting).<sup>50</sup>\n- Water Level and Pressure Sensors: These are the most direct way to monitor the state\n\nof the lake. Technologies include non-contact sensors like ultrasonic or radar gauges, which are mounted above the water surface, and submersible pressure transducers placed in the lake.<sup>32</sup> They provide continuous data on the lake's water level, allowing for the calculation of the rate of rise and the real-time tracking of hydrostatic pressure against critical thresholds.<sup>53</sup>\n\n- Geophones and Acoustic Sensors: Geophones are sensitive instruments that detect\n  ground vibrations. Deployed on or near the moraine dam and on surrounding slopes, they\n  can detect the high-frequency seismic signals generated by rockfalls, ice avalanches, or\n  the initial dam breach, providing an immediate warning of a dynamic trigger event.<sup>36</sup>\n  Acoustic sensors can similarly detect the audible sound of these events.\n- Seismic Sensors (Seismometers and Accelerometers): These instruments serve a dual purpose. A local network can detect regional earthquakes that may act as triggers.<sup>32</sup>\n   More significantly, a regional network of seismometers can be used to detect the distinct, continuous ground vibrations generated by the GLOF itself as it moves downstream, providing a powerful tool for remote detection and tracking.<sup>41</sup>\n- Ground-Based Cameras and Radar: Automatic, solar-powered cameras can provide invaluable visual information, capturing images of the glacier terminus to monitor calving activity, the dam face to identify seepage or slumping, and the lake surface to detect waves.<sup>36</sup> Ground-based interferometric radar can be used for high-precision monitoring of movement on the dam or critical slopes.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > In-Situ Monitoring Technologies and Parameters", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Framework for a GLOF Early Warning System (EWS)", "In-Situ Monitoring Technologies and Parameters"], "chunk_type": "text", "line_start": 144, "line_end": 160, "token_count_estimate": 688, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a868d48e596d2d6", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > Remote Sensing and Data Integration for Comprehensive Monitoring\nType: text\n\nRemote sensing from satellite platforms provides the essential broad-area context and long-term monitoring capability that is impossible to achieve with in-situ sensors alone. Much of this data is freely and publicly available, providing a cost-effective foundation for regional risk assessment.\n\n- Optical Satellite Imagery: Platforms like the Landsat series (USGS) and Sentinel-2 (ESA) provide multispectral imagery that is crucial for creating lake inventories and monitoring long-term changes in lake area, number, and color (which can indicate turbidity and sediment load).<sup>3</sup> The increasing application of machine learning and deep learning algorithms to this data allows for the automated and rapid delineation of thousands of lakes, making large-scale analysis feasible.<sup>54</sup>\n- Synthetic Aperture Radar (SAR): SAR satellites, such as Sentinel-1, are indispensable for GLOF monitoring due to their ability to acquire images through clouds and at night.<sup>7</sup> This overcomes the primary limitation of optical imagery in persistently cloudy mountain regions. SAR data is used to monitor changes in lake area and can detect the presence of floating ice.<sup>56</sup> An advanced technique, Interferometric SAR (InSAR), uses pairs of SAR\n\n- images to detect and measure ground surface deformation with millimeter-level precision, making it an exceptionally powerful tool for monitoring the stability of moraine dams and surrounding slopes for precursor movements.<sup>55</sup>\n- Digital Elevation Models (DEMs): DEMs provide the 3D topographic data necessary for GLOF hazard assessment. They are used to characterize the geometry of the lake basin and dam, estimate lake volume, and, crucially, to run hydrodynamic models that simulate potential flood paths, inundation depths, and travel times for downstream communities.<sup>46</sup>\n   Several global and regional DEMs are freely available for this purpose.\n\nThe following table provides a comparative overview of key sensor technologies, detailing the parameters they measure, their operational characteristics, and their availability. This information is intended to guide the selection of an appropriate suite of technologies for a comprehensive EWS.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > Remote Sensing and Data Integration for Comprehensive Monitoring", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Framework for a GLOF Early Warning System (EWS)", "Remote Sensing and Data Integration for Comprehensive Monitoring"], "chunk_type": "text", "line_start": 162, "line_end": 173, "token_count_estimate": 621, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0505cf5cce401073", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > Remote Sensing and Data Integration for Comprehensive Monitoring\nType: table\nTable\n\n| Technology Type                       | Specific Instrument                                           | Parameter(s) Measured                               | Relevance to GLOF Trigger                                                                         | Advantages                                                                        | Disadvantages/Constraints                                                              | Data Availability                                                                   |\n|---------------------------------------|---------------------------------------------------------------|-----------------------------------------------------|---------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|\n| <b>In-Situ Sensor</b>             | Radar Water Level Sensor                             | Lake water level, rate of rise             | Monitors hydrostatic pressure, overtopping risk52                                        | High temporal resolution, real-time data, non-contact              | High cost, difficult installation/maintenance, power requirements       | Commercial Purchase                                                              |\n|                                       | Submersible Pressure Transducer                         | Hydrostatic pressure, water temperature    | Direct measurement of pressure on dam53                                                  | High accuracy, continuous data                                              | In-water deployment risk (icebergs, currents), cable damage             | Commercial Purchase                                                              |\n|                                       | Geophone                                                      | High-frequency                                      | Detects rock/ice                                                                               | Highly sensitive                                                               | Can be sensitive                                                                    | Commercial                                                                          |\n|                                       |                                                               | ground vibration                                 | avalanches, dam breach onset36                                                              | to impact events, provides immediat e trigger alert                | to noise (wind, animals), requires careful placemen t                | Purchase                                                                            |\n|                                       | Seismom eter                                               | Broad-sp ectrum ground motion              | Detects earthqua ke triggers and tracks GLOF propagati on41               | Can detect events remotely (100+ km), wide-are a coverage | Requires specializ ed analysis, high initial cost                    | National/ Global Networks (often public); Commerc ial Purchase |\n|                                       | Automati c Weather Station (AWS)                  | Temperat ure, precipitat ion, radiation | Monitors meteorol ogical triggers and melt condition s50                        | Provides essential data for forecasti ng and melt modeling      | Requires regular maintena nce/calibr ation in harsh environm ents | Commerc ial Purchase                                                          |\n| Remote Sensing                     | Optical Satellite (e.g., Sentinel- 2, Landsat) | Lake area, color, snow cover            | Long-ter m monitorin g of lake expansio n and catchme nt condition s45 | Wide area coverage , long historical archive, free data         | Obscure d by clouds, limited use in winter/da rkness                 | Freely available (ESA/US GS)                                               |\n|                                       | SAR Satellite (e.g.,                                    | Lake area, surface                            | All-weath er lake/slope                                                                     | All-weath er, day/night                                                     | Complex data processin                                                           | Freely available (ESA)                                                        |\n| Sentinel-1)                           | deformation (InSAR), surface texture                          | monitoring7                                         | capability, detects subtle ground movement                                                        | geometric distortions in steep terrain                                            |                                                                                        |                                                                                     |\n| High-Resolution Commercial Satellites | Detailed imagery of dam, slopes, glacier terminus             | Detailed site inspection, change detection          | Very high spatial detail (<1 m)                                                                   | High cost per image, requires specific tasking                                    | Commercial Purchase                                                                    |                                                                                     |", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > Remote Sensing and Data Integration for Comprehensive Monitoring", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Framework for a GLOF Early Warning System (EWS)", "Remote Sensing and Data Integration for Comprehensive Monitoring"], "chunk_type": "table", "table_caption": null, "columns": ["Technology Type", "Specific Instrument", "Parameter(s) Measured", "Relevance to GLOF Trigger", "Advantages", "Disadvantages/Constraints", "Data Availability"], "table_row_start": 1, "table_row_end": 10, "line_start": 174, "line_end": 185, "token_count_estimate": 911, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f6877ed85164c47", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies\nType: text\n\nThe theoretical principles of EWS design are best understood through their application in real-world, high-risk environments. Case studies from around the globe provide invaluable lessons on successful strategies, technological challenges, and the critical importance of integrating technology with institutional and community frameworks.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "EWS in Practice: Insights from Global Case Studies"], "chunk_type": "text", "line_start": 188, "line_end": 190, "token_count_estimate": 152, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "165468a08be78903", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Kyagar Glacier, China\nType: text\n\nThe EWS implemented at the ice-dammed Kyagar Lake in the Karakoram Mountains represents a model of an effective, integrated system designed for a remote and recurring hazard. <sup>50</sup> The system combines remote sensing with a network of terrestrial stations. SAR satellite imagery is used to monitor the seasonal filling of the lake and estimate its volume. <sup>50</sup> This is complemented by a solar-powered, automatic observation station located near the ice dam, which records meteorological data and takes daily images of the glacier terminus. <sup>50</sup> Crucially, the system includes two additional monitoring stations with radar water level sensors located far downstream on the Yarkant River. This distributed network provides exceptional warning lead times: an outburst detected at the glacier station provides 34–36 hours of warning, while the downstream stations provide 22 hours and 7.5 hours of warning, respectively. <sup>50</sup> When a GLOF is detected, an automated alarm is sent via satellite to the mobile phones of authorities, enabling timely emergency response. The Kyagar system demonstrates\n\nthe power of a layered monitoring network to maximize warning time.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Kyagar Glacier, China", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "EWS in Practice: Insights from Global Case Studies", "Kyagar Glacier, China"], "chunk_type": "text", "line_start": 192, "line_end": 196, "token_count_estimate": 384, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "65b6404d542d198b", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Laguna 513, Peru\nType: text\n\nThe EWS for Laguna 513 in the Cordillera Blanca was specifically designed to address the region's primary trigger: ice and rock avalanches from the steep slopes of Mount Hualcán.<sup>36</sup> The core of the monitoring component consists of four geophones installed on the moraine dam to detect the ground vibrations from an avalanche impact, and two cameras providing a constant visual feed of the glacier face and the dam.<sup>36</sup> This data is transmitted in near real-time to a data center in the downstream city of Carhuaz. The system is built on a clear, tiered warning protocol with defined thresholds for geophone measurements that trigger different alert levels (yellow, orange, red).<sup>36</sup> This case highlights a system tailored to a specific, dominant trigger mechanism. It also underscores the importance of a well-defined institutional framework; by law, only the mayor can authorize an evacuation, so the EWS is designed to provide actionable information to a specific decision-maker.<sup>36</sup>", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Laguna 513, Peru", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "EWS in Practice: Insights from Global Case Studies", "Laguna 513, Peru"], "chunk_type": "text", "line_start": 198, "line_end": 200, "token_count_estimate": 352, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "59e45ed6081050de", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Thorthormi Lake, Bhutan\nType: text\n\nThe experience at Thorthormi Lake in the Bhutan Himalayas serves as a critical lesson in the operational challenges of maintaining an EWS in one of the world's most extreme environments. During a GLOF event in October 2023, the Automatic Water Level Station (AWLS) at the lake was completely damaged and washed away by the flood. <sup>59</sup> This event highlights the extreme vulnerability of in-situ equipment and underscores the absolute necessity of designing hardened, robust systems and ensuring redundancy. If a single point of failure (the destruction of the primary sensor) can disable the entire warning capability, the system is flawed. Furthermore, a study of a 2019 GLOF event at the same location revealed that the first warning for many residents came not from the official EWS, but from natural indicators—the unusually loud sound of the river and ground vibrations. <sup>60</sup> This demonstrates that community-based knowledge and observation are vital components of a resilient warning system and must be formally integrated with technical monitoring. The recommendations following the 2023 event—to relocate the AWLS, add more sensors for redundancy, and enhance community planning—point to a continuous process of adaptation and improvement. <sup>59</sup>", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Thorthormi Lake, Bhutan", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "EWS in Practice: Insights from Global Case Studies", "Thorthormi Lake, Bhutan"], "chunk_type": "text", "line_start": 202, "line_end": 204, "token_count_estimate": 398, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "3a1977466d653f84", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Imja Tsho and Tsho Rolpa, Nepal\nType: text\n\nThe cases of Imja Tsho and Tsho Rolpa in Nepal illustrate the long-term evolution and challenges of GLOF risk management. Tsho Rolpa was one of the first lakes in the region to have a technical EWS installed in the late 1990s, which included water level sensors and a series of 19 downstream siren towers. However, over two decades later, this system has reportedly fallen into disrepair due to a lack of sustained funding for maintenance, leaving the community vulnerable once again. This is a stark reminder that the financial commitment for an EWS cannot end with its installation; a long-term operational and maintenance budget is non-negotiable. In contrast, a pilot project at Imja Lake has demonstrated the potential of modern, lower-cost technologies. A Wi-Fi-based network was established to connect monitoring devices, including a field server collecting meteorological data and water levels, and to provide internet connectivity to remote villages. This approach not only facilitates data transmission for the EWS but also provides co-benefits to the community, which can help foster local ownership and support for the system's maintenance.\n\nSynthesizing the lessons from these diverse case studies reveals a critical conclusion: technology alone is insufficient to create a successful EWS. The most sophisticated sensor network is rendered useless if it is not supported by a robust institutional framework, a sustainable long-term maintenance plan, and deep engagement with the at-risk community. The Kyagar and Laguna 513 systems succeed because their technology is embedded within a clear operational protocol involving specific authorities. The Thorthormi case demonstrates that technology is fragile and that local, traditional knowledge can be a life-saving form of redundancy. The Nepal cases starkly illustrate that an EWS is a long-term commitment, not a one-time project; without sustained funding for upkeep, the initial investment can be entirely lost. Therefore, the design process for an EWS must extend beyond hardware and software specifications. It must include a multi-year budget for operations and maintenance, a plan for co-designing warning and response protocols with local emergency managers, and a comprehensive strategy for community training, education, and drills to build trust and ensure that a warning, when issued, leads to effective, life-saving action.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Imja Tsho and Tsho Rolpa, Nepal", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "EWS in Practice: Insights from Global Case Studies", "Imja Tsho and Tsho Rolpa, Nepal"], "chunk_type": "text", "line_start": 206, "line_end": 210, "token_count_estimate": 617, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "6e30612e2351c60b", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation\nType: text\n\nBased on the comprehensive analysis of GLOF triggers, monitoring technologies, and practical case studies, the following recommendations are provided to guide the design and\n\nimplementation of a robust and effective GLOF Early Warning System.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Recommendations for EWS Design and Implementation"], "chunk_type": "text", "line_start": 212, "line_end": 216, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83c27cdda4d27000", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Prioritizing Key Monitoring Parameters for a Tiered Warning System\nType: text\n\nA scalable and resource-efficient strategy should be adopted, focusing monitoring efforts in a tiered approach based on assessed risk levels.\n\n- Tier 1 (Regional Surveillance): This foundational layer should involve the systematic, continuous monitoring of all glacial lakes within a given region using freely available satellite data. Annual or bi-annual inventories should be created using optical (Landsat, Sentinel-2) and SAR (Sentinel-1) imagery to track changes in lake area and number. This wide-area surveillance identifies emerging threats and allows for the prioritization of resources.\n- Tier 2 (Targeted Monitoring): For lakes identified in Tier 1 as high-risk (e.g., rapid expansion, proximity to critical infrastructure, location beneath unstable slopes), a more intensive monitoring schedule should be implemented. This includes more frequent analysis of high-resolution commercial satellite imagery and the application of advanced techniques like InSAR to detect and quantify slope deformation and dam subsidence, which are precursors to failure.\n- Tier 3 (In-Situ Alarm System): For the most critical, high-consequence lakes designated as \"Potentially Dangerous Glacial Lakes\" (PDGLs), a permanent, real-time in-situ sensor network is warranted. This network must be designed with redundancy and should prioritize the continuous monitoring of the most immediate trigger parameters:\n  - 1. Rate of Lake Level Rise: The most direct indicator of rapid inflow from melt or rainfall.\n  - 2. **Ground Vibration:** Geophones placed on the dam and surrounding slopes to provide an instantaneous alert for an avalanche trigger.\n  - 3. **Real-time Meteorological Data:** An on-site AWS to correlate lake level changes with temperature and precipitation events.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Prioritizing Key Monitoring Parameters for a Tiered Warning System", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Recommendations for EWS Design and Implementation", "Prioritizing Key Monitoring Parameters for a Tiered Warning System"], "chunk_type": "text", "line_start": 218, "line_end": 227, "token_count_estimate": 520, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c701addbb75bf77d", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Integrating Data Streams for Predictive Modeling and Anomaly Detection\nType: text\n\nAn advanced EWS should move beyond simple, single-parameter threshold alarms. All data streams—from remote sensing platforms and in-situ sensors—should be transmitted to a central data processing hub. This integrated dataset enables more sophisticated analysis. Machine learning algorithms can be developed and trained on this multi-parameter data to\n\nidentify complex correlations and anomalies that may signal an impending GLOF. For example, a model could learn to recognize a hazardous signature composed of a combination of factors—such as several days of above-average temperatures, the onset of intense rainfall, and a subtle increase in microseismic activity on a nearby slope—that would be missed by individual sensor alarms. This data fusion approach enhances predictive capability and can increase warning lead times.", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Integrating Data Streams for Predictive Modeling and Anomaly Detection", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Recommendations for EWS Design and Implementation", "Integrating Data Streams for Predictive Modeling and Anomaly Detection"], "chunk_type": "text", "line_start": 229, "line_end": 233, "token_count_estimate": 293, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed2621ee48a2067a", "text": "Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems\nSection: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Establishing Actionable Thresholds and Robust Warning Protocols\nType: text\n\nThe ultimate purpose of the technology is to trigger a timely and effective human response. This requires a clear, unambiguous warning protocol.\n\n- Calibrate Actionable Thresholds: The system's alarm logic should be based on\n  empirically derived, physically meaningful thresholds. The normalized depth of >0.60 and\n  hydrostatic pressure of >510 kPa provide an excellent starting point for calibrating alarms\n  related to dam stability failure.<sup>28</sup> Thresholds for other parameters, such as rainfall\n  intensity () or the magnitude of a seismic signal from a geophone, must also be\n  established through site-specific analysis and modeling.\n- Develop a Multi-Level Warning Protocol: A tiered warning system, similar to the one\n  used in Peru (Green for normal, Yellow for alert, Orange for prepare to evacuate, Red for\n  evacuate), should be developed in close collaboration with local and national disaster\n  management authorities.<sup>36</sup> Each level must be tied to specific sensor readings or model\n  outputs and must have a corresponding, pre-agreed set of actions for authorities and\n  the public.\n- Ensure Redundancy in Monitoring and Communication: The system design must assume that components will fail. Redundancy is critical. This means deploying multiple sensors to measure the same parameter at critical locations and, equally important, establishing multiple channels for disseminating warnings. A robust communication plan should utilize sirens, mobile phone alerts (SMS and cell broadcast), radio broadcasts, and designated community messengers to ensure the warning reaches everyone at risk, even if one communication pathway fails.<sup>36</sup> Given that some GLOF triggers provide only minutes of warning, the entire system from detection to dissemination must be automated and optimized for speed.<sup>44</sup>", "metadata": {"source_file": "data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md", "document_title": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "section_path": "A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Establishing Actionable Thresholds and Robust Warning Protocols", "section_headings": ["A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems", "Recommendations for EWS Design and Implementation", "Establishing Actionable Thresholds and Robust Warning Protocols"], "chunk_type": "text", "line_start": 235, "line_end": 251, "token_count_estimate": 527, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "03e82d8184190fb4", "text": "Document: The Google Flood Forecasting Initiative\nSection: The Google Flood Forecasting Initiative\nType: text\n\nSella Nevo $^{1[0000-0002-4743-3634]}$\n\nGoogle Research, Tel Aviv\n\nAbstract. The Google Flood Forecasting Initiative is the world's first large-scale machine-learning-based operational flood forecasting system. It currently covers more than 360 million people, and provides flood warnings to governments, disaster management organizations, and individuals at risk. It combines two separate models - a hydrologic model and an inundation model - which together can provide high accuracy, actionable forecasts for flooding in upcoming days.\n\n**Keywords:** Flood forecasting · Hydrology · LSTM.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "The Google Flood Forecasting Initiative", "section_headings": ["The Google Flood Forecasting Initiative"], "chunk_type": "text", "line_start": 2, "line_end": 10, "token_count_estimate": 168, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26b19d493d1a36b0", "text": "Document: The Google Flood Forecasting Initiative\nSection: 1 Introduction\nType: text\n\nFloods are responsible for 5,000 to 60,000 fatalities [5, 18, 4, 17, 12, 11], affect between 95 million and 250 million people [5, 13, 12, 17, 4, 22], and cause between \\$21 and \\$33 billion in economic damages annually [3–5]. The majority of these harms are caused by riverine floods [17] - where a river overflows its banks and inundates the floodplain around it. The frequency and severity of floods has been increasing in recent decades [21, 10], and are expected to rise significantly in the future due to climate change, land use changes and other long term processes [16, 15]. On a brighter note, reliable early warning systems have been shown to prevent up to 43% of fatalities [2, 26], and up to 50% of economic damages [23, 1]. They have also been shown to have an impressive cost-benefit ratio of 1:9 [6], and much higher in low and middle-income countries (LMICs) [25], making them a top climate change adaptation policy recommendation by the United Nations and the World Bank.\n\nHuman costs of flooding are heavily concentrated in LMICs - for example, about half of all flood-related deaths globally occur in India and Bangladesh [17]. Despite the promise of early warning systems, many of these countries lack the resources for the comprehensive data collection, ongoing recalibration and maintenance, and computational resources that classic flood forecasting systems demand.\n\nThe Google Flood Forecasting Initiative aims to provide accurate and actionable flood warnings globally. It utilizes machine learning to create forecasting systems that are both more accurate and more scalable than classic flood forecasting systems, and then disseminates the resulting warnings via Google's various interfaces (Search, Maps, and smartphone notifications) as well as specialized interfaces to support governments and NGOs engaged in disaster management. At the time of writing, the system provides flood forecasting systems to regions covering more than 360 million people.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 12, "line_end": 18, "token_count_estimate": 478, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "92631cdca9d80a49", "text": "Document: The Google Flood Forecasting Initiative\nSection: 2 Problem Statement\nType: text\n\nFlood forecasting systems aim to provide governments, NGOs and individuals in the affected region reliable and actionable information about upcoming floods. The modeling task of riverine flood forecasting systems can often be divided into two main sub-tasks.\n\nThe first is forecasting conditions within the river, sometimes described as hydrologic modeling. In this task one traditionally uses inputs such as precipitation, temperature, soil moisture and others throughout the river basin, and produces as an output either the river discharge or the water level at specific points within the river at a specific time, usually up to two weeks into the future. Traditional hydrologic models are \"conceptual\" models - they are inspired by the actual physical processes involved, though usually are vastly simpler than the actual processes they describe. They will have somewhere between several to several dozen parameters, which are calibrated to produce outputs that fit the historical measurements in the specific river.\n\nThe second task is forecasting behavior across the floodplain, sometimes described as inundation modeling. Here we assume we already know the discharge or water level in the river (provided by a hydrologic model or a real-time measurement), and model the movement of water across the floodplain. The goal is to produce a spatially accurate map of flood extent (which areas are flooded) or flood depth (how deep is the water in each point on the map). The most common practice for these types of models are called hydraulic models - these are physics-based models which find finite-element solutions to a set of differential equations (specifically the St. Venant equations [8]) on a grid. These models can describe water behavior across the floodplain very accurately, when all their input data is accurate and they are run at sufficient resolution.\n\nThe case for incorporating AI into these two types of models is not identical, but similar principles apply. Both traditional models (hydrologic and hydraulic models) require significant manual calibration, and often continuous recalibration based on new information. This leads to high costs of deployment and operation, up to hundreds of thousands of dollars for a single basin - which significantly limits their availability in the regions that need warning systems the most, and prevents scale-up of high-quality systems to truly global scales. Relatedly, both have extremely limited spatial transferability - researchers have been largely unsuccessful in utilizing models trained in one location to improve performance in other locations, e.g. ones with less data available. Computational costs can also act as a critical limitation - especially for the hydraulic model, which is incredibly sensitive to resolution yet requires computation proportional to one over the resolution cubed. Finally, both models share a limitation that is common in physics-based and conceptual models - they can be overly rigid. Even when repeated historical data shows some assumption of the model to be incorrect, it is often difficult (and sometimes impossible) to change the model to correct its errors.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "2 Problem Statement", "section_headings": ["2 Problem Statement"], "chunk_type": "text", "line_start": 20, "line_end": 28, "token_count_estimate": 691, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b619379c48a58a4f", "text": "Document: The Google Flood Forecasting Initiative\nSection: 3 Method\nType: text\n\nOur flood forecasting system uses ML-based methods to tackle each of the tasked described above. We will describe our models for each task separately. For each task, we currently have two different architectures in production - used in different circumstances, e.g. depending on data availability and performance. See figure 1 for an overview of the full system including all components described below, and other components beyond the scope of this chapter.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "3 Method", "section_headings": ["3 Method"], "chunk_type": "text", "line_start": 30, "line_end": 32, "token_count_estimate": 115, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "598e176c3f7b3933", "text": "Document: The Google Flood Forecasting Initiative\nSection: 3 Method\nType: figure\nFigure\n\nImage /page/2/Figure/4 description: A flowchart illustrating a four-stage process for a flood alert system. The stages are labeled from left to right: Data management, Hydrologic modeling, Inundation modeling, and Alerts.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "3 Method", "section_headings": ["3 Method"], "chunk_type": "figure", "figure_caption": null, "line_start": 33, "line_end": 33, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4afa89f2865130e9", "text": "Document: The Google Flood Forecasting Initiative\nSection: 3 Method\nType: text\n\n1. \\*\\*Data management\\*\\*: This stage takes three inputs: \"Stream gauge measurements,\" \"Precipitation measurements,\" and \"Precipitation forecast.\" These inputs feed into a process labeled \"Data: Ingestion, Quality Control, Correction.\"\n\n2. \\*\\*Hydrologic modeling\\*\\*: The output from Data management flows into this stage. It contains two models in blue boxes: \"Linear\" and \"LSTM.\" A separate input, \"External stream gauge stage forecasts,\" points to a pink vertical box labeled \"Warning thresholds,\" which is positioned next to the Linear and LSTM models.\n\n3. \\*\\*Inundation modeling\\*\\*: The output from Hydrologic modeling flows into this stage. It contains two methods in green boxes: \"Thresholding\" and \"Manifold.\"\n\n4. \\*\\*Alerts\\*\\*: The output from Inundation modeling flows into the final stage. This stage lists three types of alerts that are sent out: \"Alerts to responsible authorities,\" \"Alerts to emergency units,\" and \"Alerts to population.\"\n\nEach stage title is accompanied by a small illustrative icon: a green checkmark for Data management, a line graph for Hydrologic modeling, a map of a river delta for Inundation modeling, and three smartphones displaying notifications for Alerts.\n\nFig. 1. A high-level overview of the Google flood forecasting system.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "3 Method", "section_headings": ["3 Method"], "chunk_type": "text", "line_start": 34, "line_end": 46, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b469bb79e9e83b4", "text": "Document: The Google Flood Forecasting Initiative\nSection: 3 Method > 3.1 Hydrologic Model\nType: text\n\nThe two architectures used for hydrologic modeling are linear models, and LSTMs. All our models currently forecast water levels (as opposed to discharge), and provide forecasts in gauged locations (i.e. locations where there is a stream gauge installed) - though work is ongoing to expand our reach beyond this. The training and validation scheme for both models is described in a sub-section after the models themselves.\n\nLinear Model The model input includes past river stages from the target gauge (the gauge being forecast) and its upstream gauges (typically 2-5 gauges) for each time step (usually hourly). The output is a future river stage at the target gauge for a given lead time. A multiple linear regression model is trained with historical records of the above inputs and outputs. The model is optimized", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "3 Method > 3.1 Hydrologic Model", "section_headings": ["3 Method", "3.1 Hydrologic Model"], "chunk_type": "text", "line_start": 48, "line_end": 52, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "625b5062c7a7b44e", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo\nType: text\n\nusing the mean square error (MSE) loss function with L2 regularization. Linear models are trained separately for each target gauge, with a separate linear model trained for all lead times up to the gauge's maximal lead time. For example, a target gauge with a selected maximal lead time of 24 hours and hourly resolution implies 24 trained linear models. Figure 2 illustrates the schema of the linear model.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo", "section_headings": ["4 S. Nevo"], "chunk_type": "text", "line_start": 54, "line_end": 56, "token_count_estimate": 114, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "10961c72f9ede224", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A diagram titled 'Linear Model' illustrates a predictive model's workflow. On the left, a series of inputs are shown, represented by red parallelograms. These inputs consist of 'upstream stage' and 'current stage' data for multiple past time steps, starting from (t-u) and continuing through to (t-1), indicated by vertical dots. These inputs are fed into a central blue vertical rectangle labeled 'linear regression'. This block also specifies 'u = 72 hour lookback'. The linear regression model produces an output, a red parallelogram labeled 'prediction (t)'. This prediction is then connected by an arrow to a smaller blue rectangle labeled 'MSE' (Mean Squared Error). Finally, an arrow from the MSE block points to another red parallelogram labeled 'target gauge stage (t)', representing the actual value to which the prediction is compared.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo", "section_headings": ["4 S. Nevo"], "chunk_type": "figure", "figure_caption": null, "line_start": 57, "line_end": 57, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ea17bda561a302e", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo\nType: text\n\nFig. 2. Schema of the linear model.\n\nLong Short-Term Memory Network (LSTM) Model Building on the work described in [19, 20, 14], the LSTM model consists of two LSTMs: a sequence-to-one hindcast model and a sequence-to-sequence forecast model, where the output of the hindcast model is used as the initial state of the forecast model. The hindcast model processes data from past days sequentially, taking the following variables as inputs at each timestep: (i) Precipitation measurements through the basin, (ii) near-real-time stage, and (iii) a linear combination of stage measurements from upstream gauges over some upstream \"lookback\" period (typically a few days). The linear combination of stage measurements is produced by a separate linear layer with gauge-specific weights that combines a (variable) number of upstream inputs per gauge into five features that are fed as inputs into the hindcast LSTM (similar to the linear model described above).\n\nThe hindcast LSTM runs until the current time, defined to be the time of the last available measurement. The final cell state and hidden state of the hindcast LSTM are passed through a fully connected layer and the output of this \"state handoff\" layer is used as the initial cell state and hidden state of the forecast\n\nLSTM. The forecast LSTM advances one step for every lead time, producing the relevant forecasts for all lead times up to the maximal one. The main goal of using a state handoff between two LSTMs is to distinguish between the different inputs that are available in real-time during hindcast and forecast - and specifically the availability of precipitation measurements.\n\nAll weights of the LSTMs (hindcast and forecast) are shared between all target gauges, i.e. they are regionally calibrated. The only gauge-specific weights are those of the linear upstream combiner layer which allows the architecture to support varying numbers of upstream gauges, travel times, sizes of tributaries, etc.\n\nThe system estimates the uncertainty of the water stage, or the time dependent distribution over the predicted stage, using a countable mixture of asymmetric Laplacians (CMAL). The parameters of this distribution are generated by feeding the hidden state of the forecast LSTM into a dedicated head layer for each forecasted time step. At each time step, the loss is calculated as the negative log-likelihood of the observed stages given the LSTM forecasts using the next maximal lead time values. It is important to note that the likelihood-based loss function is calculated only over the outputs of the forecast LSTM, and the hindcast LSTM is used only to initialize the forecast state. Since training is shared for all target gauges, the maximal lead time in the training phase is taken as the maximum of the gauge-specific values. Figure 3 illustrates the schema of the LSTM model.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo", "section_headings": ["4 S. Nevo"], "chunk_type": "text", "line_start": 58, "line_end": 70, "token_count_estimate": 699, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3bd01dac0ea980e7", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo\nType: figure\nFigure\n\nImage /page/4/Figure/5 description: A diagram illustrating the architecture of an LSTM (Long Short-Term Memory) model for time-series forecasting. The model processes input data through several layers to predict future values. The flowchart begins on the left with inputs for different time steps, from (t-h) to (t-0). For each time step, the inputs are 'IMERG', 'current stage', and a stack of 'upstream stage' data. These inputs are fed into a 'fully connected (linear)' layer. The outputs from these layers are then passed to a 'hindcast LSTM (seq2one)' layer. The hidden state from the hindcast LSTM is processed by another 'fully connected' layer and then used to initialize a 'forecast LSTM (seq2seq)' layer. This forecast LSTM produces a sequence of outputs, each of which goes through a 'CMAL' block to generate a probability distribution. These distributions are compared against the 'target gauge stage' for future time steps from (t+0) to (t+k). The model's performance is evaluated using a 'Σ negative log-likelihood loss'.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo", "section_headings": ["4 S. Nevo"], "chunk_type": "figure", "figure_caption": null, "line_start": 71, "line_end": 71, "token_count_estimate": 306, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4590045fea7226e2", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo\nType: text\n\nFig. 3. Schema of the LSTM model.\n\nTraining and Validation The stage forecast models are trained and validated with historical data using a cross-validation scheme, in which each fold uses one year's worth of data for validation and the rest for training (i.e., 1-year leave-out). For operational use, the models were retrained on the full data set to produce the best real-time forecasts possible.\n\nOperational Use For Alerting For each target gauge, if the maximal fore-casted river stage between the forecast's \"current time\" and the gauge's maximal lead time is above the predefined gauge-specific warning threshold, an alert is issued, and this maximal stage is used for inundation mapping (see below).", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo", "section_headings": ["4 S. Nevo"], "chunk_type": "text", "line_start": 72, "line_end": 78, "token_count_estimate": 199, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0b0704e29a3c248", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model\nType: text\n\nThe two architectures used for inundation modeling are the Thresholding model, and the Manifold model, both described below. The Thresholding model produces an inundation extent map but no forecast for flood depths. The Manifold model additional produces the water depth at each pixel, but requires a digital elevation model (DEM) and requires more effort to implement and deploy at scale. The training and validation scheme for both models is described in a sub-section after the models themselves.\n\nThresholding Model The model assumes that each pixel in the area of interest (AOI) becomes inundated when the target gauge exceeds a (pixel-specific) threshold water stage. These thresholds are learned from the series of historic stage data at the target gauge and the corresponding state of the pixel (dry/wet) during these events. Each pixel in the inundation map is treated as a separate classification task, predicting whether the pixel will be inundated or not. We refer to the \"wet\" class as the positive class.\n\nThe algorithm described below identifies pixel-specific thresholds and is aimed at maximizing some F-score using an optimized global parameter called minimal ratio. An F-score [7] refers to a weighted geometric mean of precision and recall, with  $F_1$  referring to the simple harmonic mean between them and  $F_3$ giving more weight to precision over recall, for example. The algorithm below can be optimized for any choice of F-score. To achieve this, an iterative process is applied to each pixel. In each iteration, we find the threshold that maximizes the ratio of true wet events (where the water stage at the gauge is above the threshold and the pixel was wet) to false wet events (where the water stage at the gauge is above the threshold and the pixel is dry). The threshold that maximizes this ratio is the most cost-effective threshold in the sense that it provides the most true wets per false wet instance. At the first iteration all training events are considered; then, after each selection of a threshold and its respective truefalse ratio, events with stage measurements above the threshold are discarded and a new iteration starts with the remaining events. If the new true-false ratio calculated is lower than the minimal ratio parameter value, the process stops and the final threshold for the pixel is the one found in the previous iteration.\n\nIt can be shown that for every minimal ratio parameter value, no other set of pixel-specific thresholds achieves simultaneously better precision (i.e., fraction of all flooded pixels that are predicted as being flooded) and recall (i.e., fraction of all pixels that are predicted to be flooded and are really flooded); implying it is Pareto optimal. Therefore, for any F-score there exists some value of the minimal threshold parameter which finds the thresholds that optimize this F-score.\n\nIn cases where the river stage input is higher than all past stage data, the Thresholding model's output inundation map is initialized from the most severe inundation extent seen in the historical events and expanded in all directions. The expansion distance is a linear function of the difference between the forecasted stage and the stage of the highest historical event.\n\nThis Thresholding model requires almost no site-specific data like DEMs, and no manual work, making it appealing for large-scale deployment across many areas of interest in a short amount of time.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model", "section_headings": ["4 S. Nevo", "3.2 Inundation Model"], "chunk_type": "text", "line_start": 80, "line_end": 92, "token_count_estimate": 844, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df51e95c08c65e04", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model\nType: figure\nFigure: Figure 4 illustrates the schema of the Thresholding model.\n\nFigure 4 illustrates the schema of the Thresholding model.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model", "section_headings": ["4 S. Nevo", "3.2 Inundation Model"], "chunk_type": "figure", "figure_caption": "Figure 4 illustrates the schema of the Thresholding model.", "line_start": 93, "line_end": 93, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "888b018f149d6ad7", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model\nType: figure\nFigure\n\nImage /page/6/Figure/6 description: A diagram illustrating a 'Thresholding model' for creating flood inundation maps. The process starts with 'Historic inundation maps and gauge stage data' on the left. An arrow points from a specific pixel on these maps to a timeline representing 'Historic pixel state for each flood event'. This timeline shows a sequence of 'Dry' (orange boxes) and 'Wet' (blue boxes) states. Below this, a corresponding timeline shows the 'Historic gauge stage for each flood event' with values: 2.1m, 2.8m, 3.1m, ..., 4.5m, 5.7m, 6.2m, ..., 7.3m, 8.5m. An upward arrow labeled 'Optimized stage threshold for the pixel' points from this historical data. This threshold is then used with a new 'Gauge stage' of '4.2m' to produce an 'Output flood inundation map', which is displayed at the top left of the diagram.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model", "section_headings": ["4 S. Nevo", "3.2 Inundation Model"], "chunk_type": "figure", "figure_caption": null, "line_start": 95, "line_end": 95, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1022875eb1f11133", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model\nType: text\n\nFig. 4. Schema of the Thresholding model.\n\nManifold Model The Manifold inundation model provides a machine-learning alternative to hydraulic models, by computing physically reasonable flood inundation. Its inputs are a DEM for the AOI and a target water stage. It outputs both the flood inundation extent and the inundation depth at each AOI pixel. The model is divided into two major parts, described below.\n\nFlood Extent To Water Height Algorithm\n\nThe flood extent to water height algorithm converts a DEM and an inundation\n\nextent map (i.e., wet/dry state for each pixel) into a water height map, which is a per-pixel water height in meters above sea level. The algorithm tries to find a physically reasonable water height map that best matches the input inundation map, where the physically reasonable requirement is defined as: (1) the water height surface must be smooth, i.e. we aim to find a water height map that does not change significantly between neighboring pixels; and, (2) the water height surface should not have a minimum or a maximum at the interior of flooded regions. This optimization problem is not differentiable, and thus cannot be easily solved directly. Instead, the following heuristic can be shown to produce an optimal solution to the above optimization problem. The algorithm identifies the boundaries of the inundated areas of the input inundation map. The water height at these boundaries is extracted from the corresponding DEM. In between these boundaries, the algorithm uses the Laplace differential equation to interpolate the water heights. The water height map is defined as a low-resolution image, where every pixel is set to be of 32x32 DEM pixels. This assures that the output map is smooth and does not contain high frequency changes, while also reducing the computational complexity of the process. In addition, outlier DEM pixels, which are pixels that cause high Laplace tension, are removed to assure that the overall function is smooth.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model", "section_headings": ["4 S. Nevo", "3.2 Inundation Model"], "chunk_type": "text", "line_start": 96, "line_end": 106, "token_count_estimate": 488, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a8d34324a1144a64", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm\nType: text\n\nWhen inferring inundation depth for a real-time gauge water stage forecast, we do not have access to the full current flood extent (as the extent-to-height algorithm above assumes). To be able to provide depth in this more challenging setting, we first perform some precomputation. We first apply the Thresholding model described above to all past events in our training data, producing an inundation extent map for each gauge stage measurement. We then apply the flood extent to water height algorithm described above to produce a water height map for each past event.\n\nIn real-time, when we receive an input water stage at the target gauge, the model simply performs per-pixel piecewise-linear interpolation between the water height maps of the training dataset to generate a new water height map corresponding to this input stage. The resulting water height map and the DEM are then used to generate: (1) an updated inundation extent map, by assigning a dry state to a pixel if its water height is lower than the DEM and a wet state otherwise; and, (2) an inundation depth map, as the difference between the water height and the DEM height for wet pixels. The use of the Thresholding model (as opposed to directly using satellite-based imagery of actual historical flood extents) as the input flood extent maps ensures that higher gauge stages always yield larger inundation extents, and thus removes unnecessary noise. When the model infers an inundation depth map for a gauge's water stage higher than all the events in the training set, it extrapolates the water height map by adding the gauge level difference to every pixel in the highest water height map computed from observed gauge stages, and uses the extrapolated water height and DEM to compute the water depth map.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm", "section_headings": ["4 S. Nevo", "3.2 Inundation Model", "Gauge Stage To Flood Depth Algorithm"], "chunk_type": "text", "line_start": 108, "line_end": 112, "token_count_estimate": 453, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1fe87fa0f9104851", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm\nType: figure\nFigure: Figure 5 illustrates the schema of the Manifold model.\n\nFigure 5 illustrates the schema of the Manifold model.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm", "section_headings": ["4 S. Nevo", "3.2 Inundation Model", "Gauge Stage To Flood Depth Algorithm"], "chunk_type": "figure", "figure_caption": "Figure 5 illustrates the schema of the Manifold model.", "line_start": 113, "line_end": 113, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6dbc59506c2e865d", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A diagram illustrating a workflow, labeled the \"Manifold model\", for generating an \"Output flood inundation map and water depth map\". The process starts at the bottom with a horizontal axis representing gauge stage, with points for historic flood events at 2.1m, 3.1m, 5.7m, and 7.3m. For each historic event, a \"Computed inundation map (Thresholding model)\" is shown. These maps are then used to create \"Physically-constrained water height maps\" in the middle row, a process that also incorporates Digital Elevation Model (DEM) data. The model can then take a new input, shown as a \"Gauge stage\" of 4.2m, to generate an \"Interpolated and smoothed water height map\". This map, along with DEM data, is used to produce the final two output maps: a flood inundation map showing the extent of the water in teal, and a water depth map showing varying depths in shades of blue.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm", "section_headings": ["4 S. Nevo", "3.2 Inundation Model", "Gauge Stage To Flood Depth Algorithm"], "chunk_type": "figure", "figure_caption": null, "line_start": 115, "line_end": 115, "token_count_estimate": 283, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19471c364aa33e3e", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm\nType: text\n\nFig. 5. Schema of the Manifold model.\n\nTraining and Validation The inundation models are trained and validated based on historical flood events, where flood inundation extent maps from satellite data, along with the corresponding gauge water stage measurements, are available. Similar to the stage forecast models, a 1-year leave out cross validation scheme is used for training and validation. For operational use, the models are retrained with all historical data. It should be noted that, contrary to flood inundation extent, it is much more difficult to obtain gound truth data for flood inundation depth at scale. The Manifold model is, therefore, trained and validated only on the inundation extent. However, since the Manifold model is constrained to only produce physically reasonable water height maps, accurate inundation extent metrics on the test dataset imply reasonably reliable inundation depth results (with further validation ongoing).", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 S. Nevo > 3.2 Inundation Model > Gauge Stage To Flood Depth Algorithm", "section_headings": ["4 S. Nevo", "3.2 Inundation Model", "Gauge Stage To Flood Depth Algorithm"], "chunk_type": "text", "line_start": 116, "line_end": 120, "token_count_estimate": 250, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c8ae81ab309a3617", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 Resource Requirements > 4.1 Data sets\nType: text\n\nThe system utilizes the following data sets.\n\n**Precipitation Measurements** We currently use IMERG satellite data, though other sources can be used (as long as the models are re-trained for the new dataset).\n\n**Precipitation Forecasts** We currently use Global Forecast System (GFS) precipitation forecasts, though other sources can be used (as long as the models are re-trained for the new dataset)\n\nStream Gauges Stream gauges are measurement devices that measure the water level (and sometimes estimate discharge) of the river. We use these as labels for the hydrologic model, and inputs for both the hydrologic model and the inundation model. We use both open stream gauge datasets such as GRDC, CAMELS, and Caravan - as well as proprietary datasets from the governmental hydrometeorology agencies we work with.\n\nElevation Maps Accurate elevation maps are crucial for the inundation model, especially when producing depth estimates. The publicly available global DEMs (e.g., NASA SRTM and MERIT) unfortunately lack the required spatial accuracy and resolution for detailed flood inundation simulation. Furthermore, they are based on data from over a decade ago, thus failing to capture the frequent topography changes caused by past floods. Consequently, we currently construct higher-resolution, up-to-date DEMs for each AOI from high resolution satellite optical imagery data in a process that is based on stereographic imaging. To keep the DEM up to date, the model is retrained annually based on fresh imagery in locations where flooding causes frequent topography changes. We currently use proprietary Google-generated elevation maps, yet one can use any reliable hydrologically-conditioned elevation maps of sufficient resolution, which depending on the basin could be anywhere between 0.5 to 30 meters.\n\nHistorical Flood Extent Maps We use the synthetic aperture radar ground range detected (SAR GRD) data from the Sentinel-1 satellite constellation to determine flood inundation maps at known timepoints and locations [24]. At any AOI, a SAR image is available once every several days, from which an inundation map was inferred using a binary classifier. Every pixel within a SAR image is classified as wet/dry via a Gaussian-mixture based classification algorithm. In order to calibrate and evaluate the classification algorithm, we have collected a dataset of Sentinel-2 multispectral images of flood events that coincide with the SAR image dates and locations. Reference Sentinel-2 flood maps were created by calculating per-pixel Normalized Difference Water Index (NDWI=(B3-B8)/(B3+B8), where B3 and B8 are green and near infrared bands, respectively) and applied a threshold of 0.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 Resource Requirements > 4.1 Data sets", "section_headings": ["4 Resource Requirements", "4.1 Data sets"], "chunk_type": "text", "line_start": 124, "line_end": 136, "token_count_estimate": 690, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e6592bebff32abf7", "text": "Document: The Google Flood Forecasting Initiative\nSection: 4 Resource Requirements > 4.2 Computational Resources\nType: text\n\nTraining and using these models requires computational resources as well. How-ever, significant effort has gone into minimizing the computational requirements.\n\nTraining the hydrologic models takes seconds for the linear model and approximately 3 GPU hours for the LSTM (per location). Inference time is negligible for both.\n\nTraining the inundation models takes between 10 CPU minutes to 10 CPU hours depending on the AOI for both the lightweight model and the manifold model. For comparison, calibrating hydraulic models takes about 100-1200 CPU years for the same areas of interest [9].", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "4 Resource Requirements > 4.2 Computational Resources", "section_headings": ["4 Resource Requirements", "4.2 Computational Resources"], "chunk_type": "text", "line_start": 138, "line_end": 144, "token_count_estimate": 149, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a3c9390e98b2166", "text": "Document: The Google Flood Forecasting Initiative\nSection: 5 Field Evaluation\nType: text\n\nAt the time of writing (early 2022), these models have been incorporated in operational systems for several years, currently covering about 360 million people across India and Bangladesh. So far, the models' performance in real-time operational systems is consistent with their cross-validated performance on historical data.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "5 Field Evaluation", "section_headings": ["5 Field Evaluation"], "chunk_type": "text", "line_start": 146, "line_end": 148, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0d10a4b9206ef4d2", "text": "Document: The Google Flood Forecasting Initiative\nSection: 5 Field Evaluation > 5.1 Hydrologic Models\nType: text\n\nArguably the most popular metric for evaluating the accuracy of hydrologic models is the Nash-Sutcliffe Efficiency coefficient. This metric is the  $R^2$  score of the predicted discharge, where the  $R^2$  score (or coefficient of determination) of a prediction is defined as  $1 - \\frac{\\sum (\\hat{x}_i - x_i)^2}{var(x)}$  (with  $x_i$  representing the true value of the i'th example, and  $\\hat{x}_i$  representing the prediction for the i'th example). This can be thought of as describing the percentage of target's variance explained. An  $R^2$  score of 1 means a perfect prediction (and no variance remaining), while a score of 0 means the prediction is no better than guessing the average. This trait makes this score easy to understand, regardless of the scale and behavior of the target variable. Since most of our models predict water level rather than discharge, we use the closest parallel - an  $R^2$  score over the water level predictions. Our hydrologic models have achieved an  $R^2$  score of about 0.98 in our operational systems.\n\nA metric which is less common in the professional literature but is perhaps easier for non-hydrologists to parse is simply the average error of our predictions, in centimeters. Our hydrologic models have so far seen an average error of 8.5 centimeters, across all our operational systems.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "5 Field Evaluation > 5.1 Hydrologic Models", "section_headings": ["5 Field Evaluation", "5.1 Hydrologic Models"], "chunk_type": "text", "line_start": 150, "line_end": 154, "token_count_estimate": 377, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d331119efe0a38e", "text": "Document: The Google Flood Forecasting Initiative\nSection: 5 Field Evaluation > 5.2 Inundation Models\nType: text\n\nAll of our models are evaluated at a 16x16 meter resolution. The precision of an inundation forecast is defined as the number of pixels that were both forecasted as being inundated and were indeed inundated divided by the number of pixels that were forecasted as being inundated. The recall of an inundation forecast is defined as the number of pixels that were both forecasted as being inundated and were indeed inundated over the number of pixels that were actually inundated. The f1 score of a forecast is the geometric mean of its precision and its recall.\n\nOur inundation models have been run and evaluated at a resolution of 16x16 meters per pixel, and achieve 0.69 per-pixel f1 score on average across our operational systems.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "5 Field Evaluation > 5.2 Inundation Models", "section_headings": ["5 Field Evaluation", "5.2 Inundation Models"], "chunk_type": "text", "line_start": 156, "line_end": 160, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4be417568a44c04e", "text": "Document: The Google Flood Forecasting Initiative\nSection: 5 Field Evaluation > 5.3 Impact Evaluation\nType: text\n\nIn addition to evaluating our systems' accuracy, we also aim to evaluate their effectiveness in driving protective action. In 2021, a randomized controlled trial\n\nrun by the Yale Economic Growth Center found that our collaboration with local NGOs to generate and distribute flood warnings in India increased the portion of people who received warnings prior to flooding events by 3x, and increased the portion of people who took protective action ahead of a flooding event by 4.4x.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "5 Field Evaluation > 5.3 Impact Evaluation", "section_headings": ["5 Field Evaluation", "5.3 Impact Evaluation"], "chunk_type": "text", "line_start": 162, "line_end": 166, "token_count_estimate": 126, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "354bf999bf7df049", "text": "Document: The Google Flood Forecasting Initiative\nSection: 6 Lessons Learned\nType: text\n\nIn the five years since we began this project, we have learned many lessons along the way. Here are a few highlights.\n\nFocusing on the model is often not enough to drive real-world change. he real world is complicated, and the effectiveness of systems depends on only on the quality of your model. You have to ensure you have good (real-time) data collection systems, build a good product around your model outputs, and build partnerships with the key stakeholders that would use your systems. Especially in underserved communities, many components of the service pipeline may be flawed - and so it's critical to monitor and ensure that the full process from model to on-the-ground value works properly.\n\nRelatedly, missing data and data errors drive the majority of operational inaccuracy. Academic benchmarks and analyses will focus exclusively on model quality, yet in the vast majority of operational systems errors in model inputs can completely dominate over model mistakes. As a result, investing in error detection, error correction, uncertainty estimation, out-of-distribution detection, review and oversight, and other similar tools is incredibly important.\n\nInteraction with floods is incredibly diverse and requires different communication in different locations. People have different needs and expectations from flood forecasting systems. For example, in areas that rarely experience flooding, a flood with a depth of 15 centimeters can be a critical event - and people will expect a severe warning for it. However, in areas that experience frequent flooding, shallow flood waters are considered a non-event and people may be frustrated or surprised by even an informational update for such events. In extreme cases, like in some of the most flood-affected regions in Bihar, India, people ask to be warned only if and when flood waters are expected to reach waist height. Another example of such diversity is how people communicate with others about floods. In some regions, such as in much of South Asia, communities will share with their networks any information they have about upcoming floods. In others, such as parts of South America, there is stigma associated with being affected by floods, and so people won't discuss it - including critical safety information.", "metadata": {"source_file": "data/('Google_Flood_Forecasting', '.pdf')_extraction.md", "document_title": "The Google Flood Forecasting Initiative", "section_path": "6 Lessons Learned", "section_headings": ["6 Lessons Learned"], "chunk_type": "text", "line_start": 168, "line_end": 176, "token_count_estimate": 513, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9ac017a2a5099d66", "text": "Document: identifying potentiarisk lakes\nType: text\n\n- An optimality analysis was conducted to identify potentially dangerous glacial lakes in the Himalayas.\n- We identified 207 glacial lakes as very high-hazard and 345 as high-hazard.\n- Most of the high- or very high-hazard lakes are concentrated in the eastern and central Himalayas.\n- Glacial lakes with high outburst potential have a robust hazard level, regardless of the changes in the evaluation scheme.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "identifying potentiarisk lakes", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 7, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1b31c2a83a439b88", "text": "Document: GRAPHICAL ABSTRACT\nSection: GRAPHICAL ABSTRACT\nType: figure\nFigure\n\nImage /page/0/Figure/19 description: A flowchart illustrating a three-step process for assessing Glacial Lake Outburst Flood (GLOF) hazards. The process is divided into Data, Methods, and Results. Step 1, labeled \"Data,\" includes inputs such as Glacier inventory (RGI v6.0), Glacial lake inventory, Digital Elevation Model (DEM), and High/medium-resolution remote sensing images. Step 2, labeled \"Methods,\" involves analyzing factors like the mean slope of the parent glacier, potential for mass movement into the lake, mean slope of the moraine dam, watershed area, and lake perimeter. These factors are then normalized to a common range of 0-1 and processed using a weighting scheme (fuzzy consistent matrix method). Step 3, labeled \"Results,\" displays the outcomes. A pie chart shows the distribution of hazard levels: very low (dark green, 287), low (light green, 426), medium (light yellow, 405), high (orange, 345), and very high (red, 207). Below the pie chart, a diagram illustrates the components of a GLOF hazard, with a central red circle labeled \"GLOF Hazard\" surrounded by contributing factors like \"Rock fall, landslide, or other solid mass movement,\" \"Ice avalanches, glacier collapse,\" and \"Dam instability.\"", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "GRAPHICAL ABSTRACT", "section_headings": ["GRAPHICAL ABSTRACT"], "chunk_type": "figure", "figure_caption": null, "line_start": 10, "line_end": 10, "token_count_estimate": 361, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "57093617efe1f562", "text": "Document: GRAPHICAL ABSTRACT\nSection: ARTICLE INFO\nType: text\n\nArticle history: Received 28 June 2021 Received in revised form 15 September 2021 Accepted 15 September 2021 Available online 21 September 2021\n\nEditor: Fernando A.L. Pacheco\n\nKeywords: Glacial lake outburst flood (GLOF) Hazard GLOF susceptibility Weighting scheme Himalayas", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "ARTICLE INFO", "section_headings": ["ARTICLE INFO"], "chunk_type": "text", "line_start": 13, "line_end": 19, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb54a6eb7b866d1f", "text": "Document: GRAPHICAL ABSTRACT\nSection: ABSTRACT\nType: text\n\nGlacial lakes in the Himalayas are widely distributed. Since 1900, more than 100 glacial lake outburst floods (GLOFs) have originated in the region, causing approximately 7000 deaths and considerable economic losses. Identifying potentially dangerous glacial lakes (PDGLs) is considered the first step in assessing GLOF risks. In this study, a more thorough inventory of PDGLs was presented that included numerous small-sized glacial lakes (<0.1 km<sup>2</sup>) that were generally neglected in the Himalayas for decades. Moreover, the PDGL evaluation system was improved in response to several deficiencies, such as the selection of assessment factors, which are sometimes arbitrary without a solid scientific basis. We designed an optimality experiment to select the best combination of assessment factors from 57 factors to identify PDGLs. Based on the experiments on both drained and non-drained glacial lakes in the Sunkoshi Basin, eastern Himalayas, five assessment factors were determined to be the best combination: the mean slope of the parent glacier, the potential for mass movement into the lake, the mean slope of moraine dams, the watershed area, and the lake perimeter, corresponding to the GLOF triggers for ice avalanches, rockfalls and landslides, dam instability, heavy precipitation or other liquid inflows, and lake characteristics, respectively. We then applied the best combination of assessment factors to the 1650 glacial lakes with an area greater than 0.02 km² in the Himalayas. We identified 207 glacial lakes as very high-hazard and 345 as high-hazard. It is noteworthy that in various GLOF susceptibility evaluation scenarios with different assessment factors, weighting schemes, and classification approaches, similar results for glacial lakes with high outburst potential have been obtained. The results provided here can be used as benchmark data to assess the GLOF risks for local communities.\n\n© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).\n\n\\* Corresponding authors.\n\nE-mail addresses: weicaiwang@itpcas.ac.cn (W. Wang), anbaosheng@itpcas.ac.cn (B. An).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 21, "line_end": 29, "token_count_estimate": 570, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cd793b5fb359e5be", "text": "Document: GRAPHICAL ABSTRACT\nSection: 1. Introduction\nType: text\n\nIn the past few decades, glacial lakes worldwide have rapidly increased by 50% in number, area, and volume (Shugar et al., 2020). Climate warming and high mountain environmental instability have strengthened the frequency of snow/ice avalanches and landslides (Ballesteros-Canovas et al., 2018; Yao et al., 2019; Ding et al., 2021), thus causing numerous disasters in the form of glacial lake outburst floods (GLOFs, Clague and Evans, 2000; Anacona et al., 2015; Carrivick and Tweed, 2016, 2019; Wilson et al., 2018). In the Himalayas, more than 100 GLOF events have been reported since the 1900s (Komori et al., 2012; Nie et al., 2018; Zheng et al., 2021a). Approximately 50% of GLOFs are triggered by snow/ice avalanches (Emmer and Cochachin, 2013). Based on diverse dam materials, glacial lakes are generally classified into: moraine-dammed, ice-dammed, and bedrockdammed lakes (Vilímek et al., 2013). Among these, the morainedammed lakes have garnered increased attention because of their wide distribution and the frequent disasters associated with them. For example, the deadliest GLOF event was recorded on June 16, 2013, at Chorabari glacial lake, a moraine-dammed lake in the western Himalayas, which destroyed the downstream Kedarnath village and claimed more than 6000 human lives (Allen et al., 2016a; Rafig et al., 2019). Additionally, a recent GLOF event in Gongbatongshaco (moraine-dammed), the central Himalayas, occurred on July 5, 2016, resulting in transboundary impacts on both China and Nepal, causing economic losses of more than \\$70 million (Cook et al., 2018; Nie et al.,\n\nIdentifying potentially dangerous glacial lakes (PDGLs) and establishing a PDGL inventory are prerequisites for conducting a GLOF risk assessment. To the best of our knowledge, more than 50 peer-reviewed papers on how to determine these possible sources of risk have been published. For example, Allen et al. (2019) used four characteristics of lakes, dams, parent glaciers, and topographic surroundings, and a flood path simulation model to assess the risk of glacial lakes on the Tibetan Plateau, determining a region of the central Himalayas as a hotspot where several high- and very high-risk glacial lakes are distributed. Various methods have also been applied to assess GLOF susceptibility in the Cordillera Blanca, Peruvian Andes, South America, and the Alps (Drenkhan et al., 2019; Emmer and Vilímek, 2013, 2014; Emmer et al., 2016; Frey et al., 2018; Huggel et al., 2002, 2004).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 31, "line_end": 41, "token_count_estimate": 703, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "ce175672a904cb9d", "text": "Document: GRAPHICAL ABSTRACT\nSection: 1. Introduction\nType: text\n\nfour characteristics of lakes , dams , parent glaciers , and topographic surroundings , and a flood path simulation model to assess the risk of glacial lakes on the Tibetan Plateau , determining a region of the central Himalayas as a hotspot where several high - and very high - risk glacial lakes are distributed . Various methods have also been applied to assess GLOF susceptibility in the Cordillera Blanca , Peruvian Andes , South America , and the Alps ( Drenkhan et al . , 2019 ; Emmer and Vilímek , 2013 , 2014 ; Emmer et al . , 2016 ; Frey et al . , 2018 ; Huggel et al . , 2002 , 2004 ) .\n\nOver 1600 glacial lakes with areas larger than 0.02 km² are distributed in the Himalayas (Nie et al., 2017; Wang et al., 2020a). The GLOFs in this area are characterized by transboundary impacts, which is a significant feature. However, assessing GLOF susceptibility is limited by national borders; thus, an integrative evaluation is lacking. In addition, glacial lakes are generally smaller than precipitation- or runoffed lakes. Glacial lakes with areas greater than 0.1/1.0 km² can be considered medium/large-sized lakes (Zhang et al., 2015; Nie et al., 2017; Zhang et al., 2019). In previous assessments, the threshold of the smallest lake area used to identify PDGLs was relatively large. As a result, several small lakes have been ignored. As for the abovementioned Chorabari and Gongbatongshaco GLOFs, the areas of the lakes were only 0.04 and 0.02 km², respectively, before their outburst. Such small-sized glacial lakes may not be considered in inventories for assessing potential danger, but can cause catastrophic damage.\n\nThe performance of assessment factor selection in the GLOF susceptibility evaluation system is still disorganized and chaotic, despite the rapid, decades-long development of this knowledge. For example, there are generally several factors that can be used to refer to a GLOF trigger; among them, only a few factors are used frequently. Here, we paid extra attention to data availability, ease of extraction, and usage status when selecting the assessment factors. Similarly, the diversity of assessment factors makes selection difficult. Kougkoulos et al. (2018) noticed this problem and attempted to establish a standard or paradigm for GLOF risk assessment and recommended 13 assessment factors for evaluation work. However, scholars seem to prefer using\n\nthe factors they choose in combination with actual local conditions. For instance, in small-scale watershed studies, it is possible to obtain valuable in-situ data or high-resolution remote sensing images (such as lake volume and glacier crevasses), forcing scholars to consider using these data instead of free, medium-resolution images. Considering the deficiencies of previous scenarios, this study aimed to (i) provide a complete list of all the factors that have been used in identifying PDGLs, (ii) carry out an optimality analysis to find the best combination of assessment factors, and (iii) determine the PDGLs with an area greater than 0.02 km² in the Himalayas.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 31, "line_end": 41, "token_count_estimate": 781, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "247b2dc82ade9067", "text": "Document: GRAPHICAL ABSTRACT\nSection: 2. Geographical settings\nType: text\n\nGlaciers in the Himalayas have been experiencing an extensive recession (Kääb et al., 2012; Yao et al., 2012; Azam et al., 2018; Kumar et al., 2019), and the ice loss has accelerated in recent decades (Maurer et al., 2019). Glacial retreat and increased meltwater have resulted in the formation and expansion of glacial lakes. Glacial lakes have increased approximately 8.8% in number (from 4549 to 4950) and 14.1% in area (from 398.9 to 455.3 km²) in the Himalayas during 1990–2015 (Nie et al., 2017). The Himalayan glacial lakes are characterized by large numbers and high risks compared with other glacierized regions in the Third Pole (Fig. 1a); therefore, it is of great interest to the scientific community (Fujita et al., 2013; Linsbauer et al., 2016; Song et al., 2017; Wang et al., 2017; Cook et al., 2018; King et al., 2019; Maurer et al., 2020; Veh et al., 2020; Zhang et al., 2021).\n\nThe Himalayas are one of the global GLOF hotspots. Although controversies exist regarding the frequency of GLOFs over the past several decades, there is no doubt that the frequency of GLOFs will increase in the next few decades (Harrison et al., 2018; Veh et al., 2019; Zheng et al., 2021b). In the context of climate warming and glacial retreat, the area around lakes has become more unstable, as the steep area is likely to be closer to glacial lakes, increasing the possibility of mass movement into the lake. Furthermore, the hydrostatic pressure on moraine dams would be increased, accompanied by lake expansion, which could gradually destabilize moraine dams. Such potential threats significantly impact the evolution of glacial lakes and the development of downstream communities, prompting local governments to implement prevention and mitigation measures.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "2. Geographical settings", "section_headings": ["2. Geographical settings"], "chunk_type": "text", "line_start": 43, "line_end": 47, "token_count_estimate": 503, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "554caaf33818cace", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: text\n\nThere are several comprehensive glacial lake inventories at various scales covering the Himalayan region (Zhang et al., 2015; Nie et al., 2017; Wang et al., 2020a; Shugar et al., 2020; Chen et al., 2021). We regard the available glacial lake inventories as reference data in the present study rather than using them directly. Here, we have prepared an updated glacial lake inventory, focusing on glacial lakes with the following three features: (1) mainly fed by contemporary glacier meltwater, (2) an area greater than 0.02 km², and (3) moraine-dammed lakes.\n\nSmall-sized lakes have always been neglected in previous studies on PDGL identification. However, these glacial lakes may not only cause devastating casualties and property losses but have also exhibited an increasing incidence of outbursts since the 1980s relative to medium/large-sized lakes (Fig. 2, and Supplementary materials, Table S2). Hence, we included numerous small glacial lakes in the inventory of the present study. Based on a glacial lake inventory obtained from Wang et al. (2020a), which identified glacial lakes in the Tibetan Plateau and surroundings using the Normalized Difference Water Index (NDWI) method by utilizing Landsat images (30 m), we extracted glacial lakes that fit our criteria and manually modified the lake boundaries to improve accuracy, especially for small-sized lakes. In total, there were 1650 glacial lakes in the final inventory, with a mean area of 0.175 km², and 66.4% of the lakes had an area smaller than 0.1 km² (Fig. 1b).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "text", "line_start": 51, "line_end": 55, "token_count_estimate": 417, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "164b8514182546c8", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A figure from a scientific paper showing the distribution of glacial lakes in the Himalayan region. The figure is composed of two parts, labeled 'a' and 'b'.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 56, "line_end": 56, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e46f519818e0c9b5", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: text\n\nPart 'a' is a map displaying a large area of South Asia, including the Himalayan mountain range. The map uses a color scale to represent elevation, ranging from -28 meters (greenish-blue) to 8806 meters (brown). Major river basins are outlined in purple and labeled, including B13 Indus, B10 Yamuna, B9 Alaknanda, B8 Ghaghara, B7 Gandak, B6 Sapta Koshi, B5 Teesta, B4 Sunkosh, B3 Manas, B2 Bhareli, B1 Subansiri, B12 Maquan, and B11 Yarlung. Blue dots represent glacial lakes, which are concentrated in the high-elevation areas. Several major cities are marked with red dots, including Islamabad, New Delhi, Kathmandu, Thimphu, Lhasa, and Dhaka. The legend also identifies symbols for rivers and glaciers.\n\nPart 'b' is an inset graph in the top right corner. It is a histogram with a cumulative frequency curve. The x-axis is labeled 'Lake area (km²)' and ranges from 0.0 to 1.0, with a break before the value 11.23. The left y-axis, for the histogram, is 'Frequency' and goes up to 400. The right y-axis, for the cumulative curve, is 'Number' and goes up to 1600. The teal histogram bars show that the vast majority of lakes have a small area, with the highest frequency (around 400) for lakes with an area close to 0.0 km². The frequency decreases rapidly as the lake area increases. A dashed vertical line is shown at a lake area of 0.1 km². The black line represents the cumulative number of lakes, showing a steep increase for small lake areas and leveling off around 1600.\n\nFig. 1. The distribution of glacial lakes in the Himalayas. (a) The Himalayan region is divided into 13 different subregions based on river basin borders, namely the Subansiri (B1), Bhareli (B2), Manas (B3), Sunkoshi (B4), Teesta (B5), Sapta Koshi (B6), Gandak (B7), Ghaghara (B8), Alaknanda (B9), Yamuna (B10), Yarlung (B11), Maquan (B12), and Indus River basin (B13). Inset: the distribution of lake frequency at different size intervals.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "text", "line_start": 57, "line_end": 63, "token_count_estimate": 607, "basins": ["Indus"], "subbasins": ["Gandak", "Ghaghara", "Manas", "Subansiri", "Teesta", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "7ca435e2b8c5c7d8", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: figure\nFigure\n\nImage /page/2/Figure/4 description: A scatter plot with a step function overlay, showing 'Area before outburst (km²)' on the y-axis versus 'Year' on the x-axis. The x-axis ranges from before 1985 to 2020, and the y-axis ranges from -0.1 to 1.3. The data points and the step line are color-coded, transitioning from teal and dark green in the earlier years to lighter green, yellow, and finally brown in the later years. The step line starts at approximately 0.54 km² before 1985, rises to 0.78 km² around 1986, then generally trends downwards with some fluctuations, ending at 0.1 km² from 2010 to 2020. The scatter points are clustered at various levels. Notable high-value points include one at approximately 1.04 km² around 1983, another at 1.22 km² around 1995, and one at 1.03 km² around 2009. Many points, especially after 1990, are clustered below 0.3 km².", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 64, "line_end": 64, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5d54572b51f6f7b2", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: text\n\nFig. 2. Characteristics of the area of glacial lake outbursts since the 1980s. Small-sized glacial lakes have exhibited an increased frequency of outbursts since the 1980s.\n\n Assessment factors have been proposed or used in studies of identifying PDGLs. Every factor is described as ID, represented characteristic, name, easily measured and low error (Y), usage frequency and sources, respectively.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "text", "line_start": 65, "line_end": 71, "token_count_estimate": 126, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "696d62e40b84d8ad", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: table\nTable: Table 1\n\n| ID                                                                                                                                                                                                   | Category                                                               | Assessment factor                                               | Easy                 | Frequency     | Source                                                                                           |  |   |   |        |\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|-----------------------------------------------------------------|----------------------|---------------|--------------------------------------------------------------------------------------------------|--|---|---|--------|\n| G1                                                                                                                                                                                                   | Ice avalanches, glacier collapse                                       | Distance between lake and glacier terminus                      | Y                    | 25            | 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 19, 24, 27, 34, 35, 37, 38, 39, 40, 41, 42, 45       |  |   |   |        |\n| G2                                                                                                                                                                                                   |                                                                        | Parent glacier snout steepness                                  | Y                    | 10            | 3, 6, 9, 31, 35, 38, 39, 41, 42, 45                                                              |  |   |   |        |\n| G3                                                                                                                                                                                                   |                                                                        | Glacier calving                                                 |                      | 7             | 12, 32, 33, 39, 42, 44, 45                                                                       |  |   |   |        |\n| G4                                                                                                                                                                                                   |                                                                        | Slope between lake and glacier                                  | Y                    | 6             | 3, 14, 34, 38, 39, 42                                                                            |  |   |   |        |\n| G5                                                                                                                                                                                                   |                                                                        | Avalanche volume                                                |                      | 2             | 21, 23                                                                                           |  |   |   |        |\n| G6                                                                                                                                                                                                   |                                                                        | Change of parent glacier volume                                 |                      | 2             | 25, 31                                                                                           |  |   |   |        |\n| G7                                                                                                                                                                                                   |                                                                        | Glacier velocity                                                |                      | 2             | 25, 31                                                                                           |  |   |   |        |\n| G8                                                                                                                                                                                                   |                                                                        | Width of glacier calving front                                  |                      | 2             | 38, 42                                                                                           |  |   |   |        |\n| G9                                                                                                                                                                                                   |                                                                        | Advancing and (or) retreating glacier                           | Y                    | 1             | 45                                                                                               |  |   |   |        |\n| G10                                                                                                                                                                                                  |                                                                        | Lake-glacier vertical distance                                  |                      | 1             | 42                                                                                               |  |   |   |        |\n| G11                                                                                                                                                                                                  |                                                                        | Mean slope of parent glacier                                    | Y                    | 1             | 4                                                                                                |  |   |   |        |\n| R1                                                                                                                                                                                                   | Rock fall, landslide, or other solid mass movement                     | Potential for mass movement to strike a lake                    | Y                    | 25            | 1, 9, 10, 11, 13, 17, 18, 19, 20, 21, 23, 24, 25, 28, 29, 30, 31, 36, 39, 40, 41, 42, 43, 44, 45 |  |   |   |        |\n| R2                                                                                                                                                                                                   |                                                                        | Regional seismic activity                                       |                      | 7             | 14, 15, 16, 32, 33, 35, 39                                                                       |  |   |   |        |\n| R3                                                                                                                                                                                                   |                                                                        | Maximum slope of moraine surrounding the lake                   |                      | 2             | 35, 38                                                                                           |  |   |   |        |\n| R4                                                                                                                                                                                                   |                                                                        | Distance between lake and steepest slope                        | Y                    | 1             | 35                                                                                               |  |   |   |        |\n| R5                                                                                                                                                                                                   |                                                                        | Mean slope of lake surrounding                                  | Y                    | 1             | 38                                                                                               |  |   |   |        |\n| R6                                                                                                                                                                                                   |                                                                        | Steepest slope surrounding lake                                 | Y                    | 1             | 38                                                                                               |  |   |   |        |\n| D1                                                                                                                                                                                                   | Dam instability                                                        | Dam type                                                        | Y                    | 18            | 6, 7, 8, 13, 18, 20, 27, 28, 29, 33, 34, 35, 38, 39, 40, 42, 44, 45                              |  |   |   |        |\n| D2                                                                                                                                                                                                   |                                                                        | Dam freeboard                                                   |                      | 16            | 6, 13, 14, 15, 18, 20, 25, 28, 29, 31, 33, 35, 38, 42, 44, 45                                    |  |   |   |        |\n| D3                                                                                                                                                                                                   |                                                                        | Mean slope of moraine dam                                       | Y                    | 13            | 1, 3, 10, 11, 13, 15, 18, 20, 21, 23, 39, 41, 42                                                 |  |   |   |        |\n| D4                                                                                                                                                                                                   |                                                                        | Drainage type                                                   | Y                    | 12            | 7, 15, 24, 27, 28, 29, 32, 33, 37, 38, 39, 43                                                    |  |   |   |        |\n| D5                                                                                                                                                                                                   |                                                                        | Dam width-height ratio                                          |                      | 10            | 9, 13, 14, 18, 20, 29, 39, 42, 44, 45                                                            |  |   |   |        |\n| D6                                                                                                                                                                                                   |                                                                        | Dam width                                                       | Y                    | 10            | 4, 6, 9, 14, 18, 25, 29, 31, 38, 39                                                              |  |   |   |        |\n| D7                                                                                                                                                                                                   |                                                                        | The presence of ice-cored moraine                               | Y                    | 10            | 9, 10, 21, 23, 24, 25, 31, 39, 42, 45                                                            |  |   |   |        |\n| D8                                                                                                                                                                                                   |                                                                        | Ratio of freeboard to dam height                                |                      | 4             | 9, 39, 42, 44                                                                                    |  |   |   |        |\n| D9                                                                                                                                                                                                   |                                                                        | Dam height                                                      |                      | 3             | 25, 31, 38                                                                                       |  |   |   |        |\n| D10                                                                                                                                                                                                  |                                                                        | Dam texture (consolidated or unconsolidated, bedrock, or other) |                      | 3             | 7, 8, 45                                                                                         |  |   |   |        |\n| D11                                                                                                                                                                                                  |                                                                        | Moraine vegetation coverage                                     |                      | 2             | 42, 45                                                                                           |  |   |   |        |\n| D12                                                                                                                                                                                                  |                                                                        | Permafrost susceptibility                                       |                      | 2             | 31, 32                                                                                           |  |   |   |        |\n| D13                                                                                                                                                                                                  |                                                                        | Remedial work                                                   |                      | 2             | 37, 38                                                                                           |  |   |   |        |\n| D14                                                                                                                                                                                                  |                                                                        | Extent of crest                                                 |                      | 1             | 24                                                                                               |  |   |   |        |\n| D15                                                                                                                                                                                                  |                                                                        | Mean slope between the piping spring and the nearest lakeshore  |                      | 1             | 38                                                                                               |  |   |   |        |\n| H1                                                                                                                                                                                                   | Heavy precipitation, various liquid inflows                            | Unstable lake upstream                                          | Y                    | 10            | 10, 11, 21, 23, 24, 28, 29, 30, 37, 42                                                           |  |   |   |        |\n| H2                                                                                                                                                                                                   |                                                                        | Parent glacier area                                             | Y                    | 8             | 3, 4, 7, 9, 16, 25, 31, 39                                                                       |  |   |   |        |", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "table", "table_caption": "Table 1", "columns": ["ID", "Category", "Assessment factor", "Easy", "Frequency", "Source", "", "", "", ""], "table_row_start": 1, "table_row_end": 35, "line_start": 72, "line_end": 130, "token_count_estimate": 1622, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3def910612ce342a", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: table\nTable: Table 1\n\n| ID                                                                                                                                                                                                   | Category                                                               | Assessment factor                                               | Easy                 | Frequency     | Source                                                                                           |  |   |   |        |\n| H3                                                                                                                                                                                                   |                                                                        | Intense precipitation events                                    |                      | 4             | 14, 35, 39, 44                                                                                   |  |   |   |        |\n| H4                                                                                                                                                                                                   |                                                                        | High temperature events                                         |                      | 3             | 35, 39, 44                                                                                       |  |   |   |        |\n| H5                                                                                                                                                                                                   |                                                                        | Average annual precipitation                                    |                      | 2             | 4, 9                                                                                             |  |   |   |        |\n| H6                                                                                                                                                                                                   |                                                                        | The average monthly land surface temperature                    |                      | 2             | 4, 9                                                                                             |  |   |   |        |\n| H7                                                                                                                                                                                                   |                                                                        | Watershed area                                                  | Y                    | 3             | 1, 42, 45                                                                                        |  |   |   |        |\n| H8                                                                                                                                                                                                   |                                                                        | Temperature change rate                                         |                      | 1             | 4                                                                                                |  |   |   |        |\n| H9                                                                                                                                                                                                   |                                                                        | Mean annual air temperature                                     |                      | 1             | 32                                                                                               |  |   |   |        |\n| L1                                                                                                                                                                                                   | Characteristic of lake                                                 | Lake area                                                       | Y                    | 26            | 1, 2, 4, 5, 7, 8, 11, 12, 14, 15, 16, 19, 22, 24, 28, 30, 31, 32, 33, 37, 38, 39, 40, 41, 42, 45 |  |   |   |        |\n| L2                                                                                                                                                                                                   |                                                                        | Rate of lake area increase                                      | Y                    | 22            | 2, 5, 7, 8, 11, 13, 14, 15, 19, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33, 34, 39, 45               |  |   |   |        |\n| L3                                                                                                                                                                                                   |                                                                        | Lake volume                                                     |                      | 11            | 12, 15, 16, 25, 26, 27, 31, 33, 34, 35, 40                                                       |  |   |   |        |\n| L4                                                                                                                                                                                                   |                                                                        | Lake type                                                       | Y                    | 8             | 15, 16, 26, 28, 29, 30, 32, 37                                                                   |  |   |   |        |\n| L5                                                                                                                                                                                                   |                                                                        | Lake elevation                                                  | Y                    | 4             | 15, 16, 33, 37                                                                                   |  |   |   |        |\n| L6                                                                                                                                                                                                   |                                                                        | Past GLOF event                                                 | Y                    | 3             | 15, 37, 45                                                                                       |  |   |   |        |\n| L7                                                                                                                                                                                                   |                                                                        | Width of the lake                                               | Y                    | 2             | 15, 38                                                                                           |  |   |   |        |\n| L8                                                                                                                                                                                                   |                                                                        | Average lake depth                                              |                      | 1             | 33                                                                                               |  |   |   |        |\n| L9                                                                                                                                                                                                   |                                                                        | Lake perimeter                                                  | Y                    | 1             | 38                                                                                               |  |   |   |        |\n| L10                                                                                                                                                                                                  |                                                                        | Length of the lake                                              | Y                    | 1             | 15                                                                                               |  |   |   |        |\n| L11                                                                                                                                                                                                  |                                                                        | Orientation of the lake                                         | Y                    | 1             | 15                                                                                               |  |   |   |        |\n| I1                                                                                                                                                                                                   | Influence to downstream area                                           | Distance of settlement                                          | Y                    | 8             | 12, 15, 16, 25, 30, 31, 35, 40                                                                   |  |   |   |        |\n| I2                                                                                                                                                                                                   |                                                                        | Downstream slope of dam                                         | Y                    | 4             | 4, 30, 33, 39                                                                                    |  |   |   |        |\n| I3                                                                                                                                                                                                   |                                                                        | I4                                                              | Flood peak discharge | Affected area |                                                                                                  |  | 2 | 2 | 33, 40 |\n| I5                                                                                                                                                                                                   | Position of glacial lake with respect to confluence with main River | Y                                                               | 2                    | 16, 37        |                                                                                                  |  |   |   |        |\n| Note: The source: 1: Allen et al. (2019); 2: Wang et al. (2020b); 3: Wang et al. (2011); 4: Fan et al. (2019); 5: Wang et al. (2017); 6: Liu et al. (2020); 7: Khanal et al. (2015a); 8: Wang et al. |                                                                        |                                                                 |                      |               |                                                                                                  |  |   |   |        |", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "table", "table_caption": "Table 1", "columns": ["ID", "Category", "Assessment factor", "Easy", "Frequency", "Source", "", "", "", ""], "table_row_start": 36, "table_row_end": 58, "line_start": 72, "line_end": 130, "token_count_estimate": 1027, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "262a6133c29789c4", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.1. Glacial lake inventory\nType: text\n\nNote: The source, 1: Allen et al. (2019); 2: Wang et al. (2020b); 3: Wang et al. (2011); 4: Fan et al. (2019); 5: Wang et al. (2017); 6: Liu et al. (2020); 7: Khanal et al. (2015a); 8: Wang et al. (2012); 9: Wang et al. (2008); 10: Dubey and Goyal (2020); 11: Khadka et al. (2021); 12: Washakh et al. (2019); 13: Prakash and Nagarajan (2018); 14: Prakash and Nagarajan (2017); 15: Aggarwal et al. (2017); 16: Jha and Khare (2017); 17: Allen et al. (2016b); 18: Worni et al. (2013); 19: Aggarwal et al. (2013); 20: Deswal et al. (2020); 21: Rounce et al. (2017); 22: Begam and Sen (2019); 23: Rounce et al. (2016); 24: Khanal et al. (2015b); 25: Bolch et al. (2008); 26: Nagai et al. (2017); 27: Falatkova et al. (2019); 28: Petrakov et al. (2017); 30: Kapitsa et al. (2017); 31: Bolch et al. (2011); 32: Gruber and Mergili (2013); 33: Mergili and Schneider (2011); 34: Drenkhan et al. (2019); 35: Kougkoulos et al. (2018); 36: Frey et al. (2018); 37: Emmer et al. (2016); 38: Emmer and Vilímek (2014); 39: Emmer and Vilímek (2013); 40: Cook et al. (2016); 41: Anacona et al. (2014); 42: McKillop and Clague (2007); 43: Clague and Evans (2000); 44: Huggel et al. (2004); 45: Huggel et al. (2002).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.1. Glacial lake inventory", "section_headings": ["3. Data and methods", "3.1. Glacial lake inventory"], "chunk_type": "text", "line_start": 131, "line_end": 133, "token_count_estimate": 440, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0599b3c0af9c721", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.2. Remote sensing data\nType: text\n\nA glacier inventory, RGI v6.0, (RGI-Consortium, 2017), was used to identify glacial lakes with parent glaciers. Forty-four Sentinel-2A/2B images from 2020 were used to map the Himalayan glacial lakes (Supplementary materials, Table S1) and amend parent glacier boundaries for estimating several factors related to glaciers. The 10-m spatial resolution of Sentinel-2A/2B images (Level 1C) was freely downloaded from the U.S. Geological Survey (USGS) portal (https://earthexplorer.usgs.gov/), allowing us to meticulously identify PDGLs. In addition, 40 Landsat images, including 3 Landsat2 MSS images (60 m), 1 Landsat4 TM image (30 m), 28 Landsat5 TM images (30 m), and 9 Landsat8 OLI\\_TIRS images (30 m), were used to delineate the area of outburst lakes before draining in the region. The SRTM data (30 m), downloaded from NASA Earth data (https://search.earthdata.nasa.gov/search), was used to estimate the slope of glaciers and areas around lakes and delineate the watershed area upstream of glacial lakes.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.2. Remote sensing data", "section_headings": ["3. Data and methods", "3.2. Remote sensing data"], "chunk_type": "text", "line_start": 135, "line_end": 137, "token_count_estimate": 314, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c1edb5cc60ecaadb", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.3. Optimality analysis of assessment factors\nType: text\n\nThe most important step in identifying PDGLs is the selection of assessment factors. Currently, factor selection is highly dependent on empirical judgment or employs factors that have been used in previous studies, thus lacking a solid scientific basis. Moreover, as an integrated assessment method, the best combination of factors characterize the lakes, dams, parent glaciers, and topographic surroundings.\n\nIn the present study, we obtained 57 assessment factors from 45 previous studies on the identification of PDGLs (Table 1) and then classified these factors into 6 categories based on their referential GLOF triggers. The first category consisted of 11 factors, referring to the impact of ice avalanches and glacier collapse on glacial lakes (G1–11). The second category consisted of six factors, referring to the impact of rockfalls, landslides, or other solid mass movements (R1–6). The third to sixth categories represented dam instability (D1–15), heavy precipitation or various liquid inflows (H1–9), lake characteristics (L1–11), and the impact on downstream areas (I1–5). This classification was based on previous studies (Bolch et al., 2011; Emmer and Vilímek, 2014) and is scientifically reasonable. The next step was to select the most suitable factor within the same category and find the best combination between categories.\n\nThe aforementioned assessment factors are diversified, representing the characteristics of GLOF triggers. We proposed four criteria to simplify these factors from the complicated and ensembled items (Wang et al., 2011). First, factors should be extracted from readily available data with relatively high accuracy. For example, dam freeboard (D2) requires high-resolution DEM data to ensure its accuracy, especially for small-sized lakes; thus, it was exempted from our consideration. Second, factors should be continuous-type data rather than nominal, as we hope the hazard has not only a qualitative danger level but a quantitative value. Third, factors should focus on the impacts on lake stability rather than downstream influence; hence the sixth category (I1–5) was not considered. Fourth, the factors should remain independent without redundancy. For example, the mean slope of the parent glacier (G11) and the mean slope of the surrounding lake (R5) are correlated to a\n\ncertain extent; therefore, they are not selected simultaneously. Based on the above four criteria, 17 factors were selected in the first step, namely distance between the lake and glacier terminus (G1), parent glacier snout steepness (G2), the slope between the lake and glacier (G4), lake–glacier vertical distance (G10), mean slope of parent glacier (G11), the potential for a mass movement to strike a lake (R1), mean slope of the surrounding lake (R5), mean slope of moraine dams (D3), dam width (D6), parent glacier area (H2), watershed area (H7), lake area (L1), rate of lake area increase (L2), lake elevation (L5), the width of the lake (L7), lake perimeter (L9), and length of the lake (L10).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.3. Optimality analysis of assessment factors", "section_headings": ["3. Data and methods", "3.3. Optimality analysis of assessment factors"], "chunk_type": "text", "line_start": 139, "line_end": 151, "token_count_estimate": 768, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee753dff907fea61", "text": "Document: GRAPHICAL ABSTRACT\nSection: 3. Data and methods > 3.3. Optimality analysis of assessment factors\nType: text\n\nglacier ( G4 ) , lake – glacier vertical distance ( G10 ) , mean slope of parent glacier ( G11 ) , the potential for a mass movement to strike a lake ( R1 ) , mean slope of the surrounding lake ( R5 ) , mean slope of moraine dams ( D3 ) , dam width ( D6 ) , parent glacier area ( H2 ) , watershed area ( H7 ) , lake area ( L1 ) , rate of lake area increase ( L2 ) , lake elevation ( L5 ) , the width of the lake ( L7 ) , lake perimeter ( L9 ) , and length of the lake ( L10 ) .\n\nWe selected 1 factor from every category of the 17 assessment factors, except for the sixth category. A fuzzy consistent matrix (FCM) method was used to determine the category weights (Table 2). According to previous studies, ice avalanches and landslides seem to trigger more than half of the GLOFs (Emmer and Cochachin, 2013); thus, we assigned a higher weight value to them. The Sunkoshi River basin (B4) was selected as the site for the optimality experiment. It is located in the eastern Himalayas, and 170 glacial lakes are distributed in the basin, 10 of which have drained historically (Fig. 3a, b). These outburst lakes generally have a high outburst potential; therefore, the assessment results should show higher hazard indices. In addition, all factor values were normalized to a range of 0–1 to calculate the final quantitative value.\n\nSubsequently, an optimality analysis was conducted. First, we selected the highest frequency of factors in every category to make up the initial combination (G1, R1, D3, H2, and L1) and then changed the factors in the first category in turn (G1, G2, G4, G10, and G11). The highest average hazard index of the 10 drained glacial lakes was considered the best factor in this category (Fig. 3c). Second, the factors in the other four categories were tested to obtain the best factor in each category. As a result, the best factors formed the best combination, i.e., the mean slope of the parent glacier (G11), the potential for a mass movement to strike a lake (R1), mean slope of a moraine dam (D3), watershed area (H7), and lake perimeter (L9). Finally, we applied the best combination of factors to assess GLOF susceptibility for all Himalayan glacial lakes (Table 2).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "3. Data and methods > 3.3. Optimality analysis of assessment factors", "section_headings": ["3. Data and methods", "3.3. Optimality analysis of assessment factors"], "chunk_type": "text", "line_start": 139, "line_end": 151, "token_count_estimate": 629, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab554ed67fa9e3bb", "text": "Document: GRAPHICAL ABSTRACT\nSection: 4. Results > 4.1. Assessment validation\nType: text\n\nTo display qualitative results, all glacial lake hazard indices were classified into five levels using the Natural Jenks approach (Fig. 4a), namely very low, low, medium, high, and very high. This method was also used by Allen et al. (2019) and Zheng et al. (2021b) to evaluate the Tibetan Plateau glacial lakes outburst risks.\n\nOur identified inventory comprised 72 outburst glacial lakes (Supplementary materials, Table S3). Among these, 40 lakes had an area greater than 0.1 km², while the remainder had areas lower than 0.1 km². The five selected assessment factors can better classify these large outburst lakes, with 90% of medium/large-sized lakes identified as high- or very high-hazard. Meanwhile, 69% of the small-sized lakes also fell into this classification (Fig. 4b, c). Although the hazard indices of lakes (<0.1 km²) are reduced by the lake perimeter factor, this", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "4. Results > 4.1. Assessment validation", "section_headings": ["4. Results", "4.1. Assessment validation"], "chunk_type": "text", "line_start": 155, "line_end": 161, "token_count_estimate": 256, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b8688b03a664cff", "text": "Document: GRAPHICAL ABSTRACT\nSection: 4. Results > 4.1. Assessment validation\nType: table\nTable: Table 2 Selected factors with their categories and explanations. Rank and weight of each factor are computed using the FCM method.\n\n| ID  | Category                                              | Rank | Weight | Assessment factor                            | Explanation                                                 | Unit | Reference                                  |\n|-----|-------------------------------------------------------|------|--------|----------------------------------------------|-------------------------------------------------------------|------|--------------------------------------------|\n| G11 | Ice avalanches, glacier collapse                      | 5    | 0.27   | Mean slope of parent glacier                 | Mean slope of the parent glaciers                           | °    | Fan et al., 2019                           |\n| R1  | Rock fall, landslide, or other solid mass movement | 4    | 0.25   | Potential for mass movement into the lake | Non-glacierized areas around the lake with a slope > 30° | km2  | Bolch et al., 2011; Rounce et al., 2016 |\n| D3  | Dam stability                                         | 3    | 0.21   | Mean slope of moraine dam                    | Mean slope of moraine dam in front of the lake           | °    | Worni et al., 2013                         |\n| H7  | Heavy precipitation, various types of inflows      | 2    | 0.17   | Watershed area                               | Total watershed area upstream of the lake                   | km2  | Clague and Evans, 2000                     |\n| L9  | Characteristic of lake                                | 1    | 0.1    | Lake perimeter                               | Length of the lake coast                                    | km   | Emmer and Vilímek, 2014                    |", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "4. Results > 4.1. Assessment validation", "section_headings": ["4. Results", "4.1. Assessment validation"], "chunk_type": "table", "table_caption": "Table 2 Selected factors with their categories and explanations. Rank and weight of each factor are computed using the FCM method.", "columns": ["ID", "Category", "Rank", "Weight", "Assessment factor", "Explanation", "Unit", "Reference"], "table_row_start": 1, "table_row_end": 5, "line_start": 162, "line_end": 168, "token_count_estimate": 405, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "305b46dd3c37ac30", "text": "Document: GRAPHICAL ABSTRACT\nSection: 4. Results > 4.1. Assessment validation\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A scientific figure composed of three panels labeled a, b, and c, analyzing glacial lakes in the Sunkosh River Basin. Panel a is a topographical map of the Sunkosh River Basin, showing the river system. It displays numerous glacial lakes as blue dots and specific outburst glacial lakes as red dots, labeled with IDs such as 423, 497, 517, and 522. The map's coordinates range from 27°20'N to 28°0'N latitude and 89°0'E to 90°20'E longitude. Panel b is a scatter plot titled 'Outburst glacial lake', showing the area of these specific lakes. The x-axis is 'Glacial lake ID' and the y-axis is 'Area (km²)'. Notable points include lake 423 with an area of about 1.55 km² and lake 517 with an area of about 0.7 km². A dashed line is present at an area of 0.1 km². Panel c is a bar chart showing the 'Average hazard value' for different 'Assessment factor ID's. The y-axis ranges from 0.4 to 0.8. The bars represent factors like G1, G2, R1, H7, L9, etc. The values on top of the bars are: G1 (0.671), G2 (0.671), G4 (0.698), G10 (0.652), G11 (0.704), R1 (0.704), R5 (0.678), D3 (0.704), D6 (0.616), H2 (0.704), H7 (0.714), L1 (0.714), L2 (0.706), L5 (0.71), L7 (0.723), L9 (0.726), and L10 (0.725).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "4. Results > 4.1. Assessment validation", "section_headings": ["4. Results", "4.1. Assessment validation"], "chunk_type": "figure", "figure_caption": null, "line_start": 170, "line_end": 170, "token_count_estimate": 447, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6949bcf8bad3d1fa", "text": "Document: GRAPHICAL ABSTRACT\nSection: 4. Results > 4.1. Assessment validation\nType: text\n\n**Fig. 3.** The sensitivity experiment of assessment factors conducted in the Sunkoshi River basin (B4). (a) The distribution of both drained (red circle) and non-drained (blue circle) glacial lakes in the Sunkoshi River basin. (b) The area of the 10 drained glacial lakes in the basin. (c) The factor in the same category with the highest score is selected to reveal the best combination between categories. The spline filled with blue stripes is the final selected factor.\n\ndeviation is considered reasonable. First, our GLOF inventory included 26 recently reported events (Zheng et al., 2021a) that were not recorded by any news outlet or chronicled locally, implying that the area of the glacial lakes and flood magnitudes were very small and most likely drained before the 1970s. Second, the system error determined that small- and large-sized lakes cannot be fully identified simultaneously, and only a relative balance can be achieved.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "4. Results > 4.1. Assessment validation", "section_headings": ["4. Results", "4.1. Assessment validation"], "chunk_type": "text", "line_start": 171, "line_end": 175, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "511bb27657118b1c", "text": "Document: GRAPHICAL ABSTRACT\nSection: 4. Results > 4.2. Potentially dangerous glacial lakes\nType: text\n\nAmong the 1650 glacial lakes in the Himalayas (>0.02 km²), 207 lakes were identified as very high-hazard lakes, 345 as high-hazard lakes, 405 as medium-hazard lakes, 426 as low-hazard lakes, and 267 as very low-hazard lakes. More high- or very high-hazard lakes are concentrated in the eastern and central Himalayas (Fig. 5) than in the west-ern Himalayas. This is in line with the characteristics of glacial lake distribution. The mean hazard index of lakes with an area lower (greater) than 0.1 km² is 0.425 (0.636), and that of lakes across the Himalayas is 0.496. At the high- and very high-hazard levels, 204 small-sized glacial lakes were identified, accounting for 37% of the total high- and very high-hazard lake numbers. This is likely the source of potential hazards that have not been identified or mentioned previously.\n\nThe Sapta Koshi River basin (B6) has the highest number of very high-hazard lakes (approximately one-third of the total), and 7 out of the 12 lakes with the highest levels are located in this basin (Supplementary materials, Table S4). These have several common features\n\nthat make them high-hazard, including a parent glacier belonging to a hanging glacier or a small valley glacier with a relatively small area, and a glacier snout and surrounding lake that are steep (Fig. 6).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "4. Results > 4.2. Potentially dangerous glacial lakes", "section_headings": ["4. Results", "4.2. Potentially dangerous glacial lakes"], "chunk_type": "text", "line_start": 177, "line_end": 183, "token_count_estimate": 379, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9816947577af1517", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: text\n\nWe undertook objective screening and an optimality analysis and identified 5 of the 57 assessment factors as important for PDGL identification. Our results indicate the importance of employing an optimality experiment. This method avoids inclusion of some inappropriate or fallacious factors and drives scholars to find the best combination of assessment factors in their evaluations. For example, a large number of glacial lakes with high outburst potential are situated at relatively low altitudes. The very high-hazard lakes are distributed at an average elevation of 4875 m, and the high-hazard lakes are distributed at an average elevation of 5063 m. In contrast, the low-hazard lakes have a highest average elevation of 5222 m (Fig. 7a). The visible relationship between glacial lake outburst potential and elevation shows that the lake elevation factor is unsuitable in this identification study, as it intuitively suggests that a high lake outburst potential may correspond to a high elevation. The current optimality procedure is similar to a manual training system that forces the assessment factors to intensively express the features of drained glacial lakes. In the present study, the defined assessment factors can maximally emphasize the outburst lakes in the", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "text", "line_start": 187, "line_end": 189, "token_count_estimate": 308, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36ccd241c8849c86", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A figure with three graphs labeled a, b, and c, analyzing a hazard index.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "figure", "figure_caption": null, "line_start": 190, "line_end": 190, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2946d8c7035ccad6", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: text\n\nGraph 'a' is a histogram with N=1650. The x-axis is 'Hazard index' from 0 to 1.0, and the y-axis is 'Frequency' from 0 to 40. The bars are color-coded by hazard level: dark green for 'Very low', medium green for 'Low', light green for 'Medium', orange for 'High', and red for 'Very high'. A blue curve is overlaid, showing a distribution that peaks around a hazard index of 0.45.\n\nGraph 'b' is a scatter plot with N=40. The y-axis is 'Hazard index' and the x-axis is 'Glacial lake ID'. The teal data points represent lakes with an 'Area >= 0.1 km²'. The points are scattered between a hazard index of approximately 0.4 and 1.0.\n\nGraph 'c' is a scatter plot with N=32. The y-axis is 'Hazard index' and the x-axis is 'Glacial lake ID'. The brownish-yellow data points represent lakes with an 'Area < 0.1 km²'. The points are scattered between a hazard index of approximately 0.2 and 1.0. Dashed horizontal lines indicate different hazard levels.\n\nFig. 4. Hazard index of Himalaya glacial lakes. (a) Hazard index classification based on the qualitative hazard value of 1650 glacial lakes. (b) The distribution of large-sized outburst lakes at different hazard levels. (c) The distribution of small-sized outburst lakes at different hazard levels.\n\nHimalayas via optimality analysis and promote the identification of previously undocumented high-hazard glacial lakes.\n\nA scientific classification approach, Natural Jenks, which is based on clustering, was employed to classify glacial lakes into disparate hazard levels. The differences between the levels could be highlighted using this approach. Assessment factors such as the potential for a mass movement into the lake, the total watershed area upstream of the lake, and the lake perimeter are more sensitive to the high GLOF susceptibility than the mean slopes of the parent glacier and moraine dams\n\n(Fig. 7b–f). At present, the GLOF susceptibility evaluation method essentially consists of qualitative and quantitative systems. Adopting any classification approach is, in fact, unimportant in terms of the quantitative evaluation system. The quantitative system promotes the classification of glacial lakes into hazard levels after obtaining eventual scores, which determine the rank of a hazard index among all glacial lakes evaluated. If the classification approach were altered, we could still determine which glacial lake should gain more attention using these scores. A qualitative evaluation system classifies the assessment factors", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "text", "line_start": 191, "line_end": 205, "token_count_estimate": 665, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "43a788f518326b8e", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: figure\nFigure\n\nImage /page/6/Figure/7 description: A map of a mountainous region, likely the Himalayas, displaying the distribution of glacial lakes and a related assessment for various river basins. The map uses color gradients to show elevation, with green for lower areas and white for the highest peaks. Numerous blue dots represent glacial lakes, concentrated in the high-elevation areas. The map is overlaid with a grid for latitude (from 25°23'N to 35°25'N) and longitude (from 70°50'E to 90°54'E). Several large pie charts are placed over different basins, labeled with codes (B1 to B13) and names such as Indus, Yamuna, Sapta Koshi, and Yarlung. A legend in the top right corner, titled \"Number of glacial lakes,\" indicates that the size of the pie charts corresponds to the number of lakes, with concentric circles representing <50, 100, 150, 300, and 500 lakes. A legend in the bottom left explains the colors of the pie chart segments, which represent five categories: Very low (dark green), Low (light green), Medium (pale yellow), High (orange), and Very high (red). A scale bar indicates a distance of 360 km.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "figure", "figure_caption": null, "line_start": 206, "line_end": 206, "token_count_estimate": 327, "basins": ["Indus"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "18f42d94dc97fe3e", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: text\n\nFig. 5. The glacial lake outburst hazard across the Himalayas. The red points are the locations corresponding to the 12 highest hazard level glacial lakes.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "text", "line_start": 207, "line_end": 209, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f63d889f79e9801", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A figure from a scientific paper displaying a grid of 12 satellite images of lakes in mountainous, glacial environments. The images are arranged in three rows and four columns. Each image is labeled with a unique Lake ID. The top of the image contains citation information: \"T. Zhang, W. Wang, Y. Guo et al.\" and \"Science of the Total Environment 806 (2022) 150442\". The Lake IDs for each image are as follows: Top row, from left to right: Lake ID: 1418, Lake ID: 982, Lake ID: 517, Lake ID: 1555. Middle row, from left to right: Lake ID: 860, Lake ID: 838, Lake ID: 742, Lake ID: 848. Bottom row, from left to right: Lake ID: 823, Lake ID: 837, Lake ID: 1540, Lake ID: 1140. Each image has one or more blue dots indicating the location of the lakes.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "figure", "figure_caption": null, "line_start": 210, "line_end": 210, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["150442"]}}
{"id": "fb5d5da08280afc7", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: text\n\nFig. 6. The 12 lakes with the highest hazard level in the PDGL inventory whose locations are shown in Fig. 5. For details on these glacial lakes, refer to the supplementary materials, Table S4.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "text", "line_start": 211, "line_end": 213, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2a1f137f20c9c1ca", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: figure\nFigure\n\nImage /page/7/Figure/4 description: A figure containing six box plots, labeled (a) through (f), showing the distribution of different assessment factor values across five hazard levels: Very high, High, Medium, Low, and Very low. The boxes are color-coded from red (Very high) to orange, light green, dark green, and teal (Very low).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "figure", "figure_caption": null, "line_start": 214, "line_end": 214, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53bed950963af9d8", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: text\n\n(a) Lake elevation (L5): The y-axis shows Elevation (m) from 3000 to 6000. The median elevation generally increases from the 'Very high' category (around 4800 m) to the 'Very low' category (around 5250 m).\n\n(b) Mean slope of mother glacier (G11): The y-axis shows Slope (°) from 0 to 50. The median slope decreases from 'Very high' (around 25°) to 'Very low' (around 5°).\n\n(c) Potential for mass movement to strike a lake (R1): The y-axis shows Area (km²) from 0 to 12. The median area and data spread decrease significantly from 'Very high' (median ~2.5 km²) to 'Very low' (median near 0 km²).\n\n(d) Mean slope of moraine dam (D3): The y-axis shows Slope (°) from 0 to 40. The median slope generally decreases from 'Very high' (around 13°) to 'Very low' (around 7°).\n\n(e) Watershed area (H7): The y-axis shows Area (km²) from 0 to 40. The median area and data spread decrease significantly from 'Very high' (median ~8 km²) to 'Very low' (median ~0.5 km²).\n\n(f) Lake perimeter (L9): The y-axis shows Length (m) from 0 to 9. The median length and data spread decrease from 'Very high' (median ~2 m) to 'Very low' (median ~0.8 m).\n\nFig. 7. The average assessment factor values at the five hazard levels, including the (a) lake elevation, (b) mean slope of parent glacier, (c) potential for a mass movement to strike a lake, (d) mean slope of a moraine dam, (e) total watershed area upstream of the lake, and (f) lake perimeter.\n\n(Fig. 8). The values of every factor are separated into hazard levels, and then the levels of each factor are combined to form the eventual level. The shortcoming of this classification is that it is difficult to rank glacial lakes at the same hazard level. Moreover, GLOF susceptibility evaluation is sensitive to changes in the thresholds of factor level classification (Khadka et al., 2021), and poor understanding leads to increased uncertainty in the results. On the one hand, the qualitative system can contain more assessment factors such as Boolean and discontinuous data types and is more suitable for application to small-scale studies. On the other hand, the quantitative system can be used to conduct a more robust GLOF susceptibility evaluation.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "text", "line_start": 215, "line_end": 235, "token_count_estimate": 656, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5327df750ad77bde", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: text\n\nlevels of each factor are combined to form the eventual level . The shortcoming of this classification is that it is difficult to rank glacial lakes at the same hazard level . Moreover , GLOF susceptibility evaluation is sensitive to changes in the thresholds of factor level classification ( Khadka et al . , 2021 ) , and poor understanding leads to increased uncertainty in the results . On the one hand , the qualitative system can contain more assessment factors such as Boolean and discontinuous data types and is more suitable for application to small - scale studies . On the other hand , the quantitative system can be used to conduct a more robust GLOF susceptibility evaluation .\n\nFurthermore, we conducted a sensitivity analysis of the weighting schemes. The analytical hierarchy process (AHP) and the equalweighting scheme were applied in this sensitivity analysis to enhance contrast. The AHP method is the most frequently used weighting scheme, and the calculated weight values are 0.503, 0.260, 0.134, 0.068, and 0.035, respectively, corresponding to the five ranks of the assessment factors in Table 2. The glacial lake hazard level is sensitive to variations in the weighting scheme. Accordingly, if the results of the equal-weighting scheme were used as background values, the original hazard level of 17% of the glacial lakes would change if the FCM method were adopted, whereas that of 44% of the glacial lakes would change upon adoption of the AHP method. Thus, compared with the AHP method, the FCM method used in the present study is less amenable to changes, but both schemes are equally efficient, accounting for 81% of the outburst events at the high- or very high-hazard level, higher than the equal-weighting scheme at 73%. This result revealed that assigning weights can increase the quality of the hazard level of drained glacial lakes, thus demonstrating a better GLOF susceptibility evaluation. On the other hand, glacial lakes at high- and very high-hazard levels were not as sensitive to changes in different weighting schemes compared with those at medium- and low-hazard levels (Table 3). In other words, weighting schemes have no decisive effect on the classification of high-hazard lakes.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "text", "line_start": 215, "line_end": 235, "token_count_estimate": 556, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "177047906d44d505", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation\nType: table\nTable: Table 3 A sensitivity analysis for different weighting schemes. The sample contains all the glacial lakes (>0.2 km2) in the Himalayas.\n\n| Hazard level | Equal-weighting scheme (E) | FCM method (F) | AHP method (A) | Ratio of changed level (E–F, %) | Ratio of changed level (E–A, %) |\n|-----------------|-------------------------------|----------------------|----------------------|---------------------------------------|---------------------------------------|\n| Very high       | 221                           | 207                  | 192                  | 16                                    | 35                                    |\n| High            | 304                           | 345                  | 468                  | 14                                    | 35                                    |\n| Medium          | 401                           | 405                  | 360                  | 20                                    | 57                                    |\n| Low             | 450                           | 426                  | 374                  | 20                                    | 51                                    |\n| Very low        | 274                           | 267                  | 256                  | 12                                    | 28                                    |", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.1. Methodology of GLOF susceptibility evaluation", "section_headings": ["5. Discussion", "5.1. Methodology of GLOF susceptibility evaluation"], "chunk_type": "table", "table_caption": "Table 3 A sensitivity analysis for different weighting schemes. The sample contains all the glacial lakes (>0.2 km2) in the Himalayas.", "columns": ["Hazard level", "Equal-weighting scheme (E)", "FCM method (F)", "AHP method (A)", "Ratio of changed level (E–F, %)", "Ratio of changed level (E–A, %)"], "table_row_start": 1, "table_row_end": 5, "line_start": 236, "line_end": 242, "token_count_estimate": 261, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee2f66c783052b04", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.2. Accuracy of our PDGLs identification\nType: text\n\nThe identification efficiency of large-sized drained glacial lakes (area  $\\geq 0.1 \\text{ km}^2$ ) is high, reaching 90%, and no glacial lake was assessed as low- or very low-hazard based on our method (Fig. 4b). For smallsized lakes (area < 0.1 km<sup>2</sup>), the accuracy is not as high as that of large lakes (Fig. 4c). Small glacial lakes may have shortcomings in some assessment factors, such as lake perimeter. However, lake perimeter is a necessary factor and cannot be excluded because the characteristics of lakes represent potential triggers for GLOF. As a result, we reduced the influence of this factor by giving it a lower weight value. The extracted features of drained small-sized glacial lakes have relatively lower precision than those of large-sized lakes (Fig. 4b, c), which is constrained by the resolution of remote sensing data. These deficiencies commonly cause the GLOF susceptibility evaluation system to have poor tolerance to small-sized glacial lakes. Moreover, in the assessment validation, 31% of the drained small-sized lakes were determined to have medium-, low-, or very low-hazard levels. The GLOFs originating from small-sized glacial lakes do not have a reliable outburst date or even a time range; half of these events have been reported by Zheng et al. (2021a). The latest supplementary GLOF inventory contains many small-sized drained glacial lakes that have not been reported", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.2. Accuracy of our PDGLs identification", "section_headings": ["5. Discussion", "5.2. Accuracy of our PDGLs identification"], "chunk_type": "text", "line_start": 245, "line_end": 247, "token_count_estimate": 410, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5621d8219e77519", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.2. Accuracy of our PDGLs identification\nType: figure\nFigure\n\nImage /page/8/Figure/8 description: A flowchart illustrating a framework for the quantitative and qualitative evaluation of Glacial Lake Outburst Flood (GLOF) risk. The framework is divided into two parallel systems that feed into a central GLOF hazard assessment, which is then combined with vulnerability and exposure to determine overall risk. The top half shows the 'Quantitative evaluation system of GLOF risk,' which uses 'Remote sensing technologies' (like satellite images and DEM) to assess factors such as 'Mean slope of parent glacier' and 'Lake perimeter' on a scale of 0 to 1. The bottom half shows the 'Qualitative evaluation system of GLOF risk,' which uses 'Field investigation' (like bathymetric surveys and monitoring) to assess the same factors on a scale from 'Very low' to 'Very high.' Both systems feed into a 'Weighting scheme' that contributes to a central 'GLOF Hazard' model. This model visualizes contributing factors like 'Ice avalanches,' 'Dam instability,' and 'Rock fall.' On the right, a Venn diagram defines 'Risk' as the intersection of 'Hazard,' 'Vulnerability,' and 'Exposure.' The GLOF Hazard is then combined with 'Downstream Exposure or/and Vulnerability' in a risk matrix. This matrix plots GLOF Hazard on the y-axis (from Very low to Very high) against exposure/vulnerability on the x-axis, with the resulting risk level color-coded from green (low risk) to red (high risk).", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.2. Accuracy of our PDGLs identification", "section_headings": ["5. Discussion", "5.2. Accuracy of our PDGLs identification"], "chunk_type": "figure", "figure_caption": null, "line_start": 248, "line_end": 248, "token_count_estimate": 412, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7b11fa2745d05887", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.2. Accuracy of our PDGLs identification\nType: text\n\nFig. 8. The framework of quantitative and qualitative evaluation of GLOF hazard, including data input, selection of assessment factors, weighting scheme, GLOF susceptibility, and risk assessment.\n\nbefore. Taking these newly discovered and undated GLOF events into account was initially to increase the number of small-sized drained lakes to fully evaluate their outburst features, but this also brought challenges to our validation work because the expressed outburst features in the assessment factors were not obvious.\n\nIn addition, the differences in the final glacial lake hazard levels were compared between the present study and previous studies. Zheng et al. (2021b) assessed GLOF susceptibility using four factors, including lake volume (L3), watershed area (H7), likelihood of ice and/or rock avalanches into lakes (R1), and downstream slope of the lake dam (D3), and applied an equal-weighting scheme. A comparison of our results with those of Zheng et al. (2021b) shows that the consistency in high-and very high-hazard glacial lakes is relatively high, while there are large discrepancies in the medium- and low-level hazard glacial lakes. At our experimental site, the Sunkoshi River basin (B4), the consistency of the very high-hazard levels between the different studies reached 93% (Supplementary materials, Table 5S). This is at least less controversial for determining key potential hazard sources, which is our current focus.\n\nIn the present study, the best combination of assessment factors was determined via an optimality analysis, which can effectively guarantee the authenticity and reliability of GLOF susceptibility evaluation results. Additionally, some deficiencies in the selection criteria of assessment factors have been effectively solved. We emphasize the expression effect of assessment factors on the final GLOF susceptibility rather than the status of the factors themselves. We focused on small-sized glacial lakes that were previously neglected to some extent. As a result, 37% of the small-sized glacial lakes were classified as high- and very high-hazard, an observation that was overlooked in previous studies.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.2. Accuracy of our PDGLs identification", "section_headings": ["5. Discussion", "5.2. Accuracy of our PDGLs identification"], "chunk_type": "text", "line_start": 249, "line_end": 257, "token_count_estimate": 520, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50a2759ffce59fb3", "text": "Document: GRAPHICAL ABSTRACT\nSection: 5. Discussion > 5.3. Transboundary threats\nType: text\n\nThe transboundary threat of GLOFs in the Himalayan region has recently attracted increasing attention (Rounce et al., 2017; Allen et al., 2019; Washakh et al., 2019). GLOFs frequently occur at the border between China and Nepal. Some catastrophic outbursts may intensify GLOF impacts over the 40-km river channel and cause damage to communities and infrastructure, like the Cirenmaco GLOF in 1981 (Wang et al., 2018), the Jialongco GLOF in 2002 (Li et al., 2021), and the Gongbatongshaco GLOF in 2016 (Cook et al., 2018; Nie et al., 2018), all of which displayed transboundary impacts. Although the exposure of settlements and property to GLOFs was not estimated in the present study, the inherent risk is higher than previously reported. We introduced numerous small-sized lakes in the identified inventory that are new sources of outburst potential. Further research should include an exposure and vulnerability risk assessment to enable a detailed quantitative analysis of regional transboundary flows and similar situations. In fact, we need to devote efforts to prevent and mitigate the increasing threat of GLOFs. Since GLOFs have transboundary impacts, it is necessary to establish a regional cooperation mechanism for GLOF hazard adaptation and mitigation. An early warning system covering the basin from upstream to downstream would be practical in this regard.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "5. Discussion > 5.3. Transboundary threats", "section_headings": ["5. Discussion", "5.3. Transboundary threats"], "chunk_type": "text", "line_start": 259, "line_end": 261, "token_count_estimate": 353, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "c0a48d9b9fa0dd23", "text": "Document: GRAPHICAL ABSTRACT\nSection: 6. Conclusion\nType: text\n\nHere, we present an optimality analysis of assessment factors before a formal identification of PDGLs and determine the best combination of factors from 57 factors to identify potentially dangerous moraine-dammed lakes whose areas are larger than 0.02 km² and are mainly fed by contemporary glaciers in the Himalayas. It is noteworthy that as assessment factors become increasingly diverse, their effectiveness deserves further consideration. We identified 207 glacial lakes as very high-hazard lakes, 345 as high-hazard lakes, 405 as medium-hazard lakes, 426 as low-hazard lakes, and 267 as very low-hazard lakes, most of which are located in the eastern and central Himalayas. High potential outburst lakes generally have several common features,\n\nincluding a parent glacier belonging to a hanging glacier or small valley glacier with a relatively small area and a glacier snout and lake surroundings that are steep. This study improves the scientific basis for the selection of assessment factors, which is a reference for future research on PDGL identification. Furthermore, an integrative and detailed inventory of PDGLs in the Himalayas can be used as benchmark data to respond to GLOF threats.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "6. Conclusion", "section_headings": ["6. Conclusion"], "chunk_type": "text", "line_start": 263, "line_end": 267, "token_count_estimate": 291, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3758f743baf6a54b", "text": "Document: GRAPHICAL ABSTRACT\nSection: CRediT authorship contribution statement\nType: text\n\n**Taigang Zhang:** Methodology, Data curation, Writing – original draft. **Weicai Wang:** Methodology, Supervision, Writing – review & editing. **Tanguang Gao:** Writing – review & editing. **Baosheng An:** Supervision, Writing – review & editing. **Tandong Yao:** Project administration, Funding acquisition.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "CRediT authorship contribution statement", "section_headings": ["CRediT authorship contribution statement"], "chunk_type": "text", "line_start": 269, "line_end": 271, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72a870e4b9a4b7ed", "text": "Document: GRAPHICAL ABSTRACT\nSection: Appendix A. Supplementary data\nType: text\n\nSupplementary data to this article can be found online at https://www.editorialmanager.com/stoten/download.aspx?id=5076615&guid=f344d3f5-9171-46c4-9da0-37a3f700f7bd&scheme=1.", "metadata": {"source_file": "data/('identifying_potentiarisk_lakes', '.pdf')_extraction.md", "document_title": "GRAPHICAL ABSTRACT", "section_path": "Appendix A. Supplementary data", "section_headings": ["Appendix A. Supplementary data"], "chunk_type": "text", "line_start": 281, "line_end": 283, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["5076615"]}}
{"id": "c3c960a00b050cf3", "text": "Document: Increasing risk of glacial lake outburst floods from future Third Pole deglaciation\nType: text\n\nWarming on Earth's Third Pole is leading to rapid loss of ice and the formation and expansion of glacial lakes, posing a severe threat to downstream communities. Here we provide a holistic assessment of past evolution, present state and modelled future change of glacial lakes and related glacial lake outburst flood (GLOF) risk across the Third Pole. We show that the highest GLOF risk is at present centred in the eastern Himalaya, where the current risk level is at least twice that in adjacent regions. In the future, GLOF risk will potentially almost triple as a consequence of further lake development, and additional hotspots will emerge to the west, including within transboundary regions. With apparent increases in GLOF risk already anticipated by the mid-twenty-first century in some regions, the results highlight the urgent need for forward-looking, collaborative, long-term approaches to mitigate future impacts and enhance sustainable development across the Third Pole.\n\nhe Hindu Kush-Himalaya, Tibetan Plateau and surrounding areas are widely known as the Third Pole of the Earth as it is home to the largest number of glaciers outside the polar regions<sup>1</sup>. Widespread retreat of glaciers is taking place over most of its territory and has accelerated in recent decades as one of the consequences of global warming<sup>2-4</sup>. This glacier wasting is associated with the rapid expansion and new formation of glacial lakes<sup>5-8</sup>, bringing both large opportunities and risks<sup>9,10</sup>. Particularly, when water is suddenly released, glacial lake outburst floods (GLOFs)11 can devastate lives and livelihoods up to hundreds of kilometres downstream of their source<sup>12,13</sup>. This threat is most apparent in the Third Pole<sup>14,15</sup> where the warming rates are distinctly higher than the Northern Hemisphere and global mean<sup>16</sup>, and numerous GLOFs have been recorded, originating from both moraine-dammed and ice-dammed glacial lakes<sup>15,17</sup>. While outbursts from ice-dammed glacial lakes have been concentrated in the Karakoram and Pamir regions<sup>18,19</sup>, outbursts from moraine-dammed glacial lakes are observed across the Third Pole, and most frequently along the main Himalayan arc<sup>20</sup> where glacial lakes are increasing rapidly in both size and number<sup>5,6</sup>. The impact of a GLOF can extend across international boundaries<sup>21</sup>, creating severe challenges for early warning and other risk reduction strategies, particularly in critical transboundary areas<sup>22</sup>. Despite the severe threat that such large extreme events pose for sustainable mountain development over the Third Pole<sup>4</sup>, there remains a lack of understanding regarding how and where GLOF risk will evolve in the future. GLOF assessment over the Third Pole has typically been focused on moraine-dammed proglacial lakes owing to their potentially large flood volumes<sup>23,24</sup>, weak dam composition<sup>25</sup> and clear link to climate change<sup>17</sup>. Outburst", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Increasing risk of glacial lake outburst floods from future Third Pole deglaciation", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 7, "token_count_estimate": 827, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bec938cd036db9b4", "text": "Document: Increasing risk of glacial lake outburst floods from future Third Pole deglaciation\nType: text\n\nparticularly in critical transboundary areas < sup > 22 < / sup > . Despite the severe threat that such large extreme events pose for sustainable mountain development over the Third Pole < sup > 4 < / sup > , there remains a lack of understanding regarding how and where GLOF risk will evolve in the future . GLOF assessment over the Third Pole has typically been focused on moraine - dammed proglacial lakes owing to their potentially large flood volumes < sup > 23 , 24 < / sup > , weak dam composition < sup > 25 < / sup > and clear link to climate change < sup > 17 < / sup > . Outburst\n\nfloods from moraine-dammed glacial lakes can be triggered by various mechanisms, including intense precipitation and snowmelt<sup>26,27</sup>, and most commonly, from the impact of ice and/or rock avalanches into a lake<sup>20,28</sup>. While robust long-term trends in GLOF frequency are not evident<sup>17,29</sup>, the GLOF threat is expected to increase in response to future warming as lakes expand towards steep and destabilizing mountain cliffs<sup>30,31</sup>. With the expansion of communities, tourism, hydropower, transportation and other crucial sectors into exposed areas, substantial risk reduction and adaptation strategies will be required to avoid increased impacts on sustainable development in vulnerable mountain regions. Focusing here on moraine-dammed proglacial lakes, we draw on a comprehensive inventory of glacial lakes and past GLOF events across the Third Pole, to model and evaluate how and where GLOF hazard and risk will change in response to future deglaciation, using a robust and unified evidence-based approach.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Increasing risk of glacial lake outburst floods from future Third Pole deglaciation", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 7, "token_count_estimate": 434, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90714442422b78dd", "text": "Document: Past evolution and present state of glacial lakes\nSection: Past evolution and present state of glacial lakes\nType: text\n\nTo understand the present state of glacial lakes across the Third Pole and changes over the past decades, all glacial lakes  $\\geq$ 0.01 km² were mapped based on archival Landsat satellite images from 1990 to 2015 (Methods). Our inventory reveals the existence of 26,633 glacial lakes in the Third Pole, with a total area of 1,968.8  $\\pm$  1.2 km² and approximately one-third of them being dammed by moraines (in total 7,650 with a combined area of 535.1  $\\pm$ 0.7 km²; 6,958 of these lakes were formed in proglacial areas or are in a transitional state). The dataset suggests a clear heterogeneity in their spatial distribution (Fig. 1 and Supplementary Table 1) as today's glacial lakes are principally distributed along the Hindu Kush–Himalaya–Hengduan\n\n<sup>1</sup>State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China. <sup>2</sup>Climatic Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland. <sup>3</sup>University of Chinese Academy of Sciences, Beijing, China. <sup>4</sup>Department of Geography, University of Zurich, Zurich, Switzerland. <sup>5</sup>China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad, Pakistan. <sup>6</sup>Dendrolab.ch, Department of Earth Sciences, University of Geneva, Geneva, Switzerland. <sup>7</sup>Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland. <sup>8</sup>Swiss Federal Institute for Forest Snow and Landscape Research (WSL), Birmensdorf, Switzerland. <sup>9</sup>Department of Geosciences, University of Fribourg, Fribourg, Switzerland. <sup>10</sup>Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China. <sup>11</sup>CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China. <sup>12</sup>Department of F.A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland. <sup>13</sup>These authors contributed equally: Guoxiong Zheng, Simon Keith Allen. <sup>188</sup>e-mail: baoam@ms.xjb.ac.cn; markus.stoffel@unige.ch", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Past evolution and present state of glacial lakes", "section_headings": ["Past evolution and present state of glacial lakes"], "chunk_type": "text", "line_start": 9, "line_end": 13, "token_count_estimate": 690, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "8212525ef7379ac5", "text": "Document: Past evolution and present state of glacial lakes\nSection: Past evolution and present state of glacial lakes\nType: figure\nFigure\n\nImage /page/1/Figure/1 description: A map of High Mountain Asia, showing the distribution of glacial lakes and projections for aggregated lake area under different climate scenarios. The map spans from approximately 70°E to 110°E longitude and 25°N to 45°N latitude, covering regions like the Tibetan Plateau, Himalayas, Karakoram, and Hindu Kush. The map is divided into several labeled regions, including West Tien Shan, Pamir, West Himalaya, Central Himalaya, East Himalaya, Hengduan Shan, South and East Tibet, Inner Tibet, East Kun Lun, West Kun Lun, Qilian Shan, and Hissar Alay. Bar charts are overlaid on each region, illustrating the aggregated lake area for five conditions: 'Present' (cyan), 'RCP2.6' (light blue), 'RCP4.5' (light purple), 'RCP8.5' (purple), and 'Ice free' (magenta). A scale bar indicates that a specific height corresponds to 250 km². The charts generally show an increase in lake area from present conditions to the 'Ice free' scenario. A dashed line within the bars distinguishes the 'Moraine dammed' lake area. An inset map in the top right corner shows the geographical context of the main map, outlining the area within China and India.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Past evolution and present state of glacial lakes", "section_headings": ["Past evolution and present state of glacial lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 14, "line_end": 14, "token_count_estimate": 338, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "35a2200f6fdc6769", "text": "Document: Past evolution and present state of glacial lakes\nSection: Past evolution and present state of glacial lakes\nType: text\n\n**Fig. 1** | Region-wide present and projected glacial lakes to 2050, 2100 and on an ice-free Third Pole. Map showing the geographical extent of the Third Pole and the spatial distribution of its present glacial lakes. Bar charts in different colours denote the present and potential future glacial lake areas (present plus projected results under three RCPs, and under the ice-free scenario) that were aggregated into the Global Terrestrial Network for Glaciers (GTN-G) regions<sup>46</sup>, respectively. The proportional area of present moraine-dammed glacial lakes to all present glacial lakes is shown in grey. Dashed lines show estimated changes in 2050.\n\nShan mountains and ranges of inner and southeast Tibet as well as the Tien Shan mountains. The Hengduan Shan holds the largest number of glacial lakes  $(6,571;\\ 436.2\\pm0.5\\ km^2)$ , whereas the eastern Himalaya has the most moraine-dammed glacial lakes  $(1,533;\\ 183.7\\pm0.4\\ km^2)$ . At the basin scale, glacial lakes are most abundant in the Brahmaputra River Basin  $(9,665;\\ 778.8\\pm0.8\\ km^2)$ , followed by the Indus, Yangtze, Ganges, Amu Darya and Salween river basins (all with >1,000 lakes, Supplementary Fig. 3 and Supplementary Table 2). The Brahmaputra River Basin is also home to the largest number of moraine-dammed glacial lakes  $(2,331;\\ 212.0\\pm0.4\\ km^2)$ .\n\nGlacial lakes on the Third Pole have undergone a notable increase in total area and number since the 1990s<sup>5-7</sup>. Our analyses show an overall increase of  $6.8 \\pm 0.1\\%$  in glacial lake area and 5.9% in lake number across the Third Pole between 1990 and 2015 (Supplementary Fig. 4 and Supplementary Table 1). We propose that this recent increase has principally been driven by an expansion of moraine-dammed glacial lakes that charactize many of the long, flat, debris-covered glacier tongues in the region. To test this hypothesis, we split the glacial lakes into two categories: moraine-dammed and other-dammed glacial lakes (Methods). We find that if only the moraine-dammed glacial lakes are considered, the area gain is substantially larger at  $31.3 \\pm 0.3\\%$ , with an increase in number of 26.7%. In contrast, the size of other-dammed glacial lakes remains virtually unchanged  $(-0.02 \\pm 0.2\\%)$ , with a slight decrease in number of -1.4%. Still, changes show clear heterogeneity both at regional and basin scales (Supplementary Figs. 4 and 5).", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Past evolution and present state of glacial lakes", "section_headings": ["Past evolution and present state of glacial lakes"], "chunk_type": "text", "line_start": 15, "line_end": 21, "token_count_estimate": 714, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f60203b8c792e2ab", "text": "Document: Past evolution and present state of glacial lakes\nSection: Historical GLOF activity\nType: text\n\nAs GLOF events across the Third Pole have been heavily reported in previous studies<sup>18-20,29</sup>, we systematically collected and collated these records to better understand their spatial distributions and the characteristics of different source lakes, which were categorized into moraine, ice, bedrock and complex ones depending on the dam type (Fig. 2a; see Supplementary Table 3 for all records assembled). On the basis of this dataset, we found that at least 296 GLOF events from 109 glacial lakes occurred over the Third Pole since 1560 CE. GLOFs in the eastern Himalaya are primarily from the failures of moraine-dammed glacial lakes (Fig. 2c and Supplementary Table 3) and account for ~45% of the past GLOF sources alone. GLOFs resulting from ice-dammed glacial lakes are all reported in the Pamir-Karakoram regions (Fig. 2a), where the prevalent glacier surges have led to repetitive blockage and eventual release of glacial lakes<sup>19</sup>. Although most past GLOF sources were related to moraine-dammed glacial lakes (74 out of 109, Fig. 2b), ice- or complex-dammed flood sources (34 out of 109, Fig. 2b) contributed to more than half of the past outbursts (187 out of 296, Fig. 2b) due to the repetitive and high-frequency nature of these events. This is exemplified in the case of Kyagar Tsho (see Fig. 2a for its location and Supplementary Table 3), situated in the headwaters of Yarkant River, which produced at least 33 outburst floods in the past century with an accelerating trend during recent decades<sup>32</sup>. Practical experience has proven that real-time monitoring and early warning systems are effective in forecasting such active and high-frequency floods resulting from ice dam breaches<sup>32,33</sup>. The complex-dammed", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Historical GLOF activity", "section_headings": ["Historical GLOF activity"], "chunk_type": "text", "line_start": 23, "line_end": 25, "token_count_estimate": 499, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9943d989ef978407", "text": "Document: Past evolution and present state of glacial lakes\nSection: Historical GLOF activity\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: A multi-panel figure, labeled a through e, presenting data on Glacial Lake Outburst Floods (GLOFs) in the Third Pole region of Asia.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Historical GLOF activity", "section_headings": ["Historical GLOF activity"], "chunk_type": "figure", "figure_caption": null, "line_start": 26, "line_end": 26, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7acd86900a1d9129", "text": "Document: Past evolution and present state of glacial lakes\nSection: Historical GLOF activity\nType: text\n\nPanel a is a map showing the spatial distribution of reported GLOFs across Central and South Asia. GLOFs are color-coded by dam type: Moraine (cyan), Ice (orange), Bedrock (magenta), and Complex (green). The map also indicates transboundary GLOF threats, marking present (red dots) and future (hollow red circles) potentially dangerous glacial lakes, along with modeled flow paths and cross-border points.\n\nPanel b is a donut chart illustrating GLOF frequency by dam type. The counts are: Moraine (182 total from segments of 108 and 74), Ice (187 total from segments of 113 and 74), Complex (34 total from segments of 26 and 8), and Bedrock (2 total from segments of 1 and 1).\n\nPanel c is a donut chart showing GLOF frequency by source region. The regions and their respective counts are: Hindu Kush (112, 26, 3, 1), Karakoram (96, 11, 8, 6, 4), East Himalaya (62, 49), Central Himalaya (7), South and East Tibet (5), and Hengduan Shan (3, 1). There is also a segment with a value of 2.\n\nPanel d is a bar chart comparing the number of GLOF sources. For Moraine dams, there are 74 total sources, with 19 having disappeared after a burst. For Bedrock dams, there is 1 source, with 0 disappearing. For Complex dams, there are 8 sources, with 6 disappearing.\n\nPanel e is a detailed map of the Himalayan region, showing towns like Kathmandu and Zhangmu, and a high concentration of potentially dangerous glacial lakes and their modeled flow paths, with five specific points numbered along one such path.\n\n**Fig. 2 | Reported historical GLOFs and potential transboundary threats on the Third Pole. a**, Map showing the spatial distribution of recorded GLOF sources by lake dam type as well as present and projected (ice-free scenario) glacial lakes with possible transboundary GLOF threats across the Third Pole. For ice-dammed GLOF sources, only those with known geographic coordinates are shown (see Supplementary Table 3 for complete records). The black box near the bottom of the panel is the location of **e. b**, Double doughnut chart representing the number of past GLOF sources and flood frequency by lake dam type. **c**, Double doughnut chart showing the number of past GLOF sources and flood frequency per region. **d**, Statistics of GLOF sources by lake dam type. Ice-dammed cases were not counted owing to their repetitive nature. **e**, Amplified map showing a hotspot of potential transboundary GLOF threat between China and Nepal, and a historical GLOF hotspot in the eastern Himalaya. The circled numbers represent five concentrated regions with potential transboundary GLOF threats. The capital cities of Nepal and Bhutan are indicated with yellow squares. The left black box is the location of Supplementary Fig. 13 and the right black box is the location of Supplementary Fig. 14. Base maps: Google, Europa Technologies.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Historical GLOF activity", "section_headings": ["Historical GLOF activity"], "chunk_type": "text", "line_start": 27, "line_end": 41, "token_count_estimate": 732, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "Nepal"], "lake_ids": []}}
{"id": "540ea4509d628d0f", "text": "Document: Past evolution and present state of glacial lakes\nSection: Historical GLOF activity\nType: text\n\n. * * d * * , Statistics of GLOF sources by lake dam type . Ice - dammed cases were not counted owing to their repetitive nature . * * e * * , Amplified map showing a hotspot of potential transboundary GLOF threat between China and Nepal , and a historical GLOF hotspot in the eastern Himalaya . The circled numbers represent five concentrated regions with potential transboundary GLOF threats . The capital cities of Nepal and Bhutan are indicated with yellow squares . The left black box is the location of Supplementary Fig . 13 and the right black box is the location of Supplementary Fig . 14 . Base maps : Google , Europa Technologies .\n\nglacial lakes are usually positioned on the surface of debris-covered glaciers and are held back by a mix of ice and moraine. These lakes often vanish fully after an outburst (Fig. 2d), but some exceptions exist. For instance, Merzbacher Lake (see Fig. 2a for the specific site and Supplementary Table 3), located upstream of Aksu River, has produced at least 66 recorded outbursts over the last century because of its peculiar geographic situation and water supply modes 14,34. The occurrence of floods from such lakes is sudden and difficult to monitor and mitigate, as drainage can occur within or underneath the glacier 34.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Historical GLOF activity", "section_headings": ["Historical GLOF activity"], "chunk_type": "text", "line_start": 27, "line_end": 41, "token_count_estimate": 347, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "Nepal"], "lake_ids": []}}
{"id": "183832cd9dc60ea4", "text": "Document: Past evolution and present state of glacial lakes\nSection: Present hotspots of GLOF hazard and risk\nType: text\n\nBased on a conceptual model of GLOF hazard and risk optimized for large-scale automated application (Methods), we implemented\n\nthe first quantitative assessment of potential GLOF hazard and risk for a total of 6,958 existing moraine-dammed glacial lakes across the Third Pole and provide hierarchical classifications. This study compliments numerous national- to regional-scale GLOF hazard and risk inventories that have previously been undertaken (Supplementary Table 4), allowing a comparison of GLOF hazard and risk across the entire Third Pole. The model considers GLOF hazard on the basis of well-established conditioning and triggering factors<sup>30</sup> and identifies infrastructure and communities at risk within downstream simulated flood paths. In contrast to previous work, we evaluate model reliability by validating classification results against those glacial lakes that have generated outburst floods in the past. The model successfully classified GLOF hazard as high or very high for 46 out of the 48 existing glacial lakes (that is,", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Present hotspots of GLOF hazard and risk", "section_headings": ["Present hotspots of GLOF hazard and risk"], "chunk_type": "text", "line_start": 43, "line_end": 47, "token_count_estimate": 266, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c5a8ae87f016ced", "text": "Document: Past evolution and present state of glacial lakes\nSection: Present hotspots of GLOF hazard and risk\nType: figure\nFigure\n\nImage /page/3/Figure/1 description: A figure with three panels (a, b, c) illustrating Glacial Lake Outburst Flood (GLOF) hazard and risk. Panels a and b are maps with pie charts, and panel c is a histogram.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Present hotspots of GLOF hazard and risk", "section_headings": ["Present hotspots of GLOF hazard and risk"], "chunk_type": "figure", "figure_caption": null, "line_start": 48, "line_end": 48, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "303f6c365a8e0bef", "text": "Document: Past evolution and present state of glacial lakes\nSection: Present hotspots of GLOF hazard and risk\nType: text\n\nPanel a, labeled \"GLOF hazard\", is a map showing various regions with overlaid pie charts. The size of each pie chart corresponds to the \"Aggregated hazard value\", with a scale showing concentric circles for values <10, 50, 100, 250, and 500. The slices of the pie charts are colored to represent different hazard levels: dark green for Very low (VL), light green for Low (L), yellow for Medium (M), orange for High (H), and red for Very high (VH).\n\nPanel b, labeled \"GLOF risk\", is a similar map. The size of its pie charts represents the \"Aggregated risk value\", with a scale for values <5, 25, 50, 125, and 250. The color coding for risk levels is the same as in panel a.\n\nPanel c is a histogram with the x-axis labeled \"Normalized hazard value\" (from 0 to 1.0) and the y-axis labeled \"Frequency (n = 6,958)\" (from 0 to 400). The bars are colored according to the \"Hazard class\": dark green for \"Very low\", light green for \"Low\", yellow for \"Medium\", orange for \"High\", and red for \"Very high\". The distribution is roughly bell-shaped, peaking around a normalized hazard value of 0.4. Above the histogram, brown dots represent \"Historical GLOFs (n = 48)\", with dashed lines connecting them to the corresponding hazard values. The number of historical GLOFs is indicated for some ranges, showing 1, 1, 9, and 37, with the majority occurring at high and very high hazard values.\n\n**Fig. 3** | Region-wide present GLOF hazard and risk across the Third Pole. a,b, Pie charts showing the proportion of different GLOF hazard (a) and risk (b) levels per region. VL, very low; L, low; M, medium; H, high; VH, very high. Aggregated hazard and risk values are the total sums of normalized values within each region. **c**, Histogram presenting the distribution of GLOF hazard levels across all moraine-dammed glacial lakes assessed. The vertical dashed lines indicate the distribution of assessed GLOF hazard values for glacial lakes where historical GLOFs have been recorded, which were further used to validate the overall model performance. The dots refer to the number of GLOF sources for a given hazard value.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Present hotspots of GLOF hazard and risk", "section_headings": ["Present hotspots of GLOF hazard and risk"], "chunk_type": "text", "line_start": 49, "line_end": 61, "token_count_estimate": 593, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5b79f28929031faf", "text": "Document: Past evolution and present state of glacial lakes\nSection: Present hotspots of GLOF hazard and risk\nType: text\n\na ) and risk ( b ) levels per region . VL , very low ; L , low ; M , medium ; H , high ; VH , very high . Aggregated hazard and risk values are the total sums of normalized values within each region . * * c * * , Histogram presenting the distribution of GLOF hazard levels across all moraine - dammed glacial lakes assessed . The vertical dashed lines indicate the distribution of assessed GLOF hazard values for glacial lakes where historical GLOFs have been recorded , which were further used to validate the overall model performance . The dots refer to the number of GLOF sources for a given hazard value .\n\n95.8% accuracy, Fig. 3c and Supplementary Table 6), from which the past GLOFs originated. For example, Jialong Co and Cirenma Co, located in eastern Himalaya, have been the origin of several GLOFs since 1960, and are both ranked within the 99th percentile of the most hazardous lakes. Overall, our assessment indicates that one-third (2,323) of all glacial lakes assessed have high to very high hazard levels, and one in six (1,203) glacial lakes would pose a potentially high to very high risk to downstream communities. More than one-third (2,619) of glacial lakes assessed appear not to be dangerous as no downstream infrastructures are evident within the flood paths (see Supplementary Table 5 for all infrastructures considered). On a regional scale, the eastern Himalaya has the highest value in terms of GLOF hazard, which is twice that of the adjacent Hengduan Shan, inner Tibet, central and western Himalaya, and southeast Tibet; three to four times that of the Tien Shan areas; and seven to eight times that of the Pamir, Hindu Kush and Karakoram (Fig. 3a). For GLOF risk, the eastern Himalaya likewise has the highest value, being two to three times that of the central Himalaya, Hengduan Shan and western Himalaya; four times that of southeast Tibet and western Tien Shan; and six to eight times that of inner Tibet, Hindu Kush, eastern Tien Shan and Pamir (Fig. 3b). Notably, some regions such as inner and southeast Tibet present high levels of GLOF hazard (Fig. 3a), but lower levels of GLOF risk (Fig. 3b) owing to much lower exposure values compared with the central and eastern Himalaya (Supplementary Fig. 6). While\n\nmapping of roads and other exposed elements over inner and southeast Tibet is considered less complete than elsewhere<sup>35</sup>, these results correspond well with other approaches based on gridded data including population density and cropland area that have likewise identified highest risk levels in central and eastern Himalaya<sup>36</sup>. At the basin scale, the Brahmaputra River Basin has the highest level in terms of GLOF hazard, followed by Ganges, Indus, Salween, Ili, Amu Darya river basins and Tibetan Plateau interior (Supplementary Fig. 7a). However, with respect to GLOF risk, the Ganges River Basin has the highest level, followed by Brahmaputra, Indus, Amu Darya, Ili, Salween and Syr Darya river basins (Supplementary Fig. 7b).", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Present hotspots of GLOF hazard and risk", "section_headings": ["Present hotspots of GLOF hazard and risk"], "chunk_type": "text", "line_start": 49, "line_end": 61, "token_count_estimate": 792, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac81ecab54e30834", "text": "Document: Past evolution and present state of glacial lakes\nSection: Anticipation of future changes\nType: text\n\nWe expand our assessment to consider how and where GLOF hazard and risk will evolve under continued global warming and glacier shrinking. Future glacial lake formation and associated hazard and risk are considered under three different CO<sub>2</sub> emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5)<sup>37</sup> and projections of a glacier model<sup>38</sup> for the middle (2050) and end (2100) of the twenty-first century (Methods). These results are compared with an ice-free scenario which assumes that the Third Pole's glaciers have totally vanished. Our modelling suggests that >13,000 new glacial lakes with a combined maximum area of ~1,510 km² and a combined maximum volume of ~50 km³ could potentially emerge in an ice-free environment (Supplementary\n\nTable 1). This includes the expansion of existing glacial lakes to reach their topographically constrained extent, and potential new glacial lakes forming in depressions within the former bed of glaciers. In total, ~47% of this additional lake area would have already emerged by 2050, and 86% by 2100 under RCP8.5 (or 44% and 56%, respectively, under RCP2.6) (Supplementary Table 1). This future glacial lake development is mostly clustered along the Pamir-Karakoram-Kunlun Shan-Himalaya ranges as well as the western Tien Shan mountains (Fig. 1 and Supplementary Table 1), where the existing glaciers contribute ~74% of the total Third Pole glacier area (Supplementary Table 1). There is notable regional variation in projected glacial lake development from east to west, with regions such as southeast Tibet and eastern Himalaya already revealing close to their maximum lake area by 2050, under all RCP scenarios (Fig. 1). In contrast, in Karakoram, Pamir and western Himalaya, lake area will continue to increase strikingly into the late twenty-first century and beyond. On a basin scale, the future glacial lakes will be expected chiefly at the headwaters of the Indus and Tarim river basins, followed by Amu Darya, Ganges, Brahmaputra river basins and the interior ranges of the Tibetan Plateau (~89% of all projected glacial lakes, Supplementary Fig. 3 and Supplementary Table 2). The Brahmaputra, Ganges and Yangtze river basins almost approach their maximum lake area in 2050 under the three CO<sub>2</sub> emission scenarios, whereas lakes in the Indus and Tarim river basins as well as the Tibetan Plateau interior will continue to expand beyond 2100.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Anticipation of future changes", "section_headings": ["Anticipation of future changes"], "chunk_type": "text", "line_start": 63, "line_end": 71, "token_count_estimate": 644, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "274c39be28a28a7b", "text": "Document: Past evolution and present state of glacial lakes\nSection: Anticipation of future changes\nType: text\n\ncentury and beyond . On a basin scale , the future glacial lakes will be expected chiefly at the headwaters of the Indus and Tarim river basins , followed by Amu Darya , Ganges , Brahmaputra river basins and the interior ranges of the Tibetan Plateau ( ~ 89 % of all projected glacial lakes , Supplementary Fig . 3 and Supplementary Table 2 ) . The Brahmaputra , Ganges and Yangtze river basins almost approach their maximum lake area in 2050 under the three CO < sub > 2 < / sub > emission scenarios , whereas lakes in the Indus and Tarim river basins as well as the Tibetan Plateau interior will continue to expand beyond 2100 .\n\nTo assess the possible implications of this future glacial lake development on GLOF hazard and risk, we applied the same model as was used for the present glacial lakes, with some slight modifications to account for future unknowns (Methods). For the whole Third Pole region, results indicate that future GLOF hazard and risk will increase by almost threefold relative to current conditions, with obvious regional variations (Fig. 4 and Supplementary Fig. 8). This is considered a conservative maximum estimate given that glacial lakes will appear gradually, and an unknown quantity of moraine dams could breach, and lakes thereby drain, before other lakes have emerged. Nonetheless, under the high-emission scenario RCP8.5, much of the Third Pole could already be approaching a state of 'peak risk' by the end of the twenty-first century, or even mid-century in some regions, with risk levels comparable to even the ice-free scenario (Fig. 4). In an ice-free environment, average glacial lake volumes will be ~80% larger than today, while topographic potential for ice/rock avalanche triggering will increase by ~230%, as lakes will be positioned closer to steep mountain headwalls (Supplementary Fig. 9). This suggests that the increase in potential GLOF frequency will generally be more important than the future change in magnitude. Meanwhile, in terms of risk, although overall exposure levels will increase due to the larger number of lakes, future lakes will on average have lower exposure levels (-40% compared with today) as they are located higher, and farther away from existing communities. As a consequence, average risk values per lake will be 14% lower in the future, while the overall number of lakes classified to have high or very high risk levels will increase from 1,203 to 2,963 lakes (that is, by a factor of 2.5). This analysis does not consider future changes in the distribution of population and infrastructure, particularly hydropower, that is expanding higher into alpine valleys<sup>39</sup>. Likewise, tourism expansion into remote mountain valleys could further enhance risks, while outmigration from rural areas could reduce risk in some cases<sup>40,41</sup>. Collectively, the Karakoram, Pamir and western Himalaya will be the regions with the most substantial increase in GLOF hazard, while in terms of risk, the largest increases will be in central Himalaya, Karakoram and western Himalaya (Fig. 4 and Supplementary Fig. 8). The eastern Himalaya will remain the primary GLOF hotspot under all assessed future scenarios, while the emergence of the Karakoram as an additional major hotspot of GLOF hazard and risk is most notable over longer timescales under RCP4.5 and RCP8.5, and under the ice-free", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Anticipation of future changes", "section_headings": ["Anticipation of future changes"], "chunk_type": "text", "line_start": 63, "line_end": 71, "token_count_estimate": 833, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "230a0b9f4fdc4c27", "text": "Document: Past evolution and present state of glacial lakes\nSection: Anticipation of future changes\nType: text\n\nreduce risk in some cases < sup > 40 , 41 < / sup > . Collectively , the Karakoram , Pamir and western Himalaya will be the regions with the most substantial increase in GLOF hazard , while in terms of risk , the largest increases will be in central Himalaya , Karakoram and western Himalaya ( Fig . 4 and Supplementary Fig . 8 ) . The eastern Himalaya will remain the primary GLOF hotspot under all assessed future scenarios , while the emergence of the Karakoram as an additional major hotspot of GLOF hazard and risk is most notable over longer timescales under RCP4 . 5 and RCP8 . 5 , and under the ice - free\n\nscenario. While lake volumes will on average be larger than today, it is clear that even in the future, it is not necessarily the largest lakes that will be the biggest threat to communities (Supplementary Fig. 10). At the basin scale, the future increase in GLOF risk will be greatest in the Indus, Ganges and Amu Darya river basins, with the Ganges River Basin maintaining the highest risk levels under all future scenarios (Supplementary Figs. 11 and 12). By 2100, the Indus River Basin will exhibit the highest GLOF hazard levels (currently exhibited by the Brahmaputra River Basin), with close to a fourfold increase from today under the RCP4.5 scenario.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Anticipation of future changes", "section_headings": ["Anticipation of future changes"], "chunk_type": "text", "line_start": 63, "line_end": 71, "token_count_estimate": 345, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc9c2831f422f713", "text": "Document: Past evolution and present state of glacial lakes\nSection: Potential transboundary threats\nType: text\n\nThe mountain ranges of the Tibetan Plateau and surrounding areas span 11 nations (Fig. 2a), giving rise to potential transboundary natural disasters, especially GLOFs. In fact, several relevant cases have been reported, for instance, in the transboundary Poiqu River Basin between China and Nepal<sup>22</sup> and the Shakhimardan catchment between Kyrgyzstan and Uzbekistan<sup>42</sup> (Fig. 2a). A well-known outburst from Cirenma Co (Fig. 2a) in 1981 caused serious devastation, including but not limited to the destruction of the Sino-Nepal Friendship bridge and part of the Nepal Sun Koshi hydropower station, and the loss of 200 lives in Nepal<sup>43</sup>. Recognizing the broader transboundary threat across the Third Pole, we identified where simulated flood flow paths from existing moraine-dammed glacial lakes intersected with national borders. Consequently, a total of 438 glacial lakes were found to have the potential to produce a transboundary flood, of which 191 were classified to be in the high or very high risk classes (Fig. 2a). Further analysis revealed that ~86% of these potentially dangerous lakes are located between China and Nepal, forming five hotspots of transboundary floods (Fig. 2e). It can be inferred from the illustrated example of one of these hotspots shown in Supplementary Fig. 13 that a possible outburst will have serious effects on downstream transboundary settlements and infrastructures.\n\nThe transboundary GLOF threat will amplify in the future. Analysis suggests that the number of future potential transboundary flood sources under the ice-free scenario will roughly double (an additional 464 lakes), with 211 of these lakes classified in the high or very high risk classes (Fig. 2a). The border region between China and Nepal will remain a major hotspot (~42% of all these future dangerous glacial lakes), while the border region between Tajikistan and Afghanistan emerges clearly as an additional potential transboundary hotspot (~36%, up from ~5% at present).", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Potential transboundary threats", "section_headings": ["Potential transboundary threats"], "chunk_type": "text", "line_start": 73, "line_end": 77, "token_count_estimate": 523, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": []}}
{"id": "880193c54c89d994", "text": "Document: Past evolution and present state of glacial lakes\nSection: Summary and implications\nType: text\n\nWe conclude that while the most dangerous hotspots of GLOF hazard and risk are now located within the eastern Himalaya, consistent with previous studies<sup>21,23,36,44</sup> and recorded GLOF events (Supplementary Fig. 10; see Supplementary Fig. 14 for a visual example in the eastern Himalaya), over the course of the twenty-first century, additional hotspots will emerge in the regions to the west, particularly under high-emission scenarios. For Karakoram and Pamir, where glaciers have recently exhibited a stable or slightly positive mass balance45, repetitive, high-frequency outburst events associated with ice-dammed glacial lakes will probably remain the dominant GLOF threat for several decades to come. However, the eventual depletion of glaciers in these regions projected during the twenty-first century will see conditions becoming less conducive for glacier surging and ice-dam formation<sup>19</sup>, and the threat of moraine- or bedrock-dammed glacial lakes substantially increasing. It is also important to emphasize that climate and related glacial changes as assessed here are just one driver of future GLOF risk; in some regions, future changes in exposure and societal vulnerability could potentially lead to different risk scenarios, especially where political, economic and social conditions facilitate or impede effective disaster risk reduction. Ultimately, determining exactly which lakes are the highest priority for response actions should be guided by consensus across multiple lines of evidence,", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Summary and implications", "section_headings": ["Summary and implications"], "chunk_type": "text", "line_start": 79, "line_end": 81, "token_count_estimate": 372, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b32f7127f7385e6", "text": "Document: Past evolution and present state of glacial lakes\nSection: Summary and implications\nType: figure\nFigure\n\nImage /page/5/Figure/1 description: A map of the 'Third Pole' region in Asia, showing projected aggregated risk values for various mountain ranges under different climate scenarios. The map displays longitude from 70°E to 110°E and latitude from approximately 30°N to 40°N. Bar charts are overlaid on specific regions, including West Tien Shan, Hindu Kush, Karakoram, West Himalaya, Central Himalaya, and East Himalaya, among others. Each bar chart consists of five bars representing different conditions, as explained in a legend titled 'Aggregated risk value'. The conditions are: 'Present' (light yellow), 'RCP2.6' (light pink), 'RCP4.5' (pink), 'RCP8.5' (magenta), and 'Ice free' (dark purple). A reference bar indicates a value of 600. In all regions, the aggregated risk value increases progressively from the 'Present' condition to the 'Ice free' condition. A separate legend in the top right corner shows the 'Total risk value' for the entire 'Third Pole' region. The values for the five scenarios are: 1,510, 2,705, 3,197, 3,597, and 3,861, corresponding to the increasing severity of the climate scenarios.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Summary and implications", "section_headings": ["Summary and implications"], "chunk_type": "figure", "figure_caption": null, "line_start": 82, "line_end": 82, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f201cc4102e6408", "text": "Document: Past evolution and present state of glacial lakes\nSection: Summary and implications\nType: text\n\n**Fig. 4 | Region-wide future changes in GLOF risk to 2050, 2100 and on an ice-free Third Pole.** Bar plots indicate projected changes in GLOF risk per region from the present to 2050 and 2100 (under three RCPs) as well as under the ice-free scenario. Dashed lines show estimated changes to 2050. The future GLOF risk was estimated based on currently known infrastructures that are exposed to modelled flood flow paths from potential future lakes and includes the assessed risk from present moraine-dammed glacial lakes (that is, these lakes are assumed to remain in the future). Inset: changes in GLOF risk over the whole Third Pole; note that the scale differs from the regional risk values.\n\nconsidering not only the wealth of information on existing GLOF hazard and risk (Supplementary Table 4), but also future emerging threats. Particularly where existing and emerging transboundary risks have been identified, risk reduction strategies will require high levels of international planning and cooperation. These transboundary regions represent vital economic corridors of activity, but some suffer from political tensions and challenges that can negatively affect timely data sharing, communication and coordination of early warning and disaster preparedness. We appeal to the relevant nations and international research communities to work together to prevent and mitigate the damages and losses that GLOFs bring to the Third Pole region, and to remain flexible and alert to the new challenges that could emerge under a future warmer climate.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Summary and implications", "section_headings": ["Summary and implications"], "chunk_type": "text", "line_start": 83, "line_end": 87, "token_count_estimate": 359, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e372b01805fe3553", "text": "Document: Past evolution and present state of glacial lakes\nSection: Online content\nType: text\n\nAny methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41558-021-01028-3.\n\nReceived: 2 June 2020; Accepted: 16 March 2021; Published online: 6 May 2021", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Online content", "section_headings": ["Online content"], "chunk_type": "text", "line_start": 89, "line_end": 93, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01028"]}}
{"id": "d05a579f58c1c125", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\nGlacial lake delineation and lake changes. We used Landsat satellite imagery to outline the boundaries of glacial lakes that are defined as water bodies formed primarily by glaciation and fed by glacial/snow melts, and are situated on the surface, adjacent or downstream of a glacier, or on palaeoglaciation landforms<sup>47</sup> across the Third Pole. Two time windows,  $1990 \\pm 4$  and  $2015 \\pm 1$ , were chosen to reveal the present state of glacial lakes and their changes over the last ~25 years. The selection was determined largely by the availability of Landsat datasets in such a large area. To minimize the effects of ice or seasonal snow cover on glacial lake mapping in these high elevations, we picked images mostly from the warm season (June to November), which corresponds roughly to the highest and most stable water areas/levels of the lakes in a hydrological year<sup>48</sup>. Overall, we used a total of 403 Landsat 4-5 Thematic Mapper (TM) scenes (97.8% from the warm season) for the 1990 epoch and 294 Landsat 8 Operational Land Imager (OLI) scenes (100% in the warm season) for the 2015 period (Supplementary Figs. 1 and 2a,b). We also gave priority to images with no or little cloud coverage, that is, an average cloud cover percentage of 7.7% and 4.2% for 1990 and 2015, respectively (Supplementary Figs. 1 and 2c). Landsat images were obtained from the US Geological Survey (USGS) data portal with a spatial resolution of 30 m. Images were first converted from the digital numbers to the top of atmosphere. Landsat 8 OLI images were further fused using a nearest neighbour diffusion-based pan-sharpening algorithm<sup>49</sup> to improve their spatial resolution to 15 m.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 480, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "61a77e3a768e876e", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\nb ) . We also gave priority to images with no or little cloud coverage , that is , an average cloud cover percentage of 7 . 7 % and 4 . 2 % for 1990 and 2015 , respectively ( Supplementary Figs . 1 and 2c ) . Landsat images were obtained from the US Geological Survey ( USGS ) data portal with a spatial resolution of 30 m . Images were first converted from the digital numbers to the top of atmosphere . Landsat 8 OLI images were further fused using a nearest neighbour diffusion - based pan - sharpening algorithm < sup > 49 < / sup > to improve their spatial resolution to 15 m .\n\nAn automated water body classification algorithm<sup>50</sup> based on global-local threshold segmentation for water indices greyscale images was applied to extract all water bodies from the Landsat images. A slope threshold of <20° as well as a shaded relief threshold of >0.25 were employed to alleviate the disturbances from mountain shadows<sup>50</sup>. Subsequently, careful visual inspection and manual re-editing were carried out to remove other water bodies (for example, reservoirs, rivers and small streams) and to correct the wrongly mapped glacial lakes based on corresponding Landsat scenes, online maps and other glacial lake datasets<sup>5,4</sup> available for the Third Pole. A 10 km buffer<sup>5,52</sup> around the Randolph Glacier Inventory (RGI 6.0)54 was used to preliminarily determine the distribution of what could be considered glacial lakes. However, to ensure the integrity and reliability of our inventory, we also included additional glacial lakes in areas where glaciers are missing from the RGI 6.0 dataset<sup>55</sup>. Both the glacial lake datasets for 1990 and 2015 were then cross-checked and attributed. The area of all glacial lakes was calculated based on the universal transverse mercator (UTM) projected coordinate system and their uncertainty ( $\\delta$ ) was estimated using the formula<sup>56</sup>:  $\\delta = P/G \\times G^2/2 \\times 0.6872$ , where P is the perimeter of the glacial lake and G is the spatial resolution of the images used. Lastly, we selected all glacial lakes ≥0.01 km<sup>2</sup> and superimposed them in a high-resolution Google Earth 3D view mode together with the corresponding Landsat scenes to distinguish the moraine-dammed glacial lakes separately. The remaining lakes were grouped and termed 'other-dammed glacial lakes'. Lakes dammed with ice or lying on the surface of glaciers were not included in the temporal change analyses due to their transient character and large seasonal fluctuations.\n\nThe relative change in glacial lake area between 1990 and 2015 and its uncertainty were estimated using the following equations:\n\n$$R = \\frac{A_2 - A_1}{A_1} \\times 100\\%$$\n\n$$\\delta R = \\sqrt{\\left(\\frac{\\delta A}{A_2 - A_1}\\right)^2 + \\left(\\frac{\\delta A_1}{A_1}\\right)^2} \\times R$$", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 838, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9162c7ed809178b5", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\nlying on the surface of glaciers were not included in the temporal change analyses due to their transient character and large seasonal fluctuations . The relative change in glacial lake area between 1990 and 2015 and its uncertainty were estimated using the following equations : $ $ R = \\ frac { A_2 - A_1 } { A_1 } \\ times 100 \\ % $ $ $ $ \\ delta R = \\ sqrt { \\ left ( \\ frac { \\ delta A } { A_2 - A_1 } \\ right ) ^ 2 + \\ left ( \\ frac { \\ delta A_1 } { A_1 } \\ right ) ^ 2 } \\ times R $ $\n\nwhere R is the relative change in glacial lake area and  $\\delta R$  is its uncertainty;  $A_1$ ,  $A_2$  are the lake areas at the beginning and end of the period, respectively; and  $\\delta A_1$ ,  $\\delta A_2$  are their uncertainties,  $\\delta A = \\sqrt{\\delta A_1^2 + \\delta A_2^2}$ .\n\nModelling of future glacial lakes. Glacier-bed topography, that is, ice-free digital elevation model (DEM) can be estimated using model approaches that determine local ice thickness based on the characteristics of surface topography and considerations of ice-flow dynamics8. The potential sites of future glacial lake formation under scenarios of complete deglaciation can then be projected by detecting overdeepenings on the glacier-bed. Here we used the latest global glacier ice thickness dataset<sup>57</sup> consisting of a consensus of five different ice thickness models as well as the 30 m Advanced Land Observing Satellite Global Digital Surface Model (ALOS AW3D30 v2.2) to extract the bed topography of all Third Pole glaciers. Three analytical steps were performed to determine the possible sites of future glacial lake formation: (1) glacier areas with slopes <20° were mapped first as potential locations of glacier-bed overdeepenings as lakes normally form in flat topography; (2) depressions in the glacier bed were detected by filling them using a classic filling algorithm<sup>58</sup>, while a slope raster was generated based on the filled glacier-bed topographies; and (3) depressions with a slope <1° were identified8 and intersected with the layer of possible overdeepenings obtained in the first step to generate the final layer of the possible future lakes. Ultimately, we selected all modelled overdeepenings ≥0.01 km<sup>2</sup> as the potential sites of future lake formation, to ensure comparability to the glacial lake inventories in 1990 and 2015. The area and volume of all modelled future glacial lakes were estimated based on a bathymetry raster that was derived from the difference between the", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 716, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64651c9aeafa6077", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\n, while a slope raster was generated based on the filled glacier - bed topographies ; and ( 3 ) depressions with a slope < 1 ° were identified8 and intersected with the layer of possible overdeepenings obtained in the first step to generate the final layer of the possible future lakes . Ultimately , we selected all modelled overdeepenings ≥ 0 . 01 km < sup > 2 < / sup > as the potential sites of future lake formation , to ensure comparability to the glacial lake inventories in 1990 and 2015 . The area and volume of all modelled future glacial lakes were estimated based on a bathymetry raster that was derived from the difference between the\n\noverdeepenings-filled DEM and the original glacier-bed topography. The mean and maximum depth of each glacial lake was also estimated using the detected depression extent and its bathymetry raster. In general, ice-thickness models have proven robust in detecting the locations of overdeepenings  $^{\\rm sp}$ , although local uncertainties in ice-thickness estimates (and thereby volume of the overdeepenings and associated future lake) could be on the order of  $\\pm 100\\%$  for individual cases  $^{\\rm co}$ . Likewise, dam composition (moraine or bedrock) cannot be distinguished for future lakes, and lake volumes will be ultimately determined by dam geometry and the presence of an outlet channel.\n\nTo estimate the time horizon in which potential future glacial lakes could emerge, we simulated glacier recession based on an ensemble of 14 global circulation models of the Climate Model Intercomparison Project Phase 5 (ref. 61) under three different CO<sub>2</sub> emission scenarios — RCP2.6, RCP4.5 and RCP8.5, in addition to the hypothetical end case with a completely ice-free Third Pole. Two major time horizons over the course of the twenty-first century, 2050 and 2100, were considered. The Global Glacier Evolution Model (GloGEM)38 was employed to model the evolution of all individual RGI 6.0 glaciers based on projected climate forcing until 2100. GloGEM resolves all components of the glacier surface mass balance and computes changes in glacier geometry (thickness, length) in annual time steps. Results for the ensemble of global circulation models have been averaged for the three considered RCPs. The minimum elevation for each glacier at both 2050 and 2100 under each RCP scenario was calculated and used to determine the exposed overdeepenings at each time step. We assume that the potential glacial lake is forming or has formed if the modelled minimum elevation of its parent glacier is greater than or equal to the elevation of the modelled overdeepening that was retrieved based on the filled glacier-bed topographies.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 684, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed6eb1c6ffd250eb", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\nuntil 2100 . GloGEM resolves all components of the glacier surface mass balance and computes changes in glacier geometry ( thickness , length ) in annual time steps . Results for the ensemble of global circulation models have been averaged for the three considered RCPs . The minimum elevation for each glacier at both 2050 and 2100 under each RCP scenario was calculated and used to determine the exposed overdeepenings at each time step . We assume that the potential glacial lake is forming or has formed if the modelled minimum elevation of its parent glacier is greater than or equal to the elevation of the modelled overdeepening that was retrieved based on the filled glacier - bed topographies .\n\nGLOF hazard assessment and validation. Moraine-dammed glacial lakes dominate past GLOFs recorded in most of the world17, especially in the Himalaya<sup>20,29</sup>, and their evolution can be directly linked to atmospheric warming and glacial recession17. Hence, for the current GLOF hazard and risk assessment, we focused on such lakes. A conceptual model<sup>21</sup> was used to assess hazard and risk for 6,958 existing moraine-dammed proglacial lakes across the Third Pole. The model does not apply to some glacial lakes situated on the surface or flanks of debris-covered glaciers, as predisposing and triggering factors in these cases are different. Furthermore, we recognize the transition and complexity that occurs between different lake types, meaning that some degree of subjectivity in lake classification cannot be avoided. The model defines three indices21: the GLOF risk index as a function of the hazard index (combining likelihood and potential magnitude of a GLOF) and exposure index (potential for people and infrastructure to be affected). Here we improved the model for large-scale implementation so that it can run on a single glacial lake object iteratively. The large-scale assessment was based on a single UTM grid zone. All calculations and analyses described below were based on the 90 m Multi-Error-Removed Improved-Terrain (MERIT) DEM62, which is proven to have the best performance on the Third Pole<sup>63</sup>", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 549, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f75d749d2a15a02a", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\nthree indices21 : the GLOF risk index as a function of the hazard index ( combining likelihood and potential magnitude of a GLOF ) and exposure index ( potential for people and infrastructure to be affected ) . Here we improved the model for large - scale implementation so that it can run on a single glacial lake object iteratively . The large - scale assessment was based on a single UTM grid zone . All calculations and analyses described below were based on the 90 m Multi - Error - Removed Improved - Terrain ( MERIT ) DEM62 , which is proven to have the best performance on the Third Pole < sup > 63 < / sup >\n\nThe potential hazard of a GLOF was defined based on four key factors<sup>21</sup>, which encompass well-established processes that drive GLOF triggering and magnitude30. (1) Lake volume was calculated using a lake depth-area-volume empirical relationship<sup>64</sup> and used as a proxy for possible maximum flood magnitude. (2) Total watershed area upstream of each glacial lake was used as an indicator of the potential for heavy rain and glacier/snow melt runoff to flow into the lake or cause it to overflow<sup>20,26</sup>. (3) The likelihood of ice and/or rock avalanches triggering an outburst was calculated based on the concept of topographic potential which includes: the potential for ice and/or rock to detach (parameterized by slope angle); and the potential for the ice and/or rock avalanche to reach a glacial lake (parameterized by the overall trajectory slope or angle of reach). Due to the quality of available glacier datasets at such a large scale, we did not distinguish whether the surrounding slopes were rock or ice-covered. Thus, we assumed that an avalanche was possible from any slope >30°, and the resulting avalanche could feasibly impact the lake where the overall slope trajectory is >14° (tangent angle = 0.25)65. (4) Downstream slope of the lake dam was considered to be an important factor controlling dam stability and the potential for self-destruction. It is also relevant for the erosional capacity of a GLOF event in downstream areas. This factor was calculated as the average horizontal downstream slope of each lake in three different buffer zones depending on different lake areas (A) intervals ( $A \\ge 0.1 \\text{ km}^2$ , 900 m;  $0.1 > A \\ge 0.05 \\text{ km}^2$ , 600 m;  $0.05 > A \\ge 0.01 \\text{ km}^2$ , 300 m). These intervals were derived empirically based on careful analyses of a large number of lakes using high-resolution satellite images.\n\nAll factors were then normalized to 0 to 1 using the percent ranking method and assumed to have equal weight to the hazard of a GLOF event. For display and comparative purposes, hazards of all glacial lakes were eventually classified into five levels (very low, low, medium, high, very high) using the natural breaks (Jenks) method and then were aggregated to each region and river basin based on the sum of all lake hazard levels.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 778, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7257e83656366fa7", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\ntext { km } ^ 2 $ , 300 m ) . These intervals were derived empirically based on careful analyses of a large number of lakes using high - resolution satellite images . All factors were then normalized to 0 to 1 using the percent ranking method and assumed to have equal weight to the hazard of a GLOF event . For display and comparative purposes , hazards of all glacial lakes were eventually classified into five levels ( very low , low , medium , high , very high ) using the natural breaks ( Jenks ) method and then were aggregated to each region and river basin based on the sum of all lake hazard levels .\n\nThe verification of model outcomes is highly dependent on empirical information<sup>21</sup>. Here, to verify the results from the GLOF hazard assessment and provide the historical context of GLOF activity, we compiled the most complete inventory of past GLOFs over the Third Pole. Thus, we collected and re-evaluated almost all of the historical GLOF events (Supplementary Table 3) that have been\n\nreported on the Third Pole and found a total of 296 GLOF events from 109 glacial lakes. Finally, 48 moraine-dammed glacial lakes that did not disappear after previous GLOFs and remained  $\\geq$ 0.01 km² in the 2015 epoch were used to validate the hazard assessment.\n\nGLOF exposure and risk. Exposure represents the presence of people, livestock, infrastructure and other assets that could be threatened by potential GLOF hazard<sup>21</sup>. Here we used infrastructures (such as buildings, highways, railways and historic places, see Supplementary Table 5 for all features used) to characterize the communities and populations that might be affected within a potential GLOF flow path, which was simulated using the GIS-based modified single-flow hydrological model<sup>66</sup>. All infrastructure features were downloaded from OpenStreetMap (accessed 1 August 2019) and were converted to a raster grid. The maximum downstream flow distance for each lake path was determined based on an empirically derived worst-case scenario defined by the angle of reach from the source lake, with a tangent angle = 0.05 (3° angle of reach)<sup>67</sup>. Beyond this flow distance, damages from a GLOF are not expected. The sum of the angle of reach for each OpenStreetMap raster pixel that is exposed to the lake flow path was aggregated as the quantified measure of exposure for each glacial lake. As of 2016, the OpenStreetMap was considered to be 83% globally complete and increasing rapidly, although generally lower (yet relatively homogeneous) levels of completeness are seen over the Third Pole<sup>35</sup>. The exception is Nepal where intense humanitarian mapping efforts have led to near total completeness.\n\nThe exposure values for each glacial lake were also normalized to 0 to 1 using the percent ranking method and then multiplied with the normalized hazard value to give the final risk level associated with each lake. The risk values of all glacial lakes (except those with an exposure value of zero indicating no risk to downstream communities) were divided into five classes using the natural breaks (Jenks) method and aggregated to each region and river basin to obtain their overall risk levels. In the absence of reasonable proxy variables at such a large scale, the vulnerability component of risk was not considered here.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 816, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "3968c585c873c04c", "text": "Document: Past evolution and present state of glacial lakes\nSection: Methods\nType: text\n\nefforts have led to near total completeness . The exposure values for each glacial lake were also normalized to 0 to 1 using the percent ranking method and then multiplied with the normalized hazard value to give the final risk level associated with each lake . The risk values of all glacial lakes ( except those with an exposure value of zero indicating no risk to downstream communities ) were divided into five classes using the natural breaks ( Jenks ) method and aggregated to each region and river basin to obtain their overall risk levels . In the absence of reasonable proxy variables at such a large scale , the vulnerability component of risk was not considered here .\n\nFuture GLOF hazard and risk assessments. The same modelling approach was also applied to assess future GLOF hazards and risks for all modelled glacial lakes formed at 2050 and 2100 under three different RCPs and the ice-free scenario. The difference was that the current topography of the glacier areas was substituted by the modelled glacier-bed topography and then the selected factors were calculated for these future scenarios. However, because we cannot judge whether these potential glacial lakes will be dammed by moraine or bedrock, and resulting dam geometries are highly uncertain, we used the average dam slope of present glacial lakes in each region or river basin to characterize all modelled lakes in the same region or basin. Likewise, no robust basis exists to predict the future development of downstream infrastructure and communities across all of the Third Pole; thus, the future risk assessment considers only the change in exposure of existing infrastructures and communities.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 101, "line_end": 135, "token_count_estimate": 379, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e5c442c9ccbb2ae2", "text": "Document: Past evolution and present state of glacial lakes\nSection: Code availability\nType: text\n\nThe GLOF hazard and risk assessment models are available at Zenodo under the identifier https://doi.org/10.5281/zenodo.4477947. Additional model or code used in this study are available from the corresponding authors on request.", "metadata": {"source_file": "data/('Increasing risk of glacial lake outburst floods from future Third Pole deglaciation', '.pdf')_extraction.md", "document_title": "Past evolution and present state of glacial lakes", "section_path": "Code availability", "section_headings": ["Code availability"], "chunk_type": "text", "line_start": 141, "line_end": 143, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["4477947"]}}
{"id": "52eef0062bf46f70", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: Abstract\nType: text\n\nLong-term simultaneous tracking of the dynamics of regional glacial lakes can help in identifying lakes prone to climate change and Glacial Lake Outburst Flood (GLOF). This study demonstrates a methodology of tracking the dynamics of glacial lakes using Landsat time series data and SRTM DEM of Sikkim Himalaya over three decades (1987–2020), using a random forest classifier (RFC) and an artificial neural network (ANN). The classifiers were trained with features like slope, hillshade, automated water extraction index, band ratio, modified normalized difference water index, normalized difference water index, and water ratio index. The performance of the classifiers were measured using parameters like Accuracy, Kappa, Sensitivity, Specificity, Precision, F1 score, and Area under the curve. Furthermore, imbalance tests were performed to validate the predictions of the classifiers. On average, RFC marginally outperformed ANN with an accuracy of 98%. The slope was the most important determinant in mapping the glacial lakes, followed by the automated water extraction index. Time series data generated from this method was used in forecasting the fate of numerous glacial lakes of the Sikkim Himalaya. Models like Brown's and Holt's exponential smoothing, and the random walk model were applied for forecasting. The forecasts were validated with varying degrees of accuracy. The proposed methodology helps in overcoming the challenges of mapping glacial lakes over a vast geographic area over a prolonged period and generates time series data of their spatial extent. The methodology demonstrated here will be useful for the long-term mapping and monitoring of glacial lakes all over the world.\n\n**Keywords** Glacial lake, Google earth engine, Machine learning, Random forest, Time series, Forecasting", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "text", "line_start": 4, "line_end": 8, "token_count_estimate": 477, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "298c59b167b10271", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: text\n\nGlacial Lakes (GLs) are a product of the complex interactions between the glacial terrain, topography, geology, hydrology, and climatic variables of a region [1, 2]. The formation and evolution of these GLs are driven by processes involving the accumulation", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 12, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "512c1d9018ffc955", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/12 description: The image shows the logo for Discover. On the left is a dark blue, circular, wave-like symbol. To the right of the symbol, the word \"Discover\" is written in a dark blue, sans-serif font with a capital 'D'.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 13, "line_end": 13, "token_count_estimate": 110, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c241635281a73823", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: text\n\n© The Author(s) 2025. **Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nBanerjee Discover Geoscience (2025) 3:159 Page 2 of 33\n\nof meltwater from glaciers behind natural dams like ice dams, moraines, or bedrock [3, 4]. The formation and expansion of GLs depend on the retreat of glaciers during deglaciation periods [5]. The expansion of GLs is viewed as a significant indicator of climate change, glacial lake outburst flood (GLOF), and accelerated melting of glaciers [6-12].\n\nThe intensity of change and the fate of these lakes are highly uncertain as they are very sensitive to temperature and precipitation [13, 14]. The retreating glaciers often leave behind proglacial lakes. Such lakes are fed by the melting water of the glacier till a critical volume is achieved that breaks the lake's boundary wall, leading to a glacial lake outburst flood (GLOF). The increase in ambient air temperature and land surface temperature in mountainous regions in comparison to other places in the past few decades has made the issue of GLOF a matter of concern [15, 16]. Therefore, continuous monitoring of the long-term dynamics of GLs is necessary for understanding the impacts of global warming on the glacial lakes and for assessing the GLOF risk. However, mapping the glacial lake using coarse-resolution satellite images is a challenging task due to the presence of snow, melting glaciers, debris, and mountain shadows. To address these challenges, water indices, like NDWI, derived from the image bands are used to highlight the GLs from the non-GL objects in the image [17-19]. Other derived indices like NDSI [17], MNDWI [19-21], slope and hillshade [14, 22, 23] have also been used by various researchers for the identification of GLs. These input images are used by experts as well as machine learning algorithms to discriminate pixels belonging to GL from non-GL objects.\n\nManual digitization of GLs has also been attempted using False Colour Composite (FCC) image in the Pumqu River Basin of Tibet using Landsat 8 and SRTM DEM images [24]. Similarly, manual mapping of GLs of Sikkim Himalaya was done using NDSI and NDWI derived from Resourcesat-2, LISS III sensor images, and SRTM DEM [17]. However, manual digitization of GLs over a large region and several observation periods is prone to subjective error. Often, such methods lack reliability and reproducibility due to the limitation of model validation parameters that machine learning has [17, 25, 26].", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 14, "line_end": 42, "token_count_estimate": 869, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e11898413fcbd094", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: text\n\nnon - GL objects . Manual digitization of GLs has also been attempted using False Colour Composite ( FCC ) image in the Pumqu River Basin of Tibet using Landsat 8 and SRTM DEM images [ 24 ] . Similarly , manual mapping of GLs of Sikkim Himalaya was done using NDSI and NDWI derived from Resourcesat - 2 , LISS III sensor images , and SRTM DEM [ 17 ] . However , manual digitization of GLs over a large region and several observation periods is prone to subjective error . Often , such methods lack reliability and reproducibility due to the limitation of model validation parameters that machine learning has [ 17 , 25 , 26 ] .\n\nSeveral strategies have been adopted to demarcate the GLs. For instance, global threshold as well as local threshold of NDWI were used to map GLs, along with static thresholds of slope and hillshade, iteratively to distinguish GLs from other objects using Landsat images and SRTM DEMs [14]. A similar method was used to delineate the GLs of Sikkim Himalaya using Resourcesat-1, LISS III and Cartosat-I images [27]. Threshold-based studies have been used to map high altitude GLs of Asia using NIR, SWIR bands, and NDWI derived from Landsat 8 images along with slope derived from ASTER GDEM [26]. Threshold methods like Double-Window Flexible Pace Search (DFPS) and edge detection were used to determine the NDWI threshold for mapping GLs of Gangotri glacier using Sentinel 2 A images [28]. Beyond the threshold method and manual tracing of GLs, semi-automatic methods [11, 14, 18, 22, 24, 27-29], and automatic methods [20, 26] have also been successfully applied in mapping of GLs. It is worth noting that threshold-based mapping of GLs is only appropriate for GLs that have high spectral contrast for features like NDWI, slope, etc., as compared to their surroundings [14, 27]. It becomes even more difficult to apply the threshold method for small GLs in coarseresolution imageries that have mixed pixels containing debris, snow, and glaciers along with water [30]. Furthermore, mapping of GLs using threshold-based methods gets\n\nBanerjee Discover Geoscience (2025) 3:159 Page 3 of 33\n\nmore challenging while considering variation in spatial, spectral, and temporal resolutions, due to the use of different satellites.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 14, "line_end": 42, "token_count_estimate": 641, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6d502c61e6344f6", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: text\n\nworth noting that threshold - based mapping of GLs is only appropriate for GLs that have high spectral contrast for features like NDWI , slope , etc . , as compared to their surroundings [ 14 , 27 ] . It becomes even more difficult to apply the threshold method for small GLs in coarseresolution imageries that have mixed pixels containing debris , snow , and glaciers along with water [ 30 ] . Furthermore , mapping of GLs using threshold - based methods gets Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 3 of 33 more challenging while considering variation in spatial , spectral , and temporal resolutions , due to the use of different satellites .\n\nSince 2020, GL mapping has slowly migrated away from manual digitization and threshold-based methods to machine learning methods. Threshold-based methods heavily depend on a static threshold or a fixed set of thresholds of one or two features for GL mapping [26, 31]. In contrast, machine learning-based methods can consider multiple features and have the innate adaptability to consider varied thresholds over a wide range of features to map GLs [21, 32]. This adaptability gives an advantage to machine learning in mapping glacial lakes in a coarse-resolution image. Machine learning-based techniques [33] like Random Forest Classifier (RFC) have proven highly effective in GL mapping [30]. Convolutional Neural Network (CNN) like U-Net have also yielded high-accuracy mapping of GLs [21, 34, 35]. In recent times, GL mapping has become much more reliable owing to the availability of satellite imageries, like Landsat 8 and Sentinel 1 A and 2 A, having high spatial, spectral, and temporal resolutions, in public data archives. Additionally, machine learning techniques like deep learning have further reduced the computation time and improved the accuracy of image classification. However, the performance of a neural network heavily depends on a large and balanced dataset as compared to other machine learning methods like RFC [36, 37]. Besides, such studies have only focused on the current state of the GLs and not on the long-term behaviour of the lakes, primarily due to the lack of reliable data sources [21, 34].", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 14, "line_end": 42, "token_count_estimate": 586, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a2bf3328d2318f7", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: text\n\nlike Landsat 8 and Sentinel 1 A and 2 A , having high spatial , spectral , and temporal resolutions , in public data archives . Additionally , machine learning techniques like deep learning have further reduced the computation time and improved the accuracy of image classification . However , the performance of a neural network heavily depends on a large and balanced dataset as compared to other machine learning methods like RFC [ 36 , 37 ] . Besides , such studies have only focused on the current state of the GLs and not on the long - term behaviour of the lakes , primarily due to the lack of reliable data sources [ 21 , 34 ] .\n\nThe reason for the limited number of long term studies so far on the dynamics of glacial lakes is due to the limited accessibility and heterogeneity of long-term time series data of satellite from different sensors. For instance, Landsat 5 carried 7 spectral bands, including a single thermal band at 120 m. Landsat 7, in contrast, provided 8 bands, with its thermal band operating at 60 m. Both satellites offered 8-bit radiometry, allowing 256 brightness levels [38]. A major improvement came with Landsat 8, which operates with 11 spectral bands, including two thermal bands at 100 m. The multispectral resolution across all Landsat missions remained at 30 m, while thermal bands were resampled to 30 m. The most significant leap with Landsat 8 was its radiometric depth, expanded to 12-bit, providing 4096 brightness levels [38]. Nevertheless, several regions of the world still have historically sparse Landsat coverage. Fortunately, cloud computing platforms such as Google Earth Engine (GEE) now provide access to a large volume of orthorectified surface reflectance products and georeferenced data, including the complete Landsat time series archive. Online geocomputation in GEE enables generation of georeferenced outputs over large areas in very short time frames [39–41].\n\nChange detection of GLs so far has been focused on the changes in the shape, size, number, and type of GLs over time [24, 42–44]. Many of these studies have documented decadal changes in total area and total number of GLs [29], time series analysis of selected GLs [22, 33, 45], observation period-based change detection of total number, total area and total types of GLs [46]. Change detection of regional GLs is a crucial step towards validation of the impacts of climate change on the cryosphere and hydrological regime. Moreover, understanding the dynamics of GLs is of paramount importance in GLOF mitigation. However, change detection can only provide qualitative information about the fate of the GLs under consideration. Only time series analysis and forecasting of individual lakes over a prolonged period can provide reliable data to foresee their change in surface area and volume [46]. Unfortunately, time series analysis of lakes have\n\nBanerjee Discover Geoscience (2025) 3:159 Page 4 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 14, "line_end": 42, "token_count_estimate": 740, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56982578829b8f17", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 1 Introduction\nType: text\n\n, total area and total types of GLs [ 46 ] . Change detection of regional GLs is a crucial step towards validation of the impacts of climate change on the cryosphere and hydrological regime . Moreover , understanding the dynamics of GLs is of paramount importance in GLOF mitigation . However , change detection can only provide qualitative information about the fate of the GLs under consideration . Only time series analysis and forecasting of individual lakes over a prolonged period can provide reliable data to foresee their change in surface area and volume [ 46 ] . Unfortunately , time series analysis of lakes have Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 4 of 33\n\nbeen confined to only one or very few lakes. The selection of such lakes remain highly subjective. Such studies do not shed any light on the fate of all the GLs of a region. In contrast, time series analysis of regional GLs has been performed by clubbing the GLs together in terms of the total number and total area. This kind of analysis does not shed any light towards the dynamics of individual lakes in the region. Moreover, time series analysis of selected lakes over a brief period does not provide reliable information on the long-term dynamics of the lakes. Forecasting models of the long-term dynamics of all the GLs over a regional extent have not yet been attempted [47, 48]. Most of these studies have avoided using multiple satellite data sources for time series-based forecasting models [49, 50]. This is most likely due to the lack of geospatial techniques to track individual glacial lakes over a large geographic extent and for long durations, such as several decades. Instead, most authors have manually tracked a few lakes that they considered relevant for their study. Hence, a methodology is required that provides long-term regional GL dynamics.\n\nThe objective of this study is to address this research gap by developing a new methodology of time series analysis and forecasting of GLs over a long period. For this, geocomputing using Landsat time series data on the GEE cloud computing platform was performed to prepare triennial median water indices, along with topographic indices. These indices were used to train a random forest classifier and an artificial neural network to identify the GLs. In this study, a glacial lake is defined as a lake, or a water body, formed by the action of a glacier. The dynamics of individual GLs were tracked using a unique lake ID (ULID). The ULIDs were used to prepare a time series dataset of the size of the lakes. Based on the time series dataset, the size of the lakes were projected up to 2026 using forecast models.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 14, "line_end": 42, "token_count_estimate": 660, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "45736624f911b140", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.1 Case study for demonstration\nType: text\n\nThe area of interest (AOI) in this study is the northern part of Sikkim. Sikkim is a northeastern state of India located in the eastern sector of the Himalayas. It is characterised by mountainous topography along and across its territory. The elevation varies from 280 m in the south at the border with West Bengal to 8,586 m at Mt. Kanchenjunga, bordering Nepal. Sikkim has the highest number of glaciers in comparison to other Indian states or union territories. One of the most prominent glaciers in Sikkim is the Zemu Glacier. It is the largest glacier in the Eastern Himalayas, with a length of approximately 26 km. It is a source of water for numerous rivers, including the River Teesta. Unfortunately, the Zemu Glacier has been receding since the last century at an alarming rate [51], leaving lateral moraine ridges on either side of the glacier. Many glaciers of Sikkim are debris-covered to a varying extent, making them prone to differential melting. The rapid melting of glaciers due to climate warming has resulted in the formation and expansion of many glacial lakes. Several of these lakes are supraglacial and periglacial in location. These lakes are moraine- and ice-dammed. Climate change-induced retreating glaciers [52], acute cloud bursts [53], and melting of ice dams [54] are putting several of these glacial lakes under the category of potentially dangerous glacial lakes (PDGLs). For instance, Sikkim Himalaya currently has 14 PDGLs [55] (Fig. 1).\n\nBanerjee Discover Geoscience (2025) 3:159 Page 5 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.1 Case study for demonstration", "section_headings": ["2 Materials and methods", "2.1 Case study for demonstration"], "chunk_type": "text", "line_start": 46, "line_end": 50, "token_count_estimate": 450, "basins": [], "subbasins": ["Teesta"], "countries": ["India", "Nepal"], "lake_ids": []}}
{"id": "e66be4c30f326e04", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.1 Case study for demonstration\nType: figure\nFigure\n\nImage /page/5/Figure/1 description: A figure from a scientific paper titled \"AREA OF INTEREST\" showing a map of a study area in the Sikkim Himalaya. An inset map in the upper right corner shows the location of Sikkim, highlighted in red, within India, bordering Nepal, China, and Bangladesh. An arrow points from the inset to the main map. The main map is a satellite image of a mountainous region with a grid overlay. The longitude lines range from 88.0°E to 88.8°E, and the latitude lines range from 27.2°N to 28.0°N. A legend in the top left explains the map's features: red areas are 'Glacial lakes', blue outlines indicate 'Glacier extent', and a semi-transparent overlay represents the 'Area of Interest'. The text explains that the Area of Interest (AOI) is defined by a 10 km buffer from the current glacier extent. The map includes a north arrow and a scale bar showing 0, 7.5, and 15 km. The figure caption at the bottom identifies it as \"Fig. 1\" and describes the study area as the Northern part of Sikkim.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.1 Case study for demonstration", "section_headings": ["2 Materials and methods", "2.1 Case study for demonstration"], "chunk_type": "figure", "figure_caption": null, "line_start": 51, "line_end": 51, "token_count_estimate": 328, "basins": [], "subbasins": [], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "525bee7c2c0e9e67", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.1 Case study for demonstration\nType: text\n\n**Fig. 1** The study area or AOI considered in this study is the Northern part of Sikkim, encompassing the glaciers and glacial lakes", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.1 Case study for demonstration", "section_headings": ["2 Materials and methods", "2.1 Case study for demonstration"], "chunk_type": "text", "line_start": 52, "line_end": 54, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2a42315183612f74", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine\nType: text\n\nGoogle Earth Engine (GEE) is an open cloud-based online platform that archives geospatial data and provides a coding environment for geospatial analysis. GEE was used to compute several triennial median water indices from the Landsat 5 to 8 Surface Reflectance (SR) Tier 1 (T1) image archive for the period 1987 to 2020 (Fig. 2). The computation of the indices were confined to AOI. The AOI was prepared by buffering the glacier polygons of the Global Land Ice Measurements from Space (GLIMS) – Current [56]\n\nBanerjee Discover Geoscience (2025) 3:159 Page 6 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine", "section_headings": ["2 Materials and methods", "2.2 Preparation of environmental feature raster maps in Google Earth engine"], "chunk_type": "text", "line_start": 56, "line_end": 60, "token_count_estimate": 210, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e63f8447fa01866", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine\nType: figure\nFigure\n\nImage /page/6/Figure/1 description: A dual-axis combination chart displaying the number of images per year and the cumulative sum of images from 1987 to 2020. The x-axis represents the year. The primary y-axis on the left, labeled 'Images per year', ranges from 0 to 25. The secondary y-axis on the right, labeled 'Cumulative sum of images', ranges from 0 to 500. Blue vertical bars represent the 'images per year', showing fluctuations with a general upward trend. The number of images per year varies, with notable peaks in 1992 (approx. 16), 2006 (approx. 17), and a significant increase from 2012 onwards, peaking in 2018 at about 23 images. There is a sharp drop in 2002 and 2003. An orange line represents the 'cumulative sum of images', showing a steady and accelerating increase from near 0 in 1987 to approximately 475 by 2020. The chart is annotated with brackets indicating time periods labeled 'L5' (approx. 1988-2011), 'L7' (approx. 2011-2015), and 'L8' (approx. 2015-2019).", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine", "section_headings": ["2 Materials and methods", "2.2 Preparation of environmental feature raster maps in Google Earth engine"], "chunk_type": "figure", "figure_caption": null, "line_start": 61, "line_end": 61, "token_count_estimate": 326, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b0d64f9c3b75689", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine\nType: text\n\n**Fig. 2** Landsat time series data from 1987–2020 of the Sikkim Himalaya. L5 is the Landsat 5 ETM sensor, L7 is the Landsat 7 ETM+sensor, and L8 is the Landsat 8 OLI/TIRS sensor. L5 and L7 have 11 bands, while L8 has 12 bands. The images are atmospherically corrected surface reflectance products using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS)/ Land Surface Reflectance Code (LaSRC). The images are masked from the interferences caused by cloud, shadow, water, and snow using the C Function of Mask (CFMASK), as well as a perpixel saturation mask", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine", "section_headings": ["2 Materials and methods", "2.2 Preparation of environmental feature raster maps in Google Earth engine"], "chunk_type": "text", "line_start": 62, "line_end": 64, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c84848bdb3a65a4", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine\nType: figure\nFigure\n\nImage /page/6/Figure/3 description: A flowchart illustrating a multi-step process, likely for geographic or environmental analysis, divided into 11 steps. A large, curved blue arrow indicates the overall workflow direction. Each step is represented by a box containing icons and text labels.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine", "section_headings": ["2 Materials and methods", "2.2 Preparation of environmental feature raster maps in Google Earth engine"], "chunk_type": "figure", "figure_caption": null, "line_start": 65, "line_end": 65, "token_count_estimate": 129, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "60f14d6bb94b61fb", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine\nType: text\n\nStep 1: Starts with GLIMS data, processed by GEE to create an AOI (Area of Interest).\nStep 2: Landsat Time Series Data is processed by GEE to produce Triennial Water Indices Maps.\nStep 3: SRTM DEM data is processed by GEE to create Topographic Indices Maps.\nStep 4: JRC GSW Unchanged Extent data undergoes 'Clip + Data Pruning + Ground Truthing' to produce an AOI.\nStep 5: The 'Unchanged Extent of GLs' is used to generate 'Random Points', resulting in 'Class-1 (True) Points'.\nStep 6: Openstreet Map data is used to create an AOI. This is combined with the 'Unchanged Extent of GLs' through a 'Buffer + Clip' process. 'Random Points' are then generated, resulting in 'Class-0 (False) Points'.\nStep 7: 'Triennial Water Indices Maps', 'Topographic Indices Maps', 'Class-1 (True) Points', and 'Class-0 (False) Points' are combined to create an 'ML Training-Testing Dataset'.\nStep 8: The 'ML Training-Testing Dataset' is used for 'Model Performance Tests' and 'Imbalance Tests' to generate 'Prediction Maps'.\nStep 9: 'Prediction Maps' undergo 'Vectorization' to create 'GL Vector Maps'. These are then processed by the 'ULID Method' to produce 'ULID Tagged Maps'.\nStep 10: 'ULID Tagged Maps' and 'Buffered JRC GSW Maximum Extent' data are used for 'Selection by Location' to create 'Externally Validated GLs'. These are then processed by 'Selection by Attribute' to produce a 'GL Time Series Dataset'.\nStep 11: The 'GL Time Series Dataset' is used to create 'Forecast Models' and conduct 'Model Performance Tests'.\n\n**Fig. 3** Visual summary of the methodology. *AOI* Area of Interest, *GLIMS* Global Land Ice Measurements From Space, *GL* Glacial Lake, *ML* Machine learning algorithm, *SRTM DEM* Shuttle Radar Topography Mission digital elevation model, *GEE* Google Earth Engine cloud computing, *ULID* Unique Lake Identifier, *JRC GSW* Joint Research Centre Global Surface Water Mapping\n\nby 10 km [42] and clipping them with the Sikkim administrative boundary vector map extracted from the OpenStreetMap [57] (Step 1, Fig. 3).\n\nMedian values of the triennial period were considered in this study as they are better representatives of the water indices that are influenced by temporal variabilities and missing data [7, 33]. Water indices considered for this study were selected to highlight the water body features based on the spectral signature in the visible and infrared spectra. They included Automated Water Extraction Index with No Shadow (AWEI<sub>nsh</sub>), and Automated Water Extraction Index with Shadow (AWEI<sub>sh</sub>) [58–60], Band Ratio [46, 61], Modified Normalized Difference Water Index (MNDWI) [20, 62, 63], Normalized\n\nBanerjee Discover Geoscience (2025) 3:159 Page 7 of 33\n\nDifference Water Index using the Blue band (NDWI<sub>b</sub>) and Normalized Difference Water Index using the Green band (NDWI<sub>g</sub>) [17, 30], and Water Ratio Index (WRI) [64–66]:", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine", "section_headings": ["2 Materials and methods", "2.2 Preparation of environmental feature raster maps in Google Earth engine"], "chunk_type": "text", "line_start": 66, "line_end": 106, "token_count_estimate": 898, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc659dd2e271c42d", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine\nType: text\n\n, and Automated Water Extraction Index with Shadow ( AWEI < sub > sh < / sub > ) [ 58 – 60 ] , Band Ratio [ 46 , 61 ] , Modified Normalized Difference Water Index ( MNDWI ) [ 20 , 62 , 63 ] , Normalized Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 7 of 33 Difference Water Index using the Blue band ( NDWI < sub > b < / sub > ) and Normalized Difference Water Index using the Green band ( NDWI < sub > g < / sub > ) [ 17 , 30 ] , and Water Ratio Index ( WRI ) [ 64 – 66 ] :\n\n$$AWEI_{nsh} = 4 \\times (GREEN - SWIR1) - (0.25 \\times NIR + 2.75 \\times SWIR2)$$\n (1)\n\n$$AWEI_{sh} = BLUE + 2.5 \\times GREEN - 1.5 \\times (NIR + SWIR1) - 0.25 \\times SWIR2$$\n (2)\n\n$$Band Ratio = \\frac{GREEN}{NIR} \\tag{3}$$\n\n$$MNDWI = \\frac{GREEN - SWIR1}{GREEN + SWIR1} \\tag{4}$$\n\n$$NDWI_b = \\frac{BLUE - NIR}{BLUE + NIR} \\tag{5}$$\n\n$$NDWI_g = \\frac{GREEN - NIR}{GREEN + NIR} \\tag{6}$$\n\n$$WRI = \\frac{GREEN + RED}{NIR + SWIR2} \\tag{7}$$\n\nWhere, GREEN and BLUE are representative visible bands of the Landsat bands. The NIR, SWIR 1 and 2 are the Near Infrared and Short Wavelength Infrared bands 1 and 2 of the Landsat bands (Step 2, Fig. 3). Beyond the water indices, two topographic features, namely slope [14, 22, 26] and hillshade raster maps [14, 27] were calculated in the GEE framework from the Shuttle Radar Topography Mission (SRTM) Version 3 (V3) digital elevation data of 30-meter resolution [67] (Step 3, Fig. 3). The slope and hillshade raster maps were calculated to further purify the water body feature extraction criteria (Table 1).", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.2 Preparation of environmental feature raster maps in Google Earth engine", "section_headings": ["2 Materials and methods", "2.2 Preparation of environmental feature raster maps in Google Earth engine"], "chunk_type": "text", "line_start": 66, "line_end": 106, "token_count_estimate": 616, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6c691f104af40e55", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment\nType: text\n\nJRC GSW V 1.3 provides the changes in surface water bodies from 1984 to 2020 with epochs 1984–1999 and 2000–2020, the maximum extent of global surface water, and the average monthly extent of surface water [68]. The unchanged extent of the GLs of the AOI extracted from the JRC GSW V 1.3 was clipped by the AOI, and the product was validated by ground truthing using high-resolution images of Google Earth as well as Sentinel 2 A. The main purpose of this verification was to ensure that the polygons of GLs generated from JRC GSW V 1.3 were confined within the actual images of GLs (Step 4, Fig. 3). After confirmation, these polygons were used to generate 1000 random points. These points represented instances of places with GLs in the Sikkim Himalaya. The point vector map was considered as the GL instances (Class-1) of GLs (Step 5, Fig. 3). Next, a", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment", "section_headings": ["2 Materials and methods", "2.3 Preparation of the machine learning dataset in the GIS environment"], "chunk_type": "text", "line_start": 108, "line_end": 112, "token_count_estimate": 273, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc30de2262a8d724", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment\nType: table\nTable: Table 1 Performance of RFC and ANN in the prediction of the GLs\n\n| Dataset                                                                                  | Source              | Purpose                                |\n|------------------------------------------------------------------------------------------|---------------------|----------------------------------------|\n| Global Land Ice Measurements from Space (GLIMS)                                          | Google Earth Engine | Glacier extent map                     |\n| Sikkim administrative boundary                                                           | OpenStreetMap       | Study area vector map               |\n| Landsat 5 to 8 Surface Reflectance (SR) Tier 1 (T1)                                      | Google Earth Engine | Water indices                          |\n| Shuttle Radar Topography Mission (SRTM) Version 3 (V3)                                   | Google Earth Engine | Topographic indices                    |\n| Joint Research Centre Global Surface Water Mapping Layers Version 1.3 (JRC GSW V 1.3) | Google Earth Engine | Reference map for visual validation |", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment", "section_headings": ["2 Materials and methods", "2.3 Preparation of the machine learning dataset in the GIS environment"], "chunk_type": "table", "table_caption": "Table 1 Performance of RFC and ANN in the prediction of the GLs", "columns": ["Dataset", "Source", "Purpose"], "table_row_start": 1, "table_row_end": 5, "line_start": 113, "line_end": 119, "token_count_estimate": 265, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f6c56198c8d3b18", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment\nType: text\n\nBanerjee Discover Geoscience (2025) 3:159 Page 8 of 33\n\nbuffer of 250 m was created around the unchanged surface water extent polygons. The radius of 250 m of the buffer was determined considering the maximum variation in size of most of these GLs. This buffer was used to erase portions of the AOI vector map to generate the polygon without any GLs. The latter was used to generate 3000 random points as places without GLs in the Sikkim Himalaya. The point vector map was considered as the non-GL instances (Class-0) of GLs (Step 6, Fig. 3). The normalized triennial median water indices and terrain feature raster maps were used to seed the Class-1 point and Class-0-point vector maps [69]. The tables of these point vector maps were used for training the ML algorithms (Step 7, Fig. 3). Redundant data instances, like the ones with an incomplete set of feature values, were removed from the dataset.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.3 Preparation of the machine learning dataset in the GIS environment", "section_headings": ["2 Materials and methods", "2.3 Preparation of the machine learning dataset in the GIS environment"], "chunk_type": "text", "line_start": 120, "line_end": 124, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5902b21840dccd83", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.4 Training and testing of machine learning algorithms using the dataset\nType: text\n\nThe dataset was split into 75% for training and the rest for testing the predictions. Next, the algorithms were trained on a specific triennial period (e.g., 1987–1990 or 2017–2020). Two machine learning algorithms were considered for classification, namely ANN and RFC. CNN was not considered in this study as it was not a viable classifier for the study. Unlike the classification of a single image, as has been done in most studies involving U-Net [36, 48, 63], this study required the image classification of varied images from sources like Landsat 5 to 8 and SRTM data, at various times. At different times, the same lake may have a different shape, spectral signature, and snow thickness. Thereby, instead of relying on the image properties as used by CNN for image classification, pixel properties as used by RFC and ANN were incorporated to avoid misclassification of the images.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.4 Training and testing of machine learning algorithms using the dataset", "section_headings": ["2 Materials and methods", "2.4 Training and testing of machine learning algorithms using the dataset"], "chunk_type": "text", "line_start": 126, "line_end": 128, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3293ec3d20928e05", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.5 Machine learning algorithms\nType: text\n\nA neural network is inspired by the working of the brain, where neurons, the nerve cells, create a network of communication for information processing. An ANN includes an input layer, one or several hidden layers and an output layer. The input layer contains M nodes that receive the feature values from the feature matrix X of the training dataset. The nodes of the hidden layers accept a weighted linear combination of the feature values  $\\alpha_m^T X$  along with a bias  $\\alpha_{0m}$  and uses an activation function  $\\sigma$  to generate derived features  $Z_m$  (Eq. 8) [70]:\n\n$$Z_m = \\sigma \\left( \\alpha_{0m} + \\alpha_m^T X \\right) \\tag{8}$$\n\nA weighted linear combination of the derived features generated from each hidden layer is forwarded to the next hidden layer. An activation function is used in the output layer to convert the response from the hidden layers into a classifier output as a Class-0 or Class-1 class pixel. A cost function, usually cross-entropy, is used to compare the agreement between the predicted and actual values of the target variable. The error is minimized by the backpropagation algorithm [70].\n\nIn contrast to the ANN, RF uses decision trees to classify the feature space. The decision tree is an algorithm that divides the feature space iteratively into regions  $R_m$  by selecting M splitting points called *nodes*,  $m=1,\\cdots,M$ . A node splits the feature space along a specific feature dimension, subject to the minimization of the error function. In the process  $N_m$  instances of the training dataset end up belonging into  $R_m$  region of the feature space. These instances are then classified as the kth class from a set of classes\n\nBanerjee Discover Geoscience (2025) 3:159 Page 9 of 33\n\n $k = 1, \\dots, K$ , of the target variable. Hence, the proportion  $\\widehat{p}_{mk}$  of training instances at node m representing kth class is represented as [70] (Eq. 9):\n\n$$\\widehat{p}_{mk} = \\frac{1}{N_m} \\sum_{x_i \\in R_m} I(y_i = k) \\tag{9}$$\n\nThe impurity of the classification is usually assessed by the Gini index or cross-entropy function. RF uses bootstrapping and bagging algorithms of the training dataset to reduce prediction errors [70].", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.5 Machine learning algorithms", "section_headings": ["2 Materials and methods", "2.5 Machine learning algorithms"], "chunk_type": "text", "line_start": 130, "line_end": 146, "token_count_estimate": 676, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca5187ee6faf3118", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.6 Model performance and variable importance\nType: text\n\nThe performance of the algorithms in classifying test instances correctly was estimated by model performance criteria [71, 72]. Accuracy is an overall measure of the model's performance. Sensitivity is the probability of correctly classifying Class-1 instances, while specificity is the probability of correctly classifying Class-0 instances out of the total positively classified instances. Precision is the probability of correctly classifying Class-1 instances from the total number of Class-1 instances. A model with a high F1 score indicates that the model has a balance between precision and sensitivity. Cohen's kappa is a measure of the agreement between the observed and the predicted values by considering whether the prediction is merely a chance event. Beyond the indices derived from the confusion matrix, another popular criterion is the Area Under the Curve (AUC) derived from the Receiver Operating Characteristic (ROC) curve. ROC curve plots the change in the sensitivity over the specificity of a prediction model. AUC nearing a value of one is a good measure of the model's performance. These criteria were used to select the best model to predict the category of the pixels of the entire AOI into GLs and non-GLs.\n\nAn imbalanced dataset was used to train the machine learning algorithms in this study. It has been observed that an imbalanced dataset may cause a certain degree of bias in the prediction process by a machine learning algorithm, giving favour to the majority class. To validate the use of the imbalanced dataset, four imbalance tests were performed on the dataset, namely, under-sampling [73, 74] of the majority class and over-sampling of the minority class by simple over-sampling, synthetic minority over-sampling technique (SMOTE), and random oversampling examples (ROSE) [73, 75–77] (Step 8, Fig. 3).\n\nVariable importance measures how many times a feature or a predictor variable is called by a machine learning algorithm for the prediction. The more a feature is called for prediction, the greater its importance for the algorithm. In the case of the RFC, the mean decrease in impurity by the Gini index is used for measuring the variable importance [70].", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.6 Model performance and variable importance", "section_headings": ["2 Materials and methods", "2.6 Model performance and variable importance"], "chunk_type": "text", "line_start": 148, "line_end": 154, "token_count_estimate": 577, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "70847c012d1c840f", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.7 Preparation of the time series dataset from prediction raster maps\nType: text\n\nThe vectorized prediction maps of all the triennial intervals were edited to remove issues like holes inside the polygons, clusters of polygons representing a single GL, and misclassified polygons. The editing process involved the use of an elimination tool to remove the holes.\n\nUnion of all the triennial vector maps was done to prepare the historical maximum extent of the GLs. The polygons that were overlapping and in very close proximity to each other (30 m) were aggregated to generate the final vector map of the historical\n\nBanerjee Discover Geoscience (2025) 3:159 Page 10 of 33\n\nmaximum extent of the GLs. The selection by location tool was used to link individual polygons of a specific triennial vector map with the vector map of the historical maximum extent of the GLs. In this process, each polygon of individual triennial vector maps was now linked with the unique *Object Identity (OID) of the polygons of the vector map of the historical maximum extent of the GLs, hereby called Unique Lake ID* (ULID) (Step 9, Fig. 3).\n\nAfter this process, all the triennial vector maps were joined by attributes, based on the ULID common to the polygons of the triennial vector maps. The dissolve tool was used to pool the polygons with the same ULID into a single polygon for each triennial vector map. Next, the JRC GSW V 1.3 maximum surface water extent map was vectorized and buffered by a 250 m radius. Polygons belonging to the triennial GL-vector maps that intersected with the JRC GSW V 1.3 maximum surface water extent map were selected by location and exported as vector maps. The 250 m radius was considered to eliminate any possibility of ignoring a GL polygon that has possibly been overlooked in JRC GSW V 1.3. The triennial vector maps were then joined by attributes using ULID into a single table. Each column of this table represents the time series of individual GL area over the triennial intervals from 1987 to 2020 (Step 10, Fig. 3). The uncertainty of the prediction of GL area was estimated by considering an error of one pixel on either side of the glacial lake polygon perimeter (Eqs. 10–11):\n\n$$e = n^{1/2} \\times m \\tag{10}$$\n\n$$R = \\frac{e}{A} \\times 100\\% \\tag{11}$$\n\nWhere, e is the absolute area error in  $m^2$  of the GL, n is the number of pixels on the GL perimeter, approximated to the ratio of the GL perimeter to the spatial resolution of the satellite image, which is 30 m in the case of Landsat, m is the area of each pixel, and A is the GL area.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.7 Preparation of the time series dataset from prediction raster maps", "section_headings": ["2 Materials and methods", "2.7 Preparation of the time series dataset from prediction raster maps"], "chunk_type": "text", "line_start": 156, "line_end": 172, "token_count_estimate": 743, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "324f5e11f1ba75ef", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.7 Preparation of the time series dataset from prediction raster maps > 2.7.1 Time series model-based forecasting of glacial lake areas\nType: text\n\nForecast modelling of the GLs was performed using the SPSS statistical package. The Expert Modeller extension of SPSS was used to automatically select the appropriate forecast model based on the performance criteria. The time series dataset used in this study was free of any seasonality and independent variables, apart from time. Thereby, three types of time series methods were used to forecast the dynamics of the GLs based on their performance over the time series dataset. Amongst them, Brown's Simple Exponential Smoothing [78] states that the forecast F at time t+1 is (Eq. 12) [79]:\n\n$$F_{t+1} = \\alpha A_t + (1 - \\alpha) F_t \\tag{12}$$\n\nGiven  $F_1=A_1$ . Here  $F_t$  and  $A_t$  are the forecasted and actual values of the lake area at time t.  $0 \\le \\alpha \\le 1$  is the smoothing constant. A higher  $\\alpha$  gives more weightage to the recent observations, while a lower  $\\alpha$  makes the forecast more dependent on past observations. However, Brown's model fails to show any trend. The trend of a time series is captured in Holt's Linear Exponential Smoothing Model. According to this model (Eqs. 13–14) [79, 80]:\n\n$$F_t = \\alpha A_{t-1} + (1 - \\alpha) (F_{t-1} + T_{t-1})$$\n(13)\n\nBanerjee Discover Geoscience (2025) 3:159 Page 11 of 33\n\n$$T_{t} = \\beta \\left( F_{t} - F_{t-1} \\right) + \\left( 1 - \\beta \\right) T_{t-1} \\tag{14}$$\n\n$$F_{t+1} = F_t + T_t \\tag{15}$$\n\nGiven  $F_1=A_1$  and  $T_1=0$ .  $0\\leq \\alpha \\leq 1$  and  $0\\leq \\beta \\leq 1$  are the smoothing constants. The forecast F at time t+1 is the sum of the forecast and trend T components at time t. The dependency of the trend component on the recent forecasts decline and shift towards the historical trend with the fall in  $\\beta$ . Other parameters hold the same meaning as stated in Eq. 24. In the case of non-stationary random walk time series, the value of time t+1 is expressed as (Eq. 16):\n\n$$A_{t+1} = A_t + w_t \\tag{16}$$\n\nwhere  $w_t$  is the residual. In general, a random walk model can be expressed as the sum of all the residuals of the time series. A differencing of lag 1 is used to estimate the statistics of the series. The mean of such a series will be zero, while the variance of the series will be time-dependent. Thereby, the forecast of such a series is (Eq. 17):\n\n$$F_{t+1} = F_t + d \\tag{17}$$", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.7 Preparation of the time series dataset from prediction raster maps > 2.7.1 Time series model-based forecasting of glacial lake areas", "section_headings": ["2 Materials and methods", "2.7 Preparation of the time series dataset from prediction raster maps", "2.7.1 Time series model-based forecasting of glacial lake areas"], "chunk_type": "text", "line_start": 174, "line_end": 199, "token_count_estimate": 825, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa57e6523036286f", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 2 Materials and methods > 2.7 Preparation of the time series dataset from prediction raster maps > 2.7.1 Time series model-based forecasting of glacial lake areas\nType: text\n\n$ $ A_ { t + 1 } = A_t + w_t \\ tag { 16 } $ $ where $ w_t $ is the residual . In general , a random walk model can be expressed as the sum of all the residuals of the time series . A differencing of lag 1 is used to estimate the statistics of the series . The mean of such a series will be zero , while the variance of the series will be time - dependent . Thereby , the forecast of such a series is ( Eq . 17 ) : $ $ F_ { t + 1 } = F_t + d \\ tag { 17 } $ $\n\nwhere  $d=\\frac{(A_t-A_1)}{(t-1)}$  is the drift of the series. The non-stationary random walk model can be evaluated using the Autoregressive Integrated Moving Average (ARIMA) of the (0,1,0) type. Several time series model performance criteria were considered for this study, namely, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R-squared, and Stationary R-squared [79, 81]. A time series model with RMSE and MAE close to zero, a low MAPE value and a stationary R-squared nearing one indicates a consistent time series and a good forecast model (Step 11, Fig. 3).", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "2 Materials and methods > 2.7 Preparation of the time series dataset from prediction raster maps > 2.7.1 Time series model-based forecasting of glacial lake areas", "section_headings": ["2 Materials and methods", "2.7 Preparation of the time series dataset from prediction raster maps", "2.7.1 Time series model-based forecasting of glacial lake areas"], "chunk_type": "text", "line_start": 174, "line_end": 199, "token_count_estimate": 405, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a4d585c9271d5be", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.1 Algorithm architecture\nType: text\n\nThe Classification And REgression Training (CARET) package in the R programming language was used to construct the aforesaid machine learning algorithms. The Stuttgart Neural Network Simulator with two hidden layered backpropagation perceptron was used as the ANN [82]. Each hidden layer had 18 nodes. The logistic function was used as the default activation function in the hidden layers. The Identity activation function was used in the input layer to feed the GL and non-GL instances of the dataset into the ANN. The same function was also used as the output activation function in classifying the training instances. The architecture of the ANN was based on the trial-and-error method. The architecture of the RF included 500 decision trees. The optimal performance of the RF was achieved by randomly splitting two feature variables at a time from the feature space. The RF, as well as ANN, used tenfold cross-validation on the training dataset that had nine predictors and two classes. The average Out-of-the-Box error of RF was 0.65%.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.1 Algorithm architecture", "section_headings": ["3 Results", "3.1 Algorithm architecture"], "chunk_type": "text", "line_start": 203, "line_end": 205, "token_count_estimate": 286, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c53a241ee3d6fc93", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.2 Feature variables and their relationship with the target variables\nType: text\n\nFeature variables used as inputs in the training of the machine learning classifiers showed dynamic correlations over time. The average of the water indices of the time\n\nBanerjee Discover Geoscience (2025) 3:159 Page 12 of 33\n\nseries was used for Pearson correlation analysis (Fig. 4). Hillshade was weakly correlated with all the other feature variables. Slope, in contrast, showed moderate negative correlations with Band ratio, MNDWI, NDWI<sub>b</sub>, NDWI<sub>g</sub>, and WRI, primarily attributed to the Class-1 instances responsible for GLs. AWEI<sub>nsh</sub> had a very strong positive correlation with AWEI<sub>sh</sub>, while a strong positive correlation with other water indices. AWEI<sub>sh</sub> had an overall moderate positive correlation with the Band ratio, and a strong correlation with MNDWI, NDWI<sub>b</sub>, NDWI<sub>g</sub>, and WRI. However, it was observed that AWEI<sub>sh</sub> had a moderate correlation with other water indices as far as Class-1 instances were concerned. Band ratio showed a very strong positive correlation with NDWI<sub>g</sub> and WRI, while a strong positive correlation with MNDWI and NDWI<sub>b</sub>, mainly due to the lower correlation value of Class-1 instances. MNDWI had a very strong positive correlation with NDWI<sub>g</sub> and WRI. NDWI<sub>b</sub> was very strongly correlated with NDWI<sub>g</sub>", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.2 Feature variables and their relationship with the target variables", "section_headings": ["3 Results", "3.2 Feature variables and their relationship with the target variables"], "chunk_type": "text", "line_start": 207, "line_end": 213, "token_count_estimate": 437, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c39ee296a462f535", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.2 Feature variables and their relationship with the target variables\nType: figure\nFigure\n\nImage /page/12/Figure/2 description: A scatterplot matrix, also known as a pair plot, displaying the relationships and correlations between several variables for two different groups, color-coded in red and teal. The variables analyzed are: hillshade, slope, Ave\\_AWEInsh, Ave\\_AWEIsh, Ave\\_BandRatio, Ave\\_MNDWI, Ave\\_NDWIg, Ave\\_WRI, and label. The matrix is structured as follows:", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.2 Feature variables and their relationship with the target variables", "section_headings": ["3 Results", "3.2 Feature variables and their relationship with the target variables"], "chunk_type": "figure", "figure_caption": null, "line_start": 214, "line_end": 214, "token_count_estimate": 176, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2055b255c3a7a5f0", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.2 Feature variables and their relationship with the target variables\nType: text\n\n1. \\*\\*Diagonal Panels:\\*\\* These show the density plots for each variable, illustrating the distribution for the red group and the teal group separately.\n2. \\*\\*Lower Triangle Panels:\\*\\* These contain scatter plots for each pair of variables. The points are colored red or teal, and a regression line is fitted for each color group to show the trend.\n3. \\*\\*Upper Triangle Panels:\\*\\* These display the Pearson correlation coefficient ('Corr:') for each pair of variables, calculated separately for the two groups (labeled '0:' for red and '1:' for teal). The significance of the correlation is indicated by asterisks (e.g., \\*, \\*\\*, \\*\\*\\*). For example, the correlation between 'slope' and 'hillshade' is -0.150\\*\\*\\* for group 0 and -0.164\\*\\* for group 1. The correlation between 'Ave\\_AWEIsh' and 'Ave\\_AWEInsh' is 0.903\\*\\*\\* for group 0 and 0.867\\*\\*\\* for group 1.\n4. \\*\\*Last Column ('label'):\\*\\* This column contains box plots for each variable, showing the distribution and quartiles for the two groups side-by-side.\n5. \\*\\*Last Row ('label'):\\*\\* This row contains histograms for each variable, again separated by the two color-coded groups.\n\n**Fig. 4** Image matrix of Pearson correlation analysis of the machine learning model inputs. A correlation analysis was performed on the average values of the model inputs or feature variables. The diagonal images of this image matrix show the distribution of feature variable-wise distribution of Class-0 [Red] and Class-1 [Blue] instances, representing the non-glacial lake values and glacial lake values of the feature variable, respectively. The lower triangle of the image matrix shows the scatterplots of the feature variable values and their best-fitting linear regression curves of Class-0 [Red] and Class-1 [Blue] instances, respectively. The upper triangle of the image matrix shows the overall correlation between a pair of feature variables in black, while the red and blue values are the correlation values of Class-0 and Class-1 instances, respectively. The last row of the image matrix shows a histogram of the Class-0 [Red] and Class-1 [Blue] instances of the feature variables. The last column of the image matrix shows the proportion of Class-0 [Red] and Class-1 [Blue] instances considered for the correlation analysis\n\nBanerjee Discover Geoscience (2025) 3:159 Page 13 of 33\n\nand WRI, primarily due to Class-0 instances representing non-GLs.  $NDWI_g$  was very strongly correlated with WRI.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.2 Feature variables and their relationship with the target variables", "section_headings": ["3 Results", "3.2 Feature variables and their relationship with the target variables"], "chunk_type": "text", "line_start": 215, "line_end": 229, "token_count_estimate": 759, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21cbdf067f6f2425", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.2 Feature variables and their relationship with the target variables\nType: text\n\nthe red and blue values are the correlation values of Class - 0 and Class - 1 instances , respectively . The last row of the image matrix shows a histogram of the Class - 0 [ Red ] and Class - 1 [ Blue ] instances of the feature variables . The last column of the image matrix shows the proportion of Class - 0 [ Red ] and Class - 1 [ Blue ] instances considered for the correlation analysis Banerjee Discover Geoscience ( 2025 ) 3 : 159 Page 13 of 33 and WRI , primarily due to Class - 0 instances representing non - GLs . $ NDWI_g $ was very strongly correlated with WRI .\n\nThe density plots shown as the diagonal plots in Fig. 4 have revealed that topographic indices, namely hillshade and slope, have distinct distributions for GL (Class–1) and non-GL (Class–0) instances. While the Class–1 distribution showed leptokurtosis, the Class–0 distribution had platykurtosis. Similar distributions were observed in the cases of NDWI $_{\\rm b}$ , MNDWI, and AWEI $_{\\rm nsh}$ . In contrast, platykurtosis was observed in the Class–1 distribution and leptokurtosis in the Class–0 distribution in the cases of NDWI $_{\\rm g}$  and Band ratio. Bimodal and partly overlapping distributions of Class–1 and Class–0 were observed in the cases of AWEI $_{\\rm sh}$  and WRI.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.2 Feature variables and their relationship with the target variables", "section_headings": ["3 Results", "3.2 Feature variables and their relationship with the target variables"], "chunk_type": "text", "line_start": 215, "line_end": 229, "token_count_estimate": 416, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "de2b1d501f1f8c8c", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.3 Model performances\nType: text\n\nWhen the performance is compared, both ANN and RFC models are found to be efficient. However, on average, classifications done by RFC were better than those of ANN in terms of the performance criteria over all the triennial periods. A marginal decline in the sensitivity of RFC in comparison to ANN was observed in triennials like 1990–1993 and 2011–2014. However, in comparison to ANN, the performance of RFC marginally declined for the triennial period of 2002–2005 in terms of specificity and precision (Table 2). The high values of performance criteria in the imbalance tests have shown that the imbalanced data in the dataset did not create any bias in the prediction (Supplementary Table S1). The RFC-based image classification successfully traced the boundaries of GLs over all the triennials. This can be observed by comparing the RFC-generated vector map with the True Colour Combination (TCC) and False Colour Combination", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.3 Model performances", "section_headings": ["3 Results", "3.3 Model performances"], "chunk_type": "text", "line_start": 231, "line_end": 235, "token_count_estimate": 277, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6efbe4bff8d1e95", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.3 Model performances\nType: table\nTable: Table 2 Variable importance as percentage of the total usage of the variables used by RFC in the prediction\n\n| Triennial-wise performance of the algorithms | Accuracy | Kappa | Sensitivity | Specificity | Precision | F1    | AUC   |\n|----------------------------------------------------|----------|-------|-------------|-------------|-----------|-------|-------|\n| RFC 1987–1990                                      | 0.994    | 0.983 | 0.995       | 0.989       | 0.996     | 0.996 | 0.992 |\n| ANN 1987–1990                                      | 0.989    | 0.970 | 0.990       | 0.986       | 0.995     | 0.992 | 0.988 |\n| RFC 1990–1993                                      | 0.992    | 0.978 | 0.994       | 0.983       | 0.994     | 0.994 | 0.992 |\n| ANN 1990–1993                                      | 0.989    | 0.971 | 0.995       | 0.970       | 0.990     | 0.993 | 0.983 |\n| RFC 1993–1996                                      | 0.992    | 0.978 | 0.994       | 0.983       | 0.994     | 0.994 | 0.989 |\n| ANN 1993–1996                                      | 0.988    | 0.967 | 0.994       | 0.970       | 0.990     | 0.992 | 0.982 |\n| RFC 1996–1999                                      | 0.992    | 0.978 | 0.995       | 0.981       | 0.994     | 0.994 | 0.988 |\n| ANN 1996–1999                                      | 0.990    | 0.972 | 0.995       | 0.975       | 0.992     | 0.993 | 0.985 |\n| RFC 1999–2002                                      | 0.992    | 0.980 | 0.995       | 0.985       | 0.995     | 0.995 | 0.990 |\n| ANN 1999–2002                                      | 0.987    | 0.965 | 0.992       | 0.972       | 0.991     | 0.991 | 0.982 |\n| RFC 2002–2005                                      | 0.993    | 0.980 | 0.997       | 0.979       | 0.993     | 0.995 | 0.988 |\n| ANN 2002–2005                                      | 0.987    | 0.965 | 0.989       | 0.981       | 0.994     | 0.991 | 0.985 |\n| RFC 2005–2008                                      | 0.993    | 0.980 | 0.996       | 0.984       | 0.995     | 0.995 | 0.990 |\n| ANN 2005–2008                                      | 0.988    | 0.967 | 0.993       | 0.973       | 0.991     | 0.992 | 0.983 |\n| RFC 2008–2011                                      | 0.993    | 0.981 | 0.997       | 0.980       | 0.994     | 0.995 | 0.989 |\n| ANN 2008–2011                                      | 0.987    | 0.964 | 0.995       | 0.962       | 0.987     | 0.991 | 0.978 |\n| RFC 2011–2014                                      | 0.993    | 0.982 | 0.996       | 0.986       | 0.996     | 0.996 | 0.991 |\n| ANN 2011–2014                                      | 0.988    | 0.968 | 0.996       | 0.964       | 0.988     | 0.992 | 0.980 |\n| RFC 2014–2017                                      | 0.993    | 0.981 | 0.995       | 0.986       | 0.995     | 0.995 | 0.991 |\n| ANN 2014–2017                                      | 0.987    | 0.966 | 0.994       | 0.969       | 0.990     | 0.992 | 0.981 |\n| RFC average                                        | 0.992    | 0.980 | 0.995       | 0.984       | 0.995     | 0.995 | 0.990 |\n| ANN average                                        | 0.988    | 0.968 | 0.993       | 0.972       | 0.991     | 0.992 | 0.983 |", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.3 Model performances", "section_headings": ["3 Results", "3.3 Model performances"], "chunk_type": "table", "table_caption": "Table 2 Variable importance as percentage of the total usage of the variables used by RFC in the prediction", "columns": ["Triennial-wise performance of the algorithms", "Accuracy", "Kappa", "Sensitivity", "Specificity", "Precision", "F1", "AUC"], "table_row_start": 1, "table_row_end": 22, "line_start": 236, "line_end": 259, "token_count_estimate": 1106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eaebabb59947db83", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.3 Model performances\nType: text\n\nBanerjee Discover Geoscience (2025) 3:159 Page 14 of 33\n\n(FCC) images as ground truth of selected GLs over the triennial periods (Fig. 5a, b). Furthermore, the selected GLs, predicted using the water indices generated from Landsat 8 time series data and topographic indices, were overlaid on Sentinel 2 A image for ground truthing (Fig. 6). Overall, RFC performed better than ANN in classifying AOI into GL and non-GL pixels. Thereby, the GL maps generated by RFC were considered as defaults for further analysis.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.3 Model performances", "section_headings": ["3 Results", "3.3 Model performances"], "chunk_type": "text", "line_start": 260, "line_end": 264, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4211eca7f4f5a7e2", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.4 Variable importance\nType: text\n\nBased on the performance of RFC, Slope remained the most important determinant in identifying the GLs. It scored highest on the importance scale in all the triennials. AWEI $_{\\rm sh}$  was the next most important contributor in identifying the GLs. Its contribution showed an increasing trend in the recent triennial periods. For instance, AWEI $_{\\rm sh}$  significantly contributed to the identification of GLs in the triennials like 2002–2005, 2005–2008, 2008–2011, 2011–2014, and 2017–2020. In contrast, its importance was rather low in 1987–1990. AWEI $_{\\rm nsh}$  showed the importance of the triennials like 2008–2011 and 2011–2014. NDWI $_{\\rm b}$  was more important than NDWI $_{\\rm g}$  in several triennials like 1987–1990, 1990–1993, 1993–1996, 1999–2002, 2005–2008, and 2008–2011 (Table 3).", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.4 Variable importance", "section_headings": ["3 Results", "3.4 Variable importance"], "chunk_type": "text", "line_start": 266, "line_end": 268, "token_count_estimate": 284, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24ca435cb276ed58", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.5 Maps of the glacial lakes\nType: text\n\nA total of 492 polygons were traced by the RFC and vectorized in the GIS framework. However, only 406 such polygons were considered GLs, as the remainder were considered traces of streams, a reflection of snow, an area less than 0.01 km<sup>2</sup> or other forms of misclassifications. The spatial distribution of these GLs showed that most of the larger GLs, like GL- 46, 83, 81, 174, and 9, were located at the snout of glaciers and were essentially periglacial lakes. In contrast, some GLs were not fed by glaciers. These GLs are spatially isolated from their feeder glacier. These GLs are the erosion lakes, like GL 23, 270, and 266, to name a few (Fig. 7a – d). GLs of area no less than 0.1 km<sup>2</sup> were considered for a size-altitude-wise distribution analysis. It was shown that most of the GLs were confined to altitudes above 4000 m to 6000 m. The larger lakes were arbitrarily considered as GLs with an area greater than 0.4 km<sup>2</sup>. Lakes like 3, 5, 27, 34, 39, 49, 51, 81, 110, 121, and 174, were found to be large lakes (Fig. 8).\n\nThe relative error varied between 1.1% and 43.3%, with bulk error contributed by small GLs (0.01 to 0.02 km<sup>2</sup>) (Fig. 9). Lesser variation in relative error was observed in the triennial period of 1990–1993, and from 2005 to 2020. A look at the relation of error value and GL area showed that a lower GL area had an inflating effect on the relative error, indicating an exponential relationship between error and area.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.5 Maps of the glacial lakes", "section_headings": ["3 Results", "3.5 Maps of the glacial lakes"], "chunk_type": "text", "line_start": 270, "line_end": 274, "token_count_estimate": 475, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9774a3de1816c1bf", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: text\n\nA total of 144 GLs had 11 observation periods, 39 GLs had 10 observation periods, 14 GLs had 9 observation periods, 16 GLs had 8 observation periods, 17 GLs had 7 observation periods, 20 GLs had 6 observation periods, 23 GLs had 5 observation periods, 18 GLs had 4 observation periods, 31 had 3 observation periods, 24 GLs had 2 observation periods and 60 GLs had only one observation period. The GLs showed varied patterns of change in their areas over the period considered. A detailed account of their behaviour over time can be observed in Supplementary Figure S1 a-h. Linear regression model and Lasso regression model were fit into the time series data with 11 and 10\n\nBanerjee Discover Geoscience (2025) 3:159 Page 15 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "text", "line_start": 276, "line_end": 280, "token_count_estimate": 230, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b99ec5cb58cc9808", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: figure\nFigure\n\nImage /page/15/Figure/1 description: A figure, labeled \"Fig. 5 a\", showing the temporal evolution of a glacial lake from 1990 to 2020. The figure is organized as a grid with three columns and eleven rows. The columns are titled \"TCC Image\", \"FCC Image\", and \"Vector map\". Each row corresponds to a specific year: 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2011, 2014, 2017, and 2020. The \"TCC Image\" column displays true-color composite satellite images of the lake and surrounding glacier. The \"FCC Image\" column shows false-color composite images where the lake appears in shades of blue. The \"Vector map\" column presents a map with the lake's area outlined and filled with diagonal hatching. Across the years, the images in all three columns clearly show a significant increase in the size of the lake. The caption at the bottom reads: \"Fig. 5 a Ground truth of South Lhonak Lake, GL – 83 (Coordinate: 27.9125, 88.1952) by comparing the Random...\"", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 281, "line_end": 281, "token_count_estimate": 316, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "833818249b441b35", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: text\n\n**Fig. 5** a Ground truth of South Lhonak Lake, GL – 83 (Coordinate: 27.9125, 88.1952) by comparing the Random Forest Classifier generated vector map with True Colour Combination (TCC), False Colour Combination (FCC) images. The vector map is superimposed over the high-resolution Google satellite image of the current state of the lake. TCC and FCC of the year 2014 have anomalies due to partial malfunction of the Landsat 7 Sensor in 2003, which led to the loss of a large number of satellite images (https://earthobservatory.nasa.gov/features/GlobalLandSurvey/page2.php). **b**: Ground truth of North Lhonak Lake, GL – 81 (Coordinate: 27.9193, 88.1591) by comparing the Random Forest Classifier generated vector map with True Colour Combination (TCC), False Colour Combination (FCC) images\n\nBanerjee Discover Geoscience (2025) 3:159 Page 16 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "text", "line_start": 282, "line_end": 286, "token_count_estimate": 289, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc273b2522b59db5", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: figure\nFigure\n\nImage /page/16/Picture/1 description: A figure displaying a time-series of satellite imagery of a glacier from 1990 to 2020. The figure is organized into a grid with three columns and eleven rows. Each row corresponds to a specific year: 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2011, 2014, 2017, and 2020. The three columns are labeled 'TCC' (True Color Composite), 'FCC' (False Color Composite), and 'Vector map'. The TCC column shows natural-color images of the glacier and a proglacial lake. The FCC column displays false-color images where the glacier and water are highlighted in shades of blue and cyan. The 'Vector map' column shows a grayscale image with a hatched black outline delineating the glacier's extent. Over the years, the images consistently show the glacier retreating and the lake at its terminus expanding. The images for 2014 have diagonal black stripes indicating missing data, and the images for 2020 are in grayscale. The bottom of the image has the text 'Fig. 5 (continued)'.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 287, "line_end": 287, "token_count_estimate": 327, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d524db63e76b4452", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: text\n\nFig. 5 (continued)\n\nobservation periods. Certain GLs like GL.9, GL.27, GL.49, etc. showed a robust increasing trend, while GLs like GL.19, 150, and 359 showed a decreasing trend. GLs with 11 and 10 observation periods were considered for time series analysis. The missing area value for GLs with 10 observation periods was calculated as the average of the predecessor and successor area values of the missing observation period. In most cases, GLs showed very irregular dynamics. Therefore, forecasting their future area dynamics was not viable using time series analysis (Supplementary Figure S2).\n\nA total of 48 GLs were considered in this study based on their performances according to the time series forecast criteria (Table 4). Also, GLs with no clear trend were not considered for further interpretations. Out of these GLs, time series of 26 were forecasted\n\nBanerjee Discover Geoscience (2025) 3:159 Page 17 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "text", "line_start": 288, "line_end": 296, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50d95363b0a5ca69", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: figure\nFigure\n\nImage /page/17/Figure/1 description: A figure, labeled Fig. 6, comparing satellite images of six glacial lakes from two different sources: Landsat 8 and Sentinel 2A MSI. The figure is a grid with two columns and six rows. The left column shows images from Landsat 8, and the right column shows higher-resolution images of the same lakes from Sentinel 2A MSI. Each row represents a different lake, labeled on the left as GL - 81, GL - 79, GL - 39, GL - 27, GL - 34, and GL - 3. The lakes, which are outlined in red, are shown in various shapes and sizes amidst mountainous and snowy terrain. The caption below the figure reads: 'Fig. 6. Ground truthing of glacial lakes of the Trippeal period 12/2014–12/2017 prepared from the median image.'", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 297, "line_end": 297, "token_count_estimate": 260, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f5fd804f0bb08054", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: text\n\n**Fig. 6** Ground truthing of glacial lakes of the Trinneal period 12/2014–12/2017 prepared from the median image of Landsat 8 OLI/TIRS Collection 2 atmospherically corrected surface reflectance of 30 m resolution by the median image of Sentinel-2 MSI Orthorectified Surface reflectance Level-2 A of 10 m resolution of the duration 01/2020–12/2021\n\nBanerjee Discover Geoscience (2025) 3:159 Page 18 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "text", "line_start": 298, "line_end": 304, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "95cee321def81879", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: table\nTable: Table 3 Performance of timeseries models\n\n| Triennial periods | Environmental features |         |        |           |       |       |       |      |       |  |\n|-------------------|------------------------|---------|--------|-----------|-------|-------|-------|------|-------|--|\n|                   | hillshade              | AWEInsh | AWEIsh | BandRatio | MNDWI | NDWIb | NDWIg | WRI  | slope |  |\n| 1987–1990         | 6.61                   | 9.20    | 9.21   | 8.56      | 6.50  | 8.21  | 8.60  | 6.80 | 36.33 |  |\n| 1990–1993         | 7.30                   | 6.92    | 10.66  | 7.76      | 6.82  | 8.07  | 7.75  | 8.08 | 36.64 |  |\n| 1993–1996         | 6.74                   | 5.62    | 10.31  | 5.96      | 5.71  | 7.48  | 5.91  | 6.95 | 45.32 |  |\n| 1996–1999         | 6.53                   | 6.75    | 11.12  | 6.56      | 8.38  | 6.75  | 6.45  | 6.57 | 40.90 |  |\n| 1999–2002         | 6.64                   | 7.22    | 10.42  | 6.83      | 6.74  | 9.92  | 6.19  | 8.05 | 38.00 |  |\n| 2002–2005         | 7.84                   | 8.42    | 14.17  | 8.14      | 6.78  | 8.25  | 8.60  | 8.16 | 29.65 |  |\n| 2005–2008         | 5.18                   | 6.54    | 16.85  | 6.07      | 5.95  | 6.06  | 5.57  | 6.68 | 41.10 |  |\n| 2008–2011         | 7.23                   | 9.08    | 14.33  | 10.17     | 6.92  | 9.59  | 9.44  | 8.15 | 25.10 |  |\n| 2011–2014         | 5.89                   | 10.03   | 15.63  | 7.17      | 5.73  | 10.80 | 8.19  | 5.02 | 31.56 |  |\n| 2011–2017         | 6.46                   | 6.86    | 13.08  | 5.99      | 9.05  | 6.11  | 7.47  | 9.05 | 35.93 |  |\n| 2017–2020         | 5.98                   | 7.06    | 14.74  | 7.21      | 9.53  | 6.50  | 7.28  | 8.44 | 33.26 |  |\n| Average           | 6.58                   | 7.61    | 12.77  | 7.31      | 7.10  | 7.98  | 7.40  | 7.45 | 35.80 |  |", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "table", "table_caption": "Table 3 Performance of timeseries models", "columns": ["Triennial periods", "Environmental features", "", "", "", "", "", "", "", "", ""], "table_row_start": 1, "table_row_end": 14, "line_start": 305, "line_end": 319, "token_count_estimate": 724, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "346dd3104bd5af36", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: figure\nFigure\n\nImage /page/18/Figure/3 description: A map titled \"Historical maximum extent of the glacial lakes in the North-West Quadrant.\" The main map is a topographical representation of a mountainous region, showing features marked with latitude and longitude coordinates. On the right side, there are two smaller inset maps. The top one is a circular map showing the location of the area within the Himalayas, near India and Myanmar. An arrow points from this map to a second, lower inset map that shows the full watershed with its river systems. Another arrow points from the watershed map to the main, detailed map. The main map includes a legend in the bottom right corner that defines the symbols used: a blue line for Rivers, a red outline for Glacial lakes, a blue-hatched area for Glacier extent, and a black outline for the AOI\\_fishnet\\_grid. The map is populated with numerous red-outlined glacial lakes, each labeled with a number.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 321, "line_end": 321, "token_count_estimate": 284, "basins": [], "subbasins": [], "countries": ["India", "Myanmar"], "lake_ids": []}}
{"id": "d3b9faf1d544cfec", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: text\n\nFig. 7 a Historical maximum extent of the GLs of Sikkim Himalaya in the North-West quadrant of the area of interest. The lake polygons have been constructed by the union of all the lake polygons with the same Unique Lake ID (ULID) over various observation periods. The number adjacent to the polygons indicates the ULID of the lake. **b** Historical maximum extent of the GLs of Sikkim Himalaya in the North-East quadrant of the area of interest. The lake polygons have been constructed by the union of all the lake polygons with the same Unique Lake ID (ULID) over various observation periods. The number adjacent to the polygons indicates the ULID of the lake. **c** Historical maximum extent of the GLs of Sikkim Himalaya in the South-East quadrant of the area of interest. The lake polygons have been constructed by the union of all the lake polygons with the same Unique Lake ID (ULID) over various observation periods. The number adjacent to the polygons indicates the ULID of the lake. **d** Historical maximum extent of the GLs of Sikkim Himalaya in the South-West quadrant of the area of interest. The lake polygons have been constructed by the union of all the lake polygons with the same Unique Lake ID (ULID) over various observation periods. The number adjacent to the polygons indicates the ULID of the lake\n\nby the Holt model, 10 by the Brown model, 7 by the Random Walk model and 4 by the Simple exponential model. Out of the 48 forecasts, 32 GLs were increasing, 12 were decreasing, and 4 showed irregular behaviour. On an average, larger GLs were increasing in size (Fig. 10a - d), while smaller GLs showed decreasing or irregular trends (Fig. 10e - g).\n\nBanerjee Discover Geoscience (2025) 3:159 Page 19 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "text", "line_start": 322, "line_end": 328, "token_count_estimate": 497, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac1551cb635d1f7e", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: figure\nFigure\n\nImage /page/19/Figure/1 description: A geographical map titled \"Historical maximum Extent of the glacial lakes in the North East Quadrant.\" The main map displays a mountainous region with topographical details, marked with latitude and longitude lines. It features blue lines representing rivers, numerous red-outlined shapes indicating glacial lakes (each with a number), and areas with diagonal hatching that represent glacier extent. A north arrow is in the top left, and a scale bar in kilometers is at the bottom. On the right, two inset maps provide context. The top circular inset shows the location of the area within India, near the Himalayas. Below it, a map of a larger river basin is shown, with an arrow pointing from it to the main detailed map. A legend in the bottom right corner clarifies the symbols: a blue line for Rivers, a red outline for Glacial lakes, a hatched pattern for Glacier extent, and a black outline for AOI\\_fishnet\\_grid.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 329, "line_end": 329, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8c4808a073ae3e77", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 3 Results > 3.6 Time series and forecast of glacial lakes\nType: text\n\nHistorical maximum extent of the glacial lakes in the North-East Quadrant\n\nFig. 7 (continued)", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "3 Results > 3.6 Time series and forecast of glacial lakes", "section_headings": ["3 Results", "3.6 Time series and forecast of glacial lakes"], "chunk_type": "text", "line_start": 330, "line_end": 334, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "519ad422c66384f7", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion\nType: text\n\nThe main purpose of this study was to present a methodology to map and track the spatial dynamics of GLs over a large AOI primarily through time series analysis. To achieve this goal, a time series dataset of Landsat imagery was used in the GEE platform to prepare triennial median water spectral indices from December 1987 to December 2020 along with topological indices like slope and hillshade. The dataset used for training the classifier algorithms yielded high-accuracy imageries of GLs. With the limited dataset of Landsat 5 imageries with very few bands, especially of the 1980s and 1990s, the accuracy achieved by the RFC was highly satisfactory. Unlike other contemporary studies [14, 17, 20, 43], many feature variables, namely, AWEI<sub>nsh</sub>, AWEI<sub>sh</sub>, Band Ratio, MNDWI, NDWI<sub>b</sub>, NDWI<sub>g</sub>, WRI, Slope and Hillshade, were considered in this study to encompass all the possible feature extraction for the mapping of the GLs. Median values of the water indices were considered in this study to negate the issues of limited time series images as well as annual and seasonal variations of the image qualities [33, 83].\n\nAmongst the feature variables used as inputs in this study, the slope has been the most important feature in the mapping of the GLs, followed by  $AWEI_{sh}$ ,  $AWEI_{nsh}$ ,  $NDWI_b$  and WRI. The importance of slope in mapping the GLs is mainly due to its low value and homogeneous estimates in the case of GLs as compared to the heterogeneous and higher values estimated from the surrounding complex mountain terrain [34, 84, 85].  $AWEI_{sh}$  outperformed  $AWEI_{nsh}$  in predicting the GLs as  $AWEI_{sh}$  can discriminate shadowed regions, such as those cast by mountains or clouds, from water bodies in high-relief areas. Shadows can mimic water's spectral characteristics, leading to misclassification.  $AWEI_{sh}$  incorporates additional spectral adjustments to suppress shadowed areas that might otherwise be mistaken for water, improving the detection accuracy in mountainous or high-shadow environments [58, 59]. Other water indices contributed less than 8% each in the prediction of GLs.\n\nThe correlation analysis of feature variables showed that topographic variables, especially hillshade had a very weak correlation with other feature variables. However, the\n\nBanerjee Discover Geoscience (2025) 3:159 Page 20 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 336, "line_end": 344, "token_count_estimate": 679, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff0ae70c116e2411", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion\nType: figure\nFigure\n\nImage /page/20/Figure/1 description: A map titled \"HISTORICAL MAXIMUM EXTENT OF THE GLACIAL LAKES IN THE SOUTH-EAST QUADRANT\". The map shows a mountainous, topographical region with rivers marked in blue and glacial lakes outlined in red and labeled with numbers. A legend in the bottom left indicates the symbols for Rivers, Glacial lakes, Glacier extent (a hatched pattern), and AOI\\_fishnet\\_grid. The map includes a scale in kilometers and a north arrow. On the right side, there are two inset maps. The top one is a circular map showing the location of the area within India and near the Himalayas. The bottom inset shows the larger river basin, with an arrow pointing to the section detailed in the main map.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 345, "line_end": 345, "token_count_estimate": 235, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "60339d4e60332a9c", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-East Quadrant\nType: text\n\nFig. 7 (continued)\n\nslope showed a weak negative correlation with water indices, indicating that at a lesser slope, which was true for GLs, the water indices have a higher value. MNDWI and NDWI<sub>b</sub> were strongly correlated, primarily due to their similar density distribution and the use of similar spectral information. The very strong positive correlation between the Band ratio and NDWI<sub>g</sub> was attributed to the use of the same bands in their calculations. The strong bimodal distributions of GL (Class – 1) and non-GL (Class – 0) instances in the case of slope, AWEI<sub>sh</sub> and NDWI<sub>b</sub> have been instrumental in discriminating GL pixels from non-GL pixels in this study.\n\nCertain specific considerations were made while preparing the methodology of this study. For instance, unlike comparatively popular machine learning methods like CNN, which rely on context and patch-based semantic segmentation of the image, pixel-based machine learning, like RFC, was used for two-fold reasons. First, in the case of CNN, for each triennial period, a dataset of unique patch and mask images had to be prepared for prediction. In contrast, RFC, being a pixel-based classifier, had to be provided with pixels that belonged to GLs to prepare the dataset for prediction. Secondly, the computational cost and subjective bias in identifying the GL patches for CNN would have been a trade-off with the predictive capacity of the model while considering 11 observation periods spanning three decades, and three satellite images, namely Landsat 5, 7, and 8. The next consideration of this study was the use of the Unique Lake ID, which is based on a geospatial method called 'selection by location'. The Unique Lake ID helped in tracking the GLs over the 11 observation periods. For this, an innovative method is applied by preparing the maximum extent of GLs. The maximum extent of individual GLs ensured that the GLs of various observation periods were always a proper subset of the maximum extent of individual GLs, tagging each GL with a Unique Lake ID. This method helped in tracking the dynamics of all the individual GL polygons. Finally, the JRC GSW V 1.3 image was used to externally validate the identification of the GLs. The final consideration of this study was to generate the time series dataset to observe the dynamics of individual GL and forecast GL dynamics till the year 2026 using appropriate statistical models. For instance, the rapid growth of the South Lhonak Lake of North\n\nBanerjee Discover Geoscience (2025) 3:159 Page 21 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-East Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-East Quadrant"], "chunk_type": "text", "line_start": 348, "line_end": 356, "token_count_estimate": 676, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35bf1cf6a06bfbb9", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/21/Figure/2 description: A map titled \"Historical maximum Extent of the glacial lakes in the South-West Quadrant\". The main part of the image is a detailed topographical map of a mountainous region, showing glacier extents with blue hatching, glacial lakes outlined in red, and rivers as blue lines. The map includes latitude and longitude grid lines, with latitude ranging from approximately 27°28'N to 27°44'N and longitude from 88°6'E to 88°26'E. Several peaks are labeled with their elevations, such as \"6806 m SINIOLCHU\", \"5135 m\", and \"4718 m\". A scale bar at the bottom indicates distances in kilometers. In the top left corner, there are two inset maps. The first is a circular map showing the location of the region within India and the Himalayas. An arrow points from this to a second, smaller map of a larger watershed area, with the south-west quadrant highlighted, corresponding to the main map. A legend in the bottom left clarifies the symbols for Rivers, Glacial lakes, and Glacier extent. A north arrow is present in the lower right.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 359, "line_end": 359, "token_count_estimate": 341, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "611cdd645ae3d5ad", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\nFig. 7 (continued)", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 360, "line_end": 362, "token_count_estimate": 65, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51b3196ce5cb359b", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/21/Figure/4 description: A scatter plot titled \"Lake area in terms of elevation\". The x-axis represents \"Elevation (m)\" and ranges from 3000 to 6500. The y-axis represents \"Lake area (Sq.km)\" and ranges from 0.0 to 1.8. The plot displays numerous magenta circular data points. Most of the points are clustered at the bottom of the graph, below a lake area of 0.4 Sq.km, primarily between elevations of 4000m and 5500m. A horizontal dashed line is drawn at a lake area of 0.4. Several outlier points with larger lake areas are labeled with numbers. For example, point 46 is at an elevation of approximately 5200m with a lake area of about 1.8 Sq.km. Point 9 is near 5150m with an area of about 1.4 Sq.km. Point 83 is near 6200m with an area of about 1.3 Sq.km.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 363, "line_end": 363, "token_count_estimate": 283, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "812d085471ecc24f", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\n**Fig. 8** Distribution of the glacial lake area against the elevation of the glacial lake. Selected lakes of area more than  $0.4 \\, \\mathrm{km^2}$  have been identified by their ULID to identify the larger lakes\n\nBanerjee Discover Geoscience (2025) 3:159 Page 22 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 364, "line_end": 368, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b9eabfab83d49de", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/22/Figure/1 description: A figure titled \"Error estimation of area of glacial Lakes (in %)\" displays eleven scatter plots arranged in a grid. Each plot corresponds to a specific triennial period, starting from 1987-1990 and ending with 2017-2020. The periods shown are 1987-1990, 1990-1993, 1993-1996, 1996-1999, 1999-2002, 2002-2005, 2005-2008, 2008-2011, 2011-2014, 2014-2017, and 2017-2020. In each plot, the x-axis represents \"Lake area (in Sq.Km)\" from 0 to 1.5, and the y-axis represents \"Error (in %)\" from 0 to 40. The plots contain grey data points, a red trend line, and a shaded grey confidence interval. All plots consistently show that the error percentage is high for small lake areas and decreases rapidly as the lake area increases, eventually leveling off at a low error rate for larger lakes.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 369, "line_end": 369, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f8692573dfe869f", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\n**Fig. 9** Relative error of area estimation of glacial lakes of various triennial periods. Points in the plots represent the relative error against the lake area, the red curve is the best fitting smooth curve considering error as a function of lake area, and the grey boundary represents the 95% confidence interval of the red curve\n\nSikkim, as observed in this study (Figs. 5a and 10b-GL~83), led to GLOF on 4 October 2023, causing 46 casualties, more than 77 people reported missing and impacting 88,400 people (https://iee.psu.edu/news/blog/glacier-lake-outburst-floods-loss-life-and-infrast ructure ).\n\nSimultaneous time series analysis of individual GLs of a large AOI is a new type of method. The primary hindrance to this method has been the lack of infrastructure and computational speed required for processing big geodata, such as the Landsat image time series. Manual mapping is not viable for this type of study as it can lead to subjective bias due to poor interpretability of the images, especially in the face of a large database [17, 20, 24, 28, 29, 46]. Since 2010, the power of high-speed cloud-based geocomputation of big geodata has been possible using GEE to perform this type of study [86, 87]. Moreover, the applications of machine learning are rapidly increasing in geosciences due to the affordability of high-performance PCs [88–90]. Like this study, machine learning and its subset deep learning have been intensively used in the last decade to map GLs in varied locations [21, 30, 34]. However, these studies have heavily relied on the high spatial and spectral resolutions of recently launched satellites like Landsat 8 and Sentinel 1 and 2 to map the lakes. In contrast, this study used past images of Landsat 5 and 7. To compensate for the uncertainty related to the dataset generated from past images, multiple datasets of the individual triennial period were prepared specifically for the spectral properties of the satellites that have captured the images in the past. The binding factor of all these datasets was the common minimal GL polygon area that has remained unchanged over the two epochs of observations made by JRC GSW V 1.3. This type of approach is new, as other similar studies rely only on a single training dataset for a single image classification. Unlike other studies [34, 48] that have explored CNN, this study heavily relied on RFC. This was mainly because the instances of GL pixels were\n\nBanerjee Discover Geoscience (2025) 3:159 Page 23 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 370, "line_end": 380, "token_count_estimate": 688, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34717d0d48fc2e52", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: table\nTable: Table 4 Performance of timeseries models\n\n| ULID | Trend                                                                        | Performance | statistics              | Model type | Remark  |       |               |                               |\n|------|------------------------------------------------------------------------------|-------------|-------------------------|------------|---------|-------|---------------|-------------------------------|\n|      |                                                                              | Stationary  | R-squared               | RMSE       | MAPE    | MAE   |               |                               |\n|      |                                                                              | R-squared   |                         |            |         |       | _             |                               |\n|      |                                                                              | Ideal value |                         |            |         |       |               |                               |\n|      |                                                                              | 1           | 1                       | 0          | 0       | 0     |               |                               |\n| 9    | Increasing                                                                   | 0.521       | 0.578                   | 0.087      | 6.606   | 0.065 | Brown         |                               |\n| 23   | Irregular                                                                    | -0.018      | 0.217                   | 0.01       | 3.26    | 0.007 | Simple        |                               |\n| 27   | Increasing                                                                   | 0.822       | 0.713                   | 0.011      | 1.896   | 0.007 | Holt          |                               |\n| 33   | Increasing                                                                   | 0.825       | 0.32                    | 0.002      | 19.578  | 0.001 | Holt          |                               |\n| 42   | Increasing                                                                   | 0.843       | 0.52                    | 0.002      | 6.993   | 0.002 | Holt          |                               |\n| 43   | Increasing                                                                   | 0.869       | 0.697                   | 0.004      | 22.29   | 0.003 | Holt          |                               |\n| 45   | Increasing                                                                   | 0.894       | 0.369                   | 0.014      | 31.105  | 0.01  | Holt          |                               |\n| 46   | Increasing                                                                   | 0.744       | 0.361                   | 0.04       | 1.836   | 0.029 | Holt          |                               |\n| 49   | Increasing                                                                   | 0           | 0.728                   | 0.029      | 2.154   | 0.022 | ARIMA (0,1,0) |                               |\n| 70   | Irregular                                                                    | -0.004      | 0.791                   | 0.039      | 17.282  | 0.021 | Brown         |                               |\n| 73   | Increasing                                                                   | 0.866       | 0.335                   | 0.024      | 42.608  | 0.017 | Holt          |                               |\n| 78   | Decreasing                                                                   | 0.845       | 0.436                   | 0.003      | 6.521   | 0.002 | Holt          |                               |\n| 79   | Increasing                                                                   | 0           | 0.364                   | 0.022      | 6.71    | 0.019 | ARIMA (0,1,0) |                               |\n| 81   | Increasing                                                                   | 0.824       | 0.828                   | 0.048      | 5.853   | 0.034 | Holt          |                               |\n| 83   | Increasing                                                                   | 0.74        | 0.974                   | 0.051      | 5.857   | 0.041 | Brown         | After deletion of a timestamp |\n| 86   | Increasing                                                                   | 0.659       | 0.467                   | 0.002      | 13.406  | 0.001 | Holt          |                               |\n| 94   | Decreasing                                                                   | 0.881       | 0.395                   | 0.028      | 123.879 | 0.018 | Holt          |                               |\n| 101  | Increasing                                                                   | 0.939       | 0.497                   | 0.004      | 17.956  | 0.003 | Holt          |                               |\n| 102  | Decreasing                                                                   | 0.812       | 0.588                   | 0.004      | 6.276   | 0.003 | Holt          |                               |\n| 122  | Increasing                                                                   | -0.068      | 0.355                   | 0.024      | 151.999 | 0.016 | Brown         |                               |\n| 123  | Increasing                                                                   | 0.851       | 0.308                   | 0.002      | 14.701  | 0.002 | Holt          |                               |\n| 124  | Decreasing                                                                   | 0.827       | 0.412                   | 0.033      | 38.648  | 0.024 | Holt          |                               |\n| 125  | Increasing                                                                   | 0.886       | 0.48                    | 0.007      | 11.02   | 0.005 | Holt          |                               |\n| 139  | Increasing                                                                   | 0           | 0.739                   | 0.015      | 24.701  | 0.01  | ARIMA (0,1,0) |                               |\n| 142  | Irregular                                                                    | 0.029       | 0.13                    | 0.003      | 2.985   | 0.002 | Simple        |                               |\n| 150  | Decreasing                                                                   | -0.125      | 0.465                   | 0.009      | 153.184 | 0.006 | Brown         |                               |\n| 156  | Increasing                                                                   | 0.72        | 0.916                   | 0.022      | 14.659  | 0.018 | Holt          |                               |\n| 165  | Increasing                                                                   | 0.56        | 0.451                   | 0.024      | 14.118  | 0.019 | Brown         |                               |\n| 167  | Decreasing                                                                   | 0.468       | 0.545                   | 0.013      | 62.562  | 0.01  | Brown         | After creating a timestamp    |\n| 168  | Decreasing                                                                   | 0.899       | 0.336                   | 0.009      | 140.145 | 0.007 | Holt          | After creating a timestamp    |\n| 174  | Increasing                                                                   | 0           | 0.413                   | 0.195      | 138.937 | 0.12  | ARIMA (0,1,0) |                               |", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "table", "table_caption": "Table 4 Performance of timeseries models", "columns": ["ULID", "Trend", "Performance", "statistics", "Model type", "Remark", "", "", ""], "table_row_start": 1, "table_row_end": 35, "line_start": 381, "line_end": 437, "token_count_estimate": 1431, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2a24ff9edb2a6e5f", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: table\nTable: Table 4 Performance of timeseries models\n\n| ULID | Trend                                                                        | Performance | statistics              | Model type | Remark  |       |               |                               |\n|------|------------------------------------------------------------------------------|-------------|-------------------------|------------|---------|-------|---------------|-------------------------------|\n| 175  | Decreasing                                                                   | 0.804       | 0.735                   | 0.015      | 62.607  | 0.012 | Holt          |                               |\n| 183  | Decreasing                                                                   | 0.89        | 0.364                   | 0.008      | 5.223   | 0.006 | Holt          |                               |\n| 209  | Decreasing                                                                   | 0.798       | 0.487                   | 0.017      | 72.223  | 0.014 | Holt          | After creating a timestamp    |\n| 212  | Decreasing                                                                   | -0.074      | 0.46                    | 0.018      | 321.838 | 0.013 | Simple        | After creating a timestamp    |\n| 222  | Increasing                                                                   | 0           | 0.667                   | 0.023      | 7.142   | 0.017 | ARIMA (0,1,0) | •                             |\n| 227  | Increasing                                                                   | 0           | 0.352                   | 0.008      | 13.318  | 0.006 | ARIMA (0,1,0) |                               |\n| 230  | Increasing                                                                   | 0.291       | 0.924                   | 0.029      | 44.406  | 0.024 | Brown         |                               |\n| 235  | Increasing                                                                   | -0.05       | 0.665                   | 0.046      | 87.554  | 0.033 | Brown         |                               |\n| 282  | Increasing                                                                   | 0.885       | 0.615                   | 0.004      | 3.806   | 0.003 | Holt          |                               |\n| 314  | Increasing                                                                   | 0.793       | 0.286                   | 0.006      | 23.567  | 0.004 | Holt          |                               |\n| 339  | Increasing                                                                   | 0.901       | 0.32                    | 0.004      | 15.679  | 0.003 | Holt          |                               |\n| 340  | Irregular                                                                    | -0.013      | 0.337                   | 0.005      | 16.359  | 0.003 | Simple        |                               |\n| 359  | Decreasing                                                                   | -0.174      | 0.32                    | 0.003      | 15.776  | 0.001 | Brown         |                               |\n| 360  | Increasing                                                                   | 0.171       | 0.544                   | 0.005      | 24.695  | 0.002 | ARIMA (0,1,0) |                               |\n| ULID | Trend Performance statistics Stationary R-squared R-squared Ideal value  1 1 | Performance | statistics              | Model type | Remark  |       |               |                               |\n|      |                                                                              | •           | R-squared RMSE MAPE MAE |            |         |       |               |                               |\n|      |                                                                              | Ideal value |                         |            |         |       |               |                               |\n|      |                                                                              | 0           | 0                       | 0          |         |       |               |                               |\n| 383  | Increasing                                                                   | 0.93        | 0.432                   | 0.004      | 4.839   | 0.003 | Holt          |                               |\n| 398  | Increasing                                                                   | 0.887       | 0.49                    | 0.004      | 13.956  | 0.003 | Holt          |                               |", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "table", "table_caption": "Table 4 Performance of timeseries models", "columns": ["ULID", "Trend", "Performance", "statistics", "Model type", "Remark", "", "", ""], "table_row_start": 36, "table_row_end": 55, "line_start": 381, "line_end": 437, "token_count_estimate": 889, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0cda88a7c560122", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\nBanerjee Discover Geoscience (2025) 3:159 Page 24 of 33\n\nTable 4 (continued)\n\nvery limited in contrast to the many non-GL pixels available in the AOI. RFC works very well in these types of skewed distributed training datasets [70]. ANN underperformed in this study as compared to RFC, as the former relies on a balanced set of GL and non-GL instances for effective predictions. In fact, like this study, RFC has been successfully applied in the mapping of GLs [30, 91].\n\nA total of 492 GLs were identified in this study. However, 406 GLs were considered for detailed analysis as they conformed with the JRC GSW V1.3 map. These GLs were visually validated by overlaying on historic Landsat images, Sentinel 2 A and Google Earth high-resolution images. In contrast, 320 GLs [27], 143 GLs [92], 472 GLs [17], 463 GLs [46], and 419 GLs [18] were identified in Sikkim Himalaya. The ambiguity in the identification of the GLs of Sikkim Himalaya can be attributed to the variations in the temporal and spectral resolutions of the imagery databases used. Also, the variation in the methodologies used to identify and validate the findings should be considered. Imagebased decadal dynamics of selected GLs such as GL-5, GL-83, GL-174, and GL-221 have been reported in earlier work [46]. The dynamics of selected GLs at a lower temporal resolution than this study have also been demonstrated [17]. In contrast to these previous studies, the present work provides a much higher temporal resolution and spatial coverage for tracking the GL dynamics of the AOI. Being explorative, this study is confined to relatively small AOI and triennial periods. However, this methodology can be easily amended to cover large areas like the trans-Himalayan region and at a much higher temporal resolution to capture seasonal variations of the GL area over the past several decades. By increasing the GL instances and greater automation of the methodology, near real-time mappings of the GLs will be possible to anticipate their future dynamics using time series forecasting.\n\nThe major issue faced in this study was working with low spatial and spectral resolution imagery. Moreover, for the past datasets, especially of the 1980s and 1990s, external validations by declassified high-resolution satellite imagery can further substantiate the classification results. However, visual validation of the lake area was made with FCC and TCC images derived from the Landsat time series data, as well as Sentinel 2A images. The water spectral indices used in this study showed varied degrees of strong to very strong correlation, indicating a possible variable redundancy. This issue can be effectively addressed by manually removing the weak input variables or performing principal component analysis. The relative error of area estimation showed an error range of 1% to 43% over all the triennial periods, with the majority of errors confined to small glacial lakes ranging from 0.01 km² to 0.02 km². There errores can be reduced by considering higher resolution images, like Linear Imaging Self-Scanning (LISS) satellite images, such as LISS-4 and Sentinel 2A images. The size of a GL depends on geological, geomorphological, hydrological, climatological, and topographical factors. In contrast, the\n\nBanerjee Discover Geoscience (2025) 3:159 Page 25 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 438, "line_end": 450, "token_count_estimate": 871, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11eb8b82db7900ae", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/25/Figure/1 description: A figure titled \"Increasing glacial lakes\" displays ten individual time-series plots arranged in a 5x2 grid. Each plot shows the change in area of a specific glacial lake over time. The x-axis for each plot is labeled \"Time (in triennial period)\" and ranges from 1990 to 2026. The y-axis is labeled \"Area (in Square Km)\" with varying scales for each plot. Within each plot, red dots represent the time series data, a solid black line represents the forecast, and a shaded grey ribbon represents the confidence limits. The plots are labeled as follows, from top to bottom, left to right: GL.8, GL.9, GL.27, GL.33, GL.42, GL.43, GL.45, GL.46, GL.49, and GL.73. All ten plots show a general increasing trend in the area of the glacial lakes over the time period shown. A caption at the bottom begins with \"Fig. 10. a Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs...\"", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 451, "line_end": 451, "token_count_estimate": 341, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3e7aebe83714966", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\n**Fig. 10 a** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed increasing trends. A 95% confidence interval is used. **b** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed increasing trends. A 95% confidence interval is used. **c** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed increasing trends. A 95% confidence interval is used. **d** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed increasing trends. A 95% confidence interval is used. **e** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed irregular trends. A 95% confidence interval is used. **f** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed decreasing trends. A 95% confidence interval is used. **g** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed decreasing trends. A 95% confidence interval is used. **g** Time series (Red dots), forecast (Black line) and confidence limits (Grey ribbon) of selected GLs (ULID is mentioned as the plot title) that showed decreasing trends. A 95% confidence interval is used.\n\nBanerjee Discover Geoscience (2025) 3:159 Page 26 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 452, "line_end": 456, "token_count_estimate": 503, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8a6838a23922e74", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/26/Figure/1 description: A figure titled \"Increasing glacial lakes\", labeled as \"Fig. 10 (continued)\", which displays ten individual scatter plots arranged in a 5x2 grid. Each plot shows the change in the area of a specific glacial lake over time. The x-axis for all plots is labeled \"Time (in triennial period)\" and ranges from 1990 to 2026. The y-axis is labeled \"Area (in Square Km)\", with the scale varying for each plot. Each plot contains red circular data points, a solid black trend line, and a shaded gray confidence interval. The plots are individually titled with identifiers: GL.79, GL.81, GL.83, GL.86, GL.101, GL.122, GL.123, GL.125, GL.139, and GL.156. Generally, all plots show an increasing trend in the area of the glacial lakes over the specified period, though the rate of increase and data variability differ among them. For example, GL.83 shows a strong, steady linear increase, while GL.122 shows a period of relative stability followed by a sharp increase after 2017.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 457, "line_end": 457, "token_count_estimate": 343, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46f852b985cbd419", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\nFig. 10 (continued)\n\nprediction of the size of the GLs in this study solely depended on the past size of the GL. Therefore, the methodology needs further incorporation of these factors. The forecast made by the methodology presented here must be verified and backed by expertise from such fields. However, the forecasts made by the methodology presented here can act as a red flag for an early warning system. Furthermore, this study used JRC GSW V 1.3 as the external source for validating the predictions made by RFC. Thereby, the errors in the prediction of JRC GSW V 1.3 automatically propagate into this study.\n\nTo date, most likely no methodology has been developed to perform simultaneous time series analysis of individual GLs of a large AOI. This study is the first step to effectively address this research gap. This methodology can be further extended by\n\nBanerjee Discover Geoscience (2025) 3:159 Page 27 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 458, "line_end": 466, "token_count_estimate": 271, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d030f0f3437cbb49", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/27/Figure/1 description: A figure titled \"Increasing glacial lakes\" displaying ten individual line graphs arranged in a 5x2 grid. Each graph shows the change in the area of a specific glacial lake over time. The x-axis for all graphs is labeled \"Time (in triennial period)\" and ranges from 1990 to 2026. The y-axis is labeled \"Area (in Square Km)\" and has a different scale for each graph. Each plot contains red dots representing data points, a solid black line showing the trend, and a grey shaded area representing a confidence interval. The ten graphs are labeled as follows: GL.165, GL.174, GL.222, GL.227, GL.230, GL.235, GL.282, GL.314, GL.339, and GL.360. Generally, all graphs show an increasing trend in the area of the glacial lakes over the specified time period, though with varying degrees of fluctuation and rates of increase. At the bottom left, there is a caption that reads \"Fig. 10. (continued)\".", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 467, "line_end": 467, "token_count_estimate": 335, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa817a1989fb1d45", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\nFig. 10 (continued)\n\nconsidering climate change as the cause of GL dynamics. This can be achieved firstly by closely monitoring the dynamics of GLs at regional and global scales using high-resolution imagery. The time series data prepared from such monitoring can be used as the dependent variable. Atmospheric temperature, precipitation, glacial dynamics, composition and thickness of the glacial or moraine dams, and topography can serve as the independent variables. These inputs can then be applied in deep learning based time series models such as Long Short-Term Memory networks and multiscale geographically weighted regression to analyze changes over time. This type of state-of-the-art forecast model will help in understanding the triggering factors of events like GLOF, causative factors of an increase in GL area, and geographic factors behind differential growth of\n\nBanerjee Discover Geoscience (2025) 3:159 Page 28 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 468, "line_end": 474, "token_count_estimate": 267, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7114469c97266f9", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/28/Figure/1 description: A figure titled 'Increasing glacial lakes' containing two scatter plots side-by-side, labeled 'GL.383' and 'GL.398'. Both plots show the 'Area (in Square Km)' on the y-axis against 'Time (in triennial period)' on the x-axis, which ranges from 1990 to 2026. Each plot displays red circular data points, a black linear regression line indicating an increasing trend, and a gray shaded confidence interval around the line. For plot GL.383, the area ranges from approximately 0.04 to 0.06 square kilometers. For plot GL.398, the area ranges from approximately 0.01 to 0.04 square kilometers. Both plots illustrate a general increase in the area of the respective glacial lakes over the specified time period.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 475, "line_end": 475, "token_count_estimate": 263, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "48a8d6504cf5e854", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: figure\nFigure\n\nImage /page/28/Figure/2 description: A figure titled \"Irregular glacial lakes\" containing four line graphs arranged in a 2x2 grid. Each graph plots the area of a specific glacial lake over time. The x-axis for all graphs is labeled \"Time (in triennial period)\" and ranges from 1990 to 2026. The y-axis is labeled \"Area (in Square Km)\", with varying scales for each graph. Each plot includes red circular data points, a solid black trend line, and a shaded gray confidence band.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "figure", "figure_caption": null, "line_start": 477, "line_end": 477, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9f26ed641b741e9", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant\nType: text\n\nThe top-left graph, labeled \"GL.23\", shows the area fluctuating between approximately 0.18 and 0.24 Square Km. The trend line dips around 2005 before rising again.\n\nThe top-right graph, labeled \"GL.70\", shows a wide range of area from 0.0 to 0.4 Square Km. The trend peaks around 1999, drops sharply to a low around 2008, and then gradually increases.\n\nThe bottom-left graph, labeled \"GL.142\", shows the area mostly varying between 0.07 and 0.08 Square Km, with a generally increasing trend after a slight dip around 1999.\n\nThe bottom-right graph, labeled \"GL.340\", shows the area ranging from about 0.01 to 0.05 Square Km. The trend line decreases to a minimum around 2002 and then increases, peaking around 2011.\n\nFig. 10 (continued)\n\nGLs within an AOI. Furthermore, with the availability of cloud-free high-resolution SAR time series data from Sentinel 1 A, a CNN can be trained to monitor the growth of GLs in GLOF hazard-prone areas.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "4 Discussion > Historical maximum extent of the glacial lakes in the South-West Quadrant", "section_headings": ["4 Discussion", "Historical maximum extent of the glacial lakes in the South-West Quadrant"], "chunk_type": "text", "line_start": 478, "line_end": 490, "token_count_estimate": 306, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9225cb8667e871c", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 5 Conclusion\nType: text\n\nA methodology is presented in this study to combine the GEE-based geocomputation of Landsat time series data, RFC-based image classification and time series analysis for glacial lake area dynamics and forecasts. This methodology can expedite an accurate and large-scale study of glacial lakes in a short time. Thereby, this methodology has a substantial scope of applications in identifying rapidly growing glacial lakes of a large geographic area. The mapping process, especially of past scenarios, need external validations using high-resolution imagery. The methodology presented here can be further automated and use near-real-time Landsat and Sentinel time series data with a higher spatial, temporal, and spectral resolution to predict the future dynamics of GLs of interest. With the increase in GL instances of training, RFC can be replaced by deep learning algorithms. The time series database of GLs generated by this methodology can be coupled with Long Short-Term Memory network (LSTM) deep learning models and multiscale geographically weighted regression to identify the temporal trends of the explanatory variables of GLOF and climate change-induced dynamics of GLs. This methodology is also applicable to C-band Synthetic Aperture Radar Ground Range Detected time series imageries (Sentinel-1 SAR GRD) available since 2014. A convolutional neural network Banerjee Discover Geoscience (2025) 3:159 Page 29 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "5 Conclusion", "section_headings": ["5 Conclusion"], "chunk_type": "text", "line_start": 492, "line_end": 494, "token_count_estimate": 372, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6b75855ef16407b", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 5 Conclusion\nType: figure\nFigure\n\nImage /page/29/Figure/1 description: A figure titled \"Decreasing glacial lakes\" showing ten scatter plots arranged in a 5x2 grid. The figure is labeled \"Fig. 10. (continued)\" at the bottom. Each plot tracks the area of a specific glacial lake over time. The x-axis for all plots is \"Time (in triennial period)\" from 1990 to 2026. The y-axis is \"Area (in Square Km)\" with varying scales for each plot. The data is shown with red dots, a black trend line, and a grey shaded confidence interval. The plots are titled GL.78, GL.94, GL.102, GL.124, GL.150, GL.167, GL.168, GL.175, GL.183, and GL.209. Most plots show a clear decreasing trend in area over time. However, plots GL.150 and GL.167 show more complex, non-linear trends, with periods of both increase and decrease.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "5 Conclusion", "section_headings": ["5 Conclusion"], "chunk_type": "figure", "figure_caption": null, "line_start": 495, "line_end": 495, "token_count_estimate": 286, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d32c3dc82b2da109", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 5 Conclusion\nType: text\n\nFig. 10 (continued)\n\ncan be trained using SAR images to identify the GLs irrespective of the cloud cover. The study can facilitate policy intervention towards GLOF warning, mitigation and building evidence for climate action.\n\nBanerjee Discover Geoscience (2025) 3:159 Page 30 of 33", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "5 Conclusion", "section_headings": ["5 Conclusion"], "chunk_type": "text", "line_start": 496, "line_end": 502, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "de100dac8079fedb", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 5 Conclusion\nType: figure\nFigure\n\nImage /page/30/Figure/1 description: A figure titled \"Decreasing glacial lakes\" displays two line graphs side-by-side, labeled \"GL.212\" and \"GL.359\". Both graphs plot \"Area (in Square Km)\" on the y-axis against \"Time (in triennial period)\" on the x-axis, which ranges from 1990 to 2026. Each graph contains red circular data points, a solid black trend line, and a gray shaded confidence interval. In the left graph, \"GL.212\", the area fluctuates between approximately 0.03 and 0.07 square kilometers from 1990 to 2008, after which it shows a steep decline to near zero by 2020. The y-axis for this graph ranges from 0.00 to over 0.09. In the right graph, \"GL.359\", the area remains relatively stable, hovering around 0.02 square kilometers from 1990 to 2017, before showing a sharp decrease, dropping below zero by 2026. The y-axis for this graph ranges from -0.02 to 0.03.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "5 Conclusion", "section_headings": ["5 Conclusion"], "chunk_type": "figure", "figure_caption": null, "line_start": 503, "line_end": 503, "token_count_estimate": 297, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ebd4b6314392c4cb", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: 5 Conclusion\nType: text\n\nFig. 10 (continued)", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "5 Conclusion", "section_headings": ["5 Conclusion"], "chunk_type": "text", "line_start": 504, "line_end": 506, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6bba6266ff910c73", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: Supplementary Information\nType: text\n\nThe online version contains supplementary material available at https://doi.org/10.1007/s44288-025-00280-w.\n\nSupplementary Material 1", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "Supplementary Information", "section_headings": ["Supplementary Information"], "chunk_type": "text", "line_start": 508, "line_end": 512, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00280"]}}
{"id": "962de007fcdcbacc", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: Supplementary Information > Author contributions\nType: text\n\nPolash Banerjee is the sole author of this research work.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "Supplementary Information > Author contributions", "section_headings": ["Supplementary Information", "Author contributions"], "chunk_type": "text", "line_start": 518, "line_end": 520, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "342eb1c7a48a48f1", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: Supplementary Information > Funding\nType: text\n\nNone.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "Supplementary Information > Funding", "section_headings": ["Supplementary Information", "Funding"], "chunk_type": "text", "line_start": 522, "line_end": 524, "token_count_estimate": 46, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c1bb3f50f2aa8dd", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: Supplementary Information > Declarations > Ethics approval and consent to participate\nType: text\n\nThis study did not involve human participants or animals, and therefore formal ethical approval was not required. Not applicable. No human participants were involved in this study.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "Supplementary Information > Declarations > Ethics approval and consent to participate", "section_headings": ["Supplementary Information", "Declarations", "Ethics approval and consent to participate"], "chunk_type": "text", "line_start": 532, "line_end": 534, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0aeb1a4f682e2233", "text": "Document: Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis\nSection: Supplementary Information > Declarations > Consent for publication\nType: text\n\nNot applicable.", "metadata": {"source_file": "data/('Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis', '.pdf')_extraction.md", "document_title": "Long-term monitoring and forecasting of glacial lake dynamics using Landsat time series data Google Earth Engine machine learning and geospatial analysis", "section_path": "Supplementary Information > Declarations > Consent for publication", "section_headings": ["Supplementary Information", "Declarations", "Consent for publication"], "chunk_type": "text", "line_start": 536, "line_end": 538, "token_count_estimate": 51, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed4388a51f1c5747", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: ABSTRACT\nType: text\n\nIn response to climatic change, the size and number of moraine-dammed supraglacial and proglacial lake systems have increased dramatically in recent decades. Given an appropriate trigger, the natural moraine dams that impound these proglacial lakes are breached, producing catastrophic Glacial Lake Outburst Floods (GLOFs). These floods are highly complex phenomena, with flood characteristics controlled, in the first instance, by the style of breach formation. Downstream, GLOFs typically exhibit transient, often non-Newtonian fluid dynamics as a result of high rates of sediment entrainment from the dam structure and channel boundaries. Combined, these characteristics introduce numerous modelling challenges. In this review, the historical, contemporary and emerging approaches available to model the individual stages, or components, of a GLOF event are introduced and discussed.\n\nA number of methods exist to model the stages of a GLOF event. Dam-breach models can be categorised as being empirical, analytical or numerical in nature, with each method having significant advantages and shortcomings. Empirical relationships that produce estimates of peak discharge and time to peak are straightforward to implement, but the applicability of these models is often limited by the nature of the case study data from which they are derived. Furthermore, empirical models neglect the inclusion of basic hydraulic principles that describe the mechanics of breach formation. Analytical or parametric models simulate breach development using simplified versions of the physically based equations that describe breach enlargement, whilst complex, physically-based codes represent the state-of-the-art in numerical dam-breach modelling. To date, few of the latter have been applied to investigate the moraine-dam failure problem.\n\nDespite significant advances in the physical complexity and availability of higher-order hydrodynamic solvers, the majority of published accounts that have attempted to reconstruct or predict GLOF characteristics have been limited, often by necessity, to the use of relatively simplistic models. This is in part attributable to the unavailability of terrain models of many high-mountain catchments at the fine spatial resolutions required for the effective application of numerically-sophisticated codes, and their proprietary (and often cost-prohibitive) nature. However, advanced models are experiencing increasing use in the glacial hazards literature. In particular, the suitability of emerging mesh-free, particle-based methods for simulating dam-breach and GLOF routing may represent a solution to many of the challenges associated with modelling this complex phenomenon.\n\nSources of uncertainty in the GLOF modelling chain have been identified by various workers. However, to date their significance for the robustness of reconstructive and predictive modelling efforts have been largely unexplored and quantified in detail. These sources include the geometric and material characterisation of moraine dam complexes, including lake bathymetry and the presence and extent of buried ice, initial conditions (freeboard, precise spillway dimensions), spatial discretisation of the down-valley domain, hydrodynamic model dimensionality and the dynamic coupling of successive components in the GLOF model cascade.\n\n© 2014 Elsevier B.V. All rights reserved.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 14, "token_count_estimate": 791, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8127bc361bb96e4f", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 1. Introduction\nType: text\n\nThe recession of many glaciers in response to climate warming has led to a dramatic increase in the size and number of moraine-dammed supraglacial and proglacial lake systems (e.g. Ageta et al., 2000; Sakai et al., 2000; Benn et al., 2001; Iwata et al., 2002; Komori, 2008; Röhl, 2008; Gardelle et al., 2001; Janský et al., 2009; Thompson et al., 2012a). In Peru alone, outburst floods from glacial sources caused ~32,000 deaths in the 20th century, as well as destroying vital economic infrastructure, settlements and valuable arable land (Table 1; Lliboutry et al., 1977a; Reynolds, 1992; Richardson and Reynolds, 2000a). In the Nepal Himalaya, it has been estimated that the costs associated with the destruction of a mature single hydropower installation by an outburst flood could exceed \\$500 million (Richardson and Reynolds, 2000a).\n\nGlacial Lake Outburst Floods (GLOF) are extremely complex phenomena. Each is a distinctly unique event, the characteristics of which are determined by, among other things, the triggering mechanism(s), reservoir hypsometry, the geometry, composition and structural integrity of the moraine dam, as well as the topography and geology of the flood path (Fig. 1). In this review, our definition of a GLOF refers exclusively to sudden-onset outburst floods which arise from the failure of a moraine-dam (Richardson and Reynolds, 2000; Table 1). This contrasts with other glacially sourced outburst floods, such as those resulting from the failure of an ice dam (e.g. Walder and Costa, 1996; Tweed and Russell, 1999; Roberts et al., 2003), volcanically triggered 'jökulhlaups' (e.g. Carrivick et al., 2004; Russell et al., 2010; Dunning et al., 2013), or the sudden release of water from englacial or subglacial reservoirs (Korup and Tweed, 2007).\n\nA GLOF typically requires a trigger event. Triggers include ice and/or rock avalanches (e.g. Vuichard and Zimmerman, 1986; Evans, 1987; Costa and Schuster, 1988; Clague and Evans, 1994; Hubbard et al., 2005) or calving from the terminal face of a lake-terminating glacier (e.g. Lliboutry et al., 1977a; Blown and Church, 1985) which cause a displacement or seiche wave that has the capacity to overtop the damcrest and initiate its failure. Additional triggers include the rapid input of glacial meltwater as a result of the sudden release of an englacial or subglacial reservoir (Clague and Evans, 2000; Richardson and Reynolds, 2000a,b), atmospheric triggers such as a high-intensity rainstorm or snowmelt event associated with a period of increased air temperatures (Korup and Tweed, 2007; Janský et al., 2010; Worni et al., 2012), or an earthquake that causes dam settlement or partial or full mechanical failure of the moraine dam (e.g. Osti et al., 2011).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 65, "line_end": 77, "token_count_estimate": 804, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "0c87a381e0a3a382", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 1. Introduction\nType: text\n\ncapacity to overtop the damcrest and initiate its failure . Additional triggers include the rapid input of glacial meltwater as a result of the sudden release of an englacial or subglacial reservoir ( Clague and Evans , 2000 ; Richardson and Reynolds , 2000a , b ) , atmospheric triggers such as a high - intensity rainstorm or snowmelt event associated with a period of increased air temperatures ( Korup and Tweed , 2007 ; Janský et al . , 2010 ; Worni et al . , 2012 ) , or an earthquake that causes dam settlement or partial or full mechanical failure of the moraine dam ( e . g . Osti et al . , 2011 ) .\n\nIn many cases, the rapid input of large volumes of material results in the displacement of lake water and subsequent overtopping of the dam structure, initiating breach formation (Richardson and Reynolds, 2000a; Balmforth et al., 2008). The degradation of a massive ice-core or permafrost may lower the dam crest and reduce freeboard, resulting directly in overspill and breach development, or reducing the minimum wave amplitude required to overtop the dam. Alternatively, an increase in lake volume, either suddenly, from a discrete rainfall or snowmelt event or the sudden release of water from an englacial or subglacial reservoir, or gradually, through a prolonged period of precipitation (e.g. Worni et al., 2012) may also trigger failure.\n\nBreach formation is often manifested as the progressive erosion and enlargement of an incipient channel in the downstream face of the dam (e.g. Balmforth et al., 2008; Xu and Zhang, 2009). Eventually, the dam is", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 65, "line_end": 77, "token_count_estimate": 445, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68ad1ae73fce98af", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 1. Introduction\nType: table\nTable: Table 1 Case study examples of different types of outburst floods from glacial sources.\n\n| Outburst type                         | Example                                                                                          | Description                                                                                                                                                                                                                                                                                                                                                        | References                                                                                                         |\n|---------------------------------------|--------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------|\n| Glacial Lake Outburst Flood (GLOF) | Dig Tsho, 1985 (Nepal Himalaya)                                                               | Failure triggered by overtopping from ice avalanche-triggered displacement waves. Reconstructed peak discharges of >2000 m3 s-1. Five fatalities, hydroelectric power station destroyed.                                                                                                                                                                           | Vuichard and Zimmerman (1987), Cenderelli and Wohl (2001), Richardson and Reynolds (2000a)                   |\n|                                       | Sabai Tsho, 1998 (Nepal Himalaya)                                                             | Moraine destabilisation and failure believed to have been triggered by earthquake. Reconstructed GLOF peak discharge of 10,000 m3 s-1. 4.4 × 105 m3 of sediment deposited within 14 km of breach.                                                                                                                                                                  | Osti and Egashira (2009), Osti et al. (2011)                                                                       |\n|                                       | Luggye Tsho, 1994 (Bhutan Himalaya).                                                          | Partial drainage of glacial lake, ~48 × 106 m3 of water drained into the Pho Chhu river, producing a flood wave >2 m high 200 km from the source lake. 23 fatalities and widespread damage to buildings up to 84 km downstream.                                                                                                                                    | Watanabe and Rothacher (1994), Richardson and Reynolds (2000b)                                                     |\n|                                       | Queen Bess Lake, 1997 (British Columbia)                                                      | Moraine dam overtopped by ice avalanche-triggered displacement wave. 8 × 106 m3 water released. Peak discharges >1000 m3 s-1. Elevated water stage noticeable >100 km from source.                                                                                                                                                                                 | Clague and Evans (2000), Kershaw et al. (2005)                                                                     |\n| Glacier outburst (Jökulhlaup)      | Grímsvötn lake, 1996 (Vatnajökull, Iceland) Kverkfjöll, Holocene (Vatnajökull, Iceland) | Largest documented Icelandic jökulhlaup. Triggered by subglacial eruption. Peak discharges 4 × 104 m3 s-1, total volume of water released 3.2 km2. Source of some of the largest Icelandic Holocene jokulhlaups. Reconstructed peak discharges of 0.5–10 × 104 m3 s-1. Fluvial erosion of bedrock and geomorphic work comparable to late Pleistocene 'megafloods'. | Guðmundsson et al. (1995), Russell et al. (1997), Björnsson (2002) Carrivick (2007), Carrivick et al. (2004) |\n| Glacier-dammed lake outburst       | Indus and Yarkand rivers, (Karakoram Himalaya)                                                | Numerous examples of glacier advance and damming of headwater rivers. Peak discharges believed to exceed 105 m3 s-1, causing significant erosion, sedimentation and secondary slope failure.                                                                                                                                                                       | Hewitt (1982), Hewitt and Liu (2010).                                                                              |\n|                                       | S. Tahoma Glacier, 1967– (Mt. Rainier, WA, USA)                                               | Frequent outbursts recorded since 1967. Damage to infrastructure.                                                                                                                                                                                                                                                                                                  | Walder and Driedger (1994)                                                                                         |\n|                                       | Altai Mountains (~13 ka BP) (Southern Siberia)                                                | Quaternary ice-dammed lake outbursts producing 'megafloods' with maximum discharges of 10–18 × 106 m3 s-1. Last outburst believed to have occurred ~13 ka BP; peak discharge > 1 × 106 m3 s-1.                                                                                                                                                                     | Rudoy (2002), Carling et al. (2009)                                                                                |\n|                                       | Hubbard Glacier, 1986, 2002, (Alaska, USA.)                                                   | Glacier temporarily blocked entrance to Russell Fjord twice in recent history. In 1986, catastrophic failure of ice dam resulted in the release of ~5.4 km3 of water, with peak discharges of up to 1 × 105 m3 s-1.                                                                                                                                                | Mayo (1989), Motyka and Truffer (2007), Ritchie et al. (2008)                                                   |", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "table", "table_caption": "Table 1 Case study examples of different types of outburst floods from glacial sources.", "columns": ["Outburst type", "Example", "Description", "References"], "table_row_start": 1, "table_row_end": 9, "line_start": 78, "line_end": 88, "token_count_estimate": 1050, "basins": ["Indus"], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "adf7f65934625f4b", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 1. Introduction\nType: text\n\ncompromised, and water is released through the newly-formed outflow channel. Depending on factors including the cohesiveness of the dam material, breaching may be rapid, or more prolonged, and will result in either the partial or full breaching of the dam structure. The ultimate outcome is the downstream propagation of an outburst flood wave, which may take the form of a 'clearwater' low-viscosity flow, or, as has been more commonly documented, a high-viscosity, hyperconcentrated (sediment comprising ~20% of flow volume) or debris flow arising from the entrainment of vast quantities of debris sourced from the eroded dam structure and unconsolidated channel and floodplain material (e.g. Lliboutry et al., 1977a; Blown and Church, 1985;\n\nClague et al., 1985; Vuichard and Zimmerman, 1987; Clague and Evans, 2000; O'Connor et al., 2001; Kershaw et al., 2005).\n\nOur understanding of the conditioning factors required for lake development and expansion has advanced considerably in recent decades, and can be largely attributed to advances in the capabilities of spaceborne sensor technology and Geographic Information Systems (GIS) (e.g. Wessels et al., 2002; Quincey et al., 2005, 2007; Bolch et al., 2008), combined with field-based investigations of the morphology, sedimentology and internal structure of contemporary dams using ground-penetrating radar, self-potential or resistivity techniques (e.g. Pant and Reynolds, 2000; Richardson and Reynolds, 2000a,b; Haeberli", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 89, "line_end": 95, "token_count_estimate": 419, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "03d640efc0920aaf", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 1. Introduction\nType: figure\nFigure\n\nImage /page/2/Figure/7 description: A diagram illustrates the process of a glacial lake outburst flood (GLOF) in a mountainous region. On the left, a large glacier, labeled A, terminates in a proglacial lake, labeled a. An arrow points from the glacier into the lake, indicating calving or meltwater input. At the base of the glacier is a label F. Higher in the mountains, another glacier, labeled B, and a landslide, labeled C, are also shown with arrows pointing towards the lake, indicating them as sources of water or displacement waves. The lake is contained by a natural moraine dam, labeled d. An arrow labeled 1 shows water movement within the lake. In the ground beneath the lake, a symbol for seismic activity is labeled G. The dam is shown being breached at point b, with a large arrow labeled 2 indicating the outflow of water. This creates a massive debris flow, labeled D/1, which travels down a valley. A third arrow, labeled 3, shows the direction of the flow towards a small village of four houses situated in the valley downstream. There is also a label c/E within the lake.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 96, "line_end": 96, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a33d7547edb127d0", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 1. Introduction\nType: text\n\nFig. 1. Schematic of a hazardous moraine-dammed glacial lake. Potential triggers, conditioning factors, and key 'phases' in a GLOF event are highlighted. Potential triggers include: (A) contact glacier calving; (B) icefall from hanging glaciers; (C) rock/ice/snow avalanches; (D) dam settlement and/or piping; (E) ice-cored moraine degradation; (F) rapid input of water from supra-, en-, or subglacial (including subaqueous) sources; (G) seismicity Conditioning factors for dam failure include (a) large lake volume; (b) low width-to-height dam ratio; (c) degradation of buried ice in the moraine structure; (d) limited dam freeboard. Key stages of a GLOF include (1) propagation of displacement or seiche waves in the lake, and/or piping through the dam; (2) breach initiation and breach formation; (3) propagation of resultant flood wave(s) down-valley. Adapted from Richardson and Reynolds (2000a)\n\net al., 2001; Reynolds, 2006; Hambrey et al., 2009; Langston et al., 2011; Moore et al., 2011; Reynolds, 2011; Thompson et al., 2012a,b) (Fig. 2), and surveys of expanding supraglacial lake systems (Benn et al., 2000; Richardson and Reynolds, 2000b; Benn et al., 2001; Thompson et al., 2012a) (Fig. 3).\n\nThis review considers the various modelling approaches that have been used for the reconstruction of palaeoGLOF (historical, typically ungauged) dynamics, and the prediction of potential future GLOF events. We first provide a detailed overview of the hazard, before considering hazard assessment of the lake basin and the modelling of potential triggering mechanisms. Dam overtopping and breach initiation and growth are then considered, before available hydrodynamic modelling approaches are introduced and discussed. The review concludes with a discussion of the advantages and limitations of various models and modelling approaches, including the identification of key areas for future research.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 97, "line_end": 103, "token_count_estimate": 542, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6daf5fc8cfaf5697", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.1. Neoglacial moraine dams\nType: text\n\nMoraine-dammed glacial lakes are found in the majority of alpine and high-mountain regions, with concentrations in the Andes (e.g. Lliboutry et al., 1977a; Reynolds, 1992; Emmer and Vilímek, 2013), the Hindu–Kush–Himalaya (HKH) arc (e.g. Richardson and Reynolds, 2000a; Mool et al., 2001; Komori, 2008; Bajracharya and Mool, 2009), the Tien Shan (Janský et al., 2010) and western Cordillera of North America (e.g. Clague and Evans, 1994; Evans and Clague, 1994; Clague and Evans, 2000; Kershaw et al., 2005). The majority of lateral and terminal moraine complexes, which impound present-day proglacial lakes were constructed during the 'Little Ice Age'; a globally-synchronous period of glacial advance extending from the 15th century to the end of the 19th century (Grove, 2004).\n\nThe main factors dictating the type of moraine dam that exists at a particular location are the longitudinal elevation profile of the parent glacier, or glaciers, and the availability of debris for supraglacial, englacial, or subglacial transport. Short, steep glaciers will respond to mass balance variations by advancing and retreating considerable distances. In contrast, long valley glaciers with shallow longitudinal profiles will respond to changes in mass balance by thickening and thinning, while the snout position remains relatively stable (Richardson and Reynolds, 2000a). Large, wide moraines thus tend to be associated with the latter glacier style, with steep, dynamic glaciers often lacking any significant terminal ridge at all. The mobilisation of supraglacial debris onto the former subglacial surface will result in the formation of 'dump' moraines (Benn and Owen, 2002). Most moraine-dams possess steep proximal and distal\n\nslopes, which may exceed angles of 40°, are typically devoid of any substantial vegetation cover, and are composed of poorly consolidated, unsorted, and uncohesive sediment (Costa and Schuster, 1988) (Fig. 4). Width-to-height ratios are controlled by the volume of material delivered to the glacier snout through the pathways mentioned above, and the length of time the terminus location remains stationary.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.1. Neoglacial moraine dams", "section_headings": ["2. Moraine-dammed lakes", "2.1. Neoglacial moraine dams"], "chunk_type": "text", "line_start": 107, "line_end": 113, "token_count_estimate": 608, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9129bd5cfad40344", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.2. Lake formation\nType: text\n\nProvided the moraine is sufficiently consolidated and stable, glacial meltwater will pond in the deglaciated basin between the glacier terminus and moraine. This development pathway typically requires the presence of an overdeepened glacier bed (Frey et al., 2010). In the absence of a glacial overdeepening, the ponding of glacial meltwater may not be effective enough to produce a fully formed morainedammed lake. Depending on the permeability of the moraine, the lake will either continue to increase in volume until overtopping or piping occurs, or its volume will be naturally regulated as water seeps through the structure or over the dam crest (e.g. Lliboutry et al., 1977a; Costa and Schuster, 1988; Rana et al., 2000; Hubbard et al., 2005). In the case of the latter, a stable drainage regime will persist indefinitely if flow discharge is unable to entrain the coarser fraction of morainic material, resulting in natural armouring of the outflow channel (Costa and Schuster, 1988).\n\nAlternatively, in situ glacier downwasting may result from the presence of inverted ablation gradients in the lower reaches as a result of downglacier increases in surface debris cover. This leads to a decrease in driving stresses and glacier velocity and causes, in turn, glacier stagnation (Benn et al., 2001, 2012). This, in turn, promotes the development of supraglacial ponds which, if drainage through englacial or supraglacial conduits is avoided, may coalesce to form a 'proto' moraine-dammed lake (Hochstein et al., 1995; Reynolds, 2000; Sakai et al., 2000; Benn et al., 2001; Wessels et al., 2002; Röhl, 2008; Sakai et al., 2009; Sakai and Fujita, 2010; Benn et al., 2012; Thompson et al., 2012a) (Fig. 3). Lakes that have originated in this way are particularly associated with debris-covered glaciers (Benn et al., 2000; Reynolds, 2000; Benn et al., 2001; Thompson et al., 2012a), as the irregular surface topography and typically low surface gradients of the glacier tongue are conducive to supraglacial lake formation (Reynolds, 1981, 2000; Quincey et al., 2007). Geometrically, moraines that have developed from the surface lowering of glacier tongues with thick supraglacial debris are typically large, complicated complexes with steep outwash fans (see geomorphological and sedimentological analyses in Hambrey et al., 2009, and Benn and Owen, 2002), whereas moraines and outwash fans formed by comparatively 'clean' glaciers are somewhat more subdued (Benn and Owen, 2002).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.2. Lake formation", "section_headings": ["2. Moraine-dammed lakes", "2.2. Lake formation"], "chunk_type": "text", "line_start": 115, "line_end": 119, "token_count_estimate": 710, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9ef102f45429bd6f", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.2. Lake formation\nType: figure\nFigure\n\nImage /page/3/Figure/12 description: A scientific graph showing a cross-section of model resistivity with topography. The vertical axis represents Elevation in meters (m), ranging from approximately 4940 to 5000. The horizontal axis shows distance, with markers at 0, 80, 160, 240, and 320 m. The main part of the graph is a color-coded plot representing resistivity. A color key at the bottom indicates \"Resistivity in ohm-metres,\" with values ranging from 2052 (blue) to 619729 (dark red/purple). The top of the plot is a black line showing the surface topography. Below the surface, a white dashed line is labeled \"Top of ice,\" separating an upper layer of lower resistivity (greens and yellows) from a lower layer of higher resistivity (reds and purples). The high-resistivity areas are labeled \"Ice.\" The graph also notes a \"Unit electrode spacing 5 m.\"", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.2. Lake formation", "section_headings": ["2. Moraine-dammed lakes", "2.2. Lake formation"], "chunk_type": "figure", "figure_caption": null, "line_start": 120, "line_end": 120, "token_count_estimate": 278, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["619729"]}}
{"id": "4cdd7cbca5ef5b07", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.2. Lake formation\nType: text\n\nFig. 2. Electrical resistivity profile along a transect across the terminal moraine complex of Imja Glacier, Khumbu Himal, Nepal, revealing the presence of buried ice (modified from fig. 7c, Revnolds. 2006).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.2. Lake formation", "section_headings": ["2. Moraine-dammed lakes", "2.2. Lake formation"], "chunk_type": "text", "line_start": 121, "line_end": 123, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "b5ef3fe7da32c5d7", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.2. Lake formation\nType: figure\nFigure\n\nImage /page/4/Picture/2 description: A figure from a scientific publication by M.J. Westoby et al. shows two photographs, labeled A and B, depicting glacial landscapes. Photograph A displays a glacier terminus with a steep ice cliff next to a partially frozen, light-blue proglacial lake. A vertical scale bar indicates the cliff height is 10 meters. The surrounding area is covered in rocky moraine, with snow-capped mountains in the background. Photograph B shows a wider view of a larger system of interconnected glacial lakes filled with floating icebergs. A horizontal scale bar indicates a width of 50 meters across one section of the lake. The landscape is characterized by moraine, patches of snow, and some brown vegetation in the foreground.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.2. Lake formation", "section_headings": ["2. Moraine-dammed lakes", "2.2. Lake formation"], "chunk_type": "figure", "figure_caption": null, "line_start": 124, "line_end": 124, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dd6cc86cdbe8c9ae", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.2. Lake formation\nType: text\n\nFig. 3. The temporal evolution of supraglacial ponds on Ngozumpa Glacier, Khumbu Himal, Nepal; (A) supraglacial pond with calving ice face; (B) coalesced melt ponds and subsequent development of a 'proto' glacial lake.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.2. Lake formation", "section_headings": ["2. Moraine-dammed lakes", "2.2. Lake formation"], "chunk_type": "text", "line_start": 125, "line_end": 127, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "7d9f8914d6c431a3", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 2. Moraine-dammed lakes > 2.3. Trigger mechanism\nType: text\n\nMany documented GLOFs have been initiated by overtopping waves caused by external triggers (e.g. Lliboutry et al., 1977a; Clague and Evans, 2000; Richardson and Reynolds, 2000a; Kershaw et al., 2005). Triggers include (often combined) ice and rock avalanches or landslides (e.g. Vuichard and Zimmerman, 1987; Harrison et al., 2006), or discrete glacier calving events (both sub-aerial and sub-aqueous) (e.g. Lliboutry et al., 1977a; Blown and Church, 1985). Additional triggers include dam settlement, piping, and lateral moraine collapse as a result of seismic activity (e.g. Osti et al., 2011), temporary blockage of an overflow channel (e.g. Huggel et al., 2004; Mergili and Schneider, 2011) or the flotation of submerged dead ice, resulting in water displacement (Richardson and Reynolds, 2000b). A number of conditioning factors may also predispose a particular moraine dam to fail, such as low freeboard, a high height-to-width dam ratio (Huggel et al., 2002a; McKillop and Clague, 2007; Quincey et al., 2007; Emmer and Vilímek, 2013), and sedimentological and structural characteristics (e.g. loosely consolidated, saturated sediment), as well as the presence of degrading permafrost or a massive buried ice core (Watanabe et al., 1995; Richardson and Reynolds, 2000b) (Fig. 2). A consideration of approaches to model the various triggers is presented in Section 3.1.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "2. Moraine-dammed lakes > 2.3. Trigger mechanism", "section_headings": ["2. Moraine-dammed lakes", "2.3. Trigger mechanism"], "chunk_type": "text", "line_start": 129, "line_end": 131, "token_count_estimate": 450, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d13a407018fcb8b", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling\nType: text\n\nA number of methods can be used to model the various stages or 'components' of a GLOF (Fig. 1), either empirically, analytically, or\n\nnumerically. We classify these components according to their physical location and temporality in the GLOF modelling cascade, and define them as follows:\n\n- 1. Trigger mechanism\n- 2. Breach initiation and development\n- 3. Downstream routing of the outburst flood wave(s)", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling", "section_headings": ["3. Approaches to modelling"], "chunk_type": "text", "line_start": 133, "line_end": 141, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d77a12531e47375d", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching\nType: text\n\nThe increasing capability of Geographic Information Systems (GIS), coupled with the wide availability and affordability of multi-temporal imagery and topographic data products from space- and airborne platforms has facilitated the production of largely desk-based natural hazard assessments (e.g. Huggel et al., 2002a, 2004; Salzmann et al., 2004; Kääb et al., 2002, 2005; Quincey et al., 2005; Huggel et al., 2005; McKillop and Clague, 2007; Allen et al., 2008). Potential triggering events, such as avalanche or landslide starting zones in close proximity to glacial lakes, can be identified using a relatively straightforward procedures including spectral band segmentation, thresholding, and DEM-derived slope classification, in combination with detailed aerial photography or high-resolution satellite imagery (Margreth and Funk, 1999; Salzmann et al., 2004; Quincey et al., 2005; Huggel et al., 2006; Allen et al., 2008).\n\nThe identification of potentially dangerous glaciers or rock slopes, followed by the mapping of potential avalanche run-out tracks, and", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.1. Ice, snow, and rock avalanching"], "chunk_type": "text", "line_start": 145, "line_end": 149, "token_count_estimate": 329, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb1402ea5755a920", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching\nType: figure\nFigure\n\nImage /page/5/Picture/2 description: A figure from a scientific paper, cited as \"M.J. Westoby et al. / Earth-Science Reviews 134 (2014) 137–159\", composed of three photographs labeled A, B, and C, showing a glacial landscape.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.1. Ice, snow, and rock avalanching"], "chunk_type": "figure", "figure_caption": null, "line_start": 150, "line_end": 150, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f6eca356d54d4a0", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching\nType: text\n\nPanel A is a wide-angle view of a mountainous, rocky terrain with a glacial lake in the upper right. A large moraine dam, which appears to have been breached, is prominent. A black arrow points to a section of the dam's inner wall. The slopes are covered with scree and patches of snow.\n\nPanel B shows a view from a lower elevation, looking up at a bouldery landscape with a sharp moraine ridge in the middle ground. In the background are snow-covered mountains under a light sky.\n\nPanel C is a close-up view of the side of the moraine dam, with annotations illustrating its structure. An arrow points to the top edge, labeled \"Original moraine dam surface\". Below this, a bracket indicates a large section of \"Exposed morainic material\". At the bottom, another bracket labels the accumulated debris as \"Post-GLOF stone-fall and slumping\". Snow-capped mountains are visible in the background.\n\n**Fig. 4.** Breached moraine dams in the Khumbu Himal, Nepal: (A) Dig Tsho. An ice avalanche, originating from the hanging remnants of Langmoche Glacier, entered the lake. Displacement wave(s) subsequently overtopped the terminal moraine (Vuichard and Zimmerman, 1987). A sizeable breach (centre, foreground) was formed by the escaping floodwaters. Black arrow indicates area shown in C; (B) Chukhung Glacier terminal moraine. For scale, maximum breach top width is –90 m. Little is known about the dam failure dynamics of this GLOF, although it is estimated that approximately 5.5 × 10<sup>5</sup> m³ of water was impounded before failure, based on reconstructed pre-GLOF dam and lake geometry (M Westoby, unpublished data); (C) Panoramic photograph of exposed sediment in the central section of a moraine dam breach, Dig Tsho, Nepal. Widespread, post-GLOF deposits are present at the base of the exposure. In this example, large boulder- and cobble-sized clasts are supported in a poorly-sorted, unconsolidated sand-gravel matrix. Total section is 150 m in length and 11 m high at the point of annotation.\n\nthe identification of potentially hazardous process combinations, such as an avalanche or landslide entering a glacial lake can then form the basis for more detailed investigation (Salzmann et al., 2004). The identification of subtle features such as bedrock tension cracks and jointing or overhanging glacial seracs which may provide clues as to the location of potential rock or ice avalanche starting zones remains challenging, particularly in the absence of very fine-resolution spaceborne or airborne imagery for many areas of interest (e.g. Huggel et al., 2006). GIS-based identification of contemporary or, in light of climate warming, potential future avalanche or landslide starting zones (see, e.g. Frey et al., 2010) remains the most viable approach. Mass movement trajectory modelling based on statistical parameters (e.g. Alean, 1985; Huggel et al., 2004) one-dimensional centre-of-mass models (e.g. Mergili et al., 2012), or more advanced physically based distributed models (e.g. Hungr, 1995; Sampl and Zwinger, 2004) may be used to", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.1. Ice, snow, and rock avalanching"], "chunk_type": "text", "line_start": 151, "line_end": 163, "token_count_estimate": 864, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "729236bf1979e7e9", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching\nType: text\n\net al . , 2006 ) . GIS - based identification of contemporary or , in light of climate warming , potential future avalanche or landslide starting zones ( see , e . g . Frey et al . , 2010 ) remains the most viable approach . Mass movement trajectory modelling based on statistical parameters ( e . g . Alean , 1985 ; Huggel et al . , 2004 ) one - dimensional centre - of - mass models ( e . g . Mergili et al . , 2012 ) , or more advanced physically based distributed models ( e . g . Hungr , 1995 ; Sampl and Zwinger , 2004 ) may be used to\n\npredict plausible avalanche run-out distances and inundation areas, although advanced two- and three-dimensional models based on computational fluid dynamic (CFD) theory are required if dynamic behaviour such as deformation and lateral spreading of the avalanche or landslide body are to be realistically simulated (e.g. Sampl and Zwinger, 2004; Sailer et al., 2008). Progress in our understanding of the dynamics of rock-ice avalanches has been made in recent years by workers including Schneider et al. (2010), who used a two-dimensional numerical model, RAMMS (Christen et al., 2010), in combination with seismic data recordings to infer a proportional relationship between the latter and the frictional work rate of an avalanche.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.1. Ice, snow, and rock avalanching", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.1. Ice, snow, and rock avalanching"], "chunk_type": "text", "line_start": 151, "line_end": 163, "token_count_estimate": 391, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac3af85c2977cdcc", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.2. Glacier calving\nType: text\n\nPrediction of calving rates, or more importantly for hazard assessment, the prediction of volumes of discrete calving events and\n\ncharacteristics of triggered impulse waves is far from straightforward. Progress in understanding the glaciological (e.g. Benn et al., 2007) and limnological factors governing thermo-erosional notch development (e.g. Röhl, 2006) has improved in recent years, and has provided insights into the prediction of future calving rates under future warming scenarios. Long-term monitoring of calving frequency, including impulse wave heights, may aid prediction of the likely occurrence of waves capable of overtopping terminal moraine structures. Alternatively, an artificial reduction in dam freeboard through manual intervention, specifically lake level lowering (e.g. Grabs and Hanisch, 1993), has been carried out at sites in the Peruvian Andes (e.g. Lliboutry et al., 1977a; Reynolds, 1992, 1998a,b; Reynolds et al., 1998) and Nepal Himalaya (e.g. Reynolds, 1998a,b; Yamada, 1998). As well as reducing hydrostatic pressure in the moraine dam complex, this also serves to increase the minimum wave amplitude required for overtopping, though at the risk of increasing calving activity via decreased ice-tongue support by buoyancy forces, with potentially disastrous consequences (Lliboutry et al., 1977a).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.2. Glacier calving", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.2. Glacier calving"], "chunk_type": "text", "line_start": 165, "line_end": 169, "token_count_estimate": 388, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "d1403cf508e4302b", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.3. Wave overtopping\nType: text\n\nMany documented moraine dam failures have been initiated by overtopping by waves generated from landslides or ice avalanches (e.g. Lliboutry et al., 1977a; Costa and Schuster, 1988; Clague and Evans, 2000; Richardson and Reynolds, 2000a). Numerous moraine dams, both failed and intact, show evidence of overtopping by displacement waves (Lliboutry et al., 1977a,b; Blown and Church, 1985; Costa and Schuster, 1988; Clague and Evans, 2000; Richardson and Reynolds, 2000a; Hubbard et al., 2005; Kershaw et al., 2005). Whilst these waves might not technically be a trigger for moraine-dam failure in their own right, as a preceding initiation mechanism must have occurred, it is often the erosive power of their run-up and passage across a moraine-dam that may initiate its failure.\n\nWaves generated by sub-aerial mass flows such as landslides or avalanches fall into one of two categories. The first is a solitary displacement wave formed by the 'pushing' of the top level of the water column. The second is classified as a standing wave, or 'seiche', formed when the volume and entry velocity of the external mass is large enough to trigger the mobilisation of the entire water column (Fig. 5). Resultant 'sloshing' of the water in the enclosed moraine basin causes the repeated run-up of water at the lake boundaries and may be highly effective at eroding the dam structure (e.g. Hubbard et al., 2005; Balmforth et al., 2008). Based on geomorphological evidence, Hubbard et al. (2005) undertook a comprehensive reconstruction of seiche dynamics in a moraine-dammed glacial lake in Peru. On 22 April 2002 a rock avalanche deposited 8–20 × 10<sup>6</sup> m³ of material onto the surface\n\nof the terminus of Glaciar Pucajirca,  $\\sim 5 \\times 10^6$  m³ of which directly entered moraine-dammed Laguna Safuna Alta and triggered a seiche. The lake had been artificially lowered prior to the event, though still posed a potential failure risk. The initial overtopping wave height is estimated to have exceeded 100 m. The presence of multiple erosion discontinuities on the proximal face of the latero-terminal moraine complex suggests that at least ten seiche waves reached the moraine dam, with at least one wave becoming entirely airborne. Whether more than one wave overtopped the dam structure is unknown. Following two failures in 1951 (Reynolds, 1992), evidence of multiple wave run-ups was also observed by Lliboutry et al. (1977a) at Artesoncocha, another Peruvian moraine-dammed lake.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.3. Wave overtopping", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.3. Wave overtopping"], "chunk_type": "text", "line_start": 171, "line_end": 181, "token_count_estimate": 729, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "623ab629cdb6758f", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.3. Wave overtopping\nType: text\n\nlake had been artificially lowered prior to the event , though still posed a potential failure risk . The initial overtopping wave height is estimated to have exceeded 100 m . The presence of multiple erosion discontinuities on the proximal face of the latero - terminal moraine complex suggests that at least ten seiche waves reached the moraine dam , with at least one wave becoming entirely airborne . Whether more than one wave overtopped the dam structure is unknown . Following two failures in 1951 ( Reynolds , 1992 ) , evidence of multiple wave run - ups was also observed by Lliboutry et al . ( 1977a ) at Artesoncocha , another Peruvian moraine - dammed lake .\n\nThe dimensions and propagation rate of a displacement wave are determined by the mass, geometry, velocity (both subaerial and subaqueous), and angle of entry of material into a standing body of water, as well as the lake area and bathymetry (e.g. Fritz et al., 2003a; Walder et al., 2003). For obvious reasons, empirical equations to represent impulse wave characteristics have been derived almost exclusively from physical scale, laboratory-based experiments (e.g. Fritz et al., 2003a,b; Walder et al., 2003; Ataie-Ashtiani and Nik-Khah, 2008; Balmforth et al., 2008, 2009). As a result, such experiments are typically not able to simulate the effects of, for example, observed, complex basin bathymetry on wave propagation and run-up (Synolakis, 1987). Few studies also appear to have taken into consideration the effects of lateral spreading where the source of the landslide or avalanche is the valley flanks (e.g. Risley et al., 2006), though such problems may be overcome by the use of numerical flow models (e.g. Cannata et al., 2012).\n\nFrom a hazard development perspective, the interaction of a displacement wave, or a series of seiche waves with the moraine dam structure is important. Required, therefore, are estimates of overtopped water volumes and inundation depths with which to force breach erosion and flood-routing models. Of note are recent scaled experiments conducted by Balmforth et al. (2008, 2009), who used a combination of physical and theoretical modelling to assess the impact of large displacement waves on moraine dam structures. Their results demonstrated that initiation of the breaching process ultimately amounts to a competition between erosion and rates of lake drainage and seiche damping. A threshold was identified whereby the amplitude of the initial wave, or rate of erosion, should exceed a critical value for catastrophic dam failure to be initiated (Balmforth et al., 2009).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.3. Wave overtopping", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.3. Wave overtopping"], "chunk_type": "text", "line_start": 171, "line_end": 181, "token_count_estimate": 720, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "175709125f844f59", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.4. Atmospheric triggers\nType: text\n\nHeavy precipitation (e.g. Worni et al., 2012) or a glacial outburst flood from an adjacent ice mass may trigger a rapid increase in lake volume and level and the subsequent enlargement and down-cutting of an", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.4. Atmospheric triggers", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.4. Atmospheric triggers"], "chunk_type": "text", "line_start": 183, "line_end": 185, "token_count_estimate": 101, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f851f93325f4509f", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.4. Atmospheric triggers\nType: figure\nFigure\n\nImage /page/6/Figure/11 description: A diagram illustrating a cross-section of a proglacial lake system. On the left, a light blue glacier is shown next to a body of water labeled \"Proglacial lake\". The lake is contained on the right by a brown landform labeled \"Moraine dam\". Several processes are indicated by numbered arrows. Number 1 points to two small black arrows indicating material falling from the glacier. Number 2 is a large black arrow pointing up from the lake surface near the dam. Number 3 is a curved black arrow going over the top of the moraine dam. Number 4 is a dashed white arrow pointing down into the lake near the dam, indicating sedimentation. Other unlabeled arrows show a solid black arrow pointing down from the glacier into the lake and a dashed white arrow pointing up from the submerged part of the glacier.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.4. Atmospheric triggers", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.4. Atmospheric triggers"], "chunk_type": "figure", "figure_caption": null, "line_start": 186, "line_end": 186, "token_count_estimate": 262, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26cebb2ab331dda2", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.4. Atmospheric triggers\nType: text\n\n**Fig. 5.** Dynamics of a standing wave or 'seiche': (1) input of external mass to the lake system (e.g. a large glacier calving event); (2) mobilisation of the entire water column, and; (3) subsequent overtopping of dam structure, followed by (4) continuing, but attenuating, oscillations of the water body, which may or may not result in further overtopping. Black triangle represents the water surface pivot point, around which the standing wave oscillates. Note: figure dimensions for illustrative purposes only and not necessarily to scale.\n\nexisting spillway, or the initiation of a new channel (Clague and Evans, 1992, 2000). Quantifying the minimum volume of water required to fill a lake is relatively straightforward if basin perimeter and freeboard are known and seasonal variations in water level are taken into account (e.g. Janský et al., 2010). However, it is difficult to predict the timing and volume of water released, particularly where historical outbursts from the glacier in question have not been documented.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.4. Atmospheric triggers", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.4. Atmospheric triggers"], "chunk_type": "text", "line_start": 187, "line_end": 191, "token_count_estimate": 291, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "312fb3acaca389e7", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.5. Ice-cored moraine degradation\nType: text\n\nModelling the degradation of ice-cored moraine complexes is challenging. Subsidence caused by the melting of interstitial ice or a massive ice core reduces the structural integrity of a dam, thereby increasing the propensity for failure (Richardson and Reynolds, 2000b). Consequently, dam freeboard is reduced through lowering of the dam crest, thereby reducing the minimum amplitude of displacement waves required to overtop the dam, or reducing the additional volume of glacially- or meteorologically-derived water required to fill the reservoir and overflow the dam. Quantification of moraine surface lowering has been achieved either by in situ monitoring (e.g. Reynolds, 1992; Watanabe et al., 1995; Pant and Reynolds, 2000; Janský et al., 2009), or the assessment of multi-temporal, high-resolution digital terrain models (DTMs) (cf. Irvine-Fynn et al., 2011; Bennett and Evans, 2012; Sawagaki et al., 2013). The disadvantage of the former is that it is typically extremely challenging from a logistical perspective, whilst DTM errors associated with the latter should not exceed the anticipated change in moraine surface elevation over the investigation timescale, which is often small.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.5. Ice-cored moraine degradation", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.5. Ice-cored moraine degradation"], "chunk_type": "text", "line_start": 193, "line_end": 195, "token_count_estimate": 354, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b53a5436635e1d32", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.6. Earthquake-triggered dam failure\nType: text\n\nModelling the response of a glacier-moraine-dammed lake system to an earthquake (Dai et al., 2005; Strasser et al., 2008) is incredibly difficult, not least because of the unpredictability associated with estimating the timing and magnitude of a seismic event (and its seismic signature in the vicinity of a glacial lake, which may be hundreds of kilometres from the earthquake epicentre). Analysis of geological maps may help to identify whether an area that contains morainedammed lakes lies close to active faults, and is therefore at an elevated risk of experiencing earthquake-induced GLOFs. However, by their very nature, many mountain ranges are tectonically active, and so it might be argued that this trigger should be automatically considered as a trigger in the majority of cases. Detailed field investigation in support of geotechnical characterisation of a moraine-dam may provide an indication of its susceptibility to settlement and piping in the event of an earthquake, whilst semi-quantitative analysis of the structural characteristics of calving faces of lake-terminating glaciers from field investigation or remotely sensed imagery may aid prediction of the likelihood of mass failure and the estimation of displacement wave dimensions. However, this is far from an exact science.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.1. Trigger mechanism > 3.1.6. Earthquake-triggered dam failure", "section_headings": ["3. Approaches to modelling", "3.1. Trigger mechanism", "3.1.6. Earthquake-triggered dam failure"], "chunk_type": "text", "line_start": 197, "line_end": 199, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "105e5330c0d5dcd9", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.1. Breach initiation\nType: text\n\nSudden or gradual overtopping and inundation, or internal failure as a result of piping and seepage may result in the development of a breach through the moraine dam (Fig. 4). In the case of the former, overtopping water is likely to traverse the width of the dam crest as a uniform sheet (e.g. Kershaw et al., 2005). Alternatively, flow through an existing outlet will be augmented (Costa and Schuster, 1988). Erosion of the dam will occur if the increased boundary shear stresses exerted by the overtopping or channelized flow exceed the cohesive strength of the surface material (Korup and Tweed, 2007). This permits increased discharge from the lake, which in turn may result in runaway incision. Incision will cease when one of the following occurs: i) the base-level of the moraine is reached; ii) a bedrock abutment (or a significant decrease in material erodibility) is encountered; or iii) the outflow\n\nchannel becomes sufficiently armoured to halt down-cutting (e.g. Clague and Evans, 1992).\n\nBreaches initiated by internal failure of the dam structure are considerably less well understood, although the underlying principle is that saturation and subsequent seepage of lake water through the dam structure causes the finer fraction of the sediment to be removed, forming 'pipes' and thereby reducing the local physical strength of the dam material (Clague and Evans, 2000; Korup and Tweed, 2007). In addition, thermokarst degradation of buried ice contained within the moraine structures has been observed (Richardson and Reynolds, 2000b). Collapse of subsurface cavities may lead to a lowering of dam crest elevation, thereby increasing the likelihood of an overtopping failure. The transition to open breach formation is initiated through slumping and mechanical failure and collapse of the overlying material.\n\nThe detailed simulation of breach formation processes is central to an assessment of the downstream hazards posed by a GLOF, since the shape of the breach hydrograph (Fig. 6, Table 2) will determine, in the first instance, the peak discharge at the flood source. This hydrograph will typically be used as an upstream boundary condition for subsequent flood routing. In order of increasing complexity, models used in published simulations of moraine dam failure events may be generally classed as one of the following: i) empirical regression models; ii) analytical and parametric models; iii) physically-based, numerical models.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.1. Breach initiation", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.1. Breach initiation"], "chunk_type": "text", "line_start": 203, "line_end": 211, "token_count_estimate": 642, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b3c99e3c769481d", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models\nType: text\n\nEmpirical models represent the simplest approach to dam-break modelling (Table 3). These models are not process-based and comprise a single or series of regression relationships derived from test case studies or observed historical dam failures (e.g. Macdonald and Langridge-Monopolis, 1984; Blown and Church, 1985; Costa and Schuster, 1988; Froehlich, 1995; Wahl, 1998). Examples of empirical equations used for dam-break modelling are provided in Wahl (2004), who also undertook a comprehensive investigation of uncertainty estimates surrounding the use of such equations, and also Thornton et al. (2011). Input parameters typically include a combination of the following: dam width, height, lake area and volume. If unknown, lake volume may also be approximated using a separate empirically-derived equation (see e.g. Huggel et al., 2002a,b). Model output typically comprises a single discrete value, such as peak discharge  $(Q_p)$  (e.g. Hagen, 1982; Walder and O'Connor, 1997) or time to peak  $(T_p)$  (e.g. MacDonald and Langridge-Monopolis, 1984; Froehlich, 1995). Such models have been widely used in the glacial hazards literature. One of the earliest examples is presented by Clague and Mathews (1973), who discovered that the magnitude of the peak discharge of a glacial outburst flood arising from the failure of an ice-dammed lake is approximately proportional to the available volume of stored water. An applied example of the use of empirical models is provided by Vuichard and Zimmerman (1986, 1987), who applied the relationships of Hagen (1982), MacDonald and Langridge-Monopolis (1984), and Blown and Church (1985) to", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.2. Empirical dam-breach models"], "chunk_type": "text", "line_start": 213, "line_end": 215, "token_count_estimate": 474, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26889eb3f74d39e1", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models\nType: figure\nFigure\n\nImage /page/7/Figure/15 description: A line graph plots Discharge (Q) on the vertical y-axis against Time (T) on the horizontal x-axis. The graph shows a curve that starts at a low, relatively constant discharge level from time T0 to T2. At time T2, the discharge begins to rise, increasing sharply after T3. The curve reaches its maximum value, the peak discharge labeled Qp, at time T4. After this peak, the discharge decreases until time T5. The area under the main part of the curve, representing the increased flow, is shaded in light blue. Several points on the curve are marked with a star: at time T2 corresponding to discharge Q2, at time T3 corresponding to discharge Q3, at the peak at time T4, and on the descending part of the curve at time T5. Dotted lines connect these marked points to their corresponding values on the axes.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.2. Empirical dam-breach models"], "chunk_type": "figure", "figure_caption": null, "line_start": 216, "line_end": 216, "token_count_estimate": 283, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e8ce079eed1295d", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models\nType: text\n\n**Fig. 6.** Generic dam breach outflow hydrograph. Axis annotation is explained in Table 2. Modified from fig. 2.7, Morris et al. (2009).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.2. Empirical dam-breach models"], "chunk_type": "text", "line_start": 217, "line_end": 221, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8e00b48395621ec3", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models\nType: table\nTable: Table 2 Description of phases comprising a generic dam breach outflow hydrograph (see Fig. 7) and qualitative description of our ability to model them. Adapted from Morris et al. (2009).\n\n| Time  | Description                                                                                                        | Modelling ability |\n|-------|--------------------------------------------------------------------------------------------------------------------|-------------------|\n| T0    | No breach initiation                                                                                               | -                 |\n| T1    | Start of breach initiation. Seepage over or through dam begins.                                                    | Limited           |\n| T1–T2 | Breach initiation phase. Q typically low. May last hours–months (Q2)                                               | Poor              |\n| T2–T3 | Critical transition to breach formation. Erosion reaches upstream face of dam, initiating rapid breach growth (Q3) | Limited           |\n| T3–T5 | Breach formation. Rapid vertical erosion dominates initially, followed by continued vertical and lateral erosion.  | Poor–moderate     |\n| T4    | Peak discharge (Qp)                                                                                                | Good              |", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.2. Empirical dam-breach models"], "chunk_type": "table", "table_caption": "Table 2 Description of phases comprising a generic dam breach outflow hydrograph (see Fig. 7) and qualitative description of our ability to model them. Adapted from Morris et al. (2009).", "columns": ["Time", "Description", "Modelling ability"], "table_row_start": 1, "table_row_end": 6, "line_start": 222, "line_end": 229, "token_count_estimate": 300, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f7df496182e9fbc", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models\nType: text\n\nestimate the peak discharge (6–8000  ${\\rm m}^3/{\\rm s}$ ) of the 1985 Dig Tsho GLOF, Nepal.\n\nHowever, such relations are limited in their suitability for predictive purposes, as they neglect the inclusion of basic hydraulic principles pertaining to breach initiation and enlargement and are typically derived from a range of failed dam types and settings, including artificial constructs (Walder and O'Connor, 1997). In their appraisal of preexisting empirical relations, Walder and O'Connor (1997) maintained that such relationships were flawed in their assumption that  $Q_n$  may be simply approximated as the product of volume of water released and the resulting drop in lake level, instead recognizing that hydraulic principles and constraints, such as breach erosion rate, are equally as important. Though theoretically more robust than standard empirical equations, this approach assumes that the user is able to quantify breach erosion rates. The impracticalities of quantifying the physical processes at work during breach formation are numerous, and although experimental progress in this field has been made in recent years (e.g. Morris et al., 2007), in practice these parameters are generally unknown and extremely challenging to quantify in the field. Providing the geometric characteristics of the dam structure and attendant lake basin are known, empirical relations represent an expeditious and simple approach to estimating Q<sub>p</sub>, and, at first glance, would appear suitable for relatively basic hazard assessment. Taking the above into account and without reliable estimates of time to peak flow and consideration of the form of rising and falling limbs of the outflow hydrograph, such data have limited value for the assembly of inundation maps and reach-specific hydrograph and flood attenuation data. However, whilst the derivation of individual hydrographs may be appropriate for reconstructive GLOF modelling efforts, where detailed process analysis and\n\ngeomorphological inference might be applied to constrain model boundary conditions and input parameters, the merits of adopting a deterministic approach for predictive GLOF modelling is questionable given the inherent variability of material properties and model boundary conditions. We consider the extent and significance of uncertainty in the GLOF model chain in Section 5.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.2. Empirical dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.2. Empirical dam-breach models"], "chunk_type": "text", "line_start": 230, "line_end": 236, "token_count_estimate": 585, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "891e8ff5e5534910", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.3. Analytical and parametric dam-breach models\nType: text\n\nAnalytical and parametric models are semi-physically based, given their consideration of simplified physical processes (Morris et al., 2009b). These models incorporate simplified numerical treatments of the physical processes involved during the breach development phase (e.g. Capart, 2013), and typically assume that the rate of breach growth is purely time-dependent. Accordingly, the user is required to enter morphometric boundary conditions including final breach geometry (e.g. base width and side slope angle) and the time required for full formation. The model then proceeds to iteratively calculate the outflow hydrograph as the breach develops. Another assumption is that weir equations can be used to represent flow over the dam. Such equations typically assume the form:\n\n$$Q = Cd L H^{\\frac{3}{2}}$$\n (1)\n\nwith:\n\n$$H = h_w + \\frac{V^2}{2g} \\tag{2}$$", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.3. Analytical and parametric dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.3. Analytical and parametric dam-breach models"], "chunk_type": "text", "line_start": 238, "line_end": 249, "token_count_estimate": 284, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2bef000e2e180edb", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.3. Analytical and parametric dam-breach models\nType: table\nTable: Table 3 Empirical equations used to estimate peak discharge $(Q_p)$ for natural dam failures. Examples marked with an asterisk (\\ ) have been derived entirely from case studies of moraine-dam failure\n\n|                                       | Reference                                | Type     | R2 (if known) | No. case studies |           | Empirical equation                   |\n|---------------------------------------|------------------------------------------|----------|------------------|------------------|-----------|--------------------------------------|\n|                                       |                                          |          |                  | Real             | Simulated |                                      |\n| Height of water equations             | Kirkpatrick (1977)                       | Best fit | 0.790            | 13               | 6         | $Qp = 1.268(Hw + 0.3)2.5$            |\n|                                       | US Soil Conservation (1981)              | Envelope | -                | 13               |           | $Qp = 16.6(Hw)1.85$                  |\n|                                       | US Bureau of Reclamation (1982)          | Envelope | 0.724            | 21               |           | $Qp = 19.1(Hw)1.85$                  |\n|                                       | Singh and Snorrason (1982)               | Best fit | 0.488            |                  | 8         | $Qp = 13.4(Hd)1.89$                  |\n|                                       | * Walder and O'Connor (1997)             | Best fit | 0.620            | 9                |           | $Qp = 0.045(V)0.66$                  |\n|                                       | Pierce et al. (2010)                     | Best fit | 0.633            | 72               |           | $Qp = 0.784(H)2.668$                 |\n|                                       |                                          | Best fit | 0.640            | 72               |           | $Qp = 2.325 ln(H)6.405$              |\n| Storage equations                     | Singh and Snorrason (1984)               | Best fit | 0.918            |                  | 8         | $Qp = 1.776(V)0.47$                  |\n|                                       | Evans (1986)                             | Best fit | 0.836            | 29               |           | $Qp = 0.72(V)0.53$                   |\n|                                       | * Walder and O'Connor (1997)             | Best fit | 0.090            | 9                |           | $Qp = 60.3(V)0.84$                   |\n| Height of water and storage equations | Hagen (1982)                             | Envelope | -                | 6                |           | $Qp = 0.54(V Hw)0.5$                 |\n|                                       | Macdonald and Langridge-Monopolis (1984) | Best fit | 0.788            | 23               |           | $Qp = 1.154(V Hw)0.412$              |\n|                                       |                                          | Envelope | 0.156            | 23               |           | $Qp = 3.85(VwHw)0.411$               |\n|                                       | Costa (1985)                             | Best fit | 0.745            | 31               |           | $Qp = 0.763(V Hw)0.42$               |\n|                                       | * Costa and Schuster (1988)              | Best fit | 0.780            | 8                |           | $Qp = 0.00013 (PE)0.60$              |\n|                                       |                                          | Envelope | -                |                  |           | $Qp = 0.063 (PE)0.42$                |\n|                                       | Froehlich (1995)                         | Best fit | 0.934            | 22               |           | $Qp = 0.607(V0.295 Hw1.24)$          |\n|                                       | * Walder and O'Connor (1997)             | Best fit | 0.490            | 9                |           | $Qp = 0.19(Hw V)0.47$                |\n|                                       | Pierce et al. (2010)                     | Best fit | 0.844            | 87               |           | $Qp = 0.0176(VH)0.606$               |\n|                                       |                                          | Best fit | 0.850            | 87               |           | $Qp = 0.038(V0.475H1.09)$            |\n| Other                                 | Thornton et al. (2011)                   | Best fit | 0.909            | 14               |           | $Qp = 0.1202(L)1.7856$               |\n|                                       |                                          | Best fit | 0.871            | 25               |           | $Qp = 0.863(V0.335H1.833 Wav-0.663)$ |\n|                                       |                                          | Best fit | 0.991            | 14               |           | $Qp = 0.012(V0.493H1.205L0.226)$     |", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.3. Analytical and parametric dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.3. Analytical and parametric dam-breach models"], "chunk_type": "table", "table_caption": "Table 3 Empirical equations used to estimate peak discharge $(Q_p)$ for natural dam failures. Examples marked with an asterisk (\\ ) have been derived entirely from case studies of moraine-dam failure", "columns": ["", "Reference", "Type", "R2 (if known)", "No. case studies", "", "Empirical equation"], "table_row_start": 1, "table_row_end": 24, "line_start": 250, "line_end": 275, "token_count_estimate": 1143, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00013", "205L0", "335H1", "475H1", "493H1"]}}
{"id": "1679ee6e6aacf0a2", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.3. Analytical and parametric dam-breach models\nType: text\n\nand:\n\n$$V = \\frac{Q_a}{A} \\tag{3}$$\n\nwhere Q is flow discharge (m³ s<sup>-1</sup>), Cd is a weir coefficient, L is the width of the weir,  $h_w$  is the hydraulic head of the impounded water above the dam crest, V is the average velocity (m/s) of flow immediately upstream of the weir,  $Q_a$  is the actual flow rate, and A is the cross-sectional channel area (m²). Cd can be determined by dividing  $Q_a$  by the theoretical flow rate,  $Q_t$ . Where one or more input parameters must be approximated, there exists an associated danger of over- or underestimation of dam-breach peak discharge or time to peak (or both), making sensitivity analyses of input parameter combinations an essential undertaking.\n\nEarly flood routing models developed by the United States National Weather Service (NWS), including DAMBRK (Fread, 1988a) and FLDWAV (Fread, 1993), utilise the parametric model BREACH (Fread, 1988b) to compute an upstream hydrograph. GLOF modelling studies that have adopted semi-physical breach models include Meon and Schwarz (1993), who used DAMBRK to reconstruct the 1981 GLOF from Zhangzanbo Lake, Nepal, and Shrestha et al. (2010), who used BOSS-DAMBRK (an enhanced version of the original DAMBRK model) to estimate patterns of flood inundation in the Sun Koshi basin, Nepal. Osti and Egashira (2009) used the NWS Simplified Dam Break (SMPDBK) model (see Whetmore et al., 1991), combined with an empirical predictor of failure duration (Froehlich, 1995) to reconstruct the 1998 GLOF from Sabai Tsho glacial lake in the Khumbu Himal, Nepal. Dam-breach modelling undertaken fails to account for dead ice and the heterogeneous nature of the moraine dam structure, and thereby precluding an accurate simulation of the moraine breaching process.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.3. Analytical and parametric dam-breach models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.3. Analytical and parametric dam-breach models"], "chunk_type": "text", "line_start": 276, "line_end": 284, "token_count_estimate": 562, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "d9f489fcaaf9484c", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.2. Dam-breach > 3.2.4. Fully physically based numerical models\nType: text\n\nComplex numerical models are based predominantly on the physical processes observed during failure, including breach flow hydraulics and sediment transport, as well as soil erodibility relationships and structural models to simulate breach widening (e.g. Mohamed et al., 2002; Worni et al., 2012). Purely numerical models have not seen widespread use in the dam-breach literature. This may be attributed in part to their comparatively high computational cost, although with the rapid advent of affordable, high-end desktop computers this should cease to be an issue in the near future. With respect to outbursts from moraine-dammed lakes, the most commonly used numerical models have been NWS DAMBRK (Carling and Glaister, 1987; Fread, 1988a) and BREACH (Fread, 1988b; O'Connor et al., 2001; Bajracharya et al., 2007; Xin et al., 2008; Shrestha et al., 2010), although the adoption of advanced models appears to be becoming more commonplace.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.2. Dam-breach > 3.2.4. Fully physically based numerical models", "section_headings": ["3. Approaches to modelling", "3.2. Dam-breach", "3.2.4. Fully physically based numerical models"], "chunk_type": "text", "line_start": 286, "line_end": 288, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "757e7fecdccdc423", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing\nType: text\n\nFloods from sudden dam failures, often termed 'dam-break outburst floods', are characterised by the often short-lived passage of high-magnitude floodwaters and associated transient flow hydraulics (Marren, 2005; Carrivick, 2010). The rate and style of breach formation, which, in the first instance, determines breach hydrograph form (Fig. 6), exerts a dominant control on flood dynamics in the 'near field', or through immediate downstream reaches. However, with increasing distance from the flood source, valley characteristics including topography (e.g. channel slope, bends, and channel geometry), vegetation (e.g. Lancaster et al., 2003) and sediment availability play a more dominant role (Richardson and Reynolds, 2000a). Floods which entrain vast quantities of uncohesive sediment, sourced from the moraine dam and deposits on the valley floor and sides may become transformed into debris flows (Clague et al., 1985; Evans, 1986; Rickenmann, 1999;\n\nClague and Evans, 2000). Such flows are capable of achieving far greater runout distances than typical clearwater flows, owing to the increased momentum afforded by the combination of fluid and solid forces (and resultant transition to a non-Newtonian flow regime) and continuing addition of material through bed and bank erosion (Iverson, 1997). However, evidence from historical GLOFs and debris flows suggests such flows are unlikely to form or be sustained on slopes of less than 10–15° (e.g. Clague and Evans, 1994; O'Connor et al., 1994; Clague and Evans, 2000; Procter et al., 2010).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing", "section_headings": ["3. Approaches to modelling", "3.3. Glacial Lake Outburst Flood routing"], "chunk_type": "text", "line_start": 290, "line_end": 294, "token_count_estimate": 457, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "653fd1e37da9777a", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.1. Palaeohydraulic reconstruction\nType: text\n\nThe simplest form of hydraulic modelling involves the use of palaeocompetence and palaeohydraulic techniques to reconstruct flood stage and discharges, typically at the reach scale (e.g. Costa, 1983; Williams, 1984; Kershaw et al., 2005; Bohorquez and Darby, 2008). Using this method, empirical equations are used to relate clast size to hydraulic parameters. When combined with measures of crosssectional channel geometry, longitudinal channel slope, and roughness coefficients of the reach in question (known as the 'slope-area' method), these relationships are capable of providing estimates of peak discharge and flood velocity for 'steady' hydraulic conditions, where it is assumed that hydraulic variables including flow depth, velocity, and discharge may vary at successive points downstream, but are considered as remaining temporally constant (Riggs, 1976; Williams, 1978; Costa, 1983; Baker, 1988; Baker, 2000). In reality, GLOFs are inherently 'unsteady' and are typically characterised as exhibiting spatially and temporally varying flow discharges, velocities, stage and sediment transport conditions.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.1. Palaeohydraulic reconstruction", "section_headings": ["3. Approaches to modelling", "3.3. Glacial Lake Outburst Flood routing", "3.3.1. Palaeohydraulic reconstruction"], "chunk_type": "text", "line_start": 296, "line_end": 298, "token_count_estimate": 331, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5aa67deb67080594", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.2. GIS-based methods\nType: text\n\nThe simplest computational models are GIS-based flow-routing algorithms, which transfer flow sequentially downslope across a digital elevation model (DEM) (e.g. Desmet and Govers, 1996; Liang and Mackay, 2000). Modified single-flow (MSF) models assign flow from a cell to one of its eight neighbours (the 'D8' method), based on the direction of steepest descent (O'Callaghan and Mark, 1984). The obvious limitation of this method is its inability to transfer flow to more than one adjacent cell (Tarboton, 1997). Accordingly, more advanced algorithms are capable of allocating the contents of a given cell to its neighbours (the 'multiple-flow direction' (MF) model, weighted according to slope angle. MSF and MF models also assume a critical slope angle, below which runout will cease (Huggel et al., 2003). Such models have seen an appreciable amount of use in the natural hazards literature, most likely as a result of their general applicability to many types of gravitational flows (e.g. Huggel et al., 2008). Their use in GLOF simulation has been rather more limited. Relevant studies include the work of Huggel et al. (2002b, 2003), who used a combination of MSF and MF modelling to simulate outbursts from a number of lakes in the Swiss Alps and the Peruvian Andes, and Allen et al. (2009), who used MSF modelling to investigate potential ice avalanche-debris flow interactions and flood hazard in Mount Cook National Park, New Zealand.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.2. GIS-based methods", "section_headings": ["3. Approaches to modelling", "3.3. Glacial Lake Outburst Flood routing", "3.3.2. GIS-based methods"], "chunk_type": "text", "line_start": 300, "line_end": 302, "token_count_estimate": 427, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dde03e193b2feefa", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.3. One-dimensional numerical modelling\nType: text\n\nNumerical models used for flood reconstructions may be broadly subdivided according to their dimensionality. One-, two- and three-dimensional models (hereafter referred to as 1-, 2-, and 3-D, respectively) are more process- and physically-based than their GIS counterparts, and attempt to solve modified versions of the Navier–Stokes equations (see Batchelor, 1967). 1-D flood routing models are based on a one-dimensional version of the St-Venant, or 'shallow water' equations (SWE) (Barré de St-Venant, 1871):\n\n$$\\frac{\\partial Q}{\\partial x} + \\frac{\\partial A}{\\partial t} = 0 \\tag{4}$$\n\n$$\\frac{1}{A}\\frac{\\partial Q}{\\partial t} + \\frac{1}{A}\\frac{\\partial}{\\partial x}\\left(\\frac{Q^2}{A}\\right) + g\\frac{\\partial h}{\\partial x} - g\\left(S_0 - S_f\\right) = 0 \\tag{5}$$\n\nwhere Eq. (4) and Eq. (5) are the conservation of mass and conservation of momentum equations respectively. Q is flow discharge, A the cross-section surface area, t is time, g is gravitational acceleration (9.81 m/s), h is the cross-sectional averaged water depth,  $S_0$  is the longitudinal bed slope, and  $S_f$  is the friction slope.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.3. One-dimensional numerical modelling", "section_headings": ["3. Approaches to modelling", "3.3. Glacial Lake Outburst Flood routing", "3.3.3. One-dimensional numerical modelling"], "chunk_type": "text", "line_start": 304, "line_end": 314, "token_count_estimate": 445, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b31a7fd05b927284", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.3. One-dimensional numerical modelling\nType: text\n\n} \\ right ) + g \\ frac { \\ partial h } { \\ partial x } - g \\ left ( S_0 - S_f \\ right ) = 0 \\ tag { 5 } $ $ where Eq . ( 4 ) and Eq . ( 5 ) are the conservation of mass and conservation of momentum equations respectively . Q is flow discharge , A the cross - section surface area , t is time , g is gravitational acceleration ( 9 . 81 m / s ) , h is the cross - sectional averaged water depth , $ S_0 $ is the longitudinal bed slope , and $ S_f $ is the friction slope .\n\nOne-dimensional models typically use the step-backwater procedure. This is essentially an automated, numerical adaptation of the slope-area method, with model output comprising energy-balanced water surface profiles (a function of discharge, channel roughness and channel geometry), cross-sectional averaged velocity and discharge (e.g. USACE, 2010). Cross-sectional profiles may be either derived from field surveys, or using a DEM with sufficiently high spatial resolution. Flow may be modelled as either subcritical (wave velocity > flow velocity), supercritical (wave velocity < flow velocity), or as a mixed regime. As well as predefined cross-sections, initial boundary conditions include either an input hydrograph at the uppermost cross-section, or, if unavailable, downstream water profiles calculated either from direct measurement during a flood event, or using palaeo-stage indicators (PSI). The latter approach has typically been used for GLOF reconstruction (e.g. Cenderelli and Wohl, 2003; Kershaw et al., 2005; Bohorquez and Darby, 2008), as high discharges and flow velocities, turbulence, and the inclusion of large clasts renders them virtually impossible to instrument, particularly in upper reaches. Consequently, the few directly measured GLOF hydrographs that exist are from locations tens or hundreds of kilometres downstream where relatively subdued flow dynamics permits instrumentation (Richardson and Reynolds, 2000a). Popular freely or commercially available 1-D models that have been used in the glacial hazards literature include HEC-RAS (Cenderelli and Wohl, 2001, 2003; Alho et al., 2005; Alho and Aaltonen, 2008; Carling et al., 2010; USACE, 2010), and the NWS DAMBRK (Meon and Schwarz, 1993) and FLDWAV models (Bajracharya et al., 2007).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.3. One-dimensional numerical modelling", "section_headings": ["3. Approaches to modelling", "3.3. Glacial Lake Outburst Flood routing", "3.3.3. One-dimensional numerical modelling"], "chunk_type": "text", "line_start": 304, "line_end": 314, "token_count_estimate": 689, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71721f9c36337909", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.4. Higher-order numerical modelling\nType: text\n\nTwo-dimensional models are based on depth-averaged versions of the SWE, derived by integrating the Reynolds-averaged Navier-Stokes equations over the depth of the flow (Chanson, 2004; Hervouet, 2007). Advantages of using 2-D models include their ability to simulate multi-directional and multi-channel flows (in contrast, and as their name implies, 1-D models are only capable of routing flow in one direction, i.e. downstream), super-elevation of flow around channel bends, hydraulic jumps (i.e. in-channel transitions between supercritical and subcritical flow regimes) and turbulent eddying. These are dynamic characteristics intrinsic to GLOFs. Two-dimensional models are often capable of simulating non-Newtonian flow dynamics (e.g. Pitman et al., 2013) such as those observed when a GLOF has entrained a volume of debris from the moraine-dam and valley floor to sufficiently alter its flow rheology (see e.g. O'Brien, 2003; Rickenmann et al., 2006; Armanini et al., 2008; Stoltz and Huggel, 2008). Fully 3-D models are capable of solving the full Navier–Stokes equations, resulting in calculation of variations in flow velocity across both the flow width and depth. Despite their representation of the more complex hydraulic and sediment transport characteristics of catastrophic flooding, to date 2-D and 3-D modelling applications in the flow hazards literature are rare (e.g. Worni et al., 2012), with the majority of studies focusing on glacial outburst floods (jökulhlaups) (e.g. Carrivick, 2006, 2007; Alho and Aaltonen, 2008; Bohorquez and Darby, 2008), or mudflows (e.g. Stoltz and Huggel, 2008; Boniello et al., 2010). This paucity of 2-D and 3-D model applications to GLOF simulation most likely stems from the coupling of high computational cost and the lack of DEMs at sufficiently fine resolution in alpine areas to make their use tractable.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "3. Approaches to modelling > 3.3. Glacial Lake Outburst Flood routing > 3.3.4. Higher-order numerical modelling", "section_headings": ["3. Approaches to modelling", "3.3. Glacial Lake Outburst Flood routing", "3.3.4. Higher-order numerical modelling"], "chunk_type": "text", "line_start": 316, "line_end": 318, "token_count_estimate": 570, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad9598311e1f3731", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.1. Modelling breach initiation\nType: text\n\nFrom a modelling perspective, a high degree of uncertainty surrounds the breach formation stage. This is largely attributable to the lack of incorporation of detailed simulations of erosion and breach flow in existing breach models (Wahl, 1998, 2004). During breach formation, rates and patterns of erosion and outflow vary considerably. Recent reviews (Wahl, 1998; Mohamed et al., 2002; Morris et al., 2008, 2009b) highlight the importance of the 'breach initiation' phase, defined as a period of relatively minimal, but increasing discharge, during which dam integrity is yet to be compromised (Fig. 6). Through intervention, runaway breach expansion may be avoided at this stage. Though currently lacking, this information is central to the construction of hazard assessments that aim to quantify flood wave arrival times, discharge, and patterns of inundation, all of which are often subject to a significant degree of uncertainty due to the necessary assumptions made during model parameterisation. For overtopping-type failures, breach initiation is typically manifested as the progressive development of an outflow channel by sheet flow over the dam (for non-cohesive material) or a number of channels on the downstream face of the moraine dam (for cohesive material) (Balmforth et al., 2008; Morris et al., 2008). For cohesive material, subsequent overtopping promotes the progressive enlargement of one or more of these channels through headcutting. The breach initiation phase ceases once runaway incision of an outflow channel begins. The length of this period is controlled by a number of factors, including the water depth and return period of overtopping waves, as well as the erodibility of dam material (e.g. Broich, 2002; Balmforth et al., 2008, 2009). Empirical regression equations capable of providing estimates of time to peak (e.g. MacDonald and Langridge-Monopolis, 1984) have been derived almost exclusive from case study datasets of man-made embankment dams, and provide no estimate of breach initiation time.\n\nThe delineation of the breach initiation and breach formation phases for failures initiated by seepage and piping within a natural dam is extremely challenging (Wahl, 2004). With the exception of the work of, for example, Fell et al. (2003) and Wan and Fell (2004), our understanding of breach initiation via internal piping and seepage is rather limited, though it has been established that breach initiation via subsurface pathways are typically lengthy (potentially >24 h) and less evident to the casual observer than for overtopping-type failures. The transition from 'normal', clear-water seepage outflow to cloudy seepage with minimal variation in discharge indicates a developing pipe, and thus the beginning of breach initiation (Wahl, 2004). Catastrophic failure cannot be avoided once the overlying material collapses under its own weight and subsequent erosion in the style of an overtopping failure occurs (Mohamed et al., 2002).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.1. Modelling breach initiation", "section_headings": ["4. Contemporary modelling challenges", "4.1. Dam-breaching", "4.1.1. Modelling breach initiation"], "chunk_type": "text", "line_start": 324, "line_end": 332, "token_count_estimate": 766, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62ee306e988cd86f", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.1. Modelling breach initiation\nType: text\n\nour understanding of breach initiation via internal piping and seepage is rather limited , though it has been established that breach initiation via subsurface pathways are typically lengthy ( potentially > 24 h ) and less evident to the casual observer than for overtopping - type failures . The transition from ' normal ' , clear - water seepage outflow to cloudy seepage with minimal variation in discharge indicates a developing pipe , and thus the beginning of breach initiation ( Wahl , 2004 ) . Catastrophic failure cannot be avoided once the overlying material collapses under its own weight and subsequent erosion in the style of an overtopping failure occurs ( Mohamed et al . , 2002 ) .\n\nFor moraine dams, the presence of interstitial ice, ice lenses, a massive ice core, or permafrost (e.g. Hambrey et al., 2009; Worni et al., 2012), introduce further challenges for modellers. Ice cores represent largely impermeable barriers along (or indeed, in the presence of structural discontinuities in the form of relict crevasses traces, through) which subsurface flows are concentrated and routed, accelerating thermal erosion of the ice and removal of overlying sediment (Watanabe et al., 1995; Richardson and Reynolds, 2000b; Janský et al., 2009). Following assessment of the extent of stagnant ice within moraine dams, achievable almost exclusively through the use of in situ geophysical techniques (e.g. Pant and Reynolds, 2000; Richardson and Reynolds, 2000a,b; Reynolds, 2006, 2011), dams may be treated as composite structures, much like artificial rock fill or clay-cored constructions, for the simulation of overtopping failures. However, there is little published evidence of theoretical, physical or computational experimental parameterisation of dam breach models and the exploration of the implications for failure dynamics.\n\nPresently, the breach initiation phase is poorly represented or entirely neglected in breach models (Morris et al., 2008). As an example, the 'time to failure' boundary condition in DAMBRK (Fread, 1984) is defined as the time taken for final breach dimensions to develop once downstream face erosion and subsequent breaching of the upstream dam face has already been achieved. Thus, any detailed simulation or quantification of initiation time is neglected. However, the standalone NWS-BREACH model (nested within DAMBRK), is capable of providing information on elapsed time associated with the transition from erosion of the downstream to upstream face, which may, with caution, be taken as indicative of a switch from the breach 'initiation' to 'development' phase.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.1. Modelling breach initiation", "section_headings": ["4. Contemporary modelling challenges", "4.1. Dam-breaching", "4.1.1. Modelling breach initiation"], "chunk_type": "text", "line_start": 324, "line_end": 332, "token_count_estimate": 699, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8abe1bf4c542b52e", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.2. Breach enlargement\nType: text\n\nSignificant progress has been made in the last decade through the identification of issues with dam-break model physicality by international working groups, including the CADAM, IMPACT and FLOODsite projects (e.g. Mohamed et al., 2002; Morris et al., 2008, 2009b), as well as the ongoing work of the Dam Safety Interest Group (CEATI, 2012). Although these projects focused predominantly on modelling the failure of artificially-constructed earthen embankment dams, the models considered have seen widespread use in the glacial hazards literature, and so, with careful interpretation, many findings may be equally applicable to moraine dam failure scenarios. Concerning dam-breach formation, a range of issues pertaining to, for example, the mechanics of breach enlargement, side slope evolution and breach flow hydraulics have been identified. Mohamed et al. (2002) highlighted the inability of existing models to accurately simulate the processes of breach widening and enlargement. A constant shape, typically rectangular (e.g. Wetmore and Fread, 1984), trapezoidal (e.g. Cristofano, 1965; Fread, 1984; 1988b) or parabolic (e.g. Harris and Wagner, 1967), and a linear or user-specified rates of development of increase in breach width and depth, are assumed by the majority of existing models (Wahl, 1997). However, logic dictates that subaqueous and subaerial rates of erosion are far from identical, whilst field evidence and physical scale modelling demonstrates that breach side slopes approach near-vertical angles immediately following down-cutting (e.g. Mohamed et al., 2002; Morris et al., 2008). A transition towards a more parabolic or trapezoidal geometry occurs later in the breach formation stage as mass wasting and discrete block failure of the side slopes occurs. The heterogeneous composition and relatively non-cohesive nature of moraine dam material would suggest that maintenance of steep side slopes during breach development is less likely. However, recent large-scale physical dam-break experiments have demonstrated that steep-sided breaches develop for a range of dam compositions, including both non-cohesive and cohesive materials (e.g. Vaskinn et al., 2004).\n\nFollowing recognition of the limitation of existing models, a number of advanced, commercially- or publicly-available, physically-based numerical breach models have been developed. Notable examples include HR-BREACH (Mohamed et al., 2002; Morris et al., 2008), SIMBA (Temple et al., 2005; Hanson and Hunt, 2007), and BASEMENT (Faeh et al., 2011). As an example of the improved physicality and dimensionality afforded by the use of such models, in combination with standard treatments of hydraulics, sediment transport and soil mechanics, HR-BREACH is capable of iteratively adjusting breach shape through the incorporation of an improved methodology for modelling lateral breach expansion through the simulation of both continuous erosion and discrete mass failure (Mohamed et al., 2002). With the exception of the work of Worni et al. (2012), advanced dam breach models have not been applied to the moraine dam failure problem, but have the potential to vastly improve our understanding of failure dynamics through improved physical representation of complex failure processes and outflow hydrodynamics.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.2. Breach enlargement", "section_headings": ["4. Contemporary modelling challenges", "4.1. Dam-breaching", "4.1.2. Breach enlargement"], "chunk_type": "text", "line_start": 334, "line_end": 338, "token_count_estimate": 842, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "333d623160724751", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.3. Permafrost and massive ice\nType: text\n\nAnother key parameter, also often overlooked, is the erodibility of dam material (Hanson and Cook, 2004; Hanson and Hunt, 2007;\n\nMorris et al., 2008). Sediment transport equations used within the majority of breach models consider the removal of dam material as a function of applied shear stress, water velocity, and particle diameter, which are unrepresentative of the true variables that dictate erodibility (Morris et al., 2008). Dam material texture (including soil cohesivity), compaction moisture content and compaction energy exert a profound influence over erodibility, and, along with dam geometry, determine headcut migration rates during breach initiation. Indeed, small changes in moisture content or compaction have been shown to result in orderof-magnitude changes in erodibility (Hanson and Hunt, 2007; Morris et al., 2008). Ideally, in situ investigation of the above variables is desirable, however, due to logistical challenges these are often estimated. Quantification of erodibility coefficients for materials typically used for the construction of embankment dams (soil/clay mixtures) has been attempted (Hanson and Hunt, 2007), though such material differs significantly from glacial moraine, which is typically non-cohesive, poorly consolidated, composed of a heterogeneous mixture of sand, gravel, cobbles, and boulders (e.g. Benn and Owen, 2002) (Fig. 4), and may contain discontinuous or continuous permafrost and cores of massive, relict ice (e.g. Hanisch et al., 1998; Reynolds, 2000; Richardson and Reynolds, 2000a; Delisle et al., 2003; Hambrey et al., 2009).\n\nThe potential presence of a range of permafrost concentrations and structures represents a significant modelling challenge. There is no evidence in the literature of experimental or field-based investigation into the erodibility of glacial moraine dam material, including the influence of ice content on breach development, having been undertaken to date. Recent research into the degradation of frozen river-bank sediment reveals complex degradational patterns between thawed, frozen and massive ice-cored material (Dupeyrat et al., 2011). Dupeyrat et al. (2011) discovered that the resistance of pure ice to thermal erosion by flowing water was greater than that of surrounding permafrost with lower ice content. Following the ablation of interstitial ice, cohesion and therefore substrate shear strength is vastly reduced, facilitating the rapid removal of sediment (Gatto, 1995). Equally, the importance of discrete mechanical instead of thermal erosion mechanisms increases with the bulk density of permafrost (Lick and McNeil, 2001; Dupeyrat et al., 2011). A critical ice content may therefore be identified, above which mechanical failure dominates, and below which progressive ablation and sediment removal is the main mode of erosion. At present, even the most advanced dam breach models are unable to account for the presence of permafrost or massive, 'pure' ice in the dam structure, though this may indeed prove to be a controlling factor on rates of breach development and should be a priority for future experimental and numerical modelling research.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.1. Dam-breaching > 4.1.3. Permafrost and massive ice", "section_headings": ["4. Contemporary modelling challenges", "4.1. Dam-breaching", "4.1.3. Permafrost and massive ice"], "chunk_type": "text", "line_start": 340, "line_end": 346, "token_count_estimate": 819, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73678570b4ee088e", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling\nType: text\n\nMost retrodictive or predictive GLOF modelling efforts have considered breach enlargement and the downstream routing of the escaping floodwaters as a single event. However, a number of historical GLOFs have been identified as multiple-phase events (e.g. Clague et al., 1985; Vuichard and Zimmerman, 1986; Clague and Evans, 2000; Kershaw et al., 2005). Depending on displaced water volume and the degree of flow attenuation as the moraine crest is traversed, single or multiple overtopping wave may represent self-contained flood events, regardless of whether this eventually leads to breach formation (e.g. Hubbard et al., 2005; Kershaw et al., 2005). To simulate this phenomenon, the modeller is first required to estimate the overtopping flood hydrograph for entry as an upstream boundary condition. Since contemporary dam breach or flood routing models are generally incapable of performing such calculations, overtopping volume and duration must be calculated independently using known (or estimated) input mass, basin and dam parameters applied to the empirically-derived equations of, for example, Muller (1995) or Walder et al. (2003). Results may then be used to force model applications to determine the flood characteristics of the initial escaping floodwaters. Similarly, the temporary armouring or blockage of the moraine breach by discrete mass failure of the breach sidewalls or blocks of ice (e.g. Worni et al., 2012) may contribute to the intermittency and 'pulsing' of the escaping floodwaters, such as that documented by Vuichard and Zimmerman (1986) for the 1985 Dig Tsho GLOF. Whilst physically based numerical dambreach models are capable of simulating discrete mass failure and the subsequent removal of this material by the breach flow, numerical reconstruction of the intermittency of breach expansion and hydrograph development remains challenging in a modelling environment which is typically unable to numerically resolve individual clasts and boulders.\n\nA further complication for the preservation of flow hydraulics and sediment transport is the nature of model coupling. The NWS DAMBRK and FLDWAV codes comprise both dam breach and flow routing algorithms (Fread, 1993). Similarly, HEC-RAS permits the breaching of an inline structure, either at the source of the flood (e.g. at the exit of a reservoir or storage area), or at a user-defined point along the flow path (USACE, 2010). However, both approaches use parametric-type breaching models, which, for reasons discussed above, represent rather simplified approximations of the breaching process. More traditionally, standalone breach models have been used to produce the initial outflow hydrograph, which is then considered as an upstream input to subsequent hydrodynamic modelling efforts. At present, such an approach is standard practice, though it has implications for the conservation of flow momentum at the dam foundation-valley floor boundary. Here, flow accelerates under the combination of the effects of gravity and the pressure of the overlying water contained in the reservoir (the 'pressure head') (Carrivick, 2010). In a fully-coupled, higher order model, short-lived flow acceleration, and a resulting zone of intensely turbulent flow at the dam base would be preserved. However, such effects are not considered when breaching and flood routing models are decoupled. Consequently, near-dam flow velocities calculated using this method are likely to be underestimated.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.1. Multiple flood waves and model coupling"], "chunk_type": "text", "line_start": 350, "line_end": 356, "token_count_estimate": 891, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6245a5d41926bfd8", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling\nType: text\n\nthough it has implications for the conservation of flow momentum at the dam foundation - valley floor boundary . Here , flow accelerates under the combination of the effects of gravity and the pressure of the overlying water contained in the reservoir ( the ' pressure head ' ) ( Carrivick , 2010 ) . In a fully - coupled , higher order model , short - lived flow acceleration , and a resulting zone of intensely turbulent flow at the dam base would be preserved . However , such effects are not considered when breaching and flood routing models are decoupled . Consequently , near - dam flow velocities calculated using this method are likely to be underestimated .\n\nA related and equally important consideration is the conservation of sediment outflow dynamics at the exit of the breach, and use as input to hydrodynamic modelling. Many studies have largely failed to acknowledge or account for this, yet from a geomorphological perspective, and in combination with outflow discharge and flow hydraulics, the erosion and subsequent transport of morainic material from the breach controls the transience of patterns of erosion, deposition, and flow rheology (e.g. Kershaw et al., 2005; Worni et al., 2012), particularly in near-field locales. An example of a simulated sediment outflow time series for a Himalayan GLOF, produced using HR-BREACH, is displayed in Fig. 7. In", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.1. Multiple flood waves and model coupling"], "chunk_type": "text", "line_start": 350, "line_end": 356, "token_count_estimate": 383, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5130055ff4911f73", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling\nType: figure\nFigure\n\nImage /page/12/Figure/5 description: A line graph with two y-axes plots sediment outflow and discharge against time. The x-axis is labeled 'T (mins)' and ranges from 0 to 200. The left y-axis is labeled 'Sediment outflow (Qs; m³ s⁻¹)' and ranges from 0 to 140. The right y-axis is labeled 'Discharge (Q; m³ s⁻¹)' and ranges from 0 to 1800. A jagged black line, representing sediment outflow, starts at zero, rises with significant fluctuations to a peak of approximately 100 m³/s around 90-100 minutes, and then generally decreases. Underneath the black line are thin gray vertical lines representing high-frequency data. A smooth, bell-shaped light blue curve, representing discharge, starts at zero, peaks at approximately 1700 m³/s around 135 minutes, and returns to zero around 180 minutes. The peak of the sediment outflow occurs before the peak of the discharge.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.1. Multiple flood waves and model coupling"], "chunk_type": "figure", "figure_caption": null, "line_start": 357, "line_end": 357, "token_count_estimate": 296, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0a5b9a8d8d51984", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling\nType: text\n\n**Fig. 7.** Rates of sediment evacuation ( $Q_s$ ) during moraine breaching; in this instance an experimental reconstruction of the 1985 Dig Tsho event using HR-BREACH. Sediment outflow fluctuates significantly between successive 10 second time-steps, as a result of periodic discrete breach wall failure events, which represent considerable, though short lived 'pulses' of sediment to the developing outflow channel. It is worth noting that peak sediment outflow precedes peak discharge (Note: end of sediment time series data corresponds with cessation of breach outflow).\n\nthis particular instance, sediment evacuation rates were approximated by calculating the volumetric change of the breach between successive 10 second time steps over the full duration of the simulation. The significant variability in sediment outflow over extremely short time scales (often between individual time steps) is the result of instantaneous, discrete mass failure events which represent the main lateral expansion mechanism of the breach. Such behaviour would most likely be manifest in a hydrodynamic simulation of downstream flood propagation as a series of short-lived sediment 'pulses'. The rheological, hydraulic, and geomorphological effects of temporally-varying flow sediment concentrations are discussed in further detail in Section 4.2.3.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.1. Multiple flood waves and model coupling", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.1. Multiple flood waves and model coupling"], "chunk_type": "text", "line_start": 358, "line_end": 362, "token_count_estimate": 338, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "00b0f7a00905f131", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics\nType: text\n\nAs with other sudden onset, sediment-laden outburst floods (e.g. Carrivick et al., 2010), GLOFs possess highly transient and spatially-varying flow regimes. From a modelling perspective, this introduces a range of challenges, including, but not limited to; an often rapid increase in discharge, high flow velocities and rapidly expansive patterns of inundation; the propagation of a translatory shock wave over initially 'dry' terrain; highly turbulent, typically overbank flow, the occurrence of flow super-elevation at sharp channel bends (often triggering secondary failure of colluvial slopes), and highly transitory flow rheology, which exerts a major control over intra- and post-flood channel reworking.\n\nDuring breach development, escaping floodwaters accelerate under the combination of the effects of gravity and the pressure of the overlying water contained in the reservoir (Stansby et al., 1998). This acceleration is typically short-lived, as floodwaters descending the distal face of the moraine dam encounter comparatively shallow gradients in the proglacial environment. Given a wide, largely unconfined proximal zone, flow will expand laterally as sheet flow, with depth and velocity gradually decreasing with distance from the flood source (Cenderelli and Wohl, 2003; Hogg and Pritchard, 2004). Alternatively, the continuation of steep near-field topography will promote the convergence of floodwaters, producing a higher velocity initial flood wave. As the flood wave propagates downstream, time-varying flow discharges and depths, combining to determine the overall degree of inundation, will be dictated by the breach outflow hydrograph and topographic complexity.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.2. Complex flow hydraulics"], "chunk_type": "text", "line_start": 364, "line_end": 378, "token_count_estimate": 461, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0eaabe3b42091e4d", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics\nType: text\n\nthe distal face of the moraine dam encounter comparatively shallow gradients in the proglacial environment . Given a wide , largely unconfined proximal zone , flow will expand laterally as sheet flow , with depth and velocity gradually decreasing with distance from the flood source ( Cenderelli and Wohl , 2003 ; Hogg and Pritchard , 2004 ) . Alternatively , the continuation of steep near - field topography will promote the convergence of floodwaters , producing a higher velocity initial flood wave . As the flood wave propagates downstream , time - varying flow discharges and depths , combining to determine the overall degree of inundation , will be dictated by the breach outflow hydrograph and topographic complexity .\n\nOne-dimensional hydrodynamic models are unable to simulate many key characteristics of GLOFs. Energy losses through the effects of boundary friction and turbulent eddying is handled explicitly in 2-D and 3-D flow models, though are accounted for in 1-D models through representations of channel conveyance. This is typically undertaken through manual calibration of roughness coefficients, such as Manning's n, in order to produce optimal, or 'best-fit' water surface profiles to palaeo-stage evidence, or at-a-point hydrographs which closely mirror available observed data (usually unobtainable) (e.g. Cenderelli and Wohl, 2001; Bohorquez and Darby, 2008). No studies currently exist which directly compare the application of 1-D and 2-D hydraulic models to the GLOF phenomenon. However, analogous research has been undertaken for glacial outburst floods, including the work of Alho and Aaltonen (2008) and Bohorquez and Darby (2008). The former found that, for unsteady outburst flow simulations across a relatively wide, shallow Icelandic sandur, predicted inundation extents were broadly comparable, with the greatest discrepancies occurring when flow encountered a sinusoidal gorge (Alho and Aaltonen, 2008). On the face of it, and with careful calibration, these findings appear to encourage the use of expeditious, computationally undemanding 1-D models, though *sandar* are often far less topographically complex than steep, high relief and tortuous alpine channels. However, Bohorquez and Darby (2008) found that a 1-D model compared reasonably to a 2-D approach for a glacial outburst flood in a high mountain, alpine setting. Averaged discharge estimates produced using 1-D modelling were higher than equivalent data obtained through 2-D modelling (429- $557 \\text{ m}^3 \\text{ s}^{-1}$  and  $358-454 \\text{ m}^3 \\text{ s}^{-1}$  for 1-D and 2-D models, respectively),", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.2. Complex flow hydraulics"], "chunk_type": "text", "line_start": 364, "line_end": 378, "token_count_estimate": 727, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c58c8a4f9fb9e40", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics\nType: text\n\nand tortuous alpine channels . However , Bohorquez and Darby ( 2008 ) found that a 1 - D model compared reasonably to a 2 - D approach for a glacial outburst flood in a high mountain , alpine setting . Averaged discharge estimates produced using 1 - D modelling were higher than equivalent data obtained through 2 - D modelling ( 429 - $ 557 \\ text { m } ^ 3 \\ text { s } ^ { - 1 } $ and $ 358 - 454 \\ text { m } ^ 3 \\ text { s } ^ { - 1 } $ for 1 - D and 2 - D models , respectively ) ,\n\nattributable to the reconstruction of a recirculation zone in the 2-D model, which the 1-D model was unable to reproduce due to its inherent unidirectional treatment of flow direction (Bohorquez and Darby, 2008). This particular example was undertaken over a relatively short distance (<1 km), and for an outburst event with a peak discharge an order of magnitude lower than many documented GLOFs. The natural question arising from the above therefore becomes: how do spatio-temporal fluctuations in GLOF dynamics, including the timing and maximum extent of flood inundation, vary with model dimensionality? With the increasing use of advanced hydraulic codes, a comprehensive appraisal of 1-D, 2-D and 3-D approaches to GLOF prediction is a fundamental research priority, and will be of key interest to hazard assessment and disaster mitigation bodies who are considering the use of more advanced codes.\n\nAs far as possible, and regardless of model dimensionality, particular attention should be given to the numerical discretisation of the flow path in a sufficient level of detail (Fig. 8). A sufficiently detailed topographic representation of the flow path is central to the production of meaningful, robust estimates of flood duration, discharge, velocity, and inundation extent, and is a prerequisite for the use of 2-D and 3-D hydrodynamic models. This is of particular importance in upper reaches, where, for a range of hydrograph forms, flood dynamics may vary significantly. However, results of the intercomparison of coarse\n\n(e.g. SRTM) versus fine (e.g. LiDAR) topographic data for flood inundation modelling (e.g. Sanders, 2007) have revealed that coarse-resolution elevation data possess remarkable value, particularly for rapid, 'first-pass' assessments. Similarly, Huggel et al. (2008) discovered that coarse SRTM and ASTER terrain data, in combination with GIS-based flood routing models, possess remarkable value for providing first-order hazard assessments of volcanically triggered lahars.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.2. Complex flow hydraulics"], "chunk_type": "text", "line_start": 364, "line_end": 378, "token_count_estimate": 679, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e64f3eb378858a0a", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics\nType: text\n\nwhere , for a range of hydrograph forms , flood dynamics may vary significantly . However , results of the intercomparison of coarse ( e . g . SRTM ) versus fine ( e . g . LiDAR ) topographic data for flood inundation modelling ( e . g . Sanders , 2007 ) have revealed that coarse - resolution elevation data possess remarkable value , particularly for rapid , ' first - pass ' assessments . Similarly , Huggel et al . ( 2008 ) discovered that coarse SRTM and ASTER terrain data , in combination with GIS - based flood routing models , possess remarkable value for providing first - order hazard assessments of volcanically triggered lahars .\n\nThe increasing availability of affordable, high-specification desktop computing resources means that the reconstruction and adoption of detailed floodplain topography (and higher order hydrodynamic models) is becoming increasingly viable (e.g. Alho and Aaltonen, 2008; Carrivick et al., 2010). However, the acquisition of fine-resolution topographic datasets is often significantly hindered in many remote, inaccessible, high-altitude regions due to logistical impracticalities. Recent advances in the development and provision of free, publicly-available and user-friendly photogrammetric methods (e.g. Westoby et al., 2012), and the increasing affordability of low-altitude tethered (e.g. Boike and Yoshikawa, 2003; Smith et al., 2009; Vericat et al., 2009) and autonomous surveying platforms (e.g. Lejot et al., 2007; Niethammer et al., 2012) are steadily making the production of high-resolution topographic data products for (geo)morphological analysis and grid-based representation of alpine valley floor topography more practical.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.2. Complex flow hydraulics"], "chunk_type": "text", "line_start": 364, "line_end": 378, "token_count_estimate": 481, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "289049b00953b057", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics\nType: figure\nFigure\n\nImage /page/13/Figure/6 description: A figure displaying a series of six maps that illustrate the progression of a flood over time. The maps are arranged in a 3x2 grid, showing the flood extent at different time steps: 00:30 h, 01:00 h, 01:30 h, 02:00 h, 02:30 h, and 03:00 h. A legend in the top-left panel indicates the water depth with different colors: white for 4 m, light blue for 8 m, green for 16 m, and orange-brown for 32 m. Each map is accompanied by a small inset graph in the upper right corner, plotting Q versus T with a bell-shaped curve. A vertical dashed line on this curve moves from left to right across the panels, indicating the progression of the flood hydrograph. The flood starts small at 00:30 h, expands to its maximum extent and depth around 01:30 h (when the dashed line is at the peak of the curve), and then gradually recedes in the subsequent time steps.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.2. Complex flow hydraulics"], "chunk_type": "figure", "figure_caption": null, "line_start": 379, "line_end": 379, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c2136f666ad2441", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics\nType: text\n\n**Fig. 8.** Flood inundation extent at selected time steps for the two-dimensional hydrodynamic reconstruction of a palaeoGLOF from Dig Tsho, Nepal Himalaya using a Total Variation Diminishing solver (ISIS 2D). Topographic grid discretisations of 4 m, 8 m, 16 m and 32 m were used. The 4 m and 8 m results are broadly comparable for all time steps, whereas use of 16 m and 32 m grids results in the inundation of sizeable areas of the valley floor otherwise unaffected by GLOF passage across the finer grids. Dashed line on inset box shows corresponding time step on the breach outflow hydrograph (for reference, this is identical to the 'optimal' hydrograph shown in Fig. 7). For scale, tick marks on main figure spaced at 200 m intervals.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.2. Complex flow hydraulics", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.2. Complex flow hydraulics"], "chunk_type": "text", "line_start": 380, "line_end": 382, "token_count_estimate": 239, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "71dabce31f40f703", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem\nType: text\n\nIn combination with downstream topographic complexity, flow bulking through the erosion and entrainment of valley floor and morainic sediment often results in the breach outflow hydrograph being largely unrepresentative of downstream flood discharges (e.g. Cenderelli and Wohl, 2003; Kershaw et al., 2005). This is especially the case for flows which rapidly transform into sediment-laden hyperconcentrated or debris flows. In investigating the failure of Neoglacial moraine dams in the Cascade Range, Oregon, O'Connor et al. (2001) estimated that GLOFs transformed from clear water to debris flows within 0.5 km of their source, with the total volume of water released believed to have comprised >25 percent sediment. This is unsurprising, given the vast quantities of unconsolidated glacial and glacio-fluvial material which comprises proximal, deglaciated terrain.\n\nThe entrainment of significant volumes of in-channel and overbank sediment has implications for flow mobility. Fundamentally, an increase in flow volume as a result of the addition of solid material will in turn increase flow velocity. However, consequent increases in flow viscosity and cohesion may act to reduce mobility (Breien et al., 2008). Experimental work by Iverson et al. (2011) revealed that flow mobility decreases with the passage and entrainment of sediment over initially dry terrain. However, if the flow overrides and entrainments sediment from initially wet terrain, flow bulking, a subsequent increase in flow speed and momentum, and the development of large positive pore pressures will result (Iverson et al., 2011). Research in the Swiss Alps by Haeberli (1983) and Huggel et al. (2002a) revealed that minimum average slopes of 11° and 2-3° are required for the sustained runout of debris flows and clear water flows, respectively, reinforcing the largely demobilising effect of substantial sediment entrainment and transport. Consequently, the presence of complex channel topography will result in periodic, transitory fluid dynamics.\n\nFrom a modelling perspective, simulation of the above is highly challenging. As well as neglecting the contribution of transversal velocity to the flow momentum balance, relatively simplistic MF or MSF flow routing algorithms and the majority of 1-D hydrodynamic models are incapable of simulating non-Newtonian flow rheology. This is a fundamental characteristic of sudden onset floods, and has been documented for GLOFs (e.g. Clague and Evans, 2000; O'Connor et al., 2001; Breien et al., 2008), landslide-dammed lake outburst floods (e.g. Capra and Macías, 2002; Dunning et al., 2006; Xu et al., 2012), jökulhlaups (e.g. Carrivick, 2006, 2007), mudflows (including volcanically-triggered lahars) incorporating high concentrations of volcaniclastic material (e.g. Manville, 2004; Carrivick et al., 2009, 2010), and hyperconcentrated or debris flows caused by a rock or ice avalanche (e.g. Hubbard et al., 2007).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.3. Multi-phase flow and the mobile bed problem"], "chunk_type": "text", "line_start": 384, "line_end": 396, "token_count_estimate": 793, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "32d3664d13fa1571", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem\nType: text\n\nO ' Connor et al . , 2001 ; Breien et al . , 2008 ) , landslide - dammed lake outburst floods ( e . g . Capra and Macías , 2002 ; Dunning et al . , 2006 ; Xu et al . , 2012 ) , jökulhlaups ( e . g . Carrivick , 2006 , 2007 ) , mudflows ( including volcanically - triggered lahars ) incorporating high concentrations of volcaniclastic material ( e . g . Manville , 2004 ; Carrivick et al . , 2009 , 2010 ) , and hyperconcentrated or debris flows caused by a rock or ice avalanche ( e . g . Hubbard et al . , 2007 ) .\n\nEntrained sediment affects flow density, viscosity and turbulence. Short-lived changes in hydraulic regime, including the direct influence of sediment on flow hydraulics (e.g. Carrivick, 2010) and concurrent channel reworking, represent major challenges for numerical modelling. Consequently, these effects commonly remain largely overlooked and unquantified (Carrivick et al., 2011). Research into flow-sediment interactions in sudden onset floods with a dam failure origin has been necessarily confined to laboratory-scale investigation (e.g. Chen and Simons, 1979; Capart and Young, 1998; Nsom, 2002; Rushmer, 2007; Carrivick, 2010; Laurent et al., 2011), where issues of scaling and the confined nature of the flow should be considered (Carrivick et al., 2011). A key finding has been the discovery that, in the early stages of the flow event, and without sufficient time to reach capacity, floods where suspension is the predominant mode of sediment transport exhibit a 'dynamic equilibrium' regime. This is manifested geomorphologically as alternating zones of erosion and deposition (Carrivick et al., 2010; Procter et al., 2010), whereby initially high rates of erosion and sediment entrainment and transport rapidly give way to a depositional regime as the transport capacity declines as a function of energy losses through boundary friction and geomorphic work (Carrivick et al., 2011). The results of numerical modelling presented by Xia et al. (2010) suggest that, at least in the initial stages of outburst flood propagation, rates of bed evolution and scour may approach or equal flow depth.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.3. Multi-phase flow and the mobile bed problem"], "chunk_type": "text", "line_start": 384, "line_end": 396, "token_count_estimate": 643, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db3b05f9292a490e", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem\nType: text\n\nexhibit a ' dynamic equilibrium ' regime . This is manifested geomorphologically as alternating zones of erosion and deposition ( Carrivick et al . , 2010 ; Procter et al . , 2010 ) , whereby initially high rates of erosion and sediment entrainment and transport rapidly give way to a depositional regime as the transport capacity declines as a function of energy losses through boundary friction and geomorphic work ( Carrivick et al . , 2011 ) . The results of numerical modelling presented by Xia et al . ( 2010 ) suggest that , at least in the initial stages of outburst flood propagation , rates of bed evolution and scour may approach or equal flow depth .\n\nProcesses of fluid flow and sediment transport are inherently linked to bed evolution. The vast majority of models currently in widespread use are only capable of routing floods over an immobile bed (Liao et al., 2007; Xia et al., 2010). The 'mobile' bed problem describes the propensity for the channel bed to undergo continuous morphological change over the course of an event, including the large-scale mobility of the underlying substrate through tractive or rolling motion. Consequently, the physical manifestation of the channel 'bed', as classically defined, becomes blurred, as a continuum develops comprising fluid, often intensely sediment-laden flow, a zone of active bed mobility and bed-flow sediment exchange, and underlying immobile substrate. A number of models capable of solving the mobile bed problem for dam break floods are reviewed in Xia et al. (2010), and are not described in detail here, however, advances in our understanding of the processes that govern this behaviour have been largely experimental (e.g. Cao et al., 2004; Simpson and Castelltort, 2006; Leal et al., 2009; Xia et al., 2010; Zhang and Duan, 2011). Key findings include the observation that flows over a mobile bed attenuate faster (Carrivick et al., 2011), and peak flow depth was greater and occurs earlier than for those over an immobile bed (Leal et al., 2009).\n\nGLOF modelling efforts that account for the presence of a mobile bed layer in their numerical solutions are rare. Analogous studies include the work of Xia et al. (2010), who document the development and application of a 2-D morphodynamic model capable of considering the influence of flow sediment concentration and bed evolution on flow propagation, as well as the work of Carrivick et al. (2010) who utilised a 2-D fluid dynamics model, Delft-3D, to reconstruct time-varying patterns of geomorphic change produced by a crater lake break-out lahar in New Zealand. Although the model is unable to simulate changes in flow rheology as a result of varying sediment concentration, the capability of Delft-3D to handle prior specification of zones of active (i.e. alluvial) and inactive (i.e. bedrock) channel substrate and actively update cell-specific sediment quantities renders it highly suitable for the investigation of intra- and post-event channel reworking (Carrivick et al., 2010), though has yet to be applied to the simulation of GLOFs. The above findings may have significant implications for the use of purely hydrodynamic models for GLOF reconstruction and prediction, which fail to incorporate solutions of sediment erosion and transport, including spatio-temporal variations in flow rheology and hydraulics and mobile bed evolution.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.3. Multi-phase flow and the mobile bed problem", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.3. Multi-phase flow and the mobile bed problem"], "chunk_type": "text", "line_start": 384, "line_end": 396, "token_count_estimate": 875, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abb3565b44d235f4", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.4. Mesh-free methods for dam-breach simulation\nType: text\n\nThe various methods presently thus far belong to the 'mesh-based' category of hydrodynamic models for open channel flow. Over the past decade or so, a range of particle-based, mesh-free methods for modelling complex fluid flows have been developed. Of these, the 'smoothed-particle hydrodynamics' (SPH) approach is perhaps the most popular (e.g. Bursik et al., 2003; Cleary and Prakash, 2004; Laigle et al., 2007; Pastor et al., 2009; Liu and Liu, 2010; Huang et al., 2012; Kao and Chang, 2012). SPH is a truly mesh-free, Lagrangian, particle-based method for solving the classic mechanics of fluid flow; typically the Navier–Stokes equations. Flows are discretised into a finite number of elements, or particles, possessing predefined geometric and material properties, and inter-particle contact laws (Cleary and Prakash, 2004). Particles are not truly physical, but, when grouped, are considered as a means of representing the fluid(s) under investigation (Laigle et al., 2007).\n\nFully 3-D fluid dynamics, including treatments of local flow density and vertical acceleration, are simulated through inter-particle interactions, which are determined by spatial weighting or smoothing functions (Liu and Liu, 2010). When interacting *en masse*, SPH methods have been demonstrated to reproduce complex, often transient phenomena of natural fluidal flows, including supercritical, subcritical, and\n\ntranscritical flow regimes, hydraulic jumps, shock wave propagation, wave reflection and multiple wave interactions, and splashing and flow fragmentation (Chang et al., 2011; Kao and Chang, 2012), even at relatively coarse resolutions (Cleary and Prakash, 2004). In addition, dynamic free-surface behaviour (e.g. flow superelevation) is handled with ease and without the requirement for the use of surface tracking methods (commonly required by purely mesh-based models). SPH is capable of simulating rheologically complex, non-Newtonian flows with ease, including flow solidification and fluidisation behaviour, making it ideal for application to natural flows which have been shown to demonstrate transient flow rheologies.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.4. Mesh-free methods for dam-breach simulation", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.4. Mesh-free methods for dam-breach simulation"], "chunk_type": "text", "line_start": 398, "line_end": 406, "token_count_estimate": 596, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "613048f5c456f549", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.4. Mesh-free methods for dam-breach simulation\nType: text\n\nand multiple wave interactions , and splashing and flow fragmentation ( Chang et al . , 2011 ; Kao and Chang , 2012 ) , even at relatively coarse resolutions ( Cleary and Prakash , 2004 ) . In addition , dynamic free - surface behaviour ( e . g . flow superelevation ) is handled with ease and without the requirement for the use of surface tracking methods ( commonly required by purely mesh - based models ) . SPH is capable of simulating rheologically complex , non - Newtonian flows with ease , including flow solidification and fluidisation behaviour , making it ideal for application to natural flows which have been shown to demonstrate transient flow rheologies .\n\nThe above capabilities render SPH highly suited for the investigation of sudden onset, dam-break floods. To date, the use of mesh-free methods has been largely confined mainly to experimental application against standard benchmarking problems (e.g. Chang et al., 2011), although a number of real-world case studies have been investigated. These have included the routing of dam break outburst floods over DEMs of varying topographic complexity (e.g. Chang et al., 2011) and modelling hypothetical landslide runout (Huang et al., 2011, 2012), including dynamic landslide-reservoir interactions (Pastor et al., 2009). The work of Pastor et al. (2009) is broadly analogous to the direct interaction of rock or ice avalanches with moraine-dammed lake complexes, and represents a holistic, multi-component approach to modelling all of the phenomena involved, including mass movement trajectory modelling, the interaction of a landslide or avalanche with the lake body and near- and far-field wave propagation. The addition of a treatment describing subsequent wave overtopping of a natural dam structure would complete the unified simulation of all initial stages of this particular trigger mechanism, and is an approach that merits further methodological exploration. The application of mesh-free methods to the simulation of GLOFs is a natural progression from the use of the depth-averaged 1-D and 2-D methods described above. Initial concerns over issues of model validation and computational efficiency (e.g. Richards et al., 2004) appear to have been overcome in some instances, with a number of SPH codes have been shown to perform with favourable computational efficiency and reliability (e.g. Chang et al., 2011). However, examples of SPH application to 'large-scale' simulations such as the routing of dam break outburst floods through lengthy, topographically complex catchments, such as those found in high mountain regions, are currently lacking.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.2. Hydrodynamic modelling > 4.2.4. Mesh-free methods for dam-breach simulation", "section_headings": ["4. Contemporary modelling challenges", "4.2. Hydrodynamic modelling", "4.2.4. Mesh-free methods for dam-breach simulation"], "chunk_type": "text", "line_start": 398, "line_end": 406, "token_count_estimate": 686, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "59c77f6cefd6e598", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.3. Implications for Glacial Lake Outburst Flood modelling\nType: text\n\nThe strengths and inadequacies of the models described above must be considered by workers attempting to reconstruct palaeoGLOFs or simulate future, hypothetical outburst events. Ultimately, the choice of modelling approach will be dictated by logistical and financial constraints. From a practical perspective, a relatively simple, purely rasterand DEM-based approach will be more appealing to workers tasked with the production of first-order assessment of the hazard posed by GLOFs at the regional scale (e.g. Huggel et al., 2003; Allen et al., 2009) and particularly in remote regions where high-resolution terrain data are currently unavailable. Such models represent a low-cost and computationally-undemanding method for modelling the downstream propagation of GLOFs, favour the use of simple, empirically-derived estimations of  $Q_p$  and  $T_p$  (e.g. Table 3), and are suitable for use in combination with coarse-scale terrain data for 'first-pass' hazard assessments (e.g. Huggel et al., 2003).\n\nIncreasing model complexity is traditionally associated with increases in initial start-up costs. Although also relatively computationally undemanding, more advanced models typically require a corresponding increase in user expertise and data requirements for effective parameterisation and implementation. Many input parameters, particularly material characteristics of the moraine dam, are only obtainable through labour-intensive and logistically challenging field investigation (e.g., Xin et al., 2008; Worni et al., 2012), although the well-documented\n\nheterogeneous nature of moraine dams (Benn and Owen, 2002; Bennett and Glasser, 2009; Hambrey et al., 2009) may hinder a single, definitive quantification of their material properties at all. Theoretically, improved representation of the varied and complex physical characteristics of breaching and flood propagation through the application of, for example, analytical or parametric dam breach models and 1-D depth-averaged hydrodynamic solvers are likely to improve the accuracy and robustness of spatial and temporal predictions of inundation and GLOF dynamics in some instances. However, the lack of a consideration of key dynamical aspects of GLOF behaviour, such as turbulent, often super- or transcritical flows, flow superelevation at channel bends, and transitory flow rheologies still serves to limit the veracity and precision of these approaches.\n\nHigher-order modelling approaches, including the use of nextgeneration dam breach models and 2-D, 3-D, or mesh-free hydrodynamic models currently represent the most advanced, physicallyrobust approaches to GLOF modelling. Traditionally, the use of such models has required a significant degree of user expertise, though accessibility has steadily improved in recent years with the increasing use of Graphical User Interfaces (GUI) and GIS support to simplify data input, manipulation and visualization. However, this is offset somewhat by the high initial start-up costs of many codes, the majority of which are distributed as proprietary software packages. In addition, significant computational requirements render them largely unsuitable for the investigation of input parameter uncertainty and equifinality in model output through the use of Monte Carlo methods, for example. For this purpose, simpler models are at a distinct advantage until the affordability and use of high-speed parallel computing systems or highspecification desktop computers becomes more commonplace, or models become more computationally efficient.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.3. Implications for Glacial Lake Outburst Flood modelling", "section_headings": ["4. Contemporary modelling challenges", "4.3. Implications for Glacial Lake Outburst Flood modelling"], "chunk_type": "text", "line_start": 408, "line_end": 416, "token_count_estimate": 852, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e76e8428f0e41fe7", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 4. Contemporary modelling challenges > 4.4. Challenges posed by contemporary and future climatic change\nType: text\n\nRates of glacial recession are predicted to increase in the coming decades on a scale without any known historical precedent (IPCC, 2013). Consequently, and in addition to the challenges discussed above, we will be increasingly faced with challenges associated with modelling GLOF events and glacial and geomorphological conditions to which our range of empirical observations may no longer apply.\n\nOne of the major challenges is predicting where future glacial lakes will form. On debris-covered glaciers, a specific set of glaciological conditions which predispose a glacier to develop a network of supraglacial ponds, and ultimately a fully formed moraine-dammed glacial lake, have been identified (Reynolds, 2000; Quincey et al., 2007) and form a robust basis for identifying those glaciers which are likely to develop glacial lakes in the future. Similarly, qualitative strategies have been developed for identifying the location of subglacial overdeepenings through analysis of the characteristics of glacier surface topography, which, following glacier retreat, may serve as the focal point for meltwater collection and the development of a proglacial lake (Frey et al., 2010).\n\nHowever, the rate of observed and predicted future increases in air temperature and variations in the precipitation regime of many highmountain regions are such that it remains a challenge to predict the pace of future cryospheric change, and how it relates specifically to the development of the GLOF hazard at regional and local scales. Anticipating the celerity with which various glaciological processes will operate, and the timescale required for key physical and climatological thresholds to be exceeded are hindrances to our prediction of the evolution of the GLOF hazard. A prudent approach for assessing the GLOF hazard posed by extant or future moraine-dammed lakes should be based on the regular re-analysis of glaciological, geomorphological and climatological data, with a view to identifying contemporary and future GLOF process chains, and subsequently informing the development or parameterisation of empirical and numerical dam-breach and hydrodynamic models.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "4. Contemporary modelling challenges > 4.4. Challenges posed by contemporary and future climatic change", "section_headings": ["4. Contemporary modelling challenges", "4.4. Challenges posed by contemporary and future climatic change"], "chunk_type": "text", "line_start": 418, "line_end": 424, "token_count_estimate": 530, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d40567c60900769", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: text\n\nAn appreciable degree of uncertainty surrounds the establishment of initial boundary conditions and input parameters for both reconstructive and predictive dam-breach and GLOF modelling. Few studies have undertaken detailed sensitivity analyses into the significance of this uncertainty on, for example, the form of the breach hydrograph, and subsequent implications for the assessment of the spatio-temporal evolution of the GLOF flood wave(s). In many instances, it may be the case that uncertainty associated with model parameterisation results in greater variability in model output than differences attributable to the use of, for example, 1-D, 2-D or 3-D hydrodynamic solvers. Laboratory-scale experiments (Balmforth et al., 2009) indicate that vastly different final breach widths can be reproduced from nearidentical initial breach geometries and compositions, whilst Awal et al. (2010) reported that the magnitude and timing of overtopping waves were dependent on dam geometry (specifically proximal face angle), material composition, and additional factors including ice- and rockavalanche dimensions. Similarly, in Capart's (2013) analytical dambreaching solution, peak discharge was discovered to be sensitive to the lake surface area, and highly sensitive to the initial angle of the upstream dam face, effectively highlighting the importance of quantifying such geometric descriptors of the moraine dam as precisely as possible prior to the specification of initial model boundary conditions.\n\nWith specific reference to numerical dam-breach modelling, significant uncertainty surrounds the establishment of initial conditions (e.g. dam geometry, reservoir bathymetry and hypsometry), the parameterisation of material characteristics (e.g. grain size distribution data, porosity, density, cohesion, internal angles of friction) and the establishment of suitable computational constraints (e.g. model time step and grid discretisation) (Table 4). Whilst the construction of high-resolution\n\nDTMs of moraine dam structures (Westoby et al., 2012), their attendant lake basins (e.g. Robertson et al., 2012; Yao et al., 2012), and downstream valley topography (e.g. Bremer and Sass, 2012) using photogrammetric, laser scanning, or bathymetric surveying techniques has facilitated the extraction of metric data pertaining to, and subsequent accurate characterisation of moraine and lake geometry and down-valley topography, equally robust quantification of the aforementioned material characteristics is typically achievable only through labour-intensive and logistically-demanding fieldwork (e.g. Hanson and Cook, 2004; Osti et al., 2011; Worni et al., 2012). Furthermore, the compositional heterogeneity of moraine dams further complicates the use of a single, 'all-encompassing' material parameter ensemble for applied dam-breach simulation in a range of settings.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "text", "line_start": 426, "line_end": 436, "token_count_estimate": 735, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d1c12dcdca8e1306", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: text\n\n2012 ) using photogrammetric , laser scanning , or bathymetric surveying techniques has facilitated the extraction of metric data pertaining to , and subsequent accurate characterisation of moraine and lake geometry and down - valley topography , equally robust quantification of the aforementioned material characteristics is typically achievable only through labour - intensive and logistically - demanding fieldwork ( e . g . Hanson and Cook , 2004 ; Osti et al . , 2011 ; Worni et al . , 2012 ) . Furthermore , the compositional heterogeneity of moraine dams further complicates the use of a single , ' all - encompassing ' material parameter ensemble for applied dam - breach simulation in a range of settings .\n\nVariability in hydrodynamic model output may be attributed to model dimensionality (e.g. Alho and Aaltonen, 2008; Bohorquez and Darby, 2008), spatial resolution and quality (e.g. Sanders, 2007; Huggel et al., 2008), the spacing of successive cross-sections that represent downstream topography (e.g. Castellarin et al., 2009), as well as uncertainty surrounding the parameterisation of channel and floodplain roughness coefficients (Wohl, 1998; Hall et al., 2005, Pappenberger et al., 2005), input boundary condition data (e.g. Pappenberger et al., 2006) and stage-discharge relationships (e.g. Di Baldassarre and Claps, 2011). For event-specific flood reconstruction, systematic calibration of roughness and friction coefficients to produce 'best-fit', or 'optimal' model parameters is standard practice (e.g. Kidson et al., 2006; Cao and Carling, 2002), and may serve to inform their choice for modelling hypothetical future events (e.g. Horritt and Bates, 2002). However, the application of these calibration techniques for reconstructive and predictive GLOF modelling is highly questionable, not least because of the uniqueness of each outburst event and the appreciable unlikelihood of more than one GLOF originating from a given moraine-dammed lake. Standard", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "text", "line_start": 426, "line_end": 436, "token_count_estimate": 552, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "05c6a7556688ce56", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: table\nTable: Table 4 Sources of uncertainty in the GLOF model chain, the nature of their impact and practiced or proposed measures for their quantification or assessment.\n\n|                                   | Source of uncertainty in model input (*) or output (†)         | Nature of impact or affected output variables                                                                                                                                                   | Solution or measure(s) of addressing                                                                                                                                                                                                                          |\n|-----------------------------------|-------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| <b>Trigger</b>                    | * Source and timing                                               | Dimensions and dynamics of overtopping waves (e.g. ice- or rock avalanche trigger); may determine whether runaway breach development is triggered                                         | Desk- or field-based glacial hazard assessment of moraine and surrounding topography; analysis of multi-temporal datasets required to quantify evolution of hazard (e.g. Reynolds Geo-Sciences, 2003; Huggel et al., 2004; Quincey et al., 2005). |\n| <b>Dam-breach modelling</b>   | * Moraine dam geometry                                            | Dam height, crest width and distal and proximal dam face slope influence style and rate of breach development                                                                                | Fine spatial resolution characterisation and quantification of moraine-dam geometry (e.g. Westoby et al., 2012). Ideally undertaken in situ                                                                                                             |\n|                                   | * Lake bathymetry and stage-volume relationships               | Determines reservoir pressure head and rate of inflow to breach                                                                                                                              | Bathymetric surveying (extant lakes); photogrammetric or 'data-ready' surveying (e.g. TLS) of drained basins                                                                                                                                               |\n|                                   | * Initial conditions (e.g. dam freeboard, spillway dimensions) | Influences impact of overtopping waves (freeboard); accommodation of escaping floodwaters (spillway dimensions); may ultimately determine whether runaway breach development triggered | In situ observation or analysis of fine resolution aerial or space-borne imagery                                                                                                                                                                           |\n|                                   | * Material characteristics                                        | Rate of breach development                                                                                                                                                                      | In situ quantification (preferred); literature-guided parameterisation; probabilistic sampling of parameter space                                                                                                                                       |\n|                                   | * Presence of buried ice in moraine                            | Rate of breach development; interaction of massive ice core with progression of breach development remains largely unquantified (e.g. Worni et al., 2012)                                 | Geophysical inspection of intact moraine dam; visual (and geophysical) analysis of breached structures.                                                                                                                                                    |\n| <b>Hydrodynamic modelling</b> | † Grid discretisation                                             | Decreasing grid resolution results in effective 'smoothing' of small-scale topographic elements; in the first instance determines direction of flow                                       | Undertake detailed sensitivity analysis to quantify impact on inundation extents and wetting-front travel times; attempt to validate results where possible (v. challenging)                                                                            |\n|                                   | * Channel and valley-floor roughness coefficient(s)            | Influences effectiveness of flow conveyance, influence more important at low flows; exerts little control over flow dynamics at high flow stage and velocity                              | Literature- and field investigation-guided classification; investigate and quantify any functional relationship with topographic grid resolution                                                                                                        |\n|                                   | † Complex flow rheology (model physicality)                    | Will influence flow velocities (and therefore wetting-front travel-time) and erosive potential of the flow                                                                                   | Use of advanced hydrodynamic solvers capable of simulating sediment transport, transient flow rheology and mobile bed evolution                                                                                                                         |\n|                                   | † Model dimensionality                                            | 'Simple' codes incapable of simulating lateral transfer of flow momentum and mass. Complex flow dynamics (flow superelevation, development of flow recirculation zones etc)               | If detailed GLOF hazard assessment required, use of advanced, 2-D solvers advocated. Raster-based or 1-D solvers suitable for 'first-pass' hazard assessment                                                                                            |\n|                                   | † Model coupling                                                  | Model de-coupling results in loss of flow momentum and mass transfer. Importance for determining near-field flow velocities and reworking                                                 | Use fully-coupled models to simulate breach development and cascading of floodwaters (and morainic material) from lake basin to the floodplain.                                                                                                         |", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "table", "table_caption": "Table 4 Sources of uncertainty in the GLOF model chain, the nature of their impact and practiced or proposed measures for their quantification or assessment.", "columns": ["", "Source of uncertainty in model input ( ) or output (†)", "Nature of impact or affected output variables", "Solution or measure(s) of addressing"], "table_row_start": 1, "table_row_end": 11, "line_start": 437, "line_end": 449, "token_count_estimate": 1094, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1d53326d4151e9e", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: text\n\ncalibration techniques, such as the use of palaeo-stage indicators or slackwater deposits (e.g. Cenderelli and Wohl, 2003; Kershaw et al., 2005; Bohorquez and Darby, 2008) represent one possible approach to flow calibration, but are reach-specific and typically reflect only the maximum flood stage and corresponding inundation extent. Similarly, delineation of channel- and valley-floor regions subject to GLOF passage represents a straightforward method of establishing maximum flood extents. However, a widespread lack of pre-GLOF topographic datasets (for GLOF routing), combined with the inability of many widely-used hydrodynamic codes to simulate complex and highly transient flow rheologies and sediment transport dynamics complicates the use of maximum flood extents to accurately constrain palaeoGLOF dynamics.\n\nA potential solution to the above is the adoption of probabilistic approaches to GLOF reconstruction, which embrace uncertainty in the model chain using stochastic sampling techniques and are capable of quantifying the degree of equifinality in model output. Such methods have appreciated widespread use in the distributed hydrological modelling literature (e.g. Beven and Binley, 1992; Kuczera and Parent, 1998; Lamb et al., 1998; Blazkova and Beven, 2004; Franz and Hogue, 2011), and, with limited modification, would appear to be equally suited to dam-breach reconstruction (Fig. 9) and GLOF inundation mapping (Fig. 10). As an example, quantifying the performance of dam-breach models according to their ability to reproduce the geometry of moraine breaches caused by palaeoGLOFs represents one solution to being able to identify the parameter ranges which may be deemed acceptable, or 'behavioural'. However, significant complications arise with the elucidation of a 'universal' range of input parameters for application to predictive GLOF modelling efforts given the highly site-specific nature of their derivation, and the challenges associated with identifying and reconstructing a large enough sample of GLOFs in a specific region from which to undertake a representative statistical analysis of the key modelling parameters (and their ranges) which exert the dominant influence over breach development and GLOF propagation. Specifically, and in the absence of validation of their suitability to reconstructing observed breach morphologies of other breached moraine dams, a range of tightly-constrained material characteristics that may be capable of reproducing observed breach morphologies at one site may not be readily applicable to accurately predicting the range of breach hydrographs that might arise from the failure of another. Nevertheless, the subsequent production of probability-weighted maps of inundation are arguably of more value for flood hazard and risk assessment, and the use of uncertainty-based techniques for both reconstructive and predictive GLOF modelling merits further consideration.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "text", "line_start": 450, "line_end": 454, "token_count_estimate": 729, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8a3f050fb0aca15d", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: figure\nFigure\n\nImage /page/17/Figure/4 description: A black and white line graph plots Q (in m³ s⁻¹) on the y-axis against T (in h) on the x-axis. The y-axis ranges from 0 to 2500 in increments of 500. The x-axis ranges from 0 to 6 in increments of 1. A solid black line starts at the origin (0,0), rises to a peak of approximately 1400 at T=2.6, and returns to zero around T=3.5. This black line is enclosed within a larger, shaded gray area which represents a range of values. The shaded area also starts at the origin, rises to a jagged peak of about 2000 around T=1.8, and then gradually decreases, extending to approximately T=5.5 before returning to zero.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "figure", "figure_caption": null, "line_start": 455, "line_end": 455, "token_count_estimate": 227, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0511ab189cd39b5f", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: text\n\n**Fig. 9.** Dam breach hydrographs for a reconstruction of the Dig Tsho failure. Solid black line corresponds to the 'optimal' breach outflow hydrograph, with performance conditioned on the ability of the simulation to reproduce observed geometric descriptors of post-GLOF breach morphology (including final breach depth and centreline elevation profile or slope). In contrast, grey shading represents the hydrograph envelope of all acceptable, or 'behavioural' failure simulations, reflecting a range of input parameter combinations.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "text", "line_start": 456, "line_end": 458, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f905e35172ac6af", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: figure\nFigure\n\nImage /page/17/Figure/6 description: The image contains three maps labeled A, B, and C, which visualize data for a river or floodplain. Map A shows the \"Inundation probability,\" with a color scale ranging from 0% (blue) to 100% (red). A blue arrow indicates the direction of flow. Map B displays inundation extents for the 5th percentile (white), 50th percentile (gray), and 95th percentile (black). Map C illustrates the \"Water height (m),\" with a color scale from 0 (light blue) to 13 (dark blue).", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "figure", "figure_caption": null, "line_start": 459, "line_end": 459, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7c98e2f311477d1c", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 5. Considering uncertainty in the GLOF model chain\nType: text\n\n**Fig. 10.** Communicating uncertainty in reconstructive mapping of the Dig Tsho GLOF. (A) inundation probability, calculated as the percentage of 'behavioural' simulations which inundate a given cell; (B) percentile-specific inundation extent, and; (C) an example of inundation extent and flow depth for the 95th percentile model. All data extracted at 02:00 h after breach initiation. For scale, tick marks spaced at 200 m intervals. Blue arrow in **A** indicates direction of flow.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "5. Considering uncertainty in the GLOF model chain", "section_headings": ["5. Considering uncertainty in the GLOF model chain"], "chunk_type": "text", "line_start": 460, "line_end": 462, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aefe8d09602cb379", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 6. Conclusions\nType: text\n\nHigh-magnitude outburst floods from moraine-impounded glacial lakes represent a highly-complex, multi-stage catastrophic phenomenon, capable of accomplishing significant geomorphologic reworking of channel- and valley-floor floodplain environments and posing a very real threat to infrastructure and the safety and livelihoods of communities. This review has provided a comprehensive overview of the individual 'components' that constitute a GLOF event, namely: triggering mechanism(s), moraine dam breaching, and routing of the escaping floodwaters, and the modelling approaches typically used to reconstruct or predict GLOF dynamics.\n\nProgress in understanding of the main factors controlling moraine dam breach dynamics has been relatively slow since the emergence of the first numerical models well over two decades ago. Empirical models,\n\noriginally developed for application to the failure of artificially-constructed dams and levees, commonly appear in the geosciences literature and should not be discounted as useful tools for the production of rapid, first-pass hazard assessments. The recent advent of fully physically based advanced numerical dam-breach models heralds a significant step forward in our capability to produce robust, physically based estimates of breach outflow dynamics, including quantification of sediment evacuation rates.\n\nThe requirement to model accurately GLOF hydrodynamics at both the source and downstream is central to the production of detailed GLOF inundation maps and hazard assessment. However, the high computational burden associated with the execution of advanced 2-D and 3-D hydrodynamic codes, coupled with the relative dearth of fine-resolution topographic data required for detailed discretisation of channel and floodplain domains are still major hindrances to the widespread adoption of these models for the production of detailed hazard assessments of anticipated GLOF events.\n\nIn conclusion, we highlight the following as key modelling challenges and potential areas of future GLOF research.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "6. Conclusions", "section_headings": ["6. Conclusions"], "chunk_type": "text", "line_start": 464, "line_end": 484, "token_count_estimate": 472, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "611c75461c1fe0ab", "text": "Document: Modelling outburst floods from moraine-dammed glacial lakes\nSection: 6. Conclusions\nType: text\n\ndynamics , including quantification of sediment evacuation rates . The requirement to model accurately GLOF hydrodynamics at both the source and downstream is central to the production of detailed GLOF inundation maps and hazard assessment . However , the high computational burden associated with the execution of advanced 2 - D and 3 - D hydrodynamic codes , coupled with the relative dearth of fine - resolution topographic data required for detailed discretisation of channel and floodplain domains are still major hindrances to the widespread adoption of these models for the production of detailed hazard assessments of anticipated GLOF events . In conclusion , we highlight the following as key modelling challenges and potential areas of future GLOF research .\n\n- The development of dam breach models capable of actively simulating dynamic wave behaviour in a glacial lake, such as those caused by landsliding or avalanching, and accounting for the passage of single of multiple overtopping waves in breach initiation and development;\n- Further experimental and numerical investigation into the interactions between seismic activity and the geotechnical performance of moraine dams, specifically its significance in initiating dam settlement or massive structural failure, and the evolution of breach initiation via piping and intra-morainal seepage;\n- The ability of dam breach models to use complex, real-world geometric data as input, such as irregular crest topography and arcuate dam planform, providing a true reflection of observed moraine dam geometry;\n- Quantification of the effects of spatially continuous or discontinuous permafrost in the dam structure, including massive bodies of relict glacial ice, on patterns of breach erosion, and the development of numerical models capable of accounting for often complex, user-defined spatial variations in ice content.\n- Improvements in inter-model coupling, including the preservation of breach outflow hydrodynamics, the passage of multiple overtopping flood waves or breach-flow 'pulses', and temporal variations in sediment evacuation rates from the enlarging breach;\n- Quantification of the effects of model dimensionality on reconstructed or predicted flow hydraulics and patterns of inundation and geomorphologic reworking of in-channel and overbank environments, including implications for the construction of downstream hazard assessments;\n- Wherever possible, the collection of dam material property and geometric data in situ, in order to constrain dam-breach model input parameters as rigorously as possible, and to reduce the extent of the uncertainty associated with numerical dam-breach model parameterisation;\n- The acquisition and use of high-resolution topographic datasets, in order to make the effective use of advanced hydrodynamic models viable, including, where possible, the attainment of pre- and post-GLOF DEMs to aid quantitative analysis and interpretation of net erosion and deposition;\n- The adoption of models capable of simulating spatio-temporal patterns of transitory flow rheology, including effects of flow hydraulics and capacity to perform geomorphic work, and the use of models incorporating solutions of mobile bed evolution and subsequent impacts for GLOF propagation.", "metadata": {"source_file": "data/('Modelling outburst floods from moraine-dammed glacial lakes', '.pdf')_extraction.md", "document_title": "Modelling outburst floods from moraine-dammed glacial lakes", "section_path": "6. Conclusions", "section_headings": ["6. Conclusions"], "chunk_type": "text", "line_start": 464, "line_end": 484, "token_count_estimate": 749, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f2ede6b60d88b42", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: ABSTRACT\nType: text\n\nThe rapid worldwide formation and expansion of glacial lakes has increased the likelihood of glacial lake outburst floods, threatening lives and infrastructure, particularly in vulnerable mountain communities. Given the rapid increase in the popularity of artificial intelligence methods for remote sensing of glacial lakes, a comprehensive review is essential. We survey a decade (2015-2024) of research on glacial lake monitoring from space, with a focus on classical machine learning and deep learning approaches. We identify key trends, research gaps, and best practices for future studies. Most studies rely on optical imagery, especially Landsat-8 and Sentinel-2, while Sentinel-1 serves as a complementary radar source. However, monitoring glacial lakes in mountainous regions remains a challenge on cloudy days due to the limitations of radar and the unusability of optical data. Deep learning, particularly U-Net and DeepLab derivatives, dominates learning-based glacial lake studies but remains computationally demanding. Critical challenges involve balancing performance gains against trade-offs in data availability, computational cost, and model transferability. Geographic and methodological gaps, especially in regions experiencing rapid lake growth, underscore the need for broader spatial coverage and improved spatiotemporal model generalization. Moreover, transitioning from a focus on static seasonal mapping to frequent multi-temporal monitoring is beneficial for understanding glacial lake evolution and outburst flood hazards. Adapting emerging deep learning architectures to integrate multispectral, hyperspectral, and radar data could enhance glacial lake detection capabilities. Furthermore, thorough intermethod comparisons, benchmarking with rigorous evaluation metrics, and open-sourcing datasets and code would facilitate robust, large-scale glacial lake monitoring efforts.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 5, "token_count_estimate": 433, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e3dcfa152a224218", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 1. Introduction\nType: text\n\nApproximately 1.74% of Earth's water is stored in glaciers, ice caps, and permanent snow cover, accounting for about 68.7% of global freshwater (Shiklomanov, 1993). Glacial lakes are water bodies formed by the accumulation of meltwater in depressions created by glacier retreat, including those dammed by ice, moraines, or other glacially deposited materials (Costa and Schuster, 1987). Climate change-driven glacier mass loss is accelerating the formation and expansion of glacial lakes (NASA Decadal Survey, 2007; King et al., 2019; Shugar et al., 2020)\n\nGlobally, more than 110,000 glacial lakes have been documented, with a total mapped area of approximately 15,000 km², based on studies conducted between 2006 and 2020 (Zhang et al., 2024). Glacial lakes are integral to regional freshwater systems, storing meltwater and influencing hydrological cycles (Mingwei et al., 2025). At the same time, due to their dynamic nature, some of these lakes are sources\n\nof Glacial Lake Outburst Flood (GLOF)s, endangering lives and critical infrastructure worldwide (Emmer et al., 2022; Taylor et al., 2023). Over 3000 GLOFs were recorded from the year 850 to 2022, while the total glacial lake area increased by approximately 22% per decade between 1990 and 2020 (Zhang et al., 2024).\n\nMulti-temporal monitoring of glacial lakes is beneficial for assessing GLOF hazards, developing early warning systems for the protection of downstream communities, and improving water resource management (Rinzin et al., 2023; Ahmed, 2024; Emmer, 2024). However, implementing effective and scalable monitoring strategies remains challenging due to the inherent variability in glacial lake characteristics, including differences in size, shape, depth, and turbidity. Many of these lakes are small (area <0.1 km²) and remain frozen for several months, exhibiting distinctive geomorphologic traits (Carrivick and\n\n<sup>\\*</sup> Correspondence to: 4800 Oak Grove Drive, M/S 300-331, Pasadena, CA 91109, USA. E-mail address: manu.tom@jpl.nasa.gov (M. Tom).\n\nTweed, 2013; Costa and Schuster, 1987; Clague and Evans, 2000). Beyond these physical variations, the diversity of lake formation environments makes systematic observation difficult. These include proglacial (rock-dammed, moraine-dammed, ice-dammed), ice-marginal (glacier-blocked), supraglacial, and subglacial settings (Costa and Schuster, 1987).\n\nIn situ measurements of glacial lakes capture a wide range of parameters, including lake water level, area changes, bathymetry, temperature, precipitation, and ice cover, among others (Fujita et al., 2009; Tedesco and Steiner, 2011; Sharma et al., 2018). These measurements are often sparse, as field campaigns to glacial lakes are challenging due to their remote, high-altitude, or high-latitude locations adjacent to glaciers (Treichler et al., 2019). Consequently, satellite and airborne observations often serve as complementary, and in some cases, primary methods for large-scale and continuous monitoring. These remote sensing techniques specifically focus on the geographic extent of surface lakes. However, they provide critical insights where direct measurements are limited (Huang et al., 2018).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 7, "line_end": 33, "token_count_estimate": 849, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["91109"]}}
{"id": "d50bc4ec0442b0c2", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 1. Introduction\nType: text\n\net al . , 2009 ; Tedesco and Steiner , 2011 ; Sharma et al . , 2018 ) . These measurements are often sparse , as field campaigns to glacial lakes are challenging due to their remote , high - altitude , or high - latitude locations adjacent to glaciers ( Treichler et al . , 2019 ) . Consequently , satellite and airborne observations often serve as complementary , and in some cases , primary methods for large - scale and continuous monitoring . These remote sensing techniques specifically focus on the geographic extent of surface lakes . However , they provide critical insights where direct measurements are limited ( Huang et al . , 2018 ) .\n\nSeveral approaches exist for mapping glacial lake extent using remote sensing. An example is Geographic Information System (GIS)-assisted manual lake boundary delineation and inventory creation methods. These methods leverage human expertise to interpret data and define boundaries from satellite observations (Ukita et al., 2011; Raj and Kumar, 2016; Petrov et al., 2017; Senese et al., 2018, etc.). However, such manual approaches are feasible only on a small scale and/or for a few time steps due to their labor intensive nature. In contrast, automated remote sensing methods offer greater scalability and enable the fusion of information from multiple data sources—including optical and radar imagery.\n\nThere has been a notable rise in the popularity of classical ML and DL methods for land cover classification applications over the past decade. These learning-based methods, in conjunction with remote sensing, have achieved significant breakthroughs across various subfields of geoscience (Karpatne et al., 2019; Camps-Valls et al., 2021; Ge et al., 2022). Such advancements also span hydrosphere (Sit et al., 2020) and cryosphere (Liu, 2021) monitoring.\n\nUnlike traditional non-ML approaches, data-driven ML algorithms effectively learn intricate patterns from representative remote sensing datasets, enabling more accurate and efficient analysis (Maxwell et al., 2018). DL methods, while demanding in terms of training data requirements, automatically extract features – such as texture and spatial relationships – directly from data. They learn hierarchical representations and outperform classical ML methods in many fields of Earth sciences (Tuia et al., 2024; Taylor et al., 2021). However, these data-driven methods also have limitations. These include high data and computational demands, large model sizes that hinder deployment, challenges in geographical transferability, and debatable interpretability and explainability.\n\nThe potential value and widespread adoption of diverse learningbased methods underscore the need for a detailed review. A comprehensive evaluation of existing approaches, along with a synthesis of their strengths and limitations, the establishment of best practices, and the identification of key research gaps, is essential.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 7, "line_end": 33, "token_count_estimate": 713, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72bddb977eaabafb", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 1. Introduction\nType: text\n\nThey learn hierarchical representations and outperform classical ML methods in many fields of Earth sciences ( Tuia et al . , 2024 ; Taylor et al . , 2021 ) . However , these data - driven methods also have limitations . These include high data and computational demands , large model sizes that hinder deployment , challenges in geographical transferability , and debatable interpretability and explainability . The potential value and widespread adoption of diverse learningbased methods underscore the need for a detailed review . A comprehensive evaluation of existing approaches , along with a synthesis of their strengths and limitations , the establishment of best practices , and the identification of key research gaps , is essential .\n\nThough numerous research papers in the past decade have applied ML/DL to map and monitor glacial lakes, a dedicated review remains absent. Some surveys exist on related topics—such as ML/DL for water body detection (Gautam and Singhai, 2024) and lake-water level fluctuation forecasting (Sannasi Chakravarthy et al., 2022). However, none specifically focus on remote sensing of glacial lakes. Similarly, reviews on non-ML approaches – such as bibliometric analysis of glacial lake identification (Zhengquan et al., 2023), frozen lake extraction from optical data (Jawak et al., 2015), and remote sensing applications in the mountain cryosphere (Taylor et al., 2021) – offer valuable insights. However, they do not address Artificial Intelligence (AI)-driven methodologies. The paper is structured as follows: Section 2 discusses spatiotemporal aspects in learning-based glacial lake\n\nstudies, while Section 3 provides an overview of the remote sensing data used. Section 4 explores ML/DL methodologies for studying proglacial, ice-marginal, and supraglacial lakes from space, followed by Section 5, which presents key challenges and limitations. Finally, Section 6 concludes with recommendations for future research.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 7, "line_end": 33, "token_count_estimate": 501, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2dacd4d428276b4a", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring\nType: text\n\nThe first step in a glacial lake study is mapping. This involves creating an inventory or map of lakes in a study region using an underlying ML/DL model, typically based on satellite data from a single point in time. Mapping is essential for providing a snapshot of glacial lake extents and serving as a proof-of-concept. However, it alone cannot capture seasonal fluctuations, indicate long-term trends, or assess GLOF hazard potential.\n\nThe next critical step is monitoring, which builds on repeated mapping to systematically track glacial lake evolution over time. Learning-based studies such as Banerjee and Bhuiyan (2023), Lutz et al. (2023), and Sharma and Prakash (2023), among others, have reported monitoring efforts that extend beyond one-time mapping.\n\nHowever, most studies (e.g., He et al., 2021; Wang et al., 2021) focus solely on mapping, prioritizing static lake characterization over understanding temporal dynamics. Effective monitoring requires repeated observations while balancing trade-offs between update frequency and various challenges. These challenges include data gaps due to cloud cover and inconsistent data acquisition, seasonal variability, and increased computational demands. A key constraint is the limited availability of suitable multi-temporal satellite imagery. This is especially challenging in high-mountain regions where frequent cloud cover and the lack of usable optical images (in winter) severely restrict observation windows.\n\nNotably, repeated mapping – conducted as frequently as observation conditions allow – differs from multi-temporal monitoring. The latter involves a systematic analysis of temporal patterns, often designed to capture intra-annual variations and seasonal dynamics. To ensure the accuracy and reliability of new glacial lake extent products derived from learning-based algorithms, rigorous evaluation of multi-temporal monitoring outcomes is recommended before operational deployment.\n\nEnvironmental factors such as cloud cover, haze, ice and snow cover, fog, evaporation, and surface reflectance - also referred to as sun glint or sun glitter - vary significantly over time in glaciated regions. These variations pose challenges for consistent glacial lake monitoring across all seasons (Mölg and Hardy, 2004; Hock, 2005). Frozen lakes, particularly supraglacial lakes or those in direct contact with glaciers or covered by snow, are difficult to distinguish spectrally from surrounding ice in optical satellite imagery. In some cases, spectral similarity makes detection nearly impossible during colder months. ML/DL methods can partially overcome this by learning spatial and contextual patterns beyond raw spectral values. Convolutional Neural Network (CNN)s extract multi-scale features such as texture, edges, and shape, while attention-based models use broader spatial context to improve classification. These approaches are effective when frozen lakes have distinct morphological boundaries. However, when both spectral and spatial cues are weak - such as under uniform snow cover - performance remains limited, regardless of model complexity.\n\nSeasonal surface variations also affect radar-based glacial lake detection. Flat water surfaces typically exhibit low backscatter (Bauer-Marschallinger et al., 2021). Wind or thin floating ice can roughen water surfaces, increasing backscatter (Freilich and Vanhoff, 2003; Shaposhnikova et al., 2023). Snow has higher backscatter due to its heterogeneous structure (Rott, 1984). Ideally, the dataset used to train an ML/DL model should be representative of these variations to ensure robust detection across diverse environmental conditions.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring", "section_headings": ["2. Learning-based glacial lake studies: A spatio-temporal perspective", "2.1. Beyond seasonal mapping: Toward multi-temporal monitoring"], "chunk_type": "text", "line_start": 37, "line_end": 51, "token_count_estimate": 884, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "84ca980bf1bce5df", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring\nType: table\nTable: Table 1 Examples of learning-based glacial lake studies conducted during specific seasons with favorable observation conditions.\n\n| Publication             | Season             | Primary study site           |\n|-------------------------|--------------------|------------------------------|\n| Dirscherl et al. (2020) | January-February   | Antarctica                   |\n| Dirscherl et al. (2021) | December-February  | Antarctica                   |\n| Yuan et al. (2020)      | May-September      | Southwest Greenland          |\n| Qayyum et al. (2020)    | May-November       | Hind Kush Karakoram Himalaya |\n| Wang et al. (2021)      | September-November | Himalayas                    |\n| Xu et al. (2023)        | Summer             | Eastern Himalaya             |\n| Zhao et al. (2023)      | July-November      | High Mountain Asia           |\n| Tang et al. (2024)      | June-November      | Third Pole Region            |", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring", "section_headings": ["2. Learning-based glacial lake studies: A spatio-temporal perspective", "2.1. Beyond seasonal mapping: Toward multi-temporal monitoring"], "chunk_type": "table", "table_caption": "Table 1 Examples of learning-based glacial lake studies conducted during specific seasons with favorable observation conditions.", "columns": ["Publication", "Season", "Primary study site"], "table_row_start": 1, "table_row_end": 8, "line_start": 52, "line_end": 61, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8aba8775db86e36c", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring\nType: text\n\nTo minimize the impact of seasonal challenges, learning-based glacial lake studies often target months with optimal data quality. For example, Wu et al. (2020) avoided July to September due to frequent cloud cover (southeastern Tibet), which hinders optical remote sensing. They also excluded January, when frozen conditions and lake shrinkage complicate the detection of lakes. As a result, they selected October and November, when melting slows, lake extents stabilize, and cloud cover decreases. Further examples of learning-based studies that targeted periods with favorable observation conditions are presented in Table 1.\n\nStrategies constrained by seasonal conditions improve data quality, reduce cloud interference, and capture stable lake conditions. However, they neglect temporal variability and the associated risk potential, which are essential in the context of non-stationary lakes that form during the melt season or during glacier surges. To address these limitations, future studies should expand training datasets with representative reference data spanning multiple seasons and develop models that learn key intra-annual variations. Multi-sensor fusion using all available and suitable satellite sources, including complementary sensors such as Synthetic Aperture Radar (SAR), can improve temporal coverage. SAR examples include Sentinel-1 (S1) [C-band, freely available], ICEYE-X1 (X-band, commercial). Augmenting the training data set or using generative models to simulate cloud penetrating synthetic imagery may also help.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.1. Beyond seasonal mapping: Toward multi-temporal monitoring", "section_headings": ["2. Learning-based glacial lake studies: A spatio-temporal perspective", "2.1. Beyond seasonal mapping: Toward multi-temporal monitoring"], "chunk_type": "text", "line_start": 62, "line_end": 66, "token_count_estimate": 385, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51635af475ac0ee6", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.2. Glacial lake products: types and resolutions\nType: text\n\nML/DL-based glacial lake products differ in type, temporal resolution, and spatial resolution. Maximum Lake Extent (MLE) products, such as those by Wang and Sugiyama (2024), delineate the largest observed lake boundary, making them valuable for GLOF hazard assessment and long-term trend analysis. While straightforward to interpret, MLE overlooks short-term lake fluctuations. In contrast, Per-Pixel Classification (PPC) products, used in studies like Yuan et al. (2020), He et al. (2021) and Thomas et al. (2023), etc., capture precise water extents. When applied over time, they support the analysis of seasonal and interannual variations. Some studies, such as Xu et al. (2023), integrate both PPC and per-lake classification.\n\nRegarding temporal resolution, Dirscherl et al. (2021) for example, produced monthly products, while Yuan et al. (2020) generated yearly products. Higher temporal resolutions (e.g., weekly or monthly) enhance the detection of seasonal changes. Conversely, lower resolutions (e.g., yearly) prioritize computational efficiency and broader trend analysis. The required temporal resolution should align with the study objective—whether focused on long-term trend analysis or short-term event monitoring, such as rapid GLOF response.\n\nSpatial resolution also varies based on input data. High-resolution ( $\\leq$ 3 m) products (e.g., Siddique et al., 2023; Thomas et al., 2023) are essential for mapping and monitoring small glacial lakes and resolving dynamic and detailed lake boundaries. Low-resolution ( $\\geq$ 30 m) products (e.g., Zhao et al., 2023; Yuan et al., 2020) are efficient for large-scale, long-term global assessments. However, they may miss small lakes or subtle changes in lake extent. Medium-resolution ( $\\approx$ 10 m) products (e.g., Basit et al., 2022; Xu et al., 2023) strike a balance\n\nbetween spatial detail and computational efficiency, making them well-suited for regional studies.\n\nBalancing the need for spatial and temporal detail with resource constraints and data availability is important. We recommend selecting the appropriate product type and resolution based on the study objective.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "2. Learning-based glacial lake studies: A spatio-temporal perspective > 2.2. Glacial lake products: types and resolutions", "section_headings": ["2. Learning-based glacial lake studies: A spatio-temporal perspective", "2.2. Glacial lake products: types and resolutions"], "chunk_type": "text", "line_start": 68, "line_end": 78, "token_count_estimate": 596, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4248fab64ae146c0", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview\nType: text\n\nIn learning-based glacial lake studies, optical satellite sensors are preferred [primarily Landsat-8 (L8) and Sentinel-2 (S2)] over radar sensors (Fig. 1). Of the 48 studies reviewed (Fig. 2), 43 (89.6%) used optical data, 16 (33.3%) used SAR data, and 12 (25%) used both (Appendix A).\n\nDespite radar's all-weather, day-and-night imaging capabilities, optical sensors remain preferred due to their multispectral bands, which effectively capture surface water changes over time. While radar helps enhance observation frequency, optical sensors improve lake detection accuracy. The wider range of free optical data options available over the past decade – such as L8, S2, PlanetScope – compared to SAR [S1], has further reinforced this preference. Among freely available datasets, optical sensors (S2) also offer higher spatial resolution (includes 10 m bands) than SAR (S1,  $\\approx\\!20$  m). Furthermore, SAR requires relatively extensive pre-processing before analysis (Mullissa et al., 2021). However, optical remote sensing can be affected by cast shadows and turbidity variations. On the other hand, using SAR data in mountainous regions presents challenges due to complex terrain (more details in Section 3.1). These include geometric and radiometric distortions (Rott, 1984; Wu et al., 2021) .\n\nL8 is the most commonly used (22 papers) sensor despite its moderate spatial (30–100 m) and temporal resolution (16 days). Most L8-based studies rely on Operational Land Imager (OLI) bands. These bands provide higher radiometric resolution (12-bit), improved signal-to-noise ratio, and narrower spectral bands compared to previous Landsat missions (Mancino et al., 2020). Few exceptions (e.g., He et al., 2021; Chen et al., 2022; Kaushik et al., 2022) incorporate Thermal InfraRed Sensor (TIRS) data.\n\nL8 is followed by S2 (20 studies) with slightly higher spatial (10–60 m) and temporal (5 days) resolution (Fig. 1, Appendix A). PlanetScope imagery is also preferred (5 papers) due its high spatial (3 m) and temporal (daily) resolution. However, its lower spectral resolution and commercial constraints restrict its broader applicability.\n\nAmong radar sensors, S1-SAR is the primary choice (15 studies), particularly the Interferometric Wide (IW) swath Ground Range Detected (GRD) mode. Topographic data such as Digital Elevation Model (DEM) are used to distinguish lake pixels from shadows and enhance classification accuracy. No strong preference for a specific DEM is observed (Fig. 1, Appendix A).\n\nNo existing studies have incorporated Surface Water and Ocean Topography (SWOT) data (Alsdorf and Lettenmaier, 2003; Vinogradova et al., 2025). SWOT's high-resolution swath altimetry enables precise monitoring of water level changes in lakes, rivers, and reservoirs (Biancamaria et al., 2016; Getirana et al., 2024). Its unique water surface elevation data provides an unprecedented opportunity to estimate lake", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview", "section_headings": ["3. Remote sensing data used: An overview"], "chunk_type": "text", "line_start": 80, "line_end": 92, "token_count_estimate": 807, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "647ccd214b1d5e9f", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A figure displaying three radar charts, each representing a different type of satellite data and its usage in publications. A note below the charts states, \"Each circle represents a publication.\" The first chart, titled \"Optical satellite imagery\" in blue, shows data for sources like Sentinel-2 (4 publications), Landsat-8 (5 publications), Planetscope (1 publication), ASTER (0), Corona KH-4 (0), IRS LISS III (0), Landsat-7 (2 publications), and Landsat-Other (4 publications). The most used sources are Landsat-8, Sentinel-2, and Landsat-Other. The second chart, titled \"Radar satellite imagery\" in green, shows data for Sentinel-1 (4 publications), TerraSAR-X (1 publication), and GaoFen-3 (0 publications), with Sentinel-1 being the most prominent. The third chart, titled \"Digital elevation model\" in red, shows data for NASADEM (0), SRTM (2 publications), TanDEM-X (2 publications), ArcticDEM (1 publication), ASTER (0), Copernicus DEM (2 publications), and ALOS (0).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview", "section_headings": ["3. Remote sensing data used: An overview"], "chunk_type": "figure", "figure_caption": null, "line_start": 93, "line_end": 93, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1f53b6c95de53ad9", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview\nType: text\n\nFig. 1. Distribution of input satellite sensors (optical, radar) and topography data used in ML and DL approaches for glacial lake studies. This plot is based on 47 publications. Cao et al. (2024) was excluded as it used Google Earth imagery, comprising a mix of images from IKONOS, QuickBird, GeoEye, WorldView, SPOT, and Pleiades. More details are in (Appendix A).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview", "section_headings": ["3. Remote sensing data used: An overview"], "chunk_type": "text", "line_start": 94, "line_end": 96, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c010ea1d8728dd2a", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview\nType: figure\nFigure\n\nImage /page/3/Figure/4 description: A stacked bar chart illustrating the number of published works per year, categorized into Machine Learning and Deep Learning. The y-axis, labeled \"Number of published works,\" ranges from 0 to 12. The x-axis, labeled \"Year,\" shows data for 2015, 2018, 2020, 2021, 2022, 2023, and 2024, with a break in the axis between 2015 and 2018. A legend indicates that black dots represent individual studies, light blue bars represent Deep Learning, and light green bars represent Machine Learning. Each bar is topped with the total number of works for that year, along with the percentage and count breakdown for Deep Learning (DL) and Machine Learning (ML). The data for each year is as follows: 2015: 1 total work, 100% ML (1 study: Jain\\_15). 2018: 1 total work, 100% ML (1 study: Veh\\_18). 2020: 7 total works, 57% ML (4 studies: Dirscherl\\_20, Halberstadt\\_20, Wangchuk\\_20, Zhang\\_20b) and 43% DL (3 studies: Qayyum\\_20, Wu\\_20, Yuan\\_20). 2021: 9 total works, 44% ML (4 studies: How\\_21, Rinzin\\_21, Thati\\_21, Wendleder\\_21) and 56% DL (5 studies: Chen\\_21, Dirscherl\\_21, He\\_21, Wang\\_21, Zhao\\_21). 2022: 11 total works, 27% ML (3 studies: Chen\\_22, Dell\\_22, Hu\\_22) and 73% DL (8 studies: Basit\\_22, Chatterjee\\_22, Jiang\\_22, Kaushik\\_22, Ortiz\\_22, Thati\\_22, Wang\\_22a, Wang\\_22c). 2023: 9 total works, 11% ML (1 study: Banerjee\\_23) and 89% DL (8 studies: Lutz\\_23, Niu\\_23, Sharma\\_23, Siddique\\_23, Thomas\\_23, Wei\\_23, Xu\\_23, Zhao\\_23). 2024: 10 total works, 30% ML (3 studies: Mustafa\\_24, Wang\\_24, Wu\\_24) and 70% DL (7 studies: Cao\\_24, Hardie\\_24, Hu\\_24, Sharma\\_24, Tang\\_24, Xu\\_24, Yin\\_24). The chart shows a general upward trend in the number of publications, with Deep Learning becoming the dominant category from 2021 onwards.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview", "section_headings": ["3. Remote sensing data used: An overview"], "chunk_type": "figure", "figure_caption": null, "line_start": 97, "line_end": 97, "token_count_estimate": 635, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3895db9ca403d402", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview\nType: text\n\nFig. 2. Chronological histogram (till 2024) of published glacial lake studies (total: 48) that used satellite data and learning-based approaches. Total counts, category-wise counts, and percentages are annotated above each bar.\n\nstorage variations (Wu et al., 2022). This capability is valuable for assessing potential GLOF occurrences particularly in large supraglacial lakes in Greenland and Antarctica.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview", "section_headings": ["3. Remote sensing data used: An overview"], "chunk_type": "text", "line_start": 98, "line_end": 102, "token_count_estimate": 130, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8dd1d6d6f77a994e", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview > 3.1. Single- and multi-sensor approaches: Pros and cons\nType: text\n\nML/DL approaches have effectively used optical satellite data as standalone input for glacial lake studies in both mountainous (e.g., Basit et al., 2022; Siddique et al., 2023) and polar regions (e.g., Yuan et al., 2020; Halberstadt et al., 2020). However, their reliance on cloud-free conditions limits their applicability.\n\nIn contrast, radar-only methods have shown success only in non-mountainous regions, revealing a research gap. For example, Dirscherl et al. (2021) achieved high accuracy ( $F_1$  score: 0.93) in detecting supraglacial lakes in Antarctica using S1 alone. In mountainous environments, however, radar has generally been used in combination with optical imagery (e.g., Wang et al., 2021; Wu et al., 2020). While Wang et al. (2021) achieved a satisfactory Intersection-over-Union (IoU) score (0.59) in an S1-only experiment, Wu et al. (2020) did not evaluate radar data independently.\n\nGeometric and radiometric distortions – such as foreshortening, layover, shadowing, and backscatter variability – pose significant challenges for radar remote sensing in mountainous terrain. These distortions introduce artifacts that affect both spatial structure and pixel intensities. When present in training data, the ML/DL model may learn spurious correlations – e.g., misinterpreting terrain shadow as water – leading to a corrupted decision boundary and resulting in both false positives and false negatives. Conversely, if the model is trained on clean or corrected data but applied to distorted test scenes,\n\nits predictions may still degrade due to unseen variance in geometry or backscatter. This may also result in misclassification of actual lake pixels as background (false negatives) or surrounding terrain as lake (false positives). Novel neural network architectures optimized for SAR and enhanced pre-processing techniques are needed to mitigate these issues. However, even with these strategies, matching the accuracy of optical data will be a challenge.\n\nGiven the limitations of standalone radar data, integrating optical and SAR data has emerged as an effective strategy in mountainous regions. This approach leverages their complementary strengths to enhance classification accuracy and ensure data availability. Data-driven approaches learn rich spectral information from optical data under clear conditions and geometric features from SAR data.\n\nStudies have demonstrated improved performance through fusion: Wu et al. (2020) observed a 4% increase in mIoU when adding S1 to L8. Additionally, Hu et al. (2024) achieved their highest IoU (0.84) when integrating S2, S1, and topographic data. Multi-sensor (including multi-mission) techniques typically rely on a primary sensor with one or more auxiliary sensors. In radar-optical fusion for high-mountain glacial lakes, optical data remains the primary input despite frequent cloud cover.\n\nMulti-sensor fusion introduces challenges as well. First, temporal mismatches between acquisitions can complicate fusion. This was reported by Wang et al. (2021), where a 6-day gap between optical and radar data required high-level semantic fusion. Second, absolute geolocation shifts between sensors can introduce errors. For instance, Wu et al. (2020) had to address 1–2 pixel discrepancies between S1 and L8 using a mutual information-based coregistration method. Nevertheless, the benefits of multi-sensor fusion often outweigh its challenges, making it a valuable approach for glacial lake studies.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview > 3.1. Single- and multi-sensor approaches: Pros and cons", "section_headings": ["3. Remote sensing data used: An overview", "3.1. Single- and multi-sensor approaches: Pros and cons"], "chunk_type": "text", "line_start": 104, "line_end": 118, "token_count_estimate": 893, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "15082783f6b4faf1", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview > 3.2. Role of sensor resolution in monitoring hazardous lakes\nType: text\n\nIt is relatively more important to monitor glacial lakes that are prone to outburst floods. However, hazard potential is not determined by lake size. Numerous studies have demonstrated that GLOF impacts do not correspond to lake size and that also small to very small lakes (area <0.1 km<sup>2</sup>) can trigger devastating outburst floods (Allen et al., 2016; Vilca et al., 2021; Sattar et al., 2022; Chen et al., 2023).\n\nLarger lakes are detected with relatively high accuracy by underlying ML/DL models. For instance, Wu et al. (2020) reported an overall IoU of 0.62 for all lakes using L8 and S1 data, which improved to 0.8 for lakes larger than  $0.1~\\rm km^2$ . Notably, only 17.4% of the 8262 mapped lakes exceeded this size threshold. Most glacial lakes are typically small; for example, 85.3% of Himalayan glacial lakes are under 0.1 km² (Wang et al., 2021). In the Eastern Himalayas, the average size is even smaller at 0.053 km² (Xu et al., 2023). Given their abundance, smaller lakes (i.e. smaller than 0.1 km²) statistically experience more outbursts. Yet, their mapping and monitoring remains challenging due to detection limitations, with only few learning-based studies focusing on them. The minimum lake size considered for monitoring should be guided by the intended application.\n\nDetecting glacial lakes smaller than 0.01 km² using S2 and S1 input data remains a challenge even for ML/DL approaches. This limitation, however, is more attributable to sensor resolution than to the methodologies themselves. To improve detection of these tiny lakes, integrating higher spatial-resolution satellite data, such as PlanetScope (e.g., Siddique et al., 2023; Xu et al., 2024), Pléiades, Worldview, etc., could be beneficial. Nevertheless, images from these sensors are costly, making them more suitable for individual lake studies or quality assessments rather than large-scale analysis.\n\nPan-sharpening the optical data could be useful too (Zheng et al., 2021). Additionally, combining Unmanned Aerial Vehicle (UAV) imagery with satellite data could support local-scale monitoring campaigns (Alvarez-Vanhard et al., 2021). However, scalability to country-or global-level would be extremely challenging, if not practically impossible.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview > 3.2. Role of sensor resolution in monitoring hazardous lakes", "section_headings": ["3. Remote sensing data used: An overview", "3.2. Role of sensor resolution in monitoring hazardous lakes"], "chunk_type": "text", "line_start": 120, "line_end": 128, "token_count_estimate": 642, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "458243f4ab885729", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 3. Remote sensing data used: An overview > 3.3. Importance of spectral indices, atmospheric correction\nType: text\n\nSpectral indices (Montero et al., 2023) are combinations of spectral bands (e.g., ratios, normalized differences) designed to enhance specific surface properties. In the context of glacial lakes, they help highlight features such as open water, ice, or snow while reducing noise from shadows, rocks, and other interfering factors. Integrating diverse spectral indices within learning frameworks has improved glacial lake detection. This has been observed in both high mountain (e.g., Wangchuk and Bolch, 2020; Zhao et al., 2023) and polar glacier lakes (e.g., Yuan et al., 2020; Wang and Sugiyama, 2024).\n\nZhang et al. (2020a) found that normalized indices performed better than individual bands or band ratios. However, they also reported high correlations among indices, limiting potential accuracy gains from index combinations. In their analysis, Normalized Difference Water Index (NDWI)-G (McFeeters, 1996) outperformed other indices, including Enhanced Water Index (EWI) and NDWI-B (Huggel et al., 2002). NDWI-G [Green & Near InfraRed (NIR)] is better suited for detecting deep, clear water bodies, while NDWI-B (Blue & NIR) is more sensitive to shallow and turbid waters.\n\nIn contrast, Dirscherl et al. (2020) achieved better results with S2 bands than indices. This is likely due to atmospheric correction, which removes atmospheric effects caused by the scattering and absorption of solar radiation by atmospheric gases (Gao et al., 2009). This process prepares satellite data for remote sensing applications. Zhang et al. (2020a), on the other hand, used Top of Atmosphere (ToA) reflectance. Furthermore, among the 12 indices used, Dirscherl et al. (2020) found Tasseled Cap for Wetness (TCwet, Kauth and Thomas, 1976; Schwatke et al., 2019) and Automated Water Extraction Index (*AWEI*<sub>1nsh</sub>, Feyisa\n\net al., 2014) to be more important than NDWI-G. Regional differences in pixel composition within periglacial areas, notably between Antarctica (Dirscherl et al., 2020) and Asian mountain ranges (Zhang et al., 2020a), may also have contributed to these contrasting findings.\n\nAtmospheric correction is common in data-driven glacial lake studies (Jha and Khare, 2017; Wendleder et al., 2021; He et al., 2021; Wang et al., 2022c). However, none have quantitatively assessed its direct impact on the performance of the underlying ML/DL models. This presents a significant research opportunity. At the same time, atmospheric correction has a high computational overhead, and many ML/DL models have performed well without it (Wangchuk and Bolch, 2020; Wu et al., 2020; Zhao et al., 2023; Hardie et al., 2024, etc.). Hence, an initial investigation using ToA reflectance is recommended before conducting a detailed analysis.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "3. Remote sensing data used: An overview > 3.3. Importance of spectral indices, atmospheric correction", "section_headings": ["3. Remote sensing data used: An overview", "3.3. Importance of spectral indices, atmospheric correction"], "chunk_type": "text", "line_start": 130, "line_end": 140, "token_count_estimate": 813, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91cf061ada6f6176", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies\nType: text\n\nGlacial lake studies based on remote sensing rely primarily on spectral indices and/or backscatter/reflectance thresholds. However, such approaches often require threshold adaptation for large-scale analyzes and multi-temporal monitoring (Jawak et al., 2015; Wangchuk and Bolch, 2020). While generally effective, these methods face significant challenges in mountainous environments. Complex terrain, varying weather conditions, changes in glacier dynamics, and seasonal variations in mountains contribute to notable inaccuracies in identifying glacial lakes (Bolch et al., 2011). Moreover, such methods often neglect contextual information from neighboring pixels or temporal sequences. They make decisions solely based on individual pixel values. Consequently, ML/DL approaches have become widespread.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies", "section_headings": ["4. Learning-based approaches for glacial lake studies"], "chunk_type": "text", "line_start": 142, "line_end": 144, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "07ff1a7092ea33c3", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.1. Chronological progression & methodological distribution\nType: text\n\nThe first learning-based glacial lake study (Jain et al., 2015), published a decade ago, used ASTER multispectral imagery. They used a Support Vector Machine (SVM) classifier for the semi-automatic detection of glacial lakes in the Chamkhar Chu Basin, Hindukush Himalaya, Bhutan.\n\nOf the 48 studies surveyed (Appendix B), 31 (64.6%) employed DL approaches, while 17 (35.4%) used classical ML techniques (Fig. 2). This reflects the field's growing preference for DL methodologies. Although DL is a subset of ML, they are addressed separately in our review to highlight distinct trends.\n\nMost studies on glacial lakes applying ML or DL were published in 2022 (11 papers), with at least seven DL articles consistently appearing each year since 2022. Fig. 2 provides a chronological overview of the literature. It includes both published papers and a book chapter (Thati et al., 2021) up to 2024. Fig. 3 illustrates the distribution of methodologies reported in these studies.\n\nResearch output grew substantially since 2020, coinciding with the introduction of the first DL-based approach (Yuan et al., 2020). This study used a CNN classifier and L8 imagery to detect supraglacial lakes in Southwest Greenland. 46 out of the 48 studies were published from 2020 onward, underscoring the growing focus on learning-based approaches in glacial lake remote sensing.\n\nIn ML/DL, classification predicts discrete class categories, while regression estimates continuous numerical values (Bishop, 2006). As of 2024, all learning-based approaches for glacial lake studies have been classification-based, with no reported use of regression models.\n\nAmong classification-based methods, CNN (Lecun et al., 1998) variants are predominant (Appendix C). U-Net (Ronneberger et al., 2015) is the most widely applied CNN architecture (Fig. 3). DeepLab (Chen et al., 2015, 2018a,b) variants (Appendix D), known for their advanced semantic segmentation capabilities, are relatively less explored. This is likely due to the additional resources required to train these relatively parameter-heavy models, which demand large, representative datasets. Among ML approaches, Random Forest (RF) and SVM are widely used (Cortes and Vapnik, 1995; Breiman, 2001; Mountrakis et al., 2011).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.1. Chronological progression & methodological distribution", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.1. Chronological progression & methodological distribution"], "chunk_type": "text", "line_start": 146, "line_end": 158, "token_count_estimate": 617, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "a222f17c0a5babc9", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: text\n\nGlacial lakes are predominantly located in glaciated mountain regions, particularly at medium-to-high latitudes, as well as in the polar lowlands (Shugar et al., 2020). Over 80% of these lakes are concentrated in Greenland, the Alaska Range, Southern Andes, High Mountain Asia (HMA), and the eastern Canadian Arctic (Zhang et al., 2024). Despite this broad distribution, ML/DL-based studies on glacial lake mapping and monitoring have primarily focused on a limited subset of these regions, highlighting significant research gaps. Table E.6 (Appendix E) presents key regions with glacial lakes, notable publications that investigated them, and ML/DL studies that considered these regions as primary study sites. It also includes studies that evaluated the transferability of their ML/DL models in these regions, even if they were not the primary study sites.\n\nHMA has received the most research attention from learning-based approaches, followed by Greenland and Antarctica (Appendix E). This geographic trend aligns with findings by Calamita et al. (2024). They reported that remote sensing is underutilized in studying lake ecosystem shifts in Europe (1%) and North America (5%). However, it is significantly more applied in Asia (23%) to monitor climate-related changes in lakes.\n\nIn contrast, several regions with significant GLOF activity remain understudied despite documented evidence. Historically, more than 60% of GLOFs have occurred in Alaska, HMA, and Iceland (Zhang et al., 2024). Northern Andean glacial lakes have also produced several GLOFs. The volume of Patagonian lakes (excluding the three largest) more than doubled from 1990–1999 to 2015–2018 (Shugar et al., 2020).\n\nThe risk associated to GLOF hazards is a key reason for this uneven geographic distribution of ML/DL-based glacial lake mapping studies. High-latitude regions have the highest number of glacial lakes, strongest lake growth rates (Shugar et al., 2020) and probably also experience most GLOF events. However, the impacts of GLOFs in terms of damage and loss – and thus GLOF risk – is much higher in lower- and mid-latitude mountain regions. This is due to both the higher concentration of people and infrastructure exposed to GLOFs, and, related, also higher vulnerabilities to GLOFs of societies in these densely populated mountain ranges (Taylor et al., 2023). This is further evidenced by the fact that even smaller lakes can cause major disasters in Andes (Vilca et al., 2021) or the Alps (Huggel et al., 2003). HMA is the only region that is a hotspot of GLOF risk and well studied in terms of ML/DL-based solutions for glacial lake mapping.\n\nResearch on the Andes remains limited, with only Wangchuk and Bolch (2020) conducting a dedicated study. Additionally, Qayyum et al. (2020) and Tang et al. (2024) included Andes in their transferability assessments. Similarly, the European Alps have been the focus of only one study. Wangchuk and Bolch (2020) examined the Andes and the Swiss Alps, however the study primarily focused on six locations in HMA.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "text", "line_start": 160, "line_end": 190, "token_count_estimate": 807, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "84e9a8d7f215f43e", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: text\n\nHuggel et al . , 2003 ) . HMA is the only region that is a hotspot of GLOF risk and well studied in terms of ML / DL - based solutions for glacial lake mapping . Research on the Andes remains limited , with only Wangchuk and Bolch ( 2020 ) conducting a dedicated study . Additionally , Qayyum et al . ( 2020 ) and Tang et al . ( 2024 ) included Andes in their transferability assessments . Similarly , the European Alps have been the focus of only one study . Wangchuk and Bolch ( 2020 ) examined the Andes and the Swiss Alps , however the study primarily focused on six locations in HMA .\n\nOther key regions, such as the Canadian and Russian Arctic, Scandinavia, Iceland, and New Zealand, remain unexplored using learning-based approaches, possibly due to lower risks associated with the abundant number of glacial lakes in these regions. This reveals a research opportunity not only to improve spatial coverage but also to enhance training datasets and advance algorithm development. Interestingly, the fastest-growing lakes (in terms of areal expansion) are located in Iceland, the Russian Arctic, and Scandinavia (Shugar et al., 2020). These regions offer valuable testbeds for developing and evaluating new glacial lake mapping methods, which can later be applied to high-risk regions. Comprehensive study of such regions is beneficial for building generalizable, globally robust approaches.\n\nTo further examine the spatial trends of ML/DL studies, we investigate geographical distribution of the methodologies. The reviewed approaches are categorized into six groups in Fig. 4: three for DL (U-Net, other CNNs, and other DL methods) and three for ML (RF, SVM,\n\nand other ML methods). Each continent is represented by a distinct bar plot. Each methodology is counted only once per continent, even if multiple study sites within the same continent were analyzed. For instance, Zhao et al. (2023), who assessed over 5 sites in HMA using a CNN, is considered a single entry. However, studies that applied multiple algorithms to the same site (e.g., Halberstadt et al. (2020)) are counted separately for each methodology used. Similarly, studies applying the same algorithm in different continents (e.g., Wangchuk and Bolch (2020)) are counted separately for each continent considered.\n\nTwo DL studies (Chatterjee et al., 2022; Thomas et al., 2023) were excluded from Table E.6 (Appendix E) and Fig. 4. Although Chatterjee et al. (2022) included Lake Tibet, a glacial lake, their primary focus was on non-glacial lakes. Similarly, Thomas et al. (2023) investigated supraglacial lakes across the Arctic without specifying distinct study sites, making their inclusion in our region-clustered spatial distribution analysis difficult. However, both studies remain part of the broader methodological assessment.\n\nDL techniques, particularly U-Net and other CNNs, are more commonly applied in HMA (Fig. 4). ML methods like RF and SVM are also more frequently used in HMA. However, they have a relatively stronger presence in Antarctica compared to DL.\n\nRegardless of the technique, such data-driven models, if trained on small or non-representative datasets, risk overfitting. This reduces their effectiveness when applied to previously unseen regions or time periods. Therefore, ensuring spatiotemporal transferability is crucial for enhancing model robustness.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "text", "line_start": 160, "line_end": 190, "token_count_estimate": 884, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b4315a13b957c7d4", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: text\n\n- clustered spatial distribution analysis difficult . However , both studies remain part of the broader methodological assessment . DL techniques , particularly U - Net and other CNNs , are more commonly applied in HMA ( Fig . 4 ) . ML methods like RF and SVM are also more frequently used in HMA . However , they have a relatively stronger presence in Antarctica compared to DL . Regardless of the technique , such data - driven models , if trained on small or non - representative datasets , risk overfitting . This reduces their effectiveness when applied to previously unseen regions or time periods . Therefore , ensuring spatiotemporal transferability is crucial for enhancing model robustness .\n\nMany learning-based methods have been confined to their primary study sites. However, some notable exceptions assess spatial transferability by training on one continent and testing on another (Table E.6, Appendix E). For instance, Wangchuk and Bolch (2020) developed their approach for the Himalayas and tested it in the Alps and Andes. Their qualitative results from the Andes aligned with datasets from the National Water Authority of Peru. Similarly, Dirscherl et al. (2021) evaluated their model's generalizability (trained on data from Antarctica) by applying it to supraglacial lakes in Southwest Greenland.\n\nAlthough the qualitative results of Dirscherl et al. (2021) were promising, large supraglacial lakes common in Greenland were underrepresented in their training dataset. This led to overfitting despite extensive data augmentation (Section 4.5.3). Fine-tuning with representative data from Greenland could improve the performance. However, this was not tested or reported. Likewise, Tang et al. (2024) trained their model on 15 mountain ranges in the Third Pole region and, after fine-tuning, demonstrated its qualitative generalization to the Patagonian Andes, Alaska, and Greenland. However, none of these studies provided quantitative validation in their transferability experiments. This highlights a key gap in assessment.\n\nTemporal transferability is equally critical, as models must maintain performance when applied to data from the same study site across different time periods. It is less challenging in stable environments. However, it becomes significantly harder in rapidly changing conditions, requiring models to adapt to both periodic (seasonal) and non-periodic (long-term) changes. An example is Dirscherl et al. (2020), who investigated the spatiotemporal transferability of their RF model. They trained it on supraglacial lake occurrences from summer 2019 across fourteen regions. Evaluation was conducted on eight spatially independent regions from summers 2017 and 2018 across the Antarctic ice sheet. Their model achieved impressive average  $F_1$  scores of 0.997 for the non-water class and 0.86 for the water class.\n\nModels that generalize effectively across diverse spatial and temporal contexts are essential but not yet standard practice. Future studies should prioritize comprehensive spatiotemporal transferability assessments, incorporating both quantitative and qualitative evaluations. Openly sharing datasets and ground truth annotations will further support these efforts. It will foster collaboration and enable more rigorous model development and evaluation.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "text", "line_start": 160, "line_end": 190, "token_count_estimate": 800, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "32d885705ee4649d", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A radial bar chart, also known as a sunburst chart, illustrates the distribution of machine learning and deep learning methods. The chart is divided into two main categories: Machine Learning (green) with 31 instances and Deep Learning (blue) with 39 instances. The Machine Learning section is subdivided into: Random Forest (12), Others (10) which includes LR, ME, NB, CRT, MD, EBT, ISODATA, LVQ, KNN, and X-MEANS, SVM (4), K-MEANS (2), C-K-MEANS, GAN, and DELSE. The Deep Learning section is subdivided into: ANN (2) and a larger category of CNN variants (37). The CNN variants category is further broken down into: U-Net (18), Deeplab (6), Others (9) which includes PSPNet, CoAtNet, YOLO, DCNN, Siamese CNN, HarDNet, FPN, Mask-R-CNN, and ACFNet, LinkNet (2), and CNN (2).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "figure", "figure_caption": null, "line_start": 191, "line_end": 191, "token_count_estimate": 281, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ddf70f829b90bca", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: text\n\nFig. 3. Distribution of ML (31 methodologies) and DL (39 methodologies) approaches proposed in 48 studies on glacial lakes using satellite data. Deeplab includes its variants; methods without numbers in brackets were each used once. More details are in (Appendix C). Refer to the glossary (Appendix G) for full forms.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "text", "line_start": 192, "line_end": 194, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9d649ccb274bb4b7", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: figure\nFigure\n\nImage /page/6/Figure/4 description: A world map displaying the geographical distribution of different machine learning (ML) and deep learning (DL) models across continents. The map is plotted on a longitude and latitude grid. Bar charts are overlaid on the continents to show the counts of different model types. A legend indicates the model types by color: orange for DL-U-Net, yellow for DL-Other-CNN, green for DL-Other, purple for ML-RF, blue for ML-SVM, and red for ML-Other. The total count for each continent is listed in parentheses. Asia has the highest count with 51 models (13 DL-U-Net, 16 DL-Other-CNN, 2 DL-Other, 7 ML-RF, 3 ML-SVM, 10 ML-Other). Antarctica has 10 models (2 DL-U-Net, 3 ML-RF, 1 ML-SVM, 4 ML-Other). North America has 7 models (3 DL-U-Net, 1 DL-Other-CNN, 2 ML-RF, 1 ML-Other). Europe and South America each have 1 model, both of which are ML-RF. Africa and Oceania have 0 models.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "figure", "figure_caption": null, "line_start": 195, "line_end": 195, "token_count_estimate": 345, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7a834cd51d7b99b", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability\nType: text\n\nFig. 4. Continent-wise distribution (primary study site only) of ML/DL methodologies (six categories: 3 each for DL and ML) used in glacial lake remote sensing studies. Background map credit: https://www.naturalearthdata.com/. Refer to the glossary (Appendix G) for full forms.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.2. Distribution of study sites and model transferability", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.2. Distribution of study sites and model transferability"], "chunk_type": "text", "line_start": 196, "line_end": 198, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d2f07809ab13b23", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.3. Strong vs. weak supervision: Challenges, opportunities\nType: text\n\nMost of the glacial lake studies that apply ML/DL methodologies (Fig. 3) rely on supervised learning, which depends heavily on labeled datasets. However, generating ground truth annotations is timeconsuming and resource-intensive. To mitigate this, it is essential to develop more efficient methods for generating reference labels. Another approach is to adopt weakly supervised learning (Chapelle et al., 2009; Karamanolakis et al., 2021), which uses minimal, noisy, or incomplete labels. This will reduce annotation effort, enabling broader and more scalable application of ML and DL analyses. Additionally, it will help fully leverage the vast and ever-growing volumes of available remote sensing data. An example is Ortiz et al. (2022), who applied varying degrees of weak supervision. They employed historically guided U-Net, morphological snakes, and DEep Level Set Evolution (DELSE), based on the assumption that glacial lakes evolve gradually over time. They used low-resolution historical glacial lake labels to guide the segmentation of more recent high-resolution satellite imagery and Bing maps. Similarly, Zhao et al. (2023) introduced weak supervision using NDWI within a contrastive loss-based Siamese neural network. They learned glacial lake representations by maximizing the similarity between input satellite images and their augmentations, requiring no ground truth and only minimal supervision. However, the full potential of weakly supervised learning remains systematically underexplored in glacial lake studies. This presents a significant research opportunity.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.3. Strong vs. weak supervision: Challenges, opportunities", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.3. Strong vs. weak supervision: Challenges, opportunities"], "chunk_type": "text", "line_start": 200, "line_end": 202, "token_count_estimate": 429, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "66f8ccae83136863", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance\nType: text\n\nMost supervised approaches formulate binary classification tasks to distinguish glacial lake pixels from the background. A few exceptions (Veh et al., 2018; Dirscherl et al., 2020, 2021; Halberstadt et al., 2020; Wendleder et al., 2021) incorporated additional classes like snow/ice, shadow, rock/land/sediment, debris, slush, firn, and cloud. Halberstadt et al. (2020) included even fine-grained classes such as blue ice, flowing ice, shallow lakes, deep lakes, cloud shadow, sunlit rock, and shadowed rock. In rare cases, the number of classes varied based on input data. For instance, Dirscherl et al. (2021) applied binary classification for S1 data but four-class classification for S2 data.\n\nBoth binary and multi-class classification can achieve accurate lake detection. Binary approaches are often simpler. Multi-class methods offer the added benefit of detailed surface characterization, at the cost of higher-quality training data and more complex models.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.4. Class categories, imbalance"], "chunk_type": "text", "line_start": 204, "line_end": 208, "token_count_estimate": 286, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f69312f2a0bfa32", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: An image displaying two donut charts side-by-side. The chart on the left is fully visible and shows the distribution of different categories. The largest segment is 'Cross entropy (12)' in blue-gray, followed by 'Dice (7)' in light green. The other segments are 'Others (4)' in yellow, 'Focal (3)' in teal, 'Tversky (1)' in orange, and 'Lovasz hinge (1)' in pink. The chart on the right is partially visible, showing segments for 'Adabelief' (number cut off) in green, 'SGD (7)' in a larger green segment, and 'AdaMax (1)' in blue. There is also a red segment with no visible label.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.4. Class categories, imbalance"], "chunk_type": "figure", "figure_caption": null, "line_start": 209, "line_end": 209, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "67680b8de0f50269", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance\nType: text\n\n- (a) Loss functions (based on 24 publications)\n- (b) Optimization strategies (23 publications)\n\nFig. 5. Distribution of hyperparameters in deep learning-based glacial lake studies (number of publications in brackets). Some studies reported multiple loss functions (Basit et al., 2022; Cao et al., 2024) or optimizers (Chatterjee et al., 2022). More details are in (Appendix F).\n\nWhen framed as a two-class problem, the \"lake\" class is typically underrepresented, leading to class imbalance (He and Garcia, 2009). This uneven class distribution can bias the model toward the majority class: \"background\", resulting in poor performance on the minority class\n\nStandard strategies to address class imbalance include tailored loss functions (Section 4.5.1), strict evaluation metrics (Section 4.6), class-dependent data augmentation (Section 4.5.3), and undersampling the majority class (Dirscherl et al., 2020; Yuan et al., 2020). Regardless of the strategy employed, addressing class imbalance is crucial in glacial lake studies and is strongly encouraged.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.4. Class categories, imbalance"], "chunk_type": "text", "line_start": 210, "line_end": 219, "token_count_estimate": 307, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86248118eed5cf84", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.1. Loss functions and optimization schemes\nType: text\n\nA loss function quantifies the difference between a model's predictions and the actual ground truth labels, guiding the optimization process during model training (Wang et al., 2022b). By minimizing the loss, the model iteratively adjusts its parameters to improve predictions. In glacial lake studies, cross-entropy (He et al., 2021; Kaushik et al., 2022, etc.) and Dice loss (Li et al., 2020; Hu et al., 2024, etc.) functions are the most commonly used (Fig. 5).\n\nCross-entropy is well-suited for multi-class classification, providing a straightforward approach to measure the divergence between predicted probabilities and true labels (Zhang and Sabuncu, 2018). It is also effective for binary classification. To address class imbalance, several glacial lake studies (Ortiz et al., 2022; Hardie et al., 2024, etc.) successfully employed weighted variants of cross-entropy. These variants assign higher weights to minority \"lake\" class samples during model training.\n\nFor binary semantic segmentation tasks, dice loss is particularly effective. It directly optimizes the overlap between predicted and actual foreground regions, mitigating the impact of class imbalance. While it can be extended to multi-class segmentation by computing perclass dice scores, its ability to balance foreground and background contributions makes it especially valuable in tasks like glacial lake detection (Sudre et al., 2017). Tversky loss (Wu et al., 2020), an extension of dice loss, further enhances flexibility. It adjusts penalties for false positives and false negatives, making it useful when prioritizing precision or recall (Salehi et al., 2017).\n\nOptimization strategies complement loss functions. Adaptive Moment (AdaM) is widely used (Fig. 5), followed by Stochastic Gradient Descent (SGD). AdaM combines the benefits of adaptive learning rates and momentum. This makes it particularly effective in handling sparse gradients or non-stationary objectives (Kingma and Ba, 2015).\n\nAdvanced variants like AdaMax and AdaBelief have been explored by Dirscherl et al. (2021) and Chatterjee et al. (2022), respectively. AdaMax extends AdaM to work effectively with infinite norms. It offers greater stability in sparse gradient scenarios and non-convex optimization (Kingma and Ba, 2015). AdaBelief modifies the second-moment estimation, leading to faster convergence and better general-ization (Zhuang et al., 2020).\n\nAdam (15)\n\nIn contrast, SGD is used due to its relative simplicity and effectiveness in large-scale problems. It is especially useful when computational efficiency is critical (Bottou, 2010). However, in its basic form, SGD may struggle with convergence on complex loss surfaces. Enhancements like momentum or learning rate scheduling, as demonstrated by some glacial lake studies (Yuan et al. (2020), Wang et al. (2021) and Hu et al. (2024)), can address these issues.\n\nIn glacial lake studies, depending on the number of classes, it is recommended to use any of the above loss functions that effectively tackle class imbalance. SGD optimization with momentum is recommended for large datasets or when resources are limited. AdaM variants are encouraged for complex, sparse data requiring quick model convergence.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.1. Loss functions and optimization schemes", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.5. Deep learning approaches", "4.5.1. Loss functions and optimization schemes"], "chunk_type": "text", "line_start": 223, "line_end": 239, "token_count_estimate": 849, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55f883315d0f37cb", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.2. Pre-training, transfer learning, & model adaptations\nType: text\n\nInstead of training from scratch, fine-tuning a DL model pre-trained on a large, labeled dataset mitigates issues like slow convergence and overfitting. Transfer learning allows adapting pre-trained model weights, reducing the need for extensive labeled data while enhancing generalization.\n\nIn satellite remote sensing, transfer learning is facilitated by publicly available large-scale datasets. Examples are *BigEarthNet* (Sumbul et al., 2021), *EuroSAT* (Helber et al., 2019), *SpaceNet* (Shermeyer et al., 2020), and *ImageNet* (Deng et al., 2009; Krizhevsky et al., 2017). These datasets allow models to learn fundamental spatial features – such as edges, textures, and object structures – which can be repurposed for tasks like glacial lake segmentation (Weiss et al., 2016).\n\nFor instance, Qayyum et al. (2020) employed transfer learning for glacial lake mapping. They used *EfficientNet* (Tan and Le, 2019) as the backbone for a U-Net model, fine-tuning its higher layers for glacial lake detection.\n\nTransfer learning is effective even when the original and target tasks differ significantly. While this capability has been extensively explored in related fields, such as lake ice monitoring, transfer learning remains underutilized in glacial lake studies. For instance, Tom et al. (2022) demonstrated that a DeepLabv3+ model pre-trained on closerange imagery for a computer vision task could be successfully adapted for lake ice monitoring using SAR satellite data. This exemplifies the versatility of transfer learning and presents a promising avenue for glacial lake remote sensing research.\n\nOne challenge is that most DL models used for glacial lake studies were originally designed for generic computer vision tasks. Such models typically support only 3–4 input channels (e.g., RGB, RGB-depth). This design choice simplifies transfer learning but restricts the ability to fully exploit multispectral, hyperspectral, and radar-based remote sensing data. Only a few studies used more than four channels (Wu et al., 2020; He et al., 2021; Hu et al., 2024; Tang et al., 2024). Others have worked\n\naround this limitation by empirically selecting the most relevant input spectral features (Section 3.3) or using automated feature importance analysis. However, such approaches may not have exploited the full potential of multi-source remote sensing data.\n\nExpanding model architectures to accommodate a greater number of input channels, such as a dozen or more spectral bands, is technically straightforward. However, it presents a trade-off. While such modifications enhance the model's ability to process diverse remote sensing data, they prevent the direct reuse of pre-trained weights, necessitating training from scratch. This increases computational costs and the risk of overfitting.\n\nFurthermore, standard DL architectures are not optimized for radarspecific features such as phase, backscatter, and polarization. This underscores the need for task-specific model adaptations. Therefore, to maximize the benefits of transfer learning for large-scale, data-efficient glacial lake studies, it is necessary to redesign existing architectures. These should accommodate a wider range of remote sensing inputs while still leveraging pre-trained feature representations.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.2. Pre-training, transfer learning, & model adaptations", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.5. Deep learning approaches", "4.5.2. Pre-training, transfer learning, & model adaptations"], "chunk_type": "text", "line_start": 241, "line_end": 257, "token_count_estimate": 847, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91df56ccb40d2d8e", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.3. Importance of data augmentation\nType: text\n\nAugmentation in DL is a strategy to mitigate data scarcity. By transforming existing data, this technique expands and diversifies the training dataset. It improves model robustness, generalization, and efficiency through exposure to diverse data variations, all without requiring additional data collection or labeling. However, the effectiveness of augmentation strategies remains largely empirical and highly dependent on the dataset and model.\n\nGlacial lake studies have applied spatial transformation operations such as patch flipping (Wang et al., 2022c, etc.), patch rotation (Cao et al., 2024, etc.), and patch mirroring (He et al., 2021, etc.). Spectral distribution modifications were also explored. Wu et al. (2020) adjusted image saturation and brightness. Zhao et al. (2023) employed techniques like color jittering, random erasing, blurring, noise addition, and grayscale adjustments.\n\nIn Dirscherl et al. (2021), augmentation strategies were tailored per patch depending on the number of lake pixels in each patch. To address class imbalance, they oversampled the image patches with the underrepresented 'lake' class. Additionally, they augmented challenging non-water patches (wet snow, shadow pixels, etc.).\n\nNotably, only a few papers quantitatively evaluated the performance impact of individual operations. For instance, Zhao et al. (2023) found that spatial transformations ( $F_1$  score: 0.66) slightly outperformed spectral modifications ( $F_1$  score: 0.65). Among individual techniques, image flipping was most effective ( $F_1$  score: 0.68), followed by color jittering ( $F_1$  score: 0.67) and image rotation ( $F_1$  score: 0.66). Based on these findings, they adjusted the probabilities of different augmentations to prioritize the most impactful ones.\n\nAugmentation is a common practice in glacial lake studies. However, due to the limited number of studies that evaluate the impact of specific techniques quantitatively, it is difficult to generalize their utility across models and datasets. Future studies should systematically evaluate and optimize augmentation strategies.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.3. Importance of data augmentation", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.5. Deep learning approaches", "4.5.3. Importance of data augmentation"], "chunk_type": "text", "line_start": 259, "line_end": 269, "token_count_estimate": 564, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2cb859460d2f1170", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.4. Recommended strategies\nType: text\n\nTable 2 outlines recommended DL strategies for glacial lake detection under different scenarios. While outcomes may vary with model architecture and dataset characteristics (e.g., sensor type and resolution, region, class distribution, reference label noise), these strategies serve as empirically grounded starting points for method selection and adaptation.\n\nCombining weak supervision (Section 4.3) with transfer learning (Section 4.5.2) offers a promising research direction. Ultimately, model selection should be driven by data availability. Supervised methods like U-Net are well-suited for data-rich environments if no transfer learning is involved. However, in data-scarce regions, weakly supervised approaches and pre-trained DL architectures offer viable alternatives.\n\nThough DL has emerged as an exceptionally powerful tool in glacial lake remote sensing, there are some constraints. Firstly, DL models are relatively data-hungry, requiring large amounts of annotated reference data for training. Nevertheless, given the statistical nature of DL methodologies, performance improves with a broader and more diverse training dataset. However, fine-tuning may be necessary when applying such models to new regions or time periods. Secondly, DL methods require substantial computational resources, typically demanding powerful Graphics Processing Unit(s).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.4. Recommended strategies", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.5. Deep learning approaches", "4.5.4. Recommended strategies"], "chunk_type": "text", "line_start": 271, "line_end": 277, "token_count_estimate": 357, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ddd109b92dd2bb9", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.6. Choosing right evaluation metrics\nType: text\n\nStandard metrics such as recall (producer's accuracy),  $F_1$  score, precision (user's accuracy), IoU, and overall classification accuracy are the most commonly used in learning-based glacial lake studies. These metrics are depicted by the larger circles in Fig. 6. Recall measures the proportion of actual glacial lakes correctly identified, regardless of false positives. Conversely, precision evaluates the proportion of detected lakes that are actual lakes, reducing false identifications. IoU measures the overlap between predicted and actual truth, providing a robust metric for image segmentation tasks. The  $F_1$  score balances precision and recall. Overall accuracy reflects the proportion of correctly classified pixels (both lake and background) in relation to the total number of pixels in the dataset.\n\nSome studies employ specialized metrics. For instance, the Tversky index (Wu et al., 2020) is designed to handle class imbalance while the Fréchet distance (Ortiz et al., 2022) measures shape similarity. The coefficient of determination (Kaushik et al., 2022) evaluates the agreement between the predicted and reference lake boundaries. These tailored metrics highlight a growing interest toward addressing nuanced aspects of performance evaluation.\n\nOverall classification accuracy was relatively more commonly reported in early ML/DL studies (Fig. 6). However, overall accuracy alone is inadequate for class-imbalanced datasets, as it can misleadingly inflate performance results. Metrics such as IoU,  $F_1$  score, precision, recall, and the Tversky index, which are robust to class imbalance, are strongly recommended. While overall accuracy can provide useful context when combined with these stricter metrics, it should not be reported in isolation.\n\nAdditionally, some studies (He et al., 2021; Chen et al., 2022, etc.) reported the Kappa coefficient (Fig. 6). However, Pontius and Millones (2011) highlighted that Kappa indices can be misleading or flawed, particularly in remote sensing applications. These indices compare accuracy against a baseline of randomness, which is an unrealistic reference for map construction. This makes Kappa coefficient difficult to interpret and, in some cases, undefined. Consequently, its use is not recommended. The recent trend of reduced Kappa coefficient usage relative to other metrics (Fig. 6) aligns with this recommendation.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.6. Choosing right evaluation metrics", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.6. Choosing right evaluation metrics"], "chunk_type": "text", "line_start": 279, "line_end": 287, "token_count_estimate": 633, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "369e980837fdb167", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > 4.7. Performance inter-comparison: Insights, gaps, challenges\nType: text\n\nWhen proposing a novel glacial lake product, it is essential to benchmark its performance both quantitatively and qualitatively. This comparison should be made against existing state-of-the-art methods to substantiate performance gains. However, existing comparisons often differ in datasets, learning strategies, and evaluation metrics. This makes it difficult to draw definitive conclusions.\n\nQayyum et al. (2020) compared their U-Net model with *GLakeMap* (Wangchuk and Bolch, 2020), an RF-assisted rule-based thresholding approach. Both methods performed well on large lakes. However, U-Net outperformed *GLakeMap* on small lakes. This is likely due to the higher spatial resolution of PlanetScope (3 m) compared to S2 (10 m) used by the latter.\n\nIn contrast, using S2 data, Basit et al. (2022) outperformed (IoU 0.8 vs. 0.71) Siddique et al. (2023), who relied on PlanetScope imagery. Interestingly, both used a U-Net model with an *ImageNet*-pretrained", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > 4.7. Performance inter-comparison: Insights, gaps, challenges", "section_headings": ["4. Learning-based approaches for glacial lake studies", "4.7. Performance inter-comparison: Insights, gaps, challenges"], "chunk_type": "text", "line_start": 289, "line_end": 295, "token_count_estimate": 302, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "827af4769efea283", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2\nType: text\n\nDL recommendations for glacial lake detection under common application scenarios. CE stands for Cross Entropy.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2"], "chunk_type": "text", "line_start": 297, "line_end": 299, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ef7549d66b0b77c", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Extreme class imbalance\nType: text\n\n- · Use loss functions such as Focal, Tversky, Dice, weighted CE (avoid unweighted CE).\n- · Augment the training set to increase underrepresented class samples.\n- Optimizers do not directly address class imbalance. However, Adam and SGD with momentum can support stable and faster convergence when used with imbalance-aware loss functions. Examples: Focal/Tversky + Adam, Dice + SGD (with momentum).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Extreme class imbalance", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Extreme class imbalance"], "chunk_type": "text", "line_start": 301, "line_end": 305, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a7d4b58b7b62563", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Data scarcity\nType: text\n\n- · Use transfer learning and data augmentation.\n- · When training from scratch, prefer relatively lightweight models (e.g., U-Net with MobileNetV2 or EfficientNet-BO encoders) over deeper architectures like DeepLabv3+.\n- · Optimizers like Adam can accelerate convergence on small datasets, but may require strong regularization (e.g., dropout) to prevent overfitting.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Data scarcity", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Data scarcity"], "chunk_type": "text", "line_start": 307, "line_end": 311, "token_count_estimate": 139, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc777ad31501b3da", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > imited computational resources:\nType: text\n\n- Use models with lightweight backbones (e.g., U-Net or DeepLabv3+ with MobileNetV2 or EfficientNet-B0).\n- Use computationally efficient loss functions such as weighted CE together with memory-efficient optimizers like SGD (with momentum). While Adam may converge faster, it has relatively higher memory overhead and should be used selectively.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > imited computational resources:", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "imited computational resources:"], "chunk_type": "text", "line_start": 313, "line_end": 316, "token_count_estimate": 135, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4036457bd28c17cb", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:\nType: text\n\n- · Use attention-based models (e.g., attention U-Net by He et al., 2021), noting added computational cost.\n- Use DeepLabv3+ with Atrous Spatial Pyramid Pooling (ASPP). Skip connections (in U-Net) and multi-scale context (in DeepLabv3+) enhance small target detection.\n\nEfficientNetB0 backbone. Several factors likely contributed to these performance differences. While spatial and spectral resolution both influence model performance, their impact also depends on the training strategy. EfficientNetB0, optimized for larger datasets, may have underfitted on PlanetScope's limited spectral bands, hindering its ability to learn discriminative features. While PlanetScope offers finer spatial resolution, it lacks broader spectral coverage, particularly in the ShortWave InfraRed (SWIR) range. This limitation could reduce lakebackground separability. The datasets also differed in size (1200 images for Basit et al. (2022) vs. 3525 for Siddique et al. (2023)). The loss function used also varied, with Basit et al. (2022) employing both focal and Jaccard loss, while Siddique et al. (2023) used only focal loss. The use of Jaccard loss, which directly optimizes for IoU, likely contributed to Basit et al. (2022)'s higher performance.\n\nSome studies have compared DL models with ML counterparts and thresholded spectral indices. For instance, Qayyum et al. (2020) compared their *Efficient-U-Net* (CNN) model against standard ML models. They reported an  $F_1$  score of 0.94, significantly outperforming SVM (0.78) and RF (0.75). This highlights the advantages of U-Net.\n\nSimilarly, Wu et al. (2020) compared their U-Net model with RF and thresholded Modified Normalized Difference Water Index (MNDWI). They found that both U-Net and RF outperformed MNDWI in cases involving mountain shadows and frozen lakes. Their U-Net model effectively learned the spatial relationships between neighboring pixels. U-Net exhibited fewer misclassifications in low-reflectivity areas compared to RF.\n\nSome DL-based studies have reported modest results as well. For example, Yuan et al. (2020) evaluated CNNs against RF and SVM, however observed only marginal improvements. This was because the number of test samples was not sufficient. Interestingly, all three approaches (CNN, SVM, RF) achieved more than 98.5% overall accuracy, recall and precision. This indicates a weak test set rather than an inherent shortcoming of CNN, thereby limiting the ability to discern meaningful differences in performance.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Small lake detection:"], "chunk_type": "text", "line_start": 318, "line_end": 343, "token_count_estimate": 687, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f7015ec1ba82ace", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:\nType: text\n\nthe spatial relationships between neighboring pixels . U - Net exhibited fewer misclassifications in low - reflectivity areas compared to RF . Some DL - based studies have reported modest results as well . For example , Yuan et al . ( 2020 ) evaluated CNNs against RF and SVM , however observed only marginal improvements . This was because the number of test samples was not sufficient . Interestingly , all three approaches ( CNN , SVM , RF ) achieved more than 98 . 5 % overall accuracy , recall and precision . This indicates a weak test set rather than an inherent shortcoming of CNN , thereby limiting the ability to discern meaningful differences in performance .\n\nFew studies have systematically evaluated different deep learning architectures and backbone choices. For instance, He et al. (2021) incorporated a self-attention mechanism (Vaswani et al., 2017; Ghaffarian et al., 2021) into U-Net. This resulted in only a slight improvement ( $F_1$  score: 0.69) compared to the baseline U-Net ( $F_1$  score: 0.68). Similarly, Hu et al. (2024) enhanced U-Net by integrating a residual attention mechanism. This led to a 1.5% increase in  $F_1$  score and a three-fold acceleration in convergence. Wang et al. (2021) introduced ACFNet, comparing it against Wu et al. (2020)'s U-Net and achieving a higher  $F_1$  score (0.91 vs. 0.88). While incremental, these improvements highlight the potential impact of architectural enhancements in DL models\n\nCao et al. (2024) conducted a comprehensive comparison of SVM, RF, U-Net, U-Net with an *EfficientNet* backbone, and *LinkNet* variants. SVM had the lowest IoU (0.67). L12-*LinkNet50* with heavy post-processing [SLIC superpixel and Dense Conditional Random Field\n\n(CRF)] achieved the highest (0.91). RF, U-Net, and *EfficientNet* obtained IoUs of 0.68, 0.70, and 0.78, respectively. This study highlighted the importance of post-processing. Additionally, their findings demonstrated that combining multiple loss functions, such as *Lovász hinge* and *dice* loss, improves semantic segmentation performance.\n\nVarious studies have shown that DeepLab variants outperform U-Net. For instance, on a dataset that includes glacial lakes from the Third Pole region, Tang et al. (2024) compared multiple variants of DeepLab and U-Net. They found that DeepLabv3+ with a *MobileNetV3* (Howard et al., 2019) backbone achieved the highest performance (IoU: 0.95). Their evaluation included challenging conditions such as mountain shadows, frozen lakes, and wet ice. Other DeepLabv3+ variants with *ResNet50* (He et al., 2016), *Xception* (Chollet, 2017), and *MobileNetV2* (Sandler et al., 2018) backbones followed closely (IoU: 0.94). U-Net variants performed slightly lower: *ResNet50* (0.93), *MobileNetV3* (0.92), and *MobileNetV2* (0.92). The study demonstrated that for the same backbone, DeepLabv3+ consistently outperformed U-Net.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Small lake detection:"], "chunk_type": "text", "line_start": 318, "line_end": 343, "token_count_estimate": 859, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a00f5166a602c5cf", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:\nType: text\n\n. Their evaluation included challenging conditions such as mountain shadows , frozen lakes , and wet ice . Other DeepLabv3 + variants with * ResNet50 * ( He et al . , 2016 ) , * Xception * ( Chollet , 2017 ) , and * MobileNetV2 * ( Sandler et al . , 2018 ) backbones followed closely ( IoU : 0 . 94 ) . U - Net variants performed slightly lower : * ResNet50 * ( 0 . 93 ) , * MobileNetV3 * ( 0 . 92 ) , and * MobileNetV2 * ( 0 . 92 ) . The study demonstrated that for the same backbone , DeepLabv3 + consistently outperformed U - Net .\n\nSiddique et al. (2023) also improved the IoU from 0.71 to 0.73 by switching to a DeepLabv3+ model (backbone: MobileNetV2) from U-Net (backbone: EfficientNetB0). U-Net's skip connections enable the fusion of low-level appearance details with high-level semantic features. This is important for accurately delineating lake boundaries, especially for small glacial lakes, because the skip connections preserve fine-grained spatial information by reintroducing high-resolution encoder features into the decoder (Fig. 7(a)). However, they can also propagate redundant information, potentially introducing noise irrelevant to glacial lake segmentation. In contrast, DeepLabv3+ employs ASPP, which applies parallel dilated convolutions at multiple scales to enhance multi-scale feature extraction (Fig. 7(b)). Each dilation rate captures features over a different receptive field size. This allows the model to recognize small lakes based on fine textures, large lakes based on broad spatial context, and lakes with complex geometries by combining structural information at multiple scales. This improves detection performance across a wide range of lake sizes and shapes.\n\nA notable gap remains in ensuring fair and comprehensive comparisons among methodologies. Some ML/DL methods (e.g., Xu et al., 2023) have been compared only with thresholded spectral indices rather than against other state-of-the-art ML/DL approaches. In few cases (e.g., Dirscherl et al., 2020, 2021), there is no comparative analysis at all.\n\nFor a fair comparison, the approach being proposed should be evaluated at a study site where a high-performing method has already been tested. This is important because state-of-the-art algorithms might have been developed for entirely different regions. Alternatively, at least one such method may be implemented and evaluated on the region of interest. Despite the time and effort involved, such comparisons are essential for establishing the credibility of new approaches. Accordingly, they are strongly recommended.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Small lake detection:"], "chunk_type": "text", "line_start": 318, "line_end": 343, "token_count_estimate": 722, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cdd75ffba1d19c6b", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: The image displays two related visualizations concerning evaluation metrics from scientific publications. The top part consists of seven circular charts, each representing a different metric, arranged around a central legend. The legend lists the metrics and their total counts: Recall (29) in blue, Confusion matrix (15) in orange, F1/Dice coefficient (26) in light green, Kappa (11) in purple, Precision (27) in brown, Overall accuracy (23) in gray, and IoU (22) in light yellow. Each circular chart is filled with labels representing publications (e.g., 'Wang\\_22a', 'Dirscherl\\_21'). The bottom part of the image is a bubble chart illustrating the yearly trend of these evaluation metrics from 2018 to 2024. The y-axis lists the metrics, and the x-axis shows the years. Each bubble is colored according to the metric and contains a number indicating the count for that year. The data from the bubble chart is as follows: Recall: 1 (2018), 3 (2020), 5 (2021), 6 (2022), 6 (2023), 8 (2024). Confusion matrix: 3 (2020), 3 (2021), 2 (2022), 3 (2023), 4 (2024). F1/Dice coefficient: 2 (2020), 5 (2021), 5 (2022), 7 (2023), 7 (2024). Kappa: 2 (2020), 3 (2021), 2 (2022), 3 (2023), 1 (2024). Precision: 1 (2018), 3 (2020), 5 (2021), 5 (2022), 5 (2023), 8 (2024). Overall accuracy: 1 (2018), 4 (2020), 3 (2021), 5 (2022), 6 (2023), 4 (2024). IoU: 1 (2020), 4 (2021), 4 (2022), 6 (2023), 7 (2024). A caption below the chart indicates this analysis is based on 41 publications.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Small lake detection:"], "chunk_type": "figure", "figure_caption": null, "line_start": 344, "line_end": 344, "token_count_estimate": 487, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "904db62ba90762b6", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:\nType: text\n\nFig. 6. Distribution (top) and yearly trend (bottom) of evaluation metrics based on 41 publications which reported detailed quantitative analysis. The legend indicates number of studies (in brackets) that used each metric, and circle radius reflects the usage frequency. Papers employing each metric are listed within respective circles.\n\nA comprehensive comparison of top-performing methodologies on a fixed dataset is notably absent from the literature. Comparing *ACFNet* (Wang et al., 2021) with Cao et al. (2024)'s highest-performing *LinkNet* and Tang et al. (2024)'s best-performing DeepLabv3+ would provide valuable insights. Achieving this requires representative training datasets, along with rigorous cross-regional evaluations, model exchange, and standardized benchmarking protocols. Its findings could inform a truly generalizable ML/DL approach for global-scale glacial lake mapping and monitoring.\n\nEnsuring reproducibility and transparency through open science practices is crucial for advancing intercomparisons in glacial lake research. Initiatives like NASA's Transform to OPen Science (TOPS, https://doi.org/10.5281/zenodo.10161527) lead the way in promoting open science. Some glacial lake studies have embraced this by making their code and/or data publicly accessible (Ortiz et al., 2022; Wang et al.,\n\n2022a, etc.), promoting transparency. However, majority of studies still do not, making it difficult to access datasets and reproduce results, particularly for researchers or end-users with limited programming expertise.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:", "section_headings": ["4. Learning-based approaches for glacial lake studies", "Table 2", "Small lake detection:"], "chunk_type": "text", "line_start": 345, "line_end": 353, "token_count_estimate": 423, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["10161527"]}}
{"id": "1a87f9228a67ca77", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 5. Discussion\nType: text\n\nDL methods are preferred over classical ML for glacial lake studies, comprising nearly two-thirds of reported papers and more than 50% of the proposed methodologies (Figs. 2, 3). While DL outperforms ML with sufficient data, it is slower and less interpretable. In contrast, ML methods remain valuable for their computational efficiency, relatively simpler parameter tuning, and lower risk of overfitting.\n\nThe choice of model architecture significantly influences performance and adaptability across diverse glacial environments. Applying", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "5. Discussion", "section_headings": ["5. Discussion"], "chunk_type": "text", "line_start": 355, "line_end": 359, "token_count_estimate": 146, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a5d2bdc8ffd15d7", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 5. Discussion\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: A diagram illustrating the U-Net architecture, a type of convolutional neural network for image segmentation. The architecture is symmetrical and U-shaped, consisting of a contracting path (encoder) on the left and an expansive path (decoder) on the right. The input is a 572x572 image. The contracting path involves repeated blocks of two 3x3 convolutions followed by a 2x2 max pooling operation, progressively downsampling the image and increasing the number of feature channels (64, 128, 256, 512, 1024). The expansive path uses 2x2 up-convolutions to upsample the feature maps, concatenates them with corresponding feature maps from the contracting path via skip connections, and applies two 3x3 convolutions. This process progressively decreases the number of channels (1024, 512, 256, 128, 64). The final layer is a 1x1 convolution that produces a 388x388 output segmentation map with 2 channels. A legend in the bottom right corner explains the symbols used for different operations: 3x3 Conv, ReLU (green arrow), Skip connection (pink arrow), 2x2 Max pooling (dark red arrow), 2x2 Up-Conv (blue arrow), and 1x1 Conv (yellow arrow).", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "5. Discussion", "section_headings": ["5. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 360, "line_end": 360, "token_count_estimate": 348, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1e2d8354a52d2a1c", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 5. Discussion\nType: text\n\n(a) An example U-Net architecture.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "5. Discussion", "section_headings": ["5. Discussion"], "chunk_type": "text", "line_start": 361, "line_end": 363, "token_count_estimate": 34, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0691fd7506d2d0a5", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 5. Discussion\nType: figure\nFigure\n\nImage /page/11/Figure/4 description: A diagram illustrating an example U-Net architecture, divided into an Encoder and a Decoder section. The process begins with an 'Input image' fed into the Encoder. Inside the Encoder, the image passes through a 'Backbone network' block labeled 'Atrous convolution'. The output of this block splits. One path, labeled 'Low-level Features', goes directly to the Decoder. The other path enters a module with five parallel operations: '1x1 Conv', '3x3 Conv rate 6', '3x3 Conv rate 12', '3x3 Conv rate 18', and 'Image Pooling'. The outputs of these five operations are concatenated and then passed through a '1x1 Conv' block. The result from this block is then sent to the Decoder. In the Decoder section, the features from the Encoder's main path are first processed by an 'Upsample by 4' block. Concurrently, the 'Low-level Features' from the Encoder are processed by a '1x1 Conv' block. The outputs of both these paths are then combined using a 'Concat' block. This concatenated output is passed through a '3x3 Conv' block, followed by another 'Upsample by 4' block, which produces the final 'Output segmentation map'.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "5. Discussion", "section_headings": ["5. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 364, "line_end": 364, "token_count_estimate": 361, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91b04db87d69f534", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 5. Discussion\nType: text\n\n(b) DeepLabv3+ architecture.\n\nFig. 7. Architectures of key models. Conv, ReLU, and concat stand for convolution, Rectified Linear Unit, and concatenation respectively.\n\nderivatives of U-Net or DeepLab architectures across different study sites is acceptable, as the primary focus is on studying glacial lake dynamics rather than model exploration. However, this approach prioritizes exploitation over exploration, potentially limiting the discovery of novel methods better suited to diverse environmental conditions.\n\nThe research community should embrace underexplored architectures, including those successfully applied in other domains of Earth science. For instance, Recurrent Neural Network (RNN)s, including Long Short-Term Memory (LSTM) networks (Sherstinsky, 2020; Hochreiter and Schmidhuber, 1997), are not recent innovations. However, their ability to capture fine-grained time-series dynamics (Ismail Fawaz et al., 2019) remains largely untapped in glacial lake studies. No research has applied these models to analyze temporal lake evolution. Current approaches rely on independent per-image predictions followed by simple multi-temporal analyses. RNNs typically require longer training times than CNNs. However, their potential for recognizing temporal dynamics in glacial lake monitoring warrants further exploration.\n\nA round-robin comparison of the best-performing methods would provide a clearer picture of the trade-offs between computational complexity and performance gains. Including a comprehensive costbenefit analysis would strengthen this comparison. Together, these efforts could better guide future methodological improvements.\n\nA key challenge is the impact of pre-processing choices, such as at-mospheric correction, on performance. Despite its computational cost,\n\nthe extent to which atmospheric correction improves model accuracy remains largely unquantified. Assessing its effect on classification performance could optimize processing pipelines for large-scale glacial lake studies.\n\nThe relevance of ML/DL approaches depends on the application domain. These methods are valuable for regional-scale research where large numbers of lakes need to be analyzed efficiently. However, their utility is limited for site-specific applications – such as hydropower development in discrete or transboundary river basins – where field validation, in situ monitoring, and physically-based hazard modeling are typically required.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "5. Discussion", "section_headings": ["5. Discussion"], "chunk_type": "text", "line_start": 365, "line_end": 381, "token_count_estimate": 573, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "66b5574aa79fc317", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 6. Conclusion and outlook\nType: text\n\nGlacial lakes are rapidly expanding due to climate change-driven glacier retreat, increasing the likelihood of outburst floods and impacting human lives and infrastructure. Monitoring these lakes is crucial for assessing hazards, improving early warning systems, and managing freshwater availability. However, effective large-scale studies remain a challenge due to the remoteness of glacial lakes and limited in situ data. Learning-based approaches, particularly deep learning, have emerged as powerful tools, offering numerous opportunities for automating glacial lake mapping and monitoring. However, their full potential remains underexplored.\n\nThis paper reviews the existing literature on classical machine learning and deep learning methodologies for remote sensing of glacial lakes. It surveys 48 studies, outlines their respective strengths and weaknesses, identifies key research gaps, and provides best practice recommendations.\n\nMost studies have focused on glacial lakes in Asia, Greenland, and Antarctica. Critical regions such as the Andes and European Alps have received limited attention. Optical sensors, particularly Landsat-8 and Sentinel-2, are widely used, complemented by Sentinel-1 radar data. Optical-only approaches have shown success in polar and mountainous regions but rely heavily on the pre-selection of cloud-free images. Radar-only approaches, on the other hand, have been successful in polar lowlands but are yet to prove effective in mountainous regions. Although radar data mitigates cloud-related issues, it requires extensive pre-processing to be analysis-ready. While both single- and multi-sensor approaches have been explored, multi-sensor fusion - especially SAR-optical combinations - has proven more effective. Despite these advances, mapping and monitoring glacial lakes in mountainous terrain under cloudy conditions remains an unresolved challenge. Furthermore, detecting lakes smaller than 0.01 km<sup>2</sup> remains challenging, primarily due to sensor resolution rather than methodological\n\nDeep learning methods have demonstrated significant success in glacial lake studies. Convolutional neural networks, particularly U-Net, DeepLab, and their variants, have emerged as prominent approaches. More deep learning approaches have been published compared to classical machine learning and continue to be increasingly reported. Among machine learning methods, random forests and support vector machines have been widely explored. A strong preference for pixel-wise supervised classification is evident. Weakly supervised learning approaches remain underexplored, despite their potential to reduce reliance on extensive ground truth labels. Transfer learning and data augmentation have significantly alleviated the bottleneck of limited labeled data availability. Consequently, training parameterintensive deep learning models has become more feasible. Nonetheless, high computational costs continue to limit their broader adoption in resource-constrained settings.\n\nTo advance robust and scalable glacial lake mapping and monitoring, learning-based approaches must meet several key criteria. High accuracy on primary study sites is essential. Equally important is the ability to generalize across space and time. Ideally, models should adapt to new study regions with minimal retraining. They should also accurately capture intra-annual lake dynamics.\n\nHowever, several challenges and opportunities remain. Relatively few studies investigate the transferability of their models across study sites and time periods. This raises concerns about generalizability, especially since data-driven approaches are susceptible to overfitting on less representative datasets. Rigorous spatiotemporal transferability experiments with both quantitative and qualitative evaluations must be prioritized.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "6. Conclusion and outlook", "section_headings": ["6. Conclusion and outlook"], "chunk_type": "text", "line_start": 383, "line_end": 401, "token_count_estimate": 856, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5fb5a35195fde7c0", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: 6. Conclusion and outlook\nType: text\n\nglacial lake mapping and monitoring , learning - based approaches must meet several key criteria . High accuracy on primary study sites is essential . Equally important is the ability to generalize across space and time . Ideally , models should adapt to new study regions with minimal retraining . They should also accurately capture intra - annual lake dynamics . However , several challenges and opportunities remain . Relatively few studies investigate the transferability of their models across study sites and time periods . This raises concerns about generalizability , especially since data - driven approaches are susceptible to overfitting on less representative datasets . Rigorous spatiotemporal transferability experiments with both quantitative and qualitative evaluations must be prioritized .\n\nMoreover, many studies focused on seasonal mapping of glacial lakes rather than year-round monitoring of their temporal evolution. This limits the ability to track intra-annual variations. Future studies should shift focus to multi-temporal analyses. Expanding training datasets with reference data spanning multiple seasons and years is equally important. Additionally, emerging deep learning models capable of learning seasonal and inter-annual variations need to be leveraged to capture dynamic glacial lake evolution.\n\nExisting learning-based approaches, especially deep learning, while effective, operate as black-box models that may violate hydrological and glaciological constraints. Integrating physical principles into data-driven models offers a compelling opportunity to enhance interpretability, explainability and ensure physical consistency. By aligning predictions with known physical behaviors and ensuring physically plausible outputs, unrealistic extrapolations in poorly observed regions can be prevented.\n\nFor fair and transparent performance evaluation, future studies should employ robust evaluation metrics that do not overlook class imbalance and stick to standardized benchmarking protocols. High-resolution (spatial) imagery (e.g., PlanetScope, Pléiades, UAV) may be used to improve detection of lakes smaller than 0.01 km<sup>2</sup>. However, the associated costs and limited scalability must be considered. Research should be expanded to underrepresented regions. Open-sourcing datasets, ground truth labels, and code could further advance learning-based glacial lake monitoring from space.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "6. Conclusion and outlook", "section_headings": ["6. Conclusion and outlook"], "chunk_type": "text", "line_start": 383, "line_end": 401, "token_count_estimate": 558, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72d321bd93b2758a", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: CRediT authorship contribution statement\nType: text\n\nManu Tom: Writing – review & editing, Writing – original draft, Visualization, Investigation, Conceptualization. Daniel Odermatt: Writing – review & editing, Supervision, Project administration, Funding acquisition. Cédric H. David: Writing – review & editing, Funding acquisition. Arnaud Cerbelaud: Writing – review & editing. Jeffrey Wade: Writing – review & editing. Holger Frey: Writing – review & editing, Supervision, Project administration, Funding acquisition.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "CRediT authorship contribution statement", "section_headings": ["CRediT authorship contribution statement"], "chunk_type": "text", "line_start": 403, "line_end": 405, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2ea26bf7306ee0d", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Declaration of Generative AI and AI-assisted technologies in the writing process\nType: text\n\nDuring the preparation of this work the lead author used Chat-GPT 40 to improve the readability and language of the first draft manuscript. After using this tool/service, all authors reviewed and edited the manuscript and take full responsibility for the content of the publication.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Declaration of Generative AI and AI-assisted technologies in the writing process", "section_headings": ["Declaration of Generative AI and AI-assisted technologies in the writing process"], "chunk_type": "text", "line_start": 407, "line_end": 409, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f069b5e7ccb32ac3", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Funding sources\nType: text\n\nThis work was supported by the *GLOFCA*<sup>1</sup> project funded by the Adaptation Fund and implemented by UNESCO Almaty office and the University of Zurich; and the *Alpglacier*<sup>2</sup> project funded by European Space Agency (Grant nr: 4000133436/20/INB). Manuscript preparation was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) including a grant from NASA's Terrestrial Hydrology Program.\n© 2025. All rights reserved.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Funding sources", "section_headings": ["Funding sources"], "chunk_type": "text", "line_start": 411, "line_end": 414, "token_count_estimate": 168, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["4000133436", "80NM0018D0004"]}}
{"id": "fb8275134bcbcfe0", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix A. Remote sensing data used\nType: text\n\nSee Table A.3.\n\nhttp://glofca.org/.\n\n<sup>&</sup>lt;sup>2</sup> https://alpglacier.geo.uzh.ch/.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix A. Remote sensing data used", "section_headings": ["Appendix A. Remote sensing data used"], "chunk_type": "text", "line_start": 424, "line_end": 432, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5124066b1a0b0ee9", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix A. Remote sensing data used\nType: table\nTable: Table A.3 Overview of satellite imagery and topographic data used in learning-based glacial lake studies.\n\n| Category                     | Sensor (count)     | Publication(s)                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n|------------------------------|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Optical satellite imagery | Landsat-8 (22)     | Veh et al. (2018), Halberstadt et al. (2020), Yuan et al. (2020), Wu et al. (2020), He et al. (2021), Wang et al. (2021), Thati et al. (2021), Zhao et al. (2021), Chen et al. (2022), Dell et al. (2022), Kaushik et al. (2022), Thati and Ari (2022), Wang et al. (2022a,c), Banerjee and Bhuiyan (2023), Sharma and Prakash (2023), Zhao et al. (2023), Hardie et al. (2024), Sharma et al. (2024), Tang et al. (2024), Wang and Sugiyama (2024) and Xu et al. (2024) |\n|                              | Sentinel-2 (20)    | Dirscherl et al. (2020), Wangchuk and Bolch (2020), Rinzin et al. (2021), Dirscherl et al. (2021), Wendleder et al. (2021), Basit et al. (2022), Chatterjee et al. (2022), Hu et al. (2022), Kaushik et al. (2022), Ortiz et al. (2022), Lutz et al. (2023), Niu et al. (2023), Wei et al. (2023), Xu et al. (2023), Hu et al. (2024), Mustafa et al. (2024), Wang and Sugiyama (2024), Wu et al. (2024), Xu et al. (2024) and Yin et al. (2024)                         |\n|                              | Planetscope (5)    | Qayyum et al. (2020), Wendleder et al. (2021), Siddique et al. (2023), Thomas et al. (2023) and Xu et al. (2024)                                                                                                                                                                                                                                                                                                                                                         |\n|                              | Landsat-Other (2)  | Veh et al. (2018) and Banerjee and Bhuiyan (2023)                                                                                                                                                                                                                                                                                                                                                                                                                        |\n|                              | Landsat-7 (1)      | Banerjee and Bhuiyan (2023)                                                                                                                                                                                                                                                                                                                                                                                                                                              |\n|                              | ASTER (1)          | Jain et al. (2015)                                                                                                                                                                                                                                                                                                                                                                                                                                                       |\n|                              | Corona KH-4 (1)    | Rinzin et al. (2021)                                                                                                                                                                                                                                                                                                                                                                                                                                                     |\n|                              | IRS LISS III (1)   | Sharma and Prakash (2023)                                                                                                                                                                                                                                                                                                                                                                                                                                                |\n| Radar satellite imagery   | Sentinel-1 (15)    | Wangchuk and Bolch (2020), Wu et al. (2020), Zhang et al. (2020b), Dirscherl et al. (2021), How et al. (2021), Rinzin et al. (2021), Wang et al. (2021), Jiang et al. (2022), Kaushik et al. (2022), Wendleder et al. (2021), Xu et al. (2023), Hu et al. (2024), Mustafa et al. (2024), Wu et al. (2024) and Xu et al. (2024)                                                                                                                                           |\n|                              | TerraSAR-X (1)     | Wendleder et al. (2021)                                                                                                                                                                                                                                                                                                                                                                                                                                                  |\n|                              | GaoFen-3 (1)       | Chen (2021)                                                                                                                                                                                                                                                                                                                                                                                                                                                              |\n| DEM                          | SRTM (7)           | Veh et al. (2018), Wangchuk and Bolch (2020), Rinzin et al. (2021), Wang et al. (2022c), Banerjee and Bhuiyan (2023), Mustafa et al. (2024) and Yin et al. (2024)                                                                                                                                                                                                                                                                                                        |\n|                              | ArcticDEM (5)      | How et al. (2021), Hu et al. (2022), Lutz et al. (2023), Wei et al. (2023) and Wang and Sugiyama (2024)                                                                                                                                                                                                                                                                                                                                                                  |\n|                              | ASTER (3)          | Sharma and Prakash (2023), Hardie et al. (2024) and Sharma et al. (2024)                                                                                                                                                                                                                                                                                                                                                                                                 |\n|                              | ALOS (3)           | Veh et al. (2018), Kaushik et al. (2022) and Xu et al. (2023)                                                                                                                                                                                                                                                                                                                                                                                                            |\n|                              | NASADEM (2)        | Chen et al. (2022) and Hu et al. (2024)                                                                                                                                                                                                                                                                                                                                                                                                                                  |\n|                              | TanDEM-X (2)       | Dirscherl et al. (2020, 2021)                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n|                              | Copernicus DEM (1) | Xu et al. (2024)                                                                                                                                                                                                                                                                                                                                                                                                                                                         |", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix A. Remote sensing data used", "section_headings": ["Appendix A. Remote sensing data used"], "chunk_type": "table", "table_caption": "Table A.3 Overview of satellite imagery and topographic data used in learning-based glacial lake studies.", "columns": ["Category", "Sensor (count)", "Publication(s)"], "table_row_start": 1, "table_row_end": 18, "line_start": 433, "line_end": 452, "token_count_estimate": 1039, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f99731395e589822", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix B. Search methodology for literature review\nType: text\n\nTo ensure a comprehensive review of glacial lake remote sensing studies that used ML/DL, we conducted a structured literature search using *web of science* and *google scholar*. We included all relevant studies published through the end of 2024 (inclusive).\n\nFor web of science, we used the search string: [glacial lakes OR glacier lakes] AND [deep learning OR machine learning].\n\nIn google scholar, we used the following keyword combinations: glacial lakes deep learning, glacial lakes machine learning, glacier lakes deep learning, glacier lakes machine learning, proglacial lakes, ice-dammed lakes, supraglacial lakes, glacial lakes, and glacier lakes.\n\nDuring the selection process, we first screened titles and abstracts to filter out irrelevant studies. We prioritized peer-reviewed journal articles and conference proceedings. Although *google scholar* offers an extensive search scope, it also returns non-peer-reviewed sources (e.g., preprints), which were excluded. ML/DL papers focusing on general glacier dynamics without addressing glacial lakes were also excluded. Finally, we thoroughly examined the reference lists of shortlisted papers to identify any additional relevant studies.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix B. Search methodology for literature review", "section_headings": ["Appendix B. Search methodology for literature review"], "chunk_type": "text", "line_start": 455, "line_end": 463, "token_count_estimate": 316, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea9b879892b286d8", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix C. Methodology distribution\nType: text\n\nSee Table C.4.\n\nAppendix D. Variants of U-Net and Deeplab\n\nSee Table D.5.\n\nAppendix E. Regional distribution\n\nSee Table E.6.\n\nAppendix F. Loss functions and optimization strategies used\n\nSee Tables F.7 and F.8.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix C. Methodology distribution", "section_headings": ["Appendix C. Methodology distribution"], "chunk_type": "text", "line_start": 465, "line_end": 479, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90d82141fa7d4981", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix G. Glossary\nType: text\n\nAdaM Adaptive Moment.\n\nAI Artificial Intelligence.\n\nANN Artificial Neural Network.\n\nASPP Atrous Spatial Pyramid Pooling.\n\nC-K-MEANS Cascaded K-Means.\n\nCNN Convolutional Neural Network.\n\nCRT Classification and Regression Trees.\n\n**DCNN** Deep Convolutional Neural Network.\n\n**DELSE** DEep Level Set Evolution.\n\n**DEM** Digital Elevation Model.\n\nDL Deep Learning.\n\nEBT Ensemble-Bagged Trees.\n\nFPN Feature Pyramid Network.\n\nGAN Generative Adversarial Network.\n\nGLOF Glacial Lake Outburst Flood.\n\nHMA High Mountain Asia.\n\nIoU Intersection-over-Union.\n\nKNN K-Nearest Neighbors.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix G. Glossary", "section_headings": ["Appendix G. Glossary"], "chunk_type": "text", "line_start": 481, "line_end": 519, "token_count_estimate": 192, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5720bc90d7223483", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix G. Glossary\nType: table\nTable: Table C.4 Overview of ML and DL methods used for glacial lake remote sensing.\n\n|         | Methodology [count]                           | Publication(s)                                                                                                                                                                                                                                                                                                                                                                       |\n|---------|-----------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| ML [31] | RF [12]                                       | Veh et al. (2018), Dirscherl et al. (2020), Halberstadt et al. (2020), Wangchuk and Bolch (2020), Rinzin et al. (2021), Wendleder et al. (2021), Chen et al. (2022), Dell et al. (2022), Hu et al. (2022), Banerjee and Bhuiyan (2023), Mustafa et al. (2024) and Wang and Sugiyama (2024)                                                                                           |\n|         | SVM [4]                                       | Jain et al. (2015), Halberstadt et al. (2020), Zhang et al. (2020b) and Mustafa et al. (2024)                                                                                                                                                                                                                                                                                        |\n|         | K-MEANS+ [3]                                  |                                                                                                                                                                                                                                                                                                                                                                                      |\n|         | K-MEANS [2]                                   | Thati et al. (2021) and Wu et al. (2024)                                                                                                                                                                                                                                                                                                                                             |\n|         | Cascaded K-Means (C-K-MEANS) [1]              | Wu et al. (2024)                                                                                                                                                                                                                                                                                                                                                                     |\n|         | Artificial Neural Network (ANN) [2]           | Banerjee and Bhuiyan (2023) and Mustafa et al. (2024)                                                                                                                                                                                                                                                                                                                                |\n|         | Others [10]                                   |                                                                                                                                                                                                                                                                                                                                                                                      |\n|         | Logistics Regression (LR) [1]                 | Mustafa et al. (2024)                                                                                                                                                                                                                                                                                                                                                                |\n|         | Maximum Entropy (ME) [1]                      | Halberstadt et al. (2020)                                                                                                                                                                                                                                                                                                                                                            |\n|         | Naive Bayes (NB) [1]                          | Halberstadt et al. (2020)                                                                                                                                                                                                                                                                                                                                                            |\n|         | Classification and Regression Trees (CRT) [1] | Halberstadt et al. (2020)                                                                                                                                                                                                                                                                                                                                                            |\n|         | Minimum Distance (MD) [1]                     | Halberstadt et al. (2020)                                                                                                                                                                                                                                                                                                                                                            |\n|         | Ensemble-Bagged Trees (EBT) [1]               | How et al. (2021)                                                                                                                                                                                                                                                                                                                                                                    |\n|         | ISODATA [1]                                   | Thati et al. (2021)                                                                                                                                                                                                                                                                                                                                                                  |\n|         | Learning Vector Quantization (LVQ) [1]        | Wu et al. (2024)                                                                                                                                                                                                                                                                                                                                                                     |\n|         | K-Nearest Neighbors (KNN) [1]                 | Mustafa et al. (2024)                                                                                                                                                                                                                                                                                                                                                                |\n|         | X-MEANS [1]                                   | Wu et al. (2024)                                                                                                                                                                                                                                                                                                                                                                     |\n| DL [39] | CNN Variants [37]                             |                                                                                                                                                                                                                                                                                                                                                                                      |\n|         | U-Net [18]                                    | Qayyum et al. (2020), Wu et al. (2020), Chen (2021), Dirscherl et al. (2021), He et al. (2022), Basit et al. (2022), Jiang et al. (2022), Ortiz et al. (2022), Thati and Ari (2022), Wang et al. (2022a), Lutz et al. (2023), Niu et al. (2023), Sharma and Prakash (2023), Siddique et al. (2023), Wei et al. (2023), Hu et al. (2024), Sharma et al. (2024) and Tang et al. (2024) |\n|         | DeepLab Variants [6]                          | Siddique et al. (2023), Xu et al. (2023), Hardie et al. (2024), Sharma et al. (2024), Tang et al. (2024) and Xu et al. (2024)                                                                                                                                                                                                                                                        |\n|         | CNN [2]                                       | Yuan et al. (2020) and Thomas et al. (2023)                                                                                                                                                                                                                                                                                                                                          |\n|         | LinkNet [2]                                   | Thati and Ari (2022) and Cao et al. (2024)                                                                                                                                                                                                                                                                                                                                           |\n|         | Others [9]                                    |                                                                                                                                                                                                                                                                                                                                                                                      |\n|         | ACFNet [1]                                    | Wang et al. (2021)                                                                                                                                                                                                                                                                                                                                                                   |\n|         | Mask-R-CNN [1]                                | Chatterjee et al. (2022)                                                                                                                                                                                                                                                                                                                                                             |\n|         | Feature Pyramid Network (FPN) [1]             | Thati and Ari (2022)                                                                                                                                                                                                                                                                                                                                                                 |\n|         | HarDNet (Second-order Attention Network) [1]  | Wang et al. (2022c)                                                                                                                                                                                                                                                                                                                                                                  |\n|         | Siamese CNN [1]                               | Zhao et al. (2023)                                                                                                                                                                                                                                                                                                                                                                   |\n|         | Deep Convolutional Neural Network (DCNN) [1]  | Kaushik et al. (2022)                                                                                                                                                                                                                                                                                                                                                                |\n|         | You Only Look Once (YOLO) [1]                 | Yin et al. (2024)                                                                                                                                                                                                                                                                                                                                                                    |\n|         | CoAtNet [1]                                   | Xu et al. (2023)                                                                                                                                                                                                                                                                                                                                                                     |\n|         | PSPNet [1]                                    | Thati and Ari (2022)                                                                                                                                                                                                                                                                                                                                                                 |\n|         | Others [2]                                    |                                                                                                                                                                                                                                                                                                                                                                                      |\n|         | Generative Adversarial Network (GAN) [1]      | Zhao et al. (2021)                                                                                                                                                                                                                                                                                                                                                                   |\n|         | DELSE [1]                                     | Ortiz et al. (2022)                                                                                                                                                                                                                                                                                                                                                                  |", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix G. Glossary", "section_headings": ["Appendix G. Glossary"], "chunk_type": "table", "table_caption": "Table C.4 Overview of ML and DL methods used for glacial lake remote sensing.", "columns": ["", "Methodology [count]", "Publication(s)"], "table_row_start": 1, "table_row_end": 35, "line_start": 520, "line_end": 556, "token_count_estimate": 1153, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "57463beb0f2b025a", "text": "Document: Monitoring earths glacial lakes from space with machine learning\nSection: Appendix G. Glossary\nType: text\n\nL8 Landsat-8.\n\nLR Logistics Regression.\n\nLVQ Learning Vector Quantization.\n\nMD Minimum Distance.\n\nME Maximum Entropy.\n\nML Machine Learning.\n\nMLE Maximum Lake Extent.\n\nMNDWI Modified Normalized Difference Water Index.\n\nNB Naive Bayes.\n\nNDWI Normalized Difference Water Index.\n\nNIR Near InfraRed.\n\nPPC Per-Pixel Classification.\n\nRF Random Forest.\n\nRNN Recurrent Neural Network.\n\n**S1** Sentinel-1.\n\nS2 Sentinel-2.\n\nSAR Synthetic Aperture Radar.\n\nSGD Stochastic Gradient Descent.\n\nSVM Support Vector Machine.\n\nSWOT Surface Water and Ocean Topography.\n\nToA Top of Atmosphere.\n\nUAV Unmanned Aerial Vehicle.\n\nYOLO You Only Look Once.", "metadata": {"source_file": "data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md", "document_title": "Monitoring earths glacial lakes from space with machine learning", "section_path": "Appendix G. Glossary", "section_headings": ["Appendix G. Glossary"], "chunk_type": "text", "line_start": 557, "line_end": 603, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3301919853a7b70e", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nType: text\n\nChangjun Gu, Suju Li\\*, Ming Liu, Kailong Hu and Ping Wang\n\nNational Disaster Reduction Center, Ministry of Emergency Management, Beijing 100124, China \\* Correspondence: lisuju@ndrcc.org.cn\n\nAbstract: Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km<sup>2</sup>/day) for about one month, followed by a relatively high-speed increase (0.04 km<sup>2</sup>/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system.\n\nKeywords: Lake Mercbacher; GLOF; remote sensing; early warning; climate change", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_headings": ["Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images"], "chunk_type": "text", "line_start": 4, "line_end": 12, "token_count_estimate": 619, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["100124"]}}
{"id": "2a241d902ea9b890", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nType: figure\nFigure\n\nImage /page/0/Picture/8 description: A graphic on a white background featuring a yellow circular icon with a white checkmark inside. To the right of the icon, the text 'check for updates' is displayed in two lines. The top line reads 'check for' in a regular dark gray font, and the bottom line reads 'updates' in a bold dark gray font.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_headings": ["Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images"], "chunk_type": "figure", "figure_caption": null, "line_start": 13, "line_end": 13, "token_count_estimate": 160, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf5f0d7e7f4df4df", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nType: text\n\nCitation: Gu, C.; Li, S.; Liu, M.; Hu, K.; Wang, P. Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images. *Remote Sens.* **2023**, *15*, 1941. https://doi.org/10.3390/rs15071941\n\nAcademic Editor: Yi Luo\n\nReceived: 25 February 2023 Revised: 3 April 2023 Accepted: 4 April 2023 Published: 5 April 2023", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_headings": ["Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images"], "chunk_type": "text", "line_start": 14, "line_end": 20, "token_count_estimate": 195, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "01b66c6886b18932", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nType: figure\nFigure\n\nImage /page/0/Picture/12 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top section and a black bottom section. The grey section contains two symbols in white circles with black outlines. On the left is the Creative Commons 'CC' symbol, and on the right is the attribution symbol, which is a stylized icon of a person. In the black section below the attribution symbol, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_headings": ["Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images"], "chunk_type": "figure", "figure_caption": null, "line_start": 21, "line_end": 21, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4c065bd10a8fafa3", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nType: text\n\nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_headings": ["Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images"], "chunk_type": "text", "line_start": 22, "line_end": 24, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "402a6a11bfa439dc", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 1. Introduction\nType: text\n\nGlacial deglaciation is becoming a worldwide phenomenon due to accelerating global warming [1–4]. As glaciers recede, their stability decreases, and they release stored water, which can lead to glacial hazards [5–7]. The melting water of glaciers, combined with overdeepenings in former glacier beds, has caused the global glacier lake volume and quantity to increase rapidly [8–10]. In fact, it has been suggested that the lakes formed from melting glaciers have increased by 50% in just 30 years (1990–2018). Dammed by abandoned moraines and ice, glacier lakes are susceptible to outbursts and can cause severe damage to human settlements and infrastructure downstream [7,11–15]. Statistics show more than 6300 people have died of glacier lake outburst floods (GLOFs) since the 1500s in High Mountain Asia alone [14]. Moreover, 15 million people globally are exposed to impacts from potential GLOFs, especially in High Mountains Asia (HMA) [13]. Real-time monitoring and early warning systems can effectively forecast active and high-frequency floods resulting from ice dam breaches [9]. However, GLOFs dynamics are challenging to measure and quantify due to unpredictable triggers and hard-to-reach locations [16].\n\nRemote Sens. 2023, 15, 1941 2 of 18\n\nFortunately, increasingly detailed digital topographic data and satellite imagery provide more possibilities in GLOF monitoring [17–19].\n\nLocated in the upper reaches of the Kumarik River in Kyrgyzstan, a tributary of the Aksu River in China, Lake Merzbacher is the largest glacial lake in the central Tien Shan mountain range of northwestern HMA [19]. The lake derives its water from the melt of the Inylchek Glacier and is divided into two parts: an upper lake and a lower lake [20]. The lower lake is ice-dammed and much larger than the upper one during the warm season when it is filled with water. Studies and reports have shown that there were 80 recorded outbursts of lower lake from 1932 to 2021 [21]. The average elevation of the lower lake surface is about 3380 m, and the drop from the lake to 200 km far away downstream communities is approximately 2000 km. This substantial drop in elevation makes Lake Merzbacher outburst floods to be more destructive and pose a significant threat to human settlements and infrastructure downstream.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 26, "line_end": 40, "token_count_estimate": 618, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "758380a4771fb535", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 1. Introduction\nType: text\n\ndivided into two parts : an upper lake and a lower lake [ 20 ] . The lower lake is ice - dammed and much larger than the upper one during the warm season when it is filled with water . Studies and reports have shown that there were 80 recorded outbursts of lower lake from 1932 to 2021 [ 21 ] . The average elevation of the lower lake surface is about 3380 m , and the drop from the lake to 200 km far away downstream communities is approximately 2000 km . This substantial drop in elevation makes Lake Merzbacher outburst floods to be more destructive and pose a significant threat to human settlements and infrastructure downstream .\n\nLake Merzbacher, first discovered by Gottfried Merzbacher in 1903 [22], has been the focus of research on its formation and outburst mechanism since recording its first outburst flood in 1931. The mystery of the outburst mechanism was not solved until Ajrapet demonstrated the process in 1971, revealing that the northern Inylchek Glacier dammed the lake, and a hidden stream existed under the ice dam. Lake Merzbacher exhibits a unique outburst mechanism that differs from other glacial lakes. Research has shown that the northern Inylchek Glacier dams the lake, and a hidden stream flows beneath the ice dam. As the lake fills with water, the dam gradually rises, and water flows downstream through the hidden stream [23]. This process provides a short window of opportunity for early warning. Previous studies have explored the potential methods to capture this signal. An understanding of the outburst probability is central to early warning, and thus predicting the outburst probability of GLOFs draws much academic attention. By selecting the main factors which link to dam failure, the qualitative probability of dam failure was derived and mapped over a large region [24,25]. However, identifying potentially dangerous lakes is crucial to early warning efforts. Xie et al. [19] first build an index to monitor the outburst of Lake Merzbacher based on time series remote sensing images before and after the flood. Nonetheless, the temporal-spatial resolution of available satellite images can hardly capture all the details before the outburst of Lake Merzbacher. One possible scenario is that there would not be enough satellite shreds of evidence before the outburst, especially considering the frequent clouds in this region. Therefore, continuous monitoring of the lake before an outburst is vital for the timely and accurate forecast of disasters.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 26, "line_end": 40, "token_count_estimate": 620, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69baef8bba3c6be2", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 1. Introduction\nType: text\n\n, 25 ] . However , identifying potentially dangerous lakes is crucial to early warning efforts . Xie et al . [ 19 ] first build an index to monitor the outburst of Lake Merzbacher based on time series remote sensing images before and after the flood . Nonetheless , the temporal - spatial resolution of available satellite images can hardly capture all the details before the outburst of Lake Merzbacher . One possible scenario is that there would not be enough satellite shreds of evidence before the outburst , especially considering the frequent clouds in this region . Therefore , continuous monitoring of the lake before an outburst is vital for the timely and accurate forecast of disasters .\n\nResearch has identified four distinct periods in the outburst process of Lake Merzbacher during a GLOF: icefall, quick water storage, early warning, and post-drainage. However, the characteristics and duration of the four periods are still unclear. One chief reason for this is that there are not enough available images. Glacial lakes are often located in alpine regions, where the cloudy weather decreased the availability of the time series satellite images. Furthermore, widely used satellite images, including MODIS, Landsat, and Sentinel, are deficient for the long revisit period and thus can hardly support GLOF monitoring. During the past twenty years, China's Earth observation activities have rapidly developed, creating a spaceship fleet resembling those from NASA, ESA, or JAXA, forming Chinese Earth Observation Satellite (CEOS) constellations [26,27]. Five major CEOS constellations are ready for use, including GaoFen satellites and HuanjingJianzai satellites, which have been widely used in land cover mapping [28–31]. The high resolution and short revisit period of (CEOS) constellations provide more opportunities in time series remote sensing monitoring. This study aims to obtain a comprehensive record of the Lake Merzbacher outburst using dense Chinese satellite images, and to analyze key elements during the outburst flood process, ultimately quantifying warning signals.\n\nRemote Sens. 2023, 15, 1941 3 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 26, "line_end": 40, "token_count_estimate": 551, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "9e88e3ac32ab6c2a", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.1. Study Area\nType: text\n\nLake Merzbacher, located in the Inylchek Region at 79°52′E and 42°13′N, is situated in the central Tien Shan mountain range in Kyrgyzstan and close to the Chinese border [19,20]. It is considered the largest moraine lake in Central Asia, which was formed by Inylchek Glacier and first discovered and named by the German explorer Gottfried Merzbacher in 1903 [32]. Specifically, the northern branch of the Inylchek Glacier, with a length of 41 km and an area of 247 km², retreat and the southern branch of the Inylchek Glacier, with a length of 61 km and an area of 574 km², expansion together formed this glacier lake (Figure 1). This glacial lake plays a vital role in preventing the direct runoff of North Inylchek Glacier drainage water into the valley [33]. Lake Merzbacher comprises two small lakes connected by the Merzbacher River [32]: the upper one at 3400 m and the lower one at 3300 m. Lower Lake Merzbacher, dammed by the Southern Inylchek Glacier and larger than Upper Lake Merzbacher, is responsible for the glacier lake outburst flood (GLOF) that occurs regularly and has shown a decreasing trend since the beginning of the 20th century [34].", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.1. Study Area", "section_headings": ["2. Materials and Methods", "2.1. Study Area"], "chunk_type": "text", "line_start": 44, "line_end": 46, "token_count_estimate": 378, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ec7b5f421d6798d", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.1. Study Area\nType: figure\nFigure\n\nImage /page/2/Figure/4 description: A side-by-side comparison of two maps of a mountainous region. The left panel is a topographic map showing the border between Kyrgyzstan and China. It labels Merzbacher Lake with a red dot, the Kumarik River, and a hydropower station with a red triangle. A legend explains the symbols and provides an elevation scale from 1500 to 5000 meters. The coordinates range from 79°0'E to 79°50'E longitude and 41°20'N to 42°20'N latitude. The right panel is a satellite image focusing on the glacial area. It labels the Northern Inylcheck Glacier, the Southern Inylcheck Glacier, an Upper Lake, and a Lower Lake. The glaciers are shown in white and light blue. The coordinates for this panel range from 79°49'E to 79°56'E longitude and 42°9'N to 42°15'N latitude. Each map includes a scale bar.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.1. Study Area", "section_headings": ["2. Materials and Methods", "2.1. Study Area"], "chunk_type": "figure", "figure_caption": null, "line_start": 47, "line_end": 47, "token_count_estimate": 283, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "614694f2cb56c918", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.1. Study Area\nType: figure\nFigure: Figure 1. The location of lake Merzbacher.\n\nFigure 1. The location of lake Merzbacher.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.1. Study Area", "section_headings": ["2. Materials and Methods", "2.1. Study Area"], "chunk_type": "figure", "figure_caption": "Figure 1. The location of lake Merzbacher.", "line_start": 49, "line_end": 49, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aee3163289de955f", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.1. Study Area\nType: text\n\nLocated in the upper reaches of Kunmaalik River in Aksu Prefecture, Xinjiang Uygur Autonomous Region, Lake Merzbacher's GLOF poses a significant threat to the downstream inhabitants. The lake's primary water source is from the melting of glaciers and snow during the ablation season, starting in early March and ending in September due to the semi-continental climate. This is precisely why glacial lake outburst floods (GLOFs) occur more frequently during this season [35]. To monitor the Lake Merzbacher GLOF, Xiehela hydrographic station was established in 1956, which is downstream of the lake. Since then,\n\nRemote Sens. 2023, 15, 1941 4 of 18\n\noutburst records have occurred almost every year. After extensive research by predecessors, the mechanism behind GLOFs is no longer a secret [36]. When the ice dam of a glacial lake lifts due to a continuous increase in water, the lake enters the drainage process. The concealed water channels beneath the dam open up, connecting the lake water with the outside world, and the lake water is rapidly drained.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.1. Study Area", "section_headings": ["2. Materials and Methods", "2.1. Study Area"], "chunk_type": "text", "line_start": 50, "line_end": 56, "token_count_estimate": 314, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b7ac3f0875504735", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: text\n\nSatellite data: All the satellite images were obtained from China Centre for Resources Satellite Data and Application, Beijing, China, (https://data.cresda.cn/#/home, accessed on 24 February 2023), except for Beijing-2 (BJ-2), which was obtained from Twenty First Century Aerospace Technology Co., Ltd., Beijing, China (https://www.21at.com.cn/, accessed on 24 February 2023) (Table 1).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "text", "line_start": 58, "line_end": 60, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0efc34079aebcdb1", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: table\nTable\n\n| <b>Table 1.</b> Satellite ima | ges from 2014 to 2022 |\n|-------------------------------|-----------------------|\n|-------------------------------|-----------------------|", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "table", "table_caption": null, "columns": ["Table 1. Satellite ima", "ges from 2014 to 2022"], "table_row_start": 1, "table_row_end": 1, "line_start": 61, "line_end": 63, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fada70bdc81a101a", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: table\nTable\n\n| Date           | Sensor    | Resolution | Date                      | Sensor     | Resolution |\n|----------------|-----------|------------|---------------------------|------------|------------|\n| 1 August 2014  | GF-1 WFV1 | 16 m       | 29 July 2021              | GF-1B_PMS  | 2/8 m      |\n| 10 August 2014 | GF-1 WFV3 | 16 m       | 18 May 2022               | HJ-2A_CCD2 | 16 m       |\n| 12 July 2015   | GF-1 WFV3 | 16 m       | 28 May 2022               | HJ-2A_CCD3 | 16 m       |\n| 9 August 2015  | GF-1 WFV1 | 16 m       | 3 June 2022               | HJ-2B_CCD3 | 16 m       |\n| 25 June 2016   | GF-1 WFV4 | 16 m       | 11 June 2022              | GF-1C_PMS  | 2/8 m      |\n| 2 July 2016    | GF-1 WFV1 | 16 m       | 17 June 2022              | HJ-2A_CCD4 | 16 m       |\n| 6 June 2017    | GF-1 WFV2 | 16 m       | 21 June 2022/ 8 July 2022 | HJ-2A_CCD4 | 16 m       |\n| 28 July 2017   | GF-1 WFV4 | 16 m       | 12 July 2022              | GF-1B_PMS  | 2/8 m      |\n| 6 July 2018    | GF-1 WFV2 | 16 m       | 13 July 2022              | GF-1B_PMS  | 2/8 m      |\n| 10 July 2018   | GF-1 WFV1 | 16 m       | 14 July 2022              | GF-1C_PMS  | 2/8 m      |\n| 19 July 2018   | GF-1 WFV3 | 16 m       | 14 July 2022              | HJ-2B_CCD3 | 16 m       |\n| 4 August 2018  | GF-1 WFV2 | 16 m       | 15 July 2022              | GF-1D_PMS  | 2/8 m      |\n| 13 August 2018 | GF-1 WFV4 | 16 m       | 16 July 2022              | HJ2A_CCD3  | 16 m       |\n| 5 July 2019    | GF-1D PMS | 2/8 m      | 17 July 2022              | GF1_WFV2   | 2 m        |\n| 23 July 2019   | GF-1 WFV3 | 16 m       | 18 July 2022              | BJ-2       | 0.8 m      |\n| 5 August 2020  | GF-6 PMS  | 2/8 m      | 20 July 2022              | HJ2A_CCD4  | 16 m       |\n| 3 July 2021    | GF-6 WFV  | 16 m       |                           |            |            |", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "table", "table_caption": null, "columns": ["Date", "Sensor", "Resolution", "Date", "Sensor", "Resolution"], "table_row_start": 1, "table_row_end": 17, "line_start": 65, "line_end": 83, "token_count_estimate": 744, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a19b4f7adccfec5a", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: text\n\nGaofen-1 (GF-1): The GF-1 satellite, the first satellite in the China high-resolution earth observation system, was launched on 26 April 2013, and was put into use on 30 December 2013 [37]. Two high-resolution (HR) cameras (2 m resolution panchromatic/8 m resolution multispectral cameras) and four wide field of view (WFV) cameras (16 m resolution multispectral cameras) were equipped with GF-1, which have four spectral channels (blue, green, red) distributed in the visible and NIR spectral domain ranging from 450 to 890 nm (Table 2) [38]. The repetition cycle of GF-1 WFV data can reach four days by combining the four cameras.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "text", "line_start": 84, "line_end": 88, "token_count_estimate": 221, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0309448ad779f664", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: table\nTable: Table 2. Technical parameter of GaoFen-1 (GF-1) (wide field of view (WFV) and panchromatic/multispectral high resolution (PMS)).\n\n| Payload | Spectral Type                                      | Spectral Range (nm)                              | Spatial Resolution (m) | Swath Width (km)       | Revisit Interval       |\n|---------|-------------------------------------------------------|-----------------------------------------------------|------------------------------|---------------------------|---------------------------|\n| WFV     | Blue Green Red Near infrared                 | 450~520 520~590 630~690 770~890            | 16                           | 800 (Set of 4 cameras) | Four days (side-swing) |\n| PMS     | Panchromatic Blue Green Red Near infrared | 450~900 450~520 520~590 630~690 770~890 | 2 8                       | 60 (Set of 2 cameras)  |                           |", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "table", "table_caption": "Table 2. Technical parameter of GaoFen-1 (GF-1) (wide field of view (WFV) and panchromatic/multispectral high resolution (PMS)).", "columns": ["Payload", "Spectral Type", "Spectral Range (nm)", "Spatial Resolution (m)", "Swath Width (km)", "Revisit Interval"], "table_row_start": 1, "table_row_end": 2, "line_start": 89, "line_end": 92, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "89c62a72f432db21", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: text\n\nRemote Sens. 2023, 15, 1941 5 of 18\n\nGaofen-6 (GF-6): The GF-6 satellite was launched on 2 June 2018, and was officially put into use on 21 March 2019. GF-6 is a low-orbit optical satellite equipped with a panchromatic/multispectral high-resolution (PMS) camera and a wide field view (WFV) camera (Table 3) [39,40]. This study used the GF-6 WFV and PMS images obtained on 5 August 2020 and 13 July 2021. GF-6/WFV with 16 m resolution has 8 bands, except for one coastal band (400~450 nm) and three visible bands (450~520 nm, 520~590 nm, and 630~690 nm). Same as GF-1, the red-edge I band (0.69~0.73  $\\mu$ m), red-edge II band (0.73~0.77  $\\mu$ m), a purple band (0.40~0.45  $\\mu$ m), and a yellow band (0.59~0.63  $\\mu$ m) are newly added bands. GF-6/PMS contains one panchromatic band with 2 m resolution and four multispectral bands with 8 m resolution.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "text", "line_start": 93, "line_end": 99, "token_count_estimate": 340, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44d5cb5126d1775a", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: table\nTable: Table 3. Technical parameters of GaoFen-6 (GF-6) (wide field of view (WFV) and panchromatic/multispectral high-resolution (PMS)).\n\n| Payload | Spectral Number | Spectral Type | Spectral Range (nm) | Spatial Resolution (m) | Swath Width (km) | Revisit Interval |  |\n|---------|--------------------|---------------|---------------------------|------------------------------|------------------------|---------------------|--|\n|         | 1                  | Blue          | 450~520                   |                              |                        |                     |  |\n|         | 2                  | Green         | Green 520~590             |                              |                        |                     |  |\n|         | 3                  | Red           | 630~690                   |                              | 800                    | Four days           |  |\n| WFV     | 4                  | Near infrared | 770~890                   | 17                           |                        |                     |  |\n|         | 5                  | Costal        | 400~450                   | 16                           |                        |                     |  |\n|         | 6                  | Yellow        | 590~630                   |                              |                        |                     |  |\n|         | 7                  | RedEdge1      | 690~730                   |                              |                        |                     |  |\n|         | 8                  | Rededge2      | 730~770                   |                              |                        |                     |  |\n|         | 1                  | Panchromatic  | 450~900                   | 2                            |                        | _                   |  |\n|         | 2                  | Blue          | 450~520                   |                              |                        |                     |  |\n| PMS     | 3                  | Green         | 520~590                   | 0                            | 90                     |                     |  |\n|         | 4                  | Red           | 630~690                   | 8                            |                        |                     |  |\n|         | 5                  | Near infrared | 770~890                   |                              |                        |                     |  |", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "table", "table_caption": "Table 3. Technical parameters of GaoFen-6 (GF-6) (wide field of view (WFV) and panchromatic/multispectral high-resolution (PMS)).", "columns": ["Payload", "Spectral Number", "Spectral Type", "Spectral Range (nm)", "Spatial Resolution (m)", "Swath Width (km)", "Revisit Interval", ""], "table_row_start": 1, "table_row_end": 14, "line_start": 100, "line_end": 114, "token_count_estimate": 517, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e362655817da341", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: text\n\nGaofen-7 (GF-7): The GaoFen-7 (GF-7) satellite, launched on 3 November 2019, carries two laser altimeters with the full waveform for global stereo mapping and is the first submeter-level resolution mapping satellite in the Medium- and Long-Term Development Plan for China's Civil Space Infrastructure (2015–2025) [41–43]. The GF-7 satellite operates in a sun-synchronous orbit with a designed lifespan of 8 years. Equipped with two linearray stereo cameras, it can effectively obtain full-color stereo images with a width of 20 km and a resolution better than 0.8 m and multispectral images with a resolution of 3.2 m. The two-beam laser altimeter on board conducts ground observation with a 3 Hz observation frequency, and the ground footprint diameter is less than 30 m. It acquires full-waveform data with a sampling frequency higher than 1 GHz. The laser altimeter system is designed to improve the elevation accuracy without ground control points of the two line-array stereo mapping cameras [44].\n\nHuanjingJianzai-2 A/B (HJ-2A/B): As the successors of HuanjingJianzai-1 (HJ-1) A/B satellites, HuanjingJianzai-2 A/B (HJ-2A/B) are in a sun-synchronous orbit, with both satellites having the same technical status and a design life of 5 years [45,46]. Each satellite has 4 optical payloads, including a 16 m camera, a hyperspectral imager, an infrared camera, and an atmospheric correction instrument. The 16 m camera payload consists of four visible-light CCD cameras, which can provide a multispectral image with a width of 800 km through field-of-view stitching. The hyperspectral imager has a width of 96 km, while the infrared camera has a width of 720 km. For atmospheric radiation correction, the atmospheric correction instrument can synchronously acquire atmospheric multispectral information with the same field of view as the 16 m camera in orbit.\n\nBeijing-2 (BJ-2): The Beijing-2 satellite (BJ-2) was successfully launched from the Satish Dhawan Space Center in Sriharikota, India, at 0:28 Beijing time on 11 July 2015 (16:28 UTC on 10 July 2015), carried by the PSLV-XL carrier rocket to a 651-km sun-synchronous orbit. The Beijing-2 constellation consists of three 0.8 m resolution optical remote sensing\n\nRemote Sens. 2023, 15, 1941 6 of 18\n\nsatellites [47]. The satellite, manufactured by Surrey Satellite Technology Ltd. (SSTL) in the UK, has a 7-sided structure, weighs about 450 kg, and is about 2.5 m high. The SSTL-300S1 agile platform can provide a fast-swinging ability of 45 degrees and in-orbit realization of 5 imaging modes, including multi-scene, strip, along-track stereo, cross-track stereo, and regional imaging. The VHRI-100 imaging instrument provides in-orbit panoramic and multispectral images with a swath width of about 24 km and a resolution of 0.8 m (ground sampling distance (GSD)) for panchromatic and 3.2 m for blue, green, red, and near-infrared multispectral images.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "text", "line_start": 115, "line_end": 127, "token_count_estimate": 832, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": ["300S1"]}}
{"id": "273ecf09c6b3de85", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.2. Data Sources\nType: text\n\nweighs about 450 kg , and is about 2 . 5 m high . The SSTL - 300S1 agile platform can provide a fast - swinging ability of 45 degrees and in - orbit realization of 5 imaging modes , including multi - scene , strip , along - track stereo , cross - track stereo , and regional imaging . The VHRI - 100 imaging instrument provides in - orbit panoramic and multispectral images with a swath width of about 24 km and a resolution of 0 . 8 m ( ground sampling distance ( GSD ) ) for panchromatic and 3 . 2 m for blue , green , red , and near - infrared multispectral images .\n\n2. Climate data: The land surface temperature data used in the article come from the ERA5-LAND reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) [48]. ERA5-LAND uses the simulated land-atmosphere variables from the ECMWF's fifth-generation reanalysis product ERA5 as forcing and is obtained using the modified land surface hydrology model HT-ESSEL and CY45R1. Although it has not undergone data assimilation, the observational data indirectly affect its simulation results. Compared with ERA5, ERA5-LAND has a higher spatial resolution, with a horizontal resolution of up to 0.1° (9 km) and a temporal resolution of 1 h. Due to the limitation of currently available data (https://developers.google.com/earth-engine/datasets/catalog/ECMWF\\_ERA5\\_LAND\\_MONTHLY\\_BY\\_HOUR#description, accessed on 24 February 2023), the hourly land surface temperature data of ERA5-LAND from 1 January 1981 to 1 December 2022 in the study area was used in the study.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.2. Data Sources", "section_headings": ["2. Materials and Methods", "2.2. Data Sources"], "chunk_type": "text", "line_start": 115, "line_end": 127, "token_count_estimate": 486, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["300S1"]}}
{"id": "1506e5fd83622e23", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.3. Methods\nType: text\n\n- 1. Processing of the satellite images and climate data: Preprocessing methods are shared in multispectral data of GF-1, GF-6, HJ-2 A/B, and BJ-2, including five steps: (1) Radiometric calibration; (2) Atmospheric correction; (3) Orthorectification; (4) Image fusion; (5) Image registration. The radiometric calibration and atmospheric correction were performed with the ENVI FLAASH module [44]. After finishing the preprocessing, combining 2 m resolution panchromatic and 8 m resolution multispectral data by the ENVI Gram-Schmidt Pan-Sharpening module to enhance the spatial resolution of GF-1, GF-6, and BJ-2 images [45]. DSM with a 2 m spatial resolution was derived from GF-7 by using the rational polynomial coefficient (RPC) model [41]. Time series temperature data were extracted by Google Earth Engine (GEE) cloud platform (https://earthengine.google.com/, accessed on 24 February 2023).\n- 2. Lake area extraction and changes analysis. The visual interpretation method was applied in this study. To improve the interpretation accuracy, the normalized difference water index (NDWI) [47] was introduced to better distinguish water bodies (Figure 2). The NDWI can enhance the identification of water bodies and effectively distinguish water bodies from floating ice. By using the green channel (maximum reflectance of water) and the near-infrared (NIR) channel (minimum reflectance of water), the NDWI was calculated as below:\n\n$$NDWI = \\frac{\\rho Green - \\rho NIR}{\\rho Green + \\rho NIR}$$\n (1)\n\nLake area changes, including water and ice area changes, were calculated in ArcGIS 10.8. To characterize the direction of water surface changes, the centroid shift method was used to describe the expansion process of the lake surface (Figures S1 and S2).\n\nRemote Sens. 2023, 15, 1941 7 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.3. Methods", "section_headings": ["2. Materials and Methods", "2.3. Methods"], "chunk_type": "text", "line_start": 129, "line_end": 139, "token_count_estimate": 527, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7735ef82b3912129", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.3. Methods\nType: figure\nFigure\n\nImage /page/6/Figure/1 description: A side-by-side comparison of two satellite images, labeled 'a' and 'b', of a glacial lake in a mountainous region, both dated 8 July 2022. Image 'a' shows the natural view of the dark blue lake with floating ice, surrounded by brown mountains and glaciers. Image 'b' overlays a Normalized Difference Water Index (NDWI) map on the same view. A legend for the NDWI shows a color scale from blue (Low: 0.31) to red (High: 0.58). The map displays lower NDWI values in blue over the main body of the lake and higher values in yellow and orange where the glacier meets the water. Both images include a compass rose and a scale bar indicating 1 km.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.3. Methods", "section_headings": ["2. Materials and Methods", "2.3. Methods"], "chunk_type": "figure", "figure_caption": null, "line_start": 140, "line_end": 140, "token_count_estimate": 236, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3871b6bd8dffe7ee", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.3. Methods\nType: figure\nFigure: Figure 2. Satellite image (a) and image after calculating the NDWI (b).\n\nFigure 2. Satellite image (a) and image after calculating the NDWI (b).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.3. Methods", "section_headings": ["2. Materials and Methods", "2.3. Methods"], "chunk_type": "figure", "figure_caption": "Figure 2. Satellite image (a) and image after calculating the NDWI (b).", "line_start": 142, "line_end": 142, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "429480ebd8279bea", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.3. Methods\nType: text\n\n3. Uncertainty assessment of glacier lake area. Visual interpretation was employed in extracting the lake area, and errors are unavoidable. Research has shown that mixed pixels caused by spatial resolution are a key factor in error sources. Using an error of one pixel on either side of the defined lake boundary is more appropriate [49]. Therefore, the uncertainty of the individual lake area can be calculated as follows:\n\n$$e = n^{1/2} \\times m \\tag{2}$$\n\n$$R = \\frac{e}{A} \\times 100\\% \\tag{3}$$\n\nwhere e is the absolute area error (m<sup>2</sup>) of each glacier lake, n represents the number of pixels on the lake boundary (approximately the ratio of lake circumference to spatial resolution), m is the area of each pixel in the remote sensing product, R is the relative error of a single lake, and R is the area of the lake. The area error obtained from the above equation shows that the total absolute area error of Lake Merzbacher is 0.08 km<sup>2</sup>, the average relative error is 0.22%, and the relative area error is between 0.2% and 0.68% (Table 4).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.3. Methods", "section_headings": ["2. Materials and Methods", "2.3. Methods"], "chunk_type": "text", "line_start": 143, "line_end": 151, "token_count_estimate": 352, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "247ea3aa1e36bae3", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.3. Methods\nType: table\nTable\n\n| Date | Area (km2) | Perimeter (m) | Absolute Area Error | Relative Area Error | Date | Area (km2) | Perimeter (m) | Absolute Area Error | Relative Area Error |\n|------|---------------|------------------|------------------------|------------------------|------|---------------|------------------|------------------------|------------------------|\n|------|---------------|------------------|------------------------|------------------------|------|---------------|------------------|------------------------|------------------------|", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.3. Methods", "section_headings": ["2. Materials and Methods", "2.3. Methods"], "chunk_type": "table", "table_caption": null, "columns": ["Date", "Area (km2)", "Perimeter (m)", "Absolute Area Error", "Relative Area Error", "Date", "Area (km2)", "Perimeter (m)", "Absolute Area Error", "Relative Area Error"], "table_row_start": 1, "table_row_end": 1, "line_start": 152, "line_end": 154, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc22e14b74401c56", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 2. Materials and Methods > 2.3. Methods\nType: table\nTable\n\n| Date          | Area (km2) | Perimeter (m) | Absolute Area Error (km2) | Relative Area Error (%) | Date         | Area (km2) | Perimeter (m) | Absolute Area Error (km2) | Relative Area Error (%) |\n|---------------|---------------|---------------|---------------------------------|-------------------------------|--------------|---------------|---------------|---------------------------------|-------------------------------|\n| 1 August 2014 | 2.99          | 9.94          | 0.01                            | 0.21                          | 11 June 2022 | 1.25          | 8.52          | 0                               | 0.02                          |\n| 12 July 2015  | 2.28          | 9.57          | 0.01                            | 0.27                          | 17 June 2022 | 1.45          | 7.92          | 0.01                            | 0.39                          |\n| 25 June 2016  | 1.94          | 8.82          | 0.01                            | 0.31                          | 21 June 2022 | 1.52          | 7.54          | 0.01                            | 0.37                          |\n| 6 July 2017   | 1.8           | 8.74          | 0                               | 0.01                          | 8 July 2022  | 2.07          | 8.91          | 0.01                            | 0.29                          |\n| 3 August 2018 | 2.5           | 10.15         | 0.01                            | 0.26                          | 12 July 2022 | 2.15          | 10.03         | 0                               | 0.01                          |\n| 5 July 2019   | 1.86          | 8.61          | 0                               | 0.01                          | 13 July 2022 | 2.1           | 9.91          | 0                               | 0.01                          |\n| 5 August 2020 | 2.05          | 8.25          | 0                               | 0.01                          | 14 July 2022 | 2.09          | 9.76          | 0.01                            | 0.30                          |\n| 3 July 2021   | 1.96          | 8.70          | 0.01                            | 0.30                          | 15 July 2022 | 2.08          | 9.85          | 0                               | 0.01                          |\n| 18 May 2022   | 0.72          | 5.84          | 0                               | 0.68                          | 16 July 2022 | 2.03          | 9.27          | 0.01                            | 0.30                          |\n| 28 May 2022   | 0.95          | 6.71          | 0.01                            | 0.55                          | 17 July 2022 | 2.01          | 9.12          | 0                               | 0.01                          |\n| 3 June 2022   | 1.06          | 6.82          | 0.01                            | 0.50                          | 18 July 2022 | 1.28          | 5.26          | 0                               | 0                             |", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "2. Materials and Methods > 2.3. Methods", "section_headings": ["2. Materials and Methods", "2.3. Methods"], "chunk_type": "table", "table_caption": null, "columns": ["Date", "Area (km2)", "Perimeter (m)", "Absolute Area Error (km2)", "Relative Area Error (%)", "Date", "Area (km2)", "Perimeter (m)", "Absolute Area Error (km2)", "Relative Area Error (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 156, "line_end": 168, "token_count_estimate": 639, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "deaa9d0056fdf466", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: text\n\nPrevious research has shown that the area of the lake continues to expand until the outburst occurs, and once the lake surface area starts to decrease, the GLOF event is imminent. Therefore, monitoring changes in the lake area prior to an outburst is crucial. Analysis of satellite images during the research period revealed a decreasing trend in the\n\nRemote Sens. 2023, 15, 1941 8 of 18\n\nmaximum lake area (MLA) from 2014 to 2021 (Figure 3b). Over the past nine years, the average MLA was 2.07 km<sup>2</sup>. The largest MLA was 2.99 km<sup>2</sup> on 1 August 2014 (Figure 3a), and the smallest MLA was 1.67 km<sup>2</sup> on 12 July 2016 (Figure 3d). From 2014 to 2022, the MLA occurred in July, except in 2014 and 2018 two years, which occurred in August. From the perspective of the average elevation of the lake, which shows a similar change trend as the MLA change from 2014 to 2022 (Figure 3b). The highest and the lowest average elevation of the MLA were observed in 2014 (3318.55 m) and 2019 (3289.97 m), respectively.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "text", "line_start": 173, "line_end": 179, "token_count_estimate": 349, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9fa8a6e31bdfe36", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A multi-panel figure showing the evolution of a glacial lake from 2014 to 2022. The figure is composed of ten panels labeled (a) through (j). Panel (a) is a large satellite image from October 1, 2014, showing the lake with colored outlines indicating its extent for each year from 2014 to 2022. A legend details the colors for each year: W2022 (orange), W2021 (dark blue), W2020 (green), W2019 (cyan), W2018 (blue), W2017 (magenta), W2016 (light green), W2015 (yellow), and W2014 (red). Panel (b) is a dual-axis line graph for the years 2014-2022, plotting Elevation (m) on the left y-axis (ranging from 3270 to 3330) and Area (km²) on the right y-axis (ranging from 0 to 3.5). The orange line represents elevation, and the blue line represents area. Panels (c) through (j) are smaller satellite images of the lake on different dates: (c) 2015 12 July, (d) 2016 25 June, (e) 2017 06 July, (f) 2018 04 October, (g) 2019 09 July, (h) 2020 05 October, (i) 2021 05 July, and (j) 2022 15 July, showing variations in the lake's color, ice cover, and clarity over the years.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": null, "line_start": 180, "line_end": 180, "token_count_estimate": 385, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "502f70de2109260b", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure: Figure 3. Maximum lake area (MLA) change before outburst since 2014. (a) MLA extent in each year and overlay on the image obtained on 1 August 2014; (b) MLA changes from 2014 to 2022; (c–j) MLA overlay on images obtained from 2015 to 2022.\n\n**Figure 3.** Maximum lake area (MLA) change before outburst since 2014. (a) MLA extent in each year and overlay on the image obtained on 1 August 2014; (b) MLA changes from 2014 to 2022; (c–j) MLA overlay on images obtained from 2015 to 2022.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": "Figure 3. Maximum lake area (MLA) change before outburst since 2014. (a) MLA extent in each year and overlay on the image obtained on 1 August 2014; (b) MLA changes from 2014 to 2022; (c–j) MLA overlay on images obtained from 2015 to 2022.", "line_start": 182, "line_end": 182, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5f1ac82c94c37f7", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: text\n\nBased on the observation of Lake Merzbacher GLOF in 2022, which started at least from 18 May and lasted for about two months (Figure 4). Initially, water coalesces into the southern part of the lake by gravity with an area of  $0.72~\\rm km^2$ . With the lake increasingly receiving meltwater from snow and ice, the lake area continues to increase. From 18 May to 28 May, the lake area increased at a rate of  $0.023~\\rm km^2/d$ . During the next 11 days (28 May~8 June), the lake area increased at a relatively low speed compared to the first ten days, with a rate of  $0.01~\\rm km^2/d$ . After that, the lake expanded rapidly from 8 June to 17 June, with a rate of  $0.04~\\rm km^2/d$ . For some time after that (17 June~8 July), the lake area increased rapidly, with a speed of  $0.03~\\rm km^2/d$ . The lake area reached its maximum in 12 July with a rate of  $0.03~\\rm km^2/d$  from 08 July to 12 July. However, the lake area decreased from 2.19 km² on 12 July to 2.12 km² on 14 July. In the next four days (15 July~18 July), the lake area continued to shrink with an average rate of  $0.21~\\rm km^2/d$ , especially during the period from 17 July to 18 July, and the lake area decreased rapidly from 2.01 km² to  $1.28~\\rm km^2$ .\n\nRemote Sens. 2023, 15, 1941 9 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "text", "line_start": 183, "line_end": 187, "token_count_estimate": 442, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83c399c7ae714e9f", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure\n\nImage /page/8/Figure/1 description: This figure, titled \"Figure 4. Lake extent change in 2022 before and after the outburst,\" displays a series of satellite images, a map, and a line graph to illustrate the changing size of a lake. The main part of the figure is a grid of 14 satellite images dated from May 18, 2022, to July 20, 2022. These images show a lake in a mountainous, snowy area, with its extent outlined. The lake's size is observed to increase progressively until mid-July, after which it appears to have drained. In the bottom right, there is a map with a legend showing the superimposed outlines of the lake on various dates, along with a scale bar. Next to the map is a line graph plotting the lake's area in square kilometers against the date. The y-axis, labeled \"Area(km²),\" ranges from 0 to 2.5. The x-axis, labeled \"Date,\" shows dates from May 18 (0518) to July 18 (0718). The graph shows the area increasing from approximately 0.7 km² on May 18 to a peak of about 2.2 km² on July 14, before sharply declining to around 1.2 km² by July 18, visually representing the outburst event.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": null, "line_start": 188, "line_end": 188, "token_count_estimate": 363, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aeb2d1e58bf19381", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure: Figure 4. Lake extent change in 2022 before and after the outburst.\n\nFigure 4. Lake extent change in 2022 before and after the outburst.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": "Figure 4. Lake extent change in 2022 before and after the outburst.", "line_start": 190, "line_end": 190, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9928d094de7902ce", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: text\n\nThe centroid changes of the lake area from 2014 to 2022 are presented in Figure 5. The centroid of the maximum lake area in the nine-year period was mostly located along the central line in a direction from the northeast to the southwest. When using the southwestern boundary as a reference frame, the farthest centroid of the maximum lake area was observed in 2018, measuring 1.2 km from the centroid point to the southernmost boundary. Second to 2018, the centroid in 2014 was the second farthest centroid of the maximum lake area during the research period. Simultaneously, the two largest lake areas occurred in the above two years. Notably, even in the years with the largest lake area, the centroid of the maximum lake area remained within a certain distance from the southernmost boundary. Based on the dense records in 2022 prior to the outburst, from 18 May to 28 May, the lake area expanded 0.2 km toward the northeast. In the following 14 days (28 May to 11 June), it continued to expand approximately 0.2 km to the northeast, and from 11 June to 21 June, it continued to expand northeastward with a distance of about 0.1 km. Before reaching its maximum, the lake area expanded approximately 0.3 km northeastward. Subsequently, the lake area began to shrink back toward the southwestern boundary. For the first few days (12 July to 15 July), the centroid of the lake area had little change. However, since 15 July, a significant retreat of the centroid of the lake area was observed. The most significant retreat occurred in a 24 h period from 17 July to 18 July.\n\nRemote Sens. 2023, 15, 1941 10 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "text", "line_start": 191, "line_end": 195, "token_count_estimate": 451, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cfe2e3817815f0ea", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure\n\nImage /page/9/Figure/1 description: A figure with two side-by-side maps showing changes in a geographical feature, likely a glacier, over time. Each map has an inset scatter plot. Both maps feature a scale bar from 0 to 1 km and a compass rose.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": null, "line_start": 196, "line_end": 196, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "84b8bab643ee35f0", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: text\n\nThe left panel displays annual changes from 2014 to 2022. The map shows the glacier with colored outlines and corresponding dots for each year. The legend indicates the colors: 2014 (red), 2015 (yellow), 2016 (green), 2017 (purple), 2018 (blue), 2019 (cyan), 2020 (pink), 2021 (dark blue), and 2022 (brown). The inset scatter plot shows the position of a point over these years, with the X-axis from 79.842 to 79.847 and the Y-axis from 42.2015 to 42.2055.\n\nThe right panel displays changes over specific dates, likely within a single year. The map shows the glacier with colored outlines and dots for dates like 0518, 0528, 0608, 0611, 0617, 0621, 0708, 0712, 0714, 0715, 0716, 0717, and 0718, each with a unique color as defined in the legend. The inset scatter plot shows the position of a point on these dates, with the X-axis from 79.84 to 79.845 and the Y-axis from 42.197 to 42.205.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "text", "line_start": 197, "line_end": 201, "token_count_estimate": 323, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a917c4bbb888d961", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure: Figure 5. Centroid changes of lake area in historical and 2022 before the outburst.\n\nFigure 5. Centroid changes of lake area in historical and 2022 before the outburst.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": "Figure 5. Centroid changes of lake area in historical and 2022 before the outburst.", "line_start": 202, "line_end": 202, "token_count_estimate": 102, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6eb0617edd58d3fe", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: text\n\n3.2. Using Lake Area and Ice Cover to Monitor Hazard of Glacier Lake Outburst Flood of Lake Merzbacher\n\nPrevious research has divided the entire outburst process of Lake Merzbacher into four stages: icefall (Stage I), quick water storage (Stage II), early warning (Stage III), and post-drainage (Stage IV). Therefore, the ice cover can be an essential index to predict the outburst. By setting the NDWI threshold value, we classified the lake as water and an ice area, two classes. As depicted in Figure 6, the ice area accounted for 53.3% of the total lake area on average from 2014 to 2022. The ice area proportion is the maximum (69.87%) in 2021, while that of 2016 is the minimum (23.83%). According to the records of the outburst date from 2014 to 2021, Lake Merzbacher outburst on 10 October, 9 July 2015, 25 June 2016~2 July 2016, 7 July 2017, 12 October 2018, 9 July 2019~12 July 2019, 7 October 2020, and 9 July 2021. During the first stage, the ice area is generally higher than the water area. In the second stage, the water area will expand rapidly, and the ice at the bottom of the lake will emerge from the water. Thus, the ice area will expand simultaneously before the outburst. When the dam body is raised, the lake area will shrink, and more floating ice will emerge, which indicates the lake has entered an early warning stage. As shown in Figure 6, water areas in 2014, 2015, 2018, 2019, and 2021 are smaller than ice areas, indicating that these images may have been captured during Stage II or Stage III.\n\nRemote Sens. 2023, 15, 1941 11 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "text", "line_start": 203, "line_end": 209, "token_count_estimate": 465, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14fc0a65f03cc2c3", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure\n\nImage /page/10/Figure/1 description: A stacked bar chart, labeled (a), showing the ice area and water area in square kilometers from 2014 to 2022. The x-axis represents the year, and the y-axis represents the area in square kilometers (km²), ranging from 0 to 3.5. The legend indicates that light blue bars represent 'Ice area' and dark blue bars represent 'Water area'. The data for each year is approximately as follows: 2014: Ice area ~1.7 km², Water area ~1.3 km², Total ~3.0 km². 2015: Ice area ~1.45 km², Water area ~0.8 km², Total ~2.25 km². 2016: Ice area ~0.45 km², Water area ~1.5 km², Total ~1.95 km². 2017: Ice area ~0.85 km², Water area ~0.95 km², Total ~1.8 km². 2018: Ice area ~1.55 km², Water area ~0.95 km², Total ~2.5 km². 2019: Ice area ~1.25 km², Water area ~0.6 km², Total ~1.85 km². 2020: Ice area ~0.9 km², Water area ~1.15 km², Total ~2.05 km². 2021: Ice area ~1.35 km², Water area ~0.6 km², Total ~1.95 km². 2022: Ice area ~1.0 km², Water area ~1.25 km², Total ~2.25 km².", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": null, "line_start": 210, "line_end": 210, "token_count_estimate": 380, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3d0d19d8eec2955", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A combination chart, labeled (b), showing changes in water and ice area over time. The horizontal axis is labeled 'Date' and shows dates from 0518 to 0717. The chart has two vertical axes, both labeled 'Area(km²)'. The left y-axis ranges from 0 to 2.5 and corresponds to a stacked bar chart. The right y-axis ranges from -0.4 to 0.6 and corresponds to a line chart. The legend indicates that dark blue bars represent 'Water area', light blue bars represent 'Ice area', and a red line represents 'W-I' (Water area minus Ice area). The stacked bars show a general increase in total area over the period, from approximately 0.7 km² on 0518 to a peak of about 2.1 km² on 0714. The red line fluctuates, starting near -0.2, dropping to a low of about -0.35 on 0621, and reaching a peak of approximately 0.55 on 0714.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": null, "line_start": 212, "line_end": 212, "token_count_estimate": 300, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e8b36c5d8cd34c5", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: figure\nFigure: Figure 6. Water area and Ice area of Lake Merzbacher before the outburst from 2014 to 2022 (a); Water area and Ice area daily changes before the outburst (b).\n\n**Figure 6.** Water area and Ice area of Lake Merzbacher before the outburst from 2014 to 2022 (a); Water area and Ice area daily changes before the outburst (b).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "figure", "figure_caption": "Figure 6. Water area and Ice area of Lake Merzbacher before the outburst from 2014 to 2022 (a); Water area and Ice area daily changes before the outburst (b).", "line_start": 214, "line_end": 214, "token_count_estimate": 149, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24ef70958d10dd0b", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022\nType: text\n\nTime series images captured in 2022 before and after the outburst provide more details. At the beginning of the observation period, the lake area was mainly covered by ice on 18 May. As the ice melted, the water area gradually increased and exceeded the ice area on 28 May and 3 June. During the storage process of the glacial lake, the ice originally deposited at the bottom of the lake basin was lifted by the water body and floated on the surface of the lake, resulting in ice dominating the lake area again on 17 June and 21 June. As the meltwater continued to merge, the water area surpassed the ice area again until reaching the maximum lake area on 8 July to 12 July. As the ice dam body rose from 12 July to 17 July, the gap between the water and ice area gradually increased and narrowed. During this period, ice deposited at the bottom of the ice dam was lifted by the water body, and the lake was dominated by ice again before the outburst.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022", "section_headings": ["3. Results", "3.1. Maximum Lake Area (MLA) Change before Outburst from 2014 to 2022"], "chunk_type": "text", "line_start": 215, "line_end": 217, "token_count_estimate": 293, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eae6adf01cd7a4d3", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: text\n\nAccording to the relevant records of the published studies and news reports, Lake Merzbacher has experienced nearly yearly outbursts since 1902 (Figure 7). From the perspective of the outburst date, it can be found that over 80% of outburst events occurred on 25 June, 28 July, and 14 October. From 1981 to 2022, almost every outburst event occurred on 23 July and 16 October, except in 1988 and 1996. Based on ERA5 climate data, the hottest month in every year since 1981 was extracted (Figure 7). As depicted in Figure 7, the hottest month occurred on 24 July or 20 October since 1981. From 1981 to 2022, the hottest month and the outburst date were the same in 25 years. Especially from 2015 to 2022, Lake Merzbacher outburst occurred in the hottest month every year. Lake Merzbacher outburst occurred in the second hottest month in 11 years, of which 5 years is one month ahead of the hottest month, while the rest of the 6 years are one month later than the hottest month. Only two outburst events occurred in the cold season. The main water storage source of Lake Merzbacher is the meltwater of North Inylchek Glacier, so the water storage of the glacial lake strongly depends on air temperature.\n\nFurther analysis of the outburst time reveals that since the 1930s, the outbursts of Lake Merzbacher have tended to occur earlier in the year (Figure 8). Over the period from 1902 to 2022, the Lake outburst earlier on average 6 days per decade ( $R^2 = 0.21$ ). Since the water storage of Lake Merzbacher comes from glacial meltwater, one potential reason for this fact can be climate warming, which is melting glaciers earlier. Climate change has raised average temperatures in the surrounding area of Lake Merzbacher (Figure 8). From 1981 to 2021, the average temperature increased from  $-9.59\\,^{\\circ}\\text{C}$  to  $-9.06\\,^{\\circ}\\text{C}$ , increasing at a rate of 0.18  $^{\\circ}\\text{C}$  per decade. From the perspective of April, May, June, July, and October, the average temperature increased during the same period. Among them, the fastest growth in\n\nRemote Sens. 2023, 15, 1941 12 of 18\n\naverage temperature has been seen in April, which increased at a rate of  $0.45\\,^\\circ\\text{C}$  per decade. In other words, Lake Merzbacher has shown an early warming trend.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "text", "line_start": 219, "line_end": 227, "token_count_estimate": 661, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "80bc9f2d5c73c4f6", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: The image displays three charts, labeled (a), (b), and (c), analyzing outburst data, likely related to Lake Merzbacher as mentioned in the text above the charts.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "figure", "figure_caption": null, "line_start": 228, "line_end": 228, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d68ae0eec3c766f2", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: text\n\nChart (a) is a histogram showing the frequency of outbursts by month. The x-axis is labeled 'Month' with ranges: [5, 6], (6, 7], (7, 8], (8, 9], (9, 10], (10, 11], and (11, 12]. The y-axis is 'Frequency', from 0 to 30. The frequencies for each month range are as follows: [5, 6] is 3, (6, 7] is 25, (7, 8] is 28, (8, 9] is 14, (9, 10] is 7, (10, 11] is 1, and (11, 12] is 3. The highest frequency of outbursts occurs between months 6 and 8.\n\nChart (b) is a bar chart showing the 'Outburst date' (month) over time. The x-axis is 'Year', from 1981 to 2020. The y-axis is 'Outburst date', representing the month from 0 to 14. Most outbursts occur in month 7 or 8. There are notable peaks where the outburst occurred in month 12, specifically around 1994 and 2016.\n\nChart (c) is a grouped bar chart comparing the 'Outburst Date' (blue bars) with the 'Hottest Month' (orange bars) for each year from 1981 to 2021. The x-axis is 'Year', and the y-axis is 'Month', from 0 to 14. In most years, the outburst date and the hottest month are closely aligned, typically around month 7 or 8. However, in some years, such as 1988 and 1996, the outburst date (month 12) is significantly later than the hottest month (month 7).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "text", "line_start": 229, "line_end": 235, "token_count_estimate": 456, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0775f7a60ec79d4", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: figure\nFigure: Figure 7. Date of Lake Merzbacher outburst since 1902. (a) Lake Merzbacher outburst Frequency in different months since 1902; (b) Lake Merzbacher outburst date since 1902; (c) The relationship between the outburst date and the hottest date since 1902.\n\n**Figure 7.** Date of Lake Merzbacher outburst since 1902. (a) Lake Merzbacher outburst Frequency in different months since 1902; (b) Lake Merzbacher outburst date since 1902; (c) The relationship between the outburst date and the hottest date since 1902.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "figure", "figure_caption": "Figure 7. Date of Lake Merzbacher outburst since 1902. (a) Lake Merzbacher outburst Frequency in different months since 1902; (b) Lake Merzbacher outburst date since 1902; (c) The relationship between the outburst date and the hottest date since 1902.", "line_start": 236, "line_end": 236, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7595cfaa15997932", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: figure\nFigure\n\nImage /page/11/Figure/4 description: The image displays two graphs, labeled (a) and (b).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "figure", "figure_caption": null, "line_start": 238, "line_end": 238, "token_count_estimate": 89, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a9884ec483287632", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: text\n\nGraph (a) is a scatter plot titled \"Outburst date since 1902\". The x-axis is labeled \"Year\" and ranges from 1900 to beyond 2000. The y-axis is labeled \"Days\" and ranges from 100 to 350. The plot contains numerous blue circular data points. A dotted blue trendline is drawn through the data, showing a negative slope. The equation for the trendline is given as \"y = -0.6397x + 1493.3\", and the R-squared value is \"R² = 0.21\".\n\nGraph (b) is a line graph showing temperature data over time. The x-axis is labeled \"Year\" and ranges from 1981 to 2020. There are two y-axes, both labeled \"Temperature (°C)\". The left axis ranges from 0 to 5, and the right axis ranges from -15 to 5. The graph contains five colored lines representing different months: July (yellow), April (dark blue), June (gray), October (light blue), and May (orange). A dotted gray line represents the overall \"Trendline\". The equation for this trendline is \"y = 0.0448x - 10.191\", and the R-squared value is \"R² = 0.1343\".", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "text", "line_start": 239, "line_end": 243, "token_count_estimate": 355, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0815b60f69744148", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: figure\nFigure: Figure 8. Detailed records of outburst date from Lake Merzbacher from 1902 to 2022 (a). Temperature changes in Lake Merzbacher from 1981 to 2022 (b).\n\n**Figure 8.** Detailed records of outburst date from Lake Merzbacher from 1902 to 2022 (a). Temperature changes in Lake Merzbacher from 1981 to 2022 (b).", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "figure", "figure_caption": "Figure 8. Detailed records of outburst date from Lake Merzbacher from 1902 to 2022 (a). Temperature changes in Lake Merzbacher from 1981 to 2022 (b).", "line_start": 244, "line_end": 244, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c705630a04972f0c", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980\nType: text\n\nRemote Sens. 2023, 15, 1941 13 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "3. Results > 3.3. The Relationship between the Temperature and the Outburst Date since 1980", "section_headings": ["3. Results", "3.3. The Relationship between the Temperature and the Outburst Date since 1980"], "chunk_type": "text", "line_start": 245, "line_end": 247, "token_count_estimate": 71, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44f866637cedd374", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 4. Discussion\nType: text\n\nThe glacier lake outburst flood (GLOF) significantly threatens downstream communities, infrastructure, and livelihoods [50–52]. Establishing an early warning system is crucial in mitigating disasters and reducing damage [53]. GLOFs release large amounts of water stored in glacier lakes rapidly downstream [54]. Therefore, capturing the signals of an imminent GLOF is essential for early warning. Previous studies have shown Lake Merzbacher outbursts at a high frequency, especially after entering the twenty century. Additionally, there is a certain regularity in Lake Merzbacher's outburst mechanism [19,20]. The lake area expands before reaching its maximum, after which it rapidly shrinks until the outburst. This study analyzed the lake's daily changes in area leading up to the 2022 outburst. To the best of our knowledge, this is the first study to observe these changes daily before a Lake Merzbacher outburst.\n\nWhen analyzing the maximum lake area (MLA) before the outburst of Lake Merzbacher from 2014 to 2022, it was found that the MLA did not exceed 3 km<sup>2</sup> and showed a decreasing trend, which is consistent with the findings of Li et al. [30]. In fact, the MLA has shown a decreasing trend, at least after entering the new century. On the contrary, glacier lakes showed an increasing trend in the Tienshan region reported by Wang et al. [55]. The upper lake area also showed an increasing trend [30]. The lake area expansion or shrinkage are all the consequences of climate change. On the one hand, climate change can lead to glacial ablation and thus provide more melting water for the glacier lakes. Therefore, the quantities and volume of glacier lakes showed an increasing trend. However, Lake Merzbacher was dammed by the southern branch of the Inylchek Glacier. The increase in temperature will also accelerate the piping inside the ice dam [56]. As a result, the outbursts tended to occur earlier (Figure 8). However, this does not mean Lake Merzbacher is not dangerous anymore. Under the effects of climate change, glaciers in the Tien Shan of Central Asia have generally retreated over the past half-century [57]. A general increasing trend has prevailed in the temporal variation of peak and total discharges of the jokulhlaup floods of the lake [49,58]. Furthermore, the southern branch of the Inylchek Glacier proceeds northward and flows into Lake Merzbacher, which may break the stability of the ice dam and thus significantly increase the GLOF risk for Lake Merzbacher. Therefore, the outburst of Lake Merzbacher requires our ongoing attention.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 249, "line_end": 259, "token_count_estimate": 705, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2bd231f7186a8ec", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 4. Discussion\nType: text\n\n) . However , this does not mean Lake Merzbacher is not dangerous anymore . Under the effects of climate change , glaciers in the Tien Shan of Central Asia have generally retreated over the past half - century [ 57 ] . A general increasing trend has prevailed in the temporal variation of peak and total discharges of the jokulhlaup floods of the lake [ 49 , 58 ] . Furthermore , the southern branch of the Inylchek Glacier proceeds northward and flows into Lake Merzbacher , which may break the stability of the ice dam and thus significantly increase the GLOF risk for Lake Merzbacher . Therefore , the outburst of Lake Merzbacher requires our ongoing attention .\n\nA previous study divided the entire process of the Lake Merzbacher outburst into four periods: icefall, quick water storage, early warning, and post-drainage [18]. According to satellite images in 2022, since at least 4 May, water starts to be injected into the lake (Figure 9). As the temperature keeps increasing, more and more glacier meltwater flows into Lake Merzbacher. The water area would expand at a relatively low speed during this period. This process continues for up to about one month, the same as in previous records. In the next month, the lake area would expand at a relatively high speed until reaching the maximum lake area. As the dam body gradually rises under the water pressure [20], a system of englacial channels hidden in the dam opens, and the lake water discharges through these channels. During this period, the lake area showed a relatively low decreasing speed and can be detected from some corners (S1–S3). This period lasts about 5 days and is significant to early warning. Furthermore, the entire outburst process of Lake Merzbacher will repeat nearly every year [16]. It means that Lake Merzbacher, because of its predictability, would be a promising candidate for an early warning system (EWS). Compared to other engineering measures on glacial lakes, establishing the EWS is a relatively inexpensive option. Generally, the GLOF EWS has been widely established in the HMA [58]. For example, a manual EWS was set up in 1997 by Ropal Tsho, a moraine-dammed located in the Rolwaling Valley of Nepal [49]. The manual EWS by Ropal Tsho evolved into a fully automatic system one year after its establishment, which constituted two components: a sensing system and a warning system [59]. Imja Tsho is another glacial lake located in the Solukhumbu District of Nepal, near the base of Mount Everest [60]. It is a glacial lake that formed as a result of the retreat of the Imja Glacier. The lake has grown rapidly in recent decades due to climate change, and there are\n\nRemote Sens. 2023, 15, 1941 14 of 18", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 249, "line_end": 259, "token_count_estimate": 731, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "dc195ff256fbf45b", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 4. Discussion\nType: text\n\nTsho , a moraine - dammed located in the Rolwaling Valley of Nepal [ 49 ] . The manual EWS by Ropal Tsho evolved into a fully automatic system one year after its establishment , which constituted two components : a sensing system and a warning system [ 59 ] . Imja Tsho is another glacial lake located in the Solukhumbu District of Nepal , near the base of Mount Everest [ 60 ] . It is a glacial lake that formed as a result of the retreat of the Imja Glacier . The lake has grown rapidly in recent decades due to climate change , and there are Remote Sens . 2023 , 15 , 1941 14 of 18\n\nconcerns that it could breach its natural dam and cause flooding downstream. The EWS of Imja Tsho combined two monitoring stations (web camera) and two relay stations [56]. EWSs can be complicated or easy. In theory, only the installation of water-level sensors can achieve a simple prediction scheme. However, a comprehensive monitoring system, usually composed of glacial lake monitoring, parent glaciers monitoring, the downstream river environment change detection, formulating a disaster mitigation plan, releasing early-warning information, and training for evacuation, is of great significance to reveal the GLOF generation mechanism [53]. Therefore, though the advantage of dense satellite images has been demonstrated, field-based EWS components are still required.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 249, "line_end": 259, "token_count_estimate": 377, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "573867bff5f92701", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 4. Discussion\nType: figure\nFigure\n\nImage /page/13/Picture/2 description: A low-resolution satellite image of a mountainous, snowy landscape. The image features a mix of white and light blue areas, representing snow or ice, and darker, rocky terrain. A small, dark blue body of water is visible near the center. In the top-left corner, there is a compass rose. In the bottom-left corner, a scale bar indicates a distance from 0 to 1 km. The date \"4 May 2022\" is written in the bottom-right corner.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 260, "line_end": 260, "token_count_estimate": 165, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1f7924dd79ed47f3", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 4. Discussion\nType: figure\nFigure: Figure 9. Satellite image of Lake Merzbacher captured in 4 May 2022.\n\nFigure 9. Satellite image of Lake Merzbacher captured in 4 May 2022.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 9. Satellite image of Lake Merzbacher captured in 4 May 2022.", "line_start": 262, "line_end": 262, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b9e6ad5b5f21c547", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 4. Discussion\nType: text\n\nThe difficulty in using satellite images to provide warnings of outburst floods from glacier-dammed lakes is to identify the drainage date [61]. The proportion of ice and water could also provide important information during the process. Xie et al. [16] proposed an index by calculating the ratio of floating ice and total lake area change rates. They found that when this index is less than 0.5 and the lake area is larger than 3 km<sup>2</sup>, the outburst will occur in the next 5–8 days. It is still a rough calculation. This study also calculates the proportion of ice and water before the outburst. As depicted in Figure 6, W-I shows regular fluctuations. Especially in the early warning stage, we found that the W-I showed an obvious decreasing trend, which indicates that more and more ice arose over the lake's surface. The ice and water areas are nearly the same size one day before the outburst. From the view of the satellite image, the lake area was almost dominated by ice. With the channel opening, water storage in the lake begins to flow downstream, and the floating ice over the lake's surface moves to the dam. Therefore, we can also speculate on the status of the lake according to the proportion of floating ice. Historical images from 2014 to 2021 can also help verify the assumption. According to the accurate records of outburst dates in 2020 and 2021, only two or four days are left before the outburst. As is shown in Figure 3, the floating ice almost bestrews the lake's surface in 2020 and 2021. It is speculated that the corresponding date of images in 2018 and 2019 are also near the outburst date. On the other hand, field-based EWS components may improve the timeliness and accuracy of early warning greatly.\n\nThe uncertainty and limitations in this research can be summarized below. As mentioned before, this is the first research to fully record the whole outburst process using\n\nRemote Sens. 2023, 15, 1941 15 of 18\n\nhigh-resolution images. However, it is a pity that we failed to obtain an effective image on 19 July 2022 because of the cloudy weather. It is on this day the outburst occurred. Synthetic aperture radar (SAR) is not vulnerable to weather conditions and can be applied as an effective reinforcement [62]. Even though we frequently monitor the lake area, hydrographic station data are significant for early warning. As the lake water flows out, the downstream hydrographic station would also witness the process of rapid increase followed by a rapid decrease of water [17]. Therefore, combining hydrographic station records or other field-based EWS components and dense satellite images can achieve accurate surveillance.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "4. Discussion", "section_headings": ["4. Discussion"], "chunk_type": "text", "line_start": 263, "line_end": 271, "token_count_estimate": 681, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13853ca5e15f0ba4", "text": "Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images\nSection: 5. Conclusions\nType: text\n\nThis study demonstrates the potential of using dense Chinese high-resolution satellite images to monitor glacier lake outburst flood (GLOF) and release early warning. Based on the dense high-resolution satellite images, this study monitored the whole process of the Lake Merzbacher GLOF in 2022 and further analyzed historical GLOF events in Lake Merzbacher. Our results show that the lake area would expand slowly (0.01 km<sup>2</sup>/d) and then rapidly expand  $(0.04 \\text{ km}^2/\\text{d})$  until it reaches the maximum lake area. After entering the early warning stage, which would last five days, the lake area would decrease slowly until the outburst. Further analysis shows that the GLOF is not far away when the floating ice area accounts for 50% or larger than 50% of the total lake area. The Lake Merzbacher GLOF is closely related to the temperature, and the outburst mainly occurred in the hottest months (July and October), especially in the last ten years. With the average temperature increase under climate change, Lake Merzbacher outbursted earlier and earlier (6 days earlier per decade since 1902). Our study provides essential references for monitoring Lake Merzbacher and establishing the early warning system (EWS). We also suggest that adding some field observation equipment, including a meteorological station and a hydrologic station, can improve the timeliness and accuracy of early warning.\n\n**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs15071941/s1, Figure S1: Changes in the lake area were observed at a regional scale; Figure S2: Changes in the connection between the upper and lower parts of the lake.\n\n**Author Contributions:** Conceptualization, C.G. and M.L.; methodology, C.G.; software, C.G.; validation, M.L., K.H. and P.W.; formal analysis, C.G.; investigation, C.G.; resources, S.L.; data curation, C.G.; writing—original draft preparation, C.G.; writing—review and editing, S.L.; visualization, K.H.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.\n\n**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2019YFE0127700; the National Key Research and Development Program of China, grant number 2021YFB3901200; and the China High-Resolution Earth Observation System, grant number 03-Y30F03-9001-20/22.\n\n**Data Availability Statement:** Data used in this study will be available upon request from the first author.\n\nConflicts of Interest: The authors declare no conflict of interest.", "metadata": {"source_file": "data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md", "document_title": "Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images", "section_path": "5. Conclusions", "section_headings": ["5. Conclusions"], "chunk_type": "text", "line_start": 273, "line_end": 285, "token_count_estimate": 731, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["2019YFE0127700", "2021YFB3901200"]}}
{"id": "c18808c06508ed7f", "text": "Document: ORIGINAL PAPER\nSection: Abstract\nType: text\n\nGlacial Lake Outburst Floods (GLOFs) have inflicted varying degrees of damage on the ecological environment, infrastructure, and human life in the alpine regions. Consequently, effectively predicting GLOFs has emerged as a critical research focus for disaster prevention and mitigation. This study focuses on the southern Tibetan Plateau and systematically examines the distribution characteristics of glaciers and glacial lakes, which are key contributors to GLOFs, and reviews historical GLOF events and their developmental patterns using remote sensing imagery, geographic information systems (GIS) and machine learning techniques. Based on historical GLOF data, a susceptibility evaluation system was developed by integrating Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms. This system quantitatively assesses the susceptibility of each glacial lake within the study area. The findings indicate that the majority of historical GLOF events occurred between June and September, with triggering factors including icefalls, moraine dam destabilization, and heavy precipitation. Climate warming, particularly during the 1960s and from 1990 to 2020, has significantly influenced the frequency of GLOF events, revealing substantial spatial heterogeneity, glacial dependence, and climate sensitivity of GLOFs. The evaluation results show that approximately 17.4% of the glacial lakes are situated in high and very high susceptibility classes, with the highest susceptibility observed in the central and eastern sub-regions. The MLP model demonstrated slightly higher accuracy than the SVM model, with AUC (Area Under the Receiver Operating Characteristic Curve) values of 0.96 and 0.90, respectively. This study offers a novel methodology and technical support for assessing the risk of glacial lake outbursts in the Tibetan Plateau and similar alpine mountain regions, providing a scientific basis for the development of disaster prevention and mitigation strategies.\n\n**Keywords** Glacial lake outburst flood · Spatial pattern · Susceptibility evaluation · Machine learning method · Tibetan plateau", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "text", "line_start": 5, "line_end": 9, "token_count_estimate": 504, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dd55db09429c1a19", "text": "Document: ORIGINAL PAPER\nSection: Abstract\nType: figure\nFigure\n\nImage /page/0/Picture/10 description: The Springer logo, featuring a black and white icon of a chess knight to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "figure", "figure_caption": null, "line_start": 10, "line_end": 10, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13f459d6dc21a03d", "text": "Document: ORIGINAL PAPER\nSection: Abstract\nType: text\n\nExtended author information available on the last page of the article", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "text", "line_start": 11, "line_end": 13, "token_count_estimate": 27, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71f3e4b754deff43", "text": "Document: ORIGINAL PAPER\nSection: 1 Introduction\nType: text\n\nGlacial Lake Outburst Floods (GLOFs) are catastrophic natural hazards caused by the sudden release of water from glacial lakes, often resulting in large-scale floods or debris flows. These events pose significant threats to downstream ecosystems, infrastructure, economic development, and human life (Ahmed et al. 2021; Chen et al. 2024). The Tibetan Plateau is one of the world's most densely populated regions with glaciers and glacial lakes, especially in its southern part. The complex geological structures and extreme climatic conditions in this area contribute to a high frequency of GLOFs (Harrison et al. 2018; Liu et al. 2014). In the context of global warming, the Tibetan Plateau and other alpine mountain regions have experienced severe GLOFs that have caused extensive damage to infrastructure and the ecological environment (Tang et al. 2023). Notable historical events include the 1954 Sangwang Glacial Lake outburst in Kangma County, Tibet, which resulted in approximately 400 fatalities and affected over 20,000 people downstream (Liu et al. 2019); the 1988 Guangxie Glacial Lake outburst in Bomi County, destroying 51 houses, causing five deaths and the closure of the Sichuan-Tibet Highway for over 200 days (Liu et al. 2019); the 2016 Gongbashatong Glacial Lake outburst in Nyalamu County, which flooded Zhangmu Port and caused extensive damage to infrastructure in downstream areas of Nepal, including the towns of Kodari and Tatopani; and the 2020 Jinweng Glacial Lake outburst in Nagchu City, causing significant damage to water conservancy and transportation infrastructure downstream (Yang et al. 2022). In recent years, as major engineering projects expand into alpine regions of the Tibetan Plateau, the potential threats posed by GLOFs to infrastructure, as well as their mutual feedback effects, have become increasingly prominent. Therefore, accurately predicting the likelihood of future GLOFs is crucial for developing effective disaster prevention and mitigation strategies.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 19, "token_count_estimate": 496, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "3a518bb6adbc170d", "text": "Document: ORIGINAL PAPER\nSection: 1 Introduction\nType: text\n\nGongbashatong Glacial Lake outburst in Nyalamu County , which flooded Zhangmu Port and caused extensive damage to infrastructure in downstream areas of Nepal , including the towns of Kodari and Tatopani ; and the 2020 Jinweng Glacial Lake outburst in Nagchu City , causing significant damage to water conservancy and transportation infrastructure downstream ( Yang et al . 2022 ) . In recent years , as major engineering projects expand into alpine regions of the Tibetan Plateau , the potential threats posed by GLOFs to infrastructure , as well as their mutual feedback effects , have become increasingly prominent . Therefore , accurately predicting the likelihood of future GLOFs is crucial for developing effective disaster prevention and mitigation strategies .\n\nThe formation of GLOFs is influenced by multiple factors, including glacier retreat, the morphological characteristics of glacial lakes, climate change, dynamic processes of glacier-glacial lake interactions, and geotectonic activities (Cook et al. 2018; Mou et al. 2024; Pandey et al. 2022). The interplay of these factors increases the complexity and uncertainty associated with predicting GLOFs (Shi et al. 2024; Wang et al. 2025; Westoby et al. 2014; Worni et al. 2014). Wangchuk et al. (2022) utilized Sentinel-1 SAR radar backscatter intensity data to monitor changes in glacier and lake areas and analyze the susceptibility of GLOFs. Aggarwal et al. (2016) utilized multispectral remote sensing imagery in conjunction with parameters such as lake area, distance to the parent glacier, slope, and distance from the basin outlet to identify 18 lakes with potential outburst risk. Emmer and Vilímek (2014) used decision trees and numerical calculations based on remote sensing imagery and digital terrain models to assess the susceptibility of GLOFs, providing effective monitoring tools for different GLOF scenarios. However, spatial and temporal prediction of GLOFs remains a significant challenge, particularly in the Tibetan Plateau region, characterized by high altitudes, variable climate, and complex topography (Zhang et al. 2021a; Zheng et al. 2021). Current approaches to evaluating GLOF susceptibility can be broadly categorized into statistical models and physical models. Statistical models focus on predicting the likelihood of future GLOFs by quantifying the relationships between historical events and contributing factors through statistical analysis (Chen et al. 2024; Das et al. 2024). Physical models rely on detailed topographic, hydrological, and glacial lake parameter data, employing numerical simulations to estimate the potential extent and magnitude of outburst", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 15, "line_end": 19, "token_count_estimate": 648, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "7f7b1e0a4d49c19f", "text": "Document: ORIGINAL PAPER\nSection: 1 Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/5 description: The logo for Springer, featuring a black outline of a chess knight piece to the left of the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 20, "line_end": 20, "token_count_estimate": 71, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "120b6c0fc2541119", "text": "Document: ORIGINAL PAPER\nSection: 1 Introduction\nType: text\n\nfloods with a mechanistic understanding of the processes involved (Zhang et al. 2024). These traditional risk assessment methods are often insufficient for addressing this complex environment and handling numerous evaluation units (Ahmed 2024; Niggli et al. 2024). Analyzing historical outburst events is crucial for studying GLOFs. Through the analysis of historical GLOF events, potential driving factors of glacial lake outbursts can be identified, providing valuable insights for future risk assessment and management (Ahmed 2024; Taylor et al. 2023). Moreover, data-driven, machine learning-based methods have emerged as powerful tools in geohazard prediction and have been widely applied to predict the susceptibility of landslides, avalanches, and debris flow (Chen et al. 2020; Viallon-Galinier et al. 2023; Wen et al. 2022; Zhou et al. 2024). These methods can extract hazard-related features from complex data and construct prediction models that incorporate multi-dimensional and multi-factor considerations. Compared to traditional statistical models, machine learning techniques offer significant advantages in handling large-scale data and capturing nonlinear relationships (Wen et al. 2022). Therefore, machine learning methods hold great potential for enhancing risk prediction of GLOFs.\n\nThe aim of this paper is to quantitatively predict the susceptibility of GLOFs in the southern Tibetan Plateau by integrating historical GLOF events with machine learning methods. First, we systematically analyzed the development patterns of glaciers and glacial lakes in the study area and compiled historical records of GLOFs to examine their spatial and temporal patterns. Subsequently, a system of conditioning factors was constructed, and machine learning algorithms were applied to learn from historical events and develop a predictive model for the outburst susceptibility of each glacial lake in the region. The model's prediction accuracy and the relative importance of each conditioning factor were assessed. Finally, the paper discusses the response of GLOFs to climate change, identifies highly susceptible glacial lakes, and explores their dominant control factors. The results provide methodological support for predicting GLOF risks in similar regions and offer a theoretical basis for the prevention and management of GLOF disasters in the southern Tibetan Plateau.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 21, "line_end": 25, "token_count_estimate": 544, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b7831fcba3504da", "text": "Document: ORIGINAL PAPER\nSection: 2 Study area\nType: text\n\nThe study area is situated on the southern Tibetan Plateau, encompassing the Himalayas and portions of the Hengduan Mountains, spanning a total area of 674,000 km². Based on geographical features, the study area is subdivided into western, central, and eastern subzones (Fig. 1a). The topography of the Himalayas varies significantly from north to south: the northern region consists of a high plateau and wide valley basins, with altitudes ranging from 4500 to 5200 m, while the southern part descends abruptly to below 3000 m, showcasing dramatic landscapes of deep valleys and towering mountains. The southern slopes of the Himalayas are lower in elevation, with the lowest point reaching no more than 500 m near the Ganges Plain. The southern Tibetan Plateau is primarily influenced by the Indian monsoon, the southern branch of the westerly circulation, and local climatic factors (Wen et al. 2024). Each year, around late February or early March, the Indian Ocean monsoon begins to impact the study area(Sun et al. 2022). The towering Himalayas block the warm, humid air brought by the monsoon, leading to substantial precipitation and snowfall on the southern side (Xu et al. 2022). In contrast, the southern branches of the westerly wind belt dominate the winter climate, which is relatively dry, with limited rain and snowfall. Most", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "2 Study area", "section_headings": ["2 Study area"], "chunk_type": "text", "line_start": 27, "line_end": 29, "token_count_estimate": 348, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d6d674fb6b0b7b6", "text": "Document: ORIGINAL PAPER\nSection: 2 Study area\nType: figure\nFigure\n\nImage /page/2/Picture/6 description: The image displays the logo for the publisher Springer. On the left is a black outline of a chess knight piece, specifically a horse's head facing left, positioned above two short horizontal lines. To the right of this icon, the word \"Springer\" is written in a black serif font. The entire logo is set against a plain white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "2 Study area", "section_headings": ["2 Study area"], "chunk_type": "figure", "figure_caption": null, "line_start": 30, "line_end": 30, "token_count_estimate": 110, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "212aa93677d5ac27", "text": "Document: ORIGINAL PAPER\nSection: 2 Study area\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A multi-panel figure illustrating a study on glacial lakes in the Tibetan Plateau. The figure is composed of four panels labeled a, b, c, and d. Panel a is a large map of the Tibetan Plateau and surrounding areas, spanning from 75°E to 95°E longitude and approximately 27°N to 35°N latitude. The map shows the locations of glaciers (in light blue), glacial lakes (in pink), and historical Glacial Lake Outburst Floods (GLOFs, marked with purple circles). The study area is divided into Western, Middle, and Eastern subzones. Major cities like New Delhi, Kathmandu, Thimphu, and Lasa are labeled. Panel b is a Landsat 8 satellite image from 2020-05-10 of a glacial lake named Jinwengcuo. The lake is shown in dark blue, surrounded by reddish and cyan terrain. Panel c is another Landsat 8 image of the same lake, Jinwengcuo, from 2020-11-18, showing a change in the lake's appearance and surrounding snow/ice cover. Panel d is a photograph dated 2019-07-13, showing a person in a blue jacket standing on the shore of a lake next to a monitoring station. A small boat or buoy is visible on the water in the background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "2 Study area", "section_headings": ["2 Study area"], "chunk_type": "figure", "figure_caption": null, "line_start": 32, "line_end": 32, "token_count_estimate": 328, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d55101a946cce8bb", "text": "Document: ORIGINAL PAPER\nSection: 2 Study area\nType: text\n\nFig. 1 Glacial Lake inventory map and location of the study area. (a) Glacial Lake inventory map; (b) & (c) Remote sensing images before and after Jinwucuo outburst; (d) Field survey photo of glacial lake\n\nof the winter snow is driven by oscillations in the north-south branch flow or by the intrusion of the strong Siberian cold front (Sun et al. 2022). Locally, the climate is influenced by the foehn wind effect, where the windward slopes of the southern Himalayas receive heavy precipitation, while the leeward side forms hot, dry valleys. This creates a stark contrast in climate between the northern and southern sides of the Himalayas. For example, the northern part of Gyirong County has a semi-arid plateau valley monsoon climate, characterized by an annual mean temperature of 2°C, with the warmest month averaging 10–18 °C and the coldest month averaging -10 °C. Annual precipitation in this region is approximately 300–600 mm. In contrast, the southern part of Jilong County experiences a subtropical mountain monsoon climate, with an average annual temperature of 10–13 °C, the warmest month exceeding 18 °C, and annual precipitation of around 1000 mm.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "2 Study area", "section_headings": ["2 Study area"], "chunk_type": "text", "line_start": 33, "line_end": 37, "token_count_estimate": 312, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8770dd93a8b16b30", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events\nType: text\n\nThe glacial lake data utilized in this study are sourced from the Cataloging Data Set of High Asian Ice Lakes (Wang et al. 2020). This dataset integrates glacier cataloging data and 668 Landsat series images, processed using ArcGIS and ENVI software, with boundaries extracted through manual visual interpretation. The percentage difference between the absolute lake area and GPS-measured boundaries ranges from 5.5 to 25.5%. The dataset covers two time periods: 1990 and 2018. In 1990, the study area contained 9093 glacial lakes covering a total area of 711.2 km², while in 2018, the number of glacial lakes", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events", "section_headings": ["3 Data and methodology", "3.1 Glacial lake inventory and historical GLOF events"], "chunk_type": "text", "line_start": 41, "line_end": 43, "token_count_estimate": 177, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55d8f12761291f4f", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events\nType: figure\nFigure\n\nImage /page/3/Picture/8 description: The logo for Springer, featuring a black line drawing of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events", "section_headings": ["3 Data and methodology", "3.1 Glacial lake inventory and historical GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 44, "line_end": 44, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "225f342fe3bbdb52", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events\nType: text\n\nincreased to 10,173, with a total area of 819.3 km<sup>2</sup>. The 2018 glacial lakes are illustrated in Fig. 1a. Additionally, the glacier data were derived from the best integrated quality product generated through data fusion in the Assessment of Glacier Inventories for the Third Pole Region (1990–2015) (Xia and Shiqiao 2022). This dataset employs hierarchical analysis to filter eight contributing factors and evaluates the comprehensive quality of eight existing glacier cataloging datasets for the Third Pole Region by classifying them. A new glacier catalog was created by fusing the highest-quality data within each evaluation unit (He and Zhou 2022). The eight glacier datasets used are: Randolph Glacier Inventory (RGI) updated Glacier Area Mapping for Discharge in Asian Mountains (GAMDAM), Second Chinese Glacier Inventory (CGI-2), Hindu Kush-Himalayan Glacier Inventory (HKHGI), Western Himalayas Glacier Inventory (WHGI), Karakoram-Pamir Glacier Inventory (KPGI), Second Pakistan Glacier Inventory (PGI-2), and Southeast Tibetan Plateau Glacier Inventory (SETPGI). Historical GLOFs are primarily reviewed through the literature, focusing on the timing and localization of each event, as shown in Fig. 1a. To ensure the accuracy and reliability of the reported events, all GLOF occurrences since 1986 were cross-validated using historical remote sensing imagery, primarily from Landsat series, to verify their timing, spatial extent, and geomorphic signatures (Fig. 1b&c). Furthermore, ground-truthing was carried out at several key sites. Since 2017, we have performed multiple field surveys focusing on glacial lakes with documented historical GLOF events. During these surveys, UAV (Unmanned Aerial Vehicle) were used to identify potential triggering factors, such as ice and rock avalanches, around these lakes. Additionally, unmanned boats equipped with multibeam sonar systems were employed to map the lakebed topography, providing precise measurements of the glacial lake reservoirs (Fig. 1d). The information on these historical GLOF events is detailed in Table 1. On the basis of these data, statistical analyses were conducted to examine the elevation, flow direction, and slope of glaciers, as well as the elevation, area, type, and expansion trend of glacial lakes. Additionally, temporal and spatial clustering analyses of historical GLOF events were performed using GIS tools.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.1 Glacial lake inventory and historical GLOF events", "section_headings": ["3 Data and methodology", "3.1 Glacial lake inventory and historical GLOF events"], "chunk_type": "text", "line_start": 45, "line_end": 47, "token_count_estimate": 654, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e8047ad006ca4b57", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: text\n\nSample selection plays a critical role in evaluating the GLOF susceptibility, as it directly influences the model's training effectiveness and prediction accuracy. In this study, 66 documented GLOF events within the study area were used as positive samples, excluding the Lumu Lake outburst due to unavailable localization data. An equal number of glacial lakes are randomly selected as negative samples using GIS tools to ensure an appropriate balance between positive and negative samples. Evaluation units are defined as the watersheds of individual glacial lakes within the study area. The conditioning factors must be representative, measurable, and based on readily available data. In this study, eight conditioning factors were selected: Glacial Lake Area, Glacial Lake Replenishment Area, Distance to Glacier, Elevation, Elevation Coefficient of Variation (ECV), Topographic Wetness Index (TWI), Peak Ground Acceleration (PGA), and Distance to Potential Icefall (Fig. 2). Among them, the Glacial Lake Area reflects the storage capacity of the glacial lake. The areas of glacial lakes used as a learning sample are reconstructed as comprehensively as possible using remote sensing imagery and geomorphologic evidence. Glacial Lake Replenishment Area determines the lake's ability to receive meltwater and precipitation. Distance to Glacier characterizes the extent of direct glacial activity impact. Those three factors can be", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "text", "line_start": 49, "line_end": 51, "token_count_estimate": 364, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "452f95529f38eb61", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/4/Picture/5 description: The logo for Springer, featuring a black line drawing of a chess knight piece on the left, facing left, next to the word \"Springer\" in a black serif font. The background is white.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 52, "line_end": 52, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e65698840cb37a9a", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: text\n\ncalculated and obtained using GIS. Additionally, elevation directly influences the local climate around the glacial lake, hydrological processes, and the extent and intensity of flood impacts following an outburst. Elevation data are extracted from the ASTER GDEM with 30 m resolution (https://www.gscloud.cn/). The ECV reflects the degree of terrain relief and the magnitude of elevation change within the region. The TWI is a physical indicator that captures the influence of regional topography on runoff flow direction and water accumulation. Both the ECV and TWI are derived from GIS calculations. Strong earthquakes can trigger landslides, avalanches, or ground deformation in nearby mountainous regions, leading to significant changes in glacial lake levels or even catastrophic outbursts. The seismic factor is quantitatively represented by the PGA, with data sourced from the Global Earthquake Model Global Seismic Hazard Map (Johnson et al. 2023). Icefalls are among the primary natural triggers for GLOFs. In this study, the distance to potential icefall is used to quantitatively assess their impact on GLOFs. The potential icefall data are derived from the research team's previous findings. Relative to the evaluation factors in existing studies, this study places particular emphasis on ECV and TWI to better characterize the impacts of heterogeneous mountainous terrain. These two topographic variables—ECV and TWI—are critical for understanding water routing and flood dynamics, especially within the complex geomorphological settings of the southeastern Tibetan Plateau. Furthermore, we introduce 'Distance to potential icefall' as a novel factor, informed by recent findings on icefall-triggered GLOFs in the Himalaya.\n\nAfter preparing the conditioning factors, descriptive statistics should be conducted on the values of all positive samples, along with an analysis of their degree of dispersion, to evaluate their effectiveness in differentiating GLOF susceptibility. Specifically, measures such as mean, standard deviation, and coefficient of variation will be used to quantify the variability and discriminatory power of each factor (Wen et al. 2022). A higher coefficient of variation indicates greater dispersion of the factor values, which, to some extent, suggests that the factor has weaker discriminatory power in evaluating GLOF susceptibility. In addition, the combination of conditioning factors must undergo a multicollinearity test. Multicollinearity refers to the reduced objectivity and accuracy of results caused by high correlations between explanatory variables in the model (Thompson et al. 2017). To assess the correlation between factors, the variance inflation factor (VIF) is used. The formula for calculating the VIF index is as follows (Thompson et al. 2017):\n\n$$VIF = \\frac{1}{1 - R_i^2} \\tag{1}$$\n\nwhere,  $R_j^2$  is the multiple linear regression coefficient. The VIF indicates the severity of multicollinearity. A VIF value closer to 1 signifies weaker multicollinearity, while a higher VIF value indicates stronger multicollinearity. Typically, a VIF value of 10 is used as the threshold for assessing multicollinearity. When VIF<10, there is no significant multicollinearity, which meets the requirement for model stability.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "text", "line_start": 53, "line_end": 63, "token_count_estimate": 837, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ee2c0cda29a3dcc", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: text\n\nis used . The formula for calculating the VIF index is as follows ( Thompson et al . 2017 ) : $ $ VIF = \\ frac { 1 } { 1 - R_i ^ 2 } \\ tag { 1 } $ $ where , $ R_j ^ 2 $ is the multiple linear regression coefficient . The VIF indicates the severity of multicollinearity . A VIF value closer to 1 signifies weaker multicollinearity , while a higher VIF value indicates stronger multicollinearity . Typically , a VIF value of 10 is used as the threshold for assessing multicollinearity . When VIF < 10 , there is no significant multicollinearity , which meets the requirement for model stability .\n\nTo further evaluate the relative importance of each conditioning factor and enhance model interpretability, a feature importance analysis is performed in the Results section. This analysis elucidates which factors dominate susceptibility modeling and how they interact with others to contribute to GLOF occurrence, thereby improving the transparency and physical interpretability of the model outputs. Additionally, the Discussion section provides", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "text", "line_start": 53, "line_end": 63, "token_count_estimate": 301, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "20217185cd2c6c24", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/5/Picture/7 description: The Springer logo, presented in black on a white background. On the left is a stylized icon of a chess knight facing left. To the right of the icon is the word 'Springer' written in a serif font.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 64, "line_end": 64, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e281be592d5da0e7", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: table\nTable\n\n|                     | ID Name                      | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |\n|---------------------|------------------------------|---------------------|-----------------|-------------------------------------|---------------|-----------------------------|------------------------------------------|-------------------------------|-----------------------|\n|                     |                              |                     |                 |                                     |               |                             |                                          | Data set Acquisi-             |                       |\n| _                   | Lumu Lake                    | N/A                 | N/A             | 1931/06/08 (Tibetan calendar) | 72            | Icefall                     | (Yao et al. 2014)                        |                               |                       |\n| 7                   | Taraco                       | 86.13°              | 28.29°          | 1935/08/28                          |               | Moraine dam destabilization | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |\n| 3                   | Qiongbixiamaco               | 88.92°              | 27.85°          | 1940/07/10                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |\n| 4                   | Sangwangco                   | 90.01°              | 28.24°          | 1954/07/16                          | 400           | Icefall                     | (Liu et al. 2019)                        |                               |                       |\n| 2                   | Lureco                       | 90.59°              | 28.27°          | $\\sim\\!1950s$                       |               | Icefall                     | (Yao et al. 2014)                        |                               |                       |\n| 9                   | Dacangco                     | 86.35°              | 28.19°          | 1956/06/01                          |               | Heavy rainfall              | (Tong et al. 2019)                       |                               |                       |\n| _                   | Cuoalong Glacial lake     | 90.59°              | 28.05°          | ~1956–1966                          |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |\n| ∞                   | Cirenmaco                    | .90 <sup>.</sup> 98 | 28.07°          | 1964/07                             | 200           | Icefall                     | (Veh et al. 2020)                        |                               |                       |\n| 6                   |                              | .90 <sup>.</sup> 98 | 28.07°          | 1981/07/11                          |               | Icefall                     | (Veh et al. 2020; Xu 1988)               |                               |                       |\n| 10                  |                              | .90 <sup>.</sup> 98 | $28.07^{\\circ}$ | 1983                                |               | Icefall                     | (Wang et al. 2018)                       |                               |                       |\n| 11                  | Longdaco                     | 85.35°              | $28.62^{\\circ}$ | 1964/08/25                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |\n| 12                  | Jilaico (Gehaipuco)       | 87.81°              | 27.96°          | 1964/09/21                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |\n| 13                  | Damenlahaico                 | 93.04°              | 29.87°          | 1964/09/26                          |               | Icefall                     | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |\n| 4                   | Jichudrake North 1           | 89.29°              | 27.87°          | $\\sim\\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |\n| 15                  | Upper Jiejiu Tsho            | 90.72°              | 28.27°          | $\\sim\\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |\n| 16                  | Upper Shegong Tsho        | 90.74°              | 28.30°          | $\\sim\\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al. 2021b) |                               |                       |\n| 17                  | 17 Jichudrake North 2 89.31° | 89.31°              | 27.87°          | $\\sim\\!1960s$                       |               | Icefall                     | (Komori et al. 2012; Zhang et al.        |                               |                       |\n| Table 1 (continued) |                              |                     |                 |                                     |               |                             |                                          |                               |                       |\n| ID                  | Name                         | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing |                       |\n|                     |                              |                     |                 |                                     |               |                             |                                          | Data set                      | Acquisi- tion time |\n| 18                  | Ayaco                        | 86.49°              | 28.35°          | 1968/08/15                          |               | Moraine dam destabilization | (Cui et al. 2010; Veh et al. 2020)       |                               |                       |\n| 19                  |                              | 86.49°              | 28.35°          | 1969/08/17                          |               | Moraine dam destabilization | (Yao et al. 2014)                        |                               |                       |\n| 20                  |                              | 86.49°              | 28.35°          | 1970/08/15                          |               | Moraine dam destabilization | (Yao et al. 2014)                        |                               |                       |\n| 21                  | Zhari                        | 90.61°              | 28.30°          | 1981/06/24                          |               | Icefall                     | (Yao et al. 2014)                        |                               |                       |\n| 22                  | Yindapuco (Jinco)            | 87.91°              | 27.95°          | 1982/08/27                          |               | Moraine dam destabilization | (Reynolds 2023)                          |                               |                       |\n| 23                  | Dig Tsho                     | 86.58               | 27.87           | 1985/08/4                           |               | Icefall                     | (Cenderelli and Wohl 2001)               |                               |                       |\n| 24                  |                              |                     |                 | 2015/04/25                          |               | Earthquake                  | (Byers et al. 2017)                      | Landsat-8                     | 2015/05/25            |\n| 25                  | Sanga North                  | 93.88°              | 30.12°          | ~1976–1987                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1988/10/09            |\n| 26                  | Guangxieco                   | 96.50°              | 29.46°          | 1988/07/15                          | 5             | Icefall                     | (Liu et al. 2019)                        | Landsat-5                     | 1988/10/27            |\n| 27                  | Gebumaco Upper               | 96.54°              | 29.75°          | 1991/06/12                          |               | Icefall                     | (Nie et al. 2018)                        | Landsat-5                     | 1993/10/09            |\n| 28                  | Gebumaco                     | 96.55°              | 29.75°          | 1991/06/12                          |               | Moraine dam destabilization | (Wang et al. 2011)                       | Landsat-5                     | 1993/10/09            |\n| 29                  | Chubung                      | 86.46°              | 27.87°          | 1991/07/12                          |               | Landslide                   | (Reynolds John 1999; Veh et al. 2020)    | Landsat-5                     | 1991/11/15            |\n| 30                  | Upper Luggye                 | 90.32°              | 28.08°          | 1991/09/30                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1991/09/30            |\n| 31                  |                              |                     |                 | 2010/10/04                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 2010/10/04            |", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "table", "table_caption": null, "columns": ["", "ID Name", "Longitude", "Latitude", "Date", "Death toll", "Trigger", "Reference", "Verified by remote sensing", ""], "table_row_start": 1, "table_row_end": 35, "line_start": 66, "line_end": 142, "token_count_estimate": 1881, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df5c58d5429c5ae2", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: table\nTable\n\n|                     | ID Name                      | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |\n|---------------------|------------------------------|---------------------|-----------------|-------------------------------------|---------------|-----------------------------|------------------------------------------|-------------------------------|-----------------------|\n| 32                  | Baitang Weat                 | 92.78°              | 29.55°          | 1991/10/09                          |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1991/10/09            |\n| 33                  | Langbuco Upper               | 86.42°              | 27.92°          | 1992/08                             |               | Icefall                     | (Nie et al. 2018)                        | Landsat-4                     | 1992/09/22            |\n| 34                  | Rejieco                      | 88.90°              | 27.97°          | 1992/09/10                          |               | Icefall                     | (Zhang et al. 2021b)                     | Landsat-5                     | 1992/11/10            |\n| 35                  | Zangla Tsho                  | 82.11°              | 30.35°          | 1994/08                             |               | N/A                         | (Zhang et al. 2021b)                     | Landsat-5                     | 1994/09/09            |\n| 36                  | Luggye Tsho                  | 90.30°              | 28.09°          | 1994/10/07                          |               | Moraine dam destabilization | (Veh et al. 2020; Wang et al. 2012)      | Landsat-5                     | 1994/11/09            |\n| 37                  | Xiaga                        | 91.94°              | 28.80°          | 1995/05/26                          |               | Icefall                     | (Yao et al. 2014)                        | Landsat-5                     | 1995/09/02            |\n| 38                  | Zhanabo                      | 85.37°              | 28.66°          | 1995/06/07                          |               | Moraine dam destabilization | (Yao et al. 2014)                        | Landsat-5                     | 1995/11/01            |\n| 39                  | Zanglaco West                | 81.99°              | 30.37°          | 1995/09                             |               | N/A                         | (Zhang et al. 2021b)                     | Landsat-5                     | 1995/10/30            |\n| 40                  | Kongyangmi La Tsho        | 88.78°              | 27.90°          | 1996/05/13                          |               | Icefall                     | (Nie et al. 2018)                        | Landsat-5                     | 1996/05/13            |\n| 41                  | Zhalonggabu                  | 85.48°              | 28.66°          | ~1996                               |               | N/A                         | (Veh et al. 2019)                        | Landsat-5                     | 1996/10/18            |\n| 42                  | Unnamed Lake                 | 92.38°              | 27.70°          | 1997/10/18                          |               | N/A                         | (Zhang et al. 2021b)                     | Landsat-5                     | 1997/10/18            |\n| ID                  | Name                         | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing |                       |\n|                     |                              |                     |                 |                                     |               |                             |                                          | Data set                      | Acquisi- tion time |\n| 43                  | Tam Pokhari                  | 86.84°              | 27.74°          | 1998/09/03                          |               | Icefall                     | (Lamsal et al. 2016; Veh et al. 2020)    | Landsat-5                     | 1998/09/15            |\n| 44                  | Upper Gangri Tsho1        | 90.81°              | 27.88°          | 1998/09/10                          |               | Icefall                     | (Komori et al. 2012)                     | Landsat-5                     | 1998/09/10            |\n| 45                  | Upper Gangri Tsho2        | 90.81°              | 27.89°          | 1998/09/10                          |               | Upstream inflow             | (Zhang et al. 2021b)                     | Landsat-5                     | 1998/09/10            |\n| 46                  | Dareco South                 | 85.92°              | 28.13°          | ~2001–2003                          |               | Icefall                     | (Liu et al. 2019)                        | Landsat-5                     | 2003/12/25            |\n| 47                  | Longjiuco                    | 89.74°              | 28.21°          | ~2002–2003                          |               | Heavy rainfall              | (Zhang et al. 2021b)                     | Landsat-5                     | 2003/11/18            |\n| 48                  | Jialongco                    | 85.85°              | 28.21°          | 2002/05/23                          |               | Icefall                     | (Chen et al. 2006)                       | Landsat-5                     | 2004/05/17            |\n| 49                  | Jialongco                    | 85.85°              | 28.21°          | 2002/06/29                          |               | Icefall                     | (Chen et al. 2006)                       | Landsat-5                     | 2004/05/17            |\n| 50                  | Degaco                       | 90.67°              | 28.33°          | 2002/09/18                          |               | Snow avalanche              | (Liu et al. 2013)                        | Landsat-5                     | 2003/11/18            |\n| 51                  | Jitang Sourth                | 94.32°              | 30.66°          | 2002–2003                           | 9             | N/A                         | (Liu et al. 2019)                        | Landsat-5                     | 2003/10/19            |\n| 52                  | Ouguchongguco Upper       | 93.54°              | 29.63°          | 2004/10/21                          |               | Icefall                     | (Yao et al. 2014)                        | Landsat-5                     | 2004/10/21            |\n| 53                  | Dagonglongba Lake         | 96.46°              | 29.75°          | ~2007–2008                          |               | Moraine dam destabilization | (Zhang et al. 2021b)                     | Landsat-5                     | 2008/06/28            |\n| 54                  | Sangwangco East              | 90.22°              | 28.27°          | 2008/10/31                          |               | Moraine dam destabilization | (Liu et al. 2019)                        | Landsat-5                     | 2008/12/01            |\n| 55                  | Langco                       | 91.81°              | 27.83°          | 2007/08/10                          |               | Heavy rainfall              | (Yao et al. 2014)                        | Landsat-5                     | 2008/05/16            |\n| 56                  | Zhemaico                     | 92.34°              | 28.01°          | 2009/07/03                          |               | Moraine dam destabilization | (Liu et al. 2013)                        | Landsat-5                     | 2009/10/10            |\n| 57                  | Cuoga                        | 94.00°              | 30.83°          | 2009/07/29                          | 2             | Icefall                     | (Yao et al. 2014)                        | Landsat-5                     | 2009/09/17            |\n| 58                  | Geiqu Lake                   | 87.99°              | 27.95°          | 2010/07                             |               | Heavy rainfall              | (Yao et al. 2014)                        | Landsat-5                     | 2010/11/12            |\n| 59                  | Nalongzangbu Lake         | 94.95°              | 30.53°          | 2014/06/30                          |               | Icefall                     | (Zhang et al. 2021b)                     | Landsat-8                     | 2014/08/23            |\n| 60                  | Ranzeranco (Ranzeriaco)   | 93.53°              | 30.47°          | 2013/07/05                          |               | Moraine dam destabilization | (Wang et al. 2021)                       | Landsat-8                     | 2013/09/12            |\n| 61                  | Lemthang Tsho                | 89.58°              | 28.06°          | 2015/06/28                          |               | Landslide                   | (Gurung et al. 2017; Veh et al. 2020)    | Landsat-8                     | 2015/10/18            |\n| 62                  | Yindapuco Upper              | 87.88°              | 27.93°          | 2015/10/31                          |               | N/A                         | (Reynolds 2023)                          | Landsat-8                     | 2015/11/10            |\n| 63                  | Zangbuco East                | 82.11°              | 30.16°          | ~2016                               |               | N/A                         | (Liu et al. 2019)                        | Landsat-8                     | 2016/07/10            |\n| Table 1 (continued) |                              |                     |                 |                                     |               |                             |                                          |                               |                       |", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "table", "table_caption": null, "columns": ["", "ID Name", "Longitude", "Latitude", "Date", "Death toll", "Trigger", "Reference", "Verified by remote sensing", ""], "table_row_start": 36, "table_row_end": 70, "line_start": 66, "line_end": 142, "token_count_estimate": 1958, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97b3ecef545a2018", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: table\nTable\n\n|                     | ID Name                      | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |\n|---------------------|------------------------------|---------------------|-----------------|-------------------------------------|---------------|-----------------------------|------------------------------------------|-------------------------------|-----------------------|\n| ID                  | Name                         | Longitude           | Latitude        | Date                                | Death toll | Trigger                     | Reference                                | Verified by remote sensing    |                       |\n|                     |                              |                     |                 |                                     |               |                             |                                          | Data set                      | Acquisi- tion time |\n| 64                  | Gongbatongshaco              | 86.05°              | 28.07°          | 2016/07/05                          |               | Icefall                     | (Cook et al. 2018)                       | Landsat-8                     | 2016/07/14            |\n| 65                  | Langmale Lake                | 87.14°              | 27.81°          | 2017/04/20                          |               | Rock avalanche              | (Byers et al. 2019)                      | Landsat-8                     | 2017/11/06            |\n| 66                  | Jinwengco                    | 93.63°              | 30.35°          | 2020/06/26                          |               | Landslide                   | (Yang et al. 2022)                       | Landsat-8                     | 2020/08/30            |", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "table", "table_caption": null, "columns": ["", "ID Name", "Longitude", "Latitude", "Date", "Death toll", "Trigger", "Reference", "Verified by remote sensing", ""], "table_row_start": 71, "table_row_end": 75, "line_start": 66, "line_end": 142, "token_count_estimate": 343, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "407bdbaf7a311e37", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/6/Picture/4 description: The logo for the publisher Springer, featuring a black outline of a chess knight piece to the left of the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 144, "line_end": 144, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f2b5bec4e3795576", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/7/Picture/3 description: The Springer logo, featuring a black outline of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 146, "line_end": 146, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1f0881f95daacee9", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: text\n\nTable 1 (continued)", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "text", "line_start": 147, "line_end": 149, "token_count_estimate": 33, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a2493fd250b4bb81", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/8/Picture/4 description: The logo for Springer, featuring a black and white line drawing of a chess knight's head facing left, positioned above two horizontal lines. To the right of the icon is the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 150, "line_end": 150, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0f81244978556f3", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/9/Picture/3 description: The logo for Springer, featuring a black outline of a knight chess piece to the left of the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 152, "line_end": 152, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "377e6d0ce1179414", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A figure from a scientific paper titled 'Fig. 2 GLOF conditioning factors', displaying eight different maps (labeled a through h) of a mountainous region. The figure includes a scale bar from 0 to 500 km and a north arrow. The maps are as follows:", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 154, "line_end": 154, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "32b28b5226a2f756", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: text\n\na. 'Glacial lake area': Shows the distribution of glacial lakes, represented by small pink dots within a dashed outline of the region.\n\nb. 'Glacial lake replenishment area (m²)': A map colored mostly dark blue, with a color scale ranging from 0 to 2.88303e+10.\n\nc. 'Glaciers': Displays the location of glaciers in light blue.\n\nd. 'Elevation (m)': A topographic map with a color scale from green (low elevation) to brown (high elevation), ranging from 0 to 8848 meters.\n\ne. 'Elevation variation coefficient': A map with colors from blue to yellow, with a scale from 0 to 1.628.\n\nf. 'Topographic wetness index': A map primarily in shades of green and yellow, with a scale from 1.96 to 26.5.\n\ng. 'Peak ground acceleration (m/s²)': A map with a color gradient from blue to yellow and orange, with a scale from 0.095 to 0.589.\n\nh. 'Potential icefall': Shows the location of potential icefall areas as black dots and a labeled pink shape within the region's outline.\n\nFig. 2 GLOF conditioning factors", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "text", "line_start": 155, "line_end": 173, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e047df397e49acb", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: figure\nFigure\n\nImage /page/10/Picture/4 description: The logo for the publisher Springer, featuring a black outline of a chess knight's head facing left, followed by the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 174, "line_end": 174, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "609a8b74ffacc12d", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.2 Learning samples and conditioning factors\nType: text\n\na comparison of our selected factors with those in previous studies, emphasizing both commonalities and region-specific innovations.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.2 Learning samples and conditioning factors", "section_headings": ["3 Data and methodology", "3.2 Learning samples and conditioning factors"], "chunk_type": "text", "line_start": 175, "line_end": 177, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "acf9bca1b5ad7def", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.1 Support vector machine (SVM)\nType: text\n\nSVM is a machine learning algorithm primarily employed for classification and regression tasks. The primary objective of SVM is to identify an optimal hyperplane that effectively separates data points of different classes in an N-dimensional feature space, where N denotes the number of features (Yousefi et al. 2020). Simultaneously, SVM aims to maximize the margin, defined as the distance between the hyperplane and the nearest data points from each class. Mathematically, a hyperplane is characterized by its normal vector w and offset b, and can be expressed as:\n\n$$f(x) = w^T x + b (2)$$\n\nwhere, *x* is the feature vector, serving as the condition factor in the susceptibility evaluation. SVMs can address nonlinear classification problems by employing a kernel function. The kernel function maps the data into a higher-dimensional space, enabling the identification of a linear hyperplane that separates the data. Commonly used kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. In this context, the kernel function computes the similarity between each sample in the high-dimensional space and the support vectors, which can be interpreted as weights. By analyzing the weights assigned to each feature, we can evaluate the relative importance that the model attributes to different condition factors.\n\nSVM was selected in this study due to its effectiveness in handling high-dimensional and small-sample datasets, which are characteristic of the compiled GLOF inventory. SVM often demonstrates higher generalization capacity in sparse datasets and requires fewer hyperparameters to tune (Ragab et al. 2021). Additionally, SVM is less prone to overfitting when appropriately regularized (Yousefi et al. 2020), which is critical given the imbalanced nature of GLOF event distribution.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.1 Support vector machine (SVM)", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.1 Support vector machine (SVM)"], "chunk_type": "text", "line_start": 181, "line_end": 189, "token_count_estimate": 479, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7273edeee7a827fe", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.2 Multilayer perceptron (MLP)\nType: text\n\nMLP is a feed-forward artificial neural network model primarily employed for supervised learning tasks. In classification tasks, MLP transforms raw input features through multiple fully connected layers, and the predicted probability for each class is obtained using the Softmax function (Ramchoun et al. 2016). MLP can be conceptualized as a directed graph consisting of multiple layers of nodes, where each node, except those in the input layer, represents a neuron with a nonlinear activation function. The architecture of an MLP comprises three types of layers: the input layer, hidden layers, and output layer. The input layer receives the input feature vector, with each feature corresponding to an individual input neuron. The hidden layers, which follow the input layer, may consist of one or more layers. Neurons in the hidden layers process the outputs from the previous layer by applying weights and activation functions. The output layer generates the final prediction. During", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.2 Multilayer perceptron (MLP)", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.2 Multilayer perceptron (MLP)"], "chunk_type": "text", "line_start": 191, "line_end": 193, "token_count_estimate": 264, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "396f4cabf6980324", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.2 Multilayer perceptron (MLP)\nType: figure\nFigure\n\nImage /page/11/Picture/11 description: The logo for Springer, featuring a black outline of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.2 Multilayer perceptron (MLP)", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.2 Multilayer perceptron (MLP)"], "chunk_type": "figure", "figure_caption": null, "line_start": 194, "line_end": 194, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9a4050a2ebeee2d7", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.2 Multilayer perceptron (MLP)\nType: text\n\ntraining, the MLP adjusts its weights via back-propagation and gradient descent, thereby determining the relative importance of each condition factor.\n\nThe choice of MLP was motivated by its ability to model complex nonlinear interactions among conditioning factors, which is essential in capturing the multifactorial nature of GLOF susceptibility. MLP can be more effectively adapted to address data imbalance through architecture design and loss function adjustments (Castro and Braga 2013). Furthermore, integrating two fundamentally different model architectures (kernel-based SVM and network-based MLP) enables robust cross-validation and allows us to evaluate the consistency of spatial susceptibility patterns across distinct algorithmic paradigms.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.2 Multilayer perceptron (MLP)", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.2 Multilayer perceptron (MLP)"], "chunk_type": "text", "line_start": 195, "line_end": 199, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcbc611053fcdb6d", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation\nType: text\n\nThe Receiver Operating Characteristic (ROC) curve is a graphical representation that plots the true positive rate (sensitivity) on the Y-axis and the false positive rate (1-specificity) on the X-axis at various classification thresholds (Mandrekar 2010). This plot serves as a visual tool for evaluating model performance. Sensitivity and specificity reflect the model's ability to correctly identify positive and negative samples, respectively. However, these two metrics alone do not fully capture the overall accuracy of the model. Therefore, the Area Under the Curve (AUC) value is often used to comprehensively evaluate model performance. The AUC represents the area under the ROC curve and is a widely accepted metric for assessing the performance of binary classifiers. The AUC can be calculated using the following formula (Mandrekar 2010):\n\n$$AUC = \\frac{\\sum_{i=1}^{n_0} r_i - n_0 (n_0 + 1) / 2}{n_0 n_1}$$\n (3)\n\nwhere,  $n_0$  represents the number of negative samples,  $n_1$  denotes the number of positive samples, and  $r_i$  indicates the positional rank of the i-th negative sample within the entire test dataset. The AUC value ranges from 0 to 1, with values closer to 1 indicating higher prediction accuracy of the model.\n\nFinally, the outburst susceptibility index of all glacial lakes was classified into five categories using the natural breakpoint method: very high susceptibility (VHS), high susceptibility (HS), medium susceptibility (MS), low susceptibility (LS), and very low susceptibility (VLS). Glacial lakes with VHS and HS were analyzed in detail, along with an examination of the significance of each conditioning factor. The complete methodological process of this study is illustrated in the Fig. 3 and can be summarized as follows: (1) Historical GLOF events and randomly selected non-outburst glacial lakes were used as learning samples, combined with multiple conditioning factors to construct a comprehensive dataset; (2) The dataset was randomly partitioned into training (80%) and validation subsets (20%); (3) Two machine learning algorithms—SVM and MLP—were trained using the training subset to develop susceptibility evaluation models for GLOFs; (4) The trained models were assessed through ROC curves, with their performance quantified by AUC values based on the validation subset; (5) The susceptibility index derived from the optimal models was classified using the natural breakpoint method to identify lakes prone to GLOF events. This systematic integration ensures that SVM and MLP effectively capture complementary patterns of", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.3 Model evaluation"], "chunk_type": "text", "line_start": 201, "line_end": 210, "token_count_estimate": 684, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2414e2f5fc4c5817", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation\nType: figure\nFigure\n\nImage /page/12/Picture/9 description: The logo for the publisher Springer, featuring a black outline of a chess knight piece on the left, next to the word \"Springer\" in a black serif font. The background is white.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.3 Model evaluation"], "chunk_type": "figure", "figure_caption": null, "line_start": 211, "line_end": 211, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "060573747afcc2e3", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation\nType: figure\nFigure\n\nImage /page/13/Figure/2 description: A flowchart detailing a methodology for assessing Glacial Lake Outburst Flood (GLOF) susceptibility. The flowchart has two main pathways. The first pathway takes \"Historical GLOF events\", \"Glacier\", and \"Glacial lake\" data, performs a \"Statistical analysis\" to determine \"Spatiotemporal differentiation characteristics\", and results in an understanding of \"GLOFs in response to climate change\". The second pathway uses machine learning. It starts with inputs of \"Randomly selected glacial lakes\" and \"Conditioning factors\" (which include Glacial lake area, Glacial lake replenishment area, Distance to glaciers, Elevation, Elevation variation coefficient, Topographic Wetness Index, Peak ground acceleration, and Distance to potential icefall). This data is split into a \"Training sample 80%\" and a \"Validation sample 20%\". The training sample is fed into a \"Machine learning algorithm\" to create an \"Evaluation model of GLOF susceptibility (SVM)\" and an \"Evaluation model of GLOF susceptibility (MLP)\". These models help determine the \"Importance of conditioning factors\". The validation sample is used for an \"ROC test\". Both the machine learning algorithm and the ROC test contribute to a \"GLOF susceptibility index\". This index is then processed by a \"Natural breakpoint method\" to identify \"GLOF-prone lakes\".", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.3 Model evaluation"], "chunk_type": "figure", "figure_caption": null, "line_start": 213, "line_end": 213, "token_count_estimate": 388, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62ee79fdfd6da777", "text": "Document: ORIGINAL PAPER\nSection: 3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation\nType: text\n\nFig. 3 Flowchart of the methodology employed for GLOF susceptibility analysis\n\nGLOF susceptibility, while ROC and AUC provide robust criteria for model selection and reliability assessment.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "3 Data and methodology > 3.3 GLOF modeling and model evaluation > 3.3.3 Model evaluation", "section_headings": ["3 Data and methodology", "3.3 GLOF modeling and model evaluation", "3.3.3 Model evaluation"], "chunk_type": "text", "line_start": 214, "line_end": 218, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "184fe693b557d31a", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: text\n\nThe study area contains a total of 34,151 glaciers, distributed as follows: 18,460 glaciers in the western subzone, 6759 in the central subzone, and 8932 in the eastern subzone. The elevation range of glacier spans from 2741 m to 6716 m, as shown in Fig. 4a. In this figure, the horizontal axis represents glaciers sequentially numbered from west to east. Overall, glaciers at the eastern and western ends of the southern Tibetan Plateau are found at lower elevations, while those in the central section are distributed at higher elevations. The interquartile range (25–75%) of glacier elevations is between 5110 m and 5601 m, with an average elevation of 5339 m (Fig. 4b). The primary glacier flow direction across the study area is northward, followed by northeast and northwest, while southwestward flow is the least common, followed by southward flow. The three subzones exhibit distinct glacier flow patterns: in the western subzone, the dominant direction is northward, consistent with the overall trend, while southwestward flow is the least common; in the central subzone, the dominant flow direction is westward, followed by southeastward, with northwestward flow being the least frequent; in the eastern subzone, the primary flow direction is northeastward, followed by southwestward (Fig. 4c). The overall glacier slope in the study area predominantly falls within the range of 10°-35°, with the highest concentration (22%) in the 20°-25° range. The slope distribution pattern is similar across all three subzones, with the 20°-25° interval being the most common (Fig. 4d).\n\nIn 2018, a total of 10,173 glacial lakes were identified in the study area. The western subzone, despite having the highest concentration of glaciers, contained the fewest glacial lakes, with only 1861. In contrast, the central subzone had 3573 glacial lakes, while the", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "text", "line_start": 224, "line_end": 228, "token_count_estimate": 496, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71af82ae38cb1c88", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: figure\nFigure\n\nImage /page/13/Picture/10 description: The image displays the Springer logo on a white background. On the left side of the logo is a black line drawing of a chess knight piece, with the horse's head facing to the left. Below the horse's head are two horizontal lines representing the base of the piece. To the right of the chess piece, the word 'Springer' is written in a black serif font.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 229, "line_end": 229, "token_count_estimate": 143, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "18a261dddd503d35", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: figure\nFigure\n\nImage /page/14/Figure/2 description: A figure from a scientific paper, labeled \"Natural Hazards (2025) 121:17677–17705\", containing four graphs labeled a, b, c, and d that analyze elevation and contribution ratios across different subzones.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 231, "line_end": 231, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["17677", "17705"]}}
{"id": "0d55540ee46a5ab5", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: text\n\nGraph a is a scatter plot showing Elevation (m) on the y-axis (from 3000 to 7000) versus ID on the x-axis (from 0 to over 30000). The plot is divided into a Western subzone, a Middle subzone, and an Eastern subzone. The elevation points in the Western subzone generally increase from west to east. The Middle subzone shows a high concentration of points at high elevations, mostly between 5500 m and 6500 m. The Eastern subzone shows a general decrease in elevation from west to east.\n\nGraph b is a box plot summarizing the total elevation data. The y-axis is the same as in graph a. The box, representing the 25%-75% interquartile range (IQR), is approximately between 5000 m and 5500 m. The median is around 5300 m, and the mean is slightly higher. Whiskers extend up to about 6500 m and down to 4000 m, with numerous outliers shown as dots.\n\nGraph c is a radar chart showing the Contribution ratio (%) on the radial axis (from 0 to 20) versus direction in degrees (0 to 360). It compares the total data (black line) with the Western (blue), Middle (purple), and Eastern (magenta) subzones. The Western subzone peaks around 330 degrees, the Middle subzone peaks around 270 degrees, and the Eastern subzone has peaks around 30 and 150 degrees.\n\nGraph d is a polar plot showing the Contribution ratio (%) on the radial axis (from 0 to 25) versus an angle from 0 to 90 degrees. It uses the same color scheme as graph c. All four lines show an increasing contribution ratio with the angle, peaking between 20 and 30 degrees before decreasing. The Eastern subzone shows the highest peak contribution ratio, over 10%.\n\n**Fig. 4** Elevation, orientation and slope of glaciers. (a) Elevation of glaciers from west to east; (b) Box chart of elevation distribution of all glaciers; (c) Radar map of glacier flow direction; (d) Slope map of glaciers", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "text", "line_start": 232, "line_end": 244, "token_count_estimate": 525, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["30000"]}}
{"id": "9cde59db14424b9e", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: text\n\n) on the radial axis ( from 0 to 25 ) versus an angle from 0 to 90 degrees . It uses the same color scheme as graph c . All four lines show an increasing contribution ratio with the angle , peaking between 20 and 30 degrees before decreasing . The Eastern subzone shows the highest peak contribution ratio , over 10 % . * * Fig . 4 * * Elevation , orientation and slope of glaciers . ( a ) Elevation of glaciers from west to east ; ( b ) Box chart of elevation distribution of all glaciers ; ( c ) Radar map of glacier flow direction ; ( d ) Slope map of glaciers\n\neastern subzone had the highest number, with 4739. The elevation of these glacial lakes ranged from 2174 m to 6078 m, as shown in Fig. 5a. The horizontal axis represents the sequence of glacial lakes numbered from west to east. Generally, the elevation of the glacial lakes decreases linearly from west to east. The elevation distribution of the glacial lakes predominantly fell within the interquartile range (25–75%) between 4568 m and 5232 m, with an average elevation of 4891 m (Fig. 5b), which is slightly lower than the overall elevation distribution of glaciers. The areas of the glacial lakes mainly ranged from 5400 m<sup>2</sup> to 58,137,579 m<sup>2</sup>. The western subzone featured smaller glacial lake areas, while the central subzone had the largest average glacial lake area, at 92,880 m<sup>2</sup>. The median area of glacial lakes in the eastern subzone was nearly the same as in the central subzone, with values of 28,934 m<sup>2</sup> and 28,258 m<sup>2</sup>, respectively (Fig. 5c). In terms of glacial lake types, Iceuncontacted Lakes represented the largest proportion at 46.39%, while Supraglacial Lakes accounted for the smallest proportion at 3.88%. Non-Glacier-Fed Lakes made up approximately 40.59% (Fig. 5d). From 1990 to 2018, both the number and area of glacial lakes in the three subzones showed an upward trend. The central subzone had the highest rate of increase, with a growth of 11.41% in number and 5.33% in area. The western subzone exhibited a 9.19% increase in the number of glacial lakes and a 1.38% increase in area. The eastern subzone showed an 8.73% increase in the number of glacial lakes and a 2.82% increase in area. Additionally, glacial lakes in the study area generally begin to thaw gradually at the end of April and refreeze by October.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "text", "line_start": 232, "line_end": 244, "token_count_estimate": 699, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "22fa7481b7e6eed8", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: figure\nFigure\n\nImage /page/14/Picture/5 description: The Springer logo, featuring a black and white icon of a chess knight to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 245, "line_end": 245, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "306855e396d2aa1b", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: figure\nFigure\n\nImage /page/15/Figure/2 description: A composite figure with five charts labeled a, b, c, d, and e, analyzing data on glacial lakes.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "figure", "figure_caption": null, "line_start": 247, "line_end": 247, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f2476b4660d62a5", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake\nType: text\n\nChart (a) is a scatter plot showing the elevation of glacial lakes. The y-axis, 'Elevation (m)', ranges from 3000 to 6500. The x-axis, 'Glacial Lake ID', ranges from 0 to over 10000 and is divided into 'Western subzone', 'Middle subzone', and 'Eastern subzone'. The data points are concentrated between 4000 and 6000 meters.\n\nChart (b) is a box plot of the elevation data from chart (a). The y-axis is 'Elevation (m)' from 3000 to 6500. The box shows an interquartile range from approximately 4700 to 5200 m, with a median around 5000 m.\n\nChart (c) consists of three box plots comparing the 'Area (m²)' of lakes in the 'Western subzone', 'Middle subzone', and 'Eastern subzone'. The y-axis ranges from 0 to 200000. All three subzones show a median area below 50000 m² but have numerous outliers with larger areas. A legend explains the components of the box plot: 25%-75%, 1.5IQR, Median, Mean, and Outliers.\n\nChart (d) is a pie chart showing the distribution of lake types by percentage. The categories are: 'Ice-uncontacted Lake' (46.39%), 'Non-Glacier-Fed Lake' (40.59%), 'Ice-contacted' (9.14%), and 'Supraglacial Lake' (3.88%).\n\nChart (e) is a grouped bar chart showing the percentage of the total number and area of lakes in each subzone. The y-axis is 'Percentage (%)' from 0 to 12. For the 'Western subzone', the number is about 9.2% and the area is about 1.5%. For the 'Middle subzone', the number is about 11.2% and the area is about 5.2%. For the 'Eastern subzone', the number is about 8.8% and the area is about 2.8%.\n\n**Fig. 5** Elevation, area, type and expansion trend of glacial lake. (a) Elevation of glacial lakes from west to east; (b) Box chart of elevation distribution of all glacial lakes; (c) Box chart of glacial lake areas; (d) Pie chart of glacial lake types; (e) The number and area changes of glacial lakes from 1990 to 2018", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.1 Glacier and glacial lake", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.1 Glacier and glacial lake"], "chunk_type": "text", "line_start": 248, "line_end": 260, "token_count_estimate": 612, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["10000", "200000", "50000"]}}
{"id": "07a55c02d5d00154", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events\nType: text\n\nThe earliest documented GLOF in the study area occurred on August 28, 1935 (Tibetan calendar), at Lumu Lake in Bomi County. As of 2024, a total of 66 GLOF events have been recorded in the area, as detailed in Table 1. Among these, Cirenmacuo Glacial Lake and Ayacuo Glacial Lake each experienced three outbursts, while Dig Tsho Glacial Lake and Upper Luggye Glacial Lake each experienced two outbursts. Geographically, these historical GLOF events are primarily concentrated in the central and eastern subzones of the study area. Among the three subzones, the western subzone recorded the fewest GLOF events, with only three events, while the central subzone experienced the most, with 44 events, and the eastern subzone had 19. To further evaluate the spatial distribution characteristics of these events, a spatial autocorrelation analysis using the Global Moran's I index was conducted. The results showed a Moran's I value of 0.239, a Z-score of 1.69, and a p-value of 0.091, indicating a weak spatial clustering pattern of GLOF events. However, the clustering was not statistically significant at the 95% confidence level, although marginal significance was observed at the 90% confidence threshold. Regarding temporal trends, a higher number of GLOF events occurred during the 1960s and from 1990 to 2020 (Fig. 6a). The majority of these GLOFs occurred between June and October, coinciding with the rainy season on the Tibetan Plateau (Fig. 6b). The primary causes of these events (Fig. 6c) include icefalls (48.48%), moraine dam destabilization (18.18%), and heavy rainfall (6.06%). With global warming, the frequency and variety of GLOFs on the Tibetan Plateau have increased. These", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.2 Historical GLOF events"], "chunk_type": "text", "line_start": 262, "line_end": 264, "token_count_estimate": 473, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56fff5f322b8f4d7", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events\nType: figure\nFigure\n\nImage /page/15/Picture/6 description: The image displays the Springer logo, which features a black line drawing of a chess knight piece to the left of the word \"Springer\" written in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.2 Historical GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 265, "line_end": 265, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ff67d7d7a64bdda", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events\nType: figure\nFigure\n\nImage /page/16/Figure/2 description: An image displaying three charts labeled a, b, and c, analyzing the frequency and causes of certain events. Chart a is a line graph showing the number of events per decade from the 1930s to the 2020s. The y-axis, labeled 'Number', ranges from 0 to 18. The data points are: 1930s (2), 1940s (1), 1950s (4), 1960s (10), 1970s (1), 1980s (7), 1990s (18), 2000s (12), 2010s (10), and 2020s (approximately 1). The peak occurred in the 1990s. Chart b is a bar chart showing the number of events per month. The y-axis, labeled 'Number', ranges from 0 to 14. The values for each month are: April (2), May (3), June (9), July (11), August (11), September (9), October (7), and N/A (14). The highest frequency is in July and August. Chart c is a pie chart illustrating the percentage distribution of event causes. The largest portion is 'Icefall' at 48.48%. Other causes include 'Moraine dam destabilization' (18.18%), 'N/A' (16.67%), 'Heavy rainfall' (6.06%), 'Landslide' (4.55%), 'Rock/Snow avalanche' (3.03%), 'Upstream inflow' (1.52%), and 'Earthquake' (1.52%). A legend below the pie chart provides the key for each colored segment.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.2 Historical GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 267, "line_end": 267, "token_count_estimate": 386, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "161dee10d4444f7a", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events\nType: text\n\n**Fig. 6** The timing and triggering factors of historical GLOF events. (a) Chronological distribution map of historical GLOF events; (b) Monthly distribution map of historical GLOF events; (c) Pie chart of triggering factors of historical GLOF events\n\nevents mainly result in debris flows or floods, causing extensive damage to infrastructure such as highways, bridges, and power stations. They also lead to the silting of farmland, forests, and homes, disrupting transportation and local production, and inflicting severe economic losses. For example, the 1988 Guangxie Glacial Lake outburst destroyed 23 km of the Sichuan-Tibet highway, causing a road closure that lasted more than 200 days. Some GLOFs resulted in significant casualties. For example, the 1954 Sangwang Glacial Lake outburst claimed the lives of 400 people.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.1 Characteristics of glacial lake and historical GLOF events > 4.1.2 Historical GLOF events", "section_headings": ["4 Results", "4.1 Characteristics of glacial lake and historical GLOF events", "4.1.2 Historical GLOF events"], "chunk_type": "text", "line_start": 268, "line_end": 272, "token_count_estimate": 243, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6b2a62cf2eb08137", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors\nType: text\n\nThe descriptive statistics for each conditioning factor in all positive samples (historical GLOF events) are presented in Table 2. Among these factors, elevation has the smallest coefficient of variation, approximately 0.089, while glacial lake area exhibits the largest coefficient of variation at 2.32. Other factors with relatively smaller coefficients of variation include the ECV, TWI, and PGA, indicating that historical GLOF events are more concentrated around these factors. For both positive and negative samples, the conditioning factors were used as independent variables, with the occurrence of GLOF serving as the dependent variable. These variables underwent multicollinearity diagnostics, and the results are shown in Table 2. The highest VIF value is 1.768, which meets the requirement for multicollinearity diagnostics in susceptibility evaluation. Therefore, these eight factors were ultimately selected as the conditioning factors for GLOF susceptibility.\n\nThe importance of each conditioning factor in the modeling process using machine learning algorithms for training samples is shown in Fig. 7. Due to the differing principles of each algorithm, the importance values for the conditioning factors vary. Among these factors, Distance to Glaciers, TWI, and Distance to Potential Icefall have importance greater than 0.1 in two models. Additionally, the MLP model identified Glacial Lake Area, Glacial Lake Replenishment Area, and Elevation as the most important factors, with the ECV also surpassing an importance of 0.1. The SVM model assigned the highest importance to Distance", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors", "section_headings": ["4 Results", "4.2 Generating GLOF susceptibility maps", "4.2.1 Multicollinearity and importance of GLOF conditioning factors"], "chunk_type": "text", "line_start": 276, "line_end": 280, "token_count_estimate": 399, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ffe4fe750f2103d1", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors\nType: figure\nFigure\n\nImage /page/16/Picture/9 description: The logo for the publisher Springer, featuring a black outline of a chess knight piece on the left, followed by the word \"Springer\" in a black serif font. The background is white.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors", "section_headings": ["4 Results", "4.2 Generating GLOF susceptibility maps", "4.2.1 Multicollinearity and importance of GLOF conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 281, "line_end": 281, "token_count_estimate": 102, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa28ed57aac21a6a", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors\nType: table\nTable: Table 2 Descriptive statistics and VIF of prepared conditioning factors\n\n| Conditioning factors                 | Descriptive statistics |                    |             |         |           |             |                          | VIF   |\n|--------------------------------------|------------------------|--------------------|-------------|---------|-----------|-------------|--------------------------|-------|\n|                                      | Mean                   | Standard deviation | Total       | Minimum | Median    | Maximum     | Coefficient of Variation |       |\n| Glacial lake area (m2)               | 354278.06              | 801444.42          | 19,485,300  | 6188.69 | 144929.57 | 5,813,520   | 2.32                     | 1.435 |\n| Glacial lake replenishment area (m2) | 16,644,600             | 27,457,000         | 915,452,000 | 5690.98 | 6,553,840 | 160,366,000 | 1.73                     | 1.768 |\n| Distance to glaciers (m)             | 728.68                 | 1519.38            | 40077.44    | 0       | 200.74    | 9039.55     | 2.02                     | 1.239 |\n| Elevation (m)                        | 4928.64                | 429.48             | 271075.68   | 3829.14 | 5009.42   | 5708.42     | 0.089                    | 1.578 |\n| ECV                                  | 0.0135                 | 0.00677            | 0.743       | 0.00111 | 0.013     | 0.0416      | 0.48                     | 1.580 |\n| TWI                                  | 11.25                  | 2.26               | 618.96      | 6.159   | 11.37     | 17.263      | 0.22                     | 1.584 |\n| PGA (m/s2)                           | 0.239                  | 0.0778             | 13.14       | 0.0974  | 0.246     | 0.415       | 0.34                     | 1.278 |\n| Distance to potential icefall (m)    | 14300.79               | 22711.11           | 786543.62   | 523.74  | 6219.83   | 111382.88   | 1.63                     | 1.459 |", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors", "section_headings": ["4 Results", "4.2 Generating GLOF susceptibility maps", "4.2.1 Multicollinearity and importance of GLOF conditioning factors"], "chunk_type": "table", "table_caption": "Table 2 Descriptive statistics and VIF of prepared conditioning factors", "columns": ["Conditioning factors", "Descriptive statistics", "", "", "", "", "", "", "VIF"], "table_row_start": 1, "table_row_end": 9, "line_start": 285, "line_end": 295, "token_count_estimate": 571, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00111", "00677", "111382", "14300", "144929", "22711", "271075", "354278", "40077", "786543", "801444"]}}
{"id": "44ae7ced97267403", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors\nType: figure\nFigure\n\nImage /page/17/Picture/4 description: The logo for Springer, featuring a black outline of a knight chess piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors", "section_headings": ["4 Results", "4.2 Generating GLOF susceptibility maps", "4.2.1 Multicollinearity and importance of GLOF conditioning factors"], "chunk_type": "figure", "figure_caption": null, "line_start": 297, "line_end": 297, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "65cf55bd31beacb8", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors\nType: text\n\nto Potential Icefall, with a value of 0.39. The mean importance value of Distance to Potential Icefall across both models was notably high, reaching 0.26.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.1 Multicollinearity and importance of GLOF conditioning factors", "section_headings": ["4 Results", "4.2 Generating GLOF susceptibility maps", "4.2.1 Multicollinearity and importance of GLOF conditioning factors"], "chunk_type": "text", "line_start": 298, "line_end": 300, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8aaeffbedeb42a41", "text": "Document: ORIGINAL PAPER\nSection: 4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.2 GLOF susceptibility maps\nType: text\n\nThe SVM and MLP algorithms from the Scikit-learn library were invoked through Jupyter Notebook to calculate the conditioning factor values for the training and validation samples. Additionally, the GridSearchCV function was used for parameter optimization. After executing the models, the results were imported into the GIS platform. In the SVM model, optimal hyperparameters were obtained through GridSearchCV, with C=1000 and  $\\gamma$ =0.003. The conditioning factors for each glacial lake unit in the study area were then input into the model to calculate the GLOF susceptibility index. The final GLOF susceptibility evaluation results are shown in Fig. 8a, with a value range of [0.026, 1]. Using the natural breakpoint method, the susceptibility index map was classified into five categories: very low, low, medium, high, and very high susceptibility. In the susceptibility index classification results (Table 3), 474 glacial lakes were identified as VHS, representing 4.7% of the total number of glacial lakes in the study area, with susceptibility index values ranging from 0.74 to 1.0. Additionally, 1297 glacial lakes were classified as HS, accounting for 12.7% of the total number of glacial lakes, with index values ranging from 0.528 to 0.740. In the MLP model, a three-layer neural network was constructed, consisting of an input layer, a hidden layer, and an output layer. The optimal configuration was achieved through parameter optimization, resulting in 30 neurons in the hidden layer with the Tanh activation function. The GLOF susceptibility evaluation results based on this model are illustrated in Fig. 8b, with a value range of [0.12, 0.937]. In the susceptibility index classification results (Table 3), 1286 glacial lakes were classified as VHS, accounting for 12.6% of the entire study area, with susceptibility index values ranging from 0.616 to 0.937. Additionally, 2548 glacial lakes were identified as HS, representing 25% of the study area, with index values ranging from 0.505 to 0.616. In terms of modeling accuracy, the MLP algorithm exhibits slightly higher accuracy, with an AUC value of 0.96 compared to the SVM's AUC of 0.90.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "4 Results > 4.2 Generating GLOF susceptibility maps > 4.2.2 GLOF susceptibility maps", "section_headings": ["4 Results", "4.2 Generating GLOF susceptibility maps", "4.2.2 GLOF susceptibility maps"], "chunk_type": "text", "line_start": 302, "line_end": 304, "token_count_estimate": 596, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "803fe81d58659c55", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: text\n\nThe formation of glacial lakes is closely tied to glacial movements (Liu et al. 2019). These lakes typically form at the leading edges of glaciers or in other low-lying areas, with their dams primarily composed of moraine, ice, debris, and occasionally bedrock. Over time, moraine dams can become unstable, leading to outburst events when they can no longer withstand rising water levels within the lake. This phenomenon is particularly common during warmer seasons or periods of climate warming. Global warming has led to increased temperatures in alpine and glacial regions, accelerating the melting of ice. Glaciers worldwide are retreating at an alarming rate, with many regions experiencing significant thinning and retreat (Ding et al. 2021; Jobanpreet and Chitradevi 2024). The meltwater from these glaciers replenishes glacial lakes, resulting in substantial lake expansion and destabilizing the surrounding glaciers. The debris-free glaciers on the Tibetan Plateau have retreated at", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "text", "line_start": 308, "line_end": 310, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d1c0da6caed3b86", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: figure\nFigure\n\nImage /page/18/Picture/8 description: The logo for Springer, featuring a black and white line drawing of a chess knight piece to the left of the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "figure", "figure_caption": null, "line_start": 311, "line_end": 311, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae71e8dc34fe02fc", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: figure\nFigure\n\nImage /page/19/Figure/2 description: A horizontal bar chart titled \"Conditioning factors\" shows the importance of various factors based on three different metrics: SVM (purple), MLP (green), and Mean (orange). The x-axis represents \"Importance (%)\" and ranges from 0.00 to 0.40. The y-axis lists the conditioning factors. The data presented is as follows:\n- Glacial lake area: SVM is approximately 0.07, MLP is 0.16, and Mean is 0.12.\n- Glacial lake replenishment area: SVM is approximately 0.08, MLP is 0.13, and Mean is 0.11.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "figure", "figure_caption": null, "line_start": 313, "line_end": 315, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb44b92d8e5a7cc0", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: text\n\n- Distance to glaciers: SVM is approximately 0.17, MLP is 0.13, and Mean is 0.14.\n- Elevation: SVM is approximately 0.07, MLP is 0.13, and Mean is 0.10.\n- ECV: SVM is approximately 0.04, MLP is 0.12, and Mean is 0.08.\n- TWI: SVM is approximately 0.15, MLP is 0.12, and Mean is 0.13.\n- PGA: SVM is approximately 0.06, MLP is 0.06, and Mean is 0.06.\n- Distance to potential icefall: SVM is approximately 0.39, MLP is 0.14, and Mean is 0.26.\nThe chart indicates that \"Distance to potential icefall\" is the most important factor, especially for the SVM model. The top of the image includes a citation: \"Natural Hazards (2023) 121:17677–17705\".\n\nFig. 7 Importance of GLOF susceptibility conditioning factors", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "text", "line_start": 316, "line_end": 325, "token_count_estimate": 243, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["17677", "17705"]}}
{"id": "b0749703b0132181", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: figure\nFigure\n\nImage /page/19/Figure/4 description: A figure displaying two maps, labeled 'a' and 'b', that show the Glacial Lake Outburst Flood (GLOF) susceptibility for a mountainous region. The maps cover a geographical area from 90°0'0\"E to 90°30'0\"E longitude and 28°0'0\"N to 28°10'0\"N latitude. Both maps feature a grayscale elevation background, with light blue areas indicating glaciers. A north arrow is in the top right, and a scale bar indicating 0 to 10 km is in the bottom right.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "figure", "figure_caption": null, "line_start": 326, "line_end": 326, "token_count_estimate": 176, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d721093edb829630", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: text\n\nMap 'a' is titled \"GLOF susceptibility (SVM)\". Its legend indicates an elevation range from a low of 1406 m to a high of 7452 m. The susceptibility is categorized into five levels:\n- VLS (Very Low Susceptibility): 0.026 - 0.256 (Green)\n- LS (Low Susceptibility): 0.256 - 0.392 (Light Green)\n- MS (Medium Susceptibility): 0.392 - 0.528 (Yellow)\n- HS (High Susceptibility): 0.528 - 0.740 (Orange)\n- VHS (Very High Susceptibility): 0.740 - 1 (Red)\n\nMap 'b' is titled \"GLOF susceptibility (MLP)\". It shares the same glacier and elevation legend as map 'a'. Its susceptibility categories are:\n- VLS (Very Low Susceptibility): 0.120 - 0.283 (Green)\n- LS (Low Susceptibility): 0.283 - 0.397 (Light Green)\n- MS (Medium Susceptibility): 0.397 - 0.505 (Yellow)\n- HS (High Susceptibility): 0.505 - 0.616 (Orange)\n- VHS (Very High Susceptibility): 0.616 - 0.937 (Red)\n\nBoth maps show various colored polygons representing the susceptibility levels of different glacial lakes, with red and orange areas indicating high to very high susceptibility.\n\nFig. 8 Partial Map of GLOF Susceptibility Evaluation Results. (a) GLOF Susceptibility Evaluation Result based on SVM. (b) GLOF Susceptibility Evaluation Result based on MLP\n\nan accelerated pace due to climate warming, with a reduction in area from  $94.59 \\times 10^3$  km<sup>2</sup> to  $61.16 \\times 10^3$  km<sup>2</sup> from 1988 to 2022 (Zhao et al. 2024). In the Himalayas, glaciers have shrunk by 20–30% (Nie et al. 2021). The retreat of glaciers increases the volume of glacial lakes at the glacier terminus and raises water levels, thereby increasing the risk of dam", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "text", "line_start": 327, "line_end": 347, "token_count_estimate": 547, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4fba2ea1b753d066", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: figure\nFigure\n\nImage /page/19/Picture/7 description: The Springer logo, featuring a black and white line drawing of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "figure", "figure_caption": null, "line_start": 348, "line_end": 348, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f08efcce17545ac", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: table\nTable\n\n| Susceptibility classification | SVM         |                            |                   | MLP         |                               |                             |\n|----------------------------------|-------------|----------------------------|-------------------|-------------|-------------------------------|-----------------------------|\n|                                  | Interval    | Number of glacial lakes | Percentage (%) | Interval    | Number of glacial lakes | Per- cent- age (%) |\n| VHS                              | 0.740~1     | 474                        | 4.7               | 0.616~0.937 | 1286                          | 12.6                        |\n| HS                               | 0.528~0.740 | 1297                       | 12.7              | 0.505~0.616 | 2548                          | 25.0                        |\n| MS                               | 0.392~0.528 | 5047                       | 49.6              | 0.397~0.505 | 2602                          | 25.6                        |\n| LS                               | 0.256~0.392 | 1349                       | 13.3              | 0.283~0.397 | 2142                          | 21.1                        |\n| VLS                              | 0.026~0.256 | 2006                       | 19.7              | 0.120~0.283 | 1595                          | 15.7                        |", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "table", "table_caption": null, "columns": ["Susceptibility classification", "SVM", "", "", "MLP", "", ""], "table_row_start": 1, "table_row_end": 6, "line_start": 350, "line_end": 357, "token_count_estimate": 305, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5dc89c753cb8252", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: text\n\nfailure. Marine glaciers, in particular, are highly sensitive to global warming, undergoing intense ablation, thinning, and retreat (Christian et al. 2022). This leads to the expansion of glacial lakes and the weakening of their dams, ultimately triggering GLOFs. This underscores the critical dependence of GLOF events on glacial dynamics.\n\nClimate change can significantly impact precipitation patterns, leading to increased intensity and frequency of rainfall events (Maity and Maity 2022). Such changes may result in a rapid rise in glacial lake levels, elevating the risk of outbursts (Liu et al. 2014). Additionally, climate change influences glacier dynamics, accelerating both forward and lateral glacier movements, which in turn increases the frequency of glacial lake formation and the likelihood of outburst events. GLOFs exhibit pronounced cyclicality and trends under varying climatic conditions. The 1960s, regarded as one of the coldest decades in Tibet (Gong et al. 2022), provided favorable climatic conditions for glacier accumulation and advance. However, extreme weather events and unusual climatic fluctuations during this transitional period increased the risk of GLOFs. This was particularly evident during warmer transitional years when icefalls into glacial lakes became a significant trigger for multiple outburst events. As the climate warmed in the 1970s and 1980s, the frequency of GLOFs declined considerably due to diminished conditions for glacier accumulation. Icefalls and associated factors remained the primary triggers during this time. By the 1990s, intensified glacier ablation, thinning, and retreat—driven by mean annual temperatures exceeding multi-year averages—resulted in new dynamics. The period from 2000 to 2016 marked the warmest 16 years in Tibet's recorded history (Chen et al. 2022; Gong et al. 2022). During this era of accelerated global warming, GLOF events surged dramatically. In addition to icefalls, triggers expanded to include landslides, heavy rainfall, and the melting of buried ice. These non-icefall-related factors accounted for more than half of the GLOF events during this period, underscoring the increasing climate sensitivity of GLOFs.\n\nThe spatial differentiation of glaciers, glacial lakes, and historical GLOFs across the three subzones of the study area reveals distinct regional characteristics. The western subzone hosts the highest number of glaciers, reflecting its lower temperatures and drier climatic conditions. Its greater distance from the Indian Ocean and reduced water vapor transport result in less snowfall; however, the cooler climate supports longer glacier survival. In contrast, the eastern and central subzones, being closer to the Indian Ocean (Fig. 9), benefit from stronger water vapor transport and higher precipitation levels, creating favorable conditions for the formation of glacial lakes. The relatively lower elevation of glacial lakes in the eastern subzone is likely due to the higher water vapor content and warmer climate, which promote greater ice melt at glacier termini, facilitating glacial lake forma-", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "text", "line_start": 358, "line_end": 364, "token_count_estimate": 756, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "338281fabb4eea03", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: figure\nFigure\n\nImage /page/20/Picture/7 description: The logo for Springer, featuring a black line drawing of a knight chess piece on the left, followed by the word \"Springer\" in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "figure", "figure_caption": null, "line_start": 365, "line_end": 365, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68be71bee34fbb20", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: figure\nFigure\n\nImage /page/21/Figure/2 description: A topographical map of the Tibetan plateau and surrounding regions, illustrating elevation and major atmospheric circulation patterns. The elevation is represented by a color scale in the bottom left, ranging from low (green, 0 m) to high (brown and white, 8848 m). The central feature is the snow-covered Tibetan plateau, with its southern edge divided into a 'Western subzone', 'Middle subzone', and 'Eastern subzone'. Several cities are marked, including New Delhi, Lasa, Dacca, Wulumuqi, Xining, Chengdu, and Guangzhou. Arrows indicate key weather systems: blue arrows from the northwest show the 'Westerlies circulation', purple arrows from the southwest represent the 'Indian Ocean monsoon', and blue arrows from the southeast depict the 'Southeast Asian monsoon'. A pink arrow points to the Bay of Bengal, labeled 'Study area'. The map also includes latitude lines for N20°, N30°, and N40°, and distance markers such as '~1200km' and '~545km' related to the monsoon paths.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "figure", "figure_caption": null, "line_start": 367, "line_end": 367, "token_count_estimate": 315, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dfcdac9a18df5290", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.1 GLOFs in response to climate change\nType: text\n\nFig. 9 Schematic diagram of climate background of the study area\n\ntion. The occurrence of GLOF events also varies spatially. The western subzone experiences the fewest GLOFs. In contrast, the eastern and central subzones have a higher frequency of GLOFs, driven by the greater number and larger size of glacial lakes and the higher rates of temperature increase. The varying distances from the Indian Ocean among the three subzones play a crucial role in shaping the spatial variability of water vapor transport and precipitation. The eastern and central subzones, being closer to the Indian Ocean, are more sensitive to climate change. Conversely, the western subzone, farther from the Indian Ocean and located at relatively higher latitude, experiences a drier and colder climate. Its climate is more influenced by the southern branch of the westerly circulation, resulting in a delayed response of glacial lakes to climate change. This highlights the climate sensitivity of GLOFs from another perspective.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.1 GLOFs in response to climate change", "section_headings": ["5 Discussion", "5.1 GLOFs in response to climate change"], "chunk_type": "text", "line_start": 368, "line_end": 372, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "649697ab25d14877", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: text\n\nIn the GLOF susceptibility evaluation, various machine learning models were employed to predict GLOF susceptibility and to calculate the weights of each evaluation factor. The accuracy of the models and their ability to effectively identify areas with high susceptibility demonstrated that both SVM and MLP are suitable for training with small datasets. Notably, all glacial lakes classified as HS and VHS by the SVM model were also classified as such in the MLP model. The MLP model, however, provided more conservative results, identifying a broader range of HS and VHS glacial lakes, accounting for 37.6% of the total. In contrast, the SVM model identified 17.4% of glacial lakes as HS and VHS. Figure 10 presents histograms of the susceptibility indices for all glacial lakes derived from both models. Although neither dataset passed the normal distribution test, the figure clearly shows that the", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "text", "line_start": 374, "line_end": 376, "token_count_estimate": 242, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9a41bc3d1152631", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: figure\nFigure\n\nImage /page/21/Picture/7 description: The Springer logo, featuring a black and white line drawing of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "figure", "figure_caption": null, "line_start": 377, "line_end": 377, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e05c3467d8a4870", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: figure\nFigure\n\nImage /page/22/Figure/2 description: The image contains two histograms, labeled 'a' and 'b', presented side-by-side. Both plots have a y-axis labeled 'Count' and an x-axis ranging from 0.0 to 1.0. Plot 'a' is a histogram for 'SVM' with orange, diagonally hatched bars. Its y-axis ranges from 0 to 1600. The data distribution is skewed to the right, with the highest count reaching approximately 1500 in the bin corresponding to an x-value of about 0.35. There are vertical dashed lines at x-values of approximately 0.25, 0.35, 0.5, and 0.7. Plot 'b' is a histogram for 'MLP' with purple, diagonally hatched bars. Its y-axis ranges from 0 to a value above 1200. The data distribution is more symmetric, resembling a bell curve, with its peak count of approximately 1300 in the bin corresponding to an x-value of about 0.5. There are vertical dashed lines at x-values of approximately 0.3, 0.5, and 0.6.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "figure", "figure_caption": null, "line_start": 379, "line_end": 379, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e97830b101ba05af", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: text\n\nFig. 10 Histogram of outburst susceptibility indices for each glacial lake (The vertical dashed lines represent the threshold values for susceptibility classification based on the natural break method). (a) Outburst susceptibility indices based SVM. (b) Outburst susceptibility indices based MLP\n\noverall mean of the MLP results is higher, further confirming the conservatism of the MLP model's outcomes. Overall, this analysis reveals that these glacial lakes with HS and VHS ratings are predominantly located in the central and eastern subregions of the study area, highlighting spatial patterns of GLOF risk. Field validation further supports the reliability of the model predictions. In particular, Rongxia Zangbu Glacial Lake, which was investigated during field surveys, was identified as highly susceptible to GLOF. The SVM model assigned it a susceptibility score of 0.992, while the MLP model yielded a score of 0.78, both indicating a high risk of outburst. This moraine-dammed lake, formed by glacial deposition at an elevation of approximately 5050 m, has exhibited significant changes in recent years. Field observations reveal that its surface area expanded from 1,714,497 m<sup>2</sup> in 2019 to 1,887,616 m<sup>2</sup> in 2023. Concurrently, the width of the terminal moraine dam decreased dramatically from 939 m in 2019 to 166 m in 2023. Given the continuous expansion of the lake area and the substantial narrowing of the moraine dam, Rongxia Zangbu Glacial Lake shows clear signs of destabilization and poses a substantial potential risk of future outburst. Similarly, although the Gebumaco Glacial Lake has been historically outburst (Wang et al. 2011), it still covers an area of 445,722 m<sup>2</sup>, and the glacier which it originates has developed ice crevasses that are at risk of triggering ice avalanches. In this assessment, the SVM result is 0.545 and the MLP result is 0.517, both indicating moderate susceptibility. These findings are largely consistent with field observations, underscoring the practical applicability of the model-based susceptibility evaluation.\n\nThe most critical factors influencing GLOFs are Distance to Glaciers, TWI, and Distance to Potential Icefall, highlighting their pivotal role in GLOF dynamics. These factors are physically meaningful, as they directly reflect glacier—lake interactions and the likelihood of sudden mass movements impacting lake stability. Subsequently, factors such as Glacial Lake Area, Glacial Lake Replenishment Area, Elevation, and ECV further underscore the combined influence of geographic and climatic conditions on GLOF susceptibility. This aligns with the physical mechanisms underlying GLOF formation, which commonly involve rapid meltwater influx, direct calving of glaciers into lakes, or impacts from icefalls on moraine-dammed basins (Ahmed et al. 2021). Among all factors, Distance to Glaciers and Distance to Potential Icefall are especially significant, emphasizing the intrinsic link between glacial activity and lake instability. Glacial lakes predominantly form in close proximity to glaciers,", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "text", "line_start": 380, "line_end": 386, "token_count_estimate": 820, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dfee36752ae5ad94", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: figure\nFigure\n\nImage /page/22/Picture/6 description: The Springer logo, featuring a black and white icon of a chess knight's head to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "figure", "figure_caption": null, "line_start": 387, "line_end": 387, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8d91b7c466873c8", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: text\n\nas their genesis and hydrological input are tied to glacier melt and retreat (Ahmed et al. 2021). Moreover, historical analyses confirm that icefalls frequently act as immediate triggers for GLOF events, especially in high-relief regions such as the Himalayas and Andes. Despite relatively high coefficients of variation revealed in statistical analyses for these two proximity-related factors, their physical relevance to GLOF initiation remains indisputable, marking them as essential indicators of hazard potential. Topographic factors, including TWI, Elevation, and the ECV, also play crucial roles. These factors exhibit smaller coefficients of variation in statistical analyses, indicating that historical GLOF events tend to occur within specific ranges of TWI, elevation, and ECV, enhancing their discriminative power. TWI represents the hydrological accumulation potential of a landscape, being influenced by slope, catchment water accumulation, and terrain (Chowdhury 2023). Both TWI and ECV, as topographic derivatives, reflect terrain wetness and relief, respectively, capturing the hydrological and geomorphological context in which glacial lakes evolve. Similarly, Glacial Lake Area and Replenishment Area jointly influence the lake's hydrological budget, with their effects on stability modulated by glacier proximity and elevation. Specific elevation ranges are more conducive to the formation and persistence of glacial lakes, often coinciding with climatic and glaciological thresholds relevant to GLOF risk. The Glacial Lake Replenishment Area significantly impacts the rate and volume of water accumulation in a glacial lake, with larger areas corresponding to higher potential GLOF risks. In conclusion, GLOFs are typically triggered by a combination of interrelated factors. These critical variables not only shape the formation mechanisms of GLOFs but also profoundly influence the likelihood and scale of such disasters.\n\nThis study primarily focuses on analyzing historical records to identify the characteristics and susceptibility of GLOFs. Although the study is centered on the Southern Tibetan Plateau, the proposed methodological framework—integrating historical GLOF inventories, geomorphological indicators, and machine learning classifiers—can be adapted to other regions, provided that appropriate regional datasets are available. However, the uncertainty of the current analysis is also constrained by several data- and model-related constraints. First, it does not incorporate projections or scenario modeling to assess potential trends in glacial lake expansion and GLOFs under future climate change scenarios. Given that climate change can profoundly influence glacier retreat rates, glacial lake expansion, and the frequency of GLOFs, the absence of such dynamic considerations may lead to an underestimation of future GLOF risks. Second, the spatial coverage and accuracy of historical GLOF event records are uneven, often biased toward more accessible regions with longer observation histories, introducing potential geographic bias into model training. Furthermore, the limited number of known GLOF cases creates challenges for machine learning classifiers. Although SVM and MLP demonstrate some robustness in dealing with fewer samples, their generalization ability remains limited due to the risk of overfitting. Consequently, future research should consider integrating global or regional climate models, such as the CMIP6 dataset, to project the evolution of glaciers and glacial lakes under various greenhouse gas emission scenarios. Combining these modeling results with remotely sensed monitoring and in situ observational data could facilitate the construction of a multi-source data-driven dynamic risk assessment system.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "text", "line_start": 388, "line_end": 392, "token_count_estimate": 874, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a68497d5020f3a8", "text": "Document: ORIGINAL PAPER\nSection: 5 Discussion > 5.2 GLOF-prone lakes and model performance\nType: figure\nFigure\n\nImage /page/23/Picture/4 description: The logo for the publisher Springer, featuring a black and white line drawing of a chess knight piece to the left of the word 'Springer' in a black serif font. The background is white.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "5 Discussion > 5.2 GLOF-prone lakes and model performance", "section_headings": ["5 Discussion", "5.2 GLOF-prone lakes and model performance"], "chunk_type": "figure", "figure_caption": null, "line_start": 393, "line_end": 393, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93cd4600284a656a", "text": "Document: ORIGINAL PAPER\nSection: 6 Conclusions\nType: text\n\nThis study presents a comprehensive analysis of glaciers, glacial lakes, and historical GLOF events within the southern Tibetan Plateau. The findings reveal that glaciers in the study area are abundant and widely distributed, with elevations displaying a distinct pattern: lower levels in the eastern and western extremities. Glacier flow predominantly occurs northward, with slopes most commonly ranging between 20° and 25°. Accompanying the retreat of glaciers in the region, the number of glacial lakes has shown a steady year-on-year increase, particularly in the central and eastern subzones, where both the number and area of glacial lakes have grown significantly. Glacial lake areas vary considerably, with the central subzone exhibiting the largest average glacial lake area. Historically, 66 documented GLOF events have occurred within the study area, most frequently between June and October. These events were primarily triggered by icefall (48.48%), moraine dam destabilization (18.18%), and heavy precipitation (6.06%). The impact of climate warming on GLOFs has been particularly pronounced, notably during the 1960s and from 1990 to 2020, when rising temperatures and shifting precipitation patterns intensified the frequency of these events. The study underscores the significant dependence of GLOF events on glacial dynamics and their sensitivity to climatic variations, emphasizing the interplay between glacier changes and climate shifts in shaping the risk and occurrence of these hazardous events.\n\nThe study employed two machine learning algorithms, SVM and MLP, to predict the GLOF susceptibility based on historical events. The evaluation results indicated that approximately 17.4% of glacial lakes were classified into HS and VHS, with higher susceptibility predominantly observed in the central and eastern subzones. The MLP model demonstrated slightly better accuracy than the SVM model, achieving AUC values of 0.96 and 0.90, respectively. Additionally, further analysis highlighted that the MLP model produced more conservative predictions regarding GLOF susceptibility.\n\nOverall, climate warming and glacier retreat have intensified the expansion and instability of glacial lakes, leading to an increased frequency and severity of GLOF events. The findings highlight the spatial and temporal patterns of GLOF events and their connection to climate change, providing valuable insights for guiding the prevention and mitigation efforts. The integration of machine learning technology has deepened our understanding of the complex nonlinear relationships between environmental factors and GLOF occurrences, providing an advanced and effective method for risk assessment in the region. Moving forward, future research should focus on integrating real-time remote sensing data, in situ observations, and advanced forecasting techniques to establish dynamic monitoring frameworks and early warning systems. These efforts will provide a robust scientific foundation for disaster prevention and management in alpine glacier regions.\n\n**Acknowledgements** This study was supported by the Central Guidance for Local Science and Technology Development Foundation of China (Grant No.2024ZYD0159), Open Project of Major Hazard Measurement and Control Key Laboratory of Sichuan Province (Grant No.KFKT-202306) and the Talent Introduction Project of Xihua University (Grant No.Z231013).\n\n**Funding** Sichuan Science and Technology Program (2025YFNH0008). Open Project of Major Hazard Measurement and Control Key Laboratory of Sichuan Province (KFKT-202306). Talent Introduction Project of Xihua University (Z231013).", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "6 Conclusions", "section_headings": ["6 Conclusions"], "chunk_type": "text", "line_start": 396, "line_end": 406, "token_count_estimate": 836, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["202306", "2024ZYD0159", "2025YFNH0008"]}}
{"id": "d0189a6f1ee92665", "text": "Document: ORIGINAL PAPER\nSection: 6 Conclusions\nType: figure\nFigure\n\nImage /page/24/Picture/8 description: The logo for the publisher Springer, featuring a black outline of a chess knight's head facing left, positioned to the left of the word \"Springer\" in a black serif font. The entire logo is set against a plain white background.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "6 Conclusions", "section_headings": ["6 Conclusions"], "chunk_type": "figure", "figure_caption": null, "line_start": 407, "line_end": 407, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6efc52f0e2694c96", "text": "Document: ORIGINAL PAPER\nSection: Declarations\nType: text\n\n**Competing interests** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.", "metadata": {"source_file": "data/('Predicting glacial lake outburst susceptibility on the southern Tibetan Plateau with historical events and machine learning methods', '.pdf')_extraction.md", "document_title": "ORIGINAL PAPER", "section_path": "Declarations", "section_headings": ["Declarations"], "chunk_type": "text", "line_start": 410, "line_end": 412, "token_count_estimate": 52, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab9e21b2758db60e", "text": "Document: Predicting the Risk of Glacial Lake Outburst Floods in Karakorum\nType: text\n\n**Abstract.** Glacier snouts respond to climate change by forming proglacial meltwater lakes, thereby influencing glacier mass balance and leading to advancements and surges. The positive feedback of climate change results in more frequent ice-dammed glacial lake outburst floods (GLOFs) in the Karakorum and surrounding regions, often facilitated by englacial conduits. However, the complex and multi-factor processes of conduit development are challenging to measure. Determining the lake depths that might trigger GLOFs and the numerical model specifications for breaching are still being determined. Empirical estimates of lake volumes, along with field-based monitoring of lake levels and depths and the assessment of GLOF risks, enable warnings and damage mitigation. Using historical data, remote sensing techniques, high-resolution imagery, crosscorrelation feature-tracking, and field-based data, we identified the processes of lake formation, drainage timing, and triggering depth. We developed empirical approaches to determine lake volume and trigger water pressure leading to a GLOF. The correlation of glacier surge and lake volume reveals that glacier surge velocity plays a crucial role in lake formation and controlling the size and volume of the lake. Lake volume estimation involves geometric considerations of the lake basin shape. A GLOF becomes likely when the lake's non-dimensional depth (n') exceeds 0.60, correlating with a typical water pressure on the dam face of 510 kPa. Additionally, we identified the critical risk zone of lakes, where all lake outburst floods occur, as the point where the lake volume reaches or exceeds 60% of its capacity. These field-based and empirical findings not only offer valuable insights for early warning procedures in the Karakorum but also suggest that similar approaches can be effectively applied to other mountain environments worldwide where GLOFs pose a hazard.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "Predicting the Risk of Glacial Lake Outburst Floods in Karakorum", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 3, "token_count_estimate": 472, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c8d2128117d1fc7f", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: text\n\nGlobally, glacier shrinkage is a strikingly visible sign of climate change. However, within High Mountain Asia (HMA), particularly the Karakorum, Kunlun Shan, and Eastern Pamirs, the glaciers have been gaining mass since 1970 (Berthier and Brun, 2019; Gardelle et al., 2012; Kääb et al., 2015; Minora et al., 2013; Yao et al., 2012). This positive response to climate change consequently influences glacier dynamic behaviours, with the HMA glaciers thickening, increasing glacier surges, and", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "text", "line_start": 5, "line_end": 7, "token_count_estimate": 146, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c8b83f86d4bcf30b", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a grey rectangle with a black bar at the bottom. On the grey section, there are two white circles with black symbols inside. The left circle contains the letters 'CC', and the right circle contains the attribution symbol, which is a stylized person. On the black bar at the bottom, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 8, "line_end": 8, "token_count_estimate": 122, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d2c31069d1c1d05", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/2 description: The image shows the logo for EGUsphere, a preprint repository. The logo consists of the word \"EGUsphere\" in blue. The letters \"EGU\" are capitalized and enclosed within a blue circular gear-like shape. The word \"sphere\" is in lowercase. Above the word \"sphere,\" there are two swooshes, a blue one on top and a gray one underneath. Below the main text, the words \"Preprint repository\" are written in a smaller, gray, sans-serif font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 12, "line_end": 12, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b12a17d53f78e6d8", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: text\n\nadvancing glacier termini throughout the region (Bhambri et al., 2013; Bolch et al., 2017). This behaviour contrasts neighbouring regions with negative glacier mass budgets, such as the Himalayas, Hindukush, and Tibet (Bazai et al., 2021; Bolch et al., 2011; Frey et al., 2014). In the latter areas, glaciers continue to shrink, thinning and reducing volume, showing no significant glacier advance (Dehecq et al., 2019; Farinotti et al., 2020; Yao et al., 2012). As a result, the Moraine Lake formation has increased the number of GLOFs in the glacier-retreating regions (Yong et al., 2017) as well as the number of glacier avalanches as increased (Byers et al., 2023; Kääb and Girod, 2023; You and Xu, 2022). However, in the glacier advance regions, the positive variation in regional climate feedback has prompted the rapid formation of ice-dammed lakes accompanied by sudden releases of meltwater originating from these lakes (Carling, 2013; Hewitt, 1982; Hewitt, 1998; Hewitt and Liu, 2010).\n\nThe mechanisms and frequency of ice dam glacial lake outburst floods (GLOFs) still need to be better understood, hindering accurate prediction (Bazai et al., 2021; Cook et al., 2016; Harrison et al., 2018; Richardson and Reynolds, 2000). Recent studies have investigated changes in frequency due to climate change (Rick et al., 2023; Veh et al., 2023) and conducted global assessments of flood volume and risk (Rick et al., 2023). Despite these efforts, understanding the drainage and predicting flood events from ice-dammed lakes remain challenging. Predicting these events is crucial due to their potential to cause devastating impacts on human lives and livelihoods, ecosystems, infrastructure (*e.g.*, roads, bridges, hydropower systems), river channel stability, and effects on agriculture and fisheries (Carrivick and Tweed, 2016; Cook et al., 2016; Emmer, 2017; John et al., 2000; Neupane et al., 2019; Zhang et al., 2022).", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "text", "line_start": 13, "line_end": 21, "token_count_estimate": 556, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7594167a7b6f95d4", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: text\n\nflood volume and risk ( Rick et al . , 2023 ) . Despite these efforts , understanding the drainage and predicting flood events from ice - dammed lakes remain challenging . Predicting these events is crucial due to their potential to cause devastating impacts on human lives and livelihoods , ecosystems , infrastructure ( * e . g . * , roads , bridges , hydropower systems ) , river channel stability , and effects on agriculture and fisheries ( Carrivick and Tweed , 2016 ; Cook et al . , 2016 ; Emmer , 2017 ; John et al . , 2000 ; Neupane et al . , 2019 ; Zhang et al . , 2022 ) .\n\nIce-dammed Lake floods represent the dominant hazard in cryospheric regions, comprising 70% of GLOFs. In contrast, moraine-dammed lakes contribute only 9% (with the remaining 16%, 3%, and 2 % triggered by unknown dam types, volcanic activity, and bedrock, respectively) (Carrivick and Tweed, 2016). GLOFs have been recorded causing damage up to 120 km from moraine-dammed lakes (Richardson and Reynolds, 2000) and around 500 km from ice-dammed lakes (Hewitt and Liu, 2010). The resulting impacts include hundreds of human fatalities and the other impacts noted above (Carrivick and Tweed, 2016; Cui et al., 2014; Cui et al., 2015; Kreutzmann, 1994; Mason, 1929; Stuart-Smith et al., 2021; Zhang, 1990; Zheng et al., 2021). While attempts have been made to explore the breaching mechanisms of moraine lake outburst floods, triggered by ice or debris falls, strong earthquake shaking, internal piping, or overtopping waves that exceed the shear resistance of the dam (Emmer and Vilímek, 2013; Richardson and Reynolds, 2000), the understanding of the complex and unclear mechanisms of ice dam lake outburst floods remains a challenge (Werder et al., 2010), making a prediction using complex modelling currently impossible. Therefore, there is an urgent need for simplified approaches to GLOF prediction to mitigate downstream impacts.\n\nDespite the uncertainty related to the detail of GLOF initiation, sudden glacier advances during surge cycles have a prominent role in the formation of ice-dammed lakes by creating an ice barrier in the valleys, particularly at narrow valley floor sections and at confluences (Bazai et al., 2021; Bhambri et al., 2019), damming minor and major rivers. Glacier surges are the main", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "text", "line_start": 13, "line_end": 21, "token_count_estimate": 665, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d42e1f0ee8003f4", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: figure\nFigure\n\nImage /page/2/Picture/1 description: A rectangular Creative Commons license icon. The icon has a grey background on the top and a black bar on the bottom. On the left side of the grey area is the Creative Commons 'CC' logo inside a white circle. To its right is the Attribution symbol, a black stick figure of a person inside a white circle. In the black bar below the Attribution symbol, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 22, "line_end": 22, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c9233c58fcb1c7e9", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: figure\nFigure\n\nImage /page/2/Picture/2 description: The image shows the logo for EGUsphere. The logo consists of the text \"EGUsphere\" in blue. The letters \"EGU\" are bold and enclosed within a blue circular, gear-like shape. The word \"sphere\" follows, with a capital \"U\". Above the word \"sphere,\" there are two swooshes, a blue one on top and a gray one below. Underneath the main logo text, the words \"Preprint repository\" are written in a smaller, gray font. The entire logo is on a white background.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 26, "line_end": 26, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc5d6a18f078bccd", "text": "Document: 30 1. Introduction\nSection: 30 1. Introduction\nType: text\n\nagents related to the formation of ice-dammed lakes in the Swiss Alps (Haeberli, 1983), Northern Norway (Xu et al., 2015), Argentinian Patagonia (Vandekerkhove, 2021), Alaska (Trabant et al., 2003), Karakorum, and in the Pamir (Bazai et al., 2021; Hewitt and Liu, 2010) and Tianshan regions (Ng, 2007; Shangguan et al., 2017). Recent studies reveal that the draining processes of ice-dammed lakes potentially involve subglacial breaching, overspill, rapid ice mass instability, and slow deformation of subglacial cavities (Björnsson, 2003; Haemmig et al., 2014; Round et al., 2017). Several attempts have been made to explore the drainage behaviour of ice-dammed lake outburst floods (Hewitt and Liu, 2010). However, due to the remoteness, danger, and inhospitable terrain where such lakes can be found, real-time data are few, and significant gaps remain in our knowledge of these processes.\n\nIn the Karakorum, ice-dammed lakes are found in five major valleys, three of which are densely populated and highly vulnerable to unexpected GLOF events. Recent advances in understanding have been made (Bazai et al., 2021) concerning the formation of episodic ice-dammed lakes, which, due to ice mass transfer variations, are linked to the changes in the glacier surface velocity and fluctuations in the crevasse density during the surge cycle (Rea and Evans, 2011; Sharp, 1985). Despite these advancements in knowledge, globally, the techniques for measuring and estimating lake volume (the volume of the lake before an outburst), the critical depth (for GLOF release), and timely prediction of ice-dammed lakes remain largely unexplored and unidentified (Round et al., 2017; Shangguan et al., 2016; Steiner et al., 2018). Very limited ice-dammed lake volume data are available. Still, these ice-dam lake volumes were measured either while the lake was empty (after the GLOF events) or partially filled and thus shallow (Round et al., 2017; Shangguan et al., 2016; Steiner et al., 2018). The downstream threat from those lakes is extremely high, while their full lake volumes are unknown. To measure the flood volume and flood magnitude for a deep and potentially full lake, the lake volume measurement is recognized as a critical variable that needs to be accurately calculated or at least well-estimated (Bazai et al., 2021; Bazai et al., 2022). An accurate estimate of lake volume will also help explore the timing, triggering depth of the lake, and frequency of ice-dammed lake outburst floods in relation to surge cycles. Timing information can be approximated by correlating glacier velocities and GLOF occurrences (Bazai et al., 2021; Bazai et al., 2022). The current research focuses of Bazai and colleagues aims to leverage historical records, regular field investigations, and remote sensing techniques to develop a comprehensive understanding, which should lead to better prediction of GLOFs through modelling dam failure mechanisms. Herein, the primary objective is to enhance predictive capabilities regarding GLOF event timing by refining empirical lake volume estimation and identifying critical depths for future risk reduction.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "30 1. Introduction", "section_headings": ["30 1. Introduction"], "chunk_type": "text", "line_start": 27, "line_end": 31, "token_count_estimate": 833, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c91e1a7a6261702", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.1. Data acquisition\nType: text\n\nThe identification and detection of the Shishper, Khurdopin, and Kyager ice-dammed lakes was accomplished using all available open and commercial satellite imagery sources from 1970 to 2022. The datasets include 590 images of Landsat 2-5, 7-9 and 45 images from Sentinel-2, downloaded from the United States Geological Survey (USGS) website", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.1. Data acquisition", "section_headings": ["2. Methodology:", "2.1. Data acquisition"], "chunk_type": "text", "line_start": 37, "line_end": 39, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d77c42fbcc6c98a", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.1. Data acquisition\nType: figure\nFigure\n\nImage /page/3/Picture/1 description: A rectangular Creative Commons license icon, specifically the CC BY (Attribution) license. The icon is divided horizontally into a grey upper section and a black lower section. The grey section contains two white circles with black outlines. The left circle has the letters 'CC' in black inside it. The right circle contains a black stick figure icon, representing attribution. The black lower section has the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.1. Data acquisition", "section_headings": ["2. Methodology:", "2.1. Data acquisition"], "chunk_type": "figure", "figure_caption": null, "line_start": 40, "line_end": 40, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "65632f45444e556e", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.1. Data acquisition\nType: figure\nFigure\n\nImage /page/3/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in a blue sans-serif font. The letters \"EG\" are inside a blue circular gear-like shape. Above the \"sphere\" part of the text, there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.1. Data acquisition", "section_headings": ["2. Methodology:", "2.1. Data acquisition"], "chunk_type": "figure", "figure_caption": null, "line_start": 52, "line_end": 52, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1139b7d600978e60", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.1. Data acquisition\nType: text\n\n(https://earthexplorer.usgs.gov/) (Table S1). The commercial high-resolution images consisted of 35 images from Gaofen-1 (GF-1) and Gaofen-2 (GF-2), 11 images from SPOT-6 and SPOT-7 and five images from Global Planet (https://data.cresda.cn/#/2dMap, https://earth.esa.int/eogateway and https://www.planet.com/products/planet-imagery, respectively). The following DEM datasets have been used for measuring lake volume, depth and dam height: the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Phased Array type L-band Synthetic Aperture Radar (PALSAR)-DEM data scenes from the National Aeronautics and Space Administration (NASA) Earth Science Data Center website (https://search.earthdata.nasa.gov/). KH-9 and Shuttle Radar Topography Mission (SRTM) data were downloaded from http://earthexplorer.usgs.gov/ (Table S2). For more precise and high-resolution data, a field survey of the Shishper and Khurdopin glacier lakes was conducted in 2018, 2019, 2021, and 2022 and used Unmanned Aerial Vehicles (UAV) to determine annual lake extents, lake depths, glacier heights, and thickness, termini positions, and glacier surface displacements. The glacier outlines were obtained from the Randolph Glacier Inventory (RGI 6.0) (Consortium, 2017) and modified according to surge movements with time (https://www.planet.com/products/planet-imagery/).", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.1. Data acquisition", "section_headings": ["2. Methodology:", "2.1. Data acquisition"], "chunk_type": "text", "line_start": 53, "line_end": 55, "token_count_estimate": 419, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "66782b5fdd496a18", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: text\n\nSatellite imagery had a spatial resolution of 0.8 to 30 m (Table S1). The images were selected based on the visibility of the glacier surface and lake areas, and overall, 23 ice-dammed lakes from eight surge events were identified through optical images related to the Khurdopin, Kyager, and Shishper glaciers (Table 1). The presence of lakes was determined based on the Normalized Difference Water Index (NDWI) (McFeeters, 1996), and the outlines of all 23 lakes were digitized manually using Landsat false-color composites (near-infrared, red, and green bands) to distinguish water bodies from other objects (Huggel et al., 2002). The extent of six lakes from Shishper and Khurdopin that occurred after 2017 were obtained in the field using GPS (G639; Accuracy: Single: 1 ~ 3m; SBAS: 0.6m) survey points along the lake shorelines (Fig.1a-d), as well as from Unmanned Aerial Vehicle (UAV) generated Digital Surface Models (DSMs). Field trips were conducted for Khurdopin in 2017 and 2018 and for Shishper in 2019, 2021 and 2022. Alternatively, high-resolution satellite imagery from Planet (3 m) and GF-1 and 2 (0.8 m to 4 m resolution, respectively) and SPOT-6 and SPOT-7 (1.5 m) were used to extract the lake boundaries. The use of high-resolution imagery aims to obtain accurate lake surface levels, and the latter is used for measuring volume and lake depth. The coupled lake extent and polygon help reduce the uncertainty of the lake extent obtained for Landsat 2-5 images. The above method was used to extract the extent of the previous lakes of Khurdopin and Kyager glacier, previously published (Bazai et al., 2021; Bazai et al., 2022), which data are incorporated into the current analysis, and the same method was used to extract the extent of Shishper lakes. These lake polygons and extents were further used to measure lake volume and depth mediated by field derived lake levels as appropriate (Bazai et al., 2022).", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "text", "line_start": 57, "line_end": 59, "token_count_estimate": 546, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3fcf7f54b9975ab", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: figure\nFigure\n\nImage /page/4/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top half and a black bottom half. On the left side of the grey section is the Creative Commons logo, which consists of the letters \"CC\" in black inside a white circle. To the right of that is the attribution symbol, a black stick figure inside a white circle. On the black bottom section, below the attribution symbol, the letters \"BY\" are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 60, "line_end": 60, "token_count_estimate": 148, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ef88baacf6c5adbe", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: figure\nFigure\n\nImage /page/4/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in blue, with the letters \"EGU\" enclosed in a blue, gear-like circle. Above the word \"sphere,\" there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 62, "line_end": 62, "token_count_estimate": 140, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "157cbd53b3c2b222", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: figure\nFigure\n\nImage /page/4/Figure/3 description: A figure composed of five photographic panels, labeled (a) through (e), showing observations of Shishper Lake and Khurdopin Lake.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 64, "line_end": 64, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "20a202001e9c4ca8", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: text\n\nPanel (a) is titled \"Shishper Lake March 2019\" and shows an aerial view of a murky green glacial lake surrounded by rocky, snow-dusted slopes. A blue arrow points across the water, and a dotted rectangle labeled \"Panel b\" highlights a section of the shoreline.\n\nPanel (b), titled \"Shishper Lake Shoreline,\" shows a close-up of the rocky shoreline. Four colored dashed lines indicate the receding water levels from 2019 (blue, highest), 2020 (red), 2021 (green), to 2022 (light green, lowest).\n\nPanel (c), titled \"Khurdopin Lake February 2018,\" shows a river flowing through a valley filled with jagged glacial ice. A blue wavy arrow indicates the direction of water flow.\n\nPanel (d), titled \"Khurdopin Lake March 2018,\" shows the same area as panel (c) a month later, where the river has formed a lake with a surface of broken ice chunks.\n\nPanel (e), titled \"Khurdopin Lake Shoreline,\" shows the shoreline of the lake against the glacier. Two dashed lines indicate the water levels for 2017 (black, lower) and 2018 (red, higher), with a blue wavy arrow pointing towards the lake.\n\n**Fig. 01** Shishper and Khurdopin glacial lake view in the field. a) Oblique view from a helicopter in 2019 (image captured in March 2019 during lake monitoring (Image by Gilgit-Baltistan Disaster Management Authority (GBDMA)); b) Shishper shoreline elevations of four lakes that outburst in the given years (image captured in June 2022); c and d are the regular monitoring views of the lake in the field: e) are the lake elevations in the given years. The Khurdopin Lake elevations were identified in the field in April 2018.\n\nThe surface velocities of the Khurdopin, Shishper, and Kyager glaciers from 1990 to 2022, highlighting the quiescent and surge phases, were obtained from published data (Bazai et al., 2021; Bazai et al., 2022) using image-to-image correlation open-source software COSI-Corr (Leprince et al., 2012; Leprince et al., 2007). The software effectively assesses the glacier surface velocity (Leprince et al., 2012; Steiner et al., 2018). Utilizing a displacement calculation, this technique was used to co-register and correlate surface features (Bazai et al., 2021; Steiner et al., 2018). The surface velocity and overall movement during the surge were monitored by observing changes in the GPS-registered glacier front positions every three months from March 2019 and measured for three years for the Shishper glacier in the field. When coupled with COSI-Corr measure velocities, these latter procedures gave precise results. The velocity estimation procedure generally yields an accuracy of ¼ of a pixel (Sattar et al., 2019). We estimated velocity root-mean-square errors (RMSE) to justify the image processing accuracy.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "text", "line_start": 65, "line_end": 83, "token_count_estimate": 762, "basins": [], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "7a4806c3a31ce2a1", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: figure\nFigure\n\nImage /page/5/Picture/1 description: The Creative Commons Attribution license icon, also known as CC BY. The icon is a rectangle divided horizontally. The top part is gray and features two white circles. The left circle contains the letters 'CC' in black, and the right circle contains a black stick figure icon, representing attribution. The bottom part of the rectangle is black with the white letters 'BY'.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 84, "line_end": 84, "token_count_estimate": 130, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d4170b94f73cc0e", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: figure\nFigure\n\nImage /page/5/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in blue. To the left, the letters \"EGU\" are enclosed in a blue circular gear-like symbol. Above the \"sphere\" portion of the text, there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 88, "line_end": 88, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d4456c403d2c1ed7", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity\nType: text\n\nThe Shishper, Khurdopin and Kyager glaciers are surge-type glaciers (Copland et al., 2011; Hewitt, 1998). Since 1972, eight surge events have been occurred from Khurdopin (three surges), Kyager (three surges), and Shishper (two surges) (Table 1) at an interval of 17–20 years for three glaciers. The Landsat 2-4 images from 1970 to 1990 have errors in the selected glacier area. Therefore, the initial surges for Khurdopin and Kyager between 1970 and 1989 were not considered for estimating the annual velocity. Orthorectified Landsat scenes from TM to OLI–2 and sentinel 2 were used to estimate the yearly and event-based velocities of all three glaciers from 1989 to 2022 to obtain information about the surge events and glacier front changes. Within this period (1989 to 2022), some satellite images were absent, and for precise results, cloud-free images were chosen each year.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.2. Glacier Lake mapping and glacier surface velocity", "section_headings": ["2. Methodology:", "2.2. Glacier Lake mapping and glacier surface velocity"], "chunk_type": "text", "line_start": 89, "line_end": 91, "token_count_estimate": 257, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "afcee5a3ecd6a105", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: text\n\nField observations were performed in the Shimshal and Shishper Valley for the Khurdopin glacier in 2017, 2018, and for 155 Shishper in 2019, 2021, and 2022 to investigate glacier surges, glacier front dynamics, and ice-dammed lakes: lake level and lake depth. The latter value is from shoreline elevations and the elevation of the ice dam at its lowest point (Table 1 and Figure 1). Six lakes were regularly monitored: four from the Shishper Glacier and two from the Khurdopin Glacier. 23 GLOF events from eight surge cycles that occurred during the first year of the surge, or following, are presented in Table 1, with lakes resealing after GLOFs. The field data for six events from the Khurdopin and Shishper glaciers helped to reduce the uncertainty 160 and error for the data related to 17 lake extractions through remote sensing techniques noted above. All the previously recorded lakes from Khurdopin and Shishper were drained via single subglacial conduits with stable inlets and varying outlet positions and lengths. As closely as possible, we identified the inlet and outlet positions of the drainage conduits. The inlet position of the conduit in the ice-dammed lake basin is always in the deepest position, an important factor in determining the lake's depth. 165 The lowest ice dam height also tended to be in the vicinity of the conduit. The inlet positions were geolocated in empty lake basins using GPS, and the lake depth was calculated for these locations. From the field survey, we identified the approximate position of conduits from the presence of surface depressions in the glacier. We estimated the curvilinear conduit lengths between the inlet and outlet.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "text", "line_start": 93, "line_end": 97, "token_count_estimate": 446, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5d9763d7b01399f9", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: figure\nFigure\n\nImage /page/6/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a gray upper section and a black lower section. On the left side of the gray section is a white circle containing the letters 'CC' in black. On the right side is another white circle containing a black icon of a person. The black lower section contains the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "figure", "figure_caption": null, "line_start": 98, "line_end": 98, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab429014b2948b92", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: figure\nFigure\n\nImage /page/6/Picture/2 description: The image shows the logo for EGUsphere. The text \"EGUsphere\" is written in a blue sans-serif font. The letters \"EGU\" are enclosed within a blue circular gear-like shape. Above the word \"sphere\", there are two curved lines, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "figure", "figure_caption": null, "line_start": 100, "line_end": 100, "token_count_estimate": 129, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26791350b50d5536", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: text\n\n180 **Table 1:** GLOF and Surge date and Lake volume measurement since 1970 using remote sensing technique. The average surge velocity of each of the 23 GLOFs from eight surge cycles is presented.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "text", "line_start": 101, "line_end": 103, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea13e15976afc264", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: table\nTable\n\n| No. s | Glacier Name | Date       | Sensor | Lake Area km2 | Aster/UAV Lake Volume Estimate (106 m3) | Lake Vol Uncertainty (+/-106 m3) | Vol. after [106 m3] | Average velocity (m/d) | Date of next clear image after GLOF | Type of drainage Complete (C) Partial (P) | Surge cycle and Resealed GLOF | Surge Duration in months            |\n|----------|-----------------|------------|--------|---------------------|-----------------------------------------------------|----------------------------------------|------------------------|------------------------------|----------------------------------------------|-------------------------------------------------------|----------------------------------------|-------------------------------------------|\n|          | Khurdopin       | 20/08/1977 | LM02   |                     |                                                     |                                        |                        |                              |                                              | P                                                     | 1977–1979                              | May 1977– Aug 1979; 27 Months       |\n| 1        | Khurdopin       | 15/8/1999  |        |                     |                                                     |                                        |                        | 0.33                         |                                              | C                                                     | 1998-1999                              | Jan 1995 to Sep 2002; 92 Months     |\n| 2        | Khurdopin       | 05/30/2000 | L5 TM  | 1.87                | 186                                                 | 2.1                                    | x                      | 0.33                         | 08/26/2000                                   | C                                                     | Resealed                               |                                           |\n|          | Khurdopin       | 04/07/2001 | LE07   | 0.295               | 19.5                                                | 1.5                                    | x                      | 0.44                         | 06/26/2001                                   | C                                                     | Resealed                               |                                           |\n| 4        | Khurdopin       | 07/15/2002 | LE07   | 0.60                | 52.1                                                | 1.6                                    | 2.2                    | 0.87                         | 08/16/2002                                   | P                                                     | Resealed                               |                                           |\n| 5        | Khurdopin       | 07/28/2017 | LE07   | 0.180               | 16.2                                                | 1.4                                    | x                      | 1.41                         | LC 08 08/01/2017                          | C                                                     | 2016-2018                              | June 2006 to Aug 2009: 38 months |\n| 6        | Khurdopin       | 03/18/2018 | LC08   | 0.402               | 19.8                                                | 0.9                                    | x                      | 0.53                         | 02/25/2018                                   | C                                                     | Resealed                               |                                           |\n| 7        | Kyager          | 08/01/1977 | LM02   | 1.181               | 40.73                                               | 5.8                                    | x                      |                              | 10/14/1977                                   | C                                                     | 1976-1977                              | Jan 1975 to Aug 1978; 43 Months     |\n| 8        | Kyager          | 07/18/1978 | LM02   | 2.17                | 82.12                                               | 15.6                                   | x                      |                              | 06/07/1979                                   | C                                                     | Resealed                               |                                           |\n| 9        | Kyager          | 03/08/1997 | L5 TM  | 3.30                | 127.3                                               | 2.9                                    | x                      | 0.4                          | 04/09/1997                                   | C                                                     | 1994-1996                              | Jan 1995 to Sep 2002; 92 Months     |\n| 10       | Kyager          | 09/10/1998 | L5 TM  | 3.32                | 133.5                                               | 3.5                                    | x                      | 0.3                          | 10/11/1998                                   | C                                                     | Resealed                               |                                           |\n| 11       | Kyager          | 09/07/1999 | L7ETM+ | 2.19                | 86.12                                               | 1.23                                   | x                      | 0.46                         | 08/17/1999                                   | C                                                     | Resealed                               |                                           |\n| 12       | Kyager          | 06/25/2000 | L7ETM+ | 0.91                | 23.48                                               | 1.12                                   | x                      | 0.49                         | 08/03/2000                                   | C                                                     | Resealed                               |                                           |\n| 13       | Kyager          | 09/08/2002 | L7ETM+ | 2.93                | 115.19                                              | 1.09                                   | x                      | 1.29                         | 10/09/2002                                   | C                                                     | Resealed                               |                                           |\n| 14       | Kyager          | 06/14/2008 | L5 TM  | 1.45                | 94.95                                               | 1.65                                   | x                      | 0.61                         | 23/06/2008                                   | C                                                     | Resealed                               | June 2006 to Aug 2009: 38 months |\n| 15       | Kyager          | 07/28/2009 | L5 TM  | 1.39                | 91.35                                               | 1.56                                   | x                      | 0.56                         | 04/08/2009                                   | C                                                     | Resealed                               |                                           |\n| 16       | Kyager          | 07/16/2015 | L8 OLI | 1.56                | 53.5                                                | 0.87                                   | x                      | 1.14                         | 05/08/2015                                   | C                                                     | Resealed                               | Jan 2013 to Aug 2018: 67 months     |\n| 17       | Kyager          | 07/14/2016 | L7ETM+ | 1.63                | 45.89                                               | 1.49                                   | 2.9                    | 0.53                         | 30/07/2016                                   | P                                                     | 2014-2016                              |                                           |\n| 18       | Kyager          | 07/31/2016 | L7ETM+ | 1.48                | 44.32                                               | 1.23                                   | x                      | 0.38                         | 08/09/2016                                   | C                                                     | Resealed                               |                                           |\n| 19       | Kyager          | 08/10/2017 | L8 OLI | 2.91                | 113.99                                              | 0.73                                   | 11.9                   | 0.38                         | 26/08/2017                                   | P                                                     | Resealed                               |                                           |\n| 20       | Kyager          | 08/06/2018 | L8 OLI | 2.38                | 87.98                                               | 0.53                                   | x                      | 0.29                         | 08/29/2018                                   | C                                                     | Resealed                               |                                           |\n| 21       | Shishper        | 06/23/2019 | L8 OLI | 0.37                | 24.10                                               | 2.1                                    | x                      | 0.95                         | 07/13/2019                                   | C                                                     | 2017-2019                              | Dec 2018 to June 2022: 41 Months    |\n| 22       | Shishper        | 05/29/2020 | L8 OLI | 0.50                | 24.90                                               | 1.5                                    | x                      | 0.46                         | 06/22/2020                                   | C                                                     | Resealed                               |                                           |\n| 23       | Shishper        | 05/16/2021 | L8 OLI | 0.52                | 25.77                                               | 1.4                                    | x                      | 0.29                         | 07/15/2021                                   | C                                                     | Resealed                               |                                           |\n|          | Shishper        | 05/07/2022 | L8 OLI | 0.41                | 27.66                                               | 1.1                                    | x                      | 0.24                         | 05/10/2022                                   | C                                                     | Resealed                               |                                           |", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "table", "table_caption": null, "columns": ["No. s", "Glacier Name", "Date", "Sensor", "Lake Area km2", "Aster/UAV Lake Volume Estimate (106 m3)", "Lake Vol Uncertainty (+/-106 m3)", "Vol. after [106 m3]", "Average velocity (m/d)", "Date of next clear image after GLOF", "Type of drainage Complete (C) Partial (P)", "Surge cycle and Resealed GLOF", "Surge Duration in months"], "table_row_start": 1, "table_row_end": 25, "line_start": 104, "line_end": 130, "token_count_estimate": 1653, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bff37c99e8b681c0", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: figure\nFigure\n\nImage /page/7/Picture/1 description: A rectangular icon for the Creative Commons Attribution license. The icon has a grey upper section and a black lower section. The grey section contains two white circles: the left one has the letters 'CC' in black, and the right one has a black stick figure symbol for attribution. The black lower section contains the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "figure", "figure_caption": null, "line_start": 132, "line_end": 132, "token_count_estimate": 114, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6c706e7ec294636a", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: figure\nFigure\n\nImage /page/7/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features a blue circular gear-like symbol with the letters \"EG\" inside. To the right, the name \"EGUsphere\" is written in blue, with \"EGU\" in a bold font and \"sphere\" in a lighter font. Above the word \"sphere,\" there are two swooshes, one blue and one grey. Below the main name, the words \"Preprint repository\" are written in a smaller, grey font.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "figure", "figure_caption": null, "line_start": 146, "line_end": 146, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5dbda109dae0ce6e", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.3. Field observation and lake volume measurement\nType: text\n\nIn addition, we used a UAV (DJI Mavic 2 Pro) equipped with a high-resolution camera (4000 pixels × 2250 pixels) to obtain multiple aerial photographs with a minimum of 85% image overlap (Entwistle and Heritage, 2017; Entwistle and Heritage, 2019; Tonkin and Midgley, 2016). The UAV flew at a low uniform height (500 m - to reduce the image distortion) to generate high-resolution orthomosaics and DSMs of the glacier lake surfaces, empty lake basins, and glacier termini. The integral of the difference between the elevation of the lake surface and the elevation of the bottom in the extent of the lake obtains the volume. The lake volume data for the glacial lakes of Khurdopin and Kyager were presented previously (Bazai et al., 2021; Bazai et al., 2022), and the same approach was applied to obtain the Shishper glacier lake volume and depth data (Table 1). In addition to UAV data, we utilized data from KH-9 (1974), ASTER (2000-2019), PALSAR-DEM (June 2008), and SRTM (February 2000) for the computation of lake volumes (Table S2). The SRTM DEM without voids serves as the reference dataset, and the vertical uncertainties of the SRTM DEM are reported to be ±10 m (Rodriguez et al., 2006). The corrected DEMs from Karakorum regions were used by Bazai et al. (2021) and Gardelle et al. (2013).", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.3. Field observation and lake volume measurement", "section_headings": ["2. Methodology:", "2.3. Field observation and lake volume measurement"], "chunk_type": "text", "line_start": 147, "line_end": 149, "token_count_estimate": 386, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e8c007dc54d63b9f", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.4. Geometry of lake basin\nType: text\n\nConsidering that the lake area in satellite images often exhibits a triangular planform (e.g., Fig 1a), we explored the possibility of using a geometric shape to approximate the volume of the lake basin. It is imperative to accurately extract boundary parameters, including lake elevation, length, width, and depth, and this information was manually acquired. Employing NDWI, we identify lake outliers through Landsat false-color composites, which use near-infrared, red, and green bands to distinguish water bodies from other features. We employ standard connected component analysis to (Dillencourt et al., 1992) to manually calculate the area and the perimeter and determine other dimensions of each lake. Initial calculations are pixel-based and later converted to metric units by multiplying pixel counts with their respective pixel sizes. The pixel size for high-resolution images varied from 0.8 to 3m and was cross-validated with Khurdopin and Shishper glacier lakes UAV data having a pixel size of 0.063m as well as with field survey evidence. Trials demonstrated that the known volume of the lakes determined using DEMs of the lake basins once drained could be approximated if the length of the lake from the upstream inlet to the ice-dam face (Z) and the breadth of the lake at the ice dam (C) are known. Improved estimates are obtained if the lake's depth (h) is known at the deepest point close to the dam face, which value can be obtained from a DEM of the drained basin. Alternatively, where a lake is present, this latter parameter can be obtained by plumbing the depth from a boat.\n\nThe first consideration was whether the valley sides might be considered to provide a V-shaped lake cross-section. Each lake volume (V) was approximated by an irregular tetrahedron (Fig. 2a left-hand panel) where the depth (h) is unknown, but the distance from A to B (X in Fig. 2a) and the length C are known values. Assuming the lake surface is an isosceles triangle, and the vertical face at the dam wall is an equilateral triangle, the volume can be obtained from:\n\n$$V = \\sqrt{V^2}$$\n (1)\n\n$$V^{2} = \\frac{1}{144} \\left[ Y_{1}^{2} D^{2} (Z_{2}^{2} + X^{2} + C^{2} + E^{2} - Y_{1}^{2} - D^{2}) + Y_{2}^{2} E^{2} (Y_{1}^{2} + X^{2} + C^{2} + D^{2} - Y - E^{2}) + Z^{2} C^{2} (Y_{1}^{2} + Y + D^{2} + E^{2} - Z^{2} - C^{2}) - Y_{1}^{2} Y_{2}^{2} C^{2} - Y_{2}^{2} Z^{2} D^{2} - Y_{1}^{2} Z^{2} E^{2} - C^{2} D^{2} E^{2} \\right]$$", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.4. Geometry of lake basin", "section_headings": ["2. Methodology:", "2.4. Geometry of lake basin"], "chunk_type": "text", "line_start": 151, "line_end": 160, "token_count_estimate": 842, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aeda998d520f89c9", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.4. Geometry of lake basin\nType: figure\nFigure\n\nImage /page/8/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a grey rectangle with a black bar at the bottom. On the grey part, there is a white circle with 'CC' inside on the left, and a white circle with a person icon on the right. The black bar at the bottom contains the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.4. Geometry of lake basin", "section_headings": ["2. Methodology:", "2.4. Geometry of lake basin"], "chunk_type": "figure", "figure_caption": null, "line_start": 161, "line_end": 161, "token_count_estimate": 116, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7475c529e1e4fec", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.4. Geometry of lake basin\nType: figure\nFigure\n\nImage /page/8/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in blue. The letters \"EGU\" are inside a blue, gear-like circle. Above the word \"sphere,\" there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.4. Geometry of lake basin", "section_headings": ["2. Methodology:", "2.4. Geometry of lake basin"], "chunk_type": "figure", "figure_caption": null, "line_start": 171, "line_end": 171, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f6ca0403bfeaaa9", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.4. Geometry of lake basin\nType: text\n\nWhere the values for lakeside lengths  $Y_1$  and  $Y_2$ , the main length Z, lakeside length D, and lakeside length E are defined in Fig. 2a and obtained from geometry.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.4. Geometry of lake basin", "section_headings": ["2. Methodology:", "2.4. Geometry of lake basin"], "chunk_type": "text", "line_start": 172, "line_end": 174, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "15376a1bfb7c72fb", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.4. Geometry of lake basin\nType: figure\nFigure\n\nImage /page/8/Figure/4 description: The image contains two panels, labeled (a) and (b).", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.4. Geometry of lake basin", "section_headings": ["2. Methodology:", "2.4. Geometry of lake basin"], "chunk_type": "figure", "figure_caption": null, "line_start": 175, "line_end": 175, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d970df0466557eae", "text": "Document: 30 1. Introduction\nSection: 2. Methodology: > 2.4. Geometry of lake basin\nType: text\n\nPanel (a) displays two diagrams of a three-dimensional shape, likely representing a lake basin formed by an ice dam. The diagrams show an irregular tetrahedron with vertices labeled A, B, C, D, and E. The shape is divided horizontally by a dashed line, separating the 'OPEN VALLEY' from the 'ICE DAM'. Various dimensions are labeled, including Y1, Z, X, and h. An arrow indicates 'Inflow' at the top vertex A.\n\nPanel (b) is a scatter plot comparing two methods of volume calculation. The y-axis is labeled 'Lake Volume from DEM, V\\_DEM (M m^3)' and ranges from 0 to 200. The x-axis is labeled 'Volume of Irregular Tetrahedron/10, V\\_Tet (M m^3)' and ranges from 0 to 100. The plot contains data points for three locations: Shishper (black circles), Khurdopin (blue circles), and Kyager (red circles). A light blue dotted line represents a 1:1 ratio. A red dotted trend line is fitted to the data with the equation V\\_DEM = 1.3525V\\_Tet and an R-squared value of R^2 = 0.88.\n\n**Fig. 02** (a) Definition diagram for calculating the volume of the lakes assuming (left) an irregular tetrahedral shape and (right) an irregular pentahedral shape. The blue shading represents the horizontal surface of the lake, and the white area represents the vertical ice wall. Panel (b) Relationship between the volumes of irregular tetrahedrons/10, derived from Eq. 1, and the volumes of the lakes determined using DEMs.\n\nIn passing, it can be noted that the volumes of the irregular tetrahedrons in most cases were not dissimilar to the volumes of regular tetrahedrons (*i.e.*, triangular-based pyramids), the equation for which is simple, in contrast to Equation 1. Nevertheless, using equation 1, the volume of the 'tetrahedron' lakes was around 10 times greater than the volume of the lakes determined using the DEMs (Fig. 2b). This result indicates that the actual depth of the lake (*h*) must be much less than that value associated with an equilateral triangle of side length C (Fig. 2a left-hand side).", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "2. Methodology: > 2.4. Geometry of lake basin", "section_headings": ["2. Methodology:", "2.4. Geometry of lake basin"], "chunk_type": "text", "line_start": 176, "line_end": 184, "token_count_estimate": 603, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["3525V"]}}
{"id": "a6ac24e44f77aed5", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\nThe relationship between the glacier surge velocity of (Khurdopin, Kyager, and Shishper) and 23 GLOFs is presented in Figure 3a-d and its triggering time or month in the year. In panels a to c, the GLOF occurred after the peak of the glacier surge, and the resealed lake formed while the surge velocity declined. These responses to slowing of the glacier velocity lasted 2-4 years after the surge peak. The relationship between the timing of glacier surges and the timing of GLOFs is shown in Fig. 3d. The three Karakorum glaciers can be used as regional exemplars of surge behavior controlling GLOF occurrence as there is a temporal relationship between the occurrence of periods of glacier surging and the occurrence of GLOFs (Fig. 3a-d). GLOFs occur towards the end of a surge period or immediately afterward; the detail is presented in Table 1. This pattern of behaviour prompted the hypothesis that glacier thickening and thinning during surging might control the development of ice-dammed", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 186, "line_end": 188, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a8e0491b3361664c", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/9/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top section and a black bottom section. On the left side of the grey section is the Creative Commons logo, which consists of the letters 'CC' inside a white circle. To the right of that is the attribution symbol, a stick figure inside a white circle. In the black section at the bottom, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 189, "line_end": 189, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86de4f77042f10c6", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/9/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in blue. The letters \"EGU\" are bold and enclosed in a blue circular gear-like shape. The word \"sphere\" follows, with a blue arch and a gray arch curving over it. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 191, "line_end": 191, "token_count_estimate": 128, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8481d8ebd5652715", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\nlakes (Bazai et al., 2022). Lake volumes would increase when the speed of the ice was low such that ice mass would be conserved or increased, and fracturing of the ice would reduce. The corollary pertains to when the ice speed increases, the glacier thins, and the fracturing of the ice mass increases, providing hydraulic drainage conduits.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 192, "line_end": 194, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3996839ce76f7235", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/9/Figure/4 description: The image contains four charts, labeled (a), (b), (c), and (d), which analyze glacier velocity and related events over time.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 195, "line_end": 195, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3c24c078317ff1b7", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\nCharts (a), (b), and (c) are line graphs plotting 'Average Velocity m y⁻¹' against 'Years'. Each chart has a secondary y-axis on the right labeled 'GLOF Triggered' with months from March to November. These charts show periods of increased velocity called 'Surge periods' highlighted with a grey background.\n\n- Chart (a) displays data from 1986 to 2020. The average velocity is shown by a red line, with a dashed blue line at an average of 79 m y⁻¹. There are two surge periods: 1998-2002, with an average peak velocity of 343 m y⁻¹, and 2016-2020. GLOF events are marked with blue circles.\n\n- Chart (b) covers the years 1989 to 2020. The average velocity is 29.9 m/y. It shows two main surge periods: 1994-1998 (average peak velocity 289 m/y) and 2013-2016 (average peak velocity 423 m/y). A smaller 'Mini Surge' is noted around 2008. GLOF events are marked with orange circles.\n\n- Chart (c) spans from 1989 to 2022. The average velocity is 87 m/y. It highlights two surge periods: 1997-2002 (average peak velocity 310 m/y) and 2017-2020. GLOF events are marked with grey circles.\n\n- Chart (d) is a timeline starting from 01/01/1990, with a numerical x-axis from 2000 to 12000. It plots events for different locations: Khurdopin (blue dots), Kayager (orange dots), and Shishper (grey dots). Horizontal lines indicate the season: blue for Winter and orange for Summer.\n\nFig. 03 Relationship between glacier surge and GLOF, with annual glacier velocity analysis during the surge and quiescent phases for three glaciers: (a) Khurdopin, (b) Kyager, and (c) Shishper glacier. GLOFs for these glaciers occurred between the months of March to November. The combined analysis is presented in (d), illustrating the occurrences of GLOFs (dots) in relation to periods of glacier surging (bars). Some points are plotted below the timeline to avoid coincident positions. The blue and red lines show the winter (from October to April) and summer (May to September) seasons in which the GLOF occurred dominantly in the summer months.\n\nAs a first attempt to relate glacier behaviour in a predictive sense to lake formation, we sought to determine the resultant lake volume from the prior surge speed. In Fig. 03, the relationship between surge and GLOF was developed using annual velocity data for ease of trend comprehension. However, daily measurements of glacier velocity were conducted to assess its impact on lake volume, given the velocity sensitivity to the triggering time of GLOFs. Consequently, the glacier's daily velocity was specifically recorded on the day when the GLOF was initiated, as detailed in Table 01. Despite a broad data spread, a least-squares regression analysis, with four outliers excluded from the analysis, defines a statistically significant negative power (-1) relationship between surge speed and lake volume (Fig. 4). Raising the glacier surge speed to the power of -2.0 and multiplying by 10 reproduced well the data trend for low glacier surge speeds. Similarly, -0.8 and a -3.0 power functions define the lower and upper limits of the data spreads. These results, although clearly not definitive, were a spur to investigate if there", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 196, "line_end": 210, "token_count_estimate": 835, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["12000"]}}
{"id": "0de941342a6a0f48", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/10/Picture/1 description: The image shows the Creative Commons Attribution (CC BY) license icon. It is a rectangular icon with a grey upper section and a black lower section. On the grey section, there are two white circles. The left circle contains the letters \"CC\" in black, and the right circle contains a black icon of a person. The black section at the bottom has the letters \"BY\" in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 211, "line_end": 211, "token_count_estimate": 115, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0bdb8aad35d6e6e", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/10/Picture/2 description: The image shows the logo for EGUsphere, a preprint repository. The logo consists of the text \"EGUsphere\" in blue. The letters \"EGU\" are enclosed within a blue, gear-like circle. Above the word \"sphere,\" there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 213, "line_end": 213, "token_count_estimate": 128, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d4117c7f150306a", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\nmight be a theoretical geometric relationship between the volume of the lake and the control on lake water level exerted by the surge speed. The physical basis for such a relationship is that an increase in surge speed should cause the glacier to thin and fracture after a period of ice thickening and lake volume increase. During surging, the height of the ice barrier impounding the lake would reduce, and fracturing in the ice would increase, both lowering the lake level and reducing the lake surface area. Therefore, surge speed should control lake depth, volume, and potential GLOF volumes.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 214, "line_end": 216, "token_count_estimate": 147, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "356ab35b89054413", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/10/Figure/4 description: A scatter plot showing the relationship between Glacier surge speed (U\\_s) on the x-axis and Lake Volume (V\\_DEM) on the y-axis. The x-axis, labeled \"Glacier surge speed, U\\_s (m d⁻¹)\", ranges from 0 to 1.6. The y-axis, labeled \"Lake Volume, V\\_DEM (M m³)\", ranges from 0.0 to 200.0.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 217, "line_end": 217, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eeda29ce4dcc4090", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\nThe plot displays data points for different glaciers, indicated by a legend:\n- Shishper: black circles\n- Khurdopin: blue circles\n- Kyager: red circles\n- Khurdopin drained: blue triangles\n- Kyager drained: red triangles\n\nMost of the circular data points fall within a light blue shaded area, suggesting an inverse relationship where lake volume decreases as glacier surge speed increases. Several dotted curves representing different power-law models are overlaid on the data:\n- V\\_DEM = 30U\\_s⁻³ (brown dotted line)\n- V\\_DEM = 25U\\_s⁻¹ R² = 0.33 (black dotted line)\n- V\\_DEM = 10U\\_s⁻²·⁰ (green dotted line)\n- V\\_DEM = 10U\\_s⁻⁰·⁸ (light blue dotted line)\n\nThe text above the plot reads, \"therefore, surge speed should control lake depth, volume, and potential GLOF volumes.\"\n\n**Fig. 04** Variation in glacial lake volume as a function of the glacier surge speed. Data from three glaciers. Most lakes drained completely, but three had residual volumes (triangles). Outliers are shown as black-ringed symbols. A -0.8-power function defines the lower limit to the data spread, whilst a -2.0-power function defines the central tendency of the data trend, and a -3-power function defines the upper limit to the data spread (see text for explanation).\n\nMost of the lakes investigated occur in steep-walled side valleys, the ice dam forming one side of a roughly triangular water surface (Fig. 5a-b). Projecting the valley walls downwards, the geometry of the lake basin can be considered to be either: 1) an irregular tetrahedron, with the lake surface defined as an isosceles triangle (Fig. 5a-b); and the vertical cross-section at the ice dam defined by an equilateral three-sided triangle; or, 2) an irregular pentahedron whereby the vertical cross-section at the ice dam is defined by a quadrilateral (Fig. 2a). In the former case, only the length of the lake (X) and the breadth of the lake", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 218, "line_end": 237, "token_count_estimate": 546, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d2b81af0f73efa3a", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/11/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top section and a black bottom section. The grey section contains two white circles. The left circle has the letters 'CC' in black, and the right circle has a black stick figure icon, representing attribution. The black bottom section has the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 238, "line_end": 238, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "642c47dab793416a", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/11/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in blue. The letters \"EGU\" are inside a blue circular gear-like symbol. Above the word \"sphere\", there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 244, "line_end": 244, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5648ecd41236a03e", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\n270 (C) at the ice dam are needed to estimate the lake volume, whereas for the second case, the depth (h) of the lake at the ice dam is required as well. The values for X and C are obtained from remote sensing imagery of the lake surface. Before a GLOF, the depth h can be obtained by deploying a plumb-line or echo-sounder in the lake or estimated through a topographic survey of the bounding slopes. Subsequent to GLOF drainage, h can be measured from a DEM or field survey. Assuming an irregular tetrahedron (Equation 1) implies the valley walls form a V-shaped cross-section to the lake at the ice dam face. In contrast, 275 assuming an irregular pentahedron implies the valley walls form a square-shaped section, which is more akin to typical Ushaped glacial valleys.\n\nGiven the idealized geometric properties of the water mass, and assuming C and X vary little in contrast to h, the lake's volume increases as a function of  $h^3$  (tetrahedron) or  $h^2$  (pentahedron). Given the assumption that depth reduces as surge speed increases provides an explanation for the potential -2 to -3 power function relationships between surge speeds and lake volumes. For example, concerning a tetrahedron, assuming the equilateral geometry is maintained as water depth (h) in the lake increases, then, considering Pythagoras' theorem, the volume of the lake increases as the cube of the depth and by corollary as the -3 power of the surge speed. This later function is shown in Fig. 4, and the trend closely defines the upper limit of the empirical data, whilst the -2 relationship, which should pertain to pentahedral volumes, follows the approximate trend of the central tendency of the data spread. Thus, the geometrical properties assumed in the analysis delimit both an upper limit to the relationship between surge velocity and lake volume as well as the main trend.\n\nThe appropriateness of assuming geometric shapes for the lake volume was checked by comparing the calculated geometric volumes with the measured DEM lake volumes. Utilizing Eq. 1 and only the values X and C, the volume of the 'tetrahedron' lakes was around 10 times greater than the volume of the lakes determined using the DEMs (Fig. 2a). This result indicates that the actual depth of the lake (h) must be much less than that value associated with an equilateral triangle of side length C.\n\n290 Nevertheless, this procedure provides a means to estimate lake volume from plan-view data alone.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 245, "line_end": 253, "token_count_estimate": 649, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e5e02caa8679da9c", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/12/Picture/1 description: A rectangular Creative Commons license icon, divided horizontally. The top section is gray and contains two white circles. The left circle has the letters 'CC' in black, and the right circle has a black icon of a person. The bottom section is black and has the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 254, "line_end": 254, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e7a3e70b28ac5bc", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/12/Figure/3 description: The image contains three panels labeled (a), (b), and (c). Panels (a) and (b) are photographs of glacial dammed lakes. Panel (a) is an aerial view of a large, light-blue glacial lake surrounded by dark, rocky terrain. Panel (b) is a closer view of a similar lake, showing chunks of ice or rock floating in the water within a steep valley. Panel (c) is a scatter plot comparing two volume calculation methods. The y-axis is labeled 'Lake Volume from DEM, V\\_DEM (M m³)' and the x-axis is labeled 'Volume of Pentrahedron, V\\_Pen (M m³)'. Both axes range from 0.0 to 250.0. The plot shows about 20 grey circular data points that form a strong positive linear trend. A grey dotted line represents the linear regression, with the equation 'V\\_DEM = 0.8828 V\\_Pen' and 'R² = 0.93' written next to it. A blue dotted line labeled '1:1' is also shown for reference. The caption below the figure reads: 'Figure 5: Examples of Shishper glacial dammed lakes exhibiting roughly triangular surface 2D shapes and overall pyra...'", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 256, "line_end": 256, "token_count_estimate": 335, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed9de21c4288aac5", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\n**Fig. 05:** Examples of Shishper glacial-dammed lakes exhibiting roughly triangular surface 2D shapes and overall pyramidal-shaped volumes (see Fig. 2a); (a) oblique aerial view towards the ice dam; (b) oblique aerial view from the ice dam axially up the impounded valley (image captured in March 2019 during lake monitoring (Image by (Image by Gilgit-Baltistan Disaster Management Authority (GBDMA)). Panel (c) Relationship between the volumes of irregular tetrahedrons/10, derived from Eq. 1, and the volumes of the lakes determined using DEMs.\n\nIn contrast to the assumption of a tetrahedron, improving lake volume estimates were obtained considering the measured DEM-derived values of *h* along with the values of *X* and *C*. Once again, assuming an irregular tetrahedron as in Fig. 2a, the analysis demonstrated that the tetrahedral lake volume was roughly half that of the DEM volume (not illustrated). Rather, considering", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 257, "line_end": 261, "token_count_estimate": 270, "basins": [], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "6cc3c0798c2dcef9", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/13/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a light gray top half and a black bottom half. On the gray half, there are two white circles with black symbols inside. The left circle contains the letters 'CC', and the right circle contains a stick figure person icon. On the black half, below the person icon, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 262, "line_end": 262, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2fde41ec325540e3", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: figure\nFigure\n\nImage /page/13/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in a blue sans-serif font. The letters \"EG\" are inside a blue circular gear-like shape. Above the \"sphere\" part of the text, there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "figure", "figure_caption": null, "line_start": 264, "line_end": 264, "token_count_estimate": 124, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "25857084da8ff48f", "text": "Document: 30 1. Introduction\nSection: 3. Result:\nType: text\n\nthe valley-cross section to be U-shaped (roughly quadrilateral) rather than V-shaped means that doubling the area of the triangular dam face section to form a quadrilateral gives a lake volume defined as an irregular pentahedron (square-based pyramid) (Fig. 2a right-hand side) in contrast to an irregular tetrahedron (Fig. 2a left-hand side). The volume of an irregular pentahedron is:\n\n$$V = \\frac{1}{3} (C h) X \\tag{2}$$\n\nwhich is more amenable to solving than Equation 1.\n\nThe relationship shown in Fig. 5c is preferable to that shown in Fig. 2a as it lies close to the 1:1 relationship between  $V_{Pen}$  and  $V_{DEM}$  but requires knowledge of the parameters as height or depth: h, length; X, and width; C. In contrast, the less exact relationship in Fig. 2a requires only X and C. If assuming a rectangular base to the pentahedron exactly matched the DEM volume, then the coefficient value would be unity. Thus, the coefficient of 0.8986 reflects the deviation of the cross-sectional shape of the lake at the dam face from a rectangle. Note that the relationships between both determinations of lake volume progressively deviate from a 1:1 relationship as lake volume increases. This trend might indicate that larger lakes are less well-defined as tetrahedrons or pentahedrons as the volumes increase.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result:", "section_headings": ["3. Result:"], "chunk_type": "text", "line_start": 265, "line_end": 273, "token_count_estimate": 374, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5c246902576f8fa", "text": "Document: 30 1. Introduction\nSection: 3. Result: > 3.1. Predicting the Timing of GLOF Events\nType: text\n\nThe timing of a GLOF remains difficult to determine, but the main driver is the critical depth. The critical depth is that depth that exerts sufficient pressure at the ice dam wall to induce completed connectivity within the sub-glacial GLOF drainage conduit. For the cases of the glacial lakes (Shishper, Khurdopin, and Kyager), the glacial lake depths (h) have been normalized by dividing by the minimum value of the ice dam height to give values (n') that range between 0 for a fully-drained lake to a hypothetic value of 1 if the lake level reached the height of the ice barrier. At the approximate time of GLOF occurrence, the resultant values of n' range between 0.32 and 0.95 (Fig. 6a-c). Most GLOFs occur for a range of n' values between 0.61 and 0.95 (Fig. 6d). Thus, n' = 0.60 can be regarded as a warning level value with the risk of a GLOF occurring imminently increasing as n' approaches unity.\n\nAs the water pressure (P) at the dam face increases linearly with water depth in each lake, any variation in the pressure with n' that deviates from the linear trend reflects changes in the height of the ice dam (Fig. 6d). Thus, for example, the values of pressure for the Shishper lakes around an n' value of 0.8 reflect greater overall ice dam heights in contrast to the Kyager lakes for similar values of n'. The two values of low pressure for n' < 0.6 reflect low ice dam heights and presumably low structural integrity within the ice mass, allowing ready conduit development. Low values of n' (< 0.6) likely are associated with shallow lakes of low hazard potential. Overall, the Kyager data (Fig. 6d) indicate that a minimum water pressure of around 500 kPa should be regarded as a threshold for general concern for GLOF occurrence in the region.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result: > 3.1. Predicting the Timing of GLOF Events", "section_headings": ["3. Result:", "3.1. Predicting the Timing of GLOF Events"], "chunk_type": "text", "line_start": 275, "line_end": 283, "token_count_estimate": 498, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca6221dee1446257", "text": "Document: 30 1. Introduction\nSection: 3. Result: > 3.1. Predicting the Timing of GLOF Events\nType: figure\nFigure\n\nImage /page/14/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top section and a black bottom section. On the left side of the grey section is the Creative Commons logo, which consists of the letters 'CC' inside a white circle. To the right of that is the attribution symbol, a stick figure inside a white circle. In the black section at the bottom, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result: > 3.1. Predicting the Timing of GLOF Events", "section_headings": ["3. Result:", "3.1. Predicting the Timing of GLOF Events"], "chunk_type": "figure", "figure_caption": null, "line_start": 284, "line_end": 284, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae4849042ba7277e", "text": "Document: 30 1. Introduction\nSection: 3. Result: > 3.1. Predicting the Timing of GLOF Events\nType: figure\nFigure\n\nImage /page/14/Figure/3 description: A four-panel figure, labeled (a) through (d), analyzing glacier lake characteristics. Panels (a), (b), and (c) show cross-sectional profiles of lakebeds, while panel (d) shows a scatter plot of water pressure versus lake depth.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result: > 3.1. Predicting the Timing of GLOF Events", "section_headings": ["3. Result:", "3.1. Predicting the Timing of GLOF Events"], "chunk_type": "figure", "figure_caption": null, "line_start": 286, "line_end": 286, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8bc7ffb4b38a0c43", "text": "Document: 30 1. Introduction\nSection: 3. Result: > 3.1. Predicting the Timing of GLOF Events\nType: text\n\nPanel (a) is a cross-section of the Shishper Glacier lakebed. The x-axis represents the cross-section in meters (0-2100), and the y-axis shows elevation in meters (2520-2660). A secondary top x-axis shows volume in 10^6 m^3 (0-32). The plot displays the lakebed profile, the glacier, and lake levels for years 2019 (E=2650, V=24.10), 2022 (E=2641, V=27.66), 2021 (E=2638, V=25.77), and 2020 (E=2636, V=24.90). An inset map shows the glacier and lake outlines.\n\nPanel (b) is a cross-section of the Khurdopin Glacier lakebed. The x-axis is cross-section in meters (0-3400), and the y-axis is elevation in meters (3360-3440). The top x-axis shows volume in 10^6 m^3 (0-180). It shows lake levels for years 2000 (E=3440, V=186), 2002 (E=3420, V=52.1), 2018 (E=3418, V=19.80), 2001 (E=3416, V=19.5), and 2017 (E=3415, V=16.2). An inset map is included.\n\nPanel (c) is a cross-section of the Kyager Glacier lakebed. The x-axis is cross-section in meters (0-6500), and the y-axis is elevation in meters (4750-4830). The top x-axis shows volume in 10^6 m^3 (0-130). It displays numerous lake levels from 1977 to 2018, with a detailed legend providing the year, elevation (E), and volume (V) for each.\n\nPanel (d) is a scatter plot of Water Pressure (P) in kPa versus Normalized glacier lake depth (n'). The x-axis ranges from 0.0 to 1.0, and the y-axis from 0 to 1600. Data points are shown for Shishper (black circles), Khurdopin (blue circles), and Kyager (red circles). Linear trendlines are fitted to each dataset with the equations: P = 1514.9 n' for Shishper, P = 977.1 n' for Khurdopin, and P = 835.5 n' for Kyager. A light blue shaded area from n' ≈ 0.6 to 1.0 is labeled 'Critical range'.\n\n**Fig. 06:** The relationship between lake volume and elevation and the critical depth for GLOFs. a) Shishper; b) Khurdopin; c) Kyager; d) Water pressure at dam face as a function of n'. The cross sections are denoted by (A' and A). The date of each lake flood event is shown as L, E is the lake's elevation, and V is the lake volume. The straight, solid red lines relate specific lake elevations to volumes. The UAV photographs captured in the field were used for panel a, and the images captured by the GF-2 were used for panel b.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "3. Result: > 3.1. Predicting the Timing of GLOF Events", "section_headings": ["3. Result:", "3.1. Predicting the Timing of GLOF Events"], "chunk_type": "text", "line_start": 287, "line_end": 297, "token_count_estimate": 737, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ea6aed8f95fef72d", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: text\n\nAlthough predictive models related to ice-dam lake development and subsequent GLOF risk would best be based on modelling the physics of the systems, the controlling parameters are numerous and complex. For example, the mechanisms of glacial sliding, over-burden pressure, tensile and driving stresses require consideration, as do flexure and ice fracture mechanics, thermal erosion, and water pressure, amongst other controls (Carrivick et al., 2020). Few of these controls are well-understood, and, importantly, even where there is an adequate theory, the field data required to inform modelling are absent for specific potential GLOF locations. Consequently, there is an urgent need for simpler methods to predict the likely volume of ice-dammed lakes and the probable triggering water levels that lead to GLOFs. Given that requirement, it is acknowledged that", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "text", "line_start": 299, "line_end": 303, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a963e60066bcca2", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/15/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top half and a black bottom half. The grey section contains two white circles with black outlines. The left circle has the letters 'CC' in black, and the right circle has a black icon of a person. The black section has the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 304, "line_end": 304, "token_count_estimate": 110, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c16c7a78197e62b5", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/15/Picture/2 description: The logo for EGUsphere, a preprint repository. The logo features the text \"EGUsphere\" in blue. The letters \"EGU\" are bold and enclosed in a blue circular gear-like shape. The word \"sphere\" follows, with a blue arch and a gray arch curving over it. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 308, "line_end": 308, "token_count_estimate": 129, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ba69eec2627648f", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: text\n\nthe relationships proposed herein are empirical and apply specifically to glaciers within the Karakorum region. However, there is no reason to suppose that similar functions based on geometric considerations and critical depth (Zhao et al., 2017) might not be developed elsewhere, including for moraine-dammed lakes (Yao et al., 2010). Below, the approach is explored for glacially dammed lakes worldwide.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "text", "line_start": 309, "line_end": 311, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c09c113d8e5d7e4", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/15/Figure/4 description: The image contains three plots, labeled (a), (b), and (c), which analyze data related to ice-dammed glacier lakes and Glacial Lake Outburst Flood (GLOF) events.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 312, "line_end": 312, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac31d550cce66282", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: text\n\nPlot (a) displays the maximum volume level of several glacier lakes against their elevation. The y-axis represents 'Elevation (m.a.s.l)' and the x-axis represents 'Volume M m³'. Each lake (Kyager, Khurdopin, Merzbacher, Chilinji, Medvezhiy, Shishper) is shown as a horizontal bar divided into a 'Safe lake level' (light blue) and a 'Triggering lake level' (red). Black dots indicate the volume and elevation of past 'GLOF Event's. An inset shows similar data for the Russel and Rio Colonia lakes. Each lake has an associated n' value: Kyager n'=0.67, Khurdopin n'=0.76, Merzbacher n'=0.83, Chilinji n'=0.60, Medvezhiy n'=0.71, Shishper n'=0.67, Russel n'=0.80, and Rio Colonia n'=0.79.\n\nPlot (b) is a vertical bar representing the 'Normalized Glacier Lake depth (n')'. The bar is divided into a 'Safe Zone' (light blue, for n' values below 0.6) and a 'High Risk and Critical Zone' (red, for n' values above 0.6). Dotted lines connect the n' values of various lakes to their corresponding position on this risk scale. Merzbacher, Russell, Rio Colonia, Khurdopin, and Medvezhiy fall into the high-risk zone, while Kyager, Shishper, and Chilinji are in the safe zone.\n\nPlot (c) is a scatter plot comparing 'Reference Volume (M m³)' on the y-axis to the 'Volume of Irregular Tetrahedron (M m³)' on the x-axis for various glaciers around the world. The data points, color-coded by location (e.g., Medvezhiy-Pamir, Grænalón-Iceland, Merzbacher-TianShan), show a strong positive linear correlation. A dashed line of best fit is shown with the equation y = 1.0689x and an R² value of 0.9865.\n\n**Fig. 07:** (a) Represents the maximum volume level of ice-dammed glacier lakes that experience outbursts based on specific volume and elevation criteria. In this representation, the red signifies each lake's triggering level, depth, or zone, where all GLOF events were initiated. Among these lakes, n' values represent the non-dimensional relationship between lake depth and glacier height; panels (b) illustrate each lake's normalized critical lake depth and the high-risk and critical zones for Glacial Lake Outburst Floods (GLOFs). It also highlights the high-risk and critical zones for each lake, where values exceeding n' 0.6", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "text", "line_start": 313, "line_end": 321, "token_count_estimate": 690, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d839fd7ea0eebe97", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/16/Picture/1 description: A rectangular icon representing the Creative Commons Attribution license, also known as CC BY. The icon is divided horizontally into two sections. The top, larger section is grey and contains two white circles with black outlines. The circle on the left has the letters 'CC' in black, and the circle on the right has a black pictogram of a person. The bottom, smaller section is black and has the letters 'BY' in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 322, "line_end": 322, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3147a651300cfbfc", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/16/Picture/2 description: The image shows the logo for EGUsphere, a preprint repository. The logo consists of the text \"EGUsphere\" in blue. The letters \"EGU\" are inside a blue, gear-like circle. Above the word \"sphere,\" there are two swooshes, one blue and one gray. Below the main text, the words \"Preprint repository\" are written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 338, "line_end": 338, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97b685a8f281fa2e", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: text\n\nindicate a critical threshold for the potential occurrence of an ice-dammed lake outburst flood. Panel (c) shows the relationship between the referenced volume and measured volume through a geometric approach to the location of glaciers presented in Fig. S1.\n\nGlacially dammed lakes worldwide exhibit consistent behavior in terms of lake formation, filling, and volume gain in response to low glacier velocity (Bazai et al., 2021). Additionally, specific pressure, volume, and critical lake depth values for initiating outburst floods are evident (see Figure 6). Building upon the information presented in Figure 6 for the Karakorum, Figure 7, based on 50 GLOF events from glacial dammed lakes, offers a depiction of the conditions under which glacier lake volume measurements are successfully estimated with high accuracy. Among the 50 GLOF events, 27 examples are given from Pamir, Tianshan, Greenland, and northern Patagonia (see Figure 7a-c). These relevant data are limited, often with only estimates or measurements of lake volume available. Figures 7a and b serve as key components and summaries for this discussion. identifying the lake volumes, elevations, and the critical depth values for GLOF outbursts. The measured lake volumes and the related water surface elevations are shown in Fig. 7a, together with critical depth values (n'). Critical depth values (n')exceed 0.60 in all cases of GLOFs (Fig. 7b), from which we infer that a safe lake level can be defined as  $\\leq$  0.60, and the trigger level is  $\\geq 0.60$ . Values of values n' < were associated only with slow, non-catastrophic, lake drainage. Therefore, in the case of future ice-dammed lakes, values of critical depth (n') exceeding 0.60 should be a cause for concern and would serve as a warning level. The geometric procedure outlined herein to estimate lake volume produced an excellent correlation with measured lake volumes (R<sup>2</sup> value of 0.986; Fig. 7c). These findings suggest that future exploration should concentrate on specific volume and depth parameters to determine critical thresholds associated with depth, elevation, or volume for future predictive purposes.\n\nEstimating the volume of an undrained ice-dammed lake from a field survey is dangerous due to floating ice (Fig. 05b), rugged terrain, and sudden drawdowns. The utilization of DEM measurements for lake volume estimation may also introduce high uncertainties or errors due to the difficulty in defining the lake depths (Carrivick et al., 2020; Emmer, 2018). However, for rapid response or mitigation policy purposes, the empirical model (Eq. 1) used in the current study proves to be quite efficient when applied to measure the lake volume before a GLOF.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "text", "line_start": 339, "line_end": 347, "token_count_estimate": 710, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6a88b3236101b084", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: text\n\n, elevation , or volume for future predictive purposes . Estimating the volume of an undrained ice - dammed lake from a field survey is dangerous due to floating ice ( Fig . 05b ) , rugged terrain , and sudden drawdowns . The utilization of DEM measurements for lake volume estimation may also introduce high uncertainties or errors due to the difficulty in defining the lake depths ( Carrivick et al . , 2020 ; Emmer , 2018 ) . However , for rapid response or mitigation policy purposes , the empirical model ( Eq . 1 ) used in the current study proves to be quite efficient when applied to measure the lake volume before a GLOF .\n\nUnderstanding glacier surges, lake formation, and the interactions between lakes and glaciers is crucial for advancing knowledge and developing empirical or numerical GLOF models in mountainous regions (Carrivick et al., 2020; Quincey and Luckman, 2014). In this study, the glacier and lake interactions and their relationship have been explored, and their effect on lake volume and draining processes has been examined. Glacier surge speed is routinely determined using remote-sensing imagery (Paul, 2015), as is lake surface area (Quincey and Luckman, 2014). Thus, remote sensing provides a means to develop images similar to Fig. 4 for specific locations around the globe where ice-dammed lakes form due to glacier surging. Although the data within Fig. 4 are scattered, a negative relationship between surge velocity and lake volume is strongly implied. Specifically, data points scatter around a median trend according to a theoretical -2 power function. Most values fall below a theoretical -3 power function (Fig. 4). Clearly, more data points within Fig. 4 would be desirable. Although GLOFs cannot be predicted from this approach, the likely volume of water that might be released catastrophically can be determined. For sites", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "text", "line_start": 339, "line_end": 347, "token_count_estimate": 482, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e99e7d0782032d9d", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/17/Picture/1 description: A Creative Commons Attribution (CC BY) license icon. The icon is a grey rectangle with a black bar at the bottom. On the grey section, there are two white circles with black symbols inside. The left circle contains the letters 'CC', and the right circle contains the attribution symbol, which is a stylized person. On the black bar at the bottom, the letters 'BY' are written in white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 348, "line_end": 348, "token_count_estimate": 122, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f715e33ce11986e8", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: figure\nFigure\n\nImage /page/17/Picture/2 description: The image shows the logo for EGUsphere, a preprint repository. The logo consists of the word \"EGUsphere\" in blue. The letters \"EGU\" are enclosed within a blue, gear-like circle. Above the \"sphere\" part of the word, there are two swooshes, a blue one on top and a gray one below it. Underneath the main logo, the text \"Preprint repository\" is written in a smaller, gray font. The background is white.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "figure", "figure_caption": null, "line_start": 360, "line_end": 360, "token_count_estimate": 140, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9d5d9bfc3470f359", "text": "Document: 30 1. Introduction\nSection: 4. Discussion:\nType: text\n\nthat are deemed to pose a threat to human life and infrastructure, once the lake volume is better constrained, either through DEM analysis or geometric considerations, the value of n for any specific lake provides a ready indicator of the probability of an imminent GLOF. In contrast to the lower trend for water pressures associated with the Kyager lakes, the higher water pressures required to cause the Khurdopin and Shishper lakes to empty may reflect greater structural integrity, possibly related to a greater downstream extent of the glacier dams. These structural issues can be examined in the future. Still, at this stage, if n' exceeds 0.6, an initial general warning could be issued to communities downstream of the ice dam. In principle, the estimated volume of a potential GLOF can then be routed downstream using standard hydrodynamic flood routing procedures to determine the timing, depth, and extent of flooding at locations where inundation is forecast. Thus, the severity of the likely impact on humankind can be determined, and specific warning times can be derived from the rate of travel of the GLOFs. These results represent a step forward from the observations made by Carrivick et al. (2020), who proposed the exploration of the interaction between lake water and glaciers to understand the lake formation process and identify lake depth, level, and volume. Based on this understanding, empirical models can be generated to predict GLOFs in a timely manner. The current study offers a comprehensive explanation and advanced knowledge building upon the findings of Carrivick et al. (2020). The study revealed that glacier surge and GLOF development relationship indicates that glacier dynamics control lake volume. Furthermore, the study explores the lake-draining process and identifies the relative depth above which an ice-dammed lake's sudden draining may occur. Future research should further investigate the range of n' values and the nature of ice-failure mechanisms connecting lake drawdown and GLOF progression.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "4. Discussion:", "section_headings": ["4. Discussion:"], "chunk_type": "text", "line_start": 361, "line_end": 363, "token_count_estimate": 474, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "851cfc3b76cee67a", "text": "Document: 30 1. Introduction\nSection: 410 5. Conclusion:\nType: text\n\nThe hazards associated with glacier lake outburst floods (GLOFs) in cryospheric regions are increasing worldwide in response to climate change, posing a significant threat to inhabitants in densely populated areas, particularly in the Himalayan Karakoram and Hindu Kush regions. Year on year there is a rise in human casualties and losses to residences, infrastructure, the energy sector, and local and international trade. Despite the escalating risk, the understanding of these hazards remains limited. It is imperative to determine the causes of these hazards, make timely predictions, and formulate new mitigation policies to minimize losses. This research identified the critical depth, lake volume, pressure, and elevation of ice-dammed lakes worldwide associated with GLOFs. An inverse relationship between lake volume and glacier surge speed was observed, indicating that surge speed controls lake depth, volume, and potential GLOF volumes. Comparing surveyed lake volumes with geometric estimates for 23 GLOF events from the Karakoram and 27 events from around the world, a linear regression ( $R^2 = 0.95$ ) demonstrated that geometric estimates can be robust in the absence of detailed field or remote-sensing surveys. A GLOF will likely occur when the lake's non-dimensional depth (n') exceeds 0.60, corresponding to a typical water pressure on the dam face of 510 kPa.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "410 5. Conclusion:", "section_headings": ["410 5. Conclusion:"], "chunk_type": "text", "line_start": 365, "line_end": 367, "token_count_estimate": 341, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf6a9120ded10978", "text": "Document: 30 1. Introduction\nSection: 425 Acknowledgements\nType: text\n\nThis study was supported by the Second Tibet Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK0906) and the National Natural Science Foundation of China (Grant no. 42350410445). Special thanks to the monitoring team of Gilgit-Baltistan Disaster Management Authority (GBDMA), Quaid-i-Azam University, and Karakoram International University for their support and data sharing, and thanks to the Special Research Assistant program of the Chinese Academy of Sciences.", "metadata": {"source_file": "data/('Predicting the Risk of Glacial Lake Outburst Floods in Karakorum', '.pdf')_extraction.md", "document_title": "30 1. Introduction", "section_path": "425 Acknowledgements", "section_headings": ["425 Acknowledgements"], "chunk_type": "text", "line_start": 377, "line_end": 388, "token_count_estimate": 127, "basins": [], "subbasins": ["Gilgit"], "countries": ["China"], "lake_ids": ["2019QZKK0906", "42350410445"]}}
{"id": "7b5107480c54a055", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nType: text\n\nRabindra Osti,\\* Shinji Egashira and Yognath Adikari\n\nInternational Centre for Water Hazard and Risk Management, Public Works Research Institute, Minamihara 1-6, Tsukuba 305-8516, Japan", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_headings": ["Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan"], "chunk_type": "text", "line_start": 2, "line_end": 6, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "7eee0fe99ab94fed", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan > Abstract:\nType: text\n\nIn this study, the characteristic of multiple glacial lake outburst floods (GLOFs) in the Pho Chu River basin in Bhutanese Himalayas is evaluated to help assess the potential impact. Thorthormi Cho (TC) and Lugge Cho (LC) in the east branch and two unnamed lakes labelled A and B in the west branch of Pho Chu are chosen for the study. Numerical models were employed to simulate different involved processes. The results show that the peak sediment discharge in the east branch of the Pho Chu River by the TC dam breach reached about 5000 m<sup>3</sup>/s (during the first GLOF) at 4 km whereas by the LC dam breach is about 600 m<sup>3</sup>/s (second GLOF) at 6 km. However, the highest peak hydrographs (sediment and water mixture) calculated during the first and second GLOF are about 10 000 m<sup>3</sup>/s at the 18-km section and about 23 000 m<sup>3</sup>/s at the 10-km section, respectively. In the west branch of Pho Chu, erosion and depositions are the frequent intermittent local processes during the first GLOF event from Lake A. Because the first event stabilized the irregular river bed profile, there is not much sediment discharge developed during the second GLOF from Lake B. At the 17-km section of the west branch, the peak hydrograph reached about 9000 m<sup>3</sup>/s during the first event against the peak of about 800 m<sup>3</sup>/s during the second event. The results suggest that even if multiple dam breaches occur simultaneously, GLOF surges pass through the main river channel at different times with very different flood characteristics. The differences in travel time and flood characteristics mostly depend on the distributions of bed slope and potential erosion depth along the reach. Further, the amount of sediment accumulated in and transported by each surge is reliant on the temporal geomorphologic setting of the river and therefore on the impact of the previous GLOF on riverbed profile and potential erosion depth. The robustness in peak GLOF hydrographs is associated with sediment flow dynamics. As a consequence, serious inundation of Punakha, Lobeysa and major portion of Wangdue Phodrang is anticipated. Copyright © 2011 John Wiley & Sons, Ltd.\n\nKEY WORDS moraine dam breach; GLOF; simulation; prediction; assessment; Bhutan\n\nReceived 18 November 2010; Accepted 16 September 2011", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan > Abstract:", "section_headings": ["Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "Abstract:"], "chunk_type": "text", "line_start": 8, "line_end": 14, "token_count_estimate": 661, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "9489f2e9517032c2", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: INTRODUCTION\nType: text\n\nGeologically weak, high snow-fed mountains around the globe are known as global hotspots of natural hazards. In recent decades, frequency and magnitude of high mountain hazards especially glacial hazards are increasing (Evans and Clague, 1994; Beniston et al., 1997; Kääb et al., 2005; Haeberli et al., 2007; Science Daily, 2008; Osti and Egashira, 2009; UNEP-United Nations Environment Programme, 2010) due to accelerated hydrometeorological changes resulting to glacier melting (IPCC, 2007; UNEP, 2007). Melting process leads to the development of new lakes or expansion of existing glacial lakes, increasing water volume behind fragile moraine dams. As a consequence, moraine dams of such glacial lakes can abruptly breach due to increased statistic pressure, seepage and overflow triggered by avalanches, earthquakes, landslides and other factors (Post and Mayo, 1971; Clague, 2003; RGSL, 2003; Hambrey and Alean, 2004) and in various modes e.g. head cutting, overtopping, landslides and so on (Costa and Schuster, 1988; Grabs and Hanisch, 1993; Wahl, 1998; Osti\n\nE-mail: osti55@pwri.go.jp\n\net al., 2011). The breach of moraine dam does not necessarily mean that the lake becomes stable but it is more likely that the unstable dam may burst many times together with the refilling of dam (Dussaillant et al., 2010). Moraine dam breaches pose immediate threats of landslide, debris flow, flash flood and inundation risk to downstream communities. Floods caused by the glacial lake outbursts are widely known as glacial lake outburst floods (GLOFs). Among different glacial hazards listed by Richardson and Reynolds (2000), GLOFs are considered the most damaging glacial hazard in the Hindu Kush Himalaya (UNEP, 2007).\n\nIn a changing natural, sociocultural and economical environment, GLOFs are now added threats to human life and physical infrastructures and therefore to the socioeconomic state of mountain regions (Ives, 2004; Sperling and Szekely, 2005). The increased number and magnitude of GLOF hazards and their impacts have already been reported by many researchers such as Watanabe *et al.* (1994); Beniston *et al.* (1997); ICIMOD-UNEP (2001); Kääb *et al.* (2005) and Haeberli *et al.* (2007). Although many studies have been conducted to understand triggering mechanisms as well as dam failure modes and GLOF impacts, there are only a handful of researches that really describe the mechanics of GLOF development processes, which are fundamental for prediction, assess-\n\n<sup>\\*</sup>Correspondence to: Rabindra Osti, International Centre for Water Hazard and Risk Management, Public Works Research Institute, Minamihara 1-6, Tsukuba 305-8516, Japan.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 16, "line_end": 32, "token_count_estimate": 743, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "5b02ed7a82d58e9a", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: INTRODUCTION\nType: text\n\nal . * ( 1997 ) ; ICIMOD - UNEP ( 2001 ) ; Kääb * et al . * ( 2005 ) and Haeberli * et al . * ( 2007 ) . Although many studies have been conducted to understand triggering mechanisms as well as dam failure modes and GLOF impacts , there are only a handful of researches that really describe the mechanics of GLOF development processes , which are fundamental for prediction , assess - < sup > \\ * < / sup > Correspondence to : Rabindra Osti , International Centre for Water Hazard and Risk Management , Public Works Research Institute , Minamihara 1 - 6 , Tsukuba 305 - 8516 , Japan .\n\nment and mitigation of GLOF hazards. An attempt made by Osti and Egashira (2009) to evaluate the Tam Pokhari GLOF development process in the Khumbu Region, Nepal by comparing the results from hydrodynamic models for water only and water-sediment mixture flows pointed out that the magnitude of GLOF primarily depends on the amount of sediment accumulated during the GLOF development process and therefore on the river morphology. Besides, GLOF risk in any GLOF-prone area could not be only associated with individual outbursts that are usually described in the literature but also with the possibility of simultaneous breaches of several moraine dams and therefore the multiple surges of GLOFs. There are no known hydrodynamic studies on the consequences of simultaneous moraine dam breaches triggered by any external forces such as high magnitude earthquakes. In fact, high mountain regions such as the Himalayas that consist of fragile geology are highly prone to earthquakes. Beside other reasons, the possible multiple breaches of dams due to seismic movement are discussed by Chandra (1978) and Bilham et al. (1997) and its sensitivity to the breach process was described by Tryggvason (1960), Post and Mayo (1971) and Costa and Schuster (1988). However, potential risk associated with such multiple GLOFs in a basin is rarely understood among scientists and practitioners.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 16, "line_end": 32, "token_count_estimate": 516, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "50dcf7918037e06e", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: INTRODUCTION\nType: text\n\nhydrodynamic studies on the consequences of simultaneous moraine dam breaches triggered by any external forces such as high magnitude earthquakes . In fact , high mountain regions such as the Himalayas that consist of fragile geology are highly prone to earthquakes . Beside other reasons , the possible multiple breaches of dams due to seismic movement are discussed by Chandra ( 1978 ) and Bilham et al . ( 1997 ) and its sensitivity to the breach process was described by Tryggvason ( 1960 ) , Post and Mayo ( 1971 ) and Costa and Schuster ( 1988 ) . However , potential risk associated with such multiple GLOFs in a basin is rarely understood among scientists and practitioners .\n\nThere are many unanswered questions in GLOF prediction such as what happens if two or more lakes at different locations in the same basin breach simultaneously? At any downstream locations, will the magnitude and other characteristics of GLOF triggered by each breach in such a multi-hazard scenario be the same as that predicted in a single-hazard scenario because river morphology could have been changed significantly due to multiple and consequential surges? The foremost thing to understand is that the conventional method for predicting individual GLOF events as water only flow by using river flow models (Jingshi and Fukushima, 1999; Bohorquez and Darby, 2008) may not produce a reliable result as many GLOF events observed in the past resembled debris flows (Xu and Feng, 1994; Huggel et al., 2004; Osti and Egashira, 2009). However, river flow models are useful for predicting floods due to GLOF in the downstream regions mainly in lower basin, where debris flow does not exist. Furthermore, GLOF prediction based on a debris flow model as applied by Osti and Egashira (2009) can address the problems but should be applied in a multi-hazard scenario, i.e. multiple dam breaches. In addition to that, there is a need to identify the transitional boundary where the debris flow changes into river flow after sediment deposition so that coupling two different models, i.e. debris flow model and inundation model is possible. In this paper, the GLOF prediction model developed and field tested by Osti and Egashira (2009) was applied to a multi-hazard scenario mainly to check how the characteristics of GLOF hazard change as a consequence of multiple loading of peak flows and thereby temporal variation of riverbed morphology at any downstream locations. It is hoped that the result produced through this research will be helpful to predict GLOF hazard, access\n\ncorresponding impacts and propose control measures in multiple GLOF hazards conditions of the Pho Chu River basin in Bhutan.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 16, "line_end": 32, "token_count_estimate": 672, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "ecda44a72c208ac2", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: text\n\nThe Puna Tshang Chu River in western Bhutan originates in the glacier-capped eastern Himalayas near the China (Tibet) border and flows down the Himalayan slopes crossing the border of Bhutan flowing into India and finally draining into the Bay of Bengal after mixing in the Brahmaputra River. The Puna Tshang Chu basin is characterized by three major rivers inside Bhutan: Mo Chu, Pho Chu and Dang Chu. In this study, we consider only Pho Chu (Figure 1). Among three major rivers in the basin, the Pho Chu sub-basin is highly GLOF-prone. A total of 154 glaciers and 549 lakes exist in the Pho Chu basin only (ICIMOD-UNEP, 2001). Most glacial lakes are located near the present glacier terminal at about 4150-4200 m in Lunana area, northern Pho Chu basin. The Pho Chu main (T<sub>M</sub>) stream has two branches, a western (T<sub>W</sub>) and eastern (T<sub>E</sub>) branches. Tarina and Lunana glaciers feed the western branch (T<sub>W</sub>) and the eastern branch (T<sub>E</sub>), respectively. The total catchment area of the Pho Chu River at the junction of the two tributaries is about  $1000 \\,\\text{km}^2$  ( $T_W = 410 \\,\\text{km}^2$  and  $T_E = 590 \\,\\text{km}^2$ ). The lengths of the eastern and western branches up to the merging point are 39.5 and 27 km, respectively. The length of the main river reach up to the Bhutan-India border is 185 km. There are about eight different potentially dangerous lakes only in the Pho Chu River basin (Karma, 2006). Raphstreng Tsho (28°06′16″, 90°14′45″), Thorthormi Cho (26°06′07″, 90°15′46″) and Lugge Cho (28°05′41″, 90°17′ 36\") in the eastern branch and two unnamed Lakes A  $(28^{\\circ}06'45'',\\,89^{\\circ}54'30'')$  and B  $(28^{\\circ}06'15'',\\,89^{\\circ}53'56'')$  in the western branch of the Pho Chu River are considered highly dangerous (ICIMOD-UNEP, 2001). Thorthormi Cho is aligned along the main T<sub>E</sub> about 4 km downstream from the Lugge Cho, whereas the Lake A is located about 0.5 km river distance away from Lake B in T<sub>w</sub>. The river stations showing locations of different flood-prone villages are shown in Figure 2. For clarity, distances are measured from Bhutan India border (Figure 2a) and also from the river heads (Figure 2b).\n\nThe Pho Chu basin area experienced at least four recorded GLOFs in the past (Karma, 2006). The first in 1950, which destroyed Punakha Dzong (a monastery fortress), the second in summer 1960 together with high rainfall runoff discharge, the third one in 1968 which washed away a temple and bridges and killed 12 people and the latest one was the historic Lugge Cho (partial burst) GLOF in 1994 which incurred huge damage in the Pho Chu basin (Watanabe and Rothacher, 1996). The 1994 GLOF event is the only event which is well documented in Bhutan.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "text", "line_start": 34, "line_end": 40, "token_count_estimate": 869, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["Bhutan", "China", "India"], "lake_ids": []}}
{"id": "0229261a5af494fa", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: text\n\nfrom the river heads ( Figure 2b ) . The Pho Chu basin area experienced at least four recorded GLOFs in the past ( Karma , 2006 ) . The first in 1950 , which destroyed Punakha Dzong ( a monastery fortress ) , the second in summer 1960 together with high rainfall runoff discharge , the third one in 1968 which washed away a temple and bridges and killed 12 people and the latest one was the historic Lugge Cho ( partial burst ) GLOF in 1994 which incurred huge damage in the Pho Chu basin ( Watanabe and Rothacher , 1996 ) . The 1994 GLOF event is the only event which is well documented in Bhutan .\n\nA survey of the 1994 Lugge Cho GLOF conducted by the Department of Geology and Mines, Government of Bhutan (Leber *et al.*, 2002; Brauner *et al.*, 2003) from 20 to 23 October 1994 states that 23 lives were lost and 91 households were affected on top of other damages. The peak discharge of about 2539 m<sup>3</sup>/s measured at", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "text", "line_start": 34, "line_end": 40, "token_count_estimate": 295, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "3c204549596bdef7", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A multi-panel figure illustrating the location and features of a river basin. The figure is divided into three parts labeled (a), (b), and (c).", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "figure", "figure_caption": null, "line_start": 41, "line_end": 41, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "879fb1f0e343bf91", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: text\n\nPart (a) consists of two Google satellite images. The left image shows two glacial lakes, labeled Lake-A and Lake-B, in a mountainous, snowy region. The right image shows the Rapstreng lake and the Thorthormi and Lugge glaciers. An arrow points from this area to the headwaters of a river in part (c).\n\nPart (b) displays a map of a country with its administrative boundaries outlined in red. A specific region, corresponding to the river basin, is overlaid with a black dotted grid. A scale bar at the bottom indicates distances of 0, 45, and 90 kilometers. An arrow connects this map to the more detailed map in part (c).\n\nPart (c) is a detailed elevation map of the river basin. A grayscale legend indicates elevation ranging from a low of 104 to a high of 7056. The main river, shown in blue, is formed by two tributaries, the Mo-Chu and the Pho-Chu. Downstream, the river is labeled Dang Chu and Puna Tshang Chu. Key points along the river are marked as A, B, C, D, and E. Punakha Town is located at point D. A legend for 'Reach Length' provides the following distances: A-C = 40 km, B-C = 35 km, C-D = 55 km, and D-E = 130 km. A scale bar at the bottom shows distances of 0, 15, and 30 kilometers.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "text", "line_start": 42, "line_end": 48, "token_count_estimate": 378, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "a02ba9483412b007", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: figure\nFigure: Figure 1. Location map of Pho Chu River basin in Bhutan and locations of glacial lakes on it\n\nFigure 1. Location map of Pho Chu River basin in Bhutan and locations of glacial lakes on it", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "figure", "figure_caption": "Figure 1. Location map of Pho Chu River basin in Bhutan and locations of glacial lakes on it", "line_start": 49, "line_end": 49, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "c7a87427bf26a00c", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: figure\nFigure\n\nImage /page/2/Figure/4 description: The image displays two diagrams, labeled (a) and (b), illustrating a river system. Diagram (a) is a map of the Mo Chu River Basin, showing the Puna Tshang Chu River. The river is marked with distances in kilometers, starting from 0.0 at the bottom and increasing upwards. Key locations are marked, including Wangdue Phodrang, Lobeysa, and Punakha Town at km 129.2. Further upstream, at km 184.2, the river splits into the Western Branch of Pho Chu (Tw) and the Eastern Branch of Pho Chu (Te). This upper section is circled, and an arrow points to diagram (b) for a more detailed view. Diagram (b) is a schematic of the headwaters. The Eastern Branch of Pho Chu (Te) originates from Lugge Cho (km 0.0) and Thorthormi Cho (km 3.8), with subsequent markers at 1.8, 11.0, 16.8, 19.0, and 24.0 km. The Western Branch of Pho Chu (Tw) originates from Lake A (km 0.0) and Lake B, with markers at 0.5, 8.2, 14.0, 17.5, and 18.0 km. The two branches merge, and the river continues with markers at 27.0 and 39.5.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "figure", "figure_caption": null, "line_start": 51, "line_end": 51, "token_count_estimate": 340, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "550b9c7d94782523", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: figure\nFigure: Figure 2. Flood-prone village locations and river stations along Pho Chu River measured from (a) Bhutan-India border and (b) tributaries' heads\n\nFigure 2. Flood-prone village locations and river stations along Pho Chu River measured from (a) Bhutan-India border and (b) tributaries' heads", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "figure", "figure_caption": "Figure 2. Flood-prone village locations and river stations along Pho Chu River measured from (a) Bhutan-India border and (b) tributaries' heads", "line_start": 53, "line_end": 53, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "7d409eafac6c95cc", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: STUDY AREA\nType: text\n\nWangdue Phodrang hydrological station (85 km downstream from the Lugge Cho Lake) destroyed Punakha Dzong and surrounding areas about 5 km upstream from the hydrological station (Figure 2). The GLOF scoured the valley floor and banks, triggering landslides and bringing down much sediment and woody debris. In the process, the GLOF accumulated loose sediment slowly, gaining the volume and energy of the flow and compounding the damage downstream. Lugge Cho's flood reached about 200 km downstream from the lake, i.e. somewhere near to Bhutan–India border causing 2-m-high flood waves (Richardson and Reynolds, 2000). Watanabe and Rothacher\n\n(1996) highlighted the cause of the 1994 GLOF due to excess melting of glaciers and excess water accumulation behind a moraine dam that collapsed later due to excessive hydrostatic pressure escorted by the pool of water. Lugge Cho Lake grew very fast due to the retreat of the Lugge Cho glacier about 160 m/year from 1988 to 1993 (SAARC, 2007). The glacier melting and volume expansion apply to almost all lakes in the region, which means all lakes are vulnerable. External forces such as high magnitude earthquake can also play a significant role in breaching such vulnerable moraine dams at any time as the area is highly earthquake prone. The overall moraine structure of almost\n\nall dams is resting over loose ice and sediment materials that will create a havoc if hit by a serious earthquake.\n\nBhutan is located in highly fragile earthquake zone. A review article by Kuensel (2009) shows that about 19 major earthquakes have hit Bhutan since 1713. The biggest events were in 1897, 1934 and 1950, measuring 8 or above on the Richter scale. There are still ongoing debates on what will be the worst scenario of earthquake in Bhutan, however the article (Kuensel, 2009) emphasized by quoting some past researches the probability of occurrence of an earthquake of above 8.1–8.3 magnitude in a highly seismic-risk zone of Bhutan. The most recent earthquake event in Bhutan was recorded on 24 September 2009 with the magnitude of 6.1 Richter scale (BBC, 2009). Since 1980, there have been four similar quakes in the Pho Chu River basin in Bhutan, and the alarming situation is that the upstream of the Pho Chu basin in the Lunana area is identified as a very high earthquakerisk zone (Meyer et al., 2006), which is also the home for many potentially dangerous lakes in the country. The depth and surface size of these lakes also increased in recent years (Leber et al., 2002; Brauner et al., 2003), and if these lakes burst simultaneously due to such high tremor by an earthquake, then the released volume of water and accumulated bed sediment mass could translate into a gigantic GLOF. As an aftermath the human and economic loss could be many folds of the 1994 Lugge GLOF. It is to note that the Punakha–Wangdue valley is relatively highly populated region and is under rapid development in Bhutan. There are three major hydropower stations located within the basin.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "STUDY AREA", "section_headings": ["STUDY AREA"], "chunk_type": "text", "line_start": 54, "line_end": 62, "token_count_estimate": 774, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "1be04cbeef11af4a", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\nIn this study, three different types of models, i.e. moraine dam breach, debris flow and river flow models are employed to simulate and check the effect of simultaneous dam breaches and sequential occurrences of GLOFs in the Pho Chu River basin.\n\nIn the upstream area, a numerical model is used to reproduce the debris flows, which generally form when unstable saturated non-cohesive riverbed materials entrain into the flow body. In order to determine the upstream boundary conditions to the debris flow model in the form of a dam breach hydrographs, dam breach analyses were performed using different types of dam breach models. It is considered that earthquake-triggered breaches of moraine dams occur simultaneously. However depending on several factors such as the amount of water volume released from each lake, channel geometry, sediment budget, distance between lakes and GLOF characterization including timing and magnitude of surges, the potential impact in downstream areas may vary from place to place.\n\nThe moraine dam breaches of Thorthormi Cho and Lugge Cho draining into torrent  $T_E$  and Lake A and Lake B into torrent  $T_W$  are considered for modelling. The Raphstreng Tsho Lake, which also drains into torrent  $T_E$ , shows a stable shore line and is considered stable (Leber *et al.*, 2002; Brauner *et al.*, 2003) and therefore excluded in the modelling. The characteristic of a debris flow caused by GLOFs in each case is evaluated up to the location, from\n\nwhere the erosion and deposition is not active, which is usually the place where the river gradient changes from steeper to mild slope region. Because the water flow continued further downstream even after the release of sediment by deposition, there is a chance of flooding further downstream. Therefore the hydrographs at the section, from which the sediment concentration reduced to a minimum level, are taken as input to the inundation model to check the inundation extent up to the Bhutan–India border.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 64, "line_end": 80, "token_count_estimate": 512, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "332e068072139959", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\n, 2003 ) and therefore excluded in the modelling . The characteristic of a debris flow caused by GLOFs in each case is evaluated up to the location , from where the erosion and deposition is not active , which is usually the place where the river gradient changes from steeper to mild slope region . Because the water flow continued further downstream even after the release of sediment by deposition , there is a chance of flooding further downstream . Therefore the hydrographs at the section , from which the sediment concentration reduced to a minimum level , are taken as input to the inundation model to check the inundation extent up to the Bhutan – India border .\n\nThe total water volume discharged during the event was calculated based on the assumed sink depth and the approximate area of the lake surface visible in Google Earth image and calculated with the help of a geographical information system software namely ArcGIS. The sink depth (40 m) is considered same for all the breaches and decided based on the suggestions made by the survey report (Leber et al., 2002; Brauner et al., 2003) as well as the tendency of moraine dam failures in a similar environment of Bhutan and Nepal (ICIMOD-UNEP, 2001; Mool, 2007; Osti and Egashira, 2009). The top width of the 100-m-high moraine dams is estimated at 50 m (Leber et al., 2002; Brauner et al., 2003). The longitudinal bed profiles of torrents T<sub>E</sub> and T<sub>W</sub> and the topography of the area were derived from the digital elevation model (DEM) provided by the Shuttle Radar Topography Mission (SRTM) (USGS, 2004). The SRTM data are sampled at 3\", which is about 90 m in resolution. The uncertainty lies in the vertical accuracy of SRTM-DEM, which can only be reduced by using precise DEM data that are not available for the region at present. The hydrology modelling, an extension of the ArcGIS software, is used to delineate the watershed at the Bhutan–India border and to plot the centrelines of the river as well as the tributaries. The river reach/station distances are measured relative to the distance from the tributaries' heads for all runs of simulations (Figure 2b). The time to start the breaching of individual dam and therefore the start of GLOF propagation is set at zero value for each run of simulation independently, i.e. start time of breach hydrograph is set as a start time of GLOF. The HEC-GeoRAS extension in ArcGIS is used to extract all the necessary geometric data describing the river profile and cross-sections for flood-routing purpose. Some of the typical river cross-sections are illustrated in Figure 3.\n\nDam breach modelling\n\nIn general, it is numerically difficult to reproduce moraine dam breach phenomena and therefore to predict a breach hydrograph because there are many unknown variables that may contribute to the failure of moraine dams. However, a peak dam breach flow is estimated by comparing the results from three different models namely (1) simple dam break equation, (2) the Hydrologic Engineering Center River Analysis System (HEC-RAS) and (3) the Simplified Breach Analysis (SIMBA).\n\nSimple dam break equation. A simple dam break Equation (1) is developed by the National Weather Service (NWS) of the USA, although the dam break context considered in the equation is different. This equation is", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 64, "line_end": 80, "token_count_estimate": 858, "basins": [], "subbasins": [], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": []}}
{"id": "c9cb4f4a65b1b0e9", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A figure displaying four line graphs in a 2x2 grid. Each graph plots Elevation in meters (m) on the y-axis against Station in meters (m) on the x-axis, representing different topographical cross-sections.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "figure", "figure_caption": null, "line_start": 81, "line_end": 81, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "80c9cc7812ce9f1b", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\nTop-left graph: Labeled \"200 km in T\\_W\". The y-axis ranges from 1700 to 2200 m, and the x-axis ranges from 0 to 3500 m. The line shows a U-shaped valley, starting at an elevation of approximately 2180 m, descending to a minimum of about 1750 m near station 1700, and then ascending to about 2200 m.\n\nTop-right graph: Labeled \"205 km in T\\_E\". The y-axis ranges from 1800 to 2200 m, and the x-axis ranges from 0 to 3000 m. The line shows a U-shaped valley, starting at an elevation of approximately 2180 m, descending to a minimum of about 1850 m near station 1600, and then ascending to about 2100 m.\n\nBottom-left graph: Labeled \"130 km\". The y-axis ranges from 600 to 900 m, and the x-axis ranges from 0 to 8000 m. The line starts at an elevation of about 900 m, descends to a minimum of about 610 m near station 3500, and then gradually ascends with some fluctuations to about 820 m.\n\nBottom-right graph: Labeled \"18 km\". The y-axis ranges from 0 to 600 m, and the x-axis ranges from 0 to 8000 m. The line shows a small peak of about 520 m near the start, then descends to a wide, low-elevation area around 100 m between stations 3000 and 5000, followed by a gradual, bumpy ascent to about 250 m.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 82, "line_end": 90, "token_count_estimate": 392, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e8019cfbe2831c0e", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: figure\nFigure: Figure 3. Typical cross-sections along Pho Chu River extracted from SRTM-DEM (distances measured from Bhutan-India Border)\n\nFigure 3. Typical cross-sections along Pho Chu River extracted from SRTM-DEM (distances measured from Bhutan-India Border)", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "figure", "figure_caption": "Figure 3. Typical cross-sections along Pho Chu River extracted from SRTM-DEM (distances measured from Bhutan-India Border)", "line_start": 91, "line_end": 91, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "caf7d60a7e0d10e2", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\nbased on the concept of the falling head weir flow used in the NWS full dam break model (Wetmore and Fread, 1984):\n\n$$Q_{\\rm w} = Q_{\\rm o} + 3.1 B_{\\rm r} \\left[ C / \\left( T_{\\rm f} + C / \\sqrt{H} \\right) \\right]^3$$\n (1)\n\nwhere  $Q_{\\rm w}$  is the peak water flow, i.e. the breach flow and the non-breach flow (ft³/s),  $Q_{\\rm o}$  is the non-breach flow (ft³/s),  $B_{\\rm r}$  is the final average breach width (ft), C is a coefficient that can be calculated as  $C = 23.4 * A_{\\rm s}/B_{\\rm r}$ ,  $A_{\\rm s}$  is a reservoir surface area (acres) at the maximum pool level, H is the failure depth (ft) above the final breach elevation, and  $T_{\\rm f}$  is the time to failure (h). Because the dam breach shape, especially width and height, is measured in the field, only the time of the breach must be calculated. To accomplish this, a statistically derived predictor (Equation 2) for the time of dam failure developed by Froehlich (1995) is used. From the work of Froelich in which the properties of 63 types of dam breaches ranging in height from 4 to 90 m were considered, the following predictor equation was obtained:\n\n$$T_{\\rm f} = 0.59 (V_{\\rm s}^{0.47}) / (H^{0.91})$$\n (2)\n\nwhere  $T_{\\rm f}$  is the time of failure (h) and only includes the vertical erosion of the dam,  $V_{\\rm s}$  is the storage volume (ac-ft), and H is the height (ft) of water above the bottom of the breach.\n\nHydrologic Engineering Center River Analysis System. The estimated peak flow by Equation 1 can be used to compare with the peak flow of a hydrograph produced by a dam breach model component of a hydrodynamic model named the HEC-RAS developed by the HEC of the US Army Corps of Engineers (U.S. Corps of Engineers, 2002). One of the parameters that need to be estimated for the input to HEC-RAS is the duration of the full dam breach formation, which is determined using Equation 2. The dam breach model in the HEC-RAS is used in the overtopping\n\nfailure mode; therefore, the geometry of the breached portion of the lake is critical in the calculation. Available DEMs could not accurately generate lake bottom surface profiles; therefore, the lake volumes in this study were modelled using manually drawn cross-sections that were partially extrapolated from the lake area assuming all lakes as bowl shape. HEC-RAS can perform a full unsteady flow routing through a reservoir pool and downstream of a dam (U.S. Corps of Engineers, 2002).", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 92, "line_end": 159, "token_count_estimate": 795, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "41ad485e3afe67c4", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\nthe full dam breach formation , which is determined using Equation 2 . The dam breach model in the HEC - RAS is used in the overtopping failure mode ; therefore , the geometry of the breached portion of the lake is critical in the calculation . Available DEMs could not accurately generate lake bottom surface profiles ; therefore , the lake volumes in this study were modelled using manually drawn cross - sections that were partially extrapolated from the lake area assuming all lakes as bowl shape . HEC - RAS can perform a full unsteady flow routing through a reservoir pool and downstream of a dam ( U . S . Corps of Engineers , 2002 ) .\n\nSimplified Breach Analysis. In addition to HEC-RAS, a physically based model, namely, SIMBA developed at the US Department of Agriculture is also used to produce breach hydrographs. The SIMBA model is originally developed to analyse laboratory dam breach experiment and aimed at its application in flood control and watershed protection (Hanson et al., 2005; Temple et al., 2005). In SIMBA, overtopping mode is considered and the erosion processes are divided into four primary stages, e.g. head-cut formation, deepening, upstream advancement and lateral widening. The inflow to the reservoir is an input value, which is considered minimum annual average flow 5 m<sup>3</sup>/s both in Thorthormi Cho and Lugge Cho, whereas it is assumed at 2 m3/s for Lake A and Lake B (Leber et al., 2002). The outflow and volumes can be calculated as a function of reservoir water surface elevation, which is governed by the dam erosion process. In this method, all outflow over the dam or through the breach area can be calculated by assuming negligible hydrostatic pressure exerted at the breach section and energy loss within the reservoir flow. The erosion rate of the dam can be calculated by considering normal flow depth combined with a stressbased detachment rate method (Temple et al., 2005). The erodibility coefficient of 2 cm<sup>3</sup>/N s as an input parameter for SIMBA model is used for the most erodible soil in the classification of Hanson and Simon (2001).\n\nGLOF modelling", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 92, "line_end": 159, "token_count_estimate": 584, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "d8efcf6117999d8b", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\nelevation , which is governed by the dam erosion process . In this method , all outflow over the dam or through the breach area can be calculated by assuming negligible hydrostatic pressure exerted at the breach section and energy loss within the reservoir flow . The erosion rate of the dam can be calculated by considering normal flow depth combined with a stressbased detachment rate method ( Temple et al . , 2005 ) . The erodibility coefficient of 2 cm < sup > 3 < / sup > / N s as an input parameter for SIMBA model is used for the most erodible soil in the classification of Hanson and Simon ( 2001 ) . GLOF modelling\n\nIn order to simulate a GLOF, the debris model is used with governing equations for the flow of water and sediment mixture. In order to describe water and sediment mixture flows, different approaches are usually used; some describe debris flows but some describe wash, suspension and bed loads. However, there are only a few approaches which really consider such sediment flows as a unified problem that can be described by a single set of equations. Also, many constitutive equations for the evaluation of the sediment-water mixture flow have been proposed, and they can be roughly classified into four groups (Egashira, 2007): (1) Coulomb-type shear stress (Savage and Hutter, 1989, 1991; Iverson and Denlinger, 2001), (2) Bingham type (Chen, 1988; O'Brien and Julien, 1988; Julien and Lan, 1991), (3) fluid type (Takahashi, 1980, 1991; Tsubaki et al., 1982) and (4) Coulomb-type shear plus fluid type (Egashira and Ashida, 1992; Egashira et al., 1997). There are advantages and disadvantages to each of these groups (Osti and Egashira, 2009). In this study, we employ the D-type solution as this can provide not only a steady-state solution but also can distinguish between flow over a rigid bed or an erodible bed and can explain how the flow body stops. The formulae described by Egashira et al. (1997), which are type D, are employed in the study. The model was developed to describe the flow of a mixture of water and coarse sediment under the assumption that turbulent suspension is negligible. These formulae can distinguish differences in flow over erodible and rigid beds and also be used to evaluate profiles of velocity and sediment concentration as the flow changes from a bed load layer to debris flow in response to a monotonic change in the flux sediment concentration.\n\nOsti and Egashira (2008, 2009) employed 1-D governing equations proposed by Egashira *et al.* (1997) to simulate GLOF. The constitutive equations and erosion rate formula developed by Egashira *et al.* (1997, 2001) were used to solve the governing equations.\n\nThe equations of mass conservation describing the water-sediment mixture and sediment only can be expressed as\n\n$$\\frac{\\partial h}{\\partial t} + \\frac{1}{B} \\frac{\\partial \\bar{u}hB}{\\partial x} = \\frac{E}{c_*} \\tag{3}$$\n\n$$\\frac{\\partial \\bar{c}h}{\\partial t} + \\frac{1}{B} \\frac{\\partial \\gamma \\, \\bar{c}\\bar{u}hB}{\\partial x} = E \\tag{4}$$", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 92, "line_end": 159, "token_count_estimate": 885, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "a4628540be362365", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\n$ $ \\ frac { \\ partial h } { \\ partial t } + \\ frac { 1 } { B } \\ frac { \\ partial \\ bar { u } hB } { \\ partial x } = \\ frac { E } { c_ * } \\ tag { 3 } $ $ $ $ \\ frac { \\ partial \\ bar { c } h } { \\ partial t } + \\ frac { 1 } { B } \\ frac { \\ partial \\ gamma \\ , \\ bar { c } \\ bar { u } hB } { \\ partial x } = E \\ tag { 4 } $ $\n\nThe equations for momentum conservation and bed elevation are as follows:\n\n$$\\frac{\\partial h\\bar{u}}{\\partial t} + \\frac{\\partial \\beta h\\bar{u}\\bar{u}}{\\partial x} = gh\\sin\\theta - gh\\cos\\theta\\frac{\\partial h}{\\partial x} - \\frac{\\tau_b}{\\bar{\\rho}_m}$$\n (5)\n\n$$\\frac{\\partial z_{b}}{\\partial t} = -\\frac{E}{c_{c} \\cos \\theta} \\tag{6}$$\n\nwhere h is the flow depth, t is the time, x is the coordinate toward the flow direction, E is the erosion rate of the bed sediment (its negative value denotes deposition),  $\\bar{c}$  is the\n\ndepth-averaged and volumetric sediment concentration,  $c_*$  is the sediment concentration of the underlying sediment bed,  $\\bar{u}$  is the depth-averaged velocity of the mixture, B is the river bed width, g is the acceleration due to gravity, and  $\\bar{\\rho}_{\\rm m}$  is the depth-averaged mass density of the sediment-water mixture, which is defined as\n\n$$\\bar{\\rho}_{\\rm m} = (\\sigma - \\rho)\\,\\bar{c} + \\rho \\tag{7}$$\n\nwhere  $\\sigma$  is the mass density of the sediment particle,  $\\rho$  is the mass density of water including fine sediment,  $\\tau_b$  is the bed shear stress,  $z_b$  is the bed elevation from a reference level, and  $\\theta$  is the bed slope, which is expressed as\n\n$$\\theta = \\sin^{-1} \\left[ -\\frac{\\partial z_b}{\\partial x} \\right] \\tag{8}$$\n\n$\\gamma=(1/A)\\int_A \\{(c\\,u)/(\\bar c\\,\\bar u)\\}\\,\\mathrm{d}A$  in Equation (4) is a correction factor with a value that ranges from 0 to 1 for sediment transportation, depending on the bed slope and interparticle friction angle.  $\\beta$  in Equation (5) is the momentum-correction factor, which has a value from 1.10 to 1.40 for debris flow. The following equations for the bed shear stress and erosion rate are also employed:", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 92, "line_end": 159, "token_count_estimate": 841, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "7326faf01225e422", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\n$ \\ gamma = ( 1 / A ) \\ int_A \\ { ( c \\ , u ) / ( \\ bar c \\ , \\ bar u ) \\ } \\ , \\ mathrm { d } A $ in Equation ( 4 ) is a correction factor with a value that ranges from 0 to 1 for sediment transportation , depending on the bed slope and interparticle friction angle . $ \\ beta $ in Equation ( 5 ) is the momentum - correction factor , which has a value from 1 . 10 to 1 . 40 for debris flow . The following equations for the bed shear stress and erosion rate are also employed :\n\n$$\\tau_{\\rm b} = \\tau_{\\rm v} + \\rho f \\, \\bar{u}^2 \\tag{9}$$\n\n$$\\frac{E}{\\bar{u}} = c_* \\tan(\\theta - \\theta_e) \\tag{10}$$\n\nwhere  $\\tau_y$  is the yield stress caused by particle-to-particle contacts, f is the friction factor,  $\\theta$  is the bed slope, and  $\\theta_e$  is the equilibrium bed slope corresponding to the sediment concentration  $\\bar{c}$  of a debris flow. Parameters  $\\tau_y$ ,  $\\tan\\theta_e$  and f are expressed as follows:\n\n$$\\tau_{y} = \\left(\\frac{\\bar{c}}{c_{*}}\\right)^{1/5} (\\sigma - \\rho) \\,\\bar{c} \\, g \\, h \\cos\\theta \\tan\\varphi_{s} \\tag{11}$$\n\n$$\\tan \\theta_{\\rm e} = \\frac{(\\sigma/\\rho - 1)\\,\\bar{c}}{(\\sigma/\\rho - 1)\\,\\bar{c} + 1}\\,\\tan \\varphi_{\\rm s} \\tag{12}$$\n\n$$f = \\frac{25}{4} \\left[ k_{\\rm f} \\frac{(1-\\bar{c})^{5/3}}{\\bar{c}^{2/3}} + k_{\\rm d} \\left( \\frac{\\sigma}{\\rho} \\right) (1-e^2) \\,\\bar{c}^{1/3} \\right] \\left( \\frac{h}{d} \\right)^{-2} \\tag{13}$$", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 92, "line_end": 159, "token_count_estimate": 698, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "568450176a5466b8", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY\nType: text\n\nfrac { 25 } { 4 } \\ left [ k_ { \\ rm f } \\ frac { ( 1 - \\ bar { c } ) ^ { 5 / 3 } } { \\ bar { c } ^ { 2 / 3 } } + k_ { \\ rm d } \\ left ( \\ frac { \\ sigma } { \\ rho } \\ right ) ( 1 - e ^ 2 ) \\ , \\ bar { c } ^ { 1 / 3 } \\ right ] \\ left ( \\ frac { h } { d } \\ right ) ^ { - 2 } \\ tag { 13 } $ $\n\nwhere d is the particle diameter,  $k_{\\rm f}$ =0.16,  $k_{\\rm d}$ =0.0828, and e and  $\\phi_{\\rm s}$  are the restitution coefficient (e=0.85) and the interparticle friction angle of sediment particles, respectively. In this study, parameters such as flow width, physical properties of the sediments and potential erosion depths were determined by field surveys and, in some instances, expert knowledge or data from secondary sources. The physical properties of the bed material were as follows: particle size diameter d=20 cm, interparticle friction angle of the sediment particles  $\\phi_{\\rm s}$ =38°, mass density of sediment  $\\sigma$ =2.65 g/cm³, mass density of interstitial fluid with suspended sediment  $\\rho$ =1.33 g/cm³ and the sediment concentration of the underlying stationary sediment bed  $c_*$ =0.52. The potential erosion depth ( $D_{\\rm p}$ ), which is defined\n\nas a depth of unstable sediment overlaying the bedrock was estimated to be an average of 10 m throughout the reach based on field observations (Leber *et al.*, 2002; Brauner *et al.*, 2003). In the simulation, riverbed slopes steeper than 18° are considered naturally stable as unstable saturated bed sediment cannot exist in such a steep slope. The riverbed width (*B*) varies throughout the river zones; however it is considered constant in a temporal scale. For simplicity, an average bed width for each 100-m river reach was assigned (ranges from 50 to 300 m). The finite leapfrog difference scheme was employed for computations with  $\\nabla x = 20$  m and  $\\nabla t = 0.012$  s. The computational time for each simulation was started from zero without any influence from other simulations.\n\nIn some instances, flux sediment concentrations of sediment flow could be lower than those of generally accepted debris flows. However, the word debris flow is used for simplicity because it has been explained by Egashira *et al.* (1997) that the dynamic characteristics of the debris flow change monotonically with sediment concentration and the sediment concentration can be determined uniquely in an erodible bed if sediment particles composed of the flow body are so coarse that turbulent suspension cannot take place.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY", "section_headings": ["METHODOLOGY"], "chunk_type": "text", "line_start": 92, "line_end": 159, "token_count_estimate": 756, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "21c9dd869763168c", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: METHODOLOGY > Flood inundation modelling\nType: text\n\nThe output of the debris flow model provided the upstream boundary conditions, i.e. the peak flood hydrograph for delineating the inundation in downstream areas up to the Bhutan–India border (Figure 4). The location of upstream boundary for each simulation is set at the location where the sediment concentration is reduced to a lowest level without rebounding its value at any downstream sections, and this is usually the place where the riverbed slope changes from steeper to milder\n\nslope. One-dimensional river flow model developed by the US Corps of Engineers namely HEC-RAS was used to perform one-dimensional unsteady flow calculations under mixed flow regime condition, providing upstream boundary condition as calculated hydrographs and the downstream boundary as normal water depth. All simulations were performed under mixed flow regime, which utilizes local partial inertia technique. The local partial inertia technique applies a reduction factor to the two inertia terms in the momentum equation as the Froude number goes toward 1.0 (U.S. Corps of Engineers, 2002). The model solves the Saint Venant equations formulated for natural channels using the wellknown four-point implicit box finite difference scheme. The upstream boundary conditions, i.e. the highest peak flood hydrographs, due to either first or second surge of debris flow (GLOF) in each tributary, i.e. T<sub>E</sub> and T<sub>W</sub>, are considered for inundation simulations. There are some limitations in 1-D simulation, which are well described by comparing results by 1-D and 2-D river flow models by Carling et al. (2009). However, they suggested that the difference is less significant when 1-D model deals with peak flow conditions.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "METHODOLOGY > Flood inundation modelling", "section_headings": ["METHODOLOGY", "Flood inundation modelling"], "chunk_type": "text", "line_start": 161, "line_end": 165, "token_count_estimate": 465, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "f14cd4e560d61ac8", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nThe satellite images provided the basic geometrical feature of four different lakes in the Pho Chu River basin (Table 1). Lakes Lugge and Thorthormi are two largest glacial lakes in  $T_{\\rm E}$ , whereas the two other unnamed lakes A and B in  $T_{\\rm W}$  are smaller in size. The NWS model (Equations 1 and 2) resulted in the peak dam breach discharge of over  $10\\,000\\,{\\rm m}^3/{\\rm s}$  in Lugge Cho and Thorthormi but about  $5\\,000\\,{\\rm m}^3/{\\rm s}$  in Lakes A and B. The estimated dam breach peak flows are then compared with the results produced by", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 167, "line_end": 169, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "cd8e0f39943fbd96", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/6/Figure/9 description: A diagram illustrating a hydrological model of two river systems originating from glacial lakes. On the left, a river flows from Lake A and Lake B. A corresponding graph shows the discharge (m³/sec) over time (s) at a downstream point T\\_WX. This hydrograph displays two peaks: a larger one for the \"Lake A Burst\" at time T\\_W1, and a smaller one for the \"Lake B Burst\" at time T\\_W2. On the right, another river system originates from two lakes named \"Thorthormi Cho\" and \"Lugge Cho\". A second hydrograph shows the discharge at a downstream point T\\_EY. This graph also has two peaks: a smaller one for the \"Thorthormi Cho Burst\" at time T\\_E1, and a larger one for the \"Lugge Cho Burst\" at time T\\_E2. The two river systems are shown merging further downstream. An arrow points from the right-side lakes, indicating they provide the \"Upstream boundary conditions for inundation analysis\" with the condition specified as (T\\_W2=T\\_E1=0).", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 170, "line_end": 170, "token_count_estimate": 324, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "8195e9d3ea6367a3", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 4. Sketch of the upstream boundary conditions at arbitrary debris flow termination sections $T_{WX}$ and $T_{EY}$ in $T_{W}$ and $T_{E}$ , respectively, for the worst inundation scenario\n\nFigure 4. Sketch of the upstream boundary conditions at arbitrary debris flow termination sections  $T_{WX}$  and  $T_{EY}$  in  $T_{W}$  and  $T_{E}$ , respectively, for the worst inundation scenario", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 4. Sketch of the upstream boundary conditions at arbitrary debris flow termination sections $T_{WX}$ and $T_{EY}$ in $T_{W}$ and $T_{E}$ , respectively, for the worst inundation scenario", "line_start": 172, "line_end": 172, "token_count_estimate": 178, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "a0964e80dca40f39", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: table\nTable\n\n| Table I. Features | of selected | glacial lakes | in Pho | Chu river bas | in |\n|-------------------|-------------|---------------|--------|---------------|----|\n|                   |             |               |        |               |    |", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "table", "table_caption": null, "columns": ["Table I. Features", "of selected", "glacial lakes", "in Pho", "Chu river bas", "in"], "table_row_start": 1, "table_row_end": 1, "line_start": 174, "line_end": 176, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "a9a5e602f20b5c04", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: table\nTable\n\n| Lake name   | Lake area (m2) | Breached depth (m) | Volume (m3) | Breach time (h) | Peak Flow (m3/s) |\n|-------------|----------------|--------------------|-------------|-----------------|------------------|\n| Lugge       | 1 674 000      | 40                 | 66 950 000  | 1.18            | 14 000           |\n| Thorthormi  | 1 410 000      | 40                 | 56 387 000  | 1.08            | 12 300           |\n| B (unnamed) | 424 000        | 40                 | 16 956 000  | 0.62            | 5800             |\n| A (unnamed) | 253 000        | 40                 | 10 107 000  | 0.49            | 4000             |", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "table", "table_caption": null, "columns": ["Lake name", "Lake area (m2)", "Breached depth (m)", "Volume (m3)", "Breach time (h)", "Peak Flow (m3/s)"], "table_row_start": 1, "table_row_end": 4, "line_start": 178, "line_end": 183, "token_count_estimate": 228, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "30d29769b49b81fb", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nthe HEC-RAS and SIMBA models, which in the case of Lugge Cho are plotted in Figure 5. The results produced by NWS equations that are listed in Table 1, closely match the results produced by HEC-RAS and SIMBA models (Figure 5). Dam breach hydrographs of all selected glacial lakes calculated by SIMBA are plotted in Figure 6. These hydrographs were used as upper boundary conditions for GLOF modelling in each tributary for each case of GLOF from each lake. The timing of moraine dam breaches could be same for all selected lakes but because of relative downstream locations of the Thorthormi Lake in T<sub>E</sub> and Lake B in T<sub>W</sub>, the first surges of GLOF in T<sub>E</sub> and T<sub>W</sub> are supposed to be caused by the dam breaches at Thorthormi Lake and Lake A respectively.\n\nIn the case of Thorthomi Cho in  $T_E$ , the sediment discharge at 0.5, 4, 8, 9 and 18 km (measured from the lake mouth as shown in Figure 2b) sections as well as the sediment and water mixture hydrographs at 18 km are plotted in Figure 7a–f. The upstream reaches are debris flow development and transportation regions, and the river reaches especially 9–14 and 18–21 km are mild slope regions where the debris flow usually deposits mainly for the first run of simulation. Therefore, the total hydrographs", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 184, "line_end": 188, "token_count_estimate": 395, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "d396d496017d612d", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/7/Figure/6 description: A line graph comparing two models, SIMBA and HEC-RAS, plotting Discharge (m³/s) on the y-axis against Time (min) on the x-axis. The x-axis ranges from 0 to 300 minutes, with labels at intervals of 50. The y-axis ranges from 0 to over 15000 m³/s, with labels at 0, 5000, 10000, and 15000. The SIMBA curve shows a sharp peak, reaching a maximum discharge of over 15000 m³/s at approximately 25 minutes before declining. The HEC-RAS curve has a broader peak, reaching its maximum discharge of just over 15000 m³/s at approximately 65 minutes before declining. The SIMBA curve peaks earlier and higher than the HEC-RAS curve.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 189, "line_end": 189, "token_count_estimate": 236, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["10000", "15000"]}}
{"id": "15311635f3f783e1", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 5. Calculated moraine dam breach hydrograph at Lugge Cho Lake\n\nFigure 5. Calculated moraine dam breach hydrograph at Lugge Cho Lake", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 5. Calculated moraine dam breach hydrograph at Lugge Cho Lake", "line_start": 191, "line_end": 191, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "f2ad1c4e5ab78b4f", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/7/Figure/8 description: A line graph plots Discharge in cubic meters per second (m³/s) on the y-axis against Time in minutes (min) on the x-axis. The y-axis ranges from 0 to over 15,000, with major gridlines at 5,000, 10,000, and 15,000. The x-axis ranges from 0 to 300, with major gridlines every 50 minutes. The graph displays four curves, each representing a different location: Lugge, Thorthomi, Lake B, and Lake A. All four curves show a rapid increase in discharge to a peak, followed by a more gradual decrease. The 'Lugge' curve has the highest peak, reaching approximately 17,000 m³/s at around 25 minutes. The 'Thorthomi' curve peaks slightly lower, at about 15,000 m³/s, also around 25 minutes. The 'Lake B' curve peaks at approximately 6,000 m³/s around 20 minutes. The 'Lake A' curve has the lowest peak, just over 5,000 m³/s, also around 20 minutes. All four curves show the discharge decreasing and approaching zero by the 300-minute mark.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 193, "line_end": 193, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "6ee86960fdbb20e2", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 6. SIMBA-calculated moraine dam breach hydrographs at four selected glacial lakes\n\nFigure 6. SIMBA-calculated moraine dam breach hydrographs at four selected glacial lakes", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 6. SIMBA-calculated moraine dam breach hydrographs at four selected glacial lakes", "line_start": 195, "line_end": 195, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "420e73b3e7379000", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nat these locations need to be compared and the worst case needs to be considered for inundation modelling. The results reveal that the GLOF developed as a debris flow acquired the peak value of sediment discharge of about 4500 m<sup>3</sup>/s at 4-km section (Figure 7b). Figure 7a-e indicates that the debris flow development is significant along the initial reach of 0–6 km. The sediment discharge gradually reduced from 6 to 11 km (debris flow deposition zone) and significantly reduced after the 18-km section after depositing the sediment in relatively mild slope region between 16.5 and 18 km (Figures 7e and 8a–b). In Figure 8b, the calculated final erosion and deposition situation due to the GLOF is plotted, which however does not exactly agree with the slope distribution as shown in Figure 8a. This is why the erosion and deposition take place corresponding to a temporally changed local bed slope and sediment concentration of debris flow body as shown in Equation 10. The calculated total hydrograph (sediment and water mixture) at 18 km shows that the peak flow, which characterizes the inundation and damage in downstream areas, is still very high and as high as the peak of the dam breach hydrograph at the lake location. The robustness in hydrograph exists because of accumulated sediment by riverbed erosion and sediment transportation in increased channel capacity by erosion. This is how the calculated results assuming the rigid bed river is different from the realities as described by Osti and Egashira (2009). The total hydrograph at 18 km by the first GLOF event will be compared with the second GLOF surge of Lugge Cho so that the worst case of the inundation of downstream locations can be developed.\n\nIn T<sub>E</sub> and for the simulation of the second GLOF event from the Lugge Cho Lake, the final riverbed profile calculated from the first simulation of GLOF from Thorthormi lake, potential erosion depth (+/-ve) formed during the first GLOF from Thorthormi Lake and a dam breach hydrograph at the Lugge Cho Lake are taken as initial and boundary conditions. The results are presented in Figure 9. The graphs show that the amount of sediment supplied during the second GLOF event at almost every downstream cross-section of the river is very small (Figure 9a and b) in comparison with the first event except for the initial 4-km reach in-between the Thorthormi and Lugge Cho Lakes, which was not considered in the first run simulation. In the 4-km initial reach, debris flow developed significantly but remained at minimum values in downstream sections. However, the result of second GLOF was surprising as the peak discharge of mixture flow  $(Q_T)$ , i.e. sediment and water at 10-km section exceeds 23 000 m<sup>3</sup>/s (Figure 9c), which is almost double the value of the peak breach flow at the Lugge Cho dam breach site and the peak", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 196, "line_end": 200, "token_count_estimate": 744, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e3450cdeda13b1fc", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: The image displays six line graphs, labeled (a) through (f), showing discharge over time at different downstream distances. Each graph plots discharge on the y-axis against time (T) in seconds on the x-axis.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 201, "line_end": 201, "token_count_estimate": 110, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "4505923b3003953d", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nGraph (a) is for a distance of 500m. The y-axis, Qs (m³/sec), ranges from 0 to 4000. The x-axis, T (sec), ranges from 0 to 1000. A sharp peak occurs around T=200 sec, reaching over 3000 m³/sec, followed by a rapid decrease with smaller fluctuations.\n\nGraph (b) is for a distance of 4000m. The y-axis, Qs (m³/sec), ranges from 0 to 5000. The x-axis, T (sec), ranges from 0 to 1000. Multiple sharp peaks appear between T=400 and T=500 sec, with the highest peak reaching approximately 4500 m³/sec.\n\nGraph (c) is for a distance of 8000m. The y-axis, Qs (m³/sec), ranges from 0 to 800. The x-axis, T (sec), ranges from 0 to 1000. A single prominent peak of about 750 m³/sec occurs around T=650 sec, followed by smaller peaks.\n\nGraph (d) is for a distance of 9000m. The y-axis, Qs (m³/sec), ranges from 0 to 600. The x-axis, T (sec), ranges from 0 to 1000. The main peak is around 500 m³/sec at T=700 sec, with a smaller secondary peak following.\n\nGraph (e) is for a distance of 18000m. The y-axis, Qs (m³/sec), ranges from 0 to 50. The x-axis, T (sec), ranges from 0 to 1500. The discharge peaks at approximately 45 m³/sec around T=1050 sec.\n\nGraph (f) is also for a distance of 18000m but shows total discharge, QT (m³/sec), where QT = Qs + Qw. The y-axis ranges from 0 to 10000. The x-axis, T (sec), ranges from 0 to 3000. There is a series of very high, sharp peaks between T=1000 and T=1300 sec, with the maximum peak approaching 10000 m³/sec, followed by a long tail with multiple smaller peaks.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 202, "line_end": 214, "token_count_estimate": 523, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["10000"]}}
{"id": "00ac62e92b392266", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 7. (a–e) Calculated sediment discharge graphs at different locations in $T_E$ and (f) Total hydrograph ( $Q_T$ ) at 18 km during the first GLOF event by Thorthormi Cho\n\nFigure 7. (a–e) Calculated sediment discharge graphs at different locations in  $T_E$  and (f) Total hydrograph ( $Q_T$ ) at 18 km during the first GLOF event by Thorthormi Cho", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 7. (a–e) Calculated sediment discharge graphs at different locations in $T_E$ and (f) Total hydrograph ( $Q_T$ ) at 18 km during the first GLOF event by Thorthormi Cho", "line_start": 215, "line_end": 215, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "4416aaaea6e11f48", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/8/Figure/4 description: The image contains two line graphs, labeled (a) and (b), plotted on a shared horizontal axis representing \"Distance (m)\" from 0 to 25000.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 217, "line_end": 217, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["25000"]}}
{"id": "4b66e94008c3b53b", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nGraph (a) shows \"Bed-slope (degrees)\" on the y-axis, which ranges from 0 to 25. The black line plot is highly variable, with numerous sharp peaks. The bed-slope is generally higher and more volatile from 0 to about 8000m and again from 15000m to 25000m, with several peaks approaching 20 degrees. In the intermediate section, from roughly 8000m to 15000m, the bed-slope is much lower, mostly staying below 5 degrees.\n\nGraph (b) shows \"Erosion and Deposition (m)\" on the y-axis, ranging from -15 to 15. Positive values indicate deposition, while negative values indicate erosion. The red line plot shows significant fluctuations. From 0 to about 6000m, there are several instances of erosion, reaching down to -10m. A period of net deposition occurs from about 6000m to 11000m, peaking at approximately 5m. This is followed by a major erosional phase from about 11000m to 17000m, where the value drops below -10m. For the remainder of the distance, the plot shows smaller fluctuations around the zero line.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 218, "line_end": 222, "token_count_estimate": 307, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "1d3671888e88d08f", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 8. (a) Bed slope distribution, and (b) erosion and deposition along TE\n\nFigure 8. (a) Bed slope distribution, and (b) erosion and deposition along T<sub>E</sub>", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 8. (a) Bed slope distribution, and (b) erosion and deposition along TE", "line_start": 223, "line_end": 223, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e33284910ded3ac6", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nhydrograph calculated at 18 km for the Thorthomi Cho GLOF, i.e. the first event. Despite the large size debris flow in the first GLOF event, the  $Q_T$  is much higher in the second\n\nevent because the debris flow size reduces gradually after intermittent depositions in the first event but abruptly in the second event. Also, the river storage function was not active as the channel capacity was widen by the first event to pass the debris flow developed along the 4-km initial reach during the second event.\n\nAt the 10-km section of the river, almost 30% of the released water volume from the lake passed as a peak flow and the remaining as tail water with long recession time. In all the aforementioned cases, the hydrodynamic routing procedure translated an upstream hydrograph into a subsequent downstream hydrograph. As a result, the recession time in each routed hydrograph is longer but the time that the hydrograph takes to rise is much shorter, which represents a flash flood phenomenon. Multiple peaks appear in each calculated hydrograph. In the second GLOF event, the calculated hydrographs are relatively smoother (Figure 9c) than in the first event (Figure 7f). Further, the temporal change of bed elevation at randomly selected 6-km section (Figure 9d) shows that the GLOF surge eroded the bed at first but the section was deposited later, making the final bed elevation raised by 3.5 m from 4265.2 m.\n\nLikewise, debris flow simulations are performed for the west branch of the Pho Chu river considering the possible breaches of moraine dams at the Lake A and B. The simulated results are summarized in Table 2. Unlikely, the debris flow size in the first GLOF surge (from Lake A) in  $T_{\\rm W}$  is not developed well. The maximum sediment", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 224, "line_end": 232, "token_count_estimate": 469, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "894f475a5721ebb6", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: The image displays four line graphs, arranged in a 2x2 grid and labeled (a), (b), (c), and (d).", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 233, "line_end": 233, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "b82c90fb053f40e9", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nGraph (a) is titled '6000m'. The y-axis is labeled 'Qs (m³/sec)' and ranges from 0 to 600. The x-axis is labeled 'T (sec)' and ranges from 0 to 1000. The plot shows a sharp peak reaching approximately 600 m³/sec around 250 sec, followed by a smaller peak of about 300 m³/sec, and then several smaller oscillations before returning to near zero after 600 sec.\n\nGraph (b) is titled '10000m'. The y-axis is labeled 'Qs (m³/sec)' and ranges from 0 to 80. The x-axis is labeled 'T (sec)' and ranges from 0 to 1000. The plot shows a peak of about 70 m³/sec around 420 sec, followed by a second peak of about 50 m³/sec. A much smaller, separate peak occurs around 750 sec.\n\nGraph (c) is titled '10000m'. The y-axis is labeled 'QT (m³/sec)' and ranges from 0 to 25000. The x-axis is labeled 'T (sec)' and ranges from 0 to 1000. The shape of this graph is similar to graph (b), with a large peak reaching about 23000 m³/sec around 420 sec, a second peak of about 14000 m³/sec, and a small, separate peak around 750 sec.\n\nGraph (d) is titled '6000m'. The y-axis is labeled 'Bed Elevation (m)' and ranges from 4262 to 4268. The x-axis is labeled 'T (sec)' and ranges from 0 to 1000. The plot shows the bed elevation starting at about 4264.2 m, dropping sharply to about 4262.2 m around 220 sec, then rising sharply to about 4265.5 m. After this, it oscillates and gradually increases to about 4267.5 m by 1000 sec.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 234, "line_end": 242, "token_count_estimate": 488, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["14000", "23000", "25000"]}}
{"id": "f5716241e1a7f2c2", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 9. Sediment discharge calculated at (a) 6-km and (b) 10-km sections of $T_E$ during the second GLOF event by Lugge Cho Lake, (c) hydrograph of mixture flow $Q_T$ at 10 km and (d) temporal change of bed elevation at 6 km\n\nFigure 9. Sediment discharge calculated at (a) 6-km and (b) 10-km sections of  $T_E$  during the second GLOF event by Lugge Cho Lake, (c) hydrograph of mixture flow  $Q_T$  at 10 km and (d) temporal change of bed elevation at 6 km", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 9. Sediment discharge calculated at (a) 6-km and (b) 10-km sections of $T_E$ during the second GLOF event by Lugge Cho Lake, (c) hydrograph of mixture flow $Q_T$ at 10 km and (d) temporal change of bed elevation at 6 km", "line_start": 243, "line_end": 243, "token_count_estimate": 186, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "b484a71cae46b481", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: table\nTable: Table II. Peak sediment discharges calculated at different locations due to GLOFs in $T_{\\rm W}\\,$\n\n| Location A/B |    | 2/2.5 km | 4/4.5 km | 6/6.5 km | 8/8.5 km | 12/12.5 km | 15/15.5 km | 17/17.5 km   |\n|--------------|----|----------|----------|----------|----------|------------|------------|--------------|\n| 1st GLOF A   | Qs | 500      | 1400     | 650      | 250      | 200        | 220        | 50 $QT=9700$ |\n| 2nd GLOF B   |    | 20       | 40       | 10       | 30       | 10         | 10         | 10 $QT=800$  |", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "table", "table_caption": "Table II. Peak sediment discharges calculated at different locations due to GLOFs in $T_{\\rm W}\\,$", "columns": ["Location A/B", "", "2/2.5 km", "4/4.5 km", "6/6.5 km", "8/8.5 km", "12/12.5 km", "15/15.5 km", "17/17.5 km"], "table_row_start": 1, "table_row_end": 2, "line_start": 247, "line_end": 250, "token_count_estimate": 228, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "f65996d484cba75f", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nValues are in cubic metres per second.\n\ndischarge reached  $1400 \\,\\mathrm{m}^3/\\mathrm{s}$  at the 4-km section during the first GLOF event but remains at the lowest value at the rest of the downstream sections. Figure 10 shows the riverbed slope distribution, which is relatively steep in the initial 4-km reach but mild in the rest portion. In this event, the erosion and deposition processes are also intermittent phenomenon with no significant sediment transportation because the riverbed profile is very irregular with randomly mixed steep and mild slope reaches. The peak discharge  $(Q_T)$  of the mixture flow during the first GLOF event reached at about  $9000 \\,\\mathrm{m}^3/\\mathrm{s}$  at the 17-km section (Figure 11), where the sediment discharge was reduced to lowest level, i.e.  $50 \\,\\mathrm{m}^3/\\mathrm{s}$  (Table 2). In the second GLOF event from Lake B in  $T_W$ , the sediment discharge remains at the lowest value at\n\nall the sections because the first event stabilized the irregular riverbed profile so no active erosion took place in smoothened mild slope region. Because the debris flow development is not significant in the second GLOF event throughout the river reach, the peak mixture discharge  $(Q_T)$  at 17.5 km (17 km refer to Lake A) remains at  $800\\,\\mathrm{m}^3/\\mathrm{s}$ . This means the first GLOF is destructive than the second one in  $T_W$ , which is different from the case of  $T_E$ , where the second peak was much larger than the first one. The difference also exists because no significant debris flow size was developed within 0.5-km initial reach between Lakes A and B.\n\nTwo highest peak hydrographs, one for the Lugge Cho GLOF in  $T_{\\rm E}$  calculated at 10 km and the other for the Lake A GLOF in  $T_{\\rm W}$  at 17.5 km are then as upstream", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 251, "line_end": 259, "token_count_estimate": 553, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e6a749a76a810b46", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/9/Figure/10 description: A line graph plotting Bed-slope in degrees against Distance in meters. The y-axis, labeled \"Bed-slope (degrees)\", ranges from 0 to 20. The x-axis, labeled \"Distance (m)\", ranges from 0 to 18000. The line shows a high and variable bed-slope from 0 to approximately 5000 meters, starting at about 14 degrees and fluctuating roughly between 4 and a peak of over 16 degrees. After 5000 meters, the bed-slope drops significantly and remains low, fluctuating mostly between 0 and 5 degrees for the rest of the distance up to 18000 meters.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 260, "line_end": 260, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["18000"]}}
{"id": "9ea65405d363d4c6", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 10. Bed slope distribution along TW\n\nFigure 10. Bed slope distribution along T<sub>W</sub>", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 10. Bed slope distribution along TW", "line_start": 262, "line_end": 262, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "61f33a79118cc01e", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure\n\nImage /page/9/Figure/12 description: A line graph plots Q\\_T in cubic meters per second (m^3/sec) versus Time in seconds (sec). The vertical y-axis, labeled 'Q\\_T (m^3/sec)', ranges from 0 to 12000, with tick marks every 3000 units. The horizontal x-axis, labeled 'Time (sec)', ranges from 0 to 2000, with tick marks every 500 units. The plotted line is at zero from 0 to approximately 950 seconds. It then spikes sharply to a peak of just under 10000 m^3/sec at around 980 seconds. The value then drops rapidly, has a smaller peak of about 4000 m^3/sec, and then generally decreases with several smaller, sharp peaks occurring between 1300 and 2000 seconds. The text '17000m' is written in the upper right quadrant of the graph.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": null, "line_start": 264, "line_end": 264, "token_count_estimate": 253, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": ["10000", "12000"]}}
{"id": "44bf4fdc0712cfb9", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: figure\nFigure: Figure 11. Total hydrograph ( $Q_T$ ) calculated at 17.0 km in $T_W$\n\nFigure 11. Total hydrograph ( $Q_T$ ) calculated at 17.0 km in  $T_W$", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "figure", "figure_caption": "Figure 11. Total hydrograph ( $Q_T$ ) calculated at 17.0 km in $T_W$", "line_start": 266, "line_end": 266, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "4258c4d304d9ef1c", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: RESULTS AND DISCUSSION\nType: text\n\nboundary conditions for the inundation simulation by HEC-RAS unsteady flow model. Although the exact time of dam breaches and therefore the surge arrival time at mentioned locations is not known, simultaneous loading of surges at the same time from two tributaries is assumed. The result shows that the considered scenario of GLOFs can cause a serious inundation at different locations up to the Bhutan-India border. Although the inundation extent is restricted within the close proximity of the river banks due to the existence of narrow and deep valleys throughout the reach, there are several settlements within such narrow valleys which are found to be highly prone to floodings. Among them, Punakha, Lobeysa and the major portion of Wangdue Phodrang town could be inundated by a few to several metres of flood as shown in Figure 12. The flood depth at some locations reached over 25 m especially in the narrow gorge in mild slope river reach sections of the Pho Chu and Puna Tshang Chu Rivers. The effect of river pooling in many locations causes highest flood depths such as one just downstream to the junction of  $T_E$  and  $T_W$ . On the other hand, shallow depths of flood are also observed in steep river gradient sections. The area bordering India is found to be relatively safe as the effect of the GLOF inundation is small in this area.\n\nThe reliability of results discussed in the previous sections greatly depends on the quality of observed data, which is however difficult to maintain for such a difficult terrain. Therefore there are uncertainties in the predicted results, which can however be minimized through additional field surveys and precise and regular monitoring of hydrological conditions including flood discharge measurement. A number of checks for parameter sensitivity may be required to improve the reliability of prediction by numerical model, which may also include the variation in sediment size, sediment type, channel geometry and land uses.\n\nIt is a fact that the characteristic of flood in the downstream areas is governed by the size of debris flow developed in the upstream areas but not necessarily the amount of water released from the lake. There could be many other possible scenarios, which need to be taken into account seriously for the understanding of GLOFs. For example, depending on the flow characteristics and morphological setting, there is a chance that the second GLOF surge can merge into the first surge as the velocity of second GLOF surge could be faster than the first one. In this study, two different GLOF events for each tributary, which are supposed to be triggered at the same time from the sources, are treated separately to understand how the characteristics of GLOF change with different GLOF surges passing at different times. However, the case treated in this paper could also be one of the worst scenarios. Also, despite the fact that the same dam breach triggering factor could act at the same time, there would be many uncertainties in the breaching process of moraine dams and therefore the timing of breach and water releasing process. The account should also be given to the sediment mass produced by landslides as a result of GLOF, which may add a significant amount of sediment volume in the flow. Besides, accuracy of satellitebased DEM for the derivation of topography and riverbed profile is to be improved for obtaining an applicable result.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "RESULTS AND DISCUSSION", "section_headings": ["RESULTS AND DISCUSSION"], "chunk_type": "text", "line_start": 267, "line_end": 273, "token_count_estimate": 814, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "abf7e4d8e06bda0f", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: CONCLUSIONS\nType: text\n\nGlacial lake outburst floods and associated risk are widely discussed elsewhere in the literature mainly under the scenario of increasing threats of climate change in high Himalayas. However, the flow dynamics of such outburst floods in highly fragile landscape of the Himalayas is little understood among scientists and engineers and therefore the realization of potential threat due to GLOF is", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "text", "line_start": 275, "line_end": 277, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "a964267ebdbac6d3", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: CONCLUSIONS\nType: figure\nFigure\n\nImage /page/10/Figure/7 description: A figure containing two maps illustrating flood inundation in a watershed. The map on the left shows the entire watershed with a topographical color scale for elevation, ranging from 52-400 meters (yellow-green) up to 3200-5000 meters (white). A river runs through the valley, and a legend for 'Inundation (m)' shows flood levels from 0-5 m (light blue) to 16-20 m (dark blue). A circular area on this map is highlighted and magnified in the map on the right. The right map is a close-up view showing the 'Inundation Depth (m)' with the same color scale. This map labels several locations, including 'Punakha', 'Lobeysa', 'Wangdue Phodrang', and a specific point marked as 'Fort-Monastery'. The left map has a scale from 0 to 30 kilometers, while the right map has a scale from 0 to 2.5 kilometers.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "figure", "figure_caption": null, "line_start": 278, "line_end": 278, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "ef46f9ccca5b0bf9", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: CONCLUSIONS\nType: figure\nFigure: Figure 12. GLOF inundation map based on 1-D Hydrodynamic computation\n\nFigure 12. GLOF inundation map based on 1-D Hydrodynamic computation", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "figure", "figure_caption": "Figure 12. GLOF inundation map based on 1-D Hydrodynamic computation", "line_start": 280, "line_end": 280, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "c0cfef5c6616eeac", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: CONCLUSIONS\nType: text\n\nunclear. Therefore, an approach is made to evaluate the dynamics of GLOF in a multi-hazard scenario taking an example from the Lunana region in west Bhutan. The upper Pho Chu River basin in Lunana, which is highly seismic sensitive and highly prone to GLOF hazards, is chosen for the study. The breaches of moraine dams at four different glacial lakes namely Thorthormi Cho and Lugge Cho in the east branch and Lakes A and B in the west branch of the Pho Chu River were assumed to develop series of GLOFs. Multiple GLOF surges induced by moraine dam breaches in the tributaries and main reach of the Pho Chu River and then of the Puna Tshang Chu River were reproduced by means of numerical models. Main conclusions from the study are summarized as follows:\n\n- 1. Despite the amount of water released from the lakes, the magnitudes of GLOFs are dependent of the bed slopes and potential erosion depth along the river reach and therefore the debris flow size, which usually reduces in secondary or following surges. This phenomenon is related to the change in riverbed profile associated with the temporal erosion and deposition.\n- 2. In the east branch of the Pho Chu River, the amount of sediment discharge during the first GLOF induced by the Thorthormi moraine dam breach is much higher than the amount produced during the second surge of GLOF by Lugge Cho, despite the fact that the properties of both dam breach hydrographs at the lake sites are almost identical. However, at the 10-km section, the magnitude (23 000 m<sup>3</sup>/s) of the peak flood hydrograph produced by the second GLOF from the Lugge Cho Lake is much higher than the peak (10 000 m<sup>3</sup>/s) developed during the first GLOF surge from the Thorthormi Lake. This robustness in the second GLOF surge exists due to the increased channel conveyance capacity and minimized hydraulic pooling by channel erosion caused by the first surge from Thorthormi Cho.\n- 3. In the west branch of Pho Chu, the characteristic of GLOF is different from the east branch. The total sediment volume produced through erosion is always very small due to the existence of frequently available intermittent mild slope river sections, where sediment deposition takes place immediately after the erosion. Also slopes steeper than 18° in simulation are considered rigid beds and such slopes do not produce sediment budget. The peak flood hydrograph at 17.5 km, where the sediment discharge is at the lowest value, became almost identical with the peak of the dam breach hydrograph at the lake site. Different from east branch, the second GLOF surge from Lake B could not develop a debris flow and the hydrograph attenuates significantly as a normal phenomenon.\n- 4. The previously described findings indicate that the GLOF hydrograph characteristic is greatly governed by erosion and deposition processes. In other words, the robustness of the flood peak in downstream area is influenced by the sediment flow dynamics in debris\n\nflow zone. Therefore, GLOF behaviour is greatly influenced by river morphology.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "text", "line_start": 281, "line_end": 293, "token_count_estimate": 769, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "2ef26d9d28b71377", "text": "Document: Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan\nSection: CONCLUSIONS\nType: text\n\nflood hydrograph at 17 . 5 km , where the sediment discharge is at the lowest value , became almost identical with the peak of the dam breach hydrograph at the lake site . Different from east branch , the second GLOF surge from Lake B could not develop a debris flow and the hydrograph attenuates significantly as a normal phenomenon . - 4 . The previously described findings indicate that the GLOF hydrograph characteristic is greatly governed by erosion and deposition processes . In other words , the robustness of the flood peak in downstream area is influenced by the sediment flow dynamics in debris flow zone . Therefore , GLOF behaviour is greatly influenced by river morphology .\n\nThe hydrographs with a peak value of 23 000 m<sup>3</sup>/s at 10 km in the east branch and 9700 m<sup>3</sup>/s at 17.5 km in the west branch of the Pho Chu River, which are both considered the worst cases, are used for flood inundation analysis in further downstream areas. The result shows that a serious inundation can occur at different locations up to the Bhutan–India border. Although the inundation extent is restricted within the close proximity of the river banks due to narrow and deep valleys throughout the river reaches, several towns namely Punakha, Lobeysa and the major portion of Wangdue Phodrang are under the biggest threat of GLOF with a potential inundation depth of over 10 m at some locations. The inundation map can be considered as a tool for planning GLOF disaster mitigation projects.\n\nDetail morphological settings, the number of other glacial lakes and socioeconomic conditions need to be taken into account for detail study for GLOF mitigation planning.", "metadata": {"source_file": "data/('Prediction and assessment of multiple glacial lake outburst floods scenario in Pho', '.pdf')_extraction.md", "document_title": "Prediction and assessment of multiple glacial lake outburst floods scenario in Pho Chu River basin, Bhutan", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "text", "line_start": 281, "line_end": 293, "token_count_estimate": 453, "basins": [], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "5d7bd732c6d79407", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: ABSTRACT\nType: text\n\nThermal stratification plays a key role in lakes' ecosystems. In contrast to deep lakes, the thermal structure of shallow polymictic lakes is characterized by a weak stratification with an apparent diurnal cycle. Long-term changes in stratification are governed by climate change and anthropogenic effects such as water level regulation. We developed a simple and robust model system consisting of an energy balance model to estimate depth-averaged water temperatures and an artificial neural network (ANN) model to predict stratification with high temporal resolution. One novelty of our approach is that instead of directly estimating water temperatures at different depths, we simulated the potential energy anomaly index, the indicator of stratification's strength. The ANN-based model's performance was assessed against a physical-based one-dimensional model (General Ocean Turbulence Model) by modeling a 40-year-long period from 1981 to 2020. The new model accurately predicts a shallow lake's weak stratification and its diurnal cycle. Besides, the model proved reliable on longer time scales, capturing the effect of climate change, anthropogenic water level regulation, and their synergistic interaction on the change of stratification's intensity and duration.\n\nKey words: artificial neural network, climate change, diurnal stratification, potential energy anomaly, shallow lake, weak stratification", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 7, "token_count_estimate": 357, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "760503138a7cd962", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: HIGHLIGHTS\nType: text\n\n- A simple energy balance and ANN model system is developed to simulate shallow lakes' stratification and evaluated using a physical-based model.\n- Weak stratification is modeled with high temporal resolution to capture its diurnal lifecycle.\n- The ANN-based model also reproduces the long-term synergistic impact of climate change and anthropogenic effects.\n\nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "HIGHLIGHTS", "section_headings": ["HIGHLIGHTS"], "chunk_type": "text", "line_start": 9, "line_end": 15, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d08bce224acd9e2f", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: HIGHLIGHTS\nType: figure\nFigure\n\nImage /page/1/Figure/1 description: A graphical abstract illustrating a two-part modeling process. Part 1 is titled \"Energy balance model\" and shows a diagram of a water body with meteorological driving factors. These factors include incoming net shortwave radiation (SW\\_net) and outgoing net longwave radiation (LW\\_net), sensible heat (H), and latent heat (LE). This model calculates the depth-averaged water temperature (ΔT\\_w, T\\_w), which is then plotted over time (t) in a graph below, showing temperature fluctuations. Part 2 is titled \"ANN model of stratification (φ)\". The outputs from the first model (ΔT\\_w, T\\_w) are used as inputs for this second model. The model is represented by the function φ\\_i = f(u\\_i, SW\\_i, SW\\_i-j, H\\_i, ..., h\\_i, T\\_w,i, ΔT\\_w,i-j). A diagram of an Artificial Neural Network (ANN) is shown with an input layer (yellow), a hidden layer (blue), and an output layer (pink). The inputs to the network include variables like T\\_w,i, ΔT\\_w,i-j, and u\\_i. The output of the ANN model is the stratification parameter (φ\\_i), which is plotted against time (t) in a graph at the bottom.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "HIGHLIGHTS", "section_headings": ["HIGHLIGHTS"], "chunk_type": "figure", "figure_caption": null, "line_start": 16, "line_end": 16, "token_count_estimate": 395, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "523997cfaf99a693", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 1. INTRODUCTION\nType: text\n\nThermal stratification has a significant role in lakes' ecological status, potentially causing oxygen depletion at the lake's bottom. In deep lakes, when stratification occurs, the water column can be divided into three layers: epilimnion, hypolimnion, and thermocline. Out of these, the hypolimnion plays a crucial role in maintaining stratification, as in typical circumstances, low mixing characterizes this layer, allowing stratification to persist. As a result, oxygen depletion can develop over the bottom, leading to eutrophication through nutrient release (Pettersson *et al.* 2003; Salmaso 2005). In contrast, epilimnion and hypolimnion may not develop in shallow lakes because driving forces of mixing acting at the lake's surface can reach the bottom of the lake, resulting in more frequent mixing periods and shorter stratification durations (Wilhelm & Adrian 2008). The stratification usually lasts for months in deep lakes and ranges from hours to days in shallow lakes. Despite the frequent mixing, oxygen depletion can also develop in the latter case if meteorological conditions are met in such a way that an intermittent – a few days long – but continuous stratification develops, leading to severe ecological problems, such as the record-setting algae bloom in 2019 in Lake Balaton (Istvánovics *et al.* 2022). Thus, stratification can significantly influence the trophic state of shallow lakes (Shinohara *et al.* 2023).\n\nIn shallow lakes, meteorological effects can substantially affect stratification, so it is necessary to reveal their influence and their long-term changes (Zhou *et al.* 2023). The dominant driving forces of stratification are the solar radiation, the surface air temperature, and the wind speed. The former two are responsible for building up stratification by warming the water layers with different intensities, while the latter is mainly responsible for breaking it up by current- and wave-induced mixing processes (Torma & Krámer 2017). Just mentioning two examples, as a consequence of climate change, the number of lake heatwaves increases (Wang *et al.* 2023) as well as the intensity of evaporation (Tong *et al.* 2023). In mid-latitude climates, solar radiation, and surface air temperature tend to increase, warming lakes and prolonging stratification periods in the warm seasons (Woolway *et al.* 2020).\n\nIn addition to climate change, anthropogenic effects also have a significant impact on the stratification of lakes. Among the effects, those that result in a change in the water level should be highlighted, as the water depth plays a crucial role in the stability of stratification. The higher the depth, the more stable stratification can develop (Kraemer *et al.* 2015). Due to climate change, lake and reservoir water levels may decrease if precipitation and, as a result, runoff rates from the lake's watershed decrease. Consequently, stakeholders try to keep higher water levels to provide a puffer for drought periods. However, more\n\nextended stratified periods can develop by a higher water level, increasing the possibility of oxygen depletion even in shallow, polymictic environments (Istvánovics *et al.* 2022).", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 19, "line_end": 35, "token_count_estimate": 810, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a244f6d59d61cd9", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 1. INTRODUCTION\nType: text\n\nhighlighted , as the water depth plays a crucial role in the stability of stratification . The higher the depth , the more stable stratification can develop ( Kraemer * et al . * 2015 ) . Due to climate change , lake and reservoir water levels may decrease if precipitation and , as a result , runoff rates from the lake ' s watershed decrease . Consequently , stakeholders try to keep higher water levels to provide a puffer for drought periods . However , more extended stratified periods can develop by a higher water level , increasing the possibility of oxygen depletion even in shallow , polymictic environments ( Istvánovics * et al . * 2022 ) .\n\nIn the case of shallow lakes, only weak stratification can develop with a typical diurnal cycle. This means that stratification develops over the day and breaks up due to surface cooling during the nighttime (Torma & Wu 2019). For example, in the very shallow Lake Balaton (Hungary), a stratification that forms over the day (with a maximum temperature difference of 5–6 °C between the surface and the bottom) mostly entirely disappears for the following day. Besides surface cooling, wind can also mix the water column through waves and currents, leading to even shorter stratified periods. Because of the diel cycle, at least hourly measurements are needed to capture the stratification in such environments. Nevertheless, in some cases, neither nighttime cooling nor the wind can mix the whole water column so that a stable stratification can continuously persist near the bottom for a few days. In such cases, anoxic conditions may occur with the consequences mentioned above. Overall, stratification and mixing processes in shallow lakes with a diurnal cycle must be modeled on short time scales of a few hours.\n\nPhysical-based models with different dimensions and empirical methods are also available to model stratification (Amorim et al. 2023; Naumenko & Guzivaty 2023). Due to the rapid growth of lake water temperature data, machine learning techniques, as powerful empirical tools, have gained immense potential and have been used more and more widely (Yousefi & Toffolon 2022). In contrast to physics-based models, machine learning techniques lack physical laws and theory; they make predictions based on data alone. One of their drawbacks arises from this, as their inaccuracy can increase if they are forced to make predictions for conditions outside the range used to train the model (Read et al. 2019). Nonetheless, various data-driven techniques, e.g., different artificial neural networks (ANNs), are successfully used to simulate the temperature conditions of lakes.\n\nAn ANN model can be a fast way to calculate water temperatures, and it is superior to regression models as they can accurately determine the nonlinear relationships between meteorological driving forces and water temperature changes. Nevertheless, they are usually used for deep lakes and reservoirs and mostly to predict lake water surface temperatures (Kimura *et al.* 2021; Wang *et al.* 2022). Regarding stratification, ANNs were only applied to simulate water temperatures at specific depths instead of directly determining stability conditions (Liu & Chen 2012; Saber *et al.* 2019; Saber *et al.* 2020). In short, based on a literature review, ANNs were not tested for polymictic lakes and for directly predicting stratification, even though they might be quick and reliable methods for these purposes.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 19, "line_end": 35, "token_count_estimate": 852, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9597a8c4781d949e", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 1. INTRODUCTION\nType: text\n\nand water temperature changes . Nevertheless , they are usually used for deep lakes and reservoirs and mostly to predict lake water surface temperatures ( Kimura * et al . * 2021 ; Wang * et al . * 2022 ) . Regarding stratification , ANNs were only applied to simulate water temperatures at specific depths instead of directly determining stability conditions ( Liu & Chen 2012 ; Saber * et al . * 2019 ; Saber * et al . * 2020 ) . In short , based on a literature review , ANNs were not tested for polymictic lakes and for directly predicting stratification , even though they might be quick and reliable methods for these purposes .\n\nThe aim of this study is twofold. First, to prove that a simple artificial neural network model can simulate weak diurnal stratification for a shallow lake with high temporal resolution using routine meteorological data alone. Second, to show that an ANN model can quantify the long-term effects of climate change and anthropogenic impacts based on limited observational data.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 19, "line_end": 35, "token_count_estimate": 285, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "15e44365f9139b70", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.1. Study site and data acquisition\nType: text\n\nLake Balaton is a shallow polymictic lake in Hungary, with a surface area of 596 km<sup>2</sup> and a mean depth of 3.2 m (Figure 1). Besides its ecological value, the lake is of great social and economic importance as it is highly populated along the whole shoreline and, after the capital, Hungary's second-most important tourist destination, providing many recreational activities like bathing, sailing, and fishing. It has an elongated shape, and in light of circulation and water quality patterns, it can be divided into four basins (Istvánovics & Honti 2018). The lake's main inflow is the Zala River, which accumulates the runoff from half of the total lake catchment (5.180 km<sup>2</sup>) and flows into the westernmost basin. The lake has only one outflow in the eastern basin, which is regulated. The mean resident time is more than two years, and the role of throughflow is negligible for stratification, which is, therefore, almost uniquely shaped by meteorological forces.\n\nHydrometeorological measurements were carried out in the westernmost basin of the lake in the pelagic area in 2019, 2020, and 2022 in the warm season, from late spring to early autumn. The mean depth of this location was around 3.3 m for the three periods. During the measurement campaign, routine meteorological variables and water temperature profiles were recorded with high temporal resolution. The setup was very similar in each year. For 2019, detailed information about the lake, station location, and measurement setup can be found in Lükő *et al.* 2020 and 2022. The wind speed (U) was measured by CSAT3 and Windsonic2D anemometers (Campbell Sci.). The radiation components – incoming and outgoing shortwave ( $SW_{in}$ ,  $SW_{out}$ ) and longwave radiations ( $LW_{in}$ ,  $LW_{out}$ ) – were recorded by a CNR1 net radiometer (Kipp & Zonen). The air temperature ( $T_a$ ) and relative humidity (RH) were measured by a HygroVue10 sensor (Campbell Sci.).", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.1. Study site and data acquisition", "section_headings": ["2. MATERIALS AND METHODS", "2.1. Study site and data acquisition"], "chunk_type": "text", "line_start": 39, "line_end": 43, "token_count_estimate": 577, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7081b8a90f47ee5b", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.1. Study site and data acquisition\nType: figure\nFigure\n\nImage /page/3/Figure/1 description: A location and bathymetry map of Lake Balaton, labeled as Figure 1. The map shows the depth of the lake using color gradients and contour lines. A legend in the bottom right indicates the depth in meters (m), with colors ranging from dark blue (0 m) to light green and yellow (5 m). The contour lines are labeled with depths such as 2, 2.5, 3, 3.5, 4, and 5. Two measurement sites are marked with white circles. The Zala River (Zala Riv.) is shown flowing into the southwestern end of the lake, and an outlet is marked at the eastern end. The map includes a north arrow and a scale bar in kilometers. Two inset maps in the upper right corner show the location of Hungary in Europe and the location of Lake Balaton within Hungary.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.1. Study site and data acquisition", "section_headings": ["2. MATERIALS AND METHODS", "2.1. Study site and data acquisition"], "chunk_type": "figure", "figure_caption": null, "line_start": 44, "line_end": 44, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e92123f0b9eec210", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.1. Study site and data acquisition\nType: figure\nFigure: Figure 1 | Location and bathymetry map of Lake Balaton. Measurement sites are marked by white circles.\n\nFigure 1 | Location and bathymetry map of Lake Balaton. Measurement sites are marked by white circles.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.1. Study site and data acquisition", "section_headings": ["2. MATERIALS AND METHODS", "2.1. Study site and data acquisition"], "chunk_type": "figure", "figure_caption": "Figure 1 | Location and bathymetry map of Lake Balaton. Measurement sites are marked by white circles.", "line_start": 46, "line_end": 46, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d0642f8afba171f9", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.1. Study site and data acquisition\nType: text\n\nThe water temperature profile was recorded by T107 sensors or a CS225-L thermistor string (both Campbell Sci.) at fixed depths with 0.5 m resolution. A floating sensor attached to a buoy 2 cm below the water surface measured the water surface temperature. During the campaign, both air and underwater instruments were regularly (2 weeks on average) checked, cleaned, and maintained.\n\nIn the easternmost basin of the lake, water temperature measurements were carried out by the Hungarian Meteorological Service between 1981 and 2020 near the south shoreline, where the average water depth is around 2.5 m. At this site, daily measurements were taken at a depth of 1 meter below the water surface. The stratification model was set up based on the measurements in the westernmost basin's open water area. In addition, the long-term daily data from the eastern basin was used to check the simulated daily average water temperatures (and thus the energy balance). It can be assumed that this eastern measurement is also representative due to the lake's relatively small thermal inertia and low spatial variability arising from the shallowness. Furthermore, the mean depth of the westernmost basin is around 2.5 m, which is equal to the depth at this eastern measurement location.\n\nThe long-term simulations covered a 40-year-long period from 1981 to 2020. For this period, the input meteorological data was gathered from the ECMWF ERA5 reanalysis database, including surface wind speed, wind direction, air temperature, RH, solar radiation, and downward longwave radiation with 1-h time resolution. Comparing the measured and ECMWF meteorological time series, bias correction was needed in the case of U,  $T_a$ , and  $SW_{in}$  variables. It has been performed by matching their cumulative distribution functions employing second-degree polynomials. Daily water level time series, from which monthly mean values were derived, was available from the Hungarian Water Directory.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.1. Study site and data acquisition", "section_headings": ["2. MATERIALS AND METHODS", "2.1. Study site and data acquisition"], "chunk_type": "text", "line_start": 47, "line_end": 53, "token_count_estimate": 521, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ae8abd4567aa55a", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.2. ANN-based model system\nType: text\n\nFor thermal stratification simulations, a simple model system was constructed with two submodules (Figure 2). The first submodule is a zero-dimensional (0D) energy balance model, which calculates the depth-averaged water temperatures ( $T_{zv}$ ) based on routine meteorological data following the energy balance equation:\n\n$$R_n + H + LE = \\rho_w \\cdot c_{p,w} \\cdot \\frac{\\partial T_w}{\\partial t} \\cdot h \\tag{1}$$", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.2. ANN-based model system", "section_headings": ["2. MATERIALS AND METHODS", "2.2. ANN-based model system"], "chunk_type": "text", "line_start": 55, "line_end": 59, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e67c80b29b3f9b01", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.2. ANN-based model system\nType: figure\nFigure\n\nImage /page/4/Figure/1 description: A flowchart illustrating two parallel modeling approaches originating from a single input labeled \"Meteorology\". The top path shows meteorological data (SWin, U, Ta, RH, P, cc) being fed into a model labeled \"GOTM\", which produces an output represented by the symbol Φ in a red circle. The bottom path is more complex. First, a subset of meteorological data (SWin, LWin, U, Ta, RH) is input into a \"0D EB model\", which outputs \"Tw\" in a blue circle. This output, along with another subset of meteorological data (SWin, U, Ta, RH), is then fed into an \"ANN\" model. The ANN model produces an initial output, Φ₀, which then undergoes a \"Bias correction\" step to produce the final output, Φ. A red double-headed arrow connects the final Φ outputs from both the top (GOTM) and bottom (ANN) paths, indicating a comparison or relationship between them.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.2. ANN-based model system", "section_headings": ["2. MATERIALS AND METHODS", "2.2. ANN-based model system"], "chunk_type": "figure", "figure_caption": null, "line_start": 60, "line_end": 60, "token_count_estimate": 302, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26ffcc9b24b04cce", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.2. ANN-based model system\nType: figure\nFigure: Figure 2 | Schematic drawing of the modeling process.\n\nFigure 2 | Schematic drawing of the modeling process.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.2. ANN-based model system", "section_headings": ["2. MATERIALS AND METHODS", "2.2. ANN-based model system"], "chunk_type": "figure", "figure_caption": "Figure 2 | Schematic drawing of the modeling process.", "line_start": 62, "line_end": 62, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62ac56acbc2c1475", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.2. ANN-based model system\nType: text\n\nwhere  $R_n$  (W m<sup>-2</sup>) is the net radiation, H and LE (W m<sup>-2</sup>) are the turbulent sensible and latent heat fluxes, respectively,  $\\rho_w$  (kg m<sup>-3</sup>) is the density of water,  $c_{p,w}$  (J kg<sup>-1</sup> °C<sup>-1</sup>) is the specific heat of water, t (h) is the time, and t (m) is the water depth. The turbulent heat fluxes were calculated by the following formulas (Lükő et al. 2022):\n\n$$H = \\rho_a \\cdot c_p \\cdot C_H \\cdot U_z \\cdot (T_a - T_w) \\tag{2}$$\n\n$$LE = \\rho_a \\cdot \\lambda \\cdot C_q \\cdot U_z \\cdot (q_z - q_0) \\tag{3}$$\n\nwhere  $\\rho_a$  (kg m<sup>-3</sup>) is the density of air,  $c_p$  (J kg<sup>-1</sup> °C<sup>-1</sup>) is the specific heat of air,  $\\lambda$  is the heat of vaporization (J kg<sup>-1</sup>),  $U_z$  (m s<sup>-1</sup>) is the wind speed,  $T_a$  (°C) is the air temperature,  $q_z$  (kg kg<sup>-1</sup>) is the specific humidity of the air, and  $q_0$  (kg kg<sup>-1</sup>) is the specific humidity at the surface.  $C_h$  and  $C_q$  are transfer coefficients, which were considered constants and calibrated. The 0D model works with hourly resolution\n\nThe second module is an ANN model, which uses the water temperature and its temporal change obtained from the 0D model, routine meteorological data  $(U, T_a, RH, SW_{in})$ , and water depth (h) as inputs (Figure 2) and calculates the stratification index called the potential energy anomaly  $(\\phi)$  as output (see details in the following subchapter). It also uses the preceding 6-h average of U and  $SW_{in}$  to consider the dominant antecedent meteorological conditions for mixing and stratification, respectively. In the case of water depth, monthly averaged values were used. The model has one hidden layer and consists of six neurons. It is a nonlinear input-output network without using the estimated  $\\phi$  from the previous timestep.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.2. ANN-based model system", "section_headings": ["2. MATERIALS AND METHODS", "2.2. ANN-based model system"], "chunk_type": "text", "line_start": 63, "line_end": 75, "token_count_estimate": 731, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f787fdbe99a82d0", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.2. ANN-based model system\nType: text\n\n) $ , and water depth ( h ) as inputs ( Figure 2 ) and calculates the stratification index called the potential energy anomaly $ ( \\ phi ) $ as output ( see details in the following subchapter ) . It also uses the preceding 6 - h average of U and $ SW_ { in } $ to consider the dominant antecedent meteorological conditions for mixing and stratification , respectively . In the case of water depth , monthly averaged values were used . The model has one hidden layer and consists of six neurons . It is a nonlinear input - output network without using the estimated $ \\ phi $ from the previous timestep .\n\nThe ANN model was trained for 3 years of data with a Bayesian regularization algorithm. The training and testing were done at 80–20%. Because of the Bayesian regularization, the validation process was unnecessary (Burden & Winkler 2009). The ANN model simulations were performed in the Matlab software environment. For stratification modeling, a 3-h time resolution was used as it was frequent enough to describe the daily cycle of stratification without considering the highly short-term fluctuations. After the training, historical simulations were carried out for four decades, from 1981 to 2020. Each year, the simulations were run from May to September to include the lake's warming up and cooling periods. In the results, only the summer months were examined. After the ANN simulations, a final bias correction was performed to improve the model's performance. A first-order polynomial was used for the correction, whose coefficients were calculated using the measured and trained data in 2019, 2020, and 2022.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.2. ANN-based model system", "section_headings": ["2. MATERIALS AND METHODS", "2.2. ANN-based model system"], "chunk_type": "text", "line_start": 63, "line_end": 75, "token_count_estimate": 439, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "676ffc6bbdad3e35", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.3. Benchmark model\nType: text\n\nThe calibrated 1D General Ocean Turbulence Model (GOTM, Burchard *et al.* 1999) was considered as a reference to assess the ANN m'del's reliability for long-term simulations. GOTM is a 1D model that uses a k- $\\varepsilon$  turbulence model for vertical mixing, providing stratification results on a physical basis. The model was driven by the same routine meteorological data as the 0D and the ANN model (Table 1). The sensible and latent heat fluxes were calculated by Equations (2) and (3) bulk formulas (Kondo 1975). The GOTM model has one further parameter to calibrate: the light extinction coefficient. In the absence of measurement data, we presumed it to be constant, and following a sensitivity analysis, it was set to  $0.4 \\text{ m}^{-1}$ . For water depth, monthly averaged values were used. The water column was divided into 20 layers with an increasing resolution to the water surface and the bottom. The calibration and validation of the model were conducted for the years 2019 and 2022; after that, it was run for the 1981–2020 period with an hourly time resolution.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.3. Benchmark model", "section_headings": ["2. MATERIALS AND METHODS", "2.3. Benchmark model"], "chunk_type": "text", "line_start": 77, "line_end": 79, "token_count_estimate": 332, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf832622b4ba0c2f", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.3. Benchmark model\nType: table\nTable\n\n|               |                    |       | <b>GOTM</b>           | <b>EB</b> | <b>ANN</b>     |\n|---------------|--------------------|-------|-----------------------|-----------|----------------|\n| <b>Input</b>  | <b><i>SWin</i></b> | W m-2 | 1 havg                | 1 havg    | 3 havg, 6 havg |\n|               | <b><i>LWin</i></b> | W m-2 | -                     | 1 havg    | -              |\n|               | <b><i>U</i></b>    | m s-1 | 1 havg                | 1 havg    | 3 havg, 6 havg |\n|               | <b><i>Ta</i></b>   | °C    | 1 havg                | 1 havg    | 3 havg         |\n|               | <b><i>RH</i></b>   | %     | 1 havg                | 1 havg    | 3 havg         |\n|               | <b><i>P</i></b>    | hPa   | 1 havg                | 1 havg    | -              |\n|               | <b><i>cc</i></b>   | -     | 1 havg                | -         | -              |\n|               | <b><i>Tw</i></b>   | °C    | -                     | -         | 3 havg         |\n| <b>Output</b> | <b><i>Tw</i></b>   | °C    | 3 havg (in 20 depths) | 3 havg    | -              |", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.3. Benchmark model", "section_headings": ["2. MATERIALS AND METHODS", "2.3. Benchmark model"], "chunk_type": "table", "table_caption": null, "columns": ["", "", "", "GOTM", "EB", "ANN"], "table_row_start": 1, "table_row_end": 9, "line_start": 80, "line_end": 90, "token_count_estimate": 454, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8778b01a0febd23d", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.3. Benchmark model\nType: text\n\nTable 1 | Input and output variables and their avergaing time for the 0D energy balance, the ANN, and the reference GOTM models", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.3. Benchmark model", "section_headings": ["2. MATERIALS AND METHODS", "2.3. Benchmark model"], "chunk_type": "text", "line_start": 91, "line_end": 93, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b40924fca3ed6a71", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.4. Evaluation of model performance\nType: text\n\nThe intensity and duration of stratification are assessed through the potential energy anomaly index ( $\\phi$ , J m<sup>-3</sup>), which gives the energy needed to fully mix the stratified water column (Wiles *et al.* 2006; Istvánovics *et al.* 2022). The potential energy anomaly can be calculated as follows:\n\n$$\\phi = \\frac{g}{h} \\int_{0}^{h} [\\bar{\\rho} - \\rho(z)] z dz \\tag{4}$$\n\nwhere g (m s<sup>-2</sup>) is the gravitational acceleration, h (m) is the water depth,  $\\rho$  (kg m<sup>-3</sup>) is the density of the water,  $\\bar{\\rho}$  (kg m<sup>-3</sup>) is the mean water density, and z (m) is the vertical coordinate. Figure 3 shows an example of  $\\phi$  together with the measured water temperatures at different depths.\n\nThe models' performance was evaluated through three statistical indicators: the root mean square error (RMSE), the Nash–Sutcliffe Efficiency (NS), and the relative deviation ( $\\Delta_{rel}$ ). They were calculated as follows:\n\n$$RMSE = \\sqrt{\\sum \\frac{(\\widehat{y_i} - y_i)^2}{n}}$$\n (5)\n\n$$NS = 1 - \\frac{\\sum (y_i - \\hat{y_i})^2}{\\sum (y_i - \\overline{y_i})^2}$$\n (6)\n\n$$\\Delta_{rel} = \\frac{\\sum (\\hat{y_i} - y_i)}{\\sum y_i} \\cdot 100 \\tag{7}$$\n\nwhere y and  $\\hat{y}$  are the observed and simulated data, while n is the number of observations.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.4. Evaluation of model performance", "section_headings": ["2. MATERIALS AND METHODS", "2.4. Evaluation of model performance"], "chunk_type": "text", "line_start": 95, "line_end": 113, "token_count_estimate": 551, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d2179262e6eeb9ad", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.4. Evaluation of model performance\nType: figure\nFigure\n\nImage /page/5/Figure/13 description: An image displaying two stacked line graphs that share a common horizontal axis representing time from July 31 to August 12, with labels for every two days. The top graph plots a variable labeled 'Φ [Jm⁻³]' on the y-axis, which ranges from 0 to 4. The data is shown as a single black line with circular markers, exhibiting a cyclical pattern with peaks mostly between 2 and 3, starting around August 5. The bottom graph plots 'T\\_w [°C]' on its y-axis, ranging from 24 to 28. This graph contains multiple overlapping lines in different colors (dark blue, light blue, green, yellow, purple, and red), which also show a daily cyclical fluctuation, with values ranging from approximately 23.5°C to over 28°C. The patterns in both graphs appear to be correlated.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.4. Evaluation of model performance", "section_headings": ["2. MATERIALS AND METHODS", "2.4. Evaluation of model performance"], "chunk_type": "figure", "figure_caption": null, "line_start": 114, "line_end": 114, "token_count_estimate": 266, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cd4e75d47a23adb8", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.4. Evaluation of model performance\nType: figure\nFigure: Figure 3 | Daily cycle of measured water temperatures at different depths and the corresponding potential energy anomaly (φ) time series.\n\nFigure 3 | Daily cycle of measured water temperatures at different depths and the corresponding potential energy anomaly (φ) time series.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.4. Evaluation of model performance", "section_headings": ["2. MATERIALS AND METHODS", "2.4. Evaluation of model performance"], "chunk_type": "figure", "figure_caption": "Figure 3 | Daily cycle of measured water temperatures at different depths and the corresponding potential energy anomaly (φ) time series.", "line_start": 116, "line_end": 116, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1613ffa373bb65dc", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 2. MATERIALS AND METHODS > 2.4. Evaluation of model performance\nType: text\n\n<sup>1</sup> havg stands for hourly, 3 havg for three-hourly, and 6 havg for six-hourly averages", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "2. MATERIALS AND METHODS > 2.4. Evaluation of model performance", "section_headings": ["2. MATERIALS AND METHODS", "2.4. Evaluation of model performance"], "chunk_type": "text", "line_start": 117, "line_end": 119, "token_count_estimate": 90, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1486976c56de16ad", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.1. Meteorological and hydrological conditions\nType: text\n\nIn the last 40 years, increasing trends have been experienced in the case of wind speed (0.006 m s $^{-1}$ /10 year), incoming short-wave radiation (4.44 W m $^{-2}$ /10 year), and air temperature (0.50 °C/10 year) during the summer months (June-August), which are the main factors of stratification formation and break-up. Along with these, water temperature shows a slightly weaker increasing trend than air temperature, having a warming rate of 0.44 °C/10 year (Figure 4).\n\nThe mean water depth of Lake Balaton for the summer months in the 1980s was 3.21 m, but after some drought periods in the early 2000s – between 2000 and 2003 – it dropped to 2.93 m (Figure 4). The lowest summer water depth was recorded in August 2003, at 2.63 m. After that, water level regulation was made for the lake, and the maximum control water level was increased by 10 cm. It took three years for the lake to reach the new regulation level, so its effect can only be seen starting in 2006. In 2016, due to a new regulation, it has been increased by 10 cm again. Accordingly, the mean water depth for the summer months between 2006 and 2015 was 3.31 m, while from 2016 to 2020, 3.43 m.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.1. Meteorological and hydrological conditions", "section_headings": ["3. RESULTS", "3.1. Meteorological and hydrological conditions"], "chunk_type": "text", "line_start": 123, "line_end": 127, "token_count_estimate": 346, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a21ac4261c772f88", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: text\n\nThe reference GOTM model was calibrated and validated for two parameters –  $T_{w}$  and  $\\phi$  – for 2019 and 2022. For depth-averaged water temperature, the model provides good results, with a slight overestimation in the case of sudden cooling periods. In the case of an unstable stratification (resulting in negative  $\\phi$  values), the model immediately mixes the water column and couldn't reproduce unstable situations. However, these events are negligible in the summer because they cannot last long and are very weak. Therefore, the rarely occurring negative  $\\phi$  values produced by the ANN were also neglected. Overall, the model provided satisfactory results for  $T_{w}$  and  $\\phi$ , which made it suitable for further investigations. Table 2 shows the statistical indicators for the calibration and validation periods regarding the two parameters.\n\nThe 0D energy balance model was calibrated for 2019, validated for 2020, and then run for the 1981–2020 period. The modeled hourly temperatures were averaged to daily means and compared with the measured ones in the eastern basin. Figure 5 shows the NS and RMSE values for the modeled water temperatures for each year. On average, the NS is above 0.75, and the RMSE is between 1 and 2 °C. There are 4 years of exception (1984, 1985, 1993, and 2001). In these years, the NS and RMSE values are significantly worse. According to the notice obtained from the authority, measurement inaccuracies might have occurred in these years as the temperature sensor likely sank into the sediment.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "text", "line_start": 129, "line_end": 133, "token_count_estimate": 420, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "215d035e09840b77", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure\n\nImage /page/6/Figure/8 description: A figure with four stacked line graphs, each plotting a different variable over time from 1980 to 2020. The caption reads: \"Summer averaged values of U, T\\_a, T\\_w, SW\\_in and h between 1981 and 2020 in Hungary.\"", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": null, "line_start": 134, "line_end": 134, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad70e6e1474af7b5", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: text\n\nThe top graph shows wind speed (U) in m/s on the y-axis, ranging from 3 to 4. The data points, marked with blue circles, fluctuate around a horizontal line at approximately 3.4 m/s.\n\nThe second graph shows temperature (T) in degrees Celsius on the y-axis, ranging from 18 to 27. It displays two data series: T'a (red squares) and T'w (blue circles). Both series show an increasing trend, indicated by black trend lines. The T'w values are consistently higher than the T'a values.\n\nThe third graph shows incoming shortwave radiation (SW\\_in) in W/m^2 on the y-axis, ranging from 200 to 260. The data, marked with blue circles, shows a clear increasing trend, highlighted by a black trend line.\n\nThe bottom graph shows a variable 'h' in meters on the y-axis, ranging from 2.5 to 3.5. The data, marked with blue circles, fluctuates over the years, with a noticeable dip around the year 2003.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "text", "line_start": 135, "line_end": 143, "token_count_estimate": 289, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c34772d0632f6a87", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure: Figure 4 | Yearly summer averaged values of U, $T_a$ , $T_w$ , $SW_{in}$ , and h between 1981 and 2020 in Hungary.\n\n**Figure 4** | Yearly summer averaged values of U,  $T_a$ ,  $T_w$ ,  $SW_{in}$ , and h between 1981 and 2020 in Hungary.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": "Figure 4 | Yearly summer averaged values of U, $T_a$ , $T_w$ , $SW_{in}$ , and h between 1981 and 2020 in Hungary.", "line_start": 144, "line_end": 144, "token_count_estimate": 144, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c4cf1d9110bffc9", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: table\nTable\n\n| <b>Table 2</b>   Statistical indicators of the calibration and validation periods for the OD energy balance, the | as ANNI and the reference COTM medals |\n|------------------------------------------------------------------------------------------------------------------|---------------------------------------|\n| <b>Table 2</b> Established indicators of the cambranon and validation behods for the OD energy balance. If       | ie ann, and the reference GOTM models |", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "table", "table_caption": null, "columns": ["Table 2 Statistical indicators of the calibration and validation periods for the OD energy balance, the", "as ANNI and the reference COTM medals"], "table_row_start": 1, "table_row_end": 1, "line_start": 146, "line_end": 148, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be84608a1ca2ec3b", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: table\nTable\n\n|       |        | Calibration/Training |      |                | Validation |      |                |\n|-------|--------|----------------------|------|----------------|------------|------|----------------|\n|       |        | NS                   | RMSE | $\\Delta_{rel}$ | NS         | RMSE | $\\Delta_{rel}$ |\n| 0D EB | Tw     | 0.85                 | 1.07 | 0.15           | 0.78       | 1.51 | 3.3            |\n| ANN   | $\\phi$ | 0.65                 | 0.54 | 3.99           | -          | -    | -              |\n| GOTM  | Tw     | 0.81                 | 1.37 | 0.98           | 0.91       | 0.96 | 0.98           |\n|       | $\\phi$ | 0.63                 | 0.72 | 0.76           | 0.45       | 0.57 | 0.81           |", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "table", "table_caption": null, "columns": ["", "", "Calibration/Training", "", "", "Validation", "", ""], "table_row_start": 1, "table_row_end": 5, "line_start": 150, "line_end": 156, "token_count_estimate": 289, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "002371ecaa2682e6", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: table\nTable\n\n| Axis         | Label     | Values                                               |\n|--------------|-----------|------------------------------------------------------|\n| Left Y-Axis  | NS [-]    | 1, 0.75, 0.5, 0.25                                   |\n| Right Y-Axis | RMSE [°C] | 3, 2, 1, 0                                           |\n| X-Axis       | Year      | 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020 |", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "table", "table_caption": null, "columns": ["Axis", "Label", "Values"], "table_row_start": 1, "table_row_end": 3, "line_start": 158, "line_end": 162, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64f38e52c924967c", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure: Figure 5 | NS and RMSE values for the 0D m'del's water temperature simulation from 1981 to 2020.\n\nFigure 5 | NS and RMSE values for the 0D m'del's water temperature simulation from 1981 to 2020.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": "Figure 5 | NS and RMSE values for the 0D m'del's water temperature simulation from 1981 to 2020.", "line_start": 164, "line_end": 164, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3b382ac60768ed3", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: text\n\nThe ANN model was trained and tested for the potential energy anomaly index for 2019, 2020, and 2022. The results were compared with the  $\\phi$  values calculated from the field measurements at the western basin and by the GOTM model (Figure 6). It can be seen that the ANN reproduces the measurement well, providing very similar results as the GOTM model and resulting in appropriate goodness-of-fit indices (Table 2). It must be noted that the training/testing period covered a limited range of water depths (60 cm), from 2.98 m to 3.58 m, while monthly mean water depths varied between 2.6 m–3.55 m (92 cm) during the 40-year-long period.\n\nComparing the measured and simulated potential energy anomaly time series, it was found that both GOTM and ANN can qualitatively follow the course of the diurnal cycle of weak stratification. A representative period is shown in Figure 7. In the case of higher  $\\phi$  values, the ANN underestimated the measured ones several times. This was resolved by the bias correction as a final step (Figure 2). Comparing the two models, the ANN model proved to be slightly worse overall, but there have been many periods when ANN's estimations were closer to observations, e.g., on the 5th and 7th of August (Figure 7).", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "text", "line_start": 165, "line_end": 169, "token_count_estimate": 366, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "22bd9a2c978f32ed", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure\n\nImage /page/7/Figure/7 description: Two scatter plots are displayed side-by-side, comparing model predictions against observed data. Both plots share the same x-axis, labeled \"φ\\_obs [Jm⁻³]\", and have axes ranging from 0 to 6. A diagonal line representing a 1:1 correspondence is drawn on each plot. The left plot shows blue circular data points, with the y-axis labeled \"φ\\_GOTM [Jm⁻³]\". The data points are widely scattered, particularly for observed values below 2 Jm⁻³. The right plot shows red circular data points, with the y-axis labeled \"φ\\_ANN [Jm⁻³]\". The points in this plot are more tightly clustered around the diagonal line compared to the left plot, suggesting a better correlation between the ANN model and the observed data.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": null, "line_start": 170, "line_end": 170, "token_count_estimate": 252, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50959d2b26a6b2ec", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure: Figure 6 | Comparison of the measured and simulated φ values. Right: comparing with the GOTM. Left: comparing with the ANN.\n\nFigure 6 | Comparison of the measured and simulated φ values. Right: comparing with the GOTM. Left: comparing with the ANN.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": "Figure 6 | Comparison of the measured and simulated φ values. Right: comparing with the GOTM. Left: comparing with the ANN.", "line_start": 172, "line_end": 172, "token_count_estimate": 121, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f32a793b77c49831", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure\n\nImage /page/8/Figure/1 description: A line graph plots three data series over time, from July 31 to August 12. The y-axis, labeled with the symbol Φ and units of [Jm⁻³], ranges from 0 to 4. The x-axis shows dates in two-day increments: Jul 31, Aug 02, Aug 04, Aug 06, Aug 08, Aug 10, and Aug 12. A legend in the top-left corner identifies the three series: 'Observed' is a black line with circular markers, 'GOTM' is a solid blue line, and 'ANN' is a solid red line. All three lines show a cyclical pattern with daily peaks. The 'Observed' data shows significant peaks around August 6 (approx. 2.5), August 9 (approx. 2.4), August 10 (approx. 2.5), and the highest peak on August 12 (approx. 3.5). The 'GOTM' and 'ANN' models attempt to follow the 'Observed' data, generally capturing the timing of the peaks but often underestimating their magnitude, particularly for the larger peaks.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": null, "line_start": 174, "line_end": 174, "token_count_estimate": 299, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b828090e92ad5f6c", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.2. Calibration and validation\nType: figure\nFigure: Figure 7 | Measured and simulated $\\phi$ time series between 08.01.2019 and 08.13.2019.\n\n**Figure 7** | Measured and simulated  $\\phi$  time series between 08.01.2019 and 08.13.2019.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.2. Calibration and validation", "section_headings": ["3. RESULTS", "3.2. Calibration and validation"], "chunk_type": "figure", "figure_caption": "Figure 7 | Measured and simulated $\\phi$ time series between 08.01.2019 and 08.13.2019.", "line_start": 176, "line_end": 176, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9827c6467f8f1be1", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: text\n\nAlong with the meteorological conditions, an increasing trend can also be seen in the strength of the lake's thermal stratification during the summer months over the last 40 years. This was somewhat expected since those meteorological forces  $(T_a, SW_{in})$ , which are responsible for stratification formation, possess more significant growth rates than wind speed, the main driver of mixing. However, the intensity and duration of stability changes were unknown in the absence of long-term observed water temperature profiles. Figure 8 shows the median potential energy anomaly values for the summer periods for each year. IThe whole period was divided into two to separate the effects of climate change and water level regulation. Accordingly, the first period was from 1981 to 2006 - before the water level control could have its effect - while the second one was from 2007 to 2020. In both cases, increasing trends can be observed; however, after the regulation, the steepness of growth increases, meaning that the water depth significantly affects stratification even by a few 10 cm changes. When assessing the long-term impact of climate change and water depth variations, the ANN model demonstrated the same trends as the physics-based one. Regarding the water level periods (1981-2006 and 2007-2020), the trends given by the GOTM are 0.03 and 0.14 J m<sup>-3</sup>/10 years, while the ANN model resulted in 0.04 and 0.17 J m<sup>-3</sup>/10 years trends, respectively, which are nearly identical. Looking at the cumulative distribution functions of  $\\phi$  for each summer (Figure 9), it could also be concluded that the ANN model tended to overestimate the GOTM-based values at weak stratifications ( $\\phi$  < 0.6). Finally, the early 2000s should also be noted, when a three-year-long drought period resulted in extremely low water depths, which were out of the depth range used in the training/testing. However, the ANN model could predict stratification similarly to the GOTM model.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "text", "line_start": 179, "line_end": 181, "token_count_estimate": 530, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d1859fb13d6f1316", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: figure\nFigure\n\nImage /page/8/Figure/5 description: The image displays two line graphs stacked vertically, both plotting data from 1980 to 2020. Each graph compares two models: GOTM, represented by a blue line, and ANN, represented by a red line.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "figure", "figure_caption": null, "line_start": 182, "line_end": 182, "token_count_estimate": 109, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ac13313ed1760be", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: text\n\nThe top graph shows the 'φ\\_median' in units of 'Jm⁻³' on the y-axis, which ranges from 0 to 0.8. Both the GOTM and ANN lines show fluctuating values over the years, with a general increasing trend indicated by superimposed straight trend lines. The trend line for ANN is steeper than that for GOTM.\n\nThe bottom graph shows the 'Num. of events' on the y-axis, which ranges from 0 to 15. The x-axis is also the year from 1980 to 2020. Both the GOTM and ANN lines show significant yearly fluctuations in the number of events, with values generally ranging between 2 and 12. The ANN line shows a notable peak close to 14 events around 1997. This graph does not include trend lines.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "text", "line_start": 183, "line_end": 187, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a4fabca6e48cc44", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: figure\nFigure: Figure 8 | Upper: yearly median of summer $\\phi$ values between 1981 and 2020. Lower: number of stratification periods between 1981 and 2020, when the stratification lasted at least 24 h, with a threshold of 0.5 J m-3.\n\n**Figure 8** | Upper: yearly median of summer  $\\phi$  values between 1981 and 2020. Lower: number of stratification periods between 1981 and 2020, when the stratification lasted at least 24 h, with a threshold of 0.5 J m<sup>-3</sup>.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "figure", "figure_caption": "Figure 8 | Upper: yearly median of summer $\\phi$ values between 1981 and 2020. Lower: number of stratification periods between 1981 and 2020, when the stratification lasted at least 24 h, with a threshold of 0.5 J m-3.", "line_start": 188, "line_end": 188, "token_count_estimate": 177, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b51d7fb9b70a17d3", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: figure\nFigure\n\nImage /page/9/Figure/1 description: The image contains four line graphs arranged in a 2x2 grid, each showing cumulative distribution functions. The plots are titled \"GOTM\", \"S0\", \"S1\", and \"S3\". The y-axis for each plot is labeled \"p [-]\" and ranges from 0 to 1. The x-axis is labeled \"φ [Jm⁻³]\" and ranges from 0 to 5. Each graph displays multiple overlapping lines, which are color-coded according to the year. A color bar legend is shown for the S0 and S3 plots, indicating that the colors range from green for 1985, through yellow for 1995 and 2005, to red for 2015. In all four plots, the curves start near the origin, rise steeply, and then level off at a value of p=1.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "figure", "figure_caption": null, "line_start": 190, "line_end": 190, "token_count_estimate": 244, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d80100311158f938", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: figure\nFigure: Figure 9 | Empirical cumulative distribution functions for the GOTM and the ANN training cases between 1981 and 2020.\n\nFigure 9 | Empirical cumulative distribution functions for the GOTM and the ANN training cases between 1981 and 2020.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "figure", "figure_caption": "Figure 9 | Empirical cumulative distribution functions for the GOTM and the ANN training cases between 1981 and 2020.", "line_start": 192, "line_end": 192, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a0f44d1a508433db", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 3. RESULTS > 3.3. Long-term simulations\nType: text\n\nAs mentioned in the Introduction, periods when stratification does not entirely vanish by the next day may be crucial in the lake's ecology. Thus, the number of stratification periods that lasted at least 24 h were looked at using two different thresholds, 0.5 and 1.0 J m<sup>-3</sup>. In both cases, similar increasing trends experienced. The thresholds were selected to ensure that the stratification would exhibit sufficient strength to be classified as stable. Figure 8 shows how the number of stratified periods evolved in the last 40 years for the 0.5 J/m<sup>3</sup> limit.\n\nBoth models showed the same tendencies. The GOTM resulted in 5.4 and 7.2 events per year for the two water depth periods, while the ANN gave 4.8 and 7.1 events, respectively. In contrast to the summer median  $\\phi$  values, the number of events didn't possess an increasing trend before 2006, meaning that climate change-related intensification of stratification was not strong enough to maintain a stable thermal structure that could survive by the next day. It also has to be mentioned that till 1997, the ANN model suggested a higher variability, having also years without 1-day-long stratification periods. This can be partly explained by the relationship between water depth and light extinction (see Discussion). Overall, the ANN model can assess the same trends for more extended stratification periods as the physics-based GOTM and adequately captures the interactive effect of water depth increase and climate change from 2006.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "3. RESULTS > 3.3. Long-term simulations", "section_headings": ["3. RESULTS", "3.3. Long-term simulations"], "chunk_type": "text", "line_start": 193, "line_end": 197, "token_count_estimate": 399, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8281f6c1e3971b83", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 4. DISCUSSION > 4.1. The effects of natural and anthropogenic driving forces\nType: text\n\nLake Balaton's thermal stratification is primarily driven by wind speed, incoming shortwave radiation, and air temperature, which all show an increasing trend (Figure 4).  $SW_{in}$  and  $T_a$  are working in favor of the stratification by increasing vertical water temperature and density differences. In contrast, the U works against it by inducing waves and currents, which make the water column mix and the stratification dissipate. Considering their integrated effect, an increasing trend can be seen in the strength of the lake's thermal stratification (Figure 8), which was captured by the ANN model with a suitable accuracy (Figure 7). Water depth also has a crucial effect on the stratification, as it not only increases its strength but amplifies the impact of climate change. As a result, the observed increasing trend after 2006 is a combined, synergistic effect of the varying climate and the heightened water levels. This interaction increases not just the average stability but also results in longer-lasting stratification, and with this, the 24-h-long events occur more and more often.\n\nTurbidity also greatly affects the lake's stratification. In the case of turbid water, incoming shortwave radiation can penetrate only to a limited depth, warming up only the layers it manages to traverse. This creates a temperature difference between the layers, contributing to the development of stratification. In the other case, when the water is more transparent, solar radiation can penetrate deep and warm up the whole water column, so the temperature difference between the layers will be slighter, resulting in a very weak stratification. Light extinction (or attenuation coefficient), which quantifies the efficiency of water absorbing the incoming light, depends linearly on turbidity (Brown 1984). Turbidity, and thus water transparency, is determined by the concentration of organic and inorganic substances, like phytoplankton and suspended sediments, respectively. As a result, light extinction is a highly variable parameter both on small and large time scales. However, external factors such as meteorology also directly and strongly determine it.\n\nIn the case of smaller water depths, sediment resuspension can occur more often as the wind-induced mixing can reach the lake's bottom more quickly and with higher intensity, resulting in more variable water transparency conditions. In the ANN model, this might be indirectly incorporated through nonlinear relationships with wind forcing. Regarding the 1-day-long stratification events, the ANN model showed higher variability for the first period (1981–2006) with lower water levels. In contrast, light attenuation was fixed to a constant in the GOTM model. For example, on the 4th and 5th of August, GOTM underestimated  $\\phi$ , and the ANN model provided better results (Figure 7). These two days were preceded by a wind event, likely with sediment resuspension. The suspended fine sediments require quite a long time to settle, and until that, they result in higher turbidity, light attenuation, and stratification, as explained above. This means that in some cases, the ANN may provide better results than the GOTM, even though the latter operates on a physical basis.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "4. DISCUSSION > 4.1. The effects of natural and anthropogenic driving forces", "section_headings": ["4. DISCUSSION", "4.1. The effects of natural and anthropogenic driving forces"], "chunk_type": "text", "line_start": 201, "line_end": 207, "token_count_estimate": 823, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9620cde3f2e40f8", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 4. DISCUSSION > 4.2. Sensitivity of training parameters\nType: text\n\nSensitivity analysis was carried out on three main elements of the ANN model setup: (i) the role of the water depth range in the training, (ii) the quantity of the training data, and (iii) the role of antecedent wind and solar radiation conditions. The model was trained using 3 years of data: 2019, 2020 and 2022. This version is referred to as S0. For (i) and (ii), the model was trained with only 2 years of data and then ran it for all the 3 years. The S1 model version was trained for 2019 and 2022, with a wide range of water depths, while the S2 model version was trained for 2019 and 2020 when water levels were significantly higher than in 2022. To assess the differences between the training cases the NS efficiency was used (Table 3).\n\nIn 2019 and 2020, the water level was high; the depth varied between 3.55 and 3.30 m at the measurement station, while in 2022, it was between 3.32 and 2.98 m. Due to this difference in water depths, the S2 model had to perform extrapolation for almost the whole of 2022. As a result, the S2 model mostly underestimated  $\\phi$  and provided unreliably strong, unstably stratified periods. In the S1 case, the water depth in the training period covered the whole range of the 3-years, so the model did not have to extrapolate. Compared to the accepted S0 version, the NS worsened by less than 5% due to the reduced training data providing fewer possible meteorological states (Table 3). Since the difference between S0 and S1 was not convincing for the three years, both models were run for the 1981–2020 period and compared based on the cumulative distribution functions. The distribution of the S1 case shows a more considerable variability than the one obtained using GOTM or the S0 version (Figure 9). In our case, this means that the S1 version is unreliable and usually overestimates the strength of stratification. However, the three-year dataset proved sufficient to establish reliable connections between meteorological driving factors and thermal structure.\n\nLastly, a simple sensitivity analysis was made for  $SW_{in}$  and U as the most dominant driving forces of stratification formation and break-up, respectively. In the S0 model, the input data included the mean U and  $SW_{in}$  from the preceding 6 h besides their instantaneous values. Since the NS index declined by only 8% for three years of training, the model was run without", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "4. DISCUSSION > 4.2. Sensitivity of training parameters", "section_headings": ["4. DISCUSSION", "4.2. Sensitivity of training parameters"], "chunk_type": "text", "line_start": 209, "line_end": 217, "token_count_estimate": 629, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1b0fcd4da04376ba", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 4. DISCUSSION > 4.2. Sensitivity of training parameters\nType: table\nTable: Table 3 | NS values of the different training cases\n\n| Version | Training years       | NS [-] | preceding SWin, U conditions |\n|---------|----------------------|--------|------------------------------|\n| S0      | 2019, 2020, and 2022 | 0.65   | yes                          |\n| S1      | 2019 and 2022        | 0.62   | yes                          |\n| S2      | 2019 and 2020        | 0.59   | yes                          |\n| S3      | 2019, 2020, and 2022 | 0.60   | no                           |", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "4. DISCUSSION > 4.2. Sensitivity of training parameters", "section_headings": ["4. DISCUSSION", "4.2. Sensitivity of training parameters"], "chunk_type": "table", "table_caption": "Table 3 | NS values of the different training cases", "columns": ["Version", "Training years", "NS [-]", "preceding SWin, U conditions"], "table_row_start": 1, "table_row_end": 4, "line_start": 218, "line_end": 223, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bee087ca5fcd75c1", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 4. DISCUSSION > 4.2. Sensitivity of training parameters\nType: text\n\nthe averaged values for the 1981–2020 period. As a result, the cumulative distribution functions again possess a more significant variability than the physics-based GOTM model. This means that even though the statistical indicators do not clearly support the usage of the 6-h averaged values, they are not negligible because their impacts become pronounced when we consider the long-term changes.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "4. DISCUSSION > 4.2. Sensitivity of training parameters", "section_headings": ["4. DISCUSSION", "4.2. Sensitivity of training parameters"], "chunk_type": "text", "line_start": 224, "line_end": 226, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6099cd2a1ac3d4e8", "text": "Document: Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks\nSection: 5. CONCLUSIONS\nType: text\n\nMachine learning recently became a common and efficient tool for simulating, hindcasting, and predicting lakes' water temperature. A very simple and, thus, robust model has been developed that can simulate shallow lakes' weak stratification and its diurnal cycle with similar accuracy to a complex physics-based 1D model. The presented model system possesses two submodules, both forced by the same routine meteorological variables. The first submodule is a 0D energy balance model, which estimates the depth-averaged water temperature and its temporal change as further inputs for the ANN submodule. As its output, the ANN model calculates the potential energy anomaly index, which characterizes the strength of thermal stratification. The model proved to be reliable both on short and long time scales. Besides capturing the diurnal variation and the intensity of stratification, it was able to explore the effect of climate change, anthropogenic water level regulation, and the synergistic interaction of these two.\n\nOur study provides at least two main novelties. First, it proves that a simple ANN model can simulate a delicate environment with rapidly changing and weak stratification conditions. Of course, to train the model, it is required to have a suitably long and high-frequency time series, including both temperature profiles and meteorological variables. In our case, the time series from three summer seasons proved sufficient. Second, instead of directly estimating water temperature in different depths, the potential energy anomaly index was simulated that adequately describes a shallow lake's stratification. Water temperatures are estimated based on the energy balance equation, which requires only routine meteorological data, and its calibration is also straightforward. This method also has the advantage that water temperature estimates satisfy the energy conservation in contrast to temperatures estimated by machine learning techniques.\n\nThe ANN model's predictivity can be improved in the future. Firstly, the hydrometeorological measurements have continued in Lake Balaton, so the training dataset can be extended with further seasons. Secondly, the most simple ANN structure was used for the modeling. It is conjectured that the ANN model's accuracy will increase by including estimated  $\\phi$  values from previous time steps in the inputs. Similarly, real-time in-situ measurements can be easily introduced to such a system, making it suitable for nowcasting purposes. Finally, if turbidity or light extinction data are also available, a more complex system would be worth establishing that estimates turbidity conditions first and stratification in a second step. The simultaneous availability of these two parameters would also be fruitful for simulating habitat conditions.\n\nThe proposed model system offers an efficient tool for examining several changes in shallow lake environments in the long term. The ANN model can capture intra-daily stratification and its alteration by slowly varying climate change-driven meteorological forces and by much faster human interventions like water level regulation, incorporating their synergistic effect, too. Since the system includes an energy balance submodule, evaporation changes can also be tracked. Evaporation plays a crucial role in the lake's water resource as it is the main loss component of the water balance. For water resources management, accounting for the changes in stratification and evaporation is inevitable, and this model system offers a simple and fast method for their joint calculation. In light of these changes, stakeholders can make informed decisions to ensure sustainable management of the lake's environment.", "metadata": {"source_file": "data/('Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks', '.pdf')_extraction.md", "document_title": "Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks", "section_path": "5. CONCLUSIONS", "section_headings": ["5. CONCLUSIONS"], "chunk_type": "text", "line_start": 228, "line_end": 236, "token_count_estimate": 848, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54ace62ea5a6b1ac", "text": "Document: Refining lake volume estimation and critical depth identification\nType: text\n\nAbstract. Climate change leads to changes in glacier mass balance, including steady advancements and surges that reposition the glacier snouts. Glacier advancement can dam proglacial meltwater lakes. Within the Karakoram and surrounding regions, the positive feedback of climate change has resulted in more frequent ice-dammed glacial lake outburst floods (GLOFs), often facilitated by englacial conduits. However, the complex and multi-factor processes of conduit development are difficult to measure. Determining the lake depths that might trigger GLOFs and the numerical model specifications for breaching is challenging. Empirical estimates of lake volumes, along with field-based monitoring of lake levels and depths and the assessment of GLOF hazards, enable warnings and damage mitigation. Using historical data, remote sensing techniques, high-resolution imagery, cross-correlation feature tracking, and field-based data, we identified the processes of lake formation, drainage timing, and triggering depth. We developed empirical approaches to determine lake volume and trigger water pressure leading to a GLOF. A correlation, albeit a weak one, between glacier surge velocity and lake volume reveals that glacier surge may play a crucial role in lake formation and thus controls the size and volume of the lake. Lake volume estimation involves geometric considerations of the lake basin shape. A GLOF becomes likely when the lake's normalized depth (n') exceeds\n\n0.60, equivalent to a typical water pressure on the dam face of 510 kPa. These field and remotely sensed findings not only offer valuable insights for early warning procedures in the Karakoram but also suggest that similar approaches might be effectively applied to other mountain environments worldwide where GLOFs pose a hazard.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "Refining lake volume estimation and critical depth identification", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 433, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d22273cb178bf43", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nGlobally, glacier shrinkage is a strikingly visible sign of climate change, as is an apparent increase in the number of glacier hazards such as avalanches (Byers et al., 2023; Kääb and Girod, 2023; Li et al., 2024; You and Xu, 2022) and glacial lake outburst floods (GLOFs) (Bazai et al., 2021; Bhambri et al., 2019; Emmer, 2017; Zheng et al., 2021). However, within high-mountain Asia (HMA), particularly the Karakoram, Kunlun Shan, and eastern Pamir, the glaciers have gained mass since 1970 (Berthier and Brun, 2019; Gardelle et al., 2012; Kääb et al., 2015; Minora et al., 2013; Yao et al., 2012). This positive response to climate change may now be over (Jackson et al., 2023), with many glaciers more recently displaying stability (Ali et al., 2021) or retreat (Singh et al., 2023). Nonetheless, the mass gain has influenced glacier dynamic behaviors, with the Karakoram\n\n<sup>&</sup>lt;sup>1</sup>Key Laboratory of Mountain Hazards and Earth Surface Process/Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, China\n\n<sup>&</sup>lt;sup>2</sup>China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences and HEC, Islamabad, Pakistan\n\n<sup>&</sup>lt;sup>3</sup>School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK\n\n<sup>&</sup>lt;sup>4</sup>Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China\n\n<sup>&</sup>lt;sup>5</sup>Earth Surface Process Modelling, German Research Centre for Geosciences (GFZ), Potsdam, Germany\n\n<sup>&</sup>lt;sup>6</sup>National Disaster Reduction Centre of China, Ministry of Emergency Management, Beijing, China\n\n<sup>&</sup>lt;sup>7</sup>DTU Space, Technical University of Denmark, 2800 Kongens Lyngby, Denmark", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 36, "token_count_estimate": 576, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "b7f4f4778c1fd3d8", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nsup > 4 < / sup > Institute of Geographic Sciences and Natural Resources Research , Chinese Academy of Sciences , Beijing , China < sup > & < / sup > lt ; sup > 5 < / sup > Earth Surface Process Modelling , German Research Centre for Geosciences ( GFZ ) , Potsdam , Germany < sup > & < / sup > lt ; sup > 6 < / sup > National Disaster Reduction Centre of China , Ministry of Emergency Management , Beijing , China < sup > & < / sup > lt ; sup > 7 < / sup > DTU Space , Technical University of Denmark , 2800 Kongens Lyngby , Denmark\n\nglaciers thickening, increasing glacier surges (Bazai et al., 2021, 2022; Mu et al., 2024) and advancing glacier termini throughout the region (Bhambri et al., 2013; Bolch et al., 2017). This behavior contrasts that found in neighboring regions with more sustained negative glacier mass budgets, such as the Himalaya, Hindu Kush, and Tibet (Bazai et al., 2021; Bolch et al., 2011; Frey et al., 2014). In the latter areas, glaciers continue to shrink, thin, and reduce in volume, showing no significant glacier advance (Dehecq et al., 2019; Farinotti et al., 2020; Yao et al., 2012). As a result, the increase in moraine lake formation has increased the number of GLOFs in the glacier-retreating regions (Nie et al., 2017). However, in regions where ice mass has increased, glacier advance has prompted the rapid formation of ice-dammed lakes accompanied by sudden releases of meltwater originating from these lakes (Zhang et. al., 1990; Hewitt, 1982, 1998; Hewitt and Liu, 2010; Singh et al., 2023). Although the number and size of GLOFs may decrease with progressive deglaciation (Veh et al., 2023), ice-dammed lake floods currently represent the dominant hazard in cryospheric regions (Veh et al., 2022), comprising 70 % of GLOFs through recorded history (Carrivick and Tweed, 2016). In contrast, moraine-dammed lakes contribute only 9 % (with the remaining 16 %, 3 %, and 2 % associated with unknown dam types, volcanic activity, and bedrock failure, respectively) (Carrivick and Tweed, 2016). Specific details of the GLOF hazard for HMA have been compiled by Shrestha et al. (2023).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 36, "token_count_estimate": 618, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "bec9471e45319cbb", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\nGLOFs may decrease with progressive deglaciation ( Veh et al . , 2023 ) , ice - dammed lake floods currently represent the dominant hazard in cryospheric regions ( Veh et al . , 2022 ) , comprising 70 % of GLOFs through recorded history ( Carrivick and Tweed , 2016 ) . In contrast , moraine - dammed lakes contribute only 9 % ( with the remaining 16 % , 3 % , and 2 % associated with unknown dam types , volcanic activity , and bedrock failure , respectively ) ( Carrivick and Tweed , 2016 ) . Specific details of the GLOF hazard for HMA have been compiled by Shrestha et al . ( 2023 ) .\n\nHerein, the focus is upon ice-dammed lakes. The mechanisms and frequency of dam-related GLOFs remain poorly understood, hindering accurate prediction (Bazai et al., 2021; Cook et al., 2016; Harrison et al., 2018; Richardson and Reynolds, 2000). Recent studies have investigated changes in frequency due to climate change (Rick et al., 2023; Veh et al., 2023), and there are regional assessments of flood volume and hazards (Rick et al., 2023). Despite these efforts, understanding the drainage and predicting flood events from icedammed lakes remain challenging. Nonetheless, anticipating the risks associated with these events is crucial due to their potential to have devastating impacts on human lives and livelihoods, ecosystems, infrastructure (e.g., roads, bridges, hydropower systems), and river channel stability and to have effects on agriculture and fisheries (Carrivick and Tweed, 2016; Cook et al., 2016; Emmer, 2017; Clague et al., 2000; Neupane et al., 2019; Zhang et al., 2022). GLOFs have been recorded up to 500 km from ice-dammed lakes (Hewitt and Liu, 2010), resulting in hundreds of human fatalities and the other impacts noted above (Carrivick and Tweed, 2016; Cui et al., 2014, 2015; Kreutzmann, 1994; Mason, 1929; Stuart-Smith et al., 2021; Zhang et al., 1990; Zheng et al., 2021).\n\nWhilst progress has been made in understanding the breaching mechanisms of moraine lake outburst floods, triggered by ice or debris falls, strong earthquake shaking, internal piping, or overtopping waves that exceed the shear resistance of the dam (Emmer and Vilímek, 2013; Richardson and Reynolds, 2000), understanding the mechanisms of ice-\n\ndammed lake outburst floods remains a challenge (Werder et al., 2010), making prediction using numerical modeling currently impossible. Therefore, there is an urgent need for simplified approaches to GLOF prediction to mitigate downstream impacts.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 36, "token_count_estimate": 714, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "845dfa535838af90", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\n2021 ; Zhang et al . , 1990 ; Zheng et al . , 2021 ) . Whilst progress has been made in understanding the breaching mechanisms of moraine lake outburst floods , triggered by ice or debris falls , strong earthquake shaking , internal piping , or overtopping waves that exceed the shear resistance of the dam ( Emmer and Vilímek , 2013 ; Richardson and Reynolds , 2000 ) , understanding the mechanisms of ice - dammed lake outburst floods remains a challenge ( Werder et al . , 2010 ) , making prediction using numerical modeling currently impossible . Therefore , there is an urgent need for simplified approaches to GLOF prediction to mitigate downstream impacts .\n\nDespite the uncertainty related to the details of GLOF initiation, sudden glacier advances during surge cycles have a prominent role in the formation of ice-dammed lakes by creating an ice barrier in the valleys, particularly in narrow valley floor sections and at confluences (Bazai et al., 2021; Bhambri et al., 2019), that dams rivers (Singh et al., 2023). Glacier surges have resulted in the formation of icedammed lakes in the Swiss Alps (Haeberli, 1983), northern Norway (Xu et al., 2015), Argentinian Patagonia (Vandekerkhove, 2021), Alaska (Trabant et al., 2003), Karakoram, and the Pamir (Bazai et al., 2021; Hewitt and Liu, 2010) and Tian Shan regions (Ng et al., 2007; Shangguan et al., 2017). Recent studies have revealed that the draining processes of ice-dammed lakes potentially involve one or more mechanisms: subglacial breaching, overspill, rapid ice mass instability, and slow deformation of subglacial cavities (Björnsson, 2003; Haemmig et al., 2014; Round et al., 2017). Several attempts have been made to explore the drainage behavior of ice-dammed lake outburst floods (Hewitt and Liu, 2010). However, due to the remoteness, danger, and inhospitable terrain where such lakes can be found, real-time data are few, and significant gaps remain in our knowledge of these pro-", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 36, "token_count_estimate": 577, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ea99e79d94d04c5", "text": "Document: 1 Introduction\nSection: 1 Introduction\nType: text\n\n) . Recent studies have revealed that the draining processes of ice - dammed lakes potentially involve one or more mechanisms : subglacial breaching , overspill , rapid ice mass instability , and slow deformation of subglacial cavities ( Björnsson , 2003 ; Haemmig et al . , 2014 ; Round et al . , 2017 ) . Several attempts have been made to explore the drainage behavior of ice - dammed lake outburst floods ( Hewitt and Liu , 2010 ) . However , due to the remoteness , danger , and inhospitable terrain where such lakes can be found , real - time data are few , and significant gaps remain in our knowledge of these pro -\n\nIn the Karakoram, ice-dammed lakes are found in five major valleys, three of which are densely populated and highly vulnerable to unexpected GLOFs. Recent advances in understanding have been made (Bazai et al., 2021) concerning the formation of episodic ice-dammed lakes, which, due to ice mass transfer variations, are linked to changes in the glacier surface velocity and ice thickness (Singh et al., 2023) and fluctuations in the crevasse density during the surge cycle (Rea and Evans, 2011; Sharp, 1985). Consequently, herein, we explore two main hypotheses: (1) that lake volume is related to glacier velocity and (2) that there is a critical lake depth associated with ensuing GLOFs (Thoraninsson, 1939). As lake volume can dictate the characteristics of a GLOF, a third secondary hypothesis was addressed: (3) that icedammed lakes can exhibit geometries similar to regular geometric shapes such that, in the absence of detailed lake volume data, lake volumes might be estimated from geometric consideration. Despite advancements in knowledge globally, the techniques for measuring and estimating both the volume of the lake before an outburst and the critical depth (for GLOF release), as well as for the timely prediction of GLOFs, remain largely unexplored or unidentified (Round et al., 2017; Shangguan et al., 2016; Steiner et al., 2018). Very few ice-dammed lake volume data are available. These icedammed lake volumes were measured while the lake basin was either empty (after a GLOF event) or partially filled and thus shallow (Round et al., 2017; Shangguan et al., 2016; Steiner et al., 2018). Given that their potential full volumes are unknown, the downstream threat from such lakes remains high. To measure the flood volume and flood magnitude for a deep and potentially full lake, the lake volume measurement is recognized as a critical variable that needs to be accurately calculated or at least well-estimated (Bazai et al., 2021, 2022). An accurate estimate of lake volume will also help in exploring the GLOF timing, triggering depth of the lake, and frequency of ice-dammed lake outburst floods in relation to surge cycles. Timing information can be approximated by correlating glacier velocities and GLOF occurrences (Bazai et al., 2021, 2022), which should assist in timely hazard assessment. Herein, the primary objective of this study is to enhance predictive capabilities regarding GLOF event timing by refining empirical lake volume estimation and identifying critical depths for future hazard and risk reduction. We seek to achieve the objective within a framework of adjustment of lake volume to glacier surge speed, which has implications for changes in the depth of lakes relative to the heights of the ice barriers that impound the lakes.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 36, "token_count_estimate": 893, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "319ecd8325b8cb5c", "text": "Document: 1 Introduction\nSection: 2 Study area\nType: text\n\nThe Karakoram Range in HMA is known for its complex geology, climatic variability, and denudation processes, including debris flows, mudflows, landslides, rockfalls, avalanches, and GLOFs. As was noted in the preceding section, changes in glacier dynamics, increasing glacier surges, and a trend of increases in GLOF-related disasters characterize this region. These hazards are responsible for substantial economic losses, including the destruction of residences, infrastructure such as roads and bridges, and agricultural areas, as well as blockages of transportation routes like the Karakoram Highway and other expressways (Shrestha et al., 2023).\n\nGlacier surges in the region have been recorded since the 15th century (Bazai et al., 2021). Since the application of remote sensing to the monitoring of the glaciers from 1970 to 2020, an increasing occurrence of glacier surges has been recorded since the 1990s, with some glacier surges being linked to the formation of ice-dammed lakes and subsequent GLOFs. Some lakes persist only seasonally, forming in the winter when temperatures are very low and draining slowly in the spring or summer. Other lakes are more persistent (Bhambri et al., 2019; Hewitt and Liu, 2010) and have the potential for catastrophic outbursts. The most frequent glacier surges and formation of lakes leading to outburst floods in the Karakoram region occur for the Khurdopin, Kyagar, and Shishper glaciers. For example, Round et al. (2017) concluded that surges were the main factor controlling the formation of ice-dammed lakes associated with the Kyagar Glacier, with the volumes of the lakes reaching a maximum 3 years after the surge period (Li et al., 2023). Similarly, Bazai et al. (2022) concluded that surge velocities have a significant effect on lake formation related to the Khurdopin Glacier.\n\nAlthough the foreland of the Kyagar Glacier, situated in the Shaksgam Valley, is uninhabited, GLOFs have caused damage and losses further downstream. Conversely, GLOFs from the Khurdopin and Shishper glaciers, located in the densely populated Hunza area, have resulted in casualties and substantial economic losses. Consequently, these glaciers and their lakes are selected for study. The focus of the broader investigation is to obtain the data necessary to understand the complex behavior of the glaciers and their drainage systems with a view to anticipating when the occurrence of GLOFs is imminent. Therefore, there is an urgent need to identify triggering factors for GLOFs to provide downstream warnings in a timely fashion. A better understanding of the complex process behaviors should eventually lead to improved prediction of such events, not only within the Karakoram but also worldwide.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "2 Study area", "section_headings": ["2 Study area"], "chunk_type": "text", "line_start": 38, "line_end": 44, "token_count_estimate": 682, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "80e106dee2aa16e2", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.1 Remote sensing data\nType: text\n\nThe identification and mapping of the Khurdopin, Kyagar, and Shishper ice-dammed lakes were accomplished using open and commercial satellite imagery sources from 1970 to 2022. The datasets include 590 images from Landsat 2– Landsat 5 and Landsat 7-Landsat 9 and 45 images from Sentinel-2, downloaded from the United States Geological Survey (USGS) website (http://earthexplorer.usgs.gov/, last access: 12 December 2023) (Table S1 in the Supplement). The commercial high-resolution images consisted of 35 images from Gaofen-1 (GF-1) and Gaofen-2 (GF-2), 11 images from SPOT 6 and SPOT 7, and 5 images from CRESDA (https://www.cresda.com/zgzywxyyzxeng/ cooperation/odatad/list/odatad\\_1.html, last access: 20 July 2022; https://earth.esa.int/eogateway, last access: 23 July 2022; and https://www.planet.com/products/planet-imagery, last access: 10 August 2022, respectively). The following DEM datasets have been used to estimate lake volume, depth, and dam height: the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Phased Array type L-band Synthetic Aperture Radar (PALSAR) DEM data scenes from the National Aeronautics and Space Administration (NASA) Earthdata website (https://search.earthdata. nasa.gov/, last access: 10 December 2022) and KH-9 and Shuttle Radar Topography Mission (SRTM) data downloaded from http://earthexplorer.usgs.gov/ (last access: 12 December 2022) (Table S2 in the Supplement). Field surveys were conducted of the Shishper Glacier lakes in 2019, 2021, and 2022 and of the Khurdopin Glacier lakes in 2017 and 2018 using handheld GPS devices and uncrewed aerial vehicles (UAVs) (see Sect. 3.2) to determine annual lake extents, lake depths, glacier altitudes and thickness, terminus positions, and glacier surface displacements. The purpose of the field campaigns was to obtain (i) data on processes that could not be derived from remote sensing and (ii) field data to calibrate and validate remote-sensing-derived data. The glacier outlines were obtained from the Randolph Glacier Inventory (RGI 6.0) (RGI Consortium, 2017) and modified according to surge movements with time (https://www.planet.com/products/planet-imagery/, last access: 10 October 2022).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.1 Remote sensing data", "section_headings": ["3 Data and methods", "3.1 Remote sensing data"], "chunk_type": "text", "line_start": 48, "line_end": 50, "token_count_estimate": 645, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ebcffd603f3120ee", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.2 Glacier lake surface area mapping and glacier surface velocity\nType: text\n\nSatellite imagery had a spatial resolution of 0.8 to 30 m (Table S1). High-resolution imagery is used with the aim of obtaining accurate lake surface levels. The images were selected based on the visibility of the glacier surface and lake areas, and overall, 23 ice-dammed lakes from eight surge events were identified related to the Khurdopin, Kyagar, and Shishper glaciers (Table 1, Fig. 1a). The presence of lakes was determined based on the normalized difference water index (NDWI) (McFeeters, 1996), and the outlines of all 23 lakes were digitized manually using Landsat false-color composites (near-infrared, red, and green bands) to distinguish waterbodies from other objects (Huggel et al., 2002). The extents of six Shishper and Khurdopin lakes that occurred after 2017 were obtained in the field using GPS (G639; accuracy: single, 1-3 m; Satellite Based Augmentation System: 0.6 m) survey points along the lake shorelines (Fig. 2a-d), as well as UAV-generated digital surface models (DSMs). Alternatively, high-resolution satellite imagery from Planet (3 m), GF-1 and GF-2 (0.8 and 4 m resolution, respectively), and SPOT 6 and SPOT 7 (1.5 m) was used to extract the lake boundaries. The coupled lake extent and outlines help reduce the uncertainty in the lake extent obtained for Landsat 2-Landsat 5 images. The above-described method was used to extract the extent of the Khurdopin and Kyagar glacial lakes previously reported (Bazai et al., 2021, 2022), the data of which are incorporated into the current analysis.\n\nThe Khurdopin, Kyagar, and Shishper glaciers are surgetype glaciers (Copland et al., 2011; Hewitt, 1998). Since 1972, eight surge events have occurred in total at Khurdopin (three surges), Kyagar (three surges), and Shishper (two surges) (Table 1) at an interval of 17–20 years for each glacier. The Landsat 2–Landsat 4 images from 1970 to 1990 have errors in the selected glacier area. Therefore, the initial surges for Khurdopin and Kyagar between 1970 and 1989 were not considered when estimating the annual velocity. Orthorectified Landsat scenes from the Thematic Mapper (TM) and OLI-2 and Sentinel-2 were used to estimate the yearly and event-based velocities of all three glaciers from 1989 to 2022 to obtain information about the surge events and glacier front changes. Within this period, cloud-free images were chosen each year, although some satellite images were absent. Glacier velocities were recorded as annual averages, although daily measurements of glacier velocity were also determined to assess any effect on lake volume given the possible velocity sensitivity to the triggering time of GLOFs.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.2 Glacier lake surface area mapping and glacier surface velocity", "section_headings": ["3 Data and methods", "3.2 Glacier lake surface area mapping and glacier surface velocity"], "chunk_type": "text", "line_start": 52, "line_end": 58, "token_count_estimate": 724, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2911d1d0c6cee393", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.2 Glacier lake surface area mapping and glacier surface velocity\nType: text\n\nand Kyagar between 1970 and 1989 were not considered when estimating the annual velocity . Orthorectified Landsat scenes from the Thematic Mapper ( TM ) and OLI - 2 and Sentinel - 2 were used to estimate the yearly and event - based velocities of all three glaciers from 1989 to 2022 to obtain information about the surge events and glacier front changes . Within this period , cloud - free images were chosen each year , although some satellite images were absent . Glacier velocities were recorded as annual averages , although daily measurements of glacier velocity were also determined to assess any effect on lake volume given the possible velocity sensitivity to the triggering time of GLOFs .\n\nThe surface velocities were extracted along the central line of the Khurdopin, Shishper, and Kyagar glaciers, highlighting the quiescent and surge phases obtained from published data (Bazai et al., 2021, 2022) using the image-to-image correlation, open-source software COSI-Corr (Leprince et al., 2012, 2007). The software effectively assesses the glacier surface velocity (Leprince et al., 2012; Steiner et al., 2018). Utilizing a displacement calculation, this technique was used to co-register and correlate surface features (Bazai et al., 2021; Steiner et al., 2018). The surface velocity and overall movement during the surge were measured by observing changes in the GPS-registered glacier front positions every 3 months from March 2019, and these changes were measured for 3 years for the Shishper Glacier in the field as well as for the Khurdopin Glacier from June 2017 to July 2019. When coupled with COSI-Corr-measured velocities, these latter procedures gave accurate results. The velocity estimation procedure generally yields an accuracy of 1/4 of a pixel (Sattar et al., 2019). Velocity root-mean-square errors (RM-SEs) were assessed to justify image processing accuracy. Examples of output are given in Fig. 1b-d.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.2 Glacier lake surface area mapping and glacier surface velocity", "section_headings": ["3 Data and methods", "3.2 Glacier lake surface area mapping and glacier surface velocity"], "chunk_type": "text", "line_start": 52, "line_end": 58, "token_count_estimate": 515, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8898f2aadc4a0f68", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.3 Field observation and lake volume measurement\nType: text\n\nSix lakes were regularly monitored: four from the Shishper Glacier and two from the Khurdopin Glacier. Data for 23 GLOF events from eight surge cycles that occurred during or following the first year of each surge are presented in Table 1, with lakes resealing after each GLOF. The field data for six events from the Khurdopin and Shishper glaciers helped to reduce the uncertainty or validated data for 17 lakes for which data were obtained through remote sensing techniques (as explained in Sect. 3.2).\n\nFor the Kyagar Glacier, Li et al. (2023) suspected that the drainage conduit may not have been at the deepest part of the lake basin, and its configuration changed between GLOF events. All the previously recorded lakes from Khurdopin and Shishper were drained via single subglacial conduits with stable inlet positions (i.e., the inlet was seen to be in the same place on each occasion) and varying outlet positions and conduit lengths. Therefore, as closely as possible, we identified the inlet and outlet positions of the drainage conduits. As is shown in the Results section, the inlet position of the conduit in the ice-dammed lake basin was always in the deepest position. The lowest ice dam height also tended to be in the vicinity of the conduit. The conduit inlet positions were geolocated within the empty lake basins using GPS, and the lake depths were calculated for these locations with reference to shoreline elevations (Table 1 and Fig. 2). From the field survey, we noted that the presence of alignments of surface depressions in the glacier indicated the", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.3 Field observation and lake volume measurement", "section_headings": ["3 Data and methods", "3.3 Field observation and lake volume measurement"], "chunk_type": "text", "line_start": 60, "line_end": 66, "token_count_estimate": 410, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "707fec617c4e4c78", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.3 Field observation and lake volume measurement\nType: table\nTable: Table 1. GLOF and surge date and lake volume measurement since 1970 obtained using remote sensing techniques. The average surge velocity and volume related to each of the 23 GLOFs from eight surge cycles are presented; other detail is in Table S3 in the Supplement.\n\n| Glacier name | Lake surface elevation (m) | Date (mm/dd/yyyy) | Lake area (km2) | ASTER/UAV lake volume estimate (106 m3) | Lake vol. uncertainty (±106 m3) | Vol. after GLOF (106 m3) | Average velocity (± md-1) | Date of next clear image after GLOF (mm/dd/yyyy) | Surge cycle and resealed GLOF | Surge duration in months           |\n|--------------|----------------------------|-------------------|-----------------|-----------------------------------------|---------------------------------|--------------------------|---------------------------|--------------------------------------------------|-------------------------------|------------------------------------|\n| Khurdopin    |                            | 20/08/1977        |                 |                                         |                                 |                          |                           |                                                  | 1977–1979                     | May 1977 to Aug 1979; 27 months |\n| Khurdopin    |                            | 15/08/1999        |                 |                                         |                                 |                          | 0.33                      |                                                  | 1998–1999                     | Jan 1995 to Sep 2002; 92 months |\n| Khurdopin    | 3440                       | 05/30/2000        | 1.87            | 186                                     | 2.1                             | —                        | 0.33                      | 08/26/2000                                       | Resealed                      |                                    |\n| Khurdopin    | 3416                       | 04/07/2001        | 0.295           | 19.5                                    | 1.5                             | —                        | 0.44                      | 06/26/2001                                       | Resealed                      |                                    |\n| Khurdopin    | 3420                       | 07/15/2002        | 0.60            | 52.1                                    | 1.6                             | 2.2                      | 0.87                      | 08/16/2002                                       | Resealed                      |                                    |\n| Khurdopin    | 3415                       | 07/28/2017        | 0.180           | 16.2                                    | 1.4                             | —                        | 1.41                      | 08/01/2017                                       | 2016–2018                     | Jun 2006 to Aug 2009; 38 months |\n| Khurdopin    | 3418                       | 03/18/2018        | 0.402           | 19.8                                    | 0.9                             | —                        | 0.53                      | 02/25/2018                                       | Resealed                      |                                    |\n| Kyagar       | 4785                       | 08/01/1977        | 1.181           | 40.73                                   | 5.8                             | —                        |                           | 10/14/1977                                       | 1976–1977                     | Jan 1975 to Aug 1978; 43 months |\n| Kyagar       | 4810                       | 07/18/1978        | 2.17            | 82.12                                   | 15.6                            | —                        |                           | 06/07/1979                                       | Resealed                      |                                    |\n| Kyagar       | 4823                       | 03/08/1997        | 3.30            | 127.3                                   | 2.9                             | —                        | 0.4                       | 04/09/1997                                       | 1994–1996                     | Jan 1995 to Sep 2002; 92 months |\n| Kyagar       | 4825                       | 09/10/1998        | 3.32            | 133.5                                   | 3.5                             | —                        | 0.3                       | 10/11/1998                                       | Resealed                      |                                    |\n| Kyagar       | 4813                       | 09/07/1999        | 2.19            | 86.12                                   | 1.23                            | —                        | 0.46                      | 08/17/1999                                       | Resealed                      |                                    |\n| Kyagar       | 4778                       | 06/25/2000        | 0.91            | 23.48                                   | 1.12                            | —                        | 0.49                      | 08/03/2000                                       | Resealed                      |                                    |\n| Kyagar       | 4819                       | 09/08/2002        | 2.93            | 115.19                                  | 1.09                            | —                        | 1.29                      | 10/09/2002                                       | Resealed                      |                                    |\n| Kyagar       | 4811                       | 06/14/2008        | 1.45            | 94.95                                   | 1.65                            | —                        | 0.61                      | 23/06/2008                                       | Resealed                      | Jun 2006 to Aug 2009; 38 months |\n| Kyagar       | 4808                       | 07/28/2009        | 1.39            | 91.35                                   | 1.56                            | —                        | 0.56                      | 04/08/2009                                       | Resealed                      |                                    |\n| Kyagar       | 4800                       | 07/16/2015        | 1.56            | 53.5                                    | 0.87                            | —                        | 1.14                      | 05/08/2015                                       | Resealed                      | Jan 2013 to Aug 2018; 67 months |\n| Kyagar       | 4804                       | 07/14/2016        | 1.63            | 45.89                                   | 1.49                            | 2.9                      | 0.53                      | 30/07/2016                                       | 2014–2016                     |                                    |\n| Kyagar       | 4806                       | 07/31/2016        | 1.48            | 44.32                                   | 1.23                            | —                        | 0.38                      | 08/09/2016                                       | Resealed                      |                                    |\n| Kyagar       | 4815                       | 08/10/2017        | 2.91            | 113.99                                  | 0.73                            | 11.9                     | 0.38                      | 26/08/2017                                       | Resealed                      |                                    |\n| Kyagar       | 4807                       | 08/06/2018        | 2.38            | 87.98                                   | 0.53                            | —                        | 0.29                      | 08/29/2018                                       | Resealed                      |                                    |\n| Shishper     | 2650                       | 06/23/2019        | 0.37            | 24.10                                   | 2.1                             | —                        | 0.95                      | 07/13/2019                                       | 2017–2019                     | Dec 2018 to Jun 2022; 41 months |\n| Shishper     | 2636                       | 05/29/2020        | 0.50            | 24.90                                   | 1.5                             | —                        | 0.46                      | 06/22/2020                                       | Resealed                      |                                    |\n| Shishper     | 2638                       | 05/16/2021        | 0.52            | 25.77                                   | 1.4                             | —                        | 0.29                      | 07/15/2021                                       | Resealed                      |                                    |", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.3 Field observation and lake volume measurement", "section_headings": ["3 Data and methods", "3.3 Field observation and lake volume measurement"], "chunk_type": "table", "table_caption": "Table 1. GLOF and surge date and lake volume measurement since 1970 obtained using remote sensing techniques. The average surge velocity and volume related to each of the 23 GLOFs from eight surge cycles are presented; other detail is in Table S3 in the Supplement.", "columns": ["Glacier name", "Lake surface elevation (m)", "Date (mm/dd/yyyy)", "Lake area (km2)", "ASTER/UAV lake volume estimate (106 m3)", "Lake vol. uncertainty (±106 m3)", "Vol. after GLOF (106 m3)", "Average velocity (± md-1)", "Date of next clear image after GLOF (mm/dd/yyyy)", "Surge cycle and resealed GLOF", "Surge duration in months"], "table_row_start": 1, "table_row_end": 24, "line_start": 67, "line_end": 92, "token_count_estimate": 1455, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1780c7daf4ed9425", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.3 Field observation and lake volume measurement\nType: text\n\n\\* Only the date of the GLOF is available; other details are not accessible.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.3 Field observation and lake volume measurement", "section_headings": ["3 Data and methods", "3.3 Field observation and lake volume measurement"], "chunk_type": "text", "line_start": 93, "line_end": 95, "token_count_estimate": 42, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d110985ecf0b05c", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.3 Field observation and lake volume measurement\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A multi-panel figure displaying maps of the Karakoram Range. The main, largest panel, labeled (a), is a map showing the extent of glaciers (in light blue) within the Karakoram Boundary. The map spans from 74°0'0\"E to 78°0'0\"E longitude and 35°0'0\"N to 37°0'0\"N latitude. It highlights three specific ice dam locations: Shishper, Khurdopin, and Kyagar, each marked with a black dot and a red glacier outline. The Indus River is also shown. A legend in the top left clarifies the symbols for Ice Dam, Glacier Outline, Glaciers, Indus River, and Karakoram Boundary. A scale bar at the bottom indicates distances up to 100 km. An inset map in the top right shows the location of the study area within the broader High Mountain Asia (HMA) region, with an elevation legend ranging from a low of -102 to a high of 8685. At the bottom, there are three smaller inset images, labeled (b), (c), and (d), corresponding to the Shishper, Khurdopin, and Kyagar glaciers, respectively. These insets show glacier velocity in meters per year (m/year) for different time periods, using a color scale that ranges from 0.01 (purple) to 15 (red). Image (b) is for 2016-2017, image (c) is for 2015-2016, and image (d) is for 2013-2014. Each of these insets indicates a 'Dam site'.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.3 Field observation and lake volume measurement", "section_headings": ["3 Data and methods", "3.3 Field observation and lake volume measurement"], "chunk_type": "figure", "figure_caption": null, "line_start": 96, "line_end": 96, "token_count_estimate": 404, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c68b164b96448ec", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.3 Field observation and lake volume measurement\nType: figure\nFigure: Figure 1. Overview of the study site in the Karakoram (a) and the high-mountain Asia (HMA) region; panels (b)–(d) present the extent of each glacier at a given time when surge speed has led to ice-dammed lake formation. The associated ice flow velocities are indicated. The background of panels (b)–(d) is from © Google Earth images. Publisher's remark: please note that the above figure contains disputed territories.\n\nFigure 1. Overview of the study site in the Karakoram (a) and the high-mountain Asia (HMA) region; panels (b)–(d) present the extent of each glacier at a given time when surge speed has led to ice-dammed lake formation. The associated ice flow velocities are indicated. The background of panels (b)–(d) is from © Google Earth images. Publisher's remark: please note that the above figure contains disputed territories.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.3 Field observation and lake volume measurement", "section_headings": ["3 Data and methods", "3.3 Field observation and lake volume measurement"], "chunk_type": "figure", "figure_caption": "Figure 1. Overview of the study site in the Karakoram (a) and the high-mountain Asia (HMA) region; panels (b)–(d) present the extent of each glacier at a given time when surge speed has led to ice-dammed lake formation. The associated ice flow velocities are indicated. The background of panels (b)–(d) is from © Google Earth images. Publisher's remark: please note that the above figure contains disputed territories.", "line_start": 98, "line_end": 98, "token_count_estimate": 250, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b38e95c4dcb5299", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.3 Field observation and lake volume measurement\nType: text\n\napproximate position of curvilinear conduits, from which we estimated the conduit lengths between the inlet and outlet.\n\nIn addition, we used a UAV (DJI Mavic 2 Pro) equipped with a high-resolution camera (4000 pixels × 2250 pixels) to obtain multiple aerial photographs with a minimum of 85 % image overlap (Entwistle and Heritage, 2017, 2019; Tonkin and Midgley, 2016). The UAV flew at a low uniform height (500 m - to reduce the image distortion) to generate high-resolution orthomosaics and DSMs of the glacier lake surfaces, empty lake basins, and glacier termini. In addition to UAV data, we utilized data from KH-9 (1974), ASTER (2000–2019), PALSAR DEM (June 2008), and SRTM (February 2000) for the computation of lake volumes (Table S2). The SRTM DEM without voids serves as the reference dataset, and the vertical uncertainties in the SRTM DEM are reported to be  $\\pm 10 \\,\\mathrm{m}$  (Rodriguez et al., 2006). The corrected DEMs from the Karakoram region are those used by Bazai et al. (2021) and Gardelle et al. (2013).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.3 Field observation and lake volume measurement", "section_headings": ["3 Data and methods", "3.3 Field observation and lake volume measurement"], "chunk_type": "text", "line_start": 99, "line_end": 103, "token_count_estimate": 324, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58914a1964e4f49a", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: text\n\nAlthough, in this study, we have field-derived estimates of lake depth, basin geometry, and lake surface area to calculate lake volumes, in many other applications, only remote sensing data are available for undrained lakes. Consequently, considering that the lake area in satellite images often exhibits a triangular planform (e.g., Figs. 2a and 3a), we explored the possibility of using a geometric shape to approximate the volume of undrained lake basins. Such an approach would be valuable where the depths of lakes are unknown. To this end, we employ NDWI (as in Sect. 3.2) to identify lake outlines through Landsat false-color composites, which use near-infrared, red, and green bands to distinguish waterbodies from other features. We employ standard connected component analysis (Dillencourt et al., 1992) to manually calculate each lake's area, perimeter, and other surface dimensions (as given in Fig. 3b). Initial calculations are pixelbased and are later converted into metric units by multiplying pixel counts by their respective pixel sizes. The pixel size for high-resolution images varied from 0.8 to 3 m. The output was cross-validated with Khurdopin and Shishper glacial", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "text", "line_start": 105, "line_end": 107, "token_count_estimate": 330, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "264ace56bf7fe734", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A composite image with five panels, labeled (a) through (e), showing photographs of Shishper Lake and Khurdopin Lake at different times. Panel (a), titled \"Shishper Lake March 2019\", is an aerial view of a murky green lake with a blue arrow indicating flow direction. Panel (b), titled \"Shishper Lake Shoreline\", shows a rocky slope with dashed lines indicating the shoreline for 2019 (blue, highest), 2020 (red, middle), and 2021 (green, lowest). Panel (c), titled \"Khurdopin Lake February 2018\", shows a glacier-filled valley with a river flowing through it. Panel (d), titled \"Khurdopin Lake March 2018\", shows a close-up of the broken, icy surface of the glacier. Panel (e), titled \"Khurdopin Lake Shoreline\", shows a rocky slope with dashed lines indicating the shoreline for 2017 (black, lower) and 2018 (yellow, higher).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "figure", "figure_caption": null, "line_start": 108, "line_end": 108, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dd738b4dac30b417", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: figure\nFigure: Figure 2. Shishper and Khurdopin glacial lake views in the field. (a) Oblique view from a helicopter in March 2019 (image captured during lake monitoring by Gilgit-Baltistan Disaster Management Authority); (b) the Shishper shoreline elevations of four lakes that burst out in the given years; (c, d) successive oblique views of the Khurdopin lake in the field; (e) Khurdopin lake elevations in the given years. Wiggly blue lines are flow directions.\n\n**Figure 2.** Shishper and Khurdopin glacial lake views in the field. (a) Oblique view from a helicopter in March 2019 (image captured during lake monitoring by Gilgit-Baltistan Disaster Management Authority); (b) the Shishper shoreline elevations of four lakes that burst out in the given years; (c, d) successive oblique views of the Khurdopin lake in the field; (e) Khurdopin lake elevations in the given years. Wiggly blue lines are flow directions.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "figure", "figure_caption": "Figure 2. Shishper and Khurdopin glacial lake views in the field. (a) Oblique view from a helicopter in March 2019 (image captured during lake monitoring by Gilgit-Baltistan Disaster Management Authority); (b) the Shishper shoreline elevations of four lakes that burst out in the given years; (c, d) successive oblique views of the Khurdopin lake in the field; (e) Khurdopin lake elevations in the given years. Wiggly blue lines are flow directions.", "line_start": 110, "line_end": 110, "token_count_estimate": 271, "basins": [], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "8d282e24179520c7", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: text\n\nlake UAV data that had a pixel size of 0.063 m and with field survey evidence. Trials demonstrated that the known volume of the lakes determined using DEMs of the lake basins once drained (Sect. 3.3) could be approximated if the length of the lake from the upstream inlet to the ice dam face (Z) and the breadth of the lake at the ice dam (C) were known. Given the reported image resolution, uncertainties in the characteristic length measurements (Fig. 3b), measured using 3D GIS interpolation, would translate into uncertainty in lake volume estimates of only 3% when applying Eqs. (1) and (2) if lake depth were known exactly. For undrained lakes, assuming the depth is the same as the width of the lake at the dam face (Fig. 3b) will likely overestimate lake volume. It might be expected that geometric estimates based on lake surface area alone would be improved if the lake's depth (h)is known at the deepest point close to the dam face. However, in our examples, there is uncertainty in the values of h obtained from DEMs of the drained basins such that the errors in lake depth estimates translate into errors in lake volume estimates of < 14 \\%. Alternatively, where a lake is present, this latter parameter can be obtained by plumbing the depth from a boat.\n\nGiven the triangular shape of the lake surface areas, the first consideration with regard to lake geometry was whether the valley sides might be regarded as providing a V-shaped lake cross-section or a rectangular cross-section (Fig. 3b); in either case, regular geometric shapes might provide an esti-\n\nmate of the lake volumes. A rectangular cross-section would be closer to the U-shaped valley cross-sections commonly associated with glaciated valleys. Thus, assuming a V-shaped valley, lake volume (V) can be approximated by an irregular tetrahedron (Fig. 3b, left-hand diagram) where the depth (h) is unknown but the distance from A to B (X in Fig. 3b) and the length C are known values. Assuming the lake surface is an isosceles triangle and the vertical face at the dam wall is an equilateral triangle, the volume can be obtained from\n\n$$V = \\sqrt{V^2}, \\qquad (1)$$\n$$\\begin{aligned} V^2 = \\frac{1}{144} \\biggl[ & Y_1^2 D^2 (Z_2^2 + X^2 + C^2 + E^2 - Y_1^2 - D^2) \\\\ & + Y_2^2 E^2 (Y_1^2 + X^2 + C^2 + D^2 - Y - E^2) \\\\ & + Z_2^2 C^2 (Y_1^2 + Y + D^2 + E^2 - Z^2 - C^2) \\\\ & - Y_1^2 Y_2^2 C^2 - Y_2^2 Z_2^2 D^2 - Y_1^2 Z_2^2 E^2 - C^2 D^2 E^2 \\biggr], \\end{aligned}$$\n\nwhere the values for lakeside lengths  $Y_1$  and  $Y_2$ , the main length Z, lakeside length D, and lakeside length E are defined in Fig. 3b and obtained from geometry.\n\nAlternatively, considering a pentahedral, the volume is", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "text", "line_start": 111, "line_end": 128, "token_count_estimate": 870, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "beee886f02894b4c", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: text\n\n+ Y + D ^ 2 + E ^ 2 - Z ^ 2 - C ^ 2 ) \\ \\ & - Y_1 ^ 2 Y_2 ^ 2 C ^ 2 - Y_2 ^ 2 Z_2 ^ 2 D ^ 2 - Y_1 ^ 2 Z_2 ^ 2 E ^ 2 - C ^ 2 D ^ 2 E ^ 2 \\ biggr ] , \\ end { aligned } $ $ where the values for lakeside lengths $ Y_1 $ and $ Y_2 $ , the main length Z , lakeside length D , and lakeside length E are defined in Fig . 3b and obtained from geometry . Alternatively , considering a pentahedral , the volume is\n\n$$V = \\frac{1}{3}(h^2)X. \\tag{2}$$\n\nThese shape assumptions are addressed within the Results section.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "text", "line_start": 111, "line_end": 128, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83f22625910148b5", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A composite image with two panels labeled (a) and (b). Panel (a) is an aerial photograph of a glacial lake in a steep, rocky mountain valley. The lake has a light turquoise color and contains floating ice. It is dammed by a large deposit of dark rock and debris. Panel (b) contains two schematic diagrams illustrating the cross-section of a valley with an ice dam. A dashed line separates the 'OPEN VALLEY' above from the 'ICE DAM' below. The first diagram shows a V-shaped valley cross-section. The water-filled portion in the open valley is a triangle with apex A, sides Y1, and a vertical line Z. The ice dam below is also V-shaped with sides D and E and height h. An arrow labeled 'Inflow' points to apex A. The second diagram shows a similar triangular water body in the open valley, with a vertical line X, but the ice dam below is depicted as a rectangle with height h.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "figure", "figure_caption": null, "line_start": 129, "line_end": 129, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c0c2072ec76e1ad0", "text": "Document: 1 Introduction\nSection: 3 Data and methods > 3.4 Geometry of lake basin\nType: figure\nFigure: Figure 3. (a) Example of Shishper glacier-dammed lake, with the surface exhibiting a roughly triangular 2D shape (see also Fig. 2a); (b) diagram for calculating the volume of the lakes assuming (left) an irregular tetrahedral shape and (right) an irregular pentahedral shape. The blue shading represents the horizontal surface of the lake, and the white area represents the vertical ice wall.\n\n**Figure 3.** (a) Example of Shishper glacier-dammed lake, with the surface exhibiting a roughly triangular 2D shape (see also Fig. 2a); (b) diagram for calculating the volume of the lakes assuming (left) an irregular tetrahedral shape and (right) an irregular pentahedral shape. The blue shading represents the horizontal surface of the lake, and the white area represents the vertical ice wall.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "3 Data and methods > 3.4 Geometry of lake basin", "section_headings": ["3 Data and methods", "3.4 Geometry of lake basin"], "chunk_type": "figure", "figure_caption": "Figure 3. (a) Example of Shishper glacier-dammed lake, with the surface exhibiting a roughly triangular 2D shape (see also Fig. 2a); (b) diagram for calculating the volume of the lakes assuming (left) an irregular tetrahedral shape and (right) an irregular pentahedral shape. The blue shading represents the horizontal surface of the lake, and the white area represents the vertical ice wall.", "line_start": 131, "line_end": 131, "token_count_estimate": 239, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "141ae2bdaf29c514", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 Surge velocity and ice-dammed lake volume\nType: figure\nFigure: Figure 4a-c present the relationship between the glacier surge velocity (Khurdopin, Kyagar, and Shishper) and 23 GLOFs. The relationship between surge and GLOF was developed using annual average velocity data. The glacier's daily velocity was recorded on the day the GLOF was initiated, as detailed in Table 1. In Fig. 4 panels (a) to (c), it can be seen that the GLOF occurred after the peak of the glacier surge and the resealed lake formed while the surge velocity declined. These responses to slowing of the glacier velocity lasted for 2-4 years after the surge peak. Thus, GLOFs occur toward the end of a surge period or immediately afterward; the detail is presented in Table 1. The relationship between the timing of glacier surges and the timing of GLOFs is shown in Fig. 4a-c, wherein the dates of the GLOFs are given as the month in the year. The three Karakoram glaciers can be used as regional examples of surge behavior controlling GLOF occurrence, as there is a temporal relationship between the occurrence of periods of glacier surging and the occurrence of GLOFs (Fig. 4a-d). This pattern of behavior prompted the hypothesis that glacier thickening and thinning during surging might control the development of ice-dammed lakes (Bazai et al., 2022). Lake volumes would increase when the speed of the ice is low, the ice mass would be conserved or increased, and the fracturing of the ice would be reduced. The corollary pertains to when the ice speed increases, the glacier thins, and the fracturing of the ice mass increases, providing hydraulic drainage conduits (Gao et al., 2024). This sequence of events is shown schematically within Fig. 5. A thinning glacier also minimizes potential lake depth and might increase the likelihood of a GLOF occurring over the top of the ice dam.\n\nFigure 4a-c present the relationship between the glacier surge velocity (Khurdopin, Kyagar, and Shishper) and 23 GLOFs. The relationship between surge and GLOF was developed using annual average velocity data. The glacier's daily velocity was recorded on the day the GLOF was initiated, as detailed in Table 1. In Fig. 4 panels (a) to (c), it can be seen that the GLOF occurred after the peak of the glacier surge and the resealed lake formed while the surge velocity declined. These responses to slowing of the glacier velocity lasted for 2-4 years after the surge peak. Thus, GLOFs occur toward the end of a surge period or immediately afterward; the detail is presented in Table 1. The relationship between the timing of glacier surges and the timing of GLOFs is shown in Fig. 4a-c, wherein the dates of the GLOFs are given as the month in the year. The three Karakoram glaciers can be used as regional examples of surge behavior controlling GLOF occurrence, as there is a temporal relationship between the occurrence of periods of glacier surging and the occurrence of GLOFs (Fig. 4a-d). This pattern of behavior prompted the hypothesis that glacier thickening and thinning during surging might control the development of ice-dammed lakes (Bazai et al., 2022). Lake volumes would increase when the speed of the ice is low, the ice mass would be conserved or increased, and the fracturing of the ice would be reduced. The corollary pertains to when the ice speed increases, the glacier thins, and the fracturing of the ice mass increases, providing hydraulic drainage conduits (Gao et al., 2024). This sequence of events is shown schematically within Fig. 5. A thinning glacier also minimizes potential lake depth and might increase the likelihood of a GLOF occurring over the top of the ice dam.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 Surge velocity and ice-dammed lake volume", "section_headings": ["4 Results", "4.1 Surge velocity and ice-dammed lake volume"], "chunk_type": "figure", "figure_caption": "Figure 4a-c present the relationship between the glacier surge velocity (Khurdopin, Kyagar, and Shishper) and 23 GLOFs. The relationship between surge and GLOF was developed using annual average velocity data. The glacier's daily velocity was recorded on the day the GLOF was initiated, as detailed in Table 1. In Fig. 4 panels (a) to (c), it can be seen that the GLOF occurred after the peak of the glacier surge and the resealed lake formed while the surge velocity declined. These responses to slowing of the glacier velocity lasted for 2-4 years after the surge peak. Thus, GLOFs occur toward the end of a surge period or immediately afterward; the detail is presented in Table 1. The relationship between the timing of glacier surges and the timing of GLOFs is shown in Fig. 4a-c, wherein the dates of the GLOFs are given as the month in the year. The three Karakoram glaciers can be used as regional examples of surge behavior controlling GLOF occurrence, as there is a temporal relationship between the occurrence of periods of glacier surging and the occurrence of GLOFs (Fig. 4a-d). This pattern of behavior prompted the hypothesis that glacier thickening and thinning during surging might control the development of ice-dammed lakes (Bazai et al., 2022). Lake volumes would increase when the speed of the ice is low, the ice mass would be conserved or increased, and the fracturing of the ice would be reduced. The corollary pertains to when the ice speed increases, the glacier thins, and the fracturing of the ice mass increases, providing hydraulic drainage conduits (Gao et al., 2024). This sequence of events is shown schematically within Fig. 5. A thinning glacier also minimizes potential lake depth and might increase the likelihood of a GLOF occurring over the top of the ice dam.", "line_start": 137, "line_end": 137, "token_count_estimate": 973, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "211c77bbae28ab62", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 Surge velocity and ice-dammed lake volume\nType: text\n\nAs a first attempt to relate glacier behavior in a predictive sense to lake formation, we sought to determine the relation-\n\nship between the resulting lake volume from the prior surge speed. Lake volume should be high when the glacier velocity is low and the ice mass thickens and vice versa; there is some support for this assertion (Fig. 6). Within Fig. 6, considering all the data (excluding the three drained lakes), the broad data spread prevents the fitting of a significant least-squares regression function. Nonetheless, trial curve fitting showed that a negative power function would be the best fit.\n\nIf the volume of a lake decreases as the glacier surge speed increases, as a negative power function would imply, both the lake depth and the surface area decrease; then, from an analogy with a pentahedron, the volume of a pentahedron reduces as the square of the characteristic length (Eq. 2), here the water depth. Consequently, assuming the pentahedral analogy applies, a least-squares trend line fitting procedure was used to define the constant  $\\alpha$  in the following function:  $V_{\\rm DEM} = \\alpha U_s^{-2}$  fitted to all the data excluding the drained volumes. This equation, with  $\\alpha = 13.6$ , is shown in Fig. 6 and visually is a good fit through data for a range of low values of  $U_s$  when lake depths will be greatest. Given the data dispersion and the small sample number, there are no statistical outliers (defined objectively; Carling et al., 2022).\n\nAn eye-fitted power function has been added to Fig. 6 to tentatively define the lower limit of the data spread. These results, although clearly not definitive, indicate that there probably is a relationship between the volume of the lake and the control of the lake water level exerted by the surge speed. Therefore, surge speed should exert some control on lake depth, lake volume, and potential GLOF volumes.\n\nGiven the scatter in the data within Fig. 6, additional data would be required to determine if the relationship between surge speed and lake volume does follow a negative power trend, as we have suggested.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 Surge velocity and ice-dammed lake volume", "section_headings": ["4 Results", "4.1 Surge velocity and ice-dammed lake volume"], "chunk_type": "text", "line_start": 138, "line_end": 148, "token_count_estimate": 550, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e2e6f63dab2a603b", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 Surge velocity and ice-dammed lake volume\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: The image contains four graphs, labeled (a), (b), (c), and (d), analyzing glacier velocity and Glacial Lake Outburst Flood (GLOF) events over time.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 Surge velocity and ice-dammed lake volume", "section_headings": ["4 Results", "4.1 Surge velocity and ice-dammed lake volume"], "chunk_type": "figure", "figure_caption": null, "line_start": 149, "line_end": 149, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eac539372b961f24", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 Surge velocity and ice-dammed lake volume\nType: text\n\nGraphs (a), (b), and (c) are time-series plots. Each plots 'Average Velocity m yr⁻¹' on the left y-axis as a red line with square markers, and the 'Month of GLOF Trigger' on the right y-axis, with GLOF events shown as colored circles. The x-axis represents 'Years'. Shaded gray areas indicate 'Surge periods' of high velocity.\n\n- Graph (a) shows data from roughly 1993 to 2020. The average velocity is 79 m yr⁻¹. There are two surge periods: 1998-2002, with a peak velocity around 300 m yr⁻¹, and 2016-2020, with a peak velocity over 350 m yr⁻¹. GLOF events are marked with blue circles.\n\n- Graph (b) shows data from roughly 1993 to 2019. The average velocity is 29.9 m yr⁻¹. It features a surge period from 1994-1998 with a peak velocity around 300 m yr⁻¹, a 'Mini Surge' around 2008, and another surge period from 2013-2016 with a peak velocity over 400 m yr⁻¹. GLOF events are marked with orange circles.\n\n- Graph (c) shows data from roughly 1995 to 2020. The average velocity is 87 m yr⁻¹. It has two surge periods: 1997-2002, with a peak velocity just under 300 m yr⁻¹, and 2017-2020, with a peak velocity around 350 m yr⁻¹. GLOF events are marked with gray circles.\n\n- Graph (d) is a timeline plotting GLOF events against 'Cumulative Days' from 0 to over 12,000. A legend indicates that blue circles represent Khurdopin, orange circles represent Kyagar, and gray circles represent Shishper. Horizontal bars at the top indicate surge periods for each location, and lines at the bottom distinguish between 'Winter' (blue) and 'Summer' (red) occurrences.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 Surge velocity and ice-dammed lake volume", "section_headings": ["4 Results", "4.1 Surge velocity and ice-dammed lake volume"], "chunk_type": "text", "line_start": 150, "line_end": 160, "token_count_estimate": 474, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "196d7f07cab2ed6c", "text": "Document: 1 Introduction\nSection: 4 Results > 4.1 Surge velocity and ice-dammed lake volume\nType: figure\nFigure: Figure 4. Relationship between glacier surges and GLOFs, with average annual glacier velocity during the surge and quiescent phases for three glaciers: (a) Khurdopin, (b) Kyagar, and (c) Shishper. GLOFs for these glaciers occurred between the months of March and November. The combined analysis is presented in (d), illustrating the occurrences of GLOFs (dots) and related periods of glacier surging (bars) as cumulative days since 1 January 1990. Some points are plotted below the timeline to avoid coincident positions. The blue and red lines show the winter (October to April) and summer (May to September) seasons, respectively, with the GLOFs occurring predominantly in the summer months. Within panels (a) to (c), the average surge velocity is given as the red curves, and the average velocity during the study period is given in blue text.\n\n**Figure 4.** Relationship between glacier surges and GLOFs, with average annual glacier velocity during the surge and quiescent phases for three glaciers: (a) Khurdopin, (b) Kyagar, and (c) Shishper. GLOFs for these glaciers occurred between the months of March and November. The combined analysis is presented in (d), illustrating the occurrences of GLOFs (dots) and related periods of glacier surging (bars) as cumulative days since 1 January 1990. Some points are plotted below the timeline to avoid coincident positions. The blue and red lines show the winter (October to April) and summer (May to September) seasons, respectively, with the GLOFs occurring predominantly in the summer months. Within panels (a) to (c), the average surge velocity is given as the red curves, and the average velocity during the study period is given in blue text.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.1 Surge velocity and ice-dammed lake volume", "section_headings": ["4 Results", "4.1 Surge velocity and ice-dammed lake volume"], "chunk_type": "figure", "figure_caption": "Figure 4. Relationship between glacier surges and GLOFs, with average annual glacier velocity during the surge and quiescent phases for three glaciers: (a) Khurdopin, (b) Kyagar, and (c) Shishper. GLOFs for these glaciers occurred between the months of March and November. The combined analysis is presented in (d), illustrating the occurrences of GLOFs (dots) and related periods of glacier surging (bars) as cumulative days since 1 January 1990. Some points are plotted below the timeline to avoid coincident positions. The blue and red lines show the winter (October to April) and summer (May to September) seasons, respectively, with the GLOFs occurring predominantly in the summer months. Within panels (a) to (c), the average surge velocity is given as the red curves, and the average velocity during the study period is given in blue text.", "line_start": 161, "line_end": 161, "token_count_estimate": 468, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "809d59b3f0f1a054", "text": "Document: 1 Introduction\nSection: 4 Results > 4.2 Tetrahedron assumption for lake volume\nType: text\n\nUsing Eq. (1) and assuming the ice dam face was an equilateral triangle, only the values X and C are required such that the volume of the \"tetrahedron\" lakes is around 10 times greater than the volume of the lakes determined using the DEMs (Fig. 7a). This result indicates that the actual depth of the lake (h) must be much less than that value associated with an equilateral triangle of side length C (Fig. 3b, left-hand side). Nevertheless, this procedure provides a means to estimate lake volume from plan-view data alone.\n\nIn contrast to the assumption of an equilateral triangle at the dam face, improved lake volume estimates were obtained considering the measured DEM-derived values of h along with the values of X and C. Once again, assuming an irregular tetrahedron as in Fig. 3b, the analysis demonstrated that the tetrahedral lake volume was roughly half that of the DEM volume (not illustrated). This latter result suggests that treating the valley cross-section as U-shaped (roughly quadrilateral) rather than V-shaped means that doubling the area of the triangular dam face section to form a quadrilateral should provide lake volume estimates, with the shape defined as an irregular pentahedron (square-based pyramid) (Fig. 3b, right-hand side), closer to the DEM-derived volume estimates.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.2 Tetrahedron assumption for lake volume", "section_headings": ["4 Results", "4.2 Tetrahedron assumption for lake volume"], "chunk_type": "text", "line_start": 164, "line_end": 168, "token_count_estimate": 356, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2adb68304539133e", "text": "Document: 1 Introduction\nSection: 4 Results > 4.3 Pentahedron assumption for lake volume\nType: text\n\nThe pentahedral volume estimates (Eq. 2), as shown in Fig. 7b, are preferable to those values shown in Fig. 7a. They result in a nearly 1:1 relationship between  $V_{\\rm PEN}$  and  $V_{\\rm DEM}$  but require knowledge of the parameter depth h, as well as X and C. If it were assumed that a rectangular base of the pentahedron provides an exact match for the DEM volume, the correlation coefficient value would be unity. Thus, the coefficient of 0.88 reflects the deviation of the cross-sectional shape of the lake at the dam face from a rectangle. Note that the relationships between both determinations of lake volume (Fig. 7a and b) progressively deviate from a 1:1 relationship as lake volume increases. This trend might indicate that larger lakes are less well defined as tetrahedrons or pentahedrons as the volumes increase.\n\nWhen assuming a tetrahedral shape for a lake, the lake volume can be estimated from remote sensing images alone as only the length of the lake (X) and the breadth of the lake (C) at the ice dam are needed to estimate the lake volume. When assuming a pentahedral shape, the depth (h) of the lake at the ice dam is required as well. Although, subsequent to GLOF drainage, h can be measured from a DEM or field survey,", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.3 Pentahedron assumption for lake volume", "section_headings": ["4 Results", "4.3 Pentahedron assumption for lake volume"], "chunk_type": "text", "line_start": 170, "line_end": 174, "token_count_estimate": 358, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "165122c4d6ebe481", "text": "Document: 1 Introduction\nSection: 4 Results > 4.3 Pentahedron assumption for lake volume\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A five-panel diagram illustrating the mechanism of glacier surge and its control on lake formation and outburst floods.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.3 Pentahedron assumption for lake volume", "section_headings": ["4 Results", "4.3 Pentahedron assumption for lake volume"], "chunk_type": "figure", "figure_caption": null, "line_start": 175, "line_end": 175, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aba92efc06e62fb0", "text": "Document: 1 Introduction\nSection: 4 Results > 4.3 Pentahedron assumption for lake volume\nType: text\n\nPanel (a), titled 'Glacier surge initiation (Reference glacier surface elevation)', shows a glacier with a terminus and an accumulation zone. Arrows indicate a low to medium glacier velocity.\n\nPanel (b), 'Glacier surge peak -> River blocked -> Lake formed', depicts the glacier surging forward, creating an ice dam that blocks a river and forms a lake with a level H1. The glacier has many crevasses, a high velocity, and a terminus thickness of T1.\n\nPanel (c), 'Lake outburst flood -> Terminus thin and advancement -> Peak velocity', shows the lake has drained, causing a Glacial Lake Outburst Flood (GLOF). A conduit through the glacier is now open, and the glacier is at its peak velocity. The terminus thickness is still T1.\n\nPanel (d), 'Lake re-established -> Terminus thicker -> Glacier velocity decrease', illustrates the formation of a second, larger lake with a level H2. The glacier terminus is now thicker (T2), and the glacier's velocity has decreased.\n\nPanel (e), 'Conduit open -> Outburst flood', shows the second lake draining in another outburst flood. The terminus thickness remains T2.\n\nA legend at the bottom explains the symbols used for glacier velocity and direction, glacier debris, crevasses, open conduit, lake level (H1 < H2), and glacier thickness (T1 < T2). A vertical scale next to panel (a) shows glacier velocity from low (blue) to medium (orange) to very high (red).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.3 Pentahedron assumption for lake volume", "section_headings": ["4 Results", "4.3 Pentahedron assumption for lake volume"], "chunk_type": "text", "line_start": 176, "line_end": 188, "token_count_estimate": 421, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "491f11c1a91b8dee", "text": "Document: 1 Introduction\nSection: 4 Results > 4.3 Pentahedron assumption for lake volume\nType: figure\nFigure: Figure 5. The mechanism of glacier surge controls lake formation: (a) prior to the surge, there is no stream blockage in front of a thick terminus; (b) in the first year of the surge, stream blockage occurs, leading to lake formation behind a thinning terminus; (c) during the first year, peak glacier velocity is reached, the lake drains, and the terminus thins; (d) as surge velocity decreases, the lake reforms and the terminus thickens; (e) the second lake can drain as velocity continues to decrease and the terminus thickens.\n\n**Figure 5.** The mechanism of glacier surge controls lake formation: (a) prior to the surge, there is no stream blockage in front of a thick terminus; (b) in the first year of the surge, stream blockage occurs, leading to lake formation behind a thinning terminus; (c) during the first year, peak glacier velocity is reached, the lake drains, and the terminus thins; (d) as surge velocity decreases, the lake reforms and the terminus thickens; (e) the second lake can drain as velocity continues to decrease and the terminus thickens.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.3 Pentahedron assumption for lake volume", "section_headings": ["4 Results", "4.3 Pentahedron assumption for lake volume"], "chunk_type": "figure", "figure_caption": "Figure 5. The mechanism of glacier surge controls lake formation: (a) prior to the surge, there is no stream blockage in front of a thick terminus; (b) in the first year of the surge, stream blockage occurs, leading to lake formation behind a thinning terminus; (c) during the first year, peak glacier velocity is reached, the lake drains, and the terminus thins; (d) as surge velocity decreases, the lake reforms and the terminus thickens; (e) the second lake can drain as velocity continues to decrease and the terminus thickens.", "line_start": 189, "line_end": 189, "token_count_estimate": 308, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86fbe9e9a4d07ffb", "text": "Document: 1 Introduction\nSection: 4 Results > 4.3 Pentahedron assumption for lake volume\nType: text\n\nfor the purposes of mitigation, a warning prior to a GLOF occurring is preferable.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.3 Pentahedron assumption for lake volume", "section_headings": ["4 Results", "4.3 Pentahedron assumption for lake volume"], "chunk_type": "text", "line_start": 190, "line_end": 192, "token_count_estimate": 45, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f0e32fbbacb666dd", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: text\n\nThe timing of a GLOF remains difficult to determine, but for tunnel drainage, the main driver is the critical depth\n\n(Thorarinsson, 1969). The critical depth is important as it is the depth that exerts sufficient pressure at the ice dam wall to induce completed connectivity within the subglacial GLOF drainage conduit (Gao et al., 2024; Yasuda and Furuya, 2013). For the cases of Shishper, Khurdopin, and Kyagar, the glacial lake depths (h) have been normalized by dividing them by the minimum value of each ice dam height", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "text", "line_start": 194, "line_end": 198, "token_count_estimate": 168, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ec2d51077563794", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A scatter plot showing the relationship between Lake Volume (V\\_DEM) and Glacier surge speed (U\\_s). The y-axis, labeled \"Lake Volume, V\\_DEM (M m³)\", ranges from 0.0 to 200.0. The x-axis, labeled \"Glacier surge speed, U\\_s (m d⁻¹)\", ranges from 0 to 1.6. The plot includes data points for three different locations: Shishper (black circles), Kyagar (red circles), and Khurdopin (blue circles), as well as points for when the lakes drained: Khurdopin drained (light blue triangle) and Kyagar drained (red triangle). Two downward-sloping trend lines are shown. The upper green dotted line is represented by the equation V\\_DEM = 13.69U\\_s⁻². The lower purple dotted line is represented by the equation V\\_DEM = 10U\\_s⁻⁰.⁸. The data points generally show that as glacier surge speed increases, the lake volume tends to decrease. The Khurdopin data includes a point with a high lake volume of approximately 185 M m³ at a low surge speed of about 0.3 m d⁻¹. The Kyagar data points are scattered across a wide range of volumes and speeds. The Shishper data points are clustered at lower volumes and speeds. The 'drained' data points are all located at very low lake volumes, close to zero.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 199, "line_end": 199, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09ee1f358279d176", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: figure\nFigure: Figure 6. Variation in glacial lake volume as a function of the glacier surge speed. Data from three glaciers. Most lakes drained completely, but three drained lakes had residual volumes (triangles). A -0.8 power function (purple curve) defines the lower limit to the data spread, while a -2.0 power least-squares function (green curve) defines the central tendency of the data trend (see text for explanation).\n\nFigure 6. Variation in glacial lake volume as a function of the glacier surge speed. Data from three glaciers. Most lakes drained completely, but three drained lakes had residual volumes (triangles). A -0.8 power function (purple curve) defines the lower limit to the data spread, while a -2.0 power least-squares function (green curve) defines the central tendency of the data trend (see text for explanation).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "figure", "figure_caption": "Figure 6. Variation in glacial lake volume as a function of the glacier surge speed. Data from three glaciers. Most lakes drained completely, but three drained lakes had residual volumes (triangles). A -0.8 power function (purple curve) defines the lower limit to the data spread, while a -2.0 power least-squares function (green curve) defines the central tendency of the data trend (see text for explanation).", "line_start": 201, "line_end": 201, "token_count_estimate": 229, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6d484026896469b", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: figure\nFigure\n\nImage /page/10/Figure/4 description: An image displaying two scatter plots, labeled (a) and (b).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 203, "line_end": 203, "token_count_estimate": 56, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "359e403941c784e5", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: text\n\nPlot (a) shows \"Lake Volume from DEM, V\\_DEM (M m^3)\" on the y-axis versus \"Volume of Irregular Tetrahedron/10, V\\_TET (M m^3)\" on the x-axis. The y-axis ranges from 0 to 200, and the x-axis ranges from 0 to 100. The plot contains data points for three locations: Shishper (black circles), Khurdopin (blue circles), and Kyagar (red circles). A red dotted trendline is fitted to the data with the equation V\\_DEM = 1.3525V\\_TET and an R-squared value of 0.88. A \"1:1\" label is also present on the plot.\n\nPlot (b) shows \"Lake Volume from DEM, V\\_DEM (M m^3)\" on the y-axis versus \"Volume of Pentrahedron, V\\_PEN (M m^3)\" on the x-axis. Both axes range from 0.0 to 250.0. The plot contains a single series of grey circular data points. A blue dotted trendline is fitted to the data with the equation V\\_DEM = 0.8828 V\\_PEN and an R-squared value of 0.932. A second blue dotted line represents the 1:1 relationship.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "text", "line_start": 204, "line_end": 208, "token_count_estimate": 329, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["3525V"]}}
{"id": "04220665f161c420", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: figure\nFigure: Figure 7. (a) Relationship between the volumes of irregular tetrahedrons / 10, derived from Eq. (1), and the volumes of the lakes determined using DEMs. (b) The relationship between the volumes of irregular pentahedrons and the volumes of the lakes determined using DEMs.\n\n**Figure 7. (a)** Relationship between the volumes of irregular tetrahedrons / 10, derived from Eq. (1), and the volumes of the lakes determined using DEMs. (b) The relationship between the volumes of irregular pentahedrons and the volumes of the lakes determined using DEMs.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "figure", "figure_caption": "Figure 7. (a) Relationship between the volumes of irregular tetrahedrons / 10, derived from Eq. (1), and the volumes of the lakes determined using DEMs. (b) The relationship between the volumes of irregular pentahedrons and the volumes of the lakes determined using DEMs.", "line_start": 209, "line_end": 209, "token_count_estimate": 178, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d62a8d96b26b0d7", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: text\n\nto give values (n') of normalized lake depths that range between 0 for a fully drained lake and a hypothetic value of 1 if the lake level reaches the height of the ice barrier. At the approximate time of GLOF occurrence, the resulting values of n' range between 0.32 and 0.95 (Fig. 8a–c). Most GLOFs occur for a range of n' values between 0.61 and 0.95 (Fig. 8d).\n\nThus, n' = 0.60 can be regarded as a warning level value with the potential for a GLOF occurring imminently increasing as n' approaches unity.\n\nAs the water pressure (P) at the dam face increases linearly with water depth in each lake, any variation in the pressure with n' that deviates from the linear trend reflects", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "text", "line_start": 210, "line_end": 216, "token_count_estimate": 214, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68794228cac8ec34", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: figure\nFigure\n\nImage /page/11/Figure/2 description: A figure with four panels, (a), (b), (c), and (d), analyzing glacier lake outburst floods (GLOFs) for three different glaciers: Shishper, Khurdopin, and Kyagar.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "figure", "figure_caption": null, "line_start": 217, "line_end": 217, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca2be8f2d80ef354", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: text\n\nPanels (a), (b), and (c) are cross-sectional profiles of the lakebeds for Shishper, Khurdopin, and Kyagar glaciers, respectively. Each plot has a primary x-axis for 'Cross-section (m)' and a primary y-axis for 'Elevation (m)'. A secondary top x-axis shows 'Volume 10^6 m^3', and a secondary right y-axis shows 'n''. The profiles depict the lakebed, conduit inlet/outlet, and the glacier. Each panel includes an inset satellite map showing the respective glacier and the location of the cross-section.\n- Panel (a) for Shishper shows a cross-section up to about 2200 m, with elevations from 2520 m to 2660 m. The volume scale goes up to 32 x 10^6 m^3.\n- Panel (b) for Khurdopin shows a cross-section up to about 3300 m, with elevations from 3360 m to 3440 m. The volume scale goes up to 180 x 10^6 m^3.\n- Panel (c) for Kyagar shows a cross-section up to about 6500 m, with elevations from 4750 m to 4830 m. The volume scale goes up to 130 x 10^6 m^3.\n\nPanel (d) is a scatter plot showing the relationship between 'Water Pressure, P (kPa)' on the y-axis (0 to 1600) and 'Normalized glacier lake depth, n' (-)' on the x-axis (0.0 to 1.0). It includes data for:\n- Shishper (black dots), with a trendline P = 1514.9 n'.\n- Khurdopin (blue dots), with a trendline P = 977.1 n'.\n- Kyagar (red dots), with a trendline P = 835.5 n'.\nA light blue shaded area labeled 'Critical range' covers the region where n' is approximately greater than 0.6.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "text", "line_start": 218, "line_end": 229, "token_count_estimate": 488, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3590560a2ea40466", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: figure\nFigure: Figure 8. The relationship between lake volume and elevation and the critical normalized lake depths for GLOFs: (a) Shishper, (b) Khurdopin, and (c) Kyagar. Red lines connect the relevant elevation and volume in each instance. (d) Water pressure at dam face as a function of n'. The cross-sections are denoted by A' and A. The serial number and date of each lake flood event are shown in Table 1. The straight solid red lines relate specific lake elevations to volumes. A UAV photograph captured in the field was used for panel (a), the images captured by GF-2 were used for panel (b), and Landsat 8 OLI was used for panel (c).\n\n**Figure 8.** The relationship between lake volume and elevation and the critical normalized lake depths for GLOFs: (a) Shishper, (b) Khurdopin, and (c) Kyagar. Red lines connect the relevant elevation and volume in each instance. (d) Water pressure at dam face as a function of n'. The cross-sections are denoted by A' and A. The serial number and date of each lake flood event are shown in Table 1. The straight solid red lines relate specific lake elevations to volumes. A UAV photograph captured in the field was used for panel (a), the images captured by GF-2 were used for panel (b), and Landsat 8 OLI was used for panel (c).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "figure", "figure_caption": "Figure 8. The relationship between lake volume and elevation and the critical normalized lake depths for GLOFs: (a) Shishper, (b) Khurdopin, and (c) Kyagar. Red lines connect the relevant elevation and volume in each instance. (d) Water pressure at dam face as a function of n'. The cross-sections are denoted by A' and A. The serial number and date of each lake flood event are shown in Table 1. The straight solid red lines relate specific lake elevations to volumes. A UAV photograph captured in the field was used for panel (a), the images captured by GF-2 were used for panel (b), and Landsat 8 OLI was used for panel (c).", "line_start": 230, "line_end": 230, "token_count_estimate": 360, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d8c19dadd235f5bf", "text": "Document: 1 Introduction\nSection: 4 Results > 4.4 Anticipating the timing of GLOF events\nType: text\n\nchanges in the height of the ice dam (Fig. 8d). Thus, for example, the values of pressure for the Shishper lakes around an n' value of 0.8 reflect greater overall deeper lakes and higher ice dam heights in contrast to the Kyagar lakes for similar values of n'. The two values of low pressure for n' < 0.6 are associated with relatively low ice dams and consequently reflect presumed low structural integrity within the ice mass, allowing ready conduit development. In the present examples, low values of n' (< 0.6) probably are associated with shallow lakes of low hazard potential. Overall, the Kyagar data (Fig. 8d) indicate that a minimum water pressure of around 510 kPa should be regarded as a threshold for general concern for GLOF occurrence in the region. However, consideration should be given to local conditions when applying the findings of this study to other locations around the globe.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "4 Results > 4.4 Anticipating the timing of GLOF events", "section_headings": ["4 Results", "4.4 Anticipating the timing of GLOF events"], "chunk_type": "text", "line_start": 231, "line_end": 233, "token_count_estimate": 242, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8797e62c950f3cf9", "text": "Document: 1 Introduction\nSection: 5 Discussion\nType: text\n\nAlthough predictive models related to ice-dammed lake development and subsequent GLOF hazard would best be based on modeling the physics of the systems, the controlling parameters are numerous and complex. For example, the mechanisms of glacial sliding, overburden pressure, and tensile and driving stresses require consideration, as do flexure and ice fracture mechanics, thermal erosion, and water pressure, amongst other controls (Carrivick et al., 2020), along with climatic influences (Ng et al., 2007; Richardson and Reynolds, 2000). Few of these controls are understood well, and, importantly, even where there is an adequate theory, the field data required to inform modeling are absent for specific potential GLOF locations. In this study, the glacier and lake interactions and their empirical relationships have been explored, and their effect on lake volume and draining processes has been examined. Understanding glacier surges, lake formation, and the interactions between lakes and glaciers is crucial for advancing knowledge and developing empirical or numerical GLOF models in mountainous regions (Carrivick et al., 2020; Quincey and Luckman, 2014). Glacier surge speed is routinely determined using remote sensing imagery (Paul, 2015), as is lake surface area (Quincey and Luckman, 2014). Thus, remote sensing provides a means to develop graphs similar to the graph of Fig. 6 for specific locations around the globe where ice-dammed lakes form due to glacier surging. Although the data within Fig. 6 are scattered, a negative relationship between surge velocity and lake volume is implied. Specifically, data points scatter around a median trend according to a theoretical -2power function (Fig. 6). Clearly, more data points within Fig. 6 would be desirable so that the relationship (if any) between ice surge velocity and lake volume might be better defined.\n\nThere is an urgent need for simpler methods to predict the probable triggering water levels that lead to GLOFs and the likely volume of the ice-dammed lakes that translate into GLOF hydrographs. Given that requirement, it is acknowledged that the relationships proposed herein are empirical and apply specifically to glaciers within the Karakoram region. However, there is no reason to suppose that similar functions based on geometric considerations (Zhang et al., 2023) and a critical depth (Zhao et al., 2017) might not be developed elsewhere, including for moraine-dammed lakes (Yao et al., 2010). Below, the approach is explored for glacial ice-dammed lakes worldwide.\n\nDespite the absence of long-term records, those that are available indicate that glacial ice-dammed lakes worldwide exhibit consistent behavior in terms of lake formation, filling, and volume gain in response to low glacier velocity (Bazai et al., 2021). Additionally, specific water pressure and critical normalized lake depth values for initiating outburst floods are evident (Fig. 8).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion", "section_headings": ["5 Discussion"], "chunk_type": "text", "line_start": 235, "line_end": 243, "token_count_estimate": 721, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "189fcdaf4bbede2b", "text": "Document: 1 Introduction\nSection: 5 Discussion\nType: text\n\nand a critical depth ( Zhao et al . , 2017 ) might not be developed elsewhere , including for moraine - dammed lakes ( Yao et al . , 2010 ) . Below , the approach is explored for glacial ice - dammed lakes worldwide . Despite the absence of long - term records , those that are available indicate that glacial ice - dammed lakes worldwide exhibit consistent behavior in terms of lake formation , filling , and volume gain in response to low glacier velocity ( Bazai et al . , 2021 ) . Additionally , specific water pressure and critical normalized lake depth values for initiating outburst floods are evident ( Fig . 8 ) .\n\nBuilding upon the information presented in Fig. 8 for the Karakoram, Fig. 9, based on 50 GLOF events from 10 glacial ice-dammed lakes, offers a depiction of the conditions under which glacier lake volume measurements are estimated with high accuracy. Considering lakes other than those within the Karakoram, the lake elevation (Fig. 9a) at the time of a GLOF was available for five lakes that have triggered a GLOF more than twice (Chilinji, Medvezhiy, Merzbacher, Russell, Rio Colonia), enabling the calculation of the normalized lake depths (Fig. 9b). The data points in Fig. 9c represent 27 lakes within the Pamir, Tian Shan, Greenland, and northern Patagonia for which lake volume data are available in the literature. These latter data were used to establish a relationship between the lake volume estimated using Eq. (2) and the reported volumes ( $R^2 = 0.972$ ; Fig. 9c).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion", "section_headings": ["5 Discussion"], "chunk_type": "text", "line_start": 235, "line_end": 243, "token_count_estimate": 416, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d96a0aaa6d67a39", "text": "Document: 1 Introduction\nSection: 5 Discussion\nType: figure\nFigure: Figure 9a and b serve as key components and summaries for this discussion, identifying the lake volumes, elevations, and critical normalized depth values for GLOF outbursts. Critical normalized lake depth values (n') exceed 0.60 in all\n\nFigure 9a and b serve as key components and summaries for this discussion, identifying the lake volumes, elevations, and critical normalized depth values for GLOF outbursts. Critical normalized lake depth values (n') exceed 0.60 in all", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion", "section_headings": ["5 Discussion"], "chunk_type": "figure", "figure_caption": "Figure 9a and b serve as key components and summaries for this discussion, identifying the lake volumes, elevations, and critical normalized depth values for GLOF outbursts. Critical normalized lake depth values (n') exceed 0.60 in all", "line_start": 244, "line_end": 244, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "088fee1e4e5e191b", "text": "Document: 1 Introduction\nSection: 5 Discussion\nType: text\n\ncases of GLOFs (Fig. 9b). From this result, we infer that a safe lake level can be defined as < 0.60, while the trigger level is  $\\ge 0.60$ . Values of n' < 0.60 were associated with slow, non-catastrophic lake drainage. Therefore, in the case of future ice-dammed lakes, values of critical depth (n') exceeding 0.60 should be a cause for concern, and 0.60 would serve as a warning level.\n\nEstimating the volume of an undrained ice-dammed lake from a field survey is dangerous due to floating ice, rugged terrain, and sudden drawdowns. The utilization of DEM measurements for lake volume estimation may also introduce high uncertainties or errors due to the difficulty in defining the lake depths (Carrivick et al., 2020; Emmer, 2018). However, for rapid response or mitigation policy purposes, the empirical model (Eq. 1 or Eq. 2) used in the current study proves to be quite efficient when applied to estimate the lake volume before a GLOF, not least because the errors in measurements from both satellite and UAV images are now quite small, as noted within the \"Data and methods\" section.\n\nAlthough GLOFs cannot be predicted from this approach, the likely volume of water that might be released catastrophically can be determined. For sites that are deemed to pose a threat to human life and infrastructure, once the lake volume is better constrained, through either DEM analysis or geometric considerations, the value of n' for any specific lake provides a ready indicator of the probability of an imminent GLOF. In contrast to the lower trend for water pressures associated with the Kyagar lakes, the higher water pressures required to cause the Khurdopin and Shishper lakes to empty may reflect greater structural integrity, possibly related to a greater downstream extent of the glacier dams. These structural issues can be examined in the future. Still, at this stage, if n' exceeds 0.6, an initial general warning could be issued to communities downstream of the ice dam. In principle, the estimated volume of a potential GLOF can then be routed downstream using standard hydrodynamic flood routing procedures to determine the timing, depth, and extent of flooding at locations where inundation is forecast. Thus, the severity of the likely impact on humankind can be determined, and specific warning times can be derived from the modeled rate of travel of the GLOFs. These results represent a step forward from the observations made by Carrivick et al. (2020), who proposed the exploration of the interaction between lake water and glaciers to understand the lake formation process and identify lake depth, level, and volume. Based on this understanding, empirical models can be generated to predict GLOFs in a timely manner.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "5 Discussion", "section_headings": ["5 Discussion"], "chunk_type": "text", "line_start": 245, "line_end": 251, "token_count_estimate": 682, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "997eca01491bbf1d", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: text\n\nDespite an escalating hazard from glacier lake outburst floods (GLOFs), understanding these hazards remains limited. It is imperative to determine the causes of these hazards, make timely predictions, and formulate new mitiga-", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "text", "line_start": 253, "line_end": 255, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b502fcb59b0cf92c", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: figure\nFigure\n\nImage /page/13/Figure/2 description: The image contains three plots labeled (a), (b), and (c), which analyze data related to Glacial Lake Outburst Floods (GLOFs).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "figure", "figure_caption": null, "line_start": 256, "line_end": 256, "token_count_estimate": 67, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a714c656b95139f", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: text\n\nPlot (a) displays a series of horizontal bar charts for different glacier lakes: Kyagar, Khurdopin, Merzbacher, Chilinji, Medvezhiy, Shishper, and an inset for Russel and Rio Colonia. The y-axis represents Elevation in meters above sea level (m.a.s.l.), and the x-axis represents Volume in million cubic meters (M m³). Each bar is divided into a light blue 'Safe lake level' and a red 'Triggering lake level'. Black dots indicate the volume and elevation of past GLOF events. Each lake has an associated n' value: Kyagar (n'=0.67), Khurdopin (n'=0.76), Merzbacher (n'=0.83), Chilinji (n'=0.60), Medvezhiy (n'=0.71), Shishper (n'=0.67), Russel (n'=0.80), and Rio Colonia (n'=0.79).\n\nPlot (b) is a vertical bar chart illustrating the 'Normalized Glacier Lake depth (n')'. The bar is color-coded, with a blue 'Safe Zone' for n' values up to approximately 0.6 and a red 'High Risk and Critical Zone' for values above 0.6 up to 1.0. Horizontal lines connect the n' values for each specific lake to their position on this risk scale, showing their triggering thresholds.\n\nPlot (c) is a scatter plot comparing 'Reference Volume, V\\_REF (M m³)' on the y-axis with 'Volume of Irregular Pentahedron, V\\_PEN (M m³)' on the x-axis. The plot includes data points from various locations, such as Medvezhiy-Pamir, Grænalón-Iceland, and Merzbacher-TianShan. The data points show a strong linear relationship, represented by a dashed red line with the equation V\\_REF = 1.0689 \\* V\\_PEN and an R² value of 0.972.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "text", "line_start": 257, "line_end": 263, "token_count_estimate": 495, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6304104185496c69", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: figure\nFigure: Figure 9. (a) The maximum volume of ice-dammed glacier lakes that results in GLOFs related to water surface elevation. In this representation, the red bands are defined by the range of n' values recorded just before each GLOF event was initiated. The blue bands represent lakes with volumes below the normalized lake depth of 0.60. (b) Normalized critical lake depths and the high-risk and critical zones for GLOFs, with an inferred gradation for risk within the safe zone, shown by shades of blue. (c) The relationship between the reference volume ( $V_{\\text{REF}}$ ) and the measured volume ( $V_{\\text{PEN}}$ ) obtained through a geometric approach. The locations of these glaciers are presented in Fig. S1 in the Supplement.\n\n**Figure 9.** (a) The maximum volume of ice-dammed glacier lakes that results in GLOFs related to water surface elevation. In this representation, the red bands are defined by the range of n' values recorded just before each GLOF event was initiated. The blue bands represent lakes with volumes below the normalized lake depth of 0.60. (b) Normalized critical lake depths and the high-risk and critical zones for GLOFs, with an inferred gradation for risk within the safe zone, shown by shades of blue. (c) The relationship between the reference volume ( $V_{\\text{REF}}$ ) and the measured volume ( $V_{\\text{PEN}}$ ) obtained through a geometric approach. The locations of these glaciers are presented in Fig. S1 in the Supplement.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "figure", "figure_caption": "Figure 9. (a) The maximum volume of ice-dammed glacier lakes that results in GLOFs related to water surface elevation. In this representation, the red bands are defined by the range of n' values recorded just before each GLOF event was initiated. The blue bands represent lakes with volumes below the normalized lake depth of 0.60. (b) Normalized critical lake depths and the high-risk and critical zones for GLOFs, with an inferred gradation for risk within the safe zone, shown by shades of blue. (c) The relationship between the reference volume ( $V_{\\text{REF}}$ ) and the measured volume ( $V_{\\text{PEN}}$ ) obtained through a geometric approach. The locations of these glaciers are presented in Fig. S1 in the Supplement.", "line_start": 264, "line_end": 264, "token_count_estimate": 407, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a134d778664c13b9", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: text\n\ntion policies to minimize losses. Herein, it has been shown tentatively that glacier surge speed may correlate negatively with ice-dammed lake volumes such that glacier dynamics control lake volumes. Consequently, in those cases where glacier surge speed is monitored, trends in surge speed can provide a timely indication that a lake might form, allowing the risk of a potential GLOF to be considered and mitigation measures to be reviewed. Identifying the critical depths, lake volumes, and pressures of ice-dammed lakes worldwide associated with GLOFs has indicated that GLOFs may be imminent when the normalized depth (n') for lakes exceeds a critical value (n'=0.60) with a typical water pressure on the dam face exceeding 510 kPa. Identifying a critical depth\n\nthat might lead to a GLOF is relatively straightforward and, thus, is a useful measure that provides a timely warning for downstream communities. Comparing published surveyed lake volumes with geometric volume estimates for 23 GLOF events from the Karakoram and 27 events from around the world, linear least-squares regression ( $R^2 = 0.972$ ) demonstrated that geometric estimates can be robust in the absence of detailed field or remote sensing surveys. Such an approach to determining lake volumes is useful on two accounts. Firstly, the approach can provide a quick estimation of lake volume. Then, if the lake volume is considered to be of concern, more detailed survey work can be commissioned to obtain a more accurate estimate of the volume. Sec-\n\nondly, in those situations where sufficient resources may not be available to conduct detailed volumetric surveys, the geometric approach provides a ready tool to obtain a reasonable volume estimate. Taken together, these findings suggest that future exploration should concentrate on specific volume and depth parameters to determine critical thresholds associated with normalized depth and the associated lake volume for future predictive purposes. In this respect, it should be noted that the water pressure recommended herein as potentially of concern (510 kPa) pertains to lake depths of ca. 50 m, whereas deeper lakes in other regions might drain at different values of pressure.\n\nData availability. Data and/or further information regarding the data used can be obtained from the corresponding authors upon request.\n\nSupplement. Supplementary information for this paper is available in the Supplement and at https://doi.org/10.1016/j.earscirev.2020. 103432 (Bazai et al., 2021) and https://doi.org/10.1016/j.gloplacha. 2021.103710 (Bazai et al., 2022). The supplement related to this article is available online at: https://doi.org/10.5194/tc-18-5921-2024-supplement.\n\nAuthor contributions. NAB conceptualized and designed the study and methodology, generated and compiled field data, processed data visualization, and drafted the manuscript. PAC contributed to conceptualization, methodology, interpretation, discussion, review, and editing. PC supervised funding acquisition and commented on the paper, and WH contributed to compiling the field data. NAB, ZG, LD, and JH contributed to remote sensing data analysis.\n\nCompeting interests. The contact author has declared that none of the authors has any competing interests.", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "text", "line_start": 265, "line_end": 284, "token_count_estimate": 794, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["103432", "103710"]}}
{"id": "732e64ad97c14008", "text": "Document: 1 Introduction\nSection: 6 Conclusion\nType: text\n\nonline at : https : / / doi . org / 10 . 5194 / tc - 18 - 5921 - 2024 - supplement . Author contributions . NAB conceptualized and designed the study and methodology , generated and compiled field data , processed data visualization , and drafted the manuscript . PAC contributed to conceptualization , methodology , interpretation , discussion , review , and editing . PC supervised funding acquisition and commented on the paper , and WH contributed to compiling the field data . NAB , ZG , LD , and JH contributed to remote sensing data analysis . Competing interests . The contact author has declared that none of the authors has any competing interests .\n\n*Disclaimer.* Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.\n\nAcknowledgements. Special thanks go to the monitoring team of the Gilgit-Baltistan Disaster Management Authority (GBDMA), Quaid-i-Azam University, and Karakoram International University for their support, data sharing, and technical assistance. We are also grateful to Iqtidar Hussain for sharing his expertise and to the Special Research Assistant Program of the Chinese Academy of Sciences. Additionally, we deeply thank the editor and the three reviewers for their valuable and constructive comments.\n\nFinancial support. This study was supported by the National Natural Science Foundation of China (grant no. 42350410445) and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (grant no. 2019QZKK0906).", "metadata": {"source_file": "data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md", "document_title": "1 Introduction", "section_path": "6 Conclusion", "section_headings": ["6 Conclusion"], "chunk_type": "text", "line_start": 265, "line_end": 284, "token_count_estimate": 436, "basins": [], "subbasins": ["Gilgit"], "countries": ["China"], "lake_ids": ["2019QZKK0906", "42350410445"]}}
{"id": "1a927ee08b8c834a", "text": "Document: remotesensing-13-02511-v2\nType: text\n\nAbstract: With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level's ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol and resource description framework query language (SPARQL) endpoint service is used to describe functions such as query and reasoning of the knowledge graph. The graph database is then used to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation between the fields of surveying and remote sensing.\n\nKeywords: knowledge graph; surveying; remote sensing; knowledge visualization", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "remotesensing-13-02511-v2", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 334, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["02511"]}}
{"id": "0247586765712c60", "text": "Document: remotesensing-13-02511-v2\nType: figure\nFigure\n\nImage /page/0/Picture/12 description: A graphic on a white background features a yellow circular icon with a white checkmark inside. To the right of the icon, the text 'check for updates' is displayed in black. The words 'check for' are on the top line, and the word 'updates' is on the bottom line in a bold font.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "remotesensing-13-02511-v2", "section_path": "", "section_headings": [], "chunk_type": "figure", "figure_caption": null, "line_start": 7, "line_end": 7, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["02511"]}}
{"id": "b2dc2a922fabbd3d", "text": "Document: remotesensing-13-02511-v2\nType: text\n\nCitation: Hao, X.; Ji, Z.; Li, X.; Yin, L.; Liu, L.; Sun, M.; Liu, Q.; Yang, R. Construction and Application of a Knowledge Graph. *Remote Sens.* **2021**, *13*, 2511. https://doi.org/10.3390/rs13132511\n\nAcademic Editor: Frédérique Seyler\n\nReceived: 5 June 2021 Accepted: 24 June 2021 Published: 26 June 2021\n\n**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "remotesensing-13-02511-v2", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 8, "line_end": 16, "token_count_estimate": 162, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["02511"]}}
{"id": "61235fd5e8da198a", "text": "Document: remotesensing-13-02511-v2\nType: figure\nFigure\n\nImage /page/0/Picture/17 description: The image shows the Creative Commons Attribution (CC BY) license icon. It is a horizontal rectangle with a grey top section and a black bottom section. On the left side of the grey section, there is a white circle with the letters 'CC' in black. On the right side, there is a white circle with a black stick figure icon, representing attribution. The black bottom section has the letters 'BY' in white.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "remotesensing-13-02511-v2", "section_path": "", "section_headings": [], "chunk_type": "figure", "figure_caption": null, "line_start": 17, "line_end": 17, "token_count_estimate": 121, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["02511"]}}
{"id": "6041def35d24bb6b", "text": "Document: remotesensing-13-02511-v2\nType: text\n\nCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "remotesensing-13-02511-v2", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 18, "line_end": 20, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["02511"]}}
{"id": "d8d3116d08873acc", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nIn 2012, Google officially proposed the concept of the knowledge graph, which aims to assist intelligent search engines [1]. After the knowledge graph was formally proposed, it was quickly popularized in academia and industry and was widely used in intelligent search, personalized recommendation, intelligence analysis, anti-fraud and other fields. Essentially, a knowledge graph is a semantic network and knowledge base with a directed graph structure that describes entities (concepts) and their relationships in the physical world in symbolic form. The knowledge graph is represented in the form of triples (Entity1-Relation-Entity2), where the nodes of the graph represent entities or concepts, and the edges represent the relationships between entities or concepts [2].\n\nKnowledge graphs are a new method of knowledge representation. In essence, the semantic web is an early form of the knowledge graph, which is an abstract concept that describes entities and relationships between entities in the objective world and is also a networked knowledge base composed of entities, properties, and relationships. A knowledge graph is a collection of concepts, entities, and their relationships in the abstract physical world [3]. The knowledge graph has changed the traditional method of information retrieval. On the one hand, knowledge graphs describe the semantic and attribute\n\nRemote Sens. 2021, 13, 2511 2 of 19\n\nrelationship between concepts to reason about concepts through fuzzy string matching. Conversely, knowledge graphs display the structured knowledge of classification and arrangement to users through the grid graphic information display interface. Concurrently, knowledge graphs solve the problem of manual filtering of useless information, which has practical significance for an intelligent society [4].\n\nKnowledge graphs can be divided into general knowledge graphs and domain knowledge graphs. Knowledge graphs used in surveying and remote sensing are domain knowledge graphs. To date, few studies have investigated knowledge graphs for surveying and remote sensing. Wang and others proposed a framework for remote-sensing interpretation of knowledge graphs [5]. Xie and others designed a framework for the construction of a large knowledge graph in the field of remote-sensing satellites [6]. Geoscience knowledge graphs are also used and have been studied in detail. Xu and others proposed the concept, framework, theory, and characteristics of geoscience graphs based on geoscience graphs and geoscience information graphs. Jiang proposed the process of constructing knowledge graphs and explored the key technology of geographic knowledge graphs [7]. Lu and others systematically reviewed the research progress on topics related to geographic knowledge graphs and analysed the key issues of current geoscience knowledge graph construction [8]. However, these studies all discuss the construction of knowledge graphs in theory and do not provide any real examples of constructing knowledge graphs. Based on previous research experience, this paper describes an example of constructing knowledge graphs for surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 22, "line_end": 34, "token_count_estimate": 684, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9799978a7cf8476c", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nothers proposed the concept , framework , theory , and characteristics of geoscience graphs based on geoscience graphs and geoscience information graphs . Jiang proposed the process of constructing knowledge graphs and explored the key technology of geographic knowledge graphs [ 7 ] . Lu and others systematically reviewed the research progress on topics related to geographic knowledge graphs and analysed the key issues of current geoscience knowledge graph construction [ 8 ] . However , these studies all discuss the construction of knowledge graphs in theory and do not provide any real examples of constructing knowledge graphs . Based on previous research experience , this paper describes an example of constructing knowledge graphs for surveying and remote sensing .\n\nThe construction of this knowledge graph can provide services for users of surveying and remote sensing, surveying and remote-sensing experts, and developers of surveying and remote-sensing software. Users can visualize knowledge through the knowledge graph and discover the relationship between knowledge more easily. Searches conducted through the knowledge graph improve the user's search efficiency. Remote-sensing experts gain insights and discover new rules in the field of surveying and remote sensing through the inference function of the knowledge graph. Software developers can integrate the knowledge graph into the remote-sensing product e-commerce platform, which can not only improve search efficiency, but also accurately recommend products for users. The professional field of surveying and remote sensing is the ladder of social progress. With the development of social intelligence, it is significant to study professional development technology for intelligent surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 22, "line_end": 34, "token_count_estimate": 380, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "568bb9182219080e", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs\nType: text\n\nThe system framework of a knowledge graph in the fields of surveying and remote sensing primarily refers to its construction mode structure, which describes the process of constructing a knowledge graph in the fields of surveying and remote sensing (Figure 1). The process of knowledge graphing in the field of surveying remote sensing can be divided into two parts: mode level construction and data layer construction.\n\nThe mode level is built on the data layer, and the factual expression in the data layer is standardized through the ontology library. The ontology is the conceptual template of the structured knowledge base. The knowledge base constructed through the ontology library has the advantages of a strong hierarchical structure and low redundancy. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts, and the data layer expresses knowledge in the form of triples (Entity 1-Relation-Entity 2) or (Entity-Attribute-Attribute Value). The logical structure of the knowledge graph in the field of surveying and remote sensing is shown in Figure 2.\n\nRemote Sens. **2021**, 13, 2511 3 of 19", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs"], "chunk_type": "text", "line_start": 36, "line_end": 42, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e8549b30d2a1908", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: A flowchart illustrating the process of creating a knowledge graph in the field of surveying and remote sensing. The flowchart is divided into two main parallel processes: \"Mode level construction\" and \"Data layer construction\".", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs"], "chunk_type": "figure", "figure_caption": null, "line_start": 43, "line_end": 43, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "29f01ae8f5209327", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs\nType: text\n\nUnder \"Mode level construction\", the steps are sequential: \"Application areas of knowledge graph\", followed by \"Determine the scope of ontology knowledge\", then \"Ontology construction\", and finally \"Ontology storage\".\n\nUnder \"Data layer construction\", the process begins with \"Surveying and remote sensing data\". This feeds into a sub-process labeled \"Knowledge acquisition\", which includes \"Concept and entity acquisition\" followed by \"Relationship acquisition\". The final step in this section is \"Data storage\".\n\nAn arrow connects the \"Ontology construction\" step from the mode level to the knowledge acquisition sub-process in the data layer.\n\nThe outputs of both main processes converge. An arrow from \"Ontology storage\" is labeled \"Ontology editing tool\", and an arrow from \"Data storage\" is labeled \"Relational database\". Both arrows point to a database symbol at the bottom, which represents the final product: \"Knowledge graph in the field of surveying and remote sensing\".", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs"], "chunk_type": "text", "line_start": 44, "line_end": 52, "token_count_estimate": 261, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ef7a08f459102720", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs\nType: figure\nFigure: Figure 1. The framework of the knowledge graph in the fields of surveying and remote sensing.\n\nFigure 1. The framework of the knowledge graph in the fields of surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs"], "chunk_type": "figure", "figure_caption": "Figure 1. The framework of the knowledge graph in the fields of surveying and remote sensing.", "line_start": 53, "line_end": 53, "token_count_estimate": 71, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4d4177c1def4078", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs\nType: figure\nFigure\n\nImage /page/2/Figure/3 description: A diagram illustrating a two-tiered structure, divided into an \"Ontology layer\" and a \"Data layer\". The Ontology layer at the top shows a hierarchical structure starting with \"Owl:Thing\". Below \"Owl:Thing\", connected by lines labeled \"Subclass\", are two boxes: \"Genre\" and \"Knowledge\", with an ellipsis between them. The Data layer at the bottom contains several boxes. From the \"Genre\" box, lines labeled \"Entity\" point to \"Surveying\" and \"Remote sensing\". From the \"Knowledge\" box, lines labeled \"Entity\" point to \"Collinear equation\" and \"Photogrammetry\". An ellipsis is shown between \"Remote sensing\" and \"Collinear equation\". Within the Data layer, two curved arrows labeled \"relation\" point from \"Surveying\" and \"Remote sensing\" to \"Photogrammetry\".", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs"], "chunk_type": "figure", "figure_caption": null, "line_start": 55, "line_end": 55, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "08cc911748d50f31", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs\nType: figure\nFigure: Figure 2. The logical structure of the knowledge graph in the fields of surveying and remote sensing.\n\nFigure 2. The logical structure of the knowledge graph in the fields of surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs"], "chunk_type": "figure", "figure_caption": "Figure 2. The logical structure of the knowledge graph in the fields of surveying and remote sensing.", "line_start": 57, "line_end": 57, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf87618d400ade98", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.1. Design of Disciplinary Knowledge Graph Mode in the Field of Surveying and Remote Sensing\nType: text\n\nThe mode level construction process begins from the application domain of the knowledge graph, determines the knowledge scope of the ontology, and then constructs the ontology. The construction of the ontology is the key link in the construction of the pattern layer. The ontology is an explicit description of the shared conceptual mode and is used to add semantics to semantic web pages and describe the relationship between concepts [9]. The ontology is the pattern layer, conceptual mode, and logical basis of the knowledge graph. The mode level of the knowledge graph in the field of surveying and remote sensing uses ontology to realize the storage and management of knowledge. When constructing an ontology, we mainly construct a collection of concepts from the subject words of remote-sensing teaching materials. The relationship is mainly a hierarchical relationship between the upper and lower positions. Entity filling is mainly obtained from structured data sources, and the ontology database is filled in a bottom-up manner.\n\nA domain ontology is a specialized ontology that describes concepts in a specific domain (such as remote sensing, meteorology, environment, etc.) and the relationship between concepts. The domain ontology of surveying and remote sensing, as a kind of unique ontology, can clearly describe the relationship between concepts belonging to and concepts in this domain. The basic principles of ontology construction are clarity, objectivity, consistency, minimum coding deviation, and minimum ontology constraints.\n\nRemote Sens. **2021**, 13, 2511 4 of 19\n\nOntology construction uses a seven-step method. The steps of this method are as follows: (1) determine the professional field and category of the ontology; (2) examine the possibility of reusing existing ontology; (3) list the important terms in the ontology; (4) define the hierarchical relationship between classes; (5) define the properties of the class; (6) define the constraints between the properties; and (7) create an instance [10].", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.1. Design of Disciplinary Knowledge Graph Mode in the Field of Surveying and Remote Sensing", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs", "2.1. Design of Disciplinary Knowledge Graph Mode in the Field of Surveying and Remote Sensing"], "chunk_type": "text", "line_start": 60, "line_end": 68, "token_count_estimate": 483, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "01e4070b9eec03cc", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design\nType: text\n\nIn this article, the data extraction method is used to construct the data layer. When extracting related entities, the relationship between the entities in the field of surveying and remote sensing is first defined. The relationship between entities is mainly the upper-lower relationship and the non-upper-lower relationship. Research on non-subordinate relationships in the knowledge graphs focuses on two aspects: entity attributes and entity relationships. The non-subordinate relationship of entity attributes is mainly used for triples: entity-attribute-attribute value, where the attribute depends on the corresponding entity. Each attribute will have its corresponding attribute value. In the definition of entity relationships, there is always a direct or indirect relationship between entities. Through the relationship analysis between entities, the general relationship between the entities used is defined. The relationship between entities mainly includes the relationship of belonging, containing, etc. These relationships are common relationships among entities in the field of surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs", "2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design"], "chunk_type": "text", "line_start": 70, "line_end": 72, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91951df23e784344", "text": "Document: 1. Introduction\nSection: 2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design > 2.2.2. Data Layer Construction\nType: text\n\nThe data layer construction process is based on unstructured data and uses manual, automatic, or semiautomatic techniques to extract knowledge from the data and store it in the database. Data acquisition is a key step in the construction of the data layer. The core of knowledge acquisition is how to automatically obtain knowledge elements of structured information, such as entities, relationships, and properties, from unstructured and semi-structured data sources. Typically, automatic or semi-automatic machine learning technology is used to extract entities, relationships, attributes, and other information about the knowledge graph from open multisource data [11]. Knowledge acquisition includes the extraction of entities, relationships, properties, etc. Entity acquisition is the process of automatically identifying called entities (knowledge points, type names, etc.) from text data sets. Relation extraction is the process of discovering semantic relationships between entities from data sources using methods such as machine learning. Attribute extraction is the process of extracting attribute information about entities from data sources. The difference between attribute extraction and relation extraction is not only to identify the attribute name of the entity but also to identify the attribute value of the entity. Therefore, most studies are based on rules for extraction. [12].", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design > 2.2.2. Data Layer Construction", "section_headings": ["2. The Theoretical Basis for the Construction of Knowledge Graphs", "2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design", "2.2.2. Data Layer Construction"], "chunk_type": "text", "line_start": 74, "line_end": 76, "token_count_estimate": 342, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e66d87c1510a2cb3", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs\nType: text\n\nConstruction of knowledge graphs is the core content of this article. It includes five parts: data acquisition and storage, ontology construction and storage, ontology and database mapping, query and reasoning of knowledge graphs, and visualizing the knowledge graph on Neo4j.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs", "section_headings": ["3. Construction of Knowledge Graphs"], "chunk_type": "text", "line_start": 78, "line_end": 80, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bddef5c409b52922", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: text\n\nThe data used in this article are from surveying and remote sensing. The data source is a textbook related to the field of surveying [13] and remote sensing [14]. DeepDive (http://deepdive.stanford.edu/, accessed on 6 May 2021) was used to extract semi-structured table data from unstructured text data [15]. DeepDive can extract structured data from unstructured data and perform a series of data processing steps to build a knowledge base and extract relationships. It is very good at handling data sources in different formats.\n\nRemote Sens. **2021**, 13, 2511 5 of 19\n\nDeepDive has good database support, supports PubMed and other database data sources, and is a data source that has been processed in natural language. DeepDive has established a framework to standardize the construction process of the knowledge base system and allows users to design their own extractors and markers according to the knowledge base that needs to be constructed. The relationship extraction process of DeepDive is shown in Figure 3. The DeepDive based domain text knowledge extraction method includes the following steps [16]:\n\n- (1) Data processing. First, the original corpus will be loaded. The natural language processing (NLP) tag is added. A set of candidate relationships and the sparse feature representation of each candidate relationship are extracted.\n- (2) Remote supervision of data and rules, and then various strategies will be used to supervise the data set so that we can use machine learning to learn the weight of the mode.\n- (3) Learning and inference: mode specification. Then, the advanced configuration of the mode will be specified.\n- (4) Error analysis and debugging. Finally, we will show how to use DeepDive's tags, error analysis and debugging tools.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "text", "line_start": 84, "line_end": 95, "token_count_estimate": 423, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2116e34166d42f80", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: figure\nFigure\n\nImage /page/4/Figure/6 description: A flowchart illustrating a data processing and machine learning pipeline, likely for relationship extraction. The main flow on the right begins with 'Data preparation', followed by 'Import the database', 'Text data table', and 'NLP processing'. This leads to 'POS/NER/dependency syntax analysis of text data, etc.', which then proceeds sequentially through 'Mention extraction', 'Candidate entity pair', 'Entity-pair feature extraction', and culminates in 'Data set: entity pair + feature + Label'. The final step is 'Training factor, get variable confidence'. A parallel branch on the left starts from 'Import the database' and goes to 'Entity pair data table with known relationship', which is then used to 'Matches the known relationship in the candidate entity pair'. Both the 'POS/NER...' step and the 'Matches the known relationship...' step feed into a box labeled 'Mark some data based on rules'. This box then leads to a 'Known variable table'. Finally, the 'Known variable table' feeds into the 'Data set: entity pair + feature + Label' step, merging the two branches of the flowchart.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "figure", "figure_caption": null, "line_start": 96, "line_end": 96, "token_count_estimate": 333, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5410b2018b301846", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: figure\nFigure: Figure 3. The relationship extraction process of DeepDive [15].\n\nFigure 3. The relationship extraction process of DeepDive [15].", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "figure", "figure_caption": "Figure 3. The relationship extraction process of DeepDive [15].", "line_start": 98, "line_end": 98, "token_count_estimate": 65, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3e1718dbbdd8c47b", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: text\n\nThe extraction process of domain knowledge based on DeepDive:\n\n- (1) Experiment preparation (preparation before knowledge extraction). First, DeepDive stores the involved input, intermediate and output data in a relational database. DeepDive supports many databases, such as Postgres, Greenplum and MySQL. The database used in this experiment is Postgres.\n- (2) Data processing. This part is divided into four steps: ① Loading the original input data. First, we convert the target data into an electronic text format. Only the two fields of document id and document content are reserved. We store the cleaned data in a comma-separated values (CSV) format file. Then import the original text data into the associated database. We set the data format of the document storage in the app. ddlog file. There are two text fields in the articles table, articles (id text, content text). Then, we put the compressed file of the original data into the specified input folder and run the \"DeepDive compile\" command. Then, we execute the \"DeepDive do articles\" command, which imports the original data into the articles table of the associated database. At this time, we can query the imported raw data by executing the query command. ② Adding NLP markups. We use the CoreNLP\n\nRemote Sens. 2021, 13, 2511 6 of 19\n\nnatural language processing system to add annotations to the original data. The steps of NLP are: first, the input original article is divided into sentences. The sentence is divided into words, and the part-of-speech tags, standard forms, dependency analysis and entity recognition tags of the words in the sentence are obtained. After NLP, some commonly used entities (person names, place names, etc.) can be marked. It is also necessary to ensure further entity identification of the data processed by NLP. The input are the data in the sentences table. The output are the marked data. Finally, we import the final marked data into the sentences\\_new table in the database. ③ Extracting candidate relation mentions. DeepDive proposes corresponding input and output interfaces, allowing users to design their entity or relationship extractors. Generally speaking, the SQL(Structured Query Language) statement is used as the input interface to extract data from the database; The output is the corresponding table in the database. We perform entity extraction on the data after entity recognition, and then establish the corresponding database table structure. ④ Extracting features for each candidate. First, we extract the feature description and store the feature in the func\\_feature table. The purpose is to use certain attributes or characteristics to represent each candidate pair. There is a library DDlib that can automatically generate features in DeepDive, which defines features that are not dependent on the domain. There are also many dictionaries in the DDlib library. These dictionaries contain words related to the correct classification of descriptions and relationships and are usually combined with domains and specific applications. We declare the extract\\_func\\_features function in app.ddlog. The input of this function includes the information of the two entities in the entity mention table and the NLP results in the sentence where the two entities are located. The output is the two entities and their characteristics.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "text", "line_start": 99, "line_end": 117, "token_count_estimate": 788, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e547cddd5192b175", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: text\n\nor characteristics to represent each candidate pair . There is a library DDlib that can automatically generate features in DeepDive , which defines features that are not dependent on the domain . There are also many dictionaries in the DDlib library . These dictionaries contain words related to the correct classification of descriptions and relationships and are usually combined with domains and specific applications . We declare the extract \\ _func \\ _features function in app . ddlog . The input of this function includes the information of the two entities in the entity mention table and the NLP results in the sentence where the two entities are located . The output is the two entities and their characteristics .\n\n- (3) Distant supervision with data and rules. We will use remote supervision to provide noisy label sets for candidate relationships to train machine learning models. Generally speaking, we divide the description method into two basic categories: mapping from secondary data for distant supervision and using heuristic rules for distant supervision [17]. However, we will use a simple majority voting method to solve the problem of multiple labels in each example. This method can be implemented in ddlog. In this method, first, we sum the labels (all -1, 0, or 1). Then, we simply threshold and add these labels to the decision variable table has\\_spouse. In addition, we also need to make sure that all the spouse candidates who are not marked with rules are not included in this table. Once again, we execute all the above.\n- (4) Learning and inference: model specification. We need to specify the actual model that DeepDive will perform learning and inference. DeepDive will learn the parameters of the model (the weights of features and the potential connections between variables). Then we perform statistical inferences on the learned model to determine that the probability of each variable of interest is true. ① Specifying prediction variables. In our experiment, we have a variable to predict the mention of each spouse candidate. In other words, we want DeepDive to predict the value of a Boolean variable for each mentioned spouse candidate to indicate whether the value is correct. DeepDive cannot only predict the value of these variables but also predict the marginal probability, that is, DeepDive's confidence in each prediction. ② Specifying features. We need to define the following: each has\\_spouse variable will be connected to the elements of the corresponding spouse\\_candidate row; We hope that DeepDive understands the weights of these elements from the data we remotely monitor; those weights of the element should be the same for the specific function of all instances. ③ Specifying connections between variables. We can use learning weights or given weights to specify the dependencies between predictors. In the experiment, we specify two such rules, which have fixed (given) weights. First, we define the asymmetric connection, that is, if the model considers that a person mentions p1 and another person mentions p2 as a spouse relationship in the sentence, then it should also consider the opposite.\n\nRemote Sens. **2021**, 13, 2511 7 of 19\n\nThe model should be strongly biased towards everyone mentioning a sign of marriage. Instead, we use negative weights for this operation. ④ Finally, we want to perform learning and inference using the specified model. This will build a model based on the data in the database, learn the weights, infer the expected or marginal probabilities of the variables in the model and then load it back into the database. In this way, we can see the probability of the has\\_spouse variable inferred by DeepDive.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "text", "line_start": 99, "line_end": 117, "token_count_estimate": 864, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba2ec93484b8e94b", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: text\n\n, then it should also consider the opposite . Remote Sens . * * 2021 * * , 13 , 2511 7 of 19 The model should be strongly biased towards everyone mentioning a sign of marriage . Instead , we use negative weights for this operation . ④ Finally , we want to perform learning and inference using the specified model . This will build a model based on the data in the database , learn the weights , infer the expected or marginal probabilities of the variables in the model and then load it back into the database . In this way , we can see the probability of the has \\ _spouse variable inferred by DeepDive .\n\nError analysis and debugging. To accurately analyze the experimental results, we first declare a score or a user-defined query sentence and define the part of the labeled data used for training. DeepDive uses this score to estimate the accuracy of the experiment. We declare these definitions in deepdive.conf and define deepdive.calibration.holdout\\_fraction as 0.25. The test set is 75% of the labeled data, and the test set is used to verify the correctness of the experimental results. Approximately 1400 labeled data and approximately 1000 data were used for testing. The graph on the left in Figure 4 is the correct rate graph. Under ideal conditions, the red curve should be close to the blue calibration line. However, this is not the case. It may be caused by the sparseness and noise of the training data of the test data. The middle graph in Figure 4 is the predicted number graph of the test set. The forecasted quantity map usually presents a \"U\" shape. The graph on the right in Figure 4 is the predicted probability quantity graph of the entire data set. Among them, the prediction data falling in the 0.5–0.6 probability interval indicates that there are still some hidden types of instances, and the features of DeepDive are insufficient for these instances. The predicted data whose probability does not fall at (0, 0.1) or (0.9, 1.0) are the data to be extracted. An important indicator to improve the quality of the system is to re-speculate the above data and attribute it to the probability interval (0, 0.1) or (0.9, 1.0).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "text", "line_start": 99, "line_end": 117, "token_count_estimate": 563, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d9c90ec5811a748b", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: figure\nFigure\n\nImage /page/6/Figure/3 description: The image contains three graphs arranged horizontally. The first graph on the left is titled \"Accuracy(Testing Set)\". It is a line graph plotting Accuracy against Probability. The x-axis, labeled \"Probability\", ranges from 0 to 1. The y-axis, labeled \"Accuracy\", also ranges from 0 to 1. There is a straight blue line from (0,0) to (1,1) and a red line with circular markers that generally follows the blue line but with some deviations. The second graph in the middle is a histogram titled \"Predictions(Testing Set)\". The x-axis is \"Probability\" from 0 to 1, and the y-axis is \"Predictions\" from 0 to 900. It shows a large blue bar on the far left, indicating a high number of predictions (around 900) with low probability, and a very small bar on the far right. The third graph on the right is a histogram titled \"Predictions(Whole Set)\". The x-axis is \"Probability\" from 0 to 1, and the y-axis is \"Predictions\" up to over 3000. This graph also has a large blue bar on the far left (around 3000 predictions), followed by several smaller bars, and a noticeable bar on the far right.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "figure", "figure_caption": null, "line_start": 118, "line_end": 118, "token_count_estimate": 357, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "392e18eedcab9b66", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition\nType: figure\nFigure: Figure 4. The test set correct rate calibration plot (Left), test set prediction plot (Middle), prediction plot for all data sets (Right).\n\nFigure 4. The test set correct rate calibration plot (Left), test set prediction plot (Middle), prediction plot for all data sets (Right).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.1. Data Acquisition"], "chunk_type": "figure", "figure_caption": "Figure 4. The test set correct rate calibration plot (Left), test set prediction plot (Middle), prediction plot for all data sets (Right).", "line_start": 120, "line_end": 120, "token_count_estimate": 111, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f321022de7743a59", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage\nType: text\n\nThe data of the knowledge graph is usually expressed in the form of triples, representing the relationship between entities or between entities and attribute values. In this article, the relational database MySQL and graph database Neo4j are used. The purpose of using MySQL is to realize the mapping between data and ontology and then to use existing tools to realize data query and reasoning. The purpose of using Neo4j is to update and search data. Neo4j can directly display the query results in the form of graphs to realize the function of knowledge visualization. Neo4j has local storage and data processing functions that are different from general databases, which can ensure high readability and integrity of data.\n\nThe extracted data are processed and stored in three tables, called \"knowledge\", \"knowledge\\_to\\_genre\", and \"genre\", which are imported into a MySQL database. The E-R(Entity Relationship) diagram is shown below (Figure 5).\n\nRemote Sens. **2021**, 13, 2511 8 of 19", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.2. Data Storage"], "chunk_type": "text", "line_start": 123, "line_end": 129, "token_count_estimate": 267, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1c3584032f9f3267", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage\nType: figure\nFigure\n\nImage /page/7/Figure/1 description: An entity-relationship diagram showing the structure of a database with three tables: 'knowledge', 'knowledge\\_to\\_genre', and 'genre'. The 'knowledge' table has columns 'knowledge\\_id' (primary key), 'knowledge\\_name', and 'knowledge\\_property'. The 'genre' table has columns 'genre\\_id' (primary key) and 'genre\\_name'. The 'knowledge\\_to\\_genre' table is a junction table with two foreign key columns, 'knowledge\\_id' and 'genre\\_id', linking the other two tables. This setup creates a many-to-many relationship, where a record in the 'knowledge' table can be associated with multiple genres, and a genre can be associated with multiple knowledge records.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.2. Data Storage"], "chunk_type": "figure", "figure_caption": null, "line_start": 130, "line_end": 130, "token_count_estimate": 249, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee6c9390d7352830", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage\nType: figure\nFigure: Figure 5. E-R diagram of data stored in the database.\n\nFigure 5. E-R diagram of data stored in the database.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.2. Data Storage"], "chunk_type": "figure", "figure_caption": "Figure 5. E-R diagram of data stored in the database.", "line_start": 132, "line_end": 132, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc8dda9f4fd3986c", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage\nType: text\n\nIn the entity table called \"knowledge\", each knowledge point is numbered, the attribute \"knowledge\\_id\" is created, and the attribute is used as the primary key. In the entity table called \"genre\", each category is numbered, the attribute \"genre\\_id\" is created, and the attribute is used as the primary key. In the relation table called \"knowledge\\_to\\_genre\", the properties \"genre\\_id\" and \"knowledge\\_id\" were set as the foreign keys of the entity tables \"genre\" and \"knowledge\" to create the relationship between genre and knowledge. For example, the knowledge \"collinear equation\" belongs to the field of \"surveying\"; \"genre\" is a \"class\". In the \"genre\" class, there are two entities: \"surveying\" and \"remote sensing\"; \"genre\" means the genre to which the knowledge node belongs. A knowledge node belongs to either the \"surveying\" genre or the \"remote-sensing\" genre.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.2. Data Storage", "section_headings": ["3. Construction of Knowledge Graphs", "3.1. Data Acquisition and Storage", "3.1.2. Data Storage"], "chunk_type": "text", "line_start": 133, "line_end": 135, "token_count_estimate": 280, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1419d9dacc7ea543", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction\nType: text\n\nThere are two ways to construct ontologies: top-down and bottom-up. The ontology construction of the open domain knowledge graph typically uses a bottom-up method to automatically extract concepts, types of concept, and relationships between concepts from the knowledge graph. The open world is too complex to be considered in a top-down manner. As the world changes, the corresponding concepts are still growing. Most domain knowledge graph ontology construction uses a top-down approach. On the one hand, the concept and scope of the domain knowledge graph are fixed or controllable compared to the open domain knowledge graph; on the other hand, the domain knowledge graph must yield high-accuracy results. Currently, domain knowledge graphs are widely used in voice assistants [18]. These domain knowledge graphs can meet most user needs while ensuring accuracy.\n\nThis article uses a top-down approach to construct ontologies, and the creation tool uses Protégé (https://protege.stanford.edu, accessed on 6 May 2021), an ontology editing tool [19]. The specific creation process is described as follows. First, we create the ontology class and two classes of knowledge and genre. Note that all classes are subclasses of \"Thing\", and all classes must be mutually exclusive; an instance can only be one of the two classes. Second, the relationship between the classes (i.e., the object properties) is created. This article created the object attribute \"belong\\_to\" (We classify the knowledge extracted from the \"Surveying\" textbook into the \"Surveying\" class. We define the relationship between this knowledge and the class \"surveying\" as \"belong\\_to\". Similarly, we classify the knowledge extracted from the \"Remote Sensing\" textbook as \"Remote Sensing\" class. We also define the relationship between this knowledge and the class \"Remote Sensing\" as \"belong\\_to\") to indicate that a certain knowledge point is in a certain field (type). Therefore, its attribute \"domain\" is defined as the class \"knowledge\", and its attribute \"range\" is the class \"genre\". \"Domain\" indicates which class the attribute belongs to. \"Range\" represents the value range of the attribute, which defines the inverse of this attribute as \"belong\\_to\". Thus, ontology describes reasoning rules for knowledge reasoning. The class, object properties, and data properties are shown below (Figure 6). For example, in resource description framework (RDF) data, the knowledge point \"electromagnetic waves\" belongs to the class of \"remote sensing\". When inquiring, the knowledge point \"electromagnetic wave\" can also be found in the class \"remote sensing\". Finally, class properties (data\n\nRemote Sens. **2021**, 13, 2511 9 of 19\n\nproperties) are created and are similar to object properties. Concurrently, Protégé also has a visual display function to show the structure of the ontology. The ontology structure is shown below (Figure 7).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction", "section_headings": ["3. Construction of Knowledge Graphs", "3.2. Ontology Construction and Storage", "3.2.1. Ontology Construction"], "chunk_type": "text", "line_start": 139, "line_end": 147, "token_count_estimate": 736, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f20e56ed31315287", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A composite image showing three side-by-side screenshots of an ontology editor's user interface, each displaying a different aspect of the 'Entities' tab. Red boxes highlight the selected tabs in each view. The left panel shows the 'Classes' hierarchy, with 'owl:Thing' at the top. Under 'owl:Thing' are 'genre' and 'knowledge'. 'genre' has a subclass 'Geography', which has subclasses 'Remote\\_Sensing' and 'Surveying'. The middle panel shows the 'Object properties' hierarchy, with 'owl:topObjectProperty' at the top and a sub-property 'belong\\_to'. The right panel shows the 'Data properties' hierarchy, with 'owl:topDataProperty' at the top, and sub-properties 'genre\\_id', 'knowledge\\_property', 'knowledge\\_name', 'knowledge\\_id', and 'genre\\_name'.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction", "section_headings": ["3. Construction of Knowledge Graphs", "3.2. Ontology Construction and Storage", "3.2.1. Ontology Construction"], "chunk_type": "figure", "figure_caption": null, "line_start": 148, "line_end": 148, "token_count_estimate": 296, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "78708bf823b2445a", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction\nType: figure\nFigure: Figure 6. Construction of ontology class (left), object properties (middle), and data properties (right).\n\nFigure 6. Construction of ontology class (left), object properties (middle), and data properties (right).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction", "section_headings": ["3. Construction of Knowledge Graphs", "3.2. Ontology Construction and Storage", "3.2.1. Ontology Construction"], "chunk_type": "figure", "figure_caption": "Figure 6. Construction of ontology class (left), object properties (middle), and data properties (right).", "line_start": 150, "line_end": 150, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "714586bc561cbe8e", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction\nType: figure\nFigure\n\nImage /page/8/Picture/4 description: A diagram illustrating a simple ontology with three nodes. At the top, a node labeled \"owl:Thing\" is enclosed in a light purple rectangle with a green border. Below it, two nodes are positioned side-by-side: \"knowledge\" on the left and \"genre\" on the right, both in light purple rectangles with black borders. Each node contains a yellow circle to the left of its text. There are three connections: two solid blue lines with hollow triangular arrowheads point from \"knowledge\" and \"genre\" up to \"owl:Thing\", and an orange dashed line with a hollow triangular arrowhead points from \"knowledge\" to \"genre\".", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction", "section_headings": ["3. Construction of Knowledge Graphs", "3.2. Ontology Construction and Storage", "3.2.1. Ontology Construction"], "chunk_type": "figure", "figure_caption": null, "line_start": 152, "line_end": 152, "token_count_estimate": 221, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f626671e99680802", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction\nType: figure\nFigure: Figure 7. Visual display of ontology structure.\n\nFigure 7. Visual display of ontology structure.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.1. Ontology Construction", "section_headings": ["3. Construction of Knowledge Graphs", "3.2. Ontology Construction and Storage", "3.2.1. Ontology Construction"], "chunk_type": "figure", "figure_caption": "Figure 7. Visual display of ontology structure.", "line_start": 154, "line_end": 154, "token_count_estimate": 56, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "209067c8f3ab77e9", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.2. Ontology Storage\nType: text\n\nThe ontology storage mode in this article is the Relational Database Management System (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, and then divide it into several modes according to the mapping mode, such as horizontal storage, decomposition storage, vertical storage and hybrid storage. As the storage mechanism and data management capabilities of relational databases are relatively mature, relational database storage is widely used in many storage methods.\n\nThis article uses the RDF and web ontology language (OWL) to describe ontologies [20]. RDF is used to represent any resource information and describes resources through the mode of attribute-attribute value [21]. OWL is used to express the relationship between classes, the constraints of the set cardinality, the equality relationship, the attribute type, and the characteristics of the attribute [22]. Compared with other ontology description languages, OWL has better description ability and more description vocabulary. The description vocabulary expands the reasoning and query capabilities of the ontology.\n\nThe ontology is constructed and stored using the ontology editing tool Protégé. It cannot only construct and operate the ontology but also visually display the generated ontology, including the display of the hierarchical relationship between ontology concepts, as well as the visual display of ontology conceptual entities, entity relationships, and entity attributes. When the ontology is created using the ontology description language OWL and Protégé, semiautomatic construction can be achieved. Protégé can also reason about the ontology based on the hierarchical relationship of the ontology, with the help of Jena's query mode, and can also realize the editing operation of the ontology in the RDF and OWL languages [23].", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.2. Ontology Construction and Storage > 3.2.2. Ontology Storage", "section_headings": ["3. Construction of Knowledge Graphs", "3.2. Ontology Construction and Storage", "3.2.2. Ontology Storage"], "chunk_type": "text", "line_start": 157, "line_end": 163, "token_count_estimate": 436, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a35278289556e5d", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping\nType: text\n\nTwo standards to convert the structured data of the relational database into RDF format data have been developed by the RDB2RDF studio of W3C. The process is applied by the D2RQ tool [24].\n\nRemote Sens. **2021**, 13, 2511 10 of 19\n\nThe first standard is direct mapping, which is defined by the rule that tables in the database are classes in the associated ontology. For example, there are 3 tables for the data stored in MySQL. After mapping, the ontology has 2 classes instead of the 3 classes previously defined. The columns of tables are attributes, and the rows are instances. The content in each cell of tables is text. If a certain column in the cell is a foreign key, then it will not be able to map the data in the database to the defined ontology. In this case, the shortcomings of direct mapping are marked. In response to this defect, the RDB2RDF studio proposed R2RML and allows users to flexibly edit and set mapping rules. This mapping also provides the ability to view existing relational data in the RDF data mode, which is represented by mapping the structure selected by the customer and the target vocabulary. R2RML mapping is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementations. The processor can provide virtual SPARQL protocol and resource description framework query language (SPARQL) endpoints, generate RDF dumps, or provide link data interfaces on the mapped relational data [25]. The specific mapping diagram is shown below (Figure 8).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping", "section_headings": ["3. Construction of Knowledge Graphs", "3.3. Ontology and Database Mapping"], "chunk_type": "text", "line_start": 165, "line_end": 171, "token_count_estimate": 407, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13757a9c05101bcb", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A diagram illustrating the mapping between an 'Ontology layer' and a 'Data layer', separated by a horizontal line. The Ontology layer at the top shows a hierarchical structure. A central concept 'owl:Thing' is at the apex. Below it are two concepts, 'knowledge' and 'genre', both linked to 'owl:Thing' with arrows indicating they are subclasses. There is also a dashed arrow pointing from 'knowledge' to 'genre'. The Data layer at the bottom displays a database schema with three tables. The 'knowledge' table has columns: 'knowledge\\_id', 'knowledge\\_name', and 'knowledge\\_property'. The 'genre' table has columns: 'genre\\_id' and 'genre\\_name'. A linking table, 'knowledge\\_to\\_genre', connects them with columns 'knowledge\\_id' and 'genre\\_id', representing a many-to-many relationship. Dashed arrows labeled 'mapping' connect the 'knowledge' concept in the ontology layer to the 'knowledge' table in the data layer, and the 'genre' concept to the 'genre' table.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping", "section_headings": ["3. Construction of Knowledge Graphs", "3.3. Ontology and Database Mapping"], "chunk_type": "figure", "figure_caption": null, "line_start": 172, "line_end": 172, "token_count_estimate": 344, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "320b7f0b57cd99d0", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping\nType: figure\nFigure: Figure 8. Schematic diagram of ontology to database mapping.\n\nFigure 8. Schematic diagram of ontology to database mapping.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.3. Ontology and Database Mapping", "section_headings": ["3. Construction of Knowledge Graphs", "3.3. Ontology and Database Mapping"], "chunk_type": "figure", "figure_caption": "Figure 8. Schematic diagram of ontology to database mapping.", "line_start": 174, "line_end": 174, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e345e4370ab1f01c", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.4. Query and Reasoning\nType: text\n\nThis article describes how to use D2RQ to setup a SPARQL service endpoint and use query operations in the browser. A SPARQL endpoint provides a service that is compliant with the SPARQL protocol. Through the default or defined mapping file, RDF data can be queried in the rules of RDF. In other words, to complete the final query, D2RQ converts the SPARQL query into an SQL statement based on the mapping file and then returns the result to the user [26].\n\nThe following steps are executed: (1) start the D2R server; (2) enter the browser to start the SPARQL endpoint; and (3) enter the SPARQL statement to execute the query. Concurrently, operations can also be queried by writing Python scripts. The third-party library SPARQL Wrapper of Python can easily interact with endpoints.\n\nUsing D2RQ to open the endpoint service has two disadvantages: it does not support publishing RDF data directly to the network through the endpoint and does not support the inference. To solve these problems, certain components of Fuseki, Jena, and the tuple database (TDB) in Apache Jena are investigated experimentally. Fuseki is a SPARQL server provided by Apache Jena, which is primarily run as a web application or as an embedded server. Jena provides resource description framework schema (RDFS). In the case of a single machine, storage layer technology can provide high RDF storage performance [27]. Next, we must define reasoning rules and conduct knowledge reasoning.\n\nRemote Sens. **2021**, 13, 2511 11 of 19", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.4. Query and Reasoning", "section_headings": ["3. Construction of Knowledge Graphs", "3.4. Query and Reasoning"], "chunk_type": "text", "line_start": 177, "line_end": 185, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ca5c1d76e30e8f7", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j\nType: text\n\nBased on the literature [2], the knowledge graph has basic query and reasoning functions; however, the visualization function of the knowledge graph has not been reflected. Therefore, the graph database Neo4j is used to visualize the knowledge graph [28]. Neo4j provides good visualizations of knowledge graphs. The specific implementation process and visualization are shown below. The process of generating the knowledge graph is as follows.\n\nFirst, table data are converted into the format of CSV. These files are then moved to the import directory of Neo4j because Neo4j defaults to open files in the import directory. After starting the database, the Cypher language [29] is used to import data, and the construction mode of the node is created (k: knowledge {name: \"absolute orientation\"}). This statement creates a node with a knowledge label, and this node has a name attribute, an attribute value of \"absolute orientation\", and a variable name k. The Cypher sentence for importing structured data entity data into the database is Code 1. The result of Code 1 is shown in Figure 9.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j"], "chunk_type": "text", "line_start": 187, "line_end": 191, "token_count_estimate": 284, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ffcf5b4fcd3ee56", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language\nType: text\n\nLOAD CSV WITH HEADERS FROM \"file:///knowledge.csv\" AS line MERGE (k:knowledge{id:line.knowledge\\_id, name:line.knowledge\\_name, genre:line.knowledge\\_genre})", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 1 Entity Data Imported Based on Cypher Language"], "chunk_type": "text", "line_start": 193, "line_end": 195, "token_count_estimate": 107, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f0a0381942c21f1d", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language\nType: figure\nFigure\n\nImage /page/10/Figure/6 description: A diagram on a white background illustrating a relationship between a large dataset and a specific data point with its neighbors. On the left, a large circle is filled with hundreds of small blue dots. One dot is colored red and enclosed in a red square. An arrow points from this square to the right side of the image. The right side displays a zoomed-in view, showing a central red circle with the text \"Remote Sensing\". This is surrounded by eleven blue circles, each containing a related term. The terms in the blue circles are: \"aspect map\", \"analytical\", \"aerial photog...\", \"adjacent area\", \"additive colir vie...\", another \"aerial photog...\", \"active sensor\", \"accuracy assess...\", \"adjoining\", \"altitude\", and another \"analytical\".", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 1 Entity Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": null, "line_start": 196, "line_end": 196, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa29e8f7447c930e", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language\nType: figure\nFigure: Figure 9. Part of the visualization shows the result of importing entities into the database.\n\nFigure 9. Part of the visualization shows the result of importing entities into the database.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 1 Entity Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": "Figure 9. Part of the visualization shows the result of importing entities into the database.", "line_start": 198, "line_end": 198, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae2fe1203115e795", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language\nType: text\n\nThe name of the imported file is \"knowledge.csv\", and the label is \"knowledge\". Attributes and attribute values correspond to row and column data in the table. The imports of nodes and relationships are similar. The Cypher sentence for importing structured relational data into the database is Code 2. The result of Code 1 is shown in Figure 10.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 1 Entity Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 1 Entity Data Imported Based on Cypher Language"], "chunk_type": "text", "line_start": 199, "line_end": 201, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "25cd21da964dfa23", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: text\n\nLOAD CSV WITH HEADERS FROM \"file:///knowledge\\_to\\_genre.csv\" AS line MATCH (from:knowledge{id:line.knowledge\\_id}),(to:genre{id:line.genre\\_id})\n\nMERGE (from)-[r:belong\\_to{Relation:line.knowledge\\_to\\_genre}]->(to)\n\nThe name of the imported file is \"knowledge\\_to\\_genre\". The partial result of the imported visualization is shown below (Figure 11). In this picture, the two red nodes, \"Surveying\" and \"Remote Sensing\", represent the class of knowledge, and the blue node represents knowledge.\n\nRemote Sens. 2021, 13, 2511 12 of 19", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "text", "line_start": 203, "line_end": 211, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ae85ecec9638bab", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: figure\nFigure\n\nImage /page/11/Figure/1 description: A concept map or mind map visualizing terms related to \"Remote Sensing.\" The central node is a red circle labeled \"Remote Sensing.\" Numerous lines radiate outwards from this central node, each labeled with \"belong to,\" connecting to a large number of surrounding nodes. These outer nodes are blue circles, each containing a specific term or concept. The blue nodes are arranged in several concentric rings around the central red node. Some of the terms in the blue circles include \"digital filtering,\" \"infrared imagery,\" \"satellite photo map,\" \"texture analysis,\" \"active sensor,\" \"passive sensor,\" \"image overlay,\" \"stereop.,\" and \"digital elevation model.\"", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": null, "line_start": 212, "line_end": 212, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ee90a15420b9a5e", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: text\n\n$\\textbf{Figure 10.} \\ \\ \\text{Part of the visualization shows the result of importing relationships into the database}.$", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "text", "line_start": 213, "line_end": 215, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "82824974db7c18d5", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: figure\nFigure\n\nImage /page/11/Figure/3 description: A network visualization diagram on a white background, showing a central cluster of nodes connected to a large number of peripheral nodes. In the center, there are three red circular nodes labeled 'Remote Sensing' and 'Surveys', with the third label being partially obscured. Adjacent to them is a green node labeled 'Geogra'. These central nodes are connected by thin grey lines to a multitude of light blue circular nodes that form a large, roughly circular or hexagonal shape around the center. Each blue node contains a word or short phrase, such as 'digital image', 'satellite', 'sensor', 'camera', and 'infrared'. The overall structure represents a hub-and-spoke model, illustrating the relationships between central concepts and numerous related terms.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": null, "line_start": 216, "line_end": 216, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8218c6c81043151e", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: figure\nFigure: Figure 11. Part of the visualization of the knowledge graph in the field of surveying and remote sensing.\n\nFigure 11. Part of the visualization of the knowledge graph in the field of surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": "Figure 11. Part of the visualization of the knowledge graph in the field of surveying and remote sensing.", "line_start": 218, "line_end": 218, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "acf083f524712ef3", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: text\n\nThe database contains 1024 nodes, which belong to the two classes of nodes \"knowledge\" and \"genre\". The nodes in the knowledge graph are the primary components of the knowledge graph, primarily the knowledge of surveying and remote sensing. The\n\nRemote Sens. **2021**, 13, 2511 13 of 19\n\nlibrary also contains 1295 relationships, which represent the relationship between nodes in the class \"knowledge\" and nodes in the class \"genre\". For example, \"belong\\_to\" is the belonging relationship between knowledge and type. The library also contains attribute information, such as the \"genre\" and \"name\" of the \"knowledge\" node. Certain basic information for creating the database is shown below (Figure 12).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "text", "line_start": 219, "line_end": 225, "token_count_estimate": 217, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cbd1e88b6c1e24a6", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: figure\nFigure\n\nImage /page/12/Picture/2 description: A screenshot of a user interface panel titled 'Database Information' on a dark grey background with white text. The panel is divided into three sections. The first section, 'Node Labels', contains three oval-shaped labels: a beige one showing '\\*(1,024)', a red one with 'genre', and a cyan one with 'knowledge'. The second section, 'Relationship Types', has two grey rectangular labels: one showing '\\*(1,295)' and another with 'belong\\_to'. The third section, 'Property Keys', displays four grey rectangular labels with the text 'Relation', 'genre', 'id', and 'name'.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": null, "line_start": 226, "line_end": 226, "token_count_estimate": 230, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3995790eabeaaa44", "text": "Document: 1. Introduction\nSection: 3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language\nType: figure\nFigure: Figure 12. Part of the information of the knowledge graph knowledge base in the field of surveying and remote sensing.\n\n**Figure 12.** Part of the information of the knowledge graph knowledge base in the field of surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Construction of Knowledge Graphs > 3.5. Visualizing the Knowledge Graph on Neo4j > Code 2 Relational Data Imported Based on Cypher Language", "section_headings": ["3. Construction of Knowledge Graphs", "3.5. Visualizing the Knowledge Graph on Neo4j", "Code 2 Relational Data Imported Based on Cypher Language"], "chunk_type": "figure", "figure_caption": "Figure 12. Part of the information of the knowledge graph knowledge base in the field of surveying and remote sensing.", "line_start": 228, "line_end": 228, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aac54fa5a87bb65e", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph\nType: text\n\nApplication analysis of knowledge graph is an important means to verify the value of knowledge graph. This article verifies the application value of the knowledge graph from the two application scenarios of \"domain relevance analysis\" and \"knowledge reasoning in the field of surveying and remote sensing\" in the smart campus platform.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph", "section_headings": ["4. Application Analysis of the Knowledge Graph"], "chunk_type": "text", "line_start": 231, "line_end": 233, "token_count_estimate": 89, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "98634b52feb13d5a", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis\nType: text\n\nIn many fields, there are various degrees of correlation. For example, in education, there is a common phenomenon in which pieces of knowledge are related between different disciplines of the same major. Zhou and others proposed a method for constructing a scientific knowledge graph based on the degree of interdisciplinary association, which aims to help students quickly understand the relationship between disciplines [30]. The relevance analysis based on the knowledge graph can assist students in choosing courses and improve teaching quality.\n\nIn recent years, with the development of science and technology, the \"smart campus platform\" has risen rapidly. Therefore, in the field of surveying and remote sensing, the knowledge graph can be leveraged in the context of the smart campus platform. The same knowledge is shown to exist in multiple domains, thereby highlighting the association between fields (Figure 13). The association between domains is related to the number of common knowledge between domains. The more the number of common knowledge, the higher the association between fields. In this research, if one knowledge belongs to both \"surveying\" and \"remote sensing\", then the knowledge is the common knowledge between the two fields. The more common knowledge between two fields, the higher degree of association between fields.\n\nRemote Sens. **2021**, 13, 2511 14 of 19", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.1. Domain Relevance Analysis"], "chunk_type": "text", "line_start": 235, "line_end": 241, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f88860a63bcc0514", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis\nType: figure\nFigure\n\nImage /page/13/Figure/1 description: A data visualization of a network graph on a white background, showing three separate, fan-shaped clusters of nodes. Each cluster has a central red circular node from which numerous thin grey lines radiate out to connect with hundreds of teal circular nodes. The teal nodes are arranged in dense, curved layers around their respective central red node. The cluster on the left is the smallest, the one in the middle is larger, and the one on the right is the largest. There appears to be text within each node, but it is too small to be legible.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.1. Domain Relevance Analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 242, "line_end": 242, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "60ab0b000161aae9", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis\nType: figure\nFigure: Figure 13. Display of relevance between courses.\n\nFigure 13. Display of relevance between courses.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.1. Domain Relevance Analysis"], "chunk_type": "figure", "figure_caption": "Figure 13. Display of relevance between courses.", "line_start": 244, "line_end": 244, "token_count_estimate": 51, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a993fa8573528551", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis\nType: text\n\nThe domain is the class in ontology. In this research, the domains of surveying and remote sensing respectively correspond to the classes of \"Surveying\" and \"Remote Sensing\" in the knowledge graph. Among all the knowledge, some knowledge belongs to the class of \"Surveying\", and some knowledge belongs to the class of \"Remote Sensing\". Some knowledge belongs to both the class of \"Surveying\" and the class of \"Remote Sensing\". These are the common knowledge of the two fields. Then this common knowledge determines the degree of association between the two fields (Figure 13).\n\nThe picture (Figure 13) contains nodes of \"Surveying\" and \"Remote Sensing\", as well as all the knowledge belonging to the field of remote sensing and surveying, which also contains the relationship \"belong\\_to\" between classes nodes and knowledge. The knowledge graph contains 1022 knowledge points. There are 273 knowledge points shared by the two disciplines. Its repetition rate is 26.7%. According to the degree of relevance, it can be used as a reference when selecting courses. If you were a student in a field unrelated to surveying and remote sensing and sought to have a general and comprehensive understanding of the field, then in order to save time and cost, you could just choose the two subjects of photogrammetry and remote sensing principles and applications. Accordingly, professional students in this field can quickly help such students understand the degree of coverage of knowledge content between subjects, help them quickly understand a nearby subject that has already studied a subject, and improve learning efficiency.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.1. Domain Relevance Analysis", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.1. Domain Relevance Analysis"], "chunk_type": "text", "line_start": 245, "line_end": 249, "token_count_estimate": 398, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc7f287b66db177d", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing\nType: text\n\nKnowledge reasoning is primarily based on known knowledge to infer new knowledge or distinguish incorrect knowledge. The reasoning function of knowledge is a prominent feature of the knowledge graph. Compared to traditional knowledge reasoning,\n\nRemote Sens. **2021**, 13, 2511 15 of 19\n\nknowledge reasoning based on knowledge graphs is more flexible, and its methods are more diverse. There are many methods; however, the method used in this article is primarily based on the reasoning of description logic and thus primarily introduces knowledge reasoning based on this method.\n\nThe description logic system is primarily divided into four parts: (1) three basic elements (concepts, relationships and entities); (2) the axiom set, to which the concept belongs; (3) the assertion set of the entity; and (4) the reasoning mechanism. Reasoning tools based on supporting OWL DL (description logic) language include FaCT++, Racer, and pellet [31]. In addition to existing tools, knowledge can be reasoned by writing rules.\n\nIn this knowledge graph, the reasoning of knowledge is described by writing rules based on the reasoning method of description logic. This article primarily involves two types of reasoning: entity and relational. Entity reasoning infers unknown entities based on existing entities and relationships (e.g., known entity-relation-unknown entity). Relational reasoning infers the relationship between one or more entities by editing rules under the condition of existing entities (e.g., known entity-unknown relationship-known entity).", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing"], "chunk_type": "text", "line_start": 251, "line_end": 269, "token_count_estimate": 384, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b20ca48866ff3bc", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing\nType: text\n\n, Racer , and pellet [ 31 ] . In addition to existing tools , knowledge can be reasoned by writing rules . In this knowledge graph , the reasoning of knowledge is described by writing rules based on the reasoning method of description logic . This article primarily involves two types of reasoning : entity and relational . Entity reasoning infers unknown entities based on existing entities and relationships ( e . g . , known entity - relation - unknown entity ) . Relational reasoning infers the relationship between one or more entities by editing rules under the condition of existing entities ( e . g . , known entity - unknown relationship - known entity ) .\n\nGenerally, missing knowledge is inherent to knowledge graphs, that is, the incompleteness of entities or relationships. We can apply the knowledge graph in the field of surveying and remote sensing to complement the knowledge graph in the field of surveying and remote sensing. Knowledge graph completion is an important way to acquire knowledge. The goal of knowledge graph completion is to find these missing items of knowledge and add them to the knowledge graph so that the knowledge graph tends to be complete [32]. A knowledge graph is named G, and its basic components include the entity set  $E = e_1, e_2, \\dots e_i$  (*i* is the number of entities), relation set  $R = r_1, r_2 \\dots r_j$  (*j* is the number of relations) and corresponding triple set  $T = \\{(e_m, r_k, e_n)\\}\\ (e_m, e_n \\in E, r_k \\in R)$ . Since the number of entities E and relationships R in the knowledge graph are limited, there may be some entities and relationships that are not in G. According to the content we want to complete, we can divide knowledge completion into three subtasks: ① Given a partial triplet  $T_1 = (?, r_k, e_n)$ , we predict the head entity. ② Given a partial triplet  $T_2 = (e_m, r_k, ?)$ , we predict the tail entity. ③ Given a partial triplet  $T_3 = (e_m, ?, e_n)$ , we predict the relationship between entities. According to whether the entities and relationships belong to the original knowledge graph, we can divide the knowledge graph completion into static knowledge graph completion and dynamic knowledge graph completion. The entities and relationships in the completion of the static knowledge graph are all in the original knowledge graph. The entities and relationships in the completion of the dynamic knowledge graph are not in the original knowledge graph. Through the completion of the knowledge graph, the collection of entities and relationships of the original knowledge graph can be expanded.\n\nKnowledge graph completion is the most widely used field of knowledge reasoning. The original intention of a large number of knowledge graph reasoning algorithms is to be applied to knowledge graph completion, such as the Markov logic network (MLN), translating relation embeddings (TransR), capsule network-based embedding (CapsE), and relational graph neural network with hierarchical attention (RGHAT). All the methods mentioned above can determine whether there is a certain relationship between any entities by reasoning in the vector space, and then realize the completion of the knowledge graph.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing"], "chunk_type": "text", "line_start": 251, "line_end": 269, "token_count_estimate": 848, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c276792be125f03", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing\nType: text\n\nthe collection of entities and relationships of the original knowledge graph can be expanded . Knowledge graph completion is the most widely used field of knowledge reasoning . The original intention of a large number of knowledge graph reasoning algorithms is to be applied to knowledge graph completion , such as the Markov logic network ( MLN ) , translating relation embeddings ( TransR ) , capsule network - based embedding ( CapsE ) , and relational graph neural network with hierarchical attention ( RGHAT ) . All the methods mentioned above can determine whether there is a certain relationship between any entities by reasoning in the vector space , and then realize the completion of the knowledge graph .\n\nIntegrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform can help students discover new entities or relationships between entities. For example, relational reasoning is based on the Jena tool, as shown in code 3 (based on the SPARQL language). This code defines a rule named \"rule\", which means that if there is an entity that belongs to \"Remote Sensing\", then this entity belongs to \"Geography\", which is (Remote Sensing-belong\\_to-Geography). For example: knowing (Aberration-belong\\_to-Remote Sensing), according to this code you can obtain the result: (Aberration-belong\\_to-Geography). The visual display of the whole reasoning process is shown in Figure 14.\n\nRemote Sens. **2021**, 13, 2511 16 of 19", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing"], "chunk_type": "text", "line_start": 251, "line_end": 269, "token_count_estimate": 401, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df859e20777ed3f3", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language\nType: text\n\n@prefix: <a href=\"http://www.kbdemo.com#\">\n@prefix owl: <a href=\"http://www.w3.org/2002/07/owl#\">\n@prefix rdf: <a href=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n@prefix rdf: <a href=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">http://www.w3.org/1999/02/22-rdf-syntax-ns#</a>\n\n@prefix xsd: <XML Schema>.\n\n@prefix rdfs: <a href=\"http://www.w3.org/2000/02/rdf-schema#\">http://www.w3.org/2000/02/rdf-schema#>.\n\n[rule:(?k:belong\\_to ?g)(?g:hasname ?n)(?n:genre\\_name 'Remote Sensing')->(?k rdf:type:Geography)]", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing", "Code 3 Inference Rules Based on SPARQL Language"], "chunk_type": "text", "line_start": 271, "line_end": 282, "token_count_estimate": 300, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc167fc4323e6256", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language\nType: figure\nFigure\n\nImage /page/15/Figure/6 description: A diagram illustrating a process labeled \"Inference results\". On the left, two elements are shown being added together with a plus sign. The top element is a network graph with a central red node labeled \"Remote Sensing\" connected to numerous surrounding blue nodes. The bottom element shows a relationship where the red \"Remote Sensing\" node is linked to a green node labeled \"Geogra...\" by an arrow labeled \"belong\\_to\". An arrow labeled \"Inference results\" points from the left side to the right side. On the right, the resulting network graph is displayed, which is similar to the initial graph but with the central node now being the green \"Geogra...\" node, connected to the same cluster of blue nodes.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing", "Code 3 Inference Rules Based on SPARQL Language"], "chunk_type": "figure", "figure_caption": null, "line_start": 283, "line_end": 283, "token_count_estimate": 242, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee7c5a2d4200d76d", "text": "Document: 1. Introduction\nSection: 4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language\nType: figure\nFigure: Figure 14. Visual display of the inference process.\n\nFigure 14. Visual display of the inference process.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Application Analysis of the Knowledge Graph > 4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing > Code 3 Inference Rules Based on SPARQL Language", "section_headings": ["4. Application Analysis of the Knowledge Graph", "4.2. Knowledge Reasoning in the Field of Surveying and Remote Sensing", "Code 3 Inference Rules Based on SPARQL Language"], "chunk_type": "figure", "figure_caption": "Figure 14. Visual display of the inference process.", "line_start": 285, "line_end": 285, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d26b4fc63b5769df", "text": "Document: 1. Introduction\nSection: 5. Summary and Prospect\nType: text\n\nIn the field of surveying and remote sensing, rapid acquisition, efficient processing and effective application of remote-sensing data are the core tasks. However, in the face of the massive amount of data accumulated, as well as the heterogeneous, decentralized, and dynamic update characteristics of the massive amount of data, it is difficult to realize the semantic integration, interoperability and sharing platform construction of massive data application services. Since the knowledge graph is a kind of semantic network, hierarchical interconnection and semantic processing capabilities can be realized in the form of a graphical structure, and the system and relevance of knowledge can be displayed more intuitively. The knowledge graph provides a better organization and management method for isolated information and knowledge. It describes the real-world entities, concepts, and the relationships between entities and concepts in a structured and semantic form and organizes information into a form that is easier for people to understand. The purpose is to express the objective world as a well-structured knowledge expression. It is hailed as a booster for the next generation of artificial intelligence. The contributions of this article are as follows:\n\n- (1) To quickly obtain effective data from massive amounts of heterogeneous, decentralized, and dynamically updated data. This article proposes a method for constructing a subject knowledge graph in the field of surveying and remote sensing.\n- (2) To verify the application value of the knowledge graph in the field of surveying and remote sensing. This article verifies its functions from the query, visualization and reasoning of the knowledge graph.\n  - The challenges and countermeasures encountered in the research are as follows:\n- (1) The connection between the mode layer and the data layer. The knowledge graph in the field of surveying and remote sensing is mainly divided into two parts: mode\n\nRemote Sens. **2021**, 13, 2511 17 of 19\n\nlevel construction and a data layer. The mode level is the foundation of the data layer. Through the factual expression of the ontology library standard data layer, ontology is the conceptual template of a structured knowledge base. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts. The data layer expresses knowledge in the form of triples (entity 1-relation-entity 2) or (entity-attribute-attribute value). Realizing the association between ontology and data at two levels is a major challenge in constructing a knowledge graph. This article uses the D2RQ tool to realize mapping from ontology to the database. The D2RQ tool converts the structured data of the relational database into data in RDF format. This mapping also provides the ability to view existing relational data that exist in the RDF data model, which is represented by mapping the structure selected by the customer and the target vocabulary. The R2RML mapping itself is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementation. The processor can provide virtual SPARQL endpoints on the mapped relational data, or generate RDF dumps, provide a link data interface.\n\n(2) Application and practice of the domain knowledge graph. This article gives an example of the application of integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform. The knowledge visualization application of the domain knowledge graph on the smart campus platform can assist teachers and students in selecting courses. The domain knowledge graph is applied to knowledge reasoning on the smart campus platform, which can help teachers and students discover and reason about new knowledge, as well as new relationships between knowledge.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Summary and Prospect", "section_headings": ["5. Summary and Prospect"], "chunk_type": "text", "line_start": 288, "line_end": 325, "token_count_estimate": 838, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "00433b641b66a77b", "text": "Document: 1. Introduction\nSection: 5. Summary and Prospect\nType: text\n\ncan provide virtual SPARQL endpoints on the mapped relational data , or generate RDF dumps , provide a link data interface . ( 2 ) Application and practice of the domain knowledge graph . This article gives an example of the application of integrating the knowledge graph in the field of surveying and remote sensing into the smart campus platform . The knowledge visualization application of the domain knowledge graph on the smart campus platform can assist teachers and students in selecting courses . The domain knowledge graph is applied to knowledge reasoning on the smart campus platform , which can help teachers and students discover and reason about new knowledge , as well as new relationships between knowledge .\n\nKnowledge in the field of surveying and remote sensing is diverse and complex, and knowledge graphs can be studied in more detail in the following areas. Data in these fields of study are typically images, and the recognition and acquisition of knowledge in image data are understudied; in particular, the knowledge graph of image data lacks a time dimension. Thus, future research should investigate how to add the time dimension to the knowledge graph and how to extend the time dimension to the application of the knowledge graph. If the above problems can be broken through, then the knowledge graph in the field of surveying and remote sensing will have greater potential application value:\n\n- (1) The discovery of new rules of surveying and remote sensing: The continuous increase in surveying and remote-sensing data and the continuous improvement of digital management and utilization technology have provided great convenience for scientific researchers to carry out research work. The surveying and remote-sensing knowledge graph provides support for the insight and discovery of regular knowledge of surveying and remote-sensing resources by associating a large amount of surveying and remote sensing knowledge into a network structure. Researchers can discover various knowledge and rules hidden behind the development process through the analysis of surveying and remote-sensing data to provide relevant scientific research personnel and scientific research policy makers with scientific research directions and a policy-making basis.\n- (2) Application of machine learning methods in the analysis of knowledge graphs in the field of surveying and remote sensing: From the development process of the combination of machine learning and knowledge graphs (the knowledge graph as a complex network; traditional machine learning methods to conduct graph mining and analysis on the knowledge graph; further application of deep learning methods and graph neural network methods in knowledge graphs), the value and function of the knowledge graphs have been further embodied. The surveying and remotesensing domain knowledge graph is a special domain knowledge graph, and the analysis method in the general knowledge graph is used to mine and analyse the graph, but it cannot make full use of the structure and characteristics of the surveying and remote-sensing domain knowledge graph. For the specific structural features and entity attributes in the knowledge graph of surveying and remote sensing, it\n\nRemote Sens. **2021**, 13, 2511 18 of 19\n\nis necessary to design specific machine learning methods or deep neural network structures. At the same time, for different application scenarios, different objective functions are usually designed to learn the parameters of the algorithm. Therefore, the study of machine learning and deep learning mining methods for the knowledge graph in the field of surveying and remote sensing is helpful to the further analysis and application of the knowledge graph in the field of surveying and remote sensing.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Summary and Prospect", "section_headings": ["5. Summary and Prospect"], "chunk_type": "text", "line_start": 288, "line_end": 325, "token_count_estimate": 770, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f7e58945a83301a", "text": "Document: 1. Introduction\nSection: 5. Summary and Prospect\nType: text\n\nknowledge graph . For the specific structural features and entity attributes in the knowledge graph of surveying and remote sensing , it Remote Sens . * * 2021 * * , 13 , 2511 18 of 19 is necessary to design specific machine learning methods or deep neural network structures . At the same time , for different application scenarios , different objective functions are usually designed to learn the parameters of the algorithm . Therefore , the study of machine learning and deep learning mining methods for the knowledge graph in the field of surveying and remote sensing is helpful to the further analysis and application of the knowledge graph in the field of surveying and remote sensing .\n\n(3) Construction of the service platform of the knowledge graph in the field of surveying and remote sensing: In the construction of the knowledge graph in the field of surveying and remote sensing, the work efficiency is affected due to the problem of scattered tools. The next research plan is to build a knowledge graph service platform in the field of surveying and remote sensing to realize the integration of tools and services. At the data source level, it integrates all kinds of open and available data in the field of surveying and remote sensing, as well as data unique to each demand side. Through the provided functions of surveying and remote sensing data acquisition, data storage, ontology construction, graph construction and update, a knowledge graph of the field of surveying and remote sensing that can be updated in time can be constructed. In terms of services, through the provision of knowledge service algorithms and models such as knowledge queries, knowledge visualization, and knowledge reasoning, a service platform provides targeted knowledge services for different roles.\n\n**Author Contributions:** Conceptualization, X.H. and Z.J.; methodology, X.H.; validation, X.L., Q.L. and R.Y.; formal analysis, Z.J.; investigation, L.L.; resources, X.H.; data curation, X.L.; writing—original draft preparation, X.H.; writing—review and editing, L.L., L.Y.; visualization, M.S.; supervision, X.L.; project administration, Z.J.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.\n\n**Funding:** This research was supported by the National Key Research and Development Project of China (No. 2016YFC0502106), National Science and Technology Major Project of China (No. 2018ZX07111002) and the National Natural Science Foundation of China(No. 41476161).\n\nInstitutional Review Board Statement: Not applicable.\n\nInformed Consent Statement: Not applicable.\n\n**Data Availability Statement:** The storage URL of the structured raw data to construct the knowledge graph is: https://github.com/hao1661282457/Knowledge-graphs.git (accessed on 25 June 2021).\n\n**Acknowledgments:** We thank AJE (https://www.aje.cn/ (accessed on 25 June 2021)), for editing the English text of this manuscript.\n\nConflicts of Interest: The authors declare no conflict of interest.", "metadata": {"source_file": "data/('remotesensing-13-02511-v2', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Summary and Prospect", "section_headings": ["5. Summary and Prospect"], "chunk_type": "text", "line_start": 288, "line_end": 325, "token_count_estimate": 746, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["2016YFC0502106", "2018ZX07111002", "41476161"]}}
{"id": "437fd2e5035f05b4", "text": "Document: s10346-021-01831-1\nType: text\n\nAbstract On June 25, 2020, Jinweng Co in Yiga, Tibet, experienced an outburst flood that resulted in catastrophic damage to farmland and roads. The complex causal factors for glacial lake outburst flooding (GLOF) are not fully understood. This paper provides a systematic analysis of the contributing factors leading to the GLOF disaster in terms of meteorological triggering, glacial activity, lake expansion, landslide, and glacial collapse. The analysis is based on multi-source remote sensing approaches. Pixel offset tracking of Sentinel-1 images shows changes in the glacier flow velocity from 2017 to 2020. Sentinel-2 and Landsat-8 images and inventory data have revealed the expansion of the lake since 1998. The satellite precipitation measurements revealed that the highest daily rainfall in recent years occurred approximately 4 days before the GLOF. Time series synthetic aperture radar (SAR) backscattering images and interferograms suggest that a landslide had occurred from the western lateral moraine into the lake. Additionally, SAR images suggest possible ice collapse from the glacier tongue into the lake. The causal mechanism for the June 2020 GLOF event was likely the dam failure triggered by heavy rainfall and combined with landslides and ice collapses. Our research can provide a reference to identify and mitigate glacial lake outburst disasters in mountainous regions based on satellite optical and radar images.\n\nKeywords Glacial lake outburst flood (GLOF) · Glacier threedimensional flow velocity · Outburst mechanism · Multi-source remote sensing", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 379, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "ffef1d36094186b0", "text": "Document: s10346-021-01831-1\nSection: Introduction\nType: text\n\nGlacial lake outburst flood (GLOF) is a sudden and rapid downstream discharge of a significant volume of water. Previous studies (Evans and Clague 1994; Nadim et al. 2006; Yamada 1998; Emmer and Cochachin 2013a, Emmer and Vilímek 2013b, Emmer 2017) identified two groups of factors that induce lake outbursts. The first set consists of dynamic causes, such as slope movement into the lake, earthquakes, heavy rainfall, or snowmelt. The second set includes long-term factors such as the melting of buried ice, hydrostatic pressure changes, and other long-term degradation processes. GLOFs often have catastrophic consequences for communities and infrastructure located downstream. Glacier recede and thinning in high mountains generate the formation and development of glacial lakes (Richardson and Reynolds 2000; Haeberli et al. 2001, 2002). Monitoring-related glacial movement and changes in glacier lakes and the surroundings help us better understand how the ice mass propagates in space, how their movements evolve over time, and how external factors control their\n\nbehaviors (Satyabala 2016; Ma et al. 2020). Owing to the inherently challenging landscapes, comprehensive remote sensing methods are needed to study and monitor GLOFs.\n\nThe outburst of Jinweng Co (\"Co\" refers to \"lake\" in Tibetan) in Yiga village, Tibet, caused intense and sudden flooding on June 25, 2020. This grave disaster inundated and destroyed 382.43 km<sup>2</sup> of farmland, washed away over 43.9 km of roads, and flooded 45% of the Yiga Scenic Area project (Wang et al. 2020a). Over 2,000 glacial lakes have been detected across the Himalayas (Fujita et al. 2013), so the potential for further hazards is significant. Moreover, the eastern Himalayas are considered as one of the most severely deformed areas due to intense tectonic activities and earthquakes. The melting, thinning, and receding of temperate glaciers leads to lake expansion and is related to slope movement. Extreme precipitation plays an important role in triggering slope movement, while an increase in temperature can result in increased melting, permafrost degradation, and rockfalls (Hu et al. 2019a; Wang et al. 2020a, b; Lu and Kim 2021).\n\nMany studies have documented that snow/ice accumulation/ avalanches and landslides significantly impact outburst flooding (Allen et al. 2016; Clague and Evans 2000; O'Connor et al. 2001; Worni et al. 2012). The Jinweng Co disaster originated from a parent glacier and included an outburst of the proglacial lake. The realtime monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF (Wang et al. 2020a). However, the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated. The evolution of the glacier, the proglacial lake, and the surrounding area prior to the GLOF event has not yet been analyzed. A complete assessment of triggering factors (glacier activity, precipitation, air temperature, slope movement, and ice collapse) is still lacking.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 18, "token_count_estimate": 795, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "0dce398de372bbfd", "text": "Document: s10346-021-01831-1\nSection: Introduction\nType: text\n\nCo disaster originated from a parent glacier and included an outburst of the proglacial lake . The realtime monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF ( Wang et al . 2020a ) . However , the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated . The evolution of the glacier , the proglacial lake , and the surrounding area prior to the GLOF event has not yet been analyzed . A complete assessment of triggering factors ( glacier activity , precipitation , air temperature , slope movement , and ice collapse ) is still lacking .\n\nThus, this study addresses the following key question: Can we identify precursory characteristics of the 2020 GLOF at Jinweng Co in eastern Tibet? To address this question, we aimed to understand the movement of glaciers and the change in morainedammed lakes, as well as temperature and precipitation records in the study area. We used spaceborne C-band Sentinel-1 data to retrieve the time series two-dimensional (2D) displacement and three-dimensional (3D) glacier flow velocity. The interpretation was then augmented with Sentinel-2 and Landsat-8 optical images and inventory data to construct changes to the area of the lake before and after the event. Meteorological observations were examined to understand the climatic properties of the region. Additionally, we analyzed SAR intensity images and interferograms to infer the glacier tongue changes and identify landslide activity. This led to further clarification regarding the outburst triggering mechanisms.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 8, "line_end": 18, "token_count_estimate": 411, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "49025ac6adf61ca7", "text": "Document: s10346-021-01831-1\nSection: Study area\nType: text\n\nThe study region is within the Tanggula Mountains, which are home to some of the highest mountains in the world. It is also the most humid region of the southeastern Qinghai-Tibet Plateau (QTP) (Fig. 1). The topography of the study area is affected by the compression of the Indian Ocean-Eurasian continental plate, uplift of the QTP, strong faulting activity, and fluvial incision. The climate is influenced by both East Asian and Indian monsoons (Ding 2013). Precipitation during the rainy season is mainly caused by airflow from the southwest, west, and northwest (Li et al. 2009). The temperate glaciers in this study area are small and primarily oriented in the north-south direction with large ice caps (You and Yang 2013). Consequently, the area is subject to glacial hazards such as glacier debris flows, GLOFs, and glacier surges, which are further increased by climate change (Allen et al. 2016; Bazai et al. 2021; Chiarle et al. 2007; Wang et al. 2020a, b; Kääb et al. 2021). Generally, a glacial lake outburst results from external forces such as ice or rock fall. snow avalanches, landslides, rainstorms, glacier surges, rapid snow melting, and/or internal processes such as the ablation of buried ice in moraines and the release of lake water inside the ice body (Wang et al. 2020a).\n\nJinweng Co (30.356 N°, 93.631 ° E) is a proglacial morainedammed lake (Fig. 2) and is also the largest glacial lake within the Nidou Zangbo basin (\"Zangbo\" refers to rivers in Tibetan) (Zheng et al. 2021). The lake is oriented in a north-south direction and is characterized by steep slopes and lateral moraines. The slopes are approximately 40°, and the average elevation of lateral moraines is more than 100 m above the lake level (Zheng et al. 2021). As shown in Fig. 2d, the length is approximately 1.8 km, and its width near the glacier tongue is 0.24 km, while the width near the dam was 0.33 km before the lake outburst. The parent glacier tongue enters\n\nFig. 1 Overview of the study area showing Jinweng Co and coverage of multiple datasets outlined in purple (Sentinel-2), orange (Landsat-8), blue (Ascending Sentinel-1), and red (Descending Sentinel-1)\n\nthe lake after passing through a steep ice cliff approximately 350 m long with an average slope of approximately 35 ° (Fig. 2). The parent glacier is a temperate glacier with a mean annual temperature of approximately o °C. The snow basin at high altitude serves as the accumulation zone, while the tongue is the ablation zone. This study investigates the triggering factors for the June 2020 GLOF event in Jinweng Co by analyzing the movement of the parent glaciers and changes to the lake and surroundings using multi-satellite remote sensing datasets.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Study area", "section_headings": ["Study area"], "chunk_type": "text", "line_start": 20, "line_end": 28, "token_count_estimate": 742, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "c4d6db7826e71638", "text": "Document: s10346-021-01831-1\nSection: Data and methods\nType: text\n\nThe multi-track C-band Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 and Landsat-8 optical images were used to investigate the triggering factors of the 2020 GLOF event (Fig. 3). The Copernicus Sentinel-1A/B and Sentinel-2 Level-1C images for this study are available free of cost from the Sentinel Scientific Data Hub of the European Space Agency. Multi-temporal Landsat images were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/). In addition, daily and monthly precipitation measurements from the satellite Global Precipitation Measurement (GPM) (Skofronick 2017) were obtained to analyze the meteorological factors. In addition, inventory data from 1975 to 2018 were obtained from the National Tibetan Plateau Data Center (TPDC) to evaluate temporal variations in Jinweng Co (https://data.tpdc.ac.cn) (Wang 2015).", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Data and methods", "section_headings": ["Data and methods"], "chunk_type": "text", "line_start": 30, "line_end": 32, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "5596219eb861976c", "text": "Document: s10346-021-01831-1\nSection: Data and methods > Satellite datasets\nType: text\n\nNinety-eight interferometric wide (IW) swath mode ascending Sentinel-1 and eighty-two descending Sentinel-1 images from March 2017 through October 2020 were acquired. The pixel spacing in the ground range and azimuth direction was ~ 4 m and ~ 14 m, respectively. Sentinel-2 data with multi-spectral instrument (MSI) images were acquired on May 1 and July 27, 2020, and provided a spatial resolution of 10 m. Landsat-8 MSI images from 2018 to 2019 with a spatial resolution", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "text", "line_start": 34, "line_end": 36, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "b3f2539a53f86c1f", "text": "Document: s10346-021-01831-1\nSection: Data and methods > Satellite datasets\nType: figure\nFigure\n\nImage /page/1/Figure/11 description: A satellite map of a mountainous, snow-covered region, with longitude ranging from 93°25' to 93°55' and latitude from 30°15' to 30°35'. The map features several colored rectangular overlays indicating the coverage of different satellites: a blue box for 'Ascending Sentinel-1', a red box for 'Descending Sentinel-1', an orange box for 'Landsat-8', and a smaller purple box for 'Sentinel-2'. Two locations, 'Yiga' and 'Jinweng Co', are marked with green dots. A light blue arrow indicates the 'Flood direction' from west to east. A legend in the bottom-left corner defines symbols for Glacier, Lake, and Main city. A scale bar in the bottom-right shows a scale from 0 to 6 km, and a north arrow is in the top-left corner.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "figure", "figure_caption": null, "line_start": 37, "line_end": 37, "token_count_estimate": 250, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "be5f6e23e2314056", "text": "Document: s10346-021-01831-1\nSection: Data and methods > Satellite datasets\nType: text\n\nFig. 2 Overview of Jinweng Co and its parent glacier. a An overall view of Jinweng Co and surroundings. **b** The parent glacier before the GLOF event. **c** The crevassed glacier tongue terminated in Jinweng Co. d Morphological parameters of Jinweng Co. e The eastern lateral moraine and steep slopes. f The landslide zone at the western lateral moraine. **g** The parent glacier after the GLOF event. Photos: Q. Quying Source: Guoxiong Zheng (used with permission)", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "text", "line_start": 38, "line_end": 40, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "733a73dc49f541be", "text": "Document: s10346-021-01831-1\nSection: Data and methods > Satellite datasets\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: A composite image with seven panels, labeled (a) through (g), showing various aerial and landscape views of a glacial environment. Each panel includes a compass rose indicating north. Panel (a) is an overview of a valley with snow-capped mountains, showing the Jinweng Co glacier and a dashed line indicating the 'Flow direction'. Panel (b) is a top-down view of a glacier through clouds. Panel (c) shows a glacier terminating in a milky green glacial lake. Panel (d) displays the dimensions of the lake, labeled as 1.8 km long, 0.33 km wide at one end, and 0.24 km wide at the other. Panel (e) highlights a 'Lateral moraine' with a dashed line along the side of the lake. Panel (f) points out a 'Landslide zone' in red on the mountainside next to the lake. Panel (g) is another high-altitude aerial view of the glacier and surrounding mountains, partially obscured by clouds.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "figure", "figure_caption": null, "line_start": 41, "line_end": 41, "token_count_estimate": 277, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "ffe54db6328fae96", "text": "Document: s10346-021-01831-1\nSection: Data and methods > Satellite datasets\nType: text\n\nof 30 m were added to derive the temporal changes of Jinweng Co. Information on the Sentinel-1/2 and Landsat data are listed in Table 1. and the data coverages are shown in boxes in Fig. 1.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "text", "line_start": 42, "line_end": 44, "token_count_estimate": 70, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "be1a739860adf0c8", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms\nType: text\n\nSAR intensity images can be used to detect landscape changes due to their sensitivity to terrain slope, surface roughness, and dielectric constant (e.g., Lu and Meyer 2002; Kim et al. 2017; Zribi and Dechambre 2017). Multi-temporal SAR backscattering intensity images were used in this study to infer changes in glaciers, lakes, and surroundings before the lake outburst event. Interferometric synthetic aperture radar (InSAR) can map surface deformation in the spatial-temporal view, which is a direct manifestation of slope movement (Hu et al. 2019a; Lu and Meyer 2002; Lu et al. 2003; Lu and Kim 2021).", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms"], "chunk_type": "text", "line_start": 46, "line_end": 48, "token_count_estimate": 187, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "a5e39243188f6215", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Change detection with optical images\nType: text\n\nThe interpretation of optical images is commonly employed to support GLOF mapping and inventory (Strozzi et al. 2010). There are countless glaciers in this study area, which induce enormous glacial lakes as glacier recedes. The Sentinel-2 and Landsat-8 images allow us to recognize geomorphological features related to ice mass movements, such as crevasses, debris flows, and outburst flooding. For instance, a glacial lake may have green patterns with fine texture in one optical image, and after the glacial lake outbursts or shrinks, its edge will form a gray-white submerged zone with obvious breaches and colluvial deposits. Using multi-temporal images, we can interpret the changes in glacial lakes and analyze the possibility of their collapse.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Change detection with optical images", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Change detection with optical images"], "chunk_type": "text", "line_start": 50, "line_end": 52, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "b2a26d49688023eb", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method\nType: text\n\nWe carried out the offset tracking procedure implemented by GAMMA software to estimate the two-dimensional displacement (Wegmuller et al. 1998; Werner and Wegmuller 2000; Gomez et al. 2019). Offset tracking includes intensity tracking and speckle tracking based on maximizing the cross-correlation of SAR image patches (e.g., Strozzi et al. 2002; Pritchard et al. 2005) to derive glacier displacement for pairs of images acquired at different times. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used to assist the co-registration of the SAR images to minimize geometric artifacts in offset-tracking displacements due to high topography (Kobayashi et al. 2009; Liu et al. 2020). A template of  $64 \\times 64$  and  $4 \\times 1$  was adopted in this study. Thresholds of amplitude correlation range between 0.2 and 0.4.\n\nTable.1 Basic parameters of the SAR and optical datasets used in this study", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Offset tracking method", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Offset tracking method"], "chunk_type": "text", "line_start": 54, "line_end": 58, "token_count_estimate": 283, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "da3a30c69e07f528", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method\nType: table\nTable\n\n| Data                            | Sentinel-1            |                       | Sentinel-2            | Landsat-8             |\n|---------------------------------|-----------------------|-----------------------|-----------------------|-----------------------|\n| Pixel Spacing (azimuth × range) | 14 m × 4 m            |                       | 10 m                  | 30 m                  |\n| Number of SAR images            | 98(ascending)         | 82(descending)        | 2                     | 2                     |\n| Acquisition period              | 2017/03/21-2020/10/31 | 2017/03/16-2020/10/26 | 2020/05/01-2020/07/27 | 2018/06/06-2019/06/25 |", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Offset tracking method", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Offset tracking method"], "chunk_type": "table", "table_caption": null, "columns": ["Data", "Sentinel-1", "", "Sentinel-2", "Landsat-8"], "table_row_start": 1, "table_row_end": 3, "line_start": 59, "line_end": 63, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "55dc3ea5d041446c", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method\nType: text\n\nFig. 3 Flow chart of remote sensing methods used in this study", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Offset tracking method", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Offset tracking method"], "chunk_type": "text", "line_start": 64, "line_end": 66, "token_count_estimate": 51, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "fa2f403f96174c49", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A flowchart titled 'Recent Landslides' illustrating a methodology for analyzing landslides using three different types of remote sensing data. The flowchart is organized into three parallel processing streams. The first stream starts with an 'External DEM' (Digital Elevation Model). This data undergoes 'Offset tracking', then 'Stacking', and finally '3-D inversion'. The result is the 'Derivation of 3D annual flow velocity', shown with a corresponding map image. The second stream uses 'Ascending and Descending SAR images'. These images go through 'Co-registration' to create 'Original interferograms', which are then processed by 'Interferograms filtering' to produce 'Deformation interferograms', also illustrated with a sample image. The third stream begins with 'Optical images'. These are used for 'Change detection', leading to an 'Intensity change' analysis. The final output of this stream is 'Optical interpretation', which is depicted with two comparative map images.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Offset tracking method", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Offset tracking method"], "chunk_type": "figure", "figure_caption": null, "line_start": 67, "line_end": 67, "token_count_estimate": 301, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "46658611ea5b10c0", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method > Pixel offset tracking small baseline subsets (PO-SBAS)\nType: text\n\nPixel offset (PO) tracking small baseline subsets (SBAS) (PO-SBAS) follows the same motivation as the SBAS-InSAR technique. A sequence of small baseline SAR image pairs that have been previously co-registered with respect to a common reference image was obtained as a starting point (Sansosti et al. 2006; Casu et al. 2011). Subsequently, instead of the phase difference of the selected SAR images, we used their intensities to calculate the pixel offset for both the line-of-sight (LOS) and azimuth directions. Then, the singular value decomposition (SVD) inversion method was applied for the estimated relative LOS and azimuth offsets to generate the corresponding offset-based deformation time series (Sansosti et al. 2006; Casu et al. 2011).", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Offset tracking method > Pixel offset tracking small baseline subsets (PO-SBAS)", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Offset tracking method", "Pixel offset tracking small baseline subsets (PO-SBAS)"], "chunk_type": "text", "line_start": 70, "line_end": 72, "token_count_estimate": 250, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "29899c79890988cd", "text": "Document: s10346-021-01831-1\nSection: Mapping slope movement with SAR intensity images and interferograms > Offset tracking method > Derivation of 3D Glacier velocity\nType: text\n\nTo retrieve the 3D glacier flow velocity from ascending and descending SAR images, we first obtained displacement measurements in the azimuth and LOS directions based on small baseline image pairs from both descending and ascending tracks.\n\nThe 3D velocity field can be obtained from velocity measurements in four distinct directions (Li et al. 2018; Yang et al. 2020):\n\n$$\\left\\{ \\begin{array}{l} V_{LOS}^{i} = -V_{U}cos\\:\\theta^{i} + \\bullet V_{E}sin(\\alpha^{i} - 3\\pi/2)sin\\:\\theta^{i} + V_{N}cos(\\alpha^{i} - 3\\pi/2)sin\\:\\theta^{i} \\\\ V_{AZ}^{i} = -V_{E}cos(\\alpha^{i} - 3\\pi/2) + V_{N}sin(\\alpha^{i} - 3\\pi/2) \\end{array} \\right.$$\n\nwhere i stands for the orbit direction (A indicates the ascending track, and D indicates the descending track),  $\\theta$  is the incidence\n\nangle, and  $\\alpha$  is the azimuth angle. E, N, and V refer to the east, north, and vertical directions, respectively. The matrix form is as follows:\n\n$$BX = V (2)$$\n\n $X = \\begin{bmatrix} V^{\\mathrm{U}} & V^{\\mathrm{E}} & V^{\\mathrm{N}} \\end{bmatrix}^{\\mathrm{T}}; L = \\begin{bmatrix} V_{\\mathrm{LOS}}^{\\mathrm{A}} & V_{\\mathrm{AZ}}^{\\mathrm{A}} & V_{\\mathrm{LOS}}^{\\mathrm{D}} & V_{\\mathrm{AZ}}^{\\mathrm{D}} \\end{bmatrix}^{\\mathrm{T}}; \\text{ and } B \\text{ is a}$ design matrix composed of imaging geometry parameters", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Mapping slope movement with SAR intensity images and interferograms > Offset tracking method > Derivation of 3D Glacier velocity", "section_headings": ["Mapping slope movement with SAR intensity images and interferograms", "Offset tracking method", "Derivation of 3D Glacier velocity"], "chunk_type": "text", "line_start": 74, "line_end": 88, "token_count_estimate": 602, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "c86a496527dcfe2d", "text": "Document: s10346-021-01831-1\nSection: Results > Changes in glacier velocity\nType: text\n\nIn this study, the 2D and 3D glacial movements of Jinweng Co from 2017 to 2020 were derived (Fig. 4). The 3D glacier flow velocity during this period is shown in Fig. 4a. The vector arrows indicate the horizontal velocity, whereas the color shows the vertical velocity. In the horizontal direction, the north directed velocity mainly appeared in the glacier trunk of Jinweng Co, with a mean flow velocity of up to 200 m/year. In the vertical direction, the parent glacier moved downward at a velocity of 40 m/year. Owing to sparse acquisitions from descending Sentinel-1, we only obtained the long time series displacement from the ascending track to show the temporal variation (Fig. 4b). As shown in Fig. 4b, P1 was located at the glacier tongue near the lake. The mass transport is evident in the azimuth direction (approximately in the northern direction), as seen in the cumulative displacement of up to 18.4 m. Interestingly, the total displacement during the 2019-2020 cycle (October 2019 to June 2020) at the lower part of the glacier (close to the proglacial lake) is much larger than that observed in the previous years (Fig. 4b).", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Results > Changes in glacier velocity", "section_headings": ["Results", "Changes in glacier velocity"], "chunk_type": "text", "line_start": 92, "line_end": 94, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "8f307b89741d3a53", "text": "Document: s10346-021-01831-1\nSection: Results > Changes in glacier velocity\nType: figure\nFigure\n\nImage /page/4/Figure/0 description: The image contains two plots, labeled (a) and (b).", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Results > Changes in glacier velocity", "section_headings": ["Results", "Changes in glacier velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 95, "line_end": 95, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "8f736cc22907bd9b", "text": "Document: s10346-021-01831-1\nSection: Results > Changes in glacier velocity\nType: text\n\nPlot (a) is a 3D surface plot showing velocity data. The surface depicts a green, hilly terrain with a large blue area. A compass indicates the direction of North. The color of the surface represents the 'Vertical velocity (m/year)', with a color bar at the bottom ranging from -40 (blue) to 40 (red). The blue area corresponds to negative vertical velocities. Black arrows on the surface indicate the 'Horizontal velocity (m/year)', with a scale arrow representing 200 m/year. The plot is labeled with 'Jinweng Co' and a point 'P1'. The axes are labeled with numerical values, such as 93.6 to 93.64 on one axis and 30.325 to 30.365 on the other.\n\nPlot (b) is a scatter plot showing displacement over time. The x-axis represents the 'Date (yyyy/m/d)' from 2017/3/1 to 2020/9/1. The y-axis represents 'Displacement (m)' from 0 to 20. There are two data series: 'Azimuth', plotted with red circles, and 'LOS', plotted with blue triangles. The Azimuth displacement shows a generally increasing trend, starting from approximately 2 m in 2017 and rising to over 18 m by late 2020. An event labeled 'Outburst' is marked on the Azimuth data around early 2020, when the displacement was approximately 17 m. The LOS displacement remains much lower, fluctuating between 0 and about 3 m over the same period.\n\nFig. 4 a 3D glacier flows velocity during 2017–2020. The color indicates vertical velocities, and the arrow indicates horizontal velocities. b 2D time series displacement for P1 from the ascending track. The location of P1 is shown in a", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Results > Changes in glacier velocity", "section_headings": ["Results", "Changes in glacier velocity"], "chunk_type": "text", "line_start": 96, "line_end": 102, "token_count_estimate": 450, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "3b9d6098edb0dc02", "text": "Document: s10346-021-01831-1\nSection: Results > Changes to Jinweng Co\nType: text\n\nTo investigate the historical evolution of Jinweng Co, we analyzed its extent of variation and frontal recede using the inventory data from TPDC and by interpreting optical images. Figure 5 shows the temporal variations in Jinweng Co from 1998 to 2020. By superimposing the lake boundaries in the image of 2015 (Fig. 5a), we clearly see that the lake expanded upstream, and the glacier tongue receded. From 1998 to 2020, the proglacial lake expanded dramatically at a mean rate of 21.73 m<sup>2</sup>/year, and the glacier receded at a mean rate of 0.13 m/year (Fig. 5b).\n\nTo better understand the changes to Jinweng Co before and after the catastrophic GLOF in June 2020, we interpreted four summertime optical images, including Sentinel-2 images and Landsat images, and analyzed the variations to the boundary of Jinweng Co, as shown in Fig. 6. The lake area experienced an increasing trend from 2018 to 2020. It showed 0.52 km<sup>2</sup> on June 6, 2018; 0.55 km<sup>2</sup> on June 25, 2019; and then 0.57 km<sup>2</sup> on May 1, 2020. After the GLOF event in June 2020, the lake area decreased to 0.32 km<sup>2</sup>, as reflected in the July 2020 image. This resulted in a massive  $(\\sim 0.25^2 \\text{ km})$  downstream water flow from the lake dam (Fig. 6d).", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Results > Changes to Jinweng Co", "section_headings": ["Results", "Changes to Jinweng Co"], "chunk_type": "text", "line_start": 104, "line_end": 108, "token_count_estimate": 391, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "b9c78875a6df8122", "text": "Document: s10346-021-01831-1\nSection: Results > Changes to Jinweng Co\nType: figure\nFigure\n\nImage /page/4/Figure/5 description: A two-panel figure illustrating the expansion of a glacial lake and the associated glacier retreat over time. Panel (a) is a satellite image map showing the lake's area outlined in different colors for various years between 1998 and 2020. A legend lists the acquisition dates: 1998/10/19 (light blue), 2001/10/21 (green), 2011/11/10 (yellow), 2015/11/15 (red), 2018/10/15 (red), 2019/06/25 (red), and 2020/05/01 (purple). A measurement of 357.3 m is shown on the map, indicating a distance of retreat. The map includes coordinates (around 30°21'N, 93°38'E), a scale bar, and an inset map. Panel (b) is a scatter plot graphing data against time from 1998 to 2020. It shows two datasets: 'Lake area (km²)' plotted with red squares and 'Retreat distance (km)' plotted with green circles. Linear fits are applied to both datasets. The slope for the retreat distance is 0.13 m/year, and the slope for the lake area is 21.73 m²/year. The plot also displays the 95% confidence bands for both linear fits.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Results > Changes to Jinweng Co", "section_headings": ["Results", "Changes to Jinweng Co"], "chunk_type": "figure", "figure_caption": null, "line_start": 109, "line_end": 109, "token_count_estimate": 317, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "521ea758a069ff40", "text": "Document: s10346-021-01831-1\nSection: Results > Changes to Jinweng Co\nType: text\n\nFig. 5 Temporal evolution of the Jinweng Co during 1998–2020. a Lake boundaries derived from inventory data (1998, 2001, 2011, 2015) and interpretation of Landsat-8/Sentinel-2 images (2018, 2019, 2020); the inset map shows the lake boundary after the collapse. **b** Time series of the lake areas and glacier recede from 1998 to 2020. Red squares are measurements of the lake area in km2; the red line\n\nis a linear regression fit of the area measurements, and aqua shading surrounding the red line refers to the 95% confidence interval of the lake area. Green circles are distance in km of parent glacier frontal recede; the green line is a linear regression fitting within a 95% confidence interval, indicted by pink shading", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Results > Changes to Jinweng Co", "section_headings": ["Results", "Changes to Jinweng Co"], "chunk_type": "text", "line_start": 110, "line_end": 114, "token_count_estimate": 197, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "3c7ecd439518bd45", "text": "Document: s10346-021-01831-1\nSection: Recent Landslides\nType: figure\nFigure\n\nImage /page/5/Figure/1 description: A series of four false-color satellite images, labeled (a) through (d), showing a glacial landscape at different dates. The images are dated as follows: (a) 2018/6/6, (b) 2019/6/25, (c) 2020/5/1, and (d) 2020/7/27. The images depict glaciers in bright cyan, lakes in dark blue, vegetation in green, and bare ground in reddish-brown. Each image includes annotations for the city of Yiga, and elevation markers at 3800m, 4845m, and 6838m. A legend in the bottom left explains the symbols for City, Elevation, Glacier area, Lake area, Ice dam, Natural drainage channel, and Conduit. The legend in panel (d) adds an entry for 'Breach'. The sequence of images shows changes in the glacial lakes over time, with a noticeable drainage of a lake between the images from May 2020 and July 2020. A scale bar in the bottom right of each panel indicates a distance of 2 km.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "figure", "figure_caption": null, "line_start": 117, "line_end": 117, "token_count_estimate": 273, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "20cfdce0479917e2", "text": "Document: s10346-021-01831-1\nSection: Recent Landslides\nType: text\n\nFig. 6 A comparison of the Jinweng Co area and the surrounding glaciers before and after the GLOF: a June 6, 2018, Landsat-8 image; b June 25, 2019, Landsat-8 image; c May 1, 2020, Sentinel-2 image; and d July 27, 2020, Sentinel-2 image", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "text", "line_start": 118, "line_end": 120, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "3c295fd36280ec43", "text": "Document: s10346-021-01831-1\nSection: Discussions > Meteorological conditions before the event\nType: text\n\nThe intensity, duration, and frequency of precipitation, as well as temperature rise, can affect the timing and magnitude of glacier movements and GLOF events. To further understand the meteorological conditions before the 2020 event, we processed daily precipitation, monthly precipitation, and air temperature data. Figure 7 shows the meteorological conditions at Jinweng Co. The monthly precipitation fluctuates seasonally, with the rainy season lasting from February to July and very little precipitation during the intervening winter months. The highest monthly rainfall of up to 240 mm occurred during June 2020, and the heaviest daily rainfall reached 45 mm on June 21, 2020. This was approximately 4 days before the GLOF event. Moreover, air temperature changed periodically, with the hottest period of the year from late May to mid-August. Extreme rainfall and temperature conditions are important driving factors that increased discharge into the lake and led to the lake outburst (see discussion later).\n\nIt can be seen that the June 25, 2020, outburst occurred at the highest monthly/daily precipitation. The unusually heavy rainfall combined with the warmer summer temperature provides a plausible explanation for the Jinweng Co outburst for two reasons. First, heavy rainfall increased water inflow to Jinweng Co and thus increased discharge. Second, the heavy rainfall on June 21 may have also acted as an indirect trigger of the outburst when this precipitation provoked slope movement into the lake.\n\nThe Jinweng Co outburst chain provides a strong example of the role of extreme precipitation and temperature change in a GLOF-related disaster. According to the meteorological reports, the weather has been warming at a rate of 0.5 °C per decade over the last 40 years (You et al. 2016). This suggests that the warming trend constantly persists over Tibet. Based on the results of the glacier movement and lake change, it is certain that climate warming has caused glacier tongue recede, thinning, and lake expansion.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Meteorological conditions before the event", "section_headings": ["Discussions", "Meteorological conditions before the event"], "chunk_type": "text", "line_start": 124, "line_end": 130, "token_count_estimate": 472, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "cbbdd03758748b63", "text": "Document: s10346-021-01831-1\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: text\n\nTo understand the factors associated with the June 2020 outburst, we used time series SAR intensity images and two-pass InSAR (e.g., Lu et al. 2003). Figure 8a-e show the time series SAR intensity images from May 28 to July 15 and reveal several changes. The lake expanded from May 28 to June 9, as indicated by the yellow arrows in Fig. 8a, b. In Fig. 8c-e, we found a section at the western lateral moraine moved downslope into the lake on or after June 21, and the deposit from the landslide can be\n\nFig. 7 Daily and monthly precipitation and air temperature records over the Jinweng Co area. Daily precipitation is shown as blue bars, monthly rainfall in black smoothed curve, and air temperature in purple dots", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "text", "line_start": 132, "line_end": 136, "token_count_estimate": 211, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "dd161408f4c6d96a", "text": "Document: s10346-021-01831-1\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: figure\nFigure\n\nImage /page/5/Figure/12 description: A combination chart displaying daily precipitation, monthly precipitation, and temperature data from December 2016 to mid-2020. The x-axis represents the date in yyyy/mm/d format. The left y-axis, scaled from 0 to 50, represents daily precipitation in millimeters (mm) and corresponds to the blue vertical bars. The right side has two y-axes: an inner axis for temperature in degrees Celsius (°C), scaled from -20 to 20, corresponding to the magenta scatter plot points; and an outer axis scaled from 0 to 300, which appears to correspond to the black line representing monthly precipitation in mm. All three datasets show a clear seasonal pattern, peaking in the summer months and dipping in the winter months. A red annotation labeled \"Outburst\" with a dashed vertical line points to a period of high precipitation around May 2020.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "figure", "figure_caption": null, "line_start": 137, "line_end": 137, "token_count_estimate": 249, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "f079ffe843ad6164", "text": "Document: s10346-021-01831-1\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: figure\nFigure\n\nImage /page/6/Figure/0 description: A scientific figure displaying ten panels of satellite or radar imagery, arranged in a 2x5 grid and labeled (a) through (j). The top row, panels (a) to (e), shows a time series of grayscale images of a rugged landscape, dated 2020/5/28, 2020/6/9, 2020/6/21, 2020/7/3, and 2020/7/15 respectively. The bottom row, panels (f) to (j), shows a time series of color-coded images of the same area over several years, with date ranges: 2017/5/20-2017/7/31, 2017/6/1-2017/8/24, 2018/6/20-2018/8/7, 2019/7/21-2019/8/26, and 2020/8/8-2020/8/20. Each panel features a main image with a circular magnified inset focusing on a specific area. Panel (a) includes a north arrow and a scale bar indicating 1 Km. A color scale bar in the bottom right corner shows a range from 0 to 2.83 cm, with colors progressing from blue and purple to yellow and green, corresponding to the images in the bottom row.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "figure", "figure_caption": null, "line_start": 139, "line_end": 139, "token_count_estimate": 292, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "6a9b0ef6cd1499d8", "text": "Document: s10346-021-01831-1\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: text\n\nFig. 8 Map of the landslide activity. a-e Sentinel-1 intensity images of the Jinweng Co and its surroundings representing the situation on May 28, June 9, June 21, July 3, and July 15. SAR backscattering images over the landslide are enlarged in the inset to improve visibil-\n\nity. f-i Deformation interferograms of Jinweng Co and surroundings for several periods during 2017-2020. Each fringe (a complete cycle of color variations) represents a 2.83 cm range change in the radar look direction\n\nseen in Fig. 8d, e. We used InSAR to study possible long-term deformation in the area around the landslide for several periods in 2017-2020 (Fig. 8f-j). Since interferometric coherence for C-band Sentinel-1 images are low in the Jinweng Co surroundings (where the surface is covered with snow in winter), we analyzed interferograms with high coherence acquired in summer (Fig. 8f-j). The baselines between the image pairs were less than 60 m, so the interferograms were insensitive to DEM errors. The deformation maps formed from independent image pairs with\n\nvery different atmospheric situations show essentially the same patterns at a section of the lateral moraine. This means that the fringes are a real deformation. Therefore, this section of the lateral moraine was experiencing long-term deformation and likely finally collapsed into the lake due to heavy rainfall on June 21, 2020. Therefore, the rainfall-triggered landslide that occurred on or after June 21, 2020, exerted an important effect on this lake outburst event because the water waves may have overflowed the dam and/or directly caused the rupture of the dam.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "text", "line_start": 140, "line_end": 148, "token_count_estimate": 441, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "1d8ac369375e8bae", "text": "Document: s10346-021-01831-1\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: figure\nFigure\n\nImage /page/6/Figure/5 description: A composite scientific figure presented in black and white, comparing two images of a rugged, mountainous terrain. The figure is divided into two main rows. The top row consists of a rectangular image labeled (a) on the left, and three circular magnified images labeled (a1), (a2), and (a3) to its right. The bottom row has a similar layout with a rectangular image labeled (b) and three circular magnified images labeled (b1), (b2), and (b3). Image (a) displays a mountainous landscape with a north arrow in the upper left corner and a scale bar in the lower left indicating a distance of 1 km. Three specific locations are marked with white circles and numbered 1, 2, and 3. Image (b) shows the same terrain as image (a) but without the annotations. The circular images provide close-up views of the numbered locations: (a1) and (b1) correspond to location 1, showing layered rock formations; (a2) and (b2) correspond to location 2, showing a dark, shadowed area with a bright linear feature; and (a3) and (b3) correspond to location 3, showing a high-contrast area of bright and dark patches.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "figure", "figure_caption": null, "line_start": 149, "line_end": 149, "token_count_estimate": 330, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "a12475940a0bc1bb", "text": "Document: s10346-021-01831-1\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: text\n\nFig. 9 (a) Average Sentinel-1 intensity image based on SAR images of May 28 and June 9 2020. (b) Average Sentinel-1 intensity image based on SAR images of June 21, July 3, July 15, and July 27, 2020.\n\nThe changes in the glacier tongue, the landslide, and the southern end of the lake are shown by (a1) (b1), (a2) (b2), and (a3) (b3), respectively", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "text", "line_start": 150, "line_end": 154, "token_count_estimate": 133, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "556be07c328c7c1e", "text": "Document: s10346-021-01831-1\nSection: Recent Landslides\nType: figure\nFigure\n\nImage /page/7/Picture/1 description: A diagram illustrating the causes of a landslide in a glacial environment. The diagram shows a body of water labeled \"Jinweng Co\" contained by a brown \"Lateral moraine\". On the left, a light blue \"Glacier\" is shown with a red arrow pointing to the water, labeled \"Melting\". Above the scene, gray clouds are producing \"Extreme rainfall\". A yellow arrow points upwards from the water's surface, labeled \"Water rising\". A green mass of earth is shown falling from the moraine into the lake, labeled \"Landslide\". On the right, the lake narrows into an \"Outlet\".", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "figure", "figure_caption": null, "line_start": 157, "line_end": 157, "token_count_estimate": 190, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "bc837e805fea6e57", "text": "Document: s10346-021-01831-1\nSection: Recent Landslides\nType: text\n\nFig. 10 Schematic diagrams illustrating the triggering factors and final mechanism of dam failure\n\nThe time series SAR intensity images also indicate that significant changes occurred before the landslide on the southern end of the lake between June 9 and 21. We compared the average intensity images before and after June 9 (Fig. 9) to investigate this phenomenon. Figure 9 shows a clear difference in the glacier tongue (Fig. 9a1, b1). We suggest that the change in the lake surface on June 21 (Fig. 9, b3) resulted from the collapse of ice on a steep portion of the glacier tongue (Fig. 9(a1), (b1)). Such collapse and runout could also have trapped moraine on the path into the lake. Hence, between June 9 and 21, the ice collapsed under the steep topography and transported the iceberg and moraine into the front of the lake. This caused the obvious variation seen in the June 21 SAR image (Fig. 9). Moreover, the landslide that occurred in the western lateral moraine can be observed through Fig. 9a2, b2. The distinct sliding boundary further demonstrates that the landslide might cause harm to the lake by striking the lake body and may be one of the primary GLOF triggering factors.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "text", "line_start": 158, "line_end": 162, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "39199fe780591f0b", "text": "Document: s10346-021-01831-1\nSection: Recent Landslides > Outburst mechanism of Jinweng Co\nType: text\n\nBased on the displacement features of the parent glacier, the lake changes with SAR intensity, optical images, inventory data, and extreme meteorological conditions, there is evidence that the slope of the western lateral moraine was unstable due to glacier recede. The extremely high precipitation in June 2020 facilitated the erosion of the pre-weakening slope, finally inducing landslide activity. The sketch map in Fig. 10 shows the triggering factors and outburst mechanism of Jinweng Co. First, the hydrologically controlled glacier movement and melting generated masses of destabilizing sediments. Second, due to geomorphic conditions, the steep slopes above the lake produced huge kinetic energy when ice masses slid into the lake. Third, the daily precipitation about 4 days before the GLOF event was the largest in the past 3 years. Anomalous rain events or seasonal changes in the dynamics of the surrounding glaciers might be key triggering factors causing sudden hydro-fracturing and outburst floods in summer. Increased water inflow to the lake caused increased discharge from the morainedammed lake, which may have provoked increased erosion and incision\n\nof the outflow channel into the dam body. More importantly, the landslide activity that occurred from the western lateral moraine on or after June 21 played a key role in the GLOF event because the fast slope movement into the lake is capable of producing water waves. This may have caused direct dam rupture. Overall, the causal chain for triggering the June 2020 outburst is a hydraulic connection established from a possible collapse of the glacial ice, a landslide, heavy precipitation, and warm temperature.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Recent Landslides > Outburst mechanism of Jinweng Co", "section_headings": ["Recent Landslides", "Outburst mechanism of Jinweng Co"], "chunk_type": "text", "line_start": 164, "line_end": 168, "token_count_estimate": 411, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "70068e45a4e8587f", "text": "Document: s10346-021-01831-1\nSection: Conclusions\nType: text\n\nMulti-source remote sensing datasets and processing methods combined with meteorological observations were used in this study for GLOF investigation. These were then applied to comprehend the recent movement of parent glaciers and reveal the mechanisms leading to the outburst flood. Offset tracking of multi-track Sentinel-1 images was combined to retrieve the long-term time series displacement and 3D glacier flow velocity field. Optical Sentinel-2 and Landsat-8 images and inventory data were interpreted to reconstruct the long-term changes in the glacial lake. SAR intensity images and interferograms were analyzed to characterize changes in the glacier tongue and lateral moraine collapse.\n\nIn this study, to analyze lake outburst factors, we identified two important critical stages for the Jinweng Co outburst. First, ice collapse occurred in the parent glacier from June 9 to 21, and this transported ice and moraine into the front of the lake. Second, a landslide originating from the western lateral moraine occurred forcefully from June 21 to 25 and placed additional deposits into the lake body. The water level increased due to the unusually heavy rainfall on June 21, 2020; when combined with meltwater from glaciers, it provoked anomalous water importation. Due to the availability of pre-weakening lateral moraine combined with heavy rainfall on June 21, landslide activity was a serious factor for triggering dam failure by producing displacement waves. Hence, the main mechanism may be that the landslide mobilized and entered the lake between June 21 and 25, which caused the surface waves, triggered overtopping, and finally caused dam rupture.\n\nThe kinetic movement and outburst mechanism factors of Jinweng Co revealed in this study can be used to assess the potential risks of other glacier lakes in the region. Further, with the rise in extreme precipitation and the rapid melting of glaciers that lead to the destabilization of glacial lakes, moraine instabilities under changing climate conditions could become more likely. Therefore, process chains such as Jinweng Co will become increasingly significant in the future.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Conclusions", "section_headings": ["Conclusions"], "chunk_type": "text", "line_start": 170, "line_end": 176, "token_count_estimate": 506, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831"]}}
{"id": "feb6b2cf24a831ce", "text": "Document: s10346-021-01831-1\nSection: Funding\nType: text\n\nThis research was funded by the Scientific Innovation Practice Project of Postgraduates of Chang'an University (Grants No. 300103714010, 300203211263) and the Shuler-Foscue Endowment at Southern Methodist University.", "metadata": {"source_file": "data/('s10346-021-01831-1', '.pdf')_extraction.md", "document_title": "s10346-021-01831-1", "section_path": "Funding", "section_headings": ["Funding"], "chunk_type": "text", "line_start": 182, "line_end": 183, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01831", "300103714010", "300203211263"]}}
{"id": "229a98455f6bc6cd", "text": "Document: s41467-023-36033-x\nType: text\n\nGlacial lake outburst floods (GLOFs) represent a major hazard and can result in significant loss of life. Globally, since 1990, the number and size of glacial lakes has grown rapidly along with downstream population, while socio-economic vulnerability has decreased. Nevertheless, contemporary exposure and vulnerability to GLOFs at the global scale has never been quantified. Here we show that 15 million people globally are exposed to impacts from potential GLOFs. Populations in High Mountains Asia (HMA) are the most exposed and on average live closest to glacial lakes with ~1 million people living within 10 km of a glacial lake. More than half of the globally exposed population are found in just four countries: India, Pakistan, Peru, and China. While HMA has the highest potential for GLOF impacts, we highlight the Andes as a region of concern, with similar potential for GLOF impacts to HMA but comparatively few published research studies.\n\nGlaciers are particularly sensitive to changes in climate<sup>1-3</sup> and are highly visible indicators of climate warming<sup>3-5</sup>. Over the last three decades there have been substantial decreases in global glacier mass, with ice losses between 2006 and 2016 estimated at  $-332\\pm144$  Gt y<sup>-16,7</sup>. This decline is likely to persist through the 21st century as most glaciers are out of balance with present climate;  $-36\\pm8\\%$  of current mass loss is a 'lagged response' to past climate forcing<sup>8</sup>. In many areas, overdeepenings in former glacier beds are uncovered during the course of glacier retreat, which allows melt water to collect as glacial lakes<sup>9-11</sup>. Glacial lakes can also form via the growth and coalescence of supraglacial ponds on debris-covered glaciers<sup>12,13</sup>, and in other ice-marginal settings<sup>14,15</sup>. The formation of glacial lakes can trigger positive feedbacks, whereby lakes promote further ice loss through calving and subaqueous melting, causing additional melt and retreat, and further lake expansion<sup>16-18</sup>.\n\nImportantly, these lakes can represent a substantial hazard in the form of glacial lake outburst floods (GLOFs). GLOF triggering is complex, with dam breach initiation caused by mass movement-induced impulse waves<sup>19,20</sup>, lake overfilling due to pluvial, nival and glacial runoff<sup>21</sup>, and moraine- or ice dam degradation being variably important dependent on setting<sup>22,23</sup>. Consequently, the probability of a lake\n\nreleasing a GLOF is difficult to accurately quantify without detailed and localised studies.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "s41467-023-36033-x", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 21, "token_count_estimate": 709, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": ["36033"]}}
{"id": "cd4a554e9156273b", "text": "Document: s41467-023-36033-x\nType: text\n\n- 18 < / sup > . Importantly , these lakes can represent a substantial hazard in the form of glacial lake outburst floods ( GLOFs ) . GLOF triggering is complex , with dam breach initiation caused by mass movement - induced impulse waves < sup > 19 , 20 < / sup > , lake overfilling due to pluvial , nival and glacial runoff < sup > 21 < / sup > , and moraine - or ice dam degradation being variably important dependent on setting < sup > 22 , 23 < / sup > . Consequently , the probability of a lake releasing a GLOF is difficult to accurately quantify without detailed and localised studies .\n\nGLOFs can be highly destructive and can arrive with little prior warning, causing significant damage to property, infrastructure, and agricultural land, and resulting in extensive loss of life. However, the impact varies significantly across the globe; in the last 70 years, several thousand people have been killed by GLOFs in the Cordillera Blanca alone<sup>24,25</sup>, most from a small number of events<sup>26,27</sup>, while only 393 deaths in the European Alps can be directly linked to GLOF activity over the last 1000 years<sup>28</sup>. The continued ice loss and expansion of glacial lakes due to climate change therefore represents a globally important natural hazard that requires urgent attention if future loss of life from GLOF is to be minimised<sup>29,30</sup> and the UN's Sustainable Development Goals (particularly Goal 11–Disaster Risk Reduction) are to be met.\n\nSince 1990, the number, area, and volume of glacial lakes globally has grown rapidly, increasing by 53%, 51%, and 48% respectively<sup>30</sup>. Concurrent with the rapid growth of glacial lakes, many catchments downstream have experienced rapid and large increases in population, infrastructure and hydroelectric power (HEP) schemes, while agriculture has intensified<sup>31–35</sup>. However, the socio-economic vulnerability\n\n<sup>1</sup>School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, UK. <sup>2</sup>School of Earth & Environment, University of Canterbury, Christchurch, New Zealand. <sup>3</sup>Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK.\n\n—e-mail: thomas.robinson@canterbury.ac.nz", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "s41467-023-36033-x", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 21, "token_count_estimate": 638, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["36033"]}}
{"id": "f139c0a401a38dd4", "text": "Document: s41467-023-36033-x\nType: text\n\nin population , infrastructure and hydroelectric power ( HEP ) schemes , while agriculture has intensified < sup > 31 – 35 < / sup > . However , the socio - economic vulnerability < sup > 1 < / sup > School of Geography , Politics and Sociology , Newcastle University , Newcastle upon Tyne , UK . < sup > 2 < / sup > School of Earth & Environment , University of Canterbury , Christchurch , New Zealand . < sup > 3 < / sup > Department of Geography and Environmental Sciences , Northumbria University , Newcastle upon Tyne , UK . — e - mail : thomas . robinson @ canterbury . ac . nz\n\nto climate-related hazards is thought to have decreased<sup>36</sup>, although this decrease is spatially heterogenous and it remains unclear if this heterogeneity is sufficient to offset potential increases in hazard and exposure. Contemporaneous changes in lake conditions and downstream damage potential (i.e., the combination of exposure—the proximity of populations to a potential outburst—and vulnerability—the exposed populations likelihood to be impacted by the GLOF) are all critical components of GLOF danger<sup>10,31,37,38</sup>. However, how the recent observed changes in each combine to produce contemporary global GLOF danger remains unclear<sup>29</sup>. While regional scale GLOF risk assessments have been undertaken<sup>39,40</sup>, to our knowledge, no global scale study has been attempted that considers not just the physical lake conditions, but also societal exposure and vulnerability that directly influence GLOF danger<sup>41</sup>.\n\nHere we combine the most up-to-date lake condition, exposure, and vulnerability data available to quantify and rank contemporary (2020) damage potential from GLOFs at a global scale, adding to similar recent approaches for hydrometeorological floods<sup>42,43</sup>. We analyse the spatial distribution of population exposure to determine where populations are in relation to glacial lakes, using necessarily simple estimates of potential GLOF runout paths (50 km runout, with potentially affected populations located within 1 km of a river course), therefore identifying potential GLOF danger hotspots and thus higher priority zones for mitigation and further, local-scale research. While this study captures lake conditions and damage potential as they were in 2020, the methods presented provide a framework to capture changing GLOF danger through time.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "s41467-023-36033-x", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 21, "token_count_estimate": 615, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["36033"]}}
{"id": "bdcde238987da101", "text": "Document: Results\nSection: Results > Lake conditions\nType: text\n\nAs of 2020, regional normalised GLOF lake conditions, represented in terms of the total number and area of glacial lakes, were highest in the Pacific Northwest (PNW; 1.000), and lowest in the European Alps (0.041) (Fig. S1). There was high variability between nations, with individual GLOF lake conditions highest in Greenland and Canada (1.000 and 0.685 respectively) and lowest in Ecuador (0.001). Excluding Uzbekistan (no glacial lakes, normalised hazard score of zero) the largest range in intra-regional GLOF lake condition scores were seen in High Mountain Asia (HMA), ranging from a high score in China (0.319) to a low score in Mongolia (0.006). Generally, normalised national GLOF lake condition scores in HMA are below 0.100, with the exception of China.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Results > Lake conditions", "section_headings": ["Results", "Lake conditions"], "chunk_type": "text", "line_start": 25, "line_end": 27, "token_count_estimate": 200, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "5c770b8d5e666a43", "text": "Document: Results\nSection: Results > Exposure\nType: text\n\nIn total, 90 million people across 30 countries live in 1089 basins containing glacial lakes (Fig. 1a). Our analysis indicates that of these, 15 million (16.6%) live within 50 km of a glacial lake and 1 km of potential GLOF runout tracks (Fig. 1a). We find that 62% (-9.3 million) of the globally exposed population are located in the HMA region. Globally, the proportion of exposed population varies significantly between countries; India and Pakistan contain the highest number of exposed people (-3 million and -2 million people respectively, or one-third of the global total combined) while Iceland contains the least (260 people) (Fig. 1b). Just four highly populous countries account for >50% of the globally exposed population: India, Pakistan, Peru, and China (Fig. 2a). As a result, regionally HMA has the highest normalised exposure score (1.000) while the High Arctic and Outlying Countries score the lowest (0.019). India and Pakistan are the highest individually scoring nations (1.000 and 0.701), and Sweden is the lowest (0.001).\n\nGenerally, the population exposed to GLOFs increases with distance from a glacial lake, with almost half (48%) of exposed populations globally located between 20 km and 35 km downstream of lakes (Fig. 2). Only 2% (300,000) of the global population exposed to GLOFs live within 5 km of one or more glacial lakes (Fig. 2), with the majority of these (66%; 198,000) found in HMA (Fig. 2). Populations in HMA live,\n\non average, closer to glacial lakes than anywhere else, with -1 million people living within 10 km downstream of a glacial lake, where any early warning time is likely to be low, and, uncertainty in GLOF magnitude high. In contrast, populations across the PNW and High Arctic and Outlying Countries are generally situated further than 35 km downstream from glacial lakes (Fig. 2). Analysis of exposure at the national scale reveals considerable sub-regional variability, with populations in Pakistan living closest to glacial lakes, (0.8 million within the first 15 km (Fig. S2)), while settled populations in Kyrgyzstan are living at least 35 km downstream (Fig. S2).", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Results > Exposure", "section_headings": ["Results", "Exposure"], "chunk_type": "text", "line_start": 29, "line_end": 35, "token_count_estimate": 552, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "4c9e3d79c66ef3cd", "text": "Document: Results\nSection: Results > Vulnerability\nType: text\n\nThe three indices which were used to calculate vulnerability (CPI, HDI, and SVI) showed marked variation between and within regions (Fig. S3). Generally, the Andes and HMA have the highest levels of corruption and social vulnerability and lowest levels of human development, while the contrary is true for the European Alps, PNW and High Arctic and Outlying Countries. However, regional summaries mask some substantial national variations; although the Andes region has an average corruption score of 51, country level scores vary from high corruption in Bolivia (33) to lower corruption in Ecuador (88). Similarly, the average human development score in HMA (0.671) masks a range of scores, from a low of 0.511 (Afghanistan) to a high of 0.825 (Kazakhstan). Nonetheless, HMA is identified as the most vulnerable region to GLOF in 2020 (0.768) and the PNW the least (0.336). Overall, Afghanistan and Pakistan are the most vulnerable nations (0.919 and 0.837 respectively) while Switzerland and New Zealand are the least (0.194 and 0.186 respectively).", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Results > Vulnerability", "section_headings": ["Results", "Vulnerability"], "chunk_type": "text", "line_start": 37, "line_end": 39, "token_count_estimate": 274, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dea913f301c2e8d3", "text": "Document: Results\nSection: Results > GLOF Danger\nType: text\n\nThe combined normalised scores of GLOF lake conditions, exposure and vulnerability reveal HMA to have the highest GLOF danger as of 2020 (0.313), with a total of 9.3 million people exposed to 2211 lakes covering an area 1256.09 km<sup>2</sup>. Comparatively, the High Arctic and Outlying Countries have the lowest GLOF danger (0.032) with <200,000 people exposed, albeit to a similarly high number and area of glacial lakes (1862 lakes covering an area 1166.09 km<sup>2</sup>) (Fig. 3). As with the individual components, there is substantial sub-regional variation in GLOF danger itself (Fig. 3). China and Pakistan have the highest danger globally (0.863 and 0.751 respectively). Pakistan has near double the exposed population of China (2.1 million and 1.1 million respectively) and is significantly more vulnerable (0.837 compared to 0.683 in China). However, with more numerous lakes, and of larger area (1109 lakes covering 1094.44 km²) the GLOF lake condition score in China is large enough to more than offset these differences. Both Greenland and Uzbekistan have a danger score of zero because as of 2020 they have, respectively, no exposed population and no glacial lakes.\n\nWhen all 1089 glacial basins are ranked from highest to lowest risk (Fig. 3), the top three are found in Pakistan (Khyber Pakhtunkhwa basin), Peru (Santa basin), and Bolivia (Beni basin) (Fig. S6, Table S1) containing, respectively, 1.2 million, 0.9 million and 0.1 million people who could be exposed to GLOF impacts. Interestingly, Canada and USA contain just 3 basins in the top 50 globally (Table S1) as well as the lowest ranking basin (Tyers basin, Canada), where exposure is negligible as potential GLOF runout tracks are largely unpopulated. However, Canada and USA have relatively high GLOF danger scores (0.321 and 0.059) ranking 4<sup>th</sup> and 6<sup>th</sup> respectively, mainly because they host a large number of basins with generally high GLOF lake condition scores, highlighting the importance of spatial scale in these analyses.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Results > GLOF Danger", "section_headings": ["Results", "GLOF Danger"], "chunk_type": "text", "line_start": 41, "line_end": 45, "token_count_estimate": 553, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "54dac2b5524e6f75", "text": "Document: Results\nSection: Discussion\nType: text\n\nWith an increase in interest surrounding GLOFs over the last few decades, a clear geographical disparity has emerged between where GLOFs are occurring and the hotspots of research<sup>44,45</sup>. Between 1990", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 47, "line_end": 49, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "efed10f191053001", "text": "Document: Results\nSection: Discussion\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A world map, labeled (a), displaying several major mountain regions highlighted in different colors, with corresponding pie charts showing the percentage distribution of these regions by country. A legend in the bottom right corner identifies the regions: Alps (red), Andes (blue), HMA (green), PNW (purple), HAOC (orange), and Global (white). The PNW region in North America is split between the USA (65%) and Canada (CAN, 35%). The Alps in Europe are distributed among Italy (ITA, 39%), Switzerland (CHE, 33%), France (FRA, 18%), and Austria (AUT, 10%). The Andes in South America are broken down into Peru (PER, 52%), Bolivia (BOL, 16%), Colombia (COL, 13%), Chile (CHL, 9%), Ecuador (ECU, 7%), and Argentina (ARG, 3%). The large HMA region in Asia is distributed as follows: India (IND, 32%), Pakistan (PAK, 23%), China (CHN, 12%), Kyrgyzstan (KGZ, 10%), Nepal (NPL, 9%), Kazakhstan (KAZ, 6%), Afghanistan (AFG, 4%), Uzbekistan (UZB, 2%), Bhutan (BTN, 1%), Tajikistan (TJK, 1%), with Myanmar (MMR) and Mongolia (MNG) at 0%. The HAOC region, covering high-arctic and oceanic areas, is distributed across Russia (RUS, 68%), Georgia (GEO, 25%), New Zealand (NZL, 5%), Greenland (GRL, 2%), with Iceland (ISL), Sweden (SWE), and Norway (NOR) at 0%. Inset boxes provide magnified views of the PNW, Andes, Alps, and HMA regions. A key in the bottom left shows three concentric circles representing 5%, 15%, and 50%.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 50, "line_end": 50, "token_count_estimate": 439, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "796644af2222c29a", "text": "Document: Results\nSection: Discussion\nType: figure\nFigure\n\nImage /page/2/Figure/3 description: A bar chart, labeled (b), showing the population exposed to Glacial Lake Outburst Floods (GLOF) for various countries. The x-axis lists countries by their three-letter abbreviations. The left y-axis represents the 'Population exposed to GLOF (% national total)' from 0 to 16, corresponding to white bars with gray outlines. The right y-axis represents the 'Population exposed to GLOF (millions)' from 0.00 to 3.00, corresponding to solid colored bars. The data for each country is approximately as follows, with the first value being the percentage and the second being the population in millions: AFG (2.2%, 0.5M green), ARG (0.1%, 0.2M red), AUT (3.5%, 0.5M blue), BOL (12.3%, 0.3M purple), BTN (8.5%, 0.8M red), CAN (0.2%, 0.5M purple), CHE (3.8%, 0.8M red), CHL (1.8%, 0.4M blue), CHN (0.1%, 1.2M green), COL (0.1%, 0.4M blue), ECU (0.5%, 0.3M blue), FRA (0.2%, 0.4M red), GEO (0.5%, 0.1M orange), GRL (0.1%, negligible millions), IND (0.2%, 2.8M green), ISL (0.1%, negligible millions), ITA (0.5%, 0.9M red), KAZ (0.7%, 0.6M green), KGZ (15.8%, 0.7M green), MNG (0.1%, negligible millions), NOR (0.1%, negligible millions), NPL (2.5%, 0.8M green), NZL (0.1%, negligible millions), PAK (0.3%, 2.1M green), PER (3.8%, 1.4M blue), RUS (0.1%, negligible millions), SWE (0.1%, 0.05M orange), TJK (0.3%, 0.2M green), USA (0.1%, 0.6M purple), UZB (0.1%, 0.2M green).", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 52, "line_end": 52, "token_count_estimate": 493, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d0510566edc7b2b", "text": "Document: Results\nSection: Discussion\nType: text\n\n**Fig. 1** | **Global distribution of GLOF exposure.** a Global distribution of glacial basins, colour-coded according to mountain range, with 'High Arctic and Outlying Countries' (HAOC) representing all basins outside of the four main ranges in this study (Alps, Andes, High Mountains Asia (HMA) and Pacific North West (PNW)). Pie charts show the proportion of exposed population as individual country\n\ncontributions to the mountain range total, with pie charts sized according to percentage contribution to the 2020 global total. **b** Grey bars show exposed population as a percentage of the national total (left axis). Coloured bars show the total exposed population per country (right axis).\n\nand 2015, Iceland, the North American Cordillera and Hindu-Kush-Karakoram were the most prominent GLOF research hotspots with 180, 144, and 142 published research items, respectively<sup>44</sup> (Fig. S3). Since 2015, however, the Himalayas have emerged as the primary research focus, accounting for 36% of the studies undertaken between 2017 and 2021<sup>45</sup>. As such, these 'hotspot' regions are often cited as having the highest GLOF danger. While true in part, our results also indicate that as of 2020, the potential for large GLOF impacts is also high across the Andes (Fig. 3), and as a nation, danger in Peru is third highest globally (Fig. 3b).\n\nOver the last two decades, glaciers across the Andes have undergone rapid deglaciation in response to climate changes<sup>46,47</sup> leading to the growth of many large glacial lakes and consequently a growth in overall GLOF lake conditions (Fig. 4); the number of glacial lakes across the region increased by 93% compared to just 37% in HMA across the period. Concurrent with this increase, populations living in close proximity to glacial lakes have grown (Fig. 4), increasing the overall exposure to GLOF (Fig. 2); since 1941 the population in Huaraz, Peru alone has increased by >100,000<sup>48</sup>. At the same time, regional\n\nvulnerability remains high as a result of deep-rooted corruption and poor standards of living (Fig. S4). Comparative to other regions, the number of GLOF research items across the Andes are few; less than 8% of the research conducted between 1979 and 2021 were undertaken in this region (<100 items)<sup>44,45</sup> (Fig. S3). We suggest this data sparsity across the Andes is perhaps preventing meaningful assessments of actual GLOF risk in the region and urgently requires attention, particularly given the second- and third-most dangerous basins are found in this region, and the region as a whole is ranked second for GLOF danger globally (Fig S6).", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 53, "line_end": 65, "token_count_estimate": 686, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "951dd1183e175e1b", "text": "Document: Results\nSection: Discussion\nType: text\n\nFig . S4 ) . Comparative to other regions , the number of GLOF research items across the Andes are few ; less than 8 % of the research conducted between 1979 and 2021 were undertaken in this region ( < 100 items ) < sup > 44 , 45 < / sup > ( Fig . S3 ) . We suggest this data sparsity across the Andes is perhaps preventing meaningful assessments of actual GLOF risk in the region and urgently requires attention , particularly given the second - and third - most dangerous basins are found in this region , and the region as a whole is ranked second for GLOF danger globally ( Fig S6 ) .\n\nGLOFs can have exceptional discharges and runout distances, reaching >120 km downstream<sup>49,50</sup>. Here, we have considered anyone living within 1 km of likely GLOF runout tracks up to a maximum distance of 50 km from the glacial lake to be at risk of either direct (e.g., death or injury) or indirect (e.g., loss of land, damaged infrastructure) impacts. However, peak discharge attenuates rapidly from the flood source<sup>31</sup>, meaning that impacts are generally greatest with increasing proximity to a glacial lake. We show populations in HMA (Fig. 2), and particularly those in Pakistan (Fig. S2), are living closest to glacial lakes.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 53, "line_end": 65, "token_count_estimate": 340, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f0a4ba2a9be339f0", "text": "Document: Results\nSection: Discussion\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A figure with two charts, (a) and (b), showing the spatial distribution of the population exposed to Glacial Lake Outburst Floods (GLOF).", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 66, "line_end": 66, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a8ed622fd49eedc6", "text": "Document: Results\nSection: Discussion\nType: text\n\nChart (a) is a bar chart titled \"Spatial distribution of population exposed to GLOF\". The y-axis is \"Total global population exposed to GLOF (millions)\" from 0.00 to 2.50. The x-axis is \"Distance from glacial lake (km)\" from 5 to 50. For each distance, there are colored bars representing different mountain ranges and a light gray bar for the global total. The legend indicates: Alps (red), Andes (blue), HMA (green), PNW (purple), HAOC (orange), and Global (light gray). The green bars (HMA) are the tallest among the mountain ranges at all distances, peaking at over 1 million people. The global total peaks at around 2 million people at a distance of 35 km. Two vertical dashed lines mark the \"Average GLOF runout in the Cordillera Blanca\" (approx. 14 km) and \"Average GLOF runout in HMA\" (approx. 24 km).\n\nChart (b) is a pie chart titled \"Mountain range population exposed to GLOF\". It shows the percentage distribution of the exposed population among the mountain ranges: HMA (green) is 62%, Andes (blue) is 17%, Alps (red) is 15%, PNW (purple) is 5%, and HAOC (orange) is 1%.\n\n**Fig. 2** | **Global spatial distribution of exposure.** a Spatial distribution of exposure within GLOF runout tracks up to 50 km from a glacial lake, at 5 km intervals at the global and mountain range scale. **b** Total contribution of mountain range to the\n\nglobal total exposed population. Countries are coloured according to mountain range. *HMA* High Mountains Asia, *PNW* Pacific North West, *HAOC* High Arctic and Outlying Countries.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 67, "line_end": 83, "token_count_estimate": 439, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "81d14aa50b40b558", "text": "Document: Results\nSection: Discussion\nType: text\n\n( red ) is 15 % , PNW ( purple ) is 5 % , and HAOC ( orange ) is 1 % . * * Fig . 2 * * | * * Global spatial distribution of exposure . * * a Spatial distribution of exposure within GLOF runout tracks up to 50 km from a glacial lake , at 5 km intervals at the global and mountain range scale . * * b * * Total contribution of mountain range to the global total exposed population . Countries are coloured according to mountain range . * HMA * High Mountains Asia , * PNW * Pacific North West , * HAOC * High Arctic and Outlying Countries .\n\nWith the expansion of agriculture, development of new HEP sites (and with increasing proximity to glacial lakes), and growth of the tourism sector expected to increase in this region over the next few decades, it follows that exposure is only likely to increase as people move to higher elevations to support the aforementioned development, as has been observed in other mountain regions globally<sup>24,51-53</sup>. The characteristic rapid onset and high discharge of GLOFs means there is often insufficient time to effectively warn downstream populations and for effective action to be taken, particularly for populations located within ~10–15 km of the source lake<sup>54,55</sup>. Improvements are urgently needed to Early Warning Systems (EWS) alongside evacuation drills, plus other forms of community outreach that are sympathetic to potential social and cultural barriers, to enable more rapid warnings and emergency action in these highly exposed areas. Across HMA resources for mitigation are often limited28, and residents' lack of awareness, or lack of means to affect change, inhibits their ability to prepare for, and recover from, potential GLOF disasters sourced from remote glacial lakes<sup>56</sup>. Thus, analysing the spatial distribution of exposure as presented here not only highlights where advances are needed (e.g., EWS) but could also allow for more effective mitigation strategies (e.g., land zoning, education) to be implemented. Similar to our findings for the Andes, while Pakistan is a hotspot of GLOF danger, there is a comparative lack of published research focusing on this country and despite large-scale investment (>US\\$30 million) in GLOF vulnerability projects from the United Nations Development Programme<sup>57</sup>. We suggest the area should be targeted for more detailed research.\n\nGlaciers are exhibiting negative mass balance in nearly all glaciated regions of the world<sup>5</sup>, and over the past three decades the number, area and volume of glacial lakes have increased rapidly<sup>30</sup>. Our data show that countries with the largest, or most numerous, glacial lakes do not always possess a high GLOF danger. Instead, our results show that it is the exposed population that greatly elevates the potential impact of GLOFs globally (Fig. 4), particularly across HMA and the Andes (Fig. 1). For instance, Greenland has the highest number and area of glacial lakes of any nation in this study, thus has the highest hazard score (1.000) yet no people reside along likely GLOF runout tracks giving it a danger score of zero. Documenting changes in glacial lakes and highlighting areas where GLOF lake conditions may be", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 67, "line_end": 83, "token_count_estimate": 817, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "37b62a29f2c8e5cd", "text": "Document: Results\nSection: Discussion\nType: text\n\ncountries with the largest , or most numerous , glacial lakes do not always possess a high GLOF danger . Instead , our results show that it is the exposed population that greatly elevates the potential impact of GLOFs globally ( Fig . 4 ) , particularly across HMA and the Andes ( Fig . 1 ) . For instance , Greenland has the highest number and area of glacial lakes of any nation in this study , thus has the highest hazard score ( 1 . 000 ) yet no people reside along likely GLOF runout tracks giving it a danger score of zero . Documenting changes in glacial lakes and highlighting areas where GLOF lake conditions may be\n\nincreasing, while valuable, does not therefore provide an accurate indication in terms of danger trajectories, since contemporaneous changes in population exposure may more than offset changes in lake conditions (Fig. 4). Furthermore, our data begins to highlight the degree to which natural disasters impact people; two outburst events affecting the same number of people with the same material impact (e.g., a footbridge or road washed away) can have fundamentally different consequences depending on the social, political, cultural and economic context of the country, or even catchment, in which they occur<sup>24,28,58-60</sup>. This highlights the crucial role of exposure and vulnerability in determining the impact of GLOFs. While hazard assessments dominate GLOF studies<sup>61</sup>, exposure and vulnerability assessments remain relatively unexplored topics that urgently need addressing, particularly in developing countries<sup>62</sup> where GLOF danger is generally highest (Fig. 3).\n\nHow GLOF danger might change in the future remains subject to debate. As glaciers continue to recede existing glacial lakes will expand, and many new lakes will form<sup>39</sup>, altering the spatial pattern of GLOF lake conditions<sup>63</sup>. At the same time, we will see spatiotemporal changes in populations and their vulnerability as people, goods and services migrate in response to various socioeconomic drivers, and development related to the growth of tourism, HEP and agriculture continues to expand into higher elevations closer to glacial lakes and other forms of natural hazard<sup>52</sup>. We have shown the most dangerous basins, mainly found across HMA and the Andes do not always host the most, or the largest, glacial lakes, and rather it is the high number of people and the reduced capacity of those people to cope with disaster that plays a key role in determining overall GLOF danger (Fig. 3). This finding highlights the need for a more holistic approach to GLOF risk assessment, where each component of hazard, exposure, and vulnerability are accounted for. We highlight the value of globalscale spatial danger analysis (Fig. 3) and envisage our findings to be the starting point for more targeted risk assessments at the nationaland basin-scale. Our findings are important, as they not only identify countries and basins that rank highly in terms of GLOF danger, which can allow for more targeted GLOF risk management, but also regions where more research is urgently needed to understand risk at a fundamental level. In particular, we highlight the Andes as an under-", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 67, "line_end": 83, "token_count_estimate": 771, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa285c8ce29eb91e", "text": "Document: Results\nSection: Discussion\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A figure titled \"Global GLOF danger\" is presented in two parts, (a) and (b).", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 84, "line_end": 84, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b19ea5094833e125", "text": "Document: Results\nSection: Discussion\nType: text\n\nPart (a) displays four maps showing the spatial distribution of GLOF (Glacial Lake Outburst Flood) danger at a basin scale. A color-coded legend indicates the GLOF Danger Rank, ranging from HIGH (red, rank 1-125) to LOW (dark blue, rank 1025+). The intermediate colors are shades of orange and light blue. The four maps are:\n(i) Pacific North West: Covering Alaska, western Canada, and the northwestern USA, showing high danger in red along the southern coast of Alaska and British Columbia, with lower danger areas in blue further inland and north.\n(ii) Andes: Showing high danger (red and orange) along the mountain range through Peru, Bolivia, Chile, and Argentina.\n(iii) High Mountain Asia: Displaying a large area including the Hindu Kush, Karakoram, and Himalayas, with widespread high danger (red and orange). Lower danger areas (blue) are also present.\n(iv) European Alps: Covering parts of France, Switzerland, Italy, and Austria, with a mix of danger levels, including high-danger red areas in the central Alps.\n\nPart (b) is a scatter plot showing the final normalized scores for various countries related to GLOF. The y-axis represents \"Normalised scores\" from 0 to 1, and the x-axis lists countries by their three-letter codes. The legend indicates four metrics: Hazard (red triangle), Exposure (orange square), Vulnerability (blue circle), and Danger (black 'x').\nKey data points include:\n- CHN (China): Hazard ~0.35, Exposure ~0.4, Vulnerability ~0.9, Danger ~0.85.\n- PAK (Pakistan): Hazard ~0.05, Exposure ~0.05, Vulnerability ~0.8, Danger ~0.9.\n- IND (India): Hazard ~0.65, Exposure ~0.05, Vulnerability ~0.7, Danger ~0.25.\n- USA: Hazard ~0.1, Exposure ~0.2, Vulnerability ~0.4, Danger ~0.05.\n- CHE (Switzerland): Hazard ~0.1, Exposure ~0.2, Vulnerability ~0.75, Danger ~0.25.\nOther countries listed include PER, CAN, CHL, ITA, NPL, COL, KGZ, RUS, AFG, BOL, KAZ, ARG, TJK, BTN, FRA, AUT, NZL, NOR, ECU, MNG, GEO, SWE, ISL, GRL, and UZB.\n\n**Fig. 3** | **Global GLOF danger.** a Spatial distribution of GLOF danger at basin scale from high (red) to low (blue) risk. **b** Final normalised scores of GLOF lake conditions ('hazard'), exposure, vulnerability, and danger for each country, ordered from highest danger score (left) to lowest (right).\n\nstudied hotspot of GLOF danger and suggest that the region is targeted for more detailed study. While we show the global picture of contemporary GLOF danger, it remains unclear how this danger is changing temporally and whether such changes are being driven by\n\nchanges in lake conditions, damage potential, or some combination. Thus work is required to evaluate temporal changes in lake conditions, exposure and vulnerability in order to determine the relative roles in each for GLOF danger.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 85, "line_end": 106, "token_count_estimate": 775, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "965b3b1ea094f1ec", "text": "Document: Results\nSection: Discussion\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A bubble scatter plot, labeled (a), showing the relationship between 'Population growth (% a⁻¹)' on the x-axis and 'Glacial lake area change (% a⁻¹)' on the y-axis. The x-axis ranges from -2.00 to 5.00, and the y-axis ranges from -6.00 to 12.00. Each bubble represents a country, identified by a three-letter code. The size of the bubble indicates the 'Population exposed to GLOF (x 10⁶)', with a key in the top right showing concentric circles for 0.1, 1.0, 2.0, and 3.0 million people. The color of the bubble represents a geographical region, as defined by a legend in the bottom right: red for Alps, blue for Andes, green for HMA, purple for PNW, and orange for HAOC. Most countries, particularly those in the HMA and Andes regions like India (IND), Pakistan (PAK), Peru (PER), and Nepal (NPL), are located in the upper-right quadrant, indicating both positive population growth and increasing glacial lake area. Sweden (SWE) shows the highest glacial lake area change at around 10%, while Nepal (NPL) shows one of the highest population growth rates at around 4%. India (IND) and Pakistan (PAK) have the largest bubbles, indicating the highest populations exposed to GLOF.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 107, "line_end": 107, "token_count_estimate": 340, "basins": [], "subbasins": [], "countries": ["India", "Nepal"], "lake_ids": []}}
{"id": "8a2af1122b48e070", "text": "Document: Results\nSection: Discussion\nType: figure\nFigure\n\nImage /page/5/Figure/3 description: A bubble chart, labeled (b), plotting 'Glacial lake area change (% a⁻¹)' on the y-axis versus 'Population growth (% a⁻¹)' on the x-axis. The y-axis ranges from -6.00 to 12.00, and the x-axis ranges from -2.00 to 5.00. Each bubble represents a country, identified by a three-letter code. The size of the bubble corresponds to the 'Population exposed to GLOF (% national total)', with a legend in the top right showing concentric circles for 5, 25, 35, and 50 percent. The color of the bubbles indicates the geographical region, as per the legend in the bottom right: Red for Alps (e.g., CHE, ITA), Blue for Andes (e.g., PER, CHL), Green for HMA (e.g., NPL, IND), Purple for PNW (e.g., USA, CAN), and Orange for HAOC (e.g., SWE, GEO). For example, Sweden (SWE) is near the top with a high glacial lake area change of about 10%, while Switzerland (CHE) has a large negative change of about -2.2% and a very large bubble, indicating high population exposure. Nepal (NPL) has a high population growth of nearly 4%.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 109, "line_end": 109, "token_count_estimate": 329, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "fdbaf96cebde879b", "text": "Document: Results\nSection: Discussion\nType: text\n\n**Fig. 4** | **Rate of change in glacial lake area and total population.** Rates of population change between 1990 and 2020 and glacial lake area change between 1990 and 2018 as **a** absolute population exposed to GLOFs and **b** percentage of national population exposed to GLOFs in each country. Countries are colour coded\n\naccording to Mountain Range. While our study only considers contemporary GLOF danger, this highlights that variable changes in population and lake conditions may lead to very different danger scores in the near future. *HMA* High Mountains Asia, *PNW* Pacific North West, *HAOC* High Arctic and Outlying Countries.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 110, "line_end": 114, "token_count_estimate": 163, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f12548c4b14d004", "text": "Document: Results\nSection: Methods > GLOF lake conditions\nType: text\n\nWithin natural hazard research, hazard is a critical component of risk and is defined as a function of the probability and intensity of an event, i.e., the likelihood that an event will occur from a given site based on intrinsic properties and dynamic characteristics of that site combined with the overall magnitude of the event<sup>52</sup>. Thus, the probability of a GLOF occurring at a given point in time is dependent on specific local conditions, including, but not limited to; potential topographic triggers (ice/rock/snow avalanche etc), lake-dam geometries, and lake area/volume etc<sup>10,29,33,39</sup>. Further, the likelihood of lake failure will almost certainly vary temporally. Attempts to quantify the probability of GLOFs have been undertaken at regional-scale using simple proxies for the likelihood of landslide and/or ice avalanches into lakes39,53. However, to be applied globally, these approaches require globally consistent, high-resolution DEMs, which are known to suffer from considerable artefact issues in high mountain regions where GLOFs originate<sup>64</sup>. Therefore, quantifying the probability of failure is inherently difficult at a global scale. Thus, here, we take a consequencebased approach and focus on quantifying only the intensity of a potential GLOF. We do this by using the total lake area as a proxy for intensity, where larger lakes have the potential to produce larger, more intense GLOFs. Previous regional-scale work<sup>39</sup> has sought to use lake volume as a proxy for GLOF intensity by applying simple area-volume relationships to convert mapped lake area to assumed lake volume. However, for our study any area-volume relationship would need to be globally consistent, scaling all lake hazard values consistently, and thus we prefer to use simply mapped lake area instead.\n\nAs such, we treat the probability of failure as unknown and instead focus on quantifying the impacts or effects on the potentially affected population. Consequently, we prefer the term GLOF lake conditions to GLOF hazard, with our scoring system instead highlighting conditions that may yield more intense GLOFs should a failure occur. When combined with damage potential, the basins with the largest potential impacts can then be targeted for more detailed local studies to ascertain the probability of a GLOF occurring in the first place, allowing those with the highest potential losses to be prioritised for more local\n\nstudies. While probability is a key element of risk, we note that basins with high potential impact would still be considered comparatively high risk if the probability of failure was found to be low, while basins with low potential impacts would only suffer marginal increases in comparative risk if the probability of failure was found to be high.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > GLOF lake conditions", "section_headings": ["Methods", "GLOF lake conditions"], "chunk_type": "text", "line_start": 118, "line_end": 134, "token_count_estimate": 678, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1e1565e3f335c9ac", "text": "Document: Results\nSection: Methods > GLOF lake conditions\nType: text\n\nmay yield more intense GLOFs should a failure occur . When combined with damage potential , the basins with the largest potential impacts can then be targeted for more detailed local studies to ascertain the probability of a GLOF occurring in the first place , allowing those with the highest potential losses to be prioritised for more local studies . While probability is a key element of risk , we note that basins with high potential impact would still be considered comparatively high risk if the probability of failure was found to be low , while basins with low potential impacts would only suffer marginal increases in comparative risk if the probability of failure was found to be high .\n\nWe use the Level 4 Global Water Resource Zones shapefiles<sup>65</sup> and the most recently available global inventory of glacial lakes<sup>30</sup> to identify 1089 basins containing glacial lakes. We note that these Water Resource Zones do not represent true river catchments, instead showing regions that contain several associated rivers flowing into a lake or ocean, with Level 4 representing rivers that have no tributaries larger than 100 km<sup>2</sup>. This can cause strange effects, particularly in large coastal or plains areas, such as in Chilean Patagonia (Fig. 3a). However, to our knowledge there is no suitable global dataset of river catchments, and regional and national datasets are too inconsistently derived for our globally-focussed study. We group basins into four main mountain ranges; European Alps, Andes, High Mountain Asia (HMA) and Pacific Northwest (PNW), with the remaining 131 (12%) basins outside of these ranges referred to as 'High Arctic and Outlying Countries'. We then extract the raw number and area of glacial lakes per basin/country/region to act as proxies for potential GLOF intensity, before performing a linear transformation function to produce a normalised value for each indicator (Eq. 1);\n\n$$y_{N/A} = \\frac{(X)}{(Max)} \\tag{1}$$\n\nwhere x is the absolute number/area of glacial lakes per basin/country/ region, Max is the maximum number/area of glacial lakes found out of all basins/countries/regions, and y is the normalised value of glacial lake number/area per basin/country/region. Individual normalized values of glacial lake number ( $y_N$ ) and area ( $y_A$ ) are then multiplied to produce a singular score between 0 and 1, with higher values relating to lake conditions with the potential for more intense GLOFs.\n\nFinally, we consider the potential downstream spatial extent of GLOFs by considering the expected reach. Runout distances of GLOFs primarily vary as a function of outburst volume and stream", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > GLOF lake conditions", "section_headings": ["Methods", "GLOF lake conditions"], "chunk_type": "text", "line_start": 118, "line_end": 134, "token_count_estimate": 680, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca22eddf6038bbba", "text": "Document: Results\nSection: Methods > GLOF lake conditions\nType: text\n\n/ region , Max is the maximum number / area of glacial lakes found out of all basins / countries / regions , and y is the normalised value of glacial lake number / area per basin / country / region . Individual normalized values of glacial lake number ( $ y_N $ ) and area ( $ y_A $ ) are then multiplied to produce a singular score between 0 and 1 , with higher values relating to lake conditions with the potential for more intense GLOFs . Finally , we consider the potential downstream spatial extent of GLOFs by considering the expected reach . Runout distances of GLOFs primarily vary as a function of outburst volume and stream\n\ngradient, as well as other factors such as bed roughness, sediment concentration etc<sup>66</sup>. Thus, defining a runout distance from which to assess exposed population on a global scale is difficult. Previous research<sup>29</sup> set a runout cut-off distance of 50 km, to facilitate a standardized comparison between glacial lakes. Their 50 km threshold is consistent with a number of observed runout distances of past GLOFs, such as at Dig Tsho in 1985<sup>67</sup>, Chilleon Valley in 2015<sup>68</sup> and Chorabari in 2014<sup>69</sup>. Comparisons of likely GLOF discharges with that of meteorological floods<sup>70</sup> suggest the majority (50%) of likely GLOFs that exceed the 100-year meteorological flood discharge do so to only ~20 km downstream, with 1% theoretically reaching >85 km<sup>31</sup>. However, with lake sizes increasing due to climate change, runout of future GLOFs may well exceed that of those previously observed due to the larger volume of water potentially involved. Nevertheless, although we recognise runout distances vary considerably, with some GLOF events showing runout length >200 km<sup>50</sup>, considering such distances at a global scale could lead to large overestimations of downstream impacts in many locations<sup>29</sup>. So, following the approach of Dubey & Goyal (ref. <sup>29</sup>) we use a cut off distance of 50 km, which should encapsulate the majority of runouts globally and provide a conservative estimate of potential GLOF reach accounting for potentially longer runout GLOFs in the future, while avoiding large overestimations by using observed but rare extreme runout distances. Using a 50 km cut off distance also accounts for issues arising from our use of Water Resource Zones, as only the region within 50 km of a glacial lake are assessed. In coastal and plains areas where the Water Resource Zones can incorporate areas without glacial lakes upstream, this ensures that only the area and population downstream and in proximity to glacial lakes are included in our calculations.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > GLOF lake conditions", "section_headings": ["Methods", "GLOF lake conditions"], "chunk_type": "text", "line_start": 118, "line_end": 134, "token_count_estimate": 708, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b3c33678f5b6f1b", "text": "Document: Results\nSection: Methods > Exposure\nType: text\n\nGLOF runout pathways tend to follow river channels<sup>28,71</sup>, so impact increases with proximity to the channel<sup>72</sup>. Thus, similar to previous approaches<sup>73</sup> we further constrain our potential GLOF footprint to estimate exposed populations by applying a 1 km buffer either side of any main river channel<sup>65</sup> with a glacial lake in its upper reaches, up to a distance of 50 km (Fig. S5f). We used the 2020 Gridded Population of the World version 4 (GPWv4)<sup>74</sup> to sum the population count per 1 km<sup>2</sup> cell within this buffer, obtaining exposed population (Fig. S5h). We recognise that a 1km buffer is a crude estimate for identifying potential GLOF impact zones; exposed population is likely overestimated in the upper reaches where steeper elevations and narrow river valleys likely mean populations within even 100 m of a river channel may in fact be far above the impacted zone, while in the lower reaches where valleys are flatter and wider, exposed population is likely underestimated. However, as the overall impact of a GLOF wanes with distance from the river channel<sup>72,73</sup>, and given the resolution of the population data used<sup>74</sup>, at a global scale a 1 km buffer will provide a conservative but consistent estimate of the potentially exposed population. These areas of concern can then be targeted for further, more detailed analysis using more complex GLOF runout modelling and higher resolution population data to refine our initial estimates. We use a linear transformation function to produce a normalised value of exposure for each basin (Eq. 2);\n\n$$E = \\frac{(P)}{(\\text{Max})} \\tag{2}$$\n\nWhere E is the normalised exposure score, P is the total exposed population per basin/country/region, and Max refers to the maximum exposed population per basin/country/region respectively. To add further granularity, we split the 50 km buffer into 5 km intervals and summed the population within these intervals, to determine how population is distributed along these likely GLOF runout tracks.", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > Exposure", "section_headings": ["Methods", "Exposure"], "chunk_type": "text", "line_start": 136, "line_end": 142, "token_count_estimate": 561, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "efc75002cef2f0bc", "text": "Document: Results\nSection: Methods > Vulnerability\nType: text\n\nMany factors influence human vulnerability to natural hazards<sup>75–77</sup>, and vet, due in part to the absence of sufficient data, few studies have considered the temporal trend in vulnerability<sup>78</sup>. Since the implementation of the Millennium Development Goals and the succeeding Sustainable Development Goals, there has been a vast improvement in the amount, and quality, of vulnerability data available. Here we combine qualitative information obtained from the Corruption Perception Index (CPI) at national-scale and Human Development Index (HDI) at sub-national level (first internal administrative level, e.g., state or province) with a national-scale Social Vulnerability Index (SVI) to provide a proxy for GLOF vulnerability. At a global scale, corruption and human development are indicative of population fragility<sup>79-81</sup> with higher levels of corruption and lower levels of development individually associated with larger impacts. The CPI scores and ranks countries/territories based on how corrupt a country's public sector is perceived to be by experts and business executives. It is a composite index comprised through 13 data sources and is the most widely used indicator of corruption worldwide. The HDI is a summary measure of three key dimensions of human development; health, education, and standard of living82, and is comprised of normalised indices of: life expectancy, expected years of schooling, mean years of school and Gross National Income (GNI) per capita. Both the CPI and HDI have been successfully used in previous natural hazard risk assessments<sup>51,83</sup>.\n\nWhile both the CPI and HDI provide a useful metric for assessing the development of a country/territory83, they do not reflect on many factors that influence social vulnerability<sup>75</sup>. Thus, to assess the coping capacity of downstream communities and the ability of the affected nation to effectively respond to the event, a SVI was also calculated. Drawing upon an existing flood vulnerability assessment<sup>84</sup>, the SVI used in this study initially analysed 9 indicators (Table S2) that either reduce or enhance a population's and nation's capacity to cope with a GLOF disaster. To avoid double counting, we performed a correlation study (matric-plot and correlation-matrix) to ensure variables were independent from other indicators as well as those used to calculate the HDI and CPI. To keep the sample size valid, preference was given to variables with the lowest number of missing variables. As a result, four variables from the SVI were not included when calculating the final vulnerability score; percentage of safe drinking water and percentage of good sanitation as well as percentage illiterate population and percentage unemployment. The former two were highlighted both for double counting and lack of datapoints, and the latter two for double counting with data used to calculate the HDI. Consequently, the final SVI score was based on 5 unique indicators (Eq. 3);\n\n$$SVI = \\frac{\\left(\\frac{\\text{reducing indicators}}{\\text{enhancing indicators}}\\right)}{5}$$\n (3)", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > Vulnerability", "section_headings": ["Methods", "Vulnerability"], "chunk_type": "text", "line_start": 144, "line_end": 161, "token_count_estimate": 770, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9767f9bc69c86999", "text": "Document: Results\nSection: Methods > Vulnerability\nType: text\n\nfrom the SVI were not included when calculating the final vulnerability score ; percentage of safe drinking water and percentage of good sanitation as well as percentage illiterate population and percentage unemployment . The former two were highlighted both for double counting and lack of datapoints , and the latter two for double counting with data used to calculate the HDI . Consequently , the final SVI score was based on 5 unique indicators ( Eq . 3 ) ; $ $ SVI = \\ frac { \\ left ( \\ frac { \\ text { reducing indicators } } { \\ text { enhancing indicators } } \\ right ) } { 5 } $ $ ( 3 )\n\nWe acknowledge that proxy values of vulnerability at nationaland sub-national-scale will hide more granular variations within countries. However, vulnerability data at finer resolution is largely absent globally and therefore we argue our approach provides the highest globally consistent resolution currently available for a globalscale study. Furthermore, we note that while the vulnerability of the immediately exposed population is critical to understanding the eventual impacts from a disaster, the capacity of the country as a whole to adequately respond to the disaster is also an important factor. As such, our vulnerability indicators attempt to capture both the physical vulnerability of the directly exposed populations, and the capacity of the country/region as a whole to cope with the event.\n\nFinally, we note that the relative importance of each indicator on social vulnerability will change with location, with studies often assigning weights using an analytic hierarchy process and expert knowledge to fit the specific context of the study.<sup>84</sup> Given the global scale of this study, an 'equal weighting' approach was selected with the\n\nunderstanding that the outputs should be taken as a baseline value, and exact values per country may vary. In this study, all three indicators (HDI, CPI, and SVI) are normalised and combined with equal weighting (Eq. 3) to produce a single proxy for vulnerability (Eq. 4). Final values range between 0 and 1, where 1 equates to the highest vulnerability. No scores of absolute 0 were recorded.\n\nVulnerability =\n$$1 - [HDIx(1 - CPI)x SVI]$$\n (4)", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > Vulnerability", "section_headings": ["Methods", "Vulnerability"], "chunk_type": "text", "line_start": 144, "line_end": 161, "token_count_estimate": 540, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "001aed471e80bdd4", "text": "Document: Results\nSection: Methods > GLOF danger\nType: text\n\nThe normalised results of all three parameters (GLOF lake conditions, exposure, and vulnerability) were then combined to produce a semi-quantitative metric for GLOF danger (Eq. 5). Here, we prefer the term GLOF danger over GLOF risk due to the lack of probability in our GLOF lake conditions score. Basins were then ranked from highest (1) to lowest (1089) danger to identify hotspots of GLOF danger.\n\nGLOF danger =\n$$[Lake Conditions \\times Exposure \\times Vulnerability]$$\n (5)", "metadata": {"source_file": "data/('s41467-023-36033-x', '.pdf')_extraction.md", "document_title": "Results", "section_path": "Methods > GLOF danger", "section_headings": ["Methods", "GLOF danger"], "chunk_type": "text", "line_start": 163, "line_end": 168, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d5c7e77da44dfa9", "text": "Document: s41748-021-00230-9\nSection: Abstract\nType: text\n\nClimate change is strongly affecting the Himalayas. Geologically young and fragile, the Himalayas are sensitive to even minor changes in the climate. Regional warming in Himalayas has been observed between 0.15 and 0.60 °C per decade, which is higher than the mean global warming rate of 0.74 °C per 100 years. Consequent to this warming trend, the glaciological changes in Himalayas are obvious, which has resulted in the formation, expansion and disappearance of various types of glacial lakes. The dangerous lakes among these pose threat to downstream community and infrastructure. In this study, an attempt has been made to critically analyze the progress in Glacier Lake Outburst Floods (GLOF) research and understand its dynamism from multiple approaches through a meta-analysis of peer-reviewed scholarly literature for a period of 2001–2020. The study has found that the glacial lakes in the study region are increasing in number and expanding rapidly owing to the climate change and anthropogenic causes as the Glacier Lake Outburst Floods (GLOFs) are becoming common. The expansion rates of pro-glacial lakes connected to glaciers and moraine-dammed lakes are faster than other type of lakes. Findings from the studies on area change analysis in the region reveal that a number of expanding glacial lakes can emerge as potential sites for future GLOFs, hence need immediate monitoring and observation. Geospatial tools and techniques coupled with field investigations have been found as a potent tool for mapping the evolution and propagation of GLOF hazards resulting from accelerated shrinking and thinning of glaciers and continued lake expansion. Further, the satellite-based remote sensing and modeling has been found as an excellent tool for GLOF management as well as reconstruction of previous GLOF events and prediction of future outburst potential. The idea of this study is linked to the increased incidence of GLOF events in the Himalayan region. This study will help in better understanding of glacial lake expansion and provide scope for future research in devising risk management action plans of potential GLOFs by selecting expanding glacial lakes as case studies.\n\n**Keywords** Climate change · Glacier recession · Glacial lake expansion · GLOFs · Risk management\n\nSyed Towseef Ahmad\nSyedtowseef.scholar@kashmiruniversity.net\n\nRayees Ahmed rayeesrashid84@gmail.com\n\nGowhar Farooq Wani gowhernaz@gmail.com\n\nMehebub Sahana mehebub.sahana@manchester.ac.uk\n\nHarmeet Singh harmeetgeo@gmail.com\n\nPervez Ahmed pervezahmeduok@gmail.com\n\n- Department of Geography and Disaster Management, School of Earth and Environmental Sciences, University of Kashmir, Srinagar 190 006, India\n- School of Environment, Education and Development, University of Manchester, Manchester, England", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "text", "line_start": 3, "line_end": 23, "token_count_estimate": 691, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["00230"]}}
{"id": "7e213e540f7d9cf5", "text": "Document: s41748-021-00230-9\nSection: Abstract\nType: figure\nFigure\n\nImage /page/0/Picture/18 description: The Springer logo, featuring a black outline of a chess knight piece facing left, positioned above two horizontal lines, next to the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "Abstract", "section_headings": ["Abstract"], "chunk_type": "figure", "figure_caption": null, "line_start": 24, "line_end": 24, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "4fb7cb784303b14e", "text": "Document: s41748-021-00230-9\nSection: 1 Introduction\nType: text\n\nClimate change from the past few decades has significantly impacted the lifecycle of glaciers in the high-mountainous Himalayan region. The fragile Himalayan Mountains are sensitive to even minor changes in the climatic system (Lama et al. 2009). The temperature in the Himalayas during the past three decades has shown a constant increase (Shrestha et al. 2010; Dash et al. 2007; Wang et al. 2013, 2017; Khattak et al. 2011; Ali et al. 2018; Almazroui et al. 2020) causing glacier recession (Yongping 2004; Gardelle et al. 2011; Banerjee and Shankar 2013; Bolch et al. 2012a, b; Bajracharya et al. 2011; Cogley 2016; Sakai and Fujita 2017). Consequent to this climate change-induced glacier recession, glacial lakes have shown a significant increase both in terms of number and size. This has made them susceptible to Glacial Lake Outburst Floods (GLOFs) (Nie et al. 2013; Worni et al. 2012; Veh et al. 2020) with wide recognition (Yamada and Sharma 1993; Worni et al. 2013; Liu et al. 2014). Global warming is the major reason behind the changing climate and is believed to be the biggest threat to the living world. The impact of such warming has been mostly observed over the high mountain ice packs of the earth. Retreating of mountain glaciers and the development of glacial lakes has been reflected as one of the key factors for global warming. Therefore, glaciers in the high mountain regions serve as an important indicator for the monitoring and assessment of climate change (Haeberli et al. 1999; IPCC 1992; Kääb et al. 2012). In the context of rising temperatures, most of the glaciers in the Himalayan region have been receding at a greater pace, leading to increase in the volume of the glacial lakes and a concomitant rise of GLOF events. (Bolch et al. 2011a, b; Brahmbhatt et al. 2017; Bajaracharya et al. 2007, 2008; Mir et al. 2017). Melt water from these glaciers makes an essential contribution to the development and expansion of glacial lakes in the Himalayas (Fujita et al. 2008; Harrison et al. 2018; Bolch et al. 2008). The Himalayan region which contains the largest number of glaciers outside the Polar region (Yao et al. 2012, 2014) is focus of this paper. Glacial lakes are water bodies existing in a sufficient amount influenced by the presence of glaciers and or retreating processes of a glacier (Jain et al. 2015; Fitzsimons and Howarth 2018). The sudden discharging of such lakes caused due to the ice/ moraine dam breach releases enormous volume of water and debris in a devastating manner is known as Glacial Lake Outburst Floods (GLOF's) (Cenderelli et al. 2001; worni et al. 2013).\n\nThe GLOF events are likely to cause huge consequences in terms of human and infrastructural loss in the downstream regions (Emmer et al. 2018; Nie et al. 2013; Quincey et al. 2005; Yao et al. 2012). Therefore, it is", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 27, "line_end": 31, "token_count_estimate": 766, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "9afead5167e71b3c", "text": "Document: s41748-021-00230-9\nSection: 1 Introduction\nType: figure\nFigure\n\nImage /page/1/Picture/4 description: A paragraph of text discussing the study of glaciers and glacial lakes in the Himalayas. The text highlights the difficulty of in-situ observation and proposes remote sensing, GIS, and hydrodynamic models as alternatives. The paragraph includes several citations, which are shown in blue text. The text reads: 'important to understand the behavior of the glaciers, consequent lake development and expansion in the Himalayan region under changing climate to mitigate risks to protect the society living downstream. In the Himalayas, complete in situ observation to all the glacial lakes seems impractical due to remote locations, erratic weather conditions and rough terrain. However, the combined approaches of Remote sensing, GIS and hydrodynamic models act as a viable alternative to identify and monitor such lakes regular intervals (Huggel et al. 2003; Huggel et al. 2004; Komori, 2008; Bajracharya and Mool 2009; Bolch et al. 2011a, b; Govindha 2010; Raj et al. 2013; Worni et al. 2013; Klimes et al. 2014).'", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "figure", "figure_caption": null, "line_start": 32, "line_end": 32, "token_count_estimate": 277, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "531b745844122421", "text": "Document: s41748-021-00230-9\nSection: 1 Introduction\nType: text\n\nClimate change and glacier research has traversed a long path with some path-breaking studies that give details about paleoclimate and future climate scenarios as well as keep a check on present situation. Climate crisis and water-related disasters is a world problem, It a serious challenge for developing and developed countries that are responsible for high carbon emissions. With the progress in earth, atmospheric and environmental sciences, use of ultramodern (geospatial and computer) technology and conventional (in situ) measurements, our approach towards assessing and addressing challenges posed by natural or human-induced processes that triggers disasters like GLOFs has undergone tremendous changes. The purpose of this study was to critically analyze the recent research on the subject concerning Himalayan region. To identify issues, underlying causes, gaps in knowledge, track shift in approach and provide some new insights into the management of GLOFs in the region with an intention to guide future research on the subject.\n\nThe next sections of the paper include materials and methods with description of study region and selection criteria for previous studies; analysis of findings from literature, GLOF events in the past, lake mapping and classification; discussion with a synthesis of similarities and dissimilarities on the subject and concluding remarks at the end that encapsulates this study with some suggestions to guide future research.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "1 Introduction", "section_headings": ["1 Introduction"], "chunk_type": "text", "line_start": 33, "line_end": 37, "token_count_estimate": 323, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "9711842e485dbc51", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.1 Description of Study Area\nType: text\n\nThe Himalayan mountain system, is extended over 2500 km from south of the Indus Valley to Nanga Parbat in the west and Namcha Barwa in the east. Himalayas are the loftiest and geologically young mountain ranges in the world as compared to the Alps and Appalachians. These mountain ranges are crescent shaped with a projecting southward convexity, which borders the whole northern margins of the Indian sub-continent (Roy and Purohit 2018). It spreads", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.1 Description of Study Area", "section_headings": ["2 Materials and Methods", "2.1 Description of Study Area"], "chunk_type": "text", "line_start": 41, "line_end": 43, "token_count_estimate": 141, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "83f667ee6042f6af", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.1 Description of Study Area\nType: figure\nFigure\n\nImage /page/1/Picture/10 description: The Springer logo, featuring a black and white icon of a knight chess piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.1 Description of Study Area", "section_headings": ["2 Materials and Methods", "2.1 Description of Study Area"], "chunk_type": "figure", "figure_caption": null, "line_start": 44, "line_end": 44, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "4cec17407a2a8fa1", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.1 Description of Study Area\nType: text\n\nacross the five Asian countries, namely India, China, Pakistan, Nepal and Bhutan, housing around 52.7 million people. Due to its vast size, huge altitudinal variation and complex topography, the Himalayas experience variable climate.\n\nThe Himalayan region including great ranges of central Asia is home to thousands of glaciers and glacial lakes and contains third largest snow and ice deposits in the world (Fig. 1). There are about 15,000 glaciers and 9000 glacial lakes in the Himalayan region (Mool 2005). The region contains permanent fields of snow, and during the winter season, most of the high-elevation regions receive snowfall (Kumar et al. 2005).", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.1 Description of Study Area", "section_headings": ["2 Materials and Methods", "2.1 Description of Study Area"], "chunk_type": "text", "line_start": 45, "line_end": 49, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["00230"]}}
{"id": "022d36b25eb83d19", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies\nType: text\n\nThe climate-driven glacier recession and subsequent glacial lake expansion have increased the occurrences of Glacial Lake Outburst Floods (GLOFs) in the high glacierised basins of Himalayas. In this context, an extensive systematic\n\nreview of peer-reviewed literature was conducted with a focus on glacial lake expansion and GLOFs in the Himalayan region. About 100 research articles and review papers, book chapters and institutional reports were collected from the internet-based databases like Web of Science, Google Scholar and ScienceDirect. The keywords, such as \"glaciers\", \"glacial lakes\", glacial lakes in the Himalayas\", \"glacial lake expansion or assessment\", \"GLOFs in the Himalayas\" were used to extract the research articles that refer to the period 2001-2020. Out of which only 89 most relevant research items were shortlisted and later 76 were used. Old research on the subject was used to build the foundation and background while as the recent studies were utilized to develop current state-of-the-art and knowledge. Research articles that were not administered through a half, full or double-blind peer-review process were not considered for this study. The snowball technique was used with the intention that no original contributions on glacier and glacial lake studies concerning the region is left out.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.2 Selection Criteria for Previous Studies", "section_headings": ["2 Materials and Methods", "2.2 Selection Criteria for Previous Studies"], "chunk_type": "text", "line_start": 51, "line_end": 55, "token_count_estimate": 332, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "2a0a103a4dc1b145", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies\nType: figure\nFigure\n\nImage /page/2/Figure/7 description: A location map of the Himalayan region, presented as a satellite image. The map is titled \"Fig. 1. Location map of the study area.\" The Himalayan Region is outlined in yellow and subdivided into three parts: 'a' Western Himalayas, 'b' Central Himalayas, and 'c' Eastern Himalayas, which are also indicated by pink labels. The map shows surrounding geographical features, including the Arabian Sea to the southwest and the Bay of Bengal to the southeast. A legend in the bottom-left corner explains the symbols. The map includes a scale bar in kilometers (0, 95, 190, 380), a compass rose in the top-left, and is marked with latitude and longitude lines. In the top-right corner, an inset world map shows the location of the study area, highlighted with a red outline.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.2 Selection Criteria for Previous Studies", "section_headings": ["2 Materials and Methods", "2.2 Selection Criteria for Previous Studies"], "chunk_type": "figure", "figure_caption": null, "line_start": 56, "line_end": 56, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "8487b5ef88185482", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies\nType: text\n\nFig. 1 Location map of the study area", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.2 Selection Criteria for Previous Studies", "section_headings": ["2 Materials and Methods", "2.2 Selection Criteria for Previous Studies"], "chunk_type": "text", "line_start": 57, "line_end": 59, "token_count_estimate": 40, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "fbca5b22bb3f5a5e", "text": "Document: s41748-021-00230-9\nSection: 2 Materials and Methods > 2.2 Selection Criteria for Previous Studies\nType: figure\nFigure\n\nImage /page/2/Picture/9 description: The logo for Springer, featuring a black and white icon of a chess knight piece next to the word \"Springer\" in a black serif font. The knight icon is facing left and is positioned above two horizontal lines.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "2 Materials and Methods > 2.2 Selection Criteria for Previous Studies", "section_headings": ["2 Materials and Methods", "2.2 Selection Criteria for Previous Studies"], "chunk_type": "figure", "figure_caption": null, "line_start": 60, "line_end": 60, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "4b883c317417ce56", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas\nType: text\n\nGlobal average temperatures have increased by about 1 °C since the dawn of pre-industrial times (IPCC 1992). In India alone, the mean annual temperature has increased by 0. 56 °C during 1901–2009 (Attri and Tyagi, 2010) with average temperatures expected to rise between 3.5 and 5.5 °C by 2100 (Lal 2002). Regional warming in Himalayas has been observed between 0.15 and 0.60 °C per decade, which is higher than the mean global warming rate of 0.74° C per 100 years (Shrestha et al. 2010; Negi et al. 2018; Bhutiyani et al. 2007). The warming rates are not solely attributed to the natural variability but are also a result of anthropogenic causes.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas"], "chunk_type": "text", "line_start": 65, "line_end": 67, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["00230"]}}
{"id": "0678b7028db8ec57", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.1 Western Himalayas\nType: text\n\nWestern Himalayas receive precipitation (rainfall and snowfall) from westerlies and the southwest monsoon in the winter and summer season, respectively. Changes in temperature and precipitation are key indicators of climate change. The total observed increase in mean temperature for Western Himalaya from 1991 to 2015 was 0.65 °C. (Negi et al. 2018) (Fig. 2a). Snowfall for the same period has decreased with increase in rainfall. (Negi et al. 2018) (Fig. 2b). The glacier mass loss in the region has been observed as 13% from 1962 to 2000 with the possibility of glacial lake development (Kulkarni et al. 2014).", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.1 Western Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.1 Western Himalayas"], "chunk_type": "text", "line_start": 69, "line_end": 71, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "d7961aa51c17a13c", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: text\n\nKattle and Yao (2013) analyzed three decades (1980–2009) of data on annual maximum, minimum and average temperatures in the central Himalayas. Spatial analyses of the average temperatures show a warming trend with a dramatic increase in temperature is observed in the latest decade. The", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "text", "line_start": 73, "line_end": 75, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "226fd19dedf1f7fe", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: figure\nFigure\n\nImage /page/3/Figure/9 description: A combination bar and line chart titled \"Precipitation Change in Western Himalaya\", labeled as figure (a). The x-axis shows the years from 1991 to 2015. The y-axis is labeled \"Precipitation (mm)\" and ranges from 0 to 1000. The legend indicates that black bars represent Rainfall and a red line with circular markers represents Snowfall. The chart shows a general downward trend for snowfall, indicated by a dashed red trendline, with significant annual fluctuations. The highest snowfall was in 1992, over 900 mm, and the lowest was in 1995, under 200 mm. The rainfall, represented by the black bars, shows a general upward trend, indicated by a dashed black trendline, increasing from less than 100 mm per year in the early 1990s to a peak of over 250 mm in 2015.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "figure", "figure_caption": null, "line_start": 76, "line_end": 76, "token_count_estimate": 236, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "62e200f8bf01ae9d", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: figure\nFigure\n\nImage /page/3/Figure/10 description: A line graph titled \"Temperature anomalies\" shows the mean temperature anomaly from 1990 to 2015. The y-axis, labeled \"Mean Temperature anomaly,\" ranges from -1 to 1. The x-axis represents the years from 1990 to 2015. There are two data series plotted. The first, labeled \"Western Himalaya,\" is a black line with circular markers, showing significant fluctuations with a general upward trend, starting around -0.6 in 1990 and ending around 0.4 in 2015. The second series, labeled \"Global,\" is a red line with circular markers, which also shows an upward trend but with less variability, starting around 0.4 in 1990 and ending close to 0.9 in 2015. Both series are accompanied by a dashed trend line indicating a positive slope. The label \"(b)\" is centered below the graph.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "figure", "figure_caption": null, "line_start": 78, "line_end": 78, "token_count_estimate": 242, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "64df3fd4cc1baecc", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: text\n\nFig. 2 Precipitation (a) and temperature (b) changes in Western Himalayas. Source: Adopted from Negi et al. (2018)\n\nFig. 3 Mean annual temperature and rainfall changes in the Central Himalayas. Source: Adopted from (Mohandas et al. 2015)", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "text", "line_start": 79, "line_end": 83, "token_count_estimate": 97, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "9cf7e922f81e2650", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: figure\nFigure\n\nImage /page/3/Figure/13 description: A dual-axis line graph showing the mean annual rainfall and mean annual temperature for the period 2009-2011. The x-axis displays the months of the year from January to December. The left y-axis, labeled \"Mean annual rainfall (mm)\", ranges from 0 to 400. The right y-axis, labeled \"Mean annual temperature (°C)\", ranges from 0 to 30. A solid line represents the mean annual rainfall, which starts low, peaks sharply in July at approximately 375 mm, and then decreases rapidly. A dashed line represents the mean annual temperature, which gradually rises from about 10°C in January to a peak of about 24.5°C in June, and then gradually declines.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "figure", "figure_caption": null, "line_start": 84, "line_end": 84, "token_count_estimate": 215, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "d7adfb1855f29b90", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: figure\nFigure\n\nImage /page/3/Picture/14 description: The Springer logo is displayed in black text and graphics on a white background. On the left is an icon of a knight chess piece, showing a horse's head facing left, positioned above two horizontal lines. To the right of the icon, the word \"Springer\" is written in a serif font.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "figure", "figure_caption": null, "line_start": 86, "line_end": 86, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "70ecd26c3393277b", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas\nType: text\n\nsouthern slope of the central Himalayas shows variability in temperatures, especially for the minimum temperature due natural and anthropogenic causes. In Nepal, an increasing trend of temperature has been observed with an increase in mean annual temperature by 0.06 °C per year (Shrestha et al. 2010). The maximum temperature increased by 0.7 °C (Mohandas et al. 2015) (Fig. 3).", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.2 Central Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.2 Central Himalayas"], "chunk_type": "text", "line_start": 87, "line_end": 89, "token_count_estimate": 123, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": ["00230"]}}
{"id": "856c594742faa07b", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.1 Climate Change in the Himalayas > 3.1.3 Eastern Himalayas\nType: text\n\nEastern Himalayas are experiencing widespread warming and the rate is generally greater than 0.01 °C per year. Spatial distribution of annual and seasonal temperature trends shows that most of the region is undergoing warming trends as the annual mean temperature is increasing at the rate of about 0.01 °C/year (Li et al. 2016) (Fig. 4). Dash et al. (2007) observed that maximum temperature increased in the last 100 years over the North-Eastern region of India by about 1 °C during winter and post-monsoon months.\n\nTherefore, based on the review of hydro-climatic trends and changes in observed temperatures and precipitation as highlighted through the present study, we have a strong evidence of warming over the entire Himalayan region. However, the warming rates vary across the sub-regions and seasons.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.1 Climate Change in the Himalayas > 3.1.3 Eastern Himalayas", "section_headings": ["3 Findings", "3.1 Climate Change in the Himalayas", "3.1.3 Eastern Himalayas"], "chunk_type": "text", "line_start": 91, "line_end": 95, "token_count_estimate": 224, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["00230"]}}
{"id": "5511e80c3d86fdd7", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.2 Synthesis of Existing Literature\nType: text\n\nGlacial lake expansion in the various parts of the Himalayas has been investigated with the help of satellite imageries, topographic maps and in-situ measurements (Nie et al. 2013, Ageta et al. 2000; Wang et al. 2015; Yamada 1993; Khadka et al. 2018; Komori et al. 2008). The Inventory of glaciers and glacial lakes in Nepal prepared by International Centre for Integrated Mountain Development (ICIMOD) has revealed that glacial lakes are increasing in number, size and volume due to glacier recession (Mool et al. 2001).\n\nSimilarly, the glacial lakes in Central Himalayas have shown a significant increase in area and number from 1990 to 2010. The number has increased by 123 and the total area of glacial lakes expanded by 28.81 km² (17.11%) as studied by (Nie et al. 2013). Wang et al. (2016) analyzed remote sensing data and long-term climate variables to examine the hydrological response of lakes in Nam Co Basin. The results show that the number of new formed glacier lakes increased by 36% and the area of glacier lakes increased by 36.7% (0.97 km²) from 1991 to 2011. According to Shrestha et al. (2017), in 2010 there were a total of 2168 glacial lakes with a total area of 127.61 km² and average size of 0.06 km² in the Koshi basin.\n\nThe number and area of the glacial lakes increased consistently over the study period from 1160 and 94.44 km² in 1977–2168 and 127.61 km² in 2010, with an overall growth rate of 86.9% and 35.1%, respectively. Khadka et al. (2018) employed open access Landsat imagery to map and analyze the spatio-temporal dynamics of glacial lakes in Nepal from 1977 to 2017. The results revealed that the total area of glacial lakes in Nepal expanded by 25%. The investigation of glacial lakes in the Chandra basin of Himalayas as studied by Prakash and Nagarajan (2018) shows an increase in the total number and area of glacial lakes in Chandra basin from 28 to 46 and area from 1.91 to 3.26 km².\n\nThe number of glacial lakes in Sikkim Himalayas has increased from 425 to 466 during 1975 and 2017 showing a discernible increase of about 9%. The total glacial lake area has also showed a significant increase of 24%, it has increased from 25.17 to 31.24 km² (Shukla et al. 2018). As reported by Begam et al. (2019) the glacial lakes in the central and eastern Himalayas have been identified and mapped, with a total surface area of 37.9 km², these glacial lakes (moraine-dammed) as a whole were observed to have expanded by 43.6% from 1990 to 2015 using multi-temporal Landsat data.\n\nDuring 1964–2017, the total glacial lake area in the Poiqu River basin of central Himalaya has increased by\n\nFig. 4 Annual mean temperature and precipitation changes in Eastern Himalayas (1998–2013). Source: Adopted from Li et al. (2016)", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.2 Synthesis of Existing Literature", "section_headings": ["3 Findings", "3.2 Synthesis of Existing Literature"], "chunk_type": "text", "line_start": 97, "line_end": 109, "token_count_estimate": 725, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": ["00230"]}}
{"id": "60ede66e4b762425", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.2 Synthesis of Existing Literature\nType: figure\nFigure\n\nImage /page/4/Figure/13 description: A dual-axis line graph showing annual precipitation and annual mean temperature from the year 1998 to 2013. The x-axis represents the year. The left y-axis represents annual precipitation in millimeters (mm), ranging from 500 to 1100. The right y-axis represents annual mean temperature in degrees Celsius (°C), ranging from 8.5 to 10.5. There are two data series plotted. The first, labeled \"Precipitation,\" is a solid line with solid black circles. The second, labeled \"Temperature,\" is a dashed line with open circles. A linear trendline is shown for each series. For precipitation, the trendline equation is y = 0.5007x + 836.97 with an R² value of 0.0004. For temperature, the trendline equation is y = 0.0297x + 9.0871 with an R² value of 0.1335. The precipitation data shows significant fluctuation, with a peak near 1100 mm in 2010 and a low point just above 500 mm in 2009. The temperature data also fluctuates, with a general upward trend, peaking above 10°C in 2009.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.2 Synthesis of Existing Literature", "section_headings": ["3 Findings", "3.2 Synthesis of Existing Literature"], "chunk_type": "figure", "figure_caption": null, "line_start": 110, "line_end": 110, "token_count_estimate": 296, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "918b69c4ed0246de", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.2 Synthesis of Existing Literature\nType: text\n\n110% and glaciers retreated with an average rate of 1.4 km<sup>2</sup> a – 1 between 1975 and 2015 (Zhang et al. 2019). In the three river basins that form the Ganga river, the total area of glacial lakes has increased from 179 square kilometers in 2000 to 195 square kilometers in 2015 (UNDP, Nepal). The growth of glacial lakes in the Bhutan Himalayas has continued over a 20–70 years of study period, growing at a rate of less than 70 m per year in length and less than  $0.04~\\rm km^2$  per year in area (Komori et al. 2008). The glacial lake area change in the various parts of Himalayas is reflected in Table 1.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.2 Synthesis of Existing Literature", "section_headings": ["3 Findings", "3.2 Synthesis of Existing Literature"], "chunk_type": "text", "line_start": 111, "line_end": 113, "token_count_estimate": 197, "basins": ["Ganga"], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": ["00230"]}}
{"id": "ce424c601d4051b3", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.3 GLOF Events in the Himalayas\nType: text\n\nGlacial Lake Outburst Floods (GLOFs) have emerged as a serious hazard from the past few decades due to increase in the human settlements, anthropogenic activities and unplanned infrastructure development in to the fragile ecosystem of high mountain regions, which were not earlier inhibited (Khanal et al. 2015). Himalayan region contains approximately 9000 glacial lakes and around 200 lakes which are potentially hazardous resulting in about 40 GLOF events from the last four decades (Yamada and Sharma 1993; Ives et al. 2010). The onrushing water from the 1994 Lugge Tsho outburst flood devastated the number of villages downstream, such as Chozoz, Thanza, Tenchey and Punkha (Watnabe et al. 1996). In 1929 GLOF event resulted due to the outburst from Ice-dammed lake (Chog Kumdan) in Shyok basin of Ladakh region, affected about 48 villages in the downstream regions (Gunn 1930). On 17th of June 2013 Chorabari GLOF event, resulted due the dam breach has caused widespread damage to the region (Allen et al. 2016). Similar events have been witnessed in the Tibetan Himalaya Cirenamco (1981), Ghulkin GLOF (1981) in Karakoram Himalayas, (Dig Tsho (1985) and Tampokhari (1998)", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.3 GLOF Events in the Himalayas", "section_headings": ["3 Findings", "3.3 GLOF Events in the Himalayas"], "chunk_type": "text", "line_start": 115, "line_end": 119, "token_count_estimate": 341, "basins": [], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["00230"]}}
{"id": "c92278085a96f15e", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.3 GLOF Events in the Himalayas\nType: table\nTable: Table 1 Expansion of glacial lakes in the various parts of Himalayas\n\n| References                  | Time period | Change in area (%) | Study area                                           |\n|-----------------------------|-------------|--------------------|------------------------------------------------------|\n| Khadka et al. (2018)        | 1977–2017   | 25                 | Nepal                                                |\n| Chen et al. (2007a)         | 1986–2001   | 11                 | Poiqu basin Central Himalayas                        |\n| Nie et al. (2013)           | 1990–2010   | 17                 | Central Himalayas                                    |\n| Shukla et al. (2018)        | 1975–2017   | 24                 | Central Himalayas                                    |\n| Wang et al. (2016)          | 1991–2001   | 36.7               | Namco basin                                          |\n| Wang et al. (2013)          | 1979–2009   | 173                | Central Tibet                                        |\n| Shrestha et al. (2017)      | 1975–2010   | 31.5               | Koshi basin                                          |\n| Zhang et al. (2015)         | 1990–2010   | 23                 | Entire Third Pole                                    |\n| Debnath et al. (2018)       | 1988–2014   | 8.38               | Kangchengayo- Pauhunri,Massif, Sikkim Himalaya |\n| Prakash and Nagrajan (2018) | 2000–2014   | 41.41              | Chandra basin                                        |\n| Gardelle et al. (2011)      | 1990–2009   | 20–60              | Nepal and Bhutan                                     |\n| Begam et al. (2019)         | 1990–2015   | 43.6               | Central and Eastern Himalayas                        |", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.3 GLOF Events in the Himalayas", "section_headings": ["3 Findings", "3.3 GLOF Events in the Himalayas"], "chunk_type": "table", "table_caption": "Table 1 Expansion of glacial lakes in the various parts of Himalayas", "columns": ["References", "Time period", "Change in area (%)", "Study area"], "table_row_start": 1, "table_row_end": 12, "line_start": 120, "line_end": 133, "token_count_estimate": 393, "basins": [], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": ["00230"]}}
{"id": "8592f4eff6931a32", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.3 GLOF Events in the Himalayas\nType: text\n\nin Nepal Himalayas has resulted in to the extensive human loss and property downstream (Vuichard and Zimmermann 1987; Richardson and Quincey 2009; Xu 1988).\n\nBesides, this similar destructive GLOF events have claimed large number of lives and caused enormous damage in various parts of the world, particularly Andes, European Alps, Canadian Cordillera and central Himalayan region (Huggel et al. 2003; Clague and Evans 2000; Mergili et al. 2011; Emmer and Cochachin 2013). In 1981, a GLOF event in Nepal damaged Friendship bridge of Nepal-China Highway and Koshi power station with heavy economic losses (Bajracharya et al. 2006).\n\nA GLOF event that occurred during August 2000 damaged 98 bridges in the Tibetan Plateau and destroyed around 10,000 houses accounts about 75 million US dollars in terms of financial losses (Shen 2004). The major GLOF events that have been witnessed in the various parts of the Himalayan region are reflected in the Table 2.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.3 GLOF Events in the Himalayas", "section_headings": ["3 Findings", "3.3 GLOF Events in the Himalayas"], "chunk_type": "text", "line_start": 134, "line_end": 140, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": ["China", "Nepal"], "lake_ids": ["00230"]}}
{"id": "454e93dd52edcb26", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.4 Mapping of Glacial Lakes\nType: text\n\nMapping of glacial lakes is essential to analyze the spatio-temporal distribution of the glacial lakes, which is perquisite for the management of potentially dangerous glacial lakes. Glacial lake mapping has been carried out by various researchers in the different parts of Himalayas using automated and semi-automated techniques, such as Normalized Difference Water Index (NDWI) (Prakash and Nagrajan 2018; Worni et al. 2012; Bolch et al. 2008; Zhang et al. 2020), Band ratioing (Wessels et al. 2002; Bhambri et al. 2015), supervised and unsupervised classification (Zhang et al. 2020), object-oriented mapping and manual mapping (on-screen digitization) (Bhambri et al. 2018; Zhang et al. 2015; Nie et al. 2017). All these methods and techniques have been extensively used in the", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.4 Mapping of Glacial Lakes", "section_headings": ["3 Findings", "3.4 Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 142, "line_end": 144, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "31976c557c9de238", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.4 Mapping of Glacial Lakes\nType: figure\nFigure\n\nImage /page/5/Picture/12 description: The image displays the Springer logo on a white background. The logo consists of a black icon on the left and black text on the right. The icon is a stylized representation of a chess knight's head, facing left, with two horizontal lines beneath it. To the right of the icon, the word \"Springer\" is written in a serif font.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.4 Mapping of Glacial Lakes", "section_headings": ["3 Findings", "3.4 Mapping of Glacial Lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 145, "line_end": 145, "token_count_estimate": 125, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "28ca2c5dd08f0ab0", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.4 Mapping of Glacial Lakes\nType: table\nTable: Table 2 Major GLOF events in the Himalayan region\n\n| No | Year | Lake                 | Catchment/basin/ mountain range | Source                                                                     | Country affected |\n|----|------|----------------------|------------------------------------|----------------------------------------------------------------------------|------------------|\n| 1  | 1929 | Chog Kumdan          | Shyok river                        | Gunn (1930)                                                                | India            |\n| 2  | 1981 | Cirenamco            | Tibetan Himalayas                  | Vuichard and Zimmermann (1987)                                             | China            |\n| 3  | 1985 | Dig Tsho             | Nepal Himalayas                    | Xu (1988)                                                                  | Nepal            |\n| 4  | 1998 | Tampokhari           | Nepal Himalayas                    | Yamada and Sharma (1993)                                                   | Nepal            |\n| 5  | 1994 | Luggey Tsho          | Bhutan Himalayas                   | Watanbe and Rothacher (1996)                                               | Bhutan           |\n| 7  | 1964 | Gelhaipuco           | PumQu/Arun                         | Richardson and Quincey (2009), Bajracharya et al. (2006)                   | China and Nepal  |\n| 8  | 1977 | Nare                 | Koshi                              | Yamada (1993), Ghimire (2004)                                              | Nepal            |\n| 9  | 1980 | Nagma Pokhari        | Tamor                              | Raj and Kumar (2012)                                                       | Nepal            |\n| 10 | 1981 | Zhangzangbo Boqu     | Sun Koshi                          | Xu (1988), Shrestha et al. (2010a), Chen et al. (2013), Wang et al. (2018) | China and Nepal  |\n| 11 | 1998 | Sabai Tsho           | Khumbu Himal                       | Komori et al. (2012), Dwivedi (1999), Kattelmann (2003)                    | Nepal            |\n| 12 | 2008 | Ghulkin Glacial lake | Kara                               | Hussain et al. (2021)                                                      | Pakistan         |\n| 13 | 2000 | Glacial lake         | Yigeong                            | Zhu and Li (2011)                                                          | China and India  |\n| 14 | 2005 | Glacial lake         | Parechu                            | Zhu and Li (2011)                                                          | China and India  |\n| 15 | 2013 | Chorabari            | Mandakani basin                    | Allen et al. (2016)                                                        | India            |", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.4 Mapping of Glacial Lakes", "section_headings": ["3 Findings", "3.4 Mapping of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 2 Major GLOF events in the Himalayan region", "columns": ["No", "Year", "Lake", "Catchment/basin/ mountain range", "Source", "Country affected"], "table_row_start": 1, "table_row_end": 14, "line_start": 149, "line_end": 164, "token_count_estimate": 558, "basins": [], "subbasins": ["Shyok"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["00230"]}}
{"id": "22245410d8a77506", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.4 Mapping of Glacial Lakes\nType: text\n\npast to extract the glacial lakes/water bodies in the Himalayan region (Shukla et al. 2018; Kumar et al. 2020; Mal et al. 2020; Ageta et al. 2000; Wang et al. 2015; Yamada 1993; Khadka et al. 2018). However, due to some uncertainties created by atmospheric and physical processes like mountain shadow, soil moisture, vegetation noise, cloud cover, built-up land noise and snow cover, they are sometimes misclassified as glacial lakes due to the spectral mixing (Zhang et al. 2015). In this context Xu 2006 has modified the Normalized Difference Water Index of Mc Feeters (1996), which has been extensively used by various researchers according to their purpose of study (Chen et al. 2021; Du et al. 2016; Sarp and Ozcelik 2017; Acharya et al. 2018). The Modified Normalized Difference Water Index (MNDWI) utilizes Green and Short-Wave Infrared (SWIR) bands to enhance open water features by efficiently diminishing and removing vegetation, soil and built-up land noise (Eq. 1). Accordingly, MNDWI seems to be more suitable for enhancing and extracting information of water region with a background dominated by vegetation, soil and built-up land noise.\n\n$$MNDWI = \\frac{pG - pSWIR}{pG + pSWIR},$$\n(1)\n\nwhere pGand pSWIR bands are the reflectance in Green and Short-wave infrared, respectively.\n\nA hybrid methodology which comprises the following five steps is much useful for the mapping of glacial lakes and water bodies.\n\n- Automated extraction of glacial lake layer using techniques like NDWI, MNDWI, Band ratioing and supervised/unsupervised classification.\n- Selecting the minimum threshold (based on the resolution of the data used)\n- Manual editing or on-screen digitization\n- Check and overlay with High-resolution Google earth images\n- In situ field measurements\n\nPost-checking and editing is almost necessary and suggested for all the automated methods and techniques for glacial lake mapping (Mergili et al. 2013). Therefore, automated techniques coupled with manual editing and check overlay with high-resolution Google earth images seems to be best method for the glacial lake mapping (Mool et al. 2001,2003). In addition to that, field-based observations to some of the bench mark glacial lakes may help to validate the results yielded from the remote sensing-based glacial lake mapping. Besides that, Digital Elevation Model based (DEM) derived slope, aspect and shaded relief values can be used to remove misclassifications of water bodies from topographic effects in the rugged terrain of Himalayan region. The field investigations that are essential part of the process are useful to validate the lake attributes and topographic parameters to generate the final database.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.4 Mapping of Glacial Lakes", "section_headings": ["3 Findings", "3.4 Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 165, "line_end": 182, "token_count_estimate": 707, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "c2386acabba2aeda", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.4 Mapping of Glacial Lakes\nType: figure\nFigure\n\nImage /page/6/Picture/14 description: The logo for the publisher Springer, featuring a black and white line drawing of a chess knight piece on the left, followed by the word \"Springer\" in a black serif font. The entire logo is set against a white background.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.4 Mapping of Glacial Lakes", "section_headings": ["3 Findings", "3.4 Mapping of Glacial Lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 183, "line_end": 183, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "5222a07ce7e58926", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.5 Classification of Glacial Lakes\nType: text\n\nClassification of glacial lakes plays an important role to know the location and to understand the origin and evolution of glacial lakes. Glacier proximity, moraines, outwash plains and other geomorphological features should be taken into consideration at the time of classification of glacial lakes (Bhambri et al. 2015). Although, numerous approaches of glacial lake classification (Scheffers and Kelletat 2016; Cao et al. 2016; Mool et al. 2011; Wang et al. 2016; Yao et al. 2018; Wu et al. 2011) are available in the literature. However, as of now, there has been no internationally accepted method for the classification system of the glacial lakes.\n\nSome researchers and organizations have proposed various classification systems of glacial lakes as per their own understanding and purpose of research. In the Inventory of glacial lakes in Uttarakhand, Govindha et al. (2016) have classified glacial lakes into 6 major types which include proglacial lakes, moraine-dammed lakes, blocked lakes, erosion lakes, cirque lakes and supra glacial lakes based on the methodology suggested by Mool and Bajracharya (2003) and Chen et al. (2007a, b). The schematic representation of different types of glacial lakes as given by Govindha & Kumar (2016) is depicted in (Fig. 5).\n\nPradeep et al. (2001) have also classified glacial lakes into 5 classes: glacial erosion lake, moraine-dammed lake, ice-blocked lake, Supraglacial Lake and subglacial lake. Bhambri et al. (2015) classified glacial lakes based on glacier process in to four major types as ice dammed, moraine-dammed lake, glacier erosion lake and other\n\nglacial lake. A detailed classification system which comprises 3 major classes and 10 sub-classes has been proposed by Wu et al. (2011).\n\nBased on the hydrologic relationships between glacial lakes and their feeding glacier, Jain and Mir (2019) classified glacial lakes into 5 types as supraglacial lake, pro-glacial lake, periglacial lake, glacier fed lake and non-glacier fed lake. Yi and Cui (1995) in the glacial lake study of Altay Mountains suggested multiple classification schema. As per thermal and mechanical differences the glacial lake formation, they were classified as ice-blocked lake, moraine-dammed lake, glacial erosion lake, glacial thaw lake and glacial composite lake. Furthermore, glacial lakes were divided into ice-water lake and non-icewater lake based on the water supply of glacial lakes. Rai and Mishra (2017) have categorized Glacier lakes into six types, namely blocked lake, moraine-dammed lakes, erosion lakes, cirque lakes, pro-glacial lakes and supra glacial lake. Wang et al. (2010) classified glacial lake into 7 types which include ice-blocked lake, moraine-dammed lake, glacial erosion lake, Cirque Lake, Supraglacial Lake, landslide-dammed lake and glacial valley lake. ICIMOD, 2018 has classified morphologically glacial lakes into three major types and divided them into 10 sub-classes. A complete classification schema proposed by Yao et al. 2018, which is based on the past research studies and principal of systematic, normalization, operability and scalability has incorporated almost all the types of glacial lakes in his classification system (Table 3; Fig. 6).", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.5 Classification of Glacial Lakes", "section_headings": ["3 Findings", "3.5 Classification of Glacial Lakes"], "chunk_type": "text", "line_start": 186, "line_end": 198, "token_count_estimate": 848, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "dfc3591ad3e3c471", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.5 Classification of Glacial Lakes\nType: text\n\nglacial lake . Wang et al . ( 2010 ) classified glacial lake into 7 types which include ice - blocked lake , moraine - dammed lake , glacial erosion lake , Cirque Lake , Supraglacial Lake , landslide - dammed lake and glacial valley lake . ICIMOD , 2018 has classified morphologically glacial lakes into three major types and divided them into 10 sub - classes . A complete classification schema proposed by Yao et al . 2018 , which is based on the past research studies and principal of systematic , normalization , operability and scalability has incorporated almost all the types of glacial lakes in his classification system ( Table 3 ; Fig . 6 ) .\n\nFig. 5 Types of glacial lakes: (EL) Erosion Lake, (CL) Cirque Lake, (MDL) Moraine-dammed lake (BL) Blocked lake, (SL) Supra glacial lake and (PL) Proglacial lake. Source: Adapted from (Govindha & Kumar 2016)", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.5 Classification of Glacial Lakes", "section_headings": ["3 Findings", "3.5 Classification of Glacial Lakes"], "chunk_type": "text", "line_start": 186, "line_end": 198, "token_count_estimate": 271, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "4e096e817b9b38b4", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.5 Classification of Glacial Lakes\nType: figure\nFigure\n\nImage /page/7/Picture/9 description: A diagram illustrating different types of glacial lakes, each with a corresponding abbreviation. In the lower left, a cross-section labeled 'CL' shows a 'Lake' within a 'Cirque', with a 'Zone of Plucking' and a 'Zone of Abrasion' indicated. The upper and right portions of the image display several types of lakes formed by glaciers. 'EL' shows a lake at the end of a glacier flowing into a river. 'SL' depicts lakes on the surface of a glacier. Two diagrams labeled 'PL / MDL' show a proglacial lake at the terminus of a glacier. Two diagrams labeled 'BL' illustrate lakes formed at the base of a glacier, one showing calving into the lake. Each of these diagrams includes a 'GLACIER', a 'Lake', and a 'River'.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.5 Classification of Glacial Lakes", "section_headings": ["3 Findings", "3.5 Classification of Glacial Lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 199, "line_end": 199, "token_count_estimate": 252, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "0f4591e5c69f0fae", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.5 Classification of Glacial Lakes\nType: figure\nFigure\n\nImage /page/7/Picture/10 description: The logo for the publisher Springer, featuring a black outline of a chess knight piece to the left of the word 'Springer' in a black serif font, all on a white background.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.5 Classification of Glacial Lakes", "section_headings": ["3 Findings", "3.5 Classification of Glacial Lakes"], "chunk_type": "figure", "figure_caption": null, "line_start": 201, "line_end": 201, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "f51099e0f8ce95f8", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.5 Classification of Glacial Lakes\nType: table\nTable: Table 3 Classification of glacial lakes\n\n| Class                | Sub-class                     | Description                                                 |\n|----------------------|-------------------------------|-------------------------------------------------------------|\n| Glacial Erosion lake | Cirque lake                   | The lake in one Cirque                                      |\n|                      | Glacial valley lake           | The lake in U-shaped valley by glaciation                   |\n|                      | Other glacial erosion lake    | The lake formed by glacier erosion but not                  |\n| Moraine-dammed lake  | End-moraine-dammed lake       | The lake between the end moraine ridge and glacier terminus |\n|                      | Lateral-moraine-dammed lake   | The lake beside the lateral moraine ridge                   |\n|                      | Moraine thaw lake             | The lake on the moraine ridge                               |\n| Ice-blocked lake     | Advanced glacier-blocked lake | The lake blocked by advanced glacier                        |\n|                      | Other glacier-blocked lake    | The lake with the dam being glacier ice                     |\n| Supraglacial lake    |                               | The lake on the surface of glacier                          |\n| Subglacial lake      |                               | The lake within the glacier or over the glacier bed         |\n| Other glacial lake   |                               | The lake blocked by landslide, avalanche, debris flow etc., |", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.5 Classification of Glacial Lakes", "section_headings": ["3 Findings", "3.5 Classification of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 3 Classification of glacial lakes", "columns": ["Class", "Sub-class", "Description"], "table_row_start": 1, "table_row_end": 11, "line_start": 205, "line_end": 217, "token_count_estimate": 354, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "e1565a3abfca5c41", "text": "Document: s41748-021-00230-9\nSection: 3 Findings > 3.5 Classification of Glacial Lakes\nType: text\n\nSource: Compiled from (Yao et al. 2018)", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "3 Findings > 3.5 Classification of Glacial Lakes", "section_headings": ["3 Findings", "3.5 Classification of Glacial Lakes"], "chunk_type": "text", "line_start": 218, "line_end": 220, "token_count_estimate": 42, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "3d7a3994546b71b8", "text": "Document: s41748-021-00230-9\nSection: 4 Discussion\nType: text\n\nGlacial lakes in the Himalayan region are increasing and expanding at an accelerated rate with increase in the number of potentially hazardous lakes. There is a strong evidence in the scholarly literature that the climate change-induced glacial retreat has resulted into the formation, development and expansion of different types of glacial lakes in the study region. Expansion rate of pro-glacial lakes connected to the glacier and moraine-dammed lakes is faster than other type of lakes. As reported by Begam et al. (2019), the glacial lakes in the central and eastern Himalayas have a total surface area of 37.9 km<sup>2</sup>. These moraine-dammed glacial lakes were observed to have expanded by 43.6% from 1990 to 2015. The study was conducted using multi-temporal Landsat data. This gives us an indication that a limited number of such studies have been conducted using high-resolution satellite data. Use of high-resolution data will give better results. The growth of glacial lakes in the Bhutan Himalayas has continued over a 20–70 years of study period, growing at a rate of less than 70 m per year in length and less than 0.04 km<sup>2</sup> per year in area (Komori et al. 2008). According to the glacial lake inventory prepared by Shrestha et al. (2017), there were a total of 2168 glacial lakes with a total area of 127.61 km<sup>2</sup> and average size of 0.06 km<sup>2</sup> in the Koshi basin in 2010. The number and area of the glacial lakes increased consistently over the study period from 1160 and 94.44 km<sup>2</sup> in 1977–2168 and 127.61 km<sup>2</sup> in 2010, with an overall growth rate of 86.9% and 35.1%, respectively. The results indicate that the total surface area of glacial lakes in Nepal Himalayas expanded by 25% between 1987 and 2017 as studied by Khadka et al. 2018. The number of glacial lakes in the Sikkim Himalayas has increased from 425 to 466 during 1975 and 2017 showing a discernible increase of about 9%. The total glacial lake area has also showed a significant increase of ~24%, it has increased from 25.17 to 31.24 km<sup>2</sup> (Shukla et al. 2018). Dubey and Goyal (2020) of the Indian Institute of Technology Indore, India, employed satellite data, DEM and flood models to analyze the hazard risk of 329 glacial lakes in the Indian Himalayas. The glacial lakes in the region have increased by 16% during 1993–2018. Furthermore, they estimated that about 22% of these glacial lakes possess high hazard risk both in terms of GLOF event and downstream impacts. Although this is not an exhaustive study covering all the research studies carried out so far in the study region, but on the basis of the abovecited literature, it is quite evident that glacial lakes in the Himalayan region are expanding and the formation of new glacial lakes has shown a considerable increase particularly in the form of supra glacial lakes.\n\nThe rate of growth of such glacial lakes may increase in the future due to the recession of the glaciers caused by the changing climate. The consequent changes may cause catastrophic events which can damage the people and infrastructure downstream.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "4 Discussion", "section_headings": ["4 Discussion"], "chunk_type": "text", "line_start": 222, "line_end": 226, "token_count_estimate": 802, "basins": [], "subbasins": [], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": ["00230"]}}
{"id": "83429477c1d717e8", "text": "Document: s41748-021-00230-9\nSection: 5 Concluding Remarks\nType: text\n\nHigh-mountain areas are severely affected by climate change. The present study highlighted sequentially the glacial lake changes and prioritization of risk management for potential GLOFs by investigating into major GLOF events in the Himalayan region using existing literature. Glacial lakes in the Himalayan region are increasing in number and area; hence GLOF events are becoming common in the high-glaciered basins of Himalaya. The continued recession of glaciers under changing climate may lead to the drastic increase in glacial lake area which could be possible cause for GLOFs in the region. The consequences arising from such situations are inevitable, particularly due to less availability of adequate data pertaining to intensity of rainfall, landslide locations, and outburburst potential of glacial lakes. The inferences drawn from the study suggest that there", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "5 Concluding Remarks", "section_headings": ["5 Concluding Remarks"], "chunk_type": "text", "line_start": 228, "line_end": 230, "token_count_estimate": 204, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "75f199328ffbc073", "text": "Document: s41748-021-00230-9\nSection: 5 Concluding Remarks\nType: figure\nFigure\n\nImage /page/8/Picture/11 description: The logo for the publisher Springer, shown in black text on a white background. To the left of the word \"Springer\" is an icon of a chess knight piece, which is a horse's head, facing left and resting on a base with two horizontal lines.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "5 Concluding Remarks", "section_headings": ["5 Concluding Remarks"], "chunk_type": "figure", "figure_caption": null, "line_start": 231, "line_end": 231, "token_count_estimate": 96, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "99695d456385fc8f", "text": "Document: s41748-021-00230-9\nSection: 5 Concluding Remarks\nType: figure\nFigure\n\nImage /page/9/Figure/1 description: A composite figure displaying various types of glacial lakes through satellite imagery from Google Earth and Landsat. The figure is organized into four rows, each showcasing different lake formations.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "5 Concluding Remarks", "section_headings": ["5 Concluding Remarks"], "chunk_type": "figure", "figure_caption": null, "line_start": 233, "line_end": 233, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "95f927017ea6354a", "text": "Document: s41748-021-00230-9\nSection: 5 Concluding Remarks\nType: text\n\nTop row: On the left, a Google Earth image shows a 'Cirque lake' in a mountainous, snowy region. On the right, a Landsat TM 5-4-3 band combination image displays the larger area, including 'Akkol Lake' and 'Akkol Glacier'.\n\nSecond row: This row presents three images. The first is a Landsat OLI 5-4-3 band combination showing an 'End moraine-dammed lake'. The middle image is from Google Earth, pointing out the 'End moraine'. The right image is a Landsat TM 4-3-2 band combination showing a 'Lateral moraine-dammed lake' near the 'Ngozumpa Glacier'.\n\nThird row: This row also contains three images. The left image is a wide-view Landsat OLI 5-4-3 band combination. The middle Google Earth image is a zoomed-in view identifying an 'end moraine-dammed lake', a 'lateral moraine-dammed lake', and a 'moraine thaw lake'. The right image is a Landsat OLI 5-4-3 band combination showing an 'Ice-blocked lake'.\n\nBottom row: This row consists of two images. The left is a wide-view Landsat OLI 5-4-3 band combination of the 'Rongbuk Glacier'. The right image is a magnified view showing a 'Supraglacial lake' on the glacier's surface.\n\nFig. 6 Types of glacial lakes on Landsat Images on bands 5, 4 and 3 and Google earth images. Source: Compiled from (Yao et al. 2018)", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "5 Concluding Remarks", "section_headings": ["5 Concluding Remarks"], "chunk_type": "text", "line_start": 234, "line_end": 244, "token_count_estimate": 387, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "1d276238173ae90d", "text": "Document: s41748-021-00230-9\nSection: 5 Concluding Remarks\nType: figure\nFigure\n\nImage /page/9/Picture/3 description: The Springer logo, featuring a black line drawing of a chess knight piece on the left, followed by the word \"Springer\" in a black serif font. The knight is facing left and stands on two horizontal lines. The background is white.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "5 Concluding Remarks", "section_headings": ["5 Concluding Remarks"], "chunk_type": "figure", "figure_caption": null, "line_start": 245, "line_end": 245, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "8c987c39bcdc9654", "text": "Document: s41748-021-00230-9\nSection: 5 Concluding Remarks\nType: text\n\nis an urgent need to monitor and assess the expanding glacial lakes and potentially hazardous lakes in the Himalayan region with immediate impact on the mountain communities of countries like India, China, Pakistan, Bhutan and Nepal. Although various field investigations have been conducted in the area, they are usually limited by lack of in situ glaciological and meteorological observation sites and unfavorable environmental conditions. Integration of Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) has been observed to be the best tool for mapping and monitoring dynamics of glacial lakes and the associated outburst scenarios in the Himalayan region. However, the updated and detailed glacial lake inventories for the region using high-resolution satellite data will be a resource-based for future studies to monitor the behavior of glacial lakes in the region to save many lives and reduce damages to infrastructures, such as houses, bridges, hydro-power projects, roads, etc. With the advancement in geospatial technology for polar and oceanic research, new technologies will open ways for effective management for GLOFs in the region; Geoinformatics is that specialized area where we need invest more to further the scope for cryospheric and climate research. The need has been felt to link the scientific knowledge on GLOF hazards into policy and planning at the local level as well as involve indigenous mountain communities in the GLOF risk prevention and mitigation programs.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "5 Concluding Remarks", "section_headings": ["5 Concluding Remarks"], "chunk_type": "text", "line_start": 246, "line_end": 248, "token_count_estimate": 343, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["00230"]}}
{"id": "1a20f59b527963f8", "text": "Document: s41748-021-00230-9\nSection: Declarations\nType: text\n\n**Conflict of Interest** The authors have no conflicts of interest to declare that are relevant to the content of this article.", "metadata": {"source_file": "data/('s41748-021-00230-9', '.pdf')_extraction.md", "document_title": "s41748-021-00230-9", "section_path": "Declarations", "section_headings": ["Declarations"], "chunk_type": "text", "line_start": 250, "line_end": 251, "token_count_estimate": 46, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00230"]}}
{"id": "da0df3d8496f1ea5", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nType: text\n\nby Sarah Fecht and Ben Orlove September 16, 2020\n\nVibrations in the ground may help to improve advanced warnings about sudden floods that result from glacial melting, according to a study published today in *Science Advances*.\n\nOn October 7, 1994, a natural dam that had been holding back a glacial lake burst, sending floodwaters crashing downstream into the Bhutanese village of Punakha. The sudden flood killed 21 people, destroyed 816 acres of crops and 6 tons of stored food, and washed away homes and other infrastructure. The new study, led by researchers at Columbia University's Lamont-Doherty Earth Observatory, discovered that local seismic devices unknowingly recorded this glacial lake outburst flood five hours before it reached the village.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods"], "chunk_type": "text", "line_start": 4, "line_end": 10, "token_count_estimate": 230, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a9da8e9471f21c0a", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nType: figure\nFigure\n\nImage /page/1/Picture/2 description: A figure containing two satellite maps, labeled A and B, illustrating a river system in Asia. The main map, A, shows a wide view of the Brahmaputra River, highlighted in blue, flowing from the Pho Chhu river in the Himalayas down to the Bay of Bengal. Several yellow dots in the mountains are labeled as \"Seismic stations\". A scale bar indicates 200 km. The inset map, B, is a zoomed-in view of the Pho Chhu river area, showing the locations of Punakha and Wangdue along the river. A scale bar on this map indicates 25 km.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods"], "chunk_type": "figure", "figure_caption": null, "line_start": 11, "line_end": 11, "token_count_estimate": 209, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e32cd1baf824940", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nType: text\n\nThe larger map (A) shows the Pho Chhu river as it flows from the Himalayas into the Bay of Bengal. Seismometer locations are marked with yellow dots. The inset (B) zooms in on the area inside the red box in A, indicating the area where the glacial lake outburst flood began and the location of the village of Punakha 90 kilometers downstream. Image: Maurer et al./Science Advances 2020\n\nGlacial lake outburst floods are becoming more frequent and more destructive in mountainous areas. As glaciers melt, the water pools into lakes trapped behind dams made of rocky glacial debris and ice jams. When the dam shifts or too much pressure builds behind it, the lake water rushes out in a catastrophic burst, posing a danger to downstream communities. As the planet warms, glacial lakes are becoming <u>larger and more common</u>, thus <u>increasing the potential</u> for glacial lake outburst floods (GLOFs).\n\nIn the study, led by Lamont-Doherty graduate student <u>Josh Maurer</u>, researchers discovered that a seismometer array located about 100 kilometers from the glacial lake had recorded a clear high-frequency signal at approximately 1:45am, around the time that the dam would have burst. They hypothesize that as the dam ruptured, the powerful and sudden outflow of water and/or sediments struck the riverbed, causing the vibrations that were picked up by the seismometers. The team was able to use the seismic data to reconstruct the flood as it made its way 90 kilometers downstream, reaching the village of Punakha at around 7am.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods"], "chunk_type": "text", "line_start": 12, "line_end": 18, "token_count_estimate": 454, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bcec15b738b43e0d", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A two-panel scientific figure, labeled 'A', displaying seismic data over a 12-hour period on 1994-10-07. The top panel is a seismogram showing velocity (in m s⁻¹) on the y-axis versus the hour of the day on the x-axis. The velocity scale ranges from -1 to 1, multiplied by 10⁻⁸. The bottom panel is a spectrogram showing frequency (in Hz) from 0 to 4 on the y-axis versus the same time axis. A color bar on the right indicates the Power Spectral Density (PSD), with yellow representing higher power (-180) and blue representing lower power (-200). The seismogram shows several distinct seismic events annotated with arrows: 'Kuril Islands – mb 5.0' and 'Chile – mb 4.8' before hour 2; a 'Local earthquake?' around hour 4; 'Kuril Islands – Mw 5.6' and a 'Nuclear Explosion – mb 6.0 Southern Xinjiang, China' between hours 9 and 10; and another 'Local earthquake?' around hour 11. A prolonged period of high-amplitude seismic noise is visible from approximately hour 2 to hour 8.5. The spectrogram below shows this prolonged event as a bright yellow and green band labeled 'Primary GLOF signal'. Two time intervals, '1:45 - 3:15 AM' and '5:45 - 7:15 AM', are highlighted in red text and marked with dashed red vertical lines within this GLOF signal.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods"], "chunk_type": "figure", "figure_caption": null, "line_start": 19, "line_end": 19, "token_count_estimate": 414, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "580f11f863666ff6", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nType: text\n\nThe tremors triggered by the GLOF and detected by far away seismometers: the initial outburst at 1:45 a.m., flood getting stronger at 2:15 a.m., and slowly tapering off after 7:15 a.m. Image: Maurer et al./Science Advances 2020\n\nCurrently, instruments monitor local water level in some glacial lakes and alert local communities if the lake level suddenly drops, indicating a GLOF. However, such systems are known to be somewhat unreliable and have issued false alarms in the past. The study authors suggest that with some refinement, real-time seismic monitoring could be combined with water level monitoring systems to minimize false alarms and maximize warning times. In addition, a few strategically placed seismic sensors could potentially monitor for GLOFs over a large area, whereas water level monitors must be installed lake by lake.\n\nThe authors note that more research is needed before seismic GLOF monitors would be ready for deployment. The team hopes to find and explore other instances where seismometers have captured GLOF events, to better understand how to read and analyze the signals in real time. They also caution that the Punakha flood was very large, so the signal stood out clearly in the data; in the future, they hope to better understand whether the technique can reliably detect smaller glacial lake outburst floods, which can still cause severe damage.\n\nBy reconstructing the Punakha flood, the researchers were also able to test various models of how flood waters would be expected to flow through the area, showing that seismic data could help to improve flood modeling. In addition, the paper used satellite imagery before and after the GLOF to assess its impacts on the area.\n\nExperts who were not involved in the study, including geographer Simon Allen and glaciologist Holger Frey (both from the University of Zurich), said the study represents a\n\npromising first step toward a seismology-based early warning system. Allen said that more research is needed, since the technique has only been tested on one lake so far, and cautioned that maintaining a real-time seismic monitoring network in the Himalayas or elsewhere would present financial and technical challenges.\n\n\"The algorithms need to be extremely reliable,\" said Frey. \"All events must be detected, but at the same time false alarms need to be avoided by all means.\" He also emphasized that including people from the affected communities in the design and implementation of such systems is critical in determining whether or not they are ultimately successful.\n\n\"This study is a great demonstration of the potential for long-range seismic detection of large outburst floods,\" said Kristen Cook, a geologist at the GFZ German Research Centre for Geosciences who was not involved in the study. \"This seismic detection could have important implications looking both back in time to validate flood models and better understand the processes of outburst floods, and potentially forwards in time if a seismic early warning system can be developed. Outburst floods are a big concern in the Himalaya, especially as development along river corridors increases and lakes are growing, so both more robust early warning and better modeling would have significant societal benefits.\"\n\nOther authors of the study include: Joerg Schaefer, Joshua Russell, and Nicolas Young from Columbia University; Summer Burton Rupper from the University of Utah; Norbu Wangdi from the Center for Water, Climate, and Environmental Policy in Bhutan; and Aaron Putnam from the University of Maine.\n\nLearn more about the study in a brief Q&A with study co-author <u>Joerg Schaefer</u>, below.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods"], "chunk_type": "text", "line_start": 20, "line_end": 40, "token_count_estimate": 885, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "8f8f22caff7d0aeb", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > How did the idea for this study first develop?\nType: text\n\nThis all started when we were working on the well-preserved and nearly complete moraine sequences in front of the GLOF lakes. They were in the pathway of the 1994 GLOF, and beryllium dating shows that they are old, like 4,000 years old. I was puzzled as to how such a devastating GLOF could pass these old glacial landforms without destroying them, washing them out. I asked graduate student Josh Maurer to check the spy satellite imagery and the subsequent remote sensing images for pictures of the lakes and moraines just before and just after the flood. He did that, and we documented the outburst and early phase of the 1994 GLOF. We learned that the flood was just not super dramatic right at the start, and only took out a small part of the terminal moraine section. This is a striking and scary reminder that GLOFs starting at these high altitudes pick up their devastating energy by gravity on their way downhill.\n\nJosh realized the potential, and we started to wonder if the GLOF signal should not be visible in the seismometer record. Josh got in touch with Josh Russell, a PhD student in seismology at Lamont, and together they went to work and applied a technique called 'cross-correlation based seismic analyses,' with which they could track the evolution of the GLOF with seismometers as far as 100 km away from the actual flood. They found the flood signal in stunning clarity and synthesized the seismic data with eyewitness reports and a downstream gauge station within a numerical flood model.\n\nWe also used the remote imagery before and after the flood to estimate the sediment deposition in the valley downstream to assess the damage, and traced the speed of vegetation recovery.\n\nThis is probably the most innovative earth science paper I have had the pleasure to be part of. My main role in it has been to support the work of these brilliant grad students.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > How did the idea for this study first develop?", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "How did the idea for this study first develop?"], "chunk_type": "text", "line_start": 42, "line_end": 50, "token_count_estimate": 491, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "532195859cbd6b69", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Did you encounter any obstacles in the development of this project? If so, what were they? How did you overcome them?\nType: text\n\nJosh and Josh encountered a variety of problems during their cross-correlation analyses, but they worked brilliantly and effectively as a team. Once all the results were on the table, it took us a while to organize the pieces from many different disciplines to form a coherent earth science manuscript, and to realize and formulate the potential of this technique for a new generation of GLOF early warning systems.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Did you encounter any obstacles in the development of this project? If so, what were they? How did you overcome them?", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Did you encounter any obstacles in the development of this project? If so, what were they? How did you overcome them?"], "chunk_type": "text", "line_start": 52, "line_end": 54, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a3d3f403df6381a", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > How do you think other glacial lakes could be prioritized for future research along these lines?\nType: text\n\nOne of the biggest strengths of this approach is the regional applicability. We can use this toolkit, for example, to ask the seismometer record whether or not there are similar 'GLOF-type signals' in the system. And, using Josh's satellite image processing techniques, we can search the region for the source of similar floods that might have occurred in the area over the last 40 years.\n\nBeing able to track the formation, growth and in particular increase in lake level over time is the key to evaluate and identify the most hazardous lakes in the region. Topography and sediment availability are probably similar across different GLOF-prone valleys in the region, but we should absolutely produce a map highlighting human settlements and areas that are key to their livelihoods in relation to the GLOF hazard from higher up in the Himalayas.\n\nTags: <u>climate change</u>, <u>floods</u>, <u>glacial lake outburst floods</u>, <u>GlacierHub</u>, <u>Lamont-Doherty</u>\n<u>Earth Observatory</u>, <u>seismology</u>", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > How do you think other glacial lakes could be prioritized for future research along these lines?", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "How do you think other glacial lakes could be prioritized for future research along these lines?"], "chunk_type": "text", "line_start": 56, "line_end": 63, "token_count_estimate": 345, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4f6fa92b0ce785b9", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: figure\nFigure\n\nImage /page/5/Picture/4 description: A wide shot of a flooded residential area at dusk or dawn, with several cars submerged in murky water. In the foreground, the roof of a silver car is visible above the water. The water reflects the dark silhouettes of tall evergreen trees. In the background, more cars are partially submerged, and houses or cabins can be seen amongst the trees, with a mountain visible in the far distance against a pale sky.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "figure", "figure_caption": null, "line_start": 66, "line_end": 66, "token_count_estimate": 177, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fce01c96a19b6248", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: text\n\nGlacier Flooding in Alaskan Capital Sets New Record", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "text", "line_start": 67, "line_end": 69, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1054c4a63e467f2c", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: figure\nFigure\n\nImage /page/5/Picture/6 description: A scenic, eye-level, full shot of a majestic mountain range on a sunny day. The foreground consists of a rocky, barren terrain with patches of snow. In the background, towering snow-capped mountains are partially obscured by thick, white clouds. The sky is a deep blue with some wispy clouds, and the bright sun in the upper right corner creates a lens flare effect across the image.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "figure", "figure_caption": null, "line_start": 70, "line_end": 70, "token_count_estimate": 175, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "751294b05964a7c8", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: text\n\nGlacial Melting in High Mountain Asia Has the Potential to Overwhelm Hydropower Systems", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "text", "line_start": 71, "line_end": 73, "token_count_estimate": 79, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8bbb031725c355ab", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: figure\nFigure\n\nImage /page/5/Picture/8 description: A wide shot of a rugged mountain landscape featuring a large glacier at its peak. The glacier has a jagged, icy front and sits atop a steep, rocky slope made of gray scree. To the right, a waterfall cascades down a dark rock face, likely from glacial melt. Another patch of snow is visible on the upper right side of the mountain. The sky above is a vibrant blue with scattered, wispy white clouds.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "figure", "figure_caption": null, "line_start": 74, "line_end": 74, "token_count_estimate": 180, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53ad7d5c057ff255", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: text\n\nInternational\nFramework Aims to\nProtect and Manage\nLands Recently\nExposed by Glacier\nRetreat", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "text", "line_start": 75, "line_end": 82, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e21922ac5a03a386", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: figure\nFigure\n\nImage /page/5/Picture/10 description: A promotional banner for the Columbia Climate School's UN Climate Change Conference COP30. The banner has a dark green background in the center with text. On the left is an aerial photo of a winding river through a lush green forest. On the right is a nighttime photo of an illuminated cityscape featuring a large institutional building. The text in the center reads: 'COLUMBIA CLIMATE SCHOOL' in light green, followed by 'UN CLIMATE CHANGE CONFERENCE COP30' in white and bright yellow. Below this, it says 'BELÉM, BRAZIL | NOV 10-21, 2025' in light green.", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "figure", "figure_caption": null, "line_start": 83, "line_end": 83, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1e2f3459c5c2be5", "text": "Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods\nSection: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts\nType: text\n\nDuring COP30—the 2025 UN Climate Change Conference taking place November 10–21 in Belém, Brazil—experts from Columbia Climate School and Columbia University will be contributing to key events, sharing insights, and helping shape the dialogue toward ambitious, science-based solutions. <u>Learn More</u>", "metadata": {"source_file": "data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md", "document_title": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "section_path": "Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts", "section_headings": ["Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods", "Related Posts"], "chunk_type": "text", "line_start": 84, "line_end": 86, "token_count_estimate": 128, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62a26a77ac1532ca", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nType: text\n\nGlacial lake outburst floods (GLOFs) are a substantial hazard for downstream communities in vulnerable regions, yet unpredictable triggers and remote source locations make GLOF dynamics difficult to measure and quantify. Here, we revisit a destructive GLOF that occurred in Bhutan in 1994 and apply cross-correlation-based seismic analyses to track the evolution of the GLOF remotely (~100 kilometers from the source region). We use the seismic observations along with eyewitness reports and a downstream gauge station to constrain a numerical flood model and then assess geomorphic change and current state of the unstable lakes via satellite imagery. Coherent seismic energy is evident from 1 to 5 hertz beginning approximately 5 hours before the flood impacted Punakha village, which originated at the source lake and advanced down the valley during the GLOF duration. Our analysis highlights potential benefits of using real-time seismic monitoring to improve early warning systems.\n\nCopyright © 2020\nThe Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 6, "token_count_estimate": 309, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "790fe844aaf5a293", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: INTRODUCTION\nType: text\n\nGlacial lakes in the Himalayas are rapidly growing due to climate change and acceleration of glacier melt in recent decades (1, 2). Many of these lakes are dammed by unstable, often ice-cored moraines (3), surrounded by steep topography prone to landslides, and frequently subjected to seismic events and intense monsoonal precipitation. Risks associated with glacial lake outburst floods (GLOFs) are substantial for downstream inhabitants in these regions, and thousands of fatalities have already occurred as a direct result of sudden catastrophic releases of water (4). Societies and economies of GLOF-prone regions are severely impacted, including destruction of infrastructure, disruption to communities, and loss of life (2-5). The nation of Bhutan, in particular, has been classified as having some of the greatest national-level economic consequences of glacier flood impacts, as hydropower dominates the nation's gross domestic product and socioeconomic development potential (4, 6). The electric power demands of Himalayan nations are on a steep rise with rapid economic growth, and hydropower development continues to expand into higher sites closer to glaciers (2).\n\nNumerous GLOFs have been documented in the Himalayas by firsthand observation and satellite imagery after their occurrence (7–9). The most comprehensive analysis to date of the Landsat imagery archive finds an average GLOF frequency in High-Mountain Asia (HMA) of 1.3 GLOFs per year since the 1980s (9). Despite this steady rate, quantitative in situ observations of GLOFs are scarce (3) and limited to rare situations where preinstalled instruments are coincidentally located in the same valley as the GLOF (10). Efficient strategies to mitigate outbursts, design reliable early warning systems, and minimize destructive impacts would benefit from continuous observation of flood evolution through time. While satellite observations can help identify regions of high GLOF risk and quan-\n\ntify geomorphic impacts after occurrence, they cannot capture GLOF events in real time. Numerical flood models can be used to simulate dam outbursts and flood waves (7, 11–13) yet require many physical parameters as input, which are often unknown or poorly constrained (12). For example, the shape of a breach hydrograph can strongly influence flood characteristics downstream (14). These essential upstream boundary conditions in flood routing simulations are usually unknown and must be estimated using physical or parametric dam breach models with large uncertainties (15). Here, we demonstrate how flood models can be tested using continuous time-stamped seismic observations of GLOF mass movement, thus helping to determine whether estimates of downstream flood arrival times are realistic.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 8, "line_end": 22, "token_count_estimate": 678, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "1fff7bad54dbe0f1", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: INTRODUCTION\nType: text\n\nbe used to simulate dam outbursts and flood waves ( 7 , 11 – 13 ) yet require many physical parameters as input , which are often unknown or poorly constrained ( 12 ) . For example , the shape of a breach hydrograph can strongly influence flood characteristics downstream ( 14 ) . These essential upstream boundary conditions in flood routing simulations are usually unknown and must be estimated using physical or parametric dam breach models with large uncertainties ( 15 ) . Here , we demonstrate how flood models can be tested using continuous time - stamped seismic observations of GLOF mass movement , thus helping to determine whether estimates of downstream flood arrival times are realistic .\n\nThe Pho Chhu (river) flows from the pristine mountain region of Lunana [4500 meters above sea level (masl)], southward through the Bhutan foothills where it eventually joins the Brahmaputra River in northern India (Fig. 1). The valley has a typical step-like elevation profile, alternating between sections of very steep and relatively flat terrain separated by river knickpoints. Fed by seasonal snow melt, glacier melt, and summer monsoon rains, the river plays a vital role in the welfare and livelihood of the people of Bhutan. In the Pho Chhu valley, residents of small villages live and farm along the banks of the river, with rice paddies being a primary seasonal crop. In the river source region, several large proglacial lakes are continually expanding in size as a result of accelerated glacier melting in recent decades (1, 2). Approximately 90 km downstream from the proglacial lakes is the village of Punakha (1200 masl), where a large 17th century Buddhist temple (the Punakha Dzong) is situated along the riverbank at the confluence of the Mo Chhu and Pho Chhu. This temple and surrounding region are historically and culturally important, and there is a great deal of concern about catastrophic outburst floods from the unstable lakes above.\n\nSuch an event occurred on the night of 7 October 1994, when the moraine dam of Lugge Tsho breached and debris-laden flood waters surged down the Pho Chhu valley. Twenty-one lives were lost; and the flood destroyed an estimated 12 houses, 5 water mills, 816 acres of crops, 965 acres of pasture land, 16 yaks, 6 tons of stored food grains, 4 bridges, 2 stupas, and damaged part of the Punakha Dzong (16).\n\n<sup>&</sup>lt;sup>1</sup>Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA. <sup>2</sup>Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA. <sup>3</sup>Department of Geography, University of Utah, Salt Lake City, UT 84112, USA. <sup>4</sup>Center for Water, Climate, and Environmental Policy, Bumthang, Bhutan. <sup>5</sup>School of Earth and Climate Sciences and Climate Change Institute, University of Maine, Orono, ME 04469, USA.\n\n<sup>\\*</sup>Corresponding author. Email: jmaurer@ldeo.columbia.edu", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 8, "line_end": 22, "token_count_estimate": 850, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["Bhutan", "India"], "lake_ids": ["04469", "10027", "10964", "84112"]}}
{"id": "de679412044165f1", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: INTRODUCTION\nType: figure\nFigure\n\nImage /page/1/Figure/2 description: A figure composed of five satellite images labeled A, B, C, D, and E, showing a region of study at progressively finer scales. The caption identifies Lugge Tsho as the source of a 1994 GLOF event and Raphstreng Tsho and Thorthormi Tsho as high-risk for future events.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "figure", "figure_caption": null, "line_start": 23, "line_end": 23, "token_count_estimate": 126, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d579bc4a7af41971", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: INTRODUCTION\nType: text\n\nPanel A is a wide-scale satellite map showing the Pho Chhu river flowing from the Himalayas, joining the Brahmaputra River, and emptying into the Bay of Bengal. Several seismic stations are marked with yellow dots. The scale bar is 200 km.\n\nPanel B is a zoomed-in view of the Pho Chhu river's upper reaches, showing the towns of Punakha and Wangdue. The scale bar is 25 km.\n\nPanel C is a detailed view of a glacial valley with longitude and latitude markers (90.18°E, 90.28°E, 28.08°N). It shows several glacial lakes, including Raphstreng, Thorthormi, and Lugge, as well as the settlement of Thanza. The scale bar is 2 km.\n\nPanel D is a close-up showing the Tshojo plain and features labeled 'Mid-Holocene moraines'. The scale bar is 0.75 km.\n\nPanel E is a close-up of the Lugge Tsho lake, showing the Thorthormi glacier and the location of a 'GLOF breach'. The scale bar is 0.5 km.\n\nFig. 1. Region of study. Lugge Tsho was the source of the 1994 GLOF event, while Raphstreng Tsho and Thorthormi Tsho are also considered high risk for future GLOFs. Locations of the five seismometers used in the study are denoted by the yellow circles in (A). (B) Location of the Punakha and Wangdue villages downstream of the GLOF source area. (C) Unstable glacial lakes in the Lunana region. (D) Mid-Holocene glacier moraines that were left largely intact by the GLOF. (E) Lugge Tsho and moraine breach zone.\n\nWhile the exact trigger of the Lugge Tsho breach is unknown, various causes of moraine failure have been hypothesized such as the melting of its ice core (8), a gradual increase in hydrostatic pressure as the lake depth increased due to melting (5), or collapse of part of the right lateral hillside into the lake, causing a sudden increase in hydrostatic pressure (17). After the tragic incident, much effort was put forth by the government of Bhutan to assess the risk of future GLOFs in the region, establish an emergency warning system, artificially lower lake water levels, and study Lugge Tsho in more detail. Post-GLOF field investigations found a mean lake depth of approximately 50 m, a lake volume of 58.3 million m<sup>3</sup>, and a typical discharge at\n\nthe lake outlet varying from 2.5 to 5 m<sup>3</sup> s<sup>-1</sup> during September and October 2002. The total volume of water released during the GLOF was estimated by Yamada *et al.* (18) as  $17.2 \\pm 5.3$  million m<sup>3</sup> based on a differential Global Positioning System (GPS) survey of the lowering of the lake level by  $16.9 \\pm 3.2$  m, while an integration of the Wangdue station hydrograph (fig. S1) during the GLOF yields approximately 25 million m<sup>3</sup> (19). In addition, dam breach, flood propagation, and debris flow models (sediment-water mixtures) have also been used to simulate GLOF scenarios in this region, including sequential and simultaneous (as may happen during a large earthquake) breaches occurring below high-risk lakes in Bhutan. These\n\nmodels suggest that downstream villages including Punakha and the major portion of Wangdue Phodrang are at risk for severe inundation if another large GLOF occurs (11-13, 19, 20).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 24, "line_end": 44, "token_count_estimate": 881, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "7f1d0d1424a5bd5d", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: INTRODUCTION\nType: text\n\nof the Wangdue station hydrograph ( fig . S1 ) during the GLOF yields approximately 25 million m < sup > 3 < / sup > ( 19 ) . In addition , dam breach , flood propagation , and debris flow models ( sediment - water mixtures ) have also been used to simulate GLOF scenarios in this region , including sequential and simultaneous ( as may happen during a large earthquake ) breaches occurring below high - risk lakes in Bhutan . These models suggest that downstream villages including Punakha and the major portion of Wangdue Phodrang are at risk for severe inundation if another large GLOF occurs ( 11 - 13 , 19 , 20 ) .\n\nAcross the Himalayan region as a whole, the destructive potential of GLOFs is increasing. Yet the scarcity of observational measurements deters robust validation of numerical simulations, hinders quantification of GLOF dynamics, limits real-time warning, and increases uncertainties regarding societal impacts. Continuous observational coverage offered by seismic monitoring is one potential avenue for addressing this problem. Displacement of mass at Earth's surface generates elastic seismic waves, which carry information about the source and can be recorded by seismometers at high temporal resolution across large spatial scales (21). Proof-of-concept studies have already shown the potential of seismic monitoring for diverse types of surface activities including river bedload transport and debris flows (21-24) and further demonstrate the ability of seismic records to specifically provide insight into flood mechanics (10, 23). Here, we extend the application of seismic data to a Himalayan GLOF using data from the International Deep Profiling of Tibet and the Himalaya (INDEPTH) II experiment (25, 26). These data were collected by a passive broadband seismic array situated on the Tibetan Plateau, which was coincidentally recording when the GLOF occurred in Bhutan in 1994. Insofar as the authors are aware, INDEPTH II is the only seismic data available from 1994 in the region (within a 150-km radius). We perform a time-frequency analysis of the seismic signal produced by the GLOF and use cross-correlation functions (CCFs) between seismic stations to locate and track the source of coherent seismic energy through time. With the seismic data, firsthand accounts, and gauge station measurements, we constrain the progression of the flood from initial outburst to arrival in populated villages using a numerical flood model. To further quantify geomorphic impacts of the GLOF, we apply historical spy satellite images, Landsat, and modern high-resolution imagery to analyze lake area changes over time, the extent of flood-deposited sediments, the rate of vegetation regrowth post-GLOF, and pre- and post-flood morphology of the source area dated by <sup>10</sup>Be.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 24, "line_end": 44, "token_count_estimate": 719, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "071c2a4aa6441c81", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Analysis of seismic data\nType: text\n\nSeismic energy generated during the GLOF was recorded by five seismometers (with locations ranging from approximately 75- to 130-km distance from the breach point) as a clear high-frequency (1 to 4 Hz) signal lasting several hours (Fig. 2 and fig. S2). Seismic energy at these frequencies was most likely excited by energy from turbulent flow and bedload transport processes, as observed in previous studies of seismic signals generated during high-flow regimes (10, 21–23, 27). The GLOF signal strength was 5 to 15 dB above typical background noise levels (fig. S10) and occurred during the night and early morning when local anthropogenic noise was at a minimum. We also note several earthquakes (with epicenters in the Kuril Islands and Chile) and a nuclear explosion (Southern Xinjiang, China) that were recorded by the seismic array during these hours (Fig. 2A). Spectrograms show a similar pattern on vertical and horizontal components across all five stations (fig. S2). Some interstation variability in peak frequency content is observed during the two GLOF phases, likely due to lateral heterogeneity in attenuation structure and differences in distance from the source. The first detectable high-frequency signal arrived at approximately 1:45 a.m., beginning with relatively weak amplitude and limited primarily to frequencies between 1 and 3 Hz. Over the next several hours, the seismic energy varied somewhat through time, as the flood wave passed through sections of the valley with different slope and river channel characteristics. Around 25 min after this first arrival, an increase in spectral amplitude occurred across a wider range of frequencies (1 to 4 Hz) (fig. S2) and then subsequently tapered off over the next 1.5 hours. At approximately 3:50 a.m., the signal power began increasing again and reached a maximum at 6:00 a.m. with frequency content ranging from approximately 1 to 5 Hz. During this interval, the flood wave passed through the main branch of the Pho Chhu and impacted Punakha at approximately 7:00 a.m. based on eyewitness accounts.\n\nWe further examined the signal correlation across stations to constrain the origin of the seismic energy in space and time (21, 28, 29). Computing CCFs for every station pair across a series of frequencies ranging from 1 to 5 Hz, we find strong correlation of waveforms during two distinct intervals (Fig. 2B). The first spans from approximately 1:45 to 3:15 a.m., during which a strong peak in CCF amplitude is apparent (fig. S3). Migrating the CCFs during this interval and subsequently summing them together, a region of high coherence emerges, focused directly on the GLOF breach location at Lugge Tsho (Fig. 2), indicating that during this time, the outburst event was the dominant source of seismic energy at these frequencies. Approximately 4 hours later, a second prominent interval of high coherence spans from around 5:45 to 7:15 a.m. Migration of the CCFs during this later interval indicates that the GLOF-induced seismic energy originated from a lower (downstream) section of river, indicating that the flood wave had reached this point in the valley (approximately 70 km from the breach and 20 km above Punakha) by around 5:45 a.m.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Analysis of seismic data", "section_headings": ["RESULTS", "Analysis of seismic data"], "chunk_type": "text", "line_start": 48, "line_end": 52, "token_count_estimate": 838, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "6417529e0239b5a2", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Seismic signal generation\nType: text\n\nGeneration of seismic waves from fluvial processes is understood to occur by two main processes: (i) transport of sedimentary grains that stochastically impact the riverbed (30) and (ii) turbulent fluid flow that interacts with the riverbed (31). Previous observations of seismic energy associated with bedload transport and turbulent flow processes made at much smaller distances from the seismic source (typically only hundreds of meters to a few kilometers away) show peak frequencies ranging from 1 to 100 Hz depending on distance from the source and local seismic attenuation structure (10, 21-23, 27). Here, we demonstrate that coherent 1- to 5-Hz seismic energy is generated and can propagate to distances of ~100 km from the source region during large GLOF events.\n\nNumerical models that predict seismic energy excitation due to turbulent flow and bedload transport processes suggest that >1-Hz seismic energy is difficult to produce at such large distances because of seismic attenuation of the high frequencies (30-32). However, the high river flow rates (~2500 m<sup>3</sup> s<sup>-1</sup>) and thick water flow depths associated with the GLOF represent extreme conditions that have not been explored in detail using numerical models and may well violate their underlying physical assumptions. A GLOF scenario of this magnitude may contain physics not captured by bedload transport and turbulent flow processes alone, such as flexure of the riverbed due to the initial flood wave. Additional work is needed to fully understand the physical processes responsible for the observed seismic signal generation during the GLOF event and how these observations compare with predictions from recent numerical models. In this study, we attribute the primary seismic signal to the initial flood wave as it proceeded down the valley.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Seismic signal generation", "section_headings": ["RESULTS", "Seismic signal generation"], "chunk_type": "text", "line_start": 54, "line_end": 58, "token_count_estimate": 458, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dca7cd24b0abca05", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Constraining a flood model\nType: text\n\nHydropower viability, disaster preparedness, and paleoseismic investigations have previously simulated flood events in this region using numerical models (11–13, 19, 20). Here, we build on results\n\nfrom these earlier studies by calibrating a flood model using the new set of independent observations: (i) an estimated start time and location based on the beginning of detectable seismic energy and migrated CCFs, (ii) the second interval of correlated seismic energy", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Constraining a flood model", "section_headings": ["RESULTS", "Constraining a flood model"], "chunk_type": "text", "line_start": 60, "line_end": 64, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86090d0b8681cf87", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Constraining a flood model\nType: figure\nFigure\n\nImage /page/3/Figure/5 description: A multi-part figure titled \"Observations on 7 October 1994\" showing seismic data related to a Glacial Lake Outburst Flood (GLOF).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Constraining a flood model", "section_headings": ["RESULTS", "Constraining a flood model"], "chunk_type": "figure", "figure_caption": null, "line_start": 65, "line_end": 65, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dd24a268dbae6b7d", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Constraining a flood model\nType: text\n\nPart A consists of two plots. The top plot is a seismic trace from station BB20 BHZ, filtered between 1 and 5 Hz. The x-axis represents the \"Hour of day (1994-10-07)\" from 0 to 12, and the y-axis is \"Velocity (m s⁻¹)\" from -1 to 1 (x10⁻⁸). The trace shows a significant increase in amplitude from approximately hour 2 to hour 8. Several seismic events are annotated, including earthquakes in the Kuril Islands and Chile, a nuclear explosion in China, and local earthquakes. The bottom plot is a spectrogram with the same time axis and a y-axis of \"Frequency (Hz)\" from 0 to 4. A color scale indicates power spectral density (PSD), with yellow being the highest. A strong signal, labeled \"Primary GLOF signal,\" is visible between approximately hour 2 and hour 8, primarily in the 1-4 Hz frequency range. Two time intervals are highlighted: 1:45–3:15 a.m. and 5:45–7:15 a.m.\n\nPart B is a line graph showing \"Average coherence\" versus \"Hour of day (1994-10-07)\". It compares the coherence on the day of the GLOF (red line) with a background level from the previous day (black line). The GLOF day shows a sharp peak in coherence to about 0.28 around hour 2, corresponding to the 1:45–3:15 a.m. interval, while the background level remains around 0.125.\n\nPart C displays two maps showing the spatial distribution of average coherence. The axes are \"North (km from breach)\" and \"East (km from breach)\". The left map, for 1:45–3:15 a.m., shows the highest coherence (up to 0.7) originating from the \"Breach\" location (marked with a red X). The right map, for 5:45–7:15 a.m., shows the area of high coherence (up to 0.55) has moved southwest from the breach, indicating propagation. Several station locations (BB18, BB20, BB23, SP25, SP27) and the town of Punakha are marked on the maps.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Constraining a flood model", "section_headings": ["RESULTS", "Constraining a flood model"], "chunk_type": "text", "line_start": 66, "line_end": 78, "token_count_estimate": 559, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "ddb8d0fc95471eef", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Constraining a flood model\nType: text\n\n) \" and \" East ( km from breach ) \" . The left map , for 1 : 45 – 3 : 15 a . m . , shows the highest coherence ( up to 0 . 7 ) originating from the \" Breach \" location ( marked with a red X ) . The right map , for 5 : 45 – 7 : 15 a . m . , shows the area of high coherence ( up to 0 . 55 ) has moved southwest from the breach , indicating propagation . Several station locations ( BB18 , BB20 , BB23 , SP25 , SP27 ) and the town of Punakha are marked on the maps .\n\n**Fig. 2. Key seismic observations on 7 October 1994.** (**A**) Example of a seismic trace (station BB20 BHZ) band-pass filtered between 1 and 5 Hz and spectrogram from 1 to 5 Hz during the GLOF duration. Black arrows mark seismic events that correlate in time with events in the USGS catalog, and gray arrows mark potential local earthquakes that appear on all five stations (fig. S2) but are not in the catalog.  $m_b$ , body wave magnitude;  $M_w$ , moment magnitude. (**B**) Twelve-hour time series of signal coherence for the day containing the GLOF event (red; 1994-10-07) and the day before the GLOF event (gray; 1994-10-06). The top and bottom bounds of each shaded region represent the 95th and 75th percentile of coherence values across all lag times, respectively. The blue shaded regions mark 90 min of most significant GLOF signal relative to background levels. (**C**) Migrations of the CCFs during the two intervals using a velocity of 3.0 km s<sup>-1</sup> (likely indicating regional short-period Rayleigh waves; fig. S3) for several station pairs is summed together to form these final coherence maps (see Materials and Methods and fig. S3). These two images illustrate the downstream progression of seismic energy generated by the GLOF. The coherence map on the left corresponds to seismic energy generated approximately 4 hours later and ~70 km downstream. Times are Asia/Thimphu local time [universal time coordinated (UTC) +6].", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Constraining a flood model", "section_headings": ["RESULTS", "Constraining a flood model"], "chunk_type": "text", "line_start": 66, "line_end": 78, "token_count_estimate": 595, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7985e63e801adef3", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Constraining a flood model\nType: text\n\n* ) Migrations of the CCFs during the two intervals using a velocity of 3 . 0 km s < sup > - 1 < / sup > ( likely indicating regional short - period Rayleigh waves ; fig . S3 ) for several station pairs is summed together to form these final coherence maps ( see Materials and Methods and fig . S3 ) . These two images illustrate the downstream progression of seismic energy generated by the GLOF . The coherence map on the left corresponds to seismic energy generated approximately 4 hours later and ~ 70 km downstream . Times are Asia / Thimphu local time [ universal time coordinated ( UTC ) + 6 ] .\n\nthat occurred several hours later and ~70 km downstream of the initial breach, (iii) the approximate arrival time of the flood at Punakha from firsthand observations, and (iv) the flood hydrograph from Wangdue station (around 110 km downstream from the breach). Together, these provide key constraints on the location of the flood wave through the duration of the GLOF and allow us to parameterize a model with a higher degree of confidence than previously possible. The start time of the GLOF, in particular, is a key aspect that was previously unknown. Because of the sensitivity of flood models to input parameters (fig. S1), these independent observations are especially useful for validating and selecting model runs that are most realistic (see Materials and Methods). We use the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC-RAS) software to perform a series of two-dimensional (2D) unsteady flow simulations; select model runs that agree with the independent observations within a ±30-min threshold (see Materials and Methods and Fig. 3); and report ranges of simulated breach-to-arrival times for the main populated villages along the river valley for Thanza (0.4 to 0.6 hours), Tshojo (1.0 to 1.3 hours), Lhedi (1.4 to 1.8 hours), Samdingkha (3.9 to 4.6 hours), Punakha (4.4 to 5.2 hours), and Wangdue (5.7 to 6.5 hours) (Table 1). We note that around 6:30 a.m., the peak flows in the best-fit model runs precede (in time) the peak in seismic coherence, approximately 70 km downstream from the breach. While this may reflect the actual order", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Constraining a flood model", "section_headings": ["RESULTS", "Constraining a flood model"], "chunk_type": "text", "line_start": 66, "line_end": 78, "token_count_estimate": 608, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "811a7bdc261fd3cb", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Constraining a flood model\nType: text\n\nto 0 . 6 hours ) , Tshojo ( 1 . 0 to 1 . 3 hours ) , Lhedi ( 1 . 4 to 1 . 8 hours ) , Samdingkha ( 3 . 9 to 4 . 6 hours ) , Punakha ( 4 . 4 to 5 . 2 hours ) , and Wangdue ( 5 . 7 to 6 . 5 hours ) ( Table 1 ) . We note that around 6 : 30 a . m . , the peak flows in the best - fit model runs precede ( in time ) the peak in seismic coherence , approximately 70 km downstream from the breach . While this may reflect the actual order\n\nof events, the model may also be slightly overestimating the flood wave velocity within this part of the valley. In all model runs, the duration between arrival and peak flow gradually decreases as the flood travels down the section of valley above Punakha. This consistent aspect of the flood illustrates the manner in which topography can shape a flood wave, in this case, causing the wave to become steeper and more prominent while traveling down a steep valley gorge. Furthermore, a reduction in hillslope angle of the arriving flood wave correlates in time with the second peak in coherent seismic energy at approximately 5:45 to 7:15 (fig. S13), perhaps indicating that changes in topographic slope influence the degree to which flood wave energy is transferred into the solid earth as seismic waves. Results from model runs with different breach hydrographs all converge to a similar shape before reaching the larger villages downstream. In the village of Samdingkha, for example (~8 km above Punakha), the duration between first arrival and peak flow ranges from 10 to 30 min. Without an external early warning system, this sudden rise in water level permits only a short time for inhabitants to move to safe ground, particularly if the early stages of the rise go unnoticed for several minutes.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Constraining a flood model", "section_headings": ["RESULTS", "Constraining a flood model"], "chunk_type": "text", "line_start": 66, "line_end": 78, "token_count_estimate": 507, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ef41688c395c2b64", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Geomorphic change caused by the GLOF\nType: text\n\nSatellite imagery reveals prominent changes in the Lunana region both before and after the GLOF. The most consistent change is a", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Geomorphic change caused by the GLOF", "section_headings": ["RESULTS", "Geomorphic change caused by the GLOF"], "chunk_type": "text", "line_start": 80, "line_end": 82, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2baa30a1344170fc", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Geomorphic change caused by the GLOF\nType: figure\nFigure\n\nImage /page/4/Figure/6 description: The image displays three related graphs, labeled A, B, and C, that model a flood event.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Geomorphic change caused by the GLOF", "section_headings": ["RESULTS", "Geomorphic change caused by the GLOF"], "chunk_type": "figure", "figure_caption": null, "line_start": 83, "line_end": 83, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9b38f234c0f92632", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Geomorphic change caused by the GLOF\nType: text\n\nGraph A, titled \"Hourly location of flood arrival along valley profile (modeled)\", shows an elevation profile. The y-axis represents Elevation in meters (m), from 0 to over 4000, and the x-axis represents Distance from breach in kilometers (km), from 0 to over 140. A black line depicts the decreasing elevation of the valley. A breach is marked at 1:45 at 0 km and an elevation of approximately 4200 m. Orange dots along the profile mark the hourly arrival of the flood: 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, 8:00, and 9:00. Locations such as Thanza, Tshoijo, Lhedi, Samdingkha, Punakha Dzong, and Wangdue are labeled along the profile.\n\nGraph B is a line chart plotting Distance from breach (km) on the y-axis against the Hour of day (1994-10-07) on the x-axis. It shows a flood model with three curves: a red line for 'Arrival', a blue line for 'Peak flow', and a yellow line for '37% of peak'. The red and blue lines have shaded uncertainty bands. The model is compared with observational data, including 'Onset of high coherence (seismic)' at two points, 'Punakha (eyewitnesses)', and 'Wangdue (hydrograph)' with start and max points.\n\nGraph C is a grayscale topographic map showing the path of the flood. A red 'X' marks the '1:45 breach'. A white line traces the flood's path through the valley, with hourly locations from 3:00 to 8:00 marked with orange dots. Labeled locations include Thanza, Tshoijo, Lhedi, Samdingkha, Punakha Dzong, and Wangdue. A scale bar indicates a distance of 20 km.\n\nFig. 3. Results from the HEC-RAS 2D unsteady flood model. (A) Elevation profile of the river valley, with hourly locations of flood arrival time from the best-fit model run. (B) Distance from the moraine breach versus time. The gray square symbols and brackets are independent observations from seismic, eyewitness, and gauge station sources. The color-shaded regions represent the range of model outputs that match observations within ±30 min, and the colored curves represent the single best-fit model run. The orange curve is the simulated arrival of the flood wave, the blue curve is the peak flow, and the yellow curve indicates when the flow has subsided and reached 1/e (~37%) of the peak. The horizontal separation of the orange and blue curves indicates the duration between flood arrival and peak flow for a given location. The gray (dashed) boxes indicate the intervals in Fig. 2 during which the peaks in coherent seismic energy were detected. (C) Map view of region also with modeled flood arrival times. Times are Asia/Thimphu local time (UTC+6).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Geomorphic change caused by the GLOF", "section_headings": ["RESULTS", "Geomorphic change caused by the GLOF"], "chunk_type": "text", "line_start": 84, "line_end": 92, "token_count_estimate": 770, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e7cb398a1d26f7d", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Geomorphic change caused by the GLOF\nType: table\nTable\n\n| Location         | Latitude | Longitude | Elevation (m) | Distance from breach (km) | Flood arrival |          |       |                   |          |       | Peak flow |          |       |                   |          |       |\n|------------------|----------|-----------|---------------|------------------------------------|---------------|----------|-------|-------------------|----------|-------|-----------|----------|-------|-------------------|----------|-------|\n|                  |          |           |               |                                    | Time          |          |       | Hours from breach |          |       | Time      |          |       | Hours from breach |          |       |\n|                  |          |           |               |                                    | Lower         | Best fit | Upper | Lower             | Best fit | Upper | Lower     | Best fit | Upper | Lower             | Best fit | Upper |\n| Thanza           | 28.089   | 90.213    | 4150          | 7                                  | 01:43         | 02:08    | 02:19 | 0.4               | 0.4      | 0.6   | 02:22     | 03:20    | 03:35 | 0.9               | 1.8      | 2.1   |\n| Tshojo           | 28.062   | 90.164    | 4060          | 14                                 | 02:19         | 02:41    | 03:00 | 1.0               | 1.0      | 1.3   | 03:20     | 04:10    | 04:25 | 1.8               | 2.7      | 3.0   |\n| Lhedi            | 28.034   | 90.092    | 3690          | 23                                 | 02:47         | 03:06    | 03:26 | 1.4               | 1.4      | 1.8   | 03:33     | 04:21    | 04:36 | 2.0               | 2.9      | 3.2   |\n| Samdingkha       | 27.641   | 89.865    | 1270          | 90                                 | 05:29         | 05:39    | 06:08 | 3.9               | 3.9      | 4.6   | 05:43     | 06:09    | 06:24 | 4.1               | 4.6      | 4.9   |\n| Punakha Dzong | 27.582   | 89.863    | 1210          | 98                                 | 06:01         | 06:12    | 06:38 | 4.4               | 4.5      | 5.2   | 06:37     | 06:58    | 07:13 | 5.0               | 5.5      | 5.8   |\n| Wangdue          | 27.462   | 89.901    | 1190          | 114                                | 07:19         | 07:35    | 07:54 | 5.7               | 5.9      | 6.5   | 08:47     | 09:12    | 09:27 | 7.1               | 7.7      | 8.1   |", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Geomorphic change caused by the GLOF", "section_headings": ["RESULTS", "Geomorphic change caused by the GLOF"], "chunk_type": "table", "table_caption": null, "columns": ["Location", "Latitude", "Longitude", "Elevation (m)", "Distance from breach (km)", "Flood arrival", "", "", "", "", "", "Peak flow", "", "", "", "", ""], "table_row_start": 1, "table_row_end": 8, "line_start": 93, "line_end": 102, "token_count_estimate": 680, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "61d31a4415a8eea1", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: RESULTS > Geomorphic change caused by the GLOF\nType: text\n\nsteady increase in the area of Lugge Tsho over the past 45 years, starting at approximately 0.42 km<sup>2</sup> in January 1976. The lake area increased at a rate of 0.038 km<sup>2</sup> year<sup>-1</sup> as the glacier receded and melted, reached 1.1 km<sup>2</sup> in September 1994, and then underwent a sudden decrease to 0.87 km<sup>2</sup> during the GLOF in October 1994. Subsequently, the lake area continued increasing at a rate of 0.025 km<sup>2</sup> year<sup>-1</sup> and has since reached a size of 1.42 km<sup>2</sup> as of September 2018 (fig. S4). We note that the lake expansion is primarily due to the receding glacier rather than rising lake level (see Discussion). The location of the 1994 breach is evident in both visible imagery and digital elevation model (DEM) on the lower left lateral moraine, where a cross section along the moraine crest shows a channel approximately 180 m wide and 40 m deep. Declassified satellite imagery from 1976 clearly shows that this location was a preexisting outlet for Lugge Tsho (fig. S5). Below the breach, changes in spectral reflectance are visible in post-GLOF Landsat imagery where the flood deposited debris and sediment along large regions of the valley floor. Upon first breaching the lake-fringing moraine, the flood waters flowed into another small lake approximately 500 m downstream. This seasonal lake was full at the time the GLOF occurred due to accumulated snowmelt and monsoonal precipitation from the prior summer months. The flood washed out the natural dam of this small lake basin; thus, now it no longer accumulates water as it did in years prior.\n\nAround 10 km from the breach, a prominent set of glacier moraines is situated below Thanza (Fig. 4 and table S2). Here, we analyzed three boulders on the well-preserved outermost moraine ridge using <sup>10</sup>Be surface exposure dating, following procedures in Schaefer *et al.* (33). Results yielded three consistent ages ranging from 4.4 to 4.7 thousand years (ka), indicating that the moraines were deposited during the mid-Holocene and left largely intact by the 1994 flood (fig. S5). After passing through the moraines, the flood wave spilled over the Tshojo plain, where it subsequently deposited a 2.2-km<sup>2</sup> swath of sediment. In total, we estimate approximately 4.8 km<sup>2</sup> of valley floor that was covered by sediment as a result of the GLOF. This occurred primarily along the upper 25 km of the river (fig. S6), as below this upper region, the channel is highly confined, and sediment deposition was minimal. In the decades following, the impacted vegetation has slowly recovered (fig. S7). In 1994, the mean enhanced vegetation index (EVI) of the Tshojo plain dropped to around 25% of the pre-GLOF value as a result of the flood. From 1994 to 2005, the EVI steadily increased as the vegetation began reclaiming the area, attaining around 75% of the pre-GLOF value in 2005 and remaining steady for several\n\nyears afterward. Another increase occurred from 2011 to 2013, during which time an EVI approaching that of the pre-GLOF conditions was attained.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "RESULTS > Geomorphic change caused by the GLOF", "section_headings": ["RESULTS", "Geomorphic change caused by the GLOF"], "chunk_type": "text", "line_start": 103, "line_end": 109, "token_count_estimate": 878, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "acc8f0b560e419c7", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Arrival time of flood in Punakha\nType: text\n\nAccurate estimates of GLOF arrival times are vital for disaster preparedness. Existing model estimates for Punakha include 4.75 hours (19), 5.75 hours (20), and 7 hours or more (5). Given the onset of seismic energy at 1:45 a.m. at Lugge Tsho and eyewitness accounts of rising waters around 7:00 a.m. in the village, we estimate that the flood took approximately 5 hours to reach Punakha. Assuming that the delay between the initial breach and generation of detectable seismic energy was short (see Materials and Methods), our results suggest that some previously published simulations give reasonably accurate arrival times (within ±30 min) and that the shorter (4.75 to 5.75 hours, 17 km hour<sup>-1</sup> average) times are more accurate than the 7-hour (12 km hour<sup>-1</sup> average) estimate (5). These results show how a simple estimate of the GLOF start time based on the onset of seismic energy can be highly useful for validation of numerical models, by providing an estimate of the average velocity of the flood wave. However, a future breach may have different hydrograph characteristics depending on the trigger mechanism and nature of the moraine dam failure. The 1994 GLOF also cleared out a substantial amount of blocking debris, which would allow a future GLOF to travel more quickly down the valley. Further research toward constraining probable breach hydrograph characteristics and valley roughness parameters will be crucial for refinement of GLOF models. With the vast amount of existing seismic data [in databases such as the IRIS DMC (Incorporated Research Institutions for Seismology Data Management Center), for example] and increasing number of seismic networks, it is likely that other GLOF events have been recorded by seismic instruments but have yet to be investigated. A comprehensive search and deeper analysis of any available seismic records in GLOF-prone locations may reveal new insights into quantifying and modeling GLOF trigger events and flood waves in these regions.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Arrival time of flood in Punakha", "section_headings": ["DISCUSSION", "Arrival time of flood in Punakha"], "chunk_type": "text", "line_start": 113, "line_end": 115, "token_count_estimate": 523, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93c15ce5a6ea0b35", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Sediment deposition and vegetation recovery\nType: text\n\nThe area covered by new sediment from the GLOF (4.8 km<sup>2</sup>) combined with existing sediment-covered areas (3.0 km<sup>2</sup>) amount to 7.8 km<sup>2</sup>. This approximately agrees with previous damage assessments,", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Sediment deposition and vegetation recovery", "section_headings": ["DISCUSSION", "Sediment deposition and vegetation recovery"], "chunk_type": "text", "line_start": 117, "line_end": 119, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb0467721261039a", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Sediment deposition and vegetation recovery\nType: figure\nFigure\n\nImage /page/6/Picture/2 description: A geological map is overlaid on a satellite image of a mountainous, glaciated region. The background is a Google Earth view showing snow-covered peaks and a wide river valley. The map overlay in the upper section shows large glacial features colored in solid blue, light blue with black dots, and various shades of purple. In the lower section, along the valley, there are numerous elongated formations, likely moraines, colored in red and pink. A scale bar in the bottom left indicates a distance of 2.5 km. In the lower right, three red formations are linked by white lines to a data box which lists the following sample data: '4700 ± 170 LUNA-14-07', '4590 ± 180 LUNA-14-04', and '4380 ± 170 LUNA-14-03'. The map is marked with coordinates N28.085° and E90.265°.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Sediment deposition and vegetation recovery", "section_headings": ["DISCUSSION", "Sediment deposition and vegetation recovery"], "chunk_type": "figure", "figure_caption": null, "line_start": 120, "line_end": 120, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e74ca8ebf49dd0df", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Sediment deposition and vegetation recovery\nType: text\n\n**Fig. 4. Geomorphic map of the Lunana area.** The lower (red) moraines were deposited by the glacier more than 4000 years ago during the mid-Holocene, as indicated by the  $^{10}$ Be ages of three sampled boulders given with  $1-\\sigma$  analytical error. The younger lake-fringing (purple) moraines are late-Holocene in age.\n\nwhich estimated a total of 7.2 km<sup>2</sup> of crops and pastures affected by the flood. The 2.2-km<sup>2</sup> region of the most prominent GLOF sediment deposition is located in a scrub alpine vegetation zone, which receives around 1 m year<sup>-1</sup> of precipitation primarily during the summer monsoon months (34). The flora in this area are composed of sedges, mosses, accessorial herbs, and some patches of woody vegetation (rhododendrons, junipers, and spireas) (35). The nonlinear vegetation recovery rates (fig. S7) are likely due to factors such as soil moisture, nutrient availability, competition between species, and seasonal precipitation in this high-elevation ecosystem. The observed recovery patterns may inform future studies regarding resilience of aromatic medicinal plants to changing climate, as these flora play key roles in the lives of local inhabitants (36).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Sediment deposition and vegetation recovery", "section_headings": ["DISCUSSION", "Sediment deposition and vegetation recovery"], "chunk_type": "text", "line_start": 121, "line_end": 125, "token_count_estimate": 377, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a01f4c37a9e65dee", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Comparison with other GLOFs in the Himalayas\nType: text\n\nCompared to other GLOF occurrences in HMA during the past three decades, this 1994 event is a top contender for the largest volume of water released. Yet in terms of the percentage of lake volume released, it is on the lower end (fig. S8). Yamada et al. (18) surveyed the lake bathymetry and estimated the total volume of Lugge Tsho to be 58.3 million m<sup>3</sup> in September 2002. Accounting for the lake growth between 1994 and 2002 (3 million m<sup>3</sup>) and volume of water released during the GLOF (17 to 25 million m<sup>3</sup>), we estimate that the lake volume was 73 to 80 million m<sup>3</sup> in 1994. On the basis of this approximation, around 24 to 30% of the volume of Lugge Tsho was released. Because of the large size of the lake, substantial rate of outflow during the breach, and considerable vertical relief, this smaller-than-average percentage of draining resulted in a major destructive GLOF downstream. This is consistent with the flood simulations that suggest very low dampening and low deceleration of the flood wave peak due to high relief energy and the gorge character of the Pho River (20).\n\nRegarding the seismic signal, we note that the GLOF event was exceptionally large, resulting in a high signal-to-noise ratio. Smaller\n\nGLOF events that produce less seismic energy would require seismometers to be located closer to the GLOF source to observe the weaker signal. This trade-off between geographical coverage of a seismic array versus the capability of detecting smaller events is an optimization problem, which future studies could address. There is no detectable coherent seismic energy generated from the river valley on the day before the GLOF event, suggesting that background-level river transport processes are not strong enough to be detected at these distances (fig. S12). A search for smaller GLOF events, which have occurred in other locations, along with any associated seismic signals (observed by nearby stations) may help further constrain signal strength versus distance from the source. Such observations could also be used to constrain numerical models that simulate seismic energy generated by various flood magnitudes.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Comparison with other GLOFs in the Himalayas", "section_headings": ["DISCUSSION", "Comparison with other GLOFs in the Himalayas"], "chunk_type": "text", "line_start": 127, "line_end": 133, "token_count_estimate": 573, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6e619ac837aa0f2", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Current state of high-risk lakes in Lunana\nType: text\n\nWe find that Lugge Tsho surpassed the pre-GLOF size of 1.1 km<sup>2</sup> in 2005 and reached an area of 1.4 km<sup>2</sup> in 2018 due to retreat of the glacier terminus (fig. S4). If a future event were to cause the same 16 to 23 m of lake surface lowering as occurred during the 1994 GLOF, then this would translate to 22 to 32 million m<sup>3</sup> of water released or approximately 30% more than the 1994 flood volume. While satellite imagery indicates that the lake boundaries (excluding the retreating glacier terminus) are nearly unchanged since the GLOF, the ongoing glacier retreat has also exposed unstable steep valley walls and lateral moraines above the lake (37, 38). A large mass movement into the water could result in a sudden increase in hydrostatic pressure and subsequent overtopping or structural failure of the Lugge Tsho moraine dam. The adjacent Thorthormi and Raphstreng lakes are also vulnerable to the same problem. Thorthormi sits topographically above Raphstreng by approximately 80 m, and a breach in its moraine could result in a cascading combined GLOF from both lakes (3). Efforts to artificially lower the level of Raphstreng by enlarging the\n\noutlet channel were undertaken during 2009-2012 in an attempt to reduce the risk, but this dangerous manual work by the local people was extremely difficult with uncertain effectiveness. On 20 June 2019, a minor breach occurred below Thorthormi lake, during which residents of Lunana were evacuated and no deaths or serious injuries occurred. While the increased flow was relatively minimal (water level increased by approximately 1 m during ~6:00 to 7:20 p.m.), it was large enough to wash away two bridges in Thanza and Tenchey. A field investigation after the event found that enhanced melting and basal sliding of the Thorthormi glacier had caused it to surge this displaced water from Thorthormi lake, which overtopped the primary moraine, spilled into the subsidiary lakes, and breached the lower subsidiary lake that drained completely. Satellite imagery before and after the event confirmed the draining of the subsidiary lake, but the stability of the primary Thorthormi moraine dam is uncertain (39).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Current state of high-risk lakes in Lunana", "section_headings": ["DISCUSSION", "Current state of high-risk lakes in Lunana"], "chunk_type": "text", "line_start": 135, "line_end": 139, "token_count_estimate": 589, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3307b5ef6fc7b1db", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: DISCUSSION > Potential for early warning systems\nType: text\n\nIn Bhutan and elsewhere across the Himalayas, numerous glacial lakes pose immediate GLOF threats (2). Existing warning systems usually consist of automatic water level (AWL) stations installed in priority locations to monitor lake levels and river flows and transmit data in real time using Global System for Mobile Communications (GSM) or Iridium satellite technologies. If lake levels are detected to suddenly drop and/or stream levels rise, emergency warnings are issued (via mobile text messages), and a network of warning sirens is sounded. However, AWL sensors are known to be somewhat unreliable and susceptible to false alarms (40). Our results demonstrate the feasibility of seismic monitoring as another way to remotely detect GLOFs, which could potentially improve the next generation of early warning systems. The CCF methodology would need to be automated and tested more robustly to ensure reliable distinction between GLOF seismic signatures and other tectonic, meteorological, anthropogenic, or geomorphic sources (21), but with further refinement, a network of seismometers strategically deployed across a region could hypothetically monitor for sustained signals originating from probable GLOF source locations. To detect smaller GLOF events, a priori information should be used to focus detection efforts on known hot spots. Implementation of probability density functions in coherence map calculations may help remedy artifacts due to asymmetric distribution of seismic stations. During the CCF peak at the onset of the GLOF (around 1:45 a.m.), we find that only a few minutes of seismic data are sufficient to detect the anomalous high-frequency signal originating from the upper Lunana valley (fig. S9). Unlike AWL sensors, seismometers can be installed in safer and more accessible sites with the capability to monitor large regions across multiple valleys, although further research is needed to determine the optimal trade-off between array density and event detection capabilities. The use of seismometers and AWL sensors jointly could substantially improve existing early warning systems, with cross-validation of the two independent detection methods helping to minimize occurrence of false alarms and maximize warning time.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "DISCUSSION > Potential for early warning systems", "section_headings": ["DISCUSSION", "Potential for early warning systems"], "chunk_type": "text", "line_start": 141, "line_end": 143, "token_count_estimate": 564, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "90e8f5f69c93ee82", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: Conclusion\nType: text\n\nThe risk of larger and potentially more destructive floods is rapidly increasing across the Himalayas, because of growing glacial lakes and ongoing construction of hydroelectric dams and other infrastructure in vulnerable regions (2). We have demonstrated that a GLOF can be detected remotely from seismometers located many kilome-\n\nters away from the source, which has the potential to improve efficiency and maximize warning time. Our robust spatiotemporal constraints indicate at least 5 hours between source and main impact areas in Punakha, which affords sufficient warning time for many downstream inhabitants if a GLOF is detected in its earliest phase. We find that the 1994 flood event eroded, transported, and deposited large quantities of sediment in the Lunana valley but left the local moraine ridges largely intact near the source region. We also estimate a post-GLOF recovery of two to three decades for the affected alpine scrub vegetation, which may help to quantify resilience of the local ecosystem in future studies. Given the current situation and ongoing GLOF risks in the Himalayas, future research could focus on (i) deeper analysis and characterization of tectonic, meteorological, anthropogenic, and geomorphic seismic signatures to ensure clear distinction of natural hazard signals and prevention of false alarms; (ii) continued development of efficient algorithms for automated real-time processing of environmental seismic data; and (iii) deployment of optimized seismic arrays in vulnerable regions to detect GLOF events and provide efficient early warning systems.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "Conclusion", "section_headings": ["Conclusion"], "chunk_type": "text", "line_start": 145, "line_end": 149, "token_count_estimate": 397, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56e0657b725d6164", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Seismic data\nType: text\n\nRecent proliferation of high-quality broadband seismic data in addition to developments in the analysis of the ambient seismic wavefield and other seismic signals have forged new avenues in studying characteristics of seismic energy generated by environmental processes (10, 21-24, 27). For example, time-frequency analyses of passive broadband seismic data have been used to quantify increases in high-frequency energy associated with high-flow regimes in rivers and cross-correlation between multiple stations used to isolate coherent seismic phases and provide estimates of their origin (28, 41). Here, we use seismic data from the INDEPTH II experiment as a tool to investigate the 1994 GLOF in Bhutan, which coincidentally occurred while this temporary seismic network was actively recording. INDEPTH II was a collaborative geoscience project between the Chinese Academy of Geological Sciences and investigators from U.S., German, and Canadian Geoscience institutions to investigate the deep structure and mechanics of the Himalaya-Tibet region (25). In 1994, the second phase of the project acquired passive seismic data in southern Tibet and the Himalayas, continuously recording threecomponent broadband and short-period 24-bit data along a ~350-km linear array at a sample rate of 50 Hz (42). For our analysis, we used data from a total of five broadband and short-period INDEPTH stations ranging from 75 to 135 km in distance (99 km average) to the northwest of the GLOF source area (Lugge Tsho). We downloaded the data from the IRIS DMC using a window for the approximate time of the GLOF occurrence (7 October 1994, from 00:00 to 12:00 hours, Asia/Thimphu time zone) for stations BB18, BB20, BB23, SP25, and SP27 (network code XR). The corresponding seismic traces were detrended, and instrument responses were removed to obtain units of velocity (m s<sup>-1</sup>) using the opensource Python framework ObsPy (43) and band-pass filtered between 1 and 5 Hz. This frequency range corresponded to the coherent high-frequency signal observed across all five stations (fig. S2) during the GLOF duration and also excluded lower frequency bands associated with noise sources such as ocean-generated microseisms (44, 45) as well as higher-frequency anthropogenic noise. Previous studies also observed a similar increase in seismic energy in these same frequency bands originating from turbulence and sediment\n\ntransport by rivers and flood events (10, 22, 23, 27). For the purposes of this study, we assume that the beginning of detectable seismic energy marked the initiation of the GLOF event. We note that the actual outflow may have begun slightly earlier, but the seismic energy was below the threshold of detection at first due to a gradual increase in outflow through the moraine breach. This remains difficult to constrain as the exact shape of the breach hydrograph is unknown; thus, our evaluation of the time between the breach and downstream arrival of the flood wave is a minimum estimate.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Seismic data", "section_headings": ["MATERIALS AND METHODS", "Seismic data"], "chunk_type": "text", "line_start": 153, "line_end": 157, "token_count_estimate": 773, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "2c7e99ec19f9925b", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Time-frequency analysis and CCFs\nType: text\n\nTo explore the spectral characteristics of the event and quantify the temporal variation of the seismic signal generated by the GLOF, we estimated the power spectral density of the time series for each station using Welch's averaging method. We first divided each seismic trace into 2-min segments each with 50% overlap. Then, for each 2-min segment, we used Welch's method to average modified periodograms computed using 10-s windows, also overlapping by 50%. To approximate source locations of the GLOF energy, we followed an approach similar to those outlined by previous studies for locating coherent seismic noise sources (28, 41, 46). We first applied a 1-bit normalization to reduce the influence of punctual sources of seismic energy, such as earthquakes, anthropogenic noise, or instrument issues. This simply means keeping the sign of the time series (-1 if less than 0 and +1 if greater than 0) and discarding the magnitude (46). We then calculated the normalized cross-correlation of 20-min segments (overlapping by 50%) in the time series and computed their envelopes (hereafter referred to as the CCFs) for every station pair along the seismic array for time lags ranging from -40 to +40 s for a series of frequencies ranging from 1 to 5 Hz, using a window size of 0.5 Hz. Time information from each CCF envelope was migrated to positions in space as follows: We defined a regular grid of potential source locations in the region, and for each station pair, we calculated the theoretical time delays between the two stations for every grid point. The CCF amplitude at each corresponding lag time was then mapped to positions in space. The resulting coherence map  $A_{ii}$  for stations i and j is given by\n\n$$A_{ij}(x,y) = CCF_{ij}\\left(\\frac{d_i - d_j}{v}\\right)$$\n (1)", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Time-frequency analysis and CCFs", "section_headings": ["MATERIALS AND METHODS", "Time-frequency analysis and CCFs"], "chunk_type": "text", "line_start": 159, "line_end": 166, "token_count_estimate": 521, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9dcd529dd650447", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Time-frequency analysis and CCFs\nType: text\n\nTime information from each CCF envelope was migrated to positions in space as follows : We defined a regular grid of potential source locations in the region , and for each station pair , we calculated the theoretical time delays between the two stations for every grid point . The CCF amplitude at each corresponding lag time was then mapped to positions in space . The resulting coherence map $ A_ { ii } $ for stations i and j is given by $ $ A_ { ij } ( x , y ) = CCF_ { ij } \\ left ( \\ frac { d_i - d_j } { v } \\ right ) $ $ ( 1 )\n\nwhere *d* is the distance between the hypothetical source and corresponding station and  $\\nu$  is the assumed seismic velocity (see below). Thus, each CCF delay time (along the vertical axes in fig. S3) maps to a hyperbola, where the amplitude of the hyperbola is simply the CCF amplitude. This was repeated for each 20-min segment and station pair. The resulting maps for hand-selected station pairs and frequencies (those with distinct correlation peaks; see fig. S3 and table S1) were summed together to form a final coherence map for a given interval, where high coherence values indicate the most probable source locations (Fig. 2). Similar results are also obtained if all station pairs are included in the stack (fig. S11). To determine an appropriate velocity during migration, we calculated coherence maps for a range of velocities between 1 and 5 km s<sup>-1</sup>. We found that a velocity of 3.0 km s<sup>-1</sup> resulted in the highest coherence (fig. S3) for these frequencies, similar to previous studies (28), and likely indicates short-period Rayleigh wave energy. We found that two distinct peaks in coherence occurred at approximately 2:00 and 6:30 a.m. and chose to focus on these peaks for further analysis. We defined time windows of 90-min duration spanning each respective peak, which we found to be a sufficient length for capturing the intervals of strongest GLOF signal relative to background levels. We also note that quasilinear placement of the seismometers parallel to the valley northwest of the GLOF means that the source location was well constrained along the valley but poorly constrained perpendicular to the valley, leading to blurring in that direction. This artifact may be remedied by invoking a probability density function centered on the river channel to better localize the signal, although the GLOF event studied here was large enough such that this was not necessary. While this and other sources of error such as lateral velocity heterogeneities and varying surface topography caused some blurring of the coherence maps, we found this basic methodology precise enough to clearly track the start and a subsequent down-valley shift in the location of seismic energy during the GLOF.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Time-frequency analysis and CCFs", "section_headings": ["MATERIALS AND METHODS", "Time-frequency analysis and CCFs"], "chunk_type": "text", "line_start": 159, "line_end": 166, "token_count_estimate": 756, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0fe270d24c270b0e", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Flood model\nType: text\n\nWe implemented the U.S. Army Corps of Engineers HEC-RAS software to perform a 2D unsteady flow simulation using the diffusionwave equation (47). This equation is a simplified version of a full dynamic wave model (neglects inertial force and advective accelerations) and has been found to be a satisfactory approximation in many situations (48). Simulations were run at a nominal mesh resolution of 30 m using a time step of 1 s, solved using an implicit finitevolume approach (47). We used the 30-m Advanced Land Observing Satellite (ALOS) DEM as terrain input, preprocessed using standard carve and fill operations to remove any spurious elevation artifacts that may cause unrealistic damming and pooling in localized sections where the river channel may be narrower than the DEM resolution (12, 49). To approximate the normal (pre-GLOF) river flow conditions, we specified inflows for 15 major tributaries between Lugge Tsho and Wangdue station and allowed the model to come to equilibrium. The (relative) contributions of each tributary were estimated by performing a flow accumulation analysis for the region based on upstream watershed areas (49). We then multiplied all the relative inflow values with a single scale factor to estimate (absolute) contributions, so that the river flow at Wangdue station (located downstream of all tributaries) matched the observed pre-GLOF conditions of approximately 290 m<sup>3</sup> s<sup>-1</sup> (fig. S1). In HEC-RAS, we expressed the tributaries as inflow boundary conditions and allowed the model to run for 24 hours to establish initial conditions before simulating the flood. We then performed multiple model runs using a range of Manning roughness coefficient values (n) and various breach hydrograph shapes (fig. S1). We tested values of *n* spanning from 0.05 to 0.07 (in increments of 0.01), which is the typical range for mountain streams with cobbles and large boulders (47). For the breach hydrographs, we used a simple triangular approximation scaled to have ramp-up times  $(t_{ru})$ ranging from 15 to 120 min (in increments of 15 min). On the basis of previously published differential GPS survey of the lowering of the lake level (18) and the Wangdue station GLOF hydrograph (19), we assumed 17 to 25 million m<sup>3</sup> as a probable range for the total volume of water released during the GLOF and constrained the simulated breach hydrographs to 25 million m<sup>3</sup>. As a conservative threshold, we discarded any model runs, which did not agree with all independent constraints within ±30 min, and report the corresponding range of input parameters and model outputs of the remaining ones. We note that the breach may have initiated before the seismic signal was detectable (see \"Seismic data\" section); thus, we also include simulated breach times of 1:15, 1:30, and 1:45 a.m. in our analysis. Out of a total of 72 model runs (3 values of n, 8 values of  $t_{ru}$ , and 3 breach", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Flood model", "section_headings": ["MATERIALS AND METHODS", "Flood model"], "chunk_type": "text", "line_start": 168, "line_end": 172, "token_count_estimate": 820, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bd5d1c48a1dc31e8", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Flood model\nType: text\n\n. As a conservative threshold , we discarded any model runs , which did not agree with all independent constraints within ± 30 min , and report the corresponding range of input parameters and model outputs of the remaining ones . We note that the breach may have initiated before the seismic signal was detectable ( see \" Seismic data \" section ) ; thus , we also include simulated breach times of 1 : 15 , 1 : 30 , and 1 : 45 a . m . in our analysis . Out of a total of 72 model runs ( 3 values of n , 8 values of $ t_ { ru } $ , and 3 breach\n\ntimes), 14 runs produced output that satisfied the specified  $\\pm 30$ -min threshold. Last, we quantified the model sensitivity of arrival time estimates in Punakha (fig. S1), and found that a more gradual release of water and greater channel roughness both resulted in a slower-moving flood wave. Increasing  $t_{\\rm ru}$  from 45 to 60 min delayed arrival in Punakha by 20 to 25 min (fig. S1E), and increasing n from 0.05 to 0.06 delayed arrival time in Punakha by 25 to 35 min (fig. S1F). In general, we found that the HEC-RAS model performed well in satisfying the independent observations within the specified range of model parameters. However, we stress that the overall sensitivity to breach hydrograph characteristics requires caution if external constraints are not available.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Flood model", "section_headings": ["MATERIALS AND METHODS", "Flood model"], "chunk_type": "text", "line_start": 168, "line_end": 172, "token_count_estimate": 410, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8369d69ea45e119a", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Satellite imagery\nType: text\n\nFor analyzing GLOF-induced changes in land cover, we used the U.S. Geological Survey (USGS) Landsat 5 through 8 Thematic Mapper (TM) Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance product in Google Earth Engine (50). We quantified Lugge Tsho area changes over time by manual delineation of the lake boundaries and glacier front between 1976 and 2018 using declassified spy satellite imagery (KH-9 Hexagon) and Landsat. The Hexagon images were downloaded from the USGS Earth Explorer website (https://earthexplorer. usgs.gov/) after digital scanning of the images by the USGS and have a ground resolution of approximately 15 m. To measure the extent of sediments deposited by the GLOF, we selected two Landsat 5 scenes acquired in August 1994 (pre-GLOF) and September 1995 (post-GLOF) and used supervised classification with manually defined training samples of vegetation, sediment, water, ice, and clouds from the pre-GLOF scene. We classified both images using the maximum likelihood algorithm and retained the pixels classified as sediment along the valley bottom before and after the GLOF. To analyze the post-GLOF vegetation recovery trend, we focused on the largest swath of sediment deposition over the Tshojo plain. For this region, we computed the EVI for all Landsat 5 and 7 scenes, excluding those acquired during monsoon season months (May to October). We evaluated the topography of the Lunana region using the High Mountain Asia 8-m DEM data (version 1) distributed by the National Snow and Ice Data Center (NSIDC) and obtained the valley profile from the 30-m ALOS global digital surface model dataset distributed by the Japan Aerospace Exploration Agency (JAXA).", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Satellite imagery", "section_headings": ["MATERIALS AND METHODS", "Satellite imagery"], "chunk_type": "text", "line_start": 174, "line_end": 176, "token_count_estimate": 466, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee00329e77362105", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: MATERIALS AND METHODS > Cosmogenic 10Be surface exposure dating\nType: text\n\nWe applied cosmogenic <sup>10</sup>Be dating (*51*, *52*) to three boulders sampled in 2014 from the prominent and well-preserved lateral-terminal moraines near Thanza, about 10 km downstream from the GLOF breach location (Fig. 4). Geochemical processing was performed at the Cosmogenic Nuclide Laboratory at Lamont-Doherty Earth Observatory (LDEO) following standard protocols given in Schaefer *et al.* (*33*), and <sup>10</sup>Be/<sup>9</sup>Be measurements were completed at the Center for Accelerator Mass Spectrometry at the Lawrence Livermore National Laboratory. The background correction for these measurements was below 1%. We used version 3 of the online cosmogenic nuclide calculator (*53*) with the default production rate and time-dependent Stone/Lal scaling scheme for exposure age calculations (*52*, *54*). Geographic and analytical data are given in table S2, and the geomorphic map with the <sup>10</sup>Be ages is shown in Fig. 4. This glacier chronology will be discussed in detail in forthcoming papers.", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "MATERIALS AND METHODS > Cosmogenic 10Be surface exposure dating", "section_headings": ["MATERIALS AND METHODS", "Cosmogenic 10Be surface exposure dating"], "chunk_type": "text", "line_start": 178, "line_end": 180, "token_count_estimate": 338, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d106e20a710a2943", "text": "Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas\nSection: SUPPLEMENTARY MATERIALS\nType: text\n\nSupplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/38/eaba3645/DC1", "metadata": {"source_file": "data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md", "document_title": "Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas", "section_path": "SUPPLEMENTARY MATERIALS", "section_headings": ["SUPPLEMENTARY MATERIALS"], "chunk_type": "text", "line_start": 182, "line_end": 183, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14c9149e0fd5070b", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: ABSTRACT\nType: text\n\nGlacial lake outburst floods (GLOFs) from moraine-dammed lake failures represent a significant threat to inhabitants of high mountain areas across the globe. The first part of this paper summarises the causes and mechanisms of moraine-dammed lake failures through a review of the scientific literature and unpublished reports. There are eight main causes, of which five are characterised as dynamic and three as long-term, and these are associated with around twenty failure mechanisms. The dynamic causes are slope movements into the lake, earthquakes, flood waves from a lake situated upstream, blocking of underground outflow channels, and intensive rainfall or snowmelt. The long-term causes are the melting of buried ice, the impact of hydrostatic pressure, and the effect of time. These causes (triggers) and the consequent mechanisms of dam failure are described in detail. The second part compares the historical moraine-dammed lake failures within three regions between 1900 and 2009: the Cordillera Blanca of Peru, the North American Cordillera, and the Himalayas. It has been found that dynamic causes are around four times more common than long-term causes although significant regional differences have been observed. The most frequent causes in these regions were found to be slope movements in which the displaced material was dominated by solid-state water (ice falls, ice avalanches, and snow avalanches). The other causes tended to show distinct regional patterns while the temporal distribution of events also differs according to region. In the North American Cordillera and Himalayas moraine dam failures occur exclusively during the summer season while in the Cordillera Blanca they are more evenly distributed with the exception of the dry season. This reflects the general climatic setting of each of the study regions.\n\nKeywords: moraine-dammed lakes, natural dam failures, GLOFs, natural hazards, high mountain areas", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 8, "token_count_estimate": 456, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "721730b4daa4913e", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 1. Introduction\nType: text\n\nIn this study moraine-dammed lakes are defined as natural freshwater reservoirs dammed by material accumulated from a glacier (the moraine). This type of lake is not considered to be particularly stable because the morainic material forming the dam is usually unconsolidated and, therefore, even a relatively weak trigger can cause the dam to fail, leading to a glacial lake outburst flood (GLOF) (e.g. Costa & Schuster 1988; Richardson & Reynolds 2000a; Shrestha 2010). This form of flood describes the sudden water release from any form of glacial lake (e.g. bedrock-dammed, moraine-dammed, or ice-dammed) irrespective of its cause (Benn & Evans 1998). These floods are characterised by peak discharges that are many times higher than those of a hydrometeorological flood (Clauge & Evans 2000). They, therefore, have the potential to be highly catastrophic. GLOFs may occur as a result of dam failure (moraine-dammed or icedammed) or dam overflow (all types of glacial lake).\n\nThe study of moraine-dammed lake failure is complex and requires interdisciplinary cooperation. It is appreciated that two main groups of parameters are decisive in contributing to a moraine dam failure, the first is that of dam stability and the second is the possibility of a triggering event (Richardson & Reynolds 2000a; Hegglin & Huggel 2008). These are difficult to assess and quantify which leads to problems in estimating the probability\n\nof a moraine dam failure (hazard assessment) (Emmer & Vilímek 2013). It is possible for a moraine dam failure to be total or partial and, therefore, a given lake can be subjected to repeated GLOFs (O'Connor et al. 2001; Bajracharya et al. 2007a). GLOFs represent an increasingly significant threat in high-mountain areas because the number of potentially dangerous glacial lakes is increasing as a result of global climate change and concurrent glacier retreat (e.g. Evans & Clauge 1994; Bolch et al. 2008; Ives et al. 2010).\n\nGLOFs from moraine-dammed lake failures have been studied in high-mountain regions all over the world including the Himalaya (Vuichard & Zimmerman 1987; Kattelmann & Watanabe 1997; Bajracharya et al. 2007a,b), Hindu-Kush (Iturrizaga 2005; Ives et al. 2010), Karakoram (Hewitt 1982), Tian-Shan (Janský et al. 2009; Narama et al. 2010; Bolch et al. 2011), Caucasus Mts. (Petrakov et al. 2007), Cascade Range (O'Connor et al. 2001), British Columbia (Clauge & Evans 2000; Kershaw et al. 2005), Peruvian Andes (Zapata 1984; Vilímek et al. 2005a; Carey et al. 2011; Klimeš 2012), Patagonia (Harrison et al. 2006; Dussaillant et al. 2009) as well as in the European Alps (Haeberli et al. 2001; Huggel et al. 2004) and Scandinavia (Breien et al. 2008). The objectives of this paper are to provide an overview of moraine-dammed lake failures, to investigate their causes and mechanisms, and to compare the temporal aspect of these events within three", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 20, "token_count_estimate": 808, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "86218091fa3a96f0", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 1. Introduction\nType: text\n\n. 2001 ) , British Columbia ( Clauge & Evans 2000 ; Kershaw et al . 2005 ) , Peruvian Andes ( Zapata 1984 ; Vilímek et al . 2005a ; Carey et al . 2011 ; Klimeš 2012 ) , Patagonia ( Harrison et al . 2006 ; Dussaillant et al . 2009 ) as well as in the European Alps ( Haeberli et al . 2001 ; Huggel et al . 2004 ) and Scandinavia ( Breien et al . 2008 ) . The objectives of this paper are to provide an overview of moraine - dammed lake failures , to investigate their causes and mechanisms , and to compare the temporal aspect of these events within three\n\nregions: the Cordillera Blanca of Peru, the North American Cordillera, and Himalaya. These areas were chosen because they have a long history of moraine-dammed lake research and there is, therefore, a greater chance of acquiring information about GLOFs. The flooding that occurs as a result of moraine-dammed lake failures poses a significant threat in high mountain regions and by comparing events it is possible to recognise the regional specifics that are necessary in order to build an optimal regionally focused hazard assessment.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 10, "line_end": 20, "token_count_estimate": 307, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35a19c3f451ee1f7", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 2. Materials and methods\nType: text\n\nThe causes (triggers) and mechanisms of morainedammed lake failures are summarised following an extensive search of the published scientific literature and unpublished reports from the archive of the Autoridad Nacional de Agua in Huaraz, Peru. These causes and consequent mechanisms of dam failure are described in detail with examples given for each. The similarities and disparities that exist between the particular causes have been investigated as well as the temporal characteristics of events in the Cordillera Blanca of Peru, the North American Cordillera, and Himalaya. The GLOF database (GLOFs database 2012) has been used as the basis for this comparative analysis: the details of around ninety-five moraine dam failures within the three study areas have been compiled for the period 1900 to 2009. The causes of the GLOFs are known in sixty of these cases (twenty in Cordillera Blanca of Peru, eleven in the North American Cordillera, and twenty-nine in Himalaya) but unknown in the other instances. The precise dates of the GLOFs are known in sixty-six of the cases and can be attributed to a year in seventy-five instances. The temporal characteristics of the GLOFs are considered both in terms of their monthly and annual distributions.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "2. Materials and methods", "section_headings": ["2. Materials and methods"], "chunk_type": "text", "line_start": 22, "line_end": 24, "token_count_estimate": 311, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "824ae9b6e60f06ac", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures\nType: text\n\nThere is a close relationship between various types of natural hazards and moraine-dammed lake failures that result in GLOFs. The former, the various natural hazards, often represent the cause of the latter, the lake failures. These relationships are summarised in Figure 1. Yamada (1998) divided the process of moraine dam failure into two groups: the first consisted of dam failure caused by a dynamic initiating event while the second consisted of spontaneous \"dam self-destruction\". The latter group are caused by long-term degradation of the dam without a dynamic initiating event. This grouping is followed here and the causes of moraine dam failure are classifies as either dynamic causes or long-term.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 26, "line_end": 28, "token_count_estimate": 192, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "43b76f10a6da5ae8", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: text\n\nThe dynamic causes are slope movements into the lake, earthquakes, a flood wave from a lake situated upstream, blocking of underground outflow channels, and intensive rainfall or snowmelt. In this group the time interval between the trigger and the moraine dam failure ranges from minutes in the case of, for example, a slope movement into the lake to hours or days in the case of, for example, intensive rainfall or snowmelt. These dynamic causes were termed \"quasicoincidental\" by O'Connor et al. (2001) as it is not possible to accurately predict the time or place in which they will occur.\n\nThe slope movement into the lake includes various types of mass-movement such as icefalls, avalanches, rockfalls, landslides, debris flows, and mudflows. The mass of material entering the lake causes water displacement in the form of surge (displacement) waves (Richardson & Reynolds 2000a) and these can reach heights of tens of metres (Plafker & Eyzaguirre 1979 in Costa & Schuster", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 30, "line_end": 34, "token_count_estimate": 297, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93e9a27956ec6be0", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: figure\nFigure\n\nImage /page/2/Figure/10 description: A flowchart illustrating the causes and mechanisms of dam failure. The chart is divided into a vertical axis labeled \"CAUSE OF FAILURE\" and a horizontal axis labeled \"MECHANISM\". The flowchart uses ovals of different shades and connecting arrows to show the progression from initial causes to final failure.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "figure", "figure_caption": null, "line_start": 35, "line_end": 35, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1f201faab79b4499", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: text\n\nOn the left, under \"CAUSE OF FAILURE\", are several light gray ovals representing dynamic causes: \"EARTHQUAKE\", \"DYNAMIC SLOPE MOVEMENT INTO THE LAKE\", \"INTENSIVE RAINFALL OR SNOWMELT\", \"FLOOD WAVE FROM LAKE SITUATED UPSTREAM\", and \"BLOCKING OF OUTFLOW CANALS\".\n\nThe \"MECHANISM\" section includes several stages. Medium gray ovals represent the \"interphase\": \"DISPLACEMENT (SURGE) WAVE\", \"DAM OVERFLOW\", \"RAPID RISE OF WATER LEVEL\", and \"DAM DEGRADATION\". Dark gray ovals represent the \"final phase\": \"WATER EROSION\" (subdivided into \"INTENSIVE SHORT-TERM\", \"INTERNAL (PIPING)\", and \"LONG-TERM\"), \"RUPTURE\", \"HYDROSTATIC PRESSURE\", \"BURIED ICE MELTING\", and \"TIME EFFECT\".\n\nArrows indicate the causal relationships. For example, an earthquake can lead to dynamic slope movement, which causes a displacement wave. This wave can lead to dam overflow and a rapid rise in water level. Both dam overflow and other factors like dam degradation contribute to water erosion, which, along with hydrostatic pressure, can lead to the final rupture of the dam.\n\nA legend at the bottom clarifies the symbols: a white box with a gray outline is a \"long-term cause\"; a light gray box is a \"dynamic cause\"; a medium gray box is an \"interphase\"; a dark gray box is a \"final phase\". It also distinguishes between line types: a dashed line for \"lakes without surface outflow\" and a solid line for \"lakes with surface outflow\".\n\nFig. 1 The causes and mechanisms that underpin the process of moraine dam failure.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 36, "line_end": 56, "token_count_estimate": 488, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f81c31bcb34676c", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: text\n\n, which , along with hydrostatic pressure , can lead to the final rupture of the dam . A legend at the bottom clarifies the symbols : a white box with a gray outline is a \" long - term cause \" ; a light gray box is a \" dynamic cause \" ; a medium gray box is an \" interphase \" ; a dark gray box is a \" final phase \" . It also distinguishes between line types : a dashed line for \" lakes without surface outflow \" and a solid line for \" lakes with surface outflow \" . Fig . 1 The causes and mechanisms that underpin the process of moraine dam failure .\n\n1988). The waves have a considerable impact on the stability of the moraine-dam (e.g. Kattelmann & Watanabe 1997; Clauge & Evans 2000). The slope movement into the lake and its related displacement wave may lead to two different mechanisms of dam failure. The first is that of immediate dam rupture following the impact of the displacement wave and this represents the most catastrophic scenario while the second is that of dam failure due to an increase in outflow channel discharge. The second mechanism is essentially caused by the increased water level within the lake which increases the outflow channel discharge and causes increased incision which then, in turn, increases the discharge and increases the incision. This \"positive feedback\" (Yamada 1998) continues until the outflow channel is either able to resist the incision due to structural changes or until the lake empties (Clauge & Evans 2000). However, if the displacement wave has the necessary energy and moraine dam is at the same time sufficiently resistant, flooding may result from dam overflow (Kershaw et al. 2005). This may happen without significant damage to the moraine dam and an example occurred on 22nd April 2002 as a result of rockfall into lake Safuna Alta in the Cordillera Blanca (Hubbard et al. 2005). It is possible for dam overflow and outflow channel incision caused by sudden rise of the water level to occur during the same flood event. Kershaw et al. (2005) presented stratigraphical and sedimentological evidence from the Queen Bess lake GLOF on 12th August 1997 that indicated both scenarios. The first phase of flooding was represented by dam overflow during which the bulk of the flood volume escaped (Clauge & Evans 2000) while the second phase was represented by dam incision and final failure. The time interval between these phases was in order of minutes (Evans et al. 2002).", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 36, "line_end": 56, "token_count_estimate": 632, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "17d85399a87be3c9", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: text\n\nCordillera Blanca ( Hubbard et al . 2005 ) . It is possible for dam overflow and outflow channel incision caused by sudden rise of the water level to occur during the same flood event . Kershaw et al . ( 2005 ) presented stratigraphical and sedimentological evidence from the Queen Bess lake GLOF on 12th August 1997 that indicated both scenarios . The first phase of flooding was represented by dam overflow during which the bulk of the flood volume escaped ( Clauge & Evans 2000 ) while the second phase was represented by dam incision and final failure . The time interval between these phases was in order of minutes ( Evans et al . 2002 ) .\n\nIt is possible for an earthquake to directly initiate a moraine-dammed lake failure as the shock can cause sufficient damage to a dam for it to fail (Clauge & Evans 2000) while there is also the chance that the seismic activity may initiate slope movements. In some cases it is not known whether dam failure was caused directly by an earthquake or if it was caused by slope movements initiated by an earthquake and this is particularly difficult distinguish in the case of historical events (Strasser et al. 2008). There is also the chance that an earthquake could change the internal structure of the moraine dam causing internal erosion in the form of piping which may then lead to the emptying of the lake (Lliboutry et al. 1977). In contrast, changes in the internal structure of the dam may serve to reduce infiltration through the moraine by blocking underground outflow channels. This may be dangerous in instances where the lake does not have a surface outflow as there is then a continuous rise in lake water level. It was seen at lake Parón (Cordillera Blanca) when an earthquake in 1966 inhibited flow along underground channels and the water level rose steadily until next strong earthquake in 1970 when discharge turned into the values before 1966 event (Lliboutry et al. 1977).\n\nThe flood wave from an upstream lake can cause dam failure on a lake situated downstream. The flood wave can often transform easily into different types of flows because of its high erosion and transport potential (Cenderelli & Wohl 2001; Breien et al. 2008). It produces a displacement wave or a significant rise in the water level when entering the downstream lake and this has the same consequences as a slope movement into the lake. A dam failure may occur as a result of the direct impact of the displacement wave or following incision of the outflow channels. The overall flood volume often considerable in these cases as there are inputs from two lakes. This was seen in the Cordillera Blanca following the Palcacocha outburst flood which destroyed the downstream lake Jircacocha on 13th December 1941 (Vilímek et al. 2005b) (Figure 2). The catastrophic debris-flow incorporated water from both lakes and destroyed one-third of the city of Huaraz claiming about 6000 lives (Lliboutry et al. 1977). However, in some cases, the downstream lake may absorb the flood wave if it has a sufficiently large accommodation space. It is known that lake Parón in the Cordillera Blanca has been able to absorb outburst floods from two lakes, the first from lake Chacrucocha prior to 1950 and second from lake Artesoncocha in 1951 (Lliboutry et al. 1977; Carey et al. 2012).", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 36, "line_end": 56, "token_count_estimate": 839, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be39398e850f44ba", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: text\n\n13th December 1941 ( Vilímek et al . 2005b ) ( Figure 2 ) . The catastrophic debris - flow incorporated water from both lakes and destroyed one - third of the city of Huaraz claiming about 6000 lives ( Lliboutry et al . 1977 ) . However , in some cases , the downstream lake may absorb the flood wave if it has a sufficiently large accommodation space . It is known that lake Parón in the Cordillera Blanca has been able to absorb outburst floods from two lakes , the first from lake Chacrucocha prior to 1950 and second from lake Artesoncocha in 1951 ( Lliboutry et al . 1977 ; Carey et al . 2012 ) .\n\nThe blocking of underground outflow channels can be caused by four mechanisms: clogging by sediments brought into the lake by its tributaries; clogging by material brought into the lake during mass-movements; freezing of outflow channels (O'Connor et al. 2001); and blocking of outflow channels caused by the changing internal structure of dam due to an earthquake (Lliboutry et al. 1977). The water level of the lake starts to rise if the channels are blocked and this leads to the same mechanism of dam failure as occurs during intense rainfall or snowmelt - dam rupture caused when the lithostatic pressure exceeds that of hydrostatic pressure (Richardson & Reynolds 2000a). There will also be increased erosion in instances of dam overflow. However, unlike the rise in water level caused by intense rainfall or snowmelt, the blocking of underground outflow channels usually causes the water level to increase until the lake basin is full. This occurs unless the hydrostatic pressure exceeds the lithostatic pressure as blocked channels do not usually unblock spontaneously. The moraine dam failure at lake Zhangzhanbo in Tibet on 11th July 1981 was caused by blocking of underground outflow channels (Ding & Liu 1992; Yamada 1998).\n\nIt is also clear that intense rainfall or snowmelt will lead to a rise in the water level of the lake. This cause depends on many factors of which the variability and extremity of the precipitation are perhaps the most significant or, alternatively, the variability and extremity of air temperature in relation to snowmelt (Yamada 1998). If a lake has surface runoff the increasing water level may lead to an increase in the erosion of the outflow channels and to the cycle of \"positive feedback\" (see above). If a lake does not", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 36, "line_end": 56, "token_count_estimate": 633, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "81bad7c9675afd8b", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: figure\nFigure\n\nImage /page/4/Picture/2 description: A black and white photograph of a mountainous landscape, identified by the caption as the former lake basin of Jircacocha in the Cordillera Blanca. The image shows a steep, rocky slope covered in dense, dark vegetation. At the base of the slope is a flatter, more barren area that appears to be a dried lakebed or floodplain, with a small channel winding through it. Two large, white, downward-pointing arrows are superimposed on the image, pointing towards the edges of this barren area. The caption below the image reads: \"Fig. 2 The former lake basin of Jircacocha in the Cordillera Blanca. Its dam failed after the arrival of a flood wave from lake Palcacocha. The...\"", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "figure", "figure_caption": null, "line_start": 57, "line_end": 57, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f01e59c869c0dd6", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures\nType: text\n\nFig. 2 The former lake basin of Jircacocha in the Cordillera Blanca. Its dam failed after the arrival of a flood wave from lake Palcacocha. The horizontal line indicated by arrows shows the former water level.\n\nhave surface runoff then the most important factor is the dam freeboard which is defined as the vertical elevation between the lake level and the lowest point on the dam crest. The rise in lake water level can lead to two different mechanisms of dam destruction. The first mechanism is dam rupture due to the increased hydrostatic pressure. This happens when the hydrostatic pressure overcomes the lithostatic pressure, which keeps the components of the dam together (Richardson & Reynolds 2000a). The second is dam overflow from which the subsequent erosion may induce dam failure (Kattelmann & Watanabe 1997). The failure of a dam under the Dallier glacier in the Cascade Range provides an example of one that occurred as a result of intense rainfall or snowmelt (O'Connor et al. 2001). It is also well known that intense rainfall also a major trigger for slope movements, which may initiate moraine dam failure.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.1 The dynamic causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 58, "line_end": 62, "token_count_estimate": 313, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7668c42d3446d2a4", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.2 The long-term causes of moraine-dammed lake failures\nType: text\n\nThe long-term causes of dam destruction are the ice melting of buried ice, the impact of hydrostatic pres-sure, and the effect of time. It is difficult to accurately\n\nconstrain which of these long-term issues ultimately leads to the destruction of the dam. This led Yamada (1998) to group these under the title of \"self-destruction\" due to the absence of an initial external dynamic event (Yamada 1998; Bajracharya et al. 2007b). The long-term causes also weaken the resistance of the dam to dynamic causes: while the impact of hydrostatic pressure and the effect of time have some influence on the moraine dam they are not often the main cause of dam destruction. The degradation of a moraine dam is a function of time and this function does not constitute a single process. It is, instead, a group of processes that may lead to the degradation of a dam over a protracted period and which affects the moraine slope stability, dam freeboard, internal structure etc. In combination with, for example, intense rainfall, the effect of time may lead to mass movements on moraine slopes (Awal et al. 2010). If a lake is associated with a surface outflow, its sequential erosion may cause the lake to empty, without a significant GLOF (Yamada 1998). If a lake is not associated with a surface outflow, the internal erosion of outflow channels through piping may also cause the lake to empty, without a significant GLOF (Clauge & Evans 2000; Haeberli et al. 2001).", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.2 The long-term causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.2 The long-term causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 64, "line_end": 68, "token_count_estimate": 399, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc65dc5a4fd317cd", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.2 The long-term causes of moraine-dammed lake failures\nType: figure\nFigure\n\nImage /page/5/Picture/2 description: A black and white photograph of a rugged, mountainous landscape. In the center, a waterfall or stream of meltwater flows down a dark, steep rock face through a curved groove. The water empties into a small pool at the bottom. The foreground and middle ground are covered with a chaotic pile of large, light-colored, broken rocks and debris. In the upper right, a dark, sheer rock wall rises, contrasting with the cloudy sky visible in the upper left. A partial caption at the bottom of the image reads, \"Fig. 3 An example of...\" with the rest of the text cut off.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.2 The long-term causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.2 The long-term causes of moraine-dammed lake failures"], "chunk_type": "figure", "figure_caption": null, "line_start": 69, "line_end": 69, "token_count_estimate": 212, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34a06a3645f24e4e", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 3. The causes and mechanisms of moraine-dammed lake failures > 3.2 The long-term causes of moraine-dammed lake failures\nType: text\n\n**Fig. 3** An example of an uncovered ice lens in the protruding basal moraine of the developing lake Llaca in the Cordillera Blanca.\n\nThe term \"buried ice\" describes an ice lens integrated into the body of the moraine dam (Figure 3). It is possible for this to represent up to 90% of the dam volume (Costa & Schuster 1988). The melting of this buried ice weakens the stability of the dam as it disrupts its structural integrity and may also decrease the dam freeboard (Richardson & Reynolds 2000b; Huggel et al. 2002). The disruption of the structural integrity of the dam enables rupture by hydrostatic pressure while it also decreases the ability of the dam to withstand other causes that would not normally represent a significant problem. For example, the long-term degradation of the moraine dam leads to a decrease in the freeboard which, in combination with moderate rainfall or snowmelt, may lead to dam destruction.\n\nThe hydrostatic pressure is the pressure exerted by the gravitational force acting on a water column at certain depth. The water dammed in a moraine-dammed lake affects the dam by this pressure and its long-term affect may lead to the dam failure (Yamada 1998). This cause becomes especially significant if the moraine dam is weakened by, for example, buried ice melting while deep lakes are more susceptible to rupture caused by hydrostatic pressure. The destruction caused by the systematic effect of hydrostatic pressure occurs when the moraine dam is no longer able to resist the hydrostatic pressure, i.e. the hydrostatic pressure exceeds the lithostatic pressure (Richardson & Reynolds 2000a). This may be caused by an increase in the lake water level or the protracted degradation of the moraine dam (Yamada 1998; Jaboyedoff et al. 2004 in Vilímek et al. 2005a). The increase in lake water level may be caused by intense rainfall or snowmelt or the blocking of underground outflow channels (Costa & Schuster 1988; Grabs & Hanish 1993; Janský et al. 2006). It is also possible to increase the hydrostatic pressure by basal ice melting and lake deepening (Watanabe & Rothacher 1996). In cases where dam degradation occurs as a result of buried ice melting, intense slope erosion, or changes in the internal structure of the dam, rupture by hydrostatic pressure may occur without a significant change in the pressure (Richardson & Reynolds 2000a). These all represent examples of \"dam self-destruction\" and may occur without a dynamic trigger. This happened in 1994 at lake Lugge Tsho in Bhutan (Watanabe & Rothacher 1996). The impact of hydrostatic pressure has a specific position in the categorisation of mechanisms of dam destruction as both dynamic and long-term causes can lead to dam rupture caused by hydrostatic pressure (Figure 1).", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "3. The causes and mechanisms of moraine-dammed lake failures > 3.2 The long-term causes of moraine-dammed lake failures", "section_headings": ["3. The causes and mechanisms of moraine-dammed lake failures", "3.2 The long-term causes of moraine-dammed lake failures"], "chunk_type": "text", "line_start": 70, "line_end": 76, "token_count_estimate": 730, "basins": [], "subbasins": [], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "0425586a431af611", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis\nType: text\n\nThe comparative analysis investigates historical moraine-dammed lakes failures between 1900 and 2009 from three regions: the Cordillera Blanca of Peru, the North American Cordillera, and Himalaya. The basic characteristics of these regions are listed in Table 1. These regions are commonly characterised by glacier retreat at present which is leading to the formation and\n\nTab. 1 The basic characteristics of the studied regions.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis", "section_headings": ["4. The comparative analysis"], "chunk_type": "text", "line_start": 78, "line_end": 82, "token_count_estimate": 121, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b5f7f9741400d1c7", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis\nType: table\nTable\n\n| Region                       | Mountains                                                             | Coordinates              | Highest peak(s)                                               | Climatic settings                                     |\n|------------------------------|-----------------------------------------------------------------------|--------------------------|---------------------------------------------------------------|-------------------------------------------------------|\n| Cordillera Blanca            | Cordillera Blanca (Peru) Cordillera Huayhuash (Peru)               | 8°–10° S 77°–78° W    | Huascarán (6768 m asl)                                     | Tropical High Mountain Climate – Wet & Dry Seasons |\n| North American Cordillera | Coast Mountains (CAN) Rocky Mountains (CAN) Cascade Range (USA) | 30°–50° N 120°–135° W | Mt. Waddington (4019 m asl) Mt. Rainier (4392 m asl) | Temperate High Mountain Climatic Zone – 4 Seasons  |\n| Himalaya                     | Himalaya (China, India, Nepal, Bhutan)                             | 27°–35° N 75°–95° E   | Mt. Everest/ Sagarmatha (8848 m asl)                       | Temperate High Mountain Climatic Zone – 4 Seasons  |", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis", "section_headings": ["4. The comparative analysis"], "chunk_type": "table", "table_caption": null, "columns": ["Region", "Mountains", "Coordinates", "Highest peak(s)", "Climatic settings"], "table_row_start": 1, "table_row_end": 3, "line_start": 83, "line_end": 87, "token_count_estimate": 297, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "514bbd892e1976d3", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis\nType: text\n\ndevelopment of new potentially dangerous morainedammed lakes. The threat of GLOFs in these areas is real while the downstream valleys are often settled.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis", "section_headings": ["4. The comparative analysis"], "chunk_type": "text", "line_start": 88, "line_end": 90, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2183ac5efbbc8927", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.1 The spatial analysis\nType: text\n\nIt is important to assess whether there are differences regarding the causes of moraine-dammed lake failures across the three study regions. The slope movements have been divided into two subgroups for this purpose: the first incorporates slope movements in which the displaced material was dominated by solid-state water (ice falls, ice avalanches, and snow avalanches) while the second incorporates movements in which the displaced material was dominated by rock or liquid-state water (rockfalls, landslides, and various types of flows). It should be noted that the failure mechanisms for these two subgroups are identical. Furthermore, the concept of dam failure stemming from a combination of long-term causes without an evident dynamic cause (\"dam self-destruction\" (Yamada 1998) suggests that it will always be difficult to define the precise cause of failure (i.e. there is no distinguishing between the ice melting of buried ice, the impact of hydrostatic pressure, and the effect of\n\ntime). Indeed, unless a lake is continuously monitored, it is often difficult to determine the cause of a contemporary GLOF, and even more so in the case of historical events. Therefore, while a single cause may be the most commonly ascribed, there is a possibility that it in part reflects the ease with which it can be identified. The comparison of dynamic and long-term causes (Table 2) shows that, across all three regions, dynamic causes prevail over long-term causes by a ratio of 4:1. There are, however, significant regional differences. In the Cordillera Blanca all of the twenty dam failures resulted from a dynamic event while more than two-fifths resulted from a longterm cause in Himalaya (\"dam self-destruction\").\n\nThe most frequent cause of failure in three regions of interest was found to be slope movements in which the displaced material was dominated by solid-state water (ice falls, ice avalanches, and snow avalanches) (Figure 4). This finding corresponds with those of previous studies (e.g. Costa & Schuster 1988; Ding & Liu 1992; Clauge & Evans 2000; Jiang et al. 2004; Awal et al. 2010). The second and the third most frequent causes tend to show distinct regional patterns with the second most common cause being slope movements dominated by solid rock or water in the Cordillera Blanca of Peru, intensive rainfall\n\nTab. 2 The dynamic and long-term causes of moraine-dammed lake failures (60 instances where causes of the moraine dam failures are known).", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.1 The spatial analysis", "section_headings": ["4. The comparative analysis", "4.1 The spatial analysis"], "chunk_type": "text", "line_start": 92, "line_end": 100, "token_count_estimate": 615, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a4177dd66d6817c", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.1 The spatial analysis\nType: table\nTable\n\n| Region                    | Dynamic Causes   |       | Long-term Causes |      | Total number of events |\n|---------------------------|------------------|-------|------------------|------|---------------------------|\n|                           | Number of events | %     | Number of events | %    |                           |\n| Cordillera Blanca         | 20               | 100.0 | 0                | 0.0  | 20                        |\n| North American Cordillera | 10               | 90.9  | 1                | 10.1 | 11                        |\n| Himalaya                  | 17               | 58.6  | 12               | 41.4 | 29                        |\n| Total                     | 47               | 78.3  | 13               | 21.7 | 60                        |", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.1 The spatial analysis", "section_headings": ["4. The comparative analysis", "4.1 The spatial analysis"], "chunk_type": "table", "table_caption": null, "columns": ["Region", "Dynamic Causes", "", "Long-term Causes", "", "Total number of events"], "table_row_start": 1, "table_row_end": 5, "line_start": 101, "line_end": 107, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b0fb0da3e641a73", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.1 The spatial analysis\nType: figure\nFigure\n\nImage /page/6/Figure/9 description: A black and white chart comparing data from three different mountain ranges using pie charts of varying sizes. The vertical axis is labeled 'number of events' with dashed lines at 11, 20, and 29. The horizontal axis labels the three pie charts: 'Northamerican Cordillera', 'Cordillera Blanca', and 'Himalaya'. The size of each pie chart corresponds to the number of events on the vertical axis. The first pie chart for 'Northamerican Cordillera' corresponds to 11 events and is divided into three slices: 54,5% (dark gray), 36,4% (medium gray), and 9,1% (light gray). The second pie chart for 'Cordillera Blanca' corresponds to 20 events and has four slices: 45,0% (dark gray), 35,0% (medium-dark gray), 15,0% (light gray), and 5,0% (medium-light gray). The third and largest pie chart for 'Himalaya' corresponds to 29 events and has four slices: 51,8% (dark gray), 41,4% (light gray), and two small slices both labeled 3,4%.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.1 The spatial analysis", "section_headings": ["4. The comparative analysis", "4.1 The spatial analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 109, "line_end": 109, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9948deabef6acf69", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.1 The spatial analysis\nType: text\n\n- slope movement of material with dominance of solid state water (ice falls, ice and snow avalanches)\n- slope movement of material with a dominance of rocks or liquid water (rock falls, landslides, various types of flows)\n- intense rainfall / snow melting\n- □ earthquake\n- flood wave form lake situated upstream\n- □ blocking of underground outflow tunnel □ selfdestruction\n\nFig. 4 A representation of the causes of moraine-dammed lake failures in the Cordillera Blanca, the North American Cordillera, and Himalaya.\n\nor snowmelt in the North American Cordillera, and self-destruction in Himalaya. It is of interest to note that intensive rainfall or snowmelt is only recorded as a trigger in the North American Cordillera and it may be that this reflects the particular climatic setting of the region. The moraine-dammed lakes in the North American Cordillera are situated between 1400 and 2400 m asl (O'Connor et al. 2001) whereas in the Cordillera Blanca and Himalaya they generally occur between 4000 and 5000 m asl (Lliboutry et al. 1977; Yamada 1998). The intensity of the combined rainfall and snowmelt may be greater in the North American Cordillera during the summer due to these lower elevations. Thereafter, only one moraine-dammed lake failure in the North American Cordillera was attributed to dam self-destruction.\n\nThe trigger of dam self-destruction represents the second most frequent cause of moraine dam failures in Himalaya. This may reflect the significant volumes of buried ice that occur in many moraine dam bodies in this region (Yamada 1998; Bajracharya et al. 2007b). The melting of buried ice leads to degradation of moraine dam and this may lead to its failure, especially in combination with the affect of hydrostatic pressure impact, without a dynamic triggering event (see above). Thereafter, one moraine dam failure in the region was caused by the blocking of underground outflow channels and one by a slope movement into a lake in which the displaced material was dominated by rock or liquid-state water. The second most frequent cause of moraine-dammed lake failures in the Cordillera Blanca were slope movements into lakes in which the displaced material was dominated by rock. In fact, slope movements account for 80% of all moraine dam failures in this region while 15% could be attributed to an earthquake. Thereafter, one dam failure was caused by the propagation of a flood wave which resulted from the failure of an upstream dam.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.1 The spatial analysis", "section_headings": ["4. The comparative analysis", "4.1 The spatial analysis"], "chunk_type": "text", "line_start": 110, "line_end": 123, "token_count_estimate": 627, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab3a268722b3ef45", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.2 The temporal analysis\nType: text\n\nThis section focuses on the temporal distribution of 66 moraine-dammed lake failures and GLOFs over months (Figure 5) and 75 moraine-dammed lake failures and GLOFs over years (Figure 6). The monthly differences in the distribution of GLOFs shown in Figure 5 clearly reflect the general climatic setting of each of the study regions. The temporal distribution of moraine dam failures is similar in the North American Cordillera and Himalaya as dam failures occur exclusively during the summer season from June to September. The lake water levels are higher at this time because the warmer temperatures melt glacial ice and, thereby, reduce the dam freeboard. In the Cordillera Blanca, with its alternating wet and dry seasons, moraine dam failures are more evenly distributed but cluster during the wet season from December to May. The lake water levels are again higher at this time because there is a considerable amount of precipitation while the temperatures are also slightly warmer than they are during the dry season. These data indicate that the moraine dam failures occur most commonly during the warmer times of the year and corroborate the notion the most frequent cause of moraine dam failures are dynamic slope movements in which the displaced material was dominated by solid-state water (ice falls, ice avalanches, and snow avalanches).\n\nThe annual distribution of moraine dam failures has been analysed using a dataset of seventy-five events", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.2 The temporal analysis", "section_headings": ["4. The comparative analysis", "4.2 The temporal analysis"], "chunk_type": "text", "line_start": 125, "line_end": 129, "token_count_estimate": 356, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e8e1031945fad346", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.2 The temporal analysis\nType: figure\nFigure\n\nImage /page/7/Figure/8 description: A radar chart, also known as a spider or web chart, displaying data for three different mountain ranges over twelve months. The chart has twelve spokes, labeled with Roman numerals I through XII, representing the months of the year in a clockwise direction. The concentric circles indicate a scale from 0 at the center to 50 at the outermost circle, with increments of 10. A legend on the right identifies the three data series: 'Himalaya' is represented by a thick solid black line, 'Cordillera Blanca (Peru)' by a thick dashed black line, and 'Northamerican Cordillera' by a thin double line. The 'Himalaya' data peaks in months VII (value approx. 10) and VIII (value approx. 12). The 'Cordillera Blanca (Peru)' data shows its highest peak in month I (value approx. 20), with other high points in XII (approx. 15) and II (approx. 10). The 'Northamerican Cordillera' data peaks in month VII (value approx. 15) and is similar in shape to the Himalaya data but with slightly higher values in the peak months.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.2 The temporal analysis", "section_headings": ["4. The comparative analysis", "4.2 The temporal analysis"], "chunk_type": "figure", "figure_caption": null, "line_start": 130, "line_end": 130, "token_count_estimate": 316, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31f035539322ba31", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 4. The comparative analysis > 4.2 The temporal analysis\nType: text\n\nFig. 5 The distribution of moraine dam failures plotted according to month (66 instances overall).\n\nfor which the year of failure is known. In the period between 1900 and 1924 only one moraine-dam failure was recorded - this occurred at lake Zhanlonba in China during 1902 (Ding & Liu 1992) (Figure 6). In the period between 1935 and 1944 eight moraine-dam failures were recorded in the Cordillera Blanca region. These events claimed thousands of lives and caused considerable damage to infrastructure as well as leading to a series of investigations into moraine-dammed lakes (Zapata 2002; Carey 2005). These investigations called for remedial work at thirty-five sites in the form of artificial dams, concrete outlets, tunnels, etc. (Reynolds 2003; Carey et al. 2012). It may also be one of the reasons for the decreased number of moraine dam failures in the following years. In the period between 1970 and 1974 five failures were recorded in the region. These were all triggered by the catastrophic earthquake that occurred in Cordillera Blanca on 31st May 1970. The next moraine dam failure did not occur for another twenty-seven years. This protracted period of stability is thought to reflect the extensive remedial works and rupture of the most unstable lake dams following the earthquake in 1970. The annual distribution of moraine dam failure in Himalaya is more regular with at least two failures in each five-year period between 1955 and 2004. There was a maximum of six failures in the five-year period between 1980 and 1984. The annual distribution of moraine dam failures in the North American Cordillera is also broadly constant with no peaks evident.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "4. The comparative analysis > 4.2 The temporal analysis", "section_headings": ["4. The comparative analysis", "4.2 The temporal analysis"], "chunk_type": "text", "line_start": 131, "line_end": 135, "token_count_estimate": 419, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0d54997e14cefbd1", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 5. Discussion\nType: text\n\nThere are a number of published lists of morainedammed lake failures for specific regions such as those that have been compiled for the Cordillera Blanca (Zapata 2002), the North American Cordillera (Clague & Evans 2000; O'Connor et al. 2001), and Himalaya (Yamada 1998; Ives et al. 2010). These lists have been updated and supplemented with as much data as it was possible to attain. There is, however, a paucity of events both at the beginning and at the end of the period from 1900 to 2009. The paucity of events at the beginning of this period may reflect an absence of moraine-dammed lake failures at that time or, more likely, that such events were not recorded if the areas in which they occurred were uninhabited or if no significant damage occurred. The paucity of events at the end of this period may again reflect an absence of moraine-dammed lake failures but is more likely to reflect the amount of time it takes for the data to be processed and published. In a number of instances the cause of the moraine-dammed lake failure is not known with certainty and it is only possible to attribute a probable cause. It is, nonetheless, evident that the most common causes are slope movements in which the displaced material is dominated by solid-state water (ice falls, ice avalanches, and snow avalanches). This finding corresponds with those of previous studies (e.g. Costa & Schuster 1988; Ding & Liu 1992; Clauge & Evans 2000; Jiang et al. 2004; Awal et al. 2010).\n\nFig. 6 The distribution of moraine dam failures plotted according to year (75 instances overall).", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "5. Discussion", "section_headings": ["5. Discussion"], "chunk_type": "text", "line_start": 137, "line_end": 141, "token_count_estimate": 408, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a90bc95ad9e4968", "text": "Document: The causes and mechanisms of moraine-dammed lake failures\nSection: 6. Conclusions\nType: text\n\nThere are eight main causes, of which five are characterised as dynamic and three as long-term, and these are associated with around twenty failure mechanisms. The dynamic causes are slope movements into the lake (e.g. icefalls, avalanches, rockfalls, landslides, and other types of flow), earthquakes, a flood wave from a lake situated upstream, blocking of underground outflow channels, and intensive rainfall or snowmelt. The long-term causes are the ice melting of buried ice, the impact of hydrostatic pressure, and the effect of time. It is, therefore, clear that GLOFs are associated closely to various types of natural hazard. The similarities and disparities between the particular causes were investigated as well as the temporal characteristics of events in the Cordillera Blanca of Peru, the North American Cordillera, and Himalaya. It was found that dynamic causes are around four times more common than long-term causes although significant regional differences are seen: in the Cordillera Blanca of Peru all dam failures resulted from a dynamic event while more than two-fifths resulted from a longterm cause in Himalaya. The most frequent causes of GLOFs from moraine-dammed lakes were found to be slope movements in which the displaced material was dominated by solid-state water (ice falls, ice avalanches, and snow avalanches). This accounted for around half of all events irrespective of the specific study area. The other causes tended to show distinct regional patterns with the second most common cause being slope movements dominated by solid rock or water in the Cordillera Blanca of Peru, intensive rainfall or snowmelt in the North American Cordillera, and self-destruction in Himalaya. The temporal distribution of moraine dam failures is similar in the North American Cordillera and Himalaya as dam failures occur exclusively during the summer season from June to September while in the Cordillera Blanca they are more evenly distributed but cluster during the wet season from December to May. These patterns clearly reflect the general climatic setting of each of the study regions. The annual distribution of these failures is broadly constant with no particular trends yet evident. The recognition of these regional differences is necessary in order to build optimal regionally focused methods for moraine-dammed lakes hazard assessment in the future.", "metadata": {"source_file": "data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md", "document_title": "The causes and mechanisms of moraine-dammed lake failures", "section_path": "6. Conclusions", "section_headings": ["6. Conclusions"], "chunk_type": "text", "line_start": 143, "line_end": 144, "token_count_estimate": 547, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68dcf1a273c29bd5", "text": "Document: The formation and failure of natural dams\nSection: ABSTRACT\nType: text\n\nOf the numerous kinds of dams that form by natural processes, dams formed from landslides, glacial ice, and neoglacial moraines present the greatest threat to people and property. Landslide dams form in a wide range of physiographic settings. The most common types of mass movements that form landslide dams are rock and debris avalanches, rock and soil slumps and slides, and mud, debris, and earth flows. The most common initiation mechanisms for dam-forming landslides are excessive rainfall and snowmelt and earthquakes.\n\nLandslide dams can be classified into six categories based on their relation with the valley floor. Type I dams (11 percent of the 81 landslide dams around the world that we were able to classify) do not reach from one valley side to the other. Type II dams (44 percent) span the entire valley flood, occasionally depositing material high up on opposite valley sides. Type III dams (41 percent) move considerable distances both upstream and downstream from the landslide failure. Type IV dams (1 percent) are rare and involve the contemporaneous failure of material from both sides of a valley. Type V dams (1 percent) also are rare and are created when a single landslide sends multiple tongues of debris into a valley and forms two or more landslide dams in the same reach of river. Type VI dams (3 percent) involve one or more failure surfaces that extend under the stream or valley and emerge on the opposite valley side.\n\nMany landslide dams fail shortly after formation. In our sample of 73 documented landslide-dam failures, 27 percent of the landslide dams failed less than 1 day after formation, and about 50 percent failed within 10 days. Overtopping is by far the most common cause of failure. The timing of failure and the magnitude of the resulting floods are controlled by dam size and geometry, material characteristics of the blockage, rate of inflow to the impoundment, size and depth of the impoundment, bedrock control of flow, and engineering controls such as artificial spillways, diversions, tunnels, and planned breaching by blasting or conventional excavation.\n\nGlacial-ice dams can produce at least nine kinds of ice-dammed lakes. The most dangerous are lakes formed in main valleys dammed by tributary glaciers. Failure can occur by erosion of a drainage tunnel under or through the ice dam or by a channel over the ice dam. Cold polar-ice dams generally drain supraglacially or marginally by downmelting of an outlet channel. Warmer temperate-ice dams tend to fail by sudden englacial or subglacial breaching and drainage.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 16, "token_count_estimate": 658, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c50e913c10948734", "text": "Document: The formation and failure of natural dams\nSection: ABSTRACT\nType: text\n\nand depth of the impoundment , bedrock control of flow , and engineering controls such as artificial spillways , diversions , tunnels , and planned breaching by blasting or conventional excavation . Glacial - ice dams can produce at least nine kinds of ice - dammed lakes . The most dangerous are lakes formed in main valleys dammed by tributary glaciers . Failure can occur by erosion of a drainage tunnel under or through the ice dam or by a channel over the ice dam . Cold polar - ice dams generally drain supraglacially or marginally by downmelting of an outlet channel . Warmer temperate - ice dams tend to fail by sudden englacial or subglacial breaching and drainage .\n\nLate-neoglacial moraine-dammed lakes are located in steep mountain areas affected by the advances and retreats of valley glaciers in the last several centuries. These late-neoglacial dams pose hazards because (1) they are sufficiently young that vegetation has not stabilized their slopes, (2) many dam faces are steeper than the angle of repose, (3) these dams and lakes are immediately downslope from steep crevassed glaciers and near-vertical rock slopes, and (4) downstream from these dams are steep canyons with easily erodible materials that can be incorporated into the flow and increase flood peaks. The most common reported failure mechanism is overtopping and breaching by a wave or series of waves in the lake, generated by icefalls, rockfalls, or snow or rock avalanches. Melting of ice-cores or frozen ground and piping and seepage are other possible failure mechanisms.\n\nNatural dams may cause upstream flooding as the lake rises and downstream flooding as a result of failure of the dam. Although data are few, for the same potential energy at the dam site, downstream flood peaks from the failure of glacier-ice dams are less than those from landslide, moraine, and constructed earth-fill and rock-fill dam failures. Moraine-dam failures appear to produce some of the largest downstream flood peaks for potential energy at the dam site greater than  $10^{11}$  joules. Differences in flood peaks of natural-dam failures appear to be controlled by dam characteristics and failure mechanisms.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 4, "line_end": 16, "token_count_estimate": 571, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc15a8b4fe1e9750", "text": "Document: The formation and failure of natural dams\nSection: INTRODUCTION\nType: text\n\nThere are many ways in which natural lakes and their dams can form in nature. A general but useful classification of lake basins was proposed by Davis (1882), who categorized lake basins as constructive, destructive, and obstructive. This report is concerned with only one of Davis' classifications, that of obstructive basins. Obstructive barriers include landslide dams, glacier dams, moraine dams, volcanic dams, fluviatile dams, eolian dams, coastal dams, and organic dams (table 1).\n\nThis investigation of the hazards associated with natural dams indicates that, despite the great variety of natural dams, only three kinds pose a widespread threat to people and property: landslide dams, glacier dams, and alpine moraine dams.\n\nThere are many case studies of individual natural-dam failures, but an integrated view of the phenomenon as a whole does not exist. This paper attempts to use information from a large number of individual case studies to draw comprehensive conclusions about this important natural process. The data base used for this investigation consists of information on approximately 225 natural dams whose formation and (or) failure has been documented in the literature or is know to us from our own field investigations. This data base is believed to be sufficiently large and comprehensive that the conclusions in this report will not radically change even though it is recognized that there are some reported events not known to us and far more events that have never been recorded or reported.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "INTRODUCTION", "section_headings": ["INTRODUCTION"], "chunk_type": "text", "line_start": 18, "line_end": 24, "token_count_estimate": 362, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "715cf63715f1bde7", "text": "Document: The formation and failure of natural dams\nSection: INTRODUCTION > Table 1. -- Types of obstructive dams\nType: text\n\n[Most dangerous dams are underlined. This table is modified from Hutchinson (1957) and Schuster and Costa (1986a)]", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "INTRODUCTION > Table 1. -- Types of obstructive dams", "section_headings": ["INTRODUCTION", "Table 1. -- Types of obstructive dams"], "chunk_type": "text", "line_start": 26, "line_end": 28, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf80cec0f0dbd44f", "text": "Document: The formation and failure of natural dams\nSection: INTRODUCTION > Table 1. -- Types of obstructive dams\nType: table\nTable\n\n| Type of dam                     | Example                                                              | Type of dam                               | Example                                                          |\n|---------------------------------|----------------------------------------------------------------------|-------------------------------------------|------------------------------------------------------------------|\n| Volcanic dams:                  |                                                                      | Fluviatile dams:                          |                                                                  |\n| Volcanic peaks                  | Lake Nicaragua, Nicaragua (Hutchinson, 1957)                      | Tributary sediments                       | Lake Pepin, Minnesota-Wisconsin (Davis, 1882)                 |\n| Lava flows                      | Snag Lake, California (Finch, 1937)                               | Main channel sediments (lateral lakes) | Lake Tung-ting, People's Republic of China (Hutchinson, 1957) |\n| Pyroclastic flows               | Rio Magdalena Lake, Mexico (Silva and others, 1982)               | Alluvial fans                             | Lake Tulare, California (Hutchinson, 1957)                    |\n| Landslide dams:                 |                                                                      | Deltas                                    | Blue Lakes, California (Davis, 1933)                          |\n| Rock and debris avalanches      | Castle Lake, Washington (Meyer and others, 1986)                  | Levee deposits (oxbow lakes)              | Old River, Louisiana (Campti Quadrangle USGS 1:62,500, 1957)  |\n| Slumps and slides               | Earthquake Lake, Montana (Hadley, 1964)                           | Eolian dams:                              |                                                                  |\n| Mud, debris, and earth flows    | Lake San Cristobal, Colorado (Crandell and Varnes, 1961)          | Dunes                                     | Moses Lake, Washington (Russell, 1893)                        |\n| Liquifaction of sensitive clays | Yamaska River Lake, Quebec, Canada (Clark, 1947)                  | Coastal dams:                             |                                                                  |\n| Peatslides                      | Addergoole Bog Lake, Ireland (Ousley, 1788)                       | Bay-bars                                  | Freshwater Lagoon, Eureka, California (Cotton, 1941)          |\n| Scree                           | Goatswater, United Kingdom (Marr, 1916)                           | Organic dams:                             |                                                                  |\n| Glacial dams:                   |                                                                      | Logs and other vegetation                 | Lake Okeechobee, Florida (Hutchinson, 1957)                   |\n| Ice                             | Gapshan Lake (Shyok River), Pakistan (Mason, 1929)                | Beaver dams                               | Beaver Lake, Montana (Hutchinson, 1957)                       |\n| Moraine                         | Nostetuko Lake, British Columbia, Canada (Blown and Church, 1985) |                                           |                                                                  |\n| Ice and snow avalanche          | Rio Plomo, Argentina (King, 1934)                                 |                                           |                                                                  |", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "INTRODUCTION > Table 1. -- Types of obstructive dams", "section_headings": ["INTRODUCTION", "Table 1. -- Types of obstructive dams"], "chunk_type": "table", "table_caption": null, "columns": ["Type of dam", "Example", "Type of dam", "Example"], "table_row_start": 1, "table_row_end": 15, "line_start": 29, "line_end": 45, "token_count_estimate": 671, "basins": [], "subbasins": ["Shyok"], "countries": ["China"], "lake_ids": []}}
{"id": "a59ebfe24142aa4c", "text": "Document: The formation and failure of natural dams\nSection: INTRODUCTION > Table 1. -- Types of obstructive dams\nType: text\n\nNatural dams have many economic benefits, including hydropower generation (Anderson, 1948; Adams, 1981) and recreation (Jones and others, 1985), but also can constitute serious hazards. A great deal of information is available about the types and characteristics of constructed dams and the consequences of their failure, but similar information about natural dams is almost nonexistent, despite the fact that natural dams are far more numerous than constructed dams.\n\nNatural dams hold most of the records for size and flood magnitude following failure. The largest flood known to have occurred on the surface of the earth, the \"Spokane flood,\" which carved the channeled scablands in eastern Washington State between 16,000 and 12,000 years ago, was the result of failure of a natural ice dam near Missoula. Montana (Baker, 1973). Another extraordinary documented flood was that caused by the overflow of Pleistocene Lake Bonneville at Red Rock Pass near Preston, Idaho (Malde, 1968), about 14,000 years ago (Scott and others, 1980). The flood was caused by the overtopping and failure of a natural alluvial-fan dam (Gilbert, 1878; Malde, 1968). In 1911 an earthquake in the Soviet Union triggered a rock avalanche (the Usoy landslide) with a volume of 2.0- to 2.5-billion cubic meters, which dammed the Murgab River. This landslide dam is about 550 meters high (Berg, 1950), and the impounded lake (Lake Sarez) overflows the dam (Hutchinson, 1957; Gasiev, 1984). The Usoy landslide dam is nearly three times as high as the world's largest constructed dam, the Nurek rock-fill dam, also in the Soviet Union, which has a height of 300 meters.\n\nThe purposes of this report are to (1) document the methods by which the most dangerous types of natural dams form, (2) determine what constitutes the stability or instability of the dams, (3) compare some of the physical dimensions of the dams and lakes, (4) document the mechanisms by which these natural dams fail, and (5) compare some of the characteristics of the resulting downstream flooding.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "INTRODUCTION > Table 1. -- Types of obstructive dams", "section_headings": ["INTRODUCTION", "Table 1. -- Types of obstructive dams"], "chunk_type": "text", "line_start": 46, "line_end": 52, "token_count_estimate": 527, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "189345955930331f", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS\nType: text\n\nLandslide dams are remarkably diverse in their formation, characteristics, and longevity. One of the first recorded flooding catastrophes due to a landslide dam occurred in central Java in A.D 1006 (Holmes, 1965, p. 485-487). Many landslide dams have failed catastrophically, causing major downstream flooding and loss of life. At least 2,423 people died in the 1933 flood caused by failure of the large Deixi landslide dam on the Min River in central China (Li and others, 1986). A graphic account of the consequences of the failure of a landslide dam in the Indus River valley, India, is provided by Mason (1929).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS", "section_headings": ["LANDSLIDE DAMS"], "chunk_type": "text", "line_start": 54, "line_end": 56, "token_count_estimate": 181, "basins": ["Indus"], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "48f8ab6edbf069ba", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Geomorphic Settings of Landslide Dams\nType: text\n\nLandslide dams form most frequently where narrow steep valleys are bordered by high rugged mountains. This setting is common in geologically active areas where earthquakes, volcanoes, or glacially oversteepened slopes occur. Geologically active areas usually contain abundant landslide source materials, such as sheared and fractured or hydrothermally altered bedrock, and experience triggering mechanisms to initiate landslides. Steep narrow valleys require relatively small volumes of material to form dams; thus even small mass movements present a potential for forming landslide dams. Such dams are much less common in broad open valleys, but in areas where rivers have incised lacustrine or marine deposits, slides and slumps or quick-clay failures have formed landslide dams (Evans, 1984; Clark, 1947).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Geomorphic Settings of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Geomorphic Settings of Landslide Dams"], "chunk_type": "text", "line_start": 58, "line_end": 60, "token_count_estimate": 235, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "23a30de1f7984f45", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Types of Movements that Form Landslide Dams\nType: text\n\nStudy of 184 cases of landslide dams from the literature and the authors' experience has shown that a broad range of mass-movement types can cause landslide dams (fig. 1; table 2). However, most landslide dams are caused by avalanches, slumps and slides, and flows. Relatively few dams have been caused by slope failure in sensitive clays or rock and soil falls. The number of dams resulting from failure of sensitive clays is small because of their limited extent and common occurrence in areas of low relief. The number of dams resulting from falls is small because the volumes of material constituting failures of this type are small.\n\nIn general, the highest landslide dams form in steep-walled narrow valleys because there is little area for the landslide mass to spread out. Large-volume earth and rock slumps and slides and rock and debris avalanches are particularly likely to form high dams in narrow valleys because they occur on steep slopes and usually have high velocities that allow complete stream blockage before the material can be sluiced away.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Types of Movements that Form Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Types of Movements that Form Landslide Dams"], "chunk_type": "text", "line_start": 62, "line_end": 66, "token_count_estimate": 286, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46025c62852aed15", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Types of Movements that Form Landslide Dams\nType: figure\nFigure\n\nImage /page/9/Figure/0 description: A black and white vertical bar chart displays the number of landslides by type. The y-axis, labeled \"NUMBER OF LANDSLIDES,\" ranges from 0 to 50, with tick marks every 10 units. The x-axis lists six different types of landslides. The bars are arranged in descending order of frequency from left to right. The approximate values for each category are as follows: \"Undifferentiated landslides\" is approximately 49, \"Rock and debris avalanches\" is about 44, \"Rock and soil slumps and slides\" is around 43, \"Mud, debris, and earth flows\" is approximately 37, \"Liquefaction\" is about 9, and \"Falls\" is approximately 2.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Types of Movements that Form Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Types of Movements that Form Landslide Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 67, "line_end": 67, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c4557ad6d50fbcba", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Types of Movements that Form Landslide Dams\nType: figure\nFigure: Figure 1.--Graph showing distribution of landslide dams by type of landslide, based on 183 cases from the literature and the authors' experience. Classification based on Varnes (1978)\n\nFigure 1.--Graph showing distribution of landslide dams by type of landslide, based on 183 cases from the literature and the authors' experience. Classification based on Varnes (1978)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Types of Movements that Form Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Types of Movements that Form Landslide Dams"], "chunk_type": "figure", "figure_caption": "Figure 1.--Graph showing distribution of landslide dams by type of landslide, based on 183 cases from the literature and the authors' experience. Classification based on Varnes (1978)", "line_start": 69, "line_end": 69, "token_count_estimate": 122, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "560a1548367b810b", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Types of Movements that Form Landslide Dams\nType: text\n\nCommonly, large landslide dams are caused by complex landslides that start as slumps or slides and transform into rock or debris avalanches. An outstanding example was the 2.5-cubic kilometer rockslide/debris avalanche (the world's largest historic landslide) associated with the 1980 major eruption of Mount St. Helens, Washington. This high-velocity landslide originated as a rockslide on the side of the volcano, then transformed into a debris avalanche and traveled 24 kilometers down the North Fork Toutle River valley, impounding or enlarging five large lakes, only three of which remain (Meyer and others, 1986).\n\nIf the basal failure zone of a landslide extends beneath the valley floor, upward movement of the streambed itself can alter gradients and form shallow lakes. In this situation, downstream flooding is not great because the lakes are small, outlet gradients are low, and the streambed material is not easily erodible.\n\nMud, debris, and earth flows form a significant percentage of the landslide dams reviewed. Most of these dams have been cause by relatively high-velocity debris flows issuing from tributary valleys to briefly block rivers in main valleys. Generally dams formed in this manner are not high, nor are they particularly resistant to erosion (Li and others, 1986). Thus they commonly overtop soon and breach rapidly. Other kinds of flows are much slower or may form long-lived dams. An example is the 6.5-kilometer Slumgullion earthflow that dammed the Lake Fork of the Gunnison River in Colorado about 700 years ago, impounding the 3-kilometer Lake San Cristobal (Crandell and Varnes, 1961).\n\n[Dashes indicate that authors were unable to obtain significant data]", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Types of Movements that Form Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Types of Movements that Form Landslide Dams"], "chunk_type": "text", "line_start": 70, "line_end": 80, "token_count_estimate": 461, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09c00e61929cea01", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Types of Movements that Form Landslide Dams\nType: table\nTable: Table 2.--Well-documented examples of landslide dams formed by specific classes of landslides\n\n| Landslide class and name                                                   | Year          | Dammed river                 | State/ country                            | Landslide volume (m3) | Blockage dimensions              |                         |                            | Lake dimensions         |                             | Dam failed?         | References                                                |\n|-------------------------------------------------------------------------------|---------------|---------------------------------|----------------------------------------------|-----------------------------|----------------------------------|-------------------------|----------------------------|-------------------------|-----------------------------|------------------------|-----------------------------------------------------------|\n|                                                                               |               |                                 |                                              |                             | Height (m)                    | Length (m)           | Width (m)               | Length (km)          | Volume (m3)              |                        |                                                           |\n| <b>Rock and debris avalanches</b>                                             |               |                                 |                                              |                             |                                  |                         |                            |                         |                             |                        |                                                           |\n| Usoy landslide                                                                | 1911          | Murgab River                 | Tadzhik- istan, U.S.S.R.               | 2.0-2.5x109                 | 301 (Bolt) 550 (Gasiev) | 1,000                   | 1,000                      | 53                      | --                          | Partial Failure     | Gasiev, 1984                                              |\n| Tanggudong debris slide/avalanche                                          | 1967          | Yalong River                 | Sichuan, People's Republic of China | 68x106                      | 175                              | 650                     | 3,000                      | 53                      | 680x106                     | Yes                    | Li and others, 1986                                    |\n| Mayumarca rock slide/debris avalanche                                   | 1974          | Mantaro River                | Peru                                         | 1.6x109                     | 170                              | 1,000                   | 3,800                      | 31                      | 670x106                     | Yes                    | Hutchinson and Kojan, 1975; Lee and Duncan, 1975 |\n| Mount St. Helens rock slide/debris avalanche                            | 1980          | North Fork Toutle River   | State of Washing- ton, U.S.A        | 2.8x109                     | avg.=45                          | 800 (Spirit Lake) | 24x103 (Spirit Lake) | 5.5 (Spirit Lake) | 295x106 (Spirit Lake) | No (Spirit Lake) | Meyer and others, 1986                                 |\n| <b>Rock and soil slumps and slides</b>                                        |               |                                 |                                              |                             |                                  |                         |                            |                         |                             |                        |                                                           |\n| Deixi landslide                                                               | 1933          | Min River                       | Sichuan, People's Republic of China | 150x106                     | 255                              | 400                     | 1,300                      | 17                      | 400x106                     | Yes                    | Chang, 1934; Li and others, 1986                    |\n| Lower Gros Ventre landslide                                                | 1925          | Gros Ventre River         | Wyoming, U.S.A.                           | 38x106                      | 70                               | 900                     | ~2,400                     | 6.5                     | 80x106                      | Yes                    | Emerson, 1925; Alden, 1928                             |\n| Tsao-Ling rock slide                                                       | 1941- 42   | Chin-Shui- Chi River         | Taiwan                                       | 250x106 (two slides)     | 217                              | 1,300                   | 2,000                      | --                      | 157x106                     | Yes                    | Chang, 1984                                               |\n| Cerro Condor- Sencca rock slide                                            | 1945          | Mantaro River                | Peru                                         | 5.6x106                     | 100                              | 250                     | 580                        | 21                      | 300x106                     | Yes                    | Snow, 1964                                                |\n| Madison Canyon rock slide                                                  | 1959          | Madison River                | Montana, U.S.A.                           | 21x106                      | 60-70                            | 500                     | 1,600                      | 10                      | --                          | No                     | Hadley, 1964; Knight and Bennett, 1960              |\n| Thistle earth slide                                                        | 1983          | Spanish Fork River           | Utah, U.S.A.                              | 22x106                      | ~60                              | 200                     | 600                        | 5                       | 78x106                      | No                     | Kaliser and Fleming, 1986                              |\n| Landslide class and name                                                   | Year          | Dammed river                 | State/ country                            | Landslide volume (m3) | Blockage dimensions              |                         |                            | Lake dimensions         |                             | Dam failed?         | References                                                |\n|                                                                               |               |                                 |                                              |                             | Height (m)                    | Length (m)           | Width (m)               | Length (km)          | Volume (m3)              |                        |                                                           |\n| <span style=\"text-decoration: underline;\">Mud, debris, and earth flows</span> |               |                                 |                                              |                             |                                  |                         |                            |                         |                             |                        |                                                           |\n| Slumgullion earth flow                                                     | 1200- 1300 | Lake Fork, Gunnison River | Colorado, U.S.A.                          | 50-100x106                  | 40 (estimate)                 | 500                     | 1,700                      | 3                       | --                          | No                     | Crandell and Varnes, 1961                              |\n| Gupis debris flow                                                             | 1980          | Ghizar River                 | Pakistan                                     | --                          | 30                               | 200                     | 300                        | 5                       | --                          | No                     | Nash and others, 1985                                  |\n| Polallie Creek debris flow                                                 | 1980          | East Fork, Hood River        | Oregon, U.S.A.                            | 70-100x103                  | 11                               | --                      | 230                        | --                      | 105x103                     | Yes                    | Gallino and Pierson, 198?                              |\n| <span style=\"text-decoration: underline;\">Sensitive-clay failures:</span>     |               |                                 |                                              |                             |                                  |                         |                            |                         |                             |                        |                                                           |\n|                                                                               | 1898          | Riviere Blance               | Quebec, Canada                            | 2.6x106                     | 8                                | 400                     | 3,200                      | --                      | --                          | Yes                    | Dawson, 1898                                              |\n|                                                                               | 1945          | Yamaska River                | Quebec, Canada                            | 117x103                     | 3-4                              | 75                      | 425                        | --                      | --                          | Yes                    | Clark, 1947                                               |", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Types of Movements that Form Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Types of Movements that Form Landslide Dams"], "chunk_type": "table", "table_caption": "Table 2.--Well-documented examples of landslide dams formed by specific classes of landslides", "columns": ["Landslide class and name", "Year", "Dammed river", "State/ country", "Landslide volume (m3)", "Blockage dimensions", "", "", "Lake dimensions", "", "Dam failed?", "References"], "table_row_start": 1, "table_row_end": 22, "line_start": 81, "line_end": 104, "token_count_estimate": 1521, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "ebc1af547502c0c6", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Types of Movements that Form Landslide Dams\nType: text\n\nTable 2.--Well-documented examples of landslide dams formed by specific classes of landslides -- Continued\n\nTwo sensitive-clay failures that caused landslide dams to form both occurred in shallow broad valleys in sensitive marine clays (table 2). The dams were low broad blockages that failed by overtopping within a few hours or days.\n\nThere are only two known reports of landslide dams formed by rock or earth falls. One was the 1943 \"cliff fall\" of the bank of the Grande Riviere du Chene in marine clay in Quebec (Clark, 1947); the other was a rock fall that impounded Lake Yashinkul in the central U.S.S.R in 1966 (Pushkarenko, 1982).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Types of Movements that Form Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Types of Movements that Form Landslide Dams"], "chunk_type": "text", "line_start": 105, "line_end": 111, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "beb42f7b56771f03", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Causes of Dam-forming Landslides\nType: text\n\nThe two most important processes in initiating dam-forming landslides are excessive precipitation (rainfall and snowmelt) and earthquakes (fig. 2). These account for 90 percent of the landslide dams investigated. Volcanic eruptions constitute the third most significant dam-forming process (about 8 percent). Other mechanisms, such as devegetation and stream undercutting and entrenchment, account for the remaining 2 percent.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Causes of Dam-forming Landslides", "section_headings": ["LANDSLIDE DAMS", "Causes of Dam-forming Landslides"], "chunk_type": "text", "line_start": 113, "line_end": 115, "token_count_estimate": 132, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4c53ef4f72ae1b0", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Causes of Dam-forming Landslides\nType: figure\nFigure\n\nImage /page/12/Figure/0 description: A black and white vertical bar chart showing the number of events for different causes. The y-axis is labeled \"NUMBER OF EVENTS\" and ranges from 0 to 70, with tick marks every 10 units. The x-axis categories are labeled on the bars. The first bar, labeled \"Rainstorms and snowmelt,\" shows approximately 65 events. The second bar, \"Earthquakes,\" shows 50 events. The third bar, \"Volcanic eruptions,\" shows 10 events. The fourth and final bar, \"Others,\" shows approximately 3 events.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Causes of Dam-forming Landslides", "section_headings": ["LANDSLIDE DAMS", "Causes of Dam-forming Landslides"], "chunk_type": "figure", "figure_caption": null, "line_start": 116, "line_end": 116, "token_count_estimate": 179, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fcfac5252d881ebd", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Causes of Dam-forming Landslides\nType: figure\nFigure: Figure 2.--Graph showing causes of landslides that have formed dams, based on 128 cases from the literature and the authors' experience.\n\nFigure 2.--Graph showing causes of landslides that have formed dams, based on 128 cases from the literature and the authors' experience.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Causes of Dam-forming Landslides", "section_headings": ["LANDSLIDE DAMS", "Causes of Dam-forming Landslides"], "chunk_type": "figure", "figure_caption": "Figure 2.--Graph showing causes of landslides that have formed dams, based on 128 cases from the literature and the authors' experience.", "line_start": 118, "line_end": 118, "token_count_estimate": 102, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3cc3bab2439f3a4c", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Causes of Dam-forming Landslides\nType: text\n\nNumerous landslide dams can be formed by a single rainstorm or earthquake. In 1889 in the Totsu River basin, Japan, heavy rainfall produced over 1,100 landslides in the upper 1,100 square kilometers of the watershed. About 250 of these landslides were more than 90 meters wide and 90 meters long and produced 53 landslide dams (Swanson and others, 1986). The 1929 Buller earthquake (magnitude 7.6) in the northwestern part of South Island, New Zealand, produced landslides that formed at least 11 landslide-dammed lakes (Adams, 1981). In 1783, an earthquake in Calabria, Italy, triggered mass movements that formed 215 landslide-dammed lakes (Cotecchia, 1978).\n\nAn unusual series of events forming and destroying a landslide dam has been documented for the Tsao-Ling landslide in central Taiwan (Chang, 1984). This case demonstrates just how complex natural-dam processes can be. In 1862, a major earthquake triggered a landslide that dammed the Chin-Shui-Chi River. In 1898 the natural dam failed for unknown reasons. In December 1941 a major earth quake formed another landslide dam 140 meters high at the same location. In August 1942 heavy rainfall caused reactivation of the landslide, and the natural dam increased in height from 140 to 217 meters. In May 1951 several days of intense rainfall led to the overtopping and failure of the natural dam. In the subsequent flood 154 people were killed and 564 homes damaged. On August 15, 1979, heavy rainfall again activated the landslide, which dammed the river with a natural barrier 90 meters high. Heavy precipitation continued, and 9 days later the landslide dam was overtopped and failed, causing severe downstream flooding.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Causes of Dam-forming Landslides", "section_headings": ["LANDSLIDE DAMS", "Causes of Dam-forming Landslides"], "chunk_type": "text", "line_start": 119, "line_end": 123, "token_count_estimate": 455, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b195f8288941e6d", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Classification of Landslide Dams\nType: text\n\nLandslide dams can be classified geomorphically with respect to their relations with the valley floor (Swanson and others, 1986; fig. 3). Type I dams are small with respect to the width of the valley floor and do not reach from one valley side to the other. Type II dams are larger and span the entire valley floor, occasionally depositing material high up on opposite valley sides. Type III dams fill the valley from side to side and move considerable distances upvalley and downvalley from the failure. These dams typically involve the largest volume of landslide material. Type IV landslide dams form by the contemporaneous failure of material from both sides of a valley. landslides can adjoin head-to-head in the middle of the valley, or they can juxtapose one another. Type V landslide dams form when the same landslide has multiple lobes of debris that extend across a valley floor and form two or more landslide dams in the same reach of river. landslide dams involve one or more failure surfaces that extend under the stream or river valley and emerge on the opposite valley side from These dams typically involve slow basal sliding and the landslide. slumping and form lakes by raising the elevation of the streambed, changing the local gradient of the stream.\n\nOf the 184 landslide dams classified from around the world, the most common are Types II (44 percent) and III (41 percent). The other types are much more infrequent (Type I = 11 percent; Type IV = 1percent; and Type VI = 3 percent). Type I landslide-dam lakes commonly are small, low, and usually not hazardous. Type II landslide-dam lakes are larger and much more dangerous. Type III dams not only can form large hazardous lakes behind the obstruction, but also can block tributaries to the main valley, creating additional potentially dangerous lakes. This is the case at Mount St. Helens, Washington, as a result of the 1980 debris avalanche that dammed tributaries to the North Fork Toutle River. Type IV and V landslide-dam lakes can be hazardous if the valleys are narrow and the volumes of landslide materials are large enough to form high dams. Only one example of a type IV landslide dam is known, the Yinping landslide blockage of the Min River, Sichuan Province, China, which forms Da Lake (Li and others, 1986; fig. 4). Only one example of a Type V landslide dam is known, the Slide Lake rockfall-avalanche in Glacier National Park, Montana (Butler and others, 1986). Only a few examples of Type VI are known, from Japan (Swanson and others, 1986) and one from Colorado (Muddy Creek landslide). This type of landslide dam typically poses less threat of downstream flooding than the other types because there may never be a complete blockage. The stream may continue to flow over the dam debris, so that the likelihood of abrupt overtopping and rapid incision of the dam is unlikely. Water storage usually is small and the stream gradient not relatively steep.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Classification of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Classification of Landslide Dams"], "chunk_type": "text", "line_start": 125, "line_end": 129, "token_count_estimate": 759, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "43c9bbee4a7cf1d1", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Classification of Landslide Dams\nType: figure\nFigure\n\nImage /page/14/Figure/0 description: A black and white diagram showing a classification of landslides into six types, labeled TYPE I through TYPE VI. Each type is illustrated with a cross-sectional view of geological layers.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Classification of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Classification of Landslide Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 130, "line_end": 130, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6b62b3bc2f12ab05", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Classification of Landslide Dams\nType: text\n\n- TYPE I shows a narrow, vertical column of material that has fallen or slumped through a central wavy layer. The associated text is \"Falls\" and \"Slumps\".\n- TYPE II depicts a wider vertical column that has collapsed. The text reads \"Avalanches\" and \"Slumps/Slides\".\n- TYPE III illustrates a horizontal movement of material, like a flow or avalanche, from the right side. The text is \"Flows\" and \"Avalanches\".\n- TYPE IV shows a vertical column that has dropped and been sheared along the central wavy layer. The text lists \"Falls\", \"Slumps/Slides\", and \"Avalanches\".\n- TYPE V shows a mass of material that has flowed onto the surface from above. The text is \"Falls\" and \"Avalanches\".\n- TYPE VI shows a large void or cavern above the central layer, with a \"Failure surface\" indicated below it, suggesting a collapse. The text is \"Slumps/Slides\".", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Classification of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Classification of Landslide Dams"], "chunk_type": "text", "line_start": 131, "line_end": 138, "token_count_estimate": 278, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa6110472e0e5dbb", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Classification of Landslide Dams\nType: figure\nFigure: Figure 3.--Diagrams showing classification of landslide dams. Mass-movement processes most likely to form particular landslide dams are listed in lower left corner of each category. (Modified from Swanson and others, 1986)\n\nFigure 3.--Diagrams showing classification of landslide dams. Mass-movement processes most likely to form particular landslide dams are listed in lower left corner of each category. (Modified from Swanson and others, 1986)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Classification of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Classification of Landslide Dams"], "chunk_type": "figure", "figure_caption": "Figure 3.--Diagrams showing classification of landslide dams. Mass-movement processes most likely to form particular landslide dams are listed in lower left corner of each category. (Modified from Swanson and others, 1986)", "line_start": 139, "line_end": 139, "token_count_estimate": 142, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd5e9b0e6dc26cd4", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Classification of Landslide Dams\nType: figure\nFigure\n\nImage /page/15/Picture/0 description: A black and white photograph captures a dramatic landscape of a river flowing through a deep, steep-sided gorge. The mountains are rugged and imposing. In the foreground, a lighter-colored, turbulent river flows into a larger, darker, and calmer body of water that winds its way through the canyon into the hazy distance. A road is visible carved into the mountainside on the right. A caption at the bottom of the image reads, \"Figure 4. Photograph showing a section along the Min River, Szechwan Province, Republic of China.\"", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Classification of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Classification of Landslide Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 141, "line_end": 141, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "682f5f81d77afadc", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Classification of Landslide Dams\nType: figure\nFigure: Figure 4.--Photograph view upstream along the Min River, Sichuan Province, Peoples Republic of China, showing remains of the 1933 earthquake-induced Yinping landslide dam. Xico Lake (foreground) was impounded by another landslide dam caused by the same earthquake.\n\nFigure 4.--Photograph view upstream along the Min River, Sichuan Province, Peoples Republic of China, showing remains of the 1933 earthquake-induced Yinping landslide dam. Xico Lake (foreground) was impounded by another landslide dam caused by the same earthquake.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Classification of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Classification of Landslide Dams"], "chunk_type": "figure", "figure_caption": "Figure 4.--Photograph view upstream along the Min River, Sichuan Province, Peoples Republic of China, showing remains of the 1933 earthquake-induced Yinping landslide dam. Xico Lake (foreground) was impounded by another landslide dam caused by the same earthquake.", "line_start": 143, "line_end": 143, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "33a251b38396d893", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Modes of Failure of Landslide Dams\nType: text\n\nA landslide dam in its natural state differs from a constructed embankment dam in that it is made up of a heterogeneous mass of poorly consolidated earth material and has no channelized spillway or other protected outlet. Because of the lack of a protected spillway, landslide dams commonly fail by overtopping, followed by breaching from erosion by the overflowing stream (fig. 5). In most documented cases, the breach has resulted from the fluvial erosion of the landslide material by headcutting originating at the toe of the dam and progressively moving upstream to the lake. When the headcut reaches the lake, breaching occurs (Lee and Duncan, 1975). The breach commonly does not erode down to the original river level. In this situation, smaller lakes can remain after dam failure. In some situations the landslide dam will not fail during first filling and overtopping. Subsequent failure can occur by surface erosion accompanying unusually large runoff periods. This was the case in the failure of the landslide dams at Gros Ventre, Wyoming, and Lake Elizabeth, Australia.\n\nBecause landslide dams have not undergone systematic compaction, they may be porous, and seepage through the dams potentially could lead to failure by internal erosion (piping). Seeps have been noted on the downstream faces of many landslide dams. Examples are the 1925 Lower Gros Ventre landslide in northwestern Wyoming, which failed in 1927 from overtopping (Alden, 1928), and the 1945 Cerro Condor-Sencca landslide dam in Peru, which failed in 1945, probably because of \"violent\" seepage and piping (Snow, 1964).\n\nHowever, only two cases where a landslide dam actually has failed in this manner are known: the 1945 Cerro Condor-Sencca landslide dam and the 1966 breach of the landslide dam that impounded Lake Yaskinkul on the Isfayramsay River in the south-central U.S.S.R (Glazyrin and Reyzvikh, 1968). In these failures, piping and undermining caused collapse of the dam, followed by overtopping and breaching.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Modes of Failure of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Modes of Failure of Landslide Dams"], "chunk_type": "text", "line_start": 146, "line_end": 152, "token_count_estimate": 545, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8f45edc28918c2d", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Modes of Failure of Landslide Dams\nType: figure\nFigure\n\nImage /page/16/Figure/1 description: A vertical bar chart showing the number of failures for different causes. The y-axis is labeled 'NUMBER OF FAILURES' and ranges from 0 to 60. The x-axis displays four categories. The bar for 'Overtopping' shows approximately 51 failures. The bar for 'Piping' shows approximately 2 failures. The bar for 'Slope failure' also shows approximately 2 failures. The last bar, labeled 'Physically controlled (did not fail)', shows a count of 15.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Modes of Failure of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Modes of Failure of Landslide Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 153, "line_end": 153, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ab3a2b376d4242c", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Modes of Failure of Landslide Dams\nType: figure\nFigure: Figure 5.--Graph showing modes of failure of landslide dams, based on 55 failures from the literature and the authors' experience.\n\nFigure 5.--Graph showing modes of failure of landslide dams, based on 55 failures from the literature and the authors' experience.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Modes of Failure of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Modes of Failure of Landslide Dams"], "chunk_type": "figure", "figure_caption": "Figure 5.--Graph showing modes of failure of landslide dams, based on 55 failures from the literature and the authors' experience.", "line_start": 155, "line_end": 155, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "92be1a590a1c2581", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Modes of Failure of Landslide Dams\nType: text\n\nA landslide dam with steep upstream or downstream faces is susceptible to slope failure. If the dam has a narrow cross section or if the slope failure is progressive, the crest may fail, leading to overtopping and breaching. However, nearly all upstream and downstream faces of landslide dams are at the angle of shallower, and the dams are much wider than failures are rare. A special type of slope failure involves lateral erosion of the dam by a stream or river (for example, Jackson Lake, Mount St. Helens, Washington, U.S. Geological Survey, Vancouver, Washington, unpublished data, 1985; Hole-in-the-Wall Gulch, Oregon, J. M. Geist, U.S. Forest Service, oral commun., 1986). One well-documented example exists in which failure of the downstream slope of a landslide dam may have contributed to overall failure: the 1945 Cerro Condor-Sencca blockage of the Mantaro River, Peru (Snow, 1964).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Modes of Failure of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Modes of Failure of Landslide Dams"], "chunk_type": "text", "line_start": 156, "line_end": 158, "token_count_estimate": 268, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "920805628ce64fc6", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Longevity of Landslide Dams\nType: text\n\nLandslide-dammed lakes may last for several minutes or several thousand years, depending on many factors, including volume, texture, and sorting of the blockage material; rates of seepage through the blockage; and rates of sediment and water flow into the newly formed lake. Rapid assessment of the upstream and downstream flood potential after formation of a landslide dam is essential. Seventy-three examples of landslide-dam failures in which the time to failure is known are compiled in figure 6 and indicate how dangerous landslide dams can be. Twenty-seven percent of the dams failed within 1 day of formation; 41 percent failed within 1 week; about 50 percent failed within 10 days; 80 percent failed within 6 months, and 85 percent failed within 1 year of formation. It is important to note, however, that these percentages pertain only to landslide dams that have failed. Numerous other landslide dams have formed and not failed.\n\nThe three factors that seem to be most relevant to the longevity of a landslide dam are (1) volume and rate of inflow to the impoundment, (2) size and shape of the dam, and (3) geotechnical characteristics of the dam. In many cases the amount of flow in a stream and thus the rate at which a natural lake will fill is directly proportional to the size of the upstream drainage area. The duration of a landslide dam is apt to be short whenever a small landslide blocks a stream with a large drainage area (Swanson and others, 1985). Unless seepage through the dam equals the inflow, the dam can fill to overtopping. Rapid inflow behind a low, small landslide dam means that the new natural lake probably will fill quickly to overflowing, which may lead to failure of the landslide dam.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Longevity of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Longevity of Landslide Dams"], "chunk_type": "text", "line_start": 160, "line_end": 164, "token_count_estimate": 441, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36ce33746b0210cf", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Longevity of Landslide Dams\nType: figure\nFigure\n\nImage /page/17/Figure/3 description: A line graph showing the cumulative percentage of landslide dams that have failed over time. The y-axis is labeled \"PERCENTAGE OF LANDSLIDE DAMS WHICH HAVE FAILED, BELOW INDICATED AGE\" and ranges from 0 to 100. The x-axis is labeled \"AGE OF DAM AT TIME OF FAILURE (days)\" and ranges from 0 to 400. The graph shows a single curve that starts near the origin, rises steeply, and then flattens out. Several data points are annotated along the curve: 27 percent lasted ≤ 1 day, 41 percent lasted ≤ 1 week, Half failed within 10 days (at 50 percent), 56 percent lasted ≤ 1 month, 80 percent lasted ≤ 6 months, and 85 percent lasted ≤ 1 year.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Longevity of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Longevity of Landslide Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 165, "line_end": 165, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35c259647d7e0947", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Longevity of Landslide Dams\nType: figure\nFigure: Figure 6.--Graph showing length of time before failure of landslide dams, based on 73 cases from the literature and the authors' experience.\n\nFigure 6.--Graph showing length of time before failure of landslide dams, based on 73 cases from the literature and the authors' experience.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Longevity of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Longevity of Landslide Dams"], "chunk_type": "figure", "figure_caption": "Figure 6.--Graph showing length of time before failure of landslide dams, based on 73 cases from the literature and the authors' experience.", "line_start": 167, "line_end": 167, "token_count_estimate": 101, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d7f86b59a647149", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Longevity of Landslide Dams\nType: text\n\nLandslide dams of a predominantly soft, low-density, fine-grained, or easily liquified sediment are hazardous. Landslide dams composed of these materials can be expected to have relatively low bulk densities and resistances to erosion. Failure following overtopping is common. Peak discharge from the overtopping failure of a landslide dam is dependent on cohesive strength and friction angle of the landslide material (Fread, 1985). If the dam materials are mostly saturated, shear strength may be low and the dam may not be able to withstand the increasing hydrostatic pressure resulting from the impoundment, or the dam may erode rapidly when overtopped.\n\nSome landslide dams are more resistant to all types of failure mechanisms than others. The most important characteristic in preventing failure is resistance to erosion, either at the surface of the dam from surface-water runoff or inside the dam from piping and seepage. As might be expected, landslide dams consisting of large or cohesive particles resist failure better than dams containing large percentages of soil or soft rock. Landslide dams typically are much wider than constructed earth-fill and rock-fill dams and involve larger volumes of material. The shear bulk of many such blockages provides some degree of protection against failure by any mechanism.\n\nSorting of landslide sediment also is an important factor. Natural materials with D15/D85 ratio greater than 5 are susceptible to internal erosion by piping (Sherard, 1979). If the material forming the landslide dam is permeable, easily eroded sediment, such as sand with a small proportion of silt and clay, rising water levels behind the dam may force water through the permeable beds and lead to piping and erosion that could cause the dam to fail.\n\nSlope stability of the upstream and downstream faces of a dam is inversely related to the steepness of these faces. Obviously, landslide dams with relatively gentle surface slopes are less susceptible to slope failure than those with steep slopes.\n\nIn some cases lakes have not overtopped their dams because lake inflow is less than losses due to seepage, evaporation, or withdrawals for irrigation. An example is Bitang Lake in Gansu Province, China, which has stabilized at a level considerably below the crest of the landslide dam that impounded it in 1961 (Li and others, 1986).\n\nIn a few cases landslide-dammed lakes have formed natural spillways across adjacent bedrock abutments which prevent overtopping and possible breaching of the dams. This occurs when the surface of the toe of the landslide dam is higher than the surface of the adjacent bedrock abutment. A well-known example of a natural bedrock spillway for a landslide-dammed lake is the outlet across the left abutment of the Slumgullion earthflow in southwestern Colorado (Schuster, 1985). This natural spillway has existed for 700 years.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Longevity of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Longevity of Landslide Dams"], "chunk_type": "text", "line_start": 168, "line_end": 180, "token_count_estimate": 725, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "c23b6a05b494227f", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams\nType: text\n\nConstruction of control measures (most commonly spillways) has been attempted in recent years on many major landslide dams as soon as possible after formation. However in some cases overtopping has occurred before satisfactory control measures could be constructed. This happening is particularly common in outlying areas of mountainous topography where transporting heavy construction equipment to the site is difficult.\n\nThe most simple and most common method for stabilizing landslide dams has been construction of channelized spillways, either across adjacent bedrock abutments or directly over the landslide dams. A well-known example of a successful spillway across a landslide dam is the spillway constructed in 1959 by the U.S. Army Corps of Engineers on the Madison Canyon landslide, Montana (Harrison, 1974; fig. 7).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Physical Measures to Improve the Stability of Landslide Dams"], "chunk_type": "text", "line_start": 182, "line_end": 186, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8dbb8780eaf0eba3", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams\nType: figure\nFigure\n\nImage /page/19/Picture/1 description: A black and white, high-angle photograph shows a massive landslide in a mountain valley. The landslide originates from the forested mountain on the left and has spread across the valley, damming a river to form a lake in the background. The mountain on the right is in deep shadow. The landslide area is a wide expanse of bare earth and debris.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Physical Measures to Improve the Stability of Landslide Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 187, "line_end": 187, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8683e2a404bb9192", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams\nType: figure\nFigure: Figure 7.--Photograph of the landslide dam formed by the 1959 Madison Canyon landslide, Montana, and Earthquake Lake, which it impounds. (Photograph by J. R. Stacy, U.S. Geological Survey)\n\nFigure 7.--Photograph of the landslide dam formed by the 1959 Madison Canyon landslide, Montana, and Earthquake Lake, which it impounds. (Photograph by J. R. Stacy, U.S. Geological Survey)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Physical Measures to Improve the Stability of Landslide Dams"], "chunk_type": "figure", "figure_caption": "Figure 7.--Photograph of the landslide dam formed by the 1959 Madison Canyon landslide, Montana, and Earthquake Lake, which it impounds. (Photograph by J. R. Stacy, U.S. Geological Survey)", "line_start": 189, "line_end": 189, "token_count_estimate": 155, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a8f0ded7259f19c", "text": "Document: The formation and failure of natural dams\nSection: LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams\nType: text\n\nSpillways excavated across landslide dams are not always successful in preventing dam failure and subsequent flooding because they sometimes are eroded rapidly by outflow waters. Such was the case in the 1976 landslide damming of the Rio Quemaya in Guatemala, where highway workers trenched the landslide dam to drain the lake. It drained too rapidly and caused a flood that swept away several people (Harp and others, 1981).\n\nIn a few cases, large-scale blasting has been used to excavate new river channels through landslide dams. This technique was used in 1981 to open a channel through the Zhouqu landslide dam on the Bailong River in Gansu Province, China. Other methods of stabilizing lake levels behind landslide dams and preventing overtopping include pipe and tunnel outlets and diversions. Both a pipe and a tunnel have been used to control discharge from Spirit Lake, Mount St. Helens, Washington (Sager and Chambers, 1986), and at Thistle, Utah (Kaliser and Fleming, 1986).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "LANDSLIDE DAMS > Physical Measures to Improve the Stability of Landslide Dams", "section_headings": ["LANDSLIDE DAMS", "Physical Measures to Improve the Stability of Landslide Dams"], "chunk_type": "text", "line_start": 190, "line_end": 194, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "8d13d92ee02804f9", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS\nType: text\n\nGlacier dams are dams that impound water in, on, beneath, or behind masses of glacial ice. These dams can occur in any area covered by continental or valley glaciers. Other related natural dams not discussed include icefalls (Ballantyne and McCann, 1980), snow and ice avalanches (King, 1934), and snowbanks (Church, 1972).\n\nJokulhlaup (\"glacier burst\") is the Icelandic term for a flood caused by the sudden and usually catastrophic release of water impounded within or behind glacier ice (Thorarinsson, 1953). Some of the largest floods known to have occurred in the history of the Earth were the result of the failure of ice dams during the waning stages of the Pleistocene. The failure of ice dams has caused great loss of life and property damage in many places (table 3), including Iceland (Thorarinsson, 1939, 1953, 1957); northern India (Hewitt, 1982); Pakistan (Nash and others, 1985); Peru (Lliboutry and others, 1977); Norway (Aitkenhead, 1960); Alaska (Post and Mayo, 1971); Washington and Oregon (Richardson, 1968); Switzerland, Austria, France, and Italy (Eisbacher and Clague, 1984); and Canada (Clarke, 1982; Young, 1980).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS", "section_headings": ["GLACIER DAMS"], "chunk_type": "text", "line_start": 196, "line_end": 200, "token_count_estimate": 332, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8f16270555808bac", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Geomorphic Settings of Ice Dams\nType: text\n\nIce dams can form in any area covered by or adjacent to continental or alpine glaciers (fig. 8). Glacial ice may obstruct drainage and form lakes in numerous ways (table 4; fig. 9).\n\nOne of the most hazardous kinds of ice-dammed lakes is category G in table 4, lakes formed in main valleys dammed by tributary-valley glaciers. These lakes can be large, and the ice dam can be relatively small. These dams usually form as a result of glacial surges, in which the velocity of a tributary glacier temporarily can increase 10- to 100-fold (Meier and Post, 1969). In the upper Indus River valley of Pakistan at least 18 tributary glaciers have dammed major valleys (Hewitt, 1982). The Chong Khumdan glacier on the Shyok River, a large tributary of the Indus River, is a classic example of a glacier projecting into and blocking a major valley. Numerous disastrous floods have occurred from the temporary blocking and failure of the ice dams in the valley of the Shyok River; the most notable was the flood of 1929 (Mason, 1929; Gunn, 1930).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Geomorphic Settings of Ice Dams", "section_headings": ["GLACIER DAMS", "Geomorphic Settings of Ice Dams"], "chunk_type": "text", "line_start": 202, "line_end": 206, "token_count_estimate": 312, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": []}}
{"id": "fbc02548c2b032d4", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: text\n\nIce-dam failures are complicated phenomena, involving many sets of independent factors. Glacier dams often fail periodically, with return periods of 1 to 10 years. About 95 percent of the more than 50 jokulhlaups in the Alps occurred from June through September, with maxima in June and August (Tufuell, 1984). The failure of glacier dams can occur by erosion of a drainage channel under, through, or over the ice dam. Many ice dams fail by rapid drainage through englacial or subglacial tunnels (Gilbert, 1971). Such tunnels can be tens of kilometers long (Nye, 1976), and the outlet location can change by hundreds of kilometers from one failure to another (Sturm and Benson, 1985). The ice comprising these dams behaves plastically, deforming and flowing to reestablish a blockage of drainage by closing englacial or subglacial tunnels or by squeezing breaches shut. The lake then can reform by collecting runoff from the drainage area upstream, from surface runoff from the glacier, and from subglacial flow into the reservoir.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "text", "line_start": 208, "line_end": 212, "token_count_estimate": 303, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b31d3ae39fb684dc", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: table\nTable: Table 3.--Well-documented examples of glacial-ice dams that have failed, producing jokulhlaups\n\n| Lake name                     | Location                       | Year failed                 | Dam height (m) | Lake volume (m3) | Flood peak (m3/s) | Reference                               |\n|-------------------------------|--------------------------------|--------------------------------|----------------------|------------------------|-------------------------|-----------------------------------------|\n| Missoula                      | Montana, U.S.A.             | 16,000- 12,000 yrs. B.P. | 1,078                | 2,184,000 x 106        | 21.3 x 106              | Baker, 1973; Clarke and others, 1984 |\n| Vatnsdalur                    | Iceland                        | 1898                           | 372                  | 120 x 106              | 3,000                   | Thorarinsson, 1939                      |\n| Chong Kumdan (Shyok River) | India                          | 1929                           | 120                  | 1,350 x 106            | 22,650                  | Gunn, 1930; Hewitt, 1982             |\n| Demmevatn                     | Norway                         | 1937                           | 406                  | 11.6 x 106             | 1,000                   | Clague and Mathews, 1973             |\n| Graenalon                     | Iceland                        | 1939                           | 535                  | 1,500 x 106            | 5,000                   | Thorarinsson, 1939                      |\n| Gorner                        | Switzerland                    | 1944                           | ?                    | >6 x 106               | 200                     | Haeberli, 1983                          |\n| Gjanupsvatn                   | Iceland                        | 1951                           | 167                  | 20 x 106               | 370                     | Arnborg, 1955                           |\n| Lake George                   | Alaska, U.S.A.              | 1958                           | 40                   | 1,730 x 106            | 10,100                  | Stone, 1963                             |\n| Tulsequah                     | British Columbia, Canada | 1958                           | 210                  | 229 x 106              | 1,556                   | Marcus, 1960                            |\n| Summit                        | British Columbia, Canada | 1965                           | 620                  | 251 x 106              | 3,260                   | Mathews, 1965                           |\n| Ekalugad Valley            | Baffin Island, Canada    | 1967                           | 120                  | 4.8 x 106              | 200                     | Church, 1972                            |\n| Strupvatnet                   | Norway                         | 1969                           | 186                  | 2.6 x 106              | 150                     | Whalley, 1971                           |\n| Hazard Lake                   | Yukon, Canada               | 1978                           | 300                  | 19.6 x 106             | 640                     | Clarke, 1982                            |", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "table", "table_caption": "Table 3.--Well-documented examples of glacial-ice dams that have failed, producing jokulhlaups", "columns": ["Lake name", "Location", "Year failed", "Dam height (m)", "Lake volume (m3)", "Flood peak (m3/s)", "Reference"], "table_row_start": 1, "table_row_end": 13, "line_start": 213, "line_end": 227, "token_count_estimate": 612, "basins": [], "subbasins": ["Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "76936457fdc75309", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: table\nTable: Table 4.--Classification of ice-dammed lakes (from Blachut and Ballantyne, 1976)\n\n| Type of Lake                                                                                             | Example                                                                                                               |\n|----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|\n| A. Supraglacial                                                                                          | Generally small and not hazardous                                                                                     |\n| B. Marginal-ponded                                                                                       | Unnamed lake, Greenland (Sugden and others, 1985)                                                                  |\n| C. Converging ice-stream-ponded                                                                          | Between Lake, Axel Heiberg Island, Canada (Maag, 1969)                                                             |\n| D. Tributary stream valley-ponded                                                                        | Flood Lake, British Columbia, Canada (Clarke and Waldron, 1984)                                                    |\n| E. Tributary glacier valley-ponded                                                                       | Lago Rico, Argentina (Nichols and Miller, 1952)                                                                    |\n| F. Interglacial-ponded                                                                                   | Tulsequah Lake, British Columbia, Canada (Marcus, 1960)                                                            |\n| G. Dammed by tributary glacier                                                                           | Shyok River Lake, Upper Indus Valley, Pakistan (Gunn, 1930; Mason, 1929)                                        |\n| H. Proglacial ice-dammed                                                                                 | Generally small with valley glacier can be gigantic with continental ice sheets (e.g., Glacial Lake Agassiz) |\n| I. Miscellaneous (ice-dammed craters on volcanoes) (large englacial or subglacial water bodies) | Grimsvotn, Iceland (Thorarinsson, 1953)                                                                            |", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "table", "table_caption": "Table 4.--Classification of ice-dammed lakes (from Blachut and Ballantyne, 1976)", "columns": ["Type of Lake", "Example"], "table_row_start": 1, "table_row_end": 9, "line_start": 231, "line_end": 241, "token_count_estimate": 409, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": []}}
{"id": "51fd65d4175e894c", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: text\n\nAnother commonly-evoked failure mechanism is the hydrostatic flotation hypothesis of Thorarinsson (1953), whereby subglacial drainage becomes possible when hydrostatic pressure of water in an ice-dammed lake exceeds the ice-overburden pressure in an ice dam. This excess occurs when the depth of water behind an ice dam reaches 0.9 times the height of the ice dam. The story is not so simple as measuring the lake level behind an ice dam, because some ice-dammed lakes fail before filling to 0.9 times the ice-dam height, and most lakes continue to drain well after water levels decline below this critical level. Lifting of the ice dam only may be a triggering mechanism of failure, followed by other processes that allow continued drainage of the lake, such as erosion of drainage-ways under the ice by escaping lakewater, uneven settling of the ice dam after flotation, or formation of tension crevasses from flotation (Marcus, 1960).\n\nOther mechanisms for failure of glacier dams include (1) slow plastic yielding of ice from hydrostatic-pressure differences between the lake and adjacent less-dense ice, (2) crack progression under combined shear stress from glacier flow and hydrostatic pressure, (3) water overflow erosion of a breach in the dam, (4) subglacial melting by volcanic heat, and (5) weakening of the ice dam by earthquakes (Post and Mayo, 1971).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "text", "line_start": 242, "line_end": 246, "token_count_estimate": 370, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "98ac062f6011717a", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: figure\nFigure\n\nImage /page/23/Picture/0 description: A high-angle, black and white photograph captures a large, light-colored lake situated in a deep mountain valley. The surrounding mountains are steep and rugged, with patches of snow visible on their slopes, especially on the right side of the image. The mountainsides also show areas of darker rock and some vegetation. The lake fills most of the valley floor, and at its far end, a small inlet or river can be seen flowing into it. The foreground consists of the rocky or grassy terrain from which the photo was taken, looking down into the valley.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 247, "line_end": 247, "token_count_estimate": 177, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "05ef05d68c40fc9e", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: figure\nFigure: Figure 8.--Photograph of a glacier-dammed lake in a tributary valley (type D, table 4), Coast Mountains, Alaska. Note the strand lines indicating previously higher lake levels. (Photograph by W. C. Bradley, University of Colorado)\n\nFigure 8.--Photograph of a glacier-dammed lake in a tributary valley (type D, table 4), Coast Mountains, Alaska. Note the strand lines indicating previously higher lake levels. (Photograph by W. C. Bradley, University of Colorado)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "figure", "figure_caption": "Figure 8.--Photograph of a glacier-dammed lake in a tributary valley (type D, table 4), Coast Mountains, Alaska. Note the strand lines indicating previously higher lake levels. (Photograph by W. C. Bradley, University of Colorado)", "line_start": 249, "line_end": 249, "token_count_estimate": 153, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "236202faba068a89", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: figure\nFigure\n\nImage /page/23/Picture/2 description: A black and white diagram illustrating various geographical features in a glacial environment. The diagram uses different patterns to distinguish between 'Ice' (speckled pattern), 'Land' (cross-hatched pattern), and water bodies. The image shows large areas of ice surrounding several islands of land. A large landmass is at the bottom. A 'Lake' is situated between an ice mass and the lower landmass, with a 'River valley' extending to the right. Several specific features are marked with letters A through H. A, B, C, and E are small, irregular shapes on land or in the ice. D represents a stream flowing from the ice onto the land. F is a channel through land connecting two ice masses. G is an area within the lake outlined by a dashed line. H is a channel flowing into the lake.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 251, "line_end": 251, "token_count_estimate": 246, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d0e85b3f35bec6b8", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: figure\nFigure: Figure 9.--Diagram showing classification of glacial-ice dams, keyed to table 4. (Modified from Maag, 1969; and Blackut and Ballantyne, 1976)\n\nFigure 9.--Diagram showing classification of glacial-ice dams, keyed to table 4. (Modified from Maag, 1969; and Blackut and Ballantyne, 1976)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "figure", "figure_caption": "Figure 9.--Diagram showing classification of glacial-ice dams, keyed to table 4. (Modified from Maag, 1969; and Blackut and Ballantyne, 1976)", "line_start": 253, "line_end": 253, "token_count_estimate": 119, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b66e2ea448ac392", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Modes of Failure of Ice Dams\nType: text\n\nThe type of ice forming an ice dam (cold polar and subpolar ice versus warmer temperate ice) affects the potential failure mechanisms. Glacier dams formed of cold polar and subpolar ice are relatively tight and dense, have temperatures below the pressure melting point, are dry at their bases, and generally drain supraglacially or marginally by downmelting of an outlet channel (Marcus, 1960; Maag, 1969). Warmer temperate glacier dams are more fractured and less dense, have subglacial-meltwater flow, and tend to fail by sudden englacial or subglacial breaching and drainage (Blachut and Ballantyne, 1976).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Modes of Failure of Ice Dams", "section_headings": ["GLACIER DAMS", "Modes of Failure of Ice Dams"], "chunk_type": "text", "line_start": 254, "line_end": 256, "token_count_estimate": 199, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "89b3dae35cb013ad", "text": "Document: The formation and failure of natural dams\nSection: GLACIER DAMS > Longevity and Controls of Glacier Dams\nType: text\n\nSome glacier dams fail by means of non-catastrophic seasonal drainage outlets; the ice dams at Base Camp Lake, Greenland (Clement, 1984), are an example. Others fail catastrophically on an annual or near-annual basis. An example is the glacier dam that impounded Lake George, Alaska. This dam failed annually from at least 1918 until 1966 (Bradley and others, 1972). Some glacier dams fail several times during 1 year; others fail irregularly. Irregular damming and failure are believed related to local-climate control of glacier activity, filling rates, and lake temperatures. In some situations, the overflow outlet for a glacier-dammed lake is controlled by overflow across low bedrock cols around the perimeter of the lake (Sugden and others, 1985).\n\nNumerous case studies of glacier-dammed lakes indicate that the frequency of glacier-dam failure is likely to change with time. Thorarinsson (1939) demonstrated that, as a glacier recedes and melts, failures increase in frequency but decrease in magnitude, eventually ending with establishment of a permanent outlet. This pattern of response to glacial recession, increasing ice-dam failure, and decreasing flood magnitudes has been documented for Tulsequah Lake, British Columbia (Marcus, 1960).\n\nSome factors appear to predicate the failure of a glacier dam. Filling to some characteristic level is one example. A unique lake level may signal the beginning of hydrostatic flotation or overtopping of the ice barrier. A glacier dam with a cyclical history of failure may continue to fail, provided no change occurs in the size or shape of the ice dam. At Strandline Lake, Alaska, precursor indications of a glacier-dam failure include (1) rapid iceberg calving from the glacier, producing numerous icebergs; (2) filling of a number of small supraglacial pools; and (3) lake-level rise to a pre-identified failure level (Sturm and Benson, 1985).\n\nArtificial controls of the levels of glacier-dammed lakes have been attempted in several locations. In the Alps, trenching across a glacier dam or tunneling through rock or ice has allowed control of dangerous lakes (Eisbacher and Clague, 1984). This is not always successful (Mathews, 1965). In Argentina, 500-kilogram bombs were dropped on a glacier dam in an unsuccessful attempt to destroy the dam (Nichols and Miller, 1952).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "GLACIER DAMS > Longevity and Controls of Glacier Dams", "section_headings": ["GLACIER DAMS", "Longevity and Controls of Glacier Dams"], "chunk_type": "text", "line_start": 258, "line_end": 266, "token_count_estimate": 611, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb3b09a2f25dbf2e", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS\nType: text\n\nMany moraine dams occur throughout the world, but the most dangerous of these are restricted to alpine regions affected by the advances and retreats of valley glaciers in the last several centuries in steep mountain areas. Some excellent case studies have been made of moraine-dam failures in Peru (Lliboutry and others, 1977) and in British Columbia (Blown and Church, 1985), but relatively little is known about these natural dams compared to landslide or glacier dams. Moraine-dam failures have been reported from Napal (Galay, 1985; Vuichard and Zimmermann, 1987), India and Pakistan (Burgisser and others, 1982), the U.S.S.R. (Yesenov and Degovets, 1979), Oregon (Nolf, 1966; Laenen and others, 1987), Peru (Lliboutry and others, 1977), Canada (Clague and others, 1985), Austria (Eisbacher and Clague, 1984), and Argentina (Rabassa and others, 1979).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS", "section_headings": ["MORAINE DAMS"], "chunk_type": "text", "line_start": 268, "line_end": 270, "token_count_estimate": 249, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f6394800b551ce02", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Geomorphic Settings of Moraine Dams\nType: text\n\nA globally synchronous readvance of glaciers during the last few centuries has been documented (Grove, 1979) and referred to as the Little Ice Age (Matthes, 1939) or late-neoglacial time (Porter and Denton, 1967). Neoglacial time ended in the late 19th Century, and since then many mountain glaciers have retreated significantly (Porter and Denton, 1967), leaving behind many moraine-dammed lakes.\n\nThese late-neoglacial and contemporary moraine-dammed lakes are hazards because (1) they are young and located at such high elevations that vegetation has not completely stabilized their slopes, (2) slopes are steep (some greater than 40 degrees), (3) thermal degradation melting an ice or snow core could render them unstable, and (4) they may be located close to an ice front and steep, rock-walled cirques and valleys. Anomalous meltwater inflow or ice and rock falls into the lakes may precipitate breaching and failure of these dams.\n\nA variety of types of moraines may form in alpine areas. Push moraines originate when glacier ice advances and bulldozes sediment into a ridge, typically less than 9 meters high (Sugden and John, 1976). A typical push moraine has a shallow upstream dipping surface overridden by the glacier and a steeper downstream face formed by material cascading down the glacier surface. Because at least part of the moraine dam has been overridden by the glacier, some of the dam is partially compacted; this compaction adds an increment of stability that does not exist in other kinds of moraine dams. Push moraines are not common dams, or they only contain small lakes.\n\nIce-thrust moraines originate from the collection of sediment and debris erodes from the base of a glacier and thrust along shear planes to the front margin of the ice. Moraines can form to heights of 100 meters or more if the glacier is on a steep gradient, flows rapidly, and actively erodes material from its boundary. Ice-thrust moraine sediments are relatively compact and contain more fines than other types of moraine dams.\n\nDump moraines originate from the dumping down the ice front of sediment and debris from within the ice and on the ice surface. The form and size of dump moraines are controlled by rate of ice movement, rate of surface ablation, volume of sediment in and on the ice, and meltwater effects. A dump-moraine dam will be heterogeneous mixture because material moving from the ice to the moraine comes from diverse sources, lithologies, textures, and transport mechanisms.\n\nDump moraines typically have steep ice-contact upstream slopes where the moraine was buttressed by the glacier and flatter distal downstream-facing slopes (Andrews, 1975). For large dump moraines to form, forward motion of the glacier must balance the melting rate. Till in dump moraines tends to be relatively loose, uncompacted, and free of fines. Steep slopes, and uncompacted and noncohesive sediment make dump-moraine dams the least stable.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Geomorphic Settings of Moraine Dams", "section_headings": ["MORAINE DAMS", "Geomorphic Settings of Moraine Dams"], "chunk_type": "text", "line_start": 272, "line_end": 286, "token_count_estimate": 776, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "153745df693f4e6c", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Geomorphic Settings of Moraine Dams\nType: text\n\nice , and meltwater effects . A dump - moraine dam will be heterogeneous mixture because material moving from the ice to the moraine comes from diverse sources , lithologies , textures , and transport mechanisms . Dump moraines typically have steep ice - contact upstream slopes where the moraine was buttressed by the glacier and flatter distal downstream - facing slopes ( Andrews , 1975 ) . For large dump moraines to form , forward motion of the glacier must balance the melting rate . Till in dump moraines tends to be relatively loose , uncompacted , and free of fines . Steep slopes , and uncompacted and noncohesive sediment make dump - moraine dams the least stable .\n\nIce-cored moraines can originate when sediment at the base of a glacier is moved upward toward the ice surface by compression and thrusting. Ice-cored moraines can be as much as 90 percent ice by volume, with only thin till covers. The ice core can originate as glacial ice or snow (Ostrem, 1964). Ice cores in high-altitude or subpolar continental climates can last for 1,000 years (Andrews, 1975). Naturally, the melting of an ice-cored moraine impounding a lake can lead to the collapse and failure of the moraine dam. The presence of an ice-core in a moraine may be indicated by moist areas or seeps in moraine-dam walls above lake levels late in the summer or early autumn before sub-freezing temperatures occur.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Geomorphic Settings of Moraine Dams", "section_headings": ["MORAINE DAMS", "Geomorphic Settings of Moraine Dams"], "chunk_type": "text", "line_start": 272, "line_end": 286, "token_count_estimate": 412, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e6b0ae902ca9b25f", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: text\n\nComprehensive descriptions of the failure of moraine dams are rare (table 5). The Cordillera Blanca and Cordillera Huayhuash of northcentral Peru are regions that have an especially large number of moraines enclosing narrow, steep valleys. Nearly all glaciers of the Cordillera Blanca lie behind large moraines of late-neoglacial age and contain lakes (Lliboutry and others, 1977). In 1941, Laguna Cohup, a proglacial moraine-dammed lake, drained rapidly when the moraine dam failed. The resulting flood of mud and water (known in Spanish as \"alluvion\") destroyed almost half the town of Juaraz and killed about 6,000 people (Ericksen and others, 1970; Eisbacher, 1982). This disaster led to a major effort toward control and lowering of lake levels in dangerous moraine-dammed lakes in Peru (Lliboutry and others, 1977).\n\nThe most comprehensive description of the failure of a moraine dam is the work of Blown and Church (1985) on the failure of the moraine dam of Nostetuko Lake, British Columbia, Canada (fig. 10). Another valuable account of a moraine-dam failure at Klattasine Lake, British Columbia, sometime between June 1971 and September 1973 was made by Clague and others (1985).\n\nMost of the textural data from moraine dams indicates that the moraine material is silty, sandy, bouldery till, with minimal clay (less than 3 or 4 percent). Lake levels are controlled by seepage through the barrier and open overflow channels across the top of the moraine. Of the 14 documented cases of failures of moraine dams known to us, the mechanism of failure is known or can be reasonably estimated for nine. The most common failure mechanism is overtopping by a wave or series of waves generated by icefalls or rockfalls or by snow- or rock-avalanches into the lake basin. The wave overtops the moraine dam, and augmented flow in the outlet channel causes erosion of the channel that permits increased flow from the lake as it begins to drain. Another failure mechanism is overtopping by excessive runoff during glacial retreat, snowmelt, or intense rainfall. The moraine dam impounding Lago Tempanos, Argentina, failed sometime between 1942 and 1953 because of excessive meltwater accompanying a 352-meter retreat of the glacier (Rabassa and others, 1979).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "text", "line_start": 288, "line_end": 296, "token_count_estimate": 588, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cbc92dcc37ade912", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: table\nTable: Table 5.--Well-documented examples of moraine dams that have failed\n\n| Lake or site name | Location                       | Date failed           | Change in lake level (m) | Volume discharged (m3 x 104) | Flood peak (m3/s) | Failure mechanism               | Reference                                         |\n|----------------------|--------------------------------|--------------------------|--------------------------------|------------------------------------|----------------------|------------------------------------|---------------------------------------------------|\n| Madatschferner       | Austria                        | Aug. 5, 1874             | --                             | --                                 | --                   | --                                 | Eisbacher and Clague, 1984                     |\n| Galrittferner        | Austria                        | Aug. 7, 1890             | --                             | --                                 | --                   | Ice fall                           | Eisbacher and Clague, 1984                     |\n| Cohup                | Peru                           | Dec. 13, 1941            | --                             | --                                 | --                   | --                                 | Eisbacher, 1984; Lliboutry and others, 1977 |\n| White Branch         | Oregon, U.S.A.              | July 1942                | --                             | --                                 | 360                  | --                                 | Laenen and others, 1987                        |\n| Tempanos             | Argentina                      | 1942-1953                | --                             | --                                 | --                   | Excess meltwater                | Rabassa and others, 1979                       |\n| Jancarurish          | Peru                           | 1950                     | 21                             | 600-1,000                          | 7,000-8,000          | Collapse of undercut glacier | Lliboutry and others, 1977                     |\n| Artesoncoda          | Peru                           | July 16-17, 1951      | 113                            | --                                 | --                   | Ice fall                           | Lliboutry and others, 1977                     |\n| Broken Top           | Oregon, U.S.A.              | Oct. 7, 1966             | 4.6                            | 18.9                               | 71                   | Ice fall                           | Nolf, 1966; this report                        |\n| Squaw Creek          | Oregon, U.S.A               | Sept. 7, 1970            | 25.6                           | 33.3                               | 297                  | --                                 | Laenen and others, 1987                        |\n| Safuna Alta          | Peru                           | 1970                     | 38                             | 490 (stored in lake)            | --                   | Earthquake- induced piping      | Lliboutry and others, 1977                     |\n| Klattasine           | British Columbia, Canada | June 1971- Sept. 1973 | 13                             | 170                                | >1,000               | --                                 | Clague and others, 1985                        |\n| Moraine #13          | Soviet Union                | Aug. 3, 1977             | 5.2                            | 8.64                               | 210                  | Melting of frozen soil          | Yesenov and Degovets, 1979                     |\n| Nostetuko            | British Columbia, Canada | July 19, 1983            | 38.4                           | 650                                | 11,000               | Ice fall                           | Blown and Church, 1985                         |\n| Dig Tsho             | Nepal                          | Aug. 4, 1985             | 22.5                           | 800                                | 2,000                | Ice fall                           | Galay, 1985; Vuichard and Zimmermann, 1987  |", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "table", "table_caption": "Table 5.--Well-documented examples of moraine dams that have failed", "columns": ["Lake or site name", "Location", "Date failed", "Change in lake level (m)", "Volume discharged (m3 x 104)", "Flood peak (m3/s)", "Failure mechanism", "Reference"], "table_row_start": 1, "table_row_end": 14, "line_start": 297, "line_end": 312, "token_count_estimate": 753, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "43b42c47f6e01361", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: figure\nFigure\n\nImage /page/28/Picture/0 description: A black and white, high-contrast aerial photograph of a rugged, mountainous landscape. The upper portion of the image is covered in snow and ice, with mountain peaks visible. Below the snowfields, a glacier flows down a valley between dark, steep mountain slopes. Patches of snow are scattered on the dark rock faces. In the lower right corner, a dark body of water, likely a glacial lake, can be seen at the base of the mountains.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 314, "line_end": 314, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8a86dc8870a8ce07", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: figure\nFigure: Figure 10-A.--Photograph of Nostetuko Lake, a neoglacial moraine-dammed lake, and Cumberland Glacier, British Columbia, Canada, in July 1977. (Photograph by J. M. Ryder, courtesy of Michael Church, University of British Columbia)\n\nFigure 10-A.--Photograph of Nostetuko Lake, a neoglacial moraine-dammed lake, and Cumberland Glacier, British Columbia, Canada, in July 1977. (Photograph by J. M. Ryder, courtesy of Michael Church, University of British Columbia)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "figure", "figure_caption": "Figure 10-A.--Photograph of Nostetuko Lake, a neoglacial moraine-dammed lake, and Cumberland Glacier, British Columbia, Canada, in July 1977. (Photograph by J. M. Ryder, courtesy of Michael Church, University of British Columbia)", "line_start": 316, "line_end": 316, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "afb45d8fe76b4515", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: figure\nFigure\n\nImage /page/28/Picture/2 description: A black and white panoramic photograph, labeled as Figure 12, showing the Nastaklah and Cambria Glaciers in British Columbia, Canada, during August. The image captures a high-angle view of a vast, mountainous landscape. Large glaciers flow down from snow-covered peaks, converging in a valley below. The rugged mountains have dark, rocky slopes contrasting with the white snow and ice.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 318, "line_end": 318, "token_count_estimate": 139, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a319eda2c6a827b7", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: figure\nFigure: Figure 10-B.--Photograph of Nostetuko Lake and Cumberland Glacier, British Columbia, Canada, in August 1983, after failure of the moraine dam. (Photographs by Michael Church, University of British Columbia)\n\nFigure 10-B.--Photograph of Nostetuko Lake and Cumberland Glacier, British Columbia, Canada, in August 1983, after failure of the moraine dam. (Photographs by Michael Church, University of British Columbia)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "figure", "figure_caption": "Figure 10-B.--Photograph of Nostetuko Lake and Cumberland Glacier, British Columbia, Canada, in August 1983, after failure of the moraine dam. (Photographs by Michael Church, University of British Columbia)", "line_start": 320, "line_end": 320, "token_count_estimate": 138, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "65b3340b0e40e4fb", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Modes of Failure of Moraine Dams\nType: text\n\nIn the Cordillera Blanca, Peru, all precisely dated moraine-dam failures occurred during the rainy season from October to April (Lliboutry and others, 1977). Failure during these wet months indicates that increased streamflow is an important factor in moraine-dam failures.\n\nSettlement and subsequent failure of moraine dams accompanying earthquakes is another potential failure mechanism. The large Peruvian earthquake of May 1970 resulted in the release of water from at least two moraine-dammed lakes by piping in the Cordillera Blanca (Lliboutry and others, 1977).\n\nA moraine dam in the Soviet Union collapsed from melting of frozen soil because of high air and water temperatures (Yesenov and Degovets, 1979). Although we know of no documented case, the failure of an ice-cored moraine dam from ice-melt also is a distinct possibility.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Modes of Failure of Moraine Dams", "section_headings": ["MORAINE DAMS", "Modes of Failure of Moraine Dams"], "chunk_type": "text", "line_start": 321, "line_end": 327, "token_count_estimate": 236, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e80807a2ed3e8ee4", "text": "Document: The formation and failure of natural dams\nSection: MORAINE DAMS > Longevity and Control of Moraine Dams\nType: text\n\nThe question of the longevity and stability of moraine dams is extremely complex. In our investigations, all of the known moraine dams that failed were late-neoglacial age or younger. Since ice or rock avalanches into moraine-dammed lakes are the primary failure mechanism of moraine dams, lakes located below steep, unstable, and highly-crevassed or fractured glaciers or rock slopes present obvious failure potential. In a study of the hypothetical failure and resulting flood from a late-neoglacial moraine dam in central Oregon, the annual probability of failure was estimated to be 1 to 5 percent because of the instability of the surrounding rock slopes and glacier and the history of previous moraine-dam failures in the area (Laenen and others, 1987). Ice-cored moraines or frozen-soil moraines may thaw for centuries and then become critically unstable (Ostrem, 1964; Andrews, 1975). Once this thaw occurs, the potential for a piping or overtopping failure of the moraine dam increases whenever a period of high runoff occurs.\n\nArtificial controls of the levels of moraine-dammed lakes have been attempted in several locations. The most extensive efforts probably have been undertaken in Peru (Lliboutry and others, 1977). Control efforts include draining of lakes by tunnels through ice and rock, stabilization of lake outlets with paved revetments, increasing freeboard with low earthen dams, and construction of protective retention basins--if valley gradients are sufficiently low (Lliboutry and others, 1977; Yesenov and Degovets, 1979; Eisbacher, 1982).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "MORAINE DAMS > Longevity and Control of Moraine Dams", "section_headings": ["MORAINE DAMS", "Longevity and Control of Moraine Dams"], "chunk_type": "text", "line_start": 329, "line_end": 333, "token_count_estimate": 420, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54f98afda8223a76", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS\nType: text\n\nNatural dams create the potential for two very different types of flooding: (1) upstream or backwater flooding as the reservoir fills and (2) downstream flooding as a result of failure of the dam.\n\nUpstream flooding occurs because of the relatively slow rise of water behind the dam as the basin of the natural impoundment is filled. The threat of loss of life from this kind of flooding is minimal, but property damage can be substantial as entire developments disappear under water. Upstream-flood damage from glacier and moraine dams generally is not significant because these types of natural dams commonly occur in remote areas where development is minimal. Landslide dams, however, can present significant hazards from backwater flooding.\n\nBecause landslide dams tend to occur in mountainous areas, certain types of structures, such as hydroelectric plants, may be rendered inoperable because of inundation of intakes, flumes, generators, and transportation and transmission facilities (Schuster and Costa, 1986b). A recent example of the consequences of upstream flooding from a landslide dam occurred in April 1983, when a 22-million cubic meter landslide in central Utah dammed the Spanish Fork River and flooded the town of Thistle, Utah (Kaliser and Fleming, 1986; fig. 11).\n\nIt usually is possible to accurately estimate the extent and rate of upstream flooding from natural dams. Such estimates require knowledge of height of the dam crest, rates of streamflow or icemelt into the natural reservoir, rates of seepage through or beneath the dam, and information on topography upstream from the dam.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS"], "chunk_type": "text", "line_start": 335, "line_end": 343, "token_count_estimate": 397, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f5db2c148df55e4f", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS\nType: figure\nFigure\n\nImage /page/30/Picture/2 description: A black and white aerial photograph shows a massive landslide in a mountainous region. A river or reservoir winds through a canyon on the left. On the right, a large portion of a mountainside has collapsed, leaving a wide, light-colored scar of exposed earth and rock that flows down into the valley. The debris from the landslide has spread out at the base, pushing into the river and altering its course.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS"], "chunk_type": "figure", "figure_caption": null, "line_start": 344, "line_end": 344, "token_count_estimate": 143, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68dcf769f874f244", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS\nType: figure\nFigure: Figure 11.--Photograph of the 1983 landslide at Thistle, Utah, and its temporary impoundment, Thistle Lake, in September 1983. The lake inundated the town of Thistle and was drained late in 1983 by a bedrock tunnel constructed through the mountain at lower left.\n\nFigure 11.--Photograph of the 1983 landslide at Thistle, Utah, and its temporary impoundment, Thistle Lake, in September 1983. The lake inundated the town of Thistle and was drained late in 1983 by a bedrock tunnel constructed through the mountain at lower left.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS"], "chunk_type": "figure", "figure_caption": "Figure 11.--Photograph of the 1983 landslide at Thistle, Utah, and its temporary impoundment, Thistle Lake, in September 1983. The lake inundated the town of Thistle and was drained late in 1983 by a bedrock tunnel constructed through the mountain at lower left.", "line_start": 346, "line_end": 346, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3bd3401a3d5624ef", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: text\n\nFloods resulting from the failure of natural dams usually are much larger than floods originating directly from snowmelt or rainfall. Compared with the failure of constructed dams, very little is known about the processes of natural-dam failure. Few accurate data exist about peak discharges, dimensions, or reservoir volumes of failed natural dams (Costa, 1985).\n\nMany investigations have been conducted on the safety of constructed dams (Jansen, 1980; Committee on the Safety of Existing Dams, 1983), and numerous models have been proposed to determine the outflow hydrograph and downvalley routing of floodwaters resulting from failure of constructed dams (Land, 1980; Fread, 1980). However, few similar evaluations have been made for the failures of natural dams (Ponce and Tsivoglou, 1981; Clarke, 1982), which in many cases are different from those of constructed dams (Costa, 1985).\n\nA dam failure is a complex hydrologic, hydraulic, and geologic phenomenon, controlled primarily by the failure mechanism and characteristics and properties of the dam. One way to compare different kinds of dam failures is to investigate the relationship between the specific energy of the lakewater behind the dam prior to failure and the flood-peak discharge from the failure of the dam (fig. 12). Potential energy of a lake behind a dam can be computed as the product of dam height (meters), volume (cubic meters), and specific weight of water (9,800 newtons/cubic meter). Dam-failure flood peak discharges used in figure 12 come from tables 3 and 5 and from Costa (1985).", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "text", "line_start": 349, "line_end": 355, "token_count_estimate": 419, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3eb76313ce7666d3", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: figure\nFigure\n\nImage /page/31/Figure/2 description: A scatter plot showing the relationship between Potential Energy and Peak Discharge for different types of dams. The x-axis represents \"POTENTIAL ENERGY, IN JOULES\" on a logarithmic scale from 10^7 to 10^17. The y-axis represents \"PEAK DISCHARGE, IN CUBIC METERS PER SECOND\" on a logarithmic scale from 10 to 1,000,000. The plot includes data for four types of dams, indicated by a legend: black triangles for CONSTRUCTED DAMS (includes earth and rockfill dams), black circles for LANDSLIDE DAMS, circles with a dot for MORAINE DAMS, and black diamonds for GLACIER DAMS. The data points generally show a positive correlation, where higher potential energy corresponds to higher peak discharge. Several trend lines are drawn on the plot. An \"Envelope curve\" marks the upper boundary of the data. Separate trend lines are labeled for \"Earth and rockfill dams\", \"Landslide dams\", \"Moraine dams\", and \"Glacier dams\", indicating the general trend for each category.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 356, "line_end": 356, "token_count_estimate": 333, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "220e761382850d85", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: figure\nFigure: Figure 12.--Graph showing potential energy versus peak discharge for various types of dam failures. Dashed lines are least-squares regression lines for earth- and rock-fill, moraine glacial ice, and landslide dams. Solid line is the envelope curve for all dam-failure data. Data from Costa (1985) and Table 5.\n\nFigure 12.--Graph showing potential energy versus peak discharge for various types of dam failures. Dashed lines are least-squares regression lines for earth- and rock-fill, moraine glacial ice, and landslide dams. Solid line is the envelope curve for all dam-failure data. Data from Costa (1985) and Table 5.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "figure", "figure_caption": "Figure 12.--Graph showing potential energy versus peak discharge for various types of dam failures. Dashed lines are least-squares regression lines for earth- and rock-fill, moraine glacial ice, and landslide dams. Solid line is the envelope curve for all dam-failure data. Data from Costa (1985) and Table 5.", "line_start": 358, "line_end": 358, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca450a542c2b8247", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: text\n\nRegression analysis with potential energy as the independent variable produces different equations for landslide, glacier, moraine, and earth- and rock-fill dam-failure flood peaks, with standard errors ranging from 27 percent for moraine dams to 133 percent for landslide dams (table 6). The large scatter in the data in figure 12 reflects the diverse characteristics of individual dams within each category, as well as the difficulty in computing or measuring the peak discharge from a dam failure. Direct measurements of floods are nearly impossible; thus a variety of indirect estimation methods, such as drawdown rates or measurements based on post-flood channel surveys and hydraulic formulas, are used.\n\nConcern about the large standard errors or overlapping of data from different types of dams is moot, since the variability of individual dam populations is so large and determination of the dependent variable (peak discharge) is so difficult. Individual regressions lines identify different kinds of dams in a physically meaningful way that provides insight into fundamental understanding of natural dams.\n\n[Q = peak discharge,  $m^3/s$ ; PE = potential energy (joules)]", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "text", "line_start": 359, "line_end": 367, "token_count_estimate": 316, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c14188ef4a7235a2", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: table\nTable: Table 6.--Summary of regression equations to predict peak discharge from the failure of earth- and rock-fill, moraine, glacial-ice, and landslide dams\n\n| Type of dam                | Equation                 | Number of data points | Coefficient of determination (r2) | Standard error (percent) |\n|----------------------------|--------------------------|--------------------------|-----------------------------------------|--------------------------------|\n| 1. Earth- and rock-fill | $Q = 0.0184 (PE)0.42$    | 26                       | 0.75                                    | 93                             |\n| 2. Landslide               | $Q = 0.0158 (PE)0.41$    | 12                       | .81                                     | 133                            |\n| 3. Moraine                 | $Q = 0.0000069 (PE)0.73$ | 6                        | .89                                     | 27                             |\n| 4. Glacier                 | $Q = 0.0000055 (PE)0.59$ | 11                       | .80                                     | 75                             |", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "table", "table_caption": "Table 6.--Summary of regression equations to predict peak discharge from the failure of earth- and rock-fill, moraine, glacial-ice, and landslide dams", "columns": ["Type of dam", "Equation", "Number of data points", "Coefficient of determination (r2)", "Standard error (percent)"], "table_row_start": 1, "table_row_end": 4, "line_start": 368, "line_end": 373, "token_count_estimate": 285, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["0000055", "0000069"]}}
{"id": "dd7a00af589d9e41", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: text\n\nFor the same potential energy at the time of dam failure, glacier-ice dams produce the smallest peak discharges. For potential energy less than about  $10^{11}$  joules, constructed earth- and rock-fill dams produce the largest peak discharges for a constant potential energy. For potential energy greater than about  $10^{11}$  joules, moraine-dam failures produce the largest peak discharges for a constant potential energy. Differences in peak discharges for constant potential energy of different kinds of natural and constructed dams originate because of differences in failure mechanisms, which are related to geometry and material characteristics of the dams.\n\nGlacier-dam failures commonly involve the enlargement of subglacial or englacial tunnels during failure. This enlargement requires time and produces relatively small peak discharges compared with other kinds of natural and constructed dams with the same potential energy. Landslide dams are typically much wider than constructed earth- and rock-fill dams and involve much larger volumes of material. The Madison Canyon landslide that dammed the Madison River in 1959, forming Earthquake Lake, had a base width five to eight times as great as would have been used in building a rock-fill dam of the same height (Knight and Bennett, 1960). In figure 13, transverse sections of the Mayunmarca landslide dam in Peru and the Nostetuko moraine dam in British Columbia are compared to the Oroville Dam, a large earth-fill dam in California. The landslide dam in Peru is higher at the abutments and almost as high as the constructed dam in the center, but is over three times as wide. When a landslide dam is overtopped, much more earth material commonly is present for water to erode before a full breach is developed than is the case for constructed embankment dams. This is indicated by the parallel but displaced positions of landslide and earth- and rock-fill dam regression lines shown in figure 12.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "text", "line_start": 374, "line_end": 378, "token_count_estimate": 509, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af09c3252f85caff", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: figure\nFigure\n\nImage /page/33/Figure/0 description: A black and white line drawing comparing the cross-sections of three dams: the Nostetuko Moraine Dam in British Columbia, the Oroville Dam in California, and the Mayunmarca Landslide Dam in Peru. The profiles are superimposed for direct comparison. The Nostetuko Moraine Dam is the smallest, shown as a triangular shape with a stippled pattern. The Oroville Dam is larger, depicted with a light gray shading, and its profile encompasses the Nostetuko dam. The \"Center\" and \"Abutments\" of the Oroville Dam are labeled. A dashed line is drawn above the peak of the Oroville Dam. The Mayunmarca Landslide Dam is the largest, represented by the overall outer contour line. A horizontal scale bar at the bottom of the diagram is marked from 0 to 1000 m.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "figure", "figure_caption": null, "line_start": 379, "line_end": 379, "token_count_estimate": 254, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b03e22681f11c0ae", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: figure\nFigure: Figure 13.--Diagram showing cross section of Mayunmarca landslide dam, Peru, and Nostetuko moraine dam, British Columbia, compared to the maximum cross section of a large earth-fill dam at Oroville, California. (Modified from Lee and Duncan, 1975)\n\nFigure 13.--Diagram showing cross section of Mayunmarca landslide dam, Peru, and Nostetuko moraine dam, British Columbia, compared to the maximum cross section of a large earth-fill dam at Oroville, California. (Modified from Lee and Duncan, 1975)", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "figure", "figure_caption": "Figure 13.--Diagram showing cross section of Mayunmarca landslide dam, Peru, and Nostetuko moraine dam, British Columbia, compared to the maximum cross section of a large earth-fill dam at Oroville, California. (Modified from Lee and Duncan, 1975)", "line_start": 381, "line_end": 381, "token_count_estimate": 174, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8cdf5c6f75cdf4ab", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams\nType: text\n\nMoraine dams are relatively high and narrow compared to many landslide dams and have much steeper slopes. The predominate failure mechanism for moraine dams (wave overtopping) leads to rapid erosion of cohesionless sandy and gravelly till comprising many moraine dams. The rapid breach development in moraine-dam failures, compared to other kinds of dams, is primarily responsible for the location of the regression line in figure 12.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Comparison of Floods from the Failure of Different Kinds of Dams"], "chunk_type": "text", "line_start": 382, "line_end": 384, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56fa0357045c304f", "text": "Document: The formation and failure of natural dams\nSection: FLOODS FROM THE FAILURE OF NATURAL DAMS > Prediction and Reconstruction of Floods from Dam Failures\nType: text\n\nFor purposes of rapid prediction when potential loss of life or property is involved, a conservative peak-discharge estimate based on an envelope curve developed from historic failures of landslide, glacier, moraine, and constructed earth- and rock-fill dams can be made from knowledge of the potential energy of the lake behind the dam (fig. 12). The envelope curve that includes data points from all constructed and natural-dam failures for which reasonable estimates of peak discharge exist is defined by the equation\n\n$$Q = 0.063 \\text{ PE}^{0.42}$$\n\nwhere Q is peak discharge, in cubic meters per second, and PE is potential energy, in joules.\n\nFor reconstructing past flood peaks from the failure of natural dams for paleohydrological or sedimentological investigations, regression equations for different kinds of natural dams with potential energy as the independent variable could be used (table 6).\n\nA complicating factor in downstream routing of floods from natural-dam failures is the bulking and debulking of flood waters with sediment and debris as the flood moves downvalley. The use of envelope curves, regression equations, or dam-break models allows estimation of peak flood discharges at the dam. Most commonly, flood peak discharges attenuate downvalley (Costa, 1985; fig. 8). Sometimes, however, downstream peaks can be considerably larger because easily eroded sediment is added and subtracted from the flow. So much sediment can be added to the flood flow that a debris flow forms. This seems to be an especially important process in volcanic and glacial terrains (Scott, 1985; Yesenov and Degovets, 1979; Clague and others, 1985; Lliboutry and others, 1977). Fluctuations in peak discharge downstream from the failure of a moraine dam on the Kumbel River, U.S.S.R., in 1977 are well documented. Peak discharge from the dam failure was 210 cubic meters per second, but it had bulked to 11,000 cubic meters per second about 15 kilometers downstream (Yesenov and Degovets, 1979). This problem of bulking and debulking presents one of the most difficult unsolved problems in sediment transport today, and its consequences for hazard evaluation are enormous.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "FLOODS FROM THE FAILURE OF NATURAL DAMS > Prediction and Reconstruction of Floods from Dam Failures", "section_headings": ["FLOODS FROM THE FAILURE OF NATURAL DAMS", "Prediction and Reconstruction of Floods from Dam Failures"], "chunk_type": "text", "line_start": 386, "line_end": 396, "token_count_estimate": 582, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d9af15eb2077eabe", "text": "Document: The formation and failure of natural dams\nSection: CONCLUSIONS\nType: text\n\nThree kinds of natural dams present significant hazards to people and property: landslide dams, glacier dams, and moraine dams. Using a large data base of case studies, significant generalizations about individual kinds of natural dams can be made. Landslide dams form most commonly in steep narrow valleys in geologically active areas, but they have also formed in wide, open stream valleys. Avalanches, slumps and slides, and flows triggered by excessive snowmelt or rainfall or by earthquakes are the most common landslide processes. There are at least six morphologically different kinds of landslide dams, and the danger of failure and flooding varies with the kind of landslide dam.\n\nBased on 73 examples of landslide-dam failures from around the world, landslide dams fail quickly after formation (half fail within 10 days of formation, and 15 percent last more than a year). By far the predominate failure mechanism is overtopping and breaching by headward erosion.\n\nGlacier-dam failures have produced the largest documented historic and prehistoric flood-peak discharges known on Earth. The most dangerous glacier dams are formed when tributary-valley glaciers block main valleys and form large lakes behind them. Temperate glacier dams usually fail by erosion of subglacial or englacial tunnels under or through the ice dam, aided by the hydrostatic pressure of water behind the ice dam. Cold polarice dams fail most commonly by overtopping and erosion of a channel in the ice.\n\nAll the documented cases of moraine-dam failures occurred with moraine dams formed in the last few centuries (late neoglacial). The dams originated as dump or ice-thrust moraines and may or may not have ice cores. The predominate failure mechanism is overtopping and rapid erosion by a wave or series of waves generated by rock or ice avalanches into the lake from adjacent steep cirque or valley walls.\n\nFor the same potential energy at the dam site, the failures of glacier dams produce the smallest flood peak discharges because of the time required for enlargement of outlet tunnels and channels. For constant potential energy, landslide-dam failures produce smaller flood peaks than failures of constructed earth- and rock-fill dams because of the large volume of material to be eroded before full breach development. For potential energy greater than about  $10^{11}$  joules, late-neoglacial moraine dams produce the greatest flood peak discharges of any kind of natural or earth- or rock-fill dam because of rapid breach formation associated with wave overtopping and erosion of non-cohesive till on steep slopes.\n\nAn envelope curve defining the maximum discharge produced by the failure of natural and constructed earthen dams can be used as a rapid conservative approximation of the flood peak from the failure of a potentially dangerous natural dam. In areas with abundant loosely-consolidated surficial deposits in stream valleys downstream of natural-dam failures, peak discharges may not attenuate, but could actually increase many times by incorporation of easily erodible sediment and debris. This phenomenon of sediment bulking and debulking downstream from natural-dam failures remains one of the most pressing and difficult unsolved problems in sediment transport today.", "metadata": {"source_file": "data/('The formation and failure of natural dams', '.pdf')_extraction.md", "document_title": "The formation and failure of natural dams", "section_path": "CONCLUSIONS", "section_headings": ["CONCLUSIONS"], "chunk_type": "text", "line_start": 398, "line_end": 409, "token_count_estimate": 814, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "399b7c47ad05f655", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: ABSTRACT\nType: text\n\nThere has been an increase in the adoption of Linked Data and subsequently representing data in the form of knowledge graphs across a wide spectrum of domains. There has also been significant interest in the remote sensing community to publish Earth Observation data in the form of Linked Data. As the geospatial Linked Data cloud on the internet grows, there arises a need for efficient methods of exploratory analysis of such information-rich geospatial knowledge graphs. Knowledge graph representation of remote sensing scenes has proved to add significant value for effective mining of implicit information in addition to seamless integration with other data sources. This work is geared towards visual exploration of semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). In this paper, we propose and implement an interactive web-based interface to visually explore and interact with RSS-KGs using Cesium. The proposed interface seeks to visualize the knowledge graph in the form of nodes and edges, mapped over the remote sensing scene consisting of different land use land cover regions and their inferred characteristics in addition to their spatial relationships with one another. It is envisaged that visualization in the form of nodes and edges would aid in visually validating the spatial relations in the knowledge graph, thus enhancing the understanding of the geospatial knowledge graph from the end user perspective. We demonstrate the efficacy of the interface through the visual exploration of an enriched geospatial knowledge graph of a remote sensing scene captured during an urban flood event.\n\n*Index Terms*— visualization, exploration, knowledge graphs, remote sensing scenes, earth observation, linked data", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "ABSTRACT", "section_headings": ["ABSTRACT"], "chunk_type": "text", "line_start": 3, "line_end": 7, "token_count_estimate": 420, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c404c5949ee75f2d", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: text\n\nRecently there has been a huge interest in publishing Linked Data on the web, backed by different governance initiatives [1]. The advantages of Linked Data in terms of seamless accessibility, interoperability and integration have been well understood by the research community. This has led to an increase in the size of the Linked Data cloud available on the web. The geospatial research community too has noticed and acknowledged the benefits of Linked Data and publishing", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 9, "line_end": 11, "token_count_estimate": 139, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e134998c7c29118", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: figure\nFigure\n\nImage /page/0/Figure/9 description: A flowchart titled 'Figure 1. Multi-level Semantic Enrichment of Remote Sensing Scene' illustrates a four-tiered process for enriching data. The layers, from bottom to top, are Data Mediation Layer, Spatial Knowledge Enrichment, Contextual Knowledge Enrichment, and Scene Knowledge Aggregation.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "figure", "figure_caption": null, "line_start": 12, "line_end": 12, "token_count_estimate": 129, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "77f2213504907923", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: text\n\n1. \\*\\*Data Mediation Layer\\*\\*: This foundational layer produces a 'Linked Data Representation of Remote Sensing Scene Knowledge'. An example 'RSS-LD' shows entities like 'Vehicle \"V1\"' and 'Road \"R1\"'.\n\n2. \\*\\*Spatial Knowledge Enrichment\\*\\*: This layer takes 'Spatial SWRL Rules' and a 'Remote Sensing Scene Ontology (RSSO)' as input. A 'Deductive Reasoner' processes them to create an 'Enriched Remote Sensing Scene Knowledge Graph (RSS-KG)' with 'Spatial', 'Directional', and 'Topological' components. An example 'RSS-KG' shows vehicles intersecting with a road ('geo:sfIntersects' -> 'Road \"R1\"').\n\n3. \\*\\*Contextual Knowledge Enrichment\\*\\*: This layer uses 'Contextual SWRL Rules' and a 'Flood Scene Ontology (FSO)'. A 'Deductive Reasoner' enriches the knowledge graph, which now has 'Contextual' and 'Spatial' layers. The example 'RSS-KG' shows vehicles being 'On' an 'Unaffected Road \"UR1\"'.\n\n4. \\*\\*Scene Knowledge Aggregation\\*\\*: The top layer starts with 'Spatio-Contextual Triple Aggregation'. A 'Deductive Reasoner' produces the final 'Enriched Remote Sensing Scene Knowledge Graph (RSS-KG)' with 'Aggregated', 'Contextual', and 'Spatial' layers. The example 'RSS-KG' shows an aggregated concept: 'Traffic Congestion \"TC1\"' is 'On' an 'Unaffected Road \"UR1\"'.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 13, "line_end": 21, "token_count_estimate": 475, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "95866aa06b83388a", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: figure\nFigure: Figure 1. Multi-level Semantic Enrichment of Remote Sensing Scene Knowledge Graphs (RSS-KGs) as proposed by Sem-RSSU Framework [2]\n\nFigure 1. Multi-level Semantic Enrichment of Remote Sensing Scene Knowledge Graphs (RSS-KGs) as proposed by Sem-RSSU Framework [2]", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "figure", "figure_caption": "Figure 1. Multi-level Semantic Enrichment of Remote Sensing Scene Knowledge Graphs (RSS-KGs) as proposed by Sem-RSSU Framework [2]", "line_start": 22, "line_end": 22, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "de399606c43eb1f9", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: text\n\nGeospatial Linked Data is largely being encouraged. With the adoption of Geospatial Linked Data, there is an increasing interest in leveraging such EO Linked Data through knowledge graph representation for mining the otherwise implicit knowledge. In addition to improving spatio-contextual inferencing over semantically enriched geospatial knowledge graphs there is a need for effective visual exploration of such information-rich sources.\n\nThis works builds over the Semantics driven Remote Sensing Scene Understanding (Sem-RSSU) Framework [2]. The Sem-RSSU framework advocates the transformation of remote sensing scenes as knowledge graphs. It facilitates this by proposing the Remote Sensing Scene Ontology (RSSO) – a core ontology for a generic remote sensing scene, formalizing the concepts and relations among regions in a scene. It also proposes the Flood Scene Ontology (FSO), formalizing the concepts and relations that transpire during an urban flood event. It introduces and demonstrates a holistic pipeline to translate remote sensing scenes to semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). Figure 1 depicts the multilevel Semantic Enrichment Layer of the Sem-RSSU framework for hierarchical spatial, contextual and aggregate enrichment of RSS-KGs.\n\nThis work is geared towards developing an intuitive interface for effective visual exploration of such information rich Remote Sensing Scene Knowledge Graphs. There have been some prominent research studies on visualization of geospatial linked data. Map4RDF [3] was developed as a faceted browser for visualization of RDF datasets with", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 23, "line_end": 29, "token_count_estimate": 419, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97c245050b4e0416", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: figure\nFigure\n\nImage /page/1/Figure/0 description: A screenshot of a web application titled \"Visual Exploration of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)\". The application is displayed in a web browser, with the URL visible as www.geosysiot.in/tools/rssKG-Explorer/. The interface is split into two main parts. On the left is a dark control panel with white text and toggle switches. This panel includes options like \"RSS-Knowledge Graph in 3D,\" information about the data source, ontologies, and details of the remote sensing scene: \"(Raster - WV02 [FCC]) | Srinagar, India | Sept. 17, 2014)\". It also credits the developers from IIT Bombay. On the right is the main visualization area, which shows an aerial view of a city. A large portion of this view is overlaid with a false-color composite image, where vegetation appears bright red and water or flooded areas appear in shades of cyan, surrounding buildings. A major road runs diagonally through the scene.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "figure", "figure_caption": null, "line_start": 30, "line_end": 30, "token_count_estimate": 299, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "c2569b21102b7068", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: figure\nFigure: Figure 2. Visualization of the Remote Sensing Scene of an Urban Flood Event in False Color Composite captured by WorldView 2, overlayed on Cesium's base map imagery\n\nFigure 2. Visualization of the Remote Sensing Scene of an Urban Flood Event in False Color Composite captured by WorldView 2, overlayed on Cesium's base map imagery", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "figure", "figure_caption": "Figure 2. Visualization of the Remote Sensing Scene of an Urban Flood Event in False Color Composite captured by WorldView 2, overlayed on Cesium's base map imagery", "line_start": 32, "line_end": 32, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "577e62b1ebba9f24", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 1. INTRODUCTION\nType: text\n\nsupport for visual exploration of GeoSPARQL endpoints. The LGD Browser [4] was another prominent tool for faceted browsing of structured information in OSM visualized on a slippy map. Sextant [5] has been a powerful visualization and editing tool for exploring time-evolving geospatial linked data. Another research study [6] aimed to visualize geospatial ontologies by exploiting the visualization capabilities of Cesium – a webGL based JavaScript library to render the globe in 3D on the web browser.\n\nAlthough these research studies have developed powerful visualization tools for exploring geospatial linked data, none of them focus on the inherent granular graph nature of linked data consisting of nodes and edges. Nodes and edges are fundamental building blocks of a knowledge graph. This work proposes to enable intuitive visual exploration of nodes and edges of remote sensing scene knowledge graphs in addition to a region-based visualization through an interactive web-based interface. Thus, this work aims to enable users to develop a comprehensive understanding of a Remote Sensing Scene Knowledge Graph (RSS-KG) through the web-based interface that has been developed as part of this research.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "1. INTRODUCTION", "section_headings": ["1. INTRODUCTION"], "chunk_type": "text", "line_start": 33, "line_end": 37, "token_count_estimate": 310, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d135dc773738f5b1", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 2. METHODOLOGY\nType: text\n\nThis work advocates the use of Cesium<sup>1</sup> – a WebGL based JavaScript library for rendering the globe in 3D in the web browser. The geospatial visualization capabilities of Cesium\n\nhave been studied and understood. Thus, it has been found to be an apt candidate for visualizing and enabling interactive exploration of Remote Sensing Scene Knowledge Graphs (RSS-KGs).", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "2. METHODOLOGY", "section_headings": ["2. METHODOLOGY"], "chunk_type": "text", "line_start": 39, "line_end": 43, "token_count_estimate": 136, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a7c7d7084291711d", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 2. METHODOLOGY > 2.1 JSON-LD\nType: text\n\nJSON-LD stands for JavaScript Object Notation for Linked Data. JSON-LD is a data serialization format for Linked Data. It has been conceptualized to leverage the benefits of the interoperable JSON based data serialization to the world of Linked Data and Semantic Web. The serialization in JSON-LD is similar to JSON in terms of storing data in form of key-value pairs. It must be noted that each triple has an \"@id\" key associated with it pointing to the URI of the resource that it represents. Due to its ease of consumption by web-based systems, JSON-LD forms an apt choice for knowledge graph representation and visualization with Cesium. The proposed web-based interface visualizes the \"Regions\" in the Remote Sensing Scene as Nodes and the Spatial Relations - \"Externally Connected\" of the Region Connection Calculus (RCC8) as Edges. Thus, the spatial interaction of the regions among themselves has been effectively visualized. Consequently, this leads to the visual validation of inferred spatial relations in the knowledge graph, thus improving the understanding of the knowledge graph from an end user perspective.\n\n<sup>1</sup> https://cesium.com/cesiumjs/", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "2. METHODOLOGY > 2.1 JSON-LD", "section_headings": ["2. METHODOLOGY", "2.1 JSON-LD"], "chunk_type": "text", "line_start": 45, "line_end": 49, "token_count_estimate": 345, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b4c681a69303051a", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 2. METHODOLOGY > 2.1 JSON-LD\nType: figure\nFigure\n\nImage /page/2/Figure/0 description: A screenshot of a web application titled \"Visual Exploration of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)\" running in a browser. The main view is a 3D visualization of a remote sensing scene of an urban area, overlaid with a complex network graph consisting of numerous blue circular nodes and yellow connecting lines (edges). On the left is a control panel with options for the visualization, including toggles for \"RSS-Knowledge Graph in 3D (Nodes and Edges)\" and \"RSS-Knowledge Graph (Regions-Polygons)\". This panel also provides details about the data: Data Source is \"RSS-KG [JSON-LD]\", Ontologies are \"RSSO | FSO\", the Remote Sensing Scene is from \"Srinagar, India | Sept. 17, 2014\", and the graph was generated by the \"Sem-RSSU Framework\". On the right, a panel titled \"Selected Node\" displays detailed information for a specific node, ID \"http://www.geosysiot.in/semrssu/data#R6\". The type of this node is identified as \"ApplicationSchema#FloodWater\". Its geographic coordinates are listed, including Centroid X: 74.77952999546662 and Centroid Y: 34.086458934296296. The panel also lists other externally connected nodes. The caption below the image reads: \"Figure 2. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event.\"", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "2. METHODOLOGY > 2.1 JSON-LD", "section_headings": ["2. METHODOLOGY", "2.1 JSON-LD"], "chunk_type": "figure", "figure_caption": null, "line_start": 50, "line_end": 50, "token_count_estimate": 434, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["086458934296296", "77952999546662"]}}
{"id": "eb8ac41bbdc869fb", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 2. METHODOLOGY > 2.1 JSON-LD\nType: figure\nFigure: Figure 3. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event, with an Edge representing the \"Externally Connected (EC)\" Relation of the RCC8\n\nFigure 3. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event, with an Edge representing the \"Externally Connected (EC)\" Relation of the RCC8", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "2. METHODOLOGY > 2.1 JSON-LD", "section_headings": ["2. METHODOLOGY", "2.1 JSON-LD"], "chunk_type": "figure", "figure_caption": "Figure 3. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event, with an Edge representing the \"Externally Connected (EC)\" Relation of the RCC8", "line_start": 52, "line_end": 52, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eefa1794752db1e7", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 2. METHODOLOGY > 2.1 JSON-LD\nType: text\n\nThe interface has been developed using HTML5, CSS3 and JavaScript. The Remote Sensing Scene Knowledge Graph (RSS-KG) in the JSON-LD form has been hosted on the server for consumption by the developed interface for visual exploration.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "2. METHODOLOGY > 2.1 JSON-LD", "section_headings": ["2. METHODOLOGY", "2.1 JSON-LD"], "chunk_type": "text", "line_start": 53, "line_end": 55, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13682d800b3487b7", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.1. Dataset\nType: text\n\nThe Remote Sensing Scene Knowledge Graph (RSS-KG) generated by the Sem-RSSU framework for the remote sensing scene of an urban flood event in Srinagar, India during September 2014, has been considered for this study. The snippet in Figure 4 depicts the RSS-KG in the JSON-LD format. The instance of \"Region\" class with \"id\" as \"R0\" has been depicted. The knowledge graph has been semantically enriched by inferring implicit concepts and relations using the Sem-RSSU framework. In that regard, \"ResidentialBuilding\", the inferred classes of \"FloodedResidentialBuilding\" \"AccessibleResidentialBuilding\" defined in the RSSO and FSO have been derived. The snippet also depicts the inferred spatial relation - \"Externally Connected\" of RCC8 between instances of \"Region\" classes. In addition to the LULC Classes and the Spatial Relations, the RSS-KG also constitutes the geometry-related data such as Centroid, Extent and Polygon Geometry represented in the Well-Known Text (WKT) representation.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.1. Dataset", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.1. Dataset"], "chunk_type": "text", "line_start": 59, "line_end": 95, "token_count_estimate": 313, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b72d749571b34ac3", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.1. Dataset\nType: text\n\n\" R0 \" has been depicted . The knowledge graph has been semantically enriched by inferring implicit concepts and relations using the Sem - RSSU framework . In that regard , \" ResidentialBuilding \" , the inferred classes of \" FloodedResidentialBuilding \" \" AccessibleResidentialBuilding \" defined in the RSSO and FSO have been derived . The snippet also depicts the inferred spatial relation - \" Externally Connected \" of RCC8 between instances of \" Region \" classes . In addition to the LULC Classes and the Spatial Relations , the RSS - KG also constitutes the geometry - related data such as Centroid , Extent and Polygon Geometry represented in the Well - Known Text ( WKT ) representation .\n\n```\n[ {\n    \"@id\": \"http://www.geosysiot.in/semrssu/data#RO\",\n    \"@type\": [\n    \"http://www.geosysiot.in/rsso/ApplicationSchema#Region\",\n    \"http://www.geosysiot.in/rsso/ApplicationSchema#ResidentialBuilding\",\n    \"http://www.geosysiot.in/fso/ApplicationSchema#ResidentialBuilding\",\n    \"http://www.geosysiot.in/fso/ApplicationSchema#FloodedResidentialBuilding\"],\n    \"http://www.geosysiot.in/fso/ApplicationSchema#hasInferredLULC\":\n    [ {\n        \"@value\": \"floodedBuilding\"\n      }, {\n        \"@value\": \"accessibleResidentialBuilding\"\n      }),\n      \"http://www.geosysiot.in/rsso/ApplicationSchema#hasURX\": [ {\n        \"@type\": \"http://www.w3.org/2001/XMLSchema#double\",\n        \"@value\": \"74.780932527\"\n      }),\n      \"http://www.geosysiot.in/rsso/ApplicationSchema#hasURY\": [ {\n        \"@type\": \"http://www.w3.org/2001/XMLSchema#double\",\n        \"@type\": \"http://www.w3.org/2001/XMLSchema#double\",\n        \"@type\": \"http://www.so.org/2001/XMLSchema#double\",\n        \"@type\": \"http://www.so.org/2001/XMLSchema#double\",\n        \"@type\": \"http://www.geosysiot.in/rsso/ApplicationSchema#hasURY\": [ {\n        \"@type\": \"http://www.so.org/2001/XMLSchema#double\",\n        \"@type\": \"http://www.geosysiot.in/rsso/ApplicationSchema#hasURY\": [ {\n        \"@type\": \"http://www.geosysiot.in/semrssu/data#R4\"\n      }, ...\n} \"http://www.opengis.net/ont/geosparql#rcc8ec\": [ {\n        \"@id\": \"http://www.geosysiot.in/semrssu/data#R4\"\n      }, ...\n} \"####################################\n```", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.1. Dataset", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.1. Dataset"], "chunk_type": "text", "line_start": 59, "line_end": 95, "token_count_estimate": 825, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["780932527"]}}
{"id": "ecbe651c13a60dcb", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.1. Dataset\nType: figure\nFigure: Figure 4. Snippet of the RSS-KG for a Remote Sensing Scene of an Urban Flood Event\n\nFigure 4. Snippet of the RSS-KG for a Remote Sensing Scene of an Urban Flood Event", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.1. Dataset", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.1. Dataset"], "chunk_type": "figure", "figure_caption": "Figure 4. Snippet of the RSS-KG for a Remote Sensing Scene of an Urban Flood Event", "line_start": 96, "line_end": 96, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eea1d22b2916f05f", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.1. Dataset\nType: table\nTable: Table 1. Time taken for visualizing Remote Sensing Scene Knowledge Graph (RSS-KG) for Nodes and Edges and Region Geometries\n\n| Remote Sensing Scene Knowledge Graph (RSS-KG) Visualization | Number of Entities     | Time (seconds) |\n|-------------------------------------------------------------------|---------------------------|-------------------|\n| Nodes and Edges                                                   | Nodes: 554 Edges: 2153 | 5.78              |\n| Region Geometries                                                 | Regions: 554              | 1.02              |", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.1. Dataset", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.1. Dataset"], "chunk_type": "table", "table_caption": "Table 1. Time taken for visualizing Remote Sensing Scene Knowledge Graph (RSS-KG) for Nodes and Edges and Region Geometries", "columns": ["Remote Sensing Scene Knowledge Graph (RSS-KG) Visualization", "Number of Entities", "Time (seconds)"], "table_row_start": 1, "table_row_end": 2, "line_start": 100, "line_end": 103, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c24da8c4b81494ea", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.1. Dataset\nType: figure\nFigure\n\nImage /page/3/Figure/0 description: A screenshot of a web application titled \"Visual Exploration of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)\". The main view displays a satellite image of a town, overlaid with a color-coded map showing regions in red, green, black, and brown, with a grey road running through the center. On the left is a control panel with various toggles and settings, including \"RSS-Knowledge Graph in 3D\" and \"RSS-Knowledge Graph (Regions-Polygons)\". It specifies the data source as \"Remote Sensing Scene (Raster - WV02 [FCC] | Srinagar, India | Sept. 17, 2014)\". On the right, a panel displays detailed information for a selected region, which is a road. The details include its ID, Type, Centroid coordinates (X: 74.7800655700565, Y: 34.08730995206955), corner coordinates, and a list of externally connected regions.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.1. Dataset", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.1. Dataset"], "chunk_type": "figure", "figure_caption": null, "line_start": 107, "line_end": 107, "token_count_estimate": 289, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["08730995206955", "7800655700565"]}}
{"id": "8e2d66523d75628e", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.1. Dataset\nType: figure\nFigure: Figure 5. Visualization of Regions as Polygon Geometries in a Remote Sensing Scene Knowledge Graph (RSS-KG) for a RS Scene of an Urban Flood Event\n\nFigure 5. Visualization of Regions as Polygon Geometries in a Remote Sensing Scene Knowledge Graph (RSS-KG) for a RS Scene of an Urban Flood Event", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.1. Dataset", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.1. Dataset"], "chunk_type": "figure", "figure_caption": "Figure 5. Visualization of Regions as Polygon Geometries in a Remote Sensing Scene Knowledge Graph (RSS-KG) for a RS Scene of an Urban Flood Event", "line_start": 109, "line_end": 109, "token_count_estimate": 123, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "463f438b1edb5f57", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 3. EXPERIMENTAL RESULTS > 3.2. Discussion\nType: figure\nFigure: Figure 2, figure 3 and figure 5 depict screenshots of the developed visual exploration interface. Figure 3 depicts the knowledge graph visualization in the form of nodes and edges. The nodes and edges can be interacted with through mouse clicks to be presented with more information about the clicked entities. Similarly, the figure 5 depicts the visualization of polygon geometries of the \"Region\" instances in the knowledge graph. The \"Region\" instances are visualized in a color in accordance with the Land Use Land Cover Class (LULC) they belong to. These \"Region\" instances can also be interacted with individually to be presented with inferred concepts and relations from the RSS-KG. Table 1 depicts the time taken for visual rendering of the knowledge graph by the web browser. It was observed that the node-edge visualization took significantly more time as compared to region-geometry visualization for rendering, due to the overhead of visualizing the huge number of edges, in addition to nodes.\n\nFigure 2, figure 3 and figure 5 depict screenshots of the developed visual exploration interface. Figure 3 depicts the knowledge graph visualization in the form of nodes and edges. The nodes and edges can be interacted with through mouse clicks to be presented with more information about the clicked entities. Similarly, the figure 5 depicts the visualization of polygon geometries of the \"Region\" instances in the knowledge graph. The \"Region\" instances are visualized in a color in accordance with the Land Use Land Cover Class (LULC) they belong to. These \"Region\" instances can also be interacted with individually to be presented with inferred concepts and relations from the RSS-KG. Table 1 depicts the time taken for visual rendering of the knowledge graph by the web browser. It was observed that the node-edge visualization took significantly more time as compared to region-geometry visualization for rendering, due to the overhead of visualizing the huge number of edges, in addition to nodes.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "3. EXPERIMENTAL RESULTS > 3.2. Discussion", "section_headings": ["3. EXPERIMENTAL RESULTS", "3.2. Discussion"], "chunk_type": "figure", "figure_caption": "Figure 2, figure 3 and figure 5 depict screenshots of the developed visual exploration interface. Figure 3 depicts the knowledge graph visualization in the form of nodes and edges. The nodes and edges can be interacted with through mouse clicks to be presented with more information about the clicked entities. Similarly, the figure 5 depicts the visualization of polygon geometries of the \"Region\" instances in the knowledge graph. The \"Region\" instances are visualized in a color in accordance with the Land Use Land Cover Class (LULC) they belong to. These \"Region\" instances can also be interacted with individually to be presented with inferred concepts and relations from the RSS-KG. Table 1 depicts the time taken for visual rendering of the knowledge graph by the web browser. It was observed that the node-edge visualization took significantly more time as compared to region-geometry visualization for rendering, due to the overhead of visualizing the huge number of edges, in addition to nodes.", "line_start": 113, "line_end": 113, "token_count_estimate": 529, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce4243bbb7634d30", "text": "Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs\nSection: 4. CONCLUSION\nType: text\n\nThis research proposed an intuitive interface using Cesium, for effective visual exploration of Remote Sensing Scene Knowledge Graphs (RSS-KGs) generated by the Sem-RSSU framework. The knowledge graphs comprehensively represent the different Land Use Land Cover regions contained in remote sensing scenes along with inferred spatio-contextual concepts and relations. Thus, this research aimed to enhance the comprehensive understanding of a remote sensing scene from an end user\n\nperspective. The visual exploration interface developed as a part of this research has been hosted on the web (http://www.geosysiot.in/tools/rssKG-Explorer/).\n\nFuture work on this interface would involve enabling support for user uploaded geospatial knowledge graphs in addition to support for remote GeoSPARQL endpoints.", "metadata": {"source_file": "data/('Towards_Visual_Exploration_Of_Semantically_Enriched_Remote_Sensing_Scene_Knowledge_Graphs_RSS-KGs', '.pdf')_extraction.md", "document_title": "Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs", "section_path": "4. CONCLUSION", "section_headings": ["4. CONCLUSION"], "chunk_type": "text", "line_start": 116, "line_end": 121, "token_count_estimate": 224, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "25f0ad525010c63b", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nType: text\n\nAbstract On June 25, 2020, Jinweng Co in Yiga, Tibet, experienced an outburst flood that resulted in catastrophic damage to farmland and roads. The complex causal factors for glacial lake outburst flooding (GLOF) are not fully understood. This paper provides a systematic analysis of the contributing factors leading to the GLOF disaster in terms of meteorological triggering, glacial activity, lake expansion, landslide, and glacial collapse. The analysis is based on multi-source remote sensing approaches. Pixel offset tracking of Sentinel-1 images shows changes in the glacier flow velocity from 2017 to 2020. Sentinel-2 and Landsat-8 images and inventory data have revealed the expansion of the lake since 1998. The satellite precipitation measurements revealed that the highest daily rainfall in recent years occurred approximately 4 days before the GLOF. Time series synthetic aperture radar (SAR) backscattering images and interferograms suggest that a landslide had occurred from the western lateral moraine into the lake. Additionally, SAR images suggest possible ice collapse from the glacier tongue into the lake. The causal mechanism for the June 2020 GLOF event was likely the dam failure triggered by heavy rainfall and combined with landslides and ice collapses. Our research can provide a reference to identify and mitigate glacial lake outburst disasters in mountainous regions based on satellite optical and radar images.\n\n**Keywords** Glacial lake outburst flood (GLOF)  $\\cdot$  Glacier three-dimensional flow velocity  $\\cdot$  Outburst mechanism  $\\cdot$  Multi-source remote sensing", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_headings": ["Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China"], "chunk_type": "text", "line_start": 3, "line_end": 7, "token_count_estimate": 459, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "4b2b57bfb85abc94", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Introduction\nType: text\n\nGlacial lake outburst flood (GLOF) is a sudden and rapid downstream discharge of a significant volume of water. Previous studies (Evans and Clague 1994; Nadim et al. 2006; Yamada 1998; Emmer and Cochachin 2013a, Emmer and Vilímek 2013b, Emmer 2017) identified two groups of factors that induce lake outbursts. The first set consists of dynamic causes, such as slope movement into the lake, earthquakes, heavy rainfall, or snowmelt. The second set includes long-term factors such as the melting of buried ice, hydrostatic pressure changes, and other long-term degradation processes. GLOFs often have catastrophic consequences for communities and infrastructure located downstream. Glacier recede and thinning in high mountains generate the formation and development of glacial lakes (Richardson and Reynolds 2000; Haeberli et al. 2001, 2002). Monitoring-related glacial movement and changes in glacier lakes and the surroundings help us better understand how the ice mass propagates in space, how their movements evolve over time, and how external factors control their\n\nbehaviors (Satyabala 2016; Ma et al. 2020). Owing to the inherently challenging landscapes, comprehensive remote sensing methods are needed to study and monitor GLOFs.\n\nThe outburst of Jinweng Co (\"Co\" refers to \"lake\" in Tibetan) in Yiga village, Tibet, caused intense and sudden flooding on June 25, 2020. This grave disaster inundated and destroyed 382.43 km<sup>2</sup> of farmland, washed away over 43.9 km of roads, and flooded 45% of the Yiga Scenic Area project (Wang et al. 2020a). Over 2,000 glacial lakes have been detected across the Himalayas (Fujita et al. 2013), so the potential for further hazards is significant. Moreover, the eastern Himalayas are considered as one of the most severely deformed areas due to intense tectonic activities and earthquakes. The melting, thinning, and receding of temperate glaciers leads to lake expansion and is related to slope movement. Extreme precipitation plays an important role in triggering slope movement, while an increase in temperature can result in increased melting, permafrost degradation, and rockfalls (Hu et al. 2019a; Wang et al. 2020a, b; Lu and Kim 2021).\n\nMany studies have documented that snow/ice accumulation/ avalanches and landslides significantly impact outburst flooding (Allen et al. 2016; Clague and Evans 2000; O'Connor et al. 2001; Worni et al. 2012). The Jinweng Co disaster originated from a parent glacier and included an outburst of the proglacial lake. The real-time monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF (Wang et al. 2020a). However, the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated. The evolution of the glacier, the proglacial lake, and the surrounding area prior to the GLOF event has not yet been analyzed. A complete assessment of triggering factors (glacier activity, precipitation, air temperature, slope movement, and ice collapse) is still lacking.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 23, "token_count_estimate": 823, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "784835bcd49b86e1", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Introduction\nType: text\n\noriginated from a parent glacier and included an outburst of the proglacial lake . The real - time monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF ( Wang et al . 2020a ) . However , the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated . The evolution of the glacier , the proglacial lake , and the surrounding area prior to the GLOF event has not yet been analyzed . A complete assessment of triggering factors ( glacier activity , precipitation , air temperature , slope movement , and ice collapse ) is still lacking .\n\nThus, this study addresses the following key question: Can we identify precursory characteristics of the 2020 GLOF at Jinweng Co in eastern Tibet? To address this question, we aimed to understand the movement of glaciers and the change in moraine-dammed lakes, as well as temperature and precipitation records in the study area. We used spaceborne C-band Sentinel-1 data to retrieve the time series two-dimensional (2D) displacement and three-dimensional (3D) glacier flow velocity. The interpretation was then augmented with Sentinel-2 and Landsat-8 optical images and inventory data to construct changes to the area of the lake before and after the event. Meteorological observations were examined to understand the climatic properties of the region. Additionally, we analyzed SAR intensity images and interferograms to infer the\n\nPublished online: 30 January 2022 Landslides\n\nglacier tongue changes and identify landslide activity. This led to further clarification regarding the outburst triggering mechanisms.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Introduction", "section_headings": ["Introduction"], "chunk_type": "text", "line_start": 9, "line_end": 23, "token_count_estimate": 446, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "25d5f72040e22f95", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Study area\nType: text\n\nThe study region is within the Tanggula Mountains, which are home to some of the highest mountains in the world. It is also the most humid region of the southeastern Qinghai-Tibet Plateau (QTP) (Fig. 1). The topography of the study area is affected by the compression of the Indian Ocean-Eurasian continental plate, uplift of the QTP, strong faulting activity, and fluvial incision. The climate is influenced by both East Asian and Indian monsoons (Ding 2013). Precipitation during the rainy season is mainly caused by airflow from the southwest, west, and northwest (Li et al. 2009). The temperate glaciers in this study area are small and primarily oriented in the north-south direction with large ice caps (You and Yang 2013). Consequently, the area is subject to glacial hazards such as glacier debris flows, GLOFs, and glacier surges, which are further increased by climate change (Allen et al. 2016; Bazai et al. 2021; Chiarle et al. 2007; Wang et al. 2020a, b; Kääb et al. 2021). Generally, a glacial lake outburst results from external forces such as ice or rock fall, snow avalanches, landslides, rainstorms, glacier surges, rapid snow melting, and/or internal processes such as the ablation of buried ice in moraines and the release of lake water inside the ice body (Wang et al. 2020a).\n\nJinweng Co (30.356 N°, 93.631 ° E) is a proglacial moraine-dammed lake (Fig. 2) and is also the largest glacial lake within the Nidou Zangbo basin (\"Zangbo\" refers to rivers in Tibetan) (Zheng et al. 2021). The lake is oriented in a north–south direction and is characterized by steep slopes and lateral moraines. The slopes are approximately 40°, and the average elevation of lateral moraines is more than 100 m above the lake level (Zheng et al. 2021). As shown in Fig. 2d, the length is approximately 1.8 km, and its width near the glacier tongue is 0.24 km, while the width near the dam was 0.33 km before the lake outburst. The parent glacier tongue enters\n\n**Fig. 1** Overview of the study area showing Jinweng Co and coverage of multiple datasets outlined in purple (Sentinel-2), orange (Landsat-8), blue (Ascending Sentinel-1), and red (Descending Sentinel-1)\n\nthe lake after passing through a steep ice cliff approximately 350 m long with an average slope of approximately 35 ° (Fig. 2). The parent glacier is a temperate glacier with a mean annual temperature of approximately 0 °C. The snow basin at high altitude serves as the accumulation zone, while the tongue is the ablation zone. This study investigates the triggering factors for the June 2020 GLOF event in Jinweng Co by analyzing the movement of the parent glaciers and changes to the lake and surroundings using multi-satellite remote sensing datasets.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Study area", "section_headings": ["Study area"], "chunk_type": "text", "line_start": 25, "line_end": 33, "token_count_estimate": 773, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "05bde416e830e4bb", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods\nType: text\n\nThe multi-track C-band Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 and Landsat-8 optical images were used to investigate the triggering factors of the 2020 GLOF event (Fig. 3). The Copernicus Sentinel-1A/B and Sentinel-2 Level-1C images for this study are available free of cost from the Sentinel Scientific Data Hub of the European Space Agency. Multi-temporal Landsat images were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/). In addition, daily and monthly precipitation measurements from the satellite Global Precipitation Measurement (GPM) (Skofronick 2017) were obtained to analyze the meteorological factors. In addition, inventory data from 1975 to 2018 were obtained from the National Tibetan Plateau Data Center (TPDC) to evaluate temporal variations in Jinweng Co (https://data.tpdc.ac.cn) (Wang 2015).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods", "section_headings": ["Data and methods"], "chunk_type": "text", "line_start": 35, "line_end": 37, "token_count_estimate": 267, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "3039d69e1032b7da", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Satellite datasets\nType: text\n\nNinety-eight interferometric wide (IW) swath mode ascending Sentinel-1 and eighty-two descending Sentinel-1 images from March 2017 through October 2020 were acquired. The pixel spacing in the ground range and azimuth direction was ~4 m and ~14 m, respectively. Sentinel-2 data with multi-spectral instrument (MSI) images were acquired on May 1 and July 27, 2020, and provided a spatial resolution of 10 m. Landsat-8 MSI images from 2018 to 2019 with a spatial resolution", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "text", "line_start": 39, "line_end": 41, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "33c597781840f543", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Satellite datasets\nType: figure\nFigure\n\nImage /page/1/Figure/11 description: A satellite map of a mountainous region, with longitude ranging from 93°25' to 93°55' and latitude from 30°15' to 30°35'. The map displays snow-covered mountains, glaciers, and lakes. Several colored outlines indicate the coverage areas of different satellites: a blue polygon for 'Ascending Sentinel-1', a red polygon for 'Descending Sentinel-1', an orange rectangle for 'Landsat-8', and a purple rectangle for 'Sentinel-2'. A legend in the bottom-left corner defines symbols for Glacier, Lake, and Main city. Key locations marked include the main city 'Yiga' and the lake 'Jinweng Co'. A blue arrow indicates the 'Flood direction' from west to east. A scale bar in the bottom-right shows a scale of 0 to 6 km.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "figure", "figure_caption": null, "line_start": 42, "line_end": 42, "token_count_estimate": 273, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "fe1725b42136ae73", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Satellite datasets\nType: text\n\nFig. 2 Overview of Jinweng Co and its parent glacier. a An overall view of Jinweng Co and surroundings. **b** The parent glacier before the GLOF event. **c** The crevassed glacier tongue terminated in Jinweng Co. d Morphological parameters of Jinweng Co. e The eastern lateral moraine and steep slopes. f The landslide zone at the western lateral moraine. **g** The parent glacier after the GLOF event. Photos: Q. Quying Source: Guoxiong Zheng (used with permission)", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "text", "line_start": 43, "line_end": 45, "token_count_estimate": 181, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "2d73676306ea9b6e", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Satellite datasets\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: A collage of seven images, labeled (a) through (g), showing various aerial and landscape views of a glacial environment. Each image includes a compass rose indicating the direction of North.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "figure", "figure_caption": null, "line_start": 46, "line_end": 46, "token_count_estimate": 106, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "2a8dfd60965a0763", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Satellite datasets\nType: text\n\nImage (a) is a wide aerial view of a valley with snow-capped mountains. A glacier at the far end feeds into a lake labeled \"Jinweng Co\". A dashed white line with an arrow indicates the \"Flow direction\" down the valley.\n\nImage (b) is a top-down aerial view of a mountainous, snow-covered landscape partially obscured by clouds.\n\nImage (c) shows a glacier terminating in a milky green proglacial lake, with steep, rocky mountain slopes surrounding it.\n\nImage (d) provides a view of the glacial lake with its dimensions annotated: 1.8 km in length, 0.33 km in width at the far end, and 0.24 km in width at the near end.\n\nImage (e) is a similar view to (d), with a dashed white line highlighting the \"Lateral moraine\" along the side of the lake.\n\nImage (f) shows the same lake and mountains, with a red dashed line and text identifying a \"Landslide zone\" on the slope adjacent to the lake. There is snow in the foreground.\n\nImage (g) is another high-altitude aerial view looking down on a glacier and surrounding mountains, with some cloud cover.\n\nof 30 m were added to derive the temporal changes of Jinweng Co. Information on the Sentinel-1/2 and Landsat data are listed in Table 1, and the data coverages are shown in boxes in Fig. 1.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Satellite datasets", "section_headings": ["Data and methods", "Satellite datasets"], "chunk_type": "text", "line_start": 47, "line_end": 63, "token_count_estimate": 388, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "0a254f6e5878a774", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Mapping slope movement with SAR intensity images and interferograms\nType: text\n\nSAR intensity images can be used to detect landscape changes due to their sensitivity to terrain slope, surface roughness, and dielectric constant (e.g., Lu and Meyer 2002; Kim et al. 2017; Zribi and Dechambre 2017). Multi-temporal SAR backscattering intensity images were used in this study to infer changes in glaciers, lakes, and surroundings before the lake outburst event. Interferometric synthetic aperture radar (InSAR) can map surface deformation in the spatial–temporal view, which is a direct manifestation of slope movement (Hu et al. 2019a; Lu and Meyer 2002; Lu et al. 2003; Lu and Kim 2021).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Mapping slope movement with SAR intensity images and interferograms", "section_headings": ["Data and methods", "Mapping slope movement with SAR intensity images and interferograms"], "chunk_type": "text", "line_start": 65, "line_end": 67, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "774bec88ed83ebf0", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Change detection with optical images\nType: text\n\nThe interpretation of optical images is commonly employed to support GLOF mapping and inventory (Strozzi et al. 2010). There are countless glaciers in this study area, which induce enormous glacial lakes as glacier recedes. The Sentinel-2 and Landsat-8 images\n\nallow us to recognize geomorphological features related to ice mass movements, such as crevasses, debris flows, and outburst flooding. For instance, a glacial lake may have green patterns with fine texture in one optical image, and after the glacial lake outbursts or shrinks, its edge will form a gray-white submerged zone with obvious breaches and colluvial deposits. Using multi-temporal images, we can interpret the changes in glacial lakes and analyze the possibility of their collapse.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Change detection with optical images", "section_headings": ["Data and methods", "Change detection with optical images"], "chunk_type": "text", "line_start": 69, "line_end": 73, "token_count_estimate": 241, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "c755194e08abad4d", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Offset tracking method\nType: text\n\nWe carried out the offset tracking procedure implemented by GAMMA software to estimate the two-dimensional displacement (Wegmuller et al. 1998; Werner and Wegmuller 2000; Gomez et al. 2019). Offset tracking includes intensity tracking and speckle tracking based on maximizing the cross-correlation of SAR image patches (e.g., Strozzi et al. 2002; Pritchard et al. 2005) to derive glacier displacement for pairs of images acquired at different times. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used to assist the co-registration of the SAR images to minimize geometric artifacts in offset-tracking displacements due to high topography (Kobayashi et al. 2009; Liu et al. 2020). A template of  $64 \\times 64$  and  $4 \\times 1$  was adopted in this study. Thresholds of amplitude correlation range between 0.2 and 0.4.\n\nTable.1 Basic parameters of the SAR and optical datasets used in this study", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Offset tracking method", "section_headings": ["Data and methods", "Offset tracking method"], "chunk_type": "text", "line_start": 75, "line_end": 79, "token_count_estimate": 298, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "29efca5ac84ca295", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Offset tracking method\nType: table\nTable\n\n| Data                            | Sentinel-1            |                       | Sentinel-2            | Landsat-8             |\n|---------------------------------|-----------------------|-----------------------|-----------------------|-----------------------|\n| Pixel Spacing (azimuth × range) | 14 m × 4 m            |                       | 10 m                  | 30 m                  |\n| Number of SAR images            | 98(ascending)         | 82(descending)        | 2                     | 2                     |\n| Acquisition period              | 2017/03/21-2020/10/31 | 2017/03/16-2020/10/26 | 2020/05/01-2020/07/27 | 2018/06/06-2019/06/25 |", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Offset tracking method", "section_headings": ["Data and methods", "Offset tracking method"], "chunk_type": "table", "table_caption": null, "columns": ["Data", "Sentinel-1", "", "Sentinel-2", "Landsat-8"], "table_row_start": 1, "table_row_end": 3, "line_start": 80, "line_end": 84, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "5f2ea61027263aae", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Offset tracking method\nType: text\n\n**Fig. 3** Flow chart of remote sensing methods used in this study", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Offset tracking method", "section_headings": ["Data and methods", "Offset tracking method"], "chunk_type": "text", "line_start": 85, "line_end": 87, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "913f26c83ff9b447", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Offset tracking method\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A flowchart titled 'Recent Landslides' illustrating a process for analyzing geological data from three different sources. The flowchart is organized into three parallel columns, each starting with a different data input and leading to a specific output. The first column starts with 'External DEM' (Digital Elevation Model), represented by a colorful topographic image. This data goes through 'Offset tracking', 'Stacking', and '3-D inversion' to produce 'Derivation of 3D annual flow velocity', shown as a 3D map with red and yellow areas. The middle column begins with 'Ascending and Descending SAR images', depicted as stacks of grayscale images. This data undergoes 'Co-registration' to create 'Original interferograms', which are then processed through 'Interferograms filtering' to result in 'Deformation interferograms', illustrated by a colorful, noisy-textured image. The third column uses 'Optical images', shown as a stack of color images, as input. This path involves 'Change detection', leading to 'Intensity change' (shown as a stack of grayscale images), and finally results in 'Optical interpretation', which is represented by two side-by-side maps showing changes in a landscape, possibly a glacier.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Offset tracking method", "section_headings": ["Data and methods", "Offset tracking method"], "chunk_type": "figure", "figure_caption": null, "line_start": 88, "line_end": 88, "token_count_estimate": 386, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "b238bcae01aa1c52", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Pixel offset tracking small baseline subsets (PO-SBAS)\nType: text\n\nPixel offset (PO) tracking small baseline subsets (SBAS) (PO-SBAS) follows the same motivation as the SBAS-InSAR technique. A sequence of small baseline SAR image pairs that have been previously co-registered with respect to a common reference image was obtained as a starting point (Sansosti et al. 2006; Casu et al. 2011). Subsequently, instead of the phase difference of the selected SAR images, we used their intensities to calculate the pixel offset for both the line-of-sight (LOS) and azimuth directions. Then, the singular value decomposition (SVD) inversion method was applied for the estimated relative LOS and azimuth offsets to generate the corresponding offset-based deformation time series (Sansosti et al. 2006; Casu et al. 2011).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Pixel offset tracking small baseline subsets (PO-SBAS)", "section_headings": ["Data and methods", "Pixel offset tracking small baseline subsets (PO-SBAS)"], "chunk_type": "text", "line_start": 91, "line_end": 93, "token_count_estimate": 259, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "cc4b9a124edb4fa3", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Data and methods > Derivation of 3D Glacier velocity\nType: text\n\nTo retrieve the 3D glacier flow velocity from ascending and descending SAR images, we first obtained displacement measurements in the azimuth and LOS directions based on small baseline image pairs from both descending and ascending tracks.\n\nThe 3D velocity field can be obtained from velocity measurements in four distinct directions (Li et al. 2018; Yang et al. 2020):\n\n$$\\left\\{ \\begin{array}{l} V_{LOS}^{i} = -V_{U}cos\\:\\theta^{i} + \\bullet V_{E}sin(\\alpha^{i} - 3\\pi/2)sin\\:\\theta^{i} + V_{N}cos(\\alpha^{i} - 3\\pi/2)sin\\:\\theta^{i} \\\\ V_{AZ}^{i} = -V_{E}cos(\\alpha^{i} - 3\\pi/2) + V_{N}sin(\\alpha^{i} - 3\\pi/2) \\end{array} \\right.$$\n\nwhere i stands for the orbit direction (A indicates the ascending track, and D indicates the descending track),  $\\theta$  is the incidence\n\nangle, and  $\\alpha$  is the azimuth angle. E, N, and V refer to the east, north, and vertical directions, respectively. The matrix form is as follows:\n\n$$BX = V (2)$$\n\n $X = \\begin{bmatrix} V^{\\text{U}} & V^{\\text{E}} & V^{\\text{N}} \\end{bmatrix}^{\\text{T}}$ ;  $L = \\begin{bmatrix} V_{\\text{LOS}}^{\\text{A}} & V_{\\text{AZ}}^{\\text{A}} & V_{\\text{LOS}}^{\\text{D}} & V_{\\text{AZ}}^{\\text{D}} \\end{bmatrix}^{\\text{T}}$ ; and B is a design matrix composed of imaging geometry parameters.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Data and methods > Derivation of 3D Glacier velocity", "section_headings": ["Data and methods", "Derivation of 3D Glacier velocity"], "chunk_type": "text", "line_start": 95, "line_end": 109, "token_count_estimate": 578, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "5fedbb8ebb1f96f9", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Results > Changes in glacier velocity\nType: text\n\nIn this study, the 2D and 3D glacial movements of Jinweng Co from 2017 to 2020 were derived (Fig. 4). The 3D glacier flow velocity during this period is shown in Fig. 4a. The vector arrows indicate the horizontal velocity, whereas the color shows the vertical velocity. In the horizontal direction, the north directed velocity mainly appeared in the glacier trunk of Jinweng Co, with a mean flow velocity of up to 200 m/year. In the vertical direction, the parent glacier moved downward at a velocity of 40 m/year. Owing to sparse acquisitions from descending Sentinel-1, we only obtained the long time series displacement from the ascending track to show the temporal variation (Fig. 4b). As shown in Fig. 4b, P1 was located at the glacier tongue near the lake. The mass transport is evident in the azimuth direction (approximately in the northern direction), as seen in the cumulative displacement of up to 18.4 m. Interestingly, the total displacement during the 2019-2020 cycle (October 2019 to June 2020) at the lower part of the glacier (close to the proglacial lake) is much larger than that observed in the previous years (Fig. 4b).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Results > Changes in glacier velocity", "section_headings": ["Results", "Changes in glacier velocity"], "chunk_type": "text", "line_start": 113, "line_end": 115, "token_count_estimate": 346, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "a3f89f195617130b", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Results > Changes in glacier velocity\nType: figure\nFigure\n\nImage /page/4/Figure/0 description: The image contains two plots, labeled (a) and (b).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Results > Changes in glacier velocity", "section_headings": ["Results", "Changes in glacier velocity"], "chunk_type": "figure", "figure_caption": null, "line_start": 116, "line_end": 116, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "f3c0cd39fc1b39e6", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Results > Changes in glacier velocity\nType: text\n\nPlot (a) is a 3D surface plot illustrating velocity in a geographical area. The surface is colored according to a 'Vertical velocity (m/year)' scale, which ranges from -40 (blue) to 40 (red). The plot also displays black arrows indicating 'Horizontal velocity (m/year)', with a scale arrow representing 200 m/year. A location on the map is labeled 'Jinweng Co'. The x-axis ranges from approximately 93.64 to 93.665, and the y-axis ranges from approximately 30.325 to 30.365. A compass indicates the direction of North.\n\nPlot (b) is a scatter plot showing 'Displacement (m)' on the y-axis versus 'Date (yyyy/m/d)' on the x-axis. The time period spans from 2017/3/1 to 2020/9/1. There are two data series: 'Azimuth', represented by red circles, and 'LOS', represented by blue triangles. The 'Azimuth' displacement shows a steady increase from about 2 m to over 17 m. An event labeled 'Outburst' is marked on the 'Azimuth' data around mid-2020. The 'LOS' displacement remains much lower, fluctuating between 0 m and approximately 3 m over the same period. The y-axis for displacement ranges from 0 to 20 m.\n\nFig. 4 a 3D glacier flows velocity during 2017–2020. The color indicates vertical velocities, and the arrow indicates horizontal velocities. **b** 2D time series displacement for P1 from the ascending track. The location of P1 is shown in a", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Results > Changes in glacier velocity", "section_headings": ["Results", "Changes in glacier velocity"], "chunk_type": "text", "line_start": 117, "line_end": 123, "token_count_estimate": 448, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "a651118028e72dc8", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Results > Changes to Jinweng Co\nType: text\n\nTo investigate the historical evolution of Jinweng Co, we analyzed its extent of variation and frontal recede using the inventory data from TPDC and by interpreting optical images. Figure 5 shows the temporal variations in Jinweng Co from 1998 to 2020. By superimposing the lake boundaries in the image of 2015 (Fig. 5a), we clearly see that the lake expanded upstream, and the glacier tongue receded. From 1998 to 2020, the proglacial lake expanded dramatically at a mean rate of 21.73 m²/year, and the glacier receded at a mean rate of 0.13 m/year (Fig. 5b).\n\nTo better understand the changes to Jinweng Co before and after the catastrophic GLOF in June 2020, we interpreted four summertime optical images, including Sentinel-2 images and Landsat images, and analyzed the variations to the boundary of Jinweng Co, as shown in Fig. 6. The lake area experienced an increasing trend from 2018 to 2020. It showed 0.52 km² on June 6, 2018; 0.55 km² on June 25, 2019; and then 0.57 km² on May 1, 2020. After the GLOF event in June 2020, the lake area decreased to 0.32 km², as reflected in the July 2020 image. This resulted in a massive (~0.25² km) downstream water flow from the lake dam (Fig. 6d).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Results > Changes to Jinweng Co", "section_headings": ["Results", "Changes to Jinweng Co"], "chunk_type": "text", "line_start": 125, "line_end": 129, "token_count_estimate": 367, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "d331f636ea298a5b", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Results > Changes to Jinweng Co\nType: figure\nFigure\n\nImage /page/4/Figure/5 description: A two-part figure, labeled (a) and (b), illustrating the changes in a glacial lake over time. Part (a) is a satellite image showing the expansion of a lake between 1998 and 2020. The image is marked with latitude and longitude coordinates (30°21' to 30°22' N, 93°37' to 93°38' E). A legend titled 'Acquisition Data (yyyy/mm/dd)' shows colored outlines for the lake's extent on different dates: 1998/10/19 (light blue), 2001/10/21 (green), 2011/11/10 (yellow), 2015/11/15 (red outline), 2018/10/15 (darker red outline), 2019/06/25 (pink outline), and 2020/05/01 (purple outline). An inset map shows a wider view of the lake dated 2020/7/27. Part (b) is a scatter plot showing the change in lake area and retreat distance over time, from 1998 to 2020. The x-axis represents the date. The left y-axis represents 'Lake area (km²)' and the right y-axis represents 'Retreat distance (m)'. There are two data series plotted. Red squares represent the lake area, which increases from about 0.4 km² to nearly 0.6 km². The linear fit for this data has a slope of 21.73 m²/year. Green circles represent the retreat distance, which increases from about 0.5 m to 1.2 m. The linear fit for this data has a slope of 0.13 m/year. Both linear fits are shown with their 95% confidence bands. The legend clarifies the symbols: red squares for 'Lake area (km²)', green circles for 'Retreat distance (km)', and lines and shaded areas for the linear fits and confidence bands.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Results > Changes to Jinweng Co", "section_headings": ["Results", "Changes to Jinweng Co"], "chunk_type": "figure", "figure_caption": null, "line_start": 130, "line_end": 130, "token_count_estimate": 476, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "48368b01e8191529", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Results > Changes to Jinweng Co\nType: text\n\n**Fig. 5** Temporal evolution of the Jinweng Co during 1998–2020. **a** Lake boundaries derived from inventory data (1998, 2001, 2011, 2015) and interpretation of Landsat-8/Sentinel-2 images (2018, 2019, 2020); the inset map shows the lake boundary after the collapse. **b** Time series of the lake areas and glacier recede from 1998 to 2020. Red squares are measurements of the lake area in km2; the red line\n\nis a linear regression fit of the area measurements, and aqua shading surrounding the red line refers to the 95% confidence interval of the lake area. Green circles are distance in km of parent glacier frontal recede; the green line is a linear regression fitting within a 95% confidence interval, indicted by pink shading", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Results > Changes to Jinweng Co", "section_headings": ["Results", "Changes to Jinweng Co"], "chunk_type": "text", "line_start": 131, "line_end": 135, "token_count_estimate": 229, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "7392da42b9e00cf3", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Recent Landslides\nType: figure\nFigure\n\nImage /page/5/Figure/1 description: A series of four satellite images, labeled (a) through (d), showing a glacial landscape at different points in time. Each image includes a legend and scale. The dates for the images are: (a) 2018/6/6, (b) 2019/6/25, (c) 2020/5/1, and (d) 2020/7/27. The images depict a large glacier in shades of cyan, with surrounding land in brown, green, and red. A large, dark blue glacial lake is prominent in the upper left of each frame. A smaller, higher-elevation lake is visible in panel (a), but appears to have drained in the subsequent images. Key features are labeled, including the city of Yiga and elevations such as 3800m, 4845m, and 6838m. A legend in the bottom left of each panel defines symbols for City, Elevation, Glacier area, Lake area, Ice dam, Natural drainage channel, and Conduit. The legend for panel (d) also includes a symbol for Breach.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "figure", "figure_caption": null, "line_start": 138, "line_end": 138, "token_count_estimate": 288, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "e52e3b3a99e0d0b6", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Recent Landslides\nType: text\n\nFig. 6 A comparison of the Jinweng Co area and the surrounding glaciers before and after the GLOF: **a** June 6, 2018, Landsat-8 image; **b** June 25, 2019, Landsat-8 image; **c** May 1, 2020, Sentinel-2 image; and **d** July 27, 2020, Sentinel-2 image", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "text", "line_start": 139, "line_end": 141, "token_count_estimate": 121, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "18811d0c9c2bf97b", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Meteorological conditions before the event\nType: text\n\nThe intensity, duration, and frequency of precipitation, as well as temperature rise, can affect the timing and magnitude of glacier movements and GLOF events. To further understand the meteorological conditions before the 2020 event, we processed daily precipitation, monthly precipitation, and air temperature data. Figure 7 shows the meteorological conditions at Jinweng Co. The monthly precipitation fluctuates seasonally, with the rainy season lasting from February to July and very little precipitation during the intervening winter months. The highest monthly rainfall of up to 240 mm occurred during June 2020, and the heaviest daily rainfall reached 45 mm on June 21, 2020. This was approximately 4 days before the GLOF event. Moreover, air temperature changed periodically, with the hottest period of the year from late May to mid-August. Extreme rainfall and temperature conditions are important driving factors that increased discharge into the lake and led to the lake outburst (see discussion later).\n\nIt can be seen that the June 25, 2020, outburst occurred at the highest monthly/daily precipitation. The unusually heavy rainfall combined with the warmer summer temperature provides a plausible explanation for the Jinweng Co outburst for two reasons.\n\nFirst, heavy rainfall increased water inflow to Jinweng Co and thus increased discharge. Second, the heavy rainfall on June 21 may have also acted as an indirect trigger of the outburst when this precipitation provoked slope movement into the lake.\n\nThe Jinweng Co outburst chain provides a strong example of the role of extreme precipitation and temperature change in a GLOF-related disaster. According to the meteorological reports, the weather has been warming at a rate of 0.5 °C per decade over the last 40 years (You et al. 2016). This suggests that the warming trend constantly persists over Tibet. Based on the results of the glacier movement and lake change, it is certain that climate warming has caused glacier tongue recede, thinning, and lake expansion.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Meteorological conditions before the event", "section_headings": ["Discussions", "Meteorological conditions before the event"], "chunk_type": "text", "line_start": 145, "line_end": 153, "token_count_estimate": 499, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "f319387ba5747ddb", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: text\n\nTo understand the factors associated with the June 2020 outburst, we used time series SAR intensity images and two-pass InSAR (e.g., Lu et al. 2003). Figure 8a-e show the time series SAR intensity images from May 28 to July 15 and reveal several changes. The lake expanded from May 28 to June 9, as indicated by the yellow arrows in Fig. 8a, b. In Fig. 8c-e, we found a section at the western lateral moraine moved downslope into the lake on or after June 21, and the deposit from the landslide can be\n\nFig. 7 Daily and monthly precipitation and air temperature records over the Jinweng Co area. Daily precipitation is shown as blue bars, monthly rainfall in black smoothed curve, and air temperature in purple dots", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "text", "line_start": 155, "line_end": 159, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "ad0701e9164b3b88", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: figure\nFigure\n\nImage /page/5/Figure/12 description: A combination chart displaying climate data from late 2016 to mid-2020. The x-axis represents the date in yyyy/mm/d format, starting from 2016/12/1 and extending past 2020/6/1. There are two y-axes. The left y-axis, labeled in blue, represents daily precipitation in millimeters (mm) and ranges from 0 to 50. The right y-axis has two scales: one in magenta for Temperature in degrees Celsius (°C) ranging from -20 to 20, and another in black for Monthly precipitation (mm) ranging from 0 to 300. The chart displays three data series: Daily precipitation is shown as blue vertical bars, Monthly precipitation is a black line graph, and Temperature is represented by magenta dots. All three data series show a clear seasonal pattern, with peaks in the summer months and troughs in the winter months. A specific event labeled \"Outburst\" in red text is marked with a red dashed vertical line around May 2020, coinciding with a high peak in daily precipitation.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "figure", "figure_caption": null, "line_start": 160, "line_end": 160, "token_count_estimate": 318, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "36cd8877cca685a6", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: figure\nFigure\n\nImage /page/6/Figure/0 description: A scientific figure composed of ten panels arranged in two rows and five columns, labeled (a) through (j). The top row, panels (a) to (e), displays a time series of grayscale images of a rugged terrain, dated 2020/5/28, 2020/6/9, 2020/6/21, 2020/7/3, and 2020/7/15 respectively. The bottom row, panels (f) to (j), shows colorful images of the same terrain over different time periods: (f) 2017/5/20-2017/7/31, (g) 2017/6/1-2017/8/24, (h) 2018/6/20-2018/8/7, (i) 2019/7/21-2019/8/26, and (j) 2020/8/8-2020/8/20. Each panel includes a circular magnified inset showing a detailed view of a specific area. Panel (a) contains a north arrow and a scale bar indicating 0, 0.5, and 1 Km. At the bottom right, a color scale bar for the colorful images ranges from 0 to 2.83 cm.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "figure", "figure_caption": null, "line_start": 162, "line_end": 162, "token_count_estimate": 305, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "7a6afdf2433ce645", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: text\n\n**Fig. 8** Map of the landslide activity. **a–e** Sentinel-1 intensity images of the Jinweng Co and its surroundings representing the situation on May 28, June 9, June 21, July 3, and July 15. SAR backscattering images over the landslide are enlarged in the inset to improve visibil-\n\nity. **f-j** Deformation interferograms of Jinweng Co and surroundings for several periods during 2017–2020. Each fringe (a complete cycle of color variations) represents a 2.83 cm range change in the radar look direction\n\nseen in Fig. 8d, e. We used InSAR to study possible long-term deformation in the area around the landslide for several periods in 2017–2020 (Fig. 8f-j). Since interferometric coherence for C-band Sentinel-1 images are low in the Jinweng Co surroundings (where the surface is covered with snow in winter), we analyzed interferograms with high coherence acquired in summer (Fig. 8f-j). The baselines between the image pairs were less than 60 m, so the interferograms were insensitive to DEM errors. The deformation maps formed from independent image pairs with\n\nvery different atmospheric situations show essentially the same patterns at a section of the lateral moraine. This means that the fringes are a real deformation. Therefore, this section of the lateral moraine was experiencing long-term deformation and likely finally collapsed into the lake due to heavy rainfall on June 21, 2020. Therefore, the rainfall-triggered landslide that occurred on or after June 21, 2020, exerted an important effect on this lake outburst event because the water waves may have overflowed the dam and/or directly caused the rupture of the dam.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "text", "line_start": 163, "line_end": 171, "token_count_estimate": 477, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "df211e82c0f1d800", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: figure\nFigure\n\nImage /page/6/Figure/5 description: A scientific figure presented in a grid format, comparing two grayscale images of a rugged, mountainous terrain. The figure is divided into two main columns. The left column contains two rectangular images labeled (a) and (b). Image (a) at the top left shows the terrain with a north arrow in the upper left corner, a scale bar labeled '0 0.5 1 Km' in the lower left, and three white circles numbered 1, 2, and 3 highlighting specific areas. Image (b) below it shows a similar view of the terrain without any annotations. The right column consists of a 2x3 grid of six circular, magnified images. The top row is labeled (a1), (a2), and (a3), and these correspond to the magnified views of the areas marked 1, 2, and 3 in image (a), respectively. The bottom row is labeled (b1), (b2), and (b3), which are the corresponding magnified views from image (b). Each pair of vertical images, such as (a1) and (b1), shows the same feature, allowing for a direct comparison between the two source images (a) and (b).", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "figure", "figure_caption": null, "line_start": 172, "line_end": 172, "token_count_estimate": 349, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "310f24f010d7b068", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Discussions > Slope movement and ice collapse before the GLOF event\nType: text\n\n**Fig. 9** (a) Average Sentinel-1 intensity image based on SAR images of May 28 and June 9 2020. (b) Average Sentinel-1 intensity image based on SAR images of June 21, July 3, July 15, and July 27, 2020.\n\nThe changes in the glacier tongue, the landslide, and the southern end of the lake are shown by (a1) (b1), (a2) (b2), and (a3) (b3), respectively", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Discussions > Slope movement and ice collapse before the GLOF event", "section_headings": ["Discussions", "Slope movement and ice collapse before the GLOF event"], "chunk_type": "text", "line_start": 173, "line_end": 177, "token_count_estimate": 163, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "e9c6b13570e409e2", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Recent Landslides\nType: figure\nFigure\n\nImage /page/7/Picture/1 description: A diagram illustrating the causes of recent landslides. The image shows a body of water labeled \"Jinweng Co\" surrounded by brown land labeled \"Lateral moraine\". On the left, a light blue \"Glacier\" is shown melting into the lake, indicated by a red arrow and the word \"Melting\". Above the scene, grey clouds are causing \"Extreme rainfall\". The combination of melting and rainfall leads to \"Water rising\", indicated by a yellow arrow pointing up from the water's surface. A green mass of earth, labeled \"Landslide\", is shown sliding from the moraine into the lake. On the far right, the lake has an \"Outlet\" where water flows out.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "figure", "figure_caption": null, "line_start": 180, "line_end": 180, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "7453d961cc9c7b4e", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Recent Landslides\nType: text\n\n**Fig. 10** Schematic diagrams illustrating the triggering factors and final mechanism of dam failure\n\nThe time series SAR intensity images also indicate that significant changes occurred before the landslide on the southern end of the lake between June 9 and 21. We compared the average intensity images before and after June 9 (Fig. 9) to investigate this phenomenon. Figure 9 shows a clear difference in the glacier tongue (Fig. 9a1, b1). We suggest that the change in the lake surface on June 21 (Fig. 9, b3) resulted from the collapse of ice on a steep portion of the glacier tongue (Fig. 9(a1), (b1)). Such collapse and runout could also have trapped moraine on the path into the lake. Hence, between June 9 and 21, the ice collapsed under the steep topography and transported the iceberg and moraine into the front of the lake. This caused the obvious variation seen in the June 21 SAR image (Fig. 9). Moreover, the landslide that occurred in the western lateral moraine can be observed through Fig. 9a2, b2. The distinct sliding boundary further demonstrates that the landslide might cause harm to the lake by striking the lake body and may be one of the primary GLOF triggering factors.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Recent Landslides", "section_headings": ["Recent Landslides"], "chunk_type": "text", "line_start": 181, "line_end": 185, "token_count_estimate": 348, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "fc6c4adebe4bc39d", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Recent Landslides > Outburst mechanism of Jinweng Co\nType: text\n\nBased on the displacement features of the parent glacier, the lake changes with SAR intensity, optical images, inventory data, and extreme meteorological conditions, there is evidence that the slope of the western lateral moraine was unstable due to glacier recede. The extremely high precipitation in June 2020 facilitated the erosion of the pre-weakening slope, finally inducing landslide activity. The sketch map in Fig. 10 shows the triggering factors and outburst mechanism of Jinweng Co. First, the hydrologically controlled glacier movement and melting generated masses of destabilizing sediments. Second, due to geomorphic conditions, the steep slopes above the lake produced huge kinetic energy when ice masses slid into the lake. Third, the daily precipitation about 4 days before the GLOF event was the largest in the past 3 years. Anomalous rain events or seasonal changes in the dynamics of the surrounding glaciers might be key triggering factors causing sudden hydro-fracturing and outburst floods in summer. Increased water inflow to the lake caused increased discharge from the morainedammed lake, which may have provoked increased erosion and incision\n\nof the outflow channel into the dam body. More importantly, the landslide activity that occurred from the western lateral moraine on or after June 21 played a key role in the GLOF event because the fast slope movement into the lake is capable of producing water waves. This may have caused direct dam rupture. Overall, the causal chain for triggering the June 2020 outburst is a hydraulic connection established from a possible collapse of the glacial ice, a landslide, heavy precipitation, and warm temperature.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Recent Landslides > Outburst mechanism of Jinweng Co", "section_headings": ["Recent Landslides", "Outburst mechanism of Jinweng Co"], "chunk_type": "text", "line_start": 187, "line_end": 191, "token_count_estimate": 438, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "4fd613ef8ddf9ff2", "text": "Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China\nSection: Conclusions\nType: text\n\nMulti-source remote sensing datasets and processing methods combined with meteorological observations were used in this study for GLOF investigation. These were then applied to comprehend the recent movement of parent glaciers and reveal the mechanisms leading to the outburst flood. Offset tracking of multi-track Sentinel-1 images was combined to retrieve the long-term time series displacement and 3D glacier flow velocity field. Optical Sentinel-2 and Landsat-8 images and inventory data were interpreted to reconstruct the long-term changes in the glacial lake. SAR intensity images and interferograms were analyzed to characterize changes in the glacier tongue and lateral moraine collapse.\n\nIn this study, to analyze lake outburst factors, we identified two important critical stages for the Jinweng Co outburst. First, ice collapse occurred in the parent glacier from June 9 to 21, and this transported ice and moraine into the front of the lake. Second, a landslide originating from the western lateral moraine occurred forcefully from June 21 to 25 and placed additional deposits into the lake body. The water level increased due to the unusually heavy rainfall on June 21, 2020; when combined with meltwater from glaciers, it provoked anomalous water importation. Due to the availability of pre-weakening lateral moraine combined with heavy rainfall on June 21, landslide activity was a serious factor for triggering dam failure by producing displacement waves. Hence, the main mechanism may be that the landslide mobilized and entered the lake between June 21 and 25, which caused the surface waves, triggered overtopping, and finally caused dam rupture.\n\nThe kinetic movement and outburst mechanism factors of Jinweng Co revealed in this study can be used to assess the potential risks of other glacier lakes in the region. Further, with the rise in extreme precipitation and the rapid melting of glaciers that lead to the destabilization of glacial lakes, moraine instabilities under changing climate conditions could become more likely. Therefore, process chains such as Jinweng Co will become increasingly significant in the future.", "metadata": {"source_file": "data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md", "document_title": "Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China", "section_path": "Conclusions", "section_headings": ["Conclusions"], "chunk_type": "text", "line_start": 193, "line_end": 199, "token_count_estimate": 533, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "3ba2761aaab7794a", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nType: text\n\nCheck for updates\n\nLiye Yang<sup>1,2</sup>, Zhong Lu³ ⊠, Chaoying Zhao⁴ ⊠, Qin Zhang⁴, Xie Hu⁵ & Baohang Wang<sup>6</sup>\n\nThe frequency and intensity of glacial lake outburst floods (GLOFs) are increasing with rapid glacier retreat under a warming climate, yet the processes linking triggers and downstream responses remain poorly understood. Here, we investigate the 2013 GLOF at Ranzerio lake in southeastern Tibet using multi-source remote sensing, field observations, and hydrodynamic modeling. A glacier tongue collapse, with an estimated volume of  $3.8 \\times 10^6 \\, \\text{m}^3$ , was identified as the primary trigger of the moraine dam breach. Flood routing simulations with the HEC-RAS 2D model reproduced a peak discharge of  $7930 \\pm 18 \\, \\text{m}^3/\\text{s}$  about  $25 \\pm 5 \\, \\text{min}$  after the outburst, capturing flood propagation and geomorphic impacts downstream. The results reveal the multistage process chain of the outburst and highlight the importance of monitoring lake evolution, glacier movement, terrain change, and meteorological conditions for early warning and risk management in glacierized mountain regions.\n\nHigh Mountain Asia (HMA) is experiencing rapid warming at a rate of 0.32 °C per decade, driving widespread glacier retreat and the expansion of glacial lakes<sup>1</sup>. These changes are contributing to an increased frequency and magnitude of glacial lake outburst floods (GLOFs), which pose significant threats to downstream communities and infrastructure<sup>2–5</sup>. GLOFs can be triggered by both short-term dynamic processes, such as avalanches, landslides, or extreme rainfall, and long-term destabilization mechanisms, including permafrost degradation, dam weakening, and buried ice melt<sup>6,7</sup>.\n\nIn the Himalayas, displacement waves from ice or rock avalanches are responsible for nearly half of all moraine-dam failures. Under ongoing climate warming, processes such as the rapid expansion of glacial lakes, accelerated glacier retreat, and growing instability of ice- and permafrost-affected slopes are expected to heighten the likelihood of moraine dam failure, thereby increasing both the frequency and potential magnitude of GLOF hazards<sup>6-8</sup>.\n\nDespite increasing concern among scientists and hazard management authorities about the growing frequency and potential impacts of GLOFs, many events in remote high-altitude regions remain insufficiently documented owing to limited monitoring and poor accessibility<sup>7,9</sup>. These events often involve complex, cascading processes—such as mass movement initiation, wave generation, dam overtopping and erosion, and downstream flooding—whose timing and dynamics depend on the trigger type<sup>4,10</sup>. For example, GLOFs triggered by sudden ice or rock avalanches typically evolve over seconds to minutes, whereas those induced by extreme rainfall may", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_headings": ["Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 841, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "621823ed5beb9066", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nType: text\n\nDespite increasing concern among scientists and hazard management authorities about the growing frequency and potential impacts of GLOFs , many events in remote high - altitude regions remain insufficiently documented owing to limited monitoring and poor accessibility < sup > 7 , 9 < / sup > . These events often involve complex , cascading processes — such as mass movement initiation , wave generation , dam overtopping and erosion , and downstream flooding — whose timing and dynamics depend on the trigger type < sup > 4 , 10 < / sup > . For example , GLOFs triggered by sudden ice or rock avalanches typically evolve over seconds to minutes , whereas those induced by extreme rainfall may\n\ndevelop over several hours or days. In this study, we specifically focus on GLOFs triggered by sudden events. By contrast, GLOFs associated with ice-dammed lake drainage can occur over much longer timescales, ranging from days to months, but these are not considered here.\n\nAdvancements in remote sensing now allow for the near-continuous monitoring of glacier and lake dynamics, providing valuable information for hazard assessment. Synthetic Aperture Radar (SAR) and optical offset tracking enable the quantification of multi-dimensional glacier motion, offering insights into surge and avalanche precursors<sup>11–14</sup>. Interferometric Synthetic Aperture Radar (InSAR) techniques have been widely applied to detect slope deformation linked to permafrost thaw and ground ice creep<sup>15,16</sup>. Combined with hydrodynamic models such as HEC-RAS, these datasets can help reconstruct flood processes and assess downstream impacts<sup>17–19</sup>. However, the reliability of model outputs is contingent on the quality and resolution of input parameters, highlighting the importance of integrated, multi-source observations.\n\nOn 5 July 2013, a GLOF from Ranzerio lake (30.47°N, 93.53°E) in southeastern Tibet caused severe damage to downstream infrastructure and communities. Several studies have investigated this event, with optical imagery, terrain data, and field observations suggesting that a glacier avalanche was the likely trigger<sup>20–22</sup>. However, a comprehensive assessment that combines high-resolution remote sensing and dynamic modeling to fully characterize the flood mechanism, evolution, and post-event glacier response remains lacking. A more comprehensive approach is needed to\n\n<sup>1</sup>College of Civil Engineering, Xiangtan University, Xiangtan, China. <sup>2</sup>Hunan Provincial Key Laboratory of Geomechanics and Engineering Safety, Xiangtan University, Xiangtan, China. <sup>3</sup>Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX, USA. <sup>4</sup>College of Geological Engineering and Geomatics, Chang'an University, Xi'an, China. <sup>5</sup>College of Urban and Environmental Sciences, Peking University, Beijing, China. <sup>6</sup>College of Geography and Oceanography, Minjiang University, Fuzhou, China. ⊠e-mail: zhonglu@mail.smu.edu; cyzhao@chd.edu.cn\n\ncapture the interactions between glacier dynamics and dam failure processes. Addressing this gap is critical for improving process-based understanding of GLOF initiation and propagation in paraglacial environments.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_headings": ["Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 882, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "9e16eb6c8013e43e", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nType: text\n\n< sup > 4 < / sup > College of Geological Engineering and Geomatics , Chang ' an University , Xi ' an , China . < sup > 5 < / sup > College of Urban and Environmental Sciences , Peking University , Beijing , China . < sup > 6 < / sup > College of Geography and Oceanography , Minjiang University , Fuzhou , China . ⊠ e - mail : zhonglu @ mail . smu . edu ; cyzhao @ chd . edu . cn capture the interactions between glacier dynamics and dam failure processes . Addressing this gap is critical for improving process - based understanding of GLOF initiation and propagation in paraglacial environments .\n\nThis study addresses these knowledge gaps by integrating multi-source satellite remote sensing, high-resolution terrain data, and physically based hydrodynamic modeling to investigate the 2013 Ranzerio GLOF. Specifically, we: (1) quantified temporal changes in glacial lake extent and parent glacier velocity between 2010 and 2021; (2) identified the primary trigger of the lake outburst; (3) reconstructed the flood hydrograph and downstream routing using the HEC-RAS 2D model; and (4) synthesized the GLOF process chain by combining geomorphic, glaciological, and meteorological datasets. This integrated framework enhances understanding of the multistage processes driving GLOFs in paraglacial environments and provides a transferable approach to support hazard assessment and early-warning efforts in High Mountain Asia.\n\nRanzerio lake is located in Zhongyu Township, Jiali County, south-eastern Tibet, situated above the Nidu Zangpo River (Fig. 1). The study area spans elevations from approximately 4500 m to 6386 m above sea level. It lies within a semi-humid monsoon climate zone, with a mean annual temperature of  $-0.4\\,^{\\circ}\\text{C}$  and approximately 83% of annual precipitation occurring between May and September, largely driven by monsoonal circulation<sup>20</sup>. The region is characterized by a dense distribution of glaciers and glacial lakes. Due to ongoing climate warming, the Qinghai–Tibet Plateau (QTP) has undergone widespread permafrost degradation in recent decades. According to the permafrost distribution map by Zou et al.<sup>23</sup>, the study area is underlain by permafrost (Fig. 1a).", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_headings": ["Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 627, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "49341d6c232cc06e", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nType: text\n\na mean annual temperature of $ - 0 . 4 \\ , ^ { \\ circ } \\ text { C } $ and approximately 83 % of annual precipitation occurring between May and September , largely driven by monsoonal circulation < sup > 20 < / sup > . The region is characterized by a dense distribution of glaciers and glacial lakes . Due to ongoing climate warming , the Qinghai – Tibet Plateau ( QTP ) has undergone widespread permafrost degradation in recent decades . According to the permafrost distribution map by Zou et al . < sup > 23 < / sup > , the study area is underlain by permafrost ( Fig . 1a ) .\n\nBefore the outburst, Ranzerio lake was one of the largest morainedammed glacial lakes in southeastern Tibet, with a surface area of approximately 0.58 km<sup>2</sup>, and measuring about 1.4 km in length and 0.5 km in width<sup>21</sup>. Both the lake and its parent glacier were aligned along a north-south axis. Following the GLOF, two new dammed lakes formed in the Yibu and Luoqiong valleys, with surface areas of approximately 0.13 km<sup>2</sup> and 0.33 km<sup>2</sup>, respectively<sup>20,21</sup>. These lakes were impounded in tributary valleys where GLOF-deposited debris from the main valley blocked the natural drainage pathways, effectively damming the tributary streams. High-resolution satellite imagery reveals substantial geomorphic changes on the valley slopes surrounding Ranzerio lake. Figure 1a shows the spatial coverage of Sentinel-1 SAR data and RapidEye optical imagery used in this study. Figure 1b, c illustrates lake and glacier changes before and after the outburst. In 2010, the glacier and lake areas were 5.28 km<sup>2</sup> and 0.58 km<sup>2</sup>, respectively, and a well-defined drainage channel was visible (Fig. 1b). By 12 September 2013, following the outburst, the glacier area had slightly decreased to 5.13 km<sup>2</sup>, while the lake area had been reduced by 54%, shrinking to 0.25 km2 (Fig. 1c). Inset panels show detailed views of the incised moraine dam and downstream impoundments, as captured by RapidEye imagery and confirmed through field surveys8.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_headings": ["Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 662, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d88a260e15b11544", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > Long-term development of the glacier and Ranzerio lake\nType: text\n\nLong-term changes in Ranzerio lake area and glacier tongue length were quantified from 2010 to 2021. Figure 2 presents the temporal evolution of these features over the study period. Between 2010 and 2012, the lake area expanded modestly from approximately 0.54 km² to 0.58 km². However, following the outburst flood event in July 2013, the lake area decreased sharply to about 0.27 km² by September 2013, indicating a substantial alteration of the glacier-lake system.\n\nSubsequently, the lake area gradually increased, reaching roughly  $0.29\\,\\mathrm{km^2}$  by 29 October 2021 (Fig. 2b). Concurrently, the glacier tongue exhibited continuous retreat. Prior to the outburst, the tongue length had diminished to 317 m by 2012 and further to 300 m by July 2013. After the event, retreat accelerated, with the glacier tongue length reducing to approximately 128 m by October 2021, corresponding to an average retreat rate of ~22 m/yr (Fig. 2c).", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > Long-term development of the glacier and Ranzerio lake", "section_headings": ["Results", "Long-term development of the glacier and Ranzerio lake"], "chunk_type": "text", "line_start": 36, "line_end": 40, "token_count_estimate": 290, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dad58eb9ed2b46be", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > Triggering factors of Ranzerio lake outburst\nType: text\n\nGlacier surface velocities were derived using high-resolution RapidEye imagery and the offset tracking method. Annual horizontal glacier velocities from 2010 to 2013 are shown in Fig. 3. In these maps, vector arrows depict flow direction, while color gradients indicate velocity magnitude.\n\nBetween 30 October 2010 and 24 October 2011 (Fig. 3a), horizontal glacier velocity was relatively modest. During the subsequent period (24 October 2011 to 5 September 2012; Fig. 3b), a distinct shift in flow pattern occurred. In particular, the glacier tongue on the steep slope exhibited intensified southward motion, with velocities reaching approximately 30 m/yr. From 5 September 2012 to 12 September 2013 (Fig. 3c), the glacier tongue accelerated further, advancing toward the lake at a velocity of ~40 m/yr. Although the observed acceleration of glacier flow could have increased stress at the glacier–lake interface and potentially affected the stability of the proglacial moraine, direct evidence from ice-core stratigraphy or internal deformation measurements is lacking for the 2013 event. Therefore, while glacier velocity may have contributed to moraine destabilization, DEM-derived elevation changes provide the most robust geomorphic evidence for the onset of the GLOF.\n\nRapidEye optical imagery revealed the development of a crevasse between October 2010 and September 2013. In the upper portion of the step icefall, transverse crevasses were consistently observed (Fig. 4), indicating increasing internal glacier stress. Accumulated glacier ice was observed flowing downslope toward the lake basin, cascading over steep sections of the glacier front and partially reaching the lake margin, rather than floating on the lake surface. By September 2012, imagery clearly showed glacier ice resting directly on the lake surface (Fig. 4c). Continued glacier retreat, combined with the progressive enlargement of crevasses, culminated in ice collapse from the tongue into the lake between September 2012 and September 2013. These dynamic instabilities—such as sudden mass movements or ice collapse—are considered triggers for the lake outburst event, potentially generating displacement waves that rapidly increase lake levels and erode the lake dam.\n\nFurther, we analyzed terrain changes using high-resolution 5 m TanDEM-X DEMs acquired on 22 April 2013 and 16 September 2013, representing pre- and post-GLOF conditions. Figure 5 illustrates the topographic evolution over this period. Before the outburst, the glacier tongue exhibited steep slopes averaging 39.6° and was characterized by well-developed transverse crevasses (Fig. 5a). Following the GLOF, substantial surface lowering was observed on the glacier tongue and within the lake basin (Fig. 5b). The differential DEM (Fig. 5c) revealed surface elevation losses of up to  $\\sim\\!50$  m in the steepest portions of the glacier tongue, indicating significant ice collapse. The estimated volume of collapsed glacier ice was approximately  $3.8\\times10^6\\,\\mathrm{m}^3$  based on elevation differences and DEM resolution.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > Triggering factors of Ranzerio lake outburst", "section_headings": ["Results", "Triggering factors of Ranzerio lake outburst"], "chunk_type": "text", "line_start": 42, "line_end": 52, "token_count_estimate": 806, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4a9eea204951c12", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > Triggering factors of Ranzerio lake outburst\nType: text\n\ntongue exhibited steep slopes averaging 39 . 6 ° and was characterized by well - developed transverse crevasses ( Fig . 5a ) . Following the GLOF , substantial surface lowering was observed on the glacier tongue and within the lake basin ( Fig . 5b ) . The differential DEM ( Fig . 5c ) revealed surface elevation losses of up to $ \\ sim \\ ! 50 $ m in the steepest portions of the glacier tongue , indicating significant ice collapse . The estimated volume of collapsed glacier ice was approximately $ 3 . 8 \\ times10 ^ 6 \\ , \\ mathrm { m } ^ 3 $ based on elevation differences and DEM resolution .\n\nAdditionally, the lake surface elevation dropped by approximately 45 m (inset map in Fig. 5c), indicating deep incision of the moraine spillway as a result of rapid drainage. The breach depth was therefore estimated at ~45 m, consistent with previous field assessments<sup>21</sup>. Together, these observations support the hypothesis that an ice collapse from the steep glacier tongue generated displacement waves, which induced a rapid and transient rise in lake level, thereby eroding the moraine dam and ultimately triggering its failure.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > Triggering factors of Ranzerio lake outburst", "section_headings": ["Results", "Triggering factors of Ranzerio lake outburst"], "chunk_type": "text", "line_start": 42, "line_end": 52, "token_count_estimate": 362, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "916df50473c51cee", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: text\n\nWe reconstructed the 2013 GLOF event at Ranzerio lake to quantify flood depth, flow velocity, and discharge at downstream settlements. Figure 6 illustrates the modeled maximum flow depth and velocity. The hydrograph inset in Fig. 6a shows that the peak discharge at the dam breach ( $\\sim$ 7930  $\\pm$  18 m³/s) occurred approximately 25  $\\pm$  5 min after the breach initiation.\n\nTo assess downstream impacts, we analyzed modeled flow parameters at four villages: Rongqingcun (Village 1), Zhaixongcun (Village 2), Bengdacun (Village 3), and Lingngucai (Village 4), located ~26, 28, 35, and 40 km from Ranzerio lake, respectively (Figs. 1 and 7). The modeled flood", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "text", "line_start": 54, "line_end": 58, "token_count_estimate": 233, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d8da9686919f3210", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A scientific figure composed of three panels, labeled (a), (b), and (c), illustrating changes in a glacial landscape using remote sensing data.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "figure", "figure_caption": null, "line_start": 59, "line_end": 59, "token_count_estimate": 89, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "085d84e77c783e16", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: text\n\nPanel (a) is a regional map centered around Jiali, with coordinates from 93°20'E to 93°40'E and 30°20'N to 30°40'N. It shows a main river with several numbered villages along its banks, and distinguishes between areas of \"Seasonally frozen ground\" and \"Permafrost.\" The map outlines the coverage areas for \"Sentinel-1\" (black), \"RapidEye\" (orange), and the \"Study area\" (red). A red star marks the \"Lake site.\" An inset map in the top right corner shows the location of the study area on an elevation map of the Tibetan Plateau.\n\nPanel (b) is a satellite image showing a close-up of the study area before a change event. It features a \"Glacier\" outlined in purple, a \"Lake\" outlined in blue at the glacier's terminus, and a \"Drainage channel\" outlined in orange extending from the lake.\n\nPanel (c) is a satellite image of the same area as (b), showing the landscape after the change event. It highlights a newly formed \"Dam\" (yellow line), a resulting \"Flooding area\" (reddish-brown outline), and a \"New lake\" that has formed downstream. Two inset images provide zoomed-in views of the \"Dam\" and the \"New lake.\"\n\nA comprehensive legend at the bottom defines all the symbols and outlines used across the panels.\n\nFig. 1 | Spatial coverage of multi-source remote sensing datasets and changes in the glacier and Ranzerio lake (created using ArcGIS 10.4 by the authors).\n\na Footprints of remote sensing imagery: black rectangle indicates Sentinel-1 SAR\n\na Footprints of remote sensing imagery: black rectangle indicates Sentinel-1 SAR coverage, orange indicates RapidEye optical imagery, and the red box delineates the study area. Locations of downstream villages are also shown: Rongqingcun (Village\n\n1), Zhaixongcun (Village 2), Bengdacun (Village 3), and Lingngucai (Village 4).  $\\bf b$  Pre-GLOF (30 October 2010) and  $\\bf c$  post-GLOF (12 September 2013) RapidEye images, illustrating the landscape changes triggered by the 2013 Ranzerio lake GLOF event.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "text", "line_start": 60, "line_end": 76, "token_count_estimate": 598, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2d25168c8ee3e98b", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: figure\nFigure\n\nImage /page/3/Figure/2 description: A scientific figure illustrating the changes in Ranzerio lake and its associated glacier tongue from 2010 to 2021, divided into three parts: (a), (b), and (c).", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "figure", "figure_caption": null, "line_start": 77, "line_end": 77, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c9374c7ee084f989", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: text\n\nPart (a) displays a series of nine satellite images of the lake. The top row, labeled \"Before,\" shows images from 2010/10/30, 2011/10/24, and 2012/9/5, where the lake is large. The next two rows, labeled \"After,\" show images from 2013/9/12, 2016/12/13, 2017/8/24, 2019/9/26, 2020/8/25, and 2021/10/29. The 2013 image shows a significantly smaller lake with ice debris, corresponding to a major event. Subsequent images show the lake slowly recovering but not reaching its pre-2013 size.\n\nPart (b) is a line graph plotting \"Lake area (km²)\" against \"Year\" from 2010 to 2022. The lake area increases from about 0.53 km² in 2010 to a peak of about 0.59 km² in 2012. A sharp drop, labeled \"2013 event,\" occurs in 2013, with the area decreasing to about 0.27 km². After 2013, the lake area shows a slight, gradual increase, reaching about 0.30 km² by 2022.\n\nPart (c) is a line graph plotting \"Glacier tongue (m)\" against \"Year\" from 2010 to 2022. The graph shows a consistent decrease in the length of the glacier tongue over the period. It starts at over 600 m in 2010 and drops to approximately 120 m by 2022. A label \"2013 event\" is placed near the data points for 2012 and 2013, indicating a continued retreat during that time.\n\nFig. 2 | Time series of Ranzerio lake and the glacier tongue from 2010 to 2021 (created using ArcGIS 10.4 and Origin 2021 by the authors). a Spatiotemporal evolution of the glacier tongue and lake extent. b Temporal changes in lake area. c Temporal changes in glacier tongue length.\n\nhydrographs and flow depths at these sites are shown in Fig. 7 and summarized in Table 1.\n\nResults indicate a progressive attenuation of both peak discharge and maximum flow depth with increasing downstream distance. At village 1, the peak discharge was  $\\sim 2623 \\pm 7 \\text{ m}^3/\\text{s}$ , arriving  $89 \\pm 7 \\text{ min}$\n\nafter the breach, with a flow depth of  $\\sim$ 7.7  $\\pm$  0.6 m. At village 2, the peak discharge decreased to  $\\sim$ 2485  $\\pm$  14 m³/s, occurring at 101  $\\pm$  6 min postbreach, with a depth of  $\\sim$ 5.5  $\\pm$  0.1 m. At village 3, the discharge further declined to  $\\sim$ 1056  $\\pm$  5 m³/s, reaching the site at 177  $\\pm$  7 min, with a depth of  $\\sim$ 4.3  $\\pm$  0.1 m. At village 4, the peak discharge was  $\\sim$ 242  $\\pm$  2 m³/s,", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "text", "line_start": 78, "line_end": 92, "token_count_estimate": 716, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62b52b7da4fe8734", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: figure\nFigure\n\nImage /page/4/Figure/2 description: A four-panel figure illustrating glacier horizontal velocity from 2010 to 2013. The three main panels, labeled (a), (b), and (c), show 3D topographical maps of a glacier and the adjacent Razerio Lake during different time periods. The glacier's surface is covered with colored vectors indicating the direction and speed of ice flow. The fourth panel provides a legend. Panel (a) covers the period 2010/10/30-2011/10/24. Panel (b) covers 2011/10/24-2012/9/5. Panel (c) covers 2012/9/5-2013/9/12. The legend in the bottom right indicates that a reference red vector represents a velocity of 60 meters per year (m/yr). Below this, a color bar for 'Horizontal Velocity (m/yr)' shows a scale from 0 (blue) to 60 (red), with labeled increments of 10.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "figure", "figure_caption": null, "line_start": 93, "line_end": 93, "token_count_estimate": 257, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3dc877b46d055e9c", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > GLOF reconstruction and downstream impact assessment\nType: text\n\nFig. 3 | Glacier horizontal velocities from 2010 to 2013 (created using Origin 2021 by the authors). a 30 October 2010 to 24 October 2011. b 24 October 2011 to 5 September 2012. c 5 September 2012 to 12 September 2013. Vector arrows indicate flow direction, and color shading represents the magnitude of horizontal velocity.\n\narriving  $332 \\pm 9$  min after the event, with a maximum modeled depth of  $\\sim 2.2 \\pm 0.2$  m.\n\nThese results demonstrate the rapid onset and high magnitude of the initial flood wave, followed by progressive attenuation downstream. Such dynamics underscore the need for rapid detection of upstream triggers, such as ice collapse, and for coordinated early warning systems coupled with targeted risk-reduction measures. Practical applications include continuous remote sensing of glacier and lake conditions, real-time hydrological monitoring, and automated alert systems to inform downstream communities and guide emergency response planning.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > GLOF reconstruction and downstream impact assessment", "section_headings": ["Results", "GLOF reconstruction and downstream impact assessment"], "chunk_type": "text", "line_start": 94, "line_end": 100, "token_count_estimate": 260, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "995b4b3333c448f2", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > Deposition estimation and uncertainty in flood simulation\nType: text\n\nDue to the absence of direct post-event water depth measurements, the simulated GLOF inundation extent was validated against observations from high-resolution RapidEye imagery. The flood extent was delineated from two 5 m resolution RapidEye scenes acquired on 12 September 2013 (Fig. 8a). Comparison shows that the observed inundation area was 3.67 km², while the simulated extent covered 3.89 km², representing a relative difference of ~6.0%. Likewise, the modeled new lake area (0.339 km²) closely matched field-derived measurements (0.330 km²)<sup>8,20</sup>, with a deviation of only ~2.7%. These small differences fall within the expected uncertainty range and indicate that the hydraulic model is capable of reliably reproducing the spatial extent of flooding.\n\nOwing to the limited spatial coverage of the available DEM datasets, deposition and erosion were analyzed only within the overlapping areas (Fig. 8b). The DEM difference analysis revealed a total sediment deposition of  $48.06 \\pm 0.004 \\, \\text{Mm}^3$  and a total erosion of  $25.54 \\pm 0.005 \\, \\text{Mm}^3$ , resulting in a net volumetric change of  $22.52 \\, \\text{Mm}^3$  within the analyzed area. It should be noted that these estimates here are limited to the DEM coverage and do not represent the total downstream sediment caused by the flood.\n\nFlow dynamics during GLOF events are often complex, involving transitions from clear-water to sediment-laden or debris flows with possible\n\nnon-Newtonian rheologies<sup>5,24</sup>. Due to the absence of detailed sediment data for this event, we adopted a clear-water hydraulic model following established approaches<sup>5,18,25</sup>. While this simplification may underestimate sediment-transport effects, it provides credible estimates of inundation extent, flooding discharge, and timing given the available dataset.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > Deposition estimation and uncertainty in flood simulation", "section_headings": ["Results", "Deposition estimation and uncertainty in flood simulation"], "chunk_type": "text", "line_start": 102, "line_end": 110, "token_count_estimate": 545, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74480365e8127385", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > Glacier movement after the lake outburst\nType: text\n\nTo investigate the post-outburst dynamics of the parent glacier, we derived glacier displacement time series using the PO-MSBAS method applied to Sentinel-1 SAR imagery acquired between 2017 and 2021. Azimuth displacements, representing motion along the satellite flight path, were used to approximate north-south glacier movement, consistent with the glacier's primary orientation <sup>12,26</sup>.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > Glacier movement after the lake outburst", "section_headings": ["Results", "Glacier movement after the lake outburst"], "chunk_type": "text", "line_start": 112, "line_end": 114, "token_count_estimate": 143, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d32597ebf14455c5", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Results > Glacier movement after the lake outburst\nType: figure\nFigure: Figure 9 presents the azimuth displacement time series, revealing a persistent southward glacier motion with an average velocity of $\\sim$ 6 m/yr in the accumulation zone. At point G (Fig. 9i), the cumulative LOS displacement reached $\\sim$ 7 m, while the total azimuth displacement over the 4-year period amounted to $\\sim$ 20 m, indicating sustained glacier flow following the outburst event.\n\nFigure 9 presents the azimuth displacement time series, revealing a persistent southward glacier motion with an average velocity of  $\\sim$ 6 m/yr in the accumulation zone. At point G (Fig. 9i), the cumulative LOS displacement reached  $\\sim$ 7 m, while the total azimuth displacement over the 4-year period amounted to  $\\sim$ 20 m, indicating sustained glacier flow following the outburst event.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Results > Glacier movement after the lake outburst", "section_headings": ["Results", "Glacier movement after the lake outburst"], "chunk_type": "figure", "figure_caption": "Figure 9 presents the azimuth displacement time series, revealing a persistent southward glacier motion with an average velocity of $\\sim$ 6 m/yr in the accumulation zone. At point G (Fig. 9i), the cumulative LOS displacement reached $\\sim$ 7 m, while the total azimuth displacement over the 4-year period amounted to $\\sim$ 20 m, indicating sustained glacier flow following the outburst event.", "line_start": 115, "line_end": 115, "token_count_estimate": 262, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e03312448a89f1a9", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\nThe 2013 Ranzerio lake outburst can be understood within the broader framework of moraine-dammed GLOF triggering mechanisms<sup>27–31</sup>. Such events are commonly initiated by external disturbances—such as ice or rock collapse, mass movements, or displacement waves—that erode the dam and trigger its failure. Our evidence indicates that the Ranzerio outburst was most likely driven by a sudden ice collapse and the subsequent generation of high-energy displacement waves.\n\nHigh-resolution glacier velocity mapping revealed sustained acceleration of the glacier tongue, from ~30 m/yr in 2011–2012 to ~40 m/yr in 2012–2013, accompanied by crevasse expansion and downslope extension toward the lake margin. By late 2012, the glacier", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 118, "line_end": 122, "token_count_estimate": 226, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d81618aea6e75bef", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: figure\nFigure\n\nImage /page/5/Figure/2 description: A four-panel figure displaying satellite images of a glacier tongue from 2010 to 2013, illustrating changes over time. Each panel is labeled with a letter and a date.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 123, "line_end": 123, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c9233874e83681d", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\n- Panel (a), dated 2010/10/30, shows the glacier with a red flag pointing to a feature labeled \"crevasse\". A white dashed line indicates the glacier's edge, labeled \"Glacier retreat\".\n- Panel (b), dated 2011/10/24, shows the same area a year later, with the \"crevasse\" still marked and the glacier's edge having retreated further back.\n- Panel (c), dated 2012/9/5, is a false-color image highlighting the \"crevasse\" with a red flag.\n- Panel (d), dated 2013/9/12, shows a significant \"break\" in the glacier, indicated by red text and four red arrows pointing to the fractures.\n\nAll panels are marked with coordinates 30°29' and 93°32' and include a scale bar for 0.05 km. A partial caption at the bottom reads: \"glacier crevasse on its tongue from 2010 to 2013 (created using ArcGIS September 2013. Note: The zoomed-in sections may appear less sharp du...\".\n\nFig. 4 | Glacier crevasse on its tongue from 2010 to 2013 (created using ArcGIS 10.4 by the authors). a RapidEye image on 30 October 2010. b RapidEye image on 24 October 2011. c RapidEye image on 5 September 2012. d RapidEye image on 12\n\nSeptember 2013. Note: The zoomed-in sections may appear less sharp due to the original PlanetScope spatial resolution and local magnification used to highlight details.\n\nfront was in direct contact with the lake surface. Similar pre-failure acceleration patterns have been observed in other ice-avalanche-induced GLOFs<sup>32-34</sup>, where progressive destabilization often precedes catastrophic collapse. DEM differencing between April and September\n\n2013 revealed surface lowering of up to ~50 m on the steep glacier tongue, corresponding to an estimated ice loss volume of ~3.8  $\\times$  10  $^6$  m³. This sudden collapse likely generated displacement waves capable of overtopping the moraine dam  $^{35\\text{--}37}$ .", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 124, "line_end": 139, "token_count_estimate": 531, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f5f52b3c8f65c94e", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: figure\nFigure\n\nImage /page/6/Figure/2 description: A figure composed of three maps and one line graph, illustrating the topographical changes before and after a Glacial Lake Outburst Flood (GLOF). The maps are located at approximately 93°32' E longitude and between 30°28' and 30°29' N latitude. Map (a), labeled \"Pre-GLOF\", shows a grayscale topographical view with a \"Steep Region\" and \"Crevasses\" indicated. A large, textured area is outlined in blue. Map (b), labeled \"Post-GLOF\", shows the same region after the event, with the previously blue-outlined area now outlined in pink and appearing smoother. Map (c) displays the \"Elevation change (m)\" using a color scale. The area of change is highlighted, showing a significant loss of elevation (colored yellow, orange, and red), with a total volume change of 3.8x10^6 m^3 noted. The legend for elevation change ranges from -50 to >50 meters. A \"Breach Dam\" is indicated at the lower end of the affected area. Below the maps, a line graph plots \"Dam breach depth (m)\" on the y-axis versus \"Distance (km)\" on the x-axis. The y-axis ranges from 0 to 45 m, and the x-axis from 0.00 to 0.25 km. The blue line on the graph shows the depth profile of the breach, starting at 0, rising to a peak of approximately 43 meters at a distance of about 0.12 km, and then decreasing sharply.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 140, "line_end": 140, "token_count_estimate": 423, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d241ec1b4adb53bb", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\nFig. 5 | Terrain changes of the glacier tongue and Ranzerio lake before and after the GLOF, derived from TanDEM-X datasets (created using ArcGIS 10.4 and Origin 2021 by the authors). a Pre-GLOF terrain on 22 April 2013. b Post-GLOF terrain on 16 September 2013. c Elevation difference map showing terrain changes\n\nbefore and after the event, highlighting the ice collapse that occurred from the lower glacier tongue and the incision of the moraine dam. The inset map illustrates the dam breach, with an incision depth of  $\\sim\\!45$  m.\n\nMeteorological analysis based on ERA5 reanalysis data further highlighted anomalous conditions in early July 2013, as shown in Fig. 10. Mean daily June–July temperatures over 2004–2013 ranged from 4.19–6.54 °C and 6.20–8.52 °C, respectively. Corresponding mean daily precipitation also fluctuated notably, with June averages between ~4.50 mm and 7.28 mm, and July averages between 4.94 mm and 8.48 mm. Cumulative June–July totals varied from ~293.5 mm to 481.2 mm, with the 2013 value (423.6 mm) exceeding the median but remaining below the decadal maximum. Using the 2004–2012 baseline, the 95th percentile thresholds for daily precipitation and temperature were 11.68 mm and 8.83 °C, respectively <sup>38</sup>. Importantly, the days immediately preceding the GLOF outburst included both an extreme high-temperature event (5 July 2013) and an extreme precipitation event (3 July 2013), each surpassing these thresholds. These conditions likely enhanced meltwater input and hydrological loading on the proglacial lake, thereby exacerbating dam instability <sup>29,39,40</sup>.\n\nIntegrating the geomorphological, glaciological, and meteorological evidence, we propose a multi-stage process chain, as shown in Fig. 11: (1) long-term glacier retreat and steepening of the ice front above the lake increased susceptibility to collapse; (2) short-term metrological anomalies in early July 2013 accelerated destabilization; (3) a sudden ice collapse generated displacement waves, rapidly elevating lake levels and imposing erosion on the moraine dam; and (4) overtopping and subsequent erosion breached the dam to a depth of ~45 m, releasing the lake volume as a high-magnitude flood that caused downstream geomorphic change and sediment redistribution.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 141, "line_end": 155, "token_count_estimate": 623, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a13fe067fe39d546", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\nglaciological , and meteorological evidence , we propose a multi - stage process chain , as shown in Fig . 11 : ( 1 ) long - term glacier retreat and steepening of the ice front above the lake increased susceptibility to collapse ; ( 2 ) short - term metrological anomalies in early July 2013 accelerated destabilization ; ( 3 ) a sudden ice collapse generated displacement waves , rapidly elevating lake levels and imposing erosion on the moraine dam ; and ( 4 ) overtopping and subsequent erosion breached the dam to a depth of ~ 45 m , releasing the lake volume as a high - magnitude flood that caused downstream geomorphic change and sediment redistribution .\n\nThis study provides a comprehensive reconstruction of the 2013 Ranzerio GLOF by integrating geomorphic, glaciological, and meteorological datasets with high-resolution remote sensing and physically based hydrodynamic modeling. Differential DEM analysis revealed pronounced surface lowering of up to  $\\sim\\!50$  m on the steep glacier tongue, corresponding to an ice-collapse volume of  $\\sim\\!3.8\\times10^6\\,\\mathrm{m}^3$ . This ice collapse acted as the primary trigger, generating displacement waves that abruptly raised lake levels and imposed extreme erosion on the moraine dam. Hydrodynamic simulations constrained by field-derived breach geometry successfully reproduced the outburst process, estimating a peak discharge of  $7930\\pm18\\,\\mathrm{m}^3/\\mathrm{s}$ , a breach depth of  $\\sim\\!45\\,\\mathrm{m}$ , and an inundation extent closely matching RapidEye observations (relative difference  $\\sim\\!6\\%$ ). These quantitative results validate the reliability of our reconstruction.\n\nBy explicitly linking the multistage process chain—from accelerated glacier flow and tongue destabilization, through ice collapse and displacement wave generation, to moraine-dam breach and downstream flooding—this study identifies specific indicators that can be monitored for early warning. Observables such as ice-front acceleration, sudden ice mass movement, and dam erosion provide actionable signals for hazard detection. Furthermore, the reconstruction of erosion and deposition volumes along the flood path offers quantitative input for hazard mapping and risk assessment, allowing identification of areas most susceptible to inundation and sediment impact.\n\nBeyond the event-specific insights, the integrated methodological framework—combining optical and SAR remote sensing, DEM", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 141, "line_end": 155, "token_count_estimate": 659, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "680c6e86ae1d4169", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: figure\nFigure\n\nImage /page/7/Figure/2 description: A figure with two maps, labeled (a) and (b), and an inset graph, illustrating the characteristics of the Ranzerio lake outburst flooding.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 156, "line_end": 156, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb9b4b2a36742520", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\nMap (a) displays the 'Maximum Flow depth (m)' in a river valley. The flood path is colored on a scale from light blue (0-4 m) to dark pink (36-40 m). The map includes latitude and longitude coordinates, with four numbered green circles (1, 2, 3, 4) marking locations along the river downstream from Ranzerio lake.\n\nMap (b) shows the 'Maximum Flow velocity (m/s)' for the same event and location. The flow velocity is represented by a color scale from dark red (0-2 m/s) to dark blue (18-20 m/s).\n\nAn inset graph plots 'Discharge (m³/s)' on the y-axis against 'Time (min)' on the x-axis. A red line shows the discharge hydrograph, which peaks sharply. An annotation indicates the peak discharge is 7930 m³/s at 25 minutes. Both maps include a scale bar showing 0, 2, and 4 km.\n\nFig. 6 | The maximum flowing depth and velocity of Ranzerio lake outburst flooding (created using ArcGIS 10.4 and Origin 2021 by the authors). a The maximum flooding depth. b The maximum flow velocity.\n\ndifferencing, ERA5-based meteorological reanalysis, and hydraulic modeling—provides a transferable approach for high-mountain regions. This framework supports the development of early warning systems, targeted hazard mapping, and quantitative risk assessment, contributing to climate adaptation planning in other rapidly changing cryospheric environments.\n\nSeveral limitations in this study should be acknowledged. First, due to the absence of direct post-event field measurements—such as wash limits or downstream water surface elevation profiles—it was not possible to validate modeled water surface profiles against observed data. This constrains our ability to comprehensively evaluate model performance in reproducing\n\ndownstream flow dynamics beyond the inundation extent<sup>10</sup>. To partially address this limitation, we compared the modeled inundation area with high-resolution satellite imagery, which provided reliable spatial delineation of the flood extent. Future post-event field campaigns should therefore prioritize the collection of wash limits, water surface elevations, and sediment deposition profiles to enhance model calibration and to better constrain flood dynamics in similar GLOF events.\n\nSecond, the sediment erosion and deposition analysis derived from DEM differencing provided only a partial estimate of geomorphic change associated with the 2013 Ranzerio GLOF. However, these estimates capture", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 157, "line_end": 173, "token_count_estimate": 630, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26d1c042faf30962", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: figure\nFigure\n\nImage /page/8/Figure/2 description: A figure containing four line graphs, labeled (a) Village1, (b) Village2, (c) Village3, and (d) Village4. Each graph plots Flow depth (m) on the left y-axis and Discharge (m³/s) on the right y-axis against Time (min) on the x-axis, which ranges from 0 to 1000. The legend indicates that a blue line with a shaded uncertainty band represents Flow depth, and a red line with circle markers represents Discharge. In graph (a) Village1, both flow depth and discharge show a sharp, immediate peak, with flow depth reaching about 8.5 m and discharge reaching about 2700 m³/s, before gradually decreasing. In graph (b) Village2, the pattern is similar, with flow depth peaking around 5.5 m and discharge peaking around 2700 m³/s. In graph (c) Village3, the peaks are lower and occur slightly later, with flow depth reaching about 4.5 m and discharge about 1100 m³/s. In graph (d) Village4, the rise is much more gradual, with flow depth peaking around 2.5 m and discharge around 600 m³/s at approximately 300 minutes.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 174, "line_end": 174, "token_count_estimate": 335, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6ebd8fd84461b78", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\nFig. 7 | Flooding discharge and depth of Ranzerio lake at four downstream villages (created using ArcGIS 10.4 and Origin 2021 by the authors). a–d Flow depth and discharge at four downstream villages, respectively.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 175, "line_end": 179, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7bcd2f7afe64c1c8", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: table\nTable: Table 1 | GLOF peak discharges and flow depths at four downstream villages\n\n| Routing location | Time (min) | Maximum discharge (m³/s) | Maximum flow depth(m) |\n|------------------|------------|--------------------------|-----------------------|\n| Village1         | $89 \b1 7$  | $2623 \b1 7$              | $7.7 \b1 0.6$          |\n| Village2         | $101 \b1 6$ | $2485 \b1 14$             | $5.5 \b1 0.1$          |\n| Village3         | $177 \b1 7$ | $1056 \b1 5$              | $4.3 \b1 0.1$          |\n| Village4         | $332 \b1 9$ | $242 \b1 2$               | $2.2 \b1 0.2$          |", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "table", "table_caption": "Table 1 | GLOF peak discharges and flow depths at four downstream villages", "columns": ["Routing location", "Time (min)", "Maximum discharge (m³/s)", "Maximum flow depth(m)"], "table_row_start": 1, "table_row_end": 4, "line_start": 180, "line_end": 185, "token_count_estimate": 216, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "837e5d77aa3ab218", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Discussion\nType: text\n\nonly a fraction of the total sediment budget, as extensive downstream reaches were not covered by the available DEMs. A more complete understanding of GLOF sediment budgets and flow dynamics therefore, requires extending DEM differencing to wider downstream areas. Moreover, coupling such geomorphic analyses with detailed sedimentological investigations of flood deposits will be essential for constraining flow rheology, sediment transport mechanisms, and depositional patterns<sup>2,41</sup>. This integration will provide critical insights into the physical processes governing GLOFs and improve the predictive capability of hazard assessments in high-mountain environments.\n\nFinally, while this study focused on the triggering mechanisms and flooding processes of the 2013 Ranzerio GLOF, future research should also examine hillslope dynamics surrounding proglacial lake basins. The valley slopes around Ranzerio lake are underlain by permafrost, and ongoing regional permafrost degradation driven by climatic warming may exacerbate slope instability and surface deformation<sup>5</sup>. Remote sensing techniques, particularly InSAR, offer effective means to monitor\n\nand quantify such ground movements in remote, data-scarce high-mountain environments <sup>18,42,43</sup>. Characterizing the temporal and spatial evolution of hillslope deformation could improve assessments of potential hazards threatening moraine dam stability or triggering future outburst floods. Integrating hillslope monitoring with hydrological and glaciological datasets, therefore, represents a promising approach for enhancing early warning systems and advancing comprehensive hazard evaluations in glacierized regions under climate change.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 186, "line_end": 192, "token_count_estimate": 425, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11652d3caf663b64", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods\nType: text\n\nTo investigate the 2013 Ranzerio lake GLOF event, we developed an integrated processing workflow combining multi-source satellite remote sensing data with physically based hydrodynamic modeling. The datasets used are summarized in Table 2.\n\nFour high-resolution RapidEye optical images (5 m spatial resolution) acquired between 2010 and 2013 were used to delineate lake boundaries and estimate horizontal glacier surface velocities prior to the outburst. An additional five RapidEye images (2016–2021) were analyzed to track postevent changes in glacier extent and lake area. Sentinel-1 SAR imagery (2017–2021) was further used to monitor glacier motion after the event. To quantify terrain changes and evaluate potential triggering mechanisms, we employed two TanDEM-X digital elevation models (5 m resolution) acquired on 22 April and 16 September 2013. Elevation differencing between the two DEMs enabled the identification of pre- and post-GLOF surface changes.\n\nMeteorological forcing was characterized using daily precipitation and temperature data from the ERA5 reanalysis<sup>44</sup>. Specifically, we used the post-processed daily statistics from 1940 to the present, available from the", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 194, "line_end": 200, "token_count_estimate": 307, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72eae36b21383864", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods\nType: figure\nFigure\n\nImage /page/9/Figure/2 description: A multi-panel figure comparing flooded areas and downstream elevation changes in a mountainous, snow-covered region. The figure is composed of four main panels labeled (a), (a1), (a2), and (b).", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "figure", "figure_caption": null, "line_start": 201, "line_end": 201, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "050ba0854c86cc6f", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods\nType: text\n\nPanel (a) is an overview map showing a river valley. A legend indicates three types of areas: \"Lake area\" (light purple), \"Mapped inundation area\" (magenta dashed line), and \"Simulated inundation area\" (blue dashed line). The mapped and simulated areas follow the path of a flood down the valley. The map includes latitude and longitude coordinates and a 1 km scale bar.\n\nPanel (a1) is a zoomed-in view of the upper part of the flood path, showing the \"Maximum Flow depth (m)\". A color scale indicates depths from 0-4 m (light blue) to 24-28 m (dark purple).\n\nPanel (a2) is a zoomed-in view of the lower part of the flood path, also showing the maximum flow depth. A specific area is highlighted with a dashed box and labeled \"0.339 km²\". A further magnified view of this area shows a lake-like feature labeled \"0.330 km²\".\n\nPanel (b) shows the \"Elevation change (m)\" along the same flood path. A color scale ranges from red (-50 to -40 m) to blue (90 to 100 m), representing erosion and deposition, respectively. The upper reaches of the path are shown in red, orange, and yellow, while the lower, wider area is shown in green and blue.\n\nFig. 8 | Comparison of flooded areas and downstream elevation changes (created using ArcGIS 10.4 by the authors). a Flood inundation area between simulated and mapped results. b Elevation changes occur at the downstream due to limited DEM coverage.\n\nCopernicus Climate Data Store (https://cds.climate.copernicus.eu/datasets/derived-era5-single-levels-daily-statistics). This globally consistent dataset assimilates a wide range of observations into a state-of-the-art atmospheric model, providing ~0.25° (~28 km) spatial resolution and daily temporal resolution. For this study, daily total precipitation and air temperature from June to July 2004–2013 were extracted for the study area to characterize meteorological conditions leading up to the GLOF.\n\nIn the absence of pre-event SAR acquisitions, glacier surface velocities for 2010–2013 were estimated using optical image correlation applied to the RapidEye dataset. Ice collapse volume was quantified from TanDEM-X elevation changes and subsequently used to inform the GLOF process reconstruction. The outburst flood was simulated using the HEC-RAS 2D hydrodynamic model, incorporating breach parameters derived from DEM differencing and previous field studies. HEC-RAS was selected for its proven capability in dam-break and flood routing simulations, as well as its ability to represent unsteady flow dynamics in complex terrain 5.18,19.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods", "section_headings": ["Methods"], "chunk_type": "text", "line_start": 202, "line_end": 216, "token_count_estimate": 693, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6706ce77d9ae10a7", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Lake mapping using optical imagery\nType: text\n\nTo map the temporal evolution of the Ranzerio lake and minimize classification uncertainties, we employed high-resolution RapidEye multispectral imagery from 2010 to 2021. To ensure that seasonal\n\nhydrological fluctuations did not confound interannual changes in lake area, imagery was deliberately selected from the same season each year. This seasonal consistency is a widely recognized strategy in glacial lake monitoring, as it reduces the influence of transient factors such as snowmelt or seasonal ice cover, thereby improving the comparability of multi-year measurements 45,46.\n\nLake boundaries were delineated using a semi-automated approach based on the Normalized Difference Water Index (NDWI). For images acquired under conditions of low solar illumination or partial snow/ice cover, manual editing was conducted to correct misclassified pixels, guided by careful visual inspection. The final lake polygons were further cross-checked against historical optical imagery and existing glacier lake inventories for the Tibetan Plateau 16,47 to ensure both spatial accuracy and temporal consistency. This integrated approach provided a robust time series of glacial lake outlines, suitable for quantifying area changes across the study period.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Lake mapping using optical imagery", "section_headings": ["Methods", "Lake mapping using optical imagery"], "chunk_type": "text", "line_start": 218, "line_end": 224, "token_count_estimate": 320, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c755f13fb1fc5d92", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Glacier velocity using optical and SAR images\nType: text\n\nTo quantify glacier surface displacement before and after the 2013 Ranzerio lake outburst, we applied offset tracking techniques to both optical and SAR datasets. Annual glacier velocities from 2010 to 2013", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Glacier velocity using optical and SAR images", "section_headings": ["Methods", "Glacier velocity using optical and SAR images"], "chunk_type": "text", "line_start": 226, "line_end": 228, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7206f462660b0f3e", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Glacier velocity using optical and SAR images\nType: figure\nFigure\n\nImage /page/10/Figure/2 description: A figure composed of nine panels, labeled (a) through (i), illustrating glacier displacement from 2017 to 2020. Panels (a) through (h) are a time series of 3D topographic maps showing the cumulative displacement of a glacier near Razerio Lake. The dates for the maps are: (a) 2017/03/21, (b) 2018/07/02, (c) 2018/12/29, (d) 2019/07/09, (e) 2019/12/24, (f) 2020/04/22, (g) 2020/07/15, and (h) 2020/10/31. A color scale at the bottom indicates cumulative displacement in meters (m), ranging from -15 (purple) to +15 (red), with 0 being green. Over time, the glacier area on the maps changes from green to blue and purple, indicating increasing negative displacement. Panel (i) is a scatter plot titled 'Glacier displacement (m)' versus 'Year'. The y-axis ranges from -20 to 0 m, and the x-axis ranges from 2017 to 2021. The plot shows two data series: AZI (red dots) and LOS (blue dots). Both series show a downward trend over time. The AZI displacement starts near 0 in 2017 and decreases to approximately -15 m by late 2020. The LOS displacement also starts near 0 in 2017 and decreases to about -7 m by late 2020.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Glacier velocity using optical and SAR images", "section_headings": ["Methods", "Glacier velocity using optical and SAR images"], "chunk_type": "figure", "figure_caption": null, "line_start": 229, "line_end": 229, "token_count_estimate": 390, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14e149bba1fc29ef", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Glacier velocity using optical and SAR images\nType: text\n\nFig. 9 | Glacier displacement time series from 2017 to 2020 (created using ArcGIS 10.4 and Origin 2021 by the authors). a-h Displacement on 21 March 2017, 2 July 2018, 29 December 2018, 9 July 2019, 24 December 2019, 22 April 2020, 15 July 2020,\n\n31 October 2020, respectively. The colors represent the cumulative displacement. i The two-dimensional displacement time series on point G.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Glacier velocity using optical and SAR images", "section_headings": ["Methods", "Glacier velocity using optical and SAR images"], "chunk_type": "text", "line_start": 230, "line_end": 234, "token_count_estimate": 143, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4dee1ec8dd829848", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Glacier velocity using optical and SAR images\nType: figure\nFigure\n\nImage /page/10/Figure/5 description: Fig. 10 | Daily temperature, daily precipitation, and cumulative precipitation at Ranzerio site between June and July from 2004 to 2014 (created using Python 3.12 by the authors). Daily temperature is shown in blue solid lines, daily precipitation is shown in gray bars, and cumulative precipitation is shown in gray dashed lines.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Glacier velocity using optical and SAR images", "section_headings": ["Methods", "Glacier velocity using optical and SAR images"], "chunk_type": "figure", "figure_caption": null, "line_start": 235, "line_end": 235, "token_count_estimate": 133, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "808c976346929db3", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Glacier velocity using optical and SAR images\nType: figure\nFigure\n\nImage /page/10/Figure/6 description: A multi-axis graph plots meteorological data from January 1, 2004, to January 1, 2014. The x-axis represents the date. The primary y-axis on the left, labeled 'Daily Precipitation (mm)', ranges from 0 to 25. The secondary y-axis on the right has two scales: 'Temperature (°C)' from -5 to 15, and 'Cumulative Precipitation (mm)' from 0 to over 5000. The graph displays four data series as indicated by the legend: a red dashed line for 'Event: 2013-07-5', gray bars for 'Daily Precipitation (mm)', a solid blue line for 'Temperature (°C)', and a dashed black line for 'Cumulative Precipitation (mm)'. The daily precipitation is shown as dense vertical bars, exhibiting an annual pattern. The temperature follows a cyclical annual pattern, peaking in the middle of each year. The cumulative precipitation shows a sawtooth pattern, resetting to zero at the beginning of each year and increasing throughout the year. A specific event on July 5, 2013, is marked with a vertical red dashed line, corresponding to a high daily precipitation event shown as a red bar.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Glacier velocity using optical and SAR images", "section_headings": ["Methods", "Glacier velocity using optical and SAR images"], "chunk_type": "figure", "figure_caption": null, "line_start": 237, "line_end": 237, "token_count_estimate": 342, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcd0c88b678aaf3c", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Glacier velocity using optical and SAR images\nType: text\n\nwere derived using four high-resolution (5 m) ortho-rectified RapidEye images. Displacements were estimated using the phase correlation-based image matching algorithm implemented in the COSI-Corr software package, a robust method for large-scale glacier motion analysis<sup>48–51</sup>.\n\nThe image pairs were processed by computing phase differences in the Fourier domain to detect sub-pixel offsets. Matching window sizes ranging from 32 to 256 pixels and a step size of 2 pixels were tested to optimize correlation quality. A signal-to-noise ratio (SNR) threshold of 0.9 was used to filter out low-confidence matches. Displacement fields in the east–west\n\nand north–south directions were produced, followed by noise suppression using gross error removal, non-local means filtering, and polynomial surface fitting (deramping). Horizontal surface velocities were obtained by combining the displacement components and normalizing by the acquisition interval. Given the 5 m pixel resolution, the expected measurement precision was approximately 0.25–0.5 m<sup>48</sup>.\n\nFor the post-outburst period (2017–2021), glacier motion was estimated using Sentinel-1 SAR data processed with the GAMMA software. Pixel Offset tracking was performed using a matching window of  $128 \\times 128$  pixels in range and azimuth directions, and a search step of  $4 \\times 1$  pixels. A normalized cross-correlation threshold of 0.3 was applied to exclude unreliable offset vectors<sup>12</sup>. The offset time series were then integrated using the Multi-dimensional Small Baseline Subset (MSBAS) method  $^{11,12}$ . Then, we used the Pixel Offset tracking MSBAS (PO-MSBAS) method to retrieve glacier displacement time series in both azimuth and line-of-sight (LOS) directions by singular value decomposition (SVD).", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Glacier velocity using optical and SAR images", "section_headings": ["Methods", "Glacier velocity using optical and SAR images"], "chunk_type": "text", "line_start": 238, "line_end": 246, "token_count_estimate": 522, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cd6ebc37d4c212ed", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Terrain change using TanDEM-X data\nType: text\n\nTo assess terrain changes associated with the Ranzerio lake outburst, we employed two TanDEM-X digital elevation models (DEMs) with a spatial resolution of 5 m, acquired on 22 April 2013 and 16 September 2013. DEM differencing is a widely used approach for quantifying surface elevation changes over time<sup>52,53</sup>. However, accurate estimation of elevation change requires prior co-registration of the DEMs to eliminate horizontal and vertical misalignments that can introduce systematic biases.\n\nWe adopted the gradient-based co-registration method proposed by Ye et al.<sup>54</sup>. In this approach, pixel-wise oriented gradients were extracted from the reference DEM (April 2013) and compared to the target DEM (September 2013) using the fast Fourier transform (FFT) to compute a similarity metric in the frequency domain. Control points (CPs) were identified through this similarity analysis, and a transformation was applied to align the target DEM to the reference. The co-registered DEMs were then differenced to produce an elevation change map.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Terrain change using TanDEM-X data", "section_headings": ["Methods", "Terrain change using TanDEM-X data"], "chunk_type": "text", "line_start": 248, "line_end": 252, "token_count_estimate": 300, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "099625cbbcb72a35", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Terrain change using TanDEM-X data\nType: figure\nFigure\n\nImage /page/11/Picture/7 description: A diagram illustrates the process of a glacial lake outburst flood. On the left, a large piece of ice, labeled \"Ice collapse ~3.8x10^6 m^3\", is shown breaking off from a \"Glacier\" and falling into a \"Lake\" below. The impact creates \"Displacement waves\" in the lake. These waves travel across the lake and wash over a natural dam, an event labeled \"Dam overtopping\". The water that spills over the dam flows down the other side, resulting in \"Flooding\". A sun is depicted in the upper right corner of the diagram.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Terrain change using TanDEM-X data", "section_headings": ["Methods", "Terrain change using TanDEM-X data"], "chunk_type": "figure", "figure_caption": null, "line_start": 253, "line_end": 253, "token_count_estimate": 200, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "025ae9be3b1cca16", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > Terrain change using TanDEM-X data\nType: text\n\n**Fig. 11** | Schematic diagrams showing the likely mechanisms of the disaster chains of the GLOF event for Ranzerio lake (created using Python 3.12 by the authors).\n\nThe elevation difference dataset was used to detect geomorphic changes in the glacier and surrounding slopes, supporting the analysis of surface deformation patterns. GLOF triggering zones were delineated where elevation loss exceeded 5 m and local slope gradients were steeper than 30°, following established approaches<sup>10,55</sup>. These elevation changes also served as input for assessing the dam breach parameters, supporting downstream hydrodynamic modeling of the outburst event. Moreover, to evaluate geomorphic changes caused by the 2013 Ranzerio GLOF, we performed DEM differencing between pre- and post-event high-resolution DEMs within their common overlapping spatial extent<sup>41</sup>. We calculated sediment volumes by multiplying pixel area by elevation change values and summing over deposition and erosion zones separately. Due to the limited spatial coverage of DEM data, these volume estimates represent only a partial sediment budget of the GLOF pathway and do not capture all downstream effects.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > Terrain change using TanDEM-X data", "section_headings": ["Methods", "Terrain change using TanDEM-X data"], "chunk_type": "text", "line_start": 254, "line_end": 258, "token_count_estimate": 333, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "957858c87e98bc50", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: text\n\nThe hydrodynamic simulation of the 2013 Ranzerio lake outburst was performed using HEC-RAS, a widely used two-dimensional hydraulic modeling software developed by the U.S. Army Corps of Engineers. The 2D flow module in HEC-RAS solves the shallow water equations, which are commonly applied for simulating unsteady surface water flows<sup>17–19</sup>. Key model inputs include digital elevation data, lake volume before the outburst, dam breach parameters, Manning's roughness coefficients, and upstream/downstream boundary conditions.\n\nTo ensure accurate representation of pre-flood topography, we employed 5 m resolution TanDEM-X data acquired on 22 April 2013, reflecting terrain conditions prior to the outburst event. The pre-event lake area and volume were derived from high-resolution RapidEye imagery and previously published field observations<sup>21,22</sup>. Before the outburst, the lake covered approximately 0.58 km² with an estimated volume of  $\\sim 11.7 \\times 10^6$  m³ by Peng et al. <sup>22</sup>. To estimate dam breach parameters, we applied Froehlich's empirical equations <sup>56</sup>, which are widely used in outburst flood modeling due to their relatively low uncertainty. The equation provides estimates of dam failure time ( $T_f$  in hours) and peak discharge ( $Q_p$  in  $m^3/s$ ):\n\n$$\\begin{cases}\nT_f = 0.00254(V_w)^{0.53}(h_b)^{-0.9} \\\\\nQ_p = 0.607(V_w)^{0.295}(h_w)^{1.24}\n\\end{cases}$$\n(1)\n\nwhere  $h_b$  is the dam breach depth (in m),  $V_w$  is the lake volume above  $h_b$  (in m³),  $h_w$  is the depth of water above the breach dam (in m).\n\nThe released flood volume was calculated by comparing the lake volume before and after the outburst event. Post-outburst lake depths were obtained from bathymetric measurements collected by an uncrewed automated sampling and monitoring vessel<sup>8</sup>. Lake volume after the GLOF was calculated by summing the product of lake depth and pixel area across the lake extent, as expressed in Eq. (2):\n\n$$V = D_i \\times R_i^2 \\tag{2}$$\n\nwhere  $D_i$  represents the depth for each individual pixel,  $R_i$  represents the corresponded pixel size of 5 m.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "text", "line_start": 260, "line_end": 280, "token_count_estimate": 696, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00254"]}}
{"id": "283e4de733070ef0", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: table\nTable: Table 2 | Summary of multi-source data used in the study\n\n| Data             | RapidEye                                                                                                   | TanDEM-X                         | Sentinel-1       | Precipitation                                         | Temperature |\n|------------------|------------------------------------------------------------------------------------------------------------|----------------------------------|------------------|-------------------------------------------------------|-------------|\n| Used bands       | Red/Green/Blue/Near infrared                                                                               | X                                | C                | -                                                     | -           |\n| Pixel spacing    | 5 m                                                                                                        | 5 m                              | 2.3 m × 14.0 m   | 0.25°                                                 | 0.25°       |\n| Numbers          | 9                                                                                                          | 2                                | 115              | -                                                     | -           |\n| Acquisition date | 2010/10/30;2011/10/24; 2012/09/05;2013/09/12; 2016/12/ 13;2017/08/24; 2019/09/26;2020/08/25; 2021/10/29 | 2013/04/22; 2013/09/16           | 2017–2021        | 2004–2013                                             |             |\n| Purpose          | Glacier velocity before the lake outburst; Area changes before and after the lake outburst                 | Elevation changes; GLOF modeling | Glacier velocity | Meteorological factors analysis for the lake outburst |             |", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "table", "table_caption": "Table 2 | Summary of multi-source data used in the study", "columns": ["Data", "RapidEye", "TanDEM-X", "Sentinel-1", "Precipitation", "Temperature"], "table_row_start": 1, "table_row_end": 5, "line_start": 281, "line_end": 287, "token_count_estimate": 333, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a81a1c61829fcd4d", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: text\n\nFig. 12 | Lake depth of Ranzerio lake after the outburst event (created using ArcGIS 10.4 and Origin 2021 by the authors). a The distribution of lake depth. b The lake depth along the profile.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "text", "line_start": 288, "line_end": 290, "token_count_estimate": 94, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b13b3db26533d3f", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: figure\nFigure\n\nImage /page/12/Figure/3 description: The image consists of two parts, labeled (a) and (b), illustrating the bathymetry of a lake.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "figure", "figure_caption": null, "line_start": 291, "line_end": 291, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "52745b1576d8c9a0", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: text\n\nPart (a) is a bathymetric map of the lake. The map uses a color scale to represent lake depth, ranging from blue for 0 meters to red for 40 meters. Blue contour lines are drawn at 5-meter intervals. A dashed black line labeled \"Profile\" runs longitudinally through the lake. The map includes coordinates 30°28'30\" and 93°32\", a north arrow, and a scale bar indicating 0.2 km.\n\nPart (b) is a line graph showing the depth profile along the dashed line from part (a). The x-axis represents \"Distance (km)\" from approximately 1.2 to 0, and the y-axis represents \"Depth (m)\" from 0 to 40, with depth increasing downwards. The blue line shows that the lake depth starts at 0 m, increases sharply, fluctuates in the deepest section between approximately 28 m and 35 m, and then gradually becomes shallower, reaching a depth of about 5 m at a distance of 1.2 km.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "text", "line_start": 292, "line_end": 296, "token_count_estimate": 281, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8e962fbd6f66cfac", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: figure\nFigure: Figure 12 shows the spatial distribution of lake depth after the outburst and depth variations along a selected profile. The maximum lake depth reached approximately 36 m, with depths exceeding 20 m mainly occurring between 0.05 km and 0.15 km along the profile. Using this approach, the post-outburst lake volume was estimated at $\\sim\\!3.8\\times10^6$ m³. By subtracting this from the pre-outburst volume, the total released flood volume was calculated to be approximately 7.9 $\\times$ 10 $^6$ m³. We did not explicitly include the additional water volume potentially contributed by mass movements in the simulation because reliable knowledge of collapse dynamics is limited. Moreover, it is difficult to assess how well the lake model can represent the complex interaction between the collapse and the lake $^{77,58}$ . As a result, our estimates of flood volume and peak discharge should be regarded as conservative values.\n\nFigure 12 shows the spatial distribution of lake depth after the outburst and depth variations along a selected profile. The maximum lake depth reached approximately 36 m, with depths exceeding 20 m mainly occurring between 0.05 km and 0.15 km along the profile. Using this approach, the post-outburst lake volume was estimated at  $\\sim\\!3.8\\times10^6$  m³. By subtracting this from the pre-outburst volume, the total released flood volume was calculated to be approximately 7.9  $\\times$  10  $^6$  m³. We did not explicitly include the additional water volume potentially contributed by mass movements in the simulation because reliable knowledge of collapse dynamics is limited. Moreover, it is difficult to assess how well the lake model can represent the complex interaction between the collapse and the lake  $^{77,58}$ . As a result, our estimates of flood volume and peak discharge should be regarded as conservative values.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "figure", "figure_caption": "Figure 12 shows the spatial distribution of lake depth after the outburst and depth variations along a selected profile. The maximum lake depth reached approximately 36 m, with depths exceeding 20 m mainly occurring between 0.05 km and 0.15 km along the profile. Using this approach, the post-outburst lake volume was estimated at $\\sim\\!3.8\\times10^6$ m³. By subtracting this from the pre-outburst volume, the total released flood volume was calculated to be approximately 7.9 $\\times$ 10 $^6$ m³. We did not explicitly include the additional water volume potentially contributed by mass movements in the simulation because reliable knowledge of collapse dynamics is limited. Moreover, it is difficult to assess how well the lake model can represent the complex interaction between the collapse and the lake $^{77,58}$ . As a result, our estimates of flood volume and peak discharge should be regarded as conservative values.", "line_start": 297, "line_end": 297, "token_count_estimate": 495, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e3e975b66a549f20", "text": "Document: Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake\nSection: Methods > GLOF simulation using HEC-RAS model\nType: text\n\nIn the GLOF simulations, Manning's roughness coefficients were assigned according to land cover and vegetation types, with typical values ranging from 0.025 to 0.033 and an average value of 0.03<sup>5,17</sup>. Accordingly, a roughness coefficient of 0.03 was applied to the unvegetated downstream channel. The model employed the full momentum equations to simulate two-dimensional, unsteady flow, using a time step of 1 s and a total simulation duration of 24 h. Upstream boundary conditions were derived from the estimated dam breach hydrographs, while downstream boundary conditions were specified as a normal water depth gradient of 0.01 m/m<sup>17,19</sup>. Thus, the 2013 Ranzerio lake outburst GLOF was reconstructed using observed breach parameters and released lake volume to ensure realistic simulation results. Additionally, uncertainties in the simulation parameters, lake bathymetry, model parameters, and flow transitions inevitably propagate into flood outputs<sup>5</sup>. Given the challenges in directly constraining\n\nthese parameters, we assessed the uncertainty in the hydraulic reconstruction by quantifying the spatial variability of modeled peak discharge and flow depth. Specifically, we calculated the standard deviations of these variables across neighboring grid cells surrounding downstream villages<sup>59</sup>. This approach yields a representative range of discharge and depth, reflecting the robustness and sensitivity of the hydraulic reconstruction. Furthermore, the simulated inundation extent was compared against flood mapping derived from RapidEye imagery to evaluate model uncertainty.", "metadata": {"source_file": "data/('Triggering_factors_and_flooding_processes_of_glacier', '.pdf')_extraction.md", "document_title": "Triggering factors and flooding processes of glacial lake outburst flood at Ranzerio lake", "section_path": "Methods > GLOF simulation using HEC-RAS model", "section_headings": ["Methods", "GLOF simulation using HEC-RAS model"], "chunk_type": "text", "line_start": 298, "line_end": 301, "token_count_estimate": 458, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "409877f36b7034c9", "text": "Document: veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods\nType: text\n\nSustained glacier melt in the Himalayas has gradually spawned more than 5,000 glacier lakes that are dammed by potentially unstable moraines. When such dams break, glacier lake outburst floods (GLOFs) can cause catastrophic societal and geomorphic impacts. We present a robust probabilistic estimate of average GLOFs return periods in the Himalayan region, drawing on 5.4 billion simulations. We find that the 100-y outburst flood has an average volume of  $33.5^{+3.7}/_{-3.7} \\times 10^6 \\text{ m}^3$  (posterior mean and 95% highest density interval [HDI]) with a peak discharge of  $15,600^{+\\bar{2},000}/_{-1,800}~m^{\\bar{3}}\\cdot s^{-1}.$  Our estimated GLOF hazard is tied to the rate of historic lake outbursts and the number of present lakes, which both are highest in the Eastern Himalayas. There, the estimated 100-y GLOF discharge (~14,500 m<sup>3</sup>·s<sup>-1</sup>) is more than 3 times that of the adjacent Nyainqentanglha Mountains, and at least an order of magnitude higher than in the Hindu Kush, Karakoram, and Western Himalayas. The GLOF hazard may increase in these regions that currently have large glaciers, but few lakes, if future projected ice loss generates more unstable moraine-dammed lakes than we recognize today. Flood peaks from GLOFs mostly attenuate within Himalayan headwaters, but can rival monsoon-fed discharges in major rivers hundreds to thousands of kilometers downstream. Projections of future hazard from meteorological floods need to account for the extreme runoffs during lake outbursts, given the increasing trends in population, infrastructure, and hydropower projects in Himalayan headwaters.\n\natmospheric warming  $\\mid$  meltwater lakes  $\\mid$  GLOF  $\\mid$  extreme-value statistics  $\\mid$  Bayesian modeling", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 13, "token_count_estimate": 537, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f34555c5a111752", "text": "Document: veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods\nType: text\n\nmay increase in these regions that currently have large glaciers , but few lakes , if future projected ice loss generates more unstable moraine - dammed lakes than we recognize today . Flood peaks from GLOFs mostly attenuate within Himalayan headwaters , but can rival monsoon - fed discharges in major rivers hundreds to thousands of kilometers downstream . Projections of future hazard from meteorological floods need to account for the extreme runoffs during lake outbursts , given the increasing trends in population , infrastructure , and hydropower projects in Himalayan headwaters . atmospheric warming $ \\ mid $ meltwater lakes $ \\ mid $ GLOF $ \\ mid $ extreme - value statistics $ \\ mid $ Bayesian modeling\n\nonsoonal floods are among the most destructive natural nonsonial moots are among the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater Himalayan region and the adjacent hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater hazards in the greater haz mountain ranges of the Hindu Kush, Karakoram, Nyainqentanglha, and Hengduan Shan (Fig. 1). Regional projections for the lower Indus, Ganges, and Brahmaputra rivers hold that flood frequencies will rise noticeably in the 21st century (1, 2), putting the livelihoods of 220 million people at risk (3). In Himalayan headwaters, such prognoses have disregarded episodic, but potentially destructive, floods from the sudden emptying of morainedammed lakes. Such glacier lake outburst floods (GLOFs) occur largely independently of hydrometeorological floods, but can surpass their peak discharges by orders of magnitude in the upper river reaches (4-6). Glacier lakes dammed by abandoned moraines are susceptible to outburst, triggered by ice or debris falls, strong earthquake shaking, internal piping, or overtopping waves that exceed the shear resistance of the dam (7–9). These triggers mostly happen unrecorded in remote terrain, eroding the impounding barriers within minutes to hours, and releasing sediment-laden floods that may travel >100 km downstream (7). With little to no warning, communities and infrastructure downstream have often been caught unprepared, suffering loss of human lives and livestock, and damage to roads, buildings, and hydropower facilities (10-12). An objective and reproducible hazard assessment of such dam-break floods is key to human safety and sustainable development, and is repeatedly emphasized in research and media coverage of atmospheric warming, dwindling glaciers, and growing meltwater lakes (3, 13, 14).", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 13, "token_count_estimate": 1060, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6834d9a05a79dfb2", "text": "Document: veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods\nType: text\n\nunrecorded in remote terrain , eroding the impounding barriers within minutes to hours , and releasing sediment - laden floods that may travel > 100 km downstream ( 7 ) . With little to no warning , communities and infrastructure downstream have often been caught unprepared , suffering loss of human lives and livestock , and damage to roads , buildings , and hydropower facilities ( 10 - 12 ) . An objective and reproducible hazard assessment of such dam - break floods is key to human safety and sustainable development , and is repeatedly emphasized in research and media coverage of atmospheric warming , dwindling glaciers , and growing meltwater lakes ( 3 , 13 , 14 ) .\n\nGLOFs have gained growing attention in the Himalayas (15, 16), where these disasters have had the highest death toll worldwide\n\n(17). While the distribution and dynamics of moraine-dammed lakes have been mapped extensively in recent years (12, 18–21), objectively appraising the current Himalayan GLOF hazard has remained challenging. The high-alpine conditions limit detailed fieldwork, leading researchers to extract proxies of hazard from increasingly detailed digital topographic data and satellite imagery. These data allow for readily measuring or estimating the geometry of ice and moraine dams, the possibility of avalanches or landslides entering a lake, or the water volumes released by outbursts (9, 18, 20). Ranking these diagnostics in GLOF hazard appraisals has mostly relied on expert judgment (18, 22), because triggers and conditioning factors are largely unknown for most historic GLOFs (23).\n\nGuided by studies on earthquakes, landslides, wildfires, and floods, we use the magnitude and frequency of GLOFs in a given area and period as an objective metric of hazard. GLOFs have occurred at an average rate of  $\\sim 1.3 \\text{ y}^{-1}$  in the Himalayas in the past 3 decades (23), although of differing size, which is conventionally described by the released flood volume  $V_0$  or the associated peak discharge  $Q_p$ . These parameters are difficult to measure during dam failure but can be estimated eventually from the surface areas of glacier lakes. We thus scaled the manually mapped areas of all 5,184 Himalayan moraine-dammed lakes (>0.01 km<sup>2</sup>) to volumes using a Bayesian robust regression of maximum lake depth versus area from 24 bathymetrically surveyed lakes (Materials and Methods and SI Appendix, Fig. S2 and Table S1). Our approach assumes that all lakes are equally susceptible to outburst and that any incision depth is possible, which is consistent with data from Himalayan dam breaks (SI Appendix, Fig. S3). We used a Bayesian variant of a physical dam-break model (24, 25) to predict peak discharge  $Q_p$  from the product of  $V_0$  and the breach erosion rate k (Materials and Methods and SI Appendix, Figs. S4 and", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 13, "token_count_estimate": 770, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b346e46c57c6420d", "text": "Document: Significance\nSection: Significance\nType: text\n\nGlacier lake outburst floods (GLOFs) have become emblematic of a changing mountain cryosphere. The Himalayas suffered the highest losses from these sudden pulses of meltwater but lack a quantitative appraisal of GLOF hazard. We express the hazard from Himalayan glacier lakes by the peak discharge for a given return period. The 100-y GLOF has a mean discharge of ~15,600 m³·s⁻¹, comparable to monsoonal river discharges hundreds of kilometers downstream. The Eastern Himalayas are a hotspot of GLOF hazard that is 3 times higher than in any other Himalayan region. The size of growing glacier lakes and the frequency of lake outbursts determine GLOF hazard, which needs to be acknowledged better in flood hazard studies.\n\nAuthor contributions: G.V. and O.K. designed research; G.V. and O.K. performed research; G.V. and O.K. analyzed data; and G.V., O.K., and A.W. wrote the paper.\n\nThe authors declare no competing interest.\n\nThis article is a PNAS Direct Submission.\n\nPublished under the PNAS license.\n\nData deposition: All input data are available at Zenodo (http://doi.org/10.5281/zenodo. 3523213), and model codes are available at GitHub (https://github.com/geveh/GLOFhazard).\n\n<sup>1</sup>To whom correspondence may be addressed. Email: georg.veh@uni-potsdam.de.\n\nThis article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1914898117/-/DCSupplemental.\n\nFirst published December 30, 2019.\n\nDownloaded from https://www.pnas.org by IIT MUMBAI on August 8, 2025 from IP address 103.21.125.81", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Significance", "section_headings": ["Significance"], "chunk_type": "text", "line_start": 15, "line_end": 35, "token_count_estimate": 460, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1914898117", "3523213"]}}
{"id": "42fa1bd253451fe6", "text": "Document: Significance\nSection: Significance\nType: figure\nFigure\n\nImage /page/1/Figure/1 description: A figure with three panels (A, B, C) illustrating the distribution and characteristics of moraine-dammed glacial lakes in the Himalayas. Panel A is the main map showing the study region, which includes the Hindu Kush, Karakoram, Western Himalaya, Central Himalaya, Eastern Himalaya, Nyainqen-tanglha, and Hengduan Shan mountain ranges. The map displays major rivers like the Indus, Ganges, Brahmaputra, and Yangtze, and cities such as Kabul, Islamabad, New Delhi, and Kathmandu. The distribution of lakes is shown using circles, where the size of the circle indicates the total lake area (with a key showing sizes for <3, 6, 12, 24, and 36 km²) and the color indicates the lake quantity (from 1 in light gray to 330 in dark blue). Yellow triangles mark the locations of Glacial Lake Outburst Floods (GLOFs) that occurred between 1935 and 2017. Panel B is an inset map showing the location of the study area within Asia, covering parts of India and China. Panel C is a histogram showing the frequency distribution of lake sizes. The x-axis represents lake size in square meters on a logarithmic scale (10^4, 10^5, 10^6), and the y-axis represents frequency, ranging from 0 to 500. The histogram shows that smaller lakes are most frequent, with the highest frequency (over 500) occurring for lakes around 10^4 to 2x10^4 m², and the frequency decreases as lake size increases.", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Significance", "section_headings": ["Significance"], "chunk_type": "figure", "figure_caption": null, "line_start": 36, "line_end": 36, "token_count_estimate": 394, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "1130783041dd09e0", "text": "Document: Significance\nSection: Significance\nType: text\n\nFig. 1. Moraine-dammed glacier lakes in the Himalayas. (A) Distribution of moraine-dammed lakes in our study region in 1° × 1° bins. Bubbles are scaled to the total lake area, and color-coded to abundance. Reported GLOFs (yellow triangles) have occurred most frequently in the past 8 decades in regions where glacier lakes are largest (23). (B) Location of the Himalayas between the Indian subcontinent and the Tibetan Plateau. (C) Histogram of glacier lake areas.\n\nS5). In summary, we obtained  $4.6 \\times 10^8$  and  $4.9 \\times 10^9$  scenarios of  $V_0$  and  $Q_p$ , respectively, for all moraine-dammed lakes in the Himalayas. These numbers ensure that we have sufficiently explored the physically plausible space for fitting an extreme-value distribution to these key flood diagnostics. We further stacked our simulations, accounting for contemporary mean annual GLOF rates between 0.03 and 0.71 y<sup>-1</sup> in 7 subregions of the Himalayas, and estimated GLOF return periods in terms of the 100-y flood volume  $V_{100}$  and discharge  $Q_{p100}$  (Materials and Methods) (26, 27).", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Significance", "section_headings": ["Significance"], "chunk_type": "text", "line_start": 37, "line_end": 41, "token_count_estimate": 329, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19383f64754a4787", "text": "Document: Significance\nSection: Results\nType: text\n\nThe predicted flood volumes and peak discharges span more than 7 orders of magnitude (Fig. 2). Based on a mean posterior rate of 1.26 GLOFs  $y^{-1}$  over the past 3 decades (23), we estimate a contemporary  $V_{100}$  of  $33.5^{+3.7}$ / $_{-3.7} \\times 10^6$  m<sup>3</sup> s<sup>-1</sup> and  $Q_{\\rm p100}$  of  $15,600^{+2,000}$ / $_{-1,800}$  m<sup>3</sup> s<sup>-1</sup> in the entire study area (Fig. 3). Regionally differing GLOF rates cause variation in  $V_{100}$  and  $Q_{\\rm p100}$ . For example, GLOF hazard in the Eastern Himalayas is 350% ( $V_{100}$ ) and 338% ( $Q_{p100}$ ) higher than in the next highest region (Fig. 3 and SI Appendix, Fig. S6).\n\nCorrecting for regionally varying GLOF rates, we can explore how-for a fixed rate of one GLOF y-1-hazard changes as function of the lake size distribution (Fig. 4). We find that GLOF hazard in the Eastern Himalayas, which currently have the largest lakes and have had the highest GLOF activity in the past 3 decades (23), is on average more than 87% ( $V_{100}$ ) and 57% $(Q_{p100})$  higher than the estimate from the entire Himalayas. In contrast, the Western Himalayas had no known outburst from moraine-dammed lakes in the past 30 y, while the rate-adjusted  $V_{100}$  and  $Q_{\\rm p100}$  there are still about 76% and 58% below that of the overall study area. In summary, these figures robustly support a contemporary hotspot of GLOF hazard in the southern Himalayas, mainly due to the larger abundance of glacier lakes and GLOFs in that part of the mountain belt.", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Results", "section_headings": ["Results"], "chunk_type": "text", "line_start": 43, "line_end": 47, "token_count_estimate": 521, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51b2ebd980c49b2e", "text": "Document: Significance\nSection: Discussion\nType: text\n\nWe offer a consistent and reproducible estimate of present GLOF hazard in the Himalayas. Our Bayesian estimates explore the parameter space of plausible flood volumes and associated peak discharges with roughly a million outburst scenarios for any given lake. Our approach expands previous hazard appraisals by explicitly accounting for regionally varying GLOF rates. The estimated GLOF return periods are consistent with the frequency of reported flood volumes and discharges since 1935 (Fig. 3 and SI Appendix, Fig. S6 and Table S2). The 3 largest reported flood volumes of 71.6, 19.5, and  $17.2 \\times 10^6$  m<sup>3</sup> from the lakes Sangwang Tsho (1954), Sabai Tsho (1998), and Lugge Tsho (1994), respectively, had return periods of 237, 56, and 49 y, respectively, according to our predictions. The highest reported peak discharge (15,920 m<sup>3</sup>·s<sup>-1</sup>) drained from Lake Zhangzangbo in 1981; we estimate this as a 100-y event in the greater Himalayan region. The lakes Sangwang Tsho and Sabai Tsho had similar high peak discharges of ~10,000 m<sup>3</sup>·s<sup>-1</sup>, corresponding to a return period of 60 y. Yet reported values of  $V_0$  and  $Q_p$  rely chiefly on point estimates from empirical rating curves, eyewitness accounts, flood marks, and transported clasts in river channels, or measurements several kilometers downstream of the failing dams (28). thus compromising detailed, site-specific validation of our regional predictions. These uncertainties cause some of the scatter in the data that we based our models on (SI Appendix, Figs. S4 and S5). However, our approach explicitly propagates these uncertainties and robustly estimates  $V_0$  and  $Q_p$  from the distribution of meltwater lake areas in the entire Himalayan region. The size of glacier lakes will remain the key determinant of GLOF magnitude, regardless of whether we use alternative discharge rating curves (SI Appendix, Fig. S7), more elaborate numerical dam breach simulations (29), or any other metric of flood potential (20).", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 49, "line_end": 51, "token_count_estimate": 565, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5fe4bdd76d953a6", "text": "Document: Significance\nSection: Discussion\nType: figure\nFigure\n\nImage /page/2/Figure/1 description: A vertical line of black text on a white background. The text is rotated 90 degrees counter-clockwise and is horizontally compressed, making it appear as a thin, dense line. The text reads: 'Downloaded from http://www.pnas.org by guest on August 8, 2023 from IP address 103.21.125.18.'", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 52, "line_end": 52, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "289f6bc547784386", "text": "Document: Significance\nSection: Discussion\nType: figure\nFigure\n\nImage /page/2/Figure/2 description: A figure composed of eight scatter plots arranged in a 2x4 grid, labeled A through H. All plots share the same axes. The y-axis is labeled \"Frequency density\" and ranges logarithmically from 10⁻¹¹ to 10⁻¹. The x-axis is labeled \"Peak discharge Qp [m³ s⁻¹] / Flood volume V₀ [m³]\" and ranges logarithmically from 10⁻¹ to 10⁹. Each plot displays two data series: one with orange dots and one with dark blue dots. The orange series generally shows a decreasing trend from left to right. The blue series shows a distribution that peaks around 10³ to 10⁵ on the x-axis and then decreases. Each plot is labeled with a region and contains two numbers in gray text. The details for each plot are as follows:", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 54, "line_end": 54, "token_count_estimate": 231, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb439f49c9c7c9c1", "text": "Document: Significance\nSection: Discussion\nType: text\n\nPlot A: \"All regions\", numbers 5184 and 426.8.\nPlot B: \"Hindu Kush\", numbers 650 and 30.9.\nPlot C: \"Karakoram\", numbers 351 and 17.2.\nPlot D: \"Western Himalaya\", numbers 688 and 30.8.\nPlot E: \"Central Himalaya\", numbers 958 and 77.7.\nPlot F: \"Eastern Himalaya\", numbers 1600 and 184.8.\nPlot G: \"Nyainqentanglha\", numbers 415 and 41.7.\nPlot H: \"Hengduan Shan\", numbers 527 and 44.\n\nFig. 2. Frequency densities of simulated GLOF flood volumes  $V_0$  and peak discharge  $Q_p$  for present Himalayan glacier lakes. Bubbles are log-binned densities of all simulations of flood volumes  $V_0$  (blue) and peak discharge  $Q_p$  (orange) in (A) the entire study area and (B–H) 7 subregions. Gray numbers are the number of lakes (top) and their surface areas (square kilometers) (bottom) for a given region.\n\nOur study objectively enriches previous qualitative appraisals, which contrasted in the degree and number of \"potentially dangerous\" Himalayan lakes (22). The tails of the frequency–size distributions (Fig. 2) contain valuable information for practitioners regarding the physically possible margins of  $V_0$  and  $Q_p$  in the Himalayas. We note that half of the total Himalayan lake volume  $(20.1^{+2.4}/_{-2.1} \\text{ km}^3)$  is in the largest 1% of glacier lakes (SI Appendix, Fig. S8). For example, Sangwang Tsho—the largest Himalayan moraine-dammed lake today—accounts for 5% of the total glacier lake volume ( $\\sim 1 \\text{ km}^3$ ) and could release a flood with a return period of  $5,000^{+1,400}/_{-1,000}$  y, should this volume drain entirely (SI Appendix, Fig. S9). Such flood magnitudes and associated return periods are consistent with reports on Holocene flood volumes up to 3 orders of magnitude larger in the eastern Himalayas (30).\n\nHimalayan glacier lakes are currently limited in their storage capacity to produce floods of these dimensions, particularly in the Hindu Kush, Karakoram, and Western Himalayas, where lakes are smallest today (Figs. 1 and 2 and SI Appendix, Fig. S8). Why glacier lakes occur in clusters and have become largest in the Central and Eastern Himalayas since 1990 (+7.7 to +23% in area) remains debated (19). Glaciers have had negative mass balances in all regions except the Karakoram since the 1970s at least (31), while the highest ice losses in the 21st century were in the Western Himalaya and the Nyaingentanglha (32). The Central and Eastern Himalayas have a high proportion of lakes in direct contact with debris-covered parent glaciers, possibly accelerating glacier mass loss and lake growth (33). The increasing trend of air temperature is consistent over the entire Himalayas, whereas changes in precipitation were mostly insignificant (34), so that the clustering of lakes today may be tied to local, less well-known, controls such as glacier bed topography or valley geometry (35).", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 55, "line_end": 74, "token_count_estimate": 777, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "816c64b9e3431693", "text": "Document: Significance\nSection: Discussion\nType: text\n\nthe Karakoram since the 1970s at least ( 31 ) , while the highest ice losses in the 21st century were in the Western Himalaya and the Nyaingentanglha ( 32 ) . The Central and Eastern Himalayas have a high proportion of lakes in direct contact with debris - covered parent glaciers , possibly accelerating glacier mass loss and lake growth ( 33 ) . The increasing trend of air temperature is consistent over the entire Himalayas , whereas changes in precipitation were mostly insignificant ( 34 ) , so that the clustering of lakes today may be tied to local , less well - known , controls such as glacier bed topography or valley geometry ( 35 ) .\n\nYet the distribution of glacier lakes will likely change in the future, given that a global temperature rise of 1.5 °C could melt half of the Himalayan glacier mass by the end of the 21st century (36). Models of subglacial topography suggest that a complete melting of all glaciers might provide accommodation space for another 16,000 meltwater lakes (>10<sup>4</sup> m<sup>2</sup>) with a maximum total volume of 120 km<sup>3</sup> (37), hence possibly increasing the current lake volume 6-fold. According to these projections, most of these futures lakes could form in the Karakoram and Western Himalayas, where contemporary glacier cover is highest (13). Yet the errors of the inferred bathymetries can involve tens of meters, and many lakes might fill bedrock depressions that are less prone to catastrophic outbursts. Future flood volumes may increase nevertheless, especially in regions with large ice volumes and few lakes, even if only a fraction of the total meltwater will be trapped by moraines. Regional GLOF hazard could thus change commensurately, should annual GLOF frequencies rise with a growing lake abundance in the future (16). Whether lake outbursts will become more frequent with atmospheric warming remains an open question, however. The average rate of GLOFs in the greater Himalayan region has remained unchanged in the past 3 decades (23), showing that rapid growth of glacier lakes alone is an unsuitable predictor for GLOF activity, let alone GLOF hazard (38, 39).\n\nBoth the magnitude and frequency of outburst floods are straightforward to change in our model, motivating a more dynamic assessment with regular updates of outburst frequency and lake size distribution. We also encourage future work to learn more about the triggers and drivers of historic GLOFs. With more of this information becoming available, we can improve our Bayesian framework by assigning more informed (and appropriately weighted) prior probabilities of outburst to each glacier lake. In this spirit, we offer a practical tool that is extensible and", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 55, "line_end": 74, "token_count_estimate": 667, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "22823a68698464a8", "text": "Document: Significance\nSection: Discussion\nType: figure\nFigure\n\nImage /page/3/Figure/1 description: A figure displaying estimated 100-year peak discharges from Glacial Lake Outburst Floods (GLOFs) in the greater Himalayan region. The figure includes a central map and eight surrounding line graphs.", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "figure", "figure_caption": null, "line_start": 75, "line_end": 75, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ebf4bfb63cddff0", "text": "Document: Significance\nSection: Discussion\nType: text\n\nThe map shows the Tibetan Plateau and surrounding mountain ranges: Hindu Kush, Karakoram, Western Himalaya, Central Himalaya, Eastern Himalaya, Nyainqentanglha, and Hengduan Shan. It is populated with blue circles of varying sizes, representing peak discharge (Qp) in m³ s⁻¹, and white circles representing historical GLOFs.\n\nEach of the eight line graphs plots peak discharge (Qp) on the y-axis against the return period in years on the x-axis. The y-axis ranges from 0 to 40,000 m³ s⁻¹, and the x-axis ranges from 2 to 200 years. Each graph shows an upward-curving blue line with a shaded confidence interval. The data for each region is as follows:\n\n- \\*\\*Karakoram\\*\\*: Present Qp100 is 535 (+100, -113) m³ s⁻¹; GLOF rate is 0.06.\n- \\*\\*Western Himalaya\\*\\*: Present Qp100 is 145 (+117, -166) m³ s⁻¹; GLOF rate is 0.03.\n- \\*\\*Central Himalaya\\*\\*: Present Qp100 is 2,603 (+500, -433) m³ s⁻¹; GLOF rate is 0.19.\n- \\*\\*Eastern Himalaya\\*\\*: Present Qp100 is 14,448 (+1,846, -1,891) m³ s⁻¹; GLOF rate is 0.71.\n- \\*\\*Hindu Kush\\*\\*: Present Qp100 is 574 (+125, -106) m³ s⁻¹; GLOF rate is 0.06.\n- \\*\\*Nyainqentanglha\\*\\*: Present Qp100 is 4,276 (+766, -720) m³ s⁻¹; GLOF rate is 0.23.\n- \\*\\*Hengduan Shan\\*\\*: Present Qp100 is 2,529 (+515, -449) m³ s⁻¹; GLOF rate is 0.16.\n- \\*\\*All regions\\*\\*: Present Qp100 is 15,609 (+2,013, -1,797) m³ s⁻¹; GLOF rate is 1.26.\n\nFig. 3. Estimated 100-y peak discharges from GLOFs in the greater Himalayan region. Circles on the map are present moraine-dammed lakes with colors coded and radii scaled to the modes of the predicted distributions of Q<sub>0</sub> for each lake. Plots show return periods of GLOF peak discharge for the entire study area (Lower Left) and its 7 subregions. Blue curves are estimated GLOF return periods, and numbers are the posterior mean 100-y flood discharge and 95% HDIs from 1,000 simulations for all 5,184 moraine-dammed lakes, calculated from the mean regional GLOF rate per region in the past 3 decades (brown). Thick dark blue lines are the means from 1,000 simulations (shades) drawing on sampled time series of 10,000 y each (Materials and Methods). Red ticks in Lower Left are average return periods of  $Q_p$  estimated for a sample of historic GLOFs since 1935 (SI Appendix, Table S2).", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 76, "line_end": 94, "token_count_estimate": 768, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6f0d8a9a5667998", "text": "Document: Significance\nSection: Discussion\nType: text\n\nsubregions . Blue curves are estimated GLOF return periods , and numbers are the posterior mean 100 - y flood discharge and 95 % HDIs from 1 , 000 simulations for all 5 , 184 moraine - dammed lakes , calculated from the mean regional GLOF rate per region in the past 3 decades ( brown ) . Thick dark blue lines are the means from 1 , 000 simulations ( shades ) drawing on sampled time series of 10 , 000 y each ( Materials and Methods ) . Red ticks in Lower Left are average return periods of $ Q_p $ estimated for a sample of historic GLOFs since 1935 ( SI Appendix , Table S2 ) .\n\ncompatible with flood routing models, design codes, and hazard mitigation. Our appraisal of GLOF hazard complements projections of meteorological flood hazards in a warming climate for the sparsely instrumented Himalayan drainage network. Atmospheric warming is projected to increase mean daily and annual discharges in the Indus, Ganges, and Brahmaputra rivers only by a few percent in this century (40, 41), although the return periods for a given flood stage might drop by 50 to 90% in these rivers in the 21st century. Extreme runoff in headwaters (1), and GLOFs in particular (12), has eluded such projections. We show that\n\nflood hazard due to glacier melt requires urgent attention in light of the rapidly growing population, infrastructure, and hydropower projects in the Himalayas (3, 12). Our appraisal adds to recent calls toward identifying regions that are most prone to GLOF impacts, and complements integrative concepts for climate risk adaptation on local, national, or regional levels (42).", "metadata": {"source_file": "data/('veh-et-al-hazard-from-himalayan-glacier-lake-outburst-floods', '.pdf')_extraction.md", "document_title": "Significance", "section_path": "Discussion", "section_headings": ["Discussion"], "chunk_type": "text", "line_start": 76, "line_end": 94, "token_count_estimate": 411, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8958e690fbce3462", "text": "Document: Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard Downstream Impact and Risk Over the Indian\nType: text\n\n**Abstract** Indian Himalayas are home to numerous glacial lakes, which can pose serious threat to downstream communities and lead to catastrophic socioeconomic disasters in case of a glacial lake outburst flood (GLOF). This study first identified 329 glacial lakes of size greater than 0.05 km² in the Indian Himalayas, and then a remote sensing-based hazard and risk assessment was performed on these lakes. Different factors such as avalanche, rockfall, upstream GLOF, lake expansion, identification of the presence of ice cores, and assessment of the stability of moraine were considered for the hazard modeling. Further, a stochastic inundation model was applied to quantify the potential number of buildings, bridges, and hydropower systems that could be inundated by GLOF in each lake. Finally, the hazard parameters and downstream impact were collectively considered to determine the risk linked with each lake. A total of 23 lakes were identified as very high risk lakes and 50 as high-risk lakes. The potential flood volumes associated with various triggering mechanisms were also measured and were used to identify the lakes with the most considerable risk, such as Shakho Cho and Khangchung Tso. This study is anticipated to support stakeholders and decision-makers in identifying critical glacial lakes and make well-informed decisions related to future modeling efforts, field studies, and risk mitigation measures.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard Downstream Impact and Risk Over the Indian", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 4, "token_count_estimate": 356, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "458f2559af75a882", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\nThe Himalayas have observed extensive shrinkage of glaciers with the most negative mass balance and the most significant decline in glacial length (Cogley, 2016; Gardelle et al., 2011; Kääb et al., 2012; Maurer et al., 2019; Sakai & Fujita, 2017; Yao et al., 2012). This glacial retreat is accompanied by the formation of numerous glacial lakes formed by replacing ice from glacier tongues (King et al., 2017, 2018). Linsbauer et al. (2016) predicted the future emergence of glacial lakes in Himalayas by modeling glacier bed topographies and documented 5,000 overdeepenings that may turn into glacial lakes. Sporadic outbursts in the unstable glacial lake have killed thousands of people with some of the most significant events taking place in the Himalayas (Nie et al., 2018; Veh et al., 2018). Carrivick and Tweed (2016) compiled an inventory of glacier floods and reported that the central Himalayas have observed maximum number of causalities due to glacial hazards, where Nepal and India have observed fewer flood but higher levels of damage. Glacial lake outburst flood (GLOF) is the sudden discharge of a large amount of stored water from glacial lakes (Carrivick & Rushmer, 2006), triggered majorly due to dynamic failure (mass entering the lake in the form of avalanche, rockfall, or GLOF in the upstream portion of the lake) and minorly due to self-destructive failures (settlement of ice-cored moraine or unstable moraine structure), seismic activities, earthquake, and extreme climatic events (Carrivick & Tweed, 2013; Rounce et al., 2016; Worni et al., 2013). The remote location, infrequent occurrence, and interconnection between these triggering mechanisms make it difficult to assess the potential hazard related to these lakes. Nonetheless, the estimated risk from various triggering mechanisms must be quantified, especially for the Himalayas, where 68% of the hydropower plants are located on potential GLOF tracks (Schwanghart et al., 2016).\n\nHindu Kush Himalayas have been subjected to many glacial lake studies (Emmer, 2018; Fujita et al., 2013; Ives et al., 2010; King et al., 2017, 2018; Maharjan et al., 2018; Schwanghart et al., 2016; Veh et al., 2019), including the ones carried out for specific countries such as Nepal (Mool et al., 2011; Rounce et al., 2017; Somos-Valenzuela et al., 2015), Bhutan (Komori, 2008; Ukita et al., 2011), and Tibet (Chen et al., 2007; Cui et al., 2011; Wang et al., 2011). Studies carried out for the Indian Himalayas include the work of ICIMOD (Ives et al., 2010) that created a comprehensive glacial lake inventory of Hindu Kush Himalayas and included three Indian states. Worni et al. (2013) created a glacial lake inventory for Indian Himalayas\n\n©2020. American Geophysical Union. All Rights Reserved.\n\nDUBEY AND GOYAL 1 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 6, "line_end": 22, "token_count_estimate": 779, "basins": [], "subbasins": [], "countries": ["Bhutan", "India", "Nepal"], "lake_ids": ["19447973"]}}
{"id": "0fd961003606fc01", "text": "Document: 1. Introduction\nSection: 1. Introduction\nType: text\n\n( Komori , 2008 ; Ukita et al . , 2011 ) , and Tibet ( Chen et al . , 2007 ; Cui et al . , 2011 ; Wang et al . , 2011 ) . Studies carried out for the Indian Himalayas include the work of ICIMOD ( Ives et al . , 2010 ) that created a comprehensive glacial lake inventory of Hindu Kush Himalayas and included three Indian states . Worni et al . ( 2013 ) created a glacial lake inventory for Indian Himalayas © 2020 . American Geophysical Union . All Rights Reserved . DUBEY AND GOYAL 1 of 21 19447973 , 2020 , 4 , Downloaded from https : / / agupubs\n\n(>0.01 km²) and carried out a detailed risk assessment. Indian Himalayas were also subjected to many regional studies focusing particularly on specific region or lake (Abdul Hakeem et al., 2018; Aggarwal et al., 2017; Raj et al., 2013; Sattar et al., 2019). Most of the above-mentioned studies were carried out using glacial lake data derived from medium-resolution Landsat imageries, whereas, with the proliferation of satellite data, higher spatial resolution optical satellite data are available even at no-cost public domain such as Resourcesat-2 (RS-2) Linear Imaging Self Scanning (LISS-3) and Sentinel 2 multispectral instrument (MSI) data set (Drusch et al., 2012).\n\nDocumentation of critical glacial lakes and application of remedial measures can prevent events such as the ones occurred at Ayaco Lake in 1969 and 1970 (Liu et al., 2013), Nare Lake in 1977 (Buchroithner et al., 1982), Dig Tsho in 1985 (Vuichard & Zimmermann, 1987), Sabai Tsho in 1998 (Osti & Egashira, 2009), and Chorabari in 2013 (Allen et al., 2016; Kanti et al., 2019); these events are the example of calamitous GLOF events that have caused extensive damage to lives and socioeconomic condition of inhabitants (Carrivick & Tweed, 2016). Assessment of glacial lake hazards has been performed using regional data in China (Chen et al., 2010), Nepal (Bajracharya & Mool, 2009; Mool et al., 2011), Bhutan (Nagai et al., 2017), Tibet (Chen et al., 2010), and India (Worni et al., 2013). The major distinctions among these studies are the choice of parameters and the respective weights assigned to these parameters. The application of various parameters and weights leads to contradictory hazard classification (Emmer et al., 2016; Rounce et al., 2016) which can create confusion among stakeholders. Therefore, the objective of this study is to develop a novel framework to analyze the glacial lakes holistically, that is, accounting for primary triggering mechanisms and framed with an objective approach to enable smooth decision making.\n\nTo the best of our knowledge, present investigation is the first to examine the hazard, downstream impact, and risk of glacial hazard in a holistic manner accounting for both self-destructive and dynamic failures accompanied with the determination of downstream impact in terms of public utilities such as buildings, bridges, and hydropower systems over the entire Indian Himalayas. Also, most of the earlier studies in the Indian Himalayas were based on self-destructive failures; however, to represent the complex mechanism of GLOF, more comprehensive framework that accounts for most frequent triggers is required.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "1. Introduction", "section_headings": ["1. Introduction"], "chunk_type": "text", "line_start": 6, "line_end": 22, "token_count_estimate": 835, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["19447973"]}}
{"id": "ef9ff26ce1e72d7c", "text": "Document: 1. Introduction\nSection: 2. Method > 2.1. Glacial Lake Inventory\nType: text\n\nThis study includes glacial lakes in the Indian Himalayas that are greater than 0.05 km<sup>2</sup> (Figure 1). The 0.05 km<sup>2</sup> threshold was applied to maximize the number of glacial lakes which is also consistent with past catastrophic GLOF events (Nie et al., 2018). Glacial lakes were delineated for the fall of 2018 using Sentinel-2 Multispectral Instrument (MSI) imageries, and to assess the temporal change in lake dimensions the glacial lakes were delineated for the year 1993 using Landsat 5 Thematic Mapper Plus (TM) imageries. The presence of clouds hinders the visibility of imageries. Therefore, for the cloud masked lakes, the imageries of 2017 were used to assist the lake delineation of 2018 whereas imageries of 1992 were used to assist the lake delineation of 1993. The initial period for the lake delineation was considered to be 1992 as this year represents the initial stage of Landsat 5 data availability during the months of least snow cover for the Indian Himalayas. Glacial lake boundaries were manually delineated, and the presence of glacial lake was identified using the Normalized Difference Water Index (NDWI; McFeeters, 1996). Uncertainty in lake delineation was presumed to be the lake perimeter multiplied by half the pixel size (Rounce et al., 2017; Shukla et al., 2018). Glacial lakes were categorized as moraine-dammed lake, ice-dammed lake, bedrock-dammed lake, and other glacial lakes (Maharjan et al., 2018). Glacier outlines were obtained from Randolph Glacier Inventory Version 5.0 (RGI; Arendt et al., 2012), which is based on satellite imageries acquired between 1999 and 2003. The uncertainty associated with RGI inventory has been estimated to be ~15% (Nuimura et al., 2014).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.1. Glacial Lake Inventory", "section_headings": ["2. Method", "2.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 26, "line_end": 28, "token_count_estimate": 479, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ebb95e4d2dbd0ba", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\nHazard assessment implies determination of susceptibility to various triggers such as avalanche, rockfall, upstream GLOF, lake expansion, presence of ice cores, and instability of damming moraine. The present\n\nDUBEY AND GOYAL 2 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs.onlinelibrary.\n\nwiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Term", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 30, "line_end": 38, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "3caaaae8495b188a", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: figure\nFigure\n\nImage /page/2/Figure/3 description: A collection of maps illustrating the locations of lakes in the Indian Himalayas. The main map, labeled (a), shows a large area covering the states of Jammu and Kashmir (dark green), Himachal Pradesh (light green), and Uttarakhand (grey). This map displays glacier boundaries in light blue and lakes as black dots of varying sizes. On the left, there are three smaller maps. The top map shows a portion of India with three major river basins color-coded: Brahmaputra (yellow), Ganga (light blue), and Indus (purple). This map has three boxed sections labeled (a), (b), and (c) that correspond to the other maps. Below this are two zoomed-in maps. Map (b) shows the state of Sikkim (orange) with its lakes and glacier boundaries. Map (c) shows the state of Arunachal Pradesh (red) and a part of Assam, also with lakes and glacier boundaries. Several legends are provided. The 'STATES' legend lists Arunachal Pradesh (red), Uttarakhand (grey), Himachal Pradesh (light green), Ladakh, Jammu and Kashmir (dark green), and Sikkim (orange). The 'LAKES (km.sq)' legend indicates the size of the lakes represented by the black dots: the smallest dot is 0.05 - 0.50, the next size is 0.50 - 1.00, the next is 1.00 - 1.50, and the largest is 1.50 - 1.78.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 39, "line_end": 39, "token_count_estimate": 400, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a4c0c79b4813b14c", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: figure\nFigure: Figure 1. Glacial lakes in Indian Himalayas, along with major river basins and states. Jammu Kashmir shown in the figure has been recently divided into two union territories (Jammu and Kashmir, and Ladakh).\n\nFigure 1. Glacial lakes in Indian Himalayas, along with major river basins and states. Jammu Kashmir shown in the figure has been recently divided into two union territories (Jammu and Kashmir, and Ladakh).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": "Figure 1. Glacial lakes in Indian Himalayas, along with major river basins and states. Jammu Kashmir shown in the figure has been recently divided into two union territories (Jammu and Kashmir, and Ladakh).", "line_start": 41, "line_end": 41, "token_count_estimate": 128, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "586b1f89b4a08758", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\nstudy aimed at modeling self-destructive failures using hydrostatic pressure, presence of ice core in damming moraine, and significant expansion over time. Whereas, dynamic failure (mass inflowing into the lake) using ice avalanche trajectories, landslide/rockfall, and upstream GLOFs. The workflow of the presented methodology is depicted in Figure 2a.\n\nThe combination of average steep lakefront area (SLA; Fujita et al., 2013) angle and the presence of ice-cored moraine was utilized to assess the moraine's stability. Steep lakefront area determines the lowering ( $\\rm H_p$ ) of the glacial lake in case of a breach. A threshold of 10° on the SLA angle was applied based on the implementation of SLA concept on five previously outbursted glacial lakes by Fujita et al. (2013). Google Earth and Sentinel imageries were utilized to identify supraglacial ponds or change in the outlet channel to estimate the presence of ice core in the damming structure (Rounce et al., 2016; Watson et al., 2018). Though the bedrock-dammed lakes will not produce a GLOF by dam failure but may produce a GLOF by overtopping, still to apply a standardized analysis, the SLA was determined for all the lakes irrespective of their dam type. Lakes were considered significantly expanded if the areal expansion of lake between 2018 and 1993 exceeded the errors associated with 1993 lake delineation.\n\nAdvanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 acquired between 2000 and 2010 with spatial resolution of 30 m and absolute vertical accuracy of ~17 m (Tachikawa et al., 2011), hereon referred as DEM in conjunction with RGI, was used to determine the areas that are prone to an avalanche, that is, any glaciated region with slope ranging between  $45^{\\circ}$  and  $60^{\\circ}$  (Alean, 1985; Osti et al., 2011). Avalanche-prone areas were then cumulated into maximum avalanche-prone areas using a variable kernel filter with 100% threshold. Here, a  $1 \\times 1$  grid was checked, and if all the pixels were prone to an avalanche, it was expanded to a  $2 \\times 2$  pixel grid. This expansion of the grid was continued until it fails to meet the threshold of 100%. Furthermore, three scenarios were assumed considering the avalanche thicknesses of 10, 30, and 50 m, respectively; these thicknesses are consistent with their relationship between slope and shear stress and are of the same order as observed in Switzerland, Austria, and Alaska (Alean, 1985). These thicknesses were combined with the maximum\n\nDUBEY AND GOYAL 3 of 21\n\n19447973, 2020. 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 42, "line_end": 52, "token_count_estimate": 763, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "c89f58f86972cfac", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: figure\nFigure\n\nImage /page/3/Figure/4 description: A multi-part figure illustrating a methodology for assessing Glacial Lake Outburst Flood (GLOF) risk, divided into three sections labeled (a), (b), and (c).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 53, "line_end": 53, "token_count_estimate": 82, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74a6dd34c121968c", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\nPart (a) is a flowchart detailing the process. The inputs, shown in blue boxes, are Landsat 5 Thematic Mapper (TM), Aster Global Digital Elevation Model (GDEM v2), and Sentinel 2 Multispectral Instrument (MSI). These feed into a 'Glacial lake inventory (1993, 2018)'. The flowchart then splits into four main analysis streams: 'Avalanche/Rockfall', 'Moraine's stability', 'Upstream GLOF', and 'Downstream Impact'. Each stream uses various inputs like DEM, RGI, Inventory, and Ice cores to produce outputs such as 'Dynamic failure prone lakes', 'Steep lakes with/without ice cores', 'Identification of lakes with upstream GLOF susceptibility', and 'Quantification of hydropower systems, buildings and bridges affected'.\n\nPart (b) is a Venn diagram showing the combination of four factors to determine hazard levels. The four overlapping circles are labeled 'Rockfall/Upstream GLOF', 'Avalanche Prone', 'Steep Moraine', and 'Presence of Ice Core'. The central intersection of all four is colored red, indicating 'Very High Hazard'. The intersection of three factors is light grey for 'High Hazard', two factors is darker grey for 'Moderate Hazard', and the area outside the circles is yellow for 'Low Hazard'. A legend below the diagram clarifies these color codes.\n\nPart (c) is a 'GLOF Risk Chart', a 4x4 matrix that combines 'GLOF Hazard' (on the y-axis) with 'Downstream Impact' (on the x-axis). Both axes have levels: L (Low), M (Medium), H (High), and VH (Very High). The resulting risk levels in the grid are color-coded according to the legend in part (b). The matrix reads as follows:\n- VH GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of VH, VH, M, M respectively.\n- H GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of VH, H, M, M respectively.\n- M GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of H, M, M, L respectively.\n- L GLOF Hazard combined with VH, H, M, L Downstream Impact results in risk levels of M, M, L, L respectively.\n\n\\*Randolph Glacier Inventory (RGI 5.0);", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 54, "line_end": 66, "token_count_estimate": 649, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6a3a6c71a83037a", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: figure\nFigure: Figure 2. (a) Workflow of method. (b) Hazard classification flowchart represented using Venn diagrams. (c) Risk chart represented as a function of downstream impact and GLOF hazard.\n\nFigure 2. (a) Workflow of method. (b) Hazard classification flowchart represented using Venn diagrams. (c) Risk chart represented as a function of downstream impact and GLOF hazard.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": "Figure 2. (a) Workflow of method. (b) Hazard classification flowchart represented using Venn diagrams. (c) Risk chart represented as a function of downstream impact and GLOF hazard.", "line_start": 67, "line_end": 67, "token_count_estimate": 120, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42340abb7920dcbc", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\navalanche-prone area to determine the avalanche volume. A minimum threshold of  $0.1 \\times 10^6$  m<sup>3</sup> was applied on lake volume as defined by Richardson and Reynolds (2000), a volume large enough to destroy a village. Data on ice avalanche volume triggering a GLOF are scarce. However, GLOF models attempting to model ice avalanche and wave propagation have found that an avalanche of  $0.5 \\times 10^6 \\text{ m}^3$  is required to trigger an outburst (Somos-Valenzuela et al., 2016; Worni et al., 2014). Avalanche volumes were then used to determine the average angle between the initial point of the avalanche to the last point based on the equation given by Huggel et al. (2004), that is, equation (1).\n\n$$\\tan(\\alpha) = 1.11 - 0.118\\log(V),\\tag{1}$$\n\nwhere V and  $\\alpha$  denote the volume of avalanche (m<sup>3</sup>) and the average slope trajectory (°) also referred to as \"look-up\" angle, respectively. The minimum threshold selected for the average look-up angle was 17° as avalanche rarely exceed this limit (Huggel et al., 2004). The avalanche trajectories were then estimated using a flow direction algorithm with the sink filled DEM up to the point where the average look-up angle was attained. Rockfalls were modeled similarly with rock thickness of 4 m and an average look-up angle of\n\nDUBEY AND GOYAL 4 of 21", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 68, "line_end": 97, "token_count_estimate": 404, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "08946862d7c31d61", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\ndenote the volume of avalanche ( m < sup > 3 < / sup > ) and the average slope trajectory ( ° ) also referred to as \" look - up \" angle , respectively . The minimum threshold selected for the average look - up angle was 17 ° as avalanche rarely exceed this limit ( Huggel et al . , 2004 ) . The avalanche trajectories were then estimated using a flow direction algorithm with the sink filled DEM up to the point where the average look - up angle was attained . Rockfalls were modeled similarly with rock thickness of 4 m and an average look - up angle of DUBEY AND GOYAL 4 of 21\n\n20° (Collins & Jibson, 2015). Furthermore, area prone to a rockfall was assumed to be any non-glaciated region with its slope exceeding 30° (Bolch et al., 2011; Rounce et al., 2016). Sensitivity analysis was implemented by changing the average look-up angle by ±3°. Any GLOF event in the upstream of glacial lake having the ability to cascade a series of GLOF events was modeled using Monte Carlo least cost path method (MC-LCP; Watson et al., 2015); MC-LCP uses Monte Carlo loop to model DEM uncertainty and implements an iterative least cost path analysis to produce inundation probabilities for each DEM cell. This method is computationally inexpensive and relies merely on the geometry of the downstream channel acquired from the DEM. It generates the flow path without differentiating between flash floods and debris flows. As the model has no physical basis, the model was implemented considering the conservative flood extent generated when compared to the flood extent of 1985 GLOF of Dig Tsho (Watson et al., 2015) and due to limited data availability on previous GLOFs, against the flood extents of two-dimensional debris flood model FLO-2D for Lmja Tsho (Rounce et al., 2016) and HEC-RAS for South Lhonak Lake (Sattar et al., 2019), Sikkim (supporting information Figure S1). MC-LCP model implemented with both Shuttle Radar Topography Mission (SRTM) DEM v.4 and Aster GDEM v.2 generated reasonable flood extent when compared against the flood extent generated by Sattar et al. (2019) except for few places, where the model does not capture the simulated flood extent. This could be highly problematic for downstream impact assessment if these areas were populated. More detailed analysis on the comparison of the flood extent from both the DEMs revealed that GDEM v2.0 tracked the main channel better. Therefore, MC-LCP along with Aster GDEM v2 was used to model potential GLOF from each lake.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 68, "line_end": 97, "token_count_estimate": 661, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6260920124f52299", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\nMC - LCP model implemented with both Shuttle Radar Topography Mission ( SRTM ) DEM v . 4 and Aster GDEM v . 2 generated reasonable flood extent when compared against the flood extent generated by Sattar et al . ( 2019 ) except for few places , where the model does not capture the simulated flood extent . This could be highly problematic for downstream impact assessment if these areas were populated . More detailed analysis on the comparison of the flood extent from both the DEMs revealed that GDEM v2 . 0 tracked the main channel better . Therefore , MC - LCP along with Aster GDEM v2 was used to model potential GLOF from each lake .\n\nEach lake was assessed for its downstream impact using MC-LCP model with a cutoff distance of 50 km to facilitate a standardized comparison between different lakes; the 50 km threshold was consistent with GLOF event at Dig Tsho in 1985 (Watson et al., 2015), Chilleon Valley in 2015 (Wilson et al., 2019), Chorabari in 2013 (Rafiq et al., 2019), and so forth. Although some of the GLOF events have shown runout length up to 200 km (Richardson & Reynolds, 2000), considering such outliers may lead to overestimation of downstream impact. Generated flood extents were used to assess the downstream impact of GLOF for each lake by quantifying the number of bridges, hydropower systems, and buildings that could be affected. The number of hydropower systems that could be affected was primarily assessed using the database available from 43rd report on hydropower by Ministry of India, but it was later acknowledged that the available location of various hydropower systems was representative of the system but not essentially the location of a particular part of system such as dam. To resolve this discrepancy, hydropower systems were manually marked using Google Earth and Google Maps. The locations of the buildings were retrieved from OpenStreetMap (https://www.openstreetmap.org) and were confirmed and updated using Google Earth. For the Indian Himalayas, the buildings were majorly underrepresented; more than 10,000 buildings were updated for the local inventory. Bridges were identified using OpenStreetMap as any road crossing the watercourse.\n\nThe potential flood volume (PFV) for each lake was assessed by calculating the maximum PFV of self-destructive and dynamic failures. In case of self-destructive failure, the PFV was estimated using the approach from Fujita et al. (2013), but to determine the mean depth and lake volume, equation from Cook and Quincey (2015) was utilized (equations (2) and (3)).\n\n$$D_m = 0.1217A^{0.4129}, (2)$$\n\n$$V = 0.1217A_l^{1.4129}, (3)$$", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 68, "line_end": 97, "token_count_estimate": 671, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["1217A"]}}
{"id": "6dd465860e38e0ef", "text": "Document: 1. Introduction\nSection: 2. Method > 2.2. Glacial Hazard and Downstream Impact\nType: text\n\nwatercourse . The potential flood volume ( PFV ) for each lake was assessed by calculating the maximum PFV of self - destructive and dynamic failures . In case of self - destructive failure , the PFV was estimated using the approach from Fujita et al . ( 2013 ) , but to determine the mean depth and lake volume , equation from Cook and Quincey ( 2015 ) was utilized ( equations ( 2 ) and ( 3 ) ) . $ $ D_m = 0 . 1217A ^ { 0 . 4129 } , ( 2 ) $ $ $ $ V = 0 . 1217A_l ^ { 1 . 4129 } , ( 3 ) $ $\n\nwhere  $A_l$  is the lake area in 2018 (m<sup>2</sup>),  $D_m$  is the mean depth (m), and V is the lake volume (m<sup>3</sup>). These equations were used to limit the PFV to the maximum of lake volume. In case of dynamic failure, the PFV was estimated based on the assumption that the water displaced will be equal to the volume of mass entering the lake. These estimates of PFV have significant uncertainties that need to be taken into account and propagated to the final results; Schwanghart et al. (2016) used a stochastic approach to predict outburst flood volumes and estimated that peak discharge may vary up to 2 order of magnitude for a given lake area. In this study, our aim was to provide a quantitative comparison of glacier flood risks rather than to precisely define the absolute flood of any individual flood event. Therefore, a simplistic sequential perturbation approach was used for PFV error estimation, where we evaluated PFV uncertainty (dPFV) using equation (4), whereas importance of  $d_D$  (error in depth estimation) in estimated uncertainty using equation (5).\n\nDUBEY AND GOYAL 5 of 21\n\n19447973, 2020, 4, Downloaded\n\n$$Importance = \\frac{((D+d_D)A-AD)^2}{dPFV^2}.$$\n (5)\n\nHere  $d_A$  and  $d_D$  represent the errors in area and depth estimation, respectively. For the case where higher PFV is estimated using SLA analysis, the parameters D and A represent the lake mean depth and lake area, respectively. In this case, we computed the error in-depth estimation based on root-mean-square error (RMSE) of bathymetric derived depth for 42 glacial lakes (Cook & Quincey, 2015; supporting information Table S1, Sheet 9) against the mean depth obtained from equation (2) and computed the RMSE value to be 6.7 m. The error in area estimation was determined by multiplying the shoreline value with half the pixel size of Sentinel 2 MSI data (10 m). In the case, where PFV is found to be greater for avalanche (rockfall), D represents the mean depth of avalanche (rockfall) and A represents the maximum prone area for avalanche (rockfall). Due to scarce data availability on avalanche (rockfall) depth. Mean depth of avalanche (rockfall) was assumed to be 30 m (4 m) with error in-depth estimation of 20 m (2 m), whereas the error in area estimation was found to be 15%, based on the uncertainty associated with the glacier inventory (Nuimura et al., 2014).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.2. Glacial Hazard and Downstream Impact", "section_headings": ["2. Method", "2.2. Glacial Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 68, "line_end": 97, "token_count_estimate": 859, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1217A", "19447973"]}}
{"id": "e20dd36e7dd77617", "text": "Document: 1. Introduction\nSection: 2. Method > 2.3. Risk\nType: text\n\nThe focal objective of the study was to assess the risk associated with each lake, which can be represented as a combined characteristic of hazard and downstream impact. The framework was adopted from Rounce et al. (2016) with slight variation to prioritize the lakes with the highest risk in the Indian Himalayas. The classification was carried out based on frequent trigger mechanism of GLOF events and to facilitate even distribution of lakes into very high, high, moderate, and low rankings of risk. Classification of hazards puts the lakes with avalanches hitting the lake with a steep moraine (>10°) in very high hazard category as they represent the two most frequent triggering mechanisms. The high hazard category contains the lakes which either have an avalanche hitting the lake or steep moraine with an ice core. The lakes with an ice core have been given a higher hazard ranking than a lake solely with steep moraine, as it may trigger dam settlement or piping. Moderate hazard includes any lake with either an unstable moraine or a rockfall entering the lake. Rockfall was classified in moderate ranking as there have been only three recorded GLOF events triggered by a rockfall (Byers et al., 2019; Emmer & Cochachin, 2013; Richardson & Reynolds, 2000). Lastly, lakes with low hazard are the ones with gentle (<10°) moraine and no mass entering the lake (Figure 2b).\n\nClassification of downstream impacts puts the lake threatening a large number (100) of buildings and at least one hydropower system into very high downstream impact category, the lakes impacting either a large number of buildings or a hydropower system into high downstream impact category, and the lakes threatening a small number of buildings or bridges in moderate downstream impact category. The lakes threatening no buildings and bridges were put into low downstream impact category. The criteria for the classification of risk were obtained from Rounce et al. (2017) which is an updated approach from Worni et al. (2013) and Rounce et al. (2016); it states that any combination of very high downstream impact and high hazard or vice versa will lead to the categorization of the lake in very high risk category. Subsequently, the lakes with high hazard and high downstream impact were categorized in the high-risk category. Any combination of low hazard and medium downstream impact or vice versa along with low hazard and low downstream impact were categorized in the low-risk category. Lastly, the remaining were categorized in moderate-risk category (Figure 2c).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "2. Method > 2.3. Risk", "section_headings": ["2. Method", "2.3. Risk"], "chunk_type": "text", "line_start": 99, "line_end": 103, "token_count_estimate": 604, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a440d4f223d3de8", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: text\n\nIn the Indian Himalayas, 329 glacial lakes (>0.05 km²) were inventoried for the year 2018. These lakes were scattered in four states (Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh) and two union territories (Ladakh, and Jammu and Kashmir) of India. State-wise distribution of these lakes reveals that the union territories encompass 98 (30%) glacial lakes. Himachal Pradesh comprises 36 (11%) glacial lakes, Uttarakhand comprises 22 (7%) glacial lakes, Sikkim comprises 88 (27%) glacial lakes, and Arunachal Pradesh comprises 85 (26%) glacial lakes (Figure 3a). Major river basin-wise distribution reveals that the\n\nDUBEY AND GOYAL 6 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs.omlinelibrary.witey.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "text", "line_start": 107, "line_end": 113, "token_count_estimate": 270, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "8d83e71f8660fa65", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: figure\nFigure\n\nImage /page/6/Figure/3 description: The image displays six charts, labeled (a) through (f), analyzing the distribution of lakes across different states and basins. The charts are arranged in two columns: the left column (a, b, e) shows data by state, and the right column (c, d, f) shows data by basin. The states analyzed are Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. The basins are Indus, Ganga, and Brahmaputra.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 114, "line_end": 114, "token_count_estimate": 156, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0fe4c6dc2bfbee86", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: text\n\nChart (a) is a combination bar and line graph for states. The left y-axis represents Area (km.sq) from 0 to 30, and the right y-axis represents the Number of lakes from 0 to 180. Green bars show the area in 2018, and grey bars show the area in 1993. A dashed line shows the number of lakes. For example, in Jammu and Kashmir, the area in 2018 was about 22 km.sq, and the number of lakes was about 105.\n\nChart (b) is a horizontal stacked bar chart showing the number of lakes in each state, categorized by formation type: Moraine-dammed (grey), Ice-dammed (green), Bedrock-dammed (orange), and Others (blue). Jammu and Kashmir has the highest number of lakes, over 100.\n\nChart (e) is a scatter plot showing the elevation of lakes in each state. The y-axis is Elevation (m) from 3000 to 6000. Each circle represents a lake, colored by its formation type, with the size of the circle likely indicating its area. Most lakes are located between 3500m and 5500m.\n\nChart (c) is a combination bar and line graph for basins, similar to (a). The left y-axis for Area (km.sq) goes up to 40. The Brahmaputra basin has the largest area (over 30 km.sq in 2018) and the highest number of lakes (about 165).\n\nChart (d) is a horizontal stacked bar chart showing the number of lakes by formation type for each basin. The Brahmaputra basin has the most lakes, exceeding 160.\n\nChart (f) is a scatter plot showing the elevation of lakes in each basin, similar to (e). The Brahmaputra basin shows a wide distribution of lakes from below 3500m to above 5500m.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "text", "line_start": 115, "line_end": 127, "token_count_estimate": 445, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eee990dbf22a9ab5", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: figure\nFigure: Figure 3. Areal distribution of lakes in various states (a) and basins (c). The dotted line is plotted in correspondence with the secondary axis to represent the number of lakes. Distribution of various lake types in states (b) and basins (d). Figures (e) and (f) represent glacial lakes in various elevation zones, the color of the circles represent various lake types, whereas the size of the circles represents the size of the lake.\n\n**Figure 3.** Areal distribution of lakes in various states (a) and basins (c). The dotted line is plotted in correspondence with the secondary axis to represent the number of lakes. Distribution of various lake types in states (b) and basins (d). Figures (e) and (f) represent glacial lakes in various elevation zones, the color of the circles represent various lake types, whereas the size of the circles represents the size of the lake.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "figure", "figure_caption": "Figure 3. Areal distribution of lakes in various states (a) and basins (c). The dotted line is plotted in correspondence with the secondary axis to represent the number of lakes. Distribution of various lake types in states (b) and basins (d). Figures (e) and (f) represent glacial lakes in various elevation zones, the color of the circles represent various lake types, whereas the size of the circles represents the size of the lake.", "line_start": 128, "line_end": 128, "token_count_estimate": 238, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac9700b101d320d6", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: text\n\nIndus basin contains 134 (41%) glacial lakes, Ganga basin contains 22 (7%) glacial lakes, and Brahmaputra basin contains 173 (52%) glacial lakes (Figure 3c). These lakes covered an area of  $65.80 \\pm 4.37 \\text{ km}^2$  in 2018. Lake size distribution of these lakes revealed that 129 (39%) lakes were smaller than  $0.1 \\text{ km}^2$ , 178 (54%) had size ranging between  $0.1 \\text{ and } 0.5 \\text{ km}^2$ , and only 22 (7%) were found to be greater than  $0.5 \\text{ km}^2$ . Distribution of lakes according to their type revealed that moraine-dammed lakes account for 154 (47%) lakes, followed by 57 ice-dammed lakes (17%), 31 bedrock-dammed lakes (9%), and 87 other lakes such as erosional and debris-dammed lakes (26%) (Figures 3b and 3d). Elevation profile of the lakes revealed that the lakes\n\nDUBEY AND GOYAL 7 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay,", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "text", "line_start": 129, "line_end": 135, "token_count_estimate": 331, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "9aa15d9a10f9490f", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: figure\nFigure\n\nImage /page/7/Figure/3 description: An image displaying six pie charts that analyze various environmental factors, arranged in two rows of three. The top row features charts for 'Avalanche', 'Rockfall', and 'Upstream GLOFs'. The 'Avalanche' chart shows 293 'No hit' and 36 'Avalanche hit', with the 'hit' category further broken down into 28 'Indirect hit' and 8 'Direct hit'. The 'Rockfall' chart shows 277 'No hit' and 52 'Rockfall hit', with the 'hit' category detailed as 37 'Indirect hit' and 15 'Direct hit'. The 'Upstream GLOFs' chart indicates 292 'No threat' and 37 'Potential'. The bottom row includes charts for 'Significant Growth', 'Ice Cored', and 'Moraine Stability'. The 'Significant Growth' chart shows 265 'No threat' and 64 'Potential'. The 'Ice Cored' chart displays 249 'No threat' and 80 'Potential'. The 'Moraine Stability' chart shows 82 'Stable' and 247 'Unstable'.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "figure", "figure_caption": null, "line_start": 136, "line_end": 136, "token_count_estimate": 294, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64c5634bdfd93398", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: figure\nFigure: Figure 4. Summary of hazard parameters.\n\nFigure 4. Summary of hazard parameters.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "figure", "figure_caption": "Figure 4. Summary of hazard parameters.", "line_start": 138, "line_end": 138, "token_count_estimate": 41, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6694cb00a2da532d", "text": "Document: 1. Introduction\nSection: 3. Results > 3.1. Inventory\nType: text\n\nranged from 3,000 (meters above sea level) m.a.s.l to 5,661 m.a.s.l with the mean elevation value of 4,484 m.a. s.l. The lakes present above the elevation of 5,000 m.a.s.l were predominantly ice dammed and moraine dammed. Almost all bedrock-dammed lakes were present below 5,000 m.a.s.l, whereas other lakes were uniformly distributed among all the elevation zones (Figure 3e). It was observed in the Indus and Ganga basins, large-sized lakes were present below 4,400 m.a.s.l, whereas for Brahmaputra basin all the large-sized lakes were located above the elevation 4,900 m.a.s.l (Figure 3f). All the lakes of the union territories and Himachal Pradesh were located within the Indus basin, the lakes of Uttarakhand were located within the Ganga basin, whereas all the lakes of Sikkim and Arunachal Pradesh were located within the Brahmaputra basin. In 1993, the area covered by these lakes was  $56.8 \\pm 15.69 \\text{ km}^2$  which increased to  $65.80 \\pm 4.37 \\text{ km}^2$  by 2018, that is, a 15.84% increase in lake area over 25 years. Similar increase in lake area has been observed in Tibetan Plateau (Zhang et al., 2014), Nepal (Nie et al., 2013), Bhutan (Komori, 2008), Tien Shan (Narama et al., 2010), Central Andes (Wilson et al., 2018), and Western Greenland (Carrivick & Quincey, 2014).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.1. Inventory", "section_headings": ["3. Results", "3.1. Inventory"], "chunk_type": "text", "line_start": 139, "line_end": 141, "token_count_estimate": 427, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "Nepal"], "lake_ids": []}}
{"id": "d1927cf951529573", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Hazard\nType: text\n\nThe determination of potential hazards was carried out by modeling moraine stability, avalanche, rockfall, GLOF in the upstream portion of the lake, and lake expansion. For avalanche and rockfall, a direct hit on the lake was considered to be any trajectory entering the lake with the starting point located within  $\\pm 45^{\\circ}$  of the major axis of the lake. A direct hit may have grave implications on wave run-up and moraine stability. Modeling of dynamic failure revealed that out of 329 lakes, 36 (11%) are susceptible to an avalanche entering the lake, 52 (16%) are susceptible to a rockfall, and 37 (11%) are susceptible to an upstream GLOF. It was also observed that only 8 (22%) out of 36 avalanches and 15 (29%) out of 52 rockfalls were entering the lake with a direct hit which implied that most of the mass entering the lake was along its minor axis. Concerning moraine stability, 247 (75%) lakes have an unstable moraine (average SLA angle >10°), and 80 (24) have ice cores present in their damming structure. Assessment of lake expansion revealed that from 1993 to 2018, 64 (19.5%) lakes significantly expanded and 6 (2%) lakes significantly drained (Figure 4).\n\nModeled hazards were used to classify the lakes into various hazard categories; that is, 28 lakes were categorized as very high hazard, 50 as high hazard, 198 as moderate hazard, and 53 as low hazard. Sikkim has the maximum number (20) of very high hazard lakes followed by the union territories of Ladakh, and Jammu and Kashmir (4). The state of Himachal Pradesh and Arunachal Pradesh contains two very high hazard lakes each, whereas Uttarakhand has no very high hazard lake. River basin-wise, Brahmaputra basin has 20 very high hazard lakes, followed by 6 in Indus basin and 2 in the Ganga basin. Classification of very high hazard lakes based on type revealed that 20 were moraine dammed, 7 were ice dammed, and 1 was the other lake. This is attributed due to elevation dependence and direct connection of ice-dammed and moraine-dammed lakes with their parent glaciers which make them highly susceptible to avalanches. Elevation dependence of lakes revealed that all the very high hazard lakes were lying above the elevation range of 4,000 m (Figure 5a).\n\nDUBEY AND GOYAL 8 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://online", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Hazard", "section_headings": ["3. Results", "3.2. Hazard"], "chunk_type": "text", "line_start": 143, "line_end": 151, "token_count_estimate": 654, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "1fd1daa76d7840ec", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Hazard\nType: figure\nFigure\n\nImage /page/8/Figure/3 description: The image displays three bubble charts, labeled (a), (b), and (c), which analyze hazard, downstream impact (DI), and risk across different elevation zones in five regions of the Himalayas. All three charts share the same axes. The x-axis lists the regions: Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. The y-axis represents Elevation in meters (m), ranging from 3000 to 6000. The data is represented by circles of varying sizes and colors, plotted vertically for each region.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Hazard", "section_headings": ["3. Results", "3.2. Hazard"], "chunk_type": "figure", "figure_caption": null, "line_start": 152, "line_end": 152, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "de67ccaa4c77dede", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Hazard\nType: text\n\nChart (a) illustrates Hazard levels. The legend indicates four categories: Low Hazard (blue), Moderate Hazard (light blue), High Hazard (light red), and Very High Hazard (red). Sikkim shows a high concentration of 'Very High Hazard' and 'High Hazard' circles, particularly between 5000 and 5500 meters.\n\nChart (b) shows Downstream Impact (DI). The legend is similar: Low DI (blue), Moderate DI (light blue), High DI (light red), and Very High DI (red). Jammu and Kashmir and Sikkim exhibit significant clusters of 'High DI' and 'Very High DI' circles.\n\nChart (c) depicts Risk levels. The legend follows the same color scheme: Low Risk (blue), Moderate Risk (light blue), High Risk (light red), and Very High Risk (red). Sikkim stands out with a large cluster of 'Very High Risk' and 'High Risk' circles between 5000 and 5500 meters. In contrast, Himachal Pradesh and Uttarakhand are dominated by 'Low Risk' and 'Moderate Risk' circles, including a large 'Low Risk' circle around 4200 meters in both regions.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Hazard", "section_headings": ["3. Results", "3.2. Hazard"], "chunk_type": "text", "line_start": 153, "line_end": 159, "token_count_estimate": 280, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "95025ee2031ead7f", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Hazard\nType: figure\nFigure: Figure 5. Distribution of (a) hazard, (b) downstream impact (DI), and (c) risk in various elevation zones. Color of the circle represents very high, high, moderate, and low rankings of risk, hazard, and downstream impact. The size of the circle represents the size of the lake.\n\nFigure 5. Distribution of (a) hazard, (b) downstream impact (DI), and (c) risk in various elevation zones. Color of the circle represents very high, high, moderate, and low rankings of risk, hazard, and downstream impact. The size of the circle represents the size of the lake.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Hazard", "section_headings": ["3. Results", "3.2. Hazard"], "chunk_type": "figure", "figure_caption": "Figure 5. Distribution of (a) hazard, (b) downstream impact (DI), and (c) risk in various elevation zones. Color of the circle represents very high, high, moderate, and low rankings of risk, hazard, and downstream impact. The size of the circle represents the size of the lake.", "line_start": 160, "line_end": 160, "token_count_estimate": 167, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ac3e8ba3375d0e5", "text": "Document: 1. Introduction\nSection: 3. Results > 3.2. Hazard\nType: text\n\nThe commonly known very high hazard lakes in the Indian Himalayas include Shakho Cho, Khangchung Tso, Bhale Pokhri Lake, and Goecha Lake. Other commonly known high hazard lakes include South Lhonak Lake, Basudhara Tal, Lam Dal Lake, Shaushar Lake, Gadsar Lake, and Lolgul Gali Lake. Few lakes such as Geepang Gath (moderate hazard), Chandra Tal (moderate hazard), Samudri Tapu (moderate hazard), and Gurudongmar (moderate hazard) were not classified as critical because of no avalanche trajectories entering the lake, as well as unavailability of ice cores in damming moraine.\n\nThe sensitivity test conducted by varying the \"look-up\" for avalanche by  $\\pm 3^{\\circ}$  did not alter the number of lakes prone to avalanche. In case of rockfall, the decrease in look-up angle to 17° increased the number of lakes susceptible to rockfall from 52 to 62 and categorized two low hazard lakes to moderate hazard category, whereas increasing the look-up angle to 23°, reduced the number of lakes prone to rockfall from 52 to 35 and changed the hazard ranking of four lakes from moderate to low. Unlike Rounce et al. (2017), the change in look-up angle for rockfall induced a substantial change in lake categorization.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.2. Hazard", "section_headings": ["3. Results", "3.2. Hazard"], "chunk_type": "text", "line_start": 161, "line_end": 165, "token_count_estimate": 331, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "145099bf89008802", "text": "Document: 1. Introduction\nSection: 3. Results > 3.3. Downstream Impact\nType: text\n\nThere are 217 lakes that inundated at least 20 buildings while 138 inundated at least 100 buildings; nine lakes were identified to inundate more than 1,000 buildings. Six lakes inundating more than 1,000 buildings were located in the union territories of Jammu and Kashmir, and Ladakh whereas three lakes were located in Uttarakhand (Figure 6c). The number of buildings inundated varied from 0 to 2010, with a median number of 54. The number of lakes that inundated at least one hydropower system was 67 whereas the number of\n\nDUBEY AND GOYAL 9 of 21\n\n19447973, 2020. 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.3. Downstream Impact", "section_headings": ["3. Results", "3.3. Downstream Impact"], "chunk_type": "text", "line_start": 167, "line_end": 173, "token_count_estimate": 224, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "ecaf768d87625a3d", "text": "Document: 1. Introduction\nSection: 3. Results > 3.3. Downstream Impact\nType: figure\nFigure\n\nImage /page/9/Figure/3 description: The image contains three stacked bar charts, labeled (a), (b), and (c), showing the number of lakes that threaten various infrastructures, broken down by region. A single legend applies to all charts, indicating the regions: Arunachal Pradesh (light red), Sikkim (light orange), Uttarakhand (light yellow), Himachal Pradesh (grey-blue), and Jammu and Kashmir (dark blue).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.3. Downstream Impact", "section_headings": ["3. Results", "3.3. Downstream Impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 174, "line_end": 174, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7004f48bcfac18b1", "text": "Document: 1. Introduction\nSection: 3. Results > 3.3. Downstream Impact\nType: text\n\nChart (a) displays the number of lakes versus the number of 'Bridges threatened'. The y-axis, 'Number of lakes', ranges from 0 to 140. The x-axis categories are '0', '0-5', '5-10', '10-15', and '>15'. The highest bar is in the '0-5' category, representing approximately 132 lakes. The '5-10' category shows about 100 lakes.\n\nChart (b) shows the number of lakes versus 'Hydropower systems threatened'. The y-axis, 'Number of lakes', ranges from 0 to 300. The x-axis categories are '0', '1', and '2'. The vast majority of lakes, approximately 265, threaten '0' hydropower systems.\n\nChart (c) illustrates the number of lakes versus 'Buildings threatened'. The y-axis, 'Number of lakes', ranges from 0 to 120. The x-axis categories are '<20', '20-50', '50-100', '100-500', '500-1000', and '>1000'. The categories '<20' and '100-500' have the highest bars, each representing just over 110 lakes.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.3. Downstream Impact", "section_headings": ["3. Results", "3.3. Downstream Impact"], "chunk_type": "text", "line_start": 175, "line_end": 181, "token_count_estimate": 317, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2cfd70c28024fbd2", "text": "Document: 1. Introduction\nSection: 3. Results > 3.3. Downstream Impact\nType: figure\nFigure: Figure 6. Downstream impact summary representing the potential number of (a) bridges, (b) hydropower systems, and (c) buildings threatened by GLOFs.\n\nFigure 6. Downstream impact summary representing the potential number of (a) bridges, (b) hydropower systems, and (c) buildings threatened by GLOFs.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.3. Downstream Impact", "section_headings": ["3. Results", "3.3. Downstream Impact"], "chunk_type": "figure", "figure_caption": "Figure 6. Downstream impact summary representing the potential number of (a) bridges, (b) hydropower systems, and (c) buildings threatened by GLOFs.", "line_start": 182, "line_end": 182, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f03d14d9899d6c29", "text": "Document: 1. Introduction\nSection: 3. Results > 3.3. Downstream Impact\nType: text\n\nlakes that inundated two hydropower systems was 15 (Figure 6b). The number of bridges inundated ranged from 0 to 17 with a median value of 4 (Figure 6a).\n\nThe ranking of downstream impact by considering the 100 buildings threshold to differentiate between large and small number of buildings classified 51 lakes as very high downstream impact, 87 as high downstream impact, 148 as moderate downstream impact, and 43 as low downstream impact. The threshold of 100 buildings helped in distributing the lakes uniformly. Fifty-one lakes out of 67 that inundated at least one hydropower system also inundated 100 buildings. This shows that hydropower systems are located near the areas of settlements. Altering the building threshold to 50 buildings increased the number of lakes in very high downstream impact category by 3 and high downstream category by 30. Union territories of Jammu and Kashmir, and Ladakh consist of the highest number of very high downstream impact lakes, followed by Sikkim, Uttarakhand, Himachal Pradesh, and lastly Arunachal Pradesh, which has no lake that shows very high downstream impact. Most of the lakes with very high downstream impact were located below the elevation of 5,000 m except for three lakes in Uttarakhand and one lake in Sikkim (Figure 5b). Some of the commonly known lakes with very high downstream impact are Sheshnag Lake, Tsomgo Lake, and Hangu Lake.\n\nThe uncertainty associated with PFV estimation in case where the computation was based on SLA analysis of lake was found to be  $\\pm 44\\%$ , whereas, in the case of avalanche and rockfall, the uncertainty values were found to be  $\\pm 68\\%$  and  $\\pm 52\\%$ , respectively. It was observed that these uncertainty values were majorly caused due to error in-depth estimation (importance = 98%, 95%, and 91% in case of SLA, avalanche, and rockfall, respectively) rather than the error in area estimation.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.3. Downstream Impact", "section_headings": ["3. Results", "3.3. Downstream Impact"], "chunk_type": "text", "line_start": 183, "line_end": 189, "token_count_estimate": 499, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "feefdfab5a464135", "text": "Document: 1. Introduction\nSection: 3. Results > 3.4. Risk\nType: text\n\nThe risk categorization of glacial lakes was based on the criteria described in Figure 2c. A total of 23 lakes were classified as very high risk, 50 were classified as high risk, 195 were classified as moderate risk, and 61 were classified as low risk. Out of 51 lakes that were characterized as very high downstream impact, merely 10 were categorized as very high risk due to their moderate hazard. State-wise distribution of very high risk lakes revealed that the union territories of Ladakh, and Jammu and Kashmir have 4 very high risk lakes, Uttarakhand has 2 very high risk lakes, and Sikkim has 17 very high risk lakes, whereas Himachal\n\nDUBEY AND GOYAL 10 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs\n\nwiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms\n\nPradesh and Arunachal Pradesh have no very high risk lakes (Figure 7). There was no apparent trend between risk and elevation of lakes (Figure 5c). Table 1 shows the lake details, associated hazard, downstream impact, and potential risk of the lakes recognized as very high risk lakes and their confrontation against other studies in the Indian Himalayas (Abdul Hakeem et al., 2018; Aggarwal et al., 2017; Ives et al., 2010; Raj & Kumar, 2016; Worni et al., 2013).", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "3. Results > 3.4. Risk", "section_headings": ["3. Results", "3.4. Risk"], "chunk_type": "text", "line_start": 191, "line_end": 201, "token_count_estimate": 362, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "f0846f40cdc91ce2", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.1. Glacial Lake Inventory\nType: text\n\nThis study identified 329 glacial lakes in the Indian Himalayas, which strongly agrees with previous studies (Aggarwal et al., 2017; Fujita et al., 2013; Ives et al., 2010; Worni et al., 2013; Zhang et al., 2015) (supporting information Table S1, Sheets 3–7). The threshold of lakes above the elevation of 3,000 m to select the glacial lakes was applied instead of the threshold used by previous studies (only moraine-dammed lakes for Fujita et al., 2013, and any lakes within 10 km of glacier boundaries for Worni et al., 2013), as the uncertainty associated with debris-covered glaciers in these inventories may range up to 30% and it can significantly alter the study area (Yao et al., 2012) and use of only moraine-dammed lakes may lead to underestimation of potential GLOF hazard.\n\nThe lakes that did not show any significant expansion between 1993 and 2018 were compared for the year 2018 against the lake area measurement by Fujita et al. (2013). As Fujita et al. (2013) only considered moraine-dammed lakes, the comparison was made only for 128 lakes that were common for both the studies. The mean difference in lake area between these studies was found to be  $0.0183~\\rm km^2$  which is comparable with the assumption of error linked with lake delineation which ranged from  $0.0127~\\rm to~0.1337~\\rm km^2$  with mean value of  $0.0296~\\rm km^2$ . Therefore, the assumption used to measure lake error, that is, perimeter multiplied by half the pixel size, is reasonable.\n\nThe maximum distance of lake from glaciers experiencing an avalanche and rockfall is 607.5 and 5,079.9 m, respectively; therefore, for the studies focusing on dynamic failures, the distance threshold of 10 km can be reasonably adopted. Lake distance of steep moraine lakes revealed that there are 42 lakes with steep moraine that are located beyond 10 km of glacier boundaries; therefore, for studies incorporating self-destructive failure, the distance threshold of 10 km may lead to underrepresentation of glacial lakes.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.1. Glacial Lake Inventory", "section_headings": ["4. Discussions", "4.1. Glacial Lake Inventory"], "chunk_type": "text", "line_start": 205, "line_end": 211, "token_count_estimate": 540, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc27d4e3c9ee5857", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: text\n\nThis study modeled the most frequent triggers of GLOF such as avalanche or rockfall hit, upstream GLOF, stability of the damming moraine, and presence of ice cores. Other triggers of GLOF include extreme climatic events and seismic activities. These triggers were integrally assessed by the studied hazard parameters as an extreme climatic event, or seismic activity will ultimately alter the hydrostatic pressure on the damming moraine which will more likely trigger the failure of an unstable moraine in comparison to a stable one. The impact of remediation measures was not studied as there has been only one such effort at South Lhonak Lake, Sikkim. The lowering of the lake was by 3 m, that is, well within the associated error of the DEM (RMSE = 17 m); therefore, it was not possible to make any noticeable inferences for its impact on the hazard.\n\nThe major struggle accompanied with any hazard assessment is the technique to represent the parameters in terms of its hazard category. This is especially perplexing, as these events are unpredictable and usually occur in a remote location. Therefore, the data from past events and the knowledge of their triggers are limited. Another difficulty associated with hazard assessment is that these events are interconnected; that is, one trigger can evolve into another. Falátková (2016) addresses this issue by demonstrating that the destabilization and breach of damming moraine may take place by the coupled effect of an avalanche, melting of ice cores, increase in hydrostatic pressure, and the effect over time. Additionally, Emmer and Cochachin (2013) described the combined effect of ice cores, hydrostatic pressure, and time in a single hazard parameter called as self-destruction. The SLA analysis illustrated by Fujita et al. (2013) inherently takes into account the combined impact of various triggers as it focuses on the failure of the moraine irrespective of their triggering mechanism.\n\nThese aspects of hazard analysis imply that while the underlying triggering mechanisms are understood for GLOF, there is still significant uncertainty concerning how the interconnection of these triggers affects the\n\nDUBEY AND GOYAL 11 of 21\n\n19449793, 2020, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026333 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 213, "line_end": 223, "token_count_estimate": 575, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19449793", "2019WR026333"]}}
{"id": "d67e51c57fdc2c8c", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: figure\nFigure\n\nImage /page/11/Figure/3 description: An infographic displaying a map of northern and northeastern India, showing the locations of high-risk lakes and associated risk analysis data for five regions. A legend indicates that red dots are 'Very high risk lake' and yellow circles are 'High risk lake'. Another legend explains the color coding for the pie charts: L (Low) is light green, M (Medium) is yellow, H (High) is light red, and VH (Very High) is dark red. The map shows the regions of Ladakh, Jammu and Kashmir; Himachal Pradesh; Uttarakhand; Sikkim; and Arunachal Pradesh, with a high concentration of lakes in Sikkim. Each region has a corresponding set of pie charts for Hazard, Downstream Impact, and Risk. The data is as follows: Ladakh, Jammu and Kashmir: Hazard (L=29, M=51, H=14, VH=4), Downstream Impact (L=24, M=22, H=30, VH=22), and Risk (L=25, M=55, H=14, VH=4). Himachal Pradesh: The first chart shows L=5, M=23, H=6, VH=2. The second chart shows L=9, M=24, H=2, VH=1. The third chart shows L=13, M=19, H=4. Uttarakhand: The first chart shows L=3, M=13, H=6. The second chart shows M=16, H=1, VH=5. The third chart shows M=16, H=2, VH=2. Sikkim: The first chart shows L=4, M=39, H=28, VH=17. The second chart shows L=31, M=35, H=22. The third chart shows L=5, M=48, H=15, VH=20. Arunachal Pradesh: The first chart shows L=11, M=63, H=9, VH=2. The second chart shows L=10, M=55, H=20. The third chart shows L=17, M=66, H=2, VH=2.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 224, "line_end": 224, "token_count_estimate": 510, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "1d6a583c5eebdb99", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: figure\nFigure: Figure 7. Distribution of hazard, downstream impact, and risk classification (pie charts) for various states.\n\nFigure 7. Distribution of hazard, downstream impact, and risk classification (pie charts) for various states.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": "Figure 7. Distribution of hazard, downstream impact, and risk classification (pie charts) for various states.", "line_start": 226, "line_end": 226, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e086a8f8be02f50c", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: text\n\nhazard. These inherent uncertainties prevent the quantitative hazard assessment by assignment of weights to various hazard parameters, for example, in the case of Bolch et al. (2011) and Wang et al. (2013). To overcome this, a framework was adopted to reflect the most hazardous situations by frequency of the various triggering mechanisms involved. The expansion of glacial lakes was not used as a parameter of GLOF assessment as Rounce et al. (2017) applied the expansion model to predict the further expansion of glacial lakes in Nepal Himalayas and observed that 15 out of 22 glacial lakes with significant expansion were incapable of further growth and only 1 lake out of 136 exhibited a change in its hazard assessment.\n\nFor downstream impact assessment, the GLOF extents were conservatively assessed using the stochastic MC-LCP model by quantifying potential buildings, hydropower systems, and bridges located within the inundation regions. Large-scale applicability, inexpensive computations, and consideration of DEM uncertainty are the benefits of MC-LCP model whereas no accountability for any variations in PFVs is the limitation. It should be noted that GLOFs at Lmja Tso and Dig Tsho had PFV of  $33.5 \\times 10^6$  m<sup>3</sup> (Somos-Valenzuela et al., 2016) and  $5 \\times 10^6$  m<sup>3</sup> (Vuichard & Zimmermann, 1987), respectively. Consequently, MC-LCP is producing flood extent linked with large PFV values. A good example describing this is by Khangchung Tso and Lake 228. Both of these lakes have similar flood extent despite having very different PFV of  $19.928 \\times 10^6$  m<sup>3</sup> and  $0.7612 \\times 10^6$  m<sup>3</sup>. Figure 8 describes the distribution of lakes at different PFV ranges. It shows that the number of lakes dramatically reduces beyond  $5 \\times 10^6$  m<sup>3</sup>. The figure also shows that most of the lakes with PFV higher than  $5 \\times 10^6$  m<sup>3</sup> are located in Arunachal Pradesh and Sikkim. Past GLOF events of Dig Tsho and Tam Pokhri show that PFV of  $5 \\times 10^6$  m<sup>3</sup> is significant enough to produce high socioeconomic damage to downstream communities. Conversely, a lake outburst near Chorabari glacier in Uttarakhand with PFV of just  $0.43 \\times 10^6$  m<sup>3</sup> caused catastrophic damage and killed more than 4,000 people in 2013. Therefore, the risk assessment should be based on downstream impact rather than PFV. We assumed the runout distance for each lake to be 50 km, beyond which the waves were considered to be relatively small in comparison to structures located outside the main channel. This assumption is consistent with most of the past events (Rafiq et al., 2019; Vuichard & Zimmermann, 1987). Whereas, due to exceptions such as Luggye Tsho\n\nDUBEY AND GOYAL 12 of 21", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 227, "line_end": 233, "token_count_estimate": 791, "basins": [], "subbasins": [], "countries": ["Nepal"], "lake_ids": []}}
{"id": "9d04098f25e29e4c", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: table\nTable\n\n|         | Downstream Impact, Risk Ranking, and PFV of Lakes Identified as Very High Risk Lakes |  |\n|---------|--------------------------------------------------------------------------------------|--|\n| Table 1 | Lake Details, Hazard, Downstream Impact, R                                           |  |", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "table", "table_caption": null, "columns": ["", "Downstream Impact, Risk Ranking, and PFV of Lakes Identified as Very High Risk Lakes", ""], "table_row_start": 1, "table_row_end": 2, "line_start": 234, "line_end": 236, "token_count_estimate": 87, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a1075dd4377e2df", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: table\nTable\n\n| Lake details               |                  |                  |                 |                  |               |                    |                    | Hazard                   |                   |                      |           | Lake ID (this study) | Hazard   |                             | Downstream impact |         |                    | Ranking        |                           |              | PFV (106 m3)  |\n|----------------------------|------------------|------------------|-----------------|------------------|---------------|--------------------|--------------------|--------------------------|-------------------|----------------------|-----------|-------------------------|----------|-----------------------------|-------------------|---------|--------------------|----------------|---------------------------|--------------|---------------|\n| Lake ID (this study) | Other studies | Longitude (°) | Latitude (°) | Elevation (m) | Aspect (°) | Area (km²) 1993 | Area (km²) 2018 | Significant expansion | Lakes upstream | Presence of ice core | Avalanche |                         | Rockfall | Steep moraine (slope > 10°) | Buildings         | Bridges | Hydropower systems | Hazard ranking | Downstream impact ranking | Risk ranking |               |\n| 6                          |                  | 75.373           | 34.185          | 4,294            | 35            | 0.179              | 0.121              | 0                        | 0                 | 1                    | 1         | 6                       | 0        | 1                           | 1,400             | 10      | 1                  | 3              | 3                         | 3            | 1.86 ± 0.82   |\n| 21                         |                  | 75.085           | 34.391          | 4,074            | 300           | 0.051              | 0.061              | 0                        | 0                 | 1                    | 1         | 21                      | 0        | 1                           | 200               | 5       | 0                  | 3              | 2                         | 3            | 0.7 ± 0.31    |\n| 27                         |                  | 75.058           | 34.422          | 3,580            | 280           | 0.382              | 0.399              | 0                        | 0                 | 0                    | 1         | 27                      | 0        | 0                           | 650               | 7       | 1                  | 2              | 3                         | 3            | 0.11 ± 0.07   |\n| 34                         |                  | 75.377           | 34.139          | 3,722            | 20            | 0.33               | 0.334              | 0                        | 0                 | 1                    | 0         | 34                      | 0        | 1                           | 1,000             | 10      | 1                  | 2              | 3                         | 3            | 0.19 ± 0.08   |\n| 139                        |                  | 79.487           | 30.981          | 5,661            | 30            | 0.027              | 0.051              | 1                        | 0                 | 1                    | 0         | 139                     | 1        | 1                           | 1,300             | 13      | 1                  | 2              | 3                         | 3            | 0.55 ± 0.24   |\n| 153                        | 4                | 79.46            | 30.976          | 5,574            | 220           | 0.119              | 0.177              | 1                        | 0                 | 1                    | 0         | 153                     | 0        | 1                           | 1,300             | 13      | 1                  | 2              | 3                         | 3            | 3.17 ± 1.4    |\n| 159                        | 2, 3             | 88.546           | 27.993          | 5,178            | 75            | 0.562              | 0.595              | 0                        | 0                 | 1                    | 1         | 159                     | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 8.17 ± 3.6    |\n| 160                        | 2, 3, 5          | 88.713           | 28.005          | 5,231            | 340           | 1.155              | 1.247              | 1                        | 0                 | 1                    | 1         | 160                     | 1        | 1                           | 300               | 5       | 0                  | 3              | 2                         | 3            | 49.89 ± 21.95 |\n| 161                        | 2, 3             | 88.616           | 27.975          | 5,010            | 30            | 0.574              | 0.576              | 0                        | 0                 | 0                    | 1         | 161                     | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 16.78 ± 7.38  |\n| 164                        |                  | 88.657           | 27.816          | 4,613            | 90            | 0.143              | 0.143              | 0                        | 0                 | 0                    | 1         | 164                     | 0        | 1                           | 300               | 10      | 1                  | 3              | 3                         | 3            | 2.34 ± 1.03   |\n| 173                        | 3                | 88.789           | 27.873          | 4,943            | 160           | 0.098              | 0.104              | 0                        | 0                 | 0                    | 1         | 173                     | 0        | 1                           | 275               | 8       | 1                  | 3              | 3                         | 3            | 1.49 ± 0.66   |\n| 174                        | 3                | 88.509           | 27.982          | 4,942            | 200           | 0.028              | 0.376              | 1                        | 0                 | 0                    | 1         | 174                     | 1        | 1                           | 152               | 11      | 0                  | 3              | 2                         | 3            | 1.8 ± 0.79    |\n| 176                        |                  | 88.638           | 27.873          | 5,147            | 185           | 0.078              | 0.086              | 0                        | 0                 | 0                    | 1         | 176                     | 0        | 1                           | 104               | 6       | 0                  | 3              | 2                         | 3            | 1.14 ± 0.5    |\n| 180                        |                  | 88.806           | 27.854          | 5,068            | 210           | 0.09               | 0.097              | 0                        | 0                 | 1                    | 0         | 180                     | 0        | 1                           | 275               | 8       | 1                  | 2              | 3                         | 3            | 1.36 ± 0.6    |\n| 202                        | 1, 3, 5          | 88.816           | 27.99           | 5,288            | 300           | 1.647              | 1.775              | 0                        | 0                 | 1                    | 1         | 202                     | 1        | 1                           | 150               | 3       | 0                  | 3              | 2                         | 3            | 19.93 ± 8.77  |\n| 207                        |                  | 88.761           | 27.895          | 5,232            | 280           | 0.466              | 0.476              | 0                        | 1                 | 1                    | 1         | 207                     | 0        | 1                           | 172               | 8       | 0                  | 3              | 2                         | 3            | 12.78 ± 5.62  |\n| 209                        | 1, 2             | 88.561           | 28.014          | 5,070            | 110           | 0.262              | 0.27               | 0                        | 0                 | 0                    | 1         | 209                     | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 5.06 ± 2.23   |\n| 214                        | 2                | 88.639           | 28.002          | 5,423            | 350           | 0.319              | 0.317              | 0                        | 0                 | 1                    | 1         | 214                     | 1        | 1                           | 395               | 8       | 0                  | 3              | 2                         | 3            | 7.21 ± 3.17   |\n| 218                        | 1                | 88.863           | 27.865          | 4,834            | 200           | 0.135              | 0.141              | 0                        | 0                 | 0                    | 1         | 218                     | 1        | 1                           | 195               | 8       | 1                  | 3              | 3                         | 3            | 1.76 ± 0.77   |\n| 225                        |                  | 88.672           | 27.92           | 4,857            | 20            | 0.095              | 0.097              | 0                        | 0                 | 0                    | 1         | 225                     | 0        | 1                           | 135               | 10      | 0                  | 3              | 2                         | 3            | 0.3 ± 0.13    |\n| 228                        |                  | 88.801           | 27.993          | 5,284            | 300           |                    | 0.086              | 0                        | 0                 | 0                    | 1         | 228                     | 0        | 1                           | 223               | 11      | 0                  | 3              | 2                         | 3            | 0.76 ± 0.33   |\n| 231                        | 3                | 88.747           | 27.864          | 5,089            | 280           | 0.117              | 0.185              | 1                        | 0                 | 1                    | 1         | 231                     | 0        | 1                           | 135               | 10      | 0                  | 3              | 2                         | 3            | 0.6 ± 0.26    |\n| 329                        |                  | 88.514           | 27.7            | 4,505            | 210           | 0.094              | 0.076              | 0                        | 0                 | 1                    | 0         | 329                     | 0        | 1                           | 130               | 16      | 0                  | 2              | 3                         | 3            | 0.12 ± 0.05   |", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "table", "table_caption": null, "columns": ["Lake details", "", "", "", "", "", "", "", "Hazard", "", "", "", "Lake ID (this study)", "Hazard", "", "Downstream impact", "", "", "Ranking", "", "", "PFV (106 m3)"], "table_row_start": 1, "table_row_end": 24, "line_start": 238, "line_end": 263, "token_count_estimate": 2164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e6701f6b3c7be6b6", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: text\n\nNote. In hazard assessment, 1 represents yes, and 0 represents no. Ranking of 0 represents low, 1 represents moderate, 2 represents high, and 3 represents very high. Additional details of all the 329 lakes are provided in the supporting information Table S1 (Sheet 1). 1: Ives et al., 2010 (ICIMOD); 2: Worni et al., 2013; 3: Aggarwal et al., 2017; 4: Raj & Kumar, 2016; 5: Abdul Hakeem et al., 2018.\n\n19447737, 2020, 4, Downloaded from https://gutpubs.onlinebtary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use, OA articles are governed by the applicable Creative Commons Licenseque and Conditions (https://onlinelibrary.wiley.com/terms-ad-conditions) on Wiley Online Library for nukes of use of the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the article and the artic\n\nDUBEY AND GOYAL 13 of 21\n\nNote. In hazard assessment, 1 represents yes, and 0 represents no. Ranking of 0 represents low, 1 represents moderate, 2 represents high, and 3 represents very high. Additional details of all the 329 lakes are provided in the supporting information Table S1 (Sheet 1). 1: Ives et al., 2010 (ICIMOD); 2: Worni et al., 2013; 3: Aggarwal et al., 2017; 4: Raj & Kumar, 2016; 5: Abdul Hakeem et al., 2018.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 264, "line_end": 278, "token_count_estimate": 772, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19447737", "2019WR026533"]}}
{"id": "48b2f41b357c7b7e", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: text\n\nthe article and the article and the article and the artic DUBEY AND GOYAL 13 of 21 Note . In hazard assessment , 1 represents yes , and 0 represents no . Ranking of 0 represents low , 1 represents moderate , 2 represents high , and 3 represents very high . Additional details of all the 329 lakes are provided in the supporting information Table S1 ( Sheet 1 ) . 1 : Ives et al . , 2010 ( ICIMOD ) ; 2 : Worni et al . , 2013 ; 3 : Aggarwal et al . , 2017 ; 4 : Raj & Kumar , 2016 ; 5 : Abdul Hakeem et al . , 2018 .\n\n19447973, 2020, 4, Downloaded from https://gupubs.omlinelbiary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://onlinelbiary.wiley.com/rems-and-conditions) on Wiley Online Library for rules of tuse; OA articles are governed by the applicable Creative Commons Licensea\n\nDUBEY AND GOYAL 14 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 264, "line_end": 278, "token_count_estimate": 366, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["19447973", "2019WR026533"]}}
{"id": "e4e08871d5f3b988", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: figure\nFigure\n\nImage /page/14/Figure/3 description: A stacked bar chart showing the number of lakes categorized by their potential flood volume (PVF) across five different regions in the Indian Himalayas. The y-axis, labeled \"Number of lakes,\" ranges from 0 to 100. The x-axis, labeled \"Potential flood volume (PVF; 10⁶ m³),\" has categories: 0, 0-1, 1-2, 2-3, 3-4, 4-5, 5-10, 10-15, 15-20, 20-30, 30-40, and 40-50. The legend indicates the regions: Arunachal Pradesh (red), Sikkim (light orange), Uttarakhand (light yellow), Himachal Pradesh (grey-blue), and Jammu and Kashmir (dark blue). The chart shows that the highest number of lakes have a potential flood volume between 0 and 1 million cubic meters, with that bar reaching a total of approximately 93 lakes. The second-highest category is 1-2 million cubic meters, with about 76 lakes. Generally, the number of lakes decreases as the potential flood volume increases. For the 0-1 PVF category, the approximate breakdown is: Jammu and Kashmir ~28, Himachal Pradesh ~15, Uttarakhand ~12, Sikkim ~26, and Arunachal Pradesh ~12.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": null, "line_start": 279, "line_end": 279, "token_count_estimate": 319, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "709848cc1aa617bb", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: figure\nFigure: Figure 8. Distribution of PFV for the lakes in Indian Himalayas.\n\nFigure 8. Distribution of PFV for the lakes in Indian Himalayas.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "figure", "figure_caption": "Figure 8. Distribution of PFV for the lakes in Indian Himalayas.", "line_start": 281, "line_end": 281, "token_count_estimate": 60, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "07eb6303cc840cad", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.2. Hazard and Downstream Impact\nType: text\n\n(Reynolds, 2014; Richardson & Reynolds, 2000) where the runout length of 200 km was observed, determination of runout length should be based on the application of physically based hydrodynamic model using variable discharge values, this can also be incorporated in MC-LCP model by the use of cost function based on runout distance. Additionally, due to the presence of artifacts in the DEM, the river network obtained did not coincide with the actual river path for few lakes (Lake 284, Lake 285, Lake 286, etc.), for such lakes the river network obtained using reconditioned DEM with 20 m vertical buffer was considered as flood extent instead of MC-LCP output. Improved locations of hydropower systems and updated inventory of buildings can significantly improve the downstream impact assessment. Nevertheless, this study can provide a conservative estimate of flood extents for each lake that can provide vital information for planning risk mitigation measures.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.2. Hazard and Downstream Impact", "section_headings": ["4. Discussions", "4.2. Hazard and Downstream Impact"], "chunk_type": "text", "line_start": 282, "line_end": 284, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3cb6312b336a333", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.3. Risk\nType: text\n\nThis study is meant to assist stakeholders in identifying critical glacial lake that needs an additional field survey. With the available financial resources, field investigation and hydrodynamic modeling of each of these lakes are impractical, but field-based assessment on a few of these lakes to validate remotely sensed based assessment can be useful. The framework for this study has been designed in a manner that data from hazard and downstream impact assessment can even be used by decision-makers who may have an alternate way on the classification of these glacial lakes. This study sought to balance both social and economic effects. Conversely, if economic impacts were to be prioritized, then the lakes affecting more hydropower systems should have been given higher downstream impact, for example, Bhale Pokhri Lake, Goecha La Lake, Hangu Lake, and Tsomgo Lake. Alternatively, if the social impact were to be prioritized, then the lakes affecting more number of buildings should have been given the highest downstream impact, for example, Lakes 12 and 56.\n\nThe classification showed here were established by prioritizing the lakes based on their hazard and down-stream impact, while on the contrary, a common way to prioritize the lake impact is based on higher PFV. Hazard, downstream impact, and risk of lakes having PFV higher than  $5 \\times 10^6$  m³ (PFV of Dig Tsho) are illustrated in Table 2; there are a total of 32 lakes in Indian Himalayas that have PFV exceeding  $5 \\times 10^6$  m³ among which 7 are at very high risk, 3 are at high risk, and 22 lakes are at moderate risk. Gurudongmar Lake has the largest PFV due to its large size (1.120 km²) and steep moraine (42.47°). Shakho Cho Lake being one of the most dangerous lakes in the Indian Himalayas is equipped with water level gauging instrument to monitor high rise in lake. Despite being a well-studied (Worni et al., 2013, 2014) lake in Indian Himalayas, no mitigation measures have been taken so far, and the lake appears to be at very high risk due to the potential of avalanche and rockfall. Other notable lakes that were not included in the list but are at very high risk are Lake 153, Lake 164, and Lake 218. Physically based hydrodynamic modeling of\n\nDUBEY AND GOYAL 15 of 21", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.3. Risk", "section_headings": ["4. Discussions", "4.3. Risk"], "chunk_type": "text", "line_start": 286, "line_end": 294, "token_count_estimate": 554, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85d8d2ee35efde6e", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.3. Risk\nType: table\nTable: Table 2 Details of Lakes Having PFV Higher Than $5 \\times 10^6 \\, \\mathrm{m}^3$\n\n| Lake ID | ID (this study) | Lake details      |                  |                 |                  |               |                    |                    | Hazard                   |                   |                            |           | Lake ID (this study) | Hazard   |                             | Downstream impact |         |                    | Ranking        |                           |              | PFV (106 m3)  |\n|---------|--------------------|-------------------|------------------|-----------------|------------------|---------------|--------------------|--------------------|--------------------------|-------------------|----------------------------|-----------|----------------------|----------|-----------------------------|-------------------|---------|--------------------|----------------|---------------------------|--------------|---------------|\n|         |                    | Common name    | Longitude (°) | Latitude (°) | Elevation (m) | Aspect (°) | Area (km2) 1993 | Area (km2) 2018 | Significant expansion | Lakes upstream | Presence of ice core | Avalanche |                      | Rockfall | Steep moraine (slope > 10°) | Buildings         | Bridges | Hydropower systems | Hazard ranking | Downstream impact ranking | Risk ranking |               |\n|         | 216                | Gurudongmar       | 88.710           | 28.026          | 5,130            | 350           | 1.149              | 1.12               | 0                        | 1                 | 0                          | 0         | 216                  | 0        | 1                           | 300               | 5       | 0                  | 1              | 2                         | 1            | 49.89 ± 21.95 |\n|         | 160                | Tso Lhamo         | 88.713           | 28.005          | 5,231            | 340           | 1.155              | 1.247              | 1                        | 0                 | 1                          | 1         | 160                  | 1        | 1                           | 300               | 5       | 0                  | 3              | 2                         | 3            | 49.89 ± 21.95 |\n|         | 205                | Tso Lhamo         | 88.756           | 28.012          | 5,072            | 340           | 1.111              | 1.058              | 0                        | 0                 | 0                          | 0         | 205                  | 0        | 1                           | 238               | 14      | 0                  | 1              | 2                         | 1            | 39.54 ± 17.4  |\n|         | 206                |                   | 88.698           | 28.007          | 5,228            | 20            | 0.906              | 1.018              | 1                        | 0                 | 1                          | 0         | 206                  | 1        | 1                           | 283               | 16      | 0                  | 2              | 2                         | 2            | 37.46 ± 16.48 |\n|         | 47                 | J R B Lake        | 75.179           | 34.666          | 4,252            | 35            | 0.746              | 0.757              | 0                        | 0                 | 0                          | 0         | 47                   | 0        | 1                           | 78                | 6       | 0                  | 1              | 1                         | 1            | 24.64 ± 10.84 |\n|         | 202                | Khangchung Tso | 88.816           | 27.990          | 5,288            | 300           | 1.647              | 1.775              | 0                        | 0                 | 1                          | 1         | 202                  | 1        | 1                           | 150               | 3       | 0                  | 3              | 2                         | 3            | 19.93 ± 8.77  |\n|         | 279                | Tso               | 92.033           | 27.519          | 4,272            | 10            | 0.617              | 0.588              | 0                        | 0                 | 0                          | 0         | 279                  | 0        | 1                           | 154               | 13      | 0                  | 1              | 2                         | 1            | 17.26 ± 7.59  |\n|         | 161                | Shakho Cho        | 88.616           | 27.975          | 5,010            | 30            | 0.574              | 0.576              | 0                        | 0                 | 0                          | 1         | 161                  | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 16.78 ± 7.38  |\n|         | 132                | Chandra Tal       | 77.615           | 32.483          | 4,262            | 300           | 0.53               | 0.49               | 0                        | 0                 | 0                          | 0         | 132                  | 1        | 1                           | 48                | 2       | 0                  | 1              | 1                         | 1            | 13.34 ± 5.87  |\n|         | 207                |                   | 88.761           | 27.895          | 5,232            | 280           | 0.466              | 0.476              | 0                        | 1                 | 1                          | 1         | 207                  | 0        | 1                           | 172               | 8       | 0                  | 3              | 2                         | 3            | 12.78 ± 5.62  |\n|         | 299                |                   | 93.820           | 28.616          | 3,600            | 315           | 0.467              | 0.468              | 0                        | 0                 | 0                          | 0         | 299                  | 0        | 1                           | 5                 | 5       | 0                  | 1              | 1                         | 1            | 12.49 ± 5.49  |\n|         | 71                 | Shaushar          | 75.236           | 34.991          | 4,142            | 200           | 1.296              | 1.342              | 0                        | 0                 | 1                          | 0         | 71                   | 0        | 1                           | 0                 | 0       | 0                  | 2              | 0                         | 1            | 9.02 ± 3.97   |\n|         | 324                | South Lhonak      | 88.195           | 27.912          | 5,210            | 75            | 0.59               | 1.44               | 1                        | 1                 | 1                          | 0         | 324                  | 0        | 1                           | 40                | 3       | 0                  | 2              | 1                         | 1            | 8.92 ± 3.92   |\n|         | 313                |                   | 93.578           | 28.576          | 3,879            | 30            | 0.396              | 0.363              | 0                        | 1                 | 0                          | 0         | 313                  | 0        | 1                           | 16                | 4       | 0                  | 1              | 1                         | 1            | 8.74 ± 3.85   |\n|         | 307                |                   | 93.437           | 28.305          | 3,944            | 355           | 0.196              | 0.351              | 1                        | 0                 | 0                          | 0         | 307                  | 0        | 1                           | 10                | 3       | 0                  | 1              | 1                         | 1            | 8.31 ± 3.66   |\n|         | 24                 | Lolgul Gali       | 74.892           | 34.444          | 3,864            | 350           | 0.345              | 0.35               | 0                        | 0                 | 1                          | 0         | 24                   | 0        | 1                           | 1,400             | 3       | 0                  | 2              | 2                         | 2            | 8.3 ± 3.65    |\n|         | 159                |                   | 88.546           | 27.993          | 5,178            | 75            | 0.562              | 0.595              | 0                        | 0                 | 1                          | 1         | 159                  | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 8.17 ± 3.6    |\n|         | 236                |                   | 88.512           | 27.672          | 4,525            | 70            | 0.319              | 0.333              | 0                        | 0                 | 0                          | 0         | 236                  | 0        | 1                           | 12                | 5       | 1                  | 1              | 1                         | 1            | 7.71 ± 3.39   |\n|         | 48                 |                   | 75.137           | 34.697          | 4,121            | 350           | 0.633              | 0.549              | 0                        | 0                 | 1                          | 0         | 48                   | 0        | 1                           | 78                | 6       | 0                  | 2              | 1                         | 1            | 7.32 ± 3.22   |\n|         | 214                |                   | 88.639           | 28.002          | 5,423            | 350           | 0.319              | 0.317              | 0                        | 0                 | 1                          | 1         | 214                  | 1        | 1                           | 395               | 8       | 0                  | 3              | 2                         | 3            | 7.21 ± 3.17   |\n|         | 283                |                   | 91.875           | 27.673          | 4,220            | 20            | 0.336              | 0.307              | 0                        | 0                 | 0                          | 0         | 283                  | 0        | 1                           | 40                | 11      | 0                  | 1              | 1                         | 1            | 6.89 ± 3.03   |\n|         | 133                | Geepang Gath      | 77.220           | 32.525          | 4,088            | 160           | 0.42               | 0.941              | 1                        | 0                 | 0                          | 0         | 133                  | 1        | 1                           | 3                 | 2       | 1                  | 1              | 1                         | 1            | 6.76 ± 2.97   |\n|         | 156                |                   | 78.987           | 30.745          | 4,749            | 330           | 0.229              | 0.292              | 1                        | 0                 | 0                          | 0         | 156                  | 0        | 1                           | 82                | 0       | 1                  | 1              | 1                         | 1            | 6.42 ± 2.83   |\n|         | 282                |                   | 91.870           | 27.746          | 4,371            | 20            | 0.459              | 0.439              | 0                        | 0                 | 0                          | 0         | 282                  | 0        | 1                           | 165               | 10      | 0                  | 1              | 2                         | 1            | 6.33 ± 2.79   |\n|         | 131                | Lam Dal           | 76.332           | 32.336          | 3,962            | 320           | 0.274              | 0.272              | 0                        | 0                 | 1                          | 0         | 131                  | 0        | 1                           | 9                 | 4       | 0                  | 2              | 1                         | 1            | 5.82 ± 2.56   |\n|         | 196                | Tsomgo            | 88.763           | 27.375          | 3,753            | 270           | 0.231              | 0.269              | 0                        | 0                 | 0                          | 0         | 196                  | 0        | 1                           | 275               | 9       | 2                  | 1              | 3                         | 2            | 5.45 ± 2.4    |\n|         | 130                |                   | 78.167           | 31.661          | 4,278            | 20            | 0.122              | 0.257              | 1                        | 0                 | 0                          | 0         | 130                  | 1        | 1                           | 6                 | 0       | 1                  | 1              | 1                         | 1            | 5.36 ± 2.36   |\n|         | 310                |                   | 93.726           | 28.594          | 4,218            | 260           | 0.252              | 0.25               | 0                        | 0                 | 0                          | 0         | 310                  | 0        | 1                           | 16                | 4       | 0                  | 1              | 1                         | 1            | 5.14 ± 2.26   |\n|         | 311                |                   | 93.712           | 28.585          | 3,894            | 20            | 0.135              | 0.144              | 0                        | 1                 | 0                          | 0         | 311                  | 0        | 1                           | 16                | 4       | 0                  | 1              | 1                         | 1            | 5.14 ± 2.26   |\n|         | 209                |                   | 88.561           | 28.014          | 5,070            | 110           | 0.262              | 0.27               | 0                        | 0                 | 0                          | 1         | 209                  | 1        | 1                           | 350               | 8       | 0                  | 3              | 2                         | 3            | 5.06 ± 2.23   |\n|         | 210                |                   | 88.572           | 28.007          | 4,997            | 130           | 0.275              | 0.268              | 0                        | 1                 | 0                          | 0         | 210                  | 1        | 1                           | 350               | 8       | 0                  | 1              | 2                         | 1            | 5.06 ± 2.23   |\n|         | 81                 |                   | 74.961           | 35.082          | 4,320            | 0             | 0.251              | 0.246              | 0                        | 1                 | 0                          | 0         | 81                   | 0        | 1                           | 135               | 11      | 0                  | 1              | 2                         | 1            | 5.03 ± 2.21   |", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.3. Risk", "section_headings": ["4. Discussions", "4.3. Risk"], "chunk_type": "table", "table_caption": "Table 2 Details of Lakes Having PFV Higher Than $5 \\times 10^6 \\, \\mathrm{m}^3$", "columns": ["Lake ID", "ID (this study)", "Lake details", "", "", "", "", "", "", "Hazard", "", "", "", "Lake ID (this study)", "Hazard", "", "Downstream impact", "", "", "Ranking", "", "", "PFV (106 m3)"], "table_row_start": 1, "table_row_end": 33, "line_start": 295, "line_end": 329, "token_count_estimate": 3037, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2645f489d19af398", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.3. Risk\nType: text\n\nNote. Ranking value (0) represents low, (1) represents moderate, (2) represents high, and (3) represents very high. For hazard, (1) represents yes, and (0) represents no.\n\n1944793, 2020, 4, Downloaded from https://gupubxo.ninelbhray.wiley.com/doi/10.1029/2019WR02553 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://onlinelbtrary.wiley.com/terms-und-conditions) on Wiley Online Library on (19/08/2025].\n\nDUBEY AND GOYAL 16 of 21\n\nNote: Ranking value (0) represents low, (1) represents moderate, (2) represents high, and (3) represents very high. For hazard, (1) represents yes, and (0) represents no.\n\n1944793, 2020, 4, Downloaded from https://gupubxo.ninelbhray.wiley.com/doi/10.1029/2019WR02553 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms and Conditions (https://onlinelbtrary.wiley.com/terms-und-conditions) on Wiley Online Library on (19/08/2025].\n\nDUBEY AND GOYAL 17 of 21\n\n19447973, 2020, 4, Downloaded from https://agupubs.onlinelibrary.\n\nwiley.com/doi/10.1029/2019WR026533 by Indian Institute Of Technology Bombay, Wiley Online Library on [08/08/2025]. See the Terms\n\nare governed by the applicable Creative Commons Licens\n\nGLOF from these lakes would significantly improve the hazard and downstream risk assessment and aid the stakeholders in taking decisions related to risk mitigation measures.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.3. Risk", "section_headings": ["4. Discussions", "4.3. Risk"], "chunk_type": "text", "line_start": 330, "line_end": 350, "token_count_estimate": 414, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["1944793", "19447973", "2019WR02553", "2019WR026533"]}}
{"id": "e11f702d4f65acdf", "text": "Document: 1. Introduction\nSection: 4. Discussions > 4.4. Implications for Stakeholders\nType: text\n\nAccurate identification of potentially dangerous glacial lake is challenging, but yet a crucial task and is vital to maintain the trustworthiness of the decision-makers and local communities. Generally, the regional studies assess different parameters to identify potential hazard associated with glacial lakes, and therefore, the obtained results depict very different results. Therefore, we recommend the stakeholders to base the decision criteria on the analysis based on new and more comprehensive data, more sophisticated analysis, enhanced understanding of system, and robust model simulations with wider consideration of uncertainties. The important characteristics to prioritize the glacial lake studies should be (1) credibility of the evidence that the applied model relies upon; (2) standardized application of methodology; (3) inclusion of most frequent GLOF triggers; and (4) the level of details in the estimated probable impact.\n\nThe primary objective of policy-research linkage should be to convey advanced scientific data and technology to planning and mitigation and ultimately reduce the projected risk. One crucial aspect of these hazard assessments is the consideration of worst-case scenarios, something that is not very likely and this must be appropriately communicated to the decision-makers. Second, there should be formal communication channels between policy-makers and research scientists to enable precise knowledge transfer and lastly, although the associated risk with the glacial hazards is very high, yet is mostly indeterminate, and the present tendency of grossly overstating the results in terms of both hazard and potential catastrophe needs to be realized and strictly discouraged.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "4. Discussions > 4.4. Implications for Stakeholders", "section_headings": ["4. Discussions", "4.4. Implications for Stakeholders"], "chunk_type": "text", "line_start": 352, "line_end": 356, "token_count_estimate": 367, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31cb328dc538530e", "text": "Document: 1. Introduction\nSection: 5. Conclusions\nType: text\n\nThe present study implemented a comprehensive evaluation of hazard, downstream impact, and associated risk for 329 glacial lakes with size greater than 0.05 km<sup>2</sup> in the Indian Himalayas. The implemented methodology assesses the most frequent triggers based on the detailed literature review which includes avalanche, rockfall, stability of the damming moraine, expansion rate, and presence of ice cores. The results indicated that glacial lakes expanded by 15.84% between 1993 and 2018, where 64 lakes significantly expanded and 6 lakes significantly drained; 36 lakes are susceptible to an avalanche where most of the hit is expected along the minor axis. Application of stochastic flood model reveals that 67 glacial lakes contain at least one hydropower system along their flow path. The risk assessment indicates that there are 23 very high risk lakes and 50 high-risk lakes in the Indian Himalayas. The main objective of the study was to implement an objective methodology that was least subjective as possible. Conversely, with the present state of knowledge regarding triggering mechanisms of GLOF and available remotely sensed satellite data, there is a certain level of subjectiveness that is inherently inescapable in these first pass GLOF assessments.\n\nWe recommend the use of downstream impact along with potential flood volume for both dynamic and self-destructive failures to rank glacial lakes as with sizeable flood volume, proximities of the area of settlement and socioeconomic consequence are equally essential to recognize a lake as potentially dangerous. Some of the notable lakes with very high PFV include Gurudongmar Lake, Tso Lhamo Lake, and J R B Lake, whereas some of the lakes with high PFV and very high risk include Khangchung Tso and Shakho Cho. The use of hydrodynamic model, along with field-based assessments, is strongly suggested before implementing any risk mitigation measures. Additionally, as higher temporal resolution DEMs become available, the hazard assessment should be repeated as it will enable DEM differencing which will significantly improve the identification of ice cores and the modeling of dynamic failures.", "metadata": {"source_file": "data/('Water Resources Research - 2020 - Dubey - Glacial Lake Outburst Flood Hazard  Downstream Impact  and Risk Over the Indian', '.pdf')_extraction.md", "document_title": "1. Introduction", "section_path": "5. Conclusions", "section_headings": ["5. Conclusions"], "chunk_type": "text", "line_start": 358, "line_end": 361, "token_count_estimate": 497, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc99110fac007d56", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: FOREWORD\nType: text\n\nIndian Himalayas contain the largest number of glaciers other than Polar Regions of the world. These glaciers along with seasonal snow cover are the major source of fresh water and three major river systems originating from Himalayas are nourished by their melt waters, thereby supporting the livelihood of millions of people. Glacial lakes are common features around the margins of glaciers, originating from retreating or thinning processes of glacier. The area and volume of glaciers have been changing rapidly since past few decades due to natural and anthropological effects, with most of them losing mass as they thinned and their termini retreated, resulting in the formation of new glacial lakes. These glacial lakes may at times release large quantities of glacier melt water from associated moraine or ice-dam due to breach/failure, resulting in catastrophic flood in the downstream area, with significant socio-economic impacts. It is essential to have knowledge on location of glacial lakes, their susceptibility, and the probable consequences of GLOF risk. Earth Observation satellite data is useful in mapping and monitoring of glacial lakes, which by traditional means is nearly impossible task due to highly rugged and inaccessible terrain.\n\nThe present atlas is brought out as part of an activity on “Glacial Lake Outburst Flood (GLOF) Risk Assessment of Glacial Lakes in the Himalayan Region of Indian River Basins”, taken up under the National Hydrology Project, funded by the Department of Water Resources, River Development and Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti, Government of India. This atlas is first of its kind depicting spatial distribution of glacial lakes of size greater than 0.25 ha in the Indus River basin using high resolution satellite data from Resourcesat-2 LISS-IV MX. Glacial lakes are inventoried for the Indus River basin, within both India and transboundary region. The details of glacial lakes are systematically documented at basin, subbasin, administrative, and transboundary region level, including lake type, area, and elevation distribution.\n\nThe atlas forms as an authentic and recent reference data, and is useful in monitoring glacial lake dynamics, GLOF risk assessment, and long-term climate change impact analysis.\n\nI commend the study team for taking up this initiative to bring out an exclusive “Glacial Lake Atlas of Indus River Basin”. I sincerely hope that this exhaustive atlas will be of immense value to Central/State water resources, environmental and disaster organisations, and as well as to academicians and researchers.\n\nOctober 26, 2020\n\n(Santanu Chowdhury)\n\nभारतीय अन्तरिक्ष अनुसंधान संगठन Indian Space Research Organisation", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "FOREWORD", "section_headings": ["FOREWORD"], "chunk_type": "text", "line_start": 3, "line_end": 18, "token_count_estimate": 635, "basins": ["Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "020cdd0b05af99df", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: GLACIAL LAKE ATLAS OF INDUS RIVER BASIN > SUMMARY\nType: text\n\nNational Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Hyderabad as one of the Implementing Agency under the National Hydrology Project (NHP), is carrying out hydrological studies using satellite data and geospatial techniques. As part of this, detailed glacial lake inventory, prioritization for Glacial Lake Outburst Flood (GLOF) risk, and simulation of GLOF for selected lakes are taken up for entire catchment of Indian Himalayan Rivers. Under this activity, an updated inventory of glacial lakes has been prepared for the Indus River basin using high resolution satellite data. The present glacial lake atlas is based on the inventoried glacial lakes in part of Indus River basin from its origin to foothills of Himalayas covering a catchment area of 3,42,841 Km2.\n\nThe study portion of Indus River basin covers part of India and transboundary region. Indus River basin has been divided into nine subbasins on the basis of confluence of major rivers contributing into the system viz., Satluj, Beas, Ravi, Chenab, Jhelum, and Zaskar joining the main river on the left, whereas rivers like Sengee Tsangpo, Shyok, and Gilgit joining on the right. Elevation in the river basin varies from the minimum 153 m to the maximum 8,557 m amsl. In India, Indus River basin extends in two states and two union territories (UT’s) viz., Himachal Pradesh and Uttarakhand, and Jammu & Kashmir and Ladakh, respectively.\n\nIn the present study, glacial lakes with water spread area greater than 0.25 ha have been mapped using Resourcesat-2 (RS-2) Linear Imaging Self Scanning Sensor-IV (LISS-IV) satellite data using visual interpretation techniques. Based on its process of lake formation, location, and type of damming material, glacial lakes are identified in ten different types, majorly grouped into four categories viz., Moraine-dammed, Ice-dammed, Glacier Erosion, and Other glacial lakes.\n\nA total of 5,335 glacial lakes have been mapped in the Indus River basin using a total of 157 high resolution multispectral RS-2 LISS-IV images, with a total lake water spread area of 17,395 ha. Each glacial lake has been given a 12 alpha-numeric unique glacial lake ID, along with several attributes that include hydrological, geometrical, geographical, and topographical characteristics. About 4,633 (86.84%) lakes are with < 5 ha lake area contributing to 34.04% of total lake area. The remaining lakes with > 5 ha in size are 702 (13.16%) contributing to 65.96% of total lake area in the basin. There are only 34 glacial lakes in the Indus River basin having an area of greater than 50 ha. Other Glacial Erosion lake type are found to be the maximum with 2,516 (47.16%) occupying a total lake extent of 8,247.44 ha (47.41%) in the basin. More than half (i.e. 52.43%) of the lakes are situated in the high altitude range of 4,001 - 5,000 m amsl and dominated by Other Glacial Erosion lake type i.e., 58.17%.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "GLACIAL LAKE ATLAS OF INDUS RIVER BASIN > SUMMARY", "section_headings": ["GLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "SUMMARY"], "chunk_type": "text", "line_start": 42, "line_end": 62, "token_count_estimate": 800, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "2e07afec70824366", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: GLACIAL LAKE ATLAS OF INDUS RIVER BASIN > SUMMARY\nType: text\n\nlake area in the basin . There are only 34 glacial lakes in the Indus River basin having an area of greater than 50 ha . Other Glacial Erosion lake type are found to be the maximum with 2 , 516 ( 47 . 16 % ) occupying a total lake extent of 8 , 247 . 44 ha ( 47 . 41 % ) in the basin . More than half ( i . e . 52 . 43 % ) of the lakes are situated in the high altitude range of 4 , 001 - 5 , 000 m amsl and dominated by Other Glacial Erosion lake type i . e . , 58 . 17 % .\n\nGlacial lakes are predominantly distributed in the Gilgit subbasin (18.95%) followed by Indus Upper subbasin (18.78%) with a total lake extent of 3,380.27 ha and 3,467.01 ha at 19.43% and 19.93% respectively in the entire basin. In terms of lake extent, Indus Upper subbasin has large lake area. Minimum number of glacial lakes are present in Ravi subbasin (1.14%) and then in Beas subbasin (1.80%). Other Glacial Erosion lakes, which are dominant lake type in Indus River basin are uniformly distributed in all subbasins, and found maximum in count in all subbasins (predominantly in Gilgit subbasin), except Shyok subbasin which has Supra-glacial lakes in majority. However, Glacier Ice-dammed and Glacier Trough Valley Erosion lakes are only located in Shyok and Gilgit subbasin respectively. Shyok subbasin also consists of higher number of Lateral Moraine dammed lake with ice in the entire Indus River basin.\n\nA total of 4,280 (i.e. 80.23%) glacial lakes lies within Indian region covering 79.26% of the total lake area, whereas remaining 19.77% of lakes are located in transboundary region with a 20.74% of the total lake area.\n\nIn Indian region, majority of glacial lakes are of Other Glacial Erosion type (48.11%), followed by Supra-glacial lakes (17.78%) and Other Moraine Dammed lakes (14.77%). Ladakh (UT) shares 75.21% of lake count, followed by 12.76% and 11.99% in Jammu & Kashmir (UT) and Himachal Pradesh respectively, with a total lake area of 72.28%, 20.90%, and 6.73% respectively. Whereas, remaining 0.04% of glacial lakes are found in Uttarakhand state, covering only 0.09% of lake area. Majority of lakes in Ladakh (UT) and Jammu & Kashmir (UT) are of lake area ranging in 1 - 5 ha category but lying in high (4,001 - 5,000 m) and medium altitude range (3,001 - 4,000 m) respectively. Whereas, 95.52% of lakes in Himachal Pradesh are less than 5 ha situated above 4,000 m amsl. Lakes in Uttarakhand are only situated in very high altitude range i.e. above 5,000 m amsl under two area ranges i.e. 0.25 - 5 ha and 10 - 50 ha.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "GLACIAL LAKE ATLAS OF INDUS RIVER BASIN > SUMMARY", "section_headings": ["GLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "SUMMARY"], "chunk_type": "text", "line_start": 42, "line_end": 62, "token_count_estimate": 782, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Gilgit", "Indus Upper", "Ravi", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "8be76664cd49c7a9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: GLACIAL LAKE ATLAS OF INDUS RIVER BASIN > SUMMARY\nType: text\n\nof lakes in Ladakh ( UT ) and Jammu & Kashmir ( UT ) are of lake area ranging in 1 - 5 ha category but lying in high ( 4 , 001 - 5 , 000 m ) and medium altitude range ( 3 , 001 - 4 , 000 m ) respectively . Whereas , 95 . 52 % of lakes in Himachal Pradesh are less than 5 ha situated above 4 , 000 m amsl . Lakes in Uttarakhand are only situated in very high altitude range i . e . above 5 , 000 m amsl under two area ranges i . e . 0 . 25 - 5 ha and 10 - 50 ha .\n\nIn this atlas, map sheets (plates) are prepared on the basis of the Survey of India (SOI) toposheet index (1:250,000 scale) which are 56 in number covering the entire Indus River basin. Out of 56 plates, only 46 plates have glacial lakes and corresponding plates are incorporated in atlas. The map sheets are arranged in such a way that glacial lake map is on the right page and its corresponding satellite image is on the left page. At the end of the atlas, an annexure is provided containing list of all glacial lakes inventoried in the Indus River basin with their unique glacial lake ID, latitude, longitude, subbasin, glacial lake type, area (ha), and elevation (m). Glacial Lake ID number of 12 alpha-numeric character has 3 characters bold with dark red colour depicting the corresponding toposheet number of the SOI of 1:250,000 scale.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "GLACIAL LAKE ATLAS OF INDUS RIVER BASIN > SUMMARY", "section_headings": ["GLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "SUMMARY"], "chunk_type": "text", "line_start": 42, "line_end": 62, "token_count_estimate": 421, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5ce59b477efb8378", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 1. INTRODUCTION > 1.1 About Project\nType: text\n\nThe National Hydrology Project (NHP) sponsored by Department of Water Resources, River Development and Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti, Government of India (GOI) with financial aid from the World Bank. The objective of the project is to improve the extent and accessibility of water resources information and strengthen institutional capacity to enable improved water resources planning and management across India. The mission is to establish an effective and sound hydrologic database and Hydrological Information System (HIS), together with the development of consistent and scientifically based tools and design aids, to assist in the effective water resources planning and management of the implementing agencies.\n\nNHP is intended for setting up of a system for timely and reliable water resources data acquisition, storage, collation and management. It will also provide tools/systems for informed decision making through Decision Support System (DSS) for water resources assessment, flood management, reservoir operations, drought management, etc. NHP also seeks to build capacity of the State and Central sector organisations in water resources management through the use of Information Systems and adoption of State-of-the-art technologies like Remote Sensing. NHP will improve and expand hydrology data and information systems, strengthen water resources operation and planning systems, and enhance institutional capacity for water resources management. NHP will contribute to the GOI Digital India initiative by integrating water resources information across State and Central agencies.\n\nNational Remote Sensing Centre (NRSC), as one of the Implementing Agency under NHP, is engaged with generation of geo-spatial products & services pertaining to water resources sector, generation of high resolution Digital Elevation Models (DEM), development of flood early warning systems, decision support system development for irrigation water management, modelling & dissemination of hydrological products to support water resources management and capacity building to NHP stakeholders. The satellite data based geo-spatial products & services, mainly encompassing the following:\n\n- Satellite Data/Geo-Spatial Data Hosting & Services through Bhuvan Web Portal\n- Water Resources Information Products & Services (Satellite/Model derived – Bhuvan/India- Water Resources Information System (India-WRIS)/National Water Informatics Centre (NWIC))\n- Customized Applications Development (Flood Forecasting, Irrigation Water Management)\n- Hydro-conditioned Digital Elevation Model (Satellite & Aerial)\n- Capacity Building (Customized Training & Hand Holding)\n\nAs part of various NHP technical studies carried out, NRSC has taken up “Glacial Lake Outburst Flood (GLOF) Risk Assessment of Glacial Lakes in the Himalayan Region of Indian River Basins”. In this activity, it was proposed to prepare an updated inventory of glacial lakes, prioritization and selection of critical glacial lakes based on certain characteristics (such as glacial lake, glacier, topography and others), GLOF modelling and flood inundation simulation for selected few lakes using high resolution Digital Elevation Model (DEM) for downstream of the lakes along their river reach, and to assess GLOF risk.\n\nAs a result of initial outcome of this activity, an updated inventory of glacial lakes in Indus River basin was generated using multispectral (MX) high resolution satellite data of Resourcesat-2 Linear Imaging Self Scanning\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSensor-IV (LISS-IV) for mapping lakes with size > 0.25 ha. The geo-spatial database of glacial lakes has been used to prepare atlas of “Glacial Lake Atlas of Indus River Basin”.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "1. INTRODUCTION > 1.1 About Project", "section_headings": ["1. INTRODUCTION", "1.1 About Project"], "chunk_type": "text", "line_start": 66, "line_end": 92, "token_count_estimate": 842, "basins": ["Ganga", "INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e75a94fbf7825578", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 1. INTRODUCTION > 1.2 Glacial Lakes\nType: text\n\nIndian Himalayan Region (IHR) contains the world’s largest number of glaciers and snow outside the Polar Regions and are aptly called third pole of the world. Many studies undertaken globally showed that glaciers around the world have been retreating since the industrial revolution, which began around eighteenth century. As the glaciers are thinning and retreating, resulting in associated glacier melt water lakes are expanding in size and new lakes continue to be formed. The lakes receiving melt water from glaciers are generally known as glacial lakes. A glacial lake is defined as water mass existing in a sufficient amount and extending with a free surface in, under, beside, and/or in front of a glacier and originating from glacier activities and/or retreating processes of a glacier. As glaciers retreat, the formation of glacial lakes takes place behind moraine or ice ‘dam’. These damming materials are generally weak and can breach suddenly due to various triggering factors, leading to catastrophic floods. Such outburst floods are known as GLOF.\n\nGLOFs are characterized by extreme peak discharges, with an exceptional erosion/transport potential; therefore, they can turn into flow-type movements (Emmer, 2017). Failure of such lake happens due to many factors which include erosion process, increase in water pressure, merging of an avalanche/rock into lake, nature of the damming materials etc., and this may lead to a GLOF event which could be highly disastrous in nature and create long-term degradation in the valleys, both physically and socio-economically (Mool et al., 2001b). Accordingly, Emmer et al., (2016) showed an annual nonlinear increase in the number of scientific publications focusing on GLOFs recently. Hence, monitoring of glacier associated lakes is very useful in the IHR to identify critical glacial lakes, for which a detailed inventory of glacial lakes and its type is required. According to their position relative to the glacier and damming mechanism, these glacial lakes can be classified into several types (Panda et al., 2014).\n\nInventorying glacial lakes located in these remote mountain areas with rugged terrain and inclement weather by traditional means is very tedious and difficult, hence Remote Sensing (RS) data plays a greater role in generating information on glacial lakes (Kulkarni, 1991; Berither et al., 2007; Wagnon et al., 2007; Raj, 2010; Cogley et al., 2011; Pratap et al., 2016; Gupta et al., 2019; Guru et al., 2019). Satellites with high spatial, spectral and temporal resolution sensors are useful in deriving lake information with better accuracy and repeatedly.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "1. INTRODUCTION > 1.2 Glacial Lakes", "section_headings": ["1. INTRODUCTION", "1.2 Glacial Lakes"], "chunk_type": "text", "line_start": 94, "line_end": 100, "token_count_estimate": 667, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fcb4d1fd5ef7bae8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 1. INTRODUCTION > 1.3 Previous Studies\nType: text\n\nSeveral studies have been taken up in the past to assess the glacial lake distribution in the Hindu Kush Himalayas (HKH), covering parts of eight countries viz., Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan, and lies within five river basins of Amu Darya, Indus, Ganga, Brahmaputra, and Irrawaddy (Komori, 2007; Gardelle et al., 2011; Wang et al., 2011; Wang et al., 2012; Nie et al., 2013; Raj et al., 2013; Wang et al., 2013; Worni et al., 2013; Che et al., 2014; Bambari et al., 2015; Zhang et al., 2015; Nie et al., 2017; Rounce et al., 2017; Nagai et al., 2017; Gupta et al., 2019; Guru et al., 2019; Shugar et al., 2020). But only few glacial lake inventories are available in public domain, amongst which the first inventory was prepared by the International Centre for Integrated Mountain Development (ICIMOD), Nepal, for the entire HKH region (covering the entire IHR within it), using satellite data of the Land Observation Satellite (Landsat) Thematic Mapper (TM) of the United States Geological Survey (USGS) and the Indian Remote Sensing satellite (IRS-1D) Linear Imaging and\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSelf-scanning Sensor-III (LISS-III) during 1999-2005, along with topographic maps published between the 1950s and 1982 (Mool et al., 2001a; Mool et al., 2001b; Mool et al., 2003; Bhagat et al., 2004; Roohi et al., 2005; Sah et al., 2005; Wu et al., 2005, Ives et al., 2010). This inventory has been revised in 2018 using Landsat TM and Enhanced Thematic Mapper Plus (ETM+) data of years 2004-07 ± 3 (Maharjan et al., 2018). Both glacial lake inventories prepared by the ICIMOD, have mapped lakes with size > 0.3 ha.\n\nSecond inventory of glacial lakes and water bodies in the IHR (within India only) was carried out by the NRSC, Hyderabad in collaboration with the Central Water Commission (CWC), New Delhi. Glacial lakes and water bodies located in all three major basins of Indus, Ganga, and Brahmaputra, of size > 10 ha were mapped using Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data for the year 2009 (NRSC, 2011). Subsequently, monthly monitoring of these lakes (> 50 ha) was carried out using satellite data for the months of June to October during the years 2011 to 2015.\n\nThird latest glacial lake inventory is prepared by the Space Application Centre (SAC), Ahmedabad i.e. “National Wetland Atlas: High Altitude Lakes of India”, using IRS-P6 LISS-III, comprising high altitude lake information of the IHR, within Indian administrative region only (Panigrahy et al., 2012). In this atlas, wetlands of size > 2.25 ha were mapped as a polygons and less than that were mapped as a points, using satellite data for the period of 2006-08.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "1. INTRODUCTION > 1.3 Previous Studies", "section_headings": ["1. INTRODUCTION", "1.3 Previous Studies"], "chunk_type": "text", "line_start": 102, "line_end": 113, "token_count_estimate": 783, "basins": ["Brahmaputra", "Ganga", "INDUS", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "4a010e0d18e3a7d5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 1. INTRODUCTION > 1.4 Highlights of the Atlas\nType: text\n\n**The highlights of the present atlas:**\n* The present atlas is first of its kind depicting spatial distribution of glacial lakes of size greater than 0.25 ha in Indus river basin mapped using high resolution satellite data\n* The atlas provides the details of all the glacial lakes in entire catchment of Indus River basin, both within Indian and transboundary region\n* The atlas contains details of area range-wise glacial lakes along with 10 categories of types. Further, the atlas present the distribution of glacial lakes in terms of area vs. type, elevation, area vs. elevation, and type vs. elevation, at basin, sub-basin, administrative, and transboundary regions\n* The atlas also provides comprehensive list of all glacial lakes with unique ID considering hydrological, geometrical, geographical, topographical attribute information\n\n**The expected utility of the atlas:**\n* The atlas provides a comprehensive and systematic glacial lake database for Indus River basin with size > 0.25 ha\n* In the context of climate change impact analysis, the atlas can be used as reference data for carrying out change analysis, both with respect to historical and future time periods\n* The atlas also provides authentic database for regular or periodic monitoring changes in spatial extent (expansion/shrinkage), and formation of new lakes\n* The atlas can also be used in conjunction with glacier information for its retreat and climate impact studies\n* The information on glacial lakes like their type, hydrological, topographical, and associated glaciers are useful in identifying the critical glacial lakes and consequent GLOF risk\n* Central and State Disaster Management Authorities can make use of the atlas for disaster mitigation planning and related programs\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "1. INTRODUCTION > 1.4 Highlights of the Atlas", "section_headings": ["1. INTRODUCTION", "1.4 Highlights of the Atlas"], "chunk_type": "text", "line_start": 115, "line_end": 137, "token_count_estimate": 428, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "148d8281e0cf70a5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.1 Overview\nType: text\n\nThe IHR consist of three major river systems of Indus, Ganga, and Brahmaputra, stretches over four countries viz., India, China, Nepal and Bhutan, and on the basis of physiography it has been divided into four mountain regions viz., Eastern Himalayas, Central Himalayas, Western Himalayas, and the Karakoram Mountain range. The Indus River basin is unique in the sense that it contains seven of the world’s highest peaks after Mt. Everest, among these are K2 (8,557 m), Nanga Parbat (8,100 m) and Rakaposhi (7,800 m). The Indus River flowing near Leh (Ladakh) has been shown in Figure 1. The Indus River basin from its origin to foothills of Himalayas has a catchment area of 3,42,841 Km2 (up to Indian administrative boundary). The Indus basin extends over Western Himalayas and Karakoram Mountain range in India, China, and Nepal.\n\nIndus River originates from Bokhar Chu (glacier) in northern slopes of Mt. Kailash (6,714 m) from the Tibet Autonomous Region of China (Husain, 2012; CWC, 2019). The Indus River follows a long and straight course in Ladakh region (flowing in northwest direction in India), running between the Ladakh and the Zaskar ranges. The gradient of the river is also gentle here. It forms a spectacular gorge near Gilgit (at Bunzi, north of Nanga Parbat Glacier) in Jammu & Kashmir. Downstream, the river passes by the Nanga Parbat Glacier, crossing the Himalayas, it turns into south-west and enters Pakistan near Chillar in the Dardistan region. Finally the Indus River drains into the Arabian Sea near the port city of Karachi, Pakistan after forming a huge delta. It has a total length of 2,880 Km, of which 709 Km lies in India. The present study area extends from latitude 30.32° N to 37.09° N and from longitude 72.50° E to 82.45° E.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.1 Overview", "section_headings": ["2. STUDY AREA", "2.1 Overview"], "chunk_type": "text", "line_start": 141, "line_end": 145, "token_count_estimate": 501, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Gilgit"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "f71818483caf1fe3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: text\n\nMajor rivers flowing in the Indus River basin are Satluj, Beas, Ravi, Chenab, Jhelum, and Zaskar joining the main river on the left, whereas rivers like Sengee Tsangpo, Shyok, and Gilgit joining on the right. Considering hydrological setting of the aforesaid rivers, Indus River basin is divided in 9 subbasins viz., Gilgit, Indus Middle, Jhelum, Chenab, Ravi, Beas, Shyok, Indus Upper, and Satluj. Shyok River is the largest tributary of all (640 Km long). Figure 2 shows the location of the study area with Resourcesat-2 (RS-2) Linear Imaging Self Scanner (LISS IV) satellite images. Table 1 shows the area covered by each of the above subbasins.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "text", "line_start": 147, "line_end": 151, "token_count_estimate": 221, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "291939b31c52d5f0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: table\nTable: Table 1: Details of subbasins of Indus River basin\n\n| S. No. | Subbasin | Area (Km2) | Area (%) |\n|---|---|---|---|\n| 1 | Beas | 13,963 | 4.07 |\n| 2 | Chenab | 29,258 | 8.53 |\n| 3 | Gilgit | 27,397 | 7.99 |\n| 4 | Indus Middle | 23,703 | 6.91 |\n| 5 | Indus Upper | 72,695 | 21.20 |\n| 6 | Jhelum | 29,388 | 8.57 |\n| 7 | Ravi | 9,237 | 2.69 |\n| 8 | Satluj | 57,560 | 16.79 |\n| 9 | Shyok | 79,640 | 23.23 |\n| | **Total** | **342,841** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "table", "table_caption": "Table 1: Details of subbasins of Indus River basin", "columns": ["S. No.", "Subbasin", "Area (Km2)", "Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 152, "line_end": 163, "token_count_estimate": 266, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "599bae20ab9f0084", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.3 Hydrology\nType: text\n\nAll the principal tributaries of the Indus River system are fed by snow and glaciers in the upper parts of their mountainous catchments. The snow accumulation in their upper catchments usually starts from October to March months reaching peak in January/February. The river flows are at minimum during the winter months of December to March. When snow and glaciers start melting during summer months of April to June, river flows gradually increase and accelerated further by rainy season of July to September. Annually, the upper Indus carries about 110 Billion Cubic Metre (BCM) slightly less than half the total supply of water in the Indus River system. The Jhelum and Chenab combined carry roughly one-fourth, and the Ravi, Beas, and Satluj combined constitute the remainder of the total supply of the system.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.3 Hydrology", "section_headings": ["2. STUDY AREA", "2.3 Hydrology"], "chunk_type": "text", "line_start": 166, "line_end": 171, "token_count_estimate": 232, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Jhelum", "Ravi", "Satluj"], "countries": [], "lake_ids": []}}
{"id": "36d44fb55e54f612", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.4 Topography\nType: text\n\nThe elevation of the study area ranges between 153 m and 8,557 m above mean sea level (amsl), where glaciers and glacial lakes are mostly distributed in the higher altitude region. The mean elevation of the study area is 4,355 m amsl. The transverse glaciers present in the basin at different locations and occasional landslides may also dam the river resulting in formation of temporary lakes. Downwards, the Indus crosses the Central Himalayan Range through a huge synclinal gorge. The Indus makes several deep gorges, amongst which the deepest of all is at Gilgit, which is 5,200 m in height amsl. Slope in the entire study area varies up to a maximum of 87.76°, while the mean slope in the Indus River basin is 22.23°. Hypsometric curve is a graph which shows the proportion of land area that exist at various elevations by plotting relative area against relative height, as shown in Figure 3 for the study area.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.4 Topography", "section_headings": ["2. STUDY AREA", "2.4 Topography"], "chunk_type": "text", "line_start": 173, "line_end": 175, "token_count_estimate": 253, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "d5562b7b5db2fd7e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.4 Topography\nType: figure\nFigure: Figure 3: Hypsometric curve of the study area\n\nFigure 3: Hypsometric curve of the study area", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.4 Topography", "section_headings": ["2. STUDY AREA", "2.4 Topography"], "chunk_type": "figure", "figure_caption": "Figure 3: Hypsometric curve of the study area", "line_start": 176, "line_end": 176, "token_count_estimate": 54, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e6386e8c0ed5397e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 2. STUDY AREA > 2.5 Climate\nType: text\n\nClimate over the Indus River basin varies from subtropical arid and semiarid to temperate subhumid on the plains of Sindh and Punjab provinces to alpine in the mountainous highlands of the north. Annual precipitation ranges between 100 and 500 mm in the lowlands to a maximum of 2,000 mm on mountain slopes. In the lower plains, December to February is the cold season and mean monthly temperatures vary from 14°C to 20°C. Mean monthly temperatures during March to June vary from 42°C to 44°C. Whereas, in the upper plains mean temperature ranges from 23°C to 49°C during summer and from 2°C to 23°C during winter.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "2. STUDY AREA > 2.5 Climate", "section_headings": ["2. STUDY AREA", "2.5 Climate"], "chunk_type": "text", "line_start": 179, "line_end": 194, "token_count_estimate": 214, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35aabb2fdfe141b4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 3. DATA USED\nType: text\n\nEarth observation satellites capture the data repeatedly in various spectral ranges and at different spatial and radiometric resolutions. For inventorying glacial lakes, high to medium resolution datasets are proved to be useful by many research studies (Bolch et al., 2010; Mergili et al., 2013; Wang et al., 2013; Zhang et al., 2015; Gupta et al., 2019; Guru et al., 2019). Data captured between September and December were mostly used because the presence of snow or cloud cover during this period is minimum. USGS satellite data of Landsat 5 and 7 (TM and ETM+) has been used widely for mapping glacial lakes due to free accessibility. Whereas, IRS satellite data from sensors of AWiFS, LISS-III, LISS-IV has also been used for such inventory.\n\nIn the present study, high resolution Resourcesat-2 LISS-IV satellite images with spatial resolution of 5.8 m covering a swath of 70 × 70 Km have been used for inventorying glacial lakes. Maximum of the images used for inventorying were of 2016-17 (66%) and remaining images procured were of previous years due to non-availability of cloud-free and snow-free images for the recent years. Majority of images were of September and December months (80%) due to less snow and cloud cover, and rest 20% images of other months. Figure 4 shows the layout of the RS-2 LISS-IV scenes (path-wise) procured for the Indus River basin along with its details in Table 2. The layout of satellite scenes is divided into paths (shown in separate colours) and rows (row numbers shown in the layout).\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 196, "line_end": 210, "token_count_estimate": 423, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f7ba36f0c3543423", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 3. DATA USED\nType: table\nTable: Table 2: Details of satellite scenes used for inventory\n\n| | Other Months | Sep - Dec | Total |\n|---|---|---|---|\n| Prior to 2016 | 13 | 41 | 54 |\n| 2016-17 | 17 | 86 | 103 |\n| **Total** | **30** | **127** | **157** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "table", "table_caption": "Table 2: Details of satellite scenes used for inventory", "columns": ["", "Other Months", "Sep - Dec", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 211, "line_end": 215, "token_count_estimate": 115, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51d47988a3fee0a4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 3. DATA USED\nType: text\n\nDigital Elevation Model (DEM) of Cartosat satellite with 10 m spatial resolution has been used for topographic information and watershed boundary generation. Figure 5 shows elevation range map of the study area i.e. Indus River basin. Other information like name of lakes and rivers has been gathered from digital toposheets available from University of Texas - Toposheet Library at 1:250,000 scale and Tibet Map Institute at 1:100,000 scale (U.S. Army Map Service 1955; Andre 2017).\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 216, "line_end": 221, "token_count_estimate": 148, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e02c03981360dbd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation\nType: text\n\nThe spectral reflectance curve of water in the visible spectrum starts with a low in Blue region (0.4 to 0.5 μm), reaches peak in Green region (0.5 to 0.6 μm), decreases in Red region (0.6 to 0.7 μm) and probably the most distinctive characteristic is the energy absorption at Near InfraRed (NIR) wavelengths. Identifying and delineating water bodies with remote sensing data are carried out easily in near infrared wavelengths because of this absorption property in IR region. However, various physical conditions of water bodies (water depth, turbidity, chlorophyll content, etc.) manifest spectral changes. As a result of various conditions of lakes, the water in satellite images in False Colour Composite (FCC) ranges in appearance from light to dark blue to black. In the case of frozen lakes, it appears white.\n\nGlacial lake sizes are generally small, having circular, semi-circular, or elongated shapes with very fine texture and are generally associated with glaciers in high altitude areas. Certain types of glacial lakes, like erosion and cirque lakes are not necessarily associated with glaciers. Knowledge of the physical characteristics of the glacial lakes, and their associated features is always essential for the interpretation of the images.\n\nSatellite data interpretation can be done using visual image interpretation keys such as colour, size, tone, texture, pattern, association, shape, shadow, and orientation. A number of remote sensing methods had been developed for glacial lake detection and mapping or development of inventory (Kääb 2000; Mool et al., 2001a; Huggel et al., 2002; Huggel et al., 2006; Ives et al., 2010). Manual or automated lake mapping methods have certain difficulties in identifying the lakes, which are described in the following section. An attempt was made to study the accuracy of mapping of glacial lakes using multiple automated methods along with visual interpretation, the details of which are given in Annexure-I. From this study, it was concluded that visual interpretation method was best accurate method. Hence, in the present study glacial lakes and their different types are identified and mapped using RS-2 LISS-IV multispectral images using visual interpretation method.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation"], "chunk_type": "text", "line_start": 225, "line_end": 231, "token_count_estimate": 548, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9bcf85e70da7f4a5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:\nType: text\n\nGlacial lake identification can be done either using visual interpretation or automatic mapping methods. The automatic mapping procedures have limitations due to varying terrain conditions like lakes situated in the shadow portions of mountains, presence of snow cover, cloud cover, and partly frozen lakes, etc. In the presence of snow cover on the glacier tongue or glacier’s ablation area where many Supra-glacial lakes may present, both methods have limitations and difficulties.\n\nAs lake water absorbs the incident radiation making it appear in darker tone and colour in the standard FCC of satellite data, similar response also prevails over shadow region of clouds or mountains on surface, which may lead to incorrect mapping. In fact, a mountain shadow covering a lake partly/completely within its vicinity, making it difficult to accurately map the lake boundary.\n\nMany lakes due to inclement terrain condition, can be under shadow of high peaks and will get missed in both ways of mapping. On the contrary, a lake can also present in white colour while it is in frozen form due to cold weather conditions over the area, then definitely it will not get classified while automatic mapping. Whereas, frozen lakes can be identified and mapped using visual interpretation to some extent.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Difficulties in Lake Identification:"], "chunk_type": "text", "line_start": 233, "line_end": 239, "token_count_estimate": 320, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "49c54a18389774e9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Challenges in Automatic Mapping:\nType: text\n\nIn the IHR, due to high and inclement terrain surface and due to near vertical acquisition of satellite images, some lakes get covered with shadows of mountains, which create problems in identifying glacial lakes. Also identification of lakes with high turbidity, partial ice covered lakes and the lakes in shadow areas are misclassified by automatic methods. Glacial lake mapping is always a semi-automatic approach because even after applying any of that method, it should always be followed by the post processing i.e. correcting the errors using visual interpretation. Even in all cases, automatic mapping will never give the exact and accurate boundary of the lake, leading to necessary manual corrections.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Challenges in Automatic Mapping:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Challenges in Automatic Mapping:"], "chunk_type": "text", "line_start": 241, "line_end": 243, "token_count_estimate": 194, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9bb882e0edff98d0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Reasoning for Visual Interpretation:\nType: text\n\nAlthough automatic mapping methods can speed up the detection of glacial lakes, but these methods could not be applied to the entire Himalayan region due to lot of variations in satellite scenes (seasons/years) and problems mentioned above. For example, if lakes are frozen or covered with snow or cloud and lies in a shadow area, they cannot be detected using these automatic methods. In such cases, the manual interpretation method will be helpful to map these lakes. Thus, any mapping of glacial lakes can be automated up to a certain extent only. So, visual image interpretation keys and technique will give accurate results and avoids misclassification. Therefore, in this present study, glacial lakes and its type identification, and its mapping for the entire Indus River basin (within IHR) has been done manually using visual interpretation. High resolution satellite data available on Bhuvan/Google Earth has been used on need basis in finalizing various features of glacial lake database.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Reasoning for Visual Interpretation:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Reasoning for Visual Interpretation:"], "chunk_type": "text", "line_start": 245, "line_end": 247, "token_count_estimate": 254, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "789dbafaba7183ec", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Limitations:\nType: text\n\nThe RS-2 LISS-IV MX data used for glacial lake database preparation sporadically covered with cloud and seasonal/permanent snow. Also, the Himalayan region being highly varying topography with steep slopes, the satellite data has hill shadows. Thus few glacial lakes would not have been mapped owing to the following constraints:\n\n*   Presence of snow or cloud over the glacial lakes\n*   Glacial lakes under frozen condition\n*   Glacial lakes under mountain shadow", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Limitations:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Limitations:"], "chunk_type": "text", "line_start": 249, "line_end": 255, "token_count_estimate": 144, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "32c9bdd7baef578d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes\nType: text\n\nVarious researchers have proposed glacial lakes classification schemes based on dam type, process of lake formation, topographic feature, and geographical position (Hewitt 1982; Liu and Sharma 1988; Clague and Evans 2000; Mool et al., 2001a, 2001b). Lakes located on the glacier surface can be mapped using satellite data, but there are englacial and subglacial lakes that may also exist, but cannot be mapped from aerial/optical satellite images, requires ground based instrument (Yao et al., 2018). Majorly surface glacial lakes are classified in 4 classes and 10 subclasses, i.e. Moraine-dammed lake, Ice-dammed lake, Glacier Erosion lake (also known\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nas Bed-rock lake), and Other Glacial lake. Two character symbol has been used for glacial lake classification, in which first letter (uppercase) represents lake type and second letter (lowercase) within brackets represents lake subtype, for example, M(e) for End-moraine dammed lake. Details of types of lakes are given in Table 3 and their appearance in satellite images are shown in Figure 6.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes"], "chunk_type": "text", "line_start": 257, "line_end": 268, "token_count_estimate": 316, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7b8e610f11dc5219", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes\nType: figure\nFigure: Figure 6: Types of Glacial Lakes\n\n**Figure 6: Types of Glacial Lakes**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes"], "chunk_type": "figure", "figure_caption": "Figure 6: Types of Glacial Lakes", "line_start": 269, "line_end": 269, "token_count_estimate": 61, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1e2adf5e1c701fe6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes\nType: table\nTable: Table 3: Glacial lake types and their identification keys\n\n| S. No. | Lake Type | Lake Subtype | Code | Identification Keys |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | Moraine-dammed Lake | End-moraine Dammed Lake | M(e) | Lake dammed by end (terminal) moraines |\n| 2 | Moraine-dammed Lake | Lateral Moraine Dammed Lake | M(l) | Lake dammed by lateral moraine(s) not in contact with glacial ice |\n| 3 | Moraine-dammed Lake | Lateral Moraine Dammed Lake (with Ice) | M(lg) | Lake dammed by lateral moraine(s) in contact with glacial ice |\n| 4 | Moraine-dammed Lake | Other Moraine Dammed Lake | M(o) | Lake dammed by other moraines |\n| 5 | Ice-dammed Lake | Supra-glacial Lake | I(s) | Pond or lake on the surface of a glacier |\n| 6 | Ice-dammed Lake | Glacier Ice-dammed Lake | I(d) | Lake dammed by glacier ice with no lateral moraines |\n| 7 | Glacier Erosion Lake | Cirque Erosion Lake | E(c) | A small pond occupying a cirque |\n| 8 | Glacier Erosion Lake | Glacier Trough Valley Erosion Lake | E(v) | Lakes formed in the glacier trough as a result of the glacier erosion process |\n| 9 | Glacier Erosion Lake | Other Glacial Erosion Lake | E(o) | Bodies of water occupying depressions formed by the glacial erosion process |\n| 10 | Other Glacial Lake | Other Glacial Lake | O | Lakes formed in a glaciated valley, and fed by glacial melt, but damming material not directly part of the glacial process |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 3: Glacial lake types and their identification keys", "columns": ["S. No.", "Lake Type", "Lake Subtype", "Code", "Identification Keys"], "table_row_start": 1, "table_row_end": 10, "line_start": 273, "line_end": 284, "token_count_estimate": 544, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "183178b8dd52916b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes"], "chunk_type": "text", "line_start": 285, "line_end": 291, "token_count_estimate": 45, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5eed36b969e39f79", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information\nType: text\n\nA total of 22 attributes has been given to all mapped lake features in the geodatabase, which are broadly consisting information grouped in six different categories as shows in Table 4.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 293, "line_end": 297, "token_count_estimate": 77, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee65d227c6627a5d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information\nType: table\nTable: Table 4: Details of glacial lake attributes\n\n| S. No. | Category | Attribute |\n| :--- | :--- | :--- |\n| 1 | Hydrological | Basin, subbasin, river, lake name |\n| 2 | Geometrical | Maximum length, mean width, surface area |\n| 3 | Geographical | Latitude, longitude, region, state, district, toposheet 250K, toposheet 50K |\n| 4 | Topographical | Elevation, aspect |\n| 5 | Lake Information | Feature type, glacial lake type, lake ID |\n| 6 | Data Source Information | Source of database, source of elevation, date of pass |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes", "4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 4: Details of glacial lake attributes", "columns": ["S. No.", "Category", "Attribute"], "table_row_start": 1, "table_row_end": 6, "line_start": 298, "line_end": 305, "token_count_estimate": 218, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90d499b55061011a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information\nType: text\n\nTypically, lake ID is given in 12 alpha-numeric character format like \"0142H0300080\", where first two digits '01' refers to Basin code which is Indus (02-Ganga and 03-Brahmaputra), next five characters '42H03' refers to the 1:250,000 (42H) and 1:50,000 (42H03) scale SOI Toposheet number, and the last five digits refers to the sequential number of each lake sorted from top left to bottom right. A typical example of the glacial lake database generated is given below in Table 5 along with fields and format.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 306, "line_end": 310, "token_count_estimate": 178, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142H0300080", "42H03"]}}
{"id": "6579812a50b3ac59", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information\nType: table\nTable: Table 5: Typical example of glacial lake attribute database\n\n| S. No. | Database Fields | Type | Format / Unit | Lake Attribute |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | ID No | String | Text | 0152H1103771 |\n| 2 | Toposheet 250K | String | Text | 52H |\n| 3 | Toposheet 50K | String | Text | 52H11 |\n| 4 | Latitude* | Float | Decimal Degree | 32.499 |\n| 5 | Longitude* | Float | Decimal Degree | 77.547 |\n| 6 | Basin | String | Text | Indus |\n| 7 | Sub Basin | String | Text | Chenab |\n| 8 | River | String | Text | Chandra River |\n| 9 | Type (GL/WB) | String | Text | Glacial Lake |\n| 10 | Name | String | Text | Samudra Tapu Lake |\n| 11 | Glacial Lake Type | String | Text | M(e): End-moraine Dammed Lake |\n| 12 | Surface Area | Float | ha | 128.69 |\n| 13 | Length | Float | Km | 2.381 |\n| 14 | Mean Width | Float | Km | 0.821 |\n| 15 | Elevation* | Integer | m (amsl) | 4150 |\n| 16 | Aspect | String | Text | SE |\n| 17 | Source of Database | String | Text | RS-2 LISS-IV |\n| 18 | Date of Pass | Date | DDMMYYYY | 05112016 |\n| 19 | Source of Elevation | String | Text | Cartosat DEM |\n| 20 | Region | String | Text | India |\n| 21 | State | String | Text | Himachal Pradesh |\n| 22 | District | String | Text | Lahul & Spiti |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes", "4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 5: Typical example of glacial lake attribute database", "columns": ["S. No.", "Database Fields", "Type", "Format / Unit", "Lake Attribute"], "table_row_start": 1, "table_row_end": 22, "line_start": 311, "line_end": 334, "token_count_estimate": 608, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": ["India"], "lake_ids": ["0152H1103771", "05112016", "52H11"]}}
{"id": "58d64b7fb4709903", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information\nType: text\n\n*Latitude, longitude, and elevation has been taken at the centroid of the lake\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lakes > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lakes", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 335, "line_end": 342, "token_count_estimate": 71, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7e00d261a844073c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS\nType: text\n\nThe mapped glacial lakes are analyzed for their distribution in terms of area, type, and elevation, at basin, subbasin, administrative and transboundary level. Area of mapped glacial lakes is ranging from a minimum of 0.25 ha to a maximum of 262.56 ha. Details of glacial lakes inventoried for the Indus River basin is given in Annexure-II. The results are discussed in subsequent sections:", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS", "section_headings": ["5. RESULTS"], "chunk_type": "text", "line_start": 344, "line_end": 346, "token_count_estimate": 120, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e266f265c6199495", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: text\n\n**Area range-wise Distribution**\n\nA total of 5,335 glacial lakes (greater than 0.25 ha) were identified and mapped using RS-2 LISS-IV images for the entire Indus River basin, with a total lake water spread area of 17,395.03 ha. Table 6 and Figure 7 shows the area range-wise distribution of glacial lakes for the entire basin. About 4,633 (86.84%) lakes are with < 5 ha lake area contributing to 34.04% of total lake area. The remaining lakes with > 5 ha in size are 702 (13.16%) contributing to 65.96% of total lake area in the basin. Details of lakes > 50 ha is given in Annexure-III.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "text", "line_start": 348, "line_end": 354, "token_count_estimate": 192, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b80eed1eb1df981", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: table\nTable: Table 6: Area range-wise distribution of Glacial Lakes (GL) in Indus River basin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1,378 | 496.93 | 2.86 |\n| 2 | 0.5 - 1 | 1,239 | 877.42 | 5.04 |\n| 3 | 1 - 5 | 2,016 | 4,546.28 | 26.14 |\n| 4 | 5 - 10 | 381 | 2,647.29 | 15.22 |\n| 5 | 10 - 50 | 287 | 5,302.84 | 30.48 |\n| 6 | > 50 | 34 | 3,524.27 | 20.26 |\n| | **Total** | **5,335** | **17,395.03** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 6: Area range-wise distribution of Glacial Lakes (GL) in Indus River basin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 355, "line_end": 363, "token_count_estimate": 278, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "70ad921967c1a5ee", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the entire Indus River basin is given in Table 7 and Figure 8. Out of 10 types of glacial lakes, Other Glacial Erosion lakes are found to be the maximum with 2,516 (47.16%) occupying a total lake extent of 8,247.44 ha at 47.42% in the basin. Two other types of lake, namely, Other Moraine Dammed and Supra-glacial lake are 905 (16.96%) and 809 (15.16%), extend over an area of 1,212.40 ha (6.97%) and 609.05 ha (3.50%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "text", "line_start": 364, "line_end": 376, "token_count_estimate": 192, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "41ca8cb0082e1f27", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: table\nTable: Table 7: Type-wise distribution of GL in Indus River basin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 407 | 2,267.29 | 13.03 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 225 | 317.59 | 1.83 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 45 | 111.92 | 0.64 |\n| 4 | M(o) | Other Moraine Dammed Lake | 905 | 1,212.40 | 6.97 |\n| 5 | I(s) | Supra-glacial Lake | 809 | 609.05 | 3.50 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 2 | 234.53 | 1.35 |\n| 7 | E(c) | Cirque Erosion Lake | 220 | 1,971.43 | 11.33 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 2 | 17.25 | 0.10 |\n| 9 | E(o) | Other Glacial Erosion Lake | 2,516 | 8,247.44 | 47.42 |\n| 10 | O | Other Glacial Lake | 204 | 2,406.12 | 13.83 |\n| | | **Total** | **5,335** | **17,395.03** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 7: Type-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 377, "line_end": 389, "token_count_estimate": 456, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24f292f78438b23d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 8 and Figure 9. The lakes with < 5 ha in size (86.84%) are dominant with Other Glacial Erosion lake type (46.1%) followed by Other Moraine Dammed (18.8%) and Supra-glacial lake (17.2%). The lakes with > 5 ha (13.16%) are also dominated by Other Glacial Erosion lakes (54.1%) with remaining 9 types together contributing to 45.9%. All types of Moraine-dammed glacial lakes, which constitute about 29.64% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "text", "line_start": 390, "line_end": 403, "token_count_estimate": 195, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e95c1cf34235d339", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: table\nTable: Table 8: Area range-wise vs. Type-wise distribution of GL in Indus River basin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 49 | 74 | 13 | 333 | 432 | 0 | 7 | 0 | 431 | 39 | 1,378 |\n| 2 | 0.5 - 1 | 44 | 69 | 5 | 261 | 263 | 0 | 14 | 0 | 528 | 55 | 1,239 |\n| 3 | 1 - 5 | 200 | 71 | 22 | 276 | 102 | 1 | 103 | 1 | 1,177 | 63 | 2,016 |\n| 4 | 5 - 10 | 60 | 6 | 3 | 27 | 10 | 0 | 47 | 0 | 215 | 13 | 381 |\n| 5 | 10 - 50 | 49 | 5 | 2 | 7 | 2 | 0 | 44 | 1 | 156 | 21 | 287 |\n| 6 | > 50 | 5 | 0 | 0 | 1 | 0 | 1 | 5 | 0 | 9 | 13 | 34 |\n| **Total** | | **407** | **225** | **45** | **905** | **809** | **2** | **220** | **2** | **2,516** | **204** | **5,335** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 8: Area range-wise vs. Type-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 404, "line_end": 412, "token_count_estimate": 496, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d448a2dc55cc8b1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 9 and Figure 10 shows the distribution of the glacial lakes in the Indus basin as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 4,657 (87.29% of the total lake count) with total lake area of 14,007.46 ha (80.53%) and remaining 12.71% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "text", "line_start": 413, "line_end": 427, "token_count_estimate": 210, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "27f5aa51a348ed54", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: table\nTable: Table 9: Elevation range-wise distribution of GL in Indus River basin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 21 | 47.70 | 0.27 |\n| 2 | 3,001 - 4,000 | 657 | 3,339.87 | 19.20 |\n| 3 | 4,001 - 5,000 | 2,797 | 8,301.35 | 47.73 |\n| 4 | > 5,000 | 1,860 | 5,706.11 | 32.80 |\n| **Total** | | **5,335** | **17,395.03** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 9: Elevation range-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 428, "line_end": 434, "token_count_estimate": 232, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "57c19de0076b89a4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.1 Indus Basin Level Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.1 Indus Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Indus Basin Level Statistics"], "chunk_type": "text", "line_start": 435, "line_end": 438, "token_count_estimate": 41, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "40f35342b9fe5faf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin\nType: text\n\nThe Beas subbasin of the Indus River basin is the second smallest subbasin amongst all, covering a total area of 13,963 Km² i.e. 4.07% of the total basin area (Figure 14). One of its major tributary is Parbati River which flows in the upstream area of the subbasin before it confluence with Beas River at Bhuntar of Kullu District of Himachal Pradesh. A total of 96 glacial lakes has been mapped, covering a total area of 143.83 ha i.e. 0.01% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Beas subbasin, glacial lakes has been distributed in 4 different classes of area ranges viz., 0.25 - 0.5 ha, 0.5 - 1 ha, 1 - 5 ha, and 5 - 10 ha. Table 12 and Figure 15 shows the area-wise distribution of glacial lakes for the Beas subbasin. About 92 (95.83%) lakes are with < 5 ha lake area contributing to 76.36% of total lake area. The remaining lakes with > 5 ha in size are only 4 (4.17%) contributing to 23.64% of total lake area in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Beas Subbasin"], "chunk_type": "text", "line_start": 442, "line_end": 453, "token_count_estimate": 330, "basins": ["INDUS", "Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "d1fbaa9ff318183f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin\nType: table\nTable: Table 12: Area range-wise distribution of GL in Beas subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 25 | 9.23 | 6.42 |\n| 2 | 0.5 - 1 | 24 | 16.27 | 11.31 |\n| 3 | 1 - 5 | 43 | 84.33 | 58.63 |\n| 4 | 5 - 10 | 4 | 34.00 | 23.64 |\n| 5 | 10 - 50 | 0 | 0.00 | 0.00 |\n| 6 | > 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **96** | **143.83** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Beas Subbasin"], "chunk_type": "table", "table_caption": "Table 12: Area range-wise distribution of GL in Beas subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 454, "line_end": 462, "token_count_estimate": 259, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "37474da455b4c933", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Beas subbasin is given in Table 13 and Figure 16. Out of 10 types of glacial lakes, only 6 types of lake are present in the Beas subbasin, where Other Glacial Erosion lakes are found to be the maximum with 41 (42.71%) occupying a total lake extent of 64.49 ha at 44.84% in the subbasin. Other Moraine Dammed lakes are second majority of lakes i.e. 39 (40.63%) and extend over an area of 37.58 ha (26.13%).", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Beas Subbasin"], "chunk_type": "text", "line_start": 463, "line_end": 475, "token_count_estimate": 191, "basins": ["INDUS", "Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "05d983af5b53ed7d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin\nType: table\nTable: Table 13: Type-wise distribution of GL in Beas subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 8 | 31.95 | 22.21 |\n| 2 | M(o) | Other Moraine Dammed Lake | 39 | 37.58 | 26.13 |\n| 3 | I(s) | Supra-glacial Lake | 5 | 6.49 | 4.51 |\n| 4 | E(c) | Cirque Erosion Lake | 1 | 2.52 | 1.75 |\n| 5 | E(o) | Other Glacial Erosion Lake | 41 | 64.49 | 44.84 |\n| 6 | O | Other Glacial Lake | 2 | 0.81 | 0.56 |\n| | | **Total** | **96** | **143.83** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Beas Subbasin"], "chunk_type": "table", "table_caption": "Table 13: Type-wise distribution of GL in Beas subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 476, "line_end": 484, "token_count_estimate": 313, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "55eaefc02811d732", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Beas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Beas Subbasin"], "chunk_type": "text", "line_start": 485, "line_end": 491, "token_count_estimate": 50, "basins": ["INDUS", "Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "07c4825ef56d139b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 14 and Figure 17. The lakes with < 5 ha in size (95.83%) are dominant equally with Other Glacial Erosion lake (42.40%) and Other Moraine Dammed lake type (42.40%). Lakes with > 5 ha (4.17%) are also dominated equally by Other Glacial Erosion (50.00%) and End-moraine Dammed lakes (50.00%). All types of Moraine-dammed lakes, which constitute about 48.95% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 493, "line_end": 497, "token_count_estimate": 171, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73429e4135ba8eca", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 14: Area range-wise vs. Type-wise distribution of GL in Beas subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 6 | 1 | 25 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 14 | 1 | 24 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 14 | 5 | 0 | 1 | 0 | 19 | 0 | 43 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 4 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **8** | **0** | **0** | **39** | **5** | **0** | **1** | **0** | **41** | **2** | **96** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 14: Area range-wise vs. Type-wise distribution of GL in Beas subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 498, "line_end": 506, "token_count_estimate": 486, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "079446455cc85556", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 17: Area range-wise vs. Type-wise distribution of GL in Beas subbasin\n\n**Figure 17: Area range-wise vs. Type-wise distribution of GL in Beas subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 17: Area range-wise vs. Type-wise distribution of GL in Beas subbasin", "line_start": 508, "line_end": 508, "token_count_estimate": 87, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "e8248c521fed22e7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 509, "line_end": 516, "token_count_estimate": 52, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f37442b08c1c92cd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Beas subbasin has been shown in Table 15 and Figure 18. Majority of glacial lakes are situated above 4,000 m elevation i.e. 93 (96.87% of the total lake count) with total lake area of 142.60 ha (99.15%) and remaining 3.13% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 518, "line_end": 522, "token_count_estimate": 126, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "aba13ca3e2f85968", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 15: Elevation range-wise distribution of GL in Beas subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 3 | 1.23 | 0.85 |\n| 3 | 4,001 - 5,000 | 91 | 141.36 | 98.28 |\n| 4 | > 5,000 | 2 | 1.25 | 0.87 |\n| | **Total** | **96** | **143.83** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 15: Elevation range-wise distribution of GL in Beas subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 523, "line_end": 529, "token_count_estimate": 227, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "ef7538d9d4323283", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 18: Elevation range-wise distribution of GL in Beas subbasin\n\n**Figure 18: Elevation range-wise distribution of GL in Beas subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 18: Elevation range-wise distribution of GL in Beas subbasin", "line_start": 531, "line_end": 531, "token_count_estimate": 78, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "909b0a0ff5d2dc77", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 532, "line_end": 537, "token_count_estimate": 50, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2973c60575c60a55", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 16 and Figure 19. It is noted that, 94.79% of glacial lakes (91) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 98.28%. However, very few glacial lakes (only 2) lies above 5,000 m, which are within 0.5 - 1 ha lake area range. Maximum of lakes lying in high altitude range is of size ranging 1 - 5 ha (i.e. 43), followed by lakes of size 0.25 - 0.5 ha (i.e. 23).", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 539, "line_end": 543, "token_count_estimate": 203, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d4c3cef8050722f2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 16: Area range-wise vs. Elevation range-wise distribution of GL in Beas subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 2 | 0.68 | 23 | 8.55 | 0 | 0.00 | 25 | 9.23 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 1 | 0.55 | 21 | 14.48 | 2 | 1.25 | 24 | 16.27 |\n| 3 | 1 - 5 | 0 | 0.00 | 0 | 0.00 | 43 | 84.33 | 0 | 0.00 | 43 | 84.33 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 4 | 34.00 | 0 | 0.00 | 4 | 34.00 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| Total | | 0 | 0.00 | 3 | 1.23 | 91 | 141.36 | 2 | 1.25 | 96 | 143.83 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 16: Area range-wise vs. Elevation range-wise distribution of GL in Beas subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 544, "line_end": 552, "token_count_estimate": 595, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "b9d4c2dd9e618bd1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 19: Area range-wise vs. Elevation range-wise distribution of GL in Beas subbasin\n\n**Figure 19: Area range-wise vs. Elevation range-wise distribution of GL in Beas subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 19: Area range-wise vs. Elevation range-wise distribution of GL in Beas subbasin", "line_start": 554, "line_end": 554, "token_count_estimate": 93, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "ea4e05447136e999", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 555, "line_end": 562, "token_count_estimate": 53, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee61ddd214c359d4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 17 and Figure 20. The dominant lake types in the basin i.e., Other Glacial Erosion lakes (42.40% in number and 44.84% in extent) are predominantly located in the elevation range of 4,001 - 5,000 m (40.66%). The other dominant lake type, namely, Other Moraine Dammed lakes are distributed completely in 4,001 - 5,000 m elevation range which constitutes 48.95% of the total lakes. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 21.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 564, "line_end": 568, "token_count_estimate": 192, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee20b4375c73701f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 17: Type-wise vs. Elevation range-wise distribution of GL in Beas subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 |\n| 3 | 4,001 - 5,000 | 8 | 0 | 0 | 39 | 5 | 0 | 1 | 0 | 37 | 1 | 91 |\n| 4 | > 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| Total | | 8 | 0 | 0 | 39 | 5 | 0 | 1 | 0 | 41 | 2 | 96 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 17: Type-wise vs. Elevation range-wise distribution of GL in Beas subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 569, "line_end": 575, "token_count_estimate": 466, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "7f3e2e2524979e25", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 20: Type-wise vs. Elevation range-wise distribution of GL in Beas subbasin\n\n**Figure 20: Type-wise vs. Elevation range-wise distribution of GL in Beas subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 20: Type-wise vs. Elevation range-wise distribution of GL in Beas subbasin", "line_start": 577, "line_end": 577, "token_count_estimate": 91, "basins": ["Indus"], "subbasins": ["Beas"], "countries": [], "lake_ids": []}}
{"id": "f4763fc14e75a42e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 578, "line_end": 588, "token_count_estimate": 67, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "278ef03d6a1354b2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Chenab Subbasin\nType: text\n\nThe Chenab subbasin is the fifth largest subbasin of the Indus basin covering a total area of 29,257 Km² i.e. 8.53% of the total basin area (Figure 22). Chandra and Bhaga Rivers are the two main tributaries of Chenab subbasin, and with the confluence of both rivers at Tandi in Lahul & Spiti District of Himachal Pradesh (place known as Chandra-Bhaga Sangam), from where the Chenab River starts. Later the Chenab subbasin also have two other tributaries viz., Bhut and Warwan (Marau), both joins the Chenab River from the right, at Paddar and Bandarkoot respectively, both in Kishtwar district of Jammu and Kashmir. A total of 397 glacial lakes has been mapped, covering a total area of 896.11 ha i.e. 0.03% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Chenab Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.2 Chenab Subbasin"], "chunk_type": "text", "line_start": 590, "line_end": 597, "token_count_estimate": 266, "basins": ["INDUS", "Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "79e5478192a5c2c9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nIn Chenab subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 18 and Figure 23 shows the area range-wise distribution of glacial lakes for the Chenab subbasin. About 367 (92.44%) lakes are with < 5 ha lake area contributing to 40.48% of total lake area. The remaining lakes with > 5 ha in size are only 30 (7.56%) contributing to 59.52% of total lake area in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 599, "line_end": 603, "token_count_estimate": 146, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "f0d395d8a135ad72", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 18: Area range-wise distribution of GL in Chenab subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 152 | 54.27 | 6.06 |\n| 2 | 0.5 - 1 | 99 | 68.51 | 7.65 |\n| 3 | 1 - 5 | 116 | 239.99 | 26.78 |\n| 4 | 5 - 10 | 21 | 147.32 | 16.44 |\n| 5 | 10 - 50 | 7 | 179.72 | 20.06 |\n| 6 | > 50 | 2 | 206.28 | 23.02 |\n| | **Total** | **397** | **896.11** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 18: Area range-wise distribution of GL in Chenab subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 604, "line_end": 612, "token_count_estimate": 262, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "20791a690b808537", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 613, "line_end": 621, "token_count_estimate": 50, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0e9ab28c2962bd4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Chenab subbasin is given in Table 19 and Figure 24. Out of 10 types of glacial lakes, only 7 types of lake are present in the Chenab subbasin, where Other Glacial Erosion lakes are found to be the maximum with 128 (32.24%) occupying a total lake area extent of 211.03 ha at 23.55% in the subbasin. Two other types of lake, namely, Other Moraine Dammed and Supra-glacial lakes are 97 (24.43%) and 85 (21.41%) and extend over lake area of 86.83 ha (9.69%) and 50.45 ha (5.63%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 623, "line_end": 627, "token_count_estimate": 187, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "32ba88b440ad3581", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 19: Type-wise distribution of GL in Chenab subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 32 | 349.36 | 38.99 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 26 | 22.76 | 2.54 |\n| 3 | M(o) | Other Moraine Dammed Lake | 97 | 86.83 | 9.69 |\n| 4 | I(s) | Supra-glacial Lake | 85 | 50.45 | 5.63 |\n| 5 | E(c) | Cirque Erosion Lake | 24 | 152.29 | 16.99 |\n| 6 | E(o) | Other Glacial Erosion Lake | 128 | 211.03 | 23.55 |\n| 7 | O | Other Glacial Lake | 5 | 23.39 | 2.61 |\n| | | **Total** | **397** | **896.11** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 19: Type-wise distribution of GL in Chenab subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 8, "line_start": 628, "line_end": 637, "token_count_estimate": 346, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "e4acfa96c2e20b03", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 20 and Figure 25. The lakes with < 5 ha in size (92.44%) are dominant with Other Glacial Erosion (33.52%) and Other Moraine Dammed lake (26.16%). Lakes with > 5 ha (7.56%) are dominated by Cirque Erosion (40.00%) and End-moraine Dammed lakes (33.33%). All types of Glacier Erosion lakes, which constitutes about 56.67% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 638, "line_end": 648, "token_count_estimate": 182, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "766abfaba9c969ff", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 20: Area range-wise vs. Type-wise distribution of GL in Chenab subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 5 | 8 | 0 | 37 | 57 | 0 | 0 | 0 | 45 | 0 | 152 |\n| 2 | 0.5 - 1 | 4 | 11 | 0 | 29 | 23 | 0 | 2 | 0 | 29 | 1 | 99 |\n| 3 | 1 - 5 | 13 | 7 | 0 | 30 | 4 | 0 | 10 | 0 | 49 | 3 | 116 |\n| 4 | 5 - 10 | 6 | 0 | 0 | 1 | 1 | 0 | 9 | 0 | 4 | 0 | 21 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 1 | 7 |\n| 6 | > 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| | **Total** | **32** | **26** | **0** | **97** | **85** | **0** | **24** | **0** | **128** | **5** | **397** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 20: Area range-wise vs. Type-wise distribution of GL in Chenab subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 649, "line_end": 657, "token_count_estimate": 483, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "336b0b908bfcc3b1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 25: Area range-wise vs. Type-wise distribution of GL in Chenab subbasin\n\n**Figure 25: Area range-wise vs. Type-wise distribution of GL in Chenab subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 25: Area range-wise vs. Type-wise distribution of GL in Chenab subbasin", "line_start": 659, "line_end": 659, "token_count_estimate": 84, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "955c046d38b1d3dc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Chenab subbasin has been shown in Table 21 and Figure 26. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 311 (78.34% of the total lake count) with total lake area of 738.93 ha (82.46%) and remaining 17.54% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 660, "line_end": 673, "token_count_estimate": 154, "basins": ["INDUS", "Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "28bc9dd57a51f6dd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 21: Elevation range-wise distribution of GL in Chenab subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 86 | 157.17 | 17.54 |\n| 3 | 4,001 - 5,000 | 243 | 675.70 | 75.40 |\n| 4 | > 5,000 | 68 | 63.24 | 7.06 |\n| | **Total** | **397** | **896.11** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 21: Elevation range-wise distribution of GL in Chenab subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 674, "line_end": 680, "token_count_estimate": 227, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "a2a1b3913ee3dd46", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 26: Elevation range-wise distribution of GL in Chenab subbasin\n\n**Figure 26: Elevation range-wise distribution of GL in Chenab subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 26: Elevation range-wise distribution of GL in Chenab subbasin", "line_start": 682, "line_end": 682, "token_count_estimate": 76, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "38a504ea28d720c8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 22 and Figure 27. It is noted that, 61.21% of glacial lakes (243) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 75.40%. However, 68 glacial lakes lies above 5,000 m, has majority of its lakes are < 5 ha i.e. 97.06%. Maximum lakes lying in high altitude range is of size ranging 0.25 - 0.5 ha (i.e. 87), followed by lakes of size 1 - 5 ha (i.e. 72). It has been further noticed that, 63.33% of lakes > 5 ha are lying within in high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 683, "line_end": 695, "token_count_estimate": 277, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64b06d6bea5e7abf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Chenab subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation up to 3,000 (No. of Lakes) | Elevation up to 3,000 (Total Lake Area (ha)) | Elevation 3,001 - 4,000 (No. of Lakes) | Elevation 3,001 - 4,000 (Total Lake Area (ha)) | Elevation 4,001 - 5,000 (No. of Lakes) | Elevation 4,001 - 5,000 (Total Lake Area (ha)) | Elevation > 5,000 (No. of Lakes) | Elevation > 5,000 (Total Lake Area (ha)) | Total (No. of Lakes) | Total (Lake Area (ha)) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 35 | 12.90 | 87 | 30.91 | 30 | 10.46 | 152 | 54.27 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 15 | 9.90 | 65 | 44.91 | 19 | 13.71 | 99 | 68.51 |\n| 3 | 1 - 5 | 0 | 0.00 | 27 | 60.01 | 72 | 152.04 | 17 | 27.94 | 116 | 239.99 |\n| 4 | 5 - 10 | 0 | 0.00 | 7 | 48.63 | 12 | 87.57 | 2 | 11.13 | 21 | 147.32 |\n| 5 | 10 - 50 | 0 | 0.00 | 2 | 25.73 | 5 | 153.99 | 0 | 0.00 | 7 | 179.72 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 2 | 206.28 | 0 | 0.00 | 2 | 206.28 |\n| | **Total** | **0** | **0.00** | **86** | **157.17** | **243** | **675.70** | **68** | **63.24** | **397** | **896.11** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Chenab subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation up to 3,000 (No. of Lakes)", "Elevation up to 3,000 (Total Lake Area (ha))", "Elevation 3,001 - 4,000 (No. of Lakes)", "Elevation 3,001 - 4,000 (Total Lake Area (ha))", "Elevation 4,001 - 5,000 (No. of Lakes)", "Elevation 4,001 - 5,000 (Total Lake Area (ha))", "Elevation > 5,000 (No. of Lakes)", "Elevation > 5,000 (Total Lake Area (ha))", "Total (No. of Lakes)", "Total (Lake Area (ha))"], "table_row_start": 1, "table_row_end": 7, "line_start": 696, "line_end": 704, "token_count_estimate": 603, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "dcbeecaccf213c33", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Chenab subbasin\n\n**Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Chenab subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Chenab subbasin", "line_start": 706, "line_end": 706, "token_count_estimate": 88, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "ba160f483f09e35f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 23 and Figure 28. The dominant lake types in the subbasin i.e., Other Glacial Erosion lakes (32.24%) are predominantly located in the elevation range of 4,001 - 5,000 m (29.63%). The other dominant lake type, namely, Other Moraine Dammed and Supra-glacial lakes are also distributed predominantly in 4,001 - 5,000 m elevation range i.e. 23.87% and 22.63%. 86.45% of all types of Moraine-dammed lakes, lie above 4,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 29.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 707, "line_end": 718, "token_count_estimate": 238, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a0c0e7139d01a87", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 23: Type-wise vs. Elevation range-wise distribution of GL in Chenab subbasin\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 6 | 3 | 0 | 12 | 23 | 0 | 10 | 0 | 31 | 1 | 86 |\n| 3 | 4,001 - 5,000 | 24 | 16 | 0 | 58 | 55 | 0 | 14 | 0 | 72 | 4 | 243 |\n| 4 | > 5,000 | 2 | 7 | 0 | 27 | 7 | 0 | 0 | 0 | 25 | 0 | 68 |\n| | **Total** | **32** | **26** | **0** | **97** | **85** | **0** | **24** | **0** | **128** | **5** | **397** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 23: Type-wise vs. Elevation range-wise distribution of GL in Chenab subbasin", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 719, "line_end": 725, "token_count_estimate": 405, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "d13ef399505cb2d6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Chenab subbasin\n\n**Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Chenab subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Chenab subbasin", "line_start": 727, "line_end": 727, "token_count_estimate": 86, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": []}}
{"id": "11a2d772333b7750", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 728, "line_end": 737, "token_count_estimate": 63, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26a8a2900cff1ac3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin\nType: text\n\nThe Gilgit subbasin is the fourth smallest subbasin of the Indus River basin covering a total area of 27,396 Km² i.e. 8.0% of the total basin area (Figure 30). Hunza River is a major tributary of Gilgit subbasin which confluences with the main river from the left at Gilgit in Leh District of Ladakh UT. Gilgit subbasin comprised of Gilgit and Hunza catchment, both share equal area coverage. A total of 1,011 glacial lakes has been mapped, covering a total area of 3,380.27 ha i.e. 0.12% of the total area of the subbasin.\n\n**Area range-wise Distribution**\n\nIn Gilgit subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 24 and Figure 31 shows the area range-wise distribution of glacial lakes for the Gilgit subbasin. About 887 (87.73%) lakes are with < 5 ha lake area contributing to 33.82% of total lake area. The remaining lakes with > 5 ha in size are only 124 (12.27%) contributing to 66.18% of total lake area in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.3 Gilgit Subbasin"], "chunk_type": "text", "line_start": 739, "line_end": 750, "token_count_estimate": 308, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "c302e5bae277c799", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin\nType: table\nTable: Table 24: Area range-wise distribution of GL in Gilgit subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 285 | 100.20 | 2.96 |\n| 2 | 0.5 - 1 | 218 | 152.33 | 4.51 |\n| 3 | 1 - 5 | 384 | 890.84 | 26.35 |\n| 4 | 5 - 10 | 66 | 451.17 | 13.35 |\n| 5 | 10 - 50 | 51 | 914.27 | 27.05 |\n| 6 | > 50 | 7 | 871.46 | 25.78 |\n| | **Total** | **1,011** | **3,380.27** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.3 Gilgit Subbasin"], "chunk_type": "table", "table_caption": "Table 24: Area range-wise distribution of GL in Gilgit subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 751, "line_end": 759, "token_count_estimate": 280, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "cde3a5c81b75a8e0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin\nType: text\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Gilgit subbasin is given in Table 25 and Figure 32. Out of 10 types of glacial lakes, 9 types of lake are present in the Gilgit subbasin, where Other Glacial Erosion lakes are found to be the maximum with 566 (55.98%) occupying a total lake extent of 1,862.53 ha at 55.10% in the subbasin. After that, Supra-glacial and Other Moraine Dammed lakes are in majority with 200 (19.78%) and 99 (9.79%) and extend over a total area of 168.99 ha (5.00%) and 87.59 ha (2.59%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.3 Gilgit Subbasin"], "chunk_type": "text", "line_start": 760, "line_end": 766, "token_count_estimate": 193, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "25cdf20434a33b38", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin\nType: table\nTable: Table 25: Type-wise distribution of GL in Gilgit subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 35 | 72.79 | 2.15 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 29 | 28.93 | 0.86 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 4 | 1.52 | 0.04 |\n| 4 | M(o) | Other Moraine Dammed Lake | 99 | 87.49 | 2.59 |\n| 5 | I(s) | Supra-glacial Lake | 200 | 168.99 | 5.00 |\n| 6 | E(c) | Cirque Erosion Lake | 42 | 309.87 | 9.17 |\n| 7 | E(v) | Glacier Trough Valley Erosion Lake | 2 | 17.25 | 0.51 |\n| 8 | E(o) | Other Glacial Erosion Lake | 566 | 1,862.53 | 55.10 |\n| 9 | O | Other Glacial Lake | 34 | 830.91 | 24.58 |\n| | | **Total** | **1,011** | **3,380.27** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.3 Gilgit Subbasin"], "chunk_type": "table", "table_caption": "Table 25: Type-wise distribution of GL in Gilgit subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 767, "line_end": 778, "token_count_estimate": 432, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "dacad60653e33cdf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Gilgit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.3 Gilgit Subbasin"], "chunk_type": "text", "line_start": 779, "line_end": 786, "token_count_estimate": 50, "basins": ["INDUS", "Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "bfda610c65807168", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 26 and Figure 33. The lakes with < 5 ha in size (87.73%) are dominant with Other Glacial Erosion (54.45%) and Supra-glacial lakes (22.09%). Lakes with > 5 ha (12.27%) are also dominated by Other Glacial Erosion lakes (66.93%). All types of Glacier Erosion lakes, which constitute about 60.33% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range Type-wise Distribution"], "chunk_type": "text", "line_start": 788, "line_end": 792, "token_count_estimate": 155, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb4578dc7f78c8b8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution\nType: table\nTable: Table 26: Area range-wise vs. Type-wise distribution of GL in Gilgit subbasin\n\n| S. No. | Lake Area Range (ha) | Types of Glacial Lake M(e) | Types of Glacial Lake M(l) | Types of Glacial Lake M(lg) | Types of Glacial Lake M(o) | Types of Glacial Lake I(s) | Types of Glacial Lake I(d) | Types of Glacial Lake E(c) | Types of Glacial Lake E(v) | Types of Glacial Lake E(o) | Types of Glacial Lake O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 13 | 9 | 3 | 50 | 121 | 0 | 3 | 0 | 83 | 3 | 285 |\n| 2 | 0.5 - 1 | 4 | 11 | 1 | 26 | 52 | 0 | 3 | 0 | 114 | 7 | 218 |\n| 3 | 1 - 5 | 15 | 9 | 0 | 21 | 23 | 0 | 18 | 1 | 286 | 11 | 384 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 2 | 2 | 0 | 11 | 0 | 45 | 5 | 66 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 2 | 0 | 6 | 1 | 36 | 4 | 51 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 4 | 7 |\n| | **Total** | **35** | **29** | **4** | **99** | **200** | **0** | **42** | **2** | **566** | **34** | **1,011** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 26: Area range-wise vs. Type-wise distribution of GL in Gilgit subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 793, "line_end": 801, "token_count_estimate": 560, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "7297b612bbeefd1a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range Type-wise Distribution"], "chunk_type": "text", "line_start": 802, "line_end": 810, "token_count_estimate": 52, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b500ca1fb0a8d366", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Gilgit subbasin has been shown in Table 27 and Figure 34. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 825 (81.60% of the total lake count) with total lake area of 2,685.58 ha (79.45%) and remaining 18.40% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 812, "line_end": 816, "token_count_estimate": 131, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "6bb7a5ea6c21d207", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 27: Elevation range-wise distribution of GL in Gilgit subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 13 | 24.14 | 0.71 |\n| 2 | 3,001 - 4,000 | 173 | 670.55 | 19.84 |\n| 3 | 4,001 - 5,000 | 819 | 2,680.43 | 79.30 |\n| 4 | > 5,000 | 6 | 5.14 | 0.15 |\n| | **Total** | **1,011** | **3,380.27** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 27: Elevation range-wise distribution of GL in Gilgit subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 817, "line_end": 823, "token_count_estimate": 234, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "51f583c8a9e213fa", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 28 and Figure 35. It is noted that, 81.01% of glacial lakes (819) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 79.29%. However, only 6 glacial lakes of size < 5 ha lies above 5,000 m. 86.56% of lakes lying in high altitude range are < 5 ha, predominantly of size ranging 1 - 5 ha (i.e. 344), followed by lakes of size 0.25 - 0.5 ha (i.e. 188). It has been further noticed that, 88.71% of lakes > 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 5 - 10 ha.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 824, "line_end": 834, "token_count_estimate": 276, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3a761d6dec78674", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin\n\n| S. No. | Lake Area Range (ha) | up to 3,000 (No. of lakes) | up to 3,000 (Total Lake Area (ha)) | 3,001 - 4,000 (No. of lakes) | 3,001 - 4,000 (Total Lake Area (ha)) | 4,001 - 5,000 (No. of lakes) | 4,001 - 5,000 (Total Lake Area (ha)) | > 5,000 (No. of lakes) | > 5,000 (Total Lake Area (ha)) | Total (No. of lakes) | Total (Lake Area (ha)) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 8 | 2.66 | 85 | 29.24 | 188 | 66.66 | 4 | 1.64 | 285 | 100.20 |\n| 2 | 0.5 - 1 | 2 | 1.02 | 39 | 26.19 | 177 | 125.11 | 0 | 0.00 | 218 | 152.33 |\n| 3 | 1 - 5 | 2 | 6.39 | 36 | 73.16 | 344 | 807.79 | 2 | 3.50 | 384 | 890.84 |\n| 4 | 5 - 10 | 0 | 0.00 | 4 | 27.03 | 62 | 424.14 | 0 | 0.00 | 66 | 451.17 |\n| 5 | 10 - 50 | 1 | 14.06 | 6 | 111.94 | 44 | 788.27 | 0 | 0.00 | 51 | 914.27 |\n| 6 | > 50 | 0 | 0.00 | 3 | 403.00 | 4 | 468.45 | 0 | 0.00 | 7 | 871.46 |\n| | **Total** | **13** | **24.14** | **173** | **670.55** | **819** | **2,680.43** | **6** | **5.14** | **1,011** | **3,380.27** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "up to 3,000 (No. of lakes)", "up to 3,000 (Total Lake Area (ha))", "3,001 - 4,000 (No. of lakes)", "3,001 - 4,000 (Total Lake Area (ha))", "4,001 - 5,000 (No. of lakes)", "4,001 - 5,000 (Total Lake Area (ha))", "> 5,000 (No. of lakes)", "> 5,000 (Total Lake Area (ha))", "Total (No. of lakes)", "Total (Lake Area (ha))"], "table_row_start": 1, "table_row_end": 7, "line_start": 835, "line_end": 843, "token_count_estimate": 601, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "b020d8a744c72e37", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 35: Area range-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin\n\n**Figure 35: Area range-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 35: Area range-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin", "line_start": 845, "line_end": 845, "token_count_estimate": 90, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "84098382bc232d54", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 29 and Figure 36. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (552 in number and 55.10% in extent) are predominantly located in the elevation range of 4,001 - 5,000 m (97.52%). The other dominant lake type, namely, Supra-glacial and Other Moraine Dammed lakes are distributed predominantly in 3,001 - 4,000 m and 4,001 - 5,000 m elevation range respectively i.e. 67.05% and 10.98%. Majority i.e. 82.04% of all types of Moraine-dammed lakes, lie above 4,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 37.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 846, "line_end": 859, "token_count_estimate": 261, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c87dc48d9ecc20be", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 29: Type-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 2 | 0 | 0 | 1 | 10 | 0 | 0 | 0 | 0 | 0 | 13 |\n| 2 | 3,001 - 4,000 | 8 | 13 | 0 | 6 | 116 | 0 | 0 | 1 | 14 | 15 | 173 |\n| 3 | 4,001 - 5,000 | 24 | 15 | 4 | 90 | 72 | 0 | 42 | 1 | 552 | 19 | 819 |\n| 4 | > 5,000 | 1 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 6 |\n| | **Total** | **35** | **29** | **4** | **99** | **200** | **0** | **42** | **2** | **566** | **34** | **1,011** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 29: Type-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 860, "line_end": 866, "token_count_estimate": 490, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "b7a08ffe3649176d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 36: Type-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin\n\n**Figure 36: Type-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 36: Type-wise vs. Elevation range-wise distribution of GL in Gilgit subbasin", "line_start": 868, "line_end": 868, "token_count_estimate": 88, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "8c08ff196d7e60b5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 869, "line_end": 878, "token_count_estimate": 64, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc3b4a36f8cbf924", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Indus Middle Subbasin\nType: text\n\nThe Indus Middle subbasin is the third smallest subbasin of the Indus River basin covering a total area of 23,703 Km² i.e. 6.91% of the total basin area (Figure 38). Shigar and Astor are two major tributaries of Indus Middle subbasin which confluences with the main river from the right and left at Skardu and Thelichi Gah respectively, both in Leh District of Ladakh. Indus Middle subbasin has a place known as Indus-Gilgit confluence point where Gilgit River merges the main Indus River from the right, just above Jaglot in Leh District of Ladakh. A total of 704 glacial lakes has been mapped, covering a total area of 1,833.08 ha i.e. 0.07% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Indus Middle Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.4 Indus Middle Subbasin"], "chunk_type": "text", "line_start": 880, "line_end": 887, "token_count_estimate": 244, "basins": ["INDUS", "Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "0e52a33bc880cd64", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nIn Indus Middle subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 30 and Figure 39 shows the area range-wise distribution of glacial lakes for the Indus Middle subbasin. About 615 (87.36%) lakes are with < 5 ha lake area contributing to 45.10% of total lake area. The remaining lakes with > 5 ha in size are only 89 (12.64%) contributing to 54.90% of total lake area in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 889, "line_end": 893, "token_count_estimate": 148, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "8c350b610d8790a2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 30: Area range-wise distribution of GL in Indus Middle subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 180 | 66.02 | 3.60 |\n| 2 | 0.5 - 1 | 160 | 113.58 | 6.20 |\n| 3 | 1 - 5 | 275 | 647.17 | 35.31 |\n| 4 | 5 - 10 | 50 | 338.18 | 18.45 |\n| 5 | 10 - 50 | 38 | 617.68 | 33.70 |\n| 6 | > 50 | 1 | 50.44 | 2.75 |\n| | **Total** | **704** | **1,833.08** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 30: Area range-wise distribution of GL in Indus Middle subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 894, "line_end": 902, "token_count_estimate": 269, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "5d59341e52199243", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 903, "line_end": 907, "token_count_estimate": 50, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a113fa4ef5457422", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Indus Middle subbasin is given in Table 31 and Figure 40. Out of 10 types of glacial lakes, 8 types of lake are present in the Indus Middle subbasin, where Other Glacial Erosion lakes are found to be the maximum with 372 (52.84%) occupying a total lake extent of 1,087.33 ha at 59.32% in the subbasin. After that, Supra-glacial and Lateral Moraine Dammed lakes are in majority with 170 (24.15%) and 76 (10.80%) and extend over a total lake area of 144.65 ha (7.89%) and 154.19 ha (8.41%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 909, "line_end": 913, "token_count_estimate": 191, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "45b084369207e53c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 31: Type-wise distribution of GL in Indus Middle subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 8 | 44.91 | 2.45 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 76 | 154.19 | 8.41 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 7 | 4.21 | 0.23 |\n| 4 | M(o) | Other Moraine Dammed Lake | 26 | 47.24 | 2.58 |\n| 5 | I(s) | Supra-glacial Lake | 170 | 144.65 | 7.89 |\n| 6 | E(c) | Cirque Erosion Lake | 40 | 331.02 | 18.06 |\n| 7 | E(o) | Other Glacial Erosion Lake | 372 | 1,087.33 | 59.32 |\n| 8 | O | Other Glacial Lake | 5 | 19.53 | 1.07 |\n| | | **Total** | **704** | **1,833.08** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 31: Type-wise distribution of GL in Indus Middle subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 9, "line_start": 914, "line_end": 924, "token_count_estimate": 383, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "07f3371518c02d78", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area-range Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 32 and Figure 41. The lakes with < 5 ha in size (87.36%) are dominant with Other Glacial Erosion (52.36%) and Supra-glacial lakes (26.67%). Lakes with > 5 ha (12.64%) are also dominated by Other Glacial Erosion lakes (56.18%). All types of Glacier Erosion lakes, which constitute about 58.52% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 925, "line_end": 934, "token_count_estimate": 177, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9df05d4e06634a89", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 32: Area range-wise vs. Type-wise distribution of GL in Indus Middle subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 18 | 5 | 3 | 84 | 0 | 0 | 0 | 66 | 2 | 180 |\n| 2 | 0.5 - 1 | 1 | 23 | 1 | 6 | 55 | 0 | 3 | 0 | 70 | 1 | 160 |\n| 3 | 1 - 5 | 1 | 26 | 1 | 16 | 25 | 0 | 19 | 0 | 186 | 1 | 275 |\n| 4 | 5 - 10 | 3 | 5 | 0 | 1 | 6 | 0 | 10 | 0 | 25 | 0 | 50 |\n| 5 | 10 - 50 | 1 | 4 | 0 | 0 | 0 | 0 | 7 | 0 | 25 | 1 | 38 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n| | **Total** | **8** | **76** | **7** | **26** | **170** | **0** | **40** | **0** | **372** | **5** | **704** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 32: Area range-wise vs. Type-wise distribution of GL in Indus Middle subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 935, "line_end": 943, "token_count_estimate": 485, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "a1250ac55bedbaf0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Indus Middle subbasin has been shown in Table 33 and Figure 42. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 570 (80.96% of the total lake count) with total lake area of 1,365.95 ha (74.51%) and remaining 19.04% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 944, "line_end": 956, "token_count_estimate": 154, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "e7700dddf8a08e17", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 33: Elevation range-wise distribution of GL in Indus Middle subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 8 | 23.57 | 1.29 |\n| 2 | 3,001 - 4,000 | 126 | 443.56 | 24.20 |\n| 3 | 4,001 - 5,000 | 558 | 1,359.49 | 74.16 |\n| 4 | > 5,000 | 12 | 6.47 | 0.35 |\n| | **Total** | **704** | **1,833.08** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 33: Elevation range-wise distribution of GL in Indus Middle subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 957, "line_end": 963, "token_count_estimate": 230, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "7d5a10733b91e0f9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 964, "line_end": 970, "token_count_estimate": 48, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d483712611399ab", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 34 and Figure 43. It is noted that, 79.26% of glacial lakes (558) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 74.16%. However, only 12 glacial lakes lies above 5,000 m, which are < 5 ha in size. 87.63% of lakes lying in high altitude range are < 5 ha, predominantly of size ranging 1 - 5 ha (i.e. 234), followed by lakes of size 0.25 - 0.5 ha (i.e. 132). It has been further noticed that, 77.53% of lakes > 5 ha are lying within in high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 5 - 10 ha.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 972, "line_end": 976, "token_count_estimate": 255, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6db1fa84778c643", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 34: Area range-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin\n\n| S. No. | Lake Area Range (ha) | up to 3,000 m (No. of lakes) | up to 3,000 m (Total Lake Area (ha)) | 3,001 - 4,000 m (No. of lakes) | 3,001 - 4,000 m (Total Lake Area (ha)) | 4,001 - 5,000 m (No. of lakes) | 4,001 - 5,000 m (Total Lake Area (ha)) | > 5,000 m (No. of lakes) | > 5,000 m (Total Lake Area (ha)) | Total (No. of lakes) | Total (Lake Area (ha)) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 0.82 | 39 | 14.42 | 132 | 48.30 | 7 | 2.48 | 180 | 66.02 |\n| 2 | 0.5 - 1 | 2 | 1.60 | 31 | 21.53 | 123 | 87.56 | 4 | 2.89 | 160 | 113.58 |\n| 3 | 1 - 5 | 3 | 5.79 | 37 | 76.55 | 234 | 563.74 | 1 | 1.09 | 275 | 647.17 |\n| 4 | 5 - 10 | 0 | 0.00 | 5 | 35.57 | 45 | 302.61 | 0 | 0.00 | 50 | 338.18 |\n| 5 | 10 - 50 | 1 | 15.36 | 13 | 245.05 | 24 | 357.28 | 0 | 0.00 | 38 | 617.68 |\n| 6 | > 50 | 0 | 0.00 | 1 | 50.44 | 0 | 0.00 | 0 | 0.00 | 1 | 50.44 |\n| **Total** | | **8** | **23.57** | **126** | **443.56** | **558** | **1,359.49** | **12** | **6.47** | **704** | **1,833.08** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 34: Area range-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "up to 3,000 m (No. of lakes)", "up to 3,000 m (Total Lake Area (ha))", "3,001 - 4,000 m (No. of lakes)", "3,001 - 4,000 m (Total Lake Area (ha))", "4,001 - 5,000 m (No. of lakes)", "4,001 - 5,000 m (Total Lake Area (ha))", "> 5,000 m (No. of lakes)", "> 5,000 m (Total Lake Area (ha))", "Total (No. of lakes)", "Total (Lake Area (ha))"], "table_row_start": 1, "table_row_end": 7, "line_start": 977, "line_end": 985, "token_count_estimate": 613, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "27b1c0eb44147876", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 43: Area range-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin\n\n**Figure 43: Area range-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 43: Area range-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin", "line_start": 987, "line_end": 987, "token_count_estimate": 95, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "d46cb2835aec21a7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 988, "line_end": 995, "token_count_estimate": 54, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "442d51ae1f2441c1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 35 and Figure 44. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (52.84%) are predominantly located in the elevation range of 4,001 - 5,000 m (60.22%). The other dominant lake type, namely, Supra-glacial and Lateral Moraine Dammed lakes are also distributed predominantly in 4,001 - 5,000 m elevation range i.e. 18.46% and 10.39%. Majority i.e. 80.34% of all types of Moraine-dammed lakes, lie above 4,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 45.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 997, "line_end": 1001, "token_count_estimate": 225, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc25f1fcb208e870", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 35: Type-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 1 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 8 |\n| 2 | 3,001 - 4,000 | 2 | 14 | 0 | 1 | 63 | 0 | 8 | 0 | 35 | 3 | 126 |\n| 3 | 4,001 - 5,000 | 6 | 58 | 2 | 20 | 103 | 0 | 32 | 0 | 336 | 1 | 558 |\n| 4 | > 5,000 | 0 | 3 | 5 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 12 |\n| **Total** | | **8** | **76** | **7** | **26** | **170** | **0** | **40** | **0** | **372** | **5** | **704** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 35: Type-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1002, "line_end": 1008, "token_count_estimate": 414, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "296c191c6cd300b1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 44: Type-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin\n\n**Figure 44: Type-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 44: Type-wise vs. Elevation range-wise distribution of GL in Indus Middle subbasin", "line_start": 1010, "line_end": 1010, "token_count_estimate": 93, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": []}}
{"id": "fc329d4370aad0ba", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1011, "line_end": 1022, "token_count_estimate": 68, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e8faeae6650f5680", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: text\n\nThe Indus Upper subbasin is the second largest subbasin of the Indus River basin covering a total area of 72,695 Km² i.e. 21.20% of the total basin area (Figure 46). Indus Upper subbasin has many tributaries includes Sengee Tsangpo which flows in its upstream and joins the main river from the right at Gar county in Tibet, China, and Zaskar and Shingo Rivers joins the Indus River from the left at a point before Nimmoo and a point before Marol respectively, both in Leh District of Ladakh. Shingo River also has other major tributaries before it confluence with the Indus River, viz., Dras and Suru. A total of 1,002 lakes were mapped, covering a total area of 3,467.01 ha i.e. 0.04% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Indus Upper subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 36 and Figure 47 shows the area range-wise distribution of glacial lakes for the Indus Upper subbasin. About 838 (83.63%) lakes are with < 5 ha lake area contributing to 36.32% of total lake area. The remaining lakes with > 5 ha in size are only 164 (16.01%) contributing to 63.68% of total lake area in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "text", "line_start": 1024, "line_end": 1037, "token_count_estimate": 382, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Upper"], "countries": ["China"], "lake_ids": []}}
{"id": "98faf318dfbd9366", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: table\nTable: Table 36: Area range-wise distribution of GL in Indus Upper subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 181 | 67.11 | 1.94 |\n| 2 | 0.5 - 1 | 203 | 150.54 | 4.34 |\n| 3 | 1 - 5 | 454 | 1,041.66 | 30.04 |\n| 4 | 5 - 10 | 93 | 646.88 | 18.66 |\n| 5 | 10 - 50 | 67 | 1,155.71 | 33.33 |\n| 6 | > 50 | 4 | 405.13 | 11.69 |\n| | **Total** | **1,002** | **3,467.01** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "table", "table_caption": "Table 36: Area range-wise distribution of GL in Indus Upper subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1038, "line_end": 1046, "token_count_estimate": 275, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": []}}
{"id": "b7c0de2f369af95a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Indus Upper subbasin is given in Table 37 and Figure 48. Out of 10 types of glacial lakes, 8 types of lake are present in the Indus Upper subbasin, where Other Glacial Erosion lakes are found to be the maximum with 557 (55.59%) occupying a total lake extent of 1,787.47 ha at 51.56% in the subbasin. After that, Other Moraine and End-moraine Dammed lakes are in majority with 190 (18.96%) and 120 (11.98%) and extend over a total area of 268.44 ha (7.74%) and 683.98 ha (19.73%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "text", "line_start": 1047, "line_end": 1059, "token_count_estimate": 218, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": []}}
{"id": "5ee10719d2138775", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: table\nTable: Table 37: Type-wise distribution of GL in Indus Upper subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 120 | 683.98 | 19.73 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 6 | 2.64 | 0.08 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 3 | 6.48 | 0.19 |\n| 4 | M(o) | Other Moraine Dammed Lake | 190 | 268.44 | 7.74 |\n| 5 | I(s) | Supra-glacial Lake | 55 | 32.62 | 0.94 |\n| 6 | E(c) | Cirque Erosion Lake | 26 | 104.57 | 3.02 |\n| 7 | E(o) | Other Glacial Erosion Lake | 557 | 1,787.47 | 51.56 |\n| 8 | O | Other Glacial Lake | 45 | 580.82 | 16.75 |\n| | | **Total** | **1,002** | **3,467.01** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "table", "table_caption": "Table 37: Type-wise distribution of GL in Indus Upper subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 9, "line_start": 1060, "line_end": 1070, "token_count_estimate": 390, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": []}}
{"id": "4b067a0d1a3dd1c2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Jhelum subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 42 and Figure 55 shows the area range-wise distribution of glacial lakes for the Jhelum subbasin. About 275 (71.80%) lakes are with < 5 ha lake area contributing to 15.62% of total lake area. The remaining lakes with > 5 ha in size are only 108 (28.20%) contributing to 84.38% of total lake area in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "text", "line_start": 1071, "line_end": 1082, "token_count_estimate": 174, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": []}}
{"id": "2a22348e27aa3c2a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: table\nTable: Table 42: Area range-wise distribution of GL in Jhelum subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 52 | 19.82 | 0.73 |\n| 2 | 0.5 - 1 | 85 | 62.25 | 2.29 |\n| 3 | 1 - 5 | 138 | 343.22 | 12.60 |\n| 4 | 5 - 10 | 39 | 281.64 | 10.34 |\n| 5 | 10 - 50 | 60 | 1,227.53 | 45.06 |\n| 6 | > 50 | 9 | 789.58 | 28.99 |\n| | **Total** | **383** | **2724.04** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "table", "table_caption": "Table 42: Area range-wise distribution of GL in Jhelum subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1083, "line_end": 1091, "token_count_estimate": 279, "basins": ["Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": []}}
{"id": "da3ed01ce09672f6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Jhelum subbasin is given in Table 43 and Figure 56. Out of 10 types of glacial lakes, only 7 types of lake are present in the Jhelum subbasin, where Other Glacial Erosion lakes are found to be the maximum with 283 (73.89%) occupying a total lake extent of 1,451.47 ha at 53.28% in the subbasin. After that, Cirque Erosion and Other Moraine Dammed lakes are in majority with 52 (13.58%) and 30 (7.83%) and extend over a total area of 945.98 ha (34.73%) and 63.06 ha (2.31%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "text", "line_start": 1092, "line_end": 1104, "token_count_estimate": 215, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": []}}
{"id": "58fcd8afe026844b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: table\nTable: Table 43: Type-wise distribution of GL in Jhelum subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 5 | 75.24 | 2.76 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 1 | 0.28 | 0.01 |\n| 3 | M(o) | Other Moraine Dammed Lake | 30 | 63.06 | 2.31 |\n| 4 | I(s) | Supra-glacial Lake | 7 | 4.50 | 0.17 |\n| 5 | E(c) | Cirque Erosion Lake | 52 | 945.98 | 34.73 |\n| 6 | E(o) | Other Glacial Erosion Lake | 283 | 1,451.47 | 53.28 |\n| 7 | O | Other Glacial Lake | 5 | 183.52 | 6.74 |\n| | | **Total** | **383** | **2,724.04** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "table", "table_caption": "Table 43: Type-wise distribution of GL in Jhelum subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 8, "line_start": 1105, "line_end": 1114, "token_count_estimate": 366, "basins": ["Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": []}}
{"id": "9bd6c638ecac9ac8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin\nType: text\n\n**GLACIAL LAKE ATLAS OF INDUS RIVER BASIN**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Indus Upper Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Indus Upper Subbasin"], "chunk_type": "text", "line_start": 1115, "line_end": 1121, "token_count_estimate": 54, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": []}}
{"id": "6414ac0bdbabeffc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 44 and Figure 57. The lakes with < 5 ha in size (71.80%) are dominant with Other Glacial Erosion (80.00%) and Other Moraine Dammed lakes (9.45%). Lakes with > 5 ha (28.20%) are also dominated by Other Glacial Erosion lakes (58.33%). All types of Glacier Erosion lakes, which constitute about 87.47% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range Type-wise Distribution"], "chunk_type": "text", "line_start": 1123, "line_end": 1127, "token_count_estimate": 159, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ffe8236ba4fd640", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution\nType: table\nTable: Table 44: Area range-wise vs. Type-wise distribution of GL in Jhelum subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 1 | 0 | 4 | 3 | 0 | 2 | 0 | 41 | 0 | 52 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 11 | 4 | 0 | 0 | 0 | 68 | 2 | 85 |\n| 3 | 1 - 5 | 2 | 0 | 0 | 11 | 0 | 0 | 13 | 0 | 111 | 1 | 138 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 3 | 0 | 0 | 10 | 0 | 25 | 0 | 39 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 24 | 0 | 35 | 0 | 60 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 2 | 9 |\n| | **Total** | **5** | **1** | **0** | **30** | **7** | **0** | **52** | **0** | **283** | **5** | **383** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 44: Area range-wise vs. Type-wise distribution of GL in Jhelum subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1128, "line_end": 1136, "token_count_estimate": 488, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": []}}
{"id": "a989a2207db973a7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution\nType: text\n\n***\n\n**GLACIAL LAKE ATLAS OF INDUS RIVER BASIN**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range Type-wise Distribution"], "chunk_type": "text", "line_start": 1137, "line_end": 1144, "token_count_estimate": 54, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bdb20e03cfcf7e16", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Jhelum subbasin has been shown in Table 45 and Figure 58. Majority of glacial lakes are situated below 4,000 m elevation range i.e. 208 (54.31% of the total lake count) with total lake area of 1,968.19 ha (72.25%) and remaining 45.69% glacial lakes are above 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1146, "line_end": 1150, "token_count_estimate": 131, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": []}}
{"id": "4e540d8675dedf59", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 45: Elevation range-wise distribution of GL in Jhelum subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 208 | 1,968.19 | 72.25 |\n| 3 | 4,001 - 5,000 | 175 | 755.85 | 27.75 |\n| 4 | > 5,000 | 0 | 0.00 | 0.00 |\n| | **Total** | **383** | **2724.04** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 45: Elevation range-wise distribution of GL in Jhelum subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1151, "line_end": 1157, "token_count_estimate": 229, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": []}}
{"id": "f15f00ade64f9da4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 46 and Figure 59. It is noted that, 54.31% of glacial lakes (208) are situated in medium altitude range i.e. 3,001 - 4,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 72.25%. However, no glacial lakes lies below 3,000 m and above 5,000 m elevation range. 63.46% of lakes lying in medium altitude range are < 5 ha, predominantly of size ranging 1 - 5 ha (i.e. 67), followed by lakes of size 0.5 - 1 ha (i.e. 38). It has been further noticed that, 70.37% and 29.63% of lakes > 5 ha are lying within in medium and high altitude range i.e. 3,001 - 4,000 m and 4,001 - 5,000 m respectively, majority of them falling in size range of 10 - 50 ha and in 5 - 10 ha respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1158, "line_end": 1171, "token_count_estimate": 303, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fba3a711a7a57036", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 46: Area range-wise vs. Elevation range-wise distribution of GL in Jhelum subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 27 | 10.63 | 25 | 9.19 | 0 | 0.00 | 52 | 19.82 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 38 | 27.74 | 47 | 34.52 | 0 | 0.00 | 85 | 62.25 |\n| 3 | 1 - 5 | 0 | 0.00 | 67 | 166.08 | 71 | 177.14 | 0 | 0.00 | 138 | 343.22 |\n| 4 | 5 - 10 | 0 | 0.00 | 23 | 164.09 | 16 | 117.55 | 0 | 0.00 | 39 | 281.64 |\n| 5 | 10 - 50 | 0 | 0.00 | 47 | 1,010.16 | 13 | 217.37 | 0 | 0.00 | 60 | 1,227.53 |\n| 6 | > 50 | 0 | 0.00 | 6 | 589.50 | 3 | 200.08 | 0 | 0.00 | 9 | 789.58 |\n| | Total | 0 | 0.00 | 208 | 1,968.19 | 175 | 755.85 | 0 | 0.00 | 383 | 2,724.04 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 46: Area range-wise vs. Elevation range-wise distribution of GL in Jhelum subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1172, "line_end": 1180, "token_count_estimate": 619, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": []}}
{"id": "bf90d186268e9eae", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 47 and Figure 60. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (73.89% in number and 53.28% in extent) are predominantly located in the elevation range of 3,001 - 4,000 m (74.04%). The other dominant lake type, namely, Cirque Erosion and Other Moraine Dammed lakes are distributed predominantly in medium (3,001 - 4,000 m) and high altitude range (4,001 - 5,000 m), i.e. 14.42% and 10.29%. Nearly half i.e. 54.93% of all types of Glacier Erosion lakes, lie below 4,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 61.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1181, "line_end": 1193, "token_count_estimate": 267, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "649c595e4086fcb4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 47: Type-wise vs. Elevation range-wise distribution of GL in Jhelum subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 4 | 0 | 0 | 12 | 3 | 0 | 30 | 0 | 154 | 5 | 208 |\n| 3 | 4,001 - 5,000 | 1 | 1 | 0 | 18 | 4 | 0 | 22 | 0 | 129 | 0 | 175 |\n| 4 | > 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 5 | 1 | 0 | 30 | 7 | 0 | 52 | 0 | 283 | 5 | 383 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 47: Type-wise vs. Elevation range-wise distribution of GL in Jhelum subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1194, "line_end": 1200, "token_count_estimate": 466, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": []}}
{"id": "c7470c19179fdeb5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1201, "line_end": 1208, "token_count_estimate": 64, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "76dc8656f1174cd5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Ravi Subbasin\nType: text\n\nThe Ravi subbasin is the smallest subbasin of the Indus River basin covering a total area of 9,236 Km² i.e. 2.70% of the total basin area (Figure 62). Ravi is the main river flowing all over into the subbasin with no major tributaries to form a catchment. A total of 61 glacial lakes has been mapped, covering a total area of 95.47 ha i.e. 0.01% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Ravi Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.7 Ravi Subbasin"], "chunk_type": "text", "line_start": 1210, "line_end": 1217, "token_count_estimate": 164, "basins": ["INDUS", "Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "809a102abf0b0454", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nIn Ravi subbasin, glacial lakes have been distributed in 5 classes of area ranges. Table 48 and Figure 63 shows the area range-wise distribution of glacial lakes for the Ravi subbasin. About 59 (96.73%) lakes are with < 5 ha lake area contributing to 62.97% of total lake area. The remaining lakes with > 5 ha in size are only 2 (3.27%) contributing to 37.03% of total lake area in the subbasin..", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1219, "line_end": 1223, "token_count_estimate": 144, "basins": ["Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "3affe1d275d27646", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 48: Area range-wise distribution of GL in Ravi subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 22 | 7.57 | 7.93 |\n| 2 | 0.5 - 1 | 15 | 10.18 | 10.66 |\n| 3 | 1 - 5 | 22 | 42.37 | 44.38 |\n| 4 | 5 - 10 | 1 | 9.70 | 10.16 |\n| 5 | 10 - 50 | 1 | 25.65 | 26.87 |\n| 6 | > 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **61** | **95.47** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 48: Area range-wise distribution of GL in Ravi subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1224, "line_end": 1232, "token_count_estimate": 257, "basins": ["Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "d8d27247fc011f43", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1233, "line_end": 1239, "token_count_estimate": 49, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4df7e2b8493831f8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Ravi subbasin is given in Table 49 and Figure 64. Out of 10 types of glacial lakes, only 5 types of lake are present in the Ravi subbasin, where Other Glacial Erosion and Other Moraine Dammed lakes are found to be the maximum, both with 26 (42.62%) occupying a lake extent of 54.76 ha and 26.97 ha at 57.36% and 28.25% respectively in the subbasin.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1241, "line_end": 1245, "token_count_estimate": 142, "basins": ["Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "3db5d0cdce9da3c7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 49: Type-wise distribution of GL in Ravi subbasin\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 3 | 10.54 | 11.04 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 3 | 2.10 | 2.20 |\n| 3 | M(o) | Other Moraine Dammed Lake | 26 | 26.97 | 28.25 |\n| 4 | I(s) | Supra-glacial Lake | 3 | 1.10 | 1.15 |\n| 5 | E(o) | Other Glacial Erosion Lake | 26 | 54.76 | 57.36 |\n| | | **Total** | **61** | **95.47** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 49: Type-wise distribution of GL in Ravi subbasin", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 6, "line_start": 1246, "line_end": 1253, "token_count_estimate": 284, "basins": ["Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "384d560ff5eef803", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 52 and Figure 67. It is noted that, 88.52% of glacial lakes (548) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 66.01%. However, no glacial lakes lies below 3,000 m and above 5,000 m elevation range. 98.15% of lakes lying in high altitude range are < 5 ha, predominantly of size ranging 0.25 - 0.5 ha (i.e. 20), followed by lakes of size 1 - 5 ha (i.e. 19). It has been further noticed, that > 5 ha in size, one lake each lies within medium and high altitude range i.e. 3,001 - 4,000 m and 4,001 - 5,000 m respectively. Whereas, remaining lakes in medium altitude range are < 5 ha in size.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1254, "line_end": 1265, "token_count_estimate": 293, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1684b8d86c7e3e22", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 52: Area range-wise vs. Elevation range-wise distribution of GL in Ravi subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 2 | 0.77 | 20 | 6.79 | 0 | 0.00 | 22 | 7.57 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 1 | 0.64 | 14 | 9.54 | 0 | 0.00 | 15 | 10.18 |\n| 3 | 1 - 5 | 0 | 0.00 | 3 | 5.39 | 19 | 36.98 | 0 | 0.00 | 22 | 42.37 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 1 | 9.70 | 0 | 0.00 | 1 | 9.70 |\n| 5 | 10 - 50 | 0 | 0.00 | 1 | 25.65 | 0 | 0.00 | 0 | 0.00 | 1 | 25.65 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **7** | **32.45** | **54** | **63.02** | **0** | **0.00** | **61** | **95.47** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 52: Area range-wise vs. Elevation range-wise distribution of GL in Ravi subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1266, "line_end": 1274, "token_count_estimate": 618, "basins": ["Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "d3afde243aff740b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 53 and Figure 68. The dominant lake type in the subbasin i.e., Other Glacial Erosion and Other Moraine Dammed lakes (42.62% each in number and 57.36% & 28.25% in extent respectively) are predominantly located in the elevation range of 4,001 - 5,000 m (83.33%). It has also been noticed that, only Other Glacial Erosion type of glacial lake lies in medium altitude range i.e. 3,001 m - 4,000 m amsl. Whereas, all types of Moraine-dammed lakes are lying within high altitude range i.e. 4,001 m - 5,000 m amsl. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 69.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1275, "line_end": 1283, "token_count_estimate": 260, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93ebbb3ea2c28a03", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 53: Type-wise vs. Elevation range-wise distribution of GL in Ravi subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 7 |\n| 3 | 4,001 - 5,000 | 3 | 3 | 0 | 26 | 3 | 0 | 0 | 0 | 19 | 0 | 54 |\n| 4 | > 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **3** | **3** | **0** | **26** | **3** | **0** | **0** | **0** | **26** | **0** | **61** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 53: Type-wise vs. Elevation range-wise distribution of GL in Ravi subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1284, "line_end": 1290, "token_count_estimate": 484, "basins": ["Indus"], "subbasins": ["Ravi"], "countries": [], "lake_ids": []}}
{"id": "a335696fb88e8ed6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1291, "line_end": 1294, "token_count_estimate": 48, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19b9e9bc0edc9642", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 75 and Figure 95. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (48.11% in number and 49.22% in extent) are predominantly located in the elevation range of 4,001 - 5,000 m (58.50%). The other dominant lake type, namely, Supra-glacial and Other Moraine Dammed lakes are distributed predominantly in high altitude range i.e. 4,001 - 5,000 m elevation range, i.e. 14.33% and 12.21%. Majority i.e. 91.06% of all types of Moraine-dammed lakes, lies above 4,000 m.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1296, "line_end": 1300, "token_count_estimate": 216, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d692bbfb50de374a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 75: Type-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 2 | 1 | 0 | 6 | 11 | 0 | 0 | 0 | 0 | 1 | 21 |\n| 2 | 3,001 - 4,000 | 20 | 34 | 0 | 41 | 240 | 0 | 48 | 1 | 246 | 27 | 657 |\n| 3 | 4,001 - 5,000 | 98 | 127 | 13 | 334 | 392 | 0 | 124 | 0 | 1,600 | 47 | 2,735 |\n| 4 | > 5,000 | 162 | 57 | 17 | 251 | 118 | 1 | 13 | 0 | 213 | 35 | 867 |\n| | **Total** | **282** | **219** | **30** | **632** | **761** | **1** | **185** | **1** | **2,059** | **110** | **4,280** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 75: Type-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1301, "line_end": 1307, "token_count_estimate": 446, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f9802ddbe50c882a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 95: Type-wise vs. Elevation range-wise distribution of GL in India\n\n**Figure 95: Type-wise vs. Elevation range-wise distribution of GL in India**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 95: Type-wise vs. Elevation range-wise distribution of GL in India", "line_start": 1309, "line_end": 1309, "token_count_estimate": 83, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "59a8c7ca26bbb252", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1310, "line_end": 1314, "token_count_estimate": 54, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1d4784521f5cda4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: text\n\nGlacial lakes in all 2 states and 2 union territories of Indian region are compared for State/UT-wise lake count, total lake area, lake types and their elevation ranges in the following sections.\n\n**State/UT-wise Distribution**\n\nTable 76 and Figure 96 shows the State/UT-wise distribution of glacial lakes of Indian region. Lakes are predominantly distributed in Ladakh (UT) with 3,219 (75.21%) occupying a total lake extent of 9,965.34 ha at 72.28% in the region. After that, Jammu & Kashmir (UT) and Himachal Pradesh contains majority of glacial lakes with 546 (12.76%) and 513 (11.99%) respectively, extend over an area of 2,880.79 ha (20.90%) and 927.60 ha (6.73%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1316, "line_end": 1324, "token_count_estimate": 229, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0bb568418cd4d872", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: table\nTable: Table 76: State/UT-wise distribution of GL in India\n\n| S. No. | Code | State / UT | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | HP | Himachal Pradesh | 513 | 927.60 | 6.73 |\n| 2 | JK | Jammu & Kashmir (UT) | 546 | 2,880.79 | 20.90 |\n| 3 | LA | Ladakh (UT) | 3,219 | 9,965.34 | 72.28 |\n| 4 | UK | Uttarakhand | 2 | 12.96 | 0.09 |\n| | | **Total** | **4,280** | **13,786.70** | **100** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "table", "table_caption": "Table 76: State/UT-wise distribution of GL in India", "columns": ["S. No.", "Code", "State / UT", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1325, "line_end": 1331, "token_count_estimate": 266, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "7cd9235ff72e2a3e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: figure\nFigure: Figure 96: State/UT-wise distribution of GL in India\n\n**Figure 96: State/UT-wise distribution of GL in India**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "figure", "figure_caption": "Figure 96: State/UT-wise distribution of GL in India", "line_start": 1333, "line_end": 1333, "token_count_estimate": 74, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "4ec0b1ec54476453", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: text\n\n**State/UT-Area range-wise Distribution**\n\nGlacial lakes have been distributed in all states and UT’s for 6 classes of area ranges. Table 77 and Figure 97 shows the State/UT-area range-wise distribution of glacial lakes for the Indian region. It has been observed that, glacial lakes in Ladakh (UT) are predominantly < 5 ha (87.10%), majority of which are within 1 - 5 ha in size i.e. 39.01%, followed by lakes of 0.25 - 0.5 ha in size i.e. 25.07%. Not only in Ladakh (UT), but maximum number of lakes are < 5 ha in Himachal Pradesh and Jammu & Kashmir (UT) also. Lake > 5 ha in size are maximum in Ladakh (UT) with 415 and 3,676.13 ha in area.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1334, "line_end": 1341, "token_count_estimate": 235, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6b67c96f23aee9f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: table\nTable: Table 77: State/UT-wise vs. Area range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | Himachal Pradesh - No. of lakes | Himachal Pradesh - Total Lake Area (ha) | J&K (UT) - No. of lakes | J&K (UT) - Total Lake Area (ha) | Ladakh (UT) - No. of lakes | Ladakh (UT) - Total Lake Area (ha) | Uttarakhand - No. of lakes | Uttarakhand - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 202 | 71.37 | 123 | 45.05 | 807 | 291.60 | 1 | 0.34 | 1,133 | 408.36 |\n| 2 | 0.5 - 1 | 127 | 86.61 | 125 | 88.08 | 741 | 525.86 | 0 | 0.00 | 993 | 700.55 |\n| 3 | 1 - 5 | 161 | 320.72 | 179 | 423.41 | 1,256 | 2,858.67 | 0 | 0.00 | 1,596 | 3,602.80 |\n| 4 | 5 - 10 | 15 | 110.74 | 49 | 358.24 | 237 | 1,629.17 | 0 | 0.00 | 301 | 2,098.15 |\n| 5 | 10 - 50 | 6 | 131.87 | 63 | 1,315.91 | 161 | 2,858.15 | 1 | 12.63 | 231 | 4,318.56 |\n| 6 | > 50 | 2 | 206.28 | 7 | 650.10 | 17 | 1,801.88 | 0 | 0.00 | 26 | 2,658.26 |\n| | **Total** | **513** | **927.60** | **546** | **2,880.79** | **3,219** | **9,965.34** | **2** | **12.96** | **4,280** | **13,786.70** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "table", "table_caption": "Table 77: State/UT-wise vs. Area range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "Himachal Pradesh - No. of lakes", "Himachal Pradesh - Total Lake Area (ha)", "J&K (UT) - No. of lakes", "J&K (UT) - Total Lake Area (ha)", "Ladakh (UT) - No. of lakes", "Ladakh (UT) - Total Lake Area (ha)", "Uttarakhand - No. of lakes", "Uttarakhand - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1342, "line_end": 1350, "token_count_estimate": 619, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a9dbd549ed75989f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: text\n\n**State/UT-Type-wise Distribution**\n\nGlacial lake distribution by State/UT vs. type-wise is given in Table 78 and Figure 98. It has been observed that, Ladakh (UT) contains maximum number of all types of glacial lakes in comparison with any other state/UT in the Indian region, with majority of Other Glacial Erosion lakes i.e. 49.24%), followed by Supra-glacial lakes i.e. 19.94%. Whereas, in comparison between HP and JK, all types of Moraine-dammed lakes are predominantly situated in HP and all types of Glacier Erosion lakes are predominantly situated in JK.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1351, "line_end": 1357, "token_count_estimate": 198, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "710938d11b797f9a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: table\nTable: Table 78: State/UT-wise vs. Type-wise distribution of GL in India\n\n| S. No. | State / UT | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Himachal Pradesh | 46 | 28 | 1 | 209 | 65 | 0 | 3 | 0 | 154 | 7 | 513 |\n| 2 | Jammu & Kashmir (UT) | 20 | 9 | 0 | 69 | 54 | 0 | 66 | 0 | 319 | 9 | 546 |\n| 3 | Ladakh (UT) | 215 | 182 | 29 | 354 | 642 | 1 | 116 | 1 | 1,585 | 94 | 3,219 |\n| 4 | Uttarakhand | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |\n| | **Total** | **282** | **219** | **30** | **632** | **761** | **1** | **185** | **1** | **2,059** | **110** | **4,280** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "table", "table_caption": "Table 78: State/UT-wise vs. Type-wise distribution of GL in India", "columns": ["S. No.", "State / UT", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1358, "line_end": 1364, "token_count_estimate": 418, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a43fdad8afdb83c3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**State/UT-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per State/UT vs. elevation-range wise, given in Table 79 and Figure 99. It has been observed that, majority of glacial lakes are located in high altitude range i.e. 4,001 - 5,000 m in all states and UT’s except for J&K (UT) which has its lakes predominantly situated below 4,000 m elevation and all lies within medium altitude range i.e. 3,001 - 4,000 m. Only Ladakh (UT) has lakes situated below 3,000 m. All lakes of Uttarakhand state are located above 5,000 m amsl.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1365, "line_end": 1374, "token_count_estimate": 219, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6ad839642c61c92", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: table\nTable: Table 79: State/UT-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | State / UT - Himachal Pradesh - No. of lakes | State / UT - Himachal Pradesh - Total Lake Area (ha) | State / UT - J&K (UT) - No. of lakes | State / UT - J&K (UT) - Total Lake Area (ha) | State / UT - Ladakh (UT) - No. of lakes | State / UT - Ladakh (UT) - Total Lake Area (ha) | State / UT - Uttarakhand - No. of lakes | State / UT - Uttarakhand - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0 | 0.00 | 21 | 47.70 | 0 | 0.00 | 21 | 47.70 |\n| 2 | 3,001 - 4,000 | 22 | 45.85 | 277 | 2,087.45 | 358 | 1,206.57 | 0 | 0.00 | 657 | 3,339.87 |\n| 3 | 4,001 - 5,000 | 299 | 658.37 | 268 | 792.42 | 2,168 | 6,616.71 | 0 | 0.00 | 2,735 | 8,067.50 |\n| 4 | > 5,000 | 192 | 223.38 | 1 | 0.93 | 672 | 2,094.36 | 2 | 12.96 | 867 | 2,331.63 |\n| | **Total** | **513** | **927.60** | **546** | **2,880.79** | **3,219** | **9,965.34** | **2** | **12.96** | **4,280** | **13,786.70** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "table", "table_caption": "Table 79: State/UT-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "State / UT - Himachal Pradesh - No. of lakes", "State / UT - Himachal Pradesh - Total Lake Area (ha)", "State / UT - J&K (UT) - No. of lakes", "State / UT - J&K (UT) - Total Lake Area (ha)", "State / UT - Ladakh (UT) - No. of lakes", "State / UT - Ladakh (UT) - Total Lake Area (ha)", "State / UT - Uttarakhand - No. of lakes", "State / UT - Uttarakhand - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1375, "line_end": 1381, "token_count_estimate": 561, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "d1fff8fb1740abcc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**5.5.1 District Level Statistics of Ladakh (UT)**\n\nLadakh (UT) is the largest union territory of all in terms of area, divided in two districts viz., Kargil and Leh. Amongst which, Leh covers the majority of the total area.\n\n**Area range-wise Distribution**\n\nGlacial lakes has been distributed in both districts for all 6 classes of area ranges, and area range-wise distribution for both districts has been shown in Table 80 and Figure 100. Glacial lakes in Leh district are found to be the maximum with 2,912 (90.46%) occupying a total lake extent of 9,014.22 ha at 90.45%. About 2,547 (87.46%) lakes are with < 5 ha lake area contributing to 36.64% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 12.54%, predominantly of 5 - 10 ha in size.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1382, "line_end": 1400, "token_count_estimate": 276, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb1fd32d07b389ab", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: table\nTable: Table 80: Area range-wise distribution of GL in Districts of Ladakh (UT)\n\n| S. No. | Lake Area Range (ha) | Districts of Ladakh (UT) - Kargil - No. of Lakes | Districts of Ladakh (UT) - Kargil - Total Lake Area (ha) | Districts of Ladakh (UT) - Leh - No. of lakes | Districts of Ladakh (UT) - Leh - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 59 | 20.99 | 748 | 270.61 | 807 | 291.60 |\n| 2 | 0.5 - 1 | 59 | 43.17 | 682 | 482.69 | 741 | 525.86 |\n| 3 | 1 - 5 | 139 | 308.73 | 1,117 | 2,549.94 | 1,256 | 2,858.67 |\n| 4 | 5 - 10 | 30 | 209.11 | 207 | 1,420.06 | 237 | 1,629.17 |\n| 5 | 10 - 50 | 19 | 309.33 | 142 | 2,548.82 | 161 | 2,858.15 |\n| 6 | > 50 | 1 | 59.78 | 16 | 1,742.10 | 17 | 1,801.88 |\n| | **Total** | **307** | **951.12** | **2,912** | **9,014.22** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "table", "table_caption": "Table 80: Area range-wise distribution of GL in Districts of Ladakh (UT)", "columns": ["S. No.", "Lake Area Range (ha)", "Districts of Ladakh (UT) - Kargil - No. of Lakes", "Districts of Ladakh (UT) - Kargil - Total Lake Area (ha)", "Districts of Ladakh (UT) - Leh - No. of lakes", "Districts of Ladakh (UT) - Leh - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1401, "line_end": 1409, "token_count_estimate": 468, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0bd1b9a3137429c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1410, "line_end": 1413, "token_count_estimate": 54, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d69afa3b235e1e9c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the districts of Ladakh (UT) is given in Table 81 and Figure 101. It has been observed that, Other Glacial Erosion lakes are maximum with 1,585 (49.24%) in the UT, followed by Supra-glacial lakes with 642 (19.94%). Leh district contains maximum number of all types of glacial lakes in comparison with Kargil district in the UT, with majority of Other Glacial Erosion lakes (87.63%), followed by Supra-glacial lakes i.e. 38.92%.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1415, "line_end": 1419, "token_count_estimate": 164, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1705c75a9eeeb96", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 81: Type-wise distribution of GL in Districts of Ladakh (UT)\n\n| S. No. | District | Types of Glacial Lake   M(e) | Types of Glacial Lake   M(l) | Types of Glacial Lake   M(lg) | Types of Glacial Lake   M(o) | Types of Glacial Lake   I(s) | Types of Glacial Lake   I(d) | Types of Glacial Lake   E(c) | Types of Glacial Lake   E(v) | Types of Glacial Lake   E(o) | Types of Glacial Lake   O | Total   No. of Lakes | Total   Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Kargil | 16 | 6 | 1 | 52 | 25 | 0 | 8 | 0 | 196 | 3 | 307 | 951.12 |\n| 2 | Leh | 199 | 176 | 28 | 302 | 617 | 1 | 108 | 1 | 1,389 | 91 | 2,912 | 9,014.22 |\n| | **Total** | **215** | **182** | **29** | **354** | **642** | **1** | **116** | **1** | **1,585** | **94** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 81: Type-wise distribution of GL in Districts of Ladakh (UT)", "columns": ["S. No.", "District", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total No. of Lakes", "Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 3, "line_start": 1420, "line_end": 1424, "token_count_estimate": 420, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1b12d8cd70d6744f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1425, "line_end": 1431, "token_count_estimate": 49, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af9e1c7509cb2774", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Ladakh (UT) has been shown in Table 82 and Figure 102. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 2,840 (88.22%) with total lake area of 8,711.07 ha (87.41%) and remaining 11.78% glacial lakes are below 4,000 m elevation. Leh district contains maximum number of glacial lakes above 4,000 m in comparison with other district in the UT, with majority of them falling in high altitude range i.e. 4,001 - 5,000 m. 100% low altitude lakes of Ladakh (UT) also situated in Leh. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 103.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1433, "line_end": 1437, "token_count_estimate": 218, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34740f70d3af1b9d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 82: Elevation range-wise distribution of GL in Districts of Ladakh (UT)\n\n| S. No. | Elevation Range (m) | Districts of Ladakh (UT)   Kargil   No. of Lakes | Districts of Ladakh (UT)   Kargil   Total Lake Area (ha) | Districts of Ladakh (UT)   Leh   No. of Lakes | Districts of Ladakh (UT)   Leh   Total Lake Area (ha) | Total   No. of Lakes | Total   Lake Area (ha) |\n|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 21 | 47.70 | 21 | 47.70 |\n| 2 | 3,001 - 4,000 | 2 | 4.78 | 356 | 1,201.80 | 358 | 1,206.57 |\n| 3 | 4,001 - 5,000 | 269 | 904.15 | 1,899 | 5,712.56 | 2,168 | 6,616.71 |\n| 4 | > 5,000 | 36 | 42.19 | 636 | 2,052.17 | 672 | 2,094.36 |\n| | **Total** | **307** | **951.12** | **2,912** | **9,014.22** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 82: Elevation range-wise distribution of GL in Districts of Ladakh (UT)", "columns": ["S. No.", "Elevation Range (m)", "Districts of Ladakh (UT) Kargil No. of Lakes", "Districts of Ladakh (UT) Kargil Total Lake Area (ha)", "Districts of Ladakh (UT) Leh No. of Lakes", "Districts of Ladakh (UT) Leh Total Lake Area (ha)", "Total No. of Lakes", "Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1438, "line_end": 1444, "token_count_estimate": 392, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac44104a39ea606b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1445, "line_end": 1456, "token_count_estimate": 65, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "385c17c47c529388", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: text\n\nJammu & Kashmir (UT) is the second largest union territory of all in terms of area, has total 22 districts but only 15 districts has glacial lakes.\n\n**Area-wise Distribution**\n\nGlacial lakes has been distributed in all 15 districts of J&K (UT) for all 6 classes of area ranges, and area range-wise distribution for those has been shown in Table 83 and Figure 104. Glacial lakes in Kishtwar district are found to be the maximum with 197 (36.08%) occupying a total lake extent of 392.64 ha at 13.63%. About 179 (90.86%) lakes are with < 5 ha lake area contributing to 46.57% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 9.14%, predominantly of 5 - 10 ha in size.\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the districts of J&K (UT) is given in Table 84 and Figure 105. It has been observed that, only 7 types of glacial lakes are distributed in the UT, where Other Glacial Erosion lakes are found to be the maximum with 319 (58.42%) in the UT, followed by Other Moraine Dammed lakes with 69 (12.64%). Kishtwar district contains maximum number of all types of glacial lakes in comparison with other districts in the UT, with majority of Other Glacial Erosion lakes (35.03%), followed by Supra-glacial lakes i.e. 23.86%.\n\n**Elevation-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the districts of J&K (UT) has been shown in Table 85 and Figure 106. Majority of glacial lakes are situated below 4,000 m elevation range i.e. 277 (50.73%) with total lake area of 2,087.45 ha (72.46%), all in medium altitude range only. Whereas, remaining 49.27% glacial lakes are above 4,000 m elevation, majorly in high altitude range. Kishtwar district contains maximum number of glacial lakes above and below 4,000 m elevation in comparison with any other district in the UT, with majority of them falling in high altitude range i.e. 4,001 - 5,000 m i.e. 70.01%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 107.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "text", "line_start": 1458, "line_end": 1479, "token_count_estimate": 614, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ba76ec9b78d6054", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: table\nTable: Table 83: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n| S.No. | District | 0.25 - 0.5 (No. of Lakes) | 0.25 - 0.5 (Total Lake Area (ha)) | 0.5 - 1 (No. of Lakes) | 0.5 - 1 (Total Lake Area (ha)) | 1 - 5 (No. of Lakes) | 1 - 5 (Total Lake Area (ha)) | 5 - 10 (No. of Lakes) | 5 - 10 (Total Lake Area (ha)) | 10 - 50 (No. of Lakes) | 10 - 50 (Total Lake Area (ha)) | > 50 (No. of Lakes) | > 50 (Total Lake Area (ha)) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Anantnag | 11 | 4.30 | 5 | 3.54 | 15 | 40.89 | 7 | 50.78 | 12 | 200.10 | 2 | 135.20 |\n| 2 | Badgam | 2 | 0.86 | 2 | 1.47 | 8 | 20.30 | 7 | 57.63 | 6 | 127.53 | 0 | 0.00 |\n| 3 | Bandipore | 6 | 2.44 | 10 | 7.31 | 29 | 73.84 | 7 | 50.27 | 12 | 322.77 | 0 | 0.00 |\n| 4 | Baramula | 4 | 1.64 | 2 | 1.12 | 2 | 3.67 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 5 | Doda | 2 | 0.61 | 3 | 1.78 | 6 | 8.35 | 2 | 19.20 | 0 | 0.00 | 0 | 0.00 |\n| 6 | Ganderbal | 8 | 3.03 | 9 | 6.70 | 20 | 42.83 | 2 | 13.67 | 5 | 86.00 | 1 | 161.04 |\n| 7 | Kishtwar | 70 | 24.81 | 55 | 37.26 | 54 | 120.81 | 13 | 92.22 | 5 | 117.54 | 0 | 0.00 |\n| 8 | Kulgam | 5 | 2.09 | 3 | 2.30 | 6 | 18.18 | 2 | 12.59 | 10 | 188.03 | 2 | 199.37 |\n| 9 | Kupwara | 0 | 0.00 | 1 | 0.68 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 10 | Muzaffarabad | 10 | 3.29 | 24 | 17.66 | 25 | 59.61 | 5 | 35.24 | 8 | 168.94 | 2 | 154.50 |\n| 11 | Punch | 2 | 0.79 | 9 | 6.79 | 6 | 13.41 | 2 | 12.56 | 3 | 46.65 | 0 | 0.00 |\n| 12 | Rajauri | 2 | 0.83 | 0 | 0.00 | 5 | 12.64 | 2 | 14.08 | 1 | 14.88 | 0 | 0.00 |\n| 13 | Reasi | 1 | 0.36 | 1 | 0.75 | 2 | 4.13 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 14 | Srinagar | 0 | 0.00 | 1 | 0.70 | 0 | 0.00 | 0 | 0.00 | 1 | 43.47 | 0 | 0.00 |\n| 15 | Udhampur | 0 | 0.00 | 0 | 0.00 | 1 | 4.73 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **123** | **45.05** | **125** | **88.08** | **179** | **423.41** | **49** | **358.24** | **63** | **1,315.91** | **7** | **650.10** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "table", "table_caption": "Table 83: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "columns": ["S.No.", "District", "0.25 - 0.5 (No. of Lakes)", "0.25 - 0.5 (Total Lake Area (ha))", "0.5 - 1 (No. of Lakes)", "0.5 - 1 (Total Lake Area (ha))", "1 - 5 (No. of Lakes)", "1 - 5 (Total Lake Area (ha))", "5 - 10 (No. of Lakes)", "5 - 10 (Total Lake Area (ha))", "10 - 50 (No. of Lakes)", "10 - 50 (Total Lake Area (ha))", "> 50 (No. of Lakes)", "> 50 (Total Lake Area (ha))"], "table_row_start": 1, "table_row_end": 16, "line_start": 1480, "line_end": 1497, "token_count_estimate": 1169, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1fa1904cd0d7aa9f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: figure\nFigure: Figure 104: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n**Figure 104: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "figure", "figure_caption": "Figure 104: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "line_start": 1499, "line_end": 1499, "token_count_estimate": 93, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "07c94d76cf7e93d4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "text", "line_start": 1500, "line_end": 1509, "token_count_estimate": 58, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca24fe1326a979e5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: table\nTable: Table 84: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n| S.No. | District | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total No. of Lakes | Total Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Anantnag | 1 | 1 | 0 | 3 | 0 | 0 | 4 | 0 | 42 | 1 | 52 | 434.82 |\n| 2 | Badgam | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 23 | 0 | 25 | 207.80 |\n| 3 | Bandipore | 0 | 0 | 0 | 11 | 0 | 0 | 13 | 0 | 39 | 1 | 64 | 456.63 |\n| 4 | Baramula | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 0 | 8 | 6.43 |\n| 5 | Doda | 1 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 6 | 0 | 13 | 29.94 |\n| 6 | Ganderbal | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 39 | 0 | 45 | 313.26 |\n| 7 | Kishtwar | 15 | 8 | 0 | 40 | 47 | 0 | 14 | 0 | 69 | 4 | 197 | 392.64 |\n| 8 | Kulgam | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 24 | 1 | 28 | 422.55 |\n| 9 | Kupwara | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0.68 |\n| 10 | Muzaffarabad | 2 | 0 | 0 | 12 | 6 | 0 | 13 | 0 | 39 | 2 | 74 | 439.24 |\n| 11 | Punch | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 20 | 0 | 22 | 80.21 |\n| 12 | Rajauri | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 0 | 10 | 42.43 |\n| 13 | Reasi | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 4 | 5.25 |\n| 14 | Srinagar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 44.17 |\n| 15 | Udhampur | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4.73 |\n| | **Total** | **20** | **9** | **0** | **69** | **54** | **0** | **66** | **0** | **319** | **9** | **546** | **2,880.79** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "table", "table_caption": "Table 84: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "columns": ["S.No.", "District", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total No. of Lakes", "Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1510, "line_end": 1527, "token_count_estimate": 991, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "87f2a06ff02075f4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: figure\nFigure: Figure 105: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n**Figure 105: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "figure", "figure_caption": "Figure 105: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "line_start": 1529, "line_end": 1529, "token_count_estimate": 91, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9ae160d79a0a5f98", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "text", "line_start": 1530, "line_end": 1538, "token_count_estimate": 57, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "107e94db1e702da8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: table\nTable: Table 85: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n| S. No. | District | Elevation Range (m) up to 3,000 - No. of Lakes | Elevation Range (m) up to 3,000 - Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000 - No. of Lakes | Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000 - No. of Lakes | Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) > 5,000 - No. of Lakes | Elevation Range (m) > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anantnag | 0 | 0.00 | 35 | 368.53 | 17 | 66.29 | 0 | 0.00 |\n| 2 | Badgam | 0 | 0.00 | 11 | 123.49 | 14 | 84.32 | 0 | 0.00 |\n| 3 | Bandipore | 0 | 0.00 | 46 | 413.64 | 18 | 42.99 | 0 | 0.00 |\n| 4 | Baramula | 0 | 0.00 | 7 | 6.02 | 1 | 0.41 | 0 | 0.00 |\n| 5 | Doda | 0 | 0.00 | 5 | 13.99 | 8 | 15.95 | 0 | 0.00 |\n| 6 | Ganderbal | 0 | 0.00 | 27 | 278.90 | 18 | 34.37 | 0 | 0.00 |\n| 7 | Kishtwar | 0 | 0.00 | 58 | 83.66 | 138 | 308.04 | 1 | 0.93 |\n| 8 | Kulgam | 0 | 0.00 | 23 | 365.56 | 5 | 56.98 | 0 | 0.00 |\n| 9 | Kupwara | 0 | 0.00 | 1 | 0.68 | 0 | 0.00 | 0 | 0.00 |\n| 10 | Muzaffarabad | 0 | 0.00 | 30 | 262.87 | 44 | 176.37 | 0 | 0.00 |\n| 11 | Punch | 0 | 0.00 | 18 | 76.19 | 4 | 4.02 | 0 | 0.00 |\n| 12 | Rajauri | 0 | 0.00 | 9 | 39.77 | 1 | 2.67 | 0 | 0.00 |\n| 13 | Reasi | 0 | 0.00 | 4 | 5.25 | 0 | 0.00 | 0 | 0.00 |\n| 14 | Srinagar | 0 | 0.00 | 2 | 44.17 | 0 | 0.00 | 0 | 0.00 |\n| 15 | Udhampur | 0 | 0.00 | 1 | 4.73 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **277** | **2,087.45** | **268** | **792.42** | **1** | **0.93** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "table", "table_caption": "Table 85: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "columns": ["S. No.", "District", "Elevation Range (m) up to 3,000 - No. of Lakes", "Elevation Range (m) up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000 - No. of Lakes", "Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000 - No. of Lakes", "Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) > 5,000 - No. of Lakes", "Elevation Range (m) > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1539, "line_end": 1556, "token_count_estimate": 892, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "087096b5aa555b02", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.2 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.2 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "text", "line_start": 1557, "line_end": 1567, "token_count_estimate": 71, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7a2e58e54893211c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh\nType: text\n\nHimachal Pradesh state has total 12 districts but only 6 districts has glacial lakes in it.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh"], "chunk_type": "text", "line_start": 1569, "line_end": 1571, "token_count_estimate": 64, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a48e6488463e55c1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution\nType: text\n\nGlacial lakes has been distributed in 6 districts of state for all 6 classes of area ranges, and area range-wise distribution for those has been shown in Table 86 and Figure 108. Glacial lakes in Lahul & Spiti district are found to be the maximum with 185 (36.06%) occupying a total lake extent of 470.18 ha at 50.69%. About 173 (93.51%) lakes are with < 5 ha lake area contributing to 31.75% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 6.49%, majorly of 5 - 10 ha in size.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1573, "line_end": 1577, "token_count_estimate": 191, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "38d6d5af913d4f64", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution\nType: table\nTable: Table 86: Area range-wise distribution of GL in Districts of Himachal Pradesh\n\n| S.No. | District | 0.25 - 0.5 ha : No. of Lakes | 0.25 - 0.5 ha : Total Lake Area (ha) | 0.5 - 1 ha : No. of Lakes | 0.5 - 1 ha : Total Lake Area (ha) | 1 - 5 ha : No. of Lakes | 1 - 5 ha : Total Lake Area (ha) | 5 - 10 ha : No. of Lakes | 5 - 10 ha : Total Lake Area (ha) | 10 - 50 ha : No. of Lakes | 10 - 50 ha : Total Lake Area (ha) | > 50 ha : No. of Lakes | > 50 ha : Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Chamba | 21 | 7.71 | 14 | 10.40 | 29 | 60.41 | 1 | 9.70 | 1 | 25.65 | 0 | 0.00 |\n| 2 | Kangra | 14 | 4.82 | 11 | 7.43 | 13 | 25.05 | 1 | 8.15 | 0 | 0.00 | 0 | 0.00 |\n| 3 | Kinnaur | 56 | 19.08 | 34 | 22.50 | 25 | 55.93 | 2 | 13.31 | 3 | 45.32 | 0 | 0.00 |\n| 4 | Kullu | 25 | 9.23 | 24 | 16.27 | 41 | 79.27 | 3 | 25.84 | 0 | 0.00 | 0 | 0.00 |\n| 5 | Lahul & Spiti | 82 | 29.08 | 42 | 28.28 | 49 | 91.92 | 8 | 53.73 | 2 | 60.89 | 2 | 206.28 |\n| 6 | Shimla | 4 | 1.45 | 2 | 1.72 | 4 | 8.15 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **202** | **71.37** | **127** | **86.61** | **161** | **320.72** | **15** | **110.74** | **6** | **131.87** | **2** | **206.28** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 86: Area range-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S.No.", "District", "0.25 - 0.5 ha : No. of Lakes", "0.25 - 0.5 ha : Total Lake Area (ha)", "0.5 - 1 ha : No. of Lakes", "0.5 - 1 ha : Total Lake Area (ha)", "1 - 5 ha : No. of Lakes", "1 - 5 ha : Total Lake Area (ha)", "5 - 10 ha : No. of Lakes", "5 - 10 ha : Total Lake Area (ha)", "10 - 50 ha : No. of Lakes", "10 - 50 ha : Total Lake Area (ha)", "> 50 ha : No. of Lakes", "> 50 ha : Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1578, "line_end": 1586, "token_count_estimate": 658, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f4511cddad2dd10", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1587, "line_end": 1595, "token_count_estimate": 63, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc956dbd0f34aebe", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the districts of Himachal Pradesh is given in Table 87 and Figure 109. It has been observed that, only 8 types of glacial lakes are distributed in the state, where Other Moraine Dammed lakes are found to be the maximum with 209 (40.74%) in the state, followed by Other Glacial Erosion lakes with 154 (30.02%). Lahul & Spiti district contains maximum number of types of glacial lakes in comparison with other districts in the state, with majority of Other Moraine Dammed lakes (35.14%), followed by Supra-glacial and Other Glacial Erosion lakes in equal proportion i.e. 22.70%.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1597, "line_end": 1601, "token_count_estimate": 204, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "39696ea19ab8b672", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution\nType: table\nTable: Table 87: Type-wise distribution of GL in Districts of Himachal Pradesh\n\n| S.No. | District | Types of Glacial Lake : M(e) | Types of Glacial Lake : M(l) | Types of Glacial Lake : M(lg) | Types of Glacial Lake : M(o) | Types of Glacial Lake : I(s) | Types of Glacial Lake : I(d) | Types of Glacial Lake : E(c) | Types of Glacial Lake : E(v) | Types of Glacial Lake : E(o) | Types of Glacial Lake : O | Total : No. of Lakes | Total : Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Chamba | 8 | 2 | 0 | 19 | 3 | 0 | 0 | 0 | 34 | 0 | 66 | 113.88 |\n| 2 | Kangra | 2 | 3 | 0 | 20 | 2 | 0 | 0 | 0 | 12 | 0 | 39 | 45.45 |\n| 3 | Kinnaur | 6 | 10 | 0 | 63 | 13 | 0 | 2 | 0 | 22 | 4 | 120 | 156.15 |\n| 4 | Kullu | 8 | 0 | 0 | 38 | 5 | 0 | 1 | 0 | 39 | 2 | 93 | 130.61 |\n| 5 | Lahul & Spiti | 21 | 13 | 1 | 65 | 42 | 0 | 0 | 0 | 42 | 1 | 185 | 470.18 |\n| 6 | Shimla | 1 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 5 | 0 | 10 | 11.32 |\n| | **Total** | **46** | **28** | **1** | **209** | **65** | **0** | **3** | **0** | **154** | **7** | **513** | **927.60** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 87: Type-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S.No.", "District", "Types of Glacial Lake : M(e)", "Types of Glacial Lake : M(l)", "Types of Glacial Lake : M(lg)", "Types of Glacial Lake : M(o)", "Types of Glacial Lake : I(s)", "Types of Glacial Lake : I(d)", "Types of Glacial Lake : E(c)", "Types of Glacial Lake : E(v)", "Types of Glacial Lake : E(o)", "Types of Glacial Lake : O", "Total : No. of Lakes", "Total : Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1602, "line_end": 1610, "token_count_estimate": 621, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6e6c593e17959808", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.3 District Level Statistics of Himachal Pradesh", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1611, "line_end": 1617, "token_count_estimate": 61, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ada2f7edb9012670", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Himachal Pradesh has been shown in Table 88 and Figure 110. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 491 (95.72%) with total lake area of 881.75 ha (95.06%), with majority of them situated in high altitude range i.e. 4,001 - 5,000 m. Whereas, remaining 4.28% glacial lakes are below 4,000 m elevation, all situated in medium altitude range. Lahul & Spiti district contains maximum number of glacial lakes above 4,000 m elevation in comparison with any other district in the state, with majority of them falling in very high altitude range i.e. > 5,000 m i.e. 57.84%. No lake is present in low altitude range in the state. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 111.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1619, "line_end": 1623, "token_count_estimate": 252, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cd5e867b92b45801", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 88: Elevation range-wise distribution of GL in Districts of Himachal Pradesh\n\n| S. No. | District | Elevation Range (m): up to 3,000 - No. of Lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of Lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of Lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of Lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Chamba | 0 | 0.00 | 9 | 39.47 | 50 | 69.39 | 7 | 5.02 |\n| 2 | Kangra | 0 | 0.00 | 0 | 0.00 | 39 | 45.45 | 0 | 0.00 |\n| 3 | Kinnaur | 0 | 0.00 | 3 | 2.39 | 41 | 77.87 | 76 | 75.89 |\n| 4 | Kullu | 0 | 0.00 | 3 | 1.23 | 88 | 128.14 | 2 | 1.25 |\n| 5 | Lahul & Spiti | 0 | 0.00 | 7 | 2.76 | 71 | 326.21 | 107 | 141.21 |\n| 6 | Shimla | 0 | 0.00 | 0 | 0.00 | 10 | 11.32 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **22** | **45.85** | **299** | **658.37** | **192** | **223.38** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 88: Elevation range-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S. No.", "District", "Elevation Range (m): up to 3,000 - No. of Lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of Lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of Lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of Lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1624, "line_end": 1632, "token_count_estimate": 536, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3faddfd7e891237", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1633, "line_end": 1643, "token_count_estimate": 64, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e061b2da37f70da7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand\nType: text\n\nUttarakhand state has total 13 districts but only 1 district has glacial lakes in it which is part of Indus River basin. All other glacial lakes of Uttarakhand state is majorly contributing in to the Ganga River basin. Only 2 glacial lakes are located in Pithoragarh district, with a total lake extent of 12.96 ha. One lake is with area < 5 ha falling in area range of 0.25 - 0.5 ha contributing to 2.62% of total lake area in the district, whereas another lake is > 5 ha covering a total area of 12.63 ha i.e. 97.38% falling in the area range of 10 - 50 ha, can be seen in the Table 89. It has been observed from the Table 90, district has only 2 types of glacial lakes, End-moraine Dammed and Other Glacial Erosion lake. Eventually, both lakes are also located in the elevation above > 5,000 m i.e. within the very high altitude range, as shown in Table 91. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 112.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.4 District Level Statistics of Uttarakhand"], "chunk_type": "text", "line_start": 1645, "line_end": 1649, "token_count_estimate": 289, "basins": ["Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc30a40219d0d88b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand\nType: table\nTable: Table 89: Area range-wise distribution of GL in District of Uttarakhand\n\n| S. No. | Lake Area Range (ha) | District: Pithoragarh - No. of Lakes | District: Pithoragarh - Total Lake Area (ha) | Total - No. of Lakes | Total - Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 1 | 0.34 | 1 | 0.34 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 0 | 0.00 |\n| 3 | 1 - 5 | 0 | 0.00 | 0 | 0.00 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 |\n| 5 | 10 - 50 | 1 | 12.63 | 1 | 12.63 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **2** | **12.96** | **2** | **12.96** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.4 District Level Statistics of Uttarakhand"], "chunk_type": "table", "table_caption": "Table 89: Area range-wise distribution of GL in District of Uttarakhand", "columns": ["S. No.", "Lake Area Range (ha)", "District: Pithoragarh - No. of Lakes", "District: Pithoragarh - Total Lake Area (ha)", "Total - No. of Lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1650, "line_end": 1658, "token_count_estimate": 322, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11b336ca99386980", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand\nType: table\nTable: Table 90: Type-wise distribution of GL in District of Uttarakhand\n\n| S. No. | District | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total - No. of Lakes | Total - Lakes Area(ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Pithoragarh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 12.96 |\n| | **Total** | **1** | **0** | **0** | **0** | **0** | **0** | **0** | **0** | **1** | **0** | **2** | **12.96** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.4 District Level Statistics of Uttarakhand"], "chunk_type": "table", "table_caption": "Table 90: Type-wise distribution of GL in District of Uttarakhand", "columns": ["S. No.", "District", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total - No. of Lakes", "Total - Lakes Area(ha)"], "table_row_start": 1, "table_row_end": 2, "line_start": 1662, "line_end": 1665, "token_count_estimate": 320, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c72090694875eb9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand\nType: table\nTable: Table 91: Elevation range-wise distribution of GL in District of Uttarakhand\n\n| S. No. | Elevation Range (m) | District: Pithoragarh - No. of Lakes | District: Pithoragarh - Total Lake Area (ha) | Total - No. of Lakes | Total - Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.00 | 0 | 0.00 |\n| 2 | 3,001 - 4,000 | 0 | 0.00 | 0 | 0.00 |\n| 3 | 4,001 - 5,000 | 0 | 0.00 | 0 | 0.00 |\n| 4 | > 5,000 | 2 | 12.96 | 2 | 12.96 |\n| | **Total** | **2** | **12.96** | **2** | **12.96** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.4 District Level Statistics of Uttarakhand"], "chunk_type": "table", "table_caption": "Table 91: Elevation range-wise distribution of GL in District of Uttarakhand", "columns": ["S. No.", "Elevation Range (m)", "District: Pithoragarh - No. of Lakes", "District: Pithoragarh - Total Lake Area (ha)", "Total - No. of Lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1669, "line_end": 1675, "token_count_estimate": 282, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6d49896b7aac6554", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.5.4 District Level Statistics of Uttarakhand", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.5.4 District Level Statistics of Uttarakhand"], "chunk_type": "text", "line_start": 1676, "line_end": 1681, "token_count_estimate": 54, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "569ce9624e78dbd7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\nApart from India, Indus River basin also covers part of transboundary region which has a total area of 1,00,811 Km² i.e. 29.41% of the total river basin area. This transboundary region covers part of China (Tibetan region) and Nepal. Elevation in the transboundary region varies from minimum 2,887 m to maximum 7,022 m amsl. This region has upstream part of Shyok, Indus Upper, and Satluj subbasins. A total of 1,055 glacial lakes lies within transboundary region, covering a total area of 3,608.33 ha i.e. 0.03% of the total area of the Indus River basin under transboundary region.\n\n**Area range-wise Distribution**\n\nIn Transboundary region, glacial lakes have been distributed in all 6 classes of area ranges. Table 92 and Figure 113 shows the area range-wise distribution of glacial lakes for the Transboundary region. About 911 (86.34%) lakes are with < 5 ha lake area contributing to 33.50% of total lake area. The remaining lakes with > 5 ha in size are only 144 (13.66%) contributing to 66.50% of total lake area in the region.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1683, "line_end": 1691, "token_count_estimate": 316, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "86078d4a23d779af", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: table\nTable: Table 92: Area range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 245 | 88.57 | 2.45 |\n| 2 | 0.5 - 1 | 246 | 176.87 | 4.90 |\n| 3 | 1 - 5 | 420 | 943.48 | 26.15 |\n| 4 | 5 - 10 | 80 | 549.13 | 15.22 |\n| 5 | 10 - 50 | 56 | 984.27 | 27.28 |\n| 6 | > 50 | 8 | 866.01 | 24.00 |\n| **Total** | | **1,055** | **3,608.33** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "table", "table_caption": "Table 92: Area range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1692, "line_end": 1700, "token_count_estimate": 271, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "987e5e1653b9640d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Transboundary region is given in Table 93 and Figure 114. All types of glacial lakes are present in the Transboundary region, where Other Glacial Erosion lakes are found to be the maximum with 457 (43.32%) occupying a total lake extent of 1,461.02 ha at 40.49% in the region. After that, Other Moraine Dammed and End-moraine Dammed lakes are in majority with 273 (25.88%) and 125 (11.85%) and extend over a total area of 343.48 ha (9.52%) and 756.80 ha (20.97%) respectively.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1701, "line_end": 1715, "token_count_estimate": 202, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "89c6d549397af24e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: table\nTable: Table 93: Type-wise distribution of GL in Transboundary region\n\n| S. No. | Code | GL Type | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 125 | 756.80 | 20.97 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 6 | 3.83 | 0.11 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 15 | 46.04 | 1.28 |\n| 4 | M(o) | Other Moraine Dammed Lake | 273 | 343.48 | 9.52 |\n| 5 | I(s) | Supra-glacial Lake | 48 | 35.59 | 0.99 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 1 | 232.34 | 6.44 |\n| 7 | E(c) | Cirque Erosion Lake | 35 | 77.49 | 2.14 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 1 | 13.35 | 0.37 |\n| 9 | E(o) | Other Glacial Erosion Lake | 457 | 1,461.02 | 40.49 |\n| 10 | O | Other Glacial Lake | 94 | 638.39 | 17.69 |\n| | | **Total** | **1,055** | **3,608.33** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "table", "table_caption": "Table 93: Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Code", "GL Type", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1716, "line_end": 1728, "token_count_estimate": 456, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3e2fc0f340e1485", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 94 and Figure 115. The lakes with < 5 ha in size (86.34%) are dominant with Other Glacial Erosion (43.14%) and Other Moraine Dammed lakes (28.87%). Lakes with > 5 ha (13.66%) are also dominated by Other Glacial Erosion lakes (44.44%). All types of Moraine-dammed lakes, which constitute about 39.72% are majorly with < 5 ha in water spread.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1729, "line_end": 1738, "token_count_estimate": 180, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68f1ad32eddecfba", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: table\nTable: Table 94: Area range-wise vs. Type-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 6 | 2 | 2 | 103 | 18 | 0 | 2 | 0 | 96 | 16 | 245 |\n| 2 | 0.5 - 1 | 9 | 4 | 1 | 72 | 23 | 0 | 5 | 0 | 105 | 27 | 246 |\n| 3 | 1 - 5 | 66 | 0 | 10 | 88 | 7 | 0 | 26 | 0 | 192 | 31 | 420 |\n| 4 | 5 - 10 | 21 | 0 | 1 | 7 | 0 | 0 | 1 | 0 | 43 | 7 | 80 |\n| 5 | 10 - 50 | 22 | 0 | 1 | 3 | 0 | 0 | 1 | 1 | 18 | 10 | 56 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 3 | 8 |\n| | **Total** | **125** | **6** | **15** | **273** | **48** | **1** | **35** | **1** | **457** | **94** | **1,055** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "table", "table_caption": "Table 94: Area range-wise vs. Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1739, "line_end": 1747, "token_count_estimate": 489, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f29dc9177c416593", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Transboundary region has been shown in Table 95 and Figure 116. All glacial lakes are situated above 4,000 m elevation range i.e. 1,055 (100%) with total lake area of 3608.33 ha, and no lake are located below 4,000 m elevation.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1748, "line_end": 1760, "token_count_estimate": 137, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b7a972298d48f6bb", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: table\nTable: Table 95: Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 0 | 0.00 | 0.00 |\n| 3 | 4,001 - 5,000 | 62 | 233.86 | 6.48 |\n| 4 | > 5,000 | 993 | 3,374.47 | 93.52 |\n| | **Total** | **1,055** | **3,608.33** | **100.00** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "table", "table_caption": "Table 95: Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1761, "line_end": 1767, "token_count_estimate": 232, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ad8dde66c6e2bd8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 96 and Figure 117. It is noted that, 94.12% of glacial lakes (993) are situated in very high altitude range i.e. > 5,000 m, which also constitutes majority of total lake area within that range i.e. 93.52%. However, 62 glacial lakes lies below 5,000 m, has 83.87% of its lakes < 5 ha in size. 86.51% of lakes lying in very high altitude range are < 5 ha, majorly of size ranging 1 - 5 ha (i.e. 396), followed by lakes of size 0.25 - 0.5 ha (i.e. 236). It has been further noticed that, 93.05% of lakes > 5 ha are lying within in the very high altitude range only, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1768, "line_end": 1780, "token_count_estimate": 277, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f914a31e31e5e06", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: table\nTable: Table 96: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | up to 3,000: No. of lakes | up to 3,000: Total Lake Area (ha) | 3,001 - 4,000: No. of lakes | 3,001 - 4,000: Total Lake Area (ha) | 4,001 - 5,000: No. of lakes | 4,001 - 5,000: Total Lake Area (ha) | > 5,000: No. of lakes | > 5,000: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 0 | 0.00 | 9 | 3.30 | 236 | 85.27 | 245 | 88.57 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 0 | 0.00 | 19 | 13.32 | 227 | 163.55 | 246 | 176.87 |\n| 3 | 1 - 5 | 0 | 0.00 | 0 | 0.00 | 24 | 56.08 | 396 | 887.39 | 420 | 943.48 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 4 | 24.48 | 76 | 524.65 | 80 | 549.13 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 5 | 77.83 | 51 | 906.44 | 56 | 984.27 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 1 | 58.84 | 7 | 807.17 | 8 | 866.01 |\n| | **Total** | **0** | **0.00** | **0** | **0.00** | **62** | **233.86** | **993** | **3,374.48** | **1,055** | **3,608.33** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "table", "table_caption": "Table 96: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "up to 3,000: No. of lakes", "up to 3,000: Total Lake Area (ha)", "3,001 - 4,000: No. of lakes", "3,001 - 4,000: Total Lake Area (ha)", "4,001 - 5,000: No. of lakes", "4,001 - 5,000: Total Lake Area (ha)", "> 5,000: No. of lakes", "> 5,000: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1781, "line_end": 1789, "token_count_estimate": 591, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "112e4dadb57ba112", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: figure\nFigure: Figure 117: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n**Figure 117: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "figure", "figure_caption": "Figure 117: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region", "line_start": 1791, "line_end": 1791, "token_count_estimate": 89, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b3f6d539b381c17", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 97 and Figure 118. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (43.32% in number and 40.49% in extent) are predominantly located in the elevation range > 5,000 m (43.30%). It has also been noticed that, no type of Glacier Erosion lakes lies in low and medium altitude range (i.e. below < 4,000 m). The other dominant lake type, namely, Other Moraine Dammed and End-moraine Dammed lakes are also majorly distributed in very high altitude range > 5,000 m elevation range, i.e. 26.38% and 12.19%. Majority i.e. 96.18% of all types of Moraine-dammed lakes lies in > 5,000 m.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1792, "line_end": 1806, "token_count_estimate": 270, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7875793ca09ca42", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: table\nTable: Table 97: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 4,001 - 5,000 | 4 | 1 | 0 | 11 | 4 | 0 | 1 | 1 | 27 | 13 | 62 |\n| 4 | > 5,000 | 121 | 5 | 15 | 262 | 44 | 1 | 34 | 0 | 430 | 81 | 993 |\n| | **Total** | **124** | **6** | **15** | **273** | **48** | **1** | **35** | **1** | **457** | **94** | **1,055** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "table", "table_caption": "Table 97: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1807, "line_end": 1813, "token_count_estimate": 412, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83d77213772d8570", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: figure\nFigure: Figure 118: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n**Figure 118: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "figure", "figure_caption": "Figure 118: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region", "line_start": 1815, "line_end": 1815, "token_count_estimate": 87, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b1cc17477ad55a84", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1816, "line_end": 1821, "token_count_estimate": 51, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7725075a284f1768", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 119 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Indus River basin. A total of 56 toposheets covered the entire study area, of which 46 toposheets contain glacial lakes.\n\nFigure 119 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Indus River basin. A total of 56 toposheets covered the entire study area, of which 46 toposheets contain glacial lakes.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 119 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Indus River basin. A total of 56 toposheets covered the entire study area, of which 46 toposheets contain glacial lakes.", "line_start": 1824, "line_end": 1824, "token_count_estimate": 147, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d31de0fbf4f53503", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 119: Layout of SOI 250K Toposheets and year of satellite data used\n\nFigure 119: Layout of SOI 250K Toposheets and year of satellite data used", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 119: Layout of SOI 250K Toposheets and year of satellite data used", "line_start": 1826, "line_end": 1826, "token_count_estimate": 69, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "67c6400dab9ed527", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1827, "line_end": 1833, "token_count_estimate": 39, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34caa06f918a573a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nUT: Ladakh | Map 1 | Plate No: 42D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 1835, "line_end": 1841, "token_count_estimate": 79, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "de40e85aa694a114", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 1 | 2 | 0 | 12 | 10 | 0 | 0 | 0 | 5 | 0 | 30 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 0 | 2 | 2 | 11 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 3 | 1 | 0 | 1 | 0 | 2 | 0 | 8 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| **Total** | | **3** | **3** | **0** | **19** | **13** | **0** | **1** | **0** | **9** | **2** | **50** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1842, "line_end": 1850, "token_count_estimate": 642, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85bf17f0ddee2394", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 2\nPlate No: 42D\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 1851, "line_end": 1870, "token_count_estimate": 144, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c5773032dcb241b1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 2 | 1.3 |\n| 3 | 4,001 - 5,000 | 46 | 42.7 |\n| 4 | > 5,000 | 2 | 0.7 |\n| | Total | 50 | 44.7 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1871, "line_end": 1877, "token_count_estimate": 172, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f50f09389288c668", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 3\nPlate No: 42H\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 1878, "line_end": 1909, "token_count_estimate": 201, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5a93ad43b3cb9288", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 6 | 1 | 0 | 14 | 14 | 0 | 1 | 0 | 31 | 2 | 69 |\n| 2 | 0.5 - 1 | 2 | 2 | 0 | 9 | 4 | 0 | 1 | 0 | 51 | 0 | 69 |\n| 3 | 1 - 5 | 8 | 2 | 0 | 7 | 2 | 0 | 8 | 1 | 99 | 5 | 132 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 22 | 3 | 28 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 14 | 3 | 19 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| | Total | 17 | 5 | 0 | 30 | 20 | 0 | 14 | 1 | 217 | 15 | 319 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1910, "line_end": 1918, "token_count_estimate": 538, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "756589316d04f1cf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 4\nPlate No: 42H\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 1919, "line_end": 1936, "token_count_estimate": 142, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35cb10cec75b02dc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 14.1 |\n| 2 | 3,001 - 4,000 | 29 | 166.1 |\n| 3 | 4,001 - 5,000 | 288 | 1,097.3 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 318 | 1,277.5 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1937, "line_end": 1943, "token_count_estimate": 168, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a8ae070d4577303", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 5\nPlate No: 42K\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 1944, "line_end": 1976, "token_count_estimate": 201, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "c90ff84382c706c8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1977, "line_end": 1985, "token_count_estimate": 592, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "325361b038190198", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 6\nPlate No: 42K\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 1986, "line_end": 2010, "token_count_estimate": 142, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f59af09dfcb71932", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 1 | 0.5 |\n| Total | | 1 | 0.5 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2011, "line_end": 2017, "token_count_estimate": 159, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e75da6a71c203283", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 7\nPlate No: 42L\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2018, "line_end": 2051, "token_count_estimate": 201, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "54d92a33f7197cb6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 4 | 1 | 5 | 43 | 0 | 0 | 0 | 2 | 1 | 58 |\n| 2 | 0.5 - 1 | 1 | 3 | 0 | 1 | 8 | 0 | 0 | 0 | 4 | 2 | 19 |\n| 3 | 1 - 5 | 2 | 5 | 0 | 1 | 6 | 0 | 0 | 0 | 6 | 0 | 20 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 5 | 12 | 1 | 7 | 57 | 0 | 0 | 0 | 14 | 4 | 100 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2052, "line_end": 2060, "token_count_estimate": 574, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "421c6c9b5bd42a52", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\nUT: Ladakh\nMap 8\nPlate No: 42L\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\nSubbasins:\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2061, "line_end": 2103, "token_count_estimate": 248, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "31482fda4f0de1cc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 12 | 10.1 |\n| 2 | 3,001 - 4,000 | 61 | 66.3 |\n| 3 | 4,001 - 5,000 | 27 | 30.7 |\n| 4 | > 5,000 | 0 | 0.0 |\n| **Total** | | **100** | **107.1** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2104, "line_end": 2110, "token_count_estimate": 168, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "160aa2f6ee7e75cd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\nUT: Ladakh\nMap 9\nPlate No: 42O\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2111, "line_end": 2158, "token_count_estimate": 252, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "ea1e2b11c83acf9f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| **Total** | | **2** | **0** | **0** | **0** | **0** | **0** | **0** | **0** | **0** | **0** | **2** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2159, "line_end": 2167, "token_count_estimate": 535, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44a26047c2ef5229", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 10\nPlate No: 42O\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2168, "line_end": 2191, "token_count_estimate": 170, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "986a471f3824af64", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| --- | --- | --- | --- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 1 | 0.7 |\n| 4 | > 5,000 | 1 | 1.6 |\n| Total | | 2 | 2.3 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2192, "line_end": 2198, "token_count_estimate": 164, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5426ce10d22538ce", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Subbasins**\n*   Beas\n*   Chenab\n*   Gilgit\n*   Indus Middle\n*   Indus Upper\n*   Jhelum\n*   Ravi\n*   Satluj\n*   Shyok\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 11\nPlate No: 42P\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2199, "line_end": 2264, "token_count_estimate": 331, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "a8ee6c28a400dc22", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 1 | 0.25 - 0.5 | 2 | 1 | 1 | 0 | 43 | 0 | 0 | 0 | 1 | 0 | 48 |\n| 2 | 0.5 - 1 | 0 | 2 | 1 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 36 |\n| 3 | 1 - 5 | 0 | 3 | 0 | 1 | 12 | 0 | 0 | 0 | 0 | 2 | 18 |\n| 4 | 5 - 10 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 4 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 2 | 7 | 2 | 1 | 92 | 0 | 0 | 0 | 3 | 2 | 109 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2265, "line_end": 2273, "token_count_estimate": 614, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b532a308b2a919c9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 12\nPlate No: 42P\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\nSubbasins:\n*   Beas\n*   Chenab\n*   Gilgit\n*   Indus Middle\n*   Indus Upper\n*   Jhelum\n*   Ravi\n*   Satluj\n*   Shyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2274, "line_end": 2319, "token_count_estimate": 266, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "1d366b1134970bab", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 72 | 100.9 |\n| 3 | 4,001 - 5,000 | 37 | 68.8 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 109 | 169.7 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2320, "line_end": 2326, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e87d8adc6c5c143", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\nElevation Range (m)\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 13\nPlate No: 43A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2327, "line_end": 2373, "token_count_estimate": 249, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "d83ac3258876e45f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 1 | 1 | 14 | 10 | 0 | 0 | 0 | 2 | 0 | 30 |\n| 2 | 0.5 - 1 | 0 | 2 | 0 | 7 | 5 | 0 | 1 | 0 | 3 | 2 | 20 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 5 | 2 | 0 | 1 | 0 | 7 | 2 | 18 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 3 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| | Total | 3 | 3 | 1 | 27 | 17 | 0 | 2 | 1 | 14 | 7 | 75 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2374, "line_end": 2382, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "949339b1b7a641cc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\n**UT: Ladakh**\n**Map 14**\n**Plate No: 43A**\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\n**Subbasins**\n*   Beas\n*   Chenab\n*   Gilgit\n*   Indus Middle\n*   Indus Upper\n*   Jhelum\n*   Ravi\n*   Satluj\n*   Shyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2383, "line_end": 2428, "token_count_estimate": 277, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "e2cb35bd8f8540ca", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 5 | 309.0 |\n| 3 | 4,001 - 5,000 | 68 | 98.1 |\n| 4 | > 5,000 | 2 | 2.3 |\n| | **Total** | **75** | **409.4** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2429, "line_end": 2435, "token_count_estimate": 178, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa790e1fe9a9e3f4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\n**UT: Ladakh**\n**Map 15**\n**Plate No: 43E**\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2436, "line_end": 2479, "token_count_estimate": 263, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e3091a864507725a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 4 | 0 | 0 | 2 | 0 | 55 | 0 | 61 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 71 | 2 | 79 |\n| 3 | 1 - 5 | 2 | 0 | 0 | 5 | 0 | 0 | 5 | 0 | 215 | 2 | 229 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 1 | 0 | 0 | 8 | 0 | 27 | 0 | 37 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 29 | 2 | 36 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 3 |\n| | **Total** | **3** | **0** | **0** | **15** | **0** | **0** | **22** | **0** | **399** | **6** | **445** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2480, "line_end": 2488, "token_count_estimate": 564, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d8c5027667f43ad", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 16\nPlate No: 43E\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2489, "line_end": 2538, "token_count_estimate": 250, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "87f0f2369480ca66", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 15.4 |\n| 2 | 3,001 - 4,000 | 29 | 212.8 |\n| 3 | 4,001 - 5,000 | 416 | 1,533.3 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 446 | 1,761.5 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2539, "line_end": 2545, "token_count_estimate": 167, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83973447ee629a2c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Jammu & Kashmir\nMap 17\nPlate No: 43F\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2546, "line_end": 2594, "token_count_estimate": 238, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8776a8192cd71c25", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | 5 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 4 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 1 | 0 | 0 | 6 | 2 | 0 | 0 | 0 | 5 | 0 | 14 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2595, "line_end": 2603, "token_count_estimate": 592, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "995c7f8e768f6952", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Jammu & Kashmir\nMap 18\nPlate No: 43F\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2604, "line_end": 2647, "token_count_estimate": 239, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "199322e8e7b515b0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 4 | 23.6 |\n| 3 | 4,001 - 5,000 | 10 | 11.2 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 14 | 34.8 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2648, "line_end": 2654, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abfce213332bd288", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 19\nPlate No: 43I\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2655, "line_end": 2702, "token_count_estimate": 237, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9d2e94ae6d63efed", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 3 | 0 | 1 | 15 | 0 | 0 | 0 | 35 | 1 | 56 |\n| 2 | 0.5 - 1 | 0 | 2 | 0 | 5 | 6 | 0 | 2 | 0 | 25 | 0 | 40 |\n| 3 | 1 - 5 | 0 | 2 | 0 | 5 | 2 | 0 | 10 | 0 | 73 | 0 | 92 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 2 | 1 | 0 | 7 | 0 | 12 | 0 | 24 |\n| 5 | 10 - 50 | 1 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 9 | 0 | 15 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 4 | 8 | 0 | 13 | 24 | 0 | 23 | 0 | 154 | 1 | 227 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2703, "line_end": 2711, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5b90bc3b43e1d23", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 20\nPlate No: 43I\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2712, "line_end": 2738, "token_count_estimate": 168, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e386b706fbd30d1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 26 | 53.8 |\n| 3 | 4,001 - 5,000 | 201 | 606.9 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 227 | 660.7 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2739, "line_end": 2745, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "740bd61ee33c81c1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nIndus Basin\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 21\nPlate No: 43J\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2746, "line_end": 2811, "token_count_estimate": 312, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "9e40b0fbc3531fdd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes - Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 7 | 0 | 9 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 30 | 2 | 36 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 5 | 0 | 0 | 16 | 0 | 51 | 1 | 74 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 0 | 5 | 0 | 13 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 15 | 0 | 8 | 0 | 24 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 4 |\n| Total | | 2 | 0 | 0 | 11 | 1 | 0 | 42 | 0 | 101 | 3 | 160 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes - Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2812, "line_end": 2820, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7da139b01e911e0b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\n\nMap 22\n\nPlate No: 43J\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2821, "line_end": 2844, "token_count_estimate": 158, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1aea89d4d9f88d4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 83 | 899.8 |\n| 3 | 4,001 - 5,000 | 77 | 280.7 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 160 | 1,180.5 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2845, "line_end": 2851, "token_count_estimate": 166, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a209177a09720b0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Jammu & Kashmir\n\nMap 23\n\nPlate No: 43K\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2852, "line_end": 2891, "token_count_estimate": 202, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cb2d8f51a1a0e6ed", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 14 | 0 | 15 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 16 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 22 | 0 | 28 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 11 | 0 | 13 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 15 | 0 | 20 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |\n| Total | | 1 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 79 | 1 | 94 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2892, "line_end": 2900, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5f5e1d7f81906f59", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Jammu & Kashmir\nMap 24\nPlate No: 43K\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2901, "line_end": 2921, "token_count_estimate": 143, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8dd36277dfbb27fc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 69 | 613.6 |\n| 3 | 4,001 - 5,000 | 25 | 148.4 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 94 | 762.0 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2922, "line_end": 2928, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93812d9062818267", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 25\nPlate No: 43M\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2929, "line_end": 2962, "token_count_estimate": 200, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "d41200af77aff042", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 3 | 0 | 3 | 17 | 0 | 0 | 0 | 18 | 1 | 42 |\n| 2 | 0.5 - 1 | 1 | 10 | 0 | 2 | 13 | 0 | 1 | 0 | 28 | 0 | 55 |\n| 3 | 1 - 5 | 1 | 14 | 1 | 11 | 2 | 0 | 4 | 0 | 85 | 0 | 118 |\n| 4 | 5 - 10 | 1 | 2 | 0 | 1 | 2 | 0 | 0 | 0 | 10 | 0 | 16 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10 | 0 | 11 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 3 | 29 | 1 | 17 | 34 | 0 | 6 | 0 | 151 | 1 | 242 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2963, "line_end": 2971, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46d3f528f5ad9b4d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 26\nPlate No: 43M\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\n\nSubbasins\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 2972, "line_end": 3014, "token_count_estimate": 240, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "37a642dfe4838d1a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 7 | 8.2 |\n| 2 | 3,001 - 4,000 | 28 | 34.4 |\n| 3 | 4,001 - 5,000 | 207 | 545.4 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 242 | 588.0 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3015, "line_end": 3021, "token_count_estimate": 175, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "416b6ca60cd930b2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\nElevation Range (m)\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 27\nPlate No: 43N\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3022, "line_end": 3067, "token_count_estimate": 249, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b51bb341665f75d9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 2 | 0 | 6 | 3 | 0 | 0 | 0 | 48 | 0 | 59 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 16 | 1 | 0 | 1 | 0 | 76 | 0 | 95 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 12 | 1 | 0 | 9 | 0 | 158 | 2 | 185 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 1 | 0 | 0 | 10 | 0 | 33 | 0 | 45 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 27 | 0 | 35 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 5 |\n| | Total | 6 | 2 | 0 | 35 | 5 | 0 | 28 | 0 | 344 | 4 | 424 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3068, "line_end": 3076, "token_count_estimate": 539, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "858a36626b747fc5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 28\nPlate No: 43N\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3077, "line_end": 3125, "token_count_estimate": 246, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "795b99b4dfb70cea", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 62 | 553.3 |\n| 3 | 4,001 - 5,000 | 362 | 1,361.7 |\n| 4 | > 5,000 | 0 | 0.0 |\n| **Total** | | **424** | **1,915.0** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3126, "line_end": 3132, "token_count_estimate": 172, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9f63b82083ca5054", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 29\nPlate No: 43O\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3133, "line_end": 3175, "token_count_estimate": 238, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "14343dd62feaf392", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 11 | 0 | 13 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 2 | 1 | 0 | 1 | 0 | 9 | 0 | 14 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 3 | 0 | 0 | 4 | 0 | 31 | 0 | 39 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 6 | 0 | 11 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 2 | 0 | 5 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| **Total** | | **1** | **1** | **0** | **7** | **1** | **0** | **13** | **0** | **59** | **0** | **82** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3176, "line_end": 3184, "token_count_estimate": 535, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6748bf9ff37685fa", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Jammu & Kashmir, Ladakh\nMap 30\nPlate No: 43O\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3185, "line_end": 3209, "token_count_estimate": 169, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "099ef3c271fa39f7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 33 | 110.5 |\n| 3 | 4,001 - 5,000 | 49 | 162.6 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 82 | 273.1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3210, "line_end": 3216, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f69f52a484f1056f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nIndus Basin\nIndia\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Jammu & Kashmir\nMap 31\nPlate No: 43P\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3217, "line_end": 3282, "token_count_estimate": 316, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "319b671eff4ad690", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 0 | 5 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 6 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3283, "line_end": 3291, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ea9730135610ed7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Jammu & Kashmir\nMap 32\nPlate No: 43P\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\nLocation Map\n\nIndus Basin\n\n**Subbasins**\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3292, "line_end": 3354, "token_count_estimate": 310, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "7bb0c819ef21d3ca", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 5 | 18.4 |\n| 3 | 4,001 - 5,000 | 1 | 1.8 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 6 | 20.2 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3355, "line_end": 3361, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72e8098bea15e5fa", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 33\nPlate No: 52A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3362, "line_end": 3395, "token_count_estimate": 201, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "eb0f20ae895b4d12", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake End-moraine Dammed Lake | Moraine Dammed Lake Lateral Moraine Dammed Lake | Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake Other Moraine Dammed Lake | Ice Dammed Lake Supra-glacial Lake | Ice Dammed Lake Glacier Ice-dammed Lake | Erosion Lake Cirque Erosion Lake | Erosion Lake Glacier Trough Valley Erosion Lake | Erosion Lake Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 21 | 6 | 1 | 110 | 0 | 0 | 0 | 6 | 0 | 146 |\n| 2 | 0.5 - 1 | 0 | 18 | 3 | 2 | 81 | 0 | 0 | 0 | 5 | 1 | 110 |\n| 3 | 1 - 5 | 7 | 18 | 4 | 4 | 34 | 0 | 0 | 0 | 25 | 1 | 93 |\n| 4 | 5 - 10 | 0 | 2 | 1 | 0 | 3 | 0 | 1 | 0 | 6 | 0 | 13 |\n| 5 | 10 - 50 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 9 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 10 | 62 | 14 | 7 | 228 | 0 | 2 | 0 | 45 | 3 | 371 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake End-moraine Dammed Lake", "Moraine Dammed Lake Lateral Moraine Dammed Lake", "Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake Other Moraine Dammed Lake", "Ice Dammed Lake Supra-glacial Lake", "Ice Dammed Lake Glacier Ice-dammed Lake", "Erosion Lake Cirque Erosion Lake", "Erosion Lake Glacier Trough Valley Erosion Lake", "Erosion Lake Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3396, "line_end": 3404, "token_count_estimate": 503, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b8e6afcd6eda766", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\nUT: Ladakh\nMap 34\nPlate No: 52A\n\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3405, "line_end": 3427, "token_count_estimate": 159, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "13675bdb5c042dd0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 65 | 82.2 |\n| 3 | 4,001 - 5,000 | 243 | 421.9 |\n| 4 | > 5,000 | 63 | 52.0 |\n| | Total | 371 | 556.1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3428, "line_end": 3434, "token_count_estimate": 172, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe3bcea45871c12f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\nWater Resources Group\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\nUT: Ladakh\nMap 35\nPlate No: 52B\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3435, "line_end": 3464, "token_count_estimate": 178, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "6ea7d7b41bca1281", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | End-moraine Dammed Lake | Lateral Moraine Dammed Lake | Lateral Moraine Dammed Lake with Ice | Other Moraine Dammed Lake | Supra-glacial Lake | Glacier Ice-dammed Lake | Cirque Erosion Lake | Glacier Trough Valley Erosion Lake | Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 2 | 2 | 0 | 7 | 7 | 0 | 0 | 0 | 6 | 0 | 24 |\n| 2 | 0.5 - 1 | 5 | 1 | 0 | 8 | 7 | 0 | 0 | 0 | 19 | 2 | 42 |\n| 3 | 1 - 5 | 19 | 0 | 1 | 14 | 4 | 0 | 4 | 0 | 76 | 0 | 118 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 18 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 15 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 31 | 3 | 1 | 29 | 18 | 0 | 4 | 0 | 129 | 2 | 217 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "End-moraine Dammed Lake", "Lateral Moraine Dammed Lake", "Lateral Moraine Dammed Lake with Ice", "Other Moraine Dammed Lake", "Supra-glacial Lake", "Glacier Ice-dammed Lake", "Cirque Erosion Lake", "Glacier Trough Valley Erosion Lake", "Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3465, "line_end": 3473, "token_count_estimate": 491, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f11d1ce000c9db81", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 36\nPlate No: 52B\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nIndus Basin\nIndia\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3474, "line_end": 3519, "token_count_estimate": 227, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "76e32d4d4ca70d15", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 156 | 510.6 |\n| 4 | > 5,000 | 61 | 154.4 |\n| | Total | 217 | 665.0 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3520, "line_end": 3526, "token_count_estimate": 170, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f37e49de6d2aa4e6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Jammu & Kashmir, Ladakh\nMap 37\nPlate No: 52C\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3527, "line_end": 3573, "token_count_estimate": 245, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cd8ed9b4bbf56c8b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 3 | 4 | 1 | 26 | 48 | 0 | 0 | 0 | 24 | 0 | 106 |\n| 2 | 0.5 - 1 | 4 | 5 | 0 | 14 | 22 | 0 | 1 | 0 | 18 | 1 | 65 |\n| 3 | 1 - 5 | 8 | 3 | 0 | 21 | 4 | 0 | 1 | 0 | 17 | 2 | 56 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 7 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| | Total | 23 | 12 | 1 | 63 | 75 | 0 | 3 | 0 | 59 | 4 | 240 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3574, "line_end": 3582, "token_count_estimate": 618, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ec43f128fb44777", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Jammu & Kashmir, Ladakh Map 38 Plate No: 52C\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3583, "line_end": 3603, "token_count_estimate": 165, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "67595055b3b206b7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 38 | 34.5 |\n| 3 | 4,001 - 5,000 | 171 | 379.6 |\n| 4 | > 5,000 | 31 | 27.5 |\n| | Total | 240 | 441.6 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3604, "line_end": 3610, "token_count_estimate": 171, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7a564a9c0e042e1f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Jammu & Kashmir Map 39 Plate No: 52D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3611, "line_end": 3643, "token_count_estimate": 209, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "fd5b1d8dd1fe9ae4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 3 | 4 | 0 | 6 | 10 | 0 | 0 | 0 | 10 | 0 | 33 |\n| 2 | 0.5 - 1 | 1 | 3 | 0 | 7 | 4 | 0 | 0 | 0 | 8 | 0 | 23 |\n| 3 | 1 - 5 | 3 | 3 | 0 | 15 | 1 | 0 | 0 | 0 | 9 | 1 | 32 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 8 | 10 | 0 | 28 | 15 | 0 | 0 | 0 | 29 | 1 | 91 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3644, "line_end": 3652, "token_count_estimate": 537, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d585d7111715048b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Jammu & Kashmir\nMap 40\nPlate No: 52D\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3653, "line_end": 3674, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a761ce422384b6f9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 14 | 41.3 |\n| 3 | 4,001 - 5,000 | 63 | 87.6 |\n| 4 | > 5,000 | 14 | 11.2 |\n| | Total | 91 | 140.1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3675, "line_end": 3681, "token_count_estimate": 163, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc55ec01f17f5c35", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 41\nPlate No: 52E\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3682, "line_end": 3744, "token_count_estimate": 303, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "4d8503e21d3e2089", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 9 | 1 | 4 | 47 | 0 | 0 | 0 | 3 | 4 | 69 |\n| 2 | 0.5 - 1 | 0 | 10 | 0 | 7 | 31 | 0 | 0 | 0 | 2 | 4 | 54 |\n| 3 | 1 - 5 | 1 | 11 | 6 | 4 | 12 | 0 | 0 | 0 | 0 | 4 | 38 |\n| 4 | 5 - 10 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 5 |\n| 5 | 10 - 50 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 2 | 32 | 9 | 15 | 91 | 0 | 0 | 0 | 6 | 14 | 169 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3745, "line_end": 3753, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1019e6e431d97229", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 42\nPlate No: 52E\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3754, "line_end": 3778, "token_count_estimate": 164, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "524727595f76b641", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 23 | 13.1 |\n| 3 | 4,001 - 5,000 | 91 | 134.6 |\n| 4 | > 5,000 | 58 | 98.8 |\n| Total | | 172 | 246.5 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3779, "line_end": 3785, "token_count_estimate": 163, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5db6ae3b5adf0353", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 43\nPlate No: 52F\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3786, "line_end": 3849, "token_count_estimate": 302, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "51b7a5fae8a0d521", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 7 | 1 | 0 | 5 | 6 | 0 | 0 | 0 | 11 | 1 | 31 |\n| 2 | 0.5 - 1 | 6 | 3 | 0 | 10 | 6 | 0 | 0 | 0 | 14 | 1 | 40 |\n| 3 | 1 - 5 | 22 | 6 | 0 | 4 | 2 | 0 | 1 | 0 | 24 | 3 | 62 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 13 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 41 | 10 | 0 | 19 | 14 | 0 | 1 | 0 | 58 | 5 | 148 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3850, "line_end": 3858, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8874fa9903f88082", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 44\nPlate No: 52F\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nIndus Basin\nIndia\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3859, "line_end": 3907, "token_count_estimate": 212, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "c22164935664bcbc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 35 | 56.3 |\n| 4 | > 5,000 | 113 | 259.1 |\n| | Total | 148 | 315.4 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3908, "line_end": 3914, "token_count_estimate": 165, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad6aa9a7ef610aa9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 45\nPlate No: 52G\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3915, "line_end": 3967, "token_count_estimate": 259, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "93e22f654169f38c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 2 | 2 | 9 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 5 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 5 | 0 | 8 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 7 | 0 | 0 | 6 | 2 | 0 | 1 | 0 | 9 | 5 | 30 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3968, "line_end": 3976, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ad689d2f1aa5985", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n**GLACIAL LAKE ATLAS OF INDUS RIVER BASIN**\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\nUT: Ladakh\nMap 46\nPlate No: 52G\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\n**Subbasins**\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 3977, "line_end": 4022, "token_count_estimate": 234, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "5315e4331491d4e6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 1 | 43.7 |\n| 4 | > 5,000 | 29 | 73.0 |\n| | Total | 30 | 116.7 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4023, "line_end": 4029, "token_count_estimate": 170, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "da714e957f45d220", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\n**GLACIAL LAKE ATLAS OF INDUS RIVER BASIN**\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\nState: Himachal Pradesh; UT: Ladakh\nMap 47\nPlate No: 52H\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4030, "line_end": 4077, "token_count_estimate": 291, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "50e37d732a4af7e4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 1 | 3 | 0 | 32 | 13 | 0 | 0 | 0 | 17 | 0 | 66 |\n| 2 | 0.5 - 1 | 1 | 2 | 0 | 22 | 3 | 0 | 0 | 0 | 12 | 1 | 41 |\n| 3 | 1 - 5 | 11 | 2 | 0 | 18 | 1 | 0 | 1 | 0 | 18 | 0 | 51 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 9 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 6 | > 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| Total | | 20 | 7 | 0 | 73 | 17 | 0 | 1 | 0 | 51 | 1 | 170 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4078, "line_end": 4086, "token_count_estimate": 537, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ad2884f127e6750", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4087, "line_end": 4098, "token_count_estimate": 113, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30a907fcd8dff70c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: GLACIAL LAKES IN PART OF INDUS BASIN\nType: text\n\nState: Himachal Pradesh; UT: Ladakh | Map 48 | Plate No: 52H\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "GLACIAL LAKES IN PART OF INDUS BASIN", "section_headings": ["GLACIAL LAKES IN PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4100, "line_end": 4110, "token_count_estimate": 83, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6c0071aa1a99c3cc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: GLACIAL LAKES IN PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 6 | 2.5 |\n| 3 | 4,001 - 5,000 | 84 | 357.0 |\n| 4 | > 5,000 | 80 | 109.5 |\n| | Total | 170 | 469.0 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "GLACIAL LAKES IN PART OF INDUS BASIN", "section_headings": ["GLACIAL LAKES IN PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4111, "line_end": 4117, "token_count_estimate": 164, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc3602516f2fdd2e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: GLACIAL LAKES IN PART OF INDUS BASIN\nType: text\n\nMoraine Dammed Lake • Ice Dammed Lake • Erosion Lake • Other Glacial Lake\n\nSubbasins\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\nLegend\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\nElevation Range (m)\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "GLACIAL LAKES IN PART OF INDUS BASIN", "section_headings": ["GLACIAL LAKES IN PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4118, "line_end": 4171, "token_count_estimate": 268, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "14a5200ea0d34bd5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nUT: Ladakh | Map 49 | Plate No: 52J\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4173, "line_end": 4179, "token_count_estimate": 77, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "52d0c33785e12fba", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 2 | 0 | 14 | 0 | 0 | 0 | 0 | 2 | 7 | 28 |\n| 2 | 0.5 - 1 | 4 | 1 | 0 | 10 | 0 | 0 | 0 | 0 | 5 | 4 | 24 |\n| 3 | 1 - 5 | 4 | 1 | 0 | 6 | 3 | 0 | 1 | 0 | 6 | 1 | 22 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 6 |\n| 6 | > 50 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 3 |\n| | Total | 19 | 4 | 0 | 31 | 3 | 0 | 1 | 0 | 16 | 13 | 87 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4180, "line_end": 4188, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26c56ea06f372ee6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\nUT: Ladakh | Map 50 | Plate No: 52J\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake | Ice Dammed Lake | Erosion Lake | Other Glacial Lake\n\n**Location Map**\nIndus Basin\nSubbasins: Beas, Chenab, Gilgit, Indus Middle, Indus Upper, Jhelum, Ravi, Satluj, Shyok\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4189, "line_end": 4216, "token_count_estimate": 242, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "a16352a17647ee91", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 87 | 422.3 |\n| | Total | 87 | 422.3 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4217, "line_end": 4223, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58592ec326a6f4a6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\nUT: Ladakh | Map 51 | Plate No: 52K\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4224, "line_end": 4273, "token_count_estimate": 285, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "387da5d66b1ffda9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 0 | 0 | 12 | 1 | 0 | 0 | 0 | 9 | 1 | 24 |\n| 2 | 0.5 - 1 | 5 | 0 | 0 | 13 | 2 | 0 | 1 | 0 | 4 | 2 | 27 |\n| 3 | 1 - 5 | 14 | 0 | 0 | 9 | 0 | 0 | 1 | 0 | 21 | 4 | 49 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 8 | 1 | 18 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 3 | 1 | 7 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| | Total | 25 | 0 | 0 | 40 | 3 | 0 | 3 | 0 | 45 | 11 | 127 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4274, "line_end": 4282, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7143addf806d4e1a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 52\nPlate No: 52K\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4283, "line_end": 4327, "token_count_estimate": 217, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "a41f0a5ba47c735f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 3 | 9.0 |\n| 4 | > 5,000 | 124 | 726.0 |\n| | Total | 127 | 735.0 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4328, "line_end": 4334, "token_count_estimate": 164, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e7c92c4c8a348dd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh; UT: Ladakh\nMap 53\nPlate No: 52L\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4335, "line_end": 4381, "token_count_estimate": 267, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a1824396c8a484c1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 1 | 0 | 25 | 2 | 0 | 1 | 0 | 15 | 2 | 49 |\n| 2 | 0.5 - 1 | 3 | 0 | 0 | 25 | 5 | 0 | 0 | 0 | 11 | 1 | 45 |\n| 3 | 1 - 5 | 15 | 0 | 1 | 23 | 1 | 0 | 0 | 0 | 23 | 3 | 66 |\n| 4 | 5 - 10 | 9 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 4 | 1 | 18 |\n| 5 | 10 - 50 | 6 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 36 | 1 | 1 | 77 | 8 | 0 | 2 | 0 | 53 | 7 | 185 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4382, "line_end": 4390, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4e3dea6910b9c98f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh\nMap 60\nPlate No: 52P\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4391, "line_end": 4414, "token_count_estimate": 153, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "03512bc4aeb70366", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 217 | 479.7 |\n| | Total | 217 | 479.7 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4415, "line_end": 4421, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "533bc9419d2bf20d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh, Uttarakhand\nMap 61\nPlate No: 53E\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4422, "line_end": 4459, "token_count_estimate": 206, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "21e261142ecf70db", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes   Moraine Dammed Lake   End-moraine Dammed Lake | Types of Glacial Lakes   Moraine Dammed Lake   Lateral Moraine Dammed Lake | Types of Glacial Lakes   Moraine Dammed Lake   Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes   Moraine Dammed Lake   Other Moraine Dammed Lake | Types of Glacial Lakes   Ice Dammed Lake   Supra-glacial Lake | Types of Glacial Lakes   Ice Dammed Lake   Glacier Ice-dammed Lake | Types of Glacial Lakes   Erosion Lake   Cirque Erosion Lake | Types of Glacial Lakes   Erosion Lake   Glacier Trough Valley Erosion Lake | Types of Glacial Lakes   Erosion Lake   Other Glacial Erosion Lake | Types of Glacial Lakes   Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 0 | 0 | 18 | 3 | 0 | 0 | 0 | 3 | 1 | 27 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 8 | 0 | 16 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 10 | 5 | 0 | 0 | 0 | 17 | 0 | 36 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 8 | 0 | 0 | 34 | 9 | 0 | 0 | 0 | 28 | 1 | 80 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4460, "line_end": 4468, "token_count_estimate": 582, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9688ae5511f79e05", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Himachal Pradesh, Uttarakhand\nMap 62\nPlate No: 53E\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\nSubbasins:\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4469, "line_end": 4512, "token_count_estimate": 234, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "24a76ff261225f01", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 3 | 2.4 |\n| 3 | 4,001 - 5,000 | 66 | 87.7 |\n| 4 | > 5,000 | 8 | 4.0 |\n| | Total | 77 | 94.1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4513, "line_end": 4519, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "071f5b24b0e741d2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh, Uttarakhand\nMap 63\nPlate No: 53I\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4520, "line_end": 4564, "token_count_estimate": 238, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "edc981bb6a320f58", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 9 | 0 | 32 | 12 | 0 | 0 | 0 | 13 | 2 | 68 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 25 | 9 | 0 | 0 | 0 | 6 | 2 | 42 |\n| 3 | 1 - 5 | 7 | 1 | 0 | 11 | 3 | 0 | 2 | 0 | 8 | 2 | 34 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |\n| 5 | 10 - 50 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 13 | 10 | 0 | 71 | 24 | 0 | 2 | 0 | 27 | 6 | 153 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4565, "line_end": 4573, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba2a7936e69bc691", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Himachal Pradesh, Uttarakhand\nMap 64\nPlate No: 53I\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4574, "line_end": 4600, "token_count_estimate": 169, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2ce68328febdb4a0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 37 | 78.5 |\n| 4 | > 5,000 | 116 | 170.2 |\n| | Total | 153 | 248.7 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4601, "line_end": 4607, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ac5ddfd8e50f1b1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Himachal Pradesh, Uttarakhand\nMap 65\nPlate No: 53M\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4608, "line_end": 4640, "token_count_estimate": 206, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "3bd0ccbc28463aa8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 6 | 0 | 15 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 8 | 0 | 13 |\n| 3 | 1 - 5 | 7 | 0 | 1 | 5 | 0 | 0 | 3 | 0 | 16 | 0 | 32 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 5 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 0 | 8 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 11 | 1 | 1 | 18 | 0 | 0 | 5 | 0 | 37 | 0 | 73 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4641, "line_end": 4649, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26ad6cecdf34ba13", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Himachal Pradesh, Uttarakhand\nMap 66\nPlate No: 53M\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4650, "line_end": 4671, "token_count_estimate": 147, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1c98af3a6a906c0a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 72 | 250.6 |\n| | Total | 72 | 250.6 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4672, "line_end": 4678, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "57c034027489efcd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Uttarakhand\nMap 67\nPlate No: 53N\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4679, "line_end": 4714, "token_count_estimate": 201, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "096891ed34c5127e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 4 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4715, "line_end": 4723, "token_count_estimate": 601, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae7cc84ffe0aa40b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nState: Uttarakhand\nMap 68\nPlate No: 53N\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4724, "line_end": 4748, "token_count_estimate": 153, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "040e8a3434d2a72b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 3 | 1.7 |\n| 4 | > 5,000 | 1 | 1.6 |\n| Total | | 4 | 3.3 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4749, "line_end": 4755, "token_count_estimate": 159, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d39ce25dfdf8284", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nUT: Ladakh\nMap 69\nPlate No: 61B\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4756, "line_end": 4820, "token_count_estimate": 303, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "febec10f1a74364b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 1 - 5 | 2 | 0 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |\n| 4 | 5 - 10 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 5 | 10 - 50 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |\n| Total | | 5 | 0 | 8 | 5 | 0 | 1 | 0 | 0 | 0 | 1 | 20 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4821, "line_end": 4829, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "766141fc44a095fe", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nUT: Ladakh | Map 70 | Plate No: 61B\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4830, "line_end": 4849, "token_count_estimate": 157, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e132cef0979af967", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 20 | 347.6 |\n| Total | | 20 | 347.6 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4850, "line_end": 4856, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "418be965c9bbbfb3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nTransboundary Region | Map 71 | Plate No: 61C\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4857, "line_end": 4887, "token_count_estimate": 204, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f7837d4adbedb0f4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4888, "line_end": 4896, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ec82127596a614e1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\nTransboundary Region\nMap 72\nPlate No: 61C\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4897, "line_end": 4920, "token_count_estimate": 154, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4bc4f7e71314869", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 1 | 0.9 |\n| | Total | 1 | 0.9 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4921, "line_end": 4927, "token_count_estimate": 169, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "21607e524cc28b63", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\n**Subbasins**\n*   Beas\n*   Chenab\n*   Gilgit\n*   Indus Middle\n*   Indus Upper\n*   Jhelum\n*   Ravi\n*   Satluj\n*   Shyok\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\nTransboundary Region\nMap 73\nPlate No: 61D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 4928, "line_end": 4990, "token_count_estimate": 314, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "30c5860ab6cf1fd5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 4 | 1 | 9 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 3 | 3 | 8 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 7 | 0 | 0 | 2 | 0 | 7 | 3 | 19 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| | Total | 0 | 0 | 0 | 12 | 2 | 0 | 2 | 0 | 14 | 9 | 39 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4991, "line_end": 4999, "token_count_estimate": 537, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cdc524b148697567", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nTransboundary Region\nMap 74\nPlate No: 61D\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5000, "line_end": 5023, "token_count_estimate": 167, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f657e37ac0c59592", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 7 | 68.0 |\n| 4 | > 5,000 | 32 | 54.4 |\n| | Total | 39 | 122.4 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5024, "line_end": 5030, "token_count_estimate": 163, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97598a254dc156ac", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\n**Subbasins**\n*   Beas\n*   Chenab\n*   Gilgit\n*   Indus Middle\n*   Indus Upper\n*   Jhelum\n*   Ravi\n*   Satluj\n*   Shyok\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nMoraine Dammed Lake | Ice Dammed Lake | Erosion Lake | Other Glacial Lake\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nTransboundary Region\nMap 75\nPlate No: 61F\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5031, "line_end": 5093, "token_count_estimate": 341, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "99349bf7fe0689d7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 2 | 0.5 - 1 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 6 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 4 | 12 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |\n| | Total | 8 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 4 | 6 | 23 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5094, "line_end": 5102, "token_count_estimate": 592, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aae74f578f20425e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\nTransboundary Region\nMap 76\nPlate No: 61F\n\nShyok\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nIndus Basin\nIndia\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5103, "line_end": 5149, "token_count_estimate": 248, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "43703aac118caf7c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 23 | 155.2 |\n| | Total | 23 | 155.2 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5150, "line_end": 5156, "token_count_estimate": 169, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b5701b9529cceb8d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\nTransboundary Region\nMap 77\nPlate No: 61G\n\nShyok\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5157, "line_end": 5199, "token_count_estimate": 235, "basins": ["INDUS", "Indus"], "subbasins": ["Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "c9a5c625788534de", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5200, "line_end": 5208, "token_count_estimate": 537, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2575537c340fde0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nTransboundary Region\nMap 78\nPlate No: 61G\n\nShyok\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nIndus Basin\nIndia\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5209, "line_end": 5262, "token_count_estimate": 246, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "686ace68b97a689d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 1 | 0.6 |\n| | Total | 1 | 0.6 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5263, "line_end": 5269, "token_count_estimate": 169, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b70e4bdb2b9209c6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nTransboundary Region\nMap 79\nPlate No: 61H\n\nIndus Upper\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5270, "line_end": 5318, "token_count_estimate": 240, "basins": ["INDUS", "Indus"], "subbasins": ["Indus Upper"], "countries": ["India"], "lake_ids": []}}
{"id": "974840269f7a3450", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | End-moraine Dammed Lake | Lateral Moraine Dammed Lake | Lateral Moraine Dammed Lake with Ice | Other Moraine Dammed Lake | Supra-glacial Lake | Glacier Ice-dammed Lake | Cirque Erosion Lake | Glacier Trough Valley Erosion Lake | Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 4 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "End-moraine Dammed Lake", "Lateral Moraine Dammed Lake", "Lateral Moraine Dammed Lake with Ice", "Other Moraine Dammed Lake", "Supra-glacial Lake", "Glacier Ice-dammed Lake", "Cirque Erosion Lake", "Glacier Trough Valley Erosion Lake", "Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5319, "line_end": 5327, "token_count_estimate": 491, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "23a0de6386c12b93", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nTransboundary Region\nMap 80\nPlate No: 61H\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5328, "line_end": 5355, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e44f3d6d1c786dae", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 4 | 5.0 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 4 | 5.0 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5356, "line_end": 5362, "token_count_estimate": 159, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0425a446833cc272", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nTransboundary Region\nMap 81\nPlate No: 61K\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5363, "line_end": 5396, "token_count_estimate": 200, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "d30e51c7b80d1b30", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5397, "line_end": 5405, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0d4eb6f32d18962d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nTransboundary Region Map 82 Plate No: 61K\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5406, "line_end": 5424, "token_count_estimate": 141, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9d1d164031a181cf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 1 | 0.5 |\n| Total | | 1 | 0.5 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5425, "line_end": 5431, "token_count_estimate": 167, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2a569fb1eb1f6679", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nTransboundary Region Map 83 Plate No: 62A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5432, "line_end": 5460, "token_count_estimate": 200, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "13f52e8072914d1c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 4 | 0 | 0 | 9 | 0 | 0 | 1 | 0 | 14 | 0 | 28 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 8 | 0 | 0 | 1 | 0 | 12 | 0 | 22 |\n| 3 | 1 - 5 | 8 | 0 | 0 | 4 | 0 | 0 | 2 | 0 | 16 | 2 | 32 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 1 | 6 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 5 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 15 | 0 | 0 | 22 | 0 | 0 | 4 | 0 | 46 | 6 | 93 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5461, "line_end": 5469, "token_count_estimate": 537, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7e411db96113f243", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nTransboundary Region Map 84 Plate No: 62A\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5470, "line_end": 5491, "token_count_estimate": 152, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "035abcdb11e66189", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 94 | 253.9 |\n| | Total | 94 | 253.9 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5492, "line_end": 5498, "token_count_estimate": 161, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "92d773919981f42b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nSubbasins\nBeas\nChenab\nGilgit\nIndus Middle\nIndus Upper\nJhelum\nRavi\nSatluj\nShyok\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n\nMoraine Dammed Lake  Ice Dammed Lake  Erosion Lake  Other Glacial Lake\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Uttarakhand Map 85 Plate No: 62B\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5499, "line_end": 5559, "token_count_estimate": 303, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "cb70e95bc00fa671", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 5 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 6 | 0 | 10 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 9 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 7 | 0 | 0 | 9 | 3 | 0 | 0 | 0 | 7 | 1 | 27 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5560, "line_end": 5568, "token_count_estimate": 511, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f66c21420804555", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\nState: Uttarakhand\nMap 86\nPlate No: 62B\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\n**Subbasins**\n*   Beas\n*   Chenab\n*   Gilgit\n*   Indus Middle\n*   Indus Upper\n*   Jhelum\n*   Ravi\n*   Satluj\n*   Shyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5569, "line_end": 5617, "token_count_estimate": 263, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "eea12232bd057bea", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 4 | 32.0 |\n| 4 | > 5,000 | 23 | 39.0 |\n| | Total | 27 | 71.1 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5618, "line_end": 5624, "token_count_estimate": 171, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8173ae261c0cabeb", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\nTransboundary Region\nMap 87\nPlate No: 62E\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5625, "line_end": 5673, "token_count_estimate": 253, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8bd793819931358d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 1 | 12 | 0 | 0 | 0 | 0 | 23 | 4 | 40 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 6 | 0 | 0 | 2 | 0 | 25 | 6 | 40 |\n| 3 | 1 - 5 | 12 | 0 | 0 | 19 | 0 | 0 | 7 | 0 | 45 | 6 | 89 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 8 | 3 | 17 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 10 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |\n| | Total | 19 | 1 | 1 | 38 | 0 | 0 | 10 | 0 | 105 | 24 | 198 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5674, "line_end": 5682, "token_count_estimate": 537, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "45dae15b70889f53", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKES IN PART OF INDUS BASIN\n\nTransboundary Region\nMap 88\nPlate No: 62E\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5683, "line_end": 5705, "token_count_estimate": 159, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f93b79097fcdef4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 1 | 11.8 |\n| 4 | > 5,000 | 197 | 741.8 |\n| Total | | 198 | 753.6 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5706, "line_end": 5712, "token_count_estimate": 162, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a429fc3917eedc8b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nSATELLITE IMAGE OF PART OF INDUS BASIN\n\nState: Uttarakhand\nMap 89\nPlate No: 62F\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5713, "line_end": 5747, "token_count_estimate": 201, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "ddbd9c59e11b9fb4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Types of Glacial Lakes Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 21 | 2 | 0 | 0 | 0 | 9 | 4 | 36 |\n| 2 | 0.5 - 1 | 0 | 1 | 1 | 10 | 1 | 0 | 0 | 0 | 15 | 5 | 33 |\n| 3 | 1 - 5 | 3 | 0 | 1 | 11 | 0 | 0 | 2 | 0 | 35 | 3 | 55 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 10 | 0 | 13 |\n| 5 | 10 - 50 | 6 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 6 | 1 | 15 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |\n| Total | | 11 | 1 | 2 | 46 | 3 | 0 | 2 | 0 | 76 | 13 | 154 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5748, "line_end": 5756, "token_count_estimate": 582, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "374c7cc99686fedd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\n**State:** Uttarakhand\n**Map 90**\n**Plate No:** 62F\n\n**Data Source:** Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\n**Subbasins**\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5757, "line_end": 5800, "token_count_estimate": 251, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "5908462886d22012", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 7 | 19.3 |\n| 4 | > 5,000 | 147 | 721.9 |\n| | **Total** | **154** | **741.2** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5801, "line_end": 5807, "token_count_estimate": 168, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ccaf60f66642b25", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF INDUS BASIN**\n\n**Transboundary Region**\n**Map 91**\n**Plate No:** 62J\n\n**Data Source:** Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5808, "line_end": 5856, "token_count_estimate": 297, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5529a48d517b199b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 7 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 8 |\n| 3 | 1 - 5 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 8 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **3** | **0** | **0** | **1** | **0** | **0** | **0** | **0** | **14** | **8** | **26** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5857, "line_end": 5865, "token_count_estimate": 535, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "914792a0c4ef1cc0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**GLACIAL LAKES IN PART OF INDUS BASIN**\n\n**Transboundary Region**\n**Map 92**\n**Plate No: 62J**\n\nData Source: Resourcesat-2 LISS-IV\n\n***\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\nIndus Basin\nIndia\n\n**Subbasins**\n* Beas\n* Chenab\n* Gilgit\n* Indus Middle\n* Indus Upper\n* Jhelum\n* Ravi\n* Satluj\n* Shyok\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5866, "line_end": 5916, "token_count_estimate": 277, "basins": ["INDUS", "Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": ["India"], "lake_ids": []}}
{"id": "4b8e111515b5c2f8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 26 | 53.5 |\n| | **Total** | **26** | **53.5** |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5917, "line_end": 5923, "token_count_estimate": 175, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8292b362f025754c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: SATELLITE IMAGE OF PART OF INDUS BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nCompound Basins Glacier with an End-moraine Dammed Glacial Lake at its terminus, as seen in FCC satellite image\n\nSatellite: Resourcesat - 2\nSensor: LISS-IV MX\nDate of Image: 26.08.2015\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "SATELLITE IMAGE OF PART OF INDUS BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF INDUS BASIN"], "chunk_type": "text", "line_start": 5924, "line_end": 5973, "token_count_estimate": 280, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "c0910c4ce8cea797", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: text\n\n**Overview:**\n\nThere are several automatic and semi-automatic glacial lake mapping method reported in the literature, but no method produce good and accurate results of mapping. Kääb et al. (2002), attempted the automatic classification of glacial lakes using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, but the algorithm was not robust enough to be applied to other images except ASTER images. Using LANDSAT images, Huggel et al. (2002) suggested the Normalized Difference Water Index (NDWI) according to theory low water reflectance in the NIR band and high reflectance in blue band but glacial lakes get misclassified as shadow area using this method.\n\nWangchuk et al. (2019), delineated glacial lakes using Sentinel-1 SAR images, a semi-automated approach, based on a radar signal intensity threshold between water and non-water feature classes followed by post-processing including elevations, slopes, vegetation and size thresholds, but drawback still persist as lakes which are severely affected by the wind and waves that increase the roughness and thus the backscatter would neither be identified correctly, partially or at all, due to the use of a single threshold. Hence, to ensure correct classifications of lakes, visual inspection of images and quality control is required for final accurate results.\n\n**Mapping methods:**\n\nThe NDWI, which provides an automatic way to detect water bodies including glacial lakes was adopted by many researchers for inventorying purpose. It is a ratio combining two different spectral bands that enhance water spectral signals by contrasting the reflectance between different wavelengths and removing a large portion of noise components in different wavelengths, can be expressed as:\n\nNDWI = (Green Band - NIR Band) / (Green Band + NIR Band)\n\nOther than NDWI, two more pixel-based classification techniques i.e. supervised (by giving homogeneous signature sites) and unsupervised (by giving certain number of feature classes to classify based on spectral behavior) classification techniques can also be applied. Object-based classification using eCognition software can also be done using various factors like by giving threshold values and suitable membership functions, by including indices like NDWI, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Glacier Index (NDGI), and by using layers such as slope and NIR band.\n\n**Mapping results:**\n\nA study was attempted using RS-2 LISS-IV data to compare the mapping accuracy of lakes using 4 automated methods (NDWI, Supervised, Unsupervised and Object based) with visual interpretation method. All four automatic mapping methods along with visual interpretation technique were used in an area which has deep water bodies and snow covered glacial lakes along with shadowed region (upper mountainous parts of Teesta basin). Using NDWI method, most of lakes got classified, but it also classify shadow areas as water pixels due to the similar spectral reflectance conditions. Even if the threshold value of NDWI is changed in such a way that all water pixels in a lake should get classified, many deep water bodies and shadowed portions having same spectral reflectance values will get misclassified as water pixels or in some glacial lakes water pixels are missing.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 5975, "line_end": 6002, "token_count_estimate": 804, "basins": ["Indus"], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "6dbc5644606d3c64", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: text\n\ninterpretation method . All four automatic mapping methods along with visual interpretation technique were used in an area which has deep water bodies and snow covered glacial lakes along with shadowed region ( upper mountainous parts of Teesta basin ) . Using NDWI method , most of lakes got classified , but it also classify shadow areas as water pixels due to the similar spectral reflectance conditions . Even if the threshold value of NDWI is changed in such a way that all water pixels in a lake should get classified , many deep water bodies and shadowed portions having same spectral reflectance values will get misclassified as water pixels or in some glacial lakes water pixels are missing .\n\nUnsupervised classification technique misclassifies not only shadows as lakes, but also some part of glaciers that are in retreating condition and having similar spectral reflectance values of lakes (light blue in colour). In supervised classification output, with good amount of signature sites, cloud/mountain shadows are classified as water pixels. Overall, using pixel-based classification methods, it is difficult to distinguish between deep water bodies and shadows as they have same spectral reflectance values. Pixel-based classified output of all three methods along with the total area of lakes in the study area is shown in Figure 120.\n\nUsing object-based classification method, along with various layers like slope (as the glacial lakes are located at higher elevation) and NIR band, results misclassification of shadows, though it is less in comparison to the pixel-based classified output, but at many locations water pixels are not classified. Also, if we compare the areas of lakes that is being classified using automatic method that with the area of manually mapped lakes, automatic mapped lakes has huge difference and extent of misclassification, which need to be corrected again using visual interpretation method. Figure 121 shows the comparison of the glacial lake extents of object-based classification and manual mapping using visual interpretation keys.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 5975, "line_end": 6002, "token_count_estimate": 508, "basins": ["INDUS", "Indus"], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "a957569718e0782b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nEach lake is given a unique ID, formatted in 12 alpha-numeric character. First two digit of ID refers to the basin code, next five character refers to the SOI 250k and 50K Toposheet No., and the last five digit refers to the sequential number of each lake sorted from top left to bottom right. For ex\n\n0142D1100001", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6004, "line_end": 6008, "token_count_estimate": 133, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142D1100001"]}}
{"id": "b15c1b7b9b8d0293", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| Basin Code | SOI 250K Toposheet No. | SOI 50K Toposheet No. | Lake No. |\n|---|---|---|---|\n| 01 | 42D | 11 | 00001 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["Basin Code", "SOI 250K Toposheet No.", "SOI 50K Toposheet No.", "Lake No."], "table_row_start": 1, "table_row_end": 1, "line_start": 6009, "line_end": 6011, "token_count_estimate": 111, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["00001"]}}
{"id": "e4da495d96e8d179", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nTable 98 shows the list of all glacial lakes mapped in the Indus River basin along with few important attributes.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6012, "line_end": 6016, "token_count_estimate": 75, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7bf0fa60d10eb72d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable: Table 98: List of Glacial Lakes of the Indus River Basin with few important attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0142D1100001 | 36.262 | 72.625 | Gilgit | E(o) | 0.51 | 4,537 |\n| 2 | 0142D1200002 | 36.241 | 72.616 | Gilgit | O | 0.57 | 4,298 |\n| 3 | 0142D1200003 | 36.157 | 72.608 | Gilgit | I(s) | 0.80 | 4,269 |\n| 4 | 0142D1200004 | 36.155 | 72.584 | Gilgit | I(s) | 0.30 | 4,254 |\n| 5 | 0142D1200005 | 36.151 | 72.570 | Gilgit | I(s) | 0.39 | 4,431 |\n| 6 | 0142D1200006 | 36.053 | 72.701 | Gilgit | M(o) | 1.07 | 4,648 |\n| 7 | 0142D1200007 | 36.018 | 72.678 | Gilgit | I(s) | 0.47 | 4,546 |\n| 8 | 0142D1200008 | 36.012 | 72.647 | Gilgit | I(s) | 0.28 | 4,293 |\n| 9 | 0142D1500009 | 36.412 | 72.901 | Gilgit | M(e) | 11.23 | 4,256 |\n| 10 | 0142D1500010 | 36.409 | 72.897 | Gilgit | I(s) | 0.33 | 4,334 |\n| 11 | 0142D1500011 | 36.400 | 72.908 | Gilgit | M(e) | 0.41 | 4,708 |\n| 12 | 0142D1500012 | 36.399 | 72.900 | Gilgit | I(s) | 0.73 | 4,442 |\n| 13 | 0142D1500013 | 36.310 | 72.927 | Gilgit | E(o) | 0.51 | 4,525 |\n| 14 | 0142D1500014 | 36.277 | 72.841 | Gilgit | E(o) | 1.57 | 4,825 |\n| 15 | 0142D1500015 | 36.275 | 72.840 | Gilgit | E(o) | 0.34 | 4,815 |\n| 16 | 0142D1500016 | 36.261 | 72.822 | Gilgit | M(o) | 0.62 | 4,538 |\n| 17 | 0142D1500017 | 36.250 | 72.794 | Gilgit | I(s) | 0.47 | 4,623 |\n| 18 | 0142D1600018 | 36.232 | 72.906 | Gilgit | M(o) | 0.42 | 3,679 |\n| 19 | 0142D1600019 | 36.141 | 72.860 | Gilgit | O | 0.89 | 3,598 |\n| 20 | 0142D1600020 | 36.106 | 72.941 | Gilgit | E(o) | 3.00 | 4,302 |\n| 21 | 0142D1600021 | 36.102 | 72.926 | Gilgit | E(o) | 0.45 | 4,549 |\n| 22 | 0142D1600022 | 36.101 | 72.949 | Gilgit | M(o) | 1.21 | 4,458 |\n| 23 | 0142D1600023 | 36.096 | 72.936 | Gilgit | M(o) | 0.58 | 4,631 |\n| 24 | 0142D1600024 | 36.096 | 72.867 | Gilgit | M(o) | 0.31 | 4,491 |\n| 25 | 0142D1600025 | 36.092 | 72.866 | Gilgit | M(o) | 0.27 | 4,548 |\n| 26 | 0142D1600026 | 36.081 | 72.925 | Gilgit | M(o) | 0.81 | 4,698 |\n| 27 | 0142D1600027 | 36.069 | 72.924 | Gilgit | M(o) | 1.32 | 4,810 |\n| 28 | 0142D1600028 | 36.066 | 72.874 | Gilgit | M(o) | 0.29 | 4,690 |\n| 29 | 0142D1600029 | 36.063 | 72.865 | Gilgit | M(o) | 0.31 | 4,827 |\n| 30 | 0142D1600030 | 36.054 | 72.856 | Gilgit | I(s) | 0.42 | 5,035 |\n| 31 | 0142D1600031 | 36.053 | 72.860 | Gilgit | M(o) | 0.28 | 4,970 |\n| 32 | 0142D1600032 | 36.051 | 72.939 | Gilgit | M(l) | 0.59 | 4,676 |\n| 33 | 0142D1600033 | 36.049 | 72.880 | Gilgit | I(s) | 0.35 | 4,745 |\n| 34 | 0142D1600034 | 36.046 | 72.877 | Gilgit | I(s) | 3.97 | 4,786 |\n| 35 | 0142D1600035 | 36.032 | 72.890 | Gilgit | E(o) | 0.25 | 4,619 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 98: List of Glacial Lakes of the Indus River Basin with few important attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6017, "line_end": 6118, "token_count_estimate": 1589, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142D1100001", "0142D1200002", "0142D1200003", "0142D1200004", "0142D1200005", "0142D1200006", "0142D1200007", "0142D1200008", "0142D1500009", "0142D1500010", "0142D1500011", "0142D1500012", "0142D1500013", "0142D1500014", "0142D1500015", "0142D1500016", "0142D1500017", "0142D1600018", "0142D1600019", "0142D1600020", "0142D1600021", "0142D1600022", "0142D1600023", "0142D1600024", "0142D1600025", "0142D1600026", "0142D1600027", "0142D1600028", "0142D1600029", "0142D1600030", "0142D1600031", "0142D1600032", "0142D1600033", "0142D1600034", "0142D1600035"]}}
{"id": "7da8fc0a57f7e762", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable: Table 98: List of Glacial Lakes of the Indus River Basin with few important attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 36 | 0142D1600036 | 36.032 | 72.918 | Gilgit | E(c) | 2.04 | 4,920 |\n| 37 | 0142D1600037 | 36.032 | 72.863 | Gilgit | M(l) | 0.29 | 5,055 |\n| 38 | 0142D1600038 | 36.024 | 72.839 | Gilgit | M(o) | 0.56 | 4,846 |\n| 39 | 0142D1600039 | 36.023 | 72.877 | Gilgit | M(e) | 1.21 | 4,872 |\n| 40 | 0142D1600040 | 36.022 | 72.931 | Gilgit | M(o) | 0.50 | 4,560 |\n| 41 | 0142D1600041 | 36.020 | 72.931 | Gilgit | M(o) | 0.44 | 4,549 |\n| 42 | 0142D1600042 | 36.020 | 72.877 | Gilgit | M(o) | 0.28 | 4,915 |\n| 43 | 0142D1600043 | 36.015 | 72.888 | Gilgit | E(o) | 0.44 | 4,714 |\n| 44 | 0142D1600044 | 36.014 | 72.924 | Gilgit | E(o) | 0.48 | 4,766 |\n| 45 | 0142D1600045 | 36.012 | 72.876 | Gilgit | M(o) | 0.30 | 4,937 |\n| 46 | 0142D1600046 | 36.012 | 72.891 | Gilgit | M(o) | 0.28 | 4,773 |\n| 47 | 0142D1600047 | 36.009 | 72.890 | Gilgit | M(o) | 0.31 | 4,781 |\n| 48 | 0142D1600048 | 36.009 | 72.873 | Gilgit | M(l) | 0.37 | 4,898 |\n| 49 | 0142D1600049 | 36.008 | 72.880 | Gilgit | I(s) | 0.47 | 4,878 |\n| 50 | 0142D1600050 | 36.006 | 72.825 | Gilgit | I(s) | 0.47 | 4,454 |\n| 51 | 0142H0200051 | 36.671 | 73.208 | Gilgit | M(e) | 4.58 | 4,168 |\n| 52 | 0142H0200052 | 36.659 | 73.099 | Gilgit | I(s) | 0.35 | 4,735 |\n| 53 | 0142H0200053 | 36.654 | 73.090 | Gilgit | M(o) | 0.56 | 4,664 |\n| 54 | 0142H0200054 | 36.653 | 73.091 | Gilgit | M(o) | 0.26 | 4,658 |\n| 55 | 0142H0200055 | 36.578 | 73.116 | Gilgit | M(o) | 0.37 | 3,834 |\n| 56 | 0142H0200056 | 36.574 | 73.115 | Gilgit | I(s) | 0.26 | 3,812 |\n| 57 | 0142H0200057 | 36.547 | 73.098 | Gilgit | M(l) | 0.66 | 3,873 |\n| 58 | 0142H0200058 | 36.535 | 73.140 | Gilgit | I(s) | 0.29 | 3,499 |\n| 59 | 0142H0200059 | 36.534 | 73.150 | Gilgit | I(s) | 0.42 | 3,419 |\n| 60 | 0142H0300060 | 36.466 | 73.006 | Gilgit | M(l) | 0.69 | 4,264 |\n| 61 | 0142H0300061 | 36.447 | 73.106 | Gilgit | M(e) | 1.31 | 4,509 |\n| 62 | 0142H0300062 | 36.335 | 73.183 | Gilgit | M(o) | 0.79 | 4,670 |\n| 63 | 0142H0300063 | 36.330 | 73.186 | Gilgit | I(s) | 0.48 | 4,579 |\n| 64 | 0142H0300064 | 36.329 | 73.063 | Gilgit | M(o) | 0.41 | 4,733 |\n| 65 | 0142H0300065 | 36.327 | 73.198 | Gilgit | E(o) | 3.71 | 4,246 |\n| 66 | 0142H0300066 | 36.319 | 73.207 | Gilgit | I(s) | 2.12 | 4,441 |\n| 67 | 0142H0300067 | 36.318 | 73.204 | Gilgit | I(s) | 0.85 | 4,445 |\n| 68 | 0142H0300068 | 36.316 | 73.202 | Gilgit | I(s) | 1.13 | 4,437 |\n| 69 | 0142H0300069 | 36.310 | 73.085 | Gilgit | M(l) | 0.28 | 4,564 |\n| 70 | 0142H0300070 | 36.309 | 73.232 | Gilgit | E(o) | 1.65 | 4,718 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 98: List of Glacial Lakes of the Indus River Basin with few important attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6017, "line_end": 6118, "token_count_estimate": 1598, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142D1600036", "0142D1600037", "0142D1600038", "0142D1600039", "0142D1600040", "0142D1600041", "0142D1600042", "0142D1600043", "0142D1600044", "0142D1600045", "0142D1600046", "0142D1600047", "0142D1600048", "0142D1600049", "0142D1600050", "0142H0200051", "0142H0200052", "0142H0200053", "0142H0200054", "0142H0200055", "0142H0200056", "0142H0200057", "0142H0200058", "0142H0200059", "0142H0300060", "0142H0300061", "0142H0300062", "0142H0300063", "0142H0300064", "0142H0300065", "0142H0300066", "0142H0300067", "0142H0300068", "0142H0300069", "0142H0300070"]}}
{"id": "44c4eb5f68e3a44a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable: Table 98: List of Glacial Lakes of the Indus River Basin with few important attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 71 | 0142H0300071 | 36.306 | 73.250 | Gilgit | E(c) | 9.45 | 4,619 |\n| 72 | 0142H0300072 | 36.306 | 73.090 | Gilgit | M(o) | 0.26 | 4,698 |\n| 73 | 0142H0300073 | 36.302 | 73.148 | Gilgit | M(o) | 0.40 | 4,541 |\n| 74 | 0142H0300074 | 36.302 | 73.106 | Gilgit | E(o) | 1.05 | 4,796 |\n| 75 | 0142H0300075 | 36.301 | 73.235 | Gilgit | E(o) | 1.61 | 4,481 |\n| 76 | 0142H0300076 | 36.301 | 73.119 | Gilgit | E(o) | 1.04 | 4,406 |\n| 77 | 0142H0300077 | 36.299 | 73.105 | Gilgit | M(o) | 0.25 | 4,802 |\n| 78 | 0142H0300078 | 36.298 | 73.121 | Gilgit | E(o) | 1.35 | 4,351 |\n| 79 | 0142H0300079 | 36.294 | 73.115 | Gilgit | M(o) | 1.55 | 4,634 |\n| 80 | 0142H0300080 | 36.288 | 73.167 | Gilgit | E(o) | 2.32 | 4,482 |\n| 81 | 0142H0300081 | 36.288 | 73.158 | Gilgit | E(c) | 2.59 | 4,613 |\n| 82 | 0142H0300082 | 36.284 | 73.159 | Gilgit | E(o) | 0.26 | 4,496 |\n| 83 | 0142H0300083 | 36.282 | 73.162 | Gilgit | E(o) | 1.55 | 4,441 |\n| 84 | 0142H0300084 | 36.280 | 73.180 | Gilgit | E(o) | 4.73 | 4,200 |\n| 85 | 0142H0300085 | 36.280 | 73.021 | Gilgit | M(o) | 0.47 | 4,679 |\n| 86 | 0142H0300086 | 36.279 | 73.142 | Gilgit | E(o) | 1.51 | 4,591 |\n| 87 | 0142H0300087 | 36.269 | 73.053 | Gilgit | O | 9.49 | 4,652 |\n| 88 | 0142H0300088 | 36.264 | 73.060 | Gilgit | O | 1.55 | 4,498 |\n| 89 | 0142H0300089 | 36.263 | 73.048 | Gilgit | O | 21.68 | 4,618 |\n| 90 | 0142H0300090 | 36.262 | 73.030 | Gilgit | O | 1.63 | 4,824 |\n| 91 | 0142H0300091 | 36.262 | 73.132 | Gilgit | M(o) | 0.28 | 4,625 |\n| 92 | 0142H0300092 | 36.261 | 73.006 | Gilgit | E(o) | 0.74 | 4,733 |\n| 93 | 0142H0300093 | 36.261 | 73.008 | Gilgit | E(o) | 0.31 | 4,735 |\n| 94 | 0142H0300094 | 36.260 | 73.008 | Gilgit | E(o) | 0.46 | 4,736 |\n| 95 | 0142H0300095 | 36.259 | 73.016 | Gilgit | E(o) | 2.79 | 4,692 |\n| 96 | 0142H0300096 | 36.259 | 73.039 | Gilgit | O | 4.05 | 4,718 |\n| 97 | 0142H0300097 | 36.256 | 73.035 | Gilgit | I(s) | 0.26 | 4,755 |\n| 98 | 0142H0300098 | 36.256 | 73.037 | Gilgit | M(o) | 0.43 | 4,748 |\n| 99 | 0142H0300099 | 36.256 | 73.067 | Gilgit | E(o) | 0.37 | 4,330 |\n| 100 | 0142H0300100 | 36.253 | 73.057 | Gilgit | E(o) | 4.77 | 4,423 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 98: List of Glacial Lakes of the Indus River Basin with few important attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 100, "line_start": 6017, "line_end": 6118, "token_count_estimate": 1368, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H0300071", "0142H0300072", "0142H0300073", "0142H0300074", "0142H0300075", "0142H0300076", "0142H0300077", "0142H0300078", "0142H0300079", "0142H0300080", "0142H0300081", "0142H0300082", "0142H0300083", "0142H0300084", "0142H0300085", "0142H0300086", "0142H0300087", "0142H0300088", "0142H0300089", "0142H0300090", "0142H0300091", "0142H0300092", "0142H0300093", "0142H0300094", "0142H0300095", "0142H0300096", "0142H0300097", "0142H0300098", "0142H0300099", "0142H0300100"]}}
{"id": "a5f338a2cf2d0ea5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6119, "line_end": 6121, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "75d5f8eb5e9a8e8f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 101 | 0142H0300101 | 36.252 | 73.124 | Gilgit | E(o) | 0.52 | 4,665 |\n| 102 | 0142H0400102 | 36.250 | 73.075 | Gilgit | E(o) | 0.39 | 4,352 |\n| 103 | 0142H0400103 | 36.248 | 73.133 | Gilgit | E(o) | 0.43 | 4,706 |\n| 104 | 0142H0400104 | 36.247 | 73.138 | Gilgit | E(c) | 4.27 | 4,602 |\n| 105 | 0142H0400105 | 36.246 | 73.121 | Gilgit | E(o) | 8.16 | 4,535 |\n| 106 | 0142H0400106 | 36.246 | 73.073 | Gilgit | I(s) | 0.29 | 4,521 |\n| 107 | 0142H0400107 | 36.242 | 73.011 | Gilgit | E(o) | 7.41 | 4,241 |\n| 108 | 0142H0400108 | 36.240 | 73.049 | Gilgit | E(o) | 4.10 | 4,696 |\n| 109 | 0142H0400109 | 36.238 | 73.061 | Gilgit | E(o) | 1.06 | 4,701 |\n| 110 | 0142H0400110 | 36.238 | 73.048 | Gilgit | E(o) | 1.02 | 4,696 |\n| 111 | 0142H0400111 | 36.218 | 73.184 | Gilgit | E(o) | 1.47 | 4,336 |\n| 112 | 0142H0400112 | 36.143 | 73.032 | Gilgit | M(o) | 0.29 | 4,411 |\n| 113 | 0142H0400113 | 36.132 | 73.220 | Gilgit | E(o) | 1.95 | 4,164 |\n| 114 | 0142H0400114 | 36.132 | 73.243 | Gilgit | E(o) | 0.60 | 4,247 |\n| 115 | 0142H0400115 | 36.125 | 73.239 | Gilgit | E(o) | 0.66 | 4,335 |\n| 116 | 0142H0400116 | 36.121 | 73.238 | Gilgit | M(o) | 1.15 | 4,467 |\n| 117 | 0142H0400117 | 36.114 | 73.217 | Gilgit | I(s) | 0.29 | 4,543 |\n| 118 | 0142H0400118 | 36.113 | 73.218 | Gilgit | I(s) | 0.29 | 4,549 |\n| 119 | 0142H0400119 | 36.103 | 73.072 | Gilgit | M(o) | 0.58 | 4,362 |\n| 120 | 0142H0400120 | 36.090 | 73.242 | Gilgit | E(o) | 0.65 | 4,514 |\n| 121 | 0142H0400121 | 36.082 | 73.212 | Gilgit | E(o) | 0.52 | 4,603 |\n| 122 | 0142H0400122 | 36.082 | 73.240 | Gilgit | E(o) | 3.32 | 4,513 |\n| 123 | 0142H0400123 | 36.081 | 73.211 | Gilgit | E(o) | 0.34 | 4,610 |\n| 124 | 0142H0400124 | 36.077 | 73.233 | Gilgit | E(o) | 6.94 | 4,632 |\n| 125 | 0142H0400125 | 36.073 | 73.199 | Gilgit | E(o) | 0.36 | 4,544 |\n| 126 | 0142H0400126 | 36.069 | 73.154 | Gilgit | E(o) | 0.57 | 4,417 |\n| 127 | 0142H0400127 | 36.068 | 73.204 | Gilgit | E(o) | 2.90 | 4,616 |\n| 128 | 0142H0400128 | 36.063 | 73.231 | Gilgit | E(o) | 2.09 | 4,588 |\n| 129 | 0142H0400129 | 36.061 | 73.205 | Gilgit | E(o) | 3.54 | 4,671 |\n| 130 | 0142H0400130 | 36.060 | 73.233 | Gilgit | E(o) | 1.97 | 4,499 |\n| 131 | 0142H0400131 | 36.056 | 73.196 | Gilgit | E(o) | 3.87 | 4,518 |\n| 132 | 0142H0400132 | 36.051 | 73.200 | Gilgit | E(o) | 0.58 | 4,606 |\n| 133 | 0142H0400133 | 36.049 | 73.203 | Gilgit | E(o) | 1.30 | 4,630 |\n| 134 | 0142H0400134 | 36.044 | 73.206 | Gilgit | E(o) | 0.26 | 4,728 |\n| 135 | 0142H0400135 | 36.043 | 73.206 | Gilgit | E(o) | 0.27 | 4,726 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6122, "line_end": 6269, "token_count_estimate": 1565, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H0300101", "0142H0400102", "0142H0400103", "0142H0400104", "0142H0400105", "0142H0400106", "0142H0400107", "0142H0400108", "0142H0400109", "0142H0400110", "0142H0400111", "0142H0400112", "0142H0400113", "0142H0400114", "0142H0400115", "0142H0400116", "0142H0400117", "0142H0400118", "0142H0400119", "0142H0400120", "0142H0400121", "0142H0400122", "0142H0400123", "0142H0400124", "0142H0400125", "0142H0400126", "0142H0400127", "0142H0400128", "0142H0400129", "0142H0400130", "0142H0400131", "0142H0400132", "0142H0400133", "0142H0400134", "0142H0400135"]}}
{"id": "491fe5a86512295d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 136 | 0142H0400136 | 36.040 | 73.186 | Gilgit | E(o) | 0.47 | 4,497 |\n| 137 | 0142H0400137 | 36.038 | 73.214 | Gilgit | E(o) | 0.60 | 4,768 |\n| 138 | 0142H0400138 | 36.037 | 73.193 | Gilgit | E(o) | 6.36 | 4,474 |\n| 139 | 0142H0400139 | 36.035 | 73.198 | Gilgit | E(o) | 0.63 | 4,558 |\n| 140 | 0142H0400140 | 36.032 | 73.185 | Gilgit | E(o) | 1.07 | 4,645 |\n| 141 | 0142H0400141 | 36.026 | 73.197 | Gilgit | M(o) | 1.33 | 4,755 |\n| 142 | 0142H0400142 | 36.015 | 73.230 | Gilgit | E(o) | 1.26 | 4,559 |\n| 143 | 0142H0400143 | 36.014 | 73.222 | Gilgit | E(o) | 0.78 | 4,724 |\n| 144 | 0142H0400144 | 36.011 | 73.230 | Gilgit | E(o) | 4.65 | 4,607 |\n| 145 | 0142H0400145 | 36.010 | 73.246 | Gilgit | E(o) | 0.90 | 4,331 |\n| 146 | 0142H0400146 | 36.002 | 73.248 | Gilgit | E(o) | 0.37 | 4,525 |\n| 147 | 0142H0600147 | 36.724 | 73.433 | Gilgit | E(o) | 0.44 | 4,586 |\n| 148 | 0142H0600148 | 36.692 | 73.326 | Gilgit | M(e) | 0.87 | 4,411 |\n| 149 | 0142H0600149 | 36.670 | 73.354 | Gilgit | E(o) | 0.44 | 3,753 |\n| 150 | 0142H0600150 | 36.642 | 73.407 | Gilgit | M(e) | 14.06 | 2,748 |\n| 151 | 0142H0600151 | 36.623 | 73.474 | Gilgit | E(v) | 3.90 | 3,846 |\n| 152 | 0142H0700152 | 36.301 | 73.264 | Gilgit | E(o) | 0.80 | 4,645 |\n| 153 | 0142H0700153 | 36.300 | 73.252 | Gilgit | E(c) | 1.91 | 4,382 |\n| 154 | 0142H0700154 | 36.297 | 73.252 | Gilgit | E(o) | 1.05 | 4,393 |\n| 155 | 0142H0800155 | 36.188 | 73.360 | Gilgit | E(o) | 2.63 | 4,403 |\n| 156 | 0142H0800156 | 36.175 | 73.389 | Gilgit | E(o) | 2.15 | 4,423 |\n| 157 | 0142H0800157 | 36.175 | 73.384 | Gilgit | E(o) | 0.34 | 4,480 |\n| 158 | 0142H0800158 | 36.173 | 73.386 | Gilgit | E(o) | 0.35 | 4,465 |\n| 159 | 0142H0800159 | 36.171 | 73.382 | Gilgit | E(o) | 5.25 | 4,493 |\n| 160 | 0142H0800160 | 36.158 | 73.405 | Gilgit | E(o) | 0.60 | 4,468 |\n| 161 | 0142H0800161 | 36.140 | 73.500 | Gilgit | E(o) | 2.49 | 4,358 |\n| 162 | 0142H0800162 | 36.139 | 73.381 | Gilgit | E(o) | 5.07 | 4,372 |\n| 163 | 0142H0800163 | 36.138 | 73.390 | Gilgit | E(o) | 0.58 | 4,469 |\n| 164 | 0142H0800164 | 36.136 | 73.390 | Gilgit | E(o) | 0.59 | 4,476 |\n| 165 | 0142H0800165 | 36.132 | 73.407 | Gilgit | E(o) | 0.54 | 4,508 |\n| 166 | 0142H0800166 | 36.131 | 73.384 | Gilgit | E(o) | 23.35 | 4,503 |\n| 167 | 0142H0800167 | 36.127 | 73.448 | Gilgit | E(o) | 4.72 | 4,354 |\n| 168 | 0142H0800168 | 36.126 | 73.416 | Gilgit | E(o) | 2.84 | 4,425 |\n| 169 | 0142H0800169 | 36.122 | 73.424 | Gilgit | E(o) | 0.36 | 4,443 |\n| 170 | 0142H0800170 | 36.119 | 73.449 | Gilgit | E(o) | 3.85 | 4,373 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6122, "line_end": 6269, "token_count_estimate": 1563, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H0400136", "0142H0400137", "0142H0400138", "0142H0400139", "0142H0400140", "0142H0400141", "0142H0400142", "0142H0400143", "0142H0400144", "0142H0400145", "0142H0400146", "0142H0600147", "0142H0600148", "0142H0600149", "0142H0600150", "0142H0600151", "0142H0700152", "0142H0700153", "0142H0700154", "0142H0800155", "0142H0800156", "0142H0800157", "0142H0800158", "0142H0800159", "0142H0800160", "0142H0800161", "0142H0800162", "0142H0800163", "0142H0800164", "0142H0800165", "0142H0800166", "0142H0800167", "0142H0800168", "0142H0800169", "0142H0800170"]}}
{"id": "495ad4962f382c7d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 171 | 0142H0800171 | 36.116 | 73.423 | Gilgit | E(o) | 21.76 | 4,437 |\n| 172 | 0142H0800172 | 36.116 | 73.465 | Gilgit | E(o) | 0.87 | 4,395 |\n| 173 | 0142H0800173 | 36.112 | 73.449 | Gilgit | E(o) | 11.22 | 4,427 |\n| 174 | 0142H0800174 | 36.108 | 73.465 | Gilgit | E(o) | 21.58 | 4,488 |\n| 175 | 0142H0800175 | 36.105 | 73.449 | Gilgit | E(o) | 1.42 | 4,485 |\n| 176 | 0142H0800176 | 36.103 | 73.467 | Gilgit | E(o) | 4.28 | 4,531 |\n| 177 | 0142H0800177 | 36.102 | 73.458 | Gilgit | E(c) | 1.29 | 4,687 |\n| 178 | 0142H0800178 | 36.090 | 73.470 | Gilgit | E(o) | 2.34 | 4,482 |\n| 179 | 0142H0800179 | 36.086 | 73.461 | Gilgit | E(o) | 13.98 | 4,577 |\n| 180 | 0142H0800180 | 36.052 | 73.456 | Gilgit | E(o) | 0.59 | 4,440 |\n| 181 | 0142H0800181 | 36.052 | 73.482 | Gilgit | E(o) | 1.43 | 4,546 |\n| 182 | 0142H0800182 | 36.051 | 73.433 | Gilgit | E(o) | 1.17 | 4,466 |\n| 183 | 0142H0800183 | 36.043 | 73.316 | Gilgit | E(o) | 7.10 | 4,542 |\n| 184 | 0142H0800184 | 36.037 | 73.480 | Gilgit | E(o) | 1.20 | 4,479 |\n| 185 | 0142H0800185 | 36.024 | 73.328 | Gilgit | E(o) | 10.65 | 4,559 |\n| 186 | 0142H0800186 | 36.019 | 73.253 | Gilgit | E(o) | 1.07 | 4,567 |\n| 187 | 0142H0800187 | 36.018 | 73.257 | Gilgit | E(o) | 1.10 | 4,579 |\n| 188 | 0142H0800188 | 36.018 | 73.307 | Gilgit | E(o) | 4.58 | 4,539 |\n| 189 | 0142H0800189 | 36.014 | 73.272 | Gilgit | E(o) | 1.06 | 4,457 |\n| 190 | 0142H0800190 | 36.013 | 73.334 | Gilgit | E(o) | 1.51 | 4,507 |\n| 191 | 0142H0800191 | 36.013 | 73.336 | Gilgit | E(o) | 0.75 | 4,507 |\n| 192 | 0142H0800192 | 36.009 | 73.271 | Gilgit | E(o) | 0.71 | 4,547 |\n| 193 | 0142H0800193 | 36.007 | 73.251 | Gilgit | E(o) | 0.49 | 4,438 |\n| 194 | 0142H0800194 | 36.006 | 73.304 | Gilgit | E(o) | 5.76 | 4,485 |\n| 195 | 0142H0800195 | 36.004 | 73.274 | Gilgit | E(o) | 8.10 | 4,533 |\n| 196 | 0142H0800196 | 36.002 | 73.311 | Gilgit | E(o) | 0.96 | 4,609 |\n| 197 | 0142H0800197 | 36.002 | 73.251 | Gilgit | E(o) | 0.88 | 4,513 |\n| 198 | 0142H0800198 | 36.000 | 73.312 | Gilgit | E(o) | 13.08 | 4,615 |\n| 199 | 0142H0900199 | 36.881 | 73.675 | Gilgit | O | 5.43 | 4,304 |\n| 200 | 0142H0900200 | 36.879 | 73.704 | Gilgit | O | 262.57 | 4,286 |\n| 201 | 0142H0900201 | 36.878 | 73.686 | Gilgit | O | 2.82 | 4,289 |\n| 202 | 0142H1000202 | 36.691 | 73.532 | Gilgit | E(c) | 0.44 | 4,673 |\n| 203 | 0142H1000203 | 36.676 | 73.730 | Gilgit | M(l) | 1.94 | 3,898 |\n| 204 | 0142H1000204 | 36.666 | 73.602 | Gilgit | M(o) | 0.57 | 4,610 |\n| 205 | 0142H1000205 | 36.662 | 73.622 | Gilgit | M(e) | 0.46 | 4,100 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6122, "line_end": 6269, "token_count_estimate": 1542, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H0800171", "0142H0800172", "0142H0800173", "0142H0800174", "0142H0800175", "0142H0800176", "0142H0800177", "0142H0800178", "0142H0800179", "0142H0800180", "0142H0800181", "0142H0800182", "0142H0800183", "0142H0800184", "0142H0800185", "0142H0800186", "0142H0800187", "0142H0800188", "0142H0800189", "0142H0800190", "0142H0800191", "0142H0800192", "0142H0800193", "0142H0800194", "0142H0800195", "0142H0800196", "0142H0800197", "0142H0800198", "0142H0900199", "0142H0900200", "0142H0900201", "0142H1000202", "0142H1000203", "0142H1000204", "0142H1000205"]}}
{"id": "424558a720d7479d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 206 | 0142H1000206 | 36.661 | 73.622 | Gilgit | M(e) | 1.22 | 4,087 |\n| 207 | 0142H1000207 | 36.649 | 73.663 | Gilgit | M(e) | 0.62 | 4,022 |\n| 208 | 0142H1000208 | 36.644 | 73.646 | Gilgit | O | 105.07 | 3,821 |\n| 209 | 0142H1000209 | 36.630 | 73.751 | Gilgit | M(e) | 2.94 | 4,114 |\n| 210 | 0142H1000210 | 36.619 | 73.620 | Gilgit | M(o) | 0.56 | 4,045 |\n| 211 | 0142H1000211 | 36.611 | 73.599 | Gilgit | M(o) | 0.53 | 4,504 |\n| 212 | 0142H1000212 | 36.603 | 73.669 | Gilgit | M(o) | 0.86 | 4,451 |\n| 213 | 0142H1000213 | 36.561 | 73.596 | Gilgit | M(e) | 1.80 | 3,581 |\n| 214 | 0142H1100214 | 36.489 | 73.576 | Gilgit | E(c) | 0.53 | 4,482 |\n| 215 | 0142H1100215 | 36.489 | 73.574 | Gilgit | E(o) | 0.37 | 4,487 |\n| 216 | 0142H1100216 | 36.463 | 73.577 | Gilgit | O | 0.36 | 3,848 |\n| 217 | 0142H1100217 | 36.462 | 73.665 | Gilgit | E(o) | 0.56 | 4,485 |\n| 218 | 0142H1100218 | 36.456 | 73.617 | Gilgit | M(o) | 0.27 | 4,169 |\n| 219 | 0142H1100219 | 36.446 | 73.554 | Gilgit | E(o) | 0.28 | 3,776 |\n| 220 | 0142H1100220 | 36.431 | 73.566 | Gilgit | E(o) | 12.25 | 4,030 |\n| 221 | 0142H1100221 | 36.374 | 73.610 | Gilgit | E(o) | 0.90 | 3,834 |\n| 222 | 0142H1100222 | 36.374 | 73.569 | Gilgit | E(o) | 0.26 | 4,161 |\n| 223 | 0142H1100223 | 36.373 | 73.559 | Gilgit | E(o) | 1.01 | 3,884 |\n| 224 | 0142H1100224 | 36.365 | 73.554 | Gilgit | M(o) | 0.73 | 4,110 |\n| 225 | 0142H1100225 | 36.363 | 73.573 | Gilgit | M(o) | 0.46 | 4,251 |\n| 226 | 0142H1100226 | 36.359 | 73.517 | Gilgit | E(o) | 0.59 | 4,228 |\n| 227 | 0142H1100227 | 36.355 | 73.597 | Gilgit | E(o) | 0.92 | 4,445 |\n| 228 | 0142H1100228 | 36.353 | 73.520 | Gilgit | M(o) | 1.54 | 4,356 |\n| 229 | 0142H1100229 | 36.352 | 73.522 | Gilgit | M(e) | 1.06 | 4,359 |\n| 230 | 0142H1100230 | 36.351 | 73.514 | Gilgit | E(o) | 17.25 | 4,420 |\n| 231 | 0142H1100231 | 36.348 | 73.556 | Gilgit | E(c) | 2.87 | 4,553 |\n| 232 | 0142H1100232 | 36.348 | 73.524 | Gilgit | M(o) | 2.48 | 4,503 |\n| 233 | 0142H1100233 | 36.346 | 73.518 | Gilgit | M(o) | 0.33 | 4,442 |\n| 234 | 0142H1100234 | 36.344 | 73.594 | Gilgit | E(o) | 2.23 | 4,184 |\n| 235 | 0142H1100235 | 36.343 | 73.556 | Gilgit | E(o) | 6.99 | 4,466 |\n| 236 | 0142H1100236 | 36.343 | 73.598 | Gilgit | E(o) | 3.02 | 4,149 |\n| 237 | 0142H1100237 | 36.343 | 73.568 | Gilgit | E(o) | 0.51 | 4,437 |\n| 238 | 0142H1100238 | 36.342 | 73.573 | Gilgit | E(o) | 2.60 | 4,377 |\n| 239 | 0142H1100239 | 36.340 | 73.564 | Gilgit | E(o) | 0.34 | 4,420 |\n| 240 | 0142H1100240 | 36.339 | 73.554 | Gilgit | E(o) | 5.00 | 4,423 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6122, "line_end": 6269, "token_count_estimate": 1558, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H1000206", "0142H1000207", "0142H1000208", "0142H1000209", "0142H1000210", "0142H1000211", "0142H1000212", "0142H1000213", "0142H1100214", "0142H1100215", "0142H1100216", "0142H1100217", "0142H1100218", "0142H1100219", "0142H1100220", "0142H1100221", "0142H1100222", "0142H1100223", "0142H1100224", "0142H1100225", "0142H1100226", "0142H1100227", "0142H1100228", "0142H1100229", "0142H1100230", "0142H1100231", "0142H1100232", "0142H1100233", "0142H1100234", "0142H1100235", "0142H1100236", "0142H1100237", "0142H1100238", "0142H1100239", "0142H1100240"]}}
{"id": "4dd0e72e0437b2c1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 241 | 0142H1100241 | 36.335 | 73.521 | Gilgit | E(o) | 0.56 | 4,571 |\n| 242 | 0142H1100242 | 36.331 | 73.512 | Gilgit | E(o) | 1.15 | 4,498 |\n| 243 | 0142H1100243 | 36.323 | 73.511 | Gilgit | E(o) | 0.95 | 4,287 |\n| 244 | 0142H1100244 | 36.321 | 73.544 | Gilgit | E(o) | 0.69 | 4,362 |\n| 245 | 0142H1100245 | 36.321 | 73.586 | Gilgit | E(o) | 0.26 | 4,348 |\n| 246 | 0142H1200246 | 36.195 | 73.637 | Gilgit | E(o) | 1.46 | 4,437 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6122, "line_end": 6269, "token_count_estimate": 365, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H1100241", "0142H1100242", "0142H1100243", "0142H1100244", "0142H1100245", "0142H1200246"]}}
{"id": "fc6bc43ab75b4157", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6270, "line_end": 6279, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce31bcec5f31add3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 247 | 0142H1200247 | 36.191 | 73.635 | Gilgit | E(o) | 0.74 | 4,469 |\n| 248 | 0142H1200248 | 36.158 | 73.613 | Gilgit | E(o) | 1.30 | 4,529 |\n| 249 | 0142H1200249 | 36.154 | 73.616 | Gilgit | E(o) | 13.83 | 4,545 |\n| 250 | 0142H1200250 | 36.145 | 73.639 | Gilgit | E(o) | 17.96 | 4,484 |\n| 251 | 0142H1200251 | 36.136 | 73.524 | Gilgit | E(o) | 3.85 | 4,377 |\n| 252 | 0142H1200252 | 36.133 | 73.500 | Gilgit | E(o) | 6.02 | 4,446 |\n| 253 | 0142H1200253 | 36.129 | 73.505 | Gilgit | E(o) | 23.11 | 4,482 |\n| 254 | 0142H1200254 | 36.121 | 73.674 | Gilgit | E(o) | 0.84 | 4,442 |\n| 255 | 0142H1200255 | 36.120 | 73.506 | Gilgit | E(o) | 7.36 | 4,507 |\n| 256 | 0142H1200256 | 36.118 | 73.666 | Gilgit | E(o) | 4.38 | 4,470 |\n| 257 | 0142H1200257 | 36.114 | 73.664 | Gilgit | E(o) | 2.46 | 4,509 |\n| 258 | 0142H1200258 | 36.113 | 73.666 | Gilgit | E(o) | 1.42 | 4,521 |\n| 259 | 0142H1200259 | 36.106 | 73.696 | Gilgit | E(o) | 1.07 | 4,400 |\n| 260 | 0142H1200260 | 36.101 | 73.685 | Gilgit | E(o) | 8.15 | 4,327 |\n| 261 | 0142H1200261 | 36.097 | 73.677 | Gilgit | E(o) | 1.29 | 4,334 |\n| 262 | 0142H1200262 | 36.096 | 73.668 | Gilgit | E(c) | 10.48 | 4,423 |\n| 263 | 0142H1200263 | 36.094 | 73.664 | Gilgit | E(c) | 1.68 | 4,443 |\n| 264 | 0142H1200264 | 36.092 | 73.631 | Gilgit | E(o) | 2.85 | 4,467 |\n| 265 | 0142H1200265 | 36.092 | 73.688 | Gilgit | E(o) | 3.75 | 4,422 |\n| 266 | 0142H1200266 | 36.082 | 73.634 | Gilgit | E(o) | 0.47 | 4,565 |\n| 267 | 0142H1200267 | 36.081 | 73.662 | Gilgit | E(o) | 1.11 | 4,567 |\n| 268 | 0142H1200268 | 36.080 | 73.646 | Gilgit | O | 28.87 | 4,344 |\n| 269 | 0142H1200269 | 36.072 | 73.519 | Gilgit | E(o) | 1.44 | 4,378 |\n| 270 | 0142H1200270 | 36.072 | 73.670 | Gilgit | E(o) | 0.62 | 4,397 |\n| 271 | 0142H1200271 | 36.069 | 73.736 | Gilgit | E(o) | 2.44 | 4,329 |\n| 272 | 0142H1200272 | 36.069 | 73.556 | Gilgit | E(o) | 0.95 | 4,507 |\n| 273 | 0142H1200273 | 36.069 | 73.549 | Gilgit | E(o) | 5.07 | 4,363 |\n| 274 | 0142H1200274 | 36.068 | 73.622 | Gilgit | E(o) | 2.54 | 4,515 |\n| 275 | 0142H1200275 | 36.068 | 73.741 | Gilgit | E(o) | 0.37 | 4,427 |\n| 276 | 0142H1200276 | 36.064 | 73.552 | Gilgit | E(o) | 3.23 | 4,466 |\n| 277 | 0142H1200277 | 36.061 | 73.728 | Gilgit | E(o) | 5.26 | 4,366 |\n| 278 | 0142H1200278 | 36.060 | 73.673 | Gilgit | E(o) | 0.79 | 4,408 |\n| 279 | 0142H1200279 | 36.057 | 73.517 | Gilgit | E(o) | 1.57 | 4,489 |\n| 280 | 0142H1200280 | 36.055 | 73.518 | Gilgit | E(o) | 2.13 | 4,473 |\n| 281 | 0142H1200281 | 36.052 | 73.713 | Gilgit | E(o) | 3.27 | 4,307 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6280, "line_end": 6427, "token_count_estimate": 1572, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H1200247", "0142H1200248", "0142H1200249", "0142H1200250", "0142H1200251", "0142H1200252", "0142H1200253", "0142H1200254", "0142H1200255", "0142H1200256", "0142H1200257", "0142H1200258", "0142H1200259", "0142H1200260", "0142H1200261", "0142H1200262", "0142H1200263", "0142H1200264", "0142H1200265", "0142H1200266", "0142H1200267", "0142H1200268", "0142H1200269", "0142H1200270", "0142H1200271", "0142H1200272", "0142H1200273", "0142H1200274", "0142H1200275", "0142H1200276", "0142H1200277", "0142H1200278", "0142H1200279", "0142H1200280", "0142H1200281"]}}
{"id": "9007b7825c69766d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 282 | 0142H1200282 | 36.052 | 73.648 | Gilgit | E(o) | 5.13 | 4,498 |\n| 283 | 0142H1200283 | 36.050 | 73.746 | Gilgit | E(o) | 0.73 | 4,355 |\n| 284 | 0142H1200284 | 36.050 | 73.575 | Gilgit | E(o) | 2.42 | 4,307 |\n| 285 | 0142H1200285 | 36.048 | 73.579 | Gilgit | E(o) | 1.85 | 4,315 |\n| 286 | 0142H1200286 | 36.048 | 73.553 | Gilgit | E(o) | 1.23 | 4,568 |\n| 287 | 0142H1200287 | 36.047 | 73.717 | Gilgit | E(o) | 0.62 | 4,354 |\n| 288 | 0142H1200288 | 36.047 | 73.626 | Gilgit | E(o) | 1.19 | 4,392 |\n| 289 | 0142H1200289 | 36.046 | 73.513 | Gilgit | E(o) | 2.14 | 4,572 |\n| 290 | 0142H1200290 | 36.045 | 73.582 | Gilgit | E(o) | 0.28 | 4,402 |\n| 291 | 0142H1200291 | 36.044 | 73.547 | Gilgit | E(o) | 2.31 | 4,500 |\n| 292 | 0142H1200292 | 36.043 | 73.580 | Gilgit | E(o) | 1.19 | 4,412 |\n| 293 | 0142H1200293 | 36.042 | 73.720 | Gilgit | E(o) | 6.42 | 4,474 |\n| 294 | 0142H1200294 | 36.042 | 73.522 | Gilgit | E(o) | 0.50 | 4,464 |\n| 295 | 0142H1200295 | 36.039 | 73.592 | Gilgit | E(o) | 18.87 | 4,274 |\n| 296 | 0142H1200296 | 36.037 | 73.565 | Gilgit | E(o) | 0.94 | 4,659 |\n| 297 | 0142H1200297 | 36.036 | 73.651 | Gilgit | E(o) | 4.33 | 4,467 |\n| 298 | 0142H1200298 | 36.036 | 73.584 | Gilgit | E(o) | 2.91 | 4,394 |\n| 299 | 0142H1200299 | 36.035 | 73.563 | Gilgit | E(o) | 2.14 | 4,619 |\n| 300 | 0142H1200300 | 36.034 | 73.544 | Gilgit | E(o) | 5.87 | 4,425 |\n| 301 | 0142H1200301 | 36.030 | 73.714 | Gilgit | E(o) | 10.47 | 4,320 |\n| 302 | 0142H1200302 | 36.030 | 73.631 | Gilgit | E(o) | 1.18 | 4,385 |\n| 303 | 0142H1200303 | 36.027 | 73.716 | Gilgit | E(o) | 2.71 | 4,348 |\n| 304 | 0142H1200304 | 36.027 | 73.571 | Gilgit | E(o) | 3.24 | 4,472 |\n| 305 | 0142H1200305 | 36.025 | 73.747 | Gilgit | E(o) | 1.43 | 4,368 |\n| 306 | 0142H1200306 | 36.025 | 73.606 | Gilgit | E(o) | 0.28 | 4,406 |\n| 307 | 0142H1200307 | 36.024 | 73.741 | Gilgit | E(o) | 5.14 | 4,438 |\n| 308 | 0142H1200308 | 36.024 | 73.604 | Gilgit | E(o) | 0.35 | 4,409 |\n| 309 | 0142H1200309 | 36.023 | 73.600 | Gilgit | E(o) | 6.03 | 4,410 |\n| 310 | 0142H1200310 | 36.023 | 73.655 | Gilgit | E(o) | 3.26 | 4,304 |\n| 311 | 0142H1200311 | 36.022 | 73.578 | Gilgit | E(o) | 0.69 | 4,512 |\n| 312 | 0142H1200312 | 36.022 | 73.590 | Gilgit | E(o) | 2.66 | 4,558 |\n| 313 | 0142H1200313 | 36.022 | 73.722 | Gilgit | M(o) | 1.05 | 4,450 |\n| 314 | 0142H1200314 | 36.022 | 73.747 | Gilgit | E(o) | 4.94 | 4,384 |\n| 315 | 0142H1200315 | 36.022 | 73.647 | Gilgit | E(o) | 3.23 | 4,371 |\n| 316 | 0142H1200316 | 36.021 | 73.665 | Gilgit | E(o) | 6.01 | 4,207 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6280, "line_end": 6427, "token_count_estimate": 1593, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H1200282", "0142H1200283", "0142H1200284", "0142H1200285", "0142H1200286", "0142H1200287", "0142H1200288", "0142H1200289", "0142H1200290", "0142H1200291", "0142H1200292", "0142H1200293", "0142H1200294", "0142H1200295", "0142H1200296", "0142H1200297", "0142H1200298", "0142H1200299", "0142H1200300", "0142H1200301", "0142H1200302", "0142H1200303", "0142H1200304", "0142H1200305", "0142H1200306", "0142H1200307", "0142H1200308", "0142H1200309", "0142H1200310", "0142H1200311", "0142H1200312", "0142H1200313", "0142H1200314", "0142H1200315", "0142H1200316"]}}
{"id": "2108e2e3135b7729", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 317 | 0142H1200317 | 36.018 | 73.736 | Gilgit | E(o) | 3.45 | 4,495 |\n| 318 | 0142H1200318 | 36.018 | 73.568 | Gilgit | E(o) | 0.32 | 4,449 |\n| 319 | 0142H1200319 | 36.017 | 73.620 | Gilgit | E(o) | 1.08 | 4,258 |\n| 320 | 0142H1200320 | 36.016 | 73.580 | Gilgit | E(c) | 9.13 | 4,395 |\n| 321 | 0142H1200321 | 36.015 | 73.517 | Gilgit | E(o) | 0.91 | 4,270 |\n| 322 | 0142H1200322 | 36.013 | 73.611 | Gilgit | E(o) | 3.65 | 4,367 |\n| 323 | 0142H1200323 | 36.012 | 73.715 | Gilgit | E(o) | 0.57 | 4,589 |\n| 324 | 0142H1200324 | 36.012 | 73.732 | Gilgit | E(o) | 0.86 | 4,512 |\n| 325 | 0142H1200325 | 36.012 | 73.665 | Gilgit | E(o) | 2.04 | 4,441 |\n| 326 | 0142H1200326 | 36.011 | 73.747 | Gilgit | E(o) | 0.98 | 4,397 |\n| 327 | 0142H1200327 | 36.011 | 73.558 | Gilgit | E(o) | 24.48 | 4,232 |\n| 328 | 0142H1200328 | 36.010 | 73.535 | Gilgit | E(o) | 1.94 | 4,299 |\n| 329 | 0142H1200329 | 36.010 | 73.705 | Gilgit | E(o) | 3.44 | 4,443 |\n| 330 | 0142H1200330 | 36.009 | 73.652 | Gilgit | E(o) | 4.14 | 4,485 |\n| 331 | 0142H1200331 | 36.007 | 73.734 | Gilgit | E(o) | 0.87 | 4,487 |\n| 332 | 0142H1200332 | 36.004 | 73.737 | Gilgit | E(o) | 2.35 | 4,428 |\n| 333 | 0142H1200333 | 36.004 | 73.665 | Gilgit | E(o) | 0.32 | 4,396 |\n| 334 | 0142H1200334 | 36.003 | 73.652 | Gilgit | E(o) | 0.64 | 4,430 |\n| 335 | 0142H1200335 | 36.002 | 73.614 | Gilgit | E(o) | 0.51 | 4,368 |\n| 336 | 0142H1200336 | 36.001 | 73.651 | Gilgit | E(o) | 3.61 | 4,415 |\n| 337 | 0142H1200337 | 36.000 | 73.656 | Gilgit | E(o) | 0.86 | 4,418 |\n| 338 | 0142H1300338 | 36.844 | 73.914 | Gilgit | O | 0.47 | 3,563 |\n| 339 | 0142H1300339 | 36.841 | 73.920 | Gilgit | M(e) | 4.44 | 3,547 |\n| 340 | 0142H1300340 | 36.840 | 73.912 | Gilgit | I(s) | 0.33 | 3,604 |\n| 341 | 0142H1300341 | 36.793 | 73.995 | Gilgit | I(s) | 0.52 | 3,491 |\n| 342 | 0142H1300342 | 36.791 | 73.996 | Gilgit | I(s) | 0.28 | 3,505 |\n| 343 | 0142H1300343 | 36.791 | 73.997 | Gilgit | I(s) | 0.35 | 3,498 |\n| 344 | 0142H1300344 | 36.789 | 73.998 | Gilgit | I(s) | 0.84 | 3,488 |\n| 345 | 0142H1400345 | 36.672 | 73.800 | Gilgit | M(o) | 0.29 | 4,009 |\n| 346 | 0142H1400346 | 36.633 | 73.905 | Gilgit | I(s) | 0.40 | 4,024 |\n| 347 | 0142H1400347 | 36.613 | 73.897 | Gilgit | M(e) | 0.26 | 3,776 |\n| 348 | 0142H1400348 | 36.613 | 73.895 | Gilgit | M(e) | 0.44 | 3,788 |\n| 349 | 0142H1400349 | 36.612 | 73.899 | Gilgit | M(e) | 0.28 | 3,781 |\n| 350 | 0142H1400350 | 36.610 | 73.982 | Gilgit | I(s) | 0.72 | 4,317 |\n| 351 | 0142H1400351 | 36.608 | 73.883 | Gilgit | M(e) | 2.86 | 3,732 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6280, "line_end": 6427, "token_count_estimate": 1581, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H1200317", "0142H1200318", "0142H1200319", "0142H1200320", "0142H1200321", "0142H1200322", "0142H1200323", "0142H1200324", "0142H1200325", "0142H1200326", "0142H1200327", "0142H1200328", "0142H1200329", "0142H1200330", "0142H1200331", "0142H1200332", "0142H1200333", "0142H1200334", "0142H1200335", "0142H1200336", "0142H1200337", "0142H1300338", "0142H1300339", "0142H1300340", "0142H1300341", "0142H1300342", "0142H1300343", "0142H1300344", "0142H1400345", "0142H1400346", "0142H1400347", "0142H1400348", "0142H1400349", "0142H1400350", "0142H1400351"]}}
{"id": "54019f1c29c51f9d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 352 | 0142H1400352 | 36.606 | 73.986 | Gilgit | I(s) | 0.38 | 4,281 |\n| 353 | 0142H1400353 | 36.601 | 73.862 | Gilgit | M(l) | 2.26 | 3,474 |\n| 354 | 0142H1400354 | 36.600 | 73.981 | Gilgit | M(e) | 0.31 | 4,244 |\n| 355 | 0142H1500355 | 36.328 | 73.984 | Gilgit | M(o) | 0.91 | 4,328 |\n| 356 | 0142H1500356 | 36.286 | 73.926 | Gilgit | O | 1.06 | 3,447 |\n| 357 | 0142H1500357 | 36.265 | 73.955 | Gilgit | O | 27.89 | 3,706 |\n| 358 | 0142H1500358 | 36.261 | 73.899 | Gilgit | O | 6.08 | 3,773 |\n| 359 | 0142H1600359 | 36.240 | 73.954 | Gilgit | M(o) | 1.85 | 4,084 |\n| 360 | 0142H1600360 | 36.083 | 73.770 | Gilgit | E(c) | 3.69 | 4,335 |\n| 361 | 0142H1600361 | 36.077 | 73.751 | Gilgit | E(o) | 1.43 | 4,286 |\n| 362 | 0142H1600362 | 36.036 | 73.783 | Gilgit | E(o) | 3.70 | 4,196 |\n| 363 | 0142H1600363 | 36.033 | 73.784 | Gilgit | E(o) | 0.83 | 4,213 |\n| 364 | 0142H1600364 | 36.028 | 73.780 | Gilgit | M(e) | 0.29 | 4,335 |\n| 365 | 0142H1600365 | 36.025 | 73.933 | Gilgit | E(c) | 3.65 | 4,258 |\n| 366 | 0142H1600366 | 36.024 | 73.754 | Gilgit | E(o) | 1.47 | 4,275 |\n| 367 | 0142H1600367 | 36.021 | 73.751 | Gilgit | E(o) | 1.21 | 4,355 |\n| 368 | 0142H1600368 | 36.019 | 73.922 | Gilgit | E(c) | 6.29 | 4,465 |\n| 369 | 0142H1600369 | 36.017 | 73.753 | Gilgit | E(o) | 0.31 | 4,365 |\n| 370 | 0142H1600370 | 36.001 | 73.887 | Gilgit | E(o) | 0.84 | 4,287 |\n| 371 | 0142K1600371 | 37.010 | 74.762 | Gilgit | I(s) | 0.49 | 5,030 |\n| 372 | 0142L0100372 | 36.855 | 74.154 | Gilgit | M(e) | 0.28 | 4,544 |\n| 373 | 0142L0100373 | 36.826 | 74.206 | Gilgit | O | 0.38 | 3,649 |\n| 374 | 0142L0100374 | 36.821 | 74.174 | Gilgit | I(s) | 0.29 | 3,871 |\n| 375 | 0142L0200375 | 36.701 | 74.150 | Gilgit | E(o) | 0.54 | 4,547 |\n| 376 | 0142L0200376 | 36.699 | 74.147 | Gilgit | M(l) | 0.75 | 4,426 |\n| 377 | 0142L0200377 | 36.658 | 74.146 | Gilgit | E(o) | 0.91 | 3,756 |\n| 378 | 0142L0200378 | 36.529 | 74.250 | Gilgit | M(l) | 2.49 | 3,569 |\n| 379 | 0142L0200379 | 36.504 | 74.240 | Gilgit | I(s) | 0.28 | 3,334 |\n| 380 | 0142L0300380 | 36.413 | 74.143 | Gilgit | M(l) | 0.26 | 3,759 |\n| 381 | 0142L0300381 | 36.333 | 74.007 | Gilgit | E(o) | 0.28 | 4,583 |\n| 382 | 0142L0300382 | 36.331 | 74.006 | Gilgit | M(o) | 0.26 | 4,583 |\n| 383 | 0142L0300383 | 36.331 | 74.009 | Gilgit | M(o) | 0.49 | 4,551 |\n| 384 | 0142L0300384 | 36.293 | 74.107 | Gilgit | M(o) | 0.76 | 3,509 |\n| 385 | 0142L0300385 | 36.289 | 74.109 | Gilgit | M(o) | 0.27 | 3,496 |\n| 386 | 0142L0400386 | 36.239 | 74.084 | Gilgit | E(o) | 20.57 | 3,453 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6280, "line_end": 6427, "token_count_estimate": 1586, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142H1400352", "0142H1400353", "0142H1400354", "0142H1500355", "0142H1500356", "0142H1500357", "0142H1500358", "0142H1600359", "0142H1600360", "0142H1600361", "0142H1600362", "0142H1600363", "0142H1600364", "0142H1600365", "0142H1600366", "0142H1600367", "0142H1600368", "0142H1600369", "0142H1600370", "0142K1600371", "0142L0100372", "0142L0100373", "0142L0100374", "0142L0200375", "0142L0200376", "0142L0200377", "0142L0200378", "0142L0200379", "0142L0300380", "0142L0300381", "0142L0300382", "0142L0300383", "0142L0300384", "0142L0300385", "0142L0400386"]}}
{"id": "c09f7585150c4b84", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 387 | 0142L0400387 | 36.238 | 74.103 | Gilgit | E(o) | 2.72 | 3,252 |\n| 388 | 0142L0400388 | 36.236 | 74.101 | Gilgit | E(o) | 2.50 | 3,254 |\n| 389 | 0142L0400389 | 36.236 | 74.104 | Gilgit | E(o) | 3.04 | 3,254 |\n| 390 | 0142L0400390 | 36.232 | 74.107 | Gilgit | E(o) | 1.15 | 3,225 |\n| 391 | 0142L0500391 | 36.951 | 74.374 | Gilgit | O | 0.72 | 4,698 |\n| 392 | 0142L0500392 | 36.948 | 74.369 | Gilgit | E(o) | 0.42 | 4,773 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6280, "line_end": 6427, "token_count_estimate": 361, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142L0400387", "0142L0400388", "0142L0400389", "0142L0400390", "0142L0500391", "0142L0500392"]}}
{"id": "db69649298157133", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6428, "line_end": 6436, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fff68b313be3c46f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 393 | 0142L0500393 | 36.818 | 74.373 | Gilgit | M(o) | 0.48 | 3,360 |\n| 394 | 0142L0500394 | 36.796 | 74.267 | Gilgit | M(o) | 0.31 | 4,370 |\n| 395 | 0142L0500395 | 36.791 | 74.349 | Gilgit | I(s) | 0.25 | 3,646 |\n| 396 | 0142L0500396 | 36.785 | 74.385 | Gilgit | I(s) | 0.30 | 3,769 |\n| 397 | 0142L0500397 | 36.784 | 74.378 | Gilgit | I(s) | 0.27 | 3,731 |\n| 398 | 0142L0500398 | 36.784 | 74.385 | Gilgit | I(s) | 0.32 | 3,761 |\n| 399 | 0142L0500399 | 36.783 | 74.391 | Gilgit | I(s) | 0.55 | 3,814 |\n| 400 | 0142L0500400 | 36.783 | 74.343 | Gilgit | I(s) | 0.38 | 3,710 |\n| 401 | 0142L0500401 | 36.783 | 74.350 | Gilgit | I(s) | 0.65 | 3,691 |\n| 402 | 0142L0500402 | 36.779 | 74.334 | Gilgit | I(s) | 0.34 | 3,745 |\n| 403 | 0142L0500403 | 36.777 | 74.333 | Gilgit | I(s) | 0.29 | 3,753 |\n| 404 | 0142L0500404 | 36.775 | 74.338 | Gilgit | I(s) | 0.82 | 3,768 |\n| 405 | 0142L0500405 | 36.775 | 74.343 | Gilgit | I(s) | 0.50 | 3,745 |\n| 406 | 0142L0500406 | 36.774 | 74.333 | Gilgit | I(s) | 1.22 | 3,753 |\n| 407 | 0142L0500407 | 36.769 | 74.335 | Gilgit | I(s) | 1.22 | 3,774 |\n| 408 | 0142L0500408 | 36.768 | 74.332 | Gilgit | I(s) | 0.55 | 3,769 |\n| 409 | 0142L0500409 | 36.764 | 74.330 | Gilgit | I(s) | 0.49 | 3,800 |\n| 410 | 0142L0500410 | 36.762 | 74.319 | Gilgit | I(s) | 0.27 | 3,833 |\n| 411 | 0142L0500411 | 36.761 | 74.329 | Gilgit | I(s) | 0.30 | 3,813 |\n| 412 | 0142L0500412 | 36.758 | 74.314 | Gilgit | I(s) | 0.27 | 3,866 |\n| 413 | 0142L0600413 | 36.746 | 74.318 | Gilgit | I(s) | 0.30 | 3,903 |\n| 414 | 0142L0600414 | 36.743 | 74.298 | Gilgit | M(l) | 0.70 | 3,991 |\n| 415 | 0142L0600415 | 36.732 | 74.416 | Gilgit | I(s) | 0.37 | 4,185 |\n| 416 | 0142L0600416 | 36.722 | 74.421 | Gilgit | I(s) | 0.28 | 4,242 |\n| 417 | 0142L0600417 | 36.663 | 74.494 | Gilgit | I(s) | 0.28 | 4,277 |\n| 418 | 0142L0600418 | 36.581 | 74.444 | Gilgit | I(s) | 0.26 | 4,112 |\n| 419 | 0142L0700419 | 36.486 | 74.444 | Gilgit | I(s) | 0.31 | 4,084 |\n| 420 | 0142L0900420 | 36.989 | 74.657 | Gilgit | M(e) | 1.96 | 4,556 |\n| 421 | 0142L1000421 | 36.619 | 74.523 | Gilgit | M(l) | 0.65 | 3,750 |\n| 422 | 0142L1000422 | 36.596 | 74.565 | Gilgit | M(l) | 0.32 | 3,691 |\n| 423 | 0142L1000423 | 36.594 | 74.605 | Gilgit | I(s) | 1.16 | 3,559 |\n| 424 | 0142L1000424 | 36.572 | 74.622 | Gilgit | I(s) | 0.39 | 3,501 |\n| 425 | 0142L1000425 | 36.566 | 74.625 | Gilgit | I(s) | 0.31 | 3,481 |\n| 426 | 0142L1000426 | 36.558 | 74.647 | Gilgit | I(s) | 0.42 | 3,405 |\n| 427 | 0142L1000427 | 36.544 | 74.736 | Gilgit | I(s) | 0.37 | 3,195 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6437, "line_end": 6584, "token_count_estimate": 1601, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142L0500393", "0142L0500394", "0142L0500395", "0142L0500396", "0142L0500397", "0142L0500398", "0142L0500399", "0142L0500400", "0142L0500401", "0142L0500402", "0142L0500403", "0142L0500404", "0142L0500405", "0142L0500406", "0142L0500407", "0142L0500408", "0142L0500409", "0142L0500410", "0142L0500411", "0142L0500412", "0142L0600413", "0142L0600414", "0142L0600415", "0142L0600416", "0142L0600417", "0142L0600418", "0142L0700419", "0142L0900420", "0142L1000421", "0142L1000422", "0142L1000423", "0142L1000424", "0142L1000425", "0142L1000426", "0142L1000427"]}}
{"id": "3e1f509e8e7e916a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 428 | 0142L1000428 | 36.532 | 74.717 | Gilgit | I(s) | 0.93 | 3,213 |\n| 429 | 0142L1000429 | 36.530 | 74.657 | Gilgit | E(o) | 0.63 | 3,346 |\n| 430 | 0142L1000430 | 36.527 | 74.692 | Gilgit | M(l) | 2.00 | 3,289 |\n| 431 | 0142L1100431 | 36.462 | 74.705 | Gilgit | I(s) | 2.39 | 4,820 |\n| 432 | 0142L1100432 | 36.461 | 74.392 | Gilgit | I(s) | 0.40 | 3,625 |\n| 433 | 0142L1100433 | 36.436 | 74.509 | Gilgit | I(s) | 0.47 | 3,820 |\n| 434 | 0142L1100434 | 36.413 | 74.487 | Gilgit | M(l) | 1.25 | 3,575 |\n| 435 | 0142L1100435 | 36.409 | 74.482 | Gilgit | I(s) | 0.30 | 3,633 |\n| 436 | 0142L1200436 | 36.045 | 74.571 | Gilgit | I(s) | 0.32 | 2,689 |\n| 437 | 0142L1200437 | 36.039 | 74.648 | Gilgit | I(s) | 1.17 | 3,331 |\n| 438 | 0142L1200438 | 36.038 | 74.647 | Gilgit | I(s) | 0.32 | 3,338 |\n| 439 | 0142L1200439 | 36.029 | 74.642 | Gilgit | I(s) | 0.27 | 3,300 |\n| 440 | 0142L1200440 | 36.026 | 74.649 | Gilgit | I(s) | 0.28 | 3,310 |\n| 441 | 0142L1200441 | 36.018 | 74.606 | Gilgit | I(s) | 1.19 | 3,299 |\n| 442 | 0142L1200442 | 36.014 | 74.609 | Gilgit | I(s) | 0.25 | 3,461 |\n| 443 | 0142L1300443 | 36.989 | 74.816 | Gilgit | M(e) | 0.40 | 4,669 |\n| 444 | 0142L1300444 | 36.985 | 74.776 | Gilgit | E(o) | 3.16 | 4,417 |\n| 445 | 0142L1300445 | 36.957 | 74.986 | Gilgit | O | 7.17 | 4,145 |\n| 446 | 0142L1300446 | 36.895 | 74.927 | Gilgit | M(e) | 0.63 | 4,685 |\n| 447 | 0142L1300447 | 36.888 | 74.864 | Gilgit | O | 0.54 | 4,434 |\n| 448 | 0142L1400448 | 36.531 | 74.755 | Gilgit | I(s) | 0.36 | 3,134 |\n| 449 | 0142L1400449 | 36.513 | 74.867 | Gilgit | M(o) | 3.27 | 2,521 |\n| 450 | 0142L1400450 | 36.510 | 74.798 | Gilgit | I(s) | 0.52 | 3,014 |\n| 451 | 0142L1400451 | 36.510 | 74.796 | Gilgit | I(s) | 0.34 | 3,022 |\n| 452 | 0142L1400452 | 36.509 | 74.799 | Gilgit | I(s) | 0.44 | 3,016 |\n| 453 | 0142L1400453 | 36.506 | 74.776 | Gilgit | M(l) | 1.42 | 3,050 |\n| 454 | 0142L1400454 | 36.506 | 74.805 | Gilgit | I(s) | 0.48 | 2,986 |\n| 455 | 0142L1400455 | 36.505 | 74.803 | Gilgit | I(s) | 0.32 | 3,009 |\n| 456 | 0142L1500456 | 36.500 | 74.823 | Gilgit | I(s) | 0.31 | 2,913 |\n| 457 | 0142L1500457 | 36.497 | 74.835 | Gilgit | I(s) | 0.30 | 2,859 |\n| 458 | 0142L1500458 | 36.495 | 74.831 | Gilgit | I(s) | 0.42 | 2,883 |\n| 459 | 0142L1500459 | 36.495 | 74.836 | Gilgit | I(s) | 0.28 | 2,854 |\n| 460 | 0142L1500460 | 36.495 | 74.830 | Gilgit | I(s) | 0.25 | 2,886 |\n| 461 | 0142L1500461 | 36.494 | 74.828 | Gilgit | I(s) | 0.32 | 2,879 |\n| 462 | 0142L1500462 | 36.458 | 74.882 | Gilgit | M(e) | 3.12 | 2,561 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6437, "line_end": 6584, "token_count_estimate": 1580, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142L1000428", "0142L1000429", "0142L1000430", "0142L1100431", "0142L1100432", "0142L1100433", "0142L1100434", "0142L1100435", "0142L1200436", "0142L1200437", "0142L1200438", "0142L1200439", "0142L1200440", "0142L1200441", "0142L1200442", "0142L1300443", "0142L1300444", "0142L1300445", "0142L1300446", "0142L1300447", "0142L1400448", "0142L1400449", "0142L1400450", "0142L1400451", "0142L1400452", "0142L1400453", "0142L1400454", "0142L1400455", "0142L1500456", "0142L1500457", "0142L1500458", "0142L1500459", "0142L1500460", "0142L1500461", "0142L1500462"]}}
{"id": "788a2c42355f43a3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 463 | 0142L1500463 | 36.367 | 74.764 | Gilgit | M(l) | 1.26 | 4,665 |\n| 464 | 0142L1500464 | 36.337 | 74.965 | Gilgit | M(l) | 0.33 | 4,847 |\n| 465 | 0142L1500465 | 36.255 | 74.952 | Gilgit | M(l) | 0.40 | 4,354 |\n| 466 | 0142L1500466 | 36.250 | 74.976 | Gilgit | M(lg) | 0.31 | 4,477 |\n| 467 | 0142L1600467 | 36.241 | 74.943 | Gilgit | E(o) | 0.58 | 4,860 |\n| 468 | 0142L1600468 | 36.219 | 74.802 | Gilgit | I(s) | 0.50 | 2,930 |\n| 469 | 0142L1600469 | 36.214 | 74.809 | Gilgit | I(s) | 0.52 | 2,974 |\n| 470 | 0142L1600470 | 36.174 | 74.883 | Gilgit | E(o) | 6.04 | 4,613 |\n| 471 | 0142L1600471 | 36.006 | 74.806 | Indus Middle | E(o) | 3.70 | 3,299 |\n| 472 | 0142O0400472 | 37.014 | 75.139 | Gilgit | M(e) | 1.63 | 5,062 |\n| 473 | 0142O0400473 | 37.007 | 75.136 | Gilgit | M(e) | 0.70 | 4,989 |\n| 474 | 0142P0200474 | 36.600 | 75.189 | Gilgit | I(s) | 0.28 | 4,531 |\n| 475 | 0142P0300475 | 36.471 | 75.061 | Gilgit | I(s) | 0.34 | 3,324 |\n| 476 | 0142P0300476 | 36.469 | 75.062 | Gilgit | I(s) | 0.32 | 3,321 |\n| 477 | 0142P0300477 | 36.256 | 75.158 | Gilgit | M(lg) | 0.52 | 4,423 |\n| 478 | 0142P0400478 | 36.163 | 75.067 | Gilgit | O | 1.60 | 3,428 |\n| 479 | 0142P0400479 | 36.150 | 75.047 | Gilgit | I(s) | 0.31 | 3,354 |\n| 480 | 0142P0400480 | 36.146 | 75.070 | Gilgit | I(s) | 0.94 | 3,434 |\n| 481 | 0142P0400481 | 36.137 | 75.136 | Gilgit | I(s) | 24.88 | 3,697 |\n| 482 | 0142P0400482 | 36.135 | 75.141 | Gilgit | I(s) | 0.65 | 3,701 |\n| 483 | 0142P0400483 | 36.133 | 75.130 | Gilgit | I(s) | 0.27 | 3,687 |\n| 484 | 0142P0400484 | 36.132 | 75.127 | Gilgit | I(s) | 0.40 | 3,653 |\n| 485 | 0142P0400485 | 36.132 | 75.156 | Gilgit | I(s) | 5.87 | 3,760 |\n| 486 | 0142P0400486 | 36.132 | 75.134 | Gilgit | I(s) | 0.32 | 3,691 |\n| 487 | 0142P0400487 | 36.131 | 75.146 | Gilgit | I(s) | 9.28 | 3,728 |\n| 488 | 0142P0400488 | 36.131 | 75.152 | Gilgit | I(s) | 0.32 | 3,762 |\n| 489 | 0142P0400489 | 36.131 | 75.165 | Gilgit | I(s) | 2.08 | 3,772 |\n| 490 | 0142P0400490 | 36.130 | 75.164 | Gilgit | I(s) | 0.58 | 3,777 |\n| 491 | 0142P0400491 | 36.129 | 75.161 | Gilgit | I(s) | 0.63 | 3,781 |\n| 492 | 0142P0400492 | 36.126 | 75.140 | Gilgit | I(s) | 12.99 | 3,725 |\n| 493 | 0142P0400493 | 36.125 | 75.145 | Gilgit | I(s) | 0.40 | 3,733 |\n| 494 | 0142P0400494 | 36.125 | 75.181 | Gilgit | I(s) | 0.57 | 3,818 |\n| 495 | 0142P0400495 | 36.123 | 75.180 | Gilgit | I(s) | 0.60 | 3,810 |\n| 496 | 0142P0400496 | 36.122 | 75.163 | Gilgit | I(s) | 3.70 | 3,761 |\n| 497 | 0142P0400497 | 36.122 | 75.153 | Gilgit | I(s) | 0.45 | 3,763 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6437, "line_end": 6584, "token_count_estimate": 1592, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0142L1500463", "0142L1500464", "0142L1500465", "0142L1500466", "0142L1600467", "0142L1600468", "0142L1600469", "0142L1600470", "0142L1600471", "0142O0400472", "0142O0400473", "0142P0200474", "0142P0300475", "0142P0300476", "0142P0300477", "0142P0400478", "0142P0400479", "0142P0400480", "0142P0400481", "0142P0400482", "0142P0400483", "0142P0400484", "0142P0400485", "0142P0400486", "0142P0400487", "0142P0400488", "0142P0400489", "0142P0400490", "0142P0400491", "0142P0400492", "0142P0400493", "0142P0400494", "0142P0400495", "0142P0400496", "0142P0400497"]}}
{"id": "7605baa42b84a272", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 498 | 0142P0400498 | 36.117 | 75.211 | Gilgit | I(s) | 0.29 | 3,892 |\n| 499 | 0142P0400499 | 36.116 | 75.182 | Gilgit | I(s) | 0.27 | 3,808 |\n| 500 | 0142P0400500 | 36.115 | 75.189 | Gilgit | I(s) | 0.40 | 3,843 |\n| 501 | 0142P0400501 | 36.115 | 75.228 | Gilgit | I(s) | 2.05 | 3,926 |\n| 502 | 0142P0400502 | 36.114 | 75.200 | Gilgit | I(s) | 0.49 | 3,866 |\n| 503 | 0142P0400503 | 36.114 | 75.193 | Gilgit | I(s) | 0.30 | 3,854 |\n| 504 | 0142P0400504 | 36.113 | 75.195 | Gilgit | I(s) | 0.27 | 3,856 |\n| 505 | 0142P0400505 | 36.113 | 75.192 | Gilgit | I(s) | 0.66 | 3,861 |\n| 506 | 0142P0400506 | 36.113 | 75.175 | Gilgit | I(s) | 0.52 | 3,812 |\n| 507 | 0142P0400507 | 36.112 | 75.221 | Gilgit | I(s) | 0.35 | 3,935 |\n| 508 | 0142P0400508 | 36.110 | 75.217 | Gilgit | I(s) | 0.54 | 3,941 |\n| 509 | 0142P0400509 | 36.110 | 75.220 | Gilgit | I(s) | 0.38 | 3,946 |\n| 510 | 0142P0400510 | 36.109 | 75.230 | Gilgit | I(s) | 0.98 | 3,962 |\n| 511 | 0142P0400511 | 36.109 | 75.214 | Gilgit | I(s) | 0.52 | 3,922 |\n| 512 | 0142P0400512 | 36.107 | 75.196 | Gilgit | I(s) | 0.39 | 3,877 |\n| 513 | 0142P0400513 | 36.103 | 75.232 | Gilgit | I(s) | 0.60 | 3,984 |\n| 514 | 0142P0400514 | 36.103 | 75.221 | Gilgit | I(s) | 0.55 | 3,942 |\n| 515 | 0142P0400515 | 36.101 | 75.214 | Gilgit | I(s) | 0.52 | 3,918 |\n| 516 | 0142P0400516 | 36.098 | 75.242 | Gilgit | I(s) | 0.61 | 4,016 |\n| 517 | 0142P0400517 | 36.098 | 75.223 | Gilgit | I(s) | 0.72 | 3,950 |\n| 518 | 0142P0400518 | 36.093 | 75.237 | Gilgit | I(s) | 0.26 | 4,017 |\n| 519 | 0142P0400519 | 36.049 | 75.071 | Indus Middle | M(l) | 2.42 | 4,608 |\n| 520 | 0142P0400520 | 36.022 | 75.241 | Indus Middle | M(l) | 5.52 | 4,204 |\n| 521 | 0142P0600521 | 36.548 | 75.345 | Gilgit | O | 3.72 | 4,971 |\n| 522 | 0142P0700522 | 36.415 | 75.386 | Gilgit | M(e) | 0.48 | 3,264 |\n| 523 | 0142P0700523 | 36.413 | 75.386 | Gilgit | I(s) | 0.26 | 3,278 |\n| 524 | 0142P0700524 | 36.412 | 75.386 | Gilgit | I(s) | 0.54 | 3,288 |\n| 525 | 0142P0700525 | 36.412 | 75.387 | Gilgit | I(s) | 0.41 | 3,293 |\n| 526 | 0142P0700526 | 36.412 | 75.388 | Gilgit | I(s) | 0.51 | 3,296 |\n| 527 | 0142P0700527 | 36.411 | 75.386 | Gilgit | I(s) | 0.38 | 3,291 |\n| 528 | 0142P0700528 | 36.411 | 75.388 | Gilgit | I(s) | 0.76 | 3,288 |\n| 529 | 0142P0700529 | 36.411 | 75.389 | Gilgit | I(s) | 0.51 | 3,299 |\n| 530 | 0142P0700530 | 36.410 | 75.385 | Gilgit | I(s) | 0.47 | 3,294 |\n| 531 | 0142P0700531 | 36.410 | 75.388 | Gilgit | I(s) | 0.46 | 3,299 |\n| 532 | 0142P0700532 | 36.408 | 75.391 | Gilgit | M(o) | 1.40 | 3,307 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6437, "line_end": 6584, "token_count_estimate": 1598, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0142P0400498", "0142P0400499", "0142P0400500", "0142P0400501", "0142P0400502", "0142P0400503", "0142P0400504", "0142P0400505", "0142P0400506", "0142P0400507", "0142P0400508", "0142P0400509", "0142P0400510", "0142P0400511", "0142P0400512", "0142P0400513", "0142P0400514", "0142P0400515", "0142P0400516", "0142P0400517", "0142P0400518", "0142P0400519", "0142P0400520", "0142P0600521", "0142P0700522", "0142P0700523", "0142P0700524", "0142P0700525", "0142P0700526", "0142P0700527", "0142P0700528", "0142P0700529", "0142P0700530", "0142P0700531", "0142P0700532"]}}
{"id": "37a490e441fe4ece", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 533 | 0142P0700533 | 36.408 | 75.389 | Gilgit | I(s) | 0.26 | 3,308 |\n| 534 | 0142P0700534 | 36.407 | 75.392 | Gilgit | I(s) | 0.72 | 3,315 |\n| 535 | 0142P0700535 | 36.406 | 75.386 | Gilgit | M(l) | 0.28 | 3,305 |\n| 536 | 0142P0700536 | 36.406 | 75.389 | Gilgit | I(s) | 0.44 | 3,318 |\n| 537 | 0142P0700537 | 36.402 | 75.389 | Gilgit | I(s) | 0.30 | 3,327 |\n| 538 | 0142P0700538 | 36.400 | 75.390 | Gilgit | M(l) | 2.17 | 3,345 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6437, "line_end": 6584, "token_count_estimate": 371, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142P0700533", "0142P0700534", "0142P0700535", "0142P0700536", "0142P0700537", "0142P0700538"]}}
{"id": "a33882988a7179d5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6585, "line_end": 6596, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "647e6c838d21b2d4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 539 | 0142P0700539 | 36.384 | 75.403 | Gilgit | I(s) | 0.45 | 3,325 |\n| 540 | 0142P0700540 | 36.361 | 75.446 | Gilgit | M(e) | 0.36 | 3,346 |\n| 541 | 0142P0700541 | 36.353 | 75.455 | Gilgit | I(s) | 2.01 | 3,470 |\n| 542 | 0142P0700542 | 36.351 | 75.448 | Gilgit | I(s) | 1.15 | 3,468 |\n| 543 | 0142P0700543 | 36.341 | 75.457 | Gilgit | I(s) | 0.28 | 3,549 |\n| 544 | 0142P0700544 | 36.338 | 75.455 | Gilgit | I(s) | 0.54 | 3,552 |\n| 545 | 0142P0700545 | 36.333 | 75.484 | Gilgit | I(s) | 1.29 | 3,588 |\n| 546 | 0142P0700546 | 36.331 | 75.484 | Gilgit | I(s) | 2.03 | 3,579 |\n| 547 | 0142P0700547 | 36.306 | 75.473 | Gilgit | I(s) | 0.70 | 3,678 |\n| 548 | 0142P0800548 | 36.111 | 75.415 | Gilgit | M(lg) | 0.41 | 4,655 |\n| 549 | 0142P0800549 | 36.097 | 75.257 | Gilgit | I(s) | 0.55 | 4,044 |\n| 550 | 0142P0800550 | 36.096 | 75.251 | Gilgit | I(s) | 0.85 | 4,043 |\n| 551 | 0142P0800551 | 36.095 | 75.261 | Gilgit | I(s) | 0.26 | 4,067 |\n| 552 | 0142P0800552 | 36.094 | 75.258 | Gilgit | I(s) | 0.39 | 4,051 |\n| 553 | 0142P0800553 | 36.093 | 75.256 | Gilgit | I(s) | 0.52 | 4,058 |\n| 554 | 0142P0800554 | 36.093 | 75.268 | Gilgit | I(s) | 0.42 | 4,094 |\n| 555 | 0142P0800555 | 36.089 | 75.281 | Gilgit | I(s) | 0.30 | 4,126 |\n| 556 | 0142P0800556 | 36.087 | 75.262 | Gilgit | I(s) | 1.34 | 4,078 |\n| 557 | 0142P0800557 | 36.087 | 75.284 | Gilgit | I(s) | 0.32 | 4,135 |\n| 558 | 0142P0800558 | 36.086 | 75.267 | Gilgit | I(s) | 1.13 | 4,086 |\n| 559 | 0142P0800559 | 36.086 | 75.270 | Gilgit | I(s) | 0.51 | 4,099 |\n| 560 | 0142P0800560 | 36.086 | 75.276 | Gilgit | I(s) | 0.54 | 4,136 |\n| 561 | 0142P0800561 | 36.086 | 75.265 | Gilgit | I(s) | 1.34 | 4,103 |\n| 562 | 0142P0800562 | 36.085 | 75.305 | Gilgit | I(s) | 0.29 | 4,193 |\n| 563 | 0142P0800563 | 36.084 | 75.250 | Gilgit | I(s) | 0.27 | 4,064 |\n| 564 | 0142P0800564 | 36.083 | 75.303 | Gilgit | I(s) | 0.27 | 4,184 |\n| 565 | 0142P0800565 | 36.077 | 75.259 | Gilgit | I(s) | 1.03 | 4,088 |\n| 566 | 0142P0800566 | 36.054 | 75.471 | Gilgit | I(s) | 0.60 | 4,643 |\n| 567 | 0142P0800567 | 36.045 | 75.483 | Gilgit | I(s) | 0.71 | 4,680 |\n| 568 | 0142P0800568 | 36.043 | 75.481 | Gilgit | I(s) | 0.91 | 4,664 |\n| 569 | 0142P0800569 | 36.016 | 75.350 | Indus Middle | I(s) | 0.28 | 4,632 |\n| 570 | 0142P1100570 | 36.444 | 75.679 | Gilgit | E(o) | 7.40 | 4,706 |\n| 571 | 0142P1100571 | 36.440 | 75.686 | Gilgit | E(o) | 28.09 | 4,709 |\n| 572 | 0142P1100572 | 36.334 | 75.539 | Gilgit | I(s) | 1.78 | 3,704 |\n| 573 | 0142P1100573 | 36.333 | 75.547 | Gilgit | I(s) | 0.87 | 3,757 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6597, "line_end": 6743, "token_count_estimate": 1632, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0142P0700539", "0142P0700540", "0142P0700541", "0142P0700542", "0142P0700543", "0142P0700544", "0142P0700545", "0142P0700546", "0142P0700547", "0142P0800548", "0142P0800549", "0142P0800550", "0142P0800551", "0142P0800552", "0142P0800553", "0142P0800554", "0142P0800555", "0142P0800556", "0142P0800557", "0142P0800558", "0142P0800559", "0142P0800560", "0142P0800561", "0142P0800562", "0142P0800563", "0142P0800564", "0142P0800565", "0142P0800566", "0142P0800567", "0142P0800568", "0142P0800569", "0142P1100570", "0142P1100571", "0142P1100572", "0142P1100573"]}}
{"id": "3c4dcf81ab1bfb30", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 574 | 0142P1100574 | 36.330 | 75.558 | Gilgit | I(s) | 0.26 | 3,825 |\n| 575 | 0142P1100575 | 36.329 | 75.565 | Gilgit | I(s) | 0.26 | 3,876 |\n| 576 | 0142P1100576 | 36.315 | 75.591 | Gilgit | I(s) | 0.32 | 3,990 |\n| 577 | 0142P1100577 | 36.311 | 75.603 | Gilgit | I(s) | 0.31 | 4,060 |\n| 578 | 0142P1100578 | 36.305 | 75.596 | Gilgit | M(l) | 0.70 | 4,080 |\n| 579 | 0142P1100579 | 36.304 | 75.597 | Gilgit | E(o) | 0.34 | 4,098 |\n| 580 | 0142P1100580 | 36.298 | 75.617 | Gilgit | M(l) | 3.74 | 4,120 |\n| 581 | 0142P1100581 | 36.274 | 75.638 | Gilgit | M(l) | 0.78 | 4,326 |\n| 582 | 0142P1200582 | 36.218 | 75.660 | Gilgit | I(s) | 0.90 | 4,585 |\n| 583 | 0143A0900583 | 35.994 | 72.613 | Gilgit | O | 201.58 | 3,622 |\n| 584 | 0143A0900584 | 35.965 | 72.677 | Gilgit | M(o) | 0.35 | 4,772 |\n| 585 | 0143A0900585 | 35.965 | 72.599 | Gilgit | O | 3.81 | 3,685 |\n| 586 | 0143A0900586 | 35.963 | 72.650 | Gilgit | M(e) | 0.35 | 4,749 |\n| 587 | 0143A0900587 | 35.962 | 72.595 | Gilgit | O | 5.79 | 3,673 |\n| 588 | 0143A0900588 | 35.958 | 72.644 | Gilgit | M(o) | 0.52 | 4,775 |\n| 589 | 0143A0900589 | 35.953 | 72.673 | Gilgit | M(o) | 0.34 | 4,807 |\n| 590 | 0143A0900590 | 35.946 | 72.680 | Gilgit | E(c) | 1.04 | 4,631 |\n| 591 | 0143A0900591 | 35.944 | 72.595 | Gilgit | O | 96.35 | 3,761 |\n| 592 | 0143A0900592 | 35.919 | 72.563 | Gilgit | I(s) | 0.31 | 4,168 |\n| 593 | 0143A0900593 | 35.915 | 72.657 | Gilgit | M(o) | 0.26 | 4,612 |\n| 594 | 0143A0900594 | 35.908 | 72.663 | Gilgit | M(o) | 0.27 | 4,536 |\n| 595 | 0143A0900595 | 35.908 | 72.725 | Gilgit | E(o) | 1.48 | 4,462 |\n| 596 | 0143A0900596 | 35.905 | 72.582 | Gilgit | I(s) | 1.19 | 4,744 |\n| 597 | 0143A0900597 | 35.905 | 72.544 | Gilgit | M(o) | 0.54 | 4,421 |\n| 598 | 0143A0900598 | 35.897 | 72.722 | Gilgit | I(s) | 0.29 | 4,512 |\n| 599 | 0143A0900599 | 35.887 | 72.536 | Gilgit | I(s) | 0.89 | 4,599 |\n| 600 | 0143A0900600 | 35.880 | 72.704 | Gilgit | M(o) | 0.47 | 4,815 |\n| 601 | 0143A0900601 | 35.879 | 72.705 | Gilgit | M(o) | 0.30 | 4,841 |\n| 602 | 0143A0900602 | 35.875 | 72.602 | Gilgit | M(o) | 0.56 | 4,481 |\n| 603 | 0143A0900603 | 35.875 | 72.657 | Gilgit | O | 0.56 | 4,529 |\n| 604 | 0143A0900604 | 35.860 | 72.647 | Gilgit | I(s) | 2.97 | 4,737 |\n| 605 | 0143A0900605 | 35.860 | 72.667 | Gilgit | I(s) | 0.25 | 4,401 |\n| 606 | 0143A0900606 | 35.859 | 72.668 | Gilgit | I(s) | 0.53 | 4,407 |\n| 607 | 0143A0900607 | 35.858 | 72.675 | Gilgit | I(s) | 0.84 | 4,451 |\n| 608 | 0143A0900608 | 35.858 | 72.672 | Gilgit | I(s) | 0.39 | 4,434 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6597, "line_end": 6743, "token_count_estimate": 1601, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0142P1100574", "0142P1100575", "0142P1100576", "0142P1100577", "0142P1100578", "0142P1100579", "0142P1100580", "0142P1100581", "0142P1200582", "0143A0900583", "0143A0900584", "0143A0900585", "0143A0900586", "0143A0900587", "0143A0900588", "0143A0900589", "0143A0900590", "0143A0900591", "0143A0900592", "0143A0900593", "0143A0900594", "0143A0900595", "0143A0900596", "0143A0900597", "0143A0900598", "0143A0900599", "0143A0900600", "0143A0900601", "0143A0900602", "0143A0900603", "0143A0900604", "0143A0900605", "0143A0900606", "0143A0900607", "0143A0900608"]}}
{"id": "1c694bee57c1d68a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 609 | 0143A1300609 | 35.998 | 72.888 | Gilgit | E(o) | 0.26 | 4,924 |\n| 610 | 0143A1300610 | 35.998 | 72.890 | Gilgit | E(o) | 0.31 | 4,899 |\n| 611 | 0143A1300611 | 35.992 | 72.832 | Gilgit | M(o) | 0.31 | 4,820 |\n| 612 | 0143A1300612 | 35.977 | 72.869 | Gilgit | M(l) | 0.55 | 4,983 |\n| 613 | 0143A1300613 | 35.975 | 72.870 | Gilgit | M(o) | 0.44 | 4,974 |\n| 614 | 0143A1300614 | 35.946 | 72.792 | Gilgit | M(o) | 0.27 | 4,791 |\n| 615 | 0143A1300615 | 35.940 | 72.782 | Gilgit | M(l) | 0.33 | 4,519 |\n| 616 | 0143A1300616 | 35.934 | 72.931 | Gilgit | E(o) | 1.04 | 4,407 |\n| 617 | 0143A1300617 | 35.930 | 72.930 | Gilgit | M(o) | 7.10 | 4,469 |\n| 618 | 0143A1300618 | 35.927 | 72.952 | Gilgit | E(o) | 1.58 | 4,671 |\n| 619 | 0143A1300619 | 35.923 | 72.951 | Gilgit | E(o) | 3.20 | 4,638 |\n| 620 | 0143A1300620 | 35.923 | 72.931 | Gilgit | M(o) | 0.53 | 4,622 |\n| 621 | 0143A1300621 | 35.918 | 72.950 | Gilgit | E(o) | 1.55 | 4,798 |\n| 622 | 0143A1300622 | 35.918 | 72.810 | Gilgit | M(o) | 0.44 | 5,049 |\n| 623 | 0143A1300623 | 35.916 | 72.948 | Gilgit | M(o) | 0.34 | 4,765 |\n| 624 | 0143A1300624 | 35.909 | 72.951 | Gilgit | M(o) | 1.30 | 4,664 |\n| 625 | 0143A1300625 | 35.908 | 72.915 | Gilgit | E(c) | 0.59 | 4,803 |\n| 626 | 0143A1300626 | 35.908 | 72.952 | Gilgit | M(o) | 0.29 | 4,669 |\n| 627 | 0143A1300627 | 35.907 | 72.810 | Gilgit | M(o) | 1.87 | 5,037 |\n| 628 | 0143A1300628 | 35.906 | 72.772 | Gilgit | O | 1.46 | 3,976 |\n| 629 | 0143A1300629 | 35.904 | 72.891 | Gilgit | O | 0.61 | 4,269 |\n| 630 | 0143A1300630 | 35.898 | 72.806 | Gilgit | M(e) | 0.46 | 4,751 |\n| 631 | 0143A1300631 | 35.898 | 72.905 | Gilgit | I(s) | 0.25 | 4,497 |\n| 632 | 0143A1300632 | 35.897 | 72.908 | Gilgit | I(s) | 0.41 | 4,541 |\n| 633 | 0143A1300633 | 35.895 | 72.919 | Gilgit | I(s) | 0.65 | 4,765 |\n| 634 | 0143A1300634 | 35.893 | 72.802 | Gilgit | E(o) | 1.17 | 4,564 |\n| 635 | 0143A1300635 | 35.892 | 72.900 | Gilgit | I(s) | 0.25 | 4,693 |\n| 636 | 0143A1300636 | 35.888 | 72.791 | Gilgit | I(s) | 0.31 | 4,256 |\n| 637 | 0143A1300637 | 35.886 | 72.793 | Gilgit | I(s) | 0.34 | 4,323 |\n| 638 | 0143A1300638 | 35.885 | 72.944 | Gilgit | E(o) | 0.94 | 4,577 |\n| 639 | 0143A1300639 | 35.883 | 72.900 | Gilgit | M(lg) | 0.28 | 4,808 |\n| 640 | 0143A1300640 | 35.882 | 72.897 | Gilgit | M(o) | 0.32 | 4,825 |\n| 641 | 0143A1300641 | 35.878 | 72.936 | Gilgit | I(s) | 0.30 | 4,716 |\n| 642 | 0143A1300642 | 35.878 | 72.872 | Gilgit | I(s) | 0.56 | 4,875 |\n| 643 | 0143A1300643 | 35.876 | 72.874 | Gilgit | M(o) | 1.64 | 4,866 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6597, "line_end": 6743, "token_count_estimate": 1619, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143A1300609", "0143A1300610", "0143A1300611", "0143A1300612", "0143A1300613", "0143A1300614", "0143A1300615", "0143A1300616", "0143A1300617", "0143A1300618", "0143A1300619", "0143A1300620", "0143A1300621", "0143A1300622", "0143A1300623", "0143A1300624", "0143A1300625", "0143A1300626", "0143A1300627", "0143A1300628", "0143A1300629", "0143A1300630", "0143A1300631", "0143A1300632", "0143A1300633", "0143A1300634", "0143A1300635", "0143A1300636", "0143A1300637", "0143A1300638", "0143A1300639", "0143A1300640", "0143A1300641", "0143A1300642", "0143A1300643"]}}
{"id": "27174f1aa9fd3646", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 644 | 0143A1300644 | 35.871 | 72.888 | Gilgit | M(l) | 0.64 | 4,809 |\n| 645 | 0143A1300645 | 35.869 | 72.940 | Gilgit | E(o) | 20.01 | 4,378 |\n| 646 | 0143A1300646 | 35.858 | 72.771 | Gilgit | E(o) | 0.69 | 4,752 |\n| 647 | 0143A1300647 | 35.857 | 72.926 | Gilgit | M(o) | 0.49 | 4,758 |\n| 648 | 0143A1300648 | 35.856 | 72.927 | Gilgit | M(e) | 1.56 | 4,746 |\n| 649 | 0143A1300649 | 35.855 | 72.980 | Gilgit | E(v) | 13.35 | 4,325 |\n| 650 | 0143A1300650 | 35.851 | 72.914 | Gilgit | M(o) | 0.87 | 4,731 |\n| 651 | 0143A1300651 | 35.851 | 72.918 | Gilgit | E(o) | 0.65 | 4,713 |\n| 652 | 0143A1300652 | 35.845 | 72.967 | Gilgit | M(o) | 0.74 | 4,330 |\n| 653 | 0143A1300653 | 35.844 | 72.911 | Gilgit | E(o) | 7.69 | 4,550 |\n| 654 | 0143A1300654 | 35.841 | 72.919 | Gilgit | E(o) | 2.09 | 4,437 |\n| 655 | 0143A1300655 | 35.841 | 72.996 | Gilgit | M(o) | 0.62 | 4,479 |\n| 656 | 0143A1300656 | 35.833 | 72.910 | Gilgit | M(o) | 4.36 | 4,295 |\n| 657 | 0143A1300657 | 35.827 | 72.903 | Gilgit | M(o) | 1.15 | 4,531 |\n| 658 | 0143E0100658 | 35.999 | 73.032 | Gilgit | M(o) | 0.88 | 4,718 |\n| 659 | 0143E0100659 | 35.965 | 73.021 | Gilgit | M(o) | 0.30 | 4,664 |\n| 660 | 0143E0100660 | 35.962 | 73.193 | Gilgit | E(o) | 1.32 | 4,297 |\n| 661 | 0143E0100661 | 35.959 | 73.250 | Gilgit | E(o) | 0.68 | 4,509 |\n| 662 | 0143E0100662 | 35.955 | 73.194 | Gilgit | E(o) | 3.04 | 4,428 |\n| 663 | 0143E0100663 | 35.953 | 73.155 | Gilgit | E(o) | 1.01 | 4,483 |\n| 664 | 0143E0100664 | 35.947 | 73.093 | Gilgit | M(o) | 1.72 | 4,883 |\n| 665 | 0143E0100665 | 35.947 | 73.164 | Gilgit | M(o) | 0.57 | 4,624 |\n| 666 | 0143E0100666 | 35.942 | 73.106 | Gilgit | E(o) | 3.87 | 4,791 |\n| 667 | 0143E0100667 | 35.940 | 73.192 | Gilgit | M(o) | 0.85 | 4,548 |\n| 668 | 0143E0100668 | 35.939 | 73.244 | Gilgit | E(o) | 1.37 | 4,373 |\n| 669 | 0143E0100669 | 35.936 | 73.016 | Gilgit | E(o) | 1.84 | 4,699 |\n| 670 | 0143E0100670 | 35.936 | 73.102 | Gilgit | E(o) | 0.29 | 4,719 |\n| 671 | 0143E0100671 | 35.935 | 73.173 | Gilgit | E(o) | 2.78 | 4,629 |\n| 672 | 0143E0100672 | 35.934 | 73.168 | Gilgit | E(o) | 1.81 | 4,578 |\n| 673 | 0143E0100673 | 35.934 | 73.157 | Gilgit | E(o) | 2.58 | 4,445 |\n| 674 | 0143E0100674 | 35.930 | 73.188 | Gilgit | E(o) | 2.86 | 4,693 |\n| 675 | 0143E0100675 | 35.930 | 73.247 | Gilgit | M(o) | 7.92 | 4,325 |\n| 676 | 0143E0100676 | 35.928 | 73.152 | Gilgit | E(o) | 0.48 | 4,602 |\n| 677 | 0143E0100677 | 35.926 | 73.187 | Gilgit | E(o) | 0.74 | 4,637 |\n| 678 | 0143E0100678 | 35.926 | 73.071 | Gilgit | E(o) | 1.03 | 4,605 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6597, "line_end": 6743, "token_count_estimate": 1623, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143A1300644", "0143A1300645", "0143A1300646", "0143A1300647", "0143A1300648", "0143A1300649", "0143A1300650", "0143A1300651", "0143A1300652", "0143A1300653", "0143A1300654", "0143A1300655", "0143A1300656", "0143A1300657", "0143E0100658", "0143E0100659", "0143E0100660", "0143E0100661", "0143E0100662", "0143E0100663", "0143E0100664", "0143E0100665", "0143E0100666", "0143E0100667", "0143E0100668", "0143E0100669", "0143E0100670", "0143E0100671", "0143E0100672", "0143E0100673", "0143E0100674", "0143E0100675", "0143E0100676", "0143E0100677", "0143E0100678"]}}
{"id": "31ce8c8a74cdbc0c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 679 | 0143E0100679 | 35.924 | 73.102 | Gilgit | E(o) | 0.29 | 4,659 |\n| 680 | 0143E0100680 | 35.924 | 73.113 | Gilgit | E(o) | 1.22 | 4,459 |\n| 681 | 0143E0100681 | 35.924 | 73.074 | Gilgit | E(o) | 2.24 | 4,627 |\n| 682 | 0143E0100682 | 35.920 | 73.064 | Gilgit | O | 21.34 | 4,303 |\n| 683 | 0143E0100683 | 35.917 | 73.015 | Gilgit | E(o) | 0.40 | 4,595 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 145, "line_start": 6597, "line_end": 6743, "token_count_estimate": 323, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143E0100679", "0143E0100680", "0143E0100681", "0143E0100682", "0143E0100683"]}}
{"id": "7806d1ba6b8c4b84", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6744, "line_end": 6752, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c8b82792cd4c357", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 684 | 0143E0100684 | 35.915 | 73.153 | Gilgit | E(o) | 0.42 | 4,562 |\n| 685 | 0143E0100685 | 35.914 | 73.037 | Gilgit | E(o) | 0.39 | 4,870 |\n| 686 | 0143E0100686 | 35.910 | 73.165 | Gilgit | E(o) | 0.74 | 4,631 |\n| 687 | 0143E0100687 | 35.910 | 73.146 | Gilgit | E(o) | 0.41 | 4,316 |\n| 688 | 0143E0100688 | 35.908 | 73.148 | Gilgit | E(o) | 0.56 | 4,326 |\n| 689 | 0143E0100689 | 35.906 | 73.155 | Gilgit | E(o) | 24.24 | 4,328 |\n| 690 | 0143E0100690 | 35.905 | 73.243 | Gilgit | E(o) | 0.91 | 4,133 |\n| 691 | 0143E0100691 | 35.901 | 73.206 | Gilgit | E(o) | 0.27 | 4,482 |\n| 692 | 0143E0100692 | 35.901 | 73.208 | Gilgit | E(o) | 0.37 | 4,477 |\n| 693 | 0143E0100693 | 35.899 | 73.070 | Gilgit | M(e) | 6.96 | 4,389 |\n| 694 | 0143E0100694 | 35.895 | 73.095 | Gilgit | E(o) | 1.75 | 4,629 |\n| 695 | 0143E0100695 | 35.894 | 73.246 | Gilgit | M(o) | 2.76 | 4,349 |\n| 696 | 0143E0100696 | 35.891 | 73.099 | Gilgit | E(o) | 3.76 | 4,491 |\n| 697 | 0143E0100697 | 35.891 | 73.136 | Gilgit | M(o) | 0.71 | 4,650 |\n| 698 | 0143E0100698 | 35.890 | 73.190 | Gilgit | E(o) | 1.55 | 4,766 |\n| 699 | 0143E0100699 | 35.883 | 73.046 | Gilgit | E(o) | 2.06 | 4,759 |\n| 700 | 0143E0100700 | 35.882 | 73.196 | Gilgit | E(o) | 0.98 | 4,425 |\n| 701 | 0143E0100701 | 35.881 | 73.055 | Gilgit | E(o) | 0.79 | 4,725 |\n| 702 | 0143E0100702 | 35.880 | 73.246 | Gilgit | E(o) | 1.40 | 4,589 |\n| 703 | 0143E0100703 | 35.878 | 73.058 | Gilgit | E(o) | 2.64 | 4,685 |\n| 704 | 0143E0100704 | 35.878 | 73.051 | Gilgit | E(o) | 2.14 | 4,704 |\n| 705 | 0143E0100705 | 35.876 | 73.075 | Gilgit | M(e) | 1.92 | 4,738 |\n| 706 | 0143E0100706 | 35.875 | 73.055 | Gilgit | E(o) | 3.77 | 4,627 |\n| 707 | 0143E0100707 | 35.875 | 73.058 | Gilgit | E(o) | 0.30 | 4,626 |\n| 708 | 0143E0100708 | 35.871 | 73.073 | Gilgit | E(o) | 15.95 | 4,617 |\n| 709 | 0143E0100709 | 35.870 | 73.135 | Gilgit | E(o) | 0.57 | 4,742 |\n| 710 | 0143E0100710 | 35.870 | 73.019 | Gilgit | M(o) | 0.41 | 4,519 |\n| 711 | 0143E0100711 | 35.870 | 73.015 | Gilgit | M(o) | 0.34 | 4,603 |\n| 712 | 0143E0100712 | 35.869 | 73.235 | Gilgit | E(o) | 0.98 | 4,270 |\n| 713 | 0143E0100713 | 35.867 | 73.131 | Gilgit | E(o) | 0.76 | 4,634 |\n| 714 | 0143E0100714 | 35.866 | 73.239 | Gilgit | E(o) | 0.90 | 4,347 |\n| 715 | 0143E0100715 | 35.865 | 73.138 | Gilgit | E(o) | 2.50 | 4,617 |\n| 716 | 0143E0100716 | 35.865 | 73.237 | Gilgit | E(o) | 0.57 | 4,347 |\n| 717 | 0143E0100717 | 35.858 | 73.148 | Gilgit | M(o) | 0.85 | 4,795 |\n| 718 | 0143E0100718 | 35.857 | 73.018 | Gilgit | E(o) | 6.43 | 4,571 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6753, "line_end": 6900, "token_count_estimate": 1626, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143E0100684", "0143E0100685", "0143E0100686", "0143E0100687", "0143E0100688", "0143E0100689", "0143E0100690", "0143E0100691", "0143E0100692", "0143E0100693", "0143E0100694", "0143E0100695", "0143E0100696", "0143E0100697", "0143E0100698", "0143E0100699", "0143E0100700", "0143E0100701", "0143E0100702", "0143E0100703", "0143E0100704", "0143E0100705", "0143E0100706", "0143E0100707", "0143E0100708", "0143E0100709", "0143E0100710", "0143E0100711", "0143E0100712", "0143E0100713", "0143E0100714", "0143E0100715", "0143E0100716", "0143E0100717", "0143E0100718"]}}
{"id": "247538aecc04b411", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 719 | 0143E0100719 | 35.856 | 73.225 | Indus Middle | E(o) | 1.61 | 4,385 |\n| 720 | 0143E0100720 | 35.853 | 73.149 | Gilgit | M(o) | 2.19 | 4,714 |\n| 721 | 0143E0100721 | 35.843 | 73.208 | Indus Middle | E(o) | 0.25 | 4,424 |\n| 722 | 0143E0100722 | 35.838 | 73.194 | Indus Middle | E(o) | 0.41 | 4,517 |\n| 723 | 0143E0100723 | 35.835 | 73.196 | Indus Middle | E(o) | 3.23 | 4,443 |\n| 724 | 0143E0100724 | 35.833 | 73.236 | Indus Middle | E(o) | 0.83 | 4,056 |\n| 725 | 0143E0100725 | 35.831 | 73.221 | Indus Middle | E(o) | 3.26 | 4,109 |\n| 726 | 0143E0100726 | 35.829 | 73.228 | Indus Middle | E(o) | 12.38 | 4,195 |\n| 727 | 0143E0100727 | 35.829 | 73.233 | Indus Middle | E(o) | 0.51 | 4,230 |\n| 728 | 0143E0100728 | 35.825 | 73.211 | Indus Middle | E(c) | 26.44 | 4,271 |\n| 729 | 0143E0100729 | 35.823 | 73.213 | Indus Middle | E(o) | 0.68 | 4,278 |\n| 730 | 0143E0100730 | 35.803 | 73.210 | Indus Middle | E(o) | 3.38 | 4,136 |\n| 731 | 0143E0100731 | 35.792 | 73.216 | Indus Middle | E(o) | 1.52 | 4,197 |\n| 732 | 0143E0100732 | 35.789 | 73.209 | Indus Middle | E(o) | 1.66 | 4,396 |\n| 733 | 0143E0100733 | 35.785 | 73.206 | Indus Middle | E(o) | 1.31 | 4,497 |\n| 734 | 0143E0100734 | 35.775 | 73.210 | Indus Middle | E(o) | 0.29 | 4,487 |\n| 735 | 0143E0200735 | 35.730 | 73.216 | Indus Middle | E(o) | 26.36 | 3,970 |\n| 736 | 0143E0200736 | 35.689 | 73.212 | Indus Middle | E(o) | 1.13 | 3,395 |\n| 737 | 0143E0500737 | 35.996 | 73.339 | Gilgit | E(o) | 3.58 | 4,461 |\n| 738 | 0143E0500738 | 35.993 | 73.286 | Gilgit | E(o) | 4.94 | 4,467 |\n| 739 | 0143E0500739 | 35.990 | 73.282 | Gilgit | E(o) | 3.06 | 4,528 |\n| 740 | 0143E0500740 | 35.990 | 73.320 | Gilgit | E(o) | 1.94 | 4,704 |\n| 741 | 0143E0500741 | 35.988 | 73.319 | Gilgit | E(o) | 0.43 | 4,683 |\n| 742 | 0143E0500742 | 35.987 | 73.313 | Gilgit | E(o) | 10.23 | 4,583 |\n| 743 | 0143E0500743 | 35.985 | 73.345 | Gilgit | E(o) | 0.64 | 4,434 |\n| 744 | 0143E0500744 | 35.984 | 73.306 | Gilgit | E(o) | 0.89 | 4,462 |\n| 745 | 0143E0500745 | 35.981 | 73.312 | Gilgit | E(o) | 0.89 | 4,543 |\n| 746 | 0143E0500746 | 35.979 | 73.314 | Gilgit | E(o) | 2.38 | 4,543 |\n| 747 | 0143E0500747 | 35.978 | 73.312 | Gilgit | E(o) | 1.49 | 4,538 |\n| 748 | 0143E0500748 | 35.977 | 73.391 | Gilgit | E(o) | 2.90 | 4,222 |\n| 749 | 0143E0500749 | 35.974 | 73.427 | Gilgit | E(o) | 1.48 | 4,379 |\n| 750 | 0143E0500750 | 35.972 | 73.339 | Gilgit | E(o) | 3.79 | 4,506 |\n| 751 | 0143E0500751 | 35.971 | 73.297 | Gilgit | E(o) | 1.71 | 4,518 |\n| 752 | 0143E0500752 | 35.970 | 73.292 | Gilgit | E(o) | 1.01 | 4,492 |\n| 753 | 0143E0500753 | 35.970 | 73.385 | Gilgit | E(o) | 2.34 | 4,360 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6753, "line_end": 6900, "token_count_estimate": 1634, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E0100719", "0143E0100720", "0143E0100721", "0143E0100722", "0143E0100723", "0143E0100724", "0143E0100725", "0143E0100726", "0143E0100727", "0143E0100728", "0143E0100729", "0143E0100730", "0143E0100731", "0143E0100732", "0143E0100733", "0143E0100734", "0143E0200735", "0143E0200736", "0143E0500737", "0143E0500738", "0143E0500739", "0143E0500740", "0143E0500741", "0143E0500742", "0143E0500743", "0143E0500744", "0143E0500745", "0143E0500746", "0143E0500747", "0143E0500748", "0143E0500749", "0143E0500750", "0143E0500751", "0143E0500752", "0143E0500753"]}}
{"id": "72bf1d8777f1952a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 754 | 0143E0500754 | 35.966 | 73.313 | Gilgit | E(o) | 3.44 | 4,600 |\n| 755 | 0143E0500755 | 35.966 | 73.338 | Gilgit | E(o) | 0.50 | 4,482 |\n| 756 | 0143E0500756 | 35.964 | 73.434 | Gilgit | M(o) | 1.28 | 4,333 |\n| 757 | 0143E0500757 | 35.962 | 73.397 | Gilgit | E(o) | 18.84 | 4,349 |\n| 758 | 0143E0500758 | 35.961 | 73.447 | Gilgit | E(o) | 0.84 | 4,347 |\n| 759 | 0143E0500759 | 35.960 | 73.258 | Gilgit | E(o) | 0.36 | 4,384 |\n| 760 | 0143E0500760 | 35.957 | 73.316 | Indus Middle | E(o) | 2.55 | 4,627 |\n| 761 | 0143E0500761 | 35.957 | 73.379 | Gilgit | E(o) | 1.32 | 4,364 |\n| 762 | 0143E0500762 | 35.957 | 73.249 | Gilgit | E(o) | 3.76 | 4,541 |\n| 763 | 0143E0500763 | 35.956 | 73.321 | Indus Middle | E(o) | 0.27 | 4,601 |\n| 764 | 0143E0500764 | 35.955 | 73.386 | Gilgit | E(o) | 1.20 | 4,533 |\n| 765 | 0143E0500765 | 35.954 | 73.350 | Gilgit | E(o) | 7.08 | 4,328 |\n| 766 | 0143E0500766 | 35.951 | 73.345 | Gilgit | E(o) | 0.28 | 4,386 |\n| 767 | 0143E0500767 | 35.951 | 73.305 | Gilgit | E(o) | 1.35 | 4,496 |\n| 768 | 0143E0500768 | 35.950 | 73.350 | Gilgit | E(o) | 2.49 | 4,345 |\n| 769 | 0143E0500769 | 35.949 | 73.289 | Gilgit | E(o) | 54.23 | 4,228 |\n| 770 | 0143E0500770 | 35.949 | 73.376 | Gilgit | E(o) | 1.85 | 4,422 |\n| 771 | 0143E0500771 | 35.948 | 73.317 | Indus Middle | E(o) | 5.12 | 4,494 |\n| 772 | 0143E0500772 | 35.947 | 73.336 | Gilgit | M(o) | 0.40 | 4,524 |\n| 773 | 0143E0500773 | 35.946 | 73.420 | Gilgit | E(o) | 1.17 | 4,228 |\n| 774 | 0143E0500774 | 35.945 | 73.365 | Gilgit | E(c) | 67.34 | 4,162 |\n| 775 | 0143E0500775 | 35.944 | 73.409 | Gilgit | E(o) | 1.49 | 4,376 |\n| 776 | 0143E0500776 | 35.943 | 73.310 | Indus Middle | E(o) | 0.40 | 4,463 |\n| 777 | 0143E0500777 | 35.942 | 73.405 | Gilgit | E(o) | 1.20 | 4,398 |\n| 778 | 0143E0500778 | 35.941 | 73.355 | Gilgit | E(o) | 1.79 | 4,340 |\n| 779 | 0143E0500779 | 35.939 | 73.376 | Gilgit | E(o) | 0.33 | 4,378 |\n| 780 | 0143E0500780 | 35.938 | 73.308 | Indus Middle | E(o) | 1.65 | 4,397 |\n| 781 | 0143E0500781 | 35.936 | 73.269 | Indus Middle | E(o) | 11.45 | 4,362 |\n| 782 | 0143E0500782 | 35.934 | 73.290 | Indus Middle | E(o) | 6.62 | 4,298 |\n| 783 | 0143E0500783 | 35.934 | 73.393 | Gilgit | E(o) | 3.71 | 4,247 |\n| 784 | 0143E0500784 | 35.934 | 73.385 | Gilgit | M(e) | 1.33 | 4,291 |\n| 785 | 0143E0500785 | 35.934 | 73.298 | Indus Middle | E(o) | 7.56 | 4,175 |\n| 786 | 0143E0500786 | 35.933 | 73.332 | Indus Middle | E(o) | 2.03 | 4,460 |\n| 787 | 0143E0500787 | 35.932 | 73.344 | Indus Middle | E(o) | 4.08 | 4,507 |\n| 788 | 0143E0500788 | 35.930 | 73.341 | Indus Middle | E(o) | 1.17 | 4,504 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6753, "line_end": 6900, "token_count_estimate": 1632, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E0500754", "0143E0500755", "0143E0500756", "0143E0500757", "0143E0500758", "0143E0500759", "0143E0500760", "0143E0500761", "0143E0500762", "0143E0500763", "0143E0500764", "0143E0500765", "0143E0500766", "0143E0500767", "0143E0500768", "0143E0500769", "0143E0500770", "0143E0500771", "0143E0500772", "0143E0500773", "0143E0500774", "0143E0500775", "0143E0500776", "0143E0500777", "0143E0500778", "0143E0500779", "0143E0500780", "0143E0500781", "0143E0500782", "0143E0500783", "0143E0500784", "0143E0500785", "0143E0500786", "0143E0500787", "0143E0500788"]}}
{"id": "f140ea1f20aa9577", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 789 | 0143E0500789 | 35.928 | 73.472 | Gilgit | E(o) | 1.53 | 4,285 |\n| 790 | 0143E0500790 | 35.925 | 73.274 | Indus Middle | E(o) | 11.46 | 4,227 |\n| 791 | 0143E0500791 | 35.925 | 73.342 | Indus Middle | E(o) | 11.69 | 4,396 |\n| 792 | 0143E0500792 | 35.924 | 73.479 | Gilgit | E(c) | 7.57 | 4,392 |\n| 793 | 0143E0500793 | 35.923 | 73.401 | Gilgit | E(o) | 2.27 | 4,122 |\n| 794 | 0143E0500794 | 35.922 | 73.271 | Indus Middle | E(o) | 2.13 | 4,231 |\n| 795 | 0143E0500795 | 35.918 | 73.371 | Gilgit | E(o) | 12.25 | 4,502 |\n| 796 | 0143E0500796 | 35.916 | 73.357 | Indus Middle | E(o) | 6.53 | 4,436 |\n| 797 | 0143E0500797 | 35.915 | 73.256 | Gilgit | E(o) | 0.53 | 4,418 |\n| 798 | 0143E0500798 | 35.912 | 73.480 | Gilgit | E(c) | 6.81 | 4,416 |\n| 799 | 0143E0500799 | 35.911 | 73.380 | Gilgit | E(o) | 4.98 | 4,357 |\n| 800 | 0143E0500800 | 35.911 | 73.467 | Gilgit | E(o) | 0.89 | 4,270 |\n| 801 | 0143E0500801 | 35.909 | 73.471 | Gilgit | E(o) | 2.64 | 4,316 |\n| 802 | 0143E0500802 | 35.908 | 73.257 | Indus Middle | E(o) | 1.26 | 4,276 |\n| 803 | 0143E0500803 | 35.907 | 73.358 | Indus Middle | E(o) | 3.03 | 4,324 |\n| 804 | 0143E0500804 | 35.907 | 73.497 | Gilgit | E(o) | 3.07 | 4,245 |\n| 805 | 0143E0500805 | 35.907 | 73.254 | Gilgit | E(o) | 1.48 | 4,277 |\n| 806 | 0143E0500806 | 35.903 | 73.471 | Gilgit | E(o) | 9.92 | 4,289 |\n| 807 | 0143E0500807 | 35.900 | 73.376 | Indus Middle | E(o) | 0.50 | 4,473 |\n| 808 | 0143E0500808 | 35.899 | 73.384 | Indus Middle | E(o) | 0.49 | 4,471 |\n| 809 | 0143E0500809 | 35.897 | 73.260 | Indus Middle | E(o) | 4.67 | 4,254 |\n| 810 | 0143E0500810 | 35.896 | 73.499 | Gilgit | E(o) | 0.67 | 4,302 |\n| 811 | 0143E0500811 | 35.892 | 73.267 | Indus Middle | M(o) | 1.74 | 4,299 |\n| 812 | 0143E0500812 | 35.888 | 73.498 | Gilgit | E(o) | 1.04 | 4,301 |\n| 813 | 0143E0500813 | 35.885 | 73.471 | Gilgit | E(o) | 5.41 | 4,395 |\n| 814 | 0143E0500814 | 35.884 | 73.276 | Indus Middle | E(o) | 0.36 | 4,211 |\n| 815 | 0143E0500815 | 35.879 | 73.288 | Indus Middle | E(o) | 1.44 | 3,901 |\n| 816 | 0143E0500816 | 35.878 | 73.273 | Indus Middle | E(o) | 3.99 | 4,263 |\n| 817 | 0143E0500817 | 35.877 | 73.487 | Gilgit | E(o) | 2.40 | 4,473 |\n| 818 | 0143E0500818 | 35.875 | 73.481 | Gilgit | E(o) | 4.80 | 4,389 |\n| 819 | 0143E0500819 | 35.866 | 73.264 | Indus Middle | E(o) | 2.16 | 4,430 |\n| 820 | 0143E0500820 | 35.864 | 73.269 | Indus Middle | E(o) | 0.43 | 4,342 |\n| 821 | 0143E0500821 | 35.863 | 73.275 | Indus Middle | E(o) | 5.67 | 4,256 |\n| 822 | 0143E0500822 | 35.863 | 73.281 | Indus Middle | E(o) | 4.31 | 4,220 |\n| 823 | 0143E0500823 | 35.861 | 73.288 | Indus Middle | E(o) | 4.83 | 4,144 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6753, "line_end": 6900, "token_count_estimate": 1650, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E0500789", "0143E0500790", "0143E0500791", "0143E0500792", "0143E0500793", "0143E0500794", "0143E0500795", "0143E0500796", "0143E0500797", "0143E0500798", "0143E0500799", "0143E0500800", "0143E0500801", "0143E0500802", "0143E0500803", "0143E0500804", "0143E0500805", "0143E0500806", "0143E0500807", "0143E0500808", "0143E0500809", "0143E0500810", "0143E0500811", "0143E0500812", "0143E0500813", "0143E0500814", "0143E0500815", "0143E0500816", "0143E0500817", "0143E0500818", "0143E0500819", "0143E0500820", "0143E0500821", "0143E0500822", "0143E0500823"]}}
{"id": "f45de86d8b70c006", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 824 | 0143E0500824 | 35.861 | 73.493 | Indus Middle | E(o) | 4.65 | 4,303 |\n| 825 | 0143E0500825 | 35.860 | 73.300 | Indus Middle | E(o) | 8.44 | 4,114 |\n| 826 | 0143E0500826 | 35.857 | 73.280 | Indus Middle | E(o) | 1.70 | 4,360 |\n| 827 | 0143E0500827 | 35.846 | 73.498 | Indus Middle | E(o) | 3.43 | 4,195 |\n| 828 | 0143E0500828 | 35.845 | 73.451 | Indus Middle | E(o) | 0.99 | 4,025 |\n| 829 | 0143E0500829 | 35.839 | 73.458 | Indus Middle | E(o) | 0.87 | 4,118 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6753, "line_end": 6900, "token_count_estimate": 378, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143E0500824", "0143E0500825", "0143E0500826", "0143E0500827", "0143E0500828", "0143E0500829"]}}
{"id": "8e005a3b796209a6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6901, "line_end": 6910, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "382e973387ae40da", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 830 | 0143E0500830 | 35.832 | 73.259 | Indus Middle | E(c) | 2.74 | 3,801 |\n| 831 | 0143E0500831 | 35.832 | 73.481 | Indus Middle | E(o) | 2.93 | 4,125 |\n| 832 | 0143E0500832 | 35.832 | 73.470 | Indus Middle | E(o) | 2.86 | 4,161 |\n| 833 | 0143E0500833 | 35.829 | 73.483 | Indus Middle | E(o) | 0.32 | 4,144 |\n| 834 | 0143E0500834 | 35.828 | 73.481 | Indus Middle | E(o) | 1.15 | 4,132 |\n| 835 | 0143E0500835 | 35.827 | 73.485 | Indus Middle | E(o) | 4.44 | 4,134 |\n| 836 | 0143E0500836 | 35.824 | 73.482 | Indus Middle | E(o) | 0.69 | 4,191 |\n| 837 | 0143E0500837 | 35.817 | 73.297 | Indus Middle | O | 15.36 | 2,981 |\n| 838 | 0143E0500838 | 35.816 | 73.461 | Indus Middle | E(o) | 2.62 | 4,193 |\n| 839 | 0143E0500839 | 35.816 | 73.467 | Indus Middle | E(o) | 4.17 | 4,218 |\n| 840 | 0143E0500840 | 35.811 | 73.466 | Indus Middle | E(o) | 2.66 | 4,240 |\n| 841 | 0143E0500841 | 35.808 | 73.484 | Indus Middle | E(o) | 0.60 | 4,270 |\n| 842 | 0143E0500842 | 35.806 | 73.484 | Indus Middle | E(o) | 0.70 | 4,266 |\n| 843 | 0143E0500843 | 35.795 | 73.481 | Indus Middle | E(c) | 9.25 | 4,279 |\n| 844 | 0143E0500844 | 35.777 | 73.481 | Indus Middle | E(o) | 4.61 | 4,120 |\n| 845 | 0143E0500845 | 35.771 | 73.271 | Indus Middle | E(o) | 0.38 | 4,178 |\n| 846 | 0143E0500846 | 35.770 | 73.478 | Indus Middle | E(o) | 0.43 | 4,326 |\n| 847 | 0143E0500847 | 35.770 | 73.485 | Indus Middle | E(o) | 0.27 | 4,378 |\n| 848 | 0143E0500848 | 35.769 | 73.480 | Indus Middle | E(o) | 0.40 | 4,340 |\n| 849 | 0143E0500849 | 35.767 | 73.481 | Indus Middle | E(o) | 0.58 | 4,347 |\n| 850 | 0143E0500850 | 35.767 | 73.483 | Indus Middle | E(o) | 0.90 | 4,350 |\n| 851 | 0143E0500851 | 35.765 | 73.481 | Indus Middle | E(o) | 0.70 | 4,346 |\n| 852 | 0143E0600852 | 35.749 | 73.251 | Indus Middle | E(o) | 1.34 | 3,955 |\n| 853 | 0143E0600853 | 35.747 | 73.257 | Indus Middle | E(o) | 6.95 | 3,813 |\n| 854 | 0143E0600854 | 35.739 | 73.256 | Indus Middle | E(c) | 18.92 | 4,016 |\n| 855 | 0143E0600855 | 35.675 | 73.344 | Indus Middle | E(c) | 25.12 | 3,428 |\n| 856 | 0143E0600856 | 35.652 | 73.357 | Indus Middle | E(o) | 19.01 | 3,735 |\n| 857 | 0143E0600857 | 35.647 | 73.355 | Indus Middle | E(o) | 1.16 | 3,741 |\n| 858 | 0143E0600858 | 35.643 | 73.352 | Indus Middle | E(o) | 13.95 | 3,747 |\n| 859 | 0143E0900859 | 36.000 | 73.614 | Gilgit | E(o) | 0.56 | 4,351 |\n| 860 | 0143E0900860 | 35.999 | 73.664 | Gilgit | E(o) | 6.62 | 4,256 |\n| 861 | 0143E0900861 | 35.998 | 73.653 | Gilgit | E(o) | 0.64 | 4,406 |\n| 862 | 0143E0900862 | 35.997 | 73.730 | Gilgit | E(o) | 4.85 | 4,464 |\n| 863 | 0143E0900863 | 35.996 | 73.736 | Gilgit | E(o) | 2.57 | 4,357 |\n| 864 | 0143E0900864 | 35.995 | 73.586 | Gilgit | E(o) | 0.91 | 4,217 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6911, "line_end": 7058, "token_count_estimate": 1654, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E0500830", "0143E0500831", "0143E0500832", "0143E0500833", "0143E0500834", "0143E0500835", "0143E0500836", "0143E0500837", "0143E0500838", "0143E0500839", "0143E0500840", "0143E0500841", "0143E0500842", "0143E0500843", "0143E0500844", "0143E0500845", "0143E0500846", "0143E0500847", "0143E0500848", "0143E0500849", "0143E0500850", "0143E0500851", "0143E0600852", "0143E0600853", "0143E0600854", "0143E0600855", "0143E0600856", "0143E0600857", "0143E0600858", "0143E0900859", "0143E0900860", "0143E0900861", "0143E0900862", "0143E0900863", "0143E0900864"]}}
{"id": "9d895616c4ff2860", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 865 | 0143E0900865 | 35.989 | 73.725 | Gilgit | E(o) | 1.73 | 4,495 |\n| 866 | 0143E0900866 | 35.988 | 73.713 | Gilgit | E(o) | 0.96 | 4,552 |\n| 867 | 0143E0900867 | 35.982 | 73.705 | Gilgit | E(o) | 1.43 | 4,353 |\n| 868 | 0143E0900868 | 35.980 | 73.728 | Gilgit | E(o) | 2.10 | 4,415 |\n| 869 | 0143E0900869 | 35.977 | 73.686 | Gilgit | E(o) | 1.13 | 4,361 |\n| 870 | 0143E0900870 | 35.976 | 73.682 | Gilgit | E(o) | 1.34 | 4,333 |\n| 871 | 0143E0900871 | 35.971 | 73.710 | Gilgit | E(o) | 3.86 | 4,450 |\n| 872 | 0143E0900872 | 35.970 | 73.705 | Gilgit | E(o) | 0.32 | 4,400 |\n| 873 | 0143E0900873 | 35.966 | 73.719 | Gilgit | E(o) | 0.65 | 4,467 |\n| 874 | 0143E0900874 | 35.963 | 73.709 | Gilgit | E(o) | 2.20 | 4,361 |\n| 875 | 0143E0900875 | 35.957 | 73.727 | Gilgit | E(o) | 1.40 | 4,497 |\n| 876 | 0143E0900876 | 35.957 | 73.742 | Gilgit | E(o) | 2.12 | 4,283 |\n| 877 | 0143E0900877 | 35.956 | 73.581 | Gilgit | E(o) | 0.81 | 4,116 |\n| 878 | 0143E0900878 | 35.956 | 73.583 | Gilgit | E(c) | 3.83 | 4,114 |\n| 879 | 0143E0900879 | 35.952 | 73.720 | Gilgit | E(o) | 1.52 | 4,388 |\n| 880 | 0143E0900880 | 35.951 | 73.521 | Gilgit | E(o) | 4.49 | 4,368 |\n| 881 | 0143E0900881 | 35.949 | 73.584 | Gilgit | E(c) | 5.04 | 4,217 |\n| 882 | 0143E0900882 | 35.946 | 73.728 | Gilgit | E(o) | 1.31 | 4,467 |\n| 883 | 0143E0900883 | 35.946 | 73.689 | Gilgit | E(o) | 0.57 | 4,363 |\n| 884 | 0143E0900884 | 35.946 | 73.633 | Gilgit | E(o) | 3.16 | 4,249 |\n| 885 | 0143E0900885 | 35.945 | 73.572 | Gilgit | E(c) | 0.46 | 4,461 |\n| 886 | 0143E0900886 | 35.944 | 73.704 | Gilgit | E(c) | 8.75 | 4,226 |\n| 887 | 0143E0900887 | 35.941 | 73.633 | Gilgit | E(o) | 0.85 | 4,360 |\n| 888 | 0143E0900888 | 35.938 | 73.560 | Gilgit | E(o) | 0.35 | 4,218 |\n| 889 | 0143E0900889 | 35.937 | 73.563 | Gilgit | E(o) | 0.40 | 4,222 |\n| 890 | 0143E0900890 | 35.935 | 73.641 | Gilgit | E(o) | 0.53 | 4,280 |\n| 891 | 0143E0900891 | 35.932 | 73.639 | Gilgit | E(o) | 4.61 | 4,271 |\n| 892 | 0143E0900892 | 35.932 | 73.650 | Gilgit | E(o) | 0.39 | 4,236 |\n| 893 | 0143E0900893 | 35.930 | 73.584 | Gilgit | E(o) | 0.96 | 4,258 |\n| 894 | 0143E0900894 | 35.930 | 73.589 | Gilgit | E(o) | 1.91 | 4,152 |\n| 895 | 0143E0900895 | 35.929 | 73.578 | Gilgit | E(o) | 2.43 | 4,348 |\n| 896 | 0143E0900896 | 35.928 | 73.545 | Gilgit | E(o) | 3.37 | 4,274 |\n| 897 | 0143E0900897 | 35.927 | 73.710 | Gilgit | E(o) | 0.69 | 4,378 |\n| 898 | 0143E0900898 | 35.921 | 73.545 | Gilgit | E(o) | 0.89 | 4,370 |\n| 899 | 0143E0900899 | 35.920 | 73.580 | Gilgit | E(o) | 7.16 | 4,311 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6911, "line_end": 7058, "token_count_estimate": 1634, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143E0900865", "0143E0900866", "0143E0900867", "0143E0900868", "0143E0900869", "0143E0900870", "0143E0900871", "0143E0900872", "0143E0900873", "0143E0900874", "0143E0900875", "0143E0900876", "0143E0900877", "0143E0900878", "0143E0900879", "0143E0900880", "0143E0900881", "0143E0900882", "0143E0900883", "0143E0900884", "0143E0900885", "0143E0900886", "0143E0900887", "0143E0900888", "0143E0900889", "0143E0900890", "0143E0900891", "0143E0900892", "0143E0900893", "0143E0900894", "0143E0900895", "0143E0900896", "0143E0900897", "0143E0900898", "0143E0900899"]}}
{"id": "d80113953cc3cd16", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 900 | 0143E0900900 | 35.920 | 73.643 | Gilgit | E(o) | 1.00 | 4,285 |\n| 901 | 0143E0900901 | 35.919 | 73.647 | Gilgit | E(o) | 3.61 | 4,251 |\n| 902 | 0143E0900902 | 35.918 | 73.621 | Gilgit | E(o) | 1.15 | 4,256 |\n| 903 | 0143E0900903 | 35.918 | 73.543 | Gilgit | E(o) | 4.53 | 4,410 |\n| 904 | 0143E0900904 | 35.914 | 73.549 | Gilgit | E(o) | 0.80 | 4,317 |\n| 905 | 0143E0900905 | 35.913 | 73.586 | Gilgit | E(o) | 1.20 | 4,169 |\n| 906 | 0143E0900906 | 35.910 | 73.617 | Gilgit | E(o) | 0.38 | 4,227 |\n| 907 | 0143E0900907 | 35.910 | 73.624 | Gilgit | E(o) | 2.88 | 4,276 |\n| 908 | 0143E0900908 | 35.908 | 73.620 | Gilgit | E(o) | 2.65 | 4,225 |\n| 909 | 0143E0900909 | 35.906 | 73.557 | Gilgit | E(o) | 3.14 | 4,305 |\n| 910 | 0143E0900910 | 35.906 | 73.691 | Gilgit | O | 3.07 | 3,942 |\n| 911 | 0143E0900911 | 35.905 | 73.679 | Gilgit | E(o) | 1.39 | 4,232 |\n| 912 | 0143E0900912 | 35.904 | 73.568 | Gilgit | E(o) | 18.03 | 4,109 |\n| 913 | 0143E0900913 | 35.904 | 73.552 | Gilgit | E(o) | 4.48 | 4,359 |\n| 914 | 0143E0900914 | 35.902 | 73.734 | Gilgit | E(o) | 5.81 | 4,352 |\n| 915 | 0143E0900915 | 35.897 | 73.635 | Gilgit | E(o) | 0.74 | 4,311 |\n| 916 | 0143E0900916 | 35.891 | 73.539 | Gilgit | E(o) | 4.46 | 4,146 |\n| 917 | 0143E0900917 | 35.890 | 73.545 | Gilgit | E(o) | 2.58 | 4,164 |\n| 918 | 0143E0900918 | 35.890 | 73.503 | Gilgit | E(o) | 1.24 | 4,314 |\n| 919 | 0143E0900919 | 35.889 | 73.692 | Gilgit | E(o) | 11.02 | 3,995 |\n| 920 | 0143E0900920 | 35.888 | 73.738 | Gilgit | E(o) | 4.19 | 4,396 |\n| 921 | 0143E0900921 | 35.886 | 73.567 | Gilgit | E(c) | 3.99 | 4,346 |\n| 922 | 0143E0900922 | 35.886 | 73.551 | Gilgit | E(o) | 1.71 | 4,363 |\n| 923 | 0143E0900923 | 35.885 | 73.513 | Gilgit | E(o) | 0.60 | 4,218 |\n| 924 | 0143E0900924 | 35.882 | 73.503 | Gilgit | E(o) | 1.03 | 4,341 |\n| 925 | 0143E0900925 | 35.882 | 73.510 | Gilgit | E(o) | 1.10 | 4,226 |\n| 926 | 0143E0900926 | 35.881 | 73.514 | Gilgit | E(o) | 1.21 | 4,210 |\n| 927 | 0143E0900927 | 35.881 | 73.643 | Gilgit | E(o) | 0.96 | 4,272 |\n| 928 | 0143E0900928 | 35.880 | 73.577 | Gilgit | E(c) | 30.86 | 4,059 |\n| 929 | 0143E0900929 | 35.877 | 73.615 | Gilgit | E(o) | 14.58 | 3,993 |\n| 930 | 0143E0900930 | 35.876 | 73.599 | Gilgit | E(o) | 4.19 | 4,078 |\n| 931 | 0143E0900931 | 35.876 | 73.658 | Gilgit | E(o) | 3.67 | 4,101 |\n| 932 | 0143E0900932 | 35.875 | 73.547 | Indus Middle | E(o) | 1.86 | 4,260 |\n| 933 | 0143E0900933 | 35.873 | 73.509 | Gilgit | E(o) | 1.48 | 4,290 |\n| 934 | 0143E0900934 | 35.873 | 73.555 | Indus Middle | E(o) | 0.38 | 4,382 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6911, "line_end": 7058, "token_count_estimate": 1633, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E0900900", "0143E0900901", "0143E0900902", "0143E0900903", "0143E0900904", "0143E0900905", "0143E0900906", "0143E0900907", "0143E0900908", "0143E0900909", "0143E0900910", "0143E0900911", "0143E0900912", "0143E0900913", "0143E0900914", "0143E0900915", "0143E0900916", "0143E0900917", "0143E0900918", "0143E0900919", "0143E0900920", "0143E0900921", "0143E0900922", "0143E0900923", "0143E0900924", "0143E0900925", "0143E0900926", "0143E0900927", "0143E0900928", "0143E0900929", "0143E0900930", "0143E0900931", "0143E0900932", "0143E0900933", "0143E0900934"]}}
{"id": "dd9ee8a794209f3c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 935 | 0143E0900935 | 35.872 | 73.545 | Indus Middle | E(o) | 2.77 | 4,239 |\n| 936 | 0143E0900936 | 35.868 | 73.725 | Gilgit | O | 0.55 | 4,148 |\n| 937 | 0143E0900937 | 35.868 | 73.680 | Indus Middle | E(o) | 3.23 | 4,201 |\n| 938 | 0143E0900938 | 35.867 | 73.614 | Gilgit | E(o) | 1.82 | 4,177 |\n| 939 | 0143E0900939 | 35.867 | 73.722 | Gilgit | E(o) | 1.69 | 4,137 |\n| 940 | 0143E0900940 | 35.866 | 73.625 | Gilgit | E(o) | 5.37 | 4,115 |\n| 941 | 0143E0900941 | 35.865 | 73.746 | Gilgit | E(o) | 84.31 | 4,140 |\n| 942 | 0143E0900942 | 35.861 | 73.525 | Gilgit | E(o) | 3.45 | 4,180 |\n| 943 | 0143E0900943 | 35.860 | 73.517 | Gilgit | E(o) | 0.72 | 4,217 |\n| 944 | 0143E0900944 | 35.859 | 73.519 | Gilgit | E(o) | 0.87 | 4,221 |\n| 945 | 0143E0900945 | 35.858 | 73.507 | Indus Middle | E(o) | 1.79 | 4,345 |\n| 946 | 0143E0900946 | 35.857 | 73.505 | Indus Middle | E(o) | 0.66 | 4,347 |\n| 947 | 0143E0900947 | 35.851 | 73.504 | Indus Middle | E(o) | 9.13 | 4,284 |\n| 948 | 0143E0900948 | 35.846 | 73.529 | Indus Middle | E(o) | 2.35 | 4,026 |\n| 949 | 0143E0900949 | 35.845 | 73.598 | Indus Middle | O | 0.93 | 3,435 |\n| 950 | 0143E0900950 | 35.844 | 73.541 | Indus Middle | E(o) | 1.15 | 3,988 |\n| 951 | 0143E0900951 | 35.843 | 73.536 | Indus Middle | E(o) | 0.29 | 3,999 |\n| 952 | 0143E0900952 | 35.835 | 73.729 | Indus Middle | E(o) | 0.25 | 3,711 |\n| 953 | 0143E0900953 | 35.824 | 73.605 | Indus Middle | E(c) | 7.24 | 3,702 |\n| 954 | 0143E0900954 | 35.818 | 73.597 | Indus Middle | E(o) | 3.49 | 3,972 |\n| 955 | 0143E0900955 | 35.816 | 73.600 | Indus Middle | E(o) | 4.49 | 3,975 |\n| 956 | 0143E0900956 | 35.814 | 73.612 | Indus Middle | E(o) | 2.54 | 4,085 |\n| 957 | 0143E0900957 | 35.812 | 73.604 | Indus Middle | E(o) | 8.82 | 4,054 |\n| 958 | 0143E0900958 | 35.799 | 73.577 | Indus Middle | E(o) | 0.49 | 4,274 |\n| 959 | 0143E0900959 | 35.789 | 73.566 | Indus Middle | E(o) | 0.67 | 4,049 |\n| 960 | 0143E0900960 | 35.785 | 73.569 | Indus Middle | E(o) | 0.32 | 4,083 |\n| 961 | 0143E0900961 | 35.783 | 73.507 | Indus Middle | E(o) | 12.26 | 3,918 |\n| 962 | 0143E0900962 | 35.782 | 73.572 | Indus Middle | E(o) | 22.07 | 4,082 |\n| 963 | 0143E0900963 | 35.768 | 73.567 | Indus Middle | E(o) | 1.82 | 4,144 |\n| 964 | 0143E1000964 | 35.748 | 73.551 | Indus Middle | E(o) | 2.74 | 3,942 |\n| 965 | 0143E1000965 | 35.743 | 73.560 | Indus Middle | E(c) | 7.44 | 4,128 |\n| 966 | 0143E1000966 | 35.692 | 73.723 | Indus Middle | E(o) | 0.95 | 4,077 |\n| 967 | 0143E1100967 | 35.345 | 73.696 | Indus Middle | E(c) | 0.64 | 4,302 |\n| 968 | 0143E1100968 | 35.345 | 73.700 | Indus Middle | E(o) | 2.72 | 4,245 |\n| 969 | 0143E1100969 | 35.285 | 73.713 | Indus Middle | E(o) | 3.81 | 4,305 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6911, "line_end": 7058, "token_count_estimate": 1657, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E0900935", "0143E0900936", "0143E0900937", "0143E0900938", "0143E0900939", "0143E0900940", "0143E0900941", "0143E0900942", "0143E0900943", "0143E0900944", "0143E0900945", "0143E0900946", "0143E0900947", "0143E0900948", "0143E0900949", "0143E0900950", "0143E0900951", "0143E0900952", "0143E0900953", "0143E0900954", "0143E0900955", "0143E0900956", "0143E0900957", "0143E0900958", "0143E0900959", "0143E0900960", "0143E0900961", "0143E0900962", "0143E0900963", "0143E1000964", "0143E1000965", "0143E1000966", "0143E1100967", "0143E1100968", "0143E1100969"]}}
{"id": "fe6811a619c0dfb4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 970 | 0143E1100970 | 35.282 | 73.720 | Indus Middle | E(o) | 1.99 | 4,183 |\n| 971 | 0143E1200971 | 35.243 | 73.739 | Indus Middle | E(o) | 2.32 | 4,162 |\n| 972 | 0143E1200972 | 35.243 | 73.744 | Indus Middle | E(o) | 1.03 | 4,125 |\n| 973 | 0143E1200973 | 35.242 | 73.735 | Indus Middle | E(o) | 0.46 | 4,318 |\n| 974 | 0143E1200974 | 35.240 | 73.733 | Indus Middle | E(c) | 7.72 | 4,336 |\n| 975 | 0143E1200975 | 35.239 | 73.742 | Indus Middle | E(c) | 2.41 | 4,176 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6911, "line_end": 7058, "token_count_estimate": 382, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143E1100970", "0143E1200971", "0143E1200972", "0143E1200973", "0143E1200974", "0143E1200975"]}}
{"id": "cdd5d9cb98791185", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 7059, "line_end": 7067, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7436134fe31c3fcb", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 976 | 0143E1200976 | 35.225 | 73.749 | Indus Middle | E(o) | 1.32 | 4,268 |\n| 977 | 0143E1300977 | 35.991 | 73.893 | Gilgit | E(o) | 1.11 | 4,355 |\n| 978 | 0143E1300978 | 35.984 | 73.907 | Gilgit | E(o) | 1.46 | 4,252 |\n| 979 | 0143E1300979 | 35.981 | 73.875 | Gilgit | E(o) | 2.75 | 4,325 |\n| 980 | 0143E1300980 | 35.980 | 73.923 | Gilgit | E(o) | 3.28 | 4,465 |\n| 981 | 0143E1300981 | 35.974 | 73.994 | Gilgit | E(o) | 7.82 | 4,425 |\n| 982 | 0143E1300982 | 35.974 | 73.927 | Gilgit | E(o) | 1.47 | 4,472 |\n| 983 | 0143E1300983 | 35.972 | 73.911 | Gilgit | E(c) | 29.25 | 4,426 |\n| 984 | 0143E1300984 | 35.972 | 73.894 | Gilgit | E(o) | 0.77 | 4,455 |\n| 985 | 0143E1300985 | 35.972 | 73.993 | Gilgit | E(o) | 0.46 | 4,434 |\n| 986 | 0143E1300986 | 35.972 | 73.991 | Gilgit | E(o) | 0.46 | 4,430 |\n| 987 | 0143E1300987 | 35.971 | 73.759 | Gilgit | E(o) | 3.71 | 4,259 |\n| 988 | 0143E1300988 | 35.970 | 73.894 | Gilgit | E(o) | 1.40 | 4,456 |\n| 989 | 0143E1300989 | 35.968 | 73.932 | Gilgit | E(o) | 2.09 | 4,388 |\n| 990 | 0143E1300990 | 35.962 | 73.761 | Gilgit | E(c) | 1.02 | 4,497 |\n| 991 | 0143E1300991 | 35.961 | 73.767 | Gilgit | E(o) | 2.10 | 4,376 |\n| 992 | 0143E1300992 | 35.961 | 73.984 | Gilgit | E(o) | 4.89 | 4,329 |\n| 993 | 0143E1300993 | 35.960 | 73.773 | Gilgit | E(o) | 0.87 | 4,289 |\n| 994 | 0143E1300994 | 35.950 | 73.925 | Gilgit | E(o) | 1.51 | 4,429 |\n| 995 | 0143E1300995 | 35.949 | 73.992 | Gilgit | E(o) | 0.29 | 4,364 |\n| 996 | 0143E1300996 | 35.949 | 73.758 | Gilgit | E(o) | 1.70 | 4,406 |\n| 997 | 0143E1300997 | 35.949 | 73.980 | Gilgit | E(o) | 4.76 | 4,176 |\n| 998 | 0143E1300998 | 35.948 | 73.995 | Gilgit | E(o) | 0.49 | 4,371 |\n| 999 | 0143E1300999 | 35.947 | 73.753 | Gilgit | E(o) | 0.80 | 4,414 |\n| 1000 | 0143E1301000 | 35.946 | 73.997 | Gilgit | E(o) | 0.67 | 4,371 |\n| 1001 | 0143E1301001 | 35.944 | 73.997 | Gilgit | E(o) | 0.72 | 4,370 |\n| 1002 | 0143E1301002 | 35.944 | 73.988 | Gilgit | E(o) | 1.21 | 4,265 |\n| 1003 | 0143E1301003 | 35.944 | 73.982 | Gilgit | E(o) | 1.65 | 4,262 |\n| 1004 | 0143E1301004 | 35.944 | 73.993 | Gilgit | E(o) | 2.40 | 4,312 |\n| 1005 | 0143E1301005 | 35.939 | 73.985 | Gilgit | E(o) | 19.57 | 4,258 |\n| 1006 | 0143E1301006 | 35.937 | 73.754 | Gilgit | E(o) | 0.33 | 4,458 |\n| 1007 | 0143E1301007 | 35.935 | 73.917 | Gilgit | E(o) | 1.73 | 4,365 |\n| 1008 | 0143E1301008 | 35.934 | 73.992 | Gilgit | E(o) | 3.38 | 4,285 |\n| 1009 | 0143E1301009 | 35.931 | 73.989 | Gilgit | E(o) | 2.99 | 4,282 |\n| 1010 | 0143E1301010 | 35.930 | 73.933 | Gilgit | E(o) | 2.26 | 4,452 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7068, "line_end": 7215, "token_count_estimate": 1618, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E1200976", "0143E1300977", "0143E1300978", "0143E1300979", "0143E1300980", "0143E1300981", "0143E1300982", "0143E1300983", "0143E1300984", "0143E1300985", "0143E1300986", "0143E1300987", "0143E1300988", "0143E1300989", "0143E1300990", "0143E1300991", "0143E1300992", "0143E1300993", "0143E1300994", "0143E1300995", "0143E1300996", "0143E1300997", "0143E1300998", "0143E1300999", "0143E1301000", "0143E1301001", "0143E1301002", "0143E1301003", "0143E1301004", "0143E1301005", "0143E1301006", "0143E1301007", "0143E1301008", "0143E1301009", "0143E1301010"]}}
{"id": "2f44c3c9624aad06", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1011 | 0143E1301011 | 35.929 | 73.816 | Gilgit | E(c) | 0.50 | 4,258 |\n| 1012 | 0143E1301012 | 35.917 | 73.945 | Gilgit | E(o) | 12.08 | 4,178 |\n| 1013 | 0143E1301013 | 35.916 | 73.992 | Gilgit | E(o) | 11.42 | 4,312 |\n| 1014 | 0143E1301014 | 35.915 | 73.967 | Gilgit | E(o) | 0.49 | 4,303 |\n| 1015 | 0143E1301015 | 35.913 | 73.952 | Gilgit | E(o) | 2.87 | 4,278 |\n| 1016 | 0143E1301016 | 35.912 | 73.965 | Gilgit | E(o) | 5.31 | 4,343 |\n| 1017 | 0143E1301017 | 35.911 | 73.989 | Gilgit | E(o) | 0.94 | 4,294 |\n| 1018 | 0143E1301018 | 35.908 | 73.980 | Gilgit | E(o) | 2.75 | 4,134 |\n| 1019 | 0143E1301019 | 35.907 | 73.969 | Gilgit | E(o) | 0.49 | 4,343 |\n| 1020 | 0143E1301020 | 35.906 | 73.943 | Gilgit | E(o) | 1.30 | 4,416 |\n| 1021 | 0143E1301021 | 35.906 | 73.965 | Gilgit | E(o) | 1.22 | 4,450 |\n| 1022 | 0143E1301022 | 35.906 | 73.922 | Gilgit | E(o) | 15.98 | 4,223 |\n| 1023 | 0143E1301023 | 35.905 | 73.990 | Gilgit | E(o) | 2.09 | 4,336 |\n| 1024 | 0143E1301024 | 35.904 | 73.906 | Gilgit | E(o) | 3.06 | 4,173 |\n| 1025 | 0143E1301025 | 35.904 | 73.941 | Gilgit | E(o) | 1.08 | 4,406 |\n| 1026 | 0143E1301026 | 35.900 | 73.971 | Gilgit | E(o) | 6.67 | 4,326 |\n| 1027 | 0143E1301027 | 35.895 | 73.764 | Gilgit | O | 1.18 | 4,068 |\n| 1028 | 0143E1301028 | 35.894 | 73.784 | Gilgit | E(o) | 3.21 | 4,140 |\n| 1029 | 0143E1301029 | 35.890 | 73.947 | Gilgit | E(o) | 0.73 | 4,157 |\n| 1030 | 0143E1301030 | 35.888 | 73.948 | Gilgit | E(o) | 0.94 | 4,154 |\n| 1031 | 0143E1301031 | 35.888 | 73.880 | Gilgit | E(o) | 1.86 | 4,099 |\n| 1032 | 0143E1301032 | 35.888 | 73.753 | Gilgit | E(o) | 0.63 | 4,321 |\n| 1033 | 0143E1301033 | 35.887 | 73.868 | Gilgit | E(o) | 1.96 | 4,198 |\n| 1034 | 0143E1301034 | 35.885 | 73.815 | Gilgit | E(o) | 1.38 | 4,095 |\n| 1035 | 0143E1301035 | 35.883 | 73.825 | Gilgit | E(o) | 2.51 | 4,251 |\n| 1036 | 0143E1301036 | 35.882 | 73.763 | Gilgit | E(o) | 40.94 | 4,104 |\n| 1037 | 0143E1301037 | 35.877 | 73.954 | Gilgit | E(o) | 4.69 | 4,119 |\n| 1038 | 0143E1301038 | 35.876 | 73.869 | Gilgit | E(o) | 1.57 | 4,252 |\n| 1039 | 0143E1301039 | 35.875 | 73.927 | Gilgit | E(o) | 8.04 | 4,066 |\n| 1040 | 0143E1301040 | 35.875 | 73.846 | Gilgit | E(o) | 4.41 | 4,233 |\n| 1041 | 0143E1301041 | 35.873 | 73.811 | Gilgit | E(o) | 3.44 | 4,128 |\n| 1042 | 0143E1301042 | 35.873 | 73.868 | Gilgit | E(o) | 2.13 | 4,332 |\n| 1043 | 0143E1301043 | 35.870 | 73.801 | Gilgit | E(o) | 1.45 | 4,220 |\n| 1044 | 0143E1301044 | 35.870 | 73.912 | Gilgit | E(o) | 7.34 | 4,056 |\n| 1045 | 0143E1301045 | 35.868 | 73.929 | Gilgit | E(o) | 17.55 | 4,064 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7068, "line_end": 7215, "token_count_estimate": 1637, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143E1301011", "0143E1301012", "0143E1301013", "0143E1301014", "0143E1301015", "0143E1301016", "0143E1301017", "0143E1301018", "0143E1301019", "0143E1301020", "0143E1301021", "0143E1301022", "0143E1301023", "0143E1301024", "0143E1301025", "0143E1301026", "0143E1301027", "0143E1301028", "0143E1301029", "0143E1301030", "0143E1301031", "0143E1301032", "0143E1301033", "0143E1301034", "0143E1301035", "0143E1301036", "0143E1301037", "0143E1301038", "0143E1301039", "0143E1301040", "0143E1301041", "0143E1301042", "0143E1301043", "0143E1301044", "0143E1301045"]}}
{"id": "78849a5f72597a6c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1046 | 0143E1301046 | 35.867 | 73.917 | Gilgit | E(o) | 1.83 | 4,171 |\n| 1047 | 0143E1301047 | 35.866 | 73.957 | Gilgit | E(o) | 9.95 | 4,103 |\n| 1048 | 0143E1301048 | 35.864 | 73.916 | Gilgit | E(o) | 4.31 | 4,186 |\n| 1049 | 0143E1301049 | 35.864 | 73.892 | Gilgit | E(o) | 1.12 | 4,105 |\n| 1050 | 0143E1301050 | 35.863 | 73.861 | Gilgit | E(o) | 0.49 | 4,283 |\n| 1051 | 0143E1301051 | 35.861 | 73.898 | Gilgit | E(o) | 1.60 | 4,173 |\n| 1052 | 0143E1301052 | 35.859 | 73.956 | Gilgit | E(o) | 6.20 | 4,137 |\n| 1053 | 0143E1301053 | 35.858 | 73.869 | Gilgit | E(o) | 15.26 | 4,211 |\n| 1054 | 0143E1301054 | 35.856 | 73.775 | Indus Middle | E(o) | 28.28 | 3,962 |\n| 1055 | 0143E1301055 | 35.853 | 73.784 | Indus Middle | E(o) | 0.39 | 3,978 |\n| 1056 | 0143E1301056 | 35.852 | 73.938 | Gilgit | E(o) | 1.90 | 4,236 |\n| 1057 | 0143E1301057 | 35.852 | 73.877 | Gilgit | E(o) | 19.43 | 4,166 |\n| 1058 | 0143E1301058 | 35.851 | 73.783 | Indus Middle | E(o) | 2.18 | 3,977 |\n| 1059 | 0143E1301059 | 35.851 | 73.903 | Gilgit | E(o) | 2.30 | 4,229 |\n| 1060 | 0143E1301060 | 35.851 | 73.899 | Gilgit | E(o) | 2.91 | 4,209 |\n| 1061 | 0143E1301061 | 35.849 | 73.870 | Gilgit | E(o) | 1.51 | 4,271 |\n| 1062 | 0143E1301062 | 35.848 | 73.881 | Gilgit | E(o) | 0.48 | 4,178 |\n| 1063 | 0143E1301063 | 35.844 | 73.884 | Gilgit | E(o) | 8.47 | 4,151 |\n| 1064 | 0143E1301064 | 35.835 | 73.888 | Indus Middle | E(o) | 0.56 | 4,068 |\n| 1065 | 0143E1301065 | 35.834 | 73.884 | Indus Middle | E(o) | 2.27 | 4,060 |\n| 1066 | 0143E1301066 | 35.833 | 73.885 | Indus Middle | E(o) | 0.50 | 4,062 |\n| 1067 | 0143E1301067 | 35.833 | 73.997 | Gilgit | E(o) | 1.75 | 3,950 |\n| 1068 | 0143E1301068 | 35.830 | 73.790 | Indus Middle | E(o) | 1.06 | 4,286 |\n| 1069 | 0143E1301069 | 35.825 | 73.776 | Indus Middle | E(o) | 1.17 | 4,221 |\n| 1070 | 0143E1301070 | 35.823 | 73.983 | Gilgit | E(o) | 0.34 | 4,107 |\n| 1071 | 0143E1301071 | 35.822 | 73.964 | Gilgit | E(o) | 0.75 | 4,185 |\n| 1072 | 0143E1301072 | 35.816 | 73.999 | Gilgit | E(o) | 0.80 | 4,273 |\n| 1073 | 0143E1301073 | 35.807 | 73.813 | Indus Middle | E(o) | 1.11 | 4,101 |\n| 1074 | 0143E1301074 | 35.803 | 73.813 | Indus Middle | E(o) | 0.94 | 4,177 |\n| 1075 | 0143E1301075 | 35.802 | 73.797 | Indus Middle | E(o) | 1.64 | 4,246 |\n| 1076 | 0143E1301076 | 35.790 | 73.807 | Indus Middle | E(o) | 0.27 | 4,240 |\n| 1077 | 0143E1301077 | 35.789 | 73.804 | Indus Middle | E(o) | 1.03 | 4,185 |\n| 1078 | 0143E1301078 | 35.772 | 73.765 | Indus Middle | E(o) | 1.64 | 3,991 |\n| 1079 | 0143E1501079 | 35.257 | 73.804 | Indus Middle | E(o) | 0.81 | 3,920 |\n| 1080 | 0143E1601080 | 35.250 | 73.780 | Indus Middle | E(o) | 4.75 | 4,263 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7068, "line_end": 7215, "token_count_estimate": 1649, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143E1301046", "0143E1301047", "0143E1301048", "0143E1301049", "0143E1301050", "0143E1301051", "0143E1301052", "0143E1301053", "0143E1301054", "0143E1301055", "0143E1301056", "0143E1301057", "0143E1301058", "0143E1301059", "0143E1301060", "0143E1301061", "0143E1301062", "0143E1301063", "0143E1301064", "0143E1301065", "0143E1301066", "0143E1301067", "0143E1301068", "0143E1301069", "0143E1301070", "0143E1301071", "0143E1301072", "0143E1301073", "0143E1301074", "0143E1301075", "0143E1301076", "0143E1301077", "0143E1301078", "0143E1501079", "0143E1601080"]}}
{"id": "88a3b4e71040b00c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1081 | 0143E1601081 | 35.249 | 73.899 | Indus Middle | E(o) | 2.59 | 4,182 |\n| 1082 | 0143E1601082 | 35.247 | 73.799 | Indus Middle | E(o) | 17.06 | 3,988 |\n| 1083 | 0143E1601083 | 35.242 | 73.790 | Indus Middle | E(o) | 4.18 | 4,108 |\n| 1084 | 0143E1601084 | 35.233 | 73.833 | Indus Middle | E(o) | 6.34 | 4,114 |\n| 1085 | 0143E1601085 | 35.232 | 73.770 | Indus Middle | E(o) | 1.48 | 4,120 |\n| 1086 | 0143E1601086 | 35.229 | 73.754 | Indus Middle | E(o) | 4.14 | 4,030 |\n| 1087 | 0143E1601087 | 35.229 | 73.930 | Indus Middle | E(o) | 0.79 | 4,275 |\n| 1088 | 0143E1601088 | 35.220 | 73.901 | Indus Middle | E(o) | 0.50 | 4,270 |\n| 1089 | 0143E1601089 | 35.220 | 73.855 | Indus Middle | E(o) | 0.57 | 4,191 |\n| 1090 | 0143E1601090 | 35.214 | 73.908 | Indus Middle | E(o) | 0.53 | 4,195 |\n| 1091 | 0143E1601091 | 35.212 | 73.903 | Indus Middle | E(o) | 0.32 | 4,251 |\n| 1092 | 0143E1601092 | 35.210 | 73.863 | Indus Middle | E(o) | 3.40 | 4,160 |\n| 1093 | 0143E1601093 | 35.196 | 73.940 | Indus Middle | E(o) | 1.76 | 4,242 |\n| 1094 | 0143E1601094 | 35.195 | 73.946 | Indus Middle | E(o) | 0.66 | 4,225 |\n| 1095 | 0143E1601095 | 35.185 | 73.947 | Indus Middle | E(o) | 5.01 | 4,275 |\n| 1096 | 0143E1601096 | 35.184 | 73.958 | Indus Middle | E(o) | 10.49 | 4,120 |\n| 1097 | 0143E1601097 | 35.179 | 73.968 | Indus Middle | E(o) | 1.86 | 4,244 |\n| 1098 | 0143E1601098 | 35.178 | 73.959 | Indus Middle | E(o) | 3.77 | 4,221 |\n| 1099 | 0143E1601099 | 35.174 | 73.954 | Indus Middle | E(o) | 1.19 | 4,237 |\n| 1100 | 0143E1601100 | 35.171 | 73.958 | Indus Middle | E(o) | 0.44 | 4,304 |\n| 1101 | 0143E1601101 | 35.169 | 73.958 | Indus Middle | E(o) | 0.57 | 4,308 |\n| 1102 | 0143F1001102 | 34.701 | 73.698 | Jhelum | M(o) | 0.30 | 4,304 |\n| 1103 | 0143F1001103 | 34.558 | 73.653 | Jhelum | M(o) | 0.78 | 4,323 |\n| 1104 | 0143F1001104 | 34.552 | 73.658 | Jhelum | E(o) | 0.25 | 4,173 |\n| 1105 | 0143F1301105 | 34.859 | 73.982 | Jhelum | E(o) | 0.50 | 4,166 |\n| 1106 | 0143F1301106 | 34.853 | 73.989 | Jhelum | E(o) | 2.09 | 4,076 |\n| 1107 | 0143F1301107 | 34.830 | 73.985 | Jhelum | E(o) | 20.67 | 3,873 |\n| 1108 | 0143F1301108 | 34.805 | 73.879 | Jhelum | M(o) | 1.49 | 4,287 |\n| 1109 | 0143F1301109 | 34.801 | 73.847 | Jhelum | M(o) | 0.69 | 4,413 |\n| 1110 | 0143F1301110 | 34.798 | 73.852 | Jhelum | I(s) | 0.36 | 4,224 |\n| 1111 | 0143F1301111 | 34.793 | 73.901 | Jhelum | I(s) | 0.82 | 4,112 |\n| 1112 | 0143F1301112 | 34.784 | 73.763 | Jhelum | M(o) | 3.92 | 4,293 |\n| 1113 | 0143F1301113 | 34.783 | 73.872 | Jhelum | E(o) | 0.68 | 3,914 |\n| 1114 | 0143F1301114 | 34.780 | 73.831 | Jhelum | M(o) | 1.96 | 3,937 |\n| 1115 | 0143F1301115 | 34.778 | 73.789 | Jhelum | M(e) | 0.26 | 3,830 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7068, "line_end": 7215, "token_count_estimate": 1663, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143E1601081", "0143E1601082", "0143E1601083", "0143E1601084", "0143E1601085", "0143E1601086", "0143E1601087", "0143E1601088", "0143E1601089", "0143E1601090", "0143E1601091", "0143E1601092", "0143E1601093", "0143E1601094", "0143E1601095", "0143E1601096", "0143E1601097", "0143E1601098", "0143E1601099", "0143E1601100", "0143E1601101", "0143F1001102", "0143F1001103", "0143F1001104", "0143F1301105", "0143F1301106", "0143F1301107", "0143F1301108", "0143F1301109", "0143F1301110", "0143F1301111", "0143F1301112", "0143F1301113", "0143F1301114", "0143F1301115"]}}
{"id": "f3e9d2fc56d62824", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1116 | 0143I0101116 | 35.982 | 74.004 | Gilgit | E(o) | 4.92 | 4,250 |\n| 1117 | 0143I0101117 | 35.970 | 74.005 | Gilgit | E(o) | 8.66 | 4,328 |\n| 1118 | 0143I0101118 | 35.970 | 74.071 | Gilgit | E(o) | 5.74 | 4,229 |\n| 1119 | 0143I0101119 | 35.966 | 74.016 | Gilgit | E(o) | 1.40 | 4,333 |\n| 1120 | 0143I0101120 | 35.965 | 74.003 | Gilgit | E(o) | 1.57 | 4,341 |\n| 1121 | 0143I0101121 | 35.963 | 74.013 | Gilgit | E(o) | 0.45 | 4,312 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7068, "line_end": 7215, "token_count_estimate": 372, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": ["0143I0101116", "0143I0101117", "0143I0101118", "0143I0101119", "0143I0101120", "0143I0101121"]}}
{"id": "2b6de294f771977c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 7216, "line_end": 7223, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54736cd46d73d9ba", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1122 | 0143I0101122 | 35.962 | 74.019 | Gilgit | E(o) | 15.37 | 4,285 |\n| 1123 | 0143I0101123 | 35.961 | 74.005 | Gilgit | E(o) | 3.97 | 4,413 |\n| 1124 | 0143I0101124 | 35.950 | 74.013 | Gilgit | E(o) | 2.01 | 4,346 |\n| 1125 | 0143I0101125 | 35.932 | 74.004 | Gilgit | E(o) | 1.73 | 4,295 |\n| 1126 | 0143I0101126 | 35.924 | 74.009 | Gilgit | E(o) | 4.05 | 4,159 |\n| 1127 | 0143I0101127 | 35.888 | 74.120 | Gilgit | M(o) | 0.43 | 4,355 |\n| 1128 | 0143I0101128 | 35.879 | 74.113 | Gilgit | E(o) | 3.36 | 4,289 |\n| 1129 | 0143I0101129 | 35.865 | 74.014 | Gilgit | E(o) | 2.56 | 4,192 |\n| 1130 | 0143I0101130 | 35.864 | 74.065 | Gilgit | E(o) | 1.40 | 4,176 |\n| 1131 | 0143I0101131 | 35.861 | 74.017 | Gilgit | E(o) | 1.53 | 4,243 |\n| 1132 | 0143I0101132 | 35.845 | 74.100 | Gilgit | E(c) | 6.40 | 4,374 |\n| 1133 | 0143I0101133 | 35.843 | 74.010 | Gilgit | E(c) | 3.83 | 4,236 |\n| 1134 | 0143I0101134 | 35.838 | 74.225 | Gilgit | E(o) | 0.53 | 4,309 |\n| 1135 | 0143I0101135 | 35.837 | 74.106 | Gilgit | E(c) | 2.44 | 4,264 |\n| 1136 | 0143I0101136 | 35.827 | 74.086 | Gilgit | E(o) | 3.33 | 4,310 |\n| 1137 | 0143I0101137 | 35.826 | 74.108 | Gilgit | E(o) | 6.52 | 4,261 |\n| 1138 | 0143I0101138 | 35.823 | 74.001 | Gilgit | E(o) | 4.84 | 4,140 |\n| 1139 | 0143I0101139 | 35.820 | 74.008 | Gilgit | E(o) | 0.61 | 4,222 |\n| 1140 | 0143I0101140 | 35.819 | 74.067 | Gilgit | E(o) | 0.46 | 4,216 |\n| 1141 | 0143I0101141 | 35.817 | 74.083 | Gilgit | E(c) | 16.71 | 4,269 |\n| 1142 | 0143I0101142 | 35.817 | 74.003 | Gilgit | E(o) | 1.27 | 4,267 |\n| 1143 | 0143I0101143 | 35.816 | 74.071 | Gilgit | E(o) | 0.40 | 4,335 |\n| 1144 | 0143I0101144 | 35.814 | 74.074 | Gilgit | E(c) | 1.73 | 4,385 |\n| 1145 | 0143I0101145 | 35.803 | 74.202 | Gilgit | E(o) | 0.41 | 4,198 |\n| 1146 | 0143I0101146 | 35.803 | 74.201 | Gilgit | E(o) | 0.43 | 4,195 |\n| 1147 | 0143I0101147 | 35.802 | 74.082 | Gilgit | E(o) | 0.38 | 4,392 |\n| 1148 | 0143I0101148 | 35.796 | 74.207 | Gilgit | E(o) | 0.76 | 4,344 |\n| 1149 | 0143I0101149 | 35.795 | 74.081 | Gilgit | E(o) | 0.32 | 4,294 |\n| 1150 | 0143I0101150 | 35.794 | 74.149 | Gilgit | E(o) | 0.34 | 4,237 |\n| 1151 | 0143I0101151 | 35.794 | 74.085 | Gilgit | E(o) | 0.37 | 4,370 |\n| 1152 | 0143I0101152 | 35.793 | 74.085 | Gilgit | E(o) | 3.43 | 4,368 |\n| 1153 | 0143I0101153 | 35.789 | 74.225 | Indus Middle | E(o) | 7.70 | 4,386 |\n| 1154 | 0143I0101154 | 35.787 | 74.235 | Indus Middle | E(o) | 0.73 | 4,283 |\n| 1155 | 0143I0101155 | 35.787 | 74.144 | Gilgit | E(c) | 5.10 | 4,284 |\n| 1156 | 0143I0101156 | 35.786 | 74.101 | Gilgit | E(o) | 0.52 | 4,212 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7224, "line_end": 7371, "token_count_estimate": 1642, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143I0101122", "0143I0101123", "0143I0101124", "0143I0101125", "0143I0101126", "0143I0101127", "0143I0101128", "0143I0101129", "0143I0101130", "0143I0101131", "0143I0101132", "0143I0101133", "0143I0101134", "0143I0101135", "0143I0101136", "0143I0101137", "0143I0101138", "0143I0101139", "0143I0101140", "0143I0101141", "0143I0101142", "0143I0101143", "0143I0101144", "0143I0101145", "0143I0101146", "0143I0101147", "0143I0101148", "0143I0101149", "0143I0101150", "0143I0101151", "0143I0101152", "0143I0101153", "0143I0101154", "0143I0101155", "0143I0101156"]}}
{"id": "ca940868e5532545", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1157 | 0143I0101157 | 35.780 | 74.162 | Gilgit | E(o) | 2.21 | 4,310 |\n| 1158 | 0143I0101158 | 35.779 | 74.205 | Gilgit | E(o) | 12.36 | 4,304 |\n| 1159 | 0143I0101159 | 35.773 | 74.196 | Gilgit | E(c) | 11.01 | 4,368 |\n| 1160 | 0143I0101160 | 35.772 | 74.142 | Gilgit | E(o) | 1.25 | 4,340 |\n| 1161 | 0143I0101161 | 35.768 | 74.175 | Gilgit | E(o) | 0.30 | 4,260 |\n| 1162 | 0143I0101162 | 35.766 | 74.172 | Gilgit | E(c) | 0.76 | 4,342 |\n| 1163 | 0143I0101163 | 35.766 | 74.183 | Gilgit | E(o) | 0.42 | 4,227 |\n| 1164 | 0143I0101164 | 35.764 | 74.149 | Gilgit | E(o) | 2.55 | 4,365 |\n| 1165 | 0143I0101165 | 35.761 | 74.180 | Gilgit | E(c) | 8.61 | 4,264 |\n| 1166 | 0143I0101166 | 35.757 | 74.070 | Gilgit | E(o) | 1.28 | 4,263 |\n| 1167 | 0143I0101167 | 35.756 | 74.081 | Gilgit | E(o) | 0.69 | 4,237 |\n| 1168 | 0143I0201168 | 35.747 | 74.171 | Gilgit | E(o) | 1.42 | 4,377 |\n| 1169 | 0143I0201169 | 35.747 | 74.119 | Gilgit | E(o) | 2.76 | 4,168 |\n| 1170 | 0143I0201170 | 35.747 | 74.165 | Gilgit | E(o) | 2.24 | 4,237 |\n| 1171 | 0143I0201171 | 35.741 | 74.164 | Gilgit | E(o) | 1.52 | 4,313 |\n| 1172 | 0143I0201172 | 35.741 | 74.193 | Indus Middle | E(o) | 0.99 | 4,197 |\n| 1173 | 0143I0201173 | 35.741 | 74.127 | Gilgit | E(o) | 4.18 | 4,216 |\n| 1174 | 0143I0201174 | 35.735 | 74.058 | Indus Middle | E(o) | 0.49 | 3,982 |\n| 1175 | 0143I0201175 | 35.732 | 74.061 | Indus Middle | E(o) | 1.23 | 3,985 |\n| 1176 | 0143I0201176 | 35.729 | 74.151 | Gilgit | E(o) | 0.27 | 4,238 |\n| 1177 | 0143I0201177 | 35.727 | 74.123 | Indus Middle | E(o) | 0.36 | 4,289 |\n| 1178 | 0143I0201178 | 35.726 | 74.091 | Indus Middle | E(o) | 3.99 | 4,187 |\n| 1179 | 0143I0201179 | 35.723 | 74.166 | Indus Middle | E(o) | 1.95 | 4,251 |\n| 1180 | 0143I0201180 | 35.719 | 74.166 | Indus Middle | E(o) | 6.70 | 4,249 |\n| 1181 | 0143I0201181 | 35.718 | 74.097 | Indus Middle | E(o) | 0.94 | 4,163 |\n| 1182 | 0143I0201182 | 35.716 | 74.093 | Indus Middle | E(o) | 0.39 | 4,263 |\n| 1183 | 0143I0201183 | 35.715 | 74.180 | Indus Middle | E(o) | 2.32 | 4,231 |\n| 1184 | 0143I0201184 | 35.715 | 74.146 | Gilgit | E(o) | 0.57 | 4,265 |\n| 1185 | 0143I0201185 | 35.712 | 74.239 | Indus Middle | E(o) | 4.58 | 4,231 |\n| 1186 | 0143I0201186 | 35.707 | 74.243 | Indus Middle | E(o) | 15.89 | 4,318 |\n| 1187 | 0143I0201187 | 35.707 | 74.173 | Indus Middle | E(o) | 0.33 | 4,371 |\n| 1188 | 0143I0201188 | 35.703 | 74.242 | Indus Middle | E(o) | 1.23 | 4,348 |\n| 1189 | 0143I0201189 | 35.697 | 74.170 | Indus Middle | E(o) | 1.95 | 4,322 |\n| 1190 | 0143I0201190 | 35.691 | 74.232 | Indus Middle | E(o) | 5.69 | 4,328 |\n| 1191 | 0143I0201191 | 35.690 | 74.227 | Indus Middle | E(o) | 4.51 | 4,275 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7224, "line_end": 7371, "token_count_estimate": 1657, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle"], "countries": [], "lake_ids": ["0143I0101157", "0143I0101158", "0143I0101159", "0143I0101160", "0143I0101161", "0143I0101162", "0143I0101163", "0143I0101164", "0143I0101165", "0143I0101166", "0143I0101167", "0143I0201168", "0143I0201169", "0143I0201170", "0143I0201171", "0143I0201172", "0143I0201173", "0143I0201174", "0143I0201175", "0143I0201176", "0143I0201177", "0143I0201178", "0143I0201179", "0143I0201180", "0143I0201181", "0143I0201182", "0143I0201183", "0143I0201184", "0143I0201185", "0143I0201186", "0143I0201187", "0143I0201188", "0143I0201189", "0143I0201190", "0143I0201191"]}}
{"id": "e3a3471a57d47b9b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1192 | 0143I0201192 | 35.686 | 74.230 | Indus Middle | E(o) | 14.12 | 4,336 |\n| 1193 | 0143I0201193 | 35.686 | 74.227 | Indus Middle | E(o) | 0.29 | 4,338 |\n| 1194 | 0143I0201194 | 35.686 | 74.189 | Indus Middle | E(o) | 2.84 | 4,245 |\n| 1195 | 0143I0201195 | 35.676 | 74.212 | Indus Middle | E(o) | 1.88 | 4,276 |\n| 1196 | 0143I0201196 | 35.669 | 74.207 | Indus Middle | E(o) | 3.34 | 4,231 |\n| 1197 | 0143I0201197 | 35.654 | 74.171 | Indus Middle | E(o) | 0.46 | 4,233 |\n| 1198 | 0143I0401198 | 35.173 | 74.236 | Indus Middle | E(o) | 0.36 | 4,688 |\n| 1199 | 0143I0401199 | 35.161 | 74.237 | Indus Middle | E(o) | 0.30 | 4,682 |\n| 1200 | 0143I0401200 | 35.160 | 74.067 | Indus Middle | E(o) | 0.40 | 4,034 |\n| 1201 | 0143I0401201 | 35.158 | 74.070 | Indus Middle | E(o) | 1.35 | 4,202 |\n| 1202 | 0143I0401202 | 35.155 | 74.236 | Indus Middle | E(o) | 0.43 | 4,589 |\n| 1203 | 0143I0401203 | 35.155 | 74.071 | Indus Middle | E(c) | 4.42 | 4,208 |\n| 1204 | 0143I0401204 | 35.152 | 74.081 | Indus Middle | E(o) | 6.35 | 4,132 |\n| 1205 | 0143I0401205 | 35.148 | 74.034 | Indus Middle | E(o) | 0.81 | 4,109 |\n| 1206 | 0143I0401206 | 35.126 | 74.112 | Indus Middle | E(o) | 0.25 | 4,222 |\n| 1207 | 0143I0401207 | 35.121 | 74.145 | Indus Middle | E(o) | 0.48 | 4,264 |\n| 1208 | 0143I0401208 | 35.120 | 74.133 | Indus Middle | E(o) | 0.41 | 4,231 |\n| 1209 | 0143I0401209 | 35.109 | 74.150 | Indus Middle | E(o) | 2.80 | 4,228 |\n| 1210 | 0143I0401210 | 35.105 | 74.219 | Indus Middle | M(o) | 2.52 | 4,406 |\n| 1211 | 0143I0401211 | 35.101 | 74.217 | Indus Middle | M(o) | 0.74 | 4,450 |\n| 1212 | 0143I0401212 | 35.099 | 74.158 | Indus Middle | E(o) | 0.44 | 4,145 |\n| 1213 | 0143I0401213 | 35.097 | 74.191 | Indus Middle | E(o) | 0.36 | 4,348 |\n| 1214 | 0143I0401214 | 35.094 | 74.185 | Indus Middle | E(o) | 0.61 | 4,217 |\n| 1215 | 0143I0401215 | 35.093 | 74.179 | Indus Middle | E(o) | 4.35 | 4,101 |\n| 1216 | 0143I0401216 | 35.091 | 74.148 | Indus Middle | E(o) | 1.97 | 4,315 |\n| 1217 | 0143I0401217 | 35.088 | 74.199 | Indus Middle | E(o) | 2.58 | 4,377 |\n| 1218 | 0143I0401218 | 35.086 | 74.180 | Indus Middle | E(o) | 0.33 | 4,209 |\n| 1219 | 0143I0401219 | 35.084 | 74.177 | Indus Middle | E(o) | 0.73 | 4,155 |\n| 1220 | 0143I0401220 | 35.083 | 74.164 | Indus Middle | E(o) | 1.98 | 4,204 |\n| 1221 | 0143I0401221 | 35.080 | 74.129 | Jhelum | E(o) | 1.82 | 4,172 |\n| 1222 | 0143I0401222 | 35.076 | 74.164 | Jhelum | E(o) | 1.73 | 4,387 |\n| 1223 | 0143I0401223 | 35.074 | 74.166 | Jhelum | E(o) | 0.86 | 4,369 |\n| 1224 | 0143I0401224 | 35.073 | 74.177 | Jhelum | E(c) | 11.97 | 4,042 |\n| 1225 | 0143I0401225 | 35.073 | 74.105 | Jhelum | E(o) | 2.19 | 4,028 |\n| 1226 | 0143I0401226 | 35.073 | 74.202 | Jhelum | M(o) | 0.57 | 4,345 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7224, "line_end": 7371, "token_count_estimate": 1688, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143I0201192", "0143I0201193", "0143I0201194", "0143I0201195", "0143I0201196", "0143I0201197", "0143I0401198", "0143I0401199", "0143I0401200", "0143I0401201", "0143I0401202", "0143I0401203", "0143I0401204", "0143I0401205", "0143I0401206", "0143I0401207", "0143I0401208", "0143I0401209", "0143I0401210", "0143I0401211", "0143I0401212", "0143I0401213", "0143I0401214", "0143I0401215", "0143I0401216", "0143I0401217", "0143I0401218", "0143I0401219", "0143I0401220", "0143I0401221", "0143I0401222", "0143I0401223", "0143I0401224", "0143I0401225", "0143I0401226"]}}
{"id": "e3686884f5697b81", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1227 | 0143I0401227 | 35.070 | 74.225 | Indus Middle | M(o) | 2.06 | 4,403 |\n| 1228 | 0143I0401228 | 35.070 | 74.114 | Jhelum | E(o) | 3.41 | 4,194 |\n| 1229 | 0143I0401229 | 35.062 | 74.223 | Jhelum | M(o) | 1.74 | 4,397 |\n| 1230 | 0143I0401230 | 35.061 | 74.085 | Jhelum | E(o) | 0.55 | 4,098 |\n| 1231 | 0143I0401231 | 35.058 | 74.080 | Jhelum | E(o) | 0.25 | 4,260 |\n| 1232 | 0143I0401232 | 35.054 | 74.090 | Jhelum | E(o) | 0.79 | 4,089 |\n| 1233 | 0143I0401233 | 35.051 | 74.151 | Jhelum | M(o) | 5.70 | 4,201 |\n| 1234 | 0143I0401234 | 35.046 | 74.147 | Jhelum | M(o) | 2.60 | 4,196 |\n| 1235 | 0143I0401235 | 35.042 | 74.097 | Jhelum | E(o) | 2.66 | 3,978 |\n| 1236 | 0143I0401236 | 35.040 | 74.087 | Jhelum | M(o) | 6.94 | 4,097 |\n| 1237 | 0143I0401237 | 35.028 | 74.240 | Jhelum | E(o) | 0.51 | 4,422 |\n| 1238 | 0143I0501238 | 35.830 | 74.254 | Gilgit | E(o) | 2.05 | 4,189 |\n| 1239 | 0143I0501239 | 35.821 | 74.294 | Gilgit | E(c) | 12.04 | 4,313 |\n| 1240 | 0143I0501240 | 35.819 | 74.302 | Gilgit | E(o) | 5.19 | 4,330 |\n| 1241 | 0143I0501241 | 35.819 | 74.268 | Gilgit | E(o) | 4.11 | 4,246 |\n| 1242 | 0143I0501242 | 35.818 | 74.281 | Gilgit | E(o) | 3.11 | 4,267 |\n| 1243 | 0143I0501243 | 35.817 | 74.342 | Gilgit | E(o) | 4.22 | 4,190 |\n| 1244 | 0143I0501244 | 35.817 | 74.296 | Gilgit | E(c) | 3.28 | 4,339 |\n| 1245 | 0143I0501245 | 35.814 | 74.345 | Gilgit | E(c) | 3.12 | 4,239 |\n| 1246 | 0143I0501246 | 35.813 | 74.304 | Gilgit | E(c) | 7.49 | 4,370 |\n| 1247 | 0143I0501247 | 35.797 | 74.404 | Indus Middle | E(o) | 1.14 | 4,280 |\n| 1248 | 0143I0601248 | 35.731 | 74.289 | Indus Middle | E(o) | 3.49 | 4,324 |\n| 1249 | 0143I0601249 | 35.722 | 74.256 | Indus Middle | E(o) | 10.77 | 4,254 |\n| 1250 | 0143I0601250 | 35.715 | 74.259 | Indus Middle | E(o) | 0.45 | 4,369 |\n| 1251 | 0143I0601251 | 35.711 | 74.259 | Indus Middle | E(o) | 1.05 | 4,530 |\n| 1252 | 0143I0601252 | 35.703 | 74.467 | Indus Middle | E(o) | 1.98 | 4,287 |\n| 1253 | 0143I0601253 | 35.699 | 74.479 | Indus Middle | E(o) | 2.84 | 4,302 |\n| 1254 | 0143I0601254 | 35.688 | 74.325 | Indus Middle | E(o) | 1.41 | 4,319 |\n| 1255 | 0143I0601255 | 35.674 | 74.347 | Indus Middle | E(o) | 0.51 | 4,004 |\n| 1256 | 0143I0601256 | 35.650 | 74.426 | Indus Middle | E(o) | 1.04 | 4,269 |\n| 1257 | 0143I0601257 | 35.649 | 74.324 | Indus Middle | E(o) | 1.04 | 4,337 |\n| 1258 | 0143I0601258 | 35.636 | 74.289 | Indus Middle | E(o) | 4.47 | 4,353 |\n| 1259 | 0143I0601259 | 35.636 | 74.309 | Indus Middle | E(o) | 1.12 | 4,365 |\n| 1260 | 0143I0601260 | 35.632 | 74.402 | Indus Middle | E(o) | 1.76 | 4,316 |\n| 1261 | 0143I0601261 | 35.628 | 74.399 | Indus Middle | E(o) | 2.92 | 4,420 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7224, "line_end": 7371, "token_count_estimate": 1664, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143I0401227", "0143I0401228", "0143I0401229", "0143I0401230", "0143I0401231", "0143I0401232", "0143I0401233", "0143I0401234", "0143I0401235", "0143I0401236", "0143I0401237", "0143I0501238", "0143I0501239", "0143I0501240", "0143I0501241", "0143I0501242", "0143I0501243", "0143I0501244", "0143I0501245", "0143I0501246", "0143I0501247", "0143I0601248", "0143I0601249", "0143I0601250", "0143I0601251", "0143I0601252", "0143I0601253", "0143I0601254", "0143I0601255", "0143I0601256", "0143I0601257", "0143I0601258", "0143I0601259", "0143I0601260", "0143I0601261"]}}
{"id": "bea33016f1802b8d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1262 | 0143I0601262 | 35.615 | 74.407 | Indus Middle | E(o) | 8.61 | 4,422 |\n| 1263 | 0143I0601263 | 35.614 | 74.401 | Indus Middle | E(o) | 0.94 | 4,488 |\n| 1264 | 0143I0601264 | 35.608 | 74.340 | Indus Middle | E(o) | 6.05 | 4,155 |\n| 1265 | 0143I0601265 | 35.600 | 74.418 | Indus Middle | E(o) | 6.61 | 4,430 |\n| 1266 | 0143I0601266 | 35.598 | 74.409 | Indus Middle | E(o) | 6.51 | 4,480 |\n| 1267 | 0143I0601267 | 35.588 | 74.417 | Indus Middle | E(o) | 2.00 | 4,446 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7224, "line_end": 7371, "token_count_estimate": 375, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143I0601262", "0143I0601263", "0143I0601264", "0143I0601265", "0143I0601266", "0143I0601267"]}}
{"id": "e6196b35e533347a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 7372, "line_end": 7376, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3c3a883a732ff5e7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1268 | 0143I0601268 | 35.574 | 74.447 | Indus Middle | E(o) | 0.31 | 4,291 |\n| 1269 | 0143I0601269 | 35.573 | 74.438 | Indus Middle | E(o) | 4.54 | 4,380 |\n| 1270 | 0143I0701270 | 35.271 | 74.473 | Indus Middle | I(s) | 0.35 | 3,914 |\n| 1271 | 0143I0701271 | 35.271 | 74.475 | Indus Middle | I(s) | 0.47 | 3,927 |\n| 1272 | 0143I0801272 | 35.230 | 74.442 | Indus Middle | I(s) | 0.38 | 4,544 |\n| 1273 | 0143I0801273 | 35.201 | 74.400 | Indus Middle | M(o) | 0.53 | 4,614 |\n| 1274 | 0143I0801274 | 35.197 | 74.376 | Indus Middle | I(s) | 5.61 | 4,639 |\n| 1275 | 0143I0801275 | 35.123 | 74.288 | Indus Middle | I(s) | 0.33 | 4,552 |\n| 1276 | 0143I0801276 | 35.097 | 74.270 | Indus Middle | I(s) | 0.26 | 4,311 |\n| 1277 | 0143I0801277 | 35.068 | 74.446 | Jhelum | I(s) | 0.92 | 3,770 |\n| 1278 | 0143I0801278 | 35.056 | 74.496 | Jhelum | I(s) | 0.30 | 3,859 |\n| 1279 | 0143I0801279 | 35.053 | 74.279 | Indus Middle | I(s) | 0.66 | 4,188 |\n| 1280 | 0143I0801280 | 35.044 | 74.262 | Indus Middle | I(s) | 0.45 | 4,315 |\n| 1281 | 0143I0801281 | 35.043 | 74.495 | Jhelum | I(s) | 0.28 | 3,738 |\n| 1282 | 0143I0801282 | 35.034 | 74.271 | Jhelum | M(o) | 0.57 | 4,211 |\n| 1283 | 0143I0901283 | 35.999 | 74.621 | Gilgit | I(s) | 0.26 | 3,663 |\n| 1284 | 0143I0901284 | 35.999 | 74.649 | Gilgit | M(l) | 0.84 | 4,284 |\n| 1285 | 0143I1101285 | 35.447 | 74.710 | Indus Middle | E(o) | 2.20 | 4,546 |\n| 1286 | 0143I1101286 | 35.429 | 74.709 | Indus Middle | M(e) | 0.44 | 4,761 |\n| 1287 | 0143I1101287 | 35.412 | 74.669 | Indus Middle | E(o) | 2.16 | 3,112 |\n| 1288 | 0143I1101288 | 35.407 | 74.681 | Indus Middle | E(o) | 0.52 | 3,397 |\n| 1289 | 0143I1101289 | 35.400 | 74.676 | Indus Middle | E(o) | 13.97 | 3,386 |\n| 1290 | 0143I1101290 | 35.364 | 74.682 | Indus Middle | M(l) | 1.87 | 4,205 |\n| 1291 | 0143I1101291 | 35.325 | 74.611 | Indus Middle | M(l) | 0.39 | 3,907 |\n| 1292 | 0143I1101292 | 35.281 | 74.738 | Indus Middle | E(o) | 0.34 | 4,398 |\n| 1293 | 0143I1201293 | 35.193 | 74.616 | Indus Middle | M(e) | 8.04 | 3,599 |\n| 1294 | 0143I1201294 | 35.171 | 74.559 | Indus Middle | I(s) | 0.33 | 3,775 |\n| 1295 | 0143I1201295 | 35.170 | 74.545 | Indus Middle | I(s) | 0.31 | 3,870 |\n| 1296 | 0143I1201296 | 35.169 | 74.563 | Indus Middle | I(s) | 1.90 | 3,762 |\n| 1297 | 0143I1201297 | 35.168 | 74.567 | Indus Middle | I(s) | 0.28 | 3,715 |\n| 1298 | 0143I1201298 | 35.165 | 74.551 | Indus Middle | I(s) | 0.39 | 3,850 |\n| 1299 | 0143I1201299 | 35.159 | 74.548 | Indus Middle | M(l) | 0.25 | 3,946 |\n| 1300 | 0143I1201300 | 35.153 | 74.574 | Indus Middle | I(s) | 0.88 | 3,904 |\n| 1301 | 0143I1201301 | 35.150 | 74.515 | Indus Middle | M(o) | 1.34 | 4,115 |\n| 1302 | 0143I1201302 | 35.105 | 74.612 | Indus Middle | M(l) | 0.59 | 4,245 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7377, "line_end": 7524, "token_count_estimate": 1672, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143I0601268", "0143I0601269", "0143I0701270", "0143I0701271", "0143I0801272", "0143I0801273", "0143I0801274", "0143I0801275", "0143I0801276", "0143I0801277", "0143I0801278", "0143I0801279", "0143I0801280", "0143I0801281", "0143I0801282", "0143I0901283", "0143I0901284", "0143I1101285", "0143I1101286", "0143I1101287", "0143I1101288", "0143I1101289", "0143I1101290", "0143I1101291", "0143I1101292", "0143I1201293", "0143I1201294", "0143I1201295", "0143I1201296", "0143I1201297", "0143I1201298", "0143I1201299", "0143I1201300", "0143I1201301", "0143I1201302"]}}
{"id": "b3361c65715f6a20", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1303 | 0143I1201303 | 35.103 | 74.632 | Indus Middle | I(s) | 0.30 | 4,060 |\n| 1304 | 0143I1201304 | 35.102 | 74.634 | Indus Middle | I(s) | 0.26 | 4,036 |\n| 1305 | 0143I1201305 | 35.086 | 74.602 | Indus Middle | E(o) | 0.27 | 4,551 |\n| 1306 | 0143I1201306 | 35.052 | 74.686 | Indus Middle | E(o) | 0.28 | 4,232 |\n| 1307 | 0143I1201307 | 35.044 | 74.635 | Indus Middle | E(o) | 0.92 | 4,330 |\n| 1308 | 0143I1201308 | 35.033 | 74.643 | Indus Middle | I(s) | 0.70 | 4,560 |\n| 1309 | 0143I1201309 | 35.029 | 74.678 | Indus Middle | O | 0.37 | 3,872 |\n| 1310 | 0143I1201310 | 35.013 | 74.606 | Indus Middle | E(o) | 0.53 | 4,558 |\n| 1311 | 0143I1201311 | 35.007 | 74.589 | Indus Middle | E(o) | 3.44 | 4,446 |\n| 1312 | 0143I1301312 | 35.923 | 74.972 | Indus Middle | I(s) | 0.67 | 4,775 |\n| 1313 | 0143I1401313 | 35.635 | 74.870 | Indus Middle | E(o) | 0.33 | 4,702 |\n| 1314 | 0143I1401314 | 35.601 | 74.851 | Indus Middle | E(o) | 0.55 | 4,441 |\n| 1315 | 0143I1401315 | 35.572 | 74.837 | Indus Middle | E(o) | 0.67 | 4,454 |\n| 1316 | 0143I1501316 | 35.492 | 74.983 | Indus Middle | I(s) | 1.42 | 4,214 |\n| 1317 | 0143I1501317 | 35.351 | 74.788 | Indus Middle | I(s) | 0.54 | 3,393 |\n| 1318 | 0143I1501318 | 35.332 | 74.760 | Indus Middle | M(l) | 0.31 | 3,683 |\n| 1319 | 0143I1501319 | 35.332 | 74.941 | Indus Middle | E(o) | 11.74 | 4,427 |\n| 1320 | 0143I1501320 | 35.330 | 74.786 | Indus Middle | M(l) | 15.07 | 3,475 |\n| 1321 | 0143I1501321 | 35.323 | 74.754 | Indus Middle | M(l) | 1.14 | 3,764 |\n| 1322 | 0143I1501322 | 35.315 | 74.937 | Indus Middle | M(e) | 20.13 | 4,197 |\n| 1323 | 0143I1601323 | 35.106 | 74.921 | Indus Middle | E(o) | 1.17 | 4,448 |\n| 1324 | 0143I1601324 | 35.106 | 74.907 | Indus Middle | E(o) | 0.78 | 4,562 |\n| 1325 | 0143I1601325 | 35.104 | 74.927 | Indus Middle | E(c) | 3.56 | 4,385 |\n| 1326 | 0143I1601326 | 35.101 | 74.976 | Indus Middle | E(c) | 8.00 | 4,388 |\n| 1327 | 0143I1601327 | 35.101 | 74.923 | Indus Middle | E(o) | 2.82 | 4,476 |\n| 1328 | 0143I1601328 | 35.099 | 74.899 | Indus Middle | M(o) | 0.56 | 4,363 |\n| 1329 | 0143I1601329 | 35.097 | 74.935 | Indus Middle | E(c) | 8.30 | 4,182 |\n| 1330 | 0143I1601330 | 35.096 | 74.893 | Indus Middle | E(o) | 0.92 | 4,243 |\n| 1331 | 0143I1601331 | 35.096 | 74.902 | Indus Middle | M(e) | 6.95 | 4,395 |\n| 1332 | 0143I1601332 | 35.093 | 74.882 | Indus Middle | E(c) | 1.63 | 4,475 |\n| 1333 | 0143I1601333 | 35.082 | 74.961 | Indus Middle | E(o) | 30.64 | 4,317 |\n| 1334 | 0143I1601334 | 35.075 | 74.958 | Indus Middle | E(o) | 10.45 | 4,413 |\n| 1335 | 0143I1601335 | 35.061 | 74.981 | Indus Middle | E(o) | 1.21 | 4,117 |\n| 1336 | 0143I1601336 | 35.018 | 74.900 | Indus Middle | E(o) | 1.03 | 4,331 |\n| 1337 | 0143I1601337 | 35.013 | 74.917 | Indus Middle | E(o) | 3.76 | 4,065 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7377, "line_end": 7524, "token_count_estimate": 1664, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143I1201303", "0143I1201304", "0143I1201305", "0143I1201306", "0143I1201307", "0143I1201308", "0143I1201309", "0143I1201310", "0143I1201311", "0143I1301312", "0143I1401313", "0143I1401314", "0143I1401315", "0143I1501316", "0143I1501317", "0143I1501318", "0143I1501319", "0143I1501320", "0143I1501321", "0143I1501322", "0143I1601323", "0143I1601324", "0143I1601325", "0143I1601326", "0143I1601327", "0143I1601328", "0143I1601329", "0143I1601330", "0143I1601331", "0143I1601332", "0143I1601333", "0143I1601334", "0143I1601335", "0143I1601336", "0143I1601337"]}}
{"id": "2458e5184a7fc1ac", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1338 | 0143I1601338 | 35.012 | 74.937 | Indus Middle | E(c) | 7.82 | 4,101 |\n| 1339 | 0143I1601339 | 35.004 | 74.941 | Indus Middle | E(c) | 0.93 | 4,268 |\n| 1340 | 0143I1601340 | 35.003 | 74.965 | Indus Middle | E(o) | 1.13 | 4,453 |\n| 1341 | 0143I1601341 | 35.001 | 74.941 | Indus Middle | E(c) | 2.39 | 4,333 |\n| 1342 | 0143I1601342 | 35.001 | 74.987 | Indus Middle | E(c) | 1.49 | 4,220 |\n| 1343 | 0143J0101343 | 34.976 | 74.073 | Jhelum | E(o) | 0.87 | 4,138 |\n| 1344 | 0143J0101344 | 34.970 | 74.098 | Jhelum | E(o) | 0.74 | 3,559 |\n| 1345 | 0143J0101345 | 34.962 | 74.100 | Jhelum | E(o) | 29.65 | 3,603 |\n| 1346 | 0143J0101346 | 34.959 | 74.117 | Jhelum | E(c) | 2.61 | 4,083 |\n| 1347 | 0143J0101347 | 34.957 | 74.076 | Jhelum | O | 0.66 | 3,961 |\n| 1348 | 0143J0101348 | 34.956 | 74.130 | Jhelum | E(c) | 8.48 | 3,797 |\n| 1349 | 0143J0101349 | 34.950 | 74.084 | Jhelum | E(o) | 0.94 | 4,138 |\n| 1350 | 0143J0101350 | 34.948 | 74.138 | Jhelum | E(c) | 20.03 | 4,011 |\n| 1351 | 0143J0101351 | 34.929 | 74.097 | Jhelum | E(o) | 1.67 | 3,983 |\n| 1352 | 0143J0101352 | 34.926 | 74.155 | Jhelum | E(c) | 21.64 | 3,926 |\n| 1353 | 0143J0101353 | 34.902 | 74.048 | Jhelum | E(c) | 21.58 | 3,896 |\n| 1354 | 0143J0101354 | 34.883 | 74.071 | Jhelum | E(o) | 1.24 | 4,001 |\n| 1355 | 0143J0101355 | 34.880 | 74.247 | Jhelum | E(o) | 4.07 | 3,951 |\n| 1356 | 0143J0101356 | 34.872 | 74.060 | Jhelum | E(c) | 4.37 | 4,029 |\n| 1357 | 0143J0101357 | 34.866 | 74.050 | Jhelum | E(c) | 5.93 | 3,819 |\n| 1358 | 0143J0101358 | 34.861 | 74.078 | Jhelum | E(o) | 2.42 | 3,687 |\n| 1359 | 0143J0101359 | 34.861 | 74.146 | Jhelum | E(o) | 1.31 | 3,934 |\n| 1360 | 0143J0101360 | 34.858 | 74.077 | Jhelum | E(c) | 20.51 | 3,680 |\n| 1361 | 0143J0101361 | 34.857 | 74.087 | Jhelum | E(o) | 1.59 | 3,936 |\n| 1362 | 0143J0101362 | 34.834 | 74.052 | Jhelum | O | 0.59 | 3,548 |\n| 1363 | 0143J0101363 | 34.829 | 74.062 | Jhelum | E(c) | 93.90 | 3,681 |\n| 1364 | 0143J0501364 | 34.990 | 74.288 | Jhelum | I(s) | 0.91 | 4,235 |\n| 1365 | 0143J0501365 | 34.970 | 74.429 | Jhelum | E(o) | 0.31 | 4,061 |\n| 1366 | 0143J0501366 | 34.968 | 74.377 | Jhelum | E(o) | 0.60 | 3,404 |\n| 1367 | 0143J0501367 | 34.898 | 74.342 | Jhelum | E(o) | 0.99 | 3,968 |\n| 1368 | 0143J0501368 | 34.890 | 74.286 | Jhelum | E(c) | 3.33 | 4,158 |\n| 1369 | 0143J0501369 | 34.880 | 74.361 | Jhelum | E(o) | 0.97 | 4,007 |\n| 1370 | 0143J0501370 | 34.880 | 74.448 | Jhelum | E(o) | 1.84 | 3,692 |\n| 1371 | 0143J0501371 | 34.878 | 74.287 | Jhelum | E(o) | 1.83 | 3,994 |\n| 1372 | 0143J0501372 | 34.877 | 74.281 | Jhelum | M(o) | 1.15 | 4,099 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7377, "line_end": 7524, "token_count_estimate": 1638, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143I1601338", "0143I1601339", "0143I1601340", "0143I1601341", "0143I1601342", "0143J0101343", "0143J0101344", "0143J0101345", "0143J0101346", "0143J0101347", "0143J0101348", "0143J0101349", "0143J0101350", "0143J0101351", "0143J0101352", "0143J0101353", "0143J0101354", "0143J0101355", "0143J0101356", "0143J0101357", "0143J0101358", "0143J0101359", "0143J0101360", "0143J0101361", "0143J0101362", "0143J0101363", "0143J0501364", "0143J0501365", "0143J0501366", "0143J0501367", "0143J0501368", "0143J0501369", "0143J0501370", "0143J0501371", "0143J0501372"]}}
{"id": "f02310dac537342e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1373 | 0143J0501373 | 34.877 | 74.444 | Jhelum | E(o) | 1.87 | 3,857 |\n| 1374 | 0143J0501374 | 34.876 | 74.477 | Jhelum | E(c) | 12.31 | 3,592 |\n| 1375 | 0143J0501375 | 34.874 | 74.250 | Jhelum | E(o) | 8.19 | 4,039 |\n| 1376 | 0143J0501376 | 34.873 | 74.273 | Jhelum | M(o) | 0.55 | 4,308 |\n| 1377 | 0143J0501377 | 34.868 | 74.270 | Jhelum | E(c) | 0.48 | 4,139 |\n| 1378 | 0143J0501378 | 34.867 | 74.308 | Jhelum | M(o) | 0.55 | 4,019 |\n| 1379 | 0143J0501379 | 34.865 | 74.271 | Jhelum | E(o) | 4.47 | 4,082 |\n| 1380 | 0143J0501380 | 34.864 | 74.282 | Jhelum | E(o) | 0.62 | 4,218 |\n| 1381 | 0143J0501381 | 34.859 | 74.283 | Jhelum | E(o) | 0.55 | 4,048 |\n| 1382 | 0143J0601382 | 34.741 | 74.346 | Jhelum | E(o) | 0.68 | 3,482 |\n| 1383 | 0143J0801383 | 34.020 | 74.331 | Jhelum | E(o) | 1.77 | 3,967 |\n| 1384 | 0143J0801384 | 34.020 | 74.321 | Jhelum | E(o) | 0.51 | 3,954 |\n| 1385 | 0143J0801385 | 34.020 | 74.308 | Jhelum | E(o) | 0.36 | 3,918 |\n| 1386 | 0143J0901386 | 34.995 | 74.740 | Indus Middle | E(o) | 0.72 | 4,429 |\n| 1387 | 0143J0901387 | 34.990 | 74.726 | Indus Middle | E(o) | 5.22 | 4,332 |\n| 1388 | 0143J0901388 | 34.986 | 74.569 | Jhelum | E(o) | 0.55 | 4,414 |\n| 1389 | 0143J0901389 | 34.976 | 74.625 | Indus Middle | E(o) | 1.27 | 4,094 |\n| 1390 | 0143J0901390 | 34.974 | 74.626 | Indus Middle | M(o) | 0.61 | 4,114 |\n| 1391 | 0143J0901391 | 34.947 | 74.726 | Indus Middle | M(o) | 4.48 | 4,580 |\n| 1392 | 0143J0901392 | 34.946 | 74.680 | Indus Middle | E(o) | 1.89 | 4,046 |\n| 1393 | 0143J0901393 | 34.943 | 74.728 | Indus Middle | E(o) | 0.51 | 4,524 |\n| 1394 | 0143J0901394 | 34.939 | 74.704 | Indus Middle | E(o) | 1.42 | 4,407 |\n| 1395 | 0143J0901395 | 34.939 | 74.630 | Indus Middle | E(o) | 1.29 | 4,261 |\n| 1396 | 0143J0901396 | 34.938 | 74.627 | Indus Middle | E(o) | 0.57 | 4,281 |\n| 1397 | 0143J0901397 | 34.920 | 74.521 | Jhelum | M(e) | 60.60 | 4,041 |\n| 1398 | 0143J0901398 | 34.918 | 74.617 | Indus Middle | E(o) | 1.50 | 4,093 |\n| 1399 | 0143J0901399 | 34.918 | 74.672 | Indus Middle | E(o) | 2.50 | 3,692 |\n| 1400 | 0143J0901400 | 34.917 | 74.633 | Indus Middle | E(o) | 1.40 | 4,241 |\n| 1401 | 0143J0901401 | 34.915 | 74.638 | Indus Middle | E(c) | 4.45 | 4,028 |\n| 1402 | 0143J0901402 | 34.911 | 74.657 | Indus Middle | E(o) | 0.65 | 3,933 |\n| 1403 | 0143J0901403 | 34.909 | 74.655 | Indus Middle | E(o) | 4.62 | 3,957 |\n| 1404 | 0143J0901404 | 34.908 | 74.680 | Indus Middle | E(c) | 23.48 | 3,864 |\n| 1405 | 0143J0901405 | 34.908 | 74.642 | Indus Middle | E(o) | 0.85 | 4,235 |\n| 1406 | 0143J0901406 | 34.899 | 74.663 | Jhelum | E(c) | 7.07 | 4,283 |\n| 1407 | 0143J0901407 | 34.895 | 74.661 | Jhelum | E(o) | 4.97 | 4,202 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7377, "line_end": 7524, "token_count_estimate": 1652, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143J0501373", "0143J0501374", "0143J0501375", "0143J0501376", "0143J0501377", "0143J0501378", "0143J0501379", "0143J0501380", "0143J0501381", "0143J0601382", "0143J0801383", "0143J0801384", "0143J0801385", "0143J0901386", "0143J0901387", "0143J0901388", "0143J0901389", "0143J0901390", "0143J0901391", "0143J0901392", "0143J0901393", "0143J0901394", "0143J0901395", "0143J0901396", "0143J0901397", "0143J0901398", "0143J0901399", "0143J0901400", "0143J0901401", "0143J0901402", "0143J0901403", "0143J0901404", "0143J0901405", "0143J0901406", "0143J0901407"]}}
{"id": "5458f581d5ef753a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1408 | 0143J0901408 | 34.883 | 74.508 | Jhelum | E(o) | 0.94 | 3,586 |\n| 1409 | 0143J0901409 | 34.880 | 74.586 | Jhelum | E(o) | 1.24 | 3,850 |\n| 1410 | 0143J0901410 | 34.878 | 74.586 | Jhelum | E(o) | 0.59 | 3,816 |\n| 1411 | 0143J0901411 | 34.871 | 74.602 | Jhelum | M(e) | 3.07 | 3,563 |\n| 1412 | 0143J0901412 | 34.859 | 74.695 | Jhelum | E(c) | 5.34 | 4,033 |\n| 1413 | 0143J0901413 | 34.858 | 74.734 | Indus Middle | E(o) | 0.78 | 4,198 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7377, "line_end": 7524, "token_count_estimate": 370, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143J0901408", "0143J0901409", "0143J0901410", "0143J0901411", "0143J0901412", "0143J0901413"]}}
{"id": "0999d481827bd428", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 7525, "line_end": 7534, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff4297067476a4e4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1414 | 0143J0901414 | 34.849 | 74.523 | Jhelum | E(c) | 22.54 | 4,016 |\n| 1415 | 0143J0901415 | 34.844 | 74.738 | Indus Middle | E(o) | 0.96 | 4,194 |\n| 1416 | 0143J0901416 | 34.842 | 74.735 | Indus Middle | E(c) | 2.96 | 4,189 |\n| 1417 | 0143J0901417 | 34.840 | 74.676 | Jhelum | E(c) | 10.66 | 3,868 |\n| 1418 | 0143J0901418 | 34.836 | 74.728 | Jhelum | E(c) | 5.28 | 4,088 |\n| 1419 | 0143J0901419 | 34.835 | 74.742 | Indus Middle | E(o) | 0.82 | 4,025 |\n| 1420 | 0143J0901420 | 34.831 | 74.718 | Jhelum | E(c) | 2.03 | 4,055 |\n| 1421 | 0143J0901421 | 34.826 | 74.717 | Jhelum | E(c) | 3.18 | 3,904 |\n| 1422 | 0143J0901422 | 34.820 | 74.702 | Jhelum | E(o) | 1.65 | 3,630 |\n| 1423 | 0143J1001423 | 34.620 | 74.618 | Jhelum | E(o) | 4.75 | 3,792 |\n| 1424 | 0143J1001424 | 34.618 | 74.604 | Jhelum | E(o) | 0.77 | 3,693 |\n| 1425 | 0143J1001425 | 34.597 | 74.737 | Jhelum | M(o) | 7.45 | 3,914 |\n| 1426 | 0143J1001426 | 34.577 | 74.731 | Jhelum | E(c) | 2.89 | 3,931 |\n| 1427 | 0143J1301427 | 34.988 | 74.775 | Indus Middle | E(o) | 1.05 | 4,375 |\n| 1428 | 0143J1301428 | 34.975 | 74.754 | Indus Middle | E(o) | 4.86 | 4,637 |\n| 1429 | 0143J1301429 | 34.923 | 74.771 | Indus Middle | O | 2.42 | 3,491 |\n| 1430 | 0143J1301430 | 34.921 | 74.952 | Indus Middle | E(o) | 1.20 | 4,251 |\n| 1431 | 0143J1301431 | 34.919 | 74.914 | Indus Middle | E(c) | 2.27 | 4,199 |\n| 1432 | 0143J1301432 | 34.918 | 74.955 | Indus Middle | E(c) | 1.90 | 4,246 |\n| 1433 | 0143J1301433 | 34.914 | 74.788 | Indus Middle | E(c) | 3.56 | 4,238 |\n| 1434 | 0143J1301434 | 34.914 | 74.948 | Indus Middle | E(o) | 2.73 | 4,318 |\n| 1435 | 0143J1301435 | 34.909 | 74.920 | Indus Middle | E(o) | 3.18 | 4,206 |\n| 1436 | 0143J1301436 | 34.908 | 74.930 | Indus Middle | E(o) | 0.68 | 4,145 |\n| 1437 | 0143J1301437 | 34.905 | 74.920 | Indus Middle | E(o) | 2.29 | 4,217 |\n| 1438 | 0143J1301438 | 34.903 | 74.936 | Indus Middle | E(c) | 2.03 | 4,321 |\n| 1439 | 0143J1301439 | 34.903 | 74.944 | Indus Middle | E(c) | 3.74 | 4,340 |\n| 1440 | 0143J1301440 | 34.889 | 74.940 | Indus Middle | E(o) | 1.65 | 4,205 |\n| 1441 | 0143J1301441 | 34.886 | 74.939 | Indus Middle | E(o) | 3.67 | 4,220 |\n| 1442 | 0143J1301442 | 34.878 | 74.949 | Indus Middle | E(o) | 1.98 | 4,339 |\n| 1443 | 0143J1301443 | 34.862 | 74.806 | Indus Middle | E(c) | 6.99 | 3,898 |\n| 1444 | 0143J1301444 | 34.857 | 74.992 | Indus Middle | E(o) | 6.87 | 4,099 |\n| 1445 | 0143J1301445 | 34.853 | 74.796 | Indus Middle | E(c) | 4.70 | 4,118 |\n| 1446 | 0143J1301446 | 34.851 | 74.813 | Indus Middle | E(o) | 1.08 | 4,011 |\n| 1447 | 0143J1301447 | 34.847 | 74.770 | Indus Middle | E(o) | 4.00 | 3,981 |\n| 1448 | 0143J1301448 | 34.846 | 74.787 | Indus Middle | E(o) | 14.18 | 3,883 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7535, "line_end": 7682, "token_count_estimate": 1653, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143J0901414", "0143J0901415", "0143J0901416", "0143J0901417", "0143J0901418", "0143J0901419", "0143J0901420", "0143J0901421", "0143J0901422", "0143J1001423", "0143J1001424", "0143J1001425", "0143J1001426", "0143J1301427", "0143J1301428", "0143J1301429", "0143J1301430", "0143J1301431", "0143J1301432", "0143J1301433", "0143J1301434", "0143J1301435", "0143J1301436", "0143J1301437", "0143J1301438", "0143J1301439", "0143J1301440", "0143J1301441", "0143J1301442", "0143J1301443", "0143J1301444", "0143J1301445", "0143J1301446", "0143J1301447", "0143J1301448"]}}
{"id": "bd9136affa4523cc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1449 | 0143J1301449 | 34.845 | 74.809 | Indus Middle | E(c) | 50.44 | 3,992 |\n| 1450 | 0143J1301450 | 34.840 | 74.761 | Indus Middle | E(c) | 25.43 | 3,979 |\n| 1451 | 0143J1301451 | 34.810 | 74.981 | Jhelum | E(o) | 3.57 | 4,178 |\n| 1452 | 0143J1301452 | 34.809 | 74.977 | Jhelum | E(o) | 1.57 | 4,234 |\n| 1453 | 0143J1401453 | 34.564 | 74.760 | Jhelum | E(c) | 9.78 | 3,887 |\n| 1454 | 0143J1401454 | 34.564 | 74.816 | Jhelum | E(o) | 0.86 | 4,200 |\n| 1455 | 0143J1401455 | 34.561 | 74.776 | Jhelum | E(c) | 4.32 | 3,949 |\n| 1456 | 0143J1401456 | 34.560 | 74.829 | Jhelum | E(o) | 1.35 | 4,174 |\n| 1457 | 0143J1401457 | 34.548 | 74.819 | Jhelum | E(c) | 31.47 | 3,872 |\n| 1458 | 0143J1401458 | 34.537 | 74.828 | Jhelum | E(c) | 30.51 | 3,922 |\n| 1459 | 0143J1401459 | 34.530 | 74.803 | Jhelum | E(o) | 1.79 | 3,937 |\n| 1460 | 0143J1401460 | 34.526 | 74.860 | Jhelum | E(o) | 2.51 | 3,990 |\n| 1461 | 0143J1501461 | 34.495 | 74.938 | Jhelum | E(o) | 0.65 | 4,105 |\n| 1462 | 0143J1501462 | 34.493 | 74.921 | Jhelum | E(o) | 38.39 | 3,881 |\n| 1463 | 0143J1501463 | 34.492 | 74.889 | Jhelum | E(o) | 4.62 | 3,906 |\n| 1464 | 0143J1501464 | 34.489 | 74.906 | Jhelum | E(c) | 11.89 | 4,097 |\n| 1465 | 0143J1501465 | 34.485 | 74.914 | Jhelum | E(o) | 4.90 | 4,084 |\n| 1466 | 0143J1501466 | 34.472 | 74.958 | Jhelum | M(o) | 1.51 | 3,992 |\n| 1467 | 0143J1501467 | 34.470 | 74.913 | Jhelum | E(o) | 10.44 | 3,852 |\n| 1468 | 0143J1501468 | 34.469 | 74.964 | Jhelum | M(o) | 4.49 | 3,911 |\n| 1469 | 0143J1501469 | 34.468 | 74.955 | Jhelum | M(o) | 2.50 | 4,015 |\n| 1470 | 0143J1501470 | 34.468 | 74.925 | Jhelum | E(o) | 5.73 | 3,956 |\n| 1471 | 0143J1501471 | 34.461 | 74.948 | Jhelum | E(o) | 0.94 | 4,155 |\n| 1472 | 0143J1501472 | 34.460 | 74.975 | Jhelum | E(o) | 0.26 | 4,082 |\n| 1473 | 0143J1501473 | 34.457 | 74.985 | Jhelum | M(o) | 10.33 | 3,748 |\n| 1474 | 0143J1501474 | 34.456 | 74.968 | Jhelum | E(o) | 0.36 | 3,897 |\n| 1475 | 0143J1501475 | 34.455 | 74.858 | Jhelum | M(o) | 0.42 | 3,985 |\n| 1476 | 0143J1501476 | 34.455 | 74.904 | Jhelum | E(o) | 0.46 | 4,008 |\n| 1477 | 0143J1501477 | 34.453 | 74.905 | Jhelum | E(o) | 1.73 | 4,004 |\n| 1478 | 0143J1501478 | 34.453 | 74.922 | Jhelum | E(c) | 1.71 | 4,125 |\n| 1479 | 0143J1501479 | 34.453 | 74.976 | Jhelum | E(o) | 1.34 | 3,771 |\n| 1480 | 0143J1501480 | 34.452 | 74.932 | Jhelum | E(o) | 12.30 | 3,889 |\n| 1481 | 0143J1501481 | 34.452 | 74.974 | Jhelum | E(o) | 0.60 | 3,781 |\n| 1482 | 0143J1501482 | 34.450 | 75.000 | Jhelum | E(o) | 2.79 | 3,655 |\n| 1483 | 0143J1501483 | 34.450 | 74.815 | Jhelum | E(o) | 2.76 | 3,802 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7535, "line_end": 7682, "token_count_estimate": 1643, "basins": ["Indus"], "subbasins": ["Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143J1301449", "0143J1301450", "0143J1301451", "0143J1301452", "0143J1401453", "0143J1401454", "0143J1401455", "0143J1401456", "0143J1401457", "0143J1401458", "0143J1401459", "0143J1401460", "0143J1501461", "0143J1501462", "0143J1501463", "0143J1501464", "0143J1501465", "0143J1501466", "0143J1501467", "0143J1501468", "0143J1501469", "0143J1501470", "0143J1501471", "0143J1501472", "0143J1501473", "0143J1501474", "0143J1501475", "0143J1501476", "0143J1501477", "0143J1501478", "0143J1501479", "0143J1501480", "0143J1501481", "0143J1501482", "0143J1501483"]}}
{"id": "c7b50d11896d8940", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1484 | 0143J1501484 | 34.449 | 74.941 | Jhelum | E(o) | 4.72 | 4,057 |\n| 1485 | 0143J1501485 | 34.448 | 74.909 | Jhelum | E(c) | 15.48 | 3,979 |\n| 1486 | 0143J1501486 | 34.444 | 74.892 | Jhelum | E(c) | 33.95 | 3,846 |\n| 1487 | 0143J1501487 | 34.440 | 74.997 | Jhelum | E(o) | 2.35 | 3,683 |\n| 1488 | 0143J1501488 | 34.438 | 74.996 | Jhelum | E(o) | 0.65 | 3,674 |\n| 1489 | 0143J1501489 | 34.432 | 74.924 | Jhelum | E(c) | 161.04 | 3,571 |\n| 1490 | 0143J1501490 | 34.429 | 74.871 | Jhelum | E(o) | 1.33 | 3,903 |\n| 1491 | 0143J1501491 | 34.424 | 74.872 | Jhelum | E(o) | 6.67 | 3,914 |\n| 1492 | 0143J1501492 | 34.423 | 74.881 | Jhelum | E(o) | 1.92 | 4,181 |\n| 1493 | 0143J1501493 | 34.418 | 74.936 | Jhelum | E(o) | 36.65 | 3,503 |\n| 1494 | 0143J1501494 | 34.406 | 74.930 | Jhelum | E(o) | 0.46 | 3,751 |\n| 1495 | 0143J1501495 | 34.404 | 74.928 | Jhelum | E(o) | 1.03 | 3,753 |\n| 1496 | 0143J1501496 | 34.396 | 74.927 | Jhelum | E(o) | 0.32 | 3,867 |\n| 1497 | 0143J1501497 | 34.394 | 74.880 | Jhelum | E(o) | 0.82 | 3,666 |\n| 1498 | 0143J1501498 | 34.394 | 74.886 | Jhelum | E(o) | 2.36 | 3,675 |\n| 1499 | 0143J1501499 | 34.392 | 74.874 | Jhelum | E(o) | 27.31 | 3,556 |\n| 1500 | 0143J1501500 | 34.385 | 74.944 | Jhelum | E(c) | 11.22 | 3,722 |\n| 1501 | 0143J1501501 | 34.380 | 74.876 | Jhelum | E(o) | 11.58 | 3,738 |\n| 1502 | 0143J1501502 | 34.376 | 74.939 | Jhelum | E(o) | 0.76 | 3,908 |\n| 1503 | 0143K0501503 | 33.956 | 74.361 | Jhelum | E(c) | 1.90 | 3,969 |\n| 1504 | 0143K0501504 | 33.939 | 74.411 | Jhelum | E(o) | 0.61 | 3,822 |\n| 1505 | 0143K0501505 | 33.937 | 74.410 | Jhelum | E(o) | 0.43 | 3,836 |\n| 1506 | 0143K0501506 | 33.935 | 74.436 | Jhelum | E(o) | 0.41 | 4,084 |\n| 1507 | 0143K0501507 | 33.932 | 74.408 | Jhelum | E(o) | 0.43 | 3,909 |\n| 1508 | 0143K0501508 | 33.921 | 74.444 | Jhelum | E(o) | 0.49 | 4,025 |\n| 1509 | 0143K0501509 | 33.918 | 74.392 | Jhelum | E(o) | 0.89 | 3,917 |\n| 1510 | 0143K0501510 | 33.908 | 74.385 | Jhelum | E(o) | 0.96 | 3,772 |\n| 1511 | 0143K0501511 | 33.906 | 74.386 | Jhelum | E(o) | 0.60 | 3,777 |\n| 1512 | 0143K0501512 | 33.888 | 74.433 | Jhelum | E(o) | 4.41 | 4,054 |\n| 1513 | 0143K0501513 | 33.885 | 74.414 | Jhelum | E(o) | 1.90 | 4,202 |\n| 1514 | 0143K0501514 | 33.881 | 74.396 | Jhelum | E(o) | 0.44 | 3,945 |\n| 1515 | 0143K0501515 | 33.866 | 74.417 | Jhelum | E(c) | 13.62 | 3,968 |\n| 1516 | 0143K0501516 | 33.864 | 74.407 | Jhelum | E(o) | 0.51 | 4,160 |\n| 1517 | 0143K0501517 | 33.860 | 74.429 | Jhelum | E(o) | 9.57 | 3,900 |\n| 1518 | 0143K0501518 | 33.859 | 74.406 | Jhelum | E(o) | 0.89 | 4,001 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7535, "line_end": 7682, "token_count_estimate": 1652, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": ["0143J1501484", "0143J1501485", "0143J1501486", "0143J1501487", "0143J1501488", "0143J1501489", "0143J1501490", "0143J1501491", "0143J1501492", "0143J1501493", "0143J1501494", "0143J1501495", "0143J1501496", "0143J1501497", "0143J1501498", "0143J1501499", "0143J1501500", "0143J1501501", "0143J1501502", "0143K0501503", "0143K0501504", "0143K0501505", "0143K0501506", "0143K0501507", "0143K0501508", "0143K0501509", "0143K0501510", "0143K0501511", "0143K0501512", "0143K0501513", "0143K0501514", "0143K0501515", "0143K0501516", "0143K0501517", "0143K0501518"]}}
{"id": "4fa12e3e58503336", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1519 | 0143K0501519 | 33.858 | 74.422 | Jhelum | E(o) | 9.36 | 3,941 |\n| 1520 | 0143K0501520 | 33.858 | 74.408 | Jhelum | E(o) | 0.79 | 3,997 |\n| 1521 | 0143K0501521 | 33.852 | 74.420 | Jhelum | E(o) | 1.38 | 4,023 |\n| 1522 | 0143K0501522 | 33.851 | 74.416 | Jhelum | E(o) | 8.05 | 4,012 |\n| 1523 | 0143K0501523 | 33.845 | 74.440 | Jhelum | E(o) | 0.73 | 3,887 |\n| 1524 | 0143K0501524 | 33.845 | 74.442 | Jhelum | E(o) | 0.37 | 3,888 |\n| 1525 | 0143K0501525 | 33.841 | 74.428 | Jhelum | E(o) | 45.46 | 3,954 |\n| 1526 | 0143K0501526 | 33.837 | 74.437 | Jhelum | E(o) | 6.87 | 3,940 |\n| 1527 | 0143K0501527 | 33.829 | 74.435 | Jhelum | E(o) | 15.18 | 4,043 |\n| 1528 | 0143K0501528 | 33.820 | 74.451 | Jhelum | E(c) | 29.43 | 3,997 |\n| 1529 | 0143K0501529 | 33.808 | 74.478 | Jhelum | E(o) | 2.41 | 3,942 |\n| 1530 | 0143K0501530 | 33.806 | 74.436 | Jhelum | E(o) | 2.26 | 4,020 |\n| 1531 | 0143K0501531 | 33.802 | 74.463 | Jhelum | E(o) | 4.01 | 3,975 |\n| 1532 | 0143K0501532 | 33.801 | 74.481 | Jhelum | E(o) | 1.66 | 3,974 |\n| 1533 | 0143K0601533 | 33.744 | 74.486 | Jhelum | E(o) | 6.60 | 4,063 |\n| 1534 | 0143K0601534 | 33.563 | 74.494 | Jhelum | E(o) | 2.72 | 3,711 |\n| 1535 | 0143K1001535 | 33.743 | 74.528 | Jhelum | E(o) | 8.44 | 4,013 |\n| 1536 | 0143K1001536 | 33.741 | 74.541 | Jhelum | E(o) | 2.93 | 4,090 |\n| 1537 | 0143K1001537 | 33.740 | 74.526 | Jhelum | E(o) | 10.16 | 4,018 |\n| 1538 | 0143K1001538 | 33.735 | 74.523 | Jhelum | E(o) | 13.69 | 4,064 |\n| 1539 | 0143K1001539 | 33.733 | 74.538 | Jhelum | E(o) | 0.74 | 4,141 |\n| 1540 | 0143K1001540 | 33.730 | 74.550 | Jhelum | E(o) | 1.61 | 4,013 |\n| 1541 | 0143K1001541 | 33.716 | 74.521 | Jhelum | E(o) | 8.73 | 4,103 |\n| 1542 | 0143K1001542 | 33.686 | 74.604 | Jhelum | E(o) | 2.35 | 3,993 |\n| 1543 | 0143K1001543 | 33.584 | 74.716 | Jhelum | E(o) | 1.02 | 3,955 |\n| 1544 | 0143K1001544 | 33.559 | 74.526 | Jhelum | E(o) | 22.97 | 3,851 |\n| 1545 | 0143K1001545 | 33.556 | 74.584 | Jhelum | E(o) | 0.38 | 4,166 |\n| 1546 | 0143K1001546 | 33.550 | 74.543 | Jhelum | E(o) | 16.04 | 3,912 |\n| 1547 | 0143K1001547 | 33.548 | 74.520 | Jhelum | E(o) | 1.96 | 3,850 |\n| 1548 | 0143K1001548 | 33.546 | 74.506 | Jhelum | E(o) | 0.70 | 3,710 |\n| 1549 | 0143K1001549 | 33.541 | 74.508 | Jhelum | E(o) | 7.23 | 3,692 |\n| 1550 | 0143K1001550 | 33.540 | 74.523 | Jhelum | E(o) | 10.32 | 3,812 |\n| 1551 | 0143K1001551 | 33.540 | 74.565 | Jhelum | E(o) | 21.73 | 3,967 |\n| 1552 | 0143K1001552 | 33.537 | 74.579 | Jhelum | E(o) | 0.59 | 3,910 |\n| 1553 | 0143K1001553 | 33.537 | 74.512 | Jhelum | E(o) | 2.82 | 3,695 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7535, "line_end": 7682, "token_count_estimate": 1645, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": ["0143K0501519", "0143K0501520", "0143K0501521", "0143K0501522", "0143K0501523", "0143K0501524", "0143K0501525", "0143K0501526", "0143K0501527", "0143K0501528", "0143K0501529", "0143K0501530", "0143K0501531", "0143K0501532", "0143K0601533", "0143K0601534", "0143K1001535", "0143K1001536", "0143K1001537", "0143K1001538", "0143K1001539", "0143K1001540", "0143K1001541", "0143K1001542", "0143K1001543", "0143K1001544", "0143K1001545", "0143K1001546", "0143K1001547", "0143K1001548", "0143K1001549", "0143K1001550", "0143K1001551", "0143K1001552", "0143K1001553"]}}
{"id": "e3297e5a8f715f5e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1554 | 0143K1001554 | 33.536 | 74.555 | Jhelum | E(o) | 31.85 | 4,006 |\n| 1555 | 0143K1001555 | 33.534 | 74.509 | Jhelum | E(o) | 0.77 | 3,673 |\n| 1556 | 0143K1001556 | 33.534 | 74.646 | Jhelum | E(o) | 2.25 | 4,124 |\n| 1557 | 0143K1001557 | 33.533 | 74.527 | Jhelum | E(c) | 2.12 | 3,859 |\n| 1558 | 0143K1001558 | 33.530 | 74.574 | Jhelum | E(o) | 12.27 | 3,955 |\n| 1559 | 0143K1001559 | 33.528 | 74.561 | Jhelum | E(c) | 11.15 | 4,096 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7535, "line_end": 7682, "token_count_estimate": 374, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": ["0143K1001554", "0143K1001555", "0143K1001556", "0143K1001557", "0143K1001558", "0143K1001559"]}}
{"id": "b45f787635f53e79", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 7683, "line_end": 7690, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3330311339c1847d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1560 | 0143K1001560 | 33.522 | 74.605 | Jhelum | E(o) | 12.33 | 3,844 |\n| 1561 | 0143K1001561 | 33.519 | 74.584 | Jhelum | E(o) | 70.83 | 3,934 |\n| 1562 | 0143K1001562 | 33.518 | 74.560 | Jhelum | E(o) | 0.68 | 3,965 |\n| 1563 | 0143K1001563 | 33.514 | 74.571 | Jhelum | E(c) | 0.36 | 4,013 |\n| 1564 | 0143K1001564 | 33.510 | 74.582 | Jhelum | E(o) | 0.45 | 3,971 |\n| 1565 | 0143K1001565 | 33.510 | 74.564 | Jhelum | E(o) | 25.15 | 3,909 |\n| 1566 | 0143K1001566 | 33.509 | 74.611 | Jhelum | E(o) | 6.18 | 3,893 |\n| 1567 | 0143K1001567 | 33.509 | 74.625 | Jhelum | E(o) | 33.70 | 3,937 |\n| 1568 | 0143K1001568 | 33.505 | 74.594 | Jhelum | E(c) | 11.35 | 4,089 |\n| 1569 | 0143K1101569 | 33.498 | 74.582 | Chenab | E(o) | 1.53 | 3,912 |\n| 1570 | 0143K1101570 | 33.496 | 74.579 | Chenab | E(c) | 8.98 | 3,911 |\n| 1571 | 0143K1101571 | 33.496 | 74.565 | Jhelum | E(o) | 11.18 | 3,812 |\n| 1572 | 0143K1101572 | 33.493 | 74.627 | Chenab | E(c) | 2.67 | 4,152 |\n| 1573 | 0143K1101573 | 33.492 | 74.544 | Jhelum | E(o) | 5.33 | 3,748 |\n| 1574 | 0143K1101574 | 33.487 | 74.608 | Chenab | E(o) | 3.27 | 3,869 |\n| 1575 | 0143K1101575 | 33.486 | 74.569 | Chenab | E(c) | 5.10 | 3,824 |\n| 1576 | 0143K1101576 | 33.484 | 74.621 | Chenab | E(o) | 1.08 | 3,874 |\n| 1577 | 0143K1101577 | 33.484 | 74.619 | Chenab | E(o) | 0.34 | 3,867 |\n| 1578 | 0143K1101578 | 33.477 | 74.533 | Jhelum | E(o) | 1.53 | 3,620 |\n| 1579 | 0143K1101579 | 33.476 | 74.619 | Chenab | E(o) | 0.48 | 3,817 |\n| 1580 | 0143K1101580 | 33.468 | 74.602 | Chenab | E(c) | 4.09 | 3,740 |\n| 1581 | 0143K1101581 | 33.457 | 74.626 | Chenab | E(c) | 3.06 | 3,745 |\n| 1582 | 0143K1101582 | 33.444 | 74.614 | Chenab | E(c) | 14.88 | 3,580 |\n| 1583 | 0143K1101583 | 33.442 | 74.644 | Chenab | E(o) | 0.75 | 3,474 |\n| 1584 | 0143K1401584 | 33.544 | 74.789 | Jhelum | E(o) | 4.06 | 3,921 |\n| 1585 | 0143K1401585 | 33.528 | 74.809 | Jhelum | E(o) | 0.80 | 3,809 |\n| 1586 | 0143K1401586 | 33.526 | 74.809 | Jhelum | E(o) | 0.48 | 3,810 |\n| 1587 | 0143K1401587 | 33.515 | 74.831 | Jhelum | E(o) | 6.41 | 3,522 |\n| 1588 | 0143K1401588 | 33.512 | 74.769 | Jhelum | O | 128.54 | 3,486 |\n| 1589 | 0143K1401589 | 33.505 | 74.894 | Jhelum | E(o) | 0.90 | 3,521 |\n| 1590 | 0143K1401590 | 33.503 | 74.833 | Jhelum | M(e) | 4.03 | 3,639 |\n| 1591 | 0143K1401591 | 33.503 | 74.850 | Jhelum | E(o) | 14.64 | 3,623 |\n| 1592 | 0143K1501592 | 33.495 | 74.906 | Jhelum | E(o) | 0.35 | 3,689 |\n| 1593 | 0143K1501593 | 33.493 | 74.897 | Jhelum | E(o) | 0.44 | 3,775 |\n| 1594 | 0143K1501594 | 33.490 | 74.846 | Jhelum | E(o) | 4.46 | 3,724 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7691, "line_end": 7838, "token_count_estimate": 1628, "basins": ["Indus"], "subbasins": ["Chenab", "Jhelum"], "countries": [], "lake_ids": ["0143K1001560", "0143K1001561", "0143K1001562", "0143K1001563", "0143K1001564", "0143K1001565", "0143K1001566", "0143K1001567", "0143K1001568", "0143K1101569", "0143K1101570", "0143K1101571", "0143K1101572", "0143K1101573", "0143K1101574", "0143K1101575", "0143K1101576", "0143K1101577", "0143K1101578", "0143K1101579", "0143K1101580", "0143K1101581", "0143K1101582", "0143K1101583", "0143K1401584", "0143K1401585", "0143K1401586", "0143K1401587", "0143K1401588", "0143K1401589", "0143K1401590", "0143K1401591", "0143K1501592", "0143K1501593", "0143K1501594"]}}
{"id": "17a4cc4804e427a6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1595 | 0143K1501595 | 33.477 | 74.925 | Chenab | E(o) | 0.36 | 3,680 |\n| 1596 | 0143K1501596 | 33.452 | 74.873 | Chenab | E(o) | 1.07 | 3,559 |\n| 1597 | 0143M0101597 | 35.982 | 75.050 | Indus Middle | I(s) | 0.80 | 4,003 |\n| 1598 | 0143M0101598 | 35.902 | 75.182 | Indus Middle | M(l) | 0.89 | 3,480 |\n| 1599 | 0143M0101599 | 35.889 | 75.189 | Indus Middle | M(l) | 1.49 | 3,438 |\n| 1600 | 0143M0101600 | 35.829 | 75.156 | Indus Middle | M(l) | 3.49 | 4,167 |\n| 1601 | 0143M0101601 | 35.774 | 75.238 | Indus Middle | M(l) | 0.98 | 4,193 |\n| 1602 | 0143M0201602 | 35.659 | 75.239 | Indus Middle | E(o) | 0.31 | 4,093 |\n| 1603 | 0143M0201603 | 35.645 | 75.245 | Indus Middle | E(o) | 3.80 | 4,217 |\n| 1604 | 0143M0301604 | 35.428 | 75.046 | Indus Middle | E(o) | 2.79 | 4,731 |\n| 1605 | 0143M0301605 | 35.376 | 75.091 | Indus Middle | E(o) | 2.59 | 4,621 |\n| 1606 | 0143M0301606 | 35.372 | 75.120 | Indus Middle | I(s) | 0.78 | 4,752 |\n| 1607 | 0143M0301607 | 35.371 | 75.093 | Indus Middle | E(o) | 2.94 | 4,617 |\n| 1608 | 0143M0301608 | 35.371 | 75.130 | Indus Middle | M(o) | 0.47 | 4,596 |\n| 1609 | 0143M0301609 | 35.368 | 75.134 | Indus Middle | E(o) | 0.55 | 4,741 |\n| 1610 | 0143M0301610 | 35.366 | 75.131 | Indus Middle | M(o) | 2.44 | 4,767 |\n| 1611 | 0143M0301611 | 35.345 | 75.196 | Indus Middle | E(o) | 3.49 | 4,605 |\n| 1612 | 0143M0301612 | 35.343 | 75.185 | Indus Middle | M(o) | 2.79 | 4,667 |\n| 1613 | 0143M0301613 | 35.337 | 75.192 | Indus Middle | M(o) | 1.89 | 4,529 |\n| 1614 | 0143M0301614 | 35.337 | 75.148 | Indus Middle | M(o) | 0.58 | 4,624 |\n| 1615 | 0143M0301615 | 35.334 | 75.151 | Indus Middle | E(o) | 0.27 | 4,635 |\n| 1616 | 0143M0301616 | 35.333 | 75.153 | Indus Middle | E(o) | 0.70 | 4,604 |\n| 1617 | 0143M0301617 | 35.329 | 75.182 | Indus Middle | E(o) | 1.12 | 4,890 |\n| 1618 | 0143M0301618 | 35.321 | 75.188 | Indus Middle | M(o) | 4.74 | 4,580 |\n| 1619 | 0143M0301619 | 35.314 | 75.154 | Indus Middle | M(e) | 1.97 | 4,697 |\n| 1620 | 0143M0301620 | 35.302 | 75.160 | Indus Middle | E(o) | 1.70 | 4,727 |\n| 1621 | 0143M0301621 | 35.300 | 75.190 | Indus Middle | E(o) | 6.60 | 4,758 |\n| 1622 | 0143M0301622 | 35.298 | 75.163 | Indus Middle | E(o) | 2.37 | 4,654 |\n| 1623 | 0143M0301623 | 35.295 | 75.153 | Indus Middle | E(o) | 1.12 | 4,582 |\n| 1624 | 0143M0301624 | 35.287 | 75.161 | Indus Middle | E(o) | 0.65 | 4,726 |\n| 1625 | 0143M0301625 | 35.283 | 75.155 | Indus Middle | E(c) | 2.42 | 4,557 |\n| 1626 | 0143M0301626 | 35.281 | 75.206 | Indus Middle | E(o) | 4.38 | 4,769 |\n| 1627 | 0143M0301627 | 35.278 | 75.208 | Indus Middle | E(o) | 3.41 | 4,701 |\n| 1628 | 0143M0301628 | 35.276 | 75.075 | Indus Middle | E(o) | 1.92 | 4,462 |\n| 1629 | 0143M0301629 | 35.272 | 75.163 | Indus Middle | M(e) | 6.14 | 4,756 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7691, "line_end": 7838, "token_count_estimate": 1662, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Middle"], "countries": [], "lake_ids": ["0143K1501595", "0143K1501596", "0143M0101597", "0143M0101598", "0143M0101599", "0143M0101600", "0143M0101601", "0143M0201602", "0143M0201603", "0143M0301604", "0143M0301605", "0143M0301606", "0143M0301607", "0143M0301608", "0143M0301609", "0143M0301610", "0143M0301611", "0143M0301612", "0143M0301613", "0143M0301614", "0143M0301615", "0143M0301616", "0143M0301617", "0143M0301618", "0143M0301619", "0143M0301620", "0143M0301621", "0143M0301622", "0143M0301623", "0143M0301624", "0143M0301625", "0143M0301626", "0143M0301627", "0143M0301628", "0143M0301629"]}}
{"id": "f0b879f1943abee7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1630 | 0143M0301630 | 35.271 | 75.071 | Indus Middle | E(o) | 4.53 | 4,328 |\n| 1631 | 0143M0301631 | 35.265 | 75.092 | Indus Middle | E(o) | 0.37 | 4,385 |\n| 1632 | 0143M0301632 | 35.264 | 75.194 | Indus Middle | E(o) | 19.03 | 4,551 |\n| 1633 | 0143M0301633 | 35.254 | 75.111 | Indus Middle | E(o) | 0.64 | 4,478 |\n| 1634 | 0143M0301634 | 35.252 | 75.098 | Indus Middle | E(o) | 0.28 | 4,279 |\n| 1635 | 0143M0401635 | 35.235 | 75.178 | Indus Middle | E(o) | 4.35 | 4,649 |\n| 1636 | 0143M0401636 | 35.233 | 75.242 | Indus Middle | E(o) | 0.39 | 4,707 |\n| 1637 | 0143M0401637 | 35.232 | 75.240 | Indus Middle | E(o) | 1.34 | 4,709 |\n| 1638 | 0143M0401638 | 35.231 | 75.182 | Indus Middle | E(o) | 11.26 | 4,547 |\n| 1639 | 0143M0401639 | 35.229 | 75.217 | Indus Middle | E(o) | 4.06 | 4,661 |\n| 1640 | 0143M0401640 | 35.228 | 75.153 | Indus Middle | E(o) | 0.48 | 4,574 |\n| 1641 | 0143M0401641 | 35.227 | 75.213 | Indus Middle | E(o) | 2.70 | 4,637 |\n| 1642 | 0143M0401642 | 35.227 | 75.186 | Indus Middle | E(o) | 0.51 | 4,517 |\n| 1643 | 0143M0401643 | 35.220 | 75.223 | Indus Middle | M(o) | 2.90 | 4,662 |\n| 1644 | 0143M0401644 | 35.219 | 75.226 | Indus Middle | M(o) | 1.54 | 4,685 |\n| 1645 | 0143M0401645 | 35.213 | 75.222 | Indus Middle | E(o) | 1.37 | 4,699 |\n| 1646 | 0143M0401646 | 35.213 | 75.226 | Indus Middle | M(o) | 5.78 | 4,705 |\n| 1647 | 0143M0401647 | 35.212 | 75.218 | Indus Middle | E(o) | 1.39 | 4,670 |\n| 1648 | 0143M0401648 | 35.204 | 75.230 | Indus Middle | M(o) | 1.36 | 4,730 |\n| 1649 | 0143M0401649 | 35.203 | 75.249 | Indus Middle | E(o) | 3.62 | 4,694 |\n| 1650 | 0143M0401650 | 35.198 | 75.247 | Indus Middle | E(o) | 1.62 | 4,703 |\n| 1651 | 0143M0401651 | 35.197 | 75.242 | Indus Middle | E(o) | 0.42 | 4,748 |\n| 1652 | 0143M0401652 | 35.194 | 75.232 | Indus Middle | E(o) | 1.23 | 4,737 |\n| 1653 | 0143M0401653 | 35.192 | 75.244 | Indus Middle | E(o) | 0.90 | 4,729 |\n| 1654 | 0143M0401654 | 35.191 | 75.232 | Indus Middle | E(o) | 2.18 | 4,702 |\n| 1655 | 0143M0401655 | 35.190 | 75.194 | Indus Middle | M(o) | 2.15 | 4,537 |\n| 1656 | 0143M0401656 | 35.189 | 75.248 | Indus Middle | E(o) | 3.23 | 4,648 |\n| 1657 | 0143M0401657 | 35.186 | 75.187 | Indus Middle | E(o) | 0.66 | 4,423 |\n| 1658 | 0143M0401658 | 35.169 | 75.098 | Indus Middle | E(c) | 4.45 | 4,503 |\n| 1659 | 0143M0401659 | 35.133 | 75.112 | Indus Middle | E(o) | 0.92 | 4,461 |\n| 1660 | 0143M0401660 | 35.126 | 75.145 | Indus Middle | E(o) | 5.17 | 4,619 |\n| 1661 | 0143M0401661 | 35.121 | 75.142 | Indus Middle | E(o) | 1.84 | 4,527 |\n| 1662 | 0143M0401662 | 35.120 | 75.163 | Indus Middle | E(o) | 1.33 | 4,435 |\n| 1663 | 0143M0401663 | 35.120 | 75.136 | Indus Middle | E(o) | 4.65 | 4,461 |\n| 1664 | 0143M0401664 | 35.119 | 75.228 | Indus Upper | E(o) | 17.81 | 4,586 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7691, "line_end": 7838, "token_count_estimate": 1666, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper"], "countries": [], "lake_ids": ["0143M0301630", "0143M0301631", "0143M0301632", "0143M0301633", "0143M0301634", "0143M0401635", "0143M0401636", "0143M0401637", "0143M0401638", "0143M0401639", "0143M0401640", "0143M0401641", "0143M0401642", "0143M0401643", "0143M0401644", "0143M0401645", "0143M0401646", "0143M0401647", "0143M0401648", "0143M0401649", "0143M0401650", "0143M0401651", "0143M0401652", "0143M0401653", "0143M0401654", "0143M0401655", "0143M0401656", "0143M0401657", "0143M0401658", "0143M0401659", "0143M0401660", "0143M0401661", "0143M0401662", "0143M0401663", "0143M0401664"]}}
{"id": "5e0b2885b12628f1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1665 | 0143M0401665 | 35.105 | 75.225 | Indus Upper | E(o) | 1.66 | 4,586 |\n| 1666 | 0143M0401666 | 35.104 | 75.199 | Indus Upper | E(o) | 2.13 | 4,578 |\n| 1667 | 0143M0401667 | 35.103 | 75.197 | Indus Upper | E(o) | 1.36 | 4,597 |\n| 1668 | 0143M0401668 | 35.102 | 75.233 | Indus Upper | E(o) | 6.18 | 4,517 |\n| 1669 | 0143M0401669 | 35.091 | 75.200 | Indus Upper | E(o) | 1.59 | 4,525 |\n| 1670 | 0143M0401670 | 35.084 | 75.182 | Indus Middle | E(o) | 2.31 | 4,455 |\n| 1671 | 0143M0401671 | 35.078 | 75.216 | Indus Upper | E(o) | 2.67 | 4,429 |\n| 1672 | 0143M0401672 | 35.073 | 75.024 | Indus Middle | E(c) | 1.91 | 4,386 |\n| 1673 | 0143M0401673 | 35.070 | 75.222 | Indus Upper | E(o) | 1.56 | 4,520 |\n| 1674 | 0143M0401674 | 35.067 | 75.231 | Indus Upper | E(o) | 3.00 | 4,410 |\n| 1675 | 0143M0401675 | 35.063 | 75.165 | Indus Middle | E(o) | 0.43 | 4,317 |\n| 1676 | 0143M0401676 | 35.061 | 75.204 | Indus Middle | E(o) | 0.56 | 4,482 |\n| 1677 | 0143M0401677 | 35.056 | 75.239 | Indus Upper | E(o) | 1.67 | 4,510 |\n| 1678 | 0143M0401678 | 35.050 | 75.231 | Indus Upper | E(o) | 1.66 | 4,524 |\n| 1679 | 0143M0401679 | 35.049 | 75.198 | Indus Middle | E(c) | 12.71 | 4,398 |\n| 1680 | 0143M0401680 | 35.049 | 75.238 | Indus Upper | E(o) | 4.17 | 4,461 |\n| 1681 | 0143M0401681 | 35.048 | 75.003 | Indus Middle | E(o) | 0.70 | 4,130 |\n| 1682 | 0143M0401682 | 35.016 | 75.001 | Indus Middle | E(o) | 10.87 | 3,931 |\n| 1683 | 0143M0401683 | 35.004 | 75.008 | Indus Middle | O | 0.45 | 4,025 |\n| 1684 | 0143M0501684 | 35.996 | 75.251 | Indus Middle | M(l) | 0.50 | 4,064 |\n| 1685 | 0143M0501685 | 35.984 | 75.314 | Indus Middle | M(l) | 0.61 | 3,862 |\n| 1686 | 0143M0501686 | 35.983 | 75.271 | Indus Middle | I(s) | 0.74 | 3,752 |\n| 1687 | 0143M0501687 | 35.980 | 75.328 | Indus Middle | I(s) | 0.65 | 3,867 |\n| 1688 | 0143M0501688 | 35.951 | 75.438 | Indus Middle | I(s) | 0.58 | 3,528 |\n| 1689 | 0143M0501689 | 35.871 | 75.323 | Indus Middle | M(o) | 1.22 | 2,805 |\n| 1690 | 0143M0501690 | 35.870 | 75.322 | Indus Middle | M(o) | 0.35 | 2,814 |\n| 1691 | 0143M0501691 | 35.869 | 75.325 | Indus Middle | M(o) | 1.10 | 2,776 |\n| 1692 | 0143M0501692 | 35.869 | 75.323 | Indus Middle | M(o) | 0.67 | 2,794 |\n| 1693 | 0143M0501693 | 35.869 | 75.320 | Indus Middle | M(o) | 0.47 | 2,802 |\n| 1694 | 0143M0501694 | 35.866 | 75.282 | Indus Middle | M(l) | 3.47 | 2,985 |\n| 1695 | 0143M0501695 | 35.861 | 75.260 | Indus Middle | M(l) | 2.89 | 3,115 |\n| 1696 | 0143M0501696 | 35.861 | 75.287 | Indus Middle | I(s) | 0.93 | 2,992 |\n| 1697 | 0143M0701697 | 35.404 | 75.280 | Indus Middle | I(s) | 5.20 | 4,535 |\n| 1698 | 0143M0701698 | 35.286 | 75.446 | Indus Middle | M(e) | 0.94 | 4,725 |\n| 1699 | 0143M0701699 | 35.259 | 75.427 | Indus Middle | E(o) | 1.68 | 3,891 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7691, "line_end": 7838, "token_count_estimate": 1669, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper"], "countries": [], "lake_ids": ["0143M0401665", "0143M0401666", "0143M0401667", "0143M0401668", "0143M0401669", "0143M0401670", "0143M0401671", "0143M0401672", "0143M0401673", "0143M0401674", "0143M0401675", "0143M0401676", "0143M0401677", "0143M0401678", "0143M0401679", "0143M0401680", "0143M0401681", "0143M0401682", "0143M0401683", "0143M0501684", "0143M0501685", "0143M0501686", "0143M0501687", "0143M0501688", "0143M0501689", "0143M0501690", "0143M0501691", "0143M0501692", "0143M0501693", "0143M0501694", "0143M0501695", "0143M0501696", "0143M0701697", "0143M0701698", "0143M0701699"]}}
{"id": "55c9b7f488c46450", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1700 | 0143M0701700 | 35.255 | 75.296 | Indus Middle | E(o) | 2.16 | 4,587 |\n| 1701 | 0143M0801701 | 35.239 | 75.475 | Indus Middle | M(l) | 1.41 | 4,816 |\n| 1702 | 0143M0801702 | 35.232 | 75.251 | Indus Middle | I(s) | 0.33 | 4,433 |\n| 1703 | 0143M0801703 | 35.224 | 75.346 | Indus Middle | E(o) | 3.59 | 4,595 |\n| 1704 | 0143M0801704 | 35.221 | 75.477 | Indus Middle | E(o) | 3.72 | 4,608 |\n| 1705 | 0143M0801705 | 35.220 | 75.492 | Indus Middle | E(o) | 0.36 | 4,424 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7691, "line_end": 7838, "token_count_estimate": 376, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143M0701700", "0143M0801701", "0143M0801702", "0143M0801703", "0143M0801704", "0143M0801705"]}}
{"id": "ac4783a17302962f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 7839, "line_end": 7849, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5188ae77455428c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1706 | 0143M0801706 | 35.213 | 75.336 | Indus Middle | E(o) | 1.02 | 4,587 |\n| 1707 | 0143M0801707 | 35.211 | 75.264 | Indus Middle | E(o) | 0.93 | 4,648 |\n| 1708 | 0143M0801708 | 35.207 | 75.369 | Indus Middle | E(o) | 3.89 | 4,764 |\n| 1709 | 0143M0801709 | 35.207 | 75.334 | Indus Middle | E(o) | 2.67 | 4,588 |\n| 1710 | 0143M0801710 | 35.206 | 75.364 | Indus Middle | I(s) | 0.47 | 4,778 |\n| 1711 | 0143M0801711 | 35.198 | 75.418 | Indus Middle | E(o) | 1.75 | 4,604 |\n| 1712 | 0143M0801712 | 35.194 | 75.353 | Indus Middle | E(o) | 4.01 | 4,718 |\n| 1713 | 0143M0801713 | 35.193 | 75.251 | Indus Middle | E(o) | 1.64 | 4,614 |\n| 1714 | 0143M0801714 | 35.188 | 75.453 | Indus Middle | E(o) | 2.36 | 4,552 |\n| 1715 | 0143M0801715 | 35.185 | 75.343 | Indus Middle | E(o) | 2.93 | 4,644 |\n| 1716 | 0143M0801716 | 35.183 | 75.416 | Indus Middle | E(o) | 1.63 | 4,548 |\n| 1717 | 0143M0801717 | 35.182 | 75.422 | Indus Middle | E(o) | 14.33 | 4,530 |\n| 1718 | 0143M0801718 | 35.178 | 75.353 | Indus Middle | E(o) | 5.00 | 4,582 |\n| 1719 | 0143M0801719 | 35.174 | 75.397 | Indus Upper | E(o) | 4.12 | 4,680 |\n| 1720 | 0143M0801720 | 35.169 | 75.378 | Indus Upper | E(o) | 5.15 | 4,626 |\n| 1721 | 0143M0801721 | 35.169 | 75.297 | Indus Upper | E(o) | 1.72 | 4,720 |\n| 1722 | 0143M0801722 | 35.167 | 75.381 | Indus Upper | E(o) | 2.53 | 4,678 |\n| 1723 | 0143M0801723 | 35.164 | 75.471 | Indus Upper | E(o) | 4.20 | 4,449 |\n| 1724 | 0143M0801724 | 35.163 | 75.395 | Indus Upper | E(o) | 3.19 | 4,649 |\n| 1725 | 0143M0801725 | 35.163 | 75.401 | Indus Upper | E(o) | 0.54 | 4,579 |\n| 1726 | 0143M0801726 | 35.161 | 75.471 | Indus Upper | E(o) | 3.28 | 4,451 |\n| 1727 | 0143M0801727 | 35.145 | 75.288 | Indus Upper | E(o) | 8.83 | 4,578 |\n| 1728 | 0143M0801728 | 35.143 | 75.261 | Indus Upper | E(o) | 11.84 | 4,659 |\n| 1729 | 0143M0801729 | 35.142 | 75.331 | Indus Upper | E(o) | 7.73 | 4,654 |\n| 1730 | 0143M0801730 | 35.132 | 75.321 | Indus Upper | E(o) | 19.58 | 4,526 |\n| 1731 | 0143M0801731 | 35.131 | 75.256 | Indus Upper | E(o) | 1.78 | 4,580 |\n| 1732 | 0143M0801732 | 35.126 | 75.375 | Indus Upper | E(o) | 0.91 | 4,459 |\n| 1733 | 0143M0801733 | 35.124 | 75.370 | Indus Upper | E(c) | 2.97 | 4,517 |\n| 1734 | 0143M0801734 | 35.114 | 75.342 | Indus Upper | E(o) | 0.59 | 4,451 |\n| 1735 | 0143M0801735 | 35.114 | 75.272 | Indus Upper | E(o) | 2.27 | 4,648 |\n| 1736 | 0143M0801736 | 35.105 | 75.332 | Indus Upper | E(o) | 2.81 | 4,484 |\n| 1737 | 0143M0801737 | 35.098 | 75.340 | Indus Upper | E(o) | 0.92 | 4,390 |\n| 1738 | 0143M0801738 | 35.097 | 75.336 | Indus Upper | E(o) | 1.22 | 4,435 |\n| 1739 | 0143M0801739 | 35.013 | 75.280 | Indus Upper | E(o) | 2.71 | 4,278 |\n| 1740 | 0143M0901740 | 35.997 | 75.663 | Indus Middle | I(s) | 5.04 | 4,876 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7850, "line_end": 7924, "token_count_estimate": 1702, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper"], "countries": [], "lake_ids": ["0143M0801706", "0143M0801707", "0143M0801708", "0143M0801709", "0143M0801710", "0143M0801711", "0143M0801712", "0143M0801713", "0143M0801714", "0143M0801715", "0143M0801716", "0143M0801717", "0143M0801718", "0143M0801719", "0143M0801720", "0143M0801721", "0143M0801722", "0143M0801723", "0143M0801724", "0143M0801725", "0143M0801726", "0143M0801727", "0143M0801728", "0143M0801729", "0143M0801730", "0143M0801731", "0143M0801732", "0143M0801733", "0143M0801734", "0143M0801735", "0143M0801736", "0143M0801737", "0143M0801738", "0143M0801739", "0143M0901740"]}}
{"id": "5c87b81b34725a21", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1741 | 0143M0901741 | 35.944 | 75.642 | Indus Middle | M(l) | 0.56 | 4,453 |\n| 1742 | 0143M0901742 | 35.943 | 75.643 | Indus Middle | M(l) | 0.52 | 4,457 |\n| 1743 | 0143M0901743 | 35.915 | 75.629 | Indus Middle | M(l) | 1.55 | 4,419 |\n| 1744 | 0143M0901744 | 35.881 | 75.730 | Indus Middle | M(l) | 0.54 | 4,115 |\n| 1745 | 0143M0901745 | 35.880 | 75.728 | Indus Middle | M(l) | 1.09 | 4,105 |\n| 1746 | 0143M0901746 | 35.879 | 75.730 | Indus Middle | M(lg) | 1.73 | 4,101 |\n| 1747 | 0143M0901747 | 35.878 | 75.546 | Indus Middle | M(l) | 0.25 | 4,274 |\n| 1748 | 0143M0901748 | 35.877 | 75.737 | Indus Middle | M(l) | 1.04 | 4,102 |\n| 1749 | 0143M0901749 | 35.869 | 75.684 | Indus Middle | I(s) | 0.89 | 4,170 |\n| 1750 | 0143M0901750 | 35.866 | 75.579 | Indus Middle | I(s) | 0.73 | 4,120 |\n| 1751 | 0143M0901751 | 35.861 | 75.693 | Indus Middle | M(l) | 0.79 | 4,156 |\n| 1752 | 0143M0901752 | 35.855 | 75.707 | Indus Middle | M(l) | 2.44 | 4,130 |\n| 1753 | 0143M0901753 | 35.838 | 75.730 | Indus Middle | M(l) | 7.61 | 4,025 |\n| 1754 | 0143M0901754 | 35.829 | 75.740 | Indus Middle | M(l) | 6.00 | 4,009 |\n| 1755 | 0143M1201755 | 35.214 | 75.693 | Indus Middle | E(o) | 1.60 | 4,606 |\n| 1756 | 0143M1201756 | 35.205 | 75.691 | Indus Middle | E(o) | 0.55 | 4,738 |\n| 1757 | 0143M1201757 | 35.172 | 75.507 | Indus Upper | E(o) | 3.69 | 4,544 |\n| 1758 | 0143M1201758 | 35.172 | 75.521 | Indus Upper | E(o) | 10.30 | 4,551 |\n| 1759 | 0143M1201759 | 35.166 | 75.538 | Indus Middle | E(o) | 0.78 | 4,572 |\n| 1760 | 0143M1201760 | 35.163 | 75.532 | Indus Middle | E(o) | 1.58 | 4,601 |\n| 1761 | 0143M1201761 | 35.163 | 75.536 | Indus Middle | E(o) | 0.66 | 4,585 |\n| 1762 | 0143M1201762 | 35.159 | 75.540 | Indus Middle | E(o) | 0.49 | 4,517 |\n| 1763 | 0143M1201763 | 35.156 | 75.561 | Indus Middle | E(o) | 0.46 | 4,308 |\n| 1764 | 0143M1201764 | 35.153 | 75.537 | Indus Middle | E(o) | 2.17 | 4,537 |\n| 1765 | 0143M1201765 | 35.152 | 75.554 | Indus Middle | E(o) | 0.43 | 4,347 |\n| 1766 | 0143M1201766 | 35.148 | 75.546 | Indus Middle | E(o) | 7.18 | 4,438 |\n| 1767 | 0143M1201767 | 35.140 | 75.541 | Indus Middle | E(o) | 0.86 | 4,574 |\n| 1768 | 0143M1201768 | 35.139 | 75.547 | Indus Middle | E(o) | 1.41 | 4,499 |\n| 1769 | 0143M1201769 | 35.132 | 75.632 | Indus Middle | E(o) | 3.61 | 4,360 |\n| 1770 | 0143M1201770 | 35.126 | 75.629 | Indus Middle | E(o) | 5.85 | 4,452 |\n| 1771 | 0143M1201771 | 35.125 | 75.655 | Indus Upper | E(o) | 2.89 | 4,545 |\n| 1772 | 0143M1201772 | 35.119 | 75.631 | Indus Middle | E(o) | 1.97 | 4,497 |\n| 1773 | 0143M1201773 | 35.114 | 75.663 | Indus Upper | E(o) | 5.07 | 4,395 |\n| 1774 | 0143M1201774 | 35.110 | 75.658 | Indus Upper | E(o) | 0.25 | 4,548 |\n| 1775 | 0143M1201775 | 35.108 | 75.657 | Indus Upper | E(c) | 0.72 | 4,543 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7850, "line_end": 7924, "token_count_estimate": 1658, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper"], "countries": [], "lake_ids": ["0143M0901741", "0143M0901742", "0143M0901743", "0143M0901744", "0143M0901745", "0143M0901746", "0143M0901747", "0143M0901748", "0143M0901749", "0143M0901750", "0143M0901751", "0143M0901752", "0143M0901753", "0143M0901754", "0143M1201755", "0143M1201756", "0143M1201757", "0143M1201758", "0143M1201759", "0143M1201760", "0143M1201761", "0143M1201762", "0143M1201763", "0143M1201764", "0143M1201765", "0143M1201766", "0143M1201767", "0143M1201768", "0143M1201769", "0143M1201770", "0143M1201771", "0143M1201772", "0143M1201773", "0143M1201774", "0143M1201775"]}}
{"id": "eafdb3749fd6e804", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1776 | 0143M1201776 | 35.096 | 75.579 | Indus Middle | E(o) | 10.98 | 4,375 |\n| 1777 | 0143M1201777 | 35.095 | 75.650 | Indus Middle | E(o) | 0.45 | 4,575 |\n| 1778 | 0143M1201778 | 35.095 | 75.643 | Indus Middle | E(o) | 0.80 | 4,457 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 7850, "line_end": 7924, "token_count_estimate": 243, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143M1201776", "0143M1201777", "0143M1201778"]}}
{"id": "d4a6d9dfe036d0d8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1779 | 0143M1201779 | 35.084 | 75.662 | Indus Middle | E(o) | 2.05 | 4,637 |\n| 1780 | 0143M1201780 | 35.076 | 75.647 | Indus Middle | E(o) | 0.58 | 4,624 |\n| 1781 | 0143M1201781 | 35.054 | 75.724 | Indus Upper | E(o) | 0.53 | 4,513 |\n| 1782 | 0143M1201782 | 35.049 | 75.596 | Indus Middle | E(o) | 1.08 | 4,468 |\n| 1783 | 0143M1201783 | 35.045 | 75.725 | Indus Upper | E(o) | 2.79 | 4,610 |\n| 1784 | 0143M1201784 | 35.040 | 75.728 | Indus Upper | E(o) | 0.93 | 4,618 |\n| 1785 | 0143M1201785 | 35.039 | 75.592 | Indus Middle | E(o) | 0.30 | 4,567 |\n| 1786 | 0143M1201786 | 35.037 | 75.602 | Indus Middle | E(o) | 0.60 | 4,561 |\n| 1787 | 0143M1201787 | 35.031 | 75.517 | Indus Upper | E(o) | 0.63 | 3,986 |\n| 1788 | 0143M1201788 | 35.030 | 75.708 | Indus Upper | E(o) | 2.68 | 4,488 |\n| 1789 | 0143M1201789 | 35.027 | 75.725 | Indus Upper | E(o) | 12.92 | 4,647 |\n| 1790 | 0143M1201790 | 35.027 | 75.717 | Indus Upper | E(o) | 1.46 | 4,640 |\n| 1791 | 0143M1201791 | 35.027 | 75.562 | Indus Upper | E(o) | 0.38 | 4,016 |\n| 1792 | 0143M1201792 | 35.018 | 75.672 | Indus Middle | E(o) | 3.60 | 4,609 |\n| 1793 | 0143M1201793 | 35.013 | 75.538 | Indus Upper | E(o) | 0.60 | 4,169 |\n| 1794 | 0143M1201794 | 35.010 | 75.693 | Indus Upper | E(o) | 0.77 | 4,745 |\n| 1795 | 0143M1201795 | 35.006 | 75.707 | Indus Upper | E(o) | 1.54 | 4,710 |\n| 1796 | 0143M1201796 | 35.004 | 75.695 | Indus Upper | E(o) | 1.93 | 4,718 |\n| 1797 | 0143M1201797 | 35.003 | 75.729 | Indus Upper | E(o) | 1.14 | 4,659 |\n| 1798 | 0143M1301798 | 35.912 | 75.928 | Indus Middle | I(s) | 0.41 | 4,160 |\n| 1799 | 0143M1301799 | 35.901 | 75.992 | Indus Middle | I(s) | 0.46 | 4,042 |\n| 1800 | 0143M1301800 | 35.899 | 75.981 | Indus Middle | I(s) | 0.48 | 4,004 |\n| 1801 | 0143M1301801 | 35.898 | 75.990 | Indus Middle | I(s) | 0.34 | 4,035 |\n| 1802 | 0143M1301802 | 35.897 | 75.977 | Indus Middle | I(s) | 0.37 | 4,002 |\n| 1803 | 0143M1301803 | 35.897 | 75.991 | Indus Middle | I(s) | 1.05 | 4,033 |\n| 1804 | 0143M1301804 | 35.895 | 75.978 | Indus Middle | I(s) | 0.38 | 4,001 |\n| 1805 | 0143M1301805 | 35.894 | 75.989 | Indus Middle | I(s) | 0.39 | 3,994 |\n| 1806 | 0143M1301806 | 35.887 | 75.755 | Indus Middle | M(l) | 1.55 | 4,282 |\n| 1807 | 0143M1301807 | 35.887 | 75.802 | Indus Middle | M(l) | 0.97 | 4,649 |\n| 1808 | 0143M1301808 | 35.876 | 75.980 | Indus Middle | I(s) | 0.42 | 3,915 |\n| 1809 | 0143M1301809 | 35.876 | 75.980 | Indus Middle | I(s) | 0.38 | 3,915 |\n| 1810 | 0143M1301810 | 35.864 | 75.751 | Indus Middle | I(s) | 0.66 | 4,055 |\n| 1811 | 0143M1301811 | 35.862 | 75.976 | Indus Middle | I(s) | 0.48 | 3,860 |\n| 1812 | 0143M1301812 | 35.860 | 75.969 | Indus Middle | I(s) | 0.28 | 3,838 |\n| 1813 | 0143M1301813 | 35.859 | 75.971 | Indus Middle | I(s) | 0.57 | 3,857 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7926, "line_end": 8000, "token_count_estimate": 1674, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper"], "countries": [], "lake_ids": ["0143M1201779", "0143M1201780", "0143M1201781", "0143M1201782", "0143M1201783", "0143M1201784", "0143M1201785", "0143M1201786", "0143M1201787", "0143M1201788", "0143M1201789", "0143M1201790", "0143M1201791", "0143M1201792", "0143M1201793", "0143M1201794", "0143M1201795", "0143M1201796", "0143M1201797", "0143M1301798", "0143M1301799", "0143M1301800", "0143M1301801", "0143M1301802", "0143M1301803", "0143M1301804", "0143M1301805", "0143M1301806", "0143M1301807", "0143M1301808", "0143M1301809", "0143M1301810", "0143M1301811", "0143M1301812", "0143M1301813"]}}
{"id": "951eac5ed1531a4a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1814 | 0143M1301814 | 35.858 | 75.967 | Indus Middle | I(s) | 0.28 | 3,829 |\n| 1815 | 0143M1301815 | 35.858 | 75.761 | Indus Middle | I(s) | 0.25 | 4,034 |\n| 1816 | 0143M1301816 | 35.858 | 75.764 | Indus Middle | M(l) | 0.97 | 4,030 |\n| 1817 | 0143M1301817 | 35.857 | 75.967 | Indus Middle | I(s) | 0.50 | 3,826 |\n| 1818 | 0143M1301818 | 35.857 | 75.766 | Indus Middle | M(l) | 1.02 | 4,031 |\n| 1819 | 0143M1301819 | 35.844 | 75.955 | Indus Middle | I(s) | 0.34 | 3,725 |\n| 1820 | 0143M1301820 | 35.821 | 75.752 | Indus Middle | M(l) | 1.61 | 4,007 |\n| 1821 | 0143M1301821 | 35.818 | 75.766 | Indus Middle | M(l) | 0.77 | 3,955 |\n| 1822 | 0143M1301822 | 35.815 | 75.773 | Indus Middle | M(l) | 0.43 | 3,984 |\n| 1823 | 0143M1301823 | 35.809 | 75.785 | Indus Middle | M(l) | 1.08 | 3,918 |\n| 1824 | 0143M1301824 | 35.805 | 75.792 | Indus Middle | M(l) | 1.97 | 3,908 |\n| 1825 | 0143M1401825 | 35.701 | 75.898 | Indus Middle | I(s) | 0.94 | 3,199 |\n| 1826 | 0143M1401826 | 35.694 | 75.909 | Indus Middle | I(s) | 0.55 | 3,116 |\n| 1827 | 0143M1401827 | 35.687 | 75.908 | Indus Middle | M(o) | 2.21 | 3,051 |\n| 1828 | 0143M1401828 | 35.686 | 75.901 | Indus Middle | I(s) | 1.43 | 3,076 |\n| 1829 | 0143M1401829 | 35.685 | 75.908 | Indus Middle | E(o) | 0.35 | 3,038 |\n| 1830 | 0143M1501830 | 35.374 | 75.974 | Indus Middle | I(s) | 0.53 | 4,873 |\n| 1831 | 0143M1501831 | 35.372 | 75.949 | Indus Middle | E(o) | 3.00 | 4,735 |\n| 1832 | 0143M1601832 | 35.192 | 75.763 | Indus Upper | E(o) | 1.51 | 4,518 |\n| 1833 | 0143M1601833 | 35.028 | 75.778 | Indus Upper | E(o) | 0.45 | 4,718 |\n| 1834 | 0143M1601834 | 35.025 | 75.833 | Indus Upper | E(o) | 0.58 | 4,665 |\n| 1835 | 0143M1601835 | 35.023 | 75.832 | Indus Upper | E(o) | 1.30 | 4,672 |\n| 1836 | 0143M1601836 | 35.016 | 75.804 | Indus Upper | E(o) | 1.39 | 4,630 |\n| 1837 | 0143M1601837 | 35.005 | 75.767 | Indus Upper | E(o) | 8.80 | 4,676 |\n| 1838 | 0143M1601838 | 35.001 | 75.771 | Indus Upper | E(o) | 2.77 | 4,698 |\n| 1839 | 0143N0101839 | 34.991 | 75.236 | Indus Upper | O | 129.62 | 4,138 |\n| 1840 | 0143N0101840 | 34.980 | 75.039 | Indus Middle | E(c) | 9.09 | 4,320 |\n| 1841 | 0143N0101841 | 34.975 | 75.123 | Indus Middle | E(o) | 1.01 | 4,232 |\n| 1842 | 0143N0101842 | 34.972 | 75.051 | Indus Middle | E(c) | 14.29 | 4,265 |\n| 1843 | 0143N0101843 | 34.970 | 75.079 | Indus Middle | E(o) | 0.39 | 4,422 |\n| 1844 | 0143N0101844 | 34.967 | 75.123 | Indus Middle | E(o) | 0.84 | 4,291 |\n| 1845 | 0143N0101845 | 34.963 | 75.053 | Indus Middle | E(o) | 2.00 | 4,373 |\n| 1846 | 0143N0101846 | 34.951 | 75.137 | Indus Middle | E(o) | 1.58 | 4,262 |\n| 1847 | 0143N0101847 | 34.941 | 75.148 | Indus Middle | E(o) | 0.44 | 4,393 |\n| 1848 | 0143N0101848 | 34.940 | 75.155 | Indus Middle | E(o) | 0.38 | 4,294 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7926, "line_end": 8000, "token_count_estimate": 1664, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper"], "countries": [], "lake_ids": ["0143M1301814", "0143M1301815", "0143M1301816", "0143M1301817", "0143M1301818", "0143M1301819", "0143M1301820", "0143M1301821", "0143M1301822", "0143M1301823", "0143M1301824", "0143M1401825", "0143M1401826", "0143M1401827", "0143M1401828", "0143M1401829", "0143M1501830", "0143M1501831", "0143M1601832", "0143M1601833", "0143M1601834", "0143M1601835", "0143M1601836", "0143M1601837", "0143M1601838", "0143N0101839", "0143N0101840", "0143N0101841", "0143N0101842", "0143N0101843", "0143N0101844", "0143N0101845", "0143N0101846", "0143N0101847", "0143N0101848"]}}
{"id": "3267ea5bbfacdf9d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1849 | 0143N0101849 | 34.938 | 75.155 | Indus Middle | E(o) | 0.84 | 4,293 |\n| 1850 | 0143N0101850 | 34.927 | 75.146 | Indus Middle | E(o) | 0.33 | 4,368 |\n| 1851 | 0143N0101851 | 34.926 | 75.070 | Indus Middle | E(o) | 0.67 | 4,292 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 7926, "line_end": 8000, "token_count_estimate": 245, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0143N0101849", "0143N0101850", "0143N0101851"]}}
{"id": "3f525f191019b759", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8001, "line_end": 8009, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cae56695e217561b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1852 | 0143N0101852 | 34.922 | 75.250 | Indus Upper | E(o) | 14.22 | 4,152 |\n| 1853 | 0143N0101853 | 34.920 | 75.155 | Indus Upper | E(o) | 0.48 | 4,416 |\n| 1854 | 0143N0101854 | 34.920 | 75.177 | Indus Upper | E(o) | 14.54 | 4,257 |\n| 1855 | 0143N0101855 | 34.920 | 75.143 | Indus Middle | E(c) | 5.40 | 4,320 |\n| 1856 | 0143N0101856 | 34.919 | 75.189 | Indus Upper | E(o) | 14.34 | 4,225 |\n| 1857 | 0143N0101857 | 34.916 | 75.133 | Indus Middle | E(o) | 0.96 | 4,270 |\n| 1858 | 0143N0101858 | 34.911 | 75.186 | Indus Upper | E(o) | 2.17 | 4,431 |\n| 1859 | 0143N0101859 | 34.896 | 75.079 | Jhelum | E(o) | 2.35 | 4,255 |\n| 1860 | 0143N0101860 | 34.892 | 75.089 | Jhelum | E(o) | 2.97 | 4,209 |\n| 1861 | 0143N0101861 | 34.891 | 75.113 | Indus Upper | O | 2.45 | 3,962 |\n| 1862 | 0143N0101862 | 34.889 | 75.090 | Jhelum | M(o) | 0.66 | 4,289 |\n| 1863 | 0143N0101863 | 34.880 | 75.033 | Indus Middle | E(c) | 1.64 | 4,214 |\n| 1864 | 0143N0101864 | 34.854 | 75.149 | Indus Upper | E(o) | 2.24 | 4,305 |\n| 1865 | 0143N0101865 | 34.852 | 75.139 | Jhelum | E(o) | 0.79 | 4,253 |\n| 1866 | 0143N0101866 | 34.847 | 75.161 | Indus Upper | E(o) | 0.64 | 4,119 |\n| 1867 | 0143N0101867 | 34.845 | 75.169 | Indus Upper | E(o) | 0.71 | 4,211 |\n| 1868 | 0143N0101868 | 34.843 | 75.021 | Indus Middle | E(c) | 0.73 | 3,939 |\n| 1869 | 0143N0101869 | 34.842 | 75.138 | Jhelum | E(o) | 0.35 | 4,201 |\n| 1870 | 0143N0101870 | 34.828 | 75.197 | Indus Upper | E(o) | 2.43 | 4,249 |\n| 1871 | 0143N0101871 | 34.826 | 75.154 | Jhelum | E(o) | 1.33 | 4,288 |\n| 1872 | 0143N0101872 | 34.823 | 75.157 | Jhelum | E(o) | 3.31 | 4,272 |\n| 1873 | 0143N0201873 | 34.727 | 75.097 | Jhelum | E(o) | 8.04 | 4,109 |\n| 1874 | 0143N0201874 | 34.727 | 75.029 | Jhelum | E(o) | 0.38 | 4,099 |\n| 1875 | 0143N0201875 | 34.724 | 75.028 | Jhelum | E(o) | 1.79 | 4,117 |\n| 1876 | 0143N0201876 | 34.724 | 75.097 | Jhelum | E(o) | 0.78 | 4,107 |\n| 1877 | 0143N0201877 | 34.720 | 75.239 | Jhelum | E(o) | 0.70 | 4,365 |\n| 1878 | 0143N0201878 | 34.719 | 75.118 | Jhelum | E(o) | 1.42 | 4,188 |\n| 1879 | 0143N0201879 | 34.715 | 75.043 | Jhelum | E(o) | 0.51 | 3,996 |\n| 1880 | 0143N0201880 | 34.712 | 75.189 | Jhelum | E(o) | 2.70 | 4,322 |\n| 1881 | 0143N0201881 | 34.709 | 75.248 | Jhelum | E(o) | 0.73 | 4,373 |\n| 1882 | 0143N0201882 | 34.708 | 75.126 | Jhelum | E(c) | 2.67 | 4,264 |\n| 1883 | 0143N0201883 | 34.707 | 75.249 | Jhelum | E(o) | 0.99 | 4,368 |\n| 1884 | 0143N0201884 | 34.702 | 75.141 | Jhelum | E(o) | 0.35 | 4,095 |\n| 1885 | 0143N0201885 | 34.697 | 75.137 | Jhelum | E(o) | 64.95 | 4,103 |\n| 1886 | 0143N0201886 | 34.692 | 75.198 | Jhelum | E(o) | 1.31 | 4,325 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8010, "line_end": 8084, "token_count_estimate": 1642, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper", "Jhelum"], "countries": [], "lake_ids": ["0143N0101852", "0143N0101853", "0143N0101854", "0143N0101855", "0143N0101856", "0143N0101857", "0143N0101858", "0143N0101859", "0143N0101860", "0143N0101861", "0143N0101862", "0143N0101863", "0143N0101864", "0143N0101865", "0143N0101866", "0143N0101867", "0143N0101868", "0143N0101869", "0143N0101870", "0143N0101871", "0143N0101872", "0143N0201873", "0143N0201874", "0143N0201875", "0143N0201876", "0143N0201877", "0143N0201878", "0143N0201879", "0143N0201880", "0143N0201881", "0143N0201882", "0143N0201883", "0143N0201884", "0143N0201885", "0143N0201886"]}}
{"id": "84f4ed74912a4320", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1887 | 0143N0201887 | 34.689 | 75.128 | Jhelum | E(o) | 0.71 | 4,235 |\n| 1888 | 0143N0201888 | 34.685 | 75.157 | Jhelum | E(o) | 0.29 | 4,161 |\n| 1889 | 0143N0201889 | 34.684 | 75.140 | Jhelum | E(o) | 21.41 | 4,133 |\n| 1890 | 0143N0201890 | 34.681 | 75.165 | Jhelum | E(o) | 2.61 | 4,231 |\n| 1891 | 0143N0201891 | 34.680 | 75.159 | Jhelum | E(o) | 1.44 | 4,231 |\n| 1892 | 0143N0201892 | 34.678 | 75.157 | Jhelum | E(c) | 6.00 | 4,241 |\n| 1893 | 0143N0201893 | 34.675 | 75.162 | Jhelum | E(c) | 1.09 | 4,363 |\n| 1894 | 0143N0201894 | 34.674 | 75.170 | Jhelum | E(o) | 4.49 | 4,321 |\n| 1895 | 0143N0201895 | 34.672 | 75.170 | Jhelum | E(o) | 0.74 | 4,330 |\n| 1896 | 0143N0201896 | 34.671 | 75.165 | Jhelum | E(o) | 0.87 | 4,346 |\n| 1897 | 0143N0201897 | 34.666 | 75.179 | Jhelum | E(o) | 74.54 | 4,234 |\n| 1898 | 0143N0201898 | 34.663 | 75.197 | Jhelum | E(o) | 4.37 | 4,252 |\n| 1899 | 0143N0201899 | 34.661 | 75.207 | Jhelum | E(o) | 2.98 | 4,236 |\n| 1900 | 0143N0201900 | 34.660 | 75.200 | Jhelum | E(o) | 0.96 | 4,275 |\n| 1901 | 0143N0201901 | 34.658 | 75.179 | Jhelum | E(o) | 1.63 | 4,251 |\n| 1902 | 0143N0201902 | 34.656 | 75.181 | Jhelum | E(o) | 0.66 | 4,293 |\n| 1903 | 0143N0201903 | 34.655 | 75.198 | Jhelum | E(o) | 0.76 | 4,351 |\n| 1904 | 0143N0201904 | 34.653 | 75.175 | Jhelum | E(o) | 4.22 | 4,254 |\n| 1905 | 0143N0201905 | 34.653 | 75.203 | Jhelum | E(o) | 0.85 | 4,314 |\n| 1906 | 0143N0201906 | 34.653 | 75.186 | Jhelum | E(o) | 0.84 | 4,380 |\n| 1907 | 0143N0201907 | 34.652 | 75.206 | Jhelum | E(o) | 3.98 | 4,316 |\n| 1908 | 0143N0201908 | 34.648 | 75.229 | Indus Upper | E(o) | 4.47 | 4,247 |\n| 1909 | 0143N0201909 | 34.648 | 75.222 | Indus Upper | E(o) | 2.92 | 4,268 |\n| 1910 | 0143N0201910 | 34.646 | 75.177 | Jhelum | E(o) | 9.93 | 4,250 |\n| 1911 | 0143N0201911 | 34.645 | 75.228 | Indus Upper | E(o) | 0.29 | 4,291 |\n| 1912 | 0143N0201912 | 34.644 | 75.218 | Indus Upper | E(o) | 3.22 | 4,299 |\n| 1913 | 0143N0201913 | 34.644 | 75.235 | Indus Upper | E(o) | 0.36 | 4,278 |\n| 1914 | 0143N0201914 | 34.642 | 75.229 | Indus Upper | E(o) | 1.48 | 4,294 |\n| 1915 | 0143N0201915 | 34.641 | 75.225 | Indus Upper | E(o) | 1.54 | 4,301 |\n| 1916 | 0143N0201916 | 34.641 | 75.191 | Jhelum | E(o) | 2.18 | 4,296 |\n| 1917 | 0143N0201917 | 34.633 | 75.185 | Jhelum | E(o) | 0.69 | 4,193 |\n| 1918 | 0143N0201918 | 34.622 | 75.147 | Jhelum | E(o) | 7.24 | 3,874 |\n| 1919 | 0143N0301919 | 34.459 | 75.099 | Jhelum | E(o) | 1.33 | 4,248 |\n| 1920 | 0143N0301920 | 34.447 | 75.037 | Jhelum | O | 1.46 | 3,423 |\n| 1921 | 0143N0301921 | 34.442 | 75.013 | Jhelum | E(c) | 5.62 | 4,080 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8010, "line_end": 8084, "token_count_estimate": 1616, "basins": ["Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": ["0143N0201887", "0143N0201888", "0143N0201889", "0143N0201890", "0143N0201891", "0143N0201892", "0143N0201893", "0143N0201894", "0143N0201895", "0143N0201896", "0143N0201897", "0143N0201898", "0143N0201899", "0143N0201900", "0143N0201901", "0143N0201902", "0143N0201903", "0143N0201904", "0143N0201905", "0143N0201906", "0143N0201907", "0143N0201908", "0143N0201909", "0143N0201910", "0143N0201911", "0143N0201912", "0143N0201913", "0143N0201914", "0143N0201915", "0143N0201916", "0143N0201917", "0143N0201918", "0143N0301919", "0143N0301920", "0143N0301921"]}}
{"id": "e80b5ce72bd4eaae", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1922 | 0143N0301922 | 34.436 | 75.004 | Jhelum | E(o) | 2.00 | 3,745 |\n| 1923 | 0143N0301923 | 34.435 | 75.010 | Jhelum | E(o) | 2.78 | 3,784 |\n| 1924 | 0143N0301924 | 34.427 | 75.036 | Jhelum | M(o) | 1.41 | 3,876 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 8010, "line_end": 8084, "token_count_estimate": 236, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": ["0143N0301922", "0143N0301923", "0143N0301924"]}}
{"id": "728ef11abd68a3d8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1925 | 0143N0301925 | 34.426 | 75.039 | Jhelum | M(o) | 0.87 | 3,883 |\n| 1926 | 0143N0301926 | 34.425 | 75.038 | Jhelum | M(o) | 0.46 | 3,879 |\n| 1927 | 0143N0301927 | 34.423 | 75.043 | Jhelum | M(o) | 0.59 | 3,773 |\n| 1928 | 0143N0301928 | 34.423 | 75.035 | Jhelum | E(o) | 2.48 | 3,837 |\n| 1929 | 0143N0301929 | 34.422 | 75.058 | Jhelum | E(c) | 40.12 | 3,570 |\n| 1930 | 0143N0301930 | 34.413 | 75.073 | Jhelum | E(o) | 2.64 | 3,725 |\n| 1931 | 0143N0301931 | 34.407 | 75.087 | Jhelum | E(o) | 2.16 | 3,936 |\n| 1932 | 0143N0301932 | 34.397 | 75.103 | Jhelum | E(c) | 29.55 | 3,812 |\n| 1933 | 0143N0301933 | 34.397 | 75.106 | Jhelum | E(o) | 0.79 | 3,820 |\n| 1934 | 0143N0301934 | 34.393 | 75.096 | Jhelum | E(c) | 5.43 | 3,838 |\n| 1935 | 0143N0301935 | 34.392 | 75.085 | Jhelum | E(o) | 3.11 | 4,072 |\n| 1936 | 0143N0301936 | 34.390 | 75.088 | Jhelum | E(o) | 2.12 | 4,068 |\n| 1937 | 0143N0301937 | 34.388 | 75.119 | Jhelum | E(c) | 47.25 | 3,663 |\n| 1938 | 0143N0301938 | 34.386 | 75.064 | Jhelum | E(o) | 1.47 | 4,006 |\n| 1939 | 0143N0301939 | 34.381 | 75.102 | Jhelum | E(o) | 4.09 | 3,994 |\n| 1940 | 0143N0301940 | 34.368 | 75.130 | Jhelum | M(o) | 0.72 | 3,991 |\n| 1941 | 0143N0301941 | 34.368 | 75.180 | Jhelum | E(o) | 0.64 | 4,086 |\n| 1942 | 0143N0301942 | 34.363 | 75.135 | Jhelum | E(o) | 1.90 | 4,015 |\n| 1943 | 0143N0301943 | 34.360 | 75.140 | Jhelum | E(c) | 9.59 | 3,916 |\n| 1944 | 0143N0301944 | 34.346 | 75.160 | Jhelum | E(o) | 1.04 | 4,146 |\n| 1945 | 0143N0301945 | 34.340 | 75.176 | Jhelum | E(o) | 0.38 | 3,788 |\n| 1946 | 0143N0301946 | 34.339 | 75.170 | Jhelum | E(o) | 0.57 | 4,043 |\n| 1947 | 0143N0301947 | 34.335 | 75.051 | Jhelum | E(o) | 0.35 | 3,950 |\n| 1948 | 0143N0301948 | 34.334 | 75.050 | Jhelum | E(c) | 1.33 | 3,956 |\n| 1949 | 0143N0301949 | 34.333 | 75.158 | Jhelum | I(s) | 0.90 | 4,153 |\n| 1950 | 0143N0301950 | 34.329 | 75.186 | Jhelum | E(o) | 1.72 | 4,021 |\n| 1951 | 0143N0301951 | 34.325 | 75.052 | Jhelum | E(o) | 3.30 | 4,008 |\n| 1952 | 0143N0301952 | 34.313 | 75.050 | Jhelum | E(o) | 1.52 | 4,160 |\n| 1953 | 0143N0301953 | 34.311 | 75.051 | Jhelum | E(o) | 3.78 | 4,122 |\n| 1954 | 0143N0301954 | 34.305 | 75.021 | Jhelum | E(o) | 0.36 | 3,838 |\n| 1955 | 0143N0301955 | 34.305 | 75.045 | Jhelum | E(o) | 0.60 | 4,213 |\n| 1956 | 0143N0301956 | 34.305 | 75.027 | Jhelum | E(o) | 4.53 | 3,925 |\n| 1957 | 0143N0301957 | 34.305 | 75.022 | Jhelum | E(o) | 0.38 | 3,844 |\n| 1958 | 0143N0301958 | 34.300 | 75.019 | Jhelum | E(o) | 1.43 | 3,813 |\n| 1959 | 0143N0301959 | 34.299 | 75.061 | Jhelum | E(o) | 2.98 | 3,975 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8086, "line_end": 8160, "token_count_estimate": 1579, "basins": ["Indus"], "subbasins": ["Jhelum"], "countries": [], "lake_ids": ["0143N0301925", "0143N0301926", "0143N0301927", "0143N0301928", "0143N0301929", "0143N0301930", "0143N0301931", "0143N0301932", "0143N0301933", "0143N0301934", "0143N0301935", "0143N0301936", "0143N0301937", "0143N0301938", "0143N0301939", "0143N0301940", "0143N0301941", "0143N0301942", "0143N0301943", "0143N0301944", "0143N0301945", "0143N0301946", "0143N0301947", "0143N0301948", "0143N0301949", "0143N0301950", "0143N0301951", "0143N0301952", "0143N0301953", "0143N0301954", "0143N0301955", "0143N0301956", "0143N0301957", "0143N0301958", "0143N0301959"]}}
{"id": "a59157229da64cca", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1960 | 0143N0301960 | 34.292 | 75.036 | Jhelum | E(o) | 0.58 | 4,243 |\n| 1961 | 0143N0301961 | 34.284 | 75.033 | Jhelum | E(o) | 0.34 | 4,262 |\n| 1962 | 0143N0301962 | 34.282 | 75.034 | Jhelum | E(o) | 0.80 | 4,199 |\n| 1963 | 0143N0401963 | 34.227 | 75.222 | Jhelum | E(o) | 4.73 | 4,137 |\n| 1964 | 0143N0401964 | 34.212 | 75.162 | Jhelum | E(o) | 5.89 | 3,669 |\n| 1965 | 0143N0401965 | 34.209 | 75.197 | Jhelum | E(o) | 2.41 | 3,847 |\n| 1966 | 0143N0401966 | 34.207 | 75.147 | Jhelum | E(o) | 10.35 | 3,699 |\n| 1967 | 0143N0401967 | 34.203 | 75.205 | Jhelum | E(o) | 15.92 | 3,682 |\n| 1968 | 0143N0401968 | 34.186 | 75.019 | Jhelum | E(o) | 1.09 | 3,595 |\n| 1969 | 0143N0401969 | 34.182 | 75.037 | Jhelum | E(o) | 0.44 | 3,625 |\n| 1970 | 0143N0401970 | 34.166 | 75.084 | Jhelum | E(o) | 1.17 | 4,040 |\n| 1971 | 0143N0401971 | 34.162 | 75.097 | Jhelum | E(o) | 0.91 | 3,945 |\n| 1972 | 0143N0401972 | 34.159 | 75.097 | Jhelum | E(o) | 1.44 | 3,949 |\n| 1973 | 0143N0401973 | 34.156 | 75.113 | Jhelum | E(o) | 10.27 | 3,941 |\n| 1974 | 0143N0401974 | 34.144 | 75.110 | Jhelum | E(o) | 43.47 | 3,810 |\n| 1975 | 0143N0401975 | 34.140 | 75.148 | Jhelum | E(c) | 82.93 | 3,780 |\n| 1976 | 0143N0401976 | 34.126 | 75.123 | Jhelum | E(o) | 0.70 | 3,992 |\n| 1977 | 0143N0401977 | 34.104 | 75.149 | Jhelum | E(o) | 8.06 | 3,805 |\n| 1978 | 0143N0401978 | 34.093 | 75.161 | Jhelum | E(o) | 17.80 | 3,919 |\n| 1979 | 0143N0401979 | 34.063 | 75.216 | Jhelum | E(o) | 13.36 | 3,691 |\n| 1980 | 0143N0401980 | 34.015 | 75.244 | Jhelum | E(o) | 8.05 | 3,591 |\n| 1981 | 0143N0501981 | 34.889 | 75.355 | Indus Upper | E(o) | 0.94 | 4,025 |\n| 1982 | 0143N0501982 | 34.864 | 75.282 | Indus Upper | E(o) | 1.50 | 4,172 |\n| 1983 | 0143N0501983 | 34.862 | 75.338 | Indus Upper | E(o) | 1.03 | 4,241 |\n| 1984 | 0143N0501984 | 34.860 | 75.325 | Indus Upper | E(o) | 0.70 | 4,274 |\n| 1985 | 0143N0501985 | 34.856 | 75.325 | Indus Upper | E(o) | 1.12 | 4,318 |\n| 1986 | 0143N0501986 | 34.855 | 75.333 | Indus Upper | E(o) | 4.79 | 4,317 |\n| 1987 | 0143N0501987 | 34.845 | 75.318 | Indus Upper | E(o) | 2.72 | 4,230 |\n| 1988 | 0143N0501988 | 34.835 | 75.363 | Indus Upper | E(o) | 2.35 | 4,322 |\n| 1989 | 0143N0501989 | 34.834 | 75.322 | Indus Upper | E(o) | 1.69 | 4,505 |\n| 1990 | 0143N0501990 | 34.833 | 75.319 | Indus Upper | E(o) | 0.85 | 4,510 |\n| 1991 | 0143N0501991 | 34.832 | 75.371 | Indus Upper | E(o) | 1.78 | 4,370 |\n| 1992 | 0143N0501992 | 34.832 | 75.375 | Indus Upper | E(o) | 1.15 | 4,392 |\n| 1993 | 0143N0501993 | 34.830 | 75.364 | Indus Upper | E(o) | 6.71 | 4,320 |\n| 1994 | 0143N0501994 | 34.829 | 75.439 | Indus Upper | E(o) | 0.52 | 4,582 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8086, "line_end": 8160, "token_count_estimate": 1614, "basins": ["Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": ["0143N0301960", "0143N0301961", "0143N0301962", "0143N0401963", "0143N0401964", "0143N0401965", "0143N0401966", "0143N0401967", "0143N0401968", "0143N0401969", "0143N0401970", "0143N0401971", "0143N0401972", "0143N0401973", "0143N0401974", "0143N0401975", "0143N0401976", "0143N0401977", "0143N0401978", "0143N0401979", "0143N0401980", "0143N0501981", "0143N0501982", "0143N0501983", "0143N0501984", "0143N0501985", "0143N0501986", "0143N0501987", "0143N0501988", "0143N0501989", "0143N0501990", "0143N0501991", "0143N0501992", "0143N0501993", "0143N0501994"]}}
{"id": "cfe7c00c894a7c34", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1995 | 0143N0501995 | 34.828 | 75.350 | Indus Upper | E(o) | 3.52 | 4,331 |\n| 1996 | 0143N0501996 | 34.825 | 75.363 | Indus Upper | E(o) | 3.66 | 4,332 |\n| 1997 | 0143N0501997 | 34.825 | 75.382 | Indus Upper | E(c) | 22.94 | 4,315 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 8086, "line_end": 8160, "token_count_estimate": 241, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N0501995", "0143N0501996", "0143N0501997"]}}
{"id": "51cded413fca058d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8161, "line_end": 8170, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a9d9300655da33b3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1998 | 0143N0501998 | 34.821 | 75.328 | Indus Upper | E(o) | 1.43 | 4,361 |\n| 1999 | 0143N0501999 | 34.818 | 75.374 | Indus Upper | E(o) | 0.57 | 4,479 |\n| 2000 | 0143N0502000 | 34.813 | 75.437 | Indus Upper | E(c) | 5.15 | 4,540 |\n| 2001 | 0143N0502001 | 34.811 | 75.394 | Indus Upper | E(c) | 3.08 | 4,547 |\n| 2002 | 0143N0502002 | 34.811 | 75.401 | Indus Upper | E(o) | 4.41 | 4,327 |\n| 2003 | 0143N0502003 | 34.807 | 75.421 | Indus Upper | E(o) | 0.94 | 4,501 |\n| 2004 | 0143N0502004 | 34.805 | 75.409 | Indus Upper | E(o) | 0.41 | 4,393 |\n| 2005 | 0143N0502005 | 34.805 | 75.414 | Indus Upper | E(o) | 1.02 | 4,427 |\n| 2006 | 0143N0502006 | 34.799 | 75.426 | Indus Upper | E(o) | 1.58 | 4,603 |\n| 2007 | 0143N0502007 | 34.793 | 75.436 | Indus Upper | E(o) | 0.63 | 4,529 |\n| 2008 | 0143N0502008 | 34.779 | 75.492 | Indus Upper | E(c) | 3.85 | 4,508 |\n| 2009 | 0143N0502009 | 34.777 | 75.463 | Indus Upper | E(o) | 1.14 | 4,598 |\n| 2010 | 0143N0502010 | 34.775 | 75.480 | Indus Upper | E(o) | 10.80 | 4,626 |\n| 2011 | 0143N0502011 | 34.759 | 75.430 | Indus Upper | E(o) | 1.50 | 4,482 |\n| 2012 | 0143N0502012 | 34.757 | 75.484 | Indus Upper | E(o) | 0.77 | 4,781 |\n| 2013 | 0143N0602013 | 34.713 | 75.259 | Indus Upper | E(o) | 0.81 | 4,433 |\n| 2014 | 0143N0602014 | 34.702 | 75.259 | Indus Upper | E(o) | 1.36 | 4,408 |\n| 2015 | 0143N0602015 | 34.701 | 75.255 | Indus Upper | E(o) | 1.00 | 4,429 |\n| 2016 | 0143N0602016 | 34.694 | 75.259 | Indus Upper | E(o) | 1.40 | 4,401 |\n| 2017 | 0143N0602017 | 34.694 | 75.264 | Indus Upper | E(o) | 1.58 | 4,330 |\n| 2018 | 0143N0602018 | 34.662 | 75.341 | Indus Upper | E(o) | 1.23 | 4,499 |\n| 2019 | 0143N0602019 | 34.652 | 75.370 | Indus Upper | E(o) | 4.51 | 3,981 |\n| 2020 | 0143N0602020 | 34.645 | 75.344 | Indus Upper | E(o) | 0.75 | 4,526 |\n| 2021 | 0143N0602021 | 34.644 | 75.419 | Indus Upper | E(o) | 0.60 | 4,636 |\n| 2022 | 0143N0602022 | 34.641 | 75.334 | Indus Upper | E(o) | 1.13 | 4,330 |\n| 2023 | 0143N0602023 | 34.627 | 75.360 | Indus Upper | E(o) | 0.99 | 4,430 |\n| 2024 | 0143N0602024 | 34.625 | 75.356 | Indus Upper | E(o) | 0.57 | 4,485 |\n| 2025 | 0143N0602025 | 34.621 | 75.395 | Indus Upper | E(o) | 1.60 | 4,420 |\n| 2026 | 0143N0602026 | 34.619 | 75.395 | Indus Upper | E(o) | 1.41 | 4,419 |\n| 2027 | 0143N0602027 | 34.612 | 75.400 | Indus Upper | E(c) | 7.39 | 4,457 |\n| 2028 | 0143N0602028 | 34.607 | 75.423 | Indus Upper | E(c) | 8.49 | 4,615 |\n| 2029 | 0143N0602029 | 34.593 | 75.421 | Indus Upper | E(c) | 1.60 | 4,624 |\n| 2030 | 0143N0602030 | 34.549 | 75.405 | Indus Upper | M(o) | 1.75 | 4,667 |\n| 2031 | 0143N0602031 | 34.547 | 75.496 | Indus Upper | E(c) | 1.73 | 4,682 |\n| 2032 | 0143N0602032 | 34.543 | 75.470 | Indus Upper | E(o) | 1.79 | 4,548 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8171, "line_end": 8245, "token_count_estimate": 1626, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N0501998", "0143N0501999", "0143N0502000", "0143N0502001", "0143N0502002", "0143N0502003", "0143N0502004", "0143N0502005", "0143N0502006", "0143N0502007", "0143N0502008", "0143N0502009", "0143N0502010", "0143N0502011", "0143N0502012", "0143N0602013", "0143N0602014", "0143N0602015", "0143N0602016", "0143N0602017", "0143N0602018", "0143N0602019", "0143N0602020", "0143N0602021", "0143N0602022", "0143N0602023", "0143N0602024", "0143N0602025", "0143N0602026", "0143N0602027", "0143N0602028", "0143N0602029", "0143N0602030", "0143N0602031", "0143N0602032"]}}
{"id": "5c1326b50d99eeb3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2033 | 0143N0602033 | 34.543 | 75.467 | Indus Upper | E(o) | 0.76 | 4,542 |\n| 2034 | 0143N0602034 | 34.541 | 75.469 | Indus Upper | E(o) | 0.69 | 4,548 |\n| 2035 | 0143N0602035 | 34.540 | 75.471 | Indus Upper | E(o) | 1.04 | 4,547 |\n| 2036 | 0143N0602036 | 34.539 | 75.492 | Indus Upper | M(o) | 1.08 | 4,704 |\n| 2037 | 0143N0602037 | 34.532 | 75.486 | Indus Upper | M(o) | 0.71 | 4,548 |\n| 2038 | 0143N0602038 | 34.527 | 75.482 | Indus Upper | E(o) | 0.69 | 4,597 |\n| 2039 | 0143N0602039 | 34.508 | 75.476 | Indus Upper | M(o) | 0.63 | 4,589 |\n| 2040 | 0143N0702040 | 34.470 | 75.349 | Indus Upper | E(o) | 1.29 | 4,126 |\n| 2041 | 0143N0702041 | 34.419 | 75.339 | Jhelum | E(o) | 0.47 | 4,316 |\n| 2042 | 0143N0702042 | 34.293 | 75.406 | Indus Upper | M(o) | 0.31 | 4,246 |\n| 2043 | 0143N0702043 | 34.290 | 75.412 | Indus Upper | E(o) | 1.31 | 4,193 |\n| 2044 | 0143N0802044 | 34.236 | 75.328 | Jhelum | E(o) | 1.45 | 4,170 |\n| 2045 | 0143N0802045 | 34.234 | 75.275 | Jhelum | E(o) | 18.23 | 3,830 |\n| 2046 | 0143N0802046 | 34.234 | 75.325 | Jhelum | E(o) | 7.77 | 4,279 |\n| 2047 | 0143N0802047 | 34.230 | 75.450 | Jhelum | E(o) | 0.47 | 4,178 |\n| 2048 | 0143N0802048 | 34.197 | 75.372 | Jhelum | M(l) | 0.28 | 4,354 |\n| 2049 | 0143N0802049 | 34.193 | 75.321 | Jhelum | E(c) | 10.81 | 3,734 |\n| 2050 | 0143N0802050 | 34.184 | 75.373 | Jhelum | E(c) | 16.80 | 4,276 |\n| 2051 | 0143N0802051 | 34.156 | 75.288 | Jhelum | E(o) | 1.45 | 3,540 |\n| 2052 | 0143N0802052 | 34.151 | 75.290 | Jhelum | E(o) | 0.40 | 3,559 |\n| 2053 | 0143N0802053 | 34.148 | 75.292 | Jhelum | E(o) | 0.80 | 3,611 |\n| 2054 | 0143N0802054 | 34.145 | 75.293 | Jhelum | M(o) | 1.13 | 3,626 |\n| 2055 | 0143N0802055 | 34.139 | 75.377 | Jhelum | E(o) | 33.45 | 3,709 |\n| 2056 | 0143N0802056 | 34.138 | 75.311 | Jhelum | E(o) | 0.64 | 3,827 |\n| 2057 | 0143N0802057 | 34.138 | 75.417 | Jhelum | E(o) | 9.84 | 4,083 |\n| 2058 | 0143N0802058 | 34.136 | 75.314 | Jhelum | M(e) | 7.28 | 3,856 |\n| 2059 | 0143N0802059 | 34.132 | 75.305 | Jhelum | E(o) | 0.39 | 3,744 |\n| 2060 | 0143N0802060 | 34.130 | 75.306 | Jhelum | E(o) | 3.35 | 3,741 |\n| 2061 | 0143N0802061 | 34.125 | 75.482 | Jhelum | E(o) | 0.38 | 4,159 |\n| 2062 | 0143N0802062 | 34.122 | 75.341 | Jhelum | E(o) | 0.49 | 4,044 |\n| 2063 | 0143N0802063 | 34.121 | 75.340 | Jhelum | E(o) | 0.30 | 4,036 |\n| 2064 | 0143N0802064 | 34.104 | 75.363 | Jhelum | E(o) | 0.74 | 4,096 |\n| 2065 | 0143N0802065 | 34.094 | 75.498 | Jhelum | O | 52.27 | 3,575 |\n| 2066 | 0143N0802066 | 34.067 | 75.475 | Jhelum | E(o) | 17.85 | 3,724 |\n| 2067 | 0143N0802067 | 34.039 | 75.411 | Jhelum | M(o) | 0.71 | 4,114 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8171, "line_end": 8245, "token_count_estimate": 1664, "basins": ["Indus"], "subbasins": ["Indus Upper", "Jhelum"], "countries": [], "lake_ids": ["0143N0602033", "0143N0602034", "0143N0602035", "0143N0602036", "0143N0602037", "0143N0602038", "0143N0602039", "0143N0702040", "0143N0702041", "0143N0702042", "0143N0702043", "0143N0802044", "0143N0802045", "0143N0802046", "0143N0802047", "0143N0802048", "0143N0802049", "0143N0802050", "0143N0802051", "0143N0802052", "0143N0802053", "0143N0802054", "0143N0802055", "0143N0802056", "0143N0802057", "0143N0802058", "0143N0802059", "0143N0802060", "0143N0802061", "0143N0802062", "0143N0802063", "0143N0802064", "0143N0802065", "0143N0802066", "0143N0802067"]}}
{"id": "33ee651e3d354630", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2068 | 0143N0802068 | 34.036 | 75.472 | Chenab | E(o) | 1.22 | 4,160 |\n| 2069 | 0143N0802069 | 34.003 | 75.467 | Chenab | M(o) | 0.78 | 4,023 |\n| 2070 | 0143N0802070 | 34.000 | 75.415 | Jhelum | M(o) | 0.30 | 4,349 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 8171, "line_end": 8245, "token_count_estimate": 242, "basins": ["Indus"], "subbasins": ["Chenab", "Jhelum"], "countries": [], "lake_ids": ["0143N0802068", "0143N0802069", "0143N0802070"]}}
{"id": "71196ed2c759ac29", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2071 | 0143N0902071 | 34.997 | 75.707 | Indus Upper | E(o) | 0.98 | 4,608 |\n| 2072 | 0143N0902072 | 34.994 | 75.734 | Indus Upper | I(s) | 0.34 | 4,788 |\n| 2073 | 0143N0902073 | 34.990 | 75.617 | Indus Upper | E(o) | 0.39 | 4,511 |\n| 2074 | 0143N0902074 | 34.987 | 75.553 | Indus Upper | E(o) | 17.86 | 4,269 |\n| 2075 | 0143N0902075 | 34.970 | 75.517 | Indus Upper | E(o) | 5.18 | 4,270 |\n| 2076 | 0143N0902076 | 34.969 | 75.533 | Indus Upper | E(o) | 1.72 | 4,362 |\n| 2077 | 0143N0902077 | 34.965 | 75.657 | Indus Upper | E(o) | 0.49 | 4,643 |\n| 2078 | 0143N0902078 | 34.965 | 75.545 | Indus Upper | E(o) | 13.70 | 4,412 |\n| 2079 | 0143N0902079 | 34.963 | 75.734 | Indus Upper | E(o) | 5.12 | 4,691 |\n| 2080 | 0143N0902080 | 34.962 | 75.606 | Indus Upper | E(o) | 1.68 | 4,529 |\n| 2081 | 0143N0902081 | 34.962 | 75.652 | Indus Upper | E(o) | 8.84 | 4,652 |\n| 2082 | 0143N0902082 | 34.962 | 75.532 | Indus Upper | E(o) | 5.93 | 4,387 |\n| 2083 | 0143N0902083 | 34.961 | 75.732 | Indus Upper | E(o) | 0.32 | 4,687 |\n| 2084 | 0143N0902084 | 34.956 | 75.726 | Indus Upper | E(o) | 1.50 | 4,699 |\n| 2085 | 0143N0902085 | 34.955 | 75.738 | Indus Upper | E(o) | 5.01 | 4,807 |\n| 2086 | 0143N0902086 | 34.951 | 75.729 | Indus Upper | E(o) | 6.06 | 4,677 |\n| 2087 | 0143N0902087 | 34.950 | 75.606 | Indus Upper | E(o) | 0.69 | 4,621 |\n| 2088 | 0143N0902088 | 34.949 | 75.601 | Indus Upper | E(o) | 0.75 | 4,595 |\n| 2089 | 0143N0902089 | 34.947 | 75.592 | Indus Upper | E(o) | 24.89 | 4,558 |\n| 2090 | 0143N0902090 | 34.939 | 75.709 | Indus Upper | E(o) | 9.87 | 4,644 |\n| 2091 | 0143N0902091 | 34.936 | 75.545 | Indus Upper | E(o) | 7.09 | 4,438 |\n| 2092 | 0143N0902092 | 34.935 | 75.634 | Indus Upper | E(o) | 1.01 | 4,803 |\n| 2093 | 0143N0902093 | 34.932 | 75.545 | Indus Upper | E(o) | 1.81 | 4,460 |\n| 2094 | 0143N0902094 | 34.932 | 75.615 | Indus Upper | E(o) | 1.00 | 4,729 |\n| 2095 | 0143N0902095 | 34.930 | 75.700 | Indus Upper | E(o) | 2.84 | 4,663 |\n| 2096 | 0143N0902096 | 34.929 | 75.710 | Indus Upper | E(o) | 1.22 | 4,709 |\n| 2097 | 0143N0902097 | 34.928 | 75.549 | Indus Upper | E(o) | 1.48 | 4,497 |\n| 2098 | 0143N0902098 | 34.925 | 75.597 | Indus Upper | E(o) | 7.01 | 4,549 |\n| 2099 | 0143N0902099 | 34.925 | 75.661 | Indus Upper | E(o) | 2.59 | 4,699 |\n| 2100 | 0143N0902100 | 34.924 | 75.656 | Indus Upper | E(o) | 0.84 | 4,696 |\n| 2101 | 0143N0902101 | 34.923 | 75.716 | Indus Upper | E(o) | 2.16 | 4,685 |\n| 2102 | 0143N0902102 | 34.923 | 75.518 | Indus Upper | E(o) | 3.47 | 4,359 |\n| 2103 | 0143N0902103 | 34.922 | 75.585 | Indus Upper | E(o) | 0.78 | 4,472 |\n| 2104 | 0143N0902104 | 34.921 | 75.711 | Indus Upper | E(o) | 4.32 | 4,689 |\n| 2105 | 0143N0902105 | 34.919 | 75.714 | Indus Upper | E(o) | 1.34 | 4,687 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8247, "line_end": 8321, "token_count_estimate": 1677, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N0902071", "0143N0902072", "0143N0902073", "0143N0902074", "0143N0902075", "0143N0902076", "0143N0902077", "0143N0902078", "0143N0902079", "0143N0902080", "0143N0902081", "0143N0902082", "0143N0902083", "0143N0902084", "0143N0902085", "0143N0902086", "0143N0902087", "0143N0902088", "0143N0902089", "0143N0902090", "0143N0902091", "0143N0902092", "0143N0902093", "0143N0902094", "0143N0902095", "0143N0902096", "0143N0902097", "0143N0902098", "0143N0902099", "0143N0902100", "0143N0902101", "0143N0902102", "0143N0902103", "0143N0902104", "0143N0902105"]}}
{"id": "d87134365aca34b2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2106 | 0143N0902106 | 34.917 | 75.551 | Indus Upper | E(o) | 5.27 | 4,504 |\n| 2107 | 0143N0902107 | 34.917 | 75.587 | Indus Upper | E(o) | 0.97 | 4,573 |\n| 2108 | 0143N0902108 | 34.915 | 75.740 | Indus Upper | E(o) | 1.30 | 4,740 |\n| 2109 | 0143N0902109 | 34.915 | 75.583 | Indus Upper | E(o) | 3.71 | 4,563 |\n| 2110 | 0143N0902110 | 34.914 | 75.617 | Indus Upper | E(o) | 6.86 | 4,636 |\n| 2111 | 0143N0902111 | 34.910 | 75.535 | Indus Upper | E(o) | 0.91 | 4,564 |\n| 2112 | 0143N0902112 | 34.908 | 75.685 | Indus Upper | E(o) | 3.85 | 4,745 |\n| 2113 | 0143N0902113 | 34.903 | 75.651 | Indus Upper | E(o) | 0.35 | 4,565 |\n| 2114 | 0143N0902114 | 34.899 | 75.651 | Indus Upper | M(o) | 0.99 | 4,581 |\n| 2115 | 0143N0902115 | 34.896 | 75.617 | Indus Upper | E(o) | 11.97 | 4,547 |\n| 2116 | 0143N0902116 | 34.895 | 75.604 | Indus Upper | E(o) | 0.50 | 4,508 |\n| 2117 | 0143N0902117 | 34.893 | 75.713 | Indus Upper | E(o) | 19.39 | 4,589 |\n| 2118 | 0143N0902118 | 34.891 | 75.719 | Indus Upper | E(o) | 2.67 | 4,584 |\n| 2119 | 0143N0902119 | 34.891 | 75.668 | Indus Upper | E(o) | 6.66 | 4,704 |\n| 2120 | 0143N0902120 | 34.889 | 75.618 | Indus Upper | E(o) | 3.62 | 4,557 |\n| 2121 | 0143N0902121 | 34.887 | 75.697 | Indus Upper | E(o) | 8.42 | 4,603 |\n| 2122 | 0143N0902122 | 34.884 | 75.668 | Indus Upper | E(o) | 2.96 | 4,679 |\n| 2123 | 0143N0902123 | 34.884 | 75.670 | Indus Upper | E(o) | 0.65 | 4,675 |\n| 2124 | 0143N0902124 | 34.883 | 75.680 | Indus Upper | E(o) | 13.31 | 4,627 |\n| 2125 | 0143N0902125 | 34.876 | 75.680 | Indus Upper | M(o) | 0.95 | 4,766 |\n| 2126 | 0143N0902126 | 34.873 | 75.716 | Indus Upper | E(o) | 15.83 | 4,664 |\n| 2127 | 0143N0902127 | 34.873 | 75.653 | Indus Upper | E(o) | 5.11 | 4,683 |\n| 2128 | 0143N0902128 | 34.869 | 75.709 | Indus Upper | E(o) | 4.02 | 4,757 |\n| 2129 | 0143N0902129 | 34.867 | 75.659 | Indus Upper | E(o) | 2.65 | 4,669 |\n| 2130 | 0143N0902130 | 34.865 | 75.732 | Indus Upper | E(o) | 0.47 | 4,504 |\n| 2131 | 0143N0902131 | 34.864 | 75.691 | Indus Upper | E(o) | 1.81 | 4,716 |\n| 2132 | 0143N0902132 | 34.861 | 75.689 | Indus Upper | E(o) | 2.35 | 4,661 |\n| 2133 | 0143N0902133 | 34.861 | 75.662 | Indus Upper | E(o) | 1.23 | 4,606 |\n| 2134 | 0143N0902134 | 34.858 | 75.687 | Indus Upper | E(o) | 1.15 | 4,657 |\n| 2135 | 0143N0902135 | 34.856 | 75.689 | Indus Upper | E(o) | 0.64 | 4,670 |\n| 2136 | 0143N0902136 | 34.854 | 75.690 | Indus Upper | E(o) | 2.10 | 4,673 |\n| 2137 | 0143N0902137 | 34.846 | 75.708 | Indus Upper | E(o) | 6.05 | 4,744 |\n| 2138 | 0143N0902138 | 34.845 | 75.702 | Indus Upper | E(o) | 3.94 | 4,754 |\n| 2139 | 0143N0902139 | 34.841 | 75.684 | Indus Upper | E(o) | 0.63 | 4,613 |\n| 2140 | 0143N0902140 | 34.840 | 75.716 | Indus Upper | E(o) | 9.21 | 4,627 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8247, "line_end": 8321, "token_count_estimate": 1670, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N0902106", "0143N0902107", "0143N0902108", "0143N0902109", "0143N0902110", "0143N0902111", "0143N0902112", "0143N0902113", "0143N0902114", "0143N0902115", "0143N0902116", "0143N0902117", "0143N0902118", "0143N0902119", "0143N0902120", "0143N0902121", "0143N0902122", "0143N0902123", "0143N0902124", "0143N0902125", "0143N0902126", "0143N0902127", "0143N0902128", "0143N0902129", "0143N0902130", "0143N0902131", "0143N0902132", "0143N0902133", "0143N0902134", "0143N0902135", "0143N0902136", "0143N0902137", "0143N0902138", "0143N0902139", "0143N0902140"]}}
{"id": "412b465f138ad736", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2141 | 0143N0902141 | 34.830 | 75.575 | Indus Upper | E(o) | 0.72 | 4,503 |\n| 2142 | 0143N0902142 | 34.826 | 75.599 | Indus Upper | E(o) | 2.04 | 4,378 |\n| 2143 | 0143N0902143 | 34.825 | 75.581 | Indus Upper | E(o) | 0.37 | 4,441 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 8247, "line_end": 8321, "token_count_estimate": 245, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N0902141", "0143N0902142", "0143N0902143"]}}
{"id": "c201329a97ae452f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8322, "line_end": 8330, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34a46c4f4b329b5e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2144 | 0143N0902144 | 34.823 | 75.584 | Indus Upper | E(o) | 5.31 | 4,458 |\n| 2145 | 0143N0902145 | 34.820 | 75.580 | Indus Upper | E(o) | 1.65 | 4,472 |\n| 2146 | 0143N0902146 | 34.818 | 75.582 | Indus Upper | E(o) | 0.77 | 4,484 |\n| 2147 | 0143N0902147 | 34.818 | 75.536 | Indus Upper | E(o) | 0.92 | 4,639 |\n| 2148 | 0143N0902148 | 34.813 | 75.500 | Indus Upper | E(o) | 1.21 | 4,591 |\n| 2149 | 0143N0902149 | 34.808 | 75.545 | Indus Upper | E(o) | 12.86 | 4,687 |\n| 2150 | 0143N0902150 | 34.803 | 75.553 | Indus Upper | E(o) | 0.44 | 4,668 |\n| 2151 | 0143N0902151 | 34.800 | 75.534 | Indus Upper | E(o) | 0.41 | 4,732 |\n| 2152 | 0143N0902152 | 34.793 | 75.513 | Indus Upper | E(o) | 0.75 | 4,655 |\n| 2153 | 0143N0902153 | 34.791 | 75.514 | Indus Upper | E(o) | 5.73 | 4,658 |\n| 2154 | 0143N0902154 | 34.758 | 75.503 | Indus Upper | E(o) | 1.31 | 4,504 |\n| 2155 | 0143N0902155 | 34.751 | 75.666 | Indus Upper | E(o) | 2.57 | 4,528 |\n| 2156 | 0143N1002156 | 34.742 | 75.504 | Indus Upper | E(o) | 0.36 | 4,650 |\n| 2157 | 0143N1002157 | 34.735 | 75.522 | Indus Upper | E(o) | 1.98 | 4,703 |\n| 2158 | 0143N1002158 | 34.727 | 75.564 | Indus Upper | E(o) | 2.99 | 4,605 |\n| 2159 | 0143N1002159 | 34.716 | 75.670 | Indus Upper | E(o) | 1.36 | 4,638 |\n| 2160 | 0143N1002160 | 34.705 | 75.667 | Indus Upper | E(o) | 0.55 | 4,486 |\n| 2161 | 0143N1002161 | 34.568 | 75.705 | Indus Upper | E(o) | 3.31 | 4,646 |\n| 2162 | 0143N1002162 | 34.560 | 75.706 | Indus Upper | M(o) | 3.22 | 4,755 |\n| 2163 | 0143N1002163 | 34.548 | 75.656 | Indus Upper | E(o) | 0.50 | 4,635 |\n| 2164 | 0143N1002164 | 34.544 | 75.682 | Indus Upper | M(o) | 1.06 | 4,618 |\n| 2165 | 0143N1002165 | 34.531 | 75.542 | Indus Upper | M(o) | 0.98 | 4,696 |\n| 2166 | 0143N1002166 | 34.512 | 75.750 | Indus Upper | E(c) | 9.16 | 4,693 |\n| 2167 | 0143N1002167 | 34.510 | 75.549 | Indus Upper | M(o) | 0.86 | 4,729 |\n| 2168 | 0143N1002168 | 34.508 | 75.636 | Indus Upper | E(o) | 1.02 | 4,385 |\n| 2169 | 0143N1002169 | 34.506 | 75.744 | Indus Upper | M(e) | 0.56 | 4,575 |\n| 2170 | 0143N1002170 | 34.501 | 75.643 | Indus Upper | M(o) | 0.86 | 4,541 |\n| 2171 | 0143N1102171 | 34.495 | 75.639 | Indus Upper | M(o) | 7.65 | 4,616 |\n| 2172 | 0143N1102172 | 34.492 | 75.612 | Indus Upper | M(o) | 1.90 | 4,684 |\n| 2173 | 0143N1102173 | 34.491 | 75.649 | Indus Upper | E(o) | 15.37 | 4,521 |\n| 2174 | 0143N1102174 | 34.291 | 75.649 | Indus Upper | M(o) | 0.81 | 4,500 |\n| 2175 | 0143N1202175 | 34.221 | 75.603 | Indus Upper | E(o) | 1.11 | 4,482 |\n| 2176 | 0143N1202176 | 34.179 | 75.738 | Indus Upper | M(o) | 0.27 | 3,881 |\n| 2177 | 0143N1202177 | 34.157 | 75.605 | Chenab | E(o) | 0.31 | 3,353 |\n| 2178 | 0143N1202178 | 34.145 | 75.723 | Indus Upper | M(e) | 2.93 | 4,600 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8331, "line_end": 8405, "token_count_estimate": 1672, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0143N0902144", "0143N0902145", "0143N0902146", "0143N0902147", "0143N0902148", "0143N0902149", "0143N0902150", "0143N0902151", "0143N0902152", "0143N0902153", "0143N0902154", "0143N0902155", "0143N1002156", "0143N1002157", "0143N1002158", "0143N1002159", "0143N1002160", "0143N1002161", "0143N1002162", "0143N1002163", "0143N1002164", "0143N1002165", "0143N1002166", "0143N1002167", "0143N1002168", "0143N1002169", "0143N1002170", "0143N1102171", "0143N1102172", "0143N1102173", "0143N1102174", "0143N1202175", "0143N1202176", "0143N1202177", "0143N1202178"]}}
{"id": "d7286456c96900a1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2179 | 0143N1202179 | 34.066 | 75.750 | Indus Upper | M(o) | 3.97 | 4,563 |\n| 2180 | 0143N1202180 | 34.055 | 75.718 | Chenab | M(o) | 0.60 | 4,611 |\n| 2181 | 0143N1202181 | 34.055 | 75.724 | Chenab | E(o) | 1.11 | 4,660 |\n| 2182 | 0143N1202182 | 34.051 | 75.580 | Chenab | E(o) | 0.45 | 4,055 |\n| 2183 | 0143N1202183 | 34.037 | 75.617 | Chenab | M(o) | 0.36 | 4,215 |\n| 2184 | 0143N1202184 | 34.037 | 75.614 | Chenab | E(o) | 1.21 | 4,281 |\n| 2185 | 0143N1202185 | 34.028 | 75.517 | Chenab | E(c) | 2.04 | 4,070 |\n| 2186 | 0143N1202186 | 34.000 | 75.586 | Chenab | M(o) | 1.17 | 4,335 |\n| 2187 | 0143N1302187 | 34.994 | 75.754 | Indus Upper | E(o) | 1.55 | 4,606 |\n| 2188 | 0143N1302188 | 34.987 | 75.763 | Indus Upper | M(o) | 0.39 | 4,742 |\n| 2189 | 0143N1302189 | 34.985 | 75.763 | Indus Upper | M(o) | 0.70 | 4,734 |\n| 2190 | 0143N1302190 | 34.975 | 75.854 | Indus Upper | E(o) | 0.99 | 4,631 |\n| 2191 | 0143N1302191 | 34.970 | 75.775 | Indus Upper | E(o) | 1.03 | 4,693 |\n| 2192 | 0143N1302192 | 34.968 | 75.778 | Indus Upper | E(o) | 3.65 | 4,667 |\n| 2193 | 0143N1302193 | 34.952 | 75.879 | Indus Upper | E(o) | 0.69 | 4,645 |\n| 2194 | 0143N1302194 | 34.947 | 75.763 | Indus Upper | E(o) | 25.14 | 4,778 |\n| 2195 | 0143N1302195 | 34.947 | 75.843 | Indus Upper | E(o) | 0.80 | 4,774 |\n| 2196 | 0143N1302196 | 34.946 | 75.848 | Indus Upper | E(o) | 3.12 | 4,800 |\n| 2197 | 0143N1302197 | 34.945 | 75.835 | Indus Upper | E(o) | 4.02 | 4,747 |\n| 2198 | 0143N1302198 | 34.945 | 75.823 | Indus Upper | E(o) | 2.55 | 4,558 |\n| 2199 | 0143N1302199 | 34.937 | 75.828 | Indus Upper | M(o) | 2.02 | 4,652 |\n| 2200 | 0143N1302200 | 34.923 | 75.774 | Indus Upper | E(o) | 0.28 | 4,821 |\n| 2201 | 0143N1302201 | 34.922 | 75.776 | Indus Upper | E(o) | 0.95 | 4,811 |\n| 2202 | 0143N1302202 | 34.918 | 75.765 | Indus Upper | E(o) | 8.23 | 4,764 |\n| 2203 | 0143N1302203 | 34.911 | 75.919 | Indus Upper | E(o) | 0.54 | 4,630 |\n| 2204 | 0143N1302204 | 34.903 | 75.916 | Indus Upper | E(o) | 3.59 | 4,712 |\n| 2205 | 0143N1302205 | 34.902 | 75.920 | Indus Upper | E(o) | 1.60 | 4,722 |\n| 2206 | 0143N1302206 | 34.894 | 75.891 | Indus Upper | E(o) | 1.42 | 4,943 |\n| 2207 | 0143N1302207 | 34.888 | 75.892 | Indus Upper | E(o) | 10.29 | 4,882 |\n| 2208 | 0143N1302208 | 34.888 | 75.991 | Indus Upper | E(o) | 1.46 | 4,678 |\n| 2209 | 0143N1302209 | 34.886 | 75.886 | Indus Upper | E(o) | 1.16 | 4,874 |\n| 2210 | 0143N1302210 | 34.882 | 75.907 | Indus Upper | M(e) | 4.10 | 4,929 |\n| 2211 | 0143N1302211 | 34.879 | 75.963 | Indus Upper | E(o) | 3.92 | 4,876 |\n| 2212 | 0143N1302212 | 34.878 | 75.871 | Indus Upper | E(o) | 18.96 | 4,687 |\n| 2213 | 0143N1302213 | 34.878 | 75.892 | Indus Upper | E(o) | 0.54 | 4,768 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8331, "line_end": 8405, "token_count_estimate": 1668, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0143N1202179", "0143N1202180", "0143N1202181", "0143N1202182", "0143N1202183", "0143N1202184", "0143N1202185", "0143N1202186", "0143N1302187", "0143N1302188", "0143N1302189", "0143N1302190", "0143N1302191", "0143N1302192", "0143N1302193", "0143N1302194", "0143N1302195", "0143N1302196", "0143N1302197", "0143N1302198", "0143N1302199", "0143N1302200", "0143N1302201", "0143N1302202", "0143N1302203", "0143N1302204", "0143N1302205", "0143N1302206", "0143N1302207", "0143N1302208", "0143N1302209", "0143N1302210", "0143N1302211", "0143N1302212", "0143N1302213"]}}
{"id": "17ababf17fbf7c04", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2214 | 0143N1302214 | 34.875 | 75.948 | Indus Upper | E(o) | 2.52 | 4,679 |\n| 2215 | 0143N1302215 | 34.873 | 75.927 | Indus Upper | I(s) | 1.46 | 4,803 |\n| 2216 | 0143N1302216 | 34.860 | 75.888 | Indus Upper | E(o) | 0.59 | 4,881 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 8331, "line_end": 8405, "token_count_estimate": 245, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N1302214", "0143N1302215", "0143N1302216"]}}
{"id": "41267b15bec9ec8a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2217 | 0143N1302217 | 34.860 | 75.894 | Indus Upper | E(o) | 4.27 | 4,850 |\n| 2218 | 0143N1302218 | 34.859 | 75.942 | Indus Upper | E(o) | 13.73 | 4,796 |\n| 2219 | 0143N1302219 | 34.859 | 75.782 | Indus Upper | E(o) | 1.49 | 4,662 |\n| 2220 | 0143N1302220 | 34.858 | 75.998 | Indus Upper | E(o) | 1.87 | 4,705 |\n| 2221 | 0143N1302221 | 34.858 | 75.872 | Indus Upper | E(o) | 7.11 | 4,624 |\n| 2222 | 0143N1302222 | 34.853 | 75.785 | Indus Upper | E(o) | 3.17 | 4,741 |\n| 2223 | 0143N1302223 | 34.851 | 75.810 | Indus Upper | E(o) | 3.09 | 4,625 |\n| 2224 | 0143N1302224 | 34.849 | 75.882 | Indus Upper | E(o) | 1.20 | 4,656 |\n| 2225 | 0143N1302225 | 34.847 | 75.773 | Indus Upper | E(o) | 0.88 | 4,580 |\n| 2226 | 0143N1302226 | 34.846 | 75.810 | Indus Upper | E(o) | 0.84 | 4,692 |\n| 2227 | 0143N1302227 | 34.846 | 75.806 | Indus Upper | E(o) | 0.32 | 4,719 |\n| 2228 | 0143N1302228 | 34.844 | 75.941 | Indus Upper | E(o) | 5.35 | 4,725 |\n| 2229 | 0143N1302229 | 34.843 | 75.776 | Indus Upper | E(o) | 2.62 | 4,625 |\n| 2230 | 0143N1302230 | 34.841 | 75.923 | Indus Upper | E(o) | 0.43 | 4,608 |\n| 2231 | 0143N1302231 | 34.836 | 75.920 | Indus Upper | E(o) | 0.27 | 4,550 |\n| 2232 | 0143N1302232 | 34.831 | 75.939 | Indus Upper | E(o) | 2.00 | 4,896 |\n| 2233 | 0143N1302233 | 34.825 | 75.941 | Indus Upper | E(o) | 5.61 | 4,860 |\n| 2234 | 0143N1302234 | 34.817 | 75.960 | Indus Upper | E(o) | 4.26 | 4,822 |\n| 2235 | 0143N1302235 | 34.812 | 75.979 | Indus Upper | E(o) | 0.49 | 4,838 |\n| 2236 | 0143N1302236 | 34.810 | 75.977 | Indus Upper | E(o) | 3.01 | 4,851 |\n| 2237 | 0143N1302237 | 34.806 | 75.961 | Indus Upper | E(o) | 2.52 | 4,748 |\n| 2238 | 0143N1302238 | 34.795 | 75.992 | Indus Upper | E(o) | 1.45 | 4,807 |\n| 2239 | 0143N1302239 | 34.793 | 75.998 | Indus Upper | E(o) | 3.02 | 4,782 |\n| 2240 | 0143N1302240 | 34.793 | 75.990 | Indus Upper | E(o) | 4.05 | 4,825 |\n| 2241 | 0143N1302241 | 34.793 | 75.979 | Indus Upper | E(o) | 0.41 | 4,865 |\n| 2242 | 0143N1302242 | 34.790 | 75.981 | Indus Upper | E(o) | 0.26 | 4,865 |\n| 2243 | 0143N1302243 | 34.789 | 75.995 | Indus Upper | E(o) | 4.67 | 4,728 |\n| 2244 | 0143N1302244 | 34.761 | 75.990 | Indus Upper | E(o) | 2.61 | 4,836 |\n| 2245 | 0143N1302245 | 34.760 | 75.961 | Indus Upper | E(o) | 1.54 | 4,716 |\n| 2246 | 0143N1302246 | 34.757 | 75.979 | Indus Upper | E(o) | 1.02 | 4,888 |\n| 2247 | 0143N1302247 | 34.756 | 75.983 | Indus Upper | E(o) | 0.27 | 4,861 |\n| 2248 | 0143N1302248 | 34.753 | 75.982 | Indus Upper | E(o) | 0.93 | 4,836 |\n| 2249 | 0143N1402249 | 34.748 | 75.985 | Indus Upper | E(o) | 3.25 | 4,804 |\n| 2250 | 0143N1402250 | 34.741 | 75.963 | Indus Upper | E(o) | 8.91 | 4,781 |\n| 2251 | 0143N1402251 | 34.535 | 75.903 | Indus Upper | E(o) | 0.32 | 4,797 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8407, "line_end": 8481, "token_count_estimate": 1679, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0143N1302217", "0143N1302218", "0143N1302219", "0143N1302220", "0143N1302221", "0143N1302222", "0143N1302223", "0143N1302224", "0143N1302225", "0143N1302226", "0143N1302227", "0143N1302228", "0143N1302229", "0143N1302230", "0143N1302231", "0143N1302232", "0143N1302233", "0143N1302234", "0143N1302235", "0143N1302236", "0143N1302237", "0143N1302238", "0143N1302239", "0143N1302240", "0143N1302241", "0143N1302242", "0143N1302243", "0143N1302244", "0143N1302245", "0143N1302246", "0143N1302247", "0143N1302248", "0143N1402249", "0143N1402250", "0143N1402251"]}}
{"id": "161ae37f07f13c62", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2252 | 0143N1402252 | 34.532 | 75.879 | Indus Upper | M(o) | 1.30 | 4,765 |\n| 2253 | 0143N1402253 | 34.523 | 75.772 | Indus Upper | E(o) | 7.13 | 4,583 |\n| 2254 | 0143N1402254 | 34.509 | 75.809 | Indus Upper | E(o) | 1.25 | 4,689 |\n| 2255 | 0143N1502255 | 34.483 | 75.766 | Indus Upper | E(o) | 0.44 | 4,330 |\n| 2256 | 0143N1502256 | 34.269 | 75.756 | Indus Upper | I(s) | 0.28 | 4,448 |\n| 2257 | 0143N1602257 | 34.081 | 75.756 | Indus Upper | M(l) | 0.38 | 4,353 |\n| 2258 | 0143N1602258 | 34.048 | 75.853 | Indus Upper | E(o) | 0.68 | 4,006 |\n| 2259 | 0143N1602259 | 34.040 | 75.844 | Indus Upper | M(e) | 25.26 | 4,094 |\n| 2260 | 0143N1602260 | 34.026 | 75.996 | Indus Upper | I(s) | 0.34 | 4,517 |\n| 2261 | 0143N1602261 | 34.020 | 75.827 | Indus Upper | M(o) | 3.27 | 4,294 |\n| 2262 | 0143N1602262 | 34.015 | 75.819 | Indus Upper | M(e) | 4.19 | 4,408 |\n| 2263 | 0143O0502263 | 33.990 | 75.393 | Jhelum | E(o) | 1.50 | 3,673 |\n| 2264 | 0143O0502264 | 33.987 | 75.393 | Jhelum | E(o) | 0.48 | 3,673 |\n| 2265 | 0143O0502265 | 33.971 | 75.451 | Chenab | E(o) | 1.77 | 3,988 |\n| 2266 | 0143O0502266 | 33.971 | 75.412 | Chenab | E(o) | 1.42 | 4,484 |\n| 2267 | 0143O0502267 | 33.968 | 75.449 | Chenab | E(o) | 2.45 | 3,994 |\n| 2268 | 0143O0502268 | 33.961 | 75.439 | Chenab | E(c) | 0.86 | 4,164 |\n| 2269 | 0143O0502269 | 33.953 | 75.378 | Jhelum | E(o) | 15.37 | 3,644 |\n| 2270 | 0143O0502270 | 33.952 | 75.411 | Chenab | E(c) | 26.79 | 4,082 |\n| 2271 | 0143O0502271 | 33.936 | 75.372 | Jhelum | E(o) | 0.43 | 3,800 |\n| 2272 | 0143O0502272 | 33.934 | 75.377 | Jhelum | E(o) | 4.25 | 3,810 |\n| 2273 | 0143O0502273 | 33.929 | 75.389 | Jhelum | E(c) | 19.34 | 4,048 |\n| 2274 | 0143O0502274 | 33.923 | 75.386 | Jhelum | E(o) | 2.33 | 4,164 |\n| 2275 | 0143O0502275 | 33.920 | 75.374 | Jhelum | E(o) | 0.65 | 3,802 |\n| 2276 | 0143O0502276 | 33.912 | 75.382 | Jhelum | E(o) | 0.40 | 3,990 |\n| 2277 | 0143O0502277 | 33.912 | 75.387 | Jhelum | E(o) | 3.27 | 4,009 |\n| 2278 | 0143O0502278 | 33.911 | 75.384 | Jhelum | E(o) | 5.18 | 3,994 |\n| 2279 | 0143O0502279 | 33.905 | 75.407 | Chenab | E(o) | 0.48 | 3,887 |\n| 2280 | 0143O0502280 | 33.875 | 75.426 | Chenab | E(o) | 0.26 | 3,964 |\n| 2281 | 0143O0502281 | 33.873 | 75.422 | Chenab | E(c) | 5.76 | 3,963 |\n| 2282 | 0143O0502282 | 33.869 | 75.452 | Chenab | E(c) | 2.70 | 4,040 |\n| 2283 | 0143O0502283 | 33.866 | 75.437 | Chenab | E(o) | 0.36 | 3,790 |\n| 2284 | 0143O0502284 | 33.855 | 75.421 | Jhelum | E(o) | 5.41 | 3,906 |\n| 2285 | 0143O0502285 | 33.849 | 75.432 | Chenab | E(o) | 0.39 | 4,206 |\n| 2286 | 0143O0502286 | 33.845 | 75.447 | Chenab | E(o) | 4.15 | 3,842 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8407, "line_end": 8481, "token_count_estimate": 1656, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper", "Jhelum"], "countries": [], "lake_ids": ["0143N1402252", "0143N1402253", "0143N1402254", "0143N1502255", "0143N1502256", "0143N1602257", "0143N1602258", "0143N1602259", "0143N1602260", "0143N1602261", "0143N1602262", "0143O0502263", "0143O0502264", "0143O0502265", "0143O0502266", "0143O0502267", "0143O0502268", "0143O0502269", "0143O0502270", "0143O0502271", "0143O0502272", "0143O0502273", "0143O0502274", "0143O0502275", "0143O0502276", "0143O0502277", "0143O0502278", "0143O0502279", "0143O0502280", "0143O0502281", "0143O0502282", "0143O0502283", "0143O0502284", "0143O0502285", "0143O0502286"]}}
{"id": "6071ff852a1f7432", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2287 | 0143O0502287 | 33.814 | 75.451 | Jhelum | E(o) | 3.06 | 4,053 |\n| 2288 | 0143O0502288 | 33.811 | 75.448 | Jhelum | E(o) | 1.94 | 3,904 |\n| 2289 | 0143O0502289 | 33.807 | 75.489 | Chenab | E(o) | 0.89 | 3,976 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 8407, "line_end": 8481, "token_count_estimate": 244, "basins": ["Indus"], "subbasins": ["Chenab", "Jhelum"], "countries": [], "lake_ids": ["0143O0502287", "0143O0502288", "0143O0502289"]}}
{"id": "e2650641a2d939fe", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8482, "line_end": 8491, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d0ddf21a1ac5aee0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2290 | 0143O0502290 | 33.799 | 75.465 | Chenab | E(c) | 7.54 | 3,994 |\n| 2291 | 0143O0502291 | 33.780 | 75.492 | Chenab | E(c) | 8.11 | 4,038 |\n| 2292 | 0143O0502292 | 33.769 | 75.473 | Chenab | E(o) | 2.87 | 3,986 |\n| 2293 | 0143O0502293 | 33.759 | 75.474 | Chenab | E(o) | 2.89 | 3,925 |\n| 2294 | 0143O0502294 | 33.754 | 75.470 | Jhelum | E(o) | 10.88 | 3,921 |\n| 2295 | 0143O0602295 | 33.749 | 75.470 | Jhelum | E(o) | 3.53 | 3,914 |\n| 2296 | 0143O0602296 | 33.619 | 75.537 | Chenab | E(c) | 10.85 | 3,852 |\n| 2297 | 0143O0702297 | 33.455 | 75.478 | Jhelum | E(o) | 6.97 | 3,831 |\n| 2298 | 0143O0702298 | 33.449 | 75.484 | Jhelum | E(o) | 4.68 | 3,887 |\n| 2299 | 0143O0902299 | 33.990 | 75.575 | Chenab | E(o) | 1.87 | 4,285 |\n| 2300 | 0143O0902300 | 33.980 | 75.585 | Chenab | E(o) | 3.80 | 4,271 |\n| 2301 | 0143O0902301 | 33.980 | 75.581 | Chenab | E(o) | 1.93 | 4,260 |\n| 2302 | 0143O0902302 | 33.979 | 75.578 | Chenab | E(o) | 1.19 | 4,263 |\n| 2303 | 0143O0902303 | 33.976 | 75.583 | Chenab | E(o) | 4.02 | 4,246 |\n| 2304 | 0143O0902304 | 33.974 | 75.573 | Chenab | E(o) | 9.81 | 4,118 |\n| 2305 | 0143O0902305 | 33.969 | 75.701 | Chenab | E(o) | 2.45 | 4,609 |\n| 2306 | 0143O0902306 | 33.935 | 75.617 | Chenab | E(o) | 0.92 | 4,152 |\n| 2307 | 0143O0902307 | 33.932 | 75.659 | Chenab | M(o) | 1.20 | 4,367 |\n| 2308 | 0143O0902308 | 33.932 | 75.628 | Chenab | E(c) | 7.27 | 4,151 |\n| 2309 | 0143O0902309 | 33.931 | 75.618 | Chenab | E(o) | 0.76 | 4,270 |\n| 2310 | 0143O0902310 | 33.930 | 75.623 | Chenab | E(o) | 0.29 | 4,240 |\n| 2311 | 0143O0902311 | 33.930 | 75.616 | Chenab | E(o) | 1.33 | 4,249 |\n| 2312 | 0143O0902312 | 33.926 | 75.621 | Chenab | E(o) | 0.27 | 4,412 |\n| 2313 | 0143O0902313 | 33.925 | 75.632 | Chenab | M(o) | 0.30 | 4,236 |\n| 2314 | 0143O0902314 | 33.922 | 75.632 | Chenab | M(e) | 4.08 | 4,228 |\n| 2315 | 0143O0902315 | 33.918 | 75.614 | Chenab | M(o) | 1.80 | 4,187 |\n| 2316 | 0143O0902316 | 33.917 | 75.578 | Chenab | E(c) | 2.23 | 4,130 |\n| 2317 | 0143O0902317 | 33.915 | 75.614 | Chenab | E(o) | 0.56 | 4,277 |\n| 2318 | 0143O0902318 | 33.909 | 75.681 | Chenab | E(o) | 0.74 | 4,219 |\n| 2319 | 0143O0902319 | 33.879 | 75.725 | Chenab | E(o) | 0.82 | 4,409 |\n| 2320 | 0143O0902320 | 33.850 | 75.688 | Chenab | E(c) | 3.03 | 4,255 |\n| 2321 | 0143O0902321 | 33.829 | 75.712 | Chenab | E(o) | 0.55 | 4,282 |\n| 2322 | 0143O1002322 | 33.738 | 75.521 | Chenab | E(o) | 0.48 | 3,947 |\n| 2323 | 0143O1002323 | 33.736 | 75.508 | Chenab | E(o) | 2.64 | 3,874 |\n| 2324 | 0143O1002324 | 33.730 | 75.511 | Chenab | E(o) | 1.90 | 3,931 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8492, "line_end": 8639, "token_count_estimate": 1620, "basins": ["Indus"], "subbasins": ["Chenab", "Jhelum"], "countries": [], "lake_ids": ["0143O0502290", "0143O0502291", "0143O0502292", "0143O0502293", "0143O0502294", "0143O0602295", "0143O0602296", "0143O0702297", "0143O0702298", "0143O0902299", "0143O0902300", "0143O0902301", "0143O0902302", "0143O0902303", "0143O0902304", "0143O0902305", "0143O0902306", "0143O0902307", "0143O0902308", "0143O0902309", "0143O0902310", "0143O0902311", "0143O0902312", "0143O0902313", "0143O0902314", "0143O0902315", "0143O0902316", "0143O0902317", "0143O0902318", "0143O0902319", "0143O0902320", "0143O0902321", "0143O1002322", "0143O1002323", "0143O1002324"]}}
{"id": "411dd8249bc5ed07", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2325 | 0143O1002325 | 33.723 | 75.506 | Jhelum | E(o) | 1.51 | 4,080 |\n| 2326 | 0143O1002326 | 33.715 | 75.518 | Jhelum | E(o) | 1.76 | 4,159 |\n| 2327 | 0143O1002327 | 33.633 | 75.504 | Chenab | E(c) | 2.86 | 4,056 |\n| 2328 | 0143O1002328 | 33.633 | 75.513 | Jhelum | E(o) | 1.90 | 4,123 |\n| 2329 | 0143O1002329 | 33.630 | 75.521 | Chenab | E(o) | 1.87 | 3,938 |\n| 2330 | 0143O1002330 | 33.625 | 75.514 | Chenab | E(c) | 7.19 | 4,022 |\n| 2331 | 0143O1002331 | 33.568 | 75.689 | Chenab | E(o) | 0.52 | 3,991 |\n| 2332 | 0143O1002332 | 33.549 | 75.696 | Chenab | E(o) | 1.27 | 4,078 |\n| 2333 | 0143O1302333 | 33.999 | 75.763 | Chenab | M(o) | 0.83 | 3,606 |\n| 2334 | 0143O1302334 | 33.987 | 75.849 | Chenab | M(l) | 0.92 | 4,510 |\n| 2335 | 0143O1302335 | 33.951 | 75.900 | Chenab | I(s) | 0.86 | 4,813 |\n| 2336 | 0143O1302336 | 33.904 | 75.979 | Chenab | M(o) | 2.54 | 4,752 |\n| 2337 | 0143O1302337 | 33.840 | 75.761 | Chenab | E(o) | 0.34 | 3,194 |\n| 2338 | 0143O1302338 | 33.761 | 75.815 | Chenab | E(o) | 8.93 | 4,284 |\n| 2339 | 0143O1302339 | 33.759 | 75.812 | Chenab | E(o) | 1.03 | 4,353 |\n| 2340 | 0143O1402340 | 33.718 | 75.774 | Chenab | E(o) | 5.34 | 4,209 |\n| 2341 | 0143O1402341 | 33.697 | 75.975 | Chenab | M(o) | 0.36 | 4,652 |\n| 2342 | 0143O1402342 | 33.627 | 75.886 | Chenab | E(o) | 4.18 | 4,463 |\n| 2343 | 0143O1402343 | 33.619 | 75.850 | Chenab | E(o) | 1.87 | 4,196 |\n| 2344 | 0143O1402344 | 33.617 | 75.857 | Chenab | M(o) | 0.54 | 4,477 |\n| 2345 | 0143P0902345 | 32.879 | 75.685 | Chenab | E(o) | 1.76 | 3,845 |\n| 2346 | 0143P0902346 | 32.877 | 75.686 | Chenab | E(o) | 1.03 | 3,844 |\n| 2347 | 0143P0902347 | 32.876 | 75.682 | Chenab | E(o) | 1.35 | 3,849 |\n| 2348 | 0143P0902348 | 32.872 | 75.688 | Chenab | E(c) | 9.53 | 3,916 |\n| 2349 | 0143P0902349 | 32.872 | 75.678 | Chenab | E(c) | 1.78 | 4,069 |\n| 2350 | 0143P0902350 | 32.867 | 75.669 | Chenab | E(c) | 4.73 | 3,902 |\n| 2351 | 0152A0102351 | 35.970 | 76.013 | Indus Middle | I(s) | 0.66 | 4,315 |\n| 2352 | 0152A0102352 | 35.966 | 76.022 | Indus Middle | I(s) | 0.51 | 4,289 |\n| 2353 | 0152A0102353 | 35.961 | 76.027 | Indus Middle | M(l) | 0.56 | 4,311 |\n| 2354 | 0152A0102354 | 35.954 | 76.030 | Indus Middle | M(l) | 10.62 | 4,276 |\n| 2355 | 0152A0102355 | 35.949 | 76.016 | Indus Middle | I(s) | 0.32 | 4,239 |\n| 2356 | 0152A0102356 | 35.937 | 76.026 | Indus Middle | M(l) | 7.29 | 4,188 |\n| 2357 | 0152A0102357 | 35.916 | 76.003 | Indus Middle | I(s) | 0.49 | 4,126 |\n| 2358 | 0152A0102358 | 35.915 | 76.014 | Indus Middle | I(s) | 0.41 | 4,152 |\n| 2359 | 0152A0102359 | 35.753 | 76.178 | Indus Middle | M(l) | 0.69 | 4,047 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8492, "line_end": 8639, "token_count_estimate": 1640, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["0143O1002325", "0143O1002326", "0143O1002327", "0143O1002328", "0143O1002329", "0143O1002330", "0143O1002331", "0143O1002332", "0143O1302333", "0143O1302334", "0143O1302335", "0143O1302336", "0143O1302337", "0143O1302338", "0143O1302339", "0143O1402340", "0143O1402341", "0143O1402342", "0143O1402343", "0143O1402344", "0143P0902345", "0143P0902346", "0143P0902347", "0143P0902348", "0143P0902349", "0143P0902350", "0152A0102351", "0152A0102352", "0152A0102353", "0152A0102354", "0152A0102355", "0152A0102356", "0152A0102357", "0152A0102358", "0152A0102359"]}}
{"id": "432912f75c5b321e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2360 | 0152A0202360 | 35.740 | 76.246 | Indus Middle | I(s) | 0.29 | 3,905 |\n| 2361 | 0152A0202361 | 35.735 | 76.225 | Indus Middle | I(s) | 0.50 | 3,846 |\n| 2362 | 0152A0202362 | 35.729 | 76.223 | Indus Middle | I(s) | 0.68 | 3,835 |\n| 2363 | 0152A0202363 | 35.728 | 76.249 | Indus Middle | I(s) | 0.83 | 3,898 |\n| 2364 | 0152A0202364 | 35.728 | 76.226 | Indus Middle | I(s) | 0.48 | 3,835 |\n| 2365 | 0152A0202365 | 35.728 | 76.209 | Indus Middle | I(s) | 0.27 | 3,799 |\n| 2366 | 0152A0202366 | 35.726 | 76.215 | Indus Middle | I(s) | 1.01 | 3,800 |\n| 2367 | 0152A0202367 | 35.725 | 76.205 | Indus Middle | I(s) | 0.46 | 3,785 |\n| 2368 | 0152A0202368 | 35.725 | 76.208 | Indus Middle | I(s) | 0.32 | 3,797 |\n| 2369 | 0152A0202369 | 35.724 | 76.215 | Indus Middle | I(s) | 0.76 | 3,802 |\n| 2370 | 0152A0202370 | 35.722 | 76.223 | Indus Middle | I(s) | 2.87 | 3,820 |\n| 2371 | 0152A0202371 | 35.721 | 76.216 | Indus Middle | I(s) | 0.53 | 3,802 |\n| 2372 | 0152A0202372 | 35.721 | 76.220 | Indus Middle | I(s) | 0.79 | 3,829 |\n| 2373 | 0152A0202373 | 35.721 | 76.201 | Indus Middle | I(s) | 0.57 | 3,772 |\n| 2374 | 0152A0202374 | 35.719 | 76.218 | Indus Middle | I(s) | 0.29 | 3,824 |\n| 2375 | 0152A0202375 | 35.719 | 76.209 | Indus Middle | I(s) | 1.20 | 3,774 |\n| 2376 | 0152A0202376 | 35.718 | 76.207 | Indus Middle | I(s) | 1.66 | 3,789 |\n| 2377 | 0152A0202377 | 35.718 | 76.203 | Indus Middle | I(s) | 0.47 | 3,771 |\n| 2378 | 0152A0202378 | 35.717 | 76.209 | Indus Middle | I(s) | 0.76 | 3,791 |\n| 2379 | 0152A0202379 | 35.715 | 76.200 | Indus Middle | I(s) | 0.45 | 3,740 |\n| 2380 | 0152A0202380 | 35.715 | 76.231 | Indus Middle | I(s) | 0.93 | 3,806 |\n| 2381 | 0152A0202381 | 35.715 | 76.202 | Indus Middle | I(s) | 1.21 | 3,753 |\n| 2382 | 0152A0202382 | 35.715 | 76.228 | Indus Middle | I(s) | 6.36 | 3,827 |\n| 2383 | 0152A0202383 | 35.714 | 76.184 | Indus Middle | I(s) | 0.72 | 3,722 |\n| 2384 | 0152A0202384 | 35.713 | 76.190 | Indus Middle | I(s) | 1.30 | 3,712 |\n| 2385 | 0152A0202385 | 35.712 | 76.178 | Indus Middle | I(s) | 3.89 | 3,699 |\n| 2386 | 0152A0202386 | 35.712 | 76.199 | Indus Middle | I(s) | 1.19 | 3,726 |\n| 2387 | 0152A0202387 | 35.710 | 76.199 | Indus Middle | I(s) | 0.46 | 3,748 |\n| 2388 | 0152A0202388 | 35.710 | 76.182 | Indus Middle | I(s) | 1.22 | 3,700 |\n| 2389 | 0152A0202389 | 35.707 | 76.198 | Indus Middle | I(s) | 0.62 | 3,723 |\n| 2390 | 0152A0202390 | 35.707 | 76.190 | Indus Middle | I(s) | 0.46 | 3,691 |\n| 2391 | 0152A0202391 | 35.687 | 76.155 | Indus Middle | M(e) | 0.30 | 3,406 |\n| 2392 | 0152A0302392 | 35.379 | 76.186 | Shyok | M(e) | 2.24 | 4,855 |\n| 2393 | 0152A0302393 | 35.363 | 76.042 | Shyok | E(o) | 0.44 | 4,841 |\n| 2394 | 0152A0302394 | 35.362 | 76.012 | Indus Middle | E(o) | 0.51 | 5,010 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 8492, "line_end": 8639, "token_count_estimate": 1666, "basins": ["Indus"], "subbasins": ["Indus Middle", "Shyok"], "countries": [], "lake_ids": ["0152A0202360", "0152A0202361", "0152A0202362", "0152A0202363", "0152A0202364", "0152A0202365", "0152A0202366", "0152A0202367", "0152A0202368", "0152A0202369", "0152A0202370", "0152A0202371", "0152A0202372", "0152A0202373", "0152A0202374", "0152A0202375", "0152A0202376", "0152A0202377", "0152A0202378", "0152A0202379", "0152A0202380", "0152A0202381", "0152A0202382", "0152A0202383", "0152A0202384", "0152A0202385", "0152A0202386", "0152A0202387", "0152A0202388", "0152A0202389", "0152A0202390", "0152A0202391", "0152A0302392", "0152A0302393", "0152A0302394"]}}
{"id": "ac2829dc4f5ebc9c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2395 | 0152A0302395 | 35.358 | 76.004 | Indus Middle | E(o) | 3.56 | 4,801 |\n| 2396 | 0152A0302396 | 35.345 | 76.002 | Shyok | E(o) | 0.42 | 4,846 |\n| 2397 | 0152A0302397 | 35.338 | 76.038 | Shyok | E(o) | 1.15 | 4,932 |\n| 2398 | 0152A0302398 | 35.337 | 76.028 | Shyok | E(o) | 1.76 | 4,708 |\n| 2399 | 0152A0302399 | 35.335 | 76.039 | Shyok | M(e) | 0.49 | 4,949 |\n| 2400 | 0152A0302400 | 35.321 | 76.028 | Shyok | I(s) | 0.80 | 4,837 |\n| 2401 | 0152A0302401 | 35.316 | 76.056 | Shyok | E(o) | 0.49 | 5,017 |\n| 2402 | 0152A0302402 | 35.311 | 76.056 | Shyok | E(o) | 0.58 | 4,977 |\n| 2403 | 0152A0302403 | 35.309 | 76.091 | Shyok | E(c) | 9.16 | 4,363 |\n| 2404 | 0152A0302404 | 35.306 | 76.068 | Shyok | E(o) | 2.05 | 4,807 |\n| 2405 | 0152A0302405 | 35.303 | 76.051 | Shyok | E(o) | 2.82 | 5,060 |\n| 2406 | 0152A0302406 | 35.295 | 76.091 | Shyok | E(o) | 0.83 | 4,661 |\n| 2407 | 0152A0402407 | 35.171 | 76.205 | Shyok | O | 30.72 | 3,301 |\n| 2408 | 0152A0402408 | 35.097 | 76.234 | Shyok | E(o) | 22.96 | 4,512 |\n| 2409 | 0152A0402409 | 35.096 | 76.200 | Shyok | E(o) | 9.45 | 4,591 |\n| 2410 | 0152A0402410 | 35.090 | 76.230 | Shyok | M(e) | 2.73 | 4,606 |\n| 2411 | 0152A0402411 | 35.090 | 76.217 | Shyok | E(o) | 1.77 | 4,773 |\n| 2412 | 0152A0402412 | 35.089 | 76.180 | Shyok | M(e) | 1.21 | 4,720 |\n| 2413 | 0152A0402413 | 35.086 | 76.197 | Shyok | E(o) | 1.55 | 4,720 |\n| 2414 | 0152A0402414 | 35.065 | 76.221 | Indus Upper | I(s) | 1.25 | 4,864 |\n| 2415 | 0152A0502415 | 35.759 | 76.428 | Indus Middle | M(l) | 5.55 | 4,299 |\n| 2416 | 0152A0502416 | 35.759 | 76.420 | Indus Middle | M(l) | 4.47 | 4,312 |\n| 2417 | 0152A0502417 | 35.754 | 76.324 | Indus Middle | I(s) | 6.61 | 4,077 |\n| 2418 | 0152A0502418 | 35.753 | 76.327 | Indus Middle | M(l) | 0.27 | 4,098 |\n| 2419 | 0152A0502419 | 35.752 | 76.416 | Indus Middle | I(s) | 0.46 | 4,312 |\n| 2420 | 0152A0502420 | 35.751 | 76.420 | Indus Middle | I(s) | 0.36 | 4,331 |\n| 2421 | 0152A0502421 | 35.751 | 76.412 | Indus Middle | I(s) | 0.82 | 4,319 |\n| 2422 | 0152A0602422 | 35.750 | 76.404 | Indus Middle | I(s) | 0.29 | 4,295 |\n| 2423 | 0152A0602423 | 35.750 | 76.473 | Indus Middle | M(l) | 3.05 | 4,448 |\n| 2424 | 0152A0602424 | 35.749 | 76.378 | Indus Middle | M(l) | 1.22 | 4,198 |\n| 2425 | 0152A0602425 | 35.749 | 76.475 | Indus Middle | I(s) | 0.28 | 4,447 |\n| 2426 | 0152A0602426 | 35.749 | 76.324 | Indus Middle | I(s) | 0.27 | 4,117 |\n| 2427 | 0152A0602427 | 35.748 | 76.311 | Indus Middle | I(s) | 0.91 | 4,088 |\n| 2428 | 0152A0602428 | 35.748 | 76.394 | Indus Middle | I(s) | 0.27 | 4,269 |\n| 2429 | 0152A0602429 | 35.748 | 76.343 | Indus Middle | I(s) | 0.47 | 4,169 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 8492, "line_end": 8639, "token_count_estimate": 1695, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152A0302395", "0152A0302396", "0152A0302397", "0152A0302398", "0152A0302399", "0152A0302400", "0152A0302401", "0152A0302402", "0152A0302403", "0152A0302404", "0152A0302405", "0152A0302406", "0152A0402407", "0152A0402408", "0152A0402409", "0152A0402410", "0152A0402411", "0152A0402412", "0152A0402413", "0152A0402414", "0152A0502415", "0152A0502416", "0152A0502417", "0152A0502418", "0152A0502419", "0152A0502420", "0152A0502421", "0152A0602422", "0152A0602423", "0152A0602424", "0152A0602425", "0152A0602426", "0152A0602427", "0152A0602428", "0152A0602429"]}}
{"id": "9c5cb65076daaf43", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2430 | 0152A0602430 | 35.747 | 76.360 | Indus Middle | I(s) | 0.80 | 4,190 |\n| 2431 | 0152A0602431 | 35.746 | 76.368 | Indus Middle | I(s) | 1.69 | 4,209 |\n| 2432 | 0152A0602432 | 35.746 | 76.318 | Indus Middle | I(s) | 0.25 | 4,121 |\n| 2433 | 0152A0602433 | 35.746 | 76.375 | Indus Middle | I(s) | 1.10 | 4,222 |\n| 2434 | 0152A0602434 | 35.746 | 76.312 | Indus Middle | I(s) | 0.46 | 4,098 |\n| 2435 | 0152A0602435 | 35.746 | 76.310 | Indus Middle | I(s) | 0.49 | 4,085 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 8492, "line_end": 8639, "token_count_estimate": 380, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0152A0602430", "0152A0602431", "0152A0602432", "0152A0602433", "0152A0602434", "0152A0602435"]}}
{"id": "376a15ece4453523", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8640, "line_end": 8648, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f73d71c3853b2a1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2436 | 0152A0602436 | 35.745 | 76.344 | Indus Middle | I(s) | 2.00 | 4,167 |\n| 2437 | 0152A0602437 | 35.743 | 76.273 | Indus Middle | I(s) | 0.60 | 3,989 |\n| 2438 | 0152A0602438 | 35.743 | 76.423 | Indus Middle | I(s) | 1.18 | 4,336 |\n| 2439 | 0152A0602439 | 35.742 | 76.271 | Indus Middle | I(s) | 0.72 | 3,985 |\n| 2440 | 0152A0602440 | 35.742 | 76.384 | Indus Middle | I(s) | 1.43 | 4,248 |\n| 2441 | 0152A0602441 | 35.742 | 76.381 | Indus Middle | I(s) | 0.65 | 4,248 |\n| 2442 | 0152A0602442 | 35.741 | 76.303 | Indus Middle | I(s) | 0.33 | 4,060 |\n| 2443 | 0152A0602443 | 35.740 | 76.306 | Indus Middle | I(s) | 1.29 | 4,070 |\n| 2444 | 0152A0602444 | 35.739 | 76.311 | Indus Middle | I(s) | 1.62 | 4,080 |\n| 2445 | 0152A0602445 | 35.739 | 76.275 | Indus Middle | I(s) | 0.32 | 3,985 |\n| 2446 | 0152A0602446 | 35.739 | 76.267 | Indus Middle | I(s) | 0.54 | 3,955 |\n| 2447 | 0152A0602447 | 35.739 | 76.302 | Indus Middle | I(s) | 0.34 | 4,059 |\n| 2448 | 0152A0602448 | 35.739 | 76.438 | Indus Middle | I(s) | 0.61 | 4,383 |\n| 2449 | 0152A0602449 | 35.738 | 76.271 | Indus Middle | I(s) | 0.25 | 3,974 |\n| 2450 | 0152A0602450 | 35.738 | 76.340 | Indus Middle | I(s) | 0.39 | 4,146 |\n| 2451 | 0152A0602451 | 35.738 | 76.442 | Indus Middle | I(s) | 0.32 | 4,396 |\n| 2452 | 0152A0602452 | 35.738 | 76.316 | Indus Middle | I(s) | 0.57 | 4,103 |\n| 2453 | 0152A0602453 | 35.737 | 76.340 | Indus Middle | I(s) | 0.48 | 4,153 |\n| 2454 | 0152A0602454 | 35.737 | 76.258 | Indus Middle | I(s) | 0.64 | 3,935 |\n| 2455 | 0152A0602455 | 35.736 | 76.277 | Indus Middle | I(s) | 1.13 | 4,003 |\n| 2456 | 0152A0602456 | 35.735 | 76.268 | Indus Middle | I(s) | 0.43 | 3,965 |\n| 2457 | 0152A0602457 | 35.733 | 76.252 | Indus Middle | I(s) | 0.46 | 3,907 |\n| 2458 | 0152A0602458 | 35.733 | 76.462 | Indus Middle | I(s) | 0.64 | 4,427 |\n| 2459 | 0152A0602459 | 35.732 | 76.380 | Indus Middle | I(s) | 0.61 | 4,221 |\n| 2460 | 0152A0602460 | 35.732 | 76.363 | Indus Middle | I(s) | 0.26 | 4,204 |\n| 2461 | 0152A0602461 | 35.732 | 76.364 | Indus Middle | I(s) | 0.68 | 4,205 |\n| 2462 | 0152A0602462 | 35.731 | 76.462 | Indus Middle | I(s) | 0.35 | 4,432 |\n| 2463 | 0152A0602463 | 35.730 | 76.466 | Indus Middle | I(s) | 0.30 | 4,442 |\n| 2464 | 0152A0602464 | 35.730 | 76.292 | Indus Middle | M(l) | 1.09 | 3,986 |\n| 2465 | 0152A0602465 | 35.730 | 76.410 | Indus Middle | M(l) | 13.28 | 4,161 |\n| 2466 | 0152A0602466 | 35.729 | 76.463 | Indus Middle | I(s) | 5.95 | 4,431 |\n| 2467 | 0152A0602467 | 35.729 | 76.340 | Indus Middle | M(l) | 0.58 | 4,174 |\n| 2468 | 0152A0602468 | 35.728 | 76.341 | Indus Middle | M(l) | 0.54 | 4,175 |\n| 2469 | 0152A0602469 | 35.728 | 76.396 | Indus Middle | M(l) | 0.69 | 4,249 |\n| 2470 | 0152A0602470 | 35.728 | 76.363 | Indus Middle | I(s) | 0.37 | 4,207 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8649, "line_end": 8796, "token_count_estimate": 1675, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0152A0602436", "0152A0602437", "0152A0602438", "0152A0602439", "0152A0602440", "0152A0602441", "0152A0602442", "0152A0602443", "0152A0602444", "0152A0602445", "0152A0602446", "0152A0602447", "0152A0602448", "0152A0602449", "0152A0602450", "0152A0602451", "0152A0602452", "0152A0602453", "0152A0602454", "0152A0602455", "0152A0602456", "0152A0602457", "0152A0602458", "0152A0602459", "0152A0602460", "0152A0602461", "0152A0602462", "0152A0602463", "0152A0602464", "0152A0602465", "0152A0602466", "0152A0602467", "0152A0602468", "0152A0602469", "0152A0602470"]}}
{"id": "c81f27a80d86daaa", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2471 | 0152A0602471 | 35.728 | 76.281 | Indus Middle | M(l) | 1.58 | 4,002 |\n| 2472 | 0152A0602472 | 35.727 | 76.398 | Indus Middle | M(l) | 0.40 | 4,253 |\n| 2473 | 0152A0602473 | 35.727 | 76.400 | Indus Middle | I(s) | 0.39 | 4,255 |\n| 2474 | 0152A0602474 | 35.727 | 76.393 | Indus Middle | M(l) | 0.39 | 4,250 |\n| 2475 | 0152A0602475 | 35.726 | 76.399 | Indus Middle | I(s) | 0.75 | 4,244 |\n| 2476 | 0152A0602476 | 35.726 | 76.344 | Indus Middle | M(l) | 0.97 | 4,183 |\n| 2477 | 0152A0602477 | 35.725 | 76.392 | Indus Middle | M(l) | 0.29 | 4,241 |\n| 2478 | 0152A0602478 | 35.724 | 76.386 | Indus Middle | M(l) | 0.73 | 4,205 |\n| 2479 | 0152A0602479 | 35.724 | 76.462 | Indus Middle | M(l) | 0.25 | 4,444 |\n| 2480 | 0152A0602480 | 35.723 | 76.365 | Indus Middle | M(l) | 1.12 | 4,138 |\n| 2481 | 0152A0602481 | 35.723 | 76.389 | Indus Middle | M(l) | 1.95 | 4,197 |\n| 2482 | 0152A0602482 | 35.722 | 76.369 | Indus Middle | I(s) | 0.67 | 4,147 |\n| 2483 | 0152A0602483 | 35.721 | 76.369 | Indus Middle | M(l) | 0.69 | 4,132 |\n| 2484 | 0152A0602484 | 35.721 | 76.380 | Indus Middle | M(l) | 0.66 | 4,162 |\n| 2485 | 0152A0602485 | 35.720 | 76.375 | Indus Middle | M(l) | 12.14 | 4,140 |\n| 2486 | 0152A0602486 | 35.719 | 76.348 | Indus Middle | I(s) | 0.47 | 4,261 |\n| 2487 | 0152A0602487 | 35.719 | 76.365 | Indus Middle | M(l) | 0.27 | 4,203 |\n| 2488 | 0152A0602488 | 35.717 | 76.365 | Indus Middle | M(l) | 0.63 | 4,196 |\n| 2489 | 0152A0602489 | 35.716 | 76.449 | Indus Middle | I(s) | 0.33 | 4,489 |\n| 2490 | 0152A0602490 | 35.575 | 76.297 | Shyok | I(s) | 0.33 | 4,267 |\n| 2491 | 0152A0602491 | 35.571 | 76.292 | Shyok | M(l) | 0.29 | 4,247 |\n| 2492 | 0152A0602492 | 35.563 | 76.307 | Shyok | I(s) | 0.55 | 4,182 |\n| 2493 | 0152A0602493 | 35.563 | 76.298 | Shyok | M(l) | 0.75 | 4,163 |\n| 2494 | 0152A0602494 | 35.563 | 76.302 | Shyok | I(s) | 0.34 | 4,189 |\n| 2495 | 0152A0602495 | 35.563 | 76.303 | Shyok | I(s) | 0.31 | 4,188 |\n| 2496 | 0152A0602496 | 35.561 | 76.309 | Shyok | I(s) | 0.65 | 4,171 |\n| 2497 | 0152A0602497 | 35.559 | 76.309 | Shyok | I(s) | 0.66 | 4,145 |\n| 2498 | 0152A0602498 | 35.559 | 76.320 | Shyok | M(l) | 2.11 | 4,091 |\n| 2499 | 0152A0702499 | 35.343 | 76.302 | Shyok | M(e) | 2.97 | 4,704 |\n| 2500 | 0152A0802500 | 35.103 | 76.291 | Shyok | E(o) | 1.40 | 4,594 |\n| 2501 | 0152A0802501 | 35.098 | 76.287 | Shyok | E(o) | 1.43 | 4,697 |\n| 2502 | 0152A0802502 | 35.094 | 76.284 | Shyok | E(o) | 6.17 | 4,774 |\n| 2503 | 0152A0802503 | 35.092 | 76.252 | Shyok | M(e) | 24.01 | 4,533 |\n| 2504 | 0152A0802504 | 35.089 | 76.362 | Shyok | O | 0.92 | 4,766 |\n| 2505 | 0152A0802505 | 35.085 | 76.356 | Shyok | E(o) | 4.44 | 4,844 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8649, "line_end": 8796, "token_count_estimate": 1679, "basins": ["Indus"], "subbasins": ["Indus Middle", "Shyok"], "countries": [], "lake_ids": ["0152A0602471", "0152A0602472", "0152A0602473", "0152A0602474", "0152A0602475", "0152A0602476", "0152A0602477", "0152A0602478", "0152A0602479", "0152A0602480", "0152A0602481", "0152A0602482", "0152A0602483", "0152A0602484", "0152A0602485", "0152A0602486", "0152A0602487", "0152A0602488", "0152A0602489", "0152A0602490", "0152A0602491", "0152A0602492", "0152A0602493", "0152A0602494", "0152A0602495", "0152A0602496", "0152A0602497", "0152A0602498", "0152A0702499", "0152A0802500", "0152A0802501", "0152A0802502", "0152A0802503", "0152A0802504", "0152A0802505"]}}
{"id": "3ac2b984d43193f1", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2506 | 0152A0802506 | 35.084 | 76.349 | Shyok | E(o) | 3.84 | 4,919 |\n| 2507 | 0152A0802507 | 35.084 | 76.362 | Shyok | E(o) | 0.34 | 4,822 |\n| 2508 | 0152A0802508 | 35.076 | 76.358 | Shyok | M(o) | 1.41 | 5,022 |\n| 2509 | 0152A0802509 | 35.074 | 76.293 | Shyok | E(c) | 12.08 | 4,599 |\n| 2510 | 0152A0802510 | 35.073 | 76.381 | Shyok | E(o) | 4.34 | 4,668 |\n| 2511 | 0152A0802511 | 35.064 | 76.378 | Shyok | E(o) | 7.74 | 4,771 |\n| 2512 | 0152A0802512 | 35.060 | 76.312 | Shyok | E(o) | 6.16 | 4,708 |\n| 2513 | 0152A0802513 | 35.056 | 76.261 | Indus Upper | E(o) | 16.89 | 4,881 |\n| 2514 | 0152A0802514 | 35.053 | 76.314 | Shyok | E(o) | 7.03 | 4,822 |\n| 2515 | 0152A0802515 | 35.037 | 76.414 | Shyok | I(s) | 0.89 | 4,740 |\n| 2516 | 0152A0802516 | 35.037 | 76.308 | Indus Upper | E(o) | 4.87 | 4,888 |\n| 2517 | 0152A0802517 | 35.037 | 76.352 | Shyok | M(o) | 0.76 | 4,959 |\n| 2518 | 0152A0802518 | 35.034 | 76.310 | Indus Upper | E(o) | 1.43 | 4,901 |\n| 2519 | 0152A0802519 | 35.029 | 76.323 | Indus Upper | M(o) | 2.73 | 4,901 |\n| 2520 | 0152A0802520 | 35.028 | 76.321 | Indus Upper | E(o) | 0.58 | 4,916 |\n| 2521 | 0152A0802521 | 35.024 | 76.356 | Shyok | E(o) | 2.18 | 4,838 |\n| 2522 | 0152A0802522 | 35.024 | 76.364 | Shyok | E(o) | 2.34 | 4,738 |\n| 2523 | 0152A0802523 | 35.021 | 76.319 | Indus Upper | E(o) | 4.13 | 4,757 |\n| 2524 | 0152A0802524 | 35.019 | 76.371 | Shyok | E(o) | 7.74 | 4,668 |\n| 2525 | 0152A0802525 | 35.016 | 76.303 | Indus Upper | E(o) | 3.06 | 4,515 |\n| 2526 | 0152A0802526 | 35.011 | 76.320 | Indus Upper | E(o) | 15.36 | 4,753 |\n| 2527 | 0152A0802527 | 35.010 | 76.306 | Indus Upper | E(o) | 2.84 | 4,683 |\n| 2528 | 0152A0802528 | 35.007 | 76.388 | Shyok | E(o) | 0.39 | 4,809 |\n| 2529 | 0152A0802529 | 35.006 | 76.371 | Shyok | M(e) | 1.20 | 5,093 |\n| 2530 | 0152A0802530 | 35.005 | 76.336 | Indus Upper | O | 1.14 | 4,860 |\n| 2531 | 0152A0802531 | 35.004 | 76.309 | Indus Upper | E(o) | 1.69 | 4,713 |\n| 2532 | 0152A0802532 | 35.002 | 76.376 | Shyok | M(e) | 1.72 | 5,009 |\n| 2533 | 0152A0902533 | 35.866 | 76.560 | Indus Middle | M(lg) | 0.65 | 5,374 |\n| 2534 | 0152A0902534 | 35.783 | 76.548 | Indus Middle | M(l) | 1.09 | 5,035 |\n| 2535 | 0152A0902535 | 35.767 | 76.533 | Indus Middle | M(l) | 0.36 | 4,638 |\n| 2536 | 0152A0902536 | 35.760 | 76.535 | Indus Middle | I(s) | 0.43 | 4,657 |\n| 2537 | 0152A1002537 | 35.749 | 76.532 | Indus Middle | I(s) | 0.29 | 4,594 |\n| 2538 | 0152A1002538 | 35.748 | 76.537 | Indus Middle | I(s) | 0.57 | 4,582 |\n| 2539 | 0152A1002539 | 35.748 | 76.531 | Indus Middle | I(s) | 1.23 | 4,596 |\n| 2540 | 0152A1002540 | 35.747 | 76.533 | Indus Middle | I(s) | 0.59 | 4,595 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 8649, "line_end": 8796, "token_count_estimate": 1686, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152A0802506", "0152A0802507", "0152A0802508", "0152A0802509", "0152A0802510", "0152A0802511", "0152A0802512", "0152A0802513", "0152A0802514", "0152A0802515", "0152A0802516", "0152A0802517", "0152A0802518", "0152A0802519", "0152A0802520", "0152A0802521", "0152A0802522", "0152A0802523", "0152A0802524", "0152A0802525", "0152A0802526", "0152A0802527", "0152A0802528", "0152A0802529", "0152A0802530", "0152A0802531", "0152A0802532", "0152A0902533", "0152A0902534", "0152A0902535", "0152A0902536", "0152A1002537", "0152A1002538", "0152A1002539", "0152A1002540"]}}
{"id": "3bca86f2c2822a53", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2541 | 0152A1002541 | 35.745 | 76.539 | Indus Middle | I(s) | 0.32 | 4,583 |\n| 2542 | 0152A1002542 | 35.745 | 76.533 | Indus Middle | I(s) | 0.29 | 4,603 |\n| 2543 | 0152A1002543 | 35.741 | 76.533 | Indus Middle | I(s) | 0.90 | 4,590 |\n| 2544 | 0152A1002544 | 35.720 | 76.545 | Indus Middle | I(s) | 0.28 | 4,655 |\n| 2545 | 0152A1002545 | 35.713 | 76.528 | Indus Middle | M(l) | 0.38 | 4,666 |\n| 2546 | 0152A1002546 | 35.711 | 76.528 | Indus Middle | M(l) | 0.72 | 4,660 |\n| 2547 | 0152A1002547 | 35.704 | 76.543 | Indus Middle | I(s) | 0.73 | 4,678 |\n| 2548 | 0152A1002548 | 35.693 | 76.643 | Indus Middle | M(l) | 0.27 | 5,038 |\n| 2549 | 0152A1002549 | 35.693 | 76.646 | Indus Middle | M(l) | 0.40 | 5,041 |\n| 2550 | 0152A1002550 | 35.690 | 76.668 | Indus Middle | M(lg) | 0.45 | 5,137 |\n| 2551 | 0152A1002551 | 35.689 | 76.672 | Indus Middle | M(lg) | 0.28 | 5,137 |\n| 2552 | 0152A1002552 | 35.688 | 76.675 | Indus Middle | M(lg) | 0.49 | 5,138 |\n| 2553 | 0152A1002553 | 35.685 | 76.668 | Indus Middle | I(s) | 0.26 | 5,139 |\n| 2554 | 0152A1002554 | 35.682 | 76.533 | Indus Middle | M(l) | 0.32 | 4,801 |\n| 2555 | 0152A1002555 | 35.679 | 76.670 | Indus Middle | I(s) | 0.87 | 5,135 |\n| 2556 | 0152A1002556 | 35.676 | 76.669 | Indus Middle | M(lg) | 0.33 | 5,134 |\n| 2557 | 0152A1002557 | 35.674 | 76.684 | Indus Middle | I(s) | 0.86 | 5,241 |\n| 2558 | 0152A1002558 | 35.674 | 76.594 | Indus Middle | I(s) | 0.43 | 4,856 |\n| 2559 | 0152A1002559 | 35.669 | 76.605 | Indus Middle | I(s) | 0.41 | 4,879 |\n| 2560 | 0152A1002560 | 35.667 | 76.514 | Indus Middle | M(l) | 0.38 | 4,907 |\n| 2561 | 0152A1002561 | 35.665 | 76.608 | Indus Middle | I(s) | 1.52 | 4,885 |\n| 2562 | 0152A1002562 | 35.664 | 76.606 | Indus Middle | I(s) | 0.33 | 4,889 |\n| 2563 | 0152A1002563 | 35.663 | 76.617 | Indus Middle | I(s) | 1.42 | 4,910 |\n| 2564 | 0152A1002564 | 35.663 | 76.607 | Indus Middle | I(s) | 0.45 | 4,884 |\n| 2565 | 0152A1002565 | 35.663 | 76.608 | Indus Middle | I(s) | 0.65 | 4,883 |\n| 2566 | 0152A1002566 | 35.662 | 76.621 | Indus Middle | M(l) | 0.76 | 4,918 |\n| 2567 | 0152A1002567 | 35.662 | 76.608 | Indus Middle | I(s) | 0.25 | 4,886 |\n| 2568 | 0152A1002568 | 35.661 | 76.614 | Indus Middle | I(s) | 4.13 | 4,906 |\n| 2569 | 0152A1002569 | 35.661 | 76.617 | Indus Middle | M(l) | 1.49 | 4,897 |\n| 2570 | 0152A1002570 | 35.661 | 76.608 | Indus Middle | I(s) | 0.29 | 4,889 |\n| 2571 | 0152A1002571 | 35.659 | 76.609 | Indus Middle | I(s) | 0.50 | 4,908 |\n| 2572 | 0152A1002572 | 35.658 | 76.609 | Indus Middle | I(s) | 0.44 | 4,904 |\n| 2573 | 0152A1002573 | 35.657 | 76.614 | Indus Middle | I(s) | 0.80 | 4,914 |\n| 2574 | 0152A1002574 | 35.657 | 76.609 | Indus Middle | I(s) | 0.27 | 4,931 |\n| 2575 | 0152A1002575 | 35.656 | 76.614 | Indus Middle | M(lg) | 0.28 | 4,917 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 8649, "line_end": 8796, "token_count_estimate": 1660, "basins": ["Indus"], "subbasins": ["Indus Middle"], "countries": [], "lake_ids": ["0152A1002541", "0152A1002542", "0152A1002543", "0152A1002544", "0152A1002545", "0152A1002546", "0152A1002547", "0152A1002548", "0152A1002549", "0152A1002550", "0152A1002551", "0152A1002552", "0152A1002553", "0152A1002554", "0152A1002555", "0152A1002556", "0152A1002557", "0152A1002558", "0152A1002559", "0152A1002560", "0152A1002561", "0152A1002562", "0152A1002563", "0152A1002564", "0152A1002565", "0152A1002566", "0152A1002567", "0152A1002568", "0152A1002569", "0152A1002570", "0152A1002571", "0152A1002572", "0152A1002573", "0152A1002574", "0152A1002575"]}}
{"id": "f852befe9c3db1d7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2576 | 0152A1002576 | 35.651 | 76.602 | Indus Middle | I(s) | 0.25 | 4,941 |\n| 2577 | 0152A1002577 | 35.594 | 76.740 | Shyok | I(s) | 1.74 | 4,864 |\n| 2578 | 0152A1002578 | 35.588 | 76.698 | Shyok | M(lg) | 2.60 | 4,697 |\n| 2579 | 0152A1002579 | 35.582 | 76.568 | Shyok | I(s) | 0.51 | 4,975 |\n| 2580 | 0152A1002580 | 35.574 | 76.688 | Shyok | I(s) | 1.14 | 4,670 |\n| 2581 | 0152A1002581 | 35.572 | 76.688 | Shyok | I(s) | 1.95 | 4,664 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 8649, "line_end": 8796, "token_count_estimate": 374, "basins": ["Indus"], "subbasins": ["Indus Middle", "Shyok"], "countries": [], "lake_ids": ["0152A1002576", "0152A1002577", "0152A1002578", "0152A1002579", "0152A1002580", "0152A1002581"]}}
{"id": "163b722e8fbe3ae6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8797, "line_end": 8806, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "333f858e9038936d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2582 | 0152A1002582 | 35.572 | 76.712 | Shyok | M(l) | 0.26 | 4,626 |\n| 2583 | 0152A1002583 | 35.570 | 76.709 | Shyok | I(s) | 0.68 | 4,623 |\n| 2584 | 0152A1002584 | 35.569 | 76.708 | Shyok | I(s) | 1.13 | 4,623 |\n| 2585 | 0152A1002585 | 35.564 | 76.681 | Shyok | M(l) | 0.33 | 4,739 |\n| 2586 | 0152A1002586 | 35.560 | 76.697 | Shyok | I(s) | 0.67 | 4,604 |\n| 2587 | 0152A1002587 | 35.550 | 76.598 | Shyok | I(s) | 0.94 | 4,688 |\n| 2588 | 0152A1002588 | 35.550 | 76.603 | Shyok | I(s) | 1.73 | 4,676 |\n| 2589 | 0152A1002589 | 35.547 | 76.697 | Shyok | M(l) | 1.02 | 4,531 |\n| 2590 | 0152A1002590 | 35.535 | 76.715 | Shyok | M(l) | 0.38 | 4,543 |\n| 2591 | 0152A1002591 | 35.515 | 76.705 | Shyok | M(l) | 0.70 | 4,403 |\n| 2592 | 0152A1002592 | 35.514 | 76.640 | Shyok | I(s) | 0.57 | 4,339 |\n| 2593 | 0152A1002593 | 35.509 | 76.629 | Shyok | I(s) | 0.52 | 4,311 |\n| 2594 | 0152A1002594 | 35.503 | 76.701 | Shyok | I(s) | 0.83 | 4,326 |\n| 2595 | 0152A1002595 | 35.503 | 76.625 | Shyok | I(s) | 0.30 | 4,277 |\n| 2596 | 0152A1102596 | 35.499 | 76.626 | Shyok | I(s) | 0.28 | 4,260 |\n| 2597 | 0152A1102597 | 35.497 | 76.623 | Shyok | I(s) | 0.68 | 4,247 |\n| 2598 | 0152A1102598 | 35.496 | 76.619 | Shyok | I(s) | 0.32 | 4,230 |\n| 2599 | 0152A1102599 | 35.495 | 76.618 | Shyok | M(lg) | 1.14 | 4,218 |\n| 2600 | 0152A1102600 | 35.477 | 76.526 | Shyok | I(s) | 0.37 | 4,363 |\n| 2601 | 0152A1102601 | 35.477 | 76.716 | Shyok | M(l) | 1.17 | 4,159 |\n| 2602 | 0152A1102602 | 35.476 | 76.701 | Shyok | I(s) | 0.60 | 4,191 |\n| 2603 | 0152A1102603 | 35.468 | 76.702 | Shyok | M(l) | 1.12 | 4,132 |\n| 2604 | 0152A1102604 | 35.459 | 76.640 | Shyok | I(s) | 0.47 | 3,970 |\n| 2605 | 0152A1102605 | 35.454 | 76.644 | Shyok | I(s) | 0.50 | 3,942 |\n| 2606 | 0152A1102606 | 35.453 | 76.648 | Shyok | I(s) | 0.63 | 3,908 |\n| 2607 | 0152A1102607 | 35.450 | 76.649 | Shyok | I(s) | 1.19 | 3,905 |\n| 2608 | 0152A1102608 | 35.446 | 76.658 | Shyok | I(s) | 0.65 | 3,854 |\n| 2609 | 0152A1102609 | 35.441 | 76.668 | Shyok | I(s) | 0.60 | 3,821 |\n| 2610 | 0152A1102610 | 35.438 | 76.673 | Shyok | I(s) | 0.50 | 3,831 |\n| 2611 | 0152A1102611 | 35.435 | 76.665 | Shyok | I(s) | 0.41 | 3,812 |\n| 2612 | 0152A1102612 | 35.434 | 76.664 | Shyok | I(s) | 0.44 | 3,803 |\n| 2613 | 0152A1102613 | 35.433 | 76.663 | Shyok | I(s) | 0.27 | 3,796 |\n| 2614 | 0152A1102614 | 35.429 | 76.657 | Shyok | I(s) | 0.26 | 3,772 |\n| 2615 | 0152A1102615 | 35.427 | 76.658 | Shyok | I(s) | 0.49 | 3,765 |\n| 2616 | 0152A1102616 | 35.422 | 76.649 | Shyok | I(s) | 0.31 | 3,714 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8807, "line_end": 8954, "token_count_estimate": 1673, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152A1002582", "0152A1002583", "0152A1002584", "0152A1002585", "0152A1002586", "0152A1002587", "0152A1002588", "0152A1002589", "0152A1002590", "0152A1002591", "0152A1002592", "0152A1002593", "0152A1002594", "0152A1002595", "0152A1102596", "0152A1102597", "0152A1102598", "0152A1102599", "0152A1102600", "0152A1102601", "0152A1102602", "0152A1102603", "0152A1102604", "0152A1102605", "0152A1102606", "0152A1102607", "0152A1102608", "0152A1102609", "0152A1102610", "0152A1102611", "0152A1102612", "0152A1102613", "0152A1102614", "0152A1102615", "0152A1102616"]}}
{"id": "9447adef21718af8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2617 | 0152A1102617 | 35.422 | 76.693 | Shyok | I(s) | 0.48 | 4,764 |\n| 2618 | 0152A1102618 | 35.420 | 76.651 | Shyok | I(s) | 0.39 | 3,714 |\n| 2619 | 0152A1102619 | 35.418 | 76.643 | Shyok | I(s) | 0.35 | 3,678 |\n| 2620 | 0152A1102620 | 35.415 | 76.647 | Shyok | I(s) | 0.44 | 3,675 |\n| 2621 | 0152A1102621 | 35.406 | 76.743 | Shyok | M(l) | 1.08 | 4,820 |\n| 2622 | 0152A1102622 | 35.404 | 76.649 | Shyok | I(s) | 0.40 | 3,599 |\n| 2623 | 0152A1102623 | 35.394 | 76.733 | Shyok | I(s) | 0.59 | 4,894 |\n| 2624 | 0152A1102624 | 35.393 | 76.538 | Shyok | I(s) | 1.16 | 5,152 |\n| 2625 | 0152A1102625 | 35.393 | 76.733 | Shyok | I(s) | 0.30 | 4,889 |\n| 2626 | 0152A1102626 | 35.385 | 76.642 | Shyok | I(s) | 0.90 | 3,490 |\n| 2627 | 0152A1102627 | 35.367 | 76.653 | Shyok | M(o) | 0.33 | 3,242 |\n| 2628 | 0152A1102628 | 35.339 | 76.520 | Shyok | M(lg) | 1.34 | 4,860 |\n| 2629 | 0152A1102629 | 35.338 | 76.749 | Shyok | M(l) | 0.76 | 3,716 |\n| 2630 | 0152A1202630 | 35.101 | 76.651 | Shyok | E(o) | 0.76 | 4,528 |\n| 2631 | 0152A1202631 | 35.089 | 76.605 | Shyok | E(o) | 1.05 | 5,010 |\n| 2632 | 0152A1202632 | 35.086 | 76.732 | Shyok | M(e) | 2.71 | 4,522 |\n| 2633 | 0152A1202633 | 35.085 | 76.648 | Shyok | M(l) | 0.86 | 4,788 |\n| 2634 | 0152A1202634 | 35.085 | 76.643 | Shyok | E(o) | 2.96 | 4,936 |\n| 2635 | 0152A1202635 | 35.076 | 76.610 | Shyok | M(o) | 0.52 | 5,166 |\n| 2636 | 0152A1202636 | 35.071 | 76.604 | Shyok | E(o) | 1.53 | 4,997 |\n| 2637 | 0152A1202637 | 35.067 | 76.691 | Shyok | M(o) | 1.39 | 5,045 |\n| 2638 | 0152A1202638 | 35.063 | 76.700 | Shyok | I(s) | 0.28 | 4,864 |\n| 2639 | 0152A1202639 | 35.042 | 76.634 | Shyok | E(o) | 1.04 | 4,695 |\n| 2640 | 0152A1202640 | 35.007 | 76.669 | Shyok | E(o) | 2.67 | 4,943 |\n| 2641 | 0152A1402641 | 35.593 | 76.911 | Shyok | I(s) | 0.60 | 5,159 |\n| 2642 | 0152A1402642 | 35.592 | 76.917 | Shyok | M(l) | 0.46 | 5,136 |\n| 2643 | 0152A1402643 | 35.590 | 76.921 | Shyok | I(s) | 0.55 | 5,134 |\n| 2644 | 0152A1402644 | 35.587 | 76.940 | Shyok | M(lg) | 0.58 | 5,139 |\n| 2645 | 0152A1402645 | 35.584 | 76.933 | Shyok | I(s) | 0.90 | 5,130 |\n| 2646 | 0152A1402646 | 35.581 | 76.943 | Shyok | I(s) | 0.46 | 5,115 |\n| 2647 | 0152A1402647 | 35.569 | 76.948 | Shyok | I(s) | 0.29 | 5,084 |\n| 2648 | 0152A1402648 | 35.567 | 76.953 | Shyok | I(s) | 0.29 | 5,077 |\n| 2649 | 0152A1402649 | 35.565 | 76.959 | Shyok | I(s) | 0.42 | 5,074 |\n| 2650 | 0152A1402650 | 35.560 | 76.961 | Shyok | I(s) | 0.38 | 5,062 |\n| 2651 | 0152A1402651 | 35.557 | 76.920 | Shyok | M(l) | 1.70 | 5,092 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8807, "line_end": 8954, "token_count_estimate": 1671, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152A1102617", "0152A1102618", "0152A1102619", "0152A1102620", "0152A1102621", "0152A1102622", "0152A1102623", "0152A1102624", "0152A1102625", "0152A1102626", "0152A1102627", "0152A1102628", "0152A1102629", "0152A1202630", "0152A1202631", "0152A1202632", "0152A1202633", "0152A1202634", "0152A1202635", "0152A1202636", "0152A1202637", "0152A1202638", "0152A1202639", "0152A1202640", "0152A1402641", "0152A1402642", "0152A1402643", "0152A1402644", "0152A1402645", "0152A1402646", "0152A1402647", "0152A1402648", "0152A1402649", "0152A1402650", "0152A1402651"]}}
{"id": "f28cca30f88704a9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2652 | 0152A1402652 | 35.556 | 76.962 | Shyok | I(s) | 0.35 | 5,049 |\n| 2653 | 0152A1402653 | 35.555 | 76.966 | Shyok | I(s) | 0.98 | 5,045 |\n| 2654 | 0152A1402654 | 35.554 | 76.962 | Shyok | I(s) | 0.46 | 5,057 |\n| 2655 | 0152A1402655 | 35.554 | 76.928 | Shyok | I(s) | 1.24 | 5,071 |\n| 2656 | 0152A1402656 | 35.554 | 76.927 | Shyok | I(s) | 0.32 | 5,074 |\n| 2657 | 0152A1402657 | 35.553 | 76.964 | Shyok | I(s) | 0.30 | 5,042 |\n| 2658 | 0152A1402658 | 35.552 | 76.924 | Shyok | M(lg) | 4.68 | 5,053 |\n| 2659 | 0152A1402659 | 35.549 | 76.939 | Shyok | I(s) | 0.36 | 5,058 |\n| 2660 | 0152A1402660 | 35.548 | 76.966 | Shyok | I(s) | 0.55 | 5,049 |\n| 2661 | 0152A1402661 | 35.547 | 76.956 | Shyok | I(s) | 0.41 | 5,052 |\n| 2662 | 0152A1402662 | 35.547 | 76.967 | Shyok | I(s) | 0.62 | 5,047 |\n| 2663 | 0152A1402663 | 35.546 | 76.936 | Shyok | I(s) | 0.82 | 5,050 |\n| 2664 | 0152A1402664 | 35.546 | 76.967 | Shyok | I(s) | 0.26 | 5,037 |\n| 2665 | 0152A1402665 | 35.546 | 76.937 | Shyok | I(s) | 0.51 | 5,052 |\n| 2666 | 0152A1402666 | 35.543 | 76.969 | Shyok | I(s) | 0.38 | 5,031 |\n| 2667 | 0152A1402667 | 35.534 | 76.973 | Shyok | I(s) | 0.55 | 5,009 |\n| 2668 | 0152A1402668 | 35.526 | 76.988 | Shyok | I(s) | 0.37 | 4,951 |\n| 2669 | 0152A1402669 | 35.524 | 76.987 | Shyok | I(s) | 0.27 | 4,952 |\n| 2670 | 0152A1402670 | 35.522 | 76.993 | Shyok | I(s) | 0.54 | 4,940 |\n| 2671 | 0152A1402671 | 35.521 | 76.940 | Shyok | M(l) | 1.38 | 4,987 |\n| 2672 | 0152A1402672 | 35.519 | 76.999 | Shyok | I(s) | 0.78 | 4,939 |\n| 2673 | 0152A1402673 | 35.519 | 76.940 | Shyok | I(s) | 0.47 | 5,000 |\n| 2674 | 0152A1402674 | 35.517 | 76.931 | Shyok | M(l) | 0.50 | 5,099 |\n| 2675 | 0152A1402675 | 35.509 | 76.932 | Shyok | I(s) | 0.37 | 5,030 |\n| 2676 | 0152A1402676 | 35.503 | 76.927 | Shyok | I(s) | 0.32 | 5,052 |\n| 2677 | 0152A1402677 | 35.503 | 76.927 | Shyok | I(s) | 0.37 | 5,054 |\n| 2678 | 0152A1402678 | 35.499 | 76.967 | Shyok | I(s) | 2.29 | 4,951 |\n| 2679 | 0152A1502679 | 35.500 | 76.963 | Shyok | M(l) | 1.59 | 4,966 |\n| 2680 | 0152A1502680 | 35.499 | 76.924 | Shyok | I(s) | 0.66 | 5,060 |\n| 2681 | 0152A1502681 | 35.498 | 76.958 | Shyok | I(s) | 1.08 | 5,000 |\n| 2682 | 0152A1502682 | 35.494 | 76.954 | Shyok | M(l) | 0.54 | 5,027 |\n| 2683 | 0152A1502683 | 35.494 | 76.980 | Shyok | I(s) | 0.56 | 4,935 |\n| 2684 | 0152A1502684 | 35.492 | 76.951 | Shyok | M(l) | 0.41 | 5,027 |\n| 2685 | 0152A1502685 | 35.492 | 76.987 | Shyok | I(s) | 0.63 | 4,900 |\n| 2686 | 0152A1502686 | 35.487 | 76.915 | Shyok | M(l) | 0.36 | 5,116 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 8807, "line_end": 8954, "token_count_estimate": 1683, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152A1402652", "0152A1402653", "0152A1402654", "0152A1402655", "0152A1402656", "0152A1402657", "0152A1402658", "0152A1402659", "0152A1402660", "0152A1402661", "0152A1402662", "0152A1402663", "0152A1402664", "0152A1402665", "0152A1402666", "0152A1402667", "0152A1402668", "0152A1402669", "0152A1402670", "0152A1402671", "0152A1402672", "0152A1402673", "0152A1402674", "0152A1402675", "0152A1402676", "0152A1402677", "0152A1402678", "0152A1502679", "0152A1502680", "0152A1502681", "0152A1502682", "0152A1502683", "0152A1502684", "0152A1502685", "0152A1502686"]}}
{"id": "ad6bd5d9abc2a8ba", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2687 | 0152A1502687 | 35.485 | 76.915 | Shyok | I(s) | 0.52 | 5,112 |\n| 2688 | 0152A1502688 | 35.484 | 76.990 | Shyok | I(s) | 1.12 | 4,900 |\n| 2689 | 0152A1502689 | 35.480 | 76.994 | Shyok | I(s) | 0.63 | 4,876 |\n| 2690 | 0152A1502690 | 35.478 | 76.995 | Shyok | I(s) | 0.32 | 4,859 |\n| 2691 | 0152A1502691 | 35.477 | 76.999 | Shyok | I(s) | 0.96 | 4,871 |\n| 2692 | 0152A1502692 | 35.475 | 76.905 | Shyok | M(lg) | 7.55 | 5,123 |\n| 2693 | 0152A1502693 | 35.469 | 76.893 | Shyok | M(lg) | 0.41 | 5,222 |\n| 2694 | 0152A1502694 | 35.459 | 76.921 | Shyok | M(lg) | 0.54 | 5,147 |\n| 2695 | 0152A1502695 | 35.427 | 76.990 | Shyok | I(s) | 0.76 | 5,071 |\n| 2696 | 0152A1502696 | 35.371 | 76.925 | Shyok | I(s) | 0.66 | 5,016 |\n| 2697 | 0152A1502697 | 35.367 | 76.758 | Shyok | M(l) | 1.74 | 3,847 |\n| 2698 | 0152A1502698 | 35.347 | 76.925 | Shyok | I(s) | 1.07 | 4,831 |\n| 2699 | 0152A1502699 | 35.333 | 76.809 | Shyok | M(l) | 0.96 | 4,464 |\n| 2700 | 0152A1502700 | 35.328 | 76.801 | Shyok | I(s) | 0.26 | 4,471 |\n| 2701 | 0152A1502701 | 35.325 | 76.811 | Shyok | M(l) | 0.46 | 4,566 |\n| 2702 | 0152A1502702 | 35.294 | 76.902 | Shyok | I(s) | 0.54 | 4,350 |\n| 2703 | 0152A1502703 | 35.278 | 76.882 | Shyok | I(s) | 0.27 | 4,256 |\n| 2704 | 0152A1502704 | 35.273 | 76.882 | Shyok | I(s) | 0.26 | 4,232 |\n| 2705 | 0152A1502705 | 35.267 | 76.881 | Shyok | I(s) | 0.34 | 4,200 |\n| 2706 | 0152A1502706 | 35.265 | 76.888 | Shyok | I(s) | 0.91 | 4,156 |\n| 2707 | 0152A1502707 | 35.263 | 76.888 | Shyok | I(s) | 0.41 | 4,157 |\n| 2708 | 0152A1502708 | 35.257 | 76.884 | Shyok | I(s) | 0.41 | 4,121 |\n| 2709 | 0152A1502709 | 35.255 | 76.876 | Shyok | I(s) | 0.29 | 4,084 |\n| 2710 | 0152A1502710 | 35.252 | 76.881 | Shyok | I(s) | 0.76 | 4,081 |\n| 2711 | 0152A1502711 | 35.251 | 76.871 | Shyok | I(s) | 0.35 | 4,039 |\n| 2712 | 0152A1602712 | 35.246 | 76.872 | Shyok | I(s) | 0.27 | 3,998 |\n| 2713 | 0152A1602713 | 35.243 | 76.873 | Shyok | I(s) | 0.35 | 3,982 |\n| 2714 | 0152A1602714 | 35.219 | 76.909 | Shyok | I(s) | 0.27 | 4,506 |\n| 2715 | 0152A1602715 | 35.178 | 76.979 | Shyok | I(s) | 0.49 | 4,808 |\n| 2716 | 0152A1602716 | 35.140 | 76.990 | Shyok | I(s) | 0.56 | 4,198 |\n| 2717 | 0152A1602717 | 35.104 | 76.917 | Shyok | I(s) | 0.50 | 4,981 |\n| 2718 | 0152A1602718 | 35.068 | 76.839 | Shyok | I(s) | 0.42 | 5,228 |\n| 2719 | 0152A1602719 | 35.055 | 76.759 | Shyok | M(o) | 1.01 | 4,910 |\n| 2720 | 0152A1602720 | 35.055 | 76.764 | Shyok | E(o) | 0.32 | 4,917 |\n| 2721 | 0152A1602721 | 35.014 | 76.777 | Shyok | I(s) | 0.60 | 4,905 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 8807, "line_end": 8954, "token_count_estimate": 1675, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152A1502687", "0152A1502688", "0152A1502689", "0152A1502690", "0152A1502691", "0152A1502692", "0152A1502693", "0152A1502694", "0152A1502695", "0152A1502696", "0152A1502697", "0152A1502698", "0152A1502699", "0152A1502700", "0152A1502701", "0152A1502702", "0152A1502703", "0152A1502704", "0152A1502705", "0152A1502706", "0152A1502707", "0152A1502708", "0152A1502709", "0152A1502710", "0152A1502711", "0152A1602712", "0152A1602713", "0152A1602714", "0152A1602715", "0152A1602716", "0152A1602717", "0152A1602718", "0152A1602719", "0152A1602720", "0152A1602721"]}}
{"id": "8fd66e72cf89ad47", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2722 | 0152B0102722 | 34.914 | 76.024 | Indus Upper | E(o) | 2.05 | 4,673 |\n| 2723 | 0152B0102723 | 34.912 | 76.026 | Indus Upper | E(o) | 1.80 | 4,693 |\n| 2724 | 0152B0102724 | 34.911 | 76.047 | Indus Upper | E(o) | 1.43 | 4,620 |\n| 2725 | 0152B0102725 | 34.908 | 76.017 | Indus Upper | E(o) | 9.37 | 4,781 |\n| 2726 | 0152B0102726 | 34.819 | 76.033 | Indus Upper | E(o) | 1.40 | 4,374 |\n| 2727 | 0152B0102727 | 34.796 | 76.002 | Indus Upper | E(o) | 3.37 | 4,729 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 8807, "line_end": 8954, "token_count_estimate": 385, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0152B0102722", "0152B0102723", "0152B0102724", "0152B0102725", "0152B0102726", "0152B0102727"]}}
{"id": "f6d2b493d87233c3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 8955, "line_end": 8961, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3fba4128fc463f28", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2728 | 0152B0102728 | 34.783 | 76.009 | Indus Upper | E(o) | 3.86 | 4,625 |\n| 2729 | 0152B0102729 | 34.762 | 76.000 | Indus Upper | E(o) | 1.96 | 4,678 |\n| 2730 | 0152B0102730 | 34.752 | 76.037 | Indus Upper | E(o) | 8.47 | 4,287 |\n| 2731 | 0152B0302731 | 34.351 | 76.075 | Indus Upper | M(e) | 10.63 | 4,435 |\n| 2732 | 0152B0302732 | 34.347 | 76.094 | Indus Upper | E(o) | 0.28 | 4,500 |\n| 2733 | 0152B0302733 | 34.341 | 76.084 | Indus Upper | M(e) | 1.27 | 4,639 |\n| 2734 | 0152B0302734 | 34.340 | 76.083 | Indus Upper | I(s) | 0.33 | 4,689 |\n| 2735 | 0152B0402735 | 34.158 | 76.009 | Indus Upper | M(o) | 1.77 | 4,833 |\n| 2736 | 0152B0402736 | 34.156 | 76.063 | Indus Upper | M(o) | 3.83 | 4,653 |\n| 2737 | 0152B0402737 | 34.155 | 76.066 | Indus Upper | M(o) | 0.27 | 4,705 |\n| 2738 | 0152B0402738 | 34.149 | 76.057 | Indus Upper | M(o) | 2.37 | 4,720 |\n| 2739 | 0152B0402739 | 34.129 | 76.017 | Indus Upper | I(s) | 0.47 | 4,802 |\n| 2740 | 0152B0402740 | 34.115 | 76.202 | Indus Upper | E(o) | 0.55 | 4,847 |\n| 2741 | 0152B0402741 | 34.015 | 76.157 | Indus Upper | M(o) | 0.25 | 4,041 |\n| 2742 | 0152B0402742 | 34.008 | 76.145 | Indus Upper | M(l) | 0.32 | 4,107 |\n| 2743 | 0152B0502743 | 34.999 | 76.346 | Indus Upper | E(o) | 1.27 | 4,891 |\n| 2744 | 0152B0502744 | 34.991 | 76.391 | Shyok | E(o) | 0.74 | 4,948 |\n| 2745 | 0152B0502745 | 34.987 | 76.394 | Shyok | E(o) | 0.66 | 4,810 |\n| 2746 | 0152B0502746 | 34.987 | 76.384 | Shyok | E(o) | 5.29 | 4,928 |\n| 2747 | 0152B0502747 | 34.985 | 76.381 | Shyok | E(o) | 3.90 | 4,933 |\n| 2748 | 0152B0502748 | 34.985 | 76.392 | Shyok | E(o) | 5.96 | 4,805 |\n| 2749 | 0152B0502749 | 34.982 | 76.407 | Shyok | E(o) | 2.61 | 4,657 |\n| 2750 | 0152B0502750 | 34.980 | 76.399 | Shyok | E(o) | 3.03 | 4,734 |\n| 2751 | 0152B0502751 | 34.976 | 76.389 | Shyok | E(o) | 1.78 | 4,886 |\n| 2752 | 0152B0502752 | 34.962 | 76.401 | Shyok | E(o) | 0.76 | 4,758 |\n| 2753 | 0152B0502753 | 34.959 | 76.433 | Shyok | I(s) | 0.26 | 4,800 |\n| 2754 | 0152B0502754 | 34.951 | 76.399 | Shyok | E(o) | 1.57 | 4,877 |\n| 2755 | 0152B0502755 | 34.949 | 76.395 | Shyok | E(o) | 7.28 | 4,918 |\n| 2756 | 0152B0502756 | 34.932 | 76.399 | Shyok | E(o) | 0.72 | 5,100 |\n| 2757 | 0152B0502757 | 34.931 | 76.403 | Shyok | E(o) | 1.58 | 5,023 |\n| 2758 | 0152B0502758 | 34.924 | 76.420 | Shyok | E(o) | 4.18 | 4,806 |\n| 2759 | 0152B0502759 | 34.923 | 76.426 | Shyok | E(o) | 5.50 | 4,699 |\n| 2760 | 0152B0502760 | 34.922 | 76.432 | Shyok | E(o) | 1.00 | 4,605 |\n| 2761 | 0152B0502761 | 34.915 | 76.416 | Shyok | E(o) | 0.67 | 4,916 |\n| 2762 | 0152B0502762 | 34.914 | 76.424 | Shyok | E(c) | 4.10 | 4,732 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 8962, "line_end": 9109, "token_count_estimate": 1671, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152B0102728", "0152B0102729", "0152B0102730", "0152B0302731", "0152B0302732", "0152B0302733", "0152B0302734", "0152B0402735", "0152B0402736", "0152B0402737", "0152B0402738", "0152B0402739", "0152B0402740", "0152B0402741", "0152B0402742", "0152B0502743", "0152B0502744", "0152B0502745", "0152B0502746", "0152B0502747", "0152B0502748", "0152B0502749", "0152B0502750", "0152B0502751", "0152B0502752", "0152B0502753", "0152B0502754", "0152B0502755", "0152B0502756", "0152B0502757", "0152B0502758", "0152B0502759", "0152B0502760", "0152B0502761", "0152B0502762"]}}
{"id": "7ea106a49c71a3f4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2763 | 0152B0502763 | 34.911 | 76.436 | Shyok | E(o) | 0.87 | 4,658 |\n| 2764 | 0152B0502764 | 34.906 | 76.433 | Shyok | E(o) | 1.83 | 4,742 |\n| 2765 | 0152B0502765 | 34.895 | 76.356 | Indus Upper | E(o) | 0.75 | 4,352 |\n| 2766 | 0152B0502766 | 34.880 | 76.429 | Shyok | E(o) | 1.26 | 4,896 |\n| 2767 | 0152B0502767 | 34.859 | 76.351 | Indus Upper | E(o) | 2.67 | 4,745 |\n| 2768 | 0152B0502768 | 34.859 | 76.474 | Shyok | E(o) | 5.36 | 4,820 |\n| 2769 | 0152B0502769 | 34.857 | 76.374 | Indus Upper | E(o) | 3.34 | 4,733 |\n| 2770 | 0152B0502770 | 34.855 | 76.351 | Indus Upper | M(o) | 2.49 | 4,779 |\n| 2771 | 0152B0502771 | 34.854 | 76.383 | Indus Upper | E(o) | 13.61 | 4,688 |\n| 2772 | 0152B0502772 | 34.854 | 76.368 | Indus Upper | E(o) | 13.27 | 4,821 |\n| 2773 | 0152B0502773 | 34.853 | 76.476 | Shyok | E(o) | 9.78 | 4,850 |\n| 2774 | 0152B0502774 | 34.853 | 76.373 | Indus Upper | E(o) | 1.05 | 4,800 |\n| 2775 | 0152B0502775 | 34.848 | 76.441 | Shyok | E(o) | 0.55 | 4,703 |\n| 2776 | 0152B0502776 | 34.844 | 76.441 | Shyok | E(o) | 2.75 | 4,770 |\n| 2777 | 0152B0502777 | 34.843 | 76.414 | Indus Upper | E(o) | 1.78 | 4,859 |\n| 2778 | 0152B0502778 | 34.839 | 76.415 | Indus Upper | E(o) | 5.38 | 4,845 |\n| 2779 | 0152B0502779 | 34.834 | 76.373 | Indus Upper | E(o) | 1.87 | 4,987 |\n| 2780 | 0152B0502780 | 34.833 | 76.352 | Indus Upper | E(o) | 10.34 | 4,818 |\n| 2781 | 0152B0502781 | 34.831 | 76.358 | Indus Upper | M(o) | 2.52 | 4,865 |\n| 2782 | 0152B0502782 | 34.830 | 76.310 | Indus Upper | E(o) | 1.49 | 4,616 |\n| 2783 | 0152B0502783 | 34.829 | 76.381 | Indus Upper | E(o) | 3.05 | 4,821 |\n| 2784 | 0152B0502784 | 34.827 | 76.456 | Shyok | E(o) | 19.82 | 4,648 |\n| 2785 | 0152B0502785 | 34.822 | 76.313 | Indus Upper | E(o) | 2.20 | 4,750 |\n| 2786 | 0152B0502786 | 34.821 | 76.327 | Indus Upper | E(o) | 2.25 | 4,701 |\n| 2787 | 0152B0502787 | 34.818 | 76.409 | Indus Upper | E(o) | 10.20 | 4,816 |\n| 2788 | 0152B0502788 | 34.818 | 76.395 | Indus Upper | M(o) | 0.56 | 4,851 |\n| 2789 | 0152B0502789 | 34.817 | 76.329 | Indus Upper | E(o) | 3.65 | 4,707 |\n| 2790 | 0152B0502790 | 34.815 | 76.314 | Indus Upper | I(s) | 1.62 | 4,917 |\n| 2791 | 0152B0502791 | 34.814 | 76.423 | Indus Upper | E(o) | 1.70 | 5,019 |\n| 2792 | 0152B0502792 | 34.814 | 76.495 | Shyok | E(o) | 2.08 | 4,934 |\n| 2793 | 0152B0502793 | 34.812 | 76.352 | Indus Upper | E(o) | 6.87 | 4,736 |\n| 2794 | 0152B0502794 | 34.811 | 76.456 | Shyok | E(o) | 1.32 | 4,705 |\n| 2795 | 0152B0502795 | 34.808 | 76.374 | Indus Upper | M(o) | 0.87 | 4,904 |\n| 2796 | 0152B0502796 | 34.806 | 76.381 | Indus Upper | E(o) | 1.74 | 4,911 |\n| 2797 | 0152B0502797 | 34.803 | 76.457 | Shyok | M(e) | 0.54 | 4,818 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 8962, "line_end": 9109, "token_count_estimate": 1674, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152B0502763", "0152B0502764", "0152B0502765", "0152B0502766", "0152B0502767", "0152B0502768", "0152B0502769", "0152B0502770", "0152B0502771", "0152B0502772", "0152B0502773", "0152B0502774", "0152B0502775", "0152B0502776", "0152B0502777", "0152B0502778", "0152B0502779", "0152B0502780", "0152B0502781", "0152B0502782", "0152B0502783", "0152B0502784", "0152B0502785", "0152B0502786", "0152B0502787", "0152B0502788", "0152B0502789", "0152B0502790", "0152B0502791", "0152B0502792", "0152B0502793", "0152B0502794", "0152B0502795", "0152B0502796", "0152B0502797"]}}
{"id": "d51550984547b0e2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2798 | 0152B0502798 | 34.802 | 76.382 | Indus Upper | M(o) | 0.83 | 4,909 |\n| 2799 | 0152B0502799 | 34.789 | 76.492 | Indus Upper | E(o) | 7.71 | 4,988 |\n| 2800 | 0152B0502800 | 34.784 | 76.480 | Indus Upper | E(c) | 1.81 | 5,047 |\n| 2801 | 0152B0502801 | 34.781 | 76.429 | Indus Upper | E(o) | 4.61 | 4,748 |\n| 2802 | 0152B0502802 | 34.778 | 76.447 | Indus Upper | E(o) | 1.90 | 4,869 |\n| 2803 | 0152B0502803 | 34.778 | 76.439 | Indus Upper | E(o) | 2.50 | 4,802 |\n| 2804 | 0152B0502804 | 34.777 | 76.444 | Indus Upper | E(o) | 1.24 | 4,867 |\n| 2805 | 0152B0502805 | 34.777 | 76.468 | Indus Upper | E(o) | 1.04 | 4,944 |\n| 2806 | 0152B0502806 | 34.775 | 76.481 | Indus Upper | E(o) | 7.08 | 4,877 |\n| 2807 | 0152B0502807 | 34.772 | 76.469 | Indus Upper | E(o) | 0.47 | 4,894 |\n| 2808 | 0152B0502808 | 34.772 | 76.500 | Indus Upper | E(o) | 10.53 | 4,761 |\n| 2809 | 0152B0502809 | 34.771 | 76.481 | Indus Upper | E(o) | 4.08 | 4,823 |\n| 2810 | 0152B0502810 | 34.769 | 76.475 | Indus Upper | E(o) | 6.31 | 4,853 |\n| 2811 | 0152B0502811 | 34.767 | 76.461 | Indus Upper | E(o) | 0.90 | 4,986 |\n| 2812 | 0152B0502812 | 34.765 | 76.494 | Indus Upper | E(o) | 3.00 | 4,639 |\n| 2813 | 0152B0502813 | 34.763 | 76.462 | Indus Upper | E(o) | 3.57 | 4,949 |\n| 2814 | 0152B0502814 | 34.762 | 76.449 | Indus Upper | M(o) | 1.94 | 4,988 |\n| 2815 | 0152B0502815 | 34.762 | 76.426 | Indus Upper | E(o) | 7.61 | 4,749 |\n| 2816 | 0152B0502816 | 34.762 | 76.469 | Indus Upper | E(o) | 17.67 | 4,895 |\n| 2817 | 0152B0502817 | 34.762 | 76.452 | Indus Upper | M(o) | 3.00 | 4,977 |\n| 2818 | 0152B0502818 | 34.759 | 76.487 | Indus Upper | E(o) | 4.57 | 4,660 |\n| 2819 | 0152B0502819 | 34.758 | 76.474 | Indus Upper | E(o) | 2.04 | 4,824 |\n| 2820 | 0152B0502820 | 34.758 | 76.420 | Indus Upper | E(o) | 1.42 | 4,770 |\n| 2821 | 0152B0502821 | 34.757 | 76.414 | Indus Upper | E(o) | 3.85 | 4,747 |\n| 2822 | 0152B0502822 | 34.756 | 76.481 | Indus Upper | E(o) | 2.38 | 4,773 |\n| 2823 | 0152B0502823 | 34.755 | 76.391 | Indus Upper | E(o) | 0.47 | 4,699 |\n| 2824 | 0152B0502824 | 34.755 | 76.394 | Indus Upper | E(o) | 0.99 | 4,734 |\n| 2825 | 0152B0502825 | 34.752 | 76.436 | Indus Upper | M(o) | 4.99 | 4,898 |\n| 2826 | 0152B0502826 | 34.750 | 76.423 | Indus Upper | E(o) | 1.78 | 4,785 |\n| 2827 | 0152B0602827 | 34.750 | 76.477 | Indus Upper | E(o) | 2.27 | 4,866 |\n| 2828 | 0152B0602828 | 34.748 | 76.397 | Indus Upper | E(o) | 3.81 | 4,759 |\n| 2829 | 0152B0602829 | 34.748 | 76.452 | Indus Upper | E(o) | 3.35 | 4,829 |\n| 2830 | 0152B0602830 | 34.747 | 76.400 | Indus Upper | E(o) | 1.86 | 4,775 |\n| 2831 | 0152B0602831 | 34.735 | 76.430 | Indus Upper | E(o) | 2.16 | 4,835 |\n| 2832 | 0152B0802832 | 34.130 | 76.421 | Indus Upper | E(o) | 0.66 | 5,164 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 8962, "line_end": 9109, "token_count_estimate": 1675, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0152B0502798", "0152B0502799", "0152B0502800", "0152B0502801", "0152B0502802", "0152B0502803", "0152B0502804", "0152B0502805", "0152B0502806", "0152B0502807", "0152B0502808", "0152B0502809", "0152B0502810", "0152B0502811", "0152B0502812", "0152B0502813", "0152B0502814", "0152B0502815", "0152B0502816", "0152B0502817", "0152B0502818", "0152B0502819", "0152B0502820", "0152B0502821", "0152B0502822", "0152B0502823", "0152B0502824", "0152B0502825", "0152B0502826", "0152B0602827", "0152B0602828", "0152B0602829", "0152B0602830", "0152B0602831", "0152B0802832"]}}
{"id": "53d36173173274cc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2833 | 0152B0802833 | 34.130 | 76.465 | Indus Upper | I(s) | 1.12 | 4,902 |\n| 2834 | 0152B0802834 | 34.113 | 76.427 | Indus Upper | M(o) | 1.07 | 4,965 |\n| 2835 | 0152B0802835 | 34.110 | 76.425 | Indus Upper | M(o) | 1.33 | 5,023 |\n| 2836 | 0152B0802836 | 34.108 | 76.418 | Indus Upper | M(o) | 1.69 | 4,970 |\n| 2837 | 0152B0802837 | 34.024 | 76.310 | Indus Upper | M(e) | 1.35 | 4,735 |\n| 2838 | 0152B0902838 | 34.872 | 76.505 | Shyok | E(o) | 1.17 | 4,865 |\n| 2839 | 0152B0902839 | 34.870 | 76.502 | Shyok | E(o) | 0.93 | 4,923 |\n| 2840 | 0152B0902840 | 34.866 | 76.501 | Shyok | E(o) | 7.15 | 4,908 |\n| 2841 | 0152B0902841 | 34.851 | 76.511 | Shyok | E(o) | 3.92 | 4,899 |\n| 2842 | 0152B0902842 | 34.833 | 76.522 | Shyok | E(o) | 1.77 | 4,507 |\n| 2843 | 0152B0902843 | 34.818 | 76.512 | Shyok | E(o) | 0.82 | 4,723 |\n| 2844 | 0152B0902844 | 34.813 | 76.517 | Shyok | E(o) | 20.60 | 4,771 |\n| 2845 | 0152B0902845 | 34.811 | 76.502 | Shyok | E(o) | 12.91 | 4,762 |\n| 2846 | 0152B0902846 | 34.787 | 76.535 | Shyok | E(o) | 2.57 | 4,923 |\n| 2847 | 0152B0902847 | 34.785 | 76.726 | Shyok | I(s) | 1.03 | 5,115 |\n| 2848 | 0152B0902848 | 34.784 | 76.506 | Indus Upper | E(o) | 3.60 | 5,002 |\n| 2849 | 0152B0902849 | 34.781 | 76.529 | Shyok | M(e) | 1.09 | 4,967 |\n| 2850 | 0152B0902850 | 34.774 | 76.591 | Shyok | E(o) | 1.88 | 4,799 |\n| 2851 | 0152B0902851 | 34.772 | 76.566 | Shyok | M(e) | 0.70 | 5,012 |\n| 2852 | 0152B0902852 | 34.771 | 76.577 | Shyok | E(o) | 4.29 | 4,918 |\n| 2853 | 0152B0902853 | 34.768 | 76.586 | Shyok | M(o) | 1.35 | 4,984 |\n| 2854 | 0152B0902854 | 34.765 | 76.568 | Shyok | I(s) | 0.73 | 5,139 |\n| 2855 | 0152B0902855 | 34.765 | 76.711 | Shyok | E(o) | 10.06 | 5,123 |\n| 2856 | 0152B0902856 | 34.764 | 76.571 | Shyok | I(s) | 0.85 | 5,107 |\n| 2857 | 0152B0902857 | 34.756 | 76.546 | Indus Upper | E(o) | 1.76 | 5,082 |\n| 2858 | 0152B0902858 | 34.755 | 76.539 | Indus Upper | E(c) | 3.70 | 5,213 |\n| 2859 | 0152B1002859 | 34.742 | 76.566 | Indus Upper | E(o) | 0.87 | 4,867 |\n| 2860 | 0152B1002860 | 34.742 | 76.561 | Indus Upper | E(o) | 3.44 | 4,882 |\n| 2861 | 0152B1002861 | 34.732 | 76.681 | Indus Upper | E(o) | 1.43 | 5,069 |\n| 2862 | 0152B1002862 | 34.731 | 76.689 | Indus Upper | M(o) | 0.39 | 5,010 |\n| 2863 | 0152B1002863 | 34.727 | 76.565 | Indus Upper | M(o) | 0.72 | 4,955 |\n| 2864 | 0152B1002864 | 34.727 | 76.578 | Indus Upper | E(o) | 2.84 | 4,835 |\n| 2865 | 0152B1002865 | 34.718 | 76.730 | Shyok | M(o) | 0.61 | 5,077 |\n| 2866 | 0152B1002866 | 34.713 | 76.585 | Indus Upper | M(o) | 1.13 | 4,960 |\n| 2867 | 0152B1002867 | 34.711 | 76.620 | Indus Upper | E(o) | 0.46 | 4,413 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 8962, "line_end": 9109, "token_count_estimate": 1664, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152B0802833", "0152B0802834", "0152B0802835", "0152B0802836", "0152B0802837", "0152B0902838", "0152B0902839", "0152B0902840", "0152B0902841", "0152B0902842", "0152B0902843", "0152B0902844", "0152B0902845", "0152B0902846", "0152B0902847", "0152B0902848", "0152B0902849", "0152B0902850", "0152B0902851", "0152B0902852", "0152B0902853", "0152B0902854", "0152B0902855", "0152B0902856", "0152B0902857", "0152B0902858", "0152B1002859", "0152B1002860", "0152B1002861", "0152B1002862", "0152B1002863", "0152B1002864", "0152B1002865", "0152B1002866", "0152B1002867"]}}
{"id": "556a5e8edaf49aad", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2868 | 0152B1002868 | 34.709 | 76.591 | Indus Upper | E(o) | 1.05 | 4,887 |\n| 2869 | 0152B1002869 | 34.707 | 76.534 | Indus Upper | E(o) | 0.40 | 4,611 |\n| 2870 | 0152B1002870 | 34.696 | 76.709 | Indus Upper | E(o) | 4.26 | 4,954 |\n| 2871 | 0152B1002871 | 34.695 | 76.702 | Indus Upper | E(o) | 2.98 | 4,869 |\n| 2872 | 0152B1002872 | 34.687 | 76.573 | Indus Upper | M(o) | 0.53 | 4,930 |\n| 2873 | 0152B1002873 | 34.624 | 76.725 | Indus Upper | M(e) | 1.04 | 4,963 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 8962, "line_end": 9109, "token_count_estimate": 376, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0152B1002868", "0152B1002869", "0152B1002870", "0152B1002871", "0152B1002872", "0152B1002873"]}}
{"id": "4b9d96142552ea7a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9110, "line_end": 9117, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a710e9f6dce2a161", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2874 | 0152B1002874 | 34.606 | 76.725 | Indus Upper | M(e) | 2.35 | 5,018 |\n| 2875 | 0152B1202875 | 34.098 | 76.704 | Indus Upper | M(e) | 0.31 | 5,153 |\n| 2876 | 0152B1202876 | 34.078 | 76.701 | Indus Upper | I(s) | 0.58 | 5,336 |\n| 2877 | 0152B1202877 | 34.072 | 76.651 | Indus Upper | E(o) | 1.21 | 5,166 |\n| 2878 | 0152B1202878 | 34.051 | 76.718 | Indus Upper | M(e) | 15.80 | 5,093 |\n| 2879 | 0152B1202879 | 34.006 | 76.716 | Indus Upper | M(o) | 0.40 | 5,108 |\n| 2880 | 0152B1202880 | 34.006 | 76.705 | Indus Upper | M(lg) | 1.33 | 5,257 |\n| 2881 | 0152B1202881 | 34.005 | 76.721 | Indus Upper | M(e) | 18.32 | 5,050 |\n| 2882 | 0152B1302882 | 34.998 | 76.918 | Shyok | M(e) | 1.36 | 4,973 |\n| 2883 | 0152B1302883 | 34.957 | 76.912 | Shyok | M(e) | 1.40 | 4,882 |\n| 2884 | 0152B1302884 | 34.834 | 76.757 | Shyok | I(s) | 0.48 | 4,983 |\n| 2885 | 0152B1302885 | 34.757 | 76.826 | Shyok | E(o) | 1.56 | 4,479 |\n| 2886 | 0152B1402886 | 34.748 | 76.819 | Shyok | E(o) | 3.43 | 4,566 |\n| 2887 | 0152B1402887 | 34.742 | 76.814 | Shyok | E(o) | 0.62 | 4,638 |\n| 2888 | 0152B1402888 | 34.727 | 76.777 | Shyok | E(o) | 3.00 | 4,967 |\n| 2889 | 0152B1402889 | 34.721 | 76.840 | Shyok | M(e) | 1.46 | 5,062 |\n| 2890 | 0152B1402890 | 34.714 | 76.808 | Shyok | E(o) | 2.85 | 4,870 |\n| 2891 | 0152B1402891 | 34.707 | 76.806 | Shyok | M(e) | 0.77 | 4,905 |\n| 2892 | 0152B1402892 | 34.706 | 76.806 | Shyok | M(o) | 0.27 | 4,911 |\n| 2893 | 0152B1402893 | 34.703 | 76.794 | Shyok | E(o) | 1.66 | 5,094 |\n| 2894 | 0152B1402894 | 34.700 | 76.857 | Shyok | E(o) | 2.33 | 4,771 |\n| 2895 | 0152B1402895 | 34.695 | 76.982 | Shyok | O | 0.74 | 4,538 |\n| 2896 | 0152B1402896 | 34.685 | 76.952 | Shyok | I(s) | 0.88 | 4,971 |\n| 2897 | 0152B1402897 | 34.682 | 76.751 | Shyok | E(o) | 1.03 | 5,103 |\n| 2898 | 0152B1402898 | 34.671 | 76.758 | Shyok | E(o) | 29.88 | 4,965 |\n| 2899 | 0152B1402899 | 34.636 | 76.833 | Shyok | E(o) | 0.42 | 4,807 |\n| 2900 | 0152B1402900 | 34.623 | 76.968 | Shyok | M(o) | 0.39 | 5,024 |\n| 2901 | 0152B1402901 | 34.621 | 76.968 | Shyok | M(e) | 1.88 | 5,031 |\n| 2902 | 0152B1402902 | 34.619 | 76.813 | Shyok | M(e) | 2.61 | 4,962 |\n| 2903 | 0152B1402903 | 34.606 | 76.778 | Indus Upper | M(o) | 0.66 | 4,959 |\n| 2904 | 0152B1402904 | 34.600 | 76.837 | Shyok | I(s) | 0.59 | 4,917 |\n| 2905 | 0152B1402905 | 34.593 | 76.787 | Indus Upper | E(c) | 1.28 | 5,222 |\n| 2906 | 0152B1402906 | 34.587 | 76.813 | Shyok | E(o) | 9.39 | 5,307 |\n| 2907 | 0152B1402907 | 34.586 | 76.807 | Indus Upper | M(o) | 1.67 | 5,220 |\n| 2908 | 0152B1402908 | 34.581 | 76.813 | Shyok | E(o) | 1.03 | 5,265 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9118, "line_end": 9265, "token_count_estimate": 1654, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152B1002874", "0152B1202875", "0152B1202876", "0152B1202877", "0152B1202878", "0152B1202879", "0152B1202880", "0152B1202881", "0152B1302882", "0152B1302883", "0152B1302884", "0152B1302885", "0152B1402886", "0152B1402887", "0152B1402888", "0152B1402889", "0152B1402890", "0152B1402891", "0152B1402892", "0152B1402893", "0152B1402894", "0152B1402895", "0152B1402896", "0152B1402897", "0152B1402898", "0152B1402899", "0152B1402900", "0152B1402901", "0152B1402902", "0152B1402903", "0152B1402904", "0152B1402905", "0152B1402906", "0152B1402907", "0152B1402908"]}}
{"id": "12e56d361b5b9d3d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2909 | 0152B1402909 | 34.572 | 76.779 | Indus Upper | O | 0.89 | 4,865 |\n| 2910 | 0152B1402910 | 34.572 | 76.815 | Shyok | M(e) | 1.34 | 5,171 |\n| 2911 | 0152B1402911 | 34.570 | 76.830 | Shyok | E(o) | 0.58 | 5,047 |\n| 2912 | 0152B1402912 | 34.567 | 76.817 | Shyok | M(e) | 4.44 | 5,169 |\n| 2913 | 0152B1402913 | 34.566 | 76.827 | Shyok | I(s) | 0.36 | 5,097 |\n| 2914 | 0152B1402914 | 34.562 | 76.917 | Shyok | E(o) | 5.78 | 5,335 |\n| 2915 | 0152B1402915 | 34.561 | 76.849 | Shyok | M(e) | 3.41 | 5,075 |\n| 2916 | 0152B1402916 | 34.559 | 76.925 | Shyok | M(e) | 1.09 | 5,078 |\n| 2917 | 0152B1402917 | 34.550 | 76.952 | Shyok | I(s) | 0.76 | 5,196 |\n| 2918 | 0152B1402918 | 34.548 | 76.872 | Indus Upper | M(o) | 0.27 | 5,192 |\n| 2919 | 0152B1402919 | 34.542 | 76.835 | Indus Upper | M(e) | 2.47 | 5,207 |\n| 2920 | 0152B1402920 | 34.541 | 76.865 | Indus Upper | M(o) | 0.53 | 5,067 |\n| 2921 | 0152B1402921 | 34.534 | 76.823 | Indus Upper | E(o) | 1.44 | 4,957 |\n| 2922 | 0152B1402922 | 34.532 | 76.951 | Shyok | M(e) | 2.30 | 5,219 |\n| 2923 | 0152B1402923 | 34.529 | 76.833 | Indus Upper | M(e) | 2.56 | 5,046 |\n| 2924 | 0152B1402924 | 34.513 | 76.971 | Indus Upper | M(l) | 0.50 | 5,288 |\n| 2925 | 0152B1402925 | 34.512 | 76.974 | Indus Upper | I(s) | 0.34 | 5,319 |\n| 2926 | 0152B1402926 | 34.510 | 76.971 | Indus Upper | M(e) | 2.20 | 5,251 |\n| 2927 | 0152B1502927 | 34.497 | 76.964 | Indus Upper | M(e) | 0.78 | 5,169 |\n| 2928 | 0152B1502928 | 34.487 | 76.980 | Indus Upper | E(o) | 0.74 | 5,378 |\n| 2929 | 0152B1502929 | 34.482 | 77.000 | Indus Upper | E(o) | 0.75 | 5,094 |\n| 2930 | 0152B1502930 | 34.477 | 76.972 | Indus Upper | M(e) | 9.34 | 5,195 |\n| 2931 | 0152B1502931 | 34.466 | 76.959 | Indus Upper | I(s) | 0.95 | 5,120 |\n| 2932 | 0152B1502932 | 34.460 | 76.992 | Indus Upper | I(s) | 1.06 | 5,104 |\n| 2933 | 0152B1502933 | 34.460 | 76.991 | Indus Upper | I(s) | 0.31 | 5,103 |\n| 2934 | 0152B1502934 | 34.455 | 76.932 | Indus Upper | M(e) | 0.72 | 5,066 |\n| 2935 | 0152B1502935 | 34.453 | 76.924 | Indus Upper | M(e) | 3.44 | 5,039 |\n| 2936 | 0152B1502936 | 34.448 | 76.961 | Indus Upper | M(e) | 0.50 | 5,154 |\n| 2937 | 0152B1502937 | 34.436 | 76.981 | Indus Upper | M(l) | 0.71 | 4,906 |\n| 2938 | 0152B1602938 | 34.006 | 76.788 | Indus Upper | M(e) | 14.15 | 5,126 |\n| 2939 | 0152C0102939 | 33.972 | 76.118 | Indus Upper | M(e) | 2.66 | 4,435 |\n| 2940 | 0152C0102940 | 33.967 | 76.123 | Indus Upper | M(l) | 0.27 | 4,473 |\n| 2941 | 0152C0102941 | 33.945 | 76.230 | Indus Upper | M(e) | 49.66 | 4,357 |\n| 2942 | 0152C0102942 | 33.942 | 76.019 | Chenab | M(e) | 24.05 | 4,197 |\n| 2943 | 0152C0102943 | 33.938 | 76.007 | Chenab | E(o) | 2.13 | 4,288 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9118, "line_end": 9265, "token_count_estimate": 1677, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152B1402909", "0152B1402910", "0152B1402911", "0152B1402912", "0152B1402913", "0152B1402914", "0152B1402915", "0152B1402916", "0152B1402917", "0152B1402918", "0152B1402919", "0152B1402920", "0152B1402921", "0152B1402922", "0152B1402923", "0152B1402924", "0152B1402925", "0152B1402926", "0152B1502927", "0152B1502928", "0152B1502929", "0152B1502930", "0152B1502931", "0152B1502932", "0152B1502933", "0152B1502934", "0152B1502935", "0152B1502936", "0152B1502937", "0152B1602938", "0152C0102939", "0152C0102940", "0152C0102941", "0152C0102942", "0152C0102943"]}}
{"id": "a1f2f3b97dd7bb14", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2944 | 0152C0102944 | 33.935 | 76.004 | Chenab | M(e) | 7.00 | 4,302 |\n| 2945 | 0152C0102945 | 33.890 | 76.236 | Indus Upper | E(o) | 0.79 | 4,816 |\n| 2946 | 0152C0102946 | 33.889 | 76.034 | Chenab | O | 1.19 | 3,553 |\n| 2947 | 0152C0102947 | 33.880 | 76.163 | Chenab | M(l) | 0.36 | 4,444 |\n| 2948 | 0152C0102948 | 33.875 | 76.110 | Chenab | E(o) | 3.47 | 4,073 |\n| 2949 | 0152C0102949 | 33.871 | 76.111 | Chenab | M(o) | 0.33 | 4,103 |\n| 2950 | 0152C0102950 | 33.868 | 76.121 | Chenab | M(e) | 39.44 | 4,071 |\n| 2951 | 0152C0102951 | 33.863 | 76.038 | Chenab | M(l) | 0.45 | 4,054 |\n| 2952 | 0152C0102952 | 33.861 | 76.174 | Chenab | M(l) | 0.85 | 4,602 |\n| 2953 | 0152C0102953 | 33.856 | 76.178 | Chenab | M(o) | 1.34 | 4,757 |\n| 2954 | 0152C0102954 | 33.846 | 76.015 | Chenab | M(o) | 1.17 | 4,743 |\n| 2955 | 0152C0102955 | 33.836 | 76.022 | Chenab | M(o) | 0.39 | 4,752 |\n| 2956 | 0152C0102956 | 33.799 | 76.109 | Chenab | E(o) | 0.93 | 5,158 |\n| 2957 | 0152C0102957 | 33.798 | 76.117 | Chenab | E(o) | 0.73 | 4,845 |\n| 2958 | 0152C0102958 | 33.795 | 76.117 | Chenab | E(o) | 0.78 | 4,789 |\n| 2959 | 0152C0102959 | 33.773 | 76.119 | Chenab | I(s) | 0.69 | 4,440 |\n| 2960 | 0152C0102960 | 33.759 | 76.107 | Chenab | I(s) | 0.63 | 4,458 |\n| 2961 | 0152C0202961 | 33.710 | 76.156 | Chenab | M(l) | 0.45 | 4,372 |\n| 2962 | 0152C0202962 | 33.701 | 76.182 | Chenab | I(s) | 0.38 | 3,971 |\n| 2963 | 0152C0202963 | 33.699 | 76.189 | Chenab | M(o) | 0.75 | 3,953 |\n| 2964 | 0152C0202964 | 33.698 | 76.187 | Chenab | M(o) | 1.04 | 3,950 |\n| 2965 | 0152C0202965 | 33.697 | 76.180 | Chenab | M(o) | 0.66 | 3,947 |\n| 2966 | 0152C0202966 | 33.695 | 76.218 | Chenab | I(s) | 0.30 | 4,336 |\n| 2967 | 0152C0202967 | 33.690 | 76.115 | Chenab | E(o) | 0.50 | 4,870 |\n| 2968 | 0152C0202968 | 33.683 | 76.207 | Chenab | I(s) | 0.35 | 4,508 |\n| 2969 | 0152C0202969 | 33.682 | 76.218 | Chenab | I(s) | 0.38 | 4,447 |\n| 2970 | 0152C0202970 | 33.681 | 76.215 | Chenab | I(s) | 0.32 | 4,452 |\n| 2971 | 0152C0202971 | 33.679 | 76.247 | Chenab | I(s) | 0.93 | 4,775 |\n| 2972 | 0152C0202972 | 33.677 | 76.164 | Chenab | M(o) | 0.25 | 3,923 |\n| 2973 | 0152C0202973 | 33.674 | 76.153 | Chenab | M(o) | 0.56 | 3,873 |\n| 2974 | 0152C0202974 | 33.672 | 76.151 | Chenab | M(o) | 1.05 | 3,842 |\n| 2975 | 0152C0202975 | 33.667 | 76.224 | Chenab | I(s) | 0.66 | 4,553 |\n| 2976 | 0152C0202976 | 33.663 | 76.076 | Chenab | E(o) | 0.47 | 3,656 |\n| 2977 | 0152C0202977 | 33.659 | 76.098 | Chenab | M(l) | 1.50 | 3,723 |\n| 2978 | 0152C0202978 | 33.658 | 76.091 | Chenab | M(o) | 1.43 | 3,728 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 9118, "line_end": 9265, "token_count_estimate": 1620, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0152C0102944", "0152C0102945", "0152C0102946", "0152C0102947", "0152C0102948", "0152C0102949", "0152C0102950", "0152C0102951", "0152C0102952", "0152C0102953", "0152C0102954", "0152C0102955", "0152C0102956", "0152C0102957", "0152C0102958", "0152C0102959", "0152C0102960", "0152C0202961", "0152C0202962", "0152C0202963", "0152C0202964", "0152C0202965", "0152C0202966", "0152C0202967", "0152C0202968", "0152C0202969", "0152C0202970", "0152C0202971", "0152C0202972", "0152C0202973", "0152C0202974", "0152C0202975", "0152C0202976", "0152C0202977", "0152C0202978"]}}
{"id": "a1fa6c2af99a6aea", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2979 | 0152C0202979 | 33.653 | 76.056 | Chenab | E(o) | 0.33 | 3,630 |\n| 2980 | 0152C0202980 | 33.650 | 76.057 | Chenab | E(o) | 0.33 | 3,652 |\n| 2981 | 0152C0202981 | 33.641 | 76.007 | Chenab | M(o) | 1.57 | 3,514 |\n| 2982 | 0152C0202982 | 33.628 | 76.153 | Chenab | I(s) | 0.51 | 4,486 |\n| 2983 | 0152C0202983 | 33.624 | 76.189 | Chenab | I(s) | 0.42 | 4,343 |\n| 2984 | 0152C0202984 | 33.608 | 76.194 | Chenab | M(l) | 0.63 | 4,471 |\n| 2985 | 0152C0202985 | 33.579 | 76.247 | Chenab | E(o) | 0.26 | 4,573 |\n| 2986 | 0152C0202986 | 33.559 | 76.131 | Chenab | I(s) | 0.42 | 4,157 |\n| 2987 | 0152C0202987 | 33.556 | 76.074 | Chenab | I(s) | 5.48 | 3,619 |\n| 2988 | 0152C0202988 | 33.550 | 76.068 | Chenab | I(s) | 0.29 | 3,645 |\n| 2989 | 0152C0202989 | 33.548 | 76.067 | Chenab | I(s) | 0.66 | 3,669 |\n| 2990 | 0152C0202990 | 33.539 | 76.100 | Chenab | I(s) | 0.58 | 3,808 |\n| 2991 | 0152C0202991 | 33.539 | 76.095 | Chenab | I(s) | 0.39 | 3,783 |\n| 2992 | 0152C0202992 | 33.539 | 76.087 | Chenab | I(s) | 0.64 | 3,742 |\n| 2993 | 0152C0202993 | 33.538 | 76.106 | Chenab | I(s) | 0.48 | 3,841 |\n| 2994 | 0152C0202994 | 33.537 | 76.115 | Chenab | I(s) | 0.28 | 3,893 |\n| 2995 | 0152C0202995 | 33.537 | 76.109 | Chenab | I(s) | 0.35 | 3,851 |\n| 2996 | 0152C0202996 | 33.536 | 76.097 | Chenab | I(s) | 0.38 | 3,787 |\n| 2997 | 0152C0202997 | 33.535 | 76.112 | Chenab | I(s) | 0.63 | 3,878 |\n| 2998 | 0152C0202998 | 33.533 | 76.123 | Chenab | I(s) | 0.44 | 3,935 |\n| 2999 | 0152C0202999 | 33.528 | 76.116 | Chenab | I(s) | 0.43 | 3,892 |\n| 3000 | 0152C0203000 | 33.527 | 76.113 | Chenab | I(s) | 0.30 | 3,864 |\n| 3001 | 0152C0203001 | 33.525 | 76.109 | Chenab | I(s) | 0.39 | 3,886 |\n| 3002 | 0152C0203002 | 33.524 | 76.104 | Chenab | I(s) | 0.38 | 3,878 |\n| 3003 | 0152C0203003 | 33.524 | 76.198 | Chenab | I(s) | 0.56 | 4,078 |\n| 3004 | 0152C0203004 | 33.520 | 76.103 | Chenab | I(s) | 0.41 | 3,908 |\n| 3005 | 0152C0203005 | 33.520 | 76.102 | Chenab | I(s) | 0.70 | 3,913 |\n| 3006 | 0152C0203006 | 33.518 | 76.099 | Chenab | I(s) | 0.39 | 3,929 |\n| 3007 | 0152C0303007 | 33.496 | 76.141 | Chenab | M(l) | 0.77 | 4,211 |\n| 3008 | 0152C0303008 | 33.490 | 76.119 | Chenab | E(o) | 0.42 | 4,558 |\n| 3009 | 0152C0303009 | 33.400 | 76.158 | Chenab | M(l) | 0.72 | 4,033 |\n| 3010 | 0152C0403010 | 33.233 | 76.078 | Chenab | E(o) | 0.50 | 4,170 |\n| 3011 | 0152C0403011 | 33.203 | 76.045 | Chenab | E(o) | 0.60 | 4,440 |\n| 3012 | 0152C0403012 | 33.189 | 76.116 | Chenab | E(o) | 0.39 | 4,548 |\n| 3013 | 0152C0403013 | 33.187 | 76.108 | Chenab | E(o) | 3.14 | 4,508 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 9118, "line_end": 9265, "token_count_estimate": 1606, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": ["0152C0202979", "0152C0202980", "0152C0202981", "0152C0202982", "0152C0202983", "0152C0202984", "0152C0202985", "0152C0202986", "0152C0202987", "0152C0202988", "0152C0202989", "0152C0202990", "0152C0202991", "0152C0202992", "0152C0202993", "0152C0202994", "0152C0202995", "0152C0202996", "0152C0202997", "0152C0202998", "0152C0202999", "0152C0203000", "0152C0203001", "0152C0203002", "0152C0203003", "0152C0203004", "0152C0203005", "0152C0203006", "0152C0303007", "0152C0303008", "0152C0303009", "0152C0403010", "0152C0403011", "0152C0403012", "0152C0403013"]}}
{"id": "752777c16603e50e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3014 | 0152C0403014 | 33.184 | 76.125 | Chenab | M(o) | 6.97 | 4,270 |\n| 3015 | 0152C0403015 | 33.182 | 76.113 | Chenab | M(o) | 2.62 | 4,472 |\n| 3016 | 0152C0403016 | 33.182 | 76.141 | Chenab | M(o) | 0.47 | 4,122 |\n| 3017 | 0152C0403017 | 33.175 | 76.051 | Chenab | E(o) | 0.46 | 4,512 |\n| 3018 | 0152C0403018 | 33.174 | 76.056 | Chenab | M(e) | 3.99 | 4,342 |\n| 3019 | 0152C0403019 | 33.166 | 76.083 | Chenab | M(o) | 0.42 | 4,452 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 9118, "line_end": 9265, "token_count_estimate": 370, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": ["0152C0403014", "0152C0403015", "0152C0403016", "0152C0403017", "0152C0403018", "0152C0403019"]}}
{"id": "2cc496b0d5cf36c8", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9266, "line_end": 9274, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68e571e2b36040f9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3020 | 0152C0403020 | 33.162 | 76.134 | Chenab | M(e) | 1.25 | 4,286 |\n| 3021 | 0152C0403021 | 33.147 | 76.139 | Chenab | E(c) | 9.66 | 4,338 |\n| 3022 | 0152C0403022 | 33.104 | 76.197 | Chenab | M(o) | 0.52 | 4,562 |\n| 3023 | 0152C0403023 | 33.099 | 76.198 | Chenab | E(o) | 1.18 | 4,487 |\n| 3024 | 0152C0403024 | 33.041 | 76.212 | Chenab | M(o) | 0.68 | 4,420 |\n| 3025 | 0152C0403025 | 33.039 | 76.205 | Chenab | E(o) | 0.29 | 4,324 |\n| 3026 | 0152C0403026 | 33.029 | 76.213 | Chenab | M(o) | 0.59 | 4,378 |\n| 3027 | 0152C0403027 | 33.017 | 76.169 | Chenab | M(e) | 0.88 | 4,068 |\n| 3028 | 0152C0503028 | 33.898 | 76.457 | Indus Upper | M(o) | 0.56 | 4,908 |\n| 3029 | 0152C0503029 | 33.884 | 76.461 | Indus Upper | I(s) | 0.57 | 5,211 |\n| 3030 | 0152C0503030 | 33.881 | 76.461 | Indus Upper | M(o) | 0.42 | 5,249 |\n| 3031 | 0152C0503031 | 33.879 | 76.461 | Indus Upper | I(s) | 0.33 | 5,272 |\n| 3032 | 0152C0503032 | 33.878 | 76.461 | Indus Upper | I(s) | 0.28 | 5,282 |\n| 3033 | 0152C0503033 | 33.868 | 76.354 | Indus Upper | E(o) | 2.54 | 4,465 |\n| 3034 | 0152C0503034 | 33.864 | 76.355 | Indus Upper | M(o) | 6.83 | 4,457 |\n| 3035 | 0152C0503035 | 33.863 | 76.350 | Indus Upper | M(o) | 1.92 | 4,471 |\n| 3036 | 0152C0503036 | 33.862 | 76.346 | Indus Upper | M(o) | 0.50 | 4,489 |\n| 3037 | 0152C0503037 | 33.862 | 76.358 | Indus Upper | M(o) | 0.48 | 4,455 |\n| 3038 | 0152C0503038 | 33.861 | 76.352 | Indus Upper | M(o) | 0.47 | 4,469 |\n| 3039 | 0152C0503039 | 33.860 | 76.354 | Indus Upper | M(o) | 1.43 | 4,468 |\n| 3040 | 0152C0503040 | 33.856 | 76.497 | Indus Upper | E(o) | 1.35 | 5,062 |\n| 3041 | 0152C0503041 | 33.855 | 76.500 | Indus Upper | E(o) | 0.60 | 5,059 |\n| 3042 | 0152C0503042 | 33.847 | 76.384 | Indus Upper | E(o) | 1.20 | 4,078 |\n| 3043 | 0152C0503043 | 33.846 | 76.369 | Indus Upper | M(o) | 1.04 | 4,132 |\n| 3044 | 0152C0503044 | 33.844 | 76.375 | Indus Upper | M(e) | 18.49 | 4,116 |\n| 3045 | 0152C0503045 | 33.812 | 76.390 | Indus Upper | M(o) | 0.28 | 4,957 |\n| 3046 | 0152C0603046 | 33.694 | 76.407 | Indus Upper | I(s) | 0.27 | 4,174 |\n| 3047 | 0152C0603047 | 33.690 | 76.395 | Indus Upper | I(s) | 0.26 | 4,230 |\n| 3048 | 0152C0603048 | 33.668 | 76.366 | Indus Upper | I(s) | 1.35 | 4,434 |\n| 3049 | 0152C0603049 | 33.662 | 76.352 | Indus Upper | I(s) | 0.25 | 4,503 |\n| 3050 | 0152C0603050 | 33.650 | 76.345 | Indus Upper | I(s) | 0.40 | 4,561 |\n| 3051 | 0152C0603051 | 33.647 | 76.343 | Indus Upper | I(s) | 0.90 | 4,581 |\n| 3052 | 0152C0603052 | 33.638 | 76.452 | Indus Upper | I(s) | 0.53 | 4,225 |\n| 3053 | 0152C0603053 | 33.636 | 76.454 | Indus Upper | I(s) | 0.84 | 4,221 |\n| 3054 | 0152C0603054 | 33.634 | 76.451 | Indus Upper | I(s) | 0.26 | 4,250 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9275, "line_end": 9422, "token_count_estimate": 1670, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0152C0403020", "0152C0403021", "0152C0403022", "0152C0403023", "0152C0403024", "0152C0403025", "0152C0403026", "0152C0403027", "0152C0503028", "0152C0503029", "0152C0503030", "0152C0503031", "0152C0503032", "0152C0503033", "0152C0503034", "0152C0503035", "0152C0503036", "0152C0503037", "0152C0503038", "0152C0503039", "0152C0503040", "0152C0503041", "0152C0503042", "0152C0503043", "0152C0503044", "0152C0503045", "0152C0603046", "0152C0603047", "0152C0603048", "0152C0603049", "0152C0603050", "0152C0603051", "0152C0603052", "0152C0603053", "0152C0603054"]}}
{"id": "33007b5b47db8721", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3055 | 0152C0603055 | 33.628 | 76.449 | Indus Upper | I(s) | 0.32 | 4,303 |\n| 3056 | 0152C0603056 | 33.598 | 76.443 | Indus Upper | I(s) | 0.28 | 4,454 |\n| 3057 | 0152C0603057 | 33.555 | 76.354 | Chenab | I(s) | 0.64 | 4,538 |\n| 3058 | 0152C0603058 | 33.545 | 76.334 | Chenab | I(s) | 0.33 | 4,118 |\n| 3059 | 0152C0603059 | 33.527 | 76.283 | Chenab | M(e) | 2.31 | 4,295 |\n| 3060 | 0152C0603060 | 33.512 | 76.468 | Chenab | E(o) | 0.40 | 4,790 |\n| 3061 | 0152C0603061 | 33.507 | 76.450 | Chenab | O | 16.41 | 4,219 |\n| 3062 | 0152C0703062 | 33.485 | 76.352 | Chenab | E(o) | 0.37 | 3,689 |\n| 3063 | 0152C0703063 | 33.483 | 76.466 | Chenab | O | 2.32 | 4,056 |\n| 3064 | 0152C0703064 | 33.465 | 76.474 | Chenab | M(l) | 1.69 | 3,903 |\n| 3065 | 0152C0703065 | 33.465 | 76.471 | Chenab | M(l) | 0.52 | 3,908 |\n| 3066 | 0152C0703066 | 33.460 | 76.474 | Chenab | M(e) | 6.24 | 3,876 |\n| 3067 | 0152C0703067 | 33.460 | 76.489 | Chenab | M(o) | 0.29 | 4,390 |\n| 3068 | 0152C0703068 | 33.458 | 76.485 | Chenab | E(o) | 2.42 | 4,272 |\n| 3069 | 0152C0703069 | 33.456 | 76.392 | Chenab | M(e) | 1.43 | 4,660 |\n| 3070 | 0152C0703070 | 33.454 | 76.280 | Chenab | M(e) | 0.61 | 3,975 |\n| 3071 | 0152C0703071 | 33.424 | 76.498 | Chenab | E(c) | 0.55 | 4,942 |\n| 3072 | 0152C0703072 | 33.420 | 76.498 | Chenab | M(o) | 1.80 | 4,868 |\n| 3073 | 0152C0703073 | 33.342 | 76.492 | Chenab | E(o) | 0.53 | 4,721 |\n| 3074 | 0152C0703074 | 33.312 | 76.363 | Chenab | M(e) | 6.58 | 4,115 |\n| 3075 | 0152C0703075 | 33.311 | 76.361 | Chenab | M(o) | 0.60 | 4,137 |\n| 3076 | 0152C0703076 | 33.308 | 76.362 | Chenab | M(o) | 0.35 | 4,120 |\n| 3077 | 0152C0703077 | 33.307 | 76.365 | Chenab | M(o) | 0.42 | 4,109 |\n| 3078 | 0152C0703078 | 33.306 | 76.368 | Chenab | M(e) | 0.99 | 4,142 |\n| 3079 | 0152C0703079 | 33.303 | 76.359 | Chenab | M(o) | 0.56 | 4,090 |\n| 3080 | 0152C0703080 | 33.299 | 76.358 | Chenab | M(e) | 0.29 | 4,056 |\n| 3081 | 0152C0703081 | 33.299 | 76.360 | Chenab | I(s) | 0.32 | 4,065 |\n| 3082 | 0152C0703082 | 33.250 | 76.459 | Chenab | E(o) | 0.44 | 4,829 |\n| 3083 | 0152C0803083 | 33.246 | 76.477 | Chenab | O | 0.65 | 4,431 |\n| 3084 | 0152C0803084 | 33.242 | 76.437 | Chenab | M(o) | 0.28 | 4,776 |\n| 3085 | 0152C0803085 | 33.228 | 76.489 | Chenab | M(o) | 0.52 | 4,447 |\n| 3086 | 0152C0803086 | 33.212 | 76.493 | Chenab | I(s) | 0.30 | 4,152 |\n| 3087 | 0152C0803087 | 33.208 | 76.491 | Chenab | I(s) | 0.28 | 4,121 |\n| 3088 | 0152C0803088 | 33.020 | 76.357 | Chenab | M(e) | 1.56 | 4,437 |\n| 3089 | 0152C0903089 | 33.992 | 76.718 | Indus Upper | E(o) | 1.10 | 5,316 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9275, "line_end": 9422, "token_count_estimate": 1611, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0152C0603055", "0152C0603056", "0152C0603057", "0152C0603058", "0152C0603059", "0152C0603060", "0152C0603061", "0152C0703062", "0152C0703063", "0152C0703064", "0152C0703065", "0152C0703066", "0152C0703067", "0152C0703068", "0152C0703069", "0152C0703070", "0152C0703071", "0152C0703072", "0152C0703073", "0152C0703074", "0152C0703075", "0152C0703076", "0152C0703077", "0152C0703078", "0152C0703079", "0152C0703080", "0152C0703081", "0152C0703082", "0152C0803083", "0152C0803084", "0152C0803085", "0152C0803086", "0152C0803087", "0152C0803088", "0152C0903089"]}}
{"id": "d13977c262fc2308", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3090 | 0152C0903090 | 33.753 | 76.531 | Indus Upper | E(c) | 4.86 | 5,081 |\n| 3091 | 0152C1003091 | 33.738 | 76.636 | Indus Upper | M(o) | 1.01 | 5,026 |\n| 3092 | 0152C1003092 | 33.714 | 76.669 | Indus Upper | M(o) | 1.12 | 5,167 |\n| 3093 | 0152C1003093 | 33.713 | 76.674 | Indus Upper | M(e) | 1.47 | 5,109 |\n| 3094 | 0152C1003094 | 33.515 | 76.666 | Indus Upper | E(o) | 0.43 | 4,769 |\n| 3095 | 0152C1003095 | 33.510 | 76.596 | Indus Upper | M(o) | 2.19 | 4,179 |\n| 3096 | 0152C1003096 | 33.510 | 76.602 | Indus Upper | M(o) | 0.43 | 4,171 |\n| 3097 | 0152C1103097 | 33.449 | 76.675 | Indus Upper | I(s) | 0.89 | 5,309 |\n| 3098 | 0152C1103098 | 33.422 | 76.506 | Chenab | E(o) | 0.25 | 4,727 |\n| 3099 | 0152C1103099 | 33.422 | 76.508 | Chenab | M(o) | 0.34 | 4,685 |\n| 3100 | 0152C1103100 | 33.412 | 76.727 | Indus Upper | E(o) | 0.36 | 4,119 |\n| 3101 | 0152C1103101 | 33.378 | 76.535 | Chenab | I(s) | 0.57 | 4,015 |\n| 3102 | 0152C1103102 | 33.358 | 76.700 | Indus Upper | M(lg) | 0.49 | 5,020 |\n| 3103 | 0152C1103103 | 33.328 | 76.523 | Chenab | E(o) | 0.26 | 4,882 |\n| 3104 | 0152C1103104 | 33.327 | 76.521 | Chenab | E(o) | 0.81 | 4,820 |\n| 3105 | 0152C1103105 | 33.305 | 76.669 | Chenab | M(e) | 0.64 | 4,953 |\n| 3106 | 0152C1103106 | 33.304 | 76.671 | Chenab | M(e) | 0.37 | 4,982 |\n| 3107 | 0152C1103107 | 33.270 | 76.590 | Chenab | M(o) | 0.78 | 4,556 |\n| 3108 | 0152C1103108 | 33.269 | 76.588 | Chenab | M(o) | 0.50 | 4,543 |\n| 3109 | 0152C1203109 | 33.240 | 76.737 | Chenab | E(o) | 0.31 | 4,906 |\n| 3110 | 0152C1203110 | 33.218 | 76.547 | Chenab | I(s) | 4.38 | 4,481 |\n| 3111 | 0152C1203111 | 33.202 | 76.710 | Chenab | I(s) | 0.36 | 4,373 |\n| 3112 | 0152C1203112 | 33.202 | 76.718 | Chenab | I(s) | 0.28 | 4,409 |\n| 3113 | 0152C1203113 | 33.201 | 76.707 | Chenab | I(s) | 0.26 | 4,358 |\n| 3114 | 0152C1203114 | 33.196 | 76.707 | Chenab | I(s) | 0.27 | 4,352 |\n| 3115 | 0152C1203115 | 33.195 | 76.699 | Chenab | I(s) | 0.80 | 4,309 |\n| 3116 | 0152C1203116 | 33.193 | 76.658 | Chenab | M(o) | 0.60 | 4,054 |\n| 3117 | 0152C1203117 | 33.193 | 76.659 | Chenab | M(o) | 0.26 | 4,049 |\n| 3118 | 0152C1203118 | 33.192 | 76.694 | Chenab | I(s) | 0.26 | 4,279 |\n| 3119 | 0152C1203119 | 33.192 | 76.660 | Chenab | M(o) | 0.27 | 4,063 |\n| 3120 | 0152C1203120 | 33.189 | 76.674 | Chenab | I(s) | 0.84 | 4,181 |\n| 3121 | 0152C1203121 | 33.184 | 76.591 | Chenab | E(o) | 0.49 | 4,845 |\n| 3122 | 0152C1203122 | 33.183 | 76.530 | Chenab | M(o) | 1.00 | 4,770 |\n| 3123 | 0152C1203123 | 33.165 | 76.699 | Chenab | M(o) | 0.37 | 4,946 |\n| 3124 | 0152C1203124 | 33.165 | 76.721 | Chenab | M(o) | 0.37 | 4,882 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 9275, "line_end": 9422, "token_count_estimate": 1641, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0152C0903090", "0152C1003091", "0152C1003092", "0152C1003093", "0152C1003094", "0152C1003095", "0152C1003096", "0152C1103097", "0152C1103098", "0152C1103099", "0152C1103100", "0152C1103101", "0152C1103102", "0152C1103103", "0152C1103104", "0152C1103105", "0152C1103106", "0152C1103107", "0152C1103108", "0152C1203109", "0152C1203110", "0152C1203111", "0152C1203112", "0152C1203113", "0152C1203114", "0152C1203115", "0152C1203116", "0152C1203117", "0152C1203118", "0152C1203119", "0152C1203120", "0152C1203121", "0152C1203122", "0152C1203123", "0152C1203124"]}}
{"id": "6b57c35d3ba6f5be", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3125 | 0152C1203125 | 33.164 | 76.715 | Chenab | E(o) | 1.03 | 4,799 |\n| 3126 | 0152C1203126 | 33.144 | 76.672 | Chenab | M(o) | 1.65 | 4,921 |\n| 3127 | 0152C1203127 | 33.134 | 76.602 | Chenab | M(e) | 4.13 | 4,419 |\n| 3128 | 0152C1203128 | 33.133 | 76.721 | Chenab | E(o) | 2.84 | 4,914 |\n| 3129 | 0152C1203129 | 33.132 | 76.735 | Chenab | E(o) | 0.81 | 4,826 |\n| 3130 | 0152C1203130 | 33.127 | 76.573 | Chenab | E(o) | 1.11 | 4,807 |\n| 3131 | 0152C1203131 | 33.124 | 76.714 | Chenab | M(o) | 2.19 | 4,581 |\n| 3132 | 0152C1203132 | 33.123 | 76.711 | Chenab | M(o) | 1.32 | 4,583 |\n| 3133 | 0152C1203133 | 33.114 | 76.735 | Chenab | I(s) | 0.42 | 5,172 |\n| 3134 | 0152C1203134 | 33.109 | 76.707 | Chenab | M(o) | 0.26 | 4,962 |\n| 3135 | 0152C1203135 | 33.088 | 76.700 | Chenab | M(o) | 1.65 | 4,640 |\n| 3136 | 0152C1203136 | 33.085 | 76.716 | Chenab | E(o) | 0.39 | 4,788 |\n| 3137 | 0152C1203137 | 33.068 | 76.727 | Chenab | E(o) | 0.46 | 5,017 |\n| 3138 | 0152C1203138 | 33.067 | 76.729 | Chenab | E(o) | 0.76 | 5,011 |\n| 3139 | 0152C1203139 | 33.067 | 76.722 | Chenab | E(o) | 1.23 | 4,938 |\n| 3140 | 0152C1203140 | 33.066 | 76.724 | Chenab | E(o) | 0.67 | 4,945 |\n| 3141 | 0152C1203141 | 33.066 | 76.704 | Chenab | E(o) | 0.33 | 4,990 |\n| 3142 | 0152C1203142 | 33.065 | 76.705 | Chenab | E(o) | 0.83 | 4,987 |\n| 3143 | 0152C1203143 | 33.065 | 76.723 | Chenab | E(o) | 0.50 | 4,935 |\n| 3144 | 0152C1203144 | 33.062 | 76.700 | Chenab | E(o) | 0.34 | 4,977 |\n| 3145 | 0152C1203145 | 33.040 | 76.725 | Chenab | E(o) | 0.81 | 4,861 |\n| 3146 | 0152C1203146 | 33.036 | 76.745 | Chenab | I(s) | 0.44 | 5,205 |\n| 3147 | 0152C1203147 | 33.029 | 76.740 | Chenab | E(o) | 1.64 | 4,999 |\n| 3148 | 0152C1203148 | 33.027 | 76.741 | Chenab | E(o) | 1.81 | 4,999 |\n| 3149 | 0152C1203149 | 33.025 | 76.735 | Chenab | E(o) | 0.48 | 4,859 |\n| 3150 | 0152C1203150 | 33.024 | 76.743 | Chenab | E(o) | 1.53 | 4,990 |\n| 3151 | 0152C1403151 | 33.501 | 76.757 | Indus Upper | M(e) | 0.39 | 4,810 |\n| 3152 | 0152C1503152 | 33.332 | 76.824 | Indus Upper | E(o) | 0.69 | 5,133 |\n| 3153 | 0152C1603153 | 33.182 | 76.893 | Indus Upper | M(o) | 0.39 | 4,459 |\n| 3154 | 0152C1603154 | 33.176 | 76.966 | Indus Upper | E(o) | 0.38 | 5,205 |\n| 3155 | 0152C1603155 | 33.175 | 76.967 | Indus Upper | I(s) | 0.65 | 5,211 |\n| 3156 | 0152C1603156 | 33.159 | 76.984 | Indus Upper | M(e) | 59.78 | 4,479 |\n| 3157 | 0152C1603157 | 33.110 | 76.812 | Chenab | I(s) | 0.26 | 5,231 |\n| 3158 | 0152C1603158 | 33.103 | 76.787 | Chenab | M(o) | 0.25 | 5,090 |\n| 3159 | 0152C1603159 | 33.100 | 76.786 | Chenab | M(o) | 1.39 | 5,052 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 9275, "line_end": 9422, "token_count_estimate": 1625, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0152C1203125", "0152C1203126", "0152C1203127", "0152C1203128", "0152C1203129", "0152C1203130", "0152C1203131", "0152C1203132", "0152C1203133", "0152C1203134", "0152C1203135", "0152C1203136", "0152C1203137", "0152C1203138", "0152C1203139", "0152C1203140", "0152C1203141", "0152C1203142", "0152C1203143", "0152C1203144", "0152C1203145", "0152C1203146", "0152C1203147", "0152C1203148", "0152C1203149", "0152C1203150", "0152C1403151", "0152C1503152", "0152C1603153", "0152C1603154", "0152C1603155", "0152C1603156", "0152C1603157", "0152C1603158", "0152C1603159"]}}
{"id": "e2bad852a90080f5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3160 | 0152C1603160 | 33.098 | 76.792 | Chenab | E(o) | 1.33 | 5,020 |\n| 3161 | 0152C1603161 | 33.092 | 76.961 | Indus Upper | I(s) | 0.26 | 5,430 |\n| 3162 | 0152C1603162 | 33.088 | 76.755 | Chenab | I(s) | 0.31 | 4,333 |\n| 3163 | 0152C1603163 | 33.085 | 76.756 | Chenab | I(s) | 0.73 | 4,310 |\n| 3164 | 0152C1603164 | 33.077 | 76.760 | Chenab | I(s) | 0.45 | 4,262 |\n| 3165 | 0152C1603165 | 33.075 | 76.969 | Indus Upper | E(o) | 0.32 | 5,184 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 9275, "line_end": 9422, "token_count_estimate": 377, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper"], "countries": [], "lake_ids": ["0152C1603160", "0152C1603161", "0152C1603162", "0152C1603163", "0152C1603164", "0152C1603165"]}}
{"id": "d31112cc681b3213", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9423, "line_end": 9431, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "729d09e400367ebc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3166 | 0152C1603166 | 33.071 | 76.765 | Chenab | I(s) | 0.47 | 4,226 |\n| 3167 | 0152C1603167 | 33.069 | 76.762 | Chenab | I(s) | 1.18 | 4,234 |\n| 3168 | 0152C1603168 | 33.069 | 76.823 | Chenab | M(o) | 0.26 | 5,081 |\n| 3169 | 0152C1603169 | 33.068 | 76.765 | Chenab | I(s) | 1.04 | 4,218 |\n| 3170 | 0152C1603170 | 33.066 | 76.824 | Chenab | M(l) | 1.85 | 5,021 |\n| 3171 | 0152C1603171 | 33.065 | 76.764 | Chenab | I(s) | 0.45 | 4,223 |\n| 3172 | 0152C1603172 | 33.061 | 76.772 | Chenab | I(s) | 0.42 | 4,188 |\n| 3173 | 0152C1603173 | 33.058 | 76.777 | Chenab | I(s) | 0.37 | 4,159 |\n| 3174 | 0152C1603174 | 33.043 | 76.789 | Chenab | M(o) | 0.48 | 4,012 |\n| 3175 | 0152C1603175 | 33.016 | 76.804 | Chenab | M(o) | 0.36 | 4,371 |\n| 3176 | 0152C1603176 | 33.009 | 76.757 | Chenab | M(o) | 1.47 | 5,039 |\n| 3177 | 0152C1603177 | 33.006 | 76.761 | Chenab | M(o) | 0.84 | 5,030 |\n| 3178 | 0152C1603178 | 33.004 | 76.781 | Chenab | E(o) | 0.82 | 4,956 |\n| 3179 | 0152D0103179 | 32.982 | 76.241 | Ravi | E(o) | 0.86 | 4,271 |\n| 3180 | 0152D0103180 | 32.979 | 76.103 | Ravi | E(o) | 0.32 | 3,895 |\n| 3181 | 0152D0503181 | 32.978 | 76.259 | Ravi | M(o) | 2.08 | 4,353 |\n| 3182 | 0152D0503182 | 32.872 | 76.334 | Ravi | E(o) | 0.69 | 4,290 |\n| 3183 | 0152D0603183 | 32.629 | 76.367 | Ravi | M(o) | 0.45 | 4,273 |\n| 3184 | 0152D0703184 | 32.345 | 76.305 | Ravi | E(o) | 1.33 | 3,651 |\n| 3185 | 0152D0703185 | 32.345 | 76.347 | Ravi | E(o) | 1.73 | 4,086 |\n| 3186 | 0152D0703186 | 32.344 | 76.311 | Ravi | E(o) | 0.64 | 3,731 |\n| 3187 | 0152D0703187 | 32.343 | 76.322 | Ravi | E(o) | 0.46 | 3,918 |\n| 3188 | 0152D0703188 | 32.343 | 76.316 | Ravi | E(o) | 2.58 | 3,755 |\n| 3189 | 0152D0703189 | 32.343 | 76.326 | Ravi | E(o) | 1.48 | 3,947 |\n| 3190 | 0152D0703190 | 32.336 | 76.355 | Ravi | E(o) | 1.62 | 4,191 |\n| 3191 | 0152D0703191 | 32.336 | 76.332 | Ravi | E(o) | 25.65 | 3,971 |\n| 3192 | 0152D0703192 | 32.332 | 76.345 | Ravi | E(o) | 2.71 | 4,204 |\n| 3193 | 0152D0703193 | 32.312 | 76.372 | Ravi | E(o) | 3.68 | 4,157 |\n| 3194 | 0152D0703194 | 32.269 | 76.488 | Ravi | M(o) | 1.50 | 4,200 |\n| 3195 | 0152D0703195 | 32.263 | 76.496 | Ravi | M(o) | 0.26 | 4,359 |\n| 3196 | 0152D0903196 | 32.930 | 76.672 | Chenab | M(e) | 4.83 | 4,806 |\n| 3197 | 0152D0903197 | 32.910 | 76.750 | Chenab | M(l) | 0.78 | 4,641 |\n| 3198 | 0152D0903198 | 32.888 | 76.734 | Chenab | M(o) | 1.16 | 4,929 |\n| 3199 | 0152D0903199 | 32.886 | 76.647 | Chenab | M(e) | 2.43 | 3,921 |\n| 3200 | 0152D0903200 | 32.885 | 76.584 | Chenab | M(l) | 0.36 | 4,201 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9432, "line_end": 9579, "token_count_estimate": 1609, "basins": ["Indus"], "subbasins": ["Chenab", "Ravi"], "countries": [], "lake_ids": ["0152C1603166", "0152C1603167", "0152C1603168", "0152C1603169", "0152C1603170", "0152C1603171", "0152C1603172", "0152C1603173", "0152C1603174", "0152C1603175", "0152C1603176", "0152C1603177", "0152C1603178", "0152D0103179", "0152D0103180", "0152D0503181", "0152D0503182", "0152D0603183", "0152D0703184", "0152D0703185", "0152D0703186", "0152D0703187", "0152D0703188", "0152D0703189", "0152D0703190", "0152D0703191", "0152D0703192", "0152D0703193", "0152D0703194", "0152D0703195", "0152D0903196", "0152D0903197", "0152D0903198", "0152D0903199", "0152D0903200"]}}
{"id": "a1aee125c6887f98", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3201 | 0152D0903201 | 32.882 | 76.727 | Chenab | E(o) | 0.32 | 4,899 |\n| 3202 | 0152D0903202 | 32.867 | 76.583 | Chenab | M(l) | 0.26 | 4,328 |\n| 3203 | 0152D0903203 | 32.844 | 76.540 | Chenab | M(e) | 0.31 | 3,964 |\n| 3204 | 0152D0903204 | 32.842 | 76.538 | Chenab | M(e) | 4.59 | 3,944 |\n| 3205 | 0152D1003205 | 32.688 | 76.590 | Chenab | I(s) | 0.45 | 3,997 |\n| 3206 | 0152D1003206 | 32.685 | 76.583 | Chenab | I(s) | 0.33 | 4,068 |\n| 3207 | 0152D1003207 | 32.682 | 76.578 | Chenab | I(s) | 0.28 | 4,101 |\n| 3208 | 0152D1003208 | 32.644 | 76.643 | Chenab | I(s) | 0.36 | 3,868 |\n| 3209 | 0152D1003209 | 32.641 | 76.642 | Chenab | I(s) | 0.37 | 3,912 |\n| 3210 | 0152D1003210 | 32.636 | 76.678 | Chenab | M(e) | 0.33 | 3,816 |\n| 3211 | 0152D1003211 | 32.625 | 76.671 | Chenab | I(s) | 0.33 | 4,119 |\n| 3212 | 0152D1103212 | 32.395 | 76.637 | Ravi | E(o) | 0.29 | 4,180 |\n| 3213 | 0152D1103213 | 32.380 | 76.695 | Ravi | M(o) | 0.26 | 4,196 |\n| 3214 | 0152D1103214 | 32.369 | 76.750 | Ravi | I(s) | 0.46 | 4,718 |\n| 3215 | 0152D1203215 | 32.244 | 76.524 | Ravi | E(o) | 1.29 | 4,288 |\n| 3216 | 0152D1203216 | 32.235 | 76.536 | Ravi | M(o) | 2.39 | 4,217 |\n| 3217 | 0152D1303217 | 32.991 | 76.921 | Chenab | M(o) | 0.33 | 4,372 |\n| 3218 | 0152D1303218 | 32.987 | 76.977 | Chenab | E(o) | 0.31 | 5,010 |\n| 3219 | 0152D1303219 | 32.986 | 76.968 | Chenab | M(o) | 1.35 | 5,020 |\n| 3220 | 0152D1303220 | 32.961 | 76.763 | Chenab | M(o) | 0.90 | 4,934 |\n| 3221 | 0152D1303221 | 32.960 | 76.978 | Chenab | I(s) | 0.55 | 5,157 |\n| 3222 | 0152D1303222 | 32.960 | 76.772 | Chenab | M(o) | 0.63 | 5,110 |\n| 3223 | 0152D1303223 | 32.948 | 76.974 | Chenab | M(l) | 0.38 | 5,258 |\n| 3224 | 0152D1303224 | 32.946 | 76.963 | Chenab | M(l) | 0.89 | 5,121 |\n| 3225 | 0152D1303225 | 32.932 | 76.976 | Chenab | I(s) | 2.58 | 5,375 |\n| 3226 | 0152D1303226 | 32.929 | 76.946 | Chenab | E(o) | 0.63 | 5,232 |\n| 3227 | 0152D1303227 | 32.926 | 76.944 | Chenab | M(o) | 0.95 | 5,189 |\n| 3228 | 0152D1303228 | 32.923 | 76.833 | Chenab | M(l) | 1.46 | 4,872 |\n| 3229 | 0152D1303229 | 32.909 | 76.947 | Chenab | M(o) | 0.85 | 4,999 |\n| 3230 | 0152D1303230 | 32.900 | 76.999 | Chenab | I(s) | 0.90 | 4,881 |\n| 3231 | 0152D1303231 | 32.872 | 76.983 | Chenab | E(o) | 0.25 | 5,177 |\n| 3232 | 0152D1303232 | 32.871 | 76.983 | Chenab | E(o) | 0.30 | 5,181 |\n| 3233 | 0152D1303233 | 32.867 | 76.932 | Chenab | M(o) | 2.33 | 4,991 |\n| 3234 | 0152D1303234 | 32.840 | 76.898 | Chenab | I(s) | 0.55 | 4,510 |\n| 3235 | 0152D1303235 | 32.840 | 76.896 | Chenab | I(s) | 0.29 | 4,509 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9432, "line_end": 9579, "token_count_estimate": 1604, "basins": ["Indus"], "subbasins": ["Chenab", "Ravi"], "countries": [], "lake_ids": ["0152D0903201", "0152D0903202", "0152D0903203", "0152D0903204", "0152D1003205", "0152D1003206", "0152D1003207", "0152D1003208", "0152D1003209", "0152D1003210", "0152D1003211", "0152D1103212", "0152D1103213", "0152D1103214", "0152D1203215", "0152D1203216", "0152D1303217", "0152D1303218", "0152D1303219", "0152D1303220", "0152D1303221", "0152D1303222", "0152D1303223", "0152D1303224", "0152D1303225", "0152D1303226", "0152D1303227", "0152D1303228", "0152D1303229", "0152D1303230", "0152D1303231", "0152D1303232", "0152D1303233", "0152D1303234", "0152D1303235"]}}
{"id": "474b2f2cd7ba894e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3236 | 0152D1303236 | 32.801 | 76.861 | Chenab | M(l) | 0.53 | 5,017 |\n| 3237 | 0152D1303237 | 32.787 | 76.958 | Chenab | M(o) | 0.36 | 4,460 |\n| 3238 | 0152D1303238 | 32.777 | 76.950 | Chenab | O | 2.82 | 4,416 |\n| 3239 | 0152D1303239 | 32.775 | 76.951 | Chenab | M(o) | 1.39 | 4,411 |\n| 3240 | 0152D1303240 | 32.773 | 76.939 | Chenab | I(s) | 0.51 | 4,296 |\n| 3241 | 0152D1303241 | 32.769 | 76.967 | Chenab | E(o) | 0.66 | 5,120 |\n| 3242 | 0152D1303242 | 32.769 | 76.970 | Chenab | M(o) | 1.20 | 5,072 |\n| 3243 | 0152D1303243 | 32.760 | 76.949 | Chenab | I(s) | 0.30 | 4,448 |\n| 3244 | 0152D1303244 | 32.758 | 76.953 | Chenab | M(l) | 1.48 | 4,481 |\n| 3245 | 0152D1403245 | 32.510 | 76.824 | Ravi | E(o) | 0.26 | 4,685 |\n| 3246 | 0152D1503246 | 32.422 | 76.849 | Ravi | M(l) | 1.56 | 4,026 |\n| 3247 | 0152D1503247 | 32.393 | 76.902 | Ravi | I(s) | 0.25 | 4,170 |\n| 3248 | 0152D1503248 | 32.320 | 76.908 | Ravi | M(e) | 0.33 | 4,401 |\n| 3249 | 0152D1503249 | 32.318 | 76.914 | Ravi | M(l) | 0.28 | 4,490 |\n| 3250 | 0152D1503250 | 32.296 | 76.922 | Ravi | E(o) | 0.69 | 4,629 |\n| 3251 | 0152D1503251 | 32.274 | 76.985 | Ravi | M(o) | 0.91 | 4,512 |\n| 3252 | 0152D1503252 | 32.273 | 76.986 | Ravi | M(o) | 1.50 | 4,537 |\n| 3253 | 0152D1503253 | 32.263 | 76.968 | Ravi | M(o) | 0.88 | 4,612 |\n| 3254 | 0152D1503254 | 32.256 | 76.778 | Ravi | M(o) | 2.69 | 4,563 |\n| 3255 | 0152D1603255 | 32.245 | 76.787 | Ravi | M(o) | 2.70 | 4,301 |\n| 3256 | 0152D1603256 | 32.242 | 76.787 | Ravi | M(o) | 0.30 | 4,338 |\n| 3257 | 0152D1603257 | 32.236 | 76.786 | Ravi | M(e) | 0.51 | 4,384 |\n| 3258 | 0152D1603258 | 32.234 | 76.754 | Ravi | M(e) | 9.70 | 4,350 |\n| 3259 | 0152D1603259 | 32.232 | 76.778 | Ravi | M(o) | 2.13 | 4,592 |\n| 3260 | 0152D1603260 | 32.232 | 76.808 | Ravi | E(o) | 0.64 | 4,606 |\n| 3261 | 0152D1603261 | 32.230 | 76.803 | Ravi | E(o) | 0.68 | 4,647 |\n| 3262 | 0152D1603262 | 32.228 | 76.755 | Ravi | M(o) | 0.56 | 4,513 |\n| 3263 | 0152D1603263 | 32.228 | 76.776 | Ravi | M(o) | 1.46 | 4,719 |\n| 3264 | 0152D1603264 | 32.226 | 76.809 | Ravi | M(o) | 2.09 | 4,407 |\n| 3265 | 0152D1603265 | 32.222 | 76.789 | Beas | E(o) | 8.15 | 4,577 |\n| 3266 | 0152D1603266 | 32.222 | 76.780 | Beas | E(o) | 3.58 | 4,555 |\n| 3267 | 0152D1603267 | 32.213 | 76.900 | Ravi | E(o) | 0.47 | 4,326 |\n| 3268 | 0152D1603268 | 32.208 | 76.900 | Ravi | E(o) | 0.41 | 4,392 |\n| 3269 | 0152D1603269 | 32.114 | 76.935 | Beas | M(o) | 1.48 | 4,272 |\n| 3270 | 0152E0203270 | 35.567 | 77.049 | Shyok | I(s) | 0.40 | 5,309 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 9432, "line_end": 9579, "token_count_estimate": 1584, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Ravi", "Shyok"], "countries": [], "lake_ids": ["0152D1303236", "0152D1303237", "0152D1303238", "0152D1303239", "0152D1303240", "0152D1303241", "0152D1303242", "0152D1303243", "0152D1303244", "0152D1403245", "0152D1503246", "0152D1503247", "0152D1503248", "0152D1503249", "0152D1503250", "0152D1503251", "0152D1503252", "0152D1503253", "0152D1503254", "0152D1603255", "0152D1603256", "0152D1603257", "0152D1603258", "0152D1603259", "0152D1603260", "0152D1603261", "0152D1603262", "0152D1603263", "0152D1603264", "0152D1603265", "0152D1603266", "0152D1603267", "0152D1603268", "0152D1603269", "0152E0203270"]}}
{"id": "a8390ddfa5ede13f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3271 | 0152E0203271 | 35.543 | 77.021 | Shyok | M(l) | 0.71 | 5,123 |\n| 3272 | 0152E0203272 | 35.530 | 77.004 | Shyok | I(s) | 0.72 | 4,989 |\n| 3273 | 0152E0203273 | 35.513 | 77.006 | Shyok | I(s) | 1.61 | 4,917 |\n| 3274 | 0152E0203274 | 35.512 | 77.006 | Shyok | I(s) | 0.84 | 4,918 |\n| 3275 | 0152E0203275 | 35.509 | 77.072 | Shyok | I(s) | 0.55 | 5,030 |\n| 3276 | 0152E0203276 | 35.509 | 77.091 | Shyok | I(s) | 0.54 | 5,057 |\n| 3277 | 0152E0303277 | 35.496 | 77.136 | Shyok | I(s) | 0.85 | 5,145 |\n| 3278 | 0152E0303278 | 35.496 | 77.223 | Shyok | M(lg) | 1.62 | 5,416 |\n| 3279 | 0152E0303279 | 35.483 | 77.060 | Shyok | I(s) | 0.34 | 4,748 |\n| 3280 | 0152E0303280 | 35.480 | 77.166 | Shyok | M(lg) | 1.35 | 5,240 |\n| 3281 | 0152E0303281 | 35.480 | 77.170 | Shyok | M(lg) | 1.33 | 5,256 |\n| 3282 | 0152E0303282 | 35.477 | 77.080 | Shyok | E(o) | 7.31 | 4,841 |\n| 3283 | 0152E0303283 | 35.477 | 77.084 | Shyok | E(o) | 0.68 | 4,839 |\n| 3284 | 0152E0303284 | 35.472 | 77.005 | Shyok | I(s) | 0.31 | 4,842 |\n| 3285 | 0152E0303285 | 35.471 | 77.079 | Shyok | M(lg) | 5.81 | 4,670 |\n| 3286 | 0152E0303286 | 35.470 | 77.008 | Shyok | I(s) | 1.12 | 4,845 |\n| 3287 | 0152E0303287 | 35.457 | 77.030 | Shyok | I(s) | 0.25 | 4,779 |\n| 3288 | 0152E0303288 | 35.454 | 77.032 | Shyok | I(s) | 0.91 | 4,744 |\n| 3289 | 0152E0303289 | 35.437 | 77.061 | Shyok | I(s) | 0.93 | 4,707 |\n| 3290 | 0152E0303290 | 35.435 | 77.062 | Shyok | I(s) | 3.00 | 4,706 |\n| 3291 | 0152E0303291 | 35.431 | 77.066 | Shyok | M(lg) | 1.37 | 4,646 |\n| 3292 | 0152E0303292 | 35.428 | 77.071 | Shyok | I(s) | 0.35 | 4,695 |\n| 3293 | 0152E0303293 | 35.427 | 77.071 | Shyok | I(s) | 1.42 | 4,691 |\n| 3294 | 0152E0303294 | 35.426 | 77.066 | Shyok | I(s) | 0.35 | 4,718 |\n| 3295 | 0152E0303295 | 35.425 | 77.074 | Shyok | I(s) | 0.34 | 4,705 |\n| 3296 | 0152E0303296 | 35.423 | 77.110 | Shyok | I(s) | 2.34 | 4,642 |\n| 3297 | 0152E0303297 | 35.422 | 77.081 | Shyok | I(s) | 0.44 | 4,664 |\n| 3298 | 0152E0303298 | 35.421 | 77.101 | Shyok | I(s) | 0.30 | 4,656 |\n| 3299 | 0152E0303299 | 35.421 | 77.106 | Shyok | I(s) | 0.45 | 4,644 |\n| 3300 | 0152E0303300 | 35.418 | 77.079 | Shyok | M(l) | 0.43 | 4,590 |\n| 3301 | 0152E0303301 | 35.415 | 77.083 | Shyok | I(s) | 0.47 | 4,635 |\n| 3302 | 0152E0303302 | 35.414 | 77.080 | Shyok | I(s) | 0.63 | 4,614 |\n| 3303 | 0152E0303303 | 35.412 | 77.084 | Shyok | I(s) | 2.63 | 4,628 |\n| 3304 | 0152E0303304 | 35.410 | 77.127 | Shyok | I(s) | 2.44 | 4,604 |\n| 3305 | 0152E0303305 | 35.409 | 77.077 | Shyok | I(s) | 0.26 | 4,635 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 9432, "line_end": 9579, "token_count_estimate": 1678, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152E0203271", "0152E0203272", "0152E0203273", "0152E0203274", "0152E0203275", "0152E0203276", "0152E0303277", "0152E0303278", "0152E0303279", "0152E0303280", "0152E0303281", "0152E0303282", "0152E0303283", "0152E0303284", "0152E0303285", "0152E0303286", "0152E0303287", "0152E0303288", "0152E0303289", "0152E0303290", "0152E0303291", "0152E0303292", "0152E0303293", "0152E0303294", "0152E0303295", "0152E0303296", "0152E0303297", "0152E0303298", "0152E0303299", "0152E0303300", "0152E0303301", "0152E0303302", "0152E0303303", "0152E0303304", "0152E0303305"]}}
{"id": "d76dfa1a1d1e8d05", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3306 | 0152E0303306 | 35.409 | 77.079 | Shyok | I(s) | 0.28 | 4,648 |\n| 3307 | 0152E0303307 | 35.408 | 77.084 | Shyok | I(s) | 0.36 | 4,622 |\n| 3308 | 0152E0303308 | 35.407 | 77.085 | Shyok | I(s) | 1.52 | 4,619 |\n| 3309 | 0152E0303309 | 35.400 | 77.133 | Shyok | I(s) | 0.51 | 4,570 |\n| 3310 | 0152E0303310 | 35.395 | 77.100 | Shyok | M(l) | 4.70 | 4,556 |\n| 3311 | 0152E0303311 | 35.391 | 77.111 | Shyok | M(l) | 0.35 | 4,565 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 9432, "line_end": 9579, "token_count_estimate": 373, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152E0303306", "0152E0303307", "0152E0303308", "0152E0303309", "0152E0303310", "0152E0303311"]}}
{"id": "0a2261d09ef715ea", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9580, "line_end": 9585, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5d3376c7669b112", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3312 | 0152E0303312 | 35.390 | 77.108 | Shyok | I(s) | 0.27 | 4,597 |\n| 3313 | 0152E0303313 | 35.389 | 77.113 | Shyok | I(s) | 0.67 | 4,579 |\n| 3314 | 0152E0303314 | 35.389 | 77.109 | Shyok | I(s) | 0.28 | 4,597 |\n| 3315 | 0152E0303315 | 35.387 | 77.114 | Shyok | I(s) | 0.69 | 4,574 |\n| 3316 | 0152E0303316 | 35.384 | 77.126 | Shyok | I(s) | 0.36 | 4,559 |\n| 3317 | 0152E0303317 | 35.382 | 77.121 | Shyok | I(s) | 0.72 | 4,557 |\n| 3318 | 0152E0303318 | 35.375 | 77.128 | Shyok | I(s) | 3.14 | 4,547 |\n| 3319 | 0152E0303319 | 35.364 | 77.140 | Shyok | M(lg) | 2.44 | 4,503 |\n| 3320 | 0152E0303320 | 35.330 | 77.178 | Shyok | I(s) | 0.25 | 4,338 |\n| 3321 | 0152E0303321 | 35.320 | 77.173 | Shyok | I(s) | 0.76 | 4,301 |\n| 3322 | 0152E0303322 | 35.316 | 77.174 | Shyok | I(s) | 0.29 | 4,292 |\n| 3323 | 0152E0303323 | 35.313 | 77.173 | Shyok | I(s) | 0.85 | 4,281 |\n| 3324 | 0152E0303324 | 35.300 | 77.174 | Shyok | M(l) | 2.46 | 4,189 |\n| 3325 | 0152E0303325 | 35.296 | 77.175 | Shyok | M(l) | 2.62 | 4,176 |\n| 3326 | 0152E0303326 | 35.287 | 77.184 | Shyok | M(l) | 1.04 | 4,198 |\n| 3327 | 0152E0303327 | 35.265 | 77.176 | Shyok | M(l) | 1.04 | 3,992 |\n| 3328 | 0152E0303328 | 35.256 | 77.164 | Shyok | I(s) | 0.72 | 3,987 |\n| 3329 | 0152E0403329 | 35.249 | 77.125 | Shyok | M(l) | 0.84 | 4,138 |\n| 3330 | 0152E0403330 | 35.245 | 77.110 | Shyok | M(l) | 2.82 | 4,179 |\n| 3331 | 0152E0403331 | 35.245 | 77.106 | Shyok | M(l) | 0.48 | 4,225 |\n| 3332 | 0152E0403332 | 35.244 | 77.118 | Shyok | I(s) | 0.47 | 4,146 |\n| 3333 | 0152E0403333 | 35.244 | 77.143 | Shyok | I(s) | 1.19 | 3,976 |\n| 3334 | 0152E0403334 | 35.244 | 77.140 | Shyok | I(s) | 0.26 | 4,009 |\n| 3335 | 0152E0403335 | 35.244 | 77.116 | Shyok | I(s) | 0.50 | 4,167 |\n| 3336 | 0152E0403336 | 35.244 | 77.119 | Shyok | I(s) | 0.32 | 4,135 |\n| 3337 | 0152E0403337 | 35.243 | 77.122 | Shyok | I(s) | 0.66 | 4,133 |\n| 3338 | 0152E0403338 | 35.243 | 77.147 | Shyok | I(s) | 0.57 | 3,967 |\n| 3339 | 0152E0403339 | 35.243 | 77.158 | Shyok | I(s) | 0.41 | 3,943 |\n| 3340 | 0152E0403340 | 35.243 | 77.140 | Shyok | I(s) | 0.38 | 4,007 |\n| 3341 | 0152E0403341 | 35.243 | 77.157 | Shyok | I(s) | 0.59 | 3,946 |\n| 3342 | 0152E0403342 | 35.243 | 77.144 | Shyok | I(s) | 0.29 | 3,978 |\n| 3343 | 0152E0403343 | 35.242 | 77.138 | Shyok | I(s) | 0.34 | 4,017 |\n| 3344 | 0152E0403344 | 35.242 | 77.154 | Shyok | I(s) | 0.67 | 3,925 |\n| 3345 | 0152E0403345 | 35.242 | 77.145 | Shyok | I(s) | 0.28 | 3,964 |\n| 3346 | 0152E0403346 | 35.242 | 77.148 | Shyok | I(s) | 0.47 | 3,955 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9586, "line_end": 9733, "token_count_estimate": 1686, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152E0303312", "0152E0303313", "0152E0303314", "0152E0303315", "0152E0303316", "0152E0303317", "0152E0303318", "0152E0303319", "0152E0303320", "0152E0303321", "0152E0303322", "0152E0303323", "0152E0303324", "0152E0303325", "0152E0303326", "0152E0303327", "0152E0303328", "0152E0403329", "0152E0403330", "0152E0403331", "0152E0403332", "0152E0403333", "0152E0403334", "0152E0403335", "0152E0403336", "0152E0403337", "0152E0403338", "0152E0403339", "0152E0403340", "0152E0403341", "0152E0403342", "0152E0403343", "0152E0403344", "0152E0403345", "0152E0403346"]}}
{"id": "b94444f05fdd5da7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3347 | 0152E0403347 | 35.242 | 77.112 | Shyok | I(s) | 0.36 | 4,186 |\n| 3348 | 0152E0403348 | 35.241 | 77.109 | Shyok | I(s) | 0.34 | 4,202 |\n| 3349 | 0152E0403349 | 35.240 | 77.135 | Shyok | M(l) | 0.53 | 4,051 |\n| 3350 | 0152E0403350 | 35.239 | 77.158 | Shyok | I(s) | 0.26 | 3,940 |\n| 3351 | 0152E0403351 | 35.234 | 77.161 | Shyok | I(s) | 0.36 | 3,925 |\n| 3352 | 0152E0403352 | 35.227 | 77.162 | Shyok | I(s) | 0.26 | 3,897 |\n| 3353 | 0152E0403353 | 35.213 | 77.167 | Shyok | M(l) | 1.29 | 3,804 |\n| 3354 | 0152E0403354 | 35.213 | 77.174 | Shyok | I(s) | 0.78 | 3,806 |\n| 3355 | 0152E0403355 | 35.211 | 77.179 | Shyok | I(s) | 0.28 | 3,785 |\n| 3356 | 0152E0403356 | 35.208 | 77.192 | Shyok | I(s) | 0.32 | 3,732 |\n| 3357 | 0152E0403357 | 35.206 | 77.201 | Shyok | M(o) | 0.27 | 3,671 |\n| 3358 | 0152E0403358 | 35.203 | 77.202 | Shyok | M(o) | 0.42 | 3,654 |\n| 3359 | 0152E0403359 | 35.201 | 77.201 | Shyok | M(o) | 0.57 | 3,646 |\n| 3360 | 0152E0403360 | 35.201 | 77.200 | Shyok | M(o) | 0.78 | 3,650 |\n| 3361 | 0152E0403361 | 35.201 | 77.204 | Shyok | M(o) | 0.59 | 3,620 |\n| 3362 | 0152E0403362 | 35.198 | 77.200 | Shyok | M(o) | 0.68 | 3,632 |\n| 3363 | 0152E0403363 | 35.149 | 77.074 | Shyok | I(s) | 0.35 | 5,414 |\n| 3364 | 0152E0403364 | 35.125 | 77.011 | Shyok | I(s) | 1.32 | 4,403 |\n| 3365 | 0152E0403365 | 35.123 | 77.010 | Shyok | I(s) | 0.77 | 4,407 |\n| 3366 | 0152E0403366 | 35.122 | 77.005 | Shyok | I(s) | 0.36 | 4,392 |\n| 3367 | 0152E0403367 | 35.122 | 77.014 | Shyok | I(s) | 0.53 | 4,434 |\n| 3368 | 0152E0403368 | 35.116 | 77.101 | Shyok | I(s) | 0.55 | 5,214 |\n| 3369 | 0152E0403369 | 35.114 | 77.027 | Shyok | I(s) | 0.35 | 4,530 |\n| 3370 | 0152E0403370 | 35.110 | 77.029 | Shyok | I(s) | 0.50 | 4,560 |\n| 3371 | 0152E0403371 | 35.069 | 77.018 | Shyok | M(l) | 0.50 | 4,895 |\n| 3372 | 0152E0403372 | 35.036 | 77.176 | Shyok | M(lg) | 0.38 | 4,824 |\n| 3373 | 0152E0403373 | 35.026 | 77.052 | Shyok | I(s) | 0.57 | 5,107 |\n| 3374 | 0152E0403374 | 35.010 | 77.111 | Shyok | I(s) | 0.28 | 5,148 |\n| 3375 | 0152E0503375 | 31.938 | 77.418 | Beas | E(o) | 0.32 | 4,388 |\n| 3376 | 0152E0503376 | 31.938 | 77.411 | Beas | E(o) | 0.87 | 4,264 |\n| 3377 | 0152E0503377 | 31.933 | 77.428 | Beas | M(o) | 0.42 | 4,679 |\n| 3378 | 0152E0703378 | 35.432 | 77.500 | Shyok | I(s) | 0.71 | 5,346 |\n| 3379 | 0152E0703379 | 35.420 | 77.460 | Shyok | M(l) | 0.87 | 5,409 |\n| 3380 | 0152E0703380 | 35.414 | 77.474 | Shyok | M(l) | 0.47 | 5,459 |\n| 3381 | 0152E0703381 | 35.359 | 77.427 | Shyok | M(l) | 0.29 | 5,497 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9586, "line_end": 9733, "token_count_estimate": 1667, "basins": ["Indus"], "subbasins": ["Beas", "Shyok"], "countries": [], "lake_ids": ["0152E0403347", "0152E0403348", "0152E0403349", "0152E0403350", "0152E0403351", "0152E0403352", "0152E0403353", "0152E0403354", "0152E0403355", "0152E0403356", "0152E0403357", "0152E0403358", "0152E0403359", "0152E0403360", "0152E0403361", "0152E0403362", "0152E0403363", "0152E0403364", "0152E0403365", "0152E0403366", "0152E0403367", "0152E0403368", "0152E0403369", "0152E0403370", "0152E0403371", "0152E0403372", "0152E0403373", "0152E0403374", "0152E0503375", "0152E0503376", "0152E0503377", "0152E0703378", "0152E0703379", "0152E0703380", "0152E0703381"]}}
{"id": "8c1569503a7e0e2f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3382 | 0152E0803382 | 35.248 | 77.312 | Shyok | E(o) | 0.39 | 4,330 |\n| 3383 | 0152E0803383 | 35.208 | 77.451 | Shyok | M(l) | 0.48 | 5,215 |\n| 3384 | 0152E0803384 | 35.053 | 77.425 | Shyok | M(l) | 1.11 | 5,339 |\n| 3385 | 0152E0803385 | 35.034 | 77.456 | Shyok | E(o) | 0.35 | 5,116 |\n| 3386 | 0152E0803386 | 35.029 | 77.492 | Shyok | E(o) | 0.46 | 5,442 |\n| 3387 | 0152E0803387 | 35.018 | 77.478 | Shyok | I(s) | 0.42 | 5,239 |\n| 3388 | 0152E1103388 | 35.490 | 77.505 | Shyok | M(lg) | 2.32 | 5,415 |\n| 3389 | 0152E1103389 | 35.476 | 77.513 | Shyok | M(lg) | 21.93 | 5,342 |\n| 3390 | 0152E1103390 | 35.442 | 77.546 | Shyok | I(s) | 0.84 | 5,283 |\n| 3391 | 0152E1103391 | 35.387 | 77.574 | Shyok | M(l) | 0.60 | 5,143 |\n| 3392 | 0152E1103392 | 35.386 | 77.576 | Shyok | M(l) | 1.07 | 5,145 |\n| 3393 | 0152E1103393 | 35.385 | 77.575 | Shyok | M(l) | 0.92 | 5,162 |\n| 3394 | 0152E1103394 | 35.375 | 77.584 | Shyok | M(l) | 0.29 | 5,121 |\n| 3395 | 0152E1103395 | 35.371 | 77.587 | Shyok | M(l) | 0.41 | 5,101 |\n| 3396 | 0152E1103396 | 35.364 | 77.747 | Shyok | O | 0.67 | 5,257 |\n| 3397 | 0152E1103397 | 35.363 | 77.588 | Shyok | M(l) | 0.97 | 5,057 |\n| 3398 | 0152E1103398 | 35.360 | 77.591 | Shyok | M(l) | 0.42 | 5,062 |\n| 3399 | 0152E1103399 | 35.354 | 77.591 | Shyok | I(s) | 0.35 | 5,036 |\n| 3400 | 0152E1103400 | 35.353 | 77.590 | Shyok | I(s) | 0.46 | 5,039 |\n| 3401 | 0152E1103401 | 35.353 | 77.585 | Shyok | I(s) | 0.51 | 5,041 |\n| 3402 | 0152E1103402 | 35.352 | 77.603 | Shyok | M(l) | 1.08 | 5,021 |\n| 3403 | 0152E1103403 | 35.347 | 77.593 | Shyok | I(s) | 0.30 | 5,027 |\n| 3404 | 0152E1103404 | 35.347 | 77.553 | Shyok | I(s) | 0.28 | 5,162 |\n| 3405 | 0152E1103405 | 35.347 | 77.562 | Shyok | I(s) | 0.63 | 5,144 |\n| 3406 | 0152E1103406 | 35.345 | 77.599 | Shyok | I(s) | 0.96 | 5,006 |\n| 3407 | 0152E1103407 | 35.342 | 77.564 | Shyok | I(s) | 0.33 | 5,123 |\n| 3408 | 0152E1103408 | 35.341 | 77.628 | Shyok | M(o) | 0.45 | 4,926 |\n| 3409 | 0152E1103409 | 35.341 | 77.624 | Shyok | M(o) | 0.61 | 4,950 |\n| 3410 | 0152E1103410 | 35.340 | 77.606 | Shyok | I(s) | 8.91 | 4,989 |\n| 3411 | 0152E1103411 | 35.339 | 77.670 | Shyok | O | 1.14 | 4,846 |\n| 3412 | 0152E1103412 | 35.339 | 77.619 | Shyok | M(o) | 0.62 | 4,953 |\n| 3413 | 0152E1103413 | 35.338 | 77.627 | Shyok | M(o) | 1.10 | 4,901 |\n| 3414 | 0152E1103414 | 35.337 | 77.623 | Shyok | M(o) | 2.91 | 4,922 |\n| 3415 | 0152E1103415 | 35.335 | 77.626 | Shyok | M(o) | 0.31 | 4,894 |\n| 3416 | 0152E1103416 | 35.334 | 77.607 | Shyok | M(o) | 1.09 | 4,971 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 9586, "line_end": 9733, "token_count_estimate": 1684, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152E0803382", "0152E0803383", "0152E0803384", "0152E0803385", "0152E0803386", "0152E0803387", "0152E1103388", "0152E1103389", "0152E1103390", "0152E1103391", "0152E1103392", "0152E1103393", "0152E1103394", "0152E1103395", "0152E1103396", "0152E1103397", "0152E1103398", "0152E1103399", "0152E1103400", "0152E1103401", "0152E1103402", "0152E1103403", "0152E1103404", "0152E1103405", "0152E1103406", "0152E1103407", "0152E1103408", "0152E1103409", "0152E1103410", "0152E1103411", "0152E1103412", "0152E1103413", "0152E1103414", "0152E1103415", "0152E1103416"]}}
{"id": "22ed5f9579e29ef9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3417 | 0152E1103417 | 35.334 | 77.602 | Shyok | M(o) | 0.61 | 4,993 |\n| 3418 | 0152E1103418 | 35.325 | 77.503 | Shyok | M(l) | 0.55 | 5,207 |\n| 3419 | 0152E1103419 | 35.309 | 77.693 | Shyok | O | 1.05 | 4,866 |\n| 3420 | 0152E1103420 | 35.304 | 77.681 | Shyok | O | 2.24 | 4,919 |\n| 3421 | 0152E1103421 | 35.254 | 77.568 | Shyok | I(s) | 1.50 | 5,628 |\n| 3422 | 0152E1203422 | 35.063 | 77.571 | Shyok | M(l) | 0.69 | 4,930 |\n| 3423 | 0152E1203423 | 35.037 | 77.719 | Shyok | I(s) | 0.54 | 5,259 |\n| 3424 | 0152E1203424 | 35.036 | 77.720 | Shyok | M(l) | 4.25 | 5,241 |\n| 3425 | 0152E1203425 | 35.032 | 77.700 | Shyok | M(l) | 18.02 | 5,161 |\n| 3426 | 0152E1203426 | 35.032 | 77.691 | Shyok | M(l) | 7.09 | 5,148 |\n| 3427 | 0152E1203427 | 35.030 | 77.696 | Shyok | I(s) | 0.29 | 5,195 |\n| 3428 | 0152E1203428 | 35.028 | 77.626 | Shyok | M(o) | 1.61 | 4,962 |\n| 3429 | 0152E1203429 | 35.025 | 77.660 | Shyok | M(e) | 4.72 | 5,022 |\n| 3430 | 0152E1503430 | 35.400 | 77.849 | Shyok | O | 0.37 | 5,260 |\n| 3431 | 0152E1503431 | 35.331 | 77.892 | Shyok | O | 0.34 | 5,270 |\n| 3432 | 0152E1503432 | 35.330 | 77.891 | Shyok | O | 0.59 | 5,265 |\n| 3433 | 0152E1503433 | 35.313 | 77.847 | Shyok | O | 0.78 | 5,327 |\n| 3434 | 0152E1503434 | 35.310 | 77.893 | Shyok | O | 0.50 | 5,468 |\n| 3435 | 0152E1503435 | 35.308 | 77.921 | Shyok | O | 6.96 | 5,460 |\n| 3436 | 0152E1503436 | 35.306 | 77.791 | Shyok | O | 0.38 | 4,984 |\n| 3437 | 0152E1503437 | 35.301 | 77.896 | Shyok | O | 1.68 | 5,420 |\n| 3438 | 0152E1503438 | 35.267 | 77.766 | Shyok | O | 0.56 | 4,865 |\n| 3439 | 0152E1503439 | 35.252 | 77.978 | Shyok | E(o) | 0.55 | 5,386 |\n| 3440 | 0152E1603440 | 35.205 | 77.895 | Shyok | M(e) | 0.44 | 5,294 |\n| 3441 | 0152E1603441 | 35.060 | 77.856 | Shyok | O | 32.78 | 4,679 |\n| 3442 | 0152F0103442 | 34.993 | 77.043 | Shyok | M(l) | 0.41 | 4,840 |\n| 3443 | 0152F0103443 | 34.991 | 77.117 | Shyok | M(o) | 1.14 | 5,086 |\n| 3444 | 0152F0103444 | 34.987 | 77.113 | Shyok | M(o) | 0.36 | 4,981 |\n| 3445 | 0152F0103445 | 34.974 | 77.190 | Shyok | M(e) | 1.09 | 4,868 |\n| 3446 | 0152F0103446 | 34.968 | 77.054 | Shyok | I(s) | 0.69 | 4,648 |\n| 3447 | 0152F0103447 | 34.965 | 77.055 | Shyok | I(s) | 0.37 | 4,621 |\n| 3448 | 0152F0103448 | 34.963 | 77.054 | Shyok | I(s) | 1.17 | 4,603 |\n| 3449 | 0152F0103449 | 34.947 | 77.025 | Shyok | I(s) | 0.64 | 4,884 |\n| 3450 | 0152F0103450 | 34.903 | 77.170 | Shyok | M(l) | 2.29 | 5,109 |\n| 3451 | 0152F0103451 | 34.850 | 77.240 | Shyok | M(o) | 0.66 | 5,033 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 9586, "line_end": 9733, "token_count_estimate": 1636, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152E1103417", "0152E1103418", "0152E1103419", "0152E1103420", "0152E1103421", "0152E1203422", "0152E1203423", "0152E1203424", "0152E1203425", "0152E1203426", "0152E1203427", "0152E1203428", "0152E1203429", "0152E1503430", "0152E1503431", "0152E1503432", "0152E1503433", "0152E1503434", "0152E1503435", "0152E1503436", "0152E1503437", "0152E1503438", "0152E1503439", "0152E1603440", "0152E1603441", "0152F0103442", "0152F0103443", "0152F0103444", "0152F0103445", "0152F0103446", "0152F0103447", "0152F0103448", "0152F0103449", "0152F0103450", "0152F0103451"]}}
{"id": "653d2f3380b67c7c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3452 | 0152F0203452 | 34.712 | 77.078 | Shyok | I(s) | 0.78 | 5,044 |\n| 3453 | 0152F0203453 | 34.710 | 77.075 | Shyok | I(s) | 0.61 | 5,095 |\n| 3454 | 0152F0203454 | 34.707 | 77.073 | Shyok | I(s) | 0.32 | 5,141 |\n| 3455 | 0152F0203455 | 34.693 | 77.023 | Shyok | M(e) | 1.84 | 5,204 |\n| 3456 | 0152F0203456 | 34.674 | 77.070 | Shyok | M(e) | 2.01 | 4,985 |\n| 3457 | 0152F0203457 | 34.560 | 77.061 | Shyok | M(e) | 1.92 | 5,090 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 9586, "line_end": 9733, "token_count_estimate": 375, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152F0203452", "0152F0203453", "0152F0203454", "0152F0203455", "0152F0203456", "0152F0203457"]}}
{"id": "77fa947a9c84d75a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9734, "line_end": 9743, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "da48e50328843fbb", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 3458 | 0152F0203458 | 34.553 | 77.195 | Shyok | M(e) | 0.49 | 5,143 | 3531 | 0152F0703531 | 34.366 | 77.420 | Shyok | E(o) | 1.51 | 4,991 |\n| 3459 | 0152F0203459 | 34.545 | 77.050 | Shyok | M(o) | 0.73 | 5,251 | 3532 | 0152F0703532 | 34.346 | 77.486 | Shyok | E(o) | 0.32 | 5,123 |\n| 3460 | 0152F0203460 | 34.543 | 77.049 | Shyok | M(e) | 2.76 | 5,232 | 3533 | 0152F0703533 | 34.343 | 77.489 | Shyok | E(o) | 5.14 | 5,158 |\n| 3461 | 0152F0203461 | 34.528 | 77.007 | Shyok | M(e) | 0.51 | 5,257 | 3534 | 0152F0703534 | 34.338 | 77.492 | Shyok | M(e) | 0.82 | 5,198 |\n| 3462 | 0152F0203462 | 34.527 | 77.157 | Shyok | M(e) | 1.36 | 5,245 | 3535 | 0152F0703535 | 34.335 | 77.457 | Shyok | M(e) | 2.41 | 5,202 |\n| 3463 | 0152F0203463 | 34.510 | 77.085 | Shyok | E(o) | 1.14 | 5,053 | 3536 | 0152F0703536 | 34.334 | 77.460 | Shyok | M(o) | 0.48 | 5,224 |\n| 3464 | 0152F0203464 | 34.508 | 77.033 | Shyok | M(e) | 6.37 | 5,152 | 3537 | 0152F0703537 | 34.325 | 77.466 | Shyok | M(o) | 0.51 | 5,265 |\n| 3465 | 0152F0203465 | 34.504 | 77.178 | Shyok | M(e) | 0.43 | 5,344 | 3538 | 0152F0703538 | 34.323 | 77.470 | Shyok | M(o) | 0.66 | 5,168 |\n| 3466 | 0152F0303466 | 34.499 | 77.068 | Shyok | E(o) | 2.84 | 5,209 | 3539 | 0152F0703539 | 34.317 | 77.441 | Indus Upper | E(o) | 1.96 | 5,133 |\n| 3467 | 0152F0303467 | 34.497 | 77.186 | Shyok | E(o) | 0.37 | 5,139 | 3540 | 0152F0703540 | 34.312 | 77.448 | Indus Upper | M(e) | 2.08 | 5,222 |\n| 3468 | 0152F0303468 | 34.497 | 77.072 | Shyok | E(o) | 1.42 | 5,147 | 3541 | 0152F0903541 | 34.993 | 77.618 | Shyok | E(o) | 0.59 | 5,289 |\n| 3469 | 0152F0303469 | 34.495 | 77.177 | Shyok | M(e) | 2.31 | 5,253 | 3542 | 0152F0903542 | 34.922 | 77.572 | Shyok | M(o) | 0.51 | 5,143 |\n| 3470 | 0152F0303470 | 34.494 | 77.065 | Shyok | M(e) | 0.37 | 5,248 | 3543 | 0152F0903543 | 34.912 | 77.612 | Shyok | M(l) | 0.73 | 5,087 |\n| 3471 | 0152F0303471 | 34.484 | 77.004 | Indus Upper | E(o) | 3.71 | 5,054 | 3544 | 0152F0903544 | 34.907 | 77.609 | Shyok | M(o) | 1.19 | 5,099 |\n| 3472 | 0152F0303472 | 34.484 | 77.068 | Shyok | M(e) | 0.45 | 5,114 | 3545 | 0152F0903545 | 34.905 | 77.616 | Shyok | M(e) | 14.51 | 5,125 |\n| 3473 | 0152F0303473 | 34.478 | 77.035 | Indus Upper | E(o) | 0.90 | 5,080 | 3546 | 0152F0903546 | 34.834 | 77.638 | Shyok | I(s) | 1.68 | 4,985 |\n| 3474 | 0152F0303474 | 34.476 | 77.009 | Indus Upper | E(o) | 1.22 | 4,971 | 3547 | 0152F0903547 | 34.785 | 77.665 | Shyok | E(o) | 0.42 | 5,334 |\n| 3475 | 0152F0303475 | 34.476 | 77.046 | Indus Upper | M(e) | 1.99 | 5,184 | 3548 | 0152F1003548 | 34.717 | 77.725 | Shyok | M(e) | 3.57 | 5,174 |\n| 3476 | 0152F0303476 | 34.472 | 77.002 | Indus Upper | E(o) | 0.81 | 5,049 | 3549 | 0152F1003549 | 34.658 | 77.749 | Shyok | E(o) | 8.45 | 5,139 |\n| 3477 | 0152F0303477 | 34.463 | 77.083 | Shyok | M(e) | 2.97 | 5,065 | 3550 | 0152F1003550 | 34.657 | 77.736 | Shyok | M(l) | 3.38 | 5,002 |\n| 3478 | 0152F0303478 | 34.458 | 77.149 | Shyok | M(e) | 0.62 | 5,259 | 3551 | 0152F1103551 | 34.483 | 77.516 | Shyok | E(o) | 0.35 | 4,861 |\n| 3479 | 0152F0303479 | 34.458 | 77.210 | Shyok | O | 1.00 | 4,958 | 3552 | 0152F1103552 | 34.480 | 77.524 | Shyok | M(o) | 0.59 | 4,905 |\n| 3480 | 0152F0303480 | 34.454 | 77.053 | Indus Upper | E(o) | 0.46 | 4,985 | 3553 | 0152F1103553 | 34.390 | 77.529 | Shyok | E(o) | 0.41 | 5,457 |\n| 3481 | 0152F0303481 | 34.453 | 77.004 | Indus Upper | M(e) | 0.73 | 5,076 | 3554 | 0152F1103554 | 34.387 | 77.537 | Shyok | E(o) | 0.45 | 5,326 |\n| 3482 | 0152F0303482 | 34.450 | 77.060 | Indus Upper | M(e) | 2.52 | 5,086 | 3555 | 0152F1103555 | 34.386 | 77.539 | Shyok | E(o) | 7.07 | 5,321 |\n| 3483 | 0152F0303483 | 34.440 | 77.143 | Shyok | E(o) | 2.68 | 5,159 | 3556 | 0152F1103556 | 34.381 | 77.546 | Shyok | E(o) | 2.27 | 5,245 |\n| 3484 | 0152F0303484 | 34.439 | 77.137 | Shyok | E(o) | 6.55 | 5,103 | 3557 | 0152F1103557 | 34.367 | 77.589 | Shyok | O | 0.50 | 4,718 |\n| 3485 | 0152F0303485 | 34.435 | 77.044 | Indus Upper | E(o) | 1.05 | 4,924 | 3558 | 0152F1103558 | 34.366 | 77.581 | Shyok | E(o) | 5.46 | 4,713 |\n| 3486 | 0152F0303486 | 34.435 | 77.146 | Shyok | E(o) | 0.38 | 5,181 | 3559 | 0152F1103559 | 34.360 | 77.545 | Shyok | E(o) | 1.48 | 5,120 |\n| 3487 | 0152F0303487 | 34.432 | 77.152 | Shyok | E(o) | 0.64 | 5,168 | 3560 | 0152F1103560 | 34.330 | 77.526 | Shyok | E(o) | 2.21 | 5,170 |\n| 3488 | 0152F0303488 | 34.429 | 77.136 | Shyok | M(l) | 1.78 | 5,188 | 3561 | 0152F1103561 | 34.327 | 77.535 | Shyok | E(o) | 3.39 | 5,121 |\n| 3489 | 0152F0303489 | 34.428 | 77.115 | Shyok | E(o) | 6.70 | 5,115 | 3562 | 0152F1103562 | 34.317 | 77.510 | Shyok | E(o) | 4.27 | 5,301 |\n| 3490 | 0152F0303490 | 34.423 | 77.087 | Indus Upper | M(o) | 3.38 | 5,315 | 3563 | 0152F1103563 | 34.316 | 77.523 | Shyok | E(o) | 0.57 | 5,184 |\n| 3491 | 0152F0303491 | 34.411 | 77.083 | Indus Upper | M(o) | 0.52 | 5,175 | 3564 | 0152F1103564 | 34.309 | 77.530 | Shyok | M(o) | 1.14 | 5,226 |\n| 3492 | 0152F0303492 | 34.409 | 77.089 | Indus Upper | M(e) | 2.68 | 5,084 | 3565 | 0152F1103565 | 34.309 | 77.549 | Shyok | E(o) | 0.50 | 5,159 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9744, "line_end": 9818, "token_count_estimate": 3194, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152F0203458", "0152F0203459", "0152F0203460", "0152F0203461", "0152F0203462", "0152F0203463", "0152F0203464", "0152F0203465", "0152F0303466", "0152F0303467", "0152F0303468", "0152F0303469", "0152F0303470", "0152F0303471", "0152F0303472", "0152F0303473", "0152F0303474", "0152F0303475", "0152F0303476", "0152F0303477", "0152F0303478", "0152F0303479", "0152F0303480", "0152F0303481", "0152F0303482", "0152F0303483", "0152F0303484", "0152F0303485", "0152F0303486", "0152F0303487", "0152F0303488", "0152F0303489", "0152F0303490", "0152F0303491", "0152F0303492", "0152F0703531", "0152F0703532", "0152F0703533", "0152F0703534", "0152F0703535", "0152F0703536", "0152F0703537", "0152F0703538", "0152F0703539", "0152F0703540", "0152F0903541", "0152F0903542", "0152F0903543", "0152F0903544", "0152F0903545"]}}
{"id": "0363779ee8d15022", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 3493 | 0152F0303493 | 34.409 | 77.180 | Indus Upper | M(e) | 0.83 | 5,142 | 3566 | 0152F1103566 | 34.299 | 77.629 | Shyok | E(o) | 0.85 | 4,935 |\n| 3494 | 0152F0303494 | 34.408 | 77.102 | Indus Upper | E(o) | 1.77 | 4,976 | 3567 | 0152F1103567 | 34.281 | 77.707 | Shyok | E(o) | 0.31 | 5,063 |\n| 3495 | 0152F0303495 | 34.386 | 77.249 | Indus Upper | E(o) | 3.55 | 5,143 | 3568 | 0152F1103568 | 34.280 | 77.629 | Shyok | E(o) | 3.41 | 5,060 |\n| 3496 | 0152F0303496 | 34.380 | 77.243 | Indus Upper | M(e) | 2.11 | 5,137 | 3569 | 0152F1203569 | 34.246 | 77.725 | Shyok | E(o) | 1.43 | 5,027 |\n| 3497 | 0152F0303497 | 34.370 | 77.153 | Indus Upper | E(o) | 0.71 | 4,711 | 3570 | 0152F1303570 | 34.815 | 77.997 | Shyok | I(s) | 0.32 | 4,844 |\n| 3498 | 0152F0503498 | 34.975 | 77.437 | Shyok | E(o) | 0.39 | 4,272 | 3571 | 0152F1303571 | 34.815 | 77.983 | Shyok | I(s) | 0.39 | 4,912 |\n| 3499 | 0152F0503499 | 34.892 | 77.362 | Shyok | E(o) | 0.55 | 4,622 | 3572 | 0152F1403572 | 34.674 | 77.754 | Shyok | M(e) | 1.57 | 5,387 |\n| 3500 | 0152F0503500 | 34.857 | 77.264 | Shyok | I(s) | 0.79 | 4,945 | 3573 | 0152F1403573 | 34.618 | 77.812 | Shyok | M(o) | 0.98 | 5,101 |\n| 3501 | 0152F0503501 | 34.848 | 77.265 | Shyok | M(l) | 1.08 | 5,033 | 3574 | 0152F1403574 | 34.615 | 77.808 | Shyok | M(o) | 0.27 | 5,096 |\n| 3502 | 0152F0503502 | 34.765 | 77.467 | Shyok | I(s) | 0.36 | 5,041 | 3575 | 0152F1403575 | 34.536 | 77.851 | Shyok | E(o) | 0.52 | 5,134 |\n| 3503 | 0152F0503503 | 34.756 | 77.464 | Shyok | I(s) | 0.45 | 5,112 | 3576 | 0152F1403576 | 34.513 | 77.911 | Shyok | M(l) | 3.79 | 5,388 |\n| 3504 | 0152F0603504 | 34.535 | 77.350 | Shyok | M(e) | 0.71 | 5,239 | 3577 | 0152F1403577 | 34.503 | 77.985 | Shyok | M(e) | 8.45 | 5,151 |\n| 3505 | 0152F0603505 | 34.506 | 77.298 | Shyok | M(e) | 1.48 | 5,269 | 3578 | 0152F1403578 | 34.502 | 77.982 | Shyok | M(o) | 0.57 | 5,164 |\n| 3506 | 0152F0703506 | 34.496 | 77.268 | Shyok | E(o) | 2.62 | 4,814 | 3579 | 0152F1503579 | 34.469 | 77.992 | Shyok | E(o) | 0.45 | 5,193 |\n| 3507 | 0152F0703507 | 34.470 | 77.315 | Shyok | M(e) | 1.05 | 5,271 | 3580 | 0152F1503580 | 34.410 | 77.979 | Shyok | M(l) | 1.49 | 5,526 |\n| 3508 | 0152F0703508 | 34.454 | 77.275 | Shyok | M(e) | 4.35 | 5,203 | 3581 | 0152F1503581 | 34.406 | 77.978 | Shyok | M(l) | 0.82 | 5,417 |\n| 3509 | 0152F0703509 | 34.451 | 77.262 | Shyok | E(o) | 1.66 | 4,985 | 3582 | 0152F1503582 | 34.398 | 77.983 | Shyok | M(e) | 28.02 | 5,344 |\n| 3510 | 0152F0703510 | 34.445 | 77.409 | Shyok | E(o) | 0.62 | 4,528 | 3583 | 0152F1503583 | 34.397 | 77.987 | Shyok | M(o) | 0.26 | 5,351 |\n| 3511 | 0152F0703511 | 34.444 | 77.258 | Shyok | E(o) | 1.13 | 5,025 | 3584 | 0152F1503584 | 34.395 | 77.982 | Shyok | M(o) | 0.27 | 5,338 |\n| 3512 | 0152F0703512 | 34.442 | 77.256 | Shyok | E(o) | 3.97 | 5,018 | 3585 | 0152F1503585 | 34.391 | 77.982 | Shyok | M(e) | 9.92 | 5,283 |\n| 3513 | 0152F0703513 | 34.442 | 77.407 | Shyok | E(o) | 0.58 | 4,545 | 3586 | 0152F1603586 | 34.113 | 77.930 | Shyok | E(o) | 5.85 | 5,138 |\n| 3514 | 0152F0703514 | 34.437 | 77.256 | Shyok | M(e) | 9.72 | 5,052 | 3587 | 0152F1603587 | 34.094 | 77.985 | Shyok | O | 4.75 | 4,827 |\n| 3515 | 0152F0703515 | 34.430 | 77.321 | Shyok | O | 2.80 | 4,837 | 3588 | 0152F1603588 | 34.083 | 77.958 | Shyok | O | 0.39 | 4,976 |\n| 3516 | 0152F0703516 | 34.428 | 77.250 | Shyok | I(s) | 0.75 | 5,215 | 3589 | 0152F1603589 | 34.030 | 77.949 | Shyok | E(o) | 2.59 | 5,214 |\n| 3517 | 0152F0703517 | 34.427 | 77.251 | Shyok | M(l) | 0.64 | 5,217 | 3590 | 0152G0303590 | 33.438 | 77.045 | Indus Upper | E(o) | 0.25 | 5,243 |\n| 3518 | 0152F0703518 | 34.422 | 77.278 | Shyok | E(c) | 1.75 | 5,548 | 3591 | 0152G0303591 | 33.337 | 77.078 | Indus Upper | I(s) | 0.51 | 5,362 |\n| 3519 | 0152F0703519 | 34.421 | 77.326 | Shyok | E(o) | 0.93 | 5,106 | 3592 | 0152G0303592 | 33.331 | 77.123 | Indus Upper | M(o) | 0.64 | 5,231 |\n| 3520 | 0152F0703520 | 34.417 | 77.283 | Shyok | E(o) | 0.68 | 5,496 | 3593 | 0152G0403593 | 33.189 | 77.051 | Indus Upper | O | 0.31 | 5,272 |\n| 3521 | 0152F0703521 | 34.399 | 77.315 | Shyok | M(e) | 0.41 | 5,230 | 3594 | 0152G0403594 | 33.168 | 77.027 | Indus Upper | M(e) | 0.71 | 5,362 |\n| 3522 | 0152F0703522 | 34.398 | 77.257 | Indus Upper | M(e) | 2.64 | 5,493 | 3595 | 0152G0403595 | 33.153 | 77.078 | Indus Upper | E(o) | 0.26 | 5,537 |\n| 3523 | 0152F0703523 | 34.394 | 77.337 | Shyok | M(e) | 3.24 | 5,225 | 3596 | 0152G0403596 | 33.143 | 77.053 | Indus Upper | M(e) | 6.05 | 5,326 |\n| 3524 | 0152F0703524 | 34.391 | 77.260 | Indus Upper | E(o) | 1.16 | 5,314 | 3597 | 0152G0403597 | 33.128 | 77.065 | Indus Upper | M(e) | 7.45 | 5,043 |\n| 3525 | 0152F0703525 | 34.390 | 77.445 | Shyok | M(o) | 0.68 | 5,201 | 3598 | 0152G0403598 | 33.115 | 77.183 | Indus Upper | E(o) | 0.68 | 5,127 |\n| 3526 | 0152F0703526 | 34.387 | 77.288 | Shyok | M(e) | 0.28 | 5,207 | 3599 | 0152G0503599 | 34.000 | 77.422 | Indus Upper | M(o) | 3.65 | 5,258 |\n| 3527 | 0152F0703527 | 34.386 | 77.411 | Shyok | E(o) | 9.60 | 4,879 | 3600 | 0152G0803600 | 33.054 | 77.435 | Indus Upper | E(o) | 1.88 | 5,132 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9744, "line_end": 9818, "token_count_estimate": 3195, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152F0303493", "0152F0303494", "0152F0303495", "0152F0303496", "0152F0303497", "0152F0503498", "0152F0503499", "0152F0503500", "0152F0503501", "0152F0503502", "0152F0503503", "0152F0603504", "0152F0603505", "0152F0703506", "0152F0703507", "0152F0703508", "0152F0703509", "0152F0703510", "0152F0703511", "0152F0703512", "0152F0703513", "0152F0703514", "0152F0703515", "0152F0703516", "0152F0703517", "0152F0703518", "0152F0703519", "0152F0703520", "0152F0703521", "0152F0703522", "0152F0703523", "0152F0703524", "0152F0703525", "0152F0703526", "0152F0703527", "0152F1103566", "0152F1103567", "0152F1103568", "0152F1203569", "0152F1303570", "0152F1303571", "0152F1403572", "0152F1403573", "0152F1403574", "0152F1403575", "0152F1403576", "0152F1403577", "0152F1403578", "0152F1503579", "0152F1503580"]}}
{"id": "56736979e1b3dc97", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 3528 | 0152F0703528 | 34.385 | 77.256 | Indus Upper | M(e) | 0.37 | 5,255 | 3601 | 0152G0803601 | 33.008 | 77.276 | Indus Upper | O | 0.32 | 5,298 |\n| 3529 | 0152F0703529 | 34.382 | 77.354 | Shyok | E(o) | 6.97 | 4,998 | 3602 | 0152G0803602 | 33.002 | 77.299 | Indus Upper | M(o) | 0.27 | 5,212 |\n| 3530 | 0152F0703530 | 34.374 | 77.328 | Shyok | M(e) | 1.64 | 5,293 | 3603 | 0152G1003603 | 33.724 | 77.614 | Indus Upper | I(s) | 0.26 | 5,277 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 9744, "line_end": 9818, "token_count_estimate": 426, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152F0703528", "0152F0703529", "0152F0703530", "0152G0803601", "0152G0803602", "0152G1003603"]}}
{"id": "be0ec8d0ddf21bab", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9819, "line_end": 9827, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ec6c628fdeabc33", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 3604 | 0152G1003604 | 33.723 | 77.612 | Indus Upper | M(e) | 5.20 | 5,265 | 3677 | 0152H0403677 | 32.217 | 77.034 | Ravi | M(o) | 0.52 | 4,595 |\n| 3605 | 0152G1003605 | 33.684 | 77.524 | Indus Upper | M(o) | 0.49 | 5,384 | 3678 | 0152H0403678 | 32.217 | 77.031 | Ravi | E(o) | 2.27 | 4,566 |\n| 3606 | 0152G1003606 | 33.671 | 77.606 | Indus Upper | M(e) | 7.63 | 5,243 | 3679 | 0152H0403679 | 32.216 | 77.035 | Ravi | E(o) | 1.06 | 4,598 |\n| 3607 | 0152G1003607 | 33.618 | 77.613 | Indus Upper | M(e) | 8.64 | 5,405 | 3680 | 0152H0403680 | 32.203 | 77.029 | Beas | E(o) | 0.43 | 4,577 |\n| 3608 | 0152G1003608 | 33.615 | 77.585 | Indus Upper | M(e) | 0.39 | 5,648 | 3681 | 0152H0403681 | 32.187 | 77.070 | Beas | M(o) | 0.63 | 4,323 |\n| 3609 | 0152G1003609 | 33.556 | 77.664 | Indus Upper | O | 10.15 | 5,222 | 3682 | 0152H0503682 | 32.972 | 77.297 | Indus Upper | M(o) | 2.32 | 5,352 |\n| 3610 | 0152G1003610 | 33.516 | 77.683 | Indus Upper | E(o) | 2.30 | 5,306 | 3683 | 0152H0503683 | 32.964 | 77.300 | Indus Upper | M(e) | 3.71 | 5,302 |\n| 3611 | 0152G1003611 | 33.510 | 77.664 | Indus Upper | M(o) | 0.33 | 5,633 | 3684 | 0152H0503684 | 32.941 | 77.326 | Indus Upper | M(e) | 2.15 | 5,087 |\n| 3612 | 0152G1103612 | 33.498 | 77.702 | Indus Upper | M(o) | 1.51 | 5,638 | 3685 | 0152H0503685 | 32.930 | 77.399 | Indus Upper | I(s) | 0.47 | 5,217 |\n| 3613 | 0152G1103613 | 33.476 | 77.719 | Indus Upper | E(o) | 1.05 | 5,071 | 3686 | 0152H0503686 | 32.896 | 77.269 | Indus Upper | E(o) | 0.49 | 5,053 |\n| 3614 | 0152G1103614 | 33.445 | 77.693 | Indus Upper | E(c) | 1.14 | 5,361 | 3687 | 0152H0503687 | 32.895 | 77.279 | Indus Upper | M(o) | 0.64 | 5,203 |\n| 3615 | 0152G1303615 | 33.999 | 77.979 | Shyok | O | 43.72 | 4,991 | 3688 | 0152H0503688 | 32.878 | 77.301 | Indus Upper | I(s) | 1.47 | 5,397 |\n| 3616 | 0152G1303616 | 33.941 | 77.954 | Shyok | E(o) | 1.96 | 5,500 | 3689 | 0152H0503689 | 32.860 | 77.273 | Chenab | M(o) | 0.55 | 5,162 |\n| 3617 | 0152G1303617 | 33.915 | 77.965 | Shyok | E(o) | 6.91 | 5,407 | 3690 | 0152H0503690 | 32.860 | 77.276 | Chenab | M(o) | 0.40 | 5,158 |\n| 3618 | 0152G1303618 | 33.895 | 77.926 | Indus Upper | E(o) | 1.09 | 5,306 | 3691 | 0152H0503691 | 32.859 | 77.269 | Chenab | M(o) | 0.48 | 5,163 |\n| 3619 | 0152G1403619 | 33.675 | 77.882 | Indus Upper | O | 0.91 | 5,199 | 3692 | 0152H0503692 | 32.854 | 77.399 | Indus Upper | M(o) | 0.45 | 5,248 |\n| 3620 | 0152H0103620 | 32.973 | 77.213 | Indus Upper | M(o) | 0.55 | 5,059 | 3693 | 0152H0503693 | 32.844 | 77.280 | Chenab | M(o) | 3.13 | 4,872 |\n| 3621 | 0152H0103621 | 32.964 | 77.119 | Chenab | M(o) | 0.71 | 5,322 | 3694 | 0152H0503694 | 32.834 | 77.394 | Indus Upper | E(o) | 1.57 | 5,237 |\n| 3622 | 0152H0103622 | 32.938 | 77.152 | Chenab | E(o) | 0.79 | 5,400 | 3695 | 0152H0503695 | 32.827 | 77.389 | Indus Upper | M(o) | 1.16 | 5,304 |\n| 3623 | 0152H0103623 | 32.937 | 77.156 | Chenab | E(o) | 1.55 | 5,411 | 3696 | 0152H0503696 | 32.815 | 77.368 | Chenab | E(o) | 0.36 | 5,364 |\n| 3624 | 0152H0103624 | 32.935 | 77.165 | Indus Upper | M(o) | 1.25 | 5,397 | 3697 | 0152H0503697 | 32.809 | 77.402 | Indus Upper | E(o) | 0.28 | 5,427 |\n| 3625 | 0152H0103625 | 32.935 | 77.147 | Chenab | E(o) | 1.60 | 5,336 | 3698 | 0152H0503698 | 32.795 | 77.458 | Indus Upper | E(o) | 1.51 | 4,918 |\n| 3626 | 0152H0103626 | 32.935 | 77.062 | Chenab | I(s) | 0.56 | 4,709 | 3699 | 0152H0503699 | 32.770 | 77.304 | Chenab | I(s) | 0.25 | 4,824 |\n| 3627 | 0152H0103627 | 32.922 | 77.010 | Chenab | M(o) | 1.18 | 5,263 | 3700 | 0152H0503700 | 32.769 | 77.302 | Chenab | I(s) | 0.47 | 4,823 |\n| 3628 | 0152H0103628 | 32.916 | 77.062 | Chenab | E(o) | 0.31 | 4,857 | 3701 | 0152H0503701 | 32.765 | 77.327 | Chenab | E(o) | 0.26 | 5,228 |\n| 3629 | 0152H0103629 | 32.911 | 77.020 | Chenab | E(o) | 0.25 | 5,301 | 3702 | 0152H0503702 | 32.754 | 77.442 | Indus Upper | M(e) | 1.09 | 5,219 |\n| 3630 | 0152H0103630 | 32.907 | 77.201 | Chenab | M(o) | 1.37 | 5,018 | 3703 | 0152H0603703 | 32.725 | 77.404 | Indus Upper | E(o) | 0.38 | 5,105 |\n| 3631 | 0152H0103631 | 32.892 | 77.160 | Chenab | E(o) | 0.38 | 5,350 | 3704 | 0152H0603704 | 32.724 | 77.378 | Indus Upper | M(o) | 1.42 | 5,286 |\n| 3632 | 0152H0103632 | 32.876 | 77.164 | Chenab | E(o) | 1.37 | 5,289 | 3705 | 0152H0603705 | 32.723 | 77.399 | Indus Upper | E(o) | 0.67 | 5,132 |\n| 3633 | 0152H0103633 | 32.872 | 77.174 | Chenab | M(o) | 2.36 | 5,018 | 3706 | 0152H0603706 | 32.723 | 77.330 | Chenab | M(e) | 4.53 | 4,537 |\n| 3634 | 0152H0103634 | 32.845 | 77.039 | Chenab | M(o) | 0.82 | 5,161 | 3707 | 0152H0603707 | 32.722 | 77.377 | Indus Upper | M(o) | 1.59 | 5,295 |\n| 3635 | 0152H0103635 | 32.843 | 77.015 | Chenab | M(l) | 0.25 | 5,149 | 3708 | 0152H0603708 | 32.722 | 77.413 | Indus Upper | M(e) | 10.00 | 5,128 |\n| 3636 | 0152H0103636 | 32.843 | 77.245 | Chenab | M(o) | 0.27 | 5,106 | 3709 | 0152H0603709 | 32.722 | 77.374 | Indus Upper | M(o) | 0.54 | 5,332 |\n| 3637 | 0152H0103637 | 32.837 | 77.237 | Chenab | M(o) | 0.49 | 5,143 | 3710 | 0152H0603710 | 32.721 | 77.384 | Indus Upper | M(o) | 8.52 | 5,236 |\n| 3638 | 0152H0103638 | 32.826 | 77.207 | Chenab | M(o) | 0.34 | 5,181 | 3711 | 0152H0603711 | 32.720 | 77.375 | Indus Upper | M(o) | 0.47 | 5,301 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9828, "line_end": 9902, "token_count_estimate": 3188, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Indus Upper", "Ravi", "Shyok"], "countries": [], "lake_ids": ["0152G1003604", "0152G1003605", "0152G1003606", "0152G1003607", "0152G1003608", "0152G1003609", "0152G1003610", "0152G1003611", "0152G1103612", "0152G1103613", "0152G1103614", "0152G1303615", "0152G1303616", "0152G1303617", "0152G1303618", "0152G1403619", "0152H0103620", "0152H0103621", "0152H0103622", "0152H0103623", "0152H0103624", "0152H0103625", "0152H0103626", "0152H0103627", "0152H0103628", "0152H0103629", "0152H0103630", "0152H0103631", "0152H0103632", "0152H0103633", "0152H0103634", "0152H0103635", "0152H0103636", "0152H0103637", "0152H0103638", "0152H0403677", "0152H0403678", "0152H0403679", "0152H0403680", "0152H0403681", "0152H0503682", "0152H0503683", "0152H0503684", "0152H0503685", "0152H0503686", "0152H0503687", "0152H0503688", "0152H0503689", "0152H0503690", "0152H0503691"]}}
{"id": "84f919633502b189", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 3639 | 0152H0103639 | 32.817 | 77.231 | Chenab | M(e) | 0.43 | 5,193 | 3712 | 0152H0603712 | 32.720 | 77.381 | Indus Upper | M(o) | 0.37 | 5,256 |\n| 3640 | 0152H0103640 | 32.817 | 77.229 | Chenab | I(s) | 0.33 | 5,203 | 3713 | 0152H0603713 | 32.719 | 77.381 | Indus Upper | M(o) | 0.35 | 5,265 |\n| 3641 | 0152H0103641 | 32.799 | 77.183 | Chenab | I(s) | 0.29 | 5,093 | 3714 | 0152H0603714 | 32.718 | 77.376 | Indus Upper | M(o) | 1.25 | 5,313 |\n| 3642 | 0152H0103642 | 32.788 | 77.163 | Chenab | M(o) | 0.35 | 5,207 | 3715 | 0152H0603715 | 32.717 | 77.377 | Indus Upper | M(o) | 0.27 | 5,329 |\n| 3643 | 0152H0103643 | 32.788 | 77.170 | Chenab | E(o) | 0.36 | 5,123 | 3716 | 0152H0603716 | 32.711 | 77.337 | Chenab | I(s) | 0.39 | 4,685 |\n| 3644 | 0152H0103644 | 32.787 | 77.219 | Chenab | E(o) | 2.30 | 5,111 | 3717 | 0152H0603717 | 32.708 | 77.342 | Chenab | M(l) | 2.16 | 4,671 |\n| 3645 | 0152H0103645 | 32.765 | 77.231 | Chenab | M(o) | 0.25 | 4,719 | 3718 | 0152H0603718 | 32.704 | 77.347 | Chenab | M(e) | 1.48 | 4,741 |\n| 3646 | 0152H0103646 | 32.762 | 77.196 | Chenab | M(e) | 5.38 | 4,766 | 3719 | 0152H0603719 | 32.693 | 77.366 | Chenab | M(o) | 0.42 | 5,092 |\n| 3647 | 0152H0203647 | 32.744 | 77.190 | Chenab | M(o) | 0.56 | 4,992 | 3720 | 0152H0603720 | 32.693 | 77.373 | Chenab | E(o) | 5.85 | 5,140 |\n| 3648 | 0152H0203648 | 32.682 | 77.099 | Chenab | M(l) | 0.25 | 5,087 | 3721 | 0152H0603721 | 32.693 | 77.363 | Chenab | E(o) | 0.37 | 5,069 |\n| 3649 | 0152H0203649 | 32.682 | 77.096 | Chenab | M(l) | 0.86 | 5,095 | 3722 | 0152H0603722 | 32.636 | 77.319 | Chenab | I(s) | 0.35 | 4,540 |\n| 3650 | 0152H0203650 | 32.636 | 77.103 | Chenab | E(o) | 0.47 | 4,980 | 3723 | 0152H0603723 | 32.631 | 77.307 | Chenab | M(e) | 5.32 | 4,396 |\n| 3651 | 0152H0203651 | 32.526 | 77.220 | Chenab | M(e) | 77.59 | 4,069 | 3724 | 0152H0603724 | 32.624 | 77.338 | Chenab | I(s) | 0.50 | 4,749 |\n| 3652 | 0152H0303652 | 32.398 | 77.023 | Ravi | I(s) | 0.38 | 4,380 | 3725 | 0152H0603725 | 32.582 | 77.383 | Chenab | E(o) | 1.85 | 5,199 |\n| 3653 | 0152H0303653 | 32.375 | 77.224 | Beas | E(o) | 0.64 | 4,233 | 3726 | 0152H0703726 | 32.499 | 77.454 | Chenab | M(l) | 0.52 | 4,775 |\n| 3654 | 0152H0303654 | 32.367 | 77.086 | Beas | E(o) | 0.41 | 3,686 | 3727 | 0152H0703727 | 32.393 | 77.368 | Chenab | M(o) | 0.35 | 4,885 |\n| 3655 | 0152H0303655 | 32.357 | 77.044 | Ravi | M(o) | 0.61 | 4,852 | 3728 | 0152H0703728 | 32.393 | 77.309 | Chenab | M(o) | 1.22 | 4,531 |\n| 3656 | 0152H0303656 | 32.336 | 77.014 | Ravi | M(o) | 0.26 | 4,521 | 3729 | 0152H0703729 | 32.316 | 77.324 | Beas | E(o) | 0.47 | 4,516 |\n| 3657 | 0152H0303657 | 32.307 | 77.089 | Beas | M(o) | 2.16 | 4,673 | 3730 | 0152H0703730 | 32.284 | 77.343 | Beas | M(o) | 0.44 | 4,619 |\n| 3658 | 0152H0303658 | 32.305 | 77.086 | Beas | M(o) | 1.60 | 4,620 | 3731 | 0152H0703731 | 32.279 | 77.341 | Beas | E(o) | 1.59 | 4,574 |\n| 3659 | 0152H0303659 | 32.303 | 77.095 | Beas | M(o) | 0.90 | 4,615 | 3732 | 0152H0803732 | 32.249 | 77.418 | Chenab | M(o) | 0.43 | 5,082 |\n| 3660 | 0152H0303660 | 32.294 | 77.091 | Beas | M(o) | 0.55 | 4,446 | 3733 | 0152H0803733 | 32.249 | 77.416 | Chenab | M(o) | 1.09 | 5,094 |\n| 3661 | 0152H0303661 | 32.293 | 77.243 | Beas | E(o) | 0.77 | 4,157 | 3734 | 0152H0803734 | 32.248 | 77.442 | Chenab | M(o) | 0.85 | 4,309 |\n| 3662 | 0152H0303662 | 32.292 | 77.079 | Beas | E(o) | 4.05 | 4,308 | 3735 | 0152H0803735 | 32.248 | 77.419 | Chenab | E(o) | 2.11 | 5,073 |\n| 3663 | 0152H0303663 | 32.287 | 77.080 | Beas | M(o) | 0.49 | 4,460 | 3736 | 0152H0803736 | 32.246 | 77.448 | Chenab | M(l) | 1.86 | 4,477 |\n| 3664 | 0152H0303664 | 32.285 | 77.081 | Beas | M(o) | 0.44 | 4,464 | 3737 | 0152H0803737 | 32.240 | 77.449 | Chenab | M(e) | 2.34 | 4,589 |\n| 3665 | 0152H0303665 | 32.282 | 77.080 | Beas | M(o) | 0.82 | 4,490 | 3738 | 0152H0803738 | 32.185 | 77.332 | Beas | O | 0.55 | 3,865 |\n| 3666 | 0152H0403666 | 32.236 | 77.016 | Ravi | M(o) | 0.63 | 4,757 | 3739 | 0152H0803739 | 32.180 | 77.493 | Beas | M(e) | 4.69 | 4,520 |\n| 3667 | 0152H0403667 | 32.235 | 77.018 | Ravi | M(o) | 0.84 | 4,762 | 3740 | 0152H0803740 | 32.168 | 77.465 | Beas | M(o) | 0.45 | 4,637 |\n| 3668 | 0152H0403668 | 32.232 | 77.014 | Ravi | M(l) | 0.26 | 4,763 | 3741 | 0152H0803741 | 32.168 | 77.469 | Beas | M(o) | 0.57 | 4,641 |\n| 3669 | 0152H0403669 | 32.230 | 77.019 | Ravi | M(o) | 0.43 | 4,681 | 3742 | 0152H0803742 | 32.161 | 77.308 | Beas | E(o) | 3.01 | 4,542 |\n| 3670 | 0152H0403670 | 32.227 | 77.019 | Ravi | M(o) | 0.26 | 4,635 | 3743 | 0152H0803743 | 32.157 | 77.299 | Beas | M(e) | 6.61 | 4,384 |\n| 3671 | 0152H0403671 | 32.227 | 77.024 | Ravi | E(o) | 0.44 | 4,732 | 3744 | 0152H0803744 | 32.157 | 77.297 | Beas | M(o) | 0.34 | 4,397 |\n| 3672 | 0152H0403672 | 32.226 | 77.035 | Ravi | E(o) | 1.46 | 4,686 | 3745 | 0152H0803745 | 32.152 | 77.452 | Beas | M(o) | 0.40 | 4,745 |\n| 3673 | 0152H0403673 | 32.225 | 77.033 | Ravi | E(o) | 1.05 | 4,690 | 3746 | 0152H0803746 | 32.145 | 77.301 | Beas | E(o) | 1.31 | 4,445 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9828, "line_end": 9902, "token_count_estimate": 3106, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Indus Upper", "Ravi"], "countries": [], "lake_ids": ["0152H0103639", "0152H0103640", "0152H0103641", "0152H0103642", "0152H0103643", "0152H0103644", "0152H0103645", "0152H0103646", "0152H0203647", "0152H0203648", "0152H0203649", "0152H0203650", "0152H0203651", "0152H0303652", "0152H0303653", "0152H0303654", "0152H0303655", "0152H0303656", "0152H0303657", "0152H0303658", "0152H0303659", "0152H0303660", "0152H0303661", "0152H0303662", "0152H0303663", "0152H0303664", "0152H0303665", "0152H0403666", "0152H0403667", "0152H0403668", "0152H0403669", "0152H0403670", "0152H0403671", "0152H0403672", "0152H0403673", "0152H0603712", "0152H0603713", "0152H0603714", "0152H0603715", "0152H0603716", "0152H0603717", "0152H0603718", "0152H0603719", "0152H0603720", "0152H0603721", "0152H0603722", "0152H0603723", "0152H0603724", "0152H0603725", "0152H0703726"]}}
{"id": "7e3eeaff1901b6a7", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 3674 | 0152H0403674 | 32.221 | 77.030 | Ravi | M(o) | 0.43 | 4,617 | 3747 | 0152H0803747 | 32.135 | 77.433 | Beas | M(o) | 1.16 | 4,438 |\n| 3675 | 0152H0403675 | 32.221 | 77.031 | Ravi | M(o) | 0.33 | 4,616 | 3748 | 0152H0803748 | 32.135 | 77.435 | Beas | M(o) | 1.62 | 4,446 |\n| 3676 | 0152H0403676 | 32.221 | 77.033 | Ravi | M(o) | 0.51 | 4,621 | 3749 | 0152H0803749 | 32.134 | 77.456 | Beas | E(o) | 9.52 | 4,555 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 9828, "line_end": 9902, "token_count_estimate": 415, "basins": ["Indus"], "subbasins": ["Beas", "Ravi"], "countries": [], "lake_ids": ["0152H0403674", "0152H0403675", "0152H0403676", "0152H0803747", "0152H0803748", "0152H0803749"]}}
{"id": "e53da794279ba37d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 9903, "line_end": 9913, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca4589fe1728a4be", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3750 | 0152H0803750 | 32.132 | 77.459 | Beas | M(o) | 0.41 | 4,535 |\n| 3751 | 0152H0803751 | 32.115 | 77.483 | Beas | E(o) | 0.28 | 3,846 |\n| 3752 | 0152H0803752 | 32.101 | 77.456 | Beas | E(o) | 0.60 | 4,735 |\n| 3753 | 0152H0803753 | 32.099 | 77.455 | Beas | E(o) | 1.36 | 4,735 |\n| 3754 | 0152H0803754 | 32.098 | 77.454 | Beas | M(o) | 1.26 | 4,737 |\n| 3755 | 0152H0803755 | 32.075 | 77.439 | Beas | E(o) | 0.55 | 4,715 |\n| 3756 | 0152H0803756 | 32.074 | 77.449 | Beas | E(c) | 2.52 | 4,504 |\n| 3757 | 0152H0803757 | 32.072 | 77.434 | Beas | E(o) | 0.51 | 4,840 |\n| 3758 | 0152H0803758 | 32.072 | 77.427 | Beas | E(o) | 0.75 | 4,597 |\n| 3759 | 0152H0803759 | 32.071 | 77.426 | Beas | E(o) | 0.56 | 4,599 |\n| 3760 | 0152H0803760 | 32.065 | 77.463 | Beas | E(o) | 0.72 | 4,315 |\n| 3761 | 0152H0903761 | 32.792 | 77.502 | Indus Upper | E(o) | 7.91 | 5,053 |\n| 3762 | 0152H0903762 | 32.787 | 77.589 | Indus Upper | I(s) | 0.63 | 5,320 |\n| 3763 | 0152H1003763 | 32.745 | 77.536 | Chenab | E(o) | 0.52 | 5,364 |\n| 3764 | 0152H1003764 | 32.732 | 77.550 | Chenab | M(o) | 0.40 | 5,199 |\n| 3765 | 0152H1003765 | 32.683 | 77.634 | Indus Upper | M(o) | 0.94 | 5,435 |\n| 3766 | 0152H1003766 | 32.681 | 77.637 | Indus Upper | M(o) | 0.37 | 5,449 |\n| 3767 | 0152H1003767 | 32.658 | 77.502 | Chenab | M(o) | 0.52 | 5,253 |\n| 3768 | 0152H1003768 | 32.636 | 77.593 | Chenab | M(o) | 0.87 | 5,411 |\n| 3769 | 0152H1003769 | 32.604 | 77.618 | Chenab | M(e) | 5.28 | 5,188 |\n| 3770 | 0152H1003770 | 32.504 | 77.534 | Chenab | I(s) | 0.50 | 4,236 |\n| 3771 | 0152H1103771 | 32.499 | 77.547 | Chenab | M(e) | 128.69 | 4,150 |\n| 3772 | 0152H1103772 | 32.483 | 77.615 | Chenab | E(o) | 47.30 | 4,276 |\n| 3773 | 0152H1103773 | 32.343 | 77.715 | Chenab | E(o) | 0.69 | 5,302 |\n| 3774 | 0152H1103774 | 32.298 | 77.580 | Chenab | M(o) | 0.60 | 3,902 |\n| 3775 | 0152H1103775 | 32.294 | 77.586 | Chenab | M(o) | 0.37 | 3,909 |\n| 3776 | 0152H1103776 | 32.277 | 77.589 | Chenab | M(o) | 0.28 | 3,926 |\n| 3777 | 0152H1203777 | 32.216 | 77.627 | Chenab | I(s) | 0.52 | 4,502 |\n| 3778 | 0152H1203778 | 32.215 | 77.628 | Chenab | I(s) | 0.33 | 4,505 |\n| 3779 | 0152H1203779 | 32.211 | 77.628 | Chenab | I(s) | 0.32 | 4,523 |\n| 3780 | 0152H1203780 | 32.198 | 77.645 | Chenab | I(s) | 0.42 | 4,651 |\n| 3781 | 0152H1303781 | 32.780 | 77.911 | Indus Upper | M(e) | 1.79 | 5,417 |\n| 3782 | 0152H1403782 | 32.610 | 77.912 | Indus Upper | M(e) | 2.94 | 5,348 |\n| 3783 | 0152H1403783 | 32.607 | 77.989 | Satluj | M(e) | 4.21 | 5,357 |\n| 3784 | 0152H1403784 | 32.533 | 77.966 | Satluj | M(e) | 0.74 | 5,186 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9914, "line_end": 9988, "token_count_estimate": 1639, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152H0803750", "0152H0803751", "0152H0803752", "0152H0803753", "0152H0803754", "0152H0803755", "0152H0803756", "0152H0803757", "0152H0803758", "0152H0803759", "0152H0803760", "0152H0903761", "0152H0903762", "0152H1003763", "0152H1003764", "0152H1003765", "0152H1003766", "0152H1003767", "0152H1003768", "0152H1003769", "0152H1003770", "0152H1103771", "0152H1103772", "0152H1103773", "0152H1103774", "0152H1103775", "0152H1103776", "0152H1203777", "0152H1203778", "0152H1203779", "0152H1203780", "0152H1303781", "0152H1403782", "0152H1403783", "0152H1403784"]}}
{"id": "4f7367f7c96ed60e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3785 | 0152H1403785 | 32.528 | 77.827 | Satluj | E(o) | 0.42 | 5,405 |\n| 3786 | 0152H1403786 | 32.525 | 77.942 | Satluj | M(o) | 1.99 | 5,280 |\n| 3787 | 0152H1403787 | 32.525 | 77.857 | Satluj | M(o) | 0.30 | 5,252 |\n| 3788 | 0152H1603788 | 32.137 | 77.915 | Satluj | M(e) | 1.44 | 5,048 |\n| 3789 | 0152H1603789 | 32.129 | 77.925 | Satluj | E(o) | 1.02 | 5,222 |\n| 3790 | 0152J0203790 | 34.535 | 78.103 | Shyok | M(e) | 0.39 | 5,229 |\n| 3791 | 0152J0203791 | 34.532 | 78.106 | Shyok | M(o) | 0.75 | 5,212 |\n| 3792 | 0152J0203792 | 34.523 | 78.099 | Shyok | M(o) | 1.72 | 5,280 |\n| 3793 | 0152J0203793 | 34.522 | 78.096 | Shyok | M(o) | 1.44 | 5,281 |\n| 3794 | 0152J0203794 | 34.521 | 78.090 | Shyok | M(e) | 10.62 | 5,270 |\n| 3795 | 0152J0203795 | 34.521 | 78.094 | Shyok | M(o) | 0.74 | 5,273 |\n| 3796 | 0152J0203796 | 34.520 | 78.101 | Shyok | M(e) | 10.28 | 5,265 |\n| 3797 | 0152J0203797 | 34.519 | 78.096 | Shyok | M(e) | 0.50 | 5,268 |\n| 3798 | 0152J0303798 | 34.465 | 78.121 | Shyok | I(s) | 4.12 | 5,426 |\n| 3799 | 0152J0303799 | 34.460 | 78.004 | Shyok | M(o) | 0.31 | 5,283 |\n| 3800 | 0152J0303800 | 34.460 | 78.011 | Shyok | M(o) | 1.05 | 5,305 |\n| 3801 | 0152J0303801 | 34.460 | 78.008 | Shyok | M(o) | 0.50 | 5,304 |\n| 3802 | 0152J0303802 | 34.459 | 78.005 | Shyok | M(o) | 0.63 | 5,293 |\n| 3803 | 0152J0303803 | 34.459 | 78.015 | Shyok | M(e) | 5.18 | 5,318 |\n| 3804 | 0152J0303804 | 34.458 | 78.146 | Shyok | M(o) | 0.42 | 5,336 |\n| 3805 | 0152J0303805 | 34.458 | 78.018 | Shyok | I(s) | 2.07 | 5,323 |\n| 3806 | 0152J0303806 | 34.457 | 78.009 | Shyok | M(o) | 1.38 | 5,303 |\n| 3807 | 0152J0303807 | 34.457 | 78.117 | Shyok | M(o) | 0.68 | 5,384 |\n| 3808 | 0152J0303808 | 34.457 | 78.013 | Shyok | M(e) | 5.51 | 5,331 |\n| 3809 | 0152J0303809 | 34.457 | 78.148 | Shyok | M(o) | 3.06 | 5,326 |\n| 3810 | 0152J0303810 | 34.457 | 78.152 | Shyok | M(o) | 2.00 | 5,322 |\n| 3811 | 0152J0303811 | 34.457 | 78.136 | Shyok | M(o) | 95.68 | 5,295 |\n| 3812 | 0152J0303812 | 34.447 | 78.155 | Shyok | M(o) | 0.88 | 5,269 |\n| 3813 | 0152J0303813 | 34.447 | 78.157 | Shyok | M(o) | 0.48 | 5,271 |\n| 3814 | 0152J0303814 | 34.446 | 78.025 | Shyok | M(l) | 0.36 | 5,420 |\n| 3815 | 0152J0303815 | 34.446 | 78.143 | Shyok | M(e) | 20.53 | 5,311 |\n| 3816 | 0152J0303816 | 34.445 | 78.026 | Shyok | M(l) | 1.55 | 5,411 |\n| 3817 | 0152J0303817 | 34.445 | 78.160 | Shyok | M(o) | 0.40 | 5,235 |\n| 3818 | 0152J0303818 | 34.444 | 78.148 | Shyok | M(o) | 0.99 | 5,348 |\n| 3819 | 0152J0303819 | 34.415 | 78.069 | Shyok | M(e) | 6.53 | 5,384 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9914, "line_end": 9988, "token_count_estimate": 1682, "basins": ["Indus"], "subbasins": ["Satluj", "Shyok"], "countries": [], "lake_ids": ["0152H1403785", "0152H1403786", "0152H1403787", "0152H1603788", "0152H1603789", "0152J0203790", "0152J0203791", "0152J0203792", "0152J0203793", "0152J0203794", "0152J0203795", "0152J0203796", "0152J0203797", "0152J0303798", "0152J0303799", "0152J0303800", "0152J0303801", "0152J0303802", "0152J0303803", "0152J0303804", "0152J0303805", "0152J0303806", "0152J0303807", "0152J0303808", "0152J0303809", "0152J0303810", "0152J0303811", "0152J0303812", "0152J0303813", "0152J0303814", "0152J0303815", "0152J0303816", "0152J0303817", "0152J0303818", "0152J0303819"]}}
{"id": "c4603daa96dc242e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3820 | 0152J0303820 | 34.405 | 78.080 | Shyok | M(o) | 0.35 | 5,304 |\n| 3821 | 0152J0303821 | 34.401 | 78.079 | Shyok | M(e) | 20.39 | 5,307 |\n| 3822 | 0152J0303822 | 34.390 | 78.089 | Shyok | E(o) | 11.16 | 5,226 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 9914, "line_end": 9988, "token_count_estimate": 246, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152J0303820", "0152J0303821", "0152J0303822"]}}
{"id": "2a4abfbef3c88e46", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3823 | 0152J0303823 | 34.387 | 78.113 | Shyok | E(o) | 0.48 | 5,173 |\n| 3824 | 0152J0303824 | 34.386 | 78.112 | Shyok | E(o) | 2.48 | 5,166 |\n| 3825 | 0152J0303825 | 34.333 | 78.198 | Shyok | M(l) | 0.45 | 5,330 |\n| 3826 | 0152J0303826 | 34.289 | 78.229 | Shyok | M(o) | 0.43 | 5,245 |\n| 3827 | 0152J0303827 | 34.288 | 78.228 | Shyok | M(o) | 0.62 | 5,249 |\n| 3828 | 0152J0303828 | 34.281 | 78.156 | Shyok | M(e) | 1.05 | 5,421 |\n| 3829 | 0152J0303829 | 34.279 | 78.225 | Shyok | M(o) | 0.90 | 5,250 |\n| 3830 | 0152J0303830 | 34.278 | 78.222 | Shyok | M(e) | 6.36 | 5,247 |\n| 3831 | 0152J0403831 | 34.167 | 78.212 | Shyok | E(o) | 0.28 | 5,152 |\n| 3832 | 0152J0703832 | 34.354 | 78.371 | Shyok | M(e) | 0.28 | 5,391 |\n| 3833 | 0152J0703833 | 34.349 | 78.496 | Shyok | M(e) | 1.00 | 5,372 |\n| 3834 | 0152J0803834 | 34.245 | 78.429 | Shyok | E(o) | 0.73 | 5,352 |\n| 3835 | 0152J0803835 | 34.243 | 78.429 | Shyok | E(o) | 1.45 | 5,358 |\n| 3836 | 0152J0803836 | 34.233 | 78.426 | Shyok | O | 64.78 | 5,350 |\n| 3837 | 0152J0803837 | 34.223 | 78.380 | Shyok | M(e) | 2.09 | 5,576 |\n| 3838 | 0152J0803838 | 34.215 | 78.374 | Shyok | M(o) | 0.95 | 5,568 |\n| 3839 | 0152J0803839 | 34.210 | 78.383 | Shyok | M(l) | 0.66 | 5,422 |\n| 3840 | 0152J0803840 | 34.208 | 78.375 | Shyok | M(o) | 0.31 | 5,483 |\n| 3841 | 0152J0803841 | 34.204 | 78.473 | Shyok | M(e) | 2.35 | 5,653 |\n| 3842 | 0152J0803842 | 34.200 | 78.384 | Shyok | E(o) | 0.58 | 5,331 |\n| 3843 | 0152J0803843 | 34.190 | 78.472 | Shyok | E(o) | 0.97 | 5,575 |\n| 3844 | 0152J0803844 | 34.183 | 78.459 | Shyok | E(o) | 2.00 | 5,546 |\n| 3845 | 0152J0803845 | 34.177 | 78.379 | Shyok | O | 1.06 | 5,236 |\n| 3846 | 0152J0803846 | 34.175 | 78.379 | Shyok | O | 0.26 | 5,237 |\n| 3847 | 0152J0803847 | 34.173 | 78.370 | Shyok | O | 0.55 | 5,242 |\n| 3848 | 0152J0803848 | 34.171 | 78.369 | Shyok | O | 0.35 | 5,233 |\n| 3849 | 0152J0803849 | 34.170 | 78.370 | Shyok | O | 0.52 | 5,232 |\n| 3850 | 0152J0803850 | 34.165 | 78.362 | Shyok | E(o) | 0.97 | 5,302 |\n| 3851 | 0152J0803851 | 34.128 | 78.356 | Shyok | O | 0.65 | 5,174 |\n| 3852 | 0152J0803852 | 34.125 | 78.354 | Shyok | O | 0.27 | 5,170 |\n| 3853 | 0152J0803853 | 34.123 | 78.355 | Shyok | O | 0.74 | 5,170 |\n| 3854 | 0152J0803854 | 34.122 | 78.357 | Shyok | O | 0.29 | 5,170 |\n| 3855 | 0152J0803855 | 34.120 | 78.343 | Shyok | E(o) | 1.25 | 5,253 |\n| 3856 | 0152J0803856 | 34.120 | 78.356 | Shyok | O | 0.49 | 5,172 |\n| 3857 | 0152J0803857 | 34.108 | 78.345 | Shyok | M(o) | 0.32 | 5,226 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 9990, "line_end": 10064, "token_count_estimate": 1627, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0152J0303823", "0152J0303824", "0152J0303825", "0152J0303826", "0152J0303827", "0152J0303828", "0152J0303829", "0152J0303830", "0152J0403831", "0152J0703832", "0152J0703833", "0152J0803834", "0152J0803835", "0152J0803836", "0152J0803837", "0152J0803838", "0152J0803839", "0152J0803840", "0152J0803841", "0152J0803842", "0152J0803843", "0152J0803844", "0152J0803845", "0152J0803846", "0152J0803847", "0152J0803848", "0152J0803849", "0152J0803850", "0152J0803851", "0152J0803852", "0152J0803853", "0152J0803854", "0152J0803855", "0152J0803856", "0152J0803857"]}}
{"id": "5a225499dde6dae4", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3858 | 0152J0803858 | 34.106 | 78.343 | Shyok | M(o) | 0.25 | 5,254 |\n| 3859 | 0152J0803859 | 34.103 | 78.344 | Shyok | M(o) | 0.27 | 5,291 |\n| 3860 | 0152J0803860 | 34.102 | 78.348 | Shyok | M(o) | 0.26 | 5,246 |\n| 3861 | 0152J0803861 | 34.101 | 78.341 | Shyok | I(s) | 3.07 | 5,311 |\n| 3862 | 0152J0803862 | 34.097 | 78.284 | Shyok | M(e) | 0.45 | 5,645 |\n| 3863 | 0152J1103863 | 34.471 | 78.567 | Shyok | O | 0.39 | 5,152 |\n| 3864 | 0152J1103864 | 34.347 | 78.544 | Shyok | M(e) | 0.62 | 5,573 |\n| 3865 | 0152J1203865 | 34.200 | 78.516 | Shyok | E(o) | 24.44 | 5,386 |\n| 3866 | 0152J1203866 | 34.189 | 78.532 | Shyok | E(o) | 3.90 | 5,363 |\n| 3867 | 0152J1203867 | 34.172 | 78.526 | Shyok | E(c) | 1.61 | 5,598 |\n| 3868 | 0152J1203868 | 34.151 | 78.553 | Shyok | E(o) | 65.02 | 5,566 |\n| 3869 | 0152J1203869 | 34.138 | 78.574 | Shyok | E(o) | 0.85 | 5,637 |\n| 3870 | 0152J1203870 | 34.137 | 78.571 | Shyok | E(o) | 3.07 | 5,643 |\n| 3871 | 0152J1203871 | 34.092 | 78.681 | Shyok | O | 0.44 | 5,614 |\n| 3872 | 0152J1203872 | 34.077 | 78.675 | Shyok | M(o) | 0.42 | 5,679 |\n| 3873 | 0152J1203873 | 34.044 | 78.664 | Shyok | M(e) | 3.94 | 5,700 |\n| 3874 | 0152J1203874 | 34.041 | 78.659 | Shyok | M(o) | 0.51 | 5,735 |\n| 3875 | 0152J1203875 | 34.040 | 78.658 | Shyok | M(o) | 0.34 | 5,741 |\n| 3876 | 0152J1603876 | 34.149 | 78.981 | Shyok | M(e) | 0.89 | 5,586 |\n| 3877 | 0152K0103877 | 33.862 | 78.019 | Shyok | M(o) | 0.57 | 5,436 |\n| 3878 | 0152K0103878 | 33.841 | 78.022 | Shyok | E(o) | 0.39 | 5,412 |\n| 3879 | 0152K0103879 | 33.840 | 78.028 | Shyok | E(o) | 0.55 | 5,373 |\n| 3880 | 0152K0203880 | 33.708 | 78.220 | Shyok | M(e) | 1.37 | 5,434 |\n| 3881 | 0152K0203881 | 33.702 | 78.227 | Indus Upper | M(e) | 3.44 | 5,617 |\n| 3882 | 0152K0203882 | 33.697 | 78.237 | Indus Upper | E(o) | 1.27 | 5,478 |\n| 3883 | 0152K0303883 | 33.348 | 78.199 | Indus Upper | E(o) | 6.48 | 5,807 |\n| 3884 | 0152K0303884 | 33.347 | 78.111 | Indus Upper | M(o) | 0.92 | 5,612 |\n| 3885 | 0152K0303885 | 33.332 | 78.206 | Indus Upper | M(e) | 2.38 | 5,466 |\n| 3886 | 0152K0303886 | 33.323 | 78.182 | Indus Upper | E(o) | 1.47 | 5,948 |\n| 3887 | 0152K0303887 | 33.311 | 78.227 | Indus Upper | E(o) | 7.14 | 5,591 |\n| 3888 | 0152K0303888 | 33.309 | 78.232 | Indus Upper | E(o) | 3.81 | 5,598 |\n| 3889 | 0152K0303889 | 33.303 | 78.233 | Indus Upper | M(e) | 1.51 | 5,648 |\n| 3890 | 0152K0303890 | 33.281 | 78.230 | Indus Upper | E(o) | 34.70 | 5,646 |\n| 3891 | 0152K0403891 | 33.196 | 78.159 | Indus Upper | I(s) | 0.40 | 5,605 |\n| 3892 | 0152K0403892 | 33.165 | 78.166 | Indus Upper | M(o) | 0.49 | 5,817 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 9990, "line_end": 10064, "token_count_estimate": 1656, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0152J0803858", "0152J0803859", "0152J0803860", "0152J0803861", "0152J0803862", "0152J1103863", "0152J1103864", "0152J1203865", "0152J1203866", "0152J1203867", "0152J1203868", "0152J1203869", "0152J1203870", "0152J1203871", "0152J1203872", "0152J1203873", "0152J1203874", "0152J1203875", "0152J1603876", "0152K0103877", "0152K0103878", "0152K0103879", "0152K0203880", "0152K0203881", "0152K0203882", "0152K0303883", "0152K0303884", "0152K0303885", "0152K0303886", "0152K0303887", "0152K0303888", "0152K0303889", "0152K0303890", "0152K0403891", "0152K0403892"]}}
{"id": "86647a961df1deac", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3893 | 0152K0403893 | 33.165 | 78.177 | Indus Upper | M(e) | 6.86 | 5,608 |\n| 3894 | 0152K0403894 | 33.164 | 78.151 | Indus Upper | M(e) | 1.98 | 5,606 |\n| 3895 | 0152K0403895 | 33.163 | 78.145 | Indus Upper | M(o) | 1.05 | 5,563 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 9990, "line_end": 10064, "token_count_estimate": 243, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0152K0403893", "0152K0403894", "0152K0403895"]}}
{"id": "208d24193194c698", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 10065, "line_end": 10071, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "18eaf9d9003f8b5c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3896 | 0152K0403896 | 33.159 | 78.190 | Indus Upper | M(o) | 0.49 | 5,649 |\n| 3897 | 0152K0403897 | 33.152 | 78.191 | Indus Upper | M(e) | 0.89 | 5,655 |\n| 3898 | 0152K0403898 | 33.137 | 78.197 | Indus Upper | M(o) | 10.30 | 5,733 |\n| 3899 | 0152K0403899 | 33.130 | 78.199 | Satluj | M(o) | 1.38 | 5,781 |\n| 3900 | 0152K0403900 | 33.128 | 78.197 | Satluj | I(s) | 1.00 | 5,784 |\n| 3901 | 0152K0403901 | 33.126 | 78.200 | Satluj | M(e) | 0.73 | 5,762 |\n| 3902 | 0152K0403902 | 33.124 | 78.151 | Satluj | M(e) | 3.00 | 5,609 |\n| 3903 | 0152K0403903 | 33.116 | 78.189 | Satluj | M(e) | 0.56 | 5,887 |\n| 3904 | 0152K0403904 | 33.115 | 78.009 | Indus Upper | M(e) | 9.38 | 5,521 |\n| 3905 | 0152K0403905 | 33.079 | 78.031 | Satluj | M(e) | 1.35 | 5,512 |\n| 3906 | 0152K0403906 | 33.079 | 78.029 | Satluj | M(o) | 0.49 | 5,512 |\n| 3907 | 0152K0403907 | 33.037 | 78.074 | Satluj | M(o) | 0.96 | 5,510 |\n| 3908 | 0152K0403908 | 33.020 | 78.063 | Satluj | M(e) | 3.13 | 5,561 |\n| 3909 | 0152K0403909 | 33.006 | 78.114 | Satluj | M(e) | 6.34 | 5,479 |\n| 3910 | 0152K0403910 | 33.003 | 78.081 | Satluj | M(e) | 0.68 | 5,706 |\n| 3911 | 0152K0503911 | 33.794 | 78.383 | Shyok | O | 0.95 | 4,532 |\n| 3912 | 0152K0503912 | 33.793 | 78.471 | Shyok | E(c) | 0.76 | 5,841 |\n| 3913 | 0152K0503913 | 33.782 | 78.397 | Shyok | O | 0.82 | 4,541 |\n| 3914 | 0152K0503914 | 33.777 | 78.372 | Shyok | O | 7.19 | 4,536 |\n| 3915 | 0152K0503915 | 33.753 | 78.274 | Shyok | M(e) | 1.06 | 5,639 |\n| 3916 | 0152K0603916 | 33.743 | 78.263 | Shyok | M(o) | 0.72 | 5,799 |\n| 3917 | 0152K0603917 | 33.718 | 78.284 | Shyok | E(o) | 4.43 | 5,525 |\n| 3918 | 0152K0603918 | 33.710 | 78.252 | Shyok | M(e) | 1.27 | 5,575 |\n| 3919 | 0152K0603919 | 33.704 | 78.284 | Shyok | M(o) | 1.73 | 5,541 |\n| 3920 | 0152K0603920 | 33.656 | 78.351 | Shyok | M(o) | 0.57 | 5,586 |\n| 3921 | 0152K0603921 | 33.642 | 78.356 | Shyok | E(o) | 5.19 | 5,437 |\n| 3922 | 0152K0603922 | 33.624 | 78.467 | Shyok | M(e) | 0.35 | 5,680 |\n| 3923 | 0152K0603923 | 33.622 | 78.422 | Shyok | O | 1.66 | 5,367 |\n| 3924 | 0152K0603924 | 33.619 | 78.379 | Shyok | E(o) | 2.07 | 5,573 |\n| 3925 | 0152K0603925 | 33.578 | 78.372 | Shyok | E(o) | 5.19 | 5,579 |\n| 3926 | 0152K0603926 | 33.556 | 78.307 | Indus Upper | I(s) | 0.52 | 5,754 |\n| 3927 | 0152K0603927 | 33.551 | 78.342 | Indus Upper | E(o) | 2.21 | 5,733 |\n| 3928 | 0152K0603928 | 33.548 | 78.391 | Shyok | E(o) | 6.56 | 5,446 |\n| 3929 | 0152K0603929 | 33.547 | 78.494 | Shyok | M(e) | 4.09 | 5,698 |\n| 3930 | 0152K0603930 | 33.542 | 78.410 | Shyok | M(e) | 0.86 | 5,569 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10072, "line_end": 10146, "token_count_estimate": 1633, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0152K0403896", "0152K0403897", "0152K0403898", "0152K0403899", "0152K0403900", "0152K0403901", "0152K0403902", "0152K0403903", "0152K0403904", "0152K0403905", "0152K0403906", "0152K0403907", "0152K0403908", "0152K0403909", "0152K0403910", "0152K0503911", "0152K0503912", "0152K0503913", "0152K0503914", "0152K0503915", "0152K0603916", "0152K0603917", "0152K0603918", "0152K0603919", "0152K0603920", "0152K0603921", "0152K0603922", "0152K0603923", "0152K0603924", "0152K0603925", "0152K0603926", "0152K0603927", "0152K0603928", "0152K0603929", "0152K0603930"]}}
{"id": "305d8fa1189c280a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3931 | 0152K0603931 | 33.539 | 78.287 | Indus Upper | E(o) | 0.28 | 5,625 |\n| 3932 | 0152K0603932 | 33.539 | 78.313 | Indus Upper | M(o) | 0.81 | 5,634 |\n| 3933 | 0152K0603933 | 33.535 | 78.319 | Indus Upper | E(o) | 0.56 | 5,677 |\n| 3934 | 0152K0603934 | 33.529 | 78.477 | Shyok | E(o) | 5.48 | 5,596 |\n| 3935 | 0152K0603935 | 33.521 | 78.355 | Indus Upper | E(o) | 0.44 | 5,674 |\n| 3936 | 0152K0703936 | 33.497 | 78.396 | Indus Upper | E(o) | 3.98 | 5,690 |\n| 3937 | 0152K0703937 | 33.496 | 78.498 | Shyok | E(o) | 16.54 | 5,428 |\n| 3938 | 0152K0703938 | 33.457 | 78.349 | Indus Upper | E(o) | 1.63 | 5,671 |\n| 3939 | 0152K0703939 | 33.455 | 78.498 | Shyok | O | 147.89 | 5,308 |\n| 3940 | 0152K0703940 | 33.427 | 78.488 | Shyok | O | 177.73 | 5,284 |\n| 3941 | 0152K0703941 | 33.410 | 78.475 | Shyok | O | 3.72 | 5,344 |\n| 3942 | 0152K0803942 | 33.133 | 78.431 | Indus Upper | E(o) | 0.48 | 5,849 |\n| 3943 | 0152K0803943 | 33.126 | 78.413 | Indus Upper | O | 2.14 | 5,593 |\n| 3944 | 0152K0803944 | 33.125 | 78.413 | Indus Upper | O | 0.41 | 5,586 |\n| 3945 | 0152K0803945 | 33.119 | 78.449 | Indus Upper | E(o) | 0.30 | 6,002 |\n| 3946 | 0152K0803946 | 33.116 | 78.475 | Indus Upper | E(o) | 0.62 | 5,669 |\n| 3947 | 0152K0803947 | 33.110 | 78.429 | Indus Upper | M(o) | 0.35 | 5,931 |\n| 3948 | 0152K0803948 | 33.092 | 78.453 | Satluj | M(o) | 1.45 | 5,875 |\n| 3949 | 0152K0803949 | 33.091 | 78.455 | Satluj | M(o) | 3.09 | 5,879 |\n| 3950 | 0152K0803950 | 33.082 | 78.464 | Satluj | E(o) | 0.35 | 5,907 |\n| 3951 | 0152K0803951 | 33.081 | 78.442 | Satluj | E(o) | 3.35 | 5,760 |\n| 3952 | 0152K0803952 | 33.080 | 78.464 | Satluj | M(e) | 5.52 | 5,900 |\n| 3953 | 0152K0803953 | 33.078 | 78.452 | Satluj | E(o) | 0.25 | 5,817 |\n| 3954 | 0152K0803954 | 33.077 | 78.455 | Satluj | M(o) | 5.51 | 5,811 |\n| 3955 | 0152K0803955 | 33.076 | 78.482 | Satluj | E(o) | 1.18 | 6,002 |\n| 3956 | 0152K0803956 | 33.076 | 78.452 | Satluj | M(o) | 0.84 | 5,819 |\n| 3957 | 0152K0803957 | 33.071 | 78.480 | Satluj | M(o) | 0.31 | 5,842 |\n| 3958 | 0152K0803958 | 33.070 | 78.479 | Satluj | M(o) | 0.59 | 5,837 |\n| 3959 | 0152K0803959 | 33.067 | 78.477 | Satluj | M(o) | 5.35 | 5,829 |\n| 3960 | 0152K0803960 | 33.060 | 78.446 | Satluj | M(o) | 7.09 | 5,689 |\n| 3961 | 0152K0803961 | 33.060 | 78.461 | Satluj | M(o) | 0.47 | 5,696 |\n| 3962 | 0152K0803962 | 33.060 | 78.455 | Satluj | M(o) | 8.87 | 5,694 |\n| 3963 | 0152K0803963 | 33.057 | 78.444 | Satluj | M(o) | 0.30 | 5,722 |\n| 3964 | 0152K0803964 | 33.055 | 78.470 | Satluj | M(o) | 36.49 | 5,745 |\n| 3965 | 0152K0803965 | 33.055 | 78.448 | Satluj | M(o) | 1.59 | 5,735 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10072, "line_end": 10146, "token_count_estimate": 1662, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0152K0603931", "0152K0603932", "0152K0603933", "0152K0603934", "0152K0603935", "0152K0703936", "0152K0703937", "0152K0703938", "0152K0703939", "0152K0703940", "0152K0703941", "0152K0803942", "0152K0803943", "0152K0803944", "0152K0803945", "0152K0803946", "0152K0803947", "0152K0803948", "0152K0803949", "0152K0803950", "0152K0803951", "0152K0803952", "0152K0803953", "0152K0803954", "0152K0803955", "0152K0803956", "0152K0803957", "0152K0803958", "0152K0803959", "0152K0803960", "0152K0803961", "0152K0803962", "0152K0803963", "0152K0803964", "0152K0803965"]}}
{"id": "0076c7d0c0d86ed5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3966 | 0152K0803966 | 33.055 | 78.445 | Satluj | M(o) | 0.86 | 5,738 |\n| 3967 | 0152K0803967 | 33.049 | 78.454 | Satluj | M(o) | 2.16 | 5,780 |\n| 3968 | 0152K0803968 | 33.047 | 78.492 | Indus Upper | E(o) | 3.03 | 5,893 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 10072, "line_end": 10146, "token_count_estimate": 244, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152K0803966", "0152K0803967", "0152K0803968"]}}
{"id": "01a394549ba85bd6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3969 | 0152K0803969 | 33.042 | 78.476 | Indus Upper | M(o) | 0.69 | 5,934 |\n| 3970 | 0152K0803970 | 33.033 | 78.485 | Indus Upper | E(o) | 5.05 | 5,782 |\n| 3971 | 0152K0803971 | 33.027 | 78.481 | Indus Upper | E(c) | 3.63 | 5,826 |\n| 3972 | 0152K0803972 | 33.021 | 78.498 | Indus Upper | E(o) | 2.62 | 5,587 |\n| 3973 | 0152K0803973 | 33.020 | 78.494 | Indus Upper | M(o) | 0.84 | 5,604 |\n| 3974 | 0152K0803974 | 33.019 | 78.488 | Indus Upper | M(e) | 1.99 | 5,684 |\n| 3975 | 0152K0803975 | 33.003 | 78.466 | Satluj | E(o) | 2.86 | 5,937 |\n| 3976 | 0152K0903976 | 33.891 | 78.690 | Shyok | M(o) | 0.48 | 5,761 |\n| 3977 | 0152K0903977 | 33.819 | 78.517 | Shyok | E(o) | 0.39 | 5,058 |\n| 3978 | 0152K1003978 | 33.701 | 78.545 | Shyok | M(o) | 0.42 | 5,825 |\n| 3979 | 0152K1003979 | 33.690 | 78.535 | Shyok | M(e) | 6.09 | 5,572 |\n| 3980 | 0152K1003980 | 33.608 | 78.505 | Shyok | O | 1.55 | 5,006 |\n| 3981 | 0152K1003981 | 33.570 | 78.517 | Shyok | E(o) | 1.86 | 5,585 |\n| 3982 | 0152K1003982 | 33.563 | 78.509 | Shyok | E(o) | 0.74 | 5,784 |\n| 3983 | 0152K1003983 | 33.560 | 78.518 | Shyok | M(o) | 0.88 | 5,651 |\n| 3984 | 0152K1003984 | 33.558 | 78.506 | Shyok | E(c) | 25.33 | 5,665 |\n| 3985 | 0152K1003985 | 33.539 | 78.561 | Shyok | E(o) | 3.11 | 5,393 |\n| 3986 | 0152K1003986 | 33.517 | 78.519 | Shyok | E(o) | 13.66 | 5,404 |\n| 3987 | 0152K1103987 | 33.474 | 78.502 | Shyok | O | 15.10 | 5,312 |\n| 3988 | 0152K1103988 | 33.421 | 78.649 | Shyok | E(o) | 2.61 | 5,736 |\n| 3989 | 0152K1103989 | 33.388 | 78.654 | Shyok | M(e) | 2.27 | 5,652 |\n| 3990 | 0152K1103990 | 33.380 | 78.697 | Shyok | E(o) | 6.78 | 5,809 |\n| 3991 | 0152K1103991 | 33.375 | 78.666 | Shyok | E(o) | 1.16 | 5,798 |\n| 3992 | 0152K1103992 | 33.368 | 78.662 | Indus Upper | M(e) | 1.94 | 5,767 |\n| 3993 | 0152K1103993 | 33.348 | 78.624 | Indus Upper | M(o) | 0.65 | 5,684 |\n| 3994 | 0152K1103994 | 33.332 | 78.662 | Indus Upper | M(o) | 1.74 | 5,706 |\n| 3995 | 0152K1103995 | 33.331 | 78.663 | Indus Upper | M(o) | 0.47 | 5,707 |\n| 3996 | 0152K1103996 | 33.329 | 78.722 | Indus Upper | M(o) | 0.27 | 5,661 |\n| 3997 | 0152K1103997 | 33.328 | 78.723 | Indus Upper | M(o) | 0.25 | 5,669 |\n| 3998 | 0152K1103998 | 33.325 | 78.614 | Indus Upper | E(o) | 2.74 | 5,613 |\n| 3999 | 0152K1103999 | 33.308 | 78.642 | Indus Upper | M(o) | 1.15 | 5,764 |\n| 4000 | 0152K1204000 | 33.038 | 78.507 | Indus Upper | E(o) | 1.93 | 5,754 |\n| 4001 | 0152K1204001 | 33.006 | 78.520 | Indus Upper | E(o) | 0.39 | 5,819 |\n| 4002 | 0152K1504002 | 33.391 | 78.931 | Shyok | E(o) | 2.01 | 5,787 |\n| 4003 | 0152K1504003 | 33.328 | 78.764 | Indus Upper | E(o) | 3.70 | 5,554 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10148, "line_end": 10222, "token_count_estimate": 1653, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0152K0803969", "0152K0803970", "0152K0803971", "0152K0803972", "0152K0803973", "0152K0803974", "0152K0803975", "0152K0903976", "0152K0903977", "0152K1003978", "0152K1003979", "0152K1003980", "0152K1003981", "0152K1003982", "0152K1003983", "0152K1003984", "0152K1003985", "0152K1003986", "0152K1103987", "0152K1103988", "0152K1103989", "0152K1103990", "0152K1103991", "0152K1103992", "0152K1103993", "0152K1103994", "0152K1103995", "0152K1103996", "0152K1103997", "0152K1103998", "0152K1103999", "0152K1204000", "0152K1204001", "0152K1504002", "0152K1504003"]}}
{"id": "bb0dba1429adb19d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4004 | 0152L0104004 | 32.985 | 78.149 | Satluj | M(e) | 0.61 | 5,584 |\n| 4005 | 0152L0104005 | 32.983 | 78.149 | Satluj | I(s) | 0.59 | 5,593 |\n| 4006 | 0152L0104006 | 32.975 | 78.104 | Satluj | M(o) | 0.72 | 5,732 |\n| 4007 | 0152L0104007 | 32.945 | 78.198 | Satluj | M(e) | 1.53 | 5,436 |\n| 4008 | 0152L0104008 | 32.943 | 78.197 | Satluj | M(e) | 2.20 | 5,452 |\n| 4009 | 0152L0104009 | 32.934 | 78.211 | Satluj | M(e) | 2.45 | 5,459 |\n| 4010 | 0152L0104010 | 32.918 | 78.184 | Satluj | M(o) | 1.24 | 5,643 |\n| 4011 | 0152L0104011 | 32.916 | 78.180 | Satluj | M(e) | 1.24 | 5,613 |\n| 4012 | 0152L0104012 | 32.872 | 78.229 | Satluj | E(o) | 1.71 | 5,749 |\n| 4013 | 0152L0204013 | 32.665 | 78.199 | Satluj | I(s) | 0.26 | 5,687 |\n| 4014 | 0152L0304014 | 32.494 | 78.194 | Satluj | M(e) | 0.59 | 5,496 |\n| 4015 | 0152L0304015 | 32.431 | 78.092 | Satluj | E(o) | 0.31 | 4,734 |\n| 4016 | 0152L0304016 | 32.428 | 78.090 | Satluj | E(o) | 0.66 | 4,710 |\n| 4017 | 0152L0304017 | 32.426 | 78.088 | Satluj | E(o) | 0.58 | 4,683 |\n| 4018 | 0152L0304018 | 32.423 | 78.082 | Satluj | E(o) | 2.85 | 4,646 |\n| 4019 | 0152L0304019 | 32.415 | 78.220 | Satluj | E(o) | 1.47 | 5,460 |\n| 4020 | 0152L0304020 | 32.380 | 78.121 | Satluj | M(o) | 1.77 | 4,964 |\n| 4021 | 0152L0404021 | 32.007 | 78.115 | Satluj | M(o) | 0.35 | 4,820 |\n| 4022 | 0152L0504022 | 32.987 | 78.474 | Indus Upper | M(o) | 0.42 | 5,855 |\n| 4023 | 0152L0504023 | 32.986 | 78.467 | Indus Upper | M(o) | 0.82 | 5,936 |\n| 4024 | 0152L0504024 | 32.981 | 78.482 | Indus Upper | M(o) | 2.31 | 5,733 |\n| 4025 | 0152L0504025 | 32.976 | 78.498 | Indus Upper | M(e) | 1.54 | 5,528 |\n| 4026 | 0152L0504026 | 32.972 | 78.423 | Satluj | M(o) | 1.02 | 5,892 |\n| 4027 | 0152L0504027 | 32.967 | 78.420 | Satluj | M(o) | 0.63 | 5,873 |\n| 4028 | 0152L0504028 | 32.966 | 78.422 | Satluj | M(o) | 2.77 | 5,869 |\n| 4029 | 0152L0504029 | 32.963 | 78.421 | Satluj | M(o) | 2.80 | 5,864 |\n| 4030 | 0152L0504030 | 32.947 | 78.431 | Satluj | M(o) | 0.77 | 6,079 |\n| 4031 | 0152L0504031 | 32.939 | 78.443 | Satluj | M(e) | 0.42 | 5,814 |\n| 4032 | 0152L0504032 | 32.935 | 78.488 | Indus Upper | M(o) | 0.64 | 5,872 |\n| 4033 | 0152L0504033 | 32.934 | 78.489 | Indus Upper | M(o) | 0.27 | 5,881 |\n| 4034 | 0152L0504034 | 32.887 | 78.451 | Satluj | E(c) | 7.43 | 5,948 |\n| 4035 | 0152L0604035 | 32.562 | 78.321 | Satluj | M(o) | 1.47 | 5,903 |\n| 4036 | 0152L0604036 | 32.559 | 78.325 | Satluj | M(e) | 0.45 | 5,825 |\n| 4037 | 0152L0604037 | 32.555 | 78.431 | Satluj | I(s) | 0.41 | 5,275 |\n| 4038 | 0152L0704038 | 32.476 | 78.303 | Satluj | M(lg) | 3.25 | 5,625 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10148, "line_end": 10222, "token_count_estimate": 1655, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152L0104004", "0152L0104005", "0152L0104006", "0152L0104007", "0152L0104008", "0152L0104009", "0152L0104010", "0152L0104011", "0152L0104012", "0152L0204013", "0152L0304014", "0152L0304015", "0152L0304016", "0152L0304017", "0152L0304018", "0152L0304019", "0152L0304020", "0152L0404021", "0152L0504022", "0152L0504023", "0152L0504024", "0152L0504025", "0152L0504026", "0152L0504027", "0152L0504028", "0152L0504029", "0152L0504030", "0152L0504031", "0152L0504032", "0152L0504033", "0152L0504034", "0152L0604035", "0152L0604036", "0152L0604037", "0152L0704038"]}}
{"id": "488a38213de38118", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4039 | 0152L0704039 | 32.475 | 78.305 | Satluj | E(o) | 0.70 | 5,606 |\n| 4040 | 0152L0704040 | 32.457 | 78.473 | Satluj | M(e) | 5.25 | 5,373 |\n| 4041 | 0152L0704041 | 32.381 | 78.462 | Satluj | M(o) | 0.38 | 5,569 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 10148, "line_end": 10222, "token_count_estimate": 242, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0152L0704039", "0152L0704040", "0152L0704041"]}}
{"id": "12126f4e19f8cb70", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 10223, "line_end": 10230, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ea18712eee4904a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4042 | 0152L0704042 | 32.376 | 78.481 | Satluj | M(o) | 0.54 | 5,328 |\n| 4043 | 0152L0704043 | 32.363 | 78.272 | Satluj | M(e) | 13.59 | 5,357 |\n| 4044 | 0152L0804044 | 32.204 | 78.418 | Satluj | M(e) | 3.21 | 5,609 |\n| 4045 | 0152L0804045 | 32.149 | 78.488 | Satluj | M(e) | 5.47 | 5,272 |\n| 4046 | 0152L0904046 | 32.750 | 78.696 | Satluj | E(o) | 0.38 | 5,856 |\n| 4047 | 0152L0904047 | 32.750 | 78.698 | Satluj | I(s) | 0.83 | 5,852 |\n| 4048 | 0152L1004048 | 32.747 | 78.718 | Indus Upper | E(o) | 2.33 | 5,823 |\n| 4049 | 0152L1004049 | 32.736 | 78.726 | Indus Upper | M(e) | 34.79 | 5,626 |\n| 4050 | 0152L1004050 | 32.731 | 78.689 | Satluj | M(o) | 0.55 | 5,726 |\n| 4051 | 0152L1004051 | 32.711 | 78.708 | Satluj | M(o) | 2.94 | 5,727 |\n| 4052 | 0152L1004052 | 32.708 | 78.707 | Satluj | M(o) | 0.50 | 5,700 |\n| 4053 | 0152L1004053 | 32.708 | 78.688 | Satluj | M(o) | 3.95 | 5,622 |\n| 4054 | 0152L1004054 | 32.708 | 78.703 | Satluj | M(o) | 0.29 | 5,697 |\n| 4055 | 0152L1004055 | 32.707 | 78.706 | Satluj | M(o) | 0.26 | 5,691 |\n| 4056 | 0152L1004056 | 32.707 | 78.682 | Satluj | M(o) | 0.57 | 5,612 |\n| 4057 | 0152L1004057 | 32.706 | 78.739 | Satluj | E(o) | 1.81 | 5,800 |\n| 4058 | 0152L1004058 | 32.704 | 78.698 | Satluj | M(o) | 3.53 | 5,695 |\n| 4059 | 0152L1004059 | 32.698 | 78.734 | Satluj | M(o) | 1.99 | 5,752 |\n| 4060 | 0152L1004060 | 32.698 | 78.719 | Satluj | E(o) | 0.81 | 5,865 |\n| 4061 | 0152L1004061 | 32.695 | 78.731 | Satluj | M(o) | 0.27 | 5,743 |\n| 4062 | 0152L1004062 | 32.695 | 78.711 | Satluj | E(o) | 3.24 | 5,880 |\n| 4063 | 0152L1004063 | 32.694 | 78.749 | Indus Upper | M(o) | 1.58 | 5,730 |\n| 4064 | 0152L1104064 | 32.344 | 78.502 | Satluj | I(s) | 1.03 | 5,268 |\n| 4065 | 0152L1204065 | 32.047 | 78.745 | Satluj | M(o) | 0.72 | 5,192 |\n| 4066 | 0152L1204066 | 32.046 | 78.745 | Satluj | M(o) | 0.27 | 5,204 |\n| 4067 | 0152L1204067 | 32.036 | 78.702 | Satluj | M(o) | 0.31 | 5,364 |\n| 4068 | 0152L1404068 | 32.745 | 78.761 | Indus Upper | O | 1.55 | 5,404 |\n| 4069 | 0152L1404069 | 32.740 | 78.757 | Indus Upper | E(o) | 1.87 | 5,435 |\n| 4070 | 0152L1404070 | 32.728 | 78.779 | Indus Upper | M(e) | 1.71 | 5,702 |\n| 4071 | 0152L1404071 | 32.718 | 78.751 | Indus Upper | M(e) | 8.33 | 5,552 |\n| 4072 | 0152L1404072 | 32.692 | 78.755 | Indus Upper | M(o) | 0.60 | 5,616 |\n| 4073 | 0152L1404073 | 32.690 | 78.757 | Indus Upper | M(o) | 5.56 | 5,625 |\n| 4074 | 0152L1404074 | 32.506 | 78.876 | Indus Upper | E(o) | 1.40 | 5,548 |\n| 4075 | 0152L1504075 | 32.492 | 78.852 | Indus Upper | M(e) | 11.12 | 5,706 |\n| 4076 | 0152L1504076 | 32.474 | 78.848 | Indus Upper | M(e) | 7.26 | 5,786 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10231, "line_end": 10378, "token_count_estimate": 1647, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152L0704042", "0152L0704043", "0152L0804044", "0152L0804045", "0152L0904046", "0152L0904047", "0152L1004048", "0152L1004049", "0152L1004050", "0152L1004051", "0152L1004052", "0152L1004053", "0152L1004054", "0152L1004055", "0152L1004056", "0152L1004057", "0152L1004058", "0152L1004059", "0152L1004060", "0152L1004061", "0152L1004062", "0152L1004063", "0152L1104064", "0152L1204065", "0152L1204066", "0152L1204067", "0152L1404068", "0152L1404069", "0152L1404070", "0152L1404071", "0152L1404072", "0152L1404073", "0152L1404074", "0152L1504075", "0152L1504076"]}}
{"id": "bbd70ac01ef5219f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4077 | 0152L1504077 | 32.469 | 78.836 | Indus Upper | M(o) | 0.36 | 5,663 |\n| 4078 | 0152L1504078 | 32.469 | 78.840 | Indus Upper | M(e) | 1.37 | 5,668 |\n| 4079 | 0152L1504079 | 32.445 | 78.902 | Indus Upper | M(e) | 3.63 | 5,409 |\n| 4080 | 0152L1504080 | 32.441 | 78.925 | Indus Upper | M(e) | 10.65 | 5,680 |\n| 4081 | 0152L1504081 | 32.435 | 78.866 | Indus Upper | M(e) | 1.38 | 5,271 |\n| 4082 | 0152L1504082 | 32.434 | 78.922 | Indus Upper | M(e) | 5.16 | 5,837 |\n| 4083 | 0152L1504083 | 32.434 | 78.959 | Indus Upper | E(o) | 0.52 | 5,706 |\n| 4084 | 0152L1504084 | 32.432 | 78.869 | Indus Upper | M(o) | 0.51 | 5,309 |\n| 4085 | 0152L1504085 | 32.427 | 78.868 | Indus Upper | I(s) | 0.57 | 5,411 |\n| 4086 | 0152L1504086 | 32.422 | 78.828 | Satluj | E(o) | 0.76 | 5,652 |\n| 4087 | 0152L1504087 | 32.420 | 78.816 | Satluj | M(o) | 3.51 | 5,514 |\n| 4088 | 0152L1504088 | 32.415 | 78.887 | Indus Upper | M(o) | 0.43 | 5,388 |\n| 4089 | 0152L1504089 | 32.414 | 78.827 | Satluj | E(o) | 0.27 | 5,592 |\n| 4090 | 0152L1504090 | 32.413 | 78.880 | Indus Upper | M(o) | 2.00 | 5,494 |\n| 4091 | 0152L1504091 | 32.413 | 78.953 | Indus Upper | E(o) | 3.21 | 5,550 |\n| 4092 | 0152L1504092 | 32.409 | 78.900 | Indus Upper | M(e) | 30.44 | 5,468 |\n| 4093 | 0152L1504093 | 32.408 | 78.976 | Indus Upper | M(o) | 5.27 | 5,424 |\n| 4094 | 0152L1504094 | 32.405 | 78.928 | Indus Upper | M(e) | 4.10 | 5,617 |\n| 4095 | 0152L1504095 | 32.402 | 78.960 | Indus Upper | M(o) | 0.96 | 5,685 |\n| 4096 | 0152L1504096 | 32.400 | 78.945 | Indus Upper | M(o) | 5.76 | 5,659 |\n| 4097 | 0152L1504097 | 32.397 | 78.893 | Satluj | E(c) | 0.37 | 5,754 |\n| 4098 | 0152L1504098 | 32.388 | 78.892 | Satluj | M(e) | 1.32 | 5,551 |\n| 4099 | 0152L1504099 | 32.377 | 78.923 | Indus Upper | M(e) | 5.54 | 5,502 |\n| 4100 | 0152L1504100 | 32.352 | 78.881 | Satluj | M(e) | 0.57 | 5,579 |\n| 4101 | 0152L1504101 | 32.352 | 78.970 | Indus Upper | M(o) | 0.65 | 5,411 |\n| 4102 | 0152L1504102 | 32.352 | 78.899 | Satluj | M(e) | 2.08 | 5,369 |\n| 4103 | 0152L1504103 | 32.334 | 78.909 | Satluj | M(o) | 2.03 | 5,384 |\n| 4104 | 0152L1504104 | 32.332 | 78.907 | Satluj | E(o) | 1.04 | 5,409 |\n| 4105 | 0152L1504105 | 32.332 | 78.885 | Satluj | M(o) | 0.32 | 5,474 |\n| 4106 | 0152L1504106 | 32.332 | 78.883 | Satluj | M(o) | 0.39 | 5,464 |\n| 4107 | 0152L1504107 | 32.325 | 78.973 | Satluj | E(o) | 5.79 | 5,557 |\n| 4108 | 0152L1504108 | 32.325 | 78.896 | Satluj | M(o) | 0.59 | 5,559 |\n| 4109 | 0152L1504109 | 32.320 | 78.979 | Satluj | M(o) | 10.51 | 5,498 |\n| 4110 | 0152L1504110 | 32.316 | 78.994 | Satluj | M(o) | 1.47 | 5,541 |\n| 4111 | 0152L1504111 | 32.300 | 78.985 | Satluj | M(e) | 9.66 | 5,700 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10231, "line_end": 10378, "token_count_estimate": 1654, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152L1504077", "0152L1504078", "0152L1504079", "0152L1504080", "0152L1504081", "0152L1504082", "0152L1504083", "0152L1504084", "0152L1504085", "0152L1504086", "0152L1504087", "0152L1504088", "0152L1504089", "0152L1504090", "0152L1504091", "0152L1504092", "0152L1504093", "0152L1504094", "0152L1504095", "0152L1504096", "0152L1504097", "0152L1504098", "0152L1504099", "0152L1504100", "0152L1504101", "0152L1504102", "0152L1504103", "0152L1504104", "0152L1504105", "0152L1504106", "0152L1504107", "0152L1504108", "0152L1504109", "0152L1504110", "0152L1504111"]}}
{"id": "ed52cec8e2f7f65f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4112 | 0152L1504112 | 32.258 | 78.978 | Satluj | M(o) | 2.29 | 5,764 |\n| 4113 | 0152L1504113 | 32.253 | 78.988 | Satluj | E(o) | 0.72 | 5,711 |\n| 4114 | 0152L1604114 | 32.226 | 78.983 | Satluj | E(o) | 0.78 | 5,666 |\n| 4115 | 0152L1604115 | 32.224 | 78.998 | Satluj | M(o) | 0.95 | 5,679 |\n| 4116 | 0152L1604116 | 32.222 | 78.985 | Satluj | E(o) | 0.26 | 5,686 |\n| 4117 | 0152L1604117 | 32.221 | 78.983 | Satluj | E(o) | 0.38 | 5,697 |\n| 4118 | 0152L1604118 | 32.207 | 78.976 | Satluj | E(o) | 0.25 | 5,615 |\n| 4119 | 0152L1604119 | 32.201 | 78.988 | Satluj | M(o) | 0.96 | 5,698 |\n| 4120 | 0152L1604120 | 32.191 | 78.973 | Satluj | E(o) | 3.47 | 5,663 |\n| 4121 | 0152L1604121 | 32.185 | 78.960 | Satluj | E(o) | 1.37 | 5,573 |\n| 4122 | 0152L1604122 | 32.180 | 78.969 | Satluj | M(o) | 0.58 | 5,657 |\n| 4123 | 0152L1604123 | 32.170 | 78.973 | Satluj | E(o) | 1.12 | 5,651 |\n| 4124 | 0152L1604124 | 32.161 | 78.978 | Satluj | E(o) | 6.40 | 5,580 |\n| 4125 | 0152L1604125 | 32.149 | 78.963 | Satluj | E(o) | 1.80 | 5,724 |\n| 4126 | 0152L1604126 | 32.144 | 78.907 | Satluj | M(o) | 0.95 | 5,711 |\n| 4127 | 0152L1604127 | 32.142 | 78.919 | Satluj | M(o) | 2.54 | 5,580 |\n| 4128 | 0152L1604128 | 32.139 | 78.948 | Satluj | E(o) | 0.34 | 5,710 |\n| 4129 | 0152L1604129 | 32.138 | 78.943 | Satluj | E(o) | 0.25 | 5,740 |\n| 4130 | 0152L1604130 | 32.129 | 78.970 | Satluj | E(o) | 2.55 | 5,615 |\n| 4131 | 0152L1604131 | 32.129 | 78.927 | Satluj | M(o) | 0.76 | 5,638 |\n| 4132 | 0152L1604132 | 32.125 | 78.925 | Satluj | I(s) | 0.54 | 5,730 |\n| 4133 | 0152L1604133 | 32.122 | 78.948 | Satluj | M(o) | 1.17 | 5,658 |\n| 4134 | 0152L1604134 | 32.122 | 78.943 | Satluj | M(o) | 2.40 | 5,666 |\n| 4135 | 0152L1604135 | 32.121 | 78.940 | Satluj | M(o) | 4.10 | 5,667 |\n| 4136 | 0152L1604136 | 32.121 | 78.933 | Satluj | M(o) | 0.78 | 5,799 |\n| 4137 | 0152L1604137 | 32.120 | 78.947 | Satluj | M(o) | 0.27 | 5,653 |\n| 4138 | 0152L1604138 | 32.120 | 78.951 | Satluj | M(o) | 1.12 | 5,636 |\n| 4139 | 0152L1604139 | 32.119 | 78.932 | Satluj | M(l) | 0.30 | 5,810 |\n| 4140 | 0152L1604140 | 32.119 | 78.941 | Satluj | M(o) | 0.48 | 5,717 |\n| 4141 | 0152L1604141 | 32.107 | 78.942 | Satluj | E(o) | 9.06 | 5,641 |\n| 4142 | 0152L1604142 | 32.107 | 78.933 | Satluj | E(o) | 8.82 | 5,694 |\n| 4143 | 0152L1604143 | 32.107 | 78.947 | Satluj | E(o) | 0.50 | 5,616 |\n| 4144 | 0152L1604144 | 32.104 | 78.925 | Satluj | M(o) | 0.32 | 5,812 |\n| 4145 | 0152L1604145 | 32.104 | 78.938 | Satluj | E(o) | 0.29 | 5,669 |\n| 4146 | 0152L1604146 | 32.099 | 78.908 | Satluj | E(o) | 0.30 | 5,728 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 10231, "line_end": 10378, "token_count_estimate": 1631, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0152L1504112", "0152L1504113", "0152L1604114", "0152L1604115", "0152L1604116", "0152L1604117", "0152L1604118", "0152L1604119", "0152L1604120", "0152L1604121", "0152L1604122", "0152L1604123", "0152L1604124", "0152L1604125", "0152L1604126", "0152L1604127", "0152L1604128", "0152L1604129", "0152L1604130", "0152L1604131", "0152L1604132", "0152L1604133", "0152L1604134", "0152L1604135", "0152L1604136", "0152L1604137", "0152L1604138", "0152L1604139", "0152L1604140", "0152L1604141", "0152L1604142", "0152L1604143", "0152L1604144", "0152L1604145", "0152L1604146"]}}
{"id": "07aae4638232a252", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4147 | 0152L1604147 | 32.092 | 78.947 | Satluj | E(o) | 3.01 | 5,560 |\n| 4148 | 0152L1604148 | 32.092 | 78.930 | Satluj | E(o) | 0.48 | 5,728 |\n| 4149 | 0152L1604149 | 32.092 | 78.944 | Satluj | E(o) | 0.31 | 5,561 |\n| 4150 | 0152L1604150 | 32.091 | 78.911 | Satluj | E(o) | 1.87 | 5,941 |\n| 4151 | 0152L1604151 | 32.086 | 78.864 | Satluj | O | 0.39 | 5,542 |\n| 4152 | 0152L1604152 | 32.081 | 78.803 | Satluj | M(o) | 0.42 | 5,028 |\n| 4153 | 0152L1604153 | 32.080 | 78.806 | Satluj | M(o) | 0.34 | 5,044 |\n| 4154 | 0152L1604154 | 32.076 | 78.906 | Satluj | M(o) | 0.35 | 5,657 |\n| 4155 | 0152L1604155 | 32.073 | 78.883 | Satluj | E(o) | 0.26 | 5,716 |\n| 4156 | 0152L1604156 | 32.072 | 78.885 | Satluj | E(o) | 1.36 | 5,718 |\n| 4157 | 0152L1604157 | 32.065 | 78.895 | Satluj | E(o) | 0.62 | 5,894 |\n| 4158 | 0152L1604158 | 32.063 | 78.928 | Satluj | E(o) | 1.48 | 5,518 |\n| 4159 | 0152L1604159 | 32.060 | 78.943 | Satluj | O | 6.06 | 5,411 |\n| 4160 | 0152L1604160 | 32.058 | 78.807 | Satluj | M(e) | 9.30 | 5,564 |\n| 4161 | 0152L1604161 | 32.054 | 78.904 | Satluj | E(o) | 0.66 | 5,860 |\n| 4162 | 0152L1604162 | 32.045 | 78.756 | Satluj | M(o) | 0.45 | 5,272 |\n| 4163 | 0152L1604163 | 32.045 | 78.755 | Satluj | M(o) | 0.63 | 5,271 |\n| 4164 | 0152L1604164 | 32.044 | 78.832 | Satluj | M(o) | 2.09 | 5,567 |\n| 4165 | 0152L1604165 | 32.043 | 78.903 | Satluj | O | 0.36 | 5,648 |\n| 4166 | 0152L1604166 | 32.043 | 78.850 | Satluj | E(o) | 2.09 | 5,492 |\n| 4167 | 0152L1604167 | 32.043 | 78.752 | Satluj | M(o) | 0.61 | 5,258 |\n| 4168 | 0152L1604168 | 32.042 | 78.758 | Satluj | M(o) | 0.35 | 5,289 |\n| 4169 | 0152L1604169 | 32.042 | 78.771 | Satluj | M(o) | 0.28 | 5,344 |\n| 4170 | 0152L1604170 | 32.042 | 78.758 | Satluj | M(o) | 0.83 | 5,291 |\n| 4171 | 0152L1604171 | 32.041 | 78.762 | Satluj | M(o) | 0.51 | 5,298 |\n| 4172 | 0152L1604172 | 32.040 | 78.769 | Satluj | M(o) | 0.36 | 5,345 |\n| 4173 | 0152L1604173 | 32.039 | 78.759 | Satluj | M(o) | 0.35 | 5,284 |\n| 4174 | 0152L1604174 | 32.036 | 78.835 | Satluj | I(s) | 0.64 | 5,685 |\n| 4175 | 0152L1604175 | 32.033 | 78.780 | Satluj | M(o) | 0.58 | 5,421 |\n| 4176 | 0152L1604176 | 32.033 | 78.816 | Satluj | E(o) | 1.94 | 5,970 |\n| 4177 | 0152L1604177 | 32.032 | 78.873 | Satluj | E(o) | 4.33 | 5,532 |\n| 4178 | 0152L1604178 | 32.029 | 78.845 | Satluj | M(e) | 15.62 | 5,621 |\n| 4179 | 0152L1604179 | 32.028 | 78.790 | Satluj | M(e) | 1.45 | 5,488 |\n| 4180 | 0152L1604180 | 32.020 | 78.876 | Satluj | M(e) | 1.77 | 5,644 |\n| 4181 | 0152L1604181 | 32.018 | 78.911 | Satluj | O | 3.80 | 5,579 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 10231, "line_end": 10378, "token_count_estimate": 1642, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0152L1604147", "0152L1604148", "0152L1604149", "0152L1604150", "0152L1604151", "0152L1604152", "0152L1604153", "0152L1604154", "0152L1604155", "0152L1604156", "0152L1604157", "0152L1604158", "0152L1604159", "0152L1604160", "0152L1604161", "0152L1604162", "0152L1604163", "0152L1604164", "0152L1604165", "0152L1604166", "0152L1604167", "0152L1604168", "0152L1604169", "0152L1604170", "0152L1604171", "0152L1604172", "0152L1604173", "0152L1604174", "0152L1604175", "0152L1604176", "0152L1604177", "0152L1604178", "0152L1604179", "0152L1604180", "0152L1604181"]}}
{"id": "1e220bdae58b84f0", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4182 | 0152L1604182 | 32.017 | 78.875 | Satluj | M(e) | 6.58 | 5,669 |\n| 4183 | 0152L1604183 | 32.015 | 78.926 | Satluj | O | 1.08 | 5,450 |\n| 4184 | 0152L1604184 | 32.014 | 78.823 | Satluj | M(e) | 0.32 | 5,826 |\n| 4185 | 0152L1604185 | 32.012 | 78.845 | Satluj | E(o) | 1.20 | 5,747 |\n| 4186 | 0152L1604186 | 32.009 | 78.849 | Satluj | E(o) | 0.72 | 5,723 |\n| 4187 | 0152L1604187 | 32.008 | 78.852 | Satluj | E(o) | 0.46 | 5,721 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 10231, "line_end": 10378, "token_count_estimate": 371, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0152L1604182", "0152L1604183", "0152L1604184", "0152L1604185", "0152L1604186", "0152L1604187"]}}
{"id": "c147c486e2cfe78f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 10379, "line_end": 10385, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "10b2a0aeac5116af", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4188 | 0152L1604188 | 32.004 | 78.902 | Satluj | O | 0.59 | 5,574 |\n| 4189 | 0152N0804189 | 34.039 | 79.469 | Shyok | M(e) | 1.25 | 5,508 |\n| 4190 | 0152N1104190 | 34.350 | 79.542 | Shyok | M(e) | 6.60 | 5,661 |\n| 4191 | 0152N1104191 | 34.287 | 79.524 | Shyok | M(o) | 0.30 | 5,919 |\n| 4192 | 0152N1204192 | 34.233 | 79.561 | Shyok | I(d) | 2.19 | 5,766 |\n| 4193 | 0152O0104193 | 33.958 | 79.009 | Shyok | M(e) | 0.72 | 5,721 |\n| 4194 | 0152O0404194 | 33.237 | 79.190 | Shyok | M(lg) | 0.33 | 5,924 |\n| 4195 | 0152O0404195 | 33.236 | 79.178 | Shyok | M(o) | 0.52 | 5,834 |\n| 4196 | 0152O0804196 | 33.215 | 79.388 | Shyok | I(s) | 0.54 | 5,760 |\n| 4197 | 0152O0804197 | 33.213 | 79.390 | Shyok | I(s) | 0.30 | 5,794 |\n| 4198 | 0152O0804198 | 33.184 | 79.457 | Shyok | M(o) | 0.74 | 5,737 |\n| 4199 | 0152O0804199 | 33.171 | 79.492 | Shyok | M(o) | 0.58 | 5,646 |\n| 4200 | 0152O1104200 | 33.272 | 79.587 | Shyok | I(s) | 0.62 | 5,912 |\n| 4201 | 0152O1204201 | 33.226 | 79.604 | Shyok | E(o) | 0.53 | 5,778 |\n| 4202 | 0152O1204202 | 33.223 | 79.603 | Shyok | M(e) | 0.86 | 5,808 |\n| 4203 | 0152O1204203 | 33.219 | 79.574 | Shyok | E(o) | 5.96 | 5,708 |\n| 4204 | 0152O1204204 | 33.215 | 79.574 | Shyok | M(o) | 0.36 | 5,734 |\n| 4205 | 0152O1204205 | 33.148 | 79.570 | Shyok | E(o) | 1.40 | 5,596 |\n| 4206 | 0152O1204206 | 33.113 | 79.559 | Shyok | M(o) | 0.33 | 5,651 |\n| 4207 | 0152O1204207 | 33.112 | 79.563 | Shyok | I(s) | 0.34 | 5,700 |\n| 4208 | 0152O1204208 | 33.105 | 79.546 | Shyok | I(s) | 0.36 | 5,710 |\n| 4209 | 0152O1304209 | 33.780 | 79.862 | Shyok | M(o) | 0.35 | 5,596 |\n| 4210 | 0152O1504210 | 33.287 | 79.809 | Shyok | M(o) | 0.65 | 5,649 |\n| 4211 | 0152O1504211 | 33.285 | 79.811 | Shyok | I(s) | 2.07 | 5,652 |\n| 4212 | 0152O1604212 | 33.100 | 79.938 | Shyok | E(o) | 0.28 | 5,763 |\n| 4213 | 0152O1604213 | 33.051 | 79.973 | Shyok | E(o) | 7.67 | 5,788 |\n| 4214 | 0152O1604214 | 33.040 | 79.960 | Shyok | I(s) | 0.76 | 5,781 |\n| 4215 | 0152O1604215 | 33.036 | 79.935 | Shyok | E(o) | 4.03 | 5,730 |\n| 4216 | 0152O1604216 | 33.033 | 79.937 | Shyok | E(c) | 3.32 | 5,734 |\n| 4217 | 0152O1604217 | 33.032 | 79.965 | Shyok | E(o) | 0.71 | 5,744 |\n| 4218 | 0152O1604218 | 33.029 | 79.962 | Shyok | E(o) | 6.05 | 5,751 |\n| 4219 | 0152O1604219 | 33.027 | 79.959 | Shyok | E(o) | 2.21 | 5,761 |\n| 4220 | 0152O1604220 | 33.024 | 79.955 | Shyok | M(o) | 2.04 | 5,784 |\n| 4221 | 0152O1604221 | 33.024 | 79.958 | Shyok | M(o) | 0.30 | 5,784 |\n| 4222 | 0152O1604222 | 33.006 | 79.972 | Shyok | O | 0.74 | 5,678 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10386, "line_end": 10533, "token_count_estimate": 1647, "basins": ["Indus"], "subbasins": ["Satluj", "Shyok"], "countries": [], "lake_ids": ["0152L1604188", "0152N0804189", "0152N1104190", "0152N1104191", "0152N1204192", "0152O0104193", "0152O0404194", "0152O0404195", "0152O0804196", "0152O0804197", "0152O0804198", "0152O0804199", "0152O1104200", "0152O1204201", "0152O1204202", "0152O1204203", "0152O1204204", "0152O1204205", "0152O1204206", "0152O1204207", "0152O1204208", "0152O1304209", "0152O1504210", "0152O1504211", "0152O1604212", "0152O1604213", "0152O1604214", "0152O1604215", "0152O1604216", "0152O1604217", "0152O1604218", "0152O1604219", "0152O1604220", "0152O1604221", "0152O1604222"]}}
{"id": "12c8ef3065a33c37", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4223 | 0152O1604223 | 33.005 | 79.992 | Shyok | E(o) | 1.59 | 5,705 |\n| 4224 | 0152O1604224 | 33.004 | 79.966 | Shyok | E(o) | 1.25 | 5,774 |\n| 4225 | 0152P0104225 | 32.870 | 79.075 | Indus Upper | E(o) | 0.91 | 5,716 |\n| 4226 | 0152P0204226 | 32.575 | 79.141 | Indus Upper | E(o) | 0.42 | 5,745 |\n| 4227 | 0152P0204227 | 32.541 | 79.196 | Indus Upper | E(o) | 0.99 | 5,786 |\n| 4228 | 0152P0204228 | 32.540 | 79.239 | Indus Upper | M(o) | 0.79 | 5,796 |\n| 4229 | 0152P0204229 | 32.519 | 79.162 | Indus Upper | O | 1.51 | 5,536 |\n| 4230 | 0152P0204230 | 32.507 | 79.178 | Indus Upper | E(o) | 1.80 | 5,749 |\n| 4231 | 0152P0304231 | 32.499 | 79.155 | Indus Upper | M(o) | 0.35 | 5,739 |\n| 4232 | 0152P0304232 | 32.492 | 79.172 | Satluj | E(o) | 1.25 | 5,535 |\n| 4233 | 0152P0304233 | 32.463 | 79.131 | Satluj | E(o) | 2.32 | 5,768 |\n| 4234 | 0152P0304234 | 32.459 | 79.117 | Indus Upper | M(o) | 0.41 | 5,737 |\n| 4235 | 0152P0304235 | 32.455 | 79.153 | Satluj | E(o) | 0.69 | 5,516 |\n| 4236 | 0152P0304236 | 32.452 | 79.188 | Satluj | E(c) | 2.88 | 5,622 |\n| 4237 | 0152P0304237 | 32.446 | 79.131 | Satluj | E(o) | 3.26 | 5,577 |\n| 4238 | 0152P0304238 | 32.439 | 79.121 | Satluj | M(o) | 1.36 | 5,780 |\n| 4239 | 0152P0304239 | 32.404 | 79.100 | Indus Upper | M(o) | 0.68 | 5,631 |\n| 4240 | 0152P0304240 | 32.354 | 79.104 | Satluj | E(o) | 2.80 | 5,761 |\n| 4241 | 0152P0304241 | 32.346 | 79.117 | Satluj | M(o) | 0.31 | 5,692 |\n| 4242 | 0152P0304242 | 32.338 | 79.002 | Satluj | E(c) | 4.25 | 5,617 |\n| 4243 | 0152P0304243 | 32.334 | 79.120 | Satluj | E(o) | 0.38 | 5,658 |\n| 4244 | 0152P0304244 | 32.329 | 79.002 | Satluj | E(o) | 1.71 | 5,665 |\n| 4245 | 0152P0304245 | 32.277 | 79.017 | Satluj | O | 3.08 | 5,604 |\n| 4246 | 0152P0304246 | 32.252 | 79.044 | Satluj | E(o) | 1.17 | 5,806 |\n| 4247 | 0152P0404247 | 32.244 | 79.017 | Satluj | E(o) | 0.37 | 5,791 |\n| 4248 | 0152P0404248 | 32.238 | 79.034 | Satluj | E(o) | 1.58 | 5,728 |\n| 4249 | 0152P0404249 | 32.225 | 79.006 | Satluj | E(o) | 1.79 | 5,581 |\n| 4250 | 0152P0404250 | 32.224 | 79.026 | Satluj | E(o) | 5.03 | 5,533 |\n| 4251 | 0152P0404251 | 32.215 | 79.010 | Satluj | E(o) | 1.18 | 5,514 |\n| 4252 | 0152P0404252 | 32.210 | 79.044 | Satluj | O | 21.55 | 5,272 |\n| 4253 | 0152P0404253 | 32.208 | 79.019 | Satluj | E(o) | 2.11 | 5,575 |\n| 4254 | 0152P0404254 | 32.203 | 79.017 | Satluj | M(o) | 0.45 | 5,678 |\n| 4255 | 0152P0604255 | 32.604 | 79.296 | Indus Upper | E(o) | 1.66 | 5,827 |\n| 4256 | 0152P0604256 | 32.591 | 79.463 | Indus Upper | M(o) | 0.90 | 5,719 |\n| 4257 | 0152P0604257 | 32.591 | 79.432 | Indus Upper | M(e) | 1.34 | 5,693 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10386, "line_end": 10533, "token_count_estimate": 1653, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0152O1604223", "0152O1604224", "0152P0104225", "0152P0204226", "0152P0204227", "0152P0204228", "0152P0204229", "0152P0204230", "0152P0304231", "0152P0304232", "0152P0304233", "0152P0304234", "0152P0304235", "0152P0304236", "0152P0304237", "0152P0304238", "0152P0304239", "0152P0304240", "0152P0304241", "0152P0304242", "0152P0304243", "0152P0304244", "0152P0304245", "0152P0304246", "0152P0404247", "0152P0404248", "0152P0404249", "0152P0404250", "0152P0404251", "0152P0404252", "0152P0404253", "0152P0404254", "0152P0604255", "0152P0604256", "0152P0604257"]}}
{"id": "ad3275788480820b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4258 | 0152P0604258 | 32.588 | 79.458 | Indus Upper | M(e) | 2.27 | 5,769 |\n| 4259 | 0152P0604259 | 32.587 | 79.477 | Indus Upper | E(o) | 0.47 | 5,612 |\n| 4260 | 0152P0604260 | 32.587 | 79.434 | Indus Upper | I(s) | 0.38 | 5,790 |\n| 4261 | 0152P0604261 | 32.584 | 79.473 | Indus Upper | I(s) | 0.32 | 5,627 |\n| 4262 | 0152P0604262 | 32.584 | 79.338 | Indus Upper | M(o) | 1.57 | 5,928 |\n| 4263 | 0152P0604263 | 32.583 | 79.340 | Indus Upper | M(o) | 0.48 | 5,920 |\n| 4264 | 0152P0604264 | 32.582 | 79.359 | Indus Upper | E(o) | 5.45 | 5,529 |\n| 4265 | 0152P0604265 | 32.581 | 79.339 | Indus Upper | I(s) | 0.35 | 5,925 |\n| 4266 | 0152P0604266 | 32.577 | 79.482 | Indus Upper | I(s) | 0.92 | 5,933 |\n| 4267 | 0152P0604267 | 32.577 | 79.486 | Indus Upper | M(e) | 2.30 | 5,897 |\n| 4268 | 0152P0604268 | 32.576 | 79.428 | Indus Upper | M(e) | 1.79 | 5,749 |\n| 4269 | 0152P0604269 | 32.576 | 79.447 | Indus Upper | M(o) | 1.62 | 5,879 |\n| 4270 | 0152P0604270 | 32.568 | 79.268 | Indus Upper | E(o) | 1.64 | 5,778 |\n| 4271 | 0152P0604271 | 32.567 | 79.338 | Indus Upper | M(o) | 0.80 | 5,739 |\n| 4272 | 0152P0604272 | 32.563 | 79.436 | Indus Upper | M(o) | 0.69 | 5,871 |\n| 4273 | 0152P0604273 | 32.560 | 79.331 | Satluj | E(c) | 2.56 | 5,736 |\n| 4274 | 0152P0604274 | 32.559 | 79.299 | Indus Upper | M(o) | 0.26 | 5,532 |\n| 4275 | 0152P0604275 | 32.557 | 79.447 | Indus Upper | M(e) | 4.29 | 5,532 |\n| 4276 | 0152P0604276 | 32.555 | 79.297 | Indus Upper | M(e) | 4.79 | 5,545 |\n| 4277 | 0152P0604277 | 32.554 | 79.332 | Satluj | M(o) | 1.34 | 5,597 |\n| 4278 | 0152P0604278 | 32.554 | 79.320 | Satluj | M(o) | 0.28 | 5,629 |\n| 4279 | 0152P0604279 | 32.541 | 79.261 | Indus Upper | I(s) | 0.43 | 5,789 |\n| 4280 | 0152P0604280 | 32.537 | 79.421 | Indus Upper | M(o) | 5.36 | 5,601 |\n| 4281 | 0152P0604281 | 32.537 | 79.424 | Indus Upper | M(o) | 4.65 | 5,602 |\n| 4282 | 0152P0604282 | 32.536 | 79.467 | Indus Upper | M(o) | 0.37 | 5,459 |\n| 4283 | 0152P0604283 | 32.533 | 79.355 | Indus Upper | I(s) | 0.82 | 5,483 |\n| 4284 | 0152P0604284 | 32.528 | 79.271 | Satluj | E(o) | 1.98 | 5,589 |\n| 4285 | 0152P0604285 | 32.528 | 79.412 | Indus Upper | I(s) | 0.57 | 5,589 |\n| 4286 | 0152P0604286 | 32.525 | 79.289 | Satluj | O | 1.59 | 5,464 |\n| 4287 | 0152P0604287 | 32.525 | 79.365 | Satluj | M(o) | 0.26 | 5,479 |\n| 4288 | 0152P0604288 | 32.524 | 79.369 | Satluj | E(o) | 1.40 | 5,460 |\n| 4289 | 0152P0604289 | 32.520 | 79.288 | Satluj | M(o) | 0.93 | 5,545 |\n| 4290 | 0152P0604290 | 32.516 | 79.286 | Satluj | M(o) | 0.81 | 5,548 |\n| 4291 | 0152P0604291 | 32.516 | 79.255 | Satluj | E(o) | 0.64 | 5,731 |\n| 4292 | 0152P0604292 | 32.515 | 79.262 | Satluj | O | 0.98 | 5,567 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 10386, "line_end": 10533, "token_count_estimate": 1661, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152P0604258", "0152P0604259", "0152P0604260", "0152P0604261", "0152P0604262", "0152P0604263", "0152P0604264", "0152P0604265", "0152P0604266", "0152P0604267", "0152P0604268", "0152P0604269", "0152P0604270", "0152P0604271", "0152P0604272", "0152P0604273", "0152P0604274", "0152P0604275", "0152P0604276", "0152P0604277", "0152P0604278", "0152P0604279", "0152P0604280", "0152P0604281", "0152P0604282", "0152P0604283", "0152P0604284", "0152P0604285", "0152P0604286", "0152P0604287", "0152P0604288", "0152P0604289", "0152P0604290", "0152P0604291", "0152P0604292"]}}
{"id": "257f26a4759dac33", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4293 | 0152P0604293 | 32.513 | 79.292 | Satluj | E(o) | 2.22 | 5,603 |\n| 4294 | 0152P0604294 | 32.512 | 79.298 | Satluj | M(o) | 1.12 | 5,551 |\n| 4295 | 0152P0604295 | 32.511 | 79.446 | Indus Upper | I(s) | 0.35 | 5,451 |\n| 4296 | 0152P0604296 | 32.510 | 79.445 | Indus Upper | M(o) | 1.09 | 5,463 |\n| 4297 | 0152P0604297 | 32.509 | 79.264 | Satluj | O | 0.71 | 5,518 |\n| 4298 | 0152P0604298 | 32.506 | 79.254 | Satluj | E(o) | 4.95 | 5,450 |\n| 4299 | 0152P0604299 | 32.505 | 79.476 | Indus Upper | E(c) | 1.63 | 5,551 |\n| 4300 | 0152P0704300 | 32.500 | 79.372 | Satluj | O | 1.03 | 5,196 |\n| 4301 | 0152P0704301 | 32.477 | 79.279 | Satluj | M(o) | 0.59 | 5,433 |\n| 4302 | 0152P0704302 | 32.477 | 79.270 | Satluj | E(o) | 0.48 | 5,439 |\n| 4303 | 0152P0704303 | 32.474 | 79.447 | Satluj | O | 0.76 | 5,053 |\n| 4304 | 0152P0704304 | 32.421 | 79.478 | Satluj | E(o) | 0.90 | 5,391 |\n| 4305 | 0152P0704305 | 32.418 | 79.477 | Satluj | E(o) | 0.89 | 5,388 |\n| 4306 | 0152P0704306 | 32.418 | 79.472 | Satluj | E(o) | 0.42 | 5,363 |\n| 4307 | 0152P0704307 | 32.390 | 79.417 | Satluj | E(o) | 9.61 | 5,475 |\n| 4308 | 0152P0704308 | 32.377 | 79.393 | Satluj | O | 24.86 | 5,448 |\n| 4309 | 0152P0704309 | 32.377 | 79.419 | Satluj | I(s) | 0.77 | 5,670 |\n| 4310 | 0152P0704310 | 32.374 | 79.418 | Satluj | I(s) | 1.65 | 5,665 |\n| 4311 | 0152P0704311 | 32.356 | 79.426 | Satluj | E(o) | 2.41 | 5,465 |\n| 4312 | 0152P0704312 | 32.353 | 79.395 | Satluj | M(o) | 0.48 | 5,586 |\n| 4313 | 0152P0704313 | 32.352 | 79.394 | Satluj | M(o) | 0.39 | 5,598 |\n| 4314 | 0152P0704314 | 32.350 | 79.406 | Satluj | E(o) | 4.42 | 5,544 |\n| 4315 | 0152P0704315 | 32.346 | 79.404 | Satluj | E(o) | 0.66 | 5,555 |\n| 4316 | 0152P1104316 | 32.412 | 79.527 | Indus Upper | E(o) | 4.48 | 5,555 |\n| 4317 | 0152P1104317 | 32.411 | 79.589 | Indus Upper | M(o) | 1.07 | 5,588 |\n| 4318 | 0152P1104318 | 32.410 | 79.585 | Indus Upper | M(e) | 3.31 | 5,609 |\n| 4319 | 0152P1104319 | 32.410 | 79.676 | Indus Upper | M(o) | 0.97 | 5,531 |\n| 4320 | 0152P1104320 | 32.410 | 79.604 | Indus Upper | M(e) | 5.92 | 5,400 |\n| 4321 | 0152P1104321 | 32.401 | 79.591 | Indus Upper | E(o) | 6.51 | 5,635 |\n| 4322 | 0152P1104322 | 32.399 | 79.622 | Indus Upper | E(o) | 23.72 | 5,058 |\n| 4323 | 0152P1104323 | 32.394 | 79.545 | Indus Upper | E(o) | 4.94 | 5,439 |\n| 4324 | 0152P1104324 | 32.392 | 79.508 | Satluj | E(o) | 2.21 | 5,368 |\n| 4325 | 0152P1104325 | 32.390 | 79.530 | Satluj | M(o) | 0.59 | 5,469 |\n| 4326 | 0152P1104326 | 32.389 | 79.659 | Indus Upper | M(e) | 12.30 | 5,720 |\n| 4327 | 0152P1104327 | 32.386 | 79.502 | Satluj | E(o) | 0.62 | 5,360 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 10386, "line_end": 10533, "token_count_estimate": 1645, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152P0604293", "0152P0604294", "0152P0604295", "0152P0604296", "0152P0604297", "0152P0604298", "0152P0604299", "0152P0704300", "0152P0704301", "0152P0704302", "0152P0704303", "0152P0704304", "0152P0704305", "0152P0704306", "0152P0704307", "0152P0704308", "0152P0704309", "0152P0704310", "0152P0704311", "0152P0704312", "0152P0704313", "0152P0704314", "0152P0704315", "0152P1104316", "0152P1104317", "0152P1104318", "0152P1104319", "0152P1104320", "0152P1104321", "0152P1104322", "0152P1104323", "0152P1104324", "0152P1104325", "0152P1104326", "0152P1104327"]}}
{"id": "78393b91dd921c76", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4328 | 0152P1104328 | 32.385 | 79.680 | Indus Upper | M(o) | 0.45 | 5,444 |\n| 4329 | 0152P1104329 | 32.385 | 79.669 | Indus Upper | M(e) | 6.46 | 5,591 |\n| 4330 | 0152P1104330 | 32.384 | 79.593 | Indus Upper | E(o) | 2.57 | 5,468 |\n| 4331 | 0152P1104331 | 32.384 | 79.640 | Indus Upper | M(o) | 0.26 | 5,652 |\n| 4332 | 0152P1104332 | 32.382 | 79.509 | Satluj | E(c) | 2.08 | 5,499 |\n| 4333 | 0152P1104333 | 32.377 | 79.521 | Satluj | E(o) | 0.65 | 5,422 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 10386, "line_end": 10533, "token_count_estimate": 376, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152P1104328", "0152P1104329", "0152P1104330", "0152P1104331", "0152P1104332", "0152P1104333"]}}
{"id": "2f02a55166330fdc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 10534, "line_end": 10541, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "79408f231534c52a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4334 | 0152P1104334 | 32.376 | 79.647 | Indus Upper | M(e) | 1.91 | 5,649 |\n| 4335 | 0152P1104335 | 32.375 | 79.559 | Indus Upper | M(o) | 0.65 | 5,401 |\n| 4336 | 0152P1104336 | 32.374 | 79.636 | Indus Upper | E(o) | 0.46 | 5,581 |\n| 4337 | 0152P1104337 | 32.370 | 79.534 | Satluj | E(o) | 2.26 | 5,366 |\n| 4338 | 0152P1104338 | 32.367 | 79.567 | Indus Upper | E(c) | 1.48 | 5,394 |\n| 4339 | 0152P1104339 | 32.366 | 79.652 | Indus Upper | M(o) | 1.26 | 5,657 |\n| 4340 | 0152P1104340 | 32.365 | 79.667 | Indus Upper | O | 0.40 | 5,474 |\n| 4341 | 0152P1104341 | 32.362 | 79.589 | Indus Upper | M(o) | 1.09 | 5,454 |\n| 4342 | 0152P1104342 | 32.362 | 79.616 | Indus Upper | E(o) | 1.15 | 5,369 |\n| 4343 | 0152P1104343 | 32.360 | 79.580 | Indus Upper | M(o) | 5.42 | 5,413 |\n| 4344 | 0152P1104344 | 32.356 | 79.704 | Indus Upper | M(e) | 4.08 | 5,645 |\n| 4345 | 0152P1104345 | 32.354 | 79.590 | Satluj | E(o) | 1.17 | 5,424 |\n| 4346 | 0152P1104346 | 32.354 | 79.625 | Indus Upper | I(s) | 0.83 | 5,575 |\n| 4347 | 0152P1104347 | 32.353 | 79.634 | Indus Upper | M(o) | 1.13 | 5,549 |\n| 4348 | 0152P1104348 | 32.353 | 79.628 | Indus Upper | I(s) | 0.59 | 5,562 |\n| 4349 | 0152P1104349 | 32.353 | 79.594 | Satluj | E(o) | 4.71 | 5,421 |\n| 4350 | 0152P1104350 | 32.342 | 79.659 | Satluj | M(o) | 0.50 | 5,686 |\n| 4351 | 0152P1104351 | 32.342 | 79.654 | Satluj | M(o) | 0.66 | 5,690 |\n| 4352 | 0152P1104352 | 32.341 | 79.668 | Indus Upper | E(o) | 2.40 | 5,676 |\n| 4353 | 0152P1104353 | 32.339 | 79.674 | Indus Upper | M(o) | 1.84 | 5,674 |\n| 4354 | 0152P1104354 | 32.337 | 79.673 | Indus Upper | I(s) | 0.38 | 5,675 |\n| 4355 | 0152P1104355 | 32.336 | 79.672 | Indus Upper | I(s) | 0.57 | 5,678 |\n| 4356 | 0152P1104356 | 32.336 | 79.675 | Indus Upper | I(s) | 0.33 | 5,674 |\n| 4357 | 0152P1104357 | 32.336 | 79.679 | Indus Upper | I(s) | 0.40 | 5,670 |\n| 4358 | 0152P1104358 | 32.336 | 79.674 | Indus Upper | I(s) | 0.97 | 5,676 |\n| 4359 | 0152P1104359 | 32.333 | 79.619 | Satluj | E(o) | 0.74 | 5,378 |\n| 4360 | 0152P1104360 | 32.332 | 79.653 | Satluj | O | 1.87 | 5,564 |\n| 4361 | 0152P1104361 | 32.332 | 79.573 | Satluj | E(o) | 0.95 | 5,390 |\n| 4362 | 0152P1104362 | 32.327 | 79.620 | Satluj | E(o) | 1.92 | 5,477 |\n| 4363 | 0152P1104363 | 32.326 | 79.582 | Satluj | E(o) | 0.69 | 5,353 |\n| 4364 | 0152P1104364 | 32.320 | 79.643 | Satluj | E(o) | 1.15 | 5,516 |\n| 4365 | 0152P1104365 | 32.317 | 79.638 | Satluj | M(e) | 5.07 | 5,506 |\n| 4366 | 0152P1104366 | 32.314 | 79.658 | Indus Upper | M(o) | 0.71 | 5,515 |\n| 4367 | 0152P1104367 | 32.313 | 79.657 | Indus Upper | M(e) | 4.45 | 5,497 |\n| 4368 | 0152P1104368 | 32.298 | 79.662 | Indus Upper | E(o) | 17.15 | 5,690 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10542, "line_end": 10689, "token_count_estimate": 1655, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152P1104334", "0152P1104335", "0152P1104336", "0152P1104337", "0152P1104338", "0152P1104339", "0152P1104340", "0152P1104341", "0152P1104342", "0152P1104343", "0152P1104344", "0152P1104345", "0152P1104346", "0152P1104347", "0152P1104348", "0152P1104349", "0152P1104350", "0152P1104351", "0152P1104352", "0152P1104353", "0152P1104354", "0152P1104355", "0152P1104356", "0152P1104357", "0152P1104358", "0152P1104359", "0152P1104360", "0152P1104361", "0152P1104362", "0152P1104363", "0152P1104364", "0152P1104365", "0152P1104366", "0152P1104367", "0152P1104368"]}}
{"id": "d096f87cf8218cbd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4369 | 0152P1104369 | 32.296 | 79.679 | Indus Upper | M(e) | 2.10 | 5,472 |\n| 4370 | 0152P1104370 | 32.288 | 79.645 | Satluj | M(o) | 0.30 | 5,504 |\n| 4371 | 0152P1104371 | 32.284 | 79.676 | Indus Upper | M(e) | 4.26 | 5,559 |\n| 4372 | 0152P1104372 | 32.283 | 79.694 | Indus Upper | M(e) | 5.06 | 5,296 |\n| 4373 | 0152P1104373 | 32.283 | 79.680 | Indus Upper | M(e) | 2.73 | 5,560 |\n| 4374 | 0152P1104374 | 32.279 | 79.694 | Indus Upper | M(o) | 0.60 | 5,392 |\n| 4375 | 0152P1104375 | 32.277 | 79.676 | Indus Upper | E(o) | 6.15 | 5,589 |\n| 4376 | 0152P1104376 | 32.267 | 79.721 | Indus Upper | E(o) | 1.86 | 5,389 |\n| 4377 | 0152P1104377 | 32.259 | 79.680 | Satluj | M(e) | 2.24 | 5,651 |\n| 4378 | 0152P1104378 | 32.254 | 79.683 | Satluj | E(o) | 5.10 | 5,607 |\n| 4379 | 0152P1204379 | 32.235 | 79.654 | Satluj | E(o) | 0.72 | 5,581 |\n| 4380 | 0152P1204380 | 32.215 | 79.705 | Satluj | O | 1.15 | 5,123 |\n| 4381 | 0152P1304381 | 32.996 | 79.981 | Shyok | M(e) | 3.61 | 5,751 |\n| 4382 | 0152P1304382 | 32.982 | 79.929 | Shyok | M(o) | 4.36 | 5,817 |\n| 4383 | 0152P1304383 | 32.980 | 79.928 | Shyok | I(s) | 0.66 | 5,822 |\n| 4384 | 0152P1304384 | 32.977 | 79.972 | Shyok | M(e) | 2.39 | 5,720 |\n| 4385 | 0152P1304385 | 32.971 | 79.957 | Shyok | M(e) | 1.27 | 5,745 |\n| 4386 | 0152P1304386 | 32.965 | 79.948 | Shyok | E(o) | 6.79 | 5,648 |\n| 4387 | 0152P1304387 | 32.961 | 79.952 | Shyok | M(o) | 2.06 | 5,688 |\n| 4388 | 0152P1304388 | 32.949 | 79.991 | Shyok | I(s) | 0.79 | 5,703 |\n| 4389 | 0152P1304389 | 32.933 | 79.992 | Shyok | E(o) | 1.24 | 5,650 |\n| 4390 | 0152P1604390 | 32.207 | 79.843 | Indus Upper | M(e) | 0.34 | 5,764 |\n| 4391 | 0152P1604391 | 32.198 | 79.852 | Indus Upper | M(o) | 0.42 | 5,626 |\n| 4392 | 0152P1604392 | 32.193 | 79.792 | Indus Upper | M(e) | 2.24 | 5,783 |\n| 4393 | 0152P1604393 | 32.177 | 79.846 | Indus Upper | M(o) | 0.42 | 5,703 |\n| 4394 | 0152P1604394 | 32.173 | 79.797 | Satluj | E(o) | 0.93 | 5,834 |\n| 4395 | 0152P1604395 | 32.145 | 79.751 | Satluj | E(c) | 1.03 | 5,573 |\n| 4396 | 0152P1604396 | 32.134 | 79.763 | Satluj | E(o) | 0.27 | 5,746 |\n| 4397 | 0152P1604397 | 32.133 | 79.770 | Satluj | E(o) | 1.25 | 5,681 |\n| 4398 | 0152P1604398 | 32.130 | 79.799 | Satluj | E(o) | 1.32 | 5,875 |\n| 4399 | 0152P1604399 | 32.128 | 79.797 | Satluj | E(o) | 3.06 | 5,795 |\n| 4400 | 0152P1604400 | 32.125 | 79.801 | Satluj | E(o) | 1.45 | 5,762 |\n| 4401 | 0152P1604401 | 32.122 | 79.794 | Satluj | M(e) | 3.70 | 5,731 |\n| 4402 | 0152P1604402 | 32.120 | 79.774 | Satluj | M(o) | 0.27 | 5,690 |\n| 4403 | 0152P1604403 | 32.119 | 79.778 | Satluj | M(o) | 0.54 | 5,721 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10542, "line_end": 10689, "token_count_estimate": 1644, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0152P1104369", "0152P1104370", "0152P1104371", "0152P1104372", "0152P1104373", "0152P1104374", "0152P1104375", "0152P1104376", "0152P1104377", "0152P1104378", "0152P1204379", "0152P1204380", "0152P1304381", "0152P1304382", "0152P1304383", "0152P1304384", "0152P1304385", "0152P1304386", "0152P1304387", "0152P1304388", "0152P1304389", "0152P1604390", "0152P1604391", "0152P1604392", "0152P1604393", "0152P1604394", "0152P1604395", "0152P1604396", "0152P1604397", "0152P1604398", "0152P1604399", "0152P1604400", "0152P1604401", "0152P1604402", "0152P1604403"]}}
{"id": "210b16676d4c736f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4404 | 0152P1604404 | 32.117 | 79.804 | Satluj | E(o) | 2.03 | 5,673 |\n| 4405 | 0152P1604405 | 32.113 | 79.802 | Satluj | E(o) | 0.57 | 5,666 |\n| 4406 | 0152P1604406 | 32.110 | 79.801 | Satluj | M(o) | 0.25 | 5,660 |\n| 4407 | 0152P1604407 | 32.108 | 79.907 | Indus Upper | M(o) | 0.37 | 5,485 |\n| 4408 | 0152P1604408 | 32.107 | 79.801 | Satluj | M(o) | 1.21 | 5,697 |\n| 4409 | 0152P1604409 | 32.107 | 79.785 | Satluj | E(o) | 0.43 | 5,502 |\n| 4410 | 0152P1604410 | 32.106 | 79.798 | Satluj | E(o) | 0.43 | 5,709 |\n| 4411 | 0152P1604411 | 32.101 | 79.908 | Indus Upper | M(o) | 1.24 | 5,489 |\n| 4412 | 0152P1604412 | 32.099 | 79.871 | Indus Upper | M(lg) | 4.66 | 5,950 |\n| 4413 | 0152P1604413 | 32.096 | 79.773 | Satluj | E(o) | 0.27 | 5,680 |\n| 4414 | 0152P1604414 | 32.095 | 79.773 | Satluj | E(c) | 2.08 | 5,665 |\n| 4415 | 0152P1604415 | 32.078 | 79.803 | Satluj | E(o) | 3.45 | 5,763 |\n| 4416 | 0152P1604416 | 32.078 | 79.859 | Indus Upper | E(o) | 1.07 | 5,651 |\n| 4417 | 0152P1604417 | 32.059 | 79.800 | Satluj | E(o) | 0.67 | 5,679 |\n| 4418 | 0152P1604418 | 32.058 | 79.844 | Indus Upper | E(o) | 1.38 | 5,708 |\n| 4419 | 0152P1604419 | 32.054 | 79.829 | Indus Upper | E(o) | 4.91 | 5,695 |\n| 4420 | 0152P1604420 | 32.053 | 79.839 | Indus Upper | E(o) | 3.37 | 5,641 |\n| 4421 | 0152P1604421 | 32.050 | 79.825 | Indus Upper | M(o) | 1.46 | 5,714 |\n| 4422 | 0152P1604422 | 32.048 | 79.845 | Indus Upper | E(o) | 2.31 | 5,565 |\n| 4423 | 0152P1604423 | 32.047 | 79.841 | Indus Upper | E(o) | 1.12 | 5,614 |\n| 4424 | 0152P1604424 | 32.047 | 79.832 | Indus Upper | M(o) | 3.90 | 5,674 |\n| 4425 | 0152P1604425 | 32.044 | 79.835 | Indus Upper | M(o) | 2.38 | 5,658 |\n| 4426 | 0152P1604426 | 32.043 | 79.901 | Indus Upper | M(e) | 4.58 | 5,423 |\n| 4427 | 0152P1604427 | 32.040 | 79.812 | Satluj | E(o) | 0.30 | 5,697 |\n| 4428 | 0152P1604428 | 32.039 | 79.849 | Indus Upper | M(o) | 0.65 | 5,477 |\n| 4429 | 0152P1604429 | 32.039 | 79.918 | Indus Upper | M(e) | 3.59 | 5,801 |\n| 4430 | 0152P1604430 | 32.030 | 79.871 | Indus Upper | M(l) | 0.45 | 5,782 |\n| 4431 | 0152P1604431 | 32.030 | 79.813 | Satluj | E(o) | 0.45 | 5,607 |\n| 4432 | 0152P1604432 | 32.028 | 79.810 | Satluj | E(o) | 0.55 | 5,605 |\n| 4433 | 0152P1604433 | 32.026 | 79.835 | Indus Upper | I(s) | 0.49 | 5,590 |\n| 4434 | 0152P1604434 | 32.021 | 79.818 | Satluj | E(o) | 0.59 | 5,673 |\n| 4435 | 0152P1604435 | 32.018 | 79.826 | Satluj | E(o) | 2.64 | 5,655 |\n| 4436 | 0152P1604436 | 32.008 | 79.890 | Indus Upper | E(o) | 0.86 | 5,643 |\n| 4437 | 0152P1604437 | 32.006 | 79.896 | Indus Upper | E(o) | 1.29 | 5,586 |\n| 4438 | 0152P1604438 | 32.004 | 79.896 | Indus Upper | E(o) | 9.41 | 5,589 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 10542, "line_end": 10689, "token_count_estimate": 1679, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152P1604404", "0152P1604405", "0152P1604406", "0152P1604407", "0152P1604408", "0152P1604409", "0152P1604410", "0152P1604411", "0152P1604412", "0152P1604413", "0152P1604414", "0152P1604415", "0152P1604416", "0152P1604417", "0152P1604418", "0152P1604419", "0152P1604420", "0152P1604421", "0152P1604422", "0152P1604423", "0152P1604424", "0152P1604425", "0152P1604426", "0152P1604427", "0152P1604428", "0152P1604429", "0152P1604430", "0152P1604431", "0152P1604432", "0152P1604433", "0152P1604434", "0152P1604435", "0152P1604436", "0152P1604437", "0152P1604438"]}}
{"id": "361c6382139ada8c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4439 | 0152P1604439 | 32.002 | 79.886 | Indus Upper | E(o) | 0.77 | 5,637 |\n| 4440 | 0152P1604440 | 32.001 | 79.855 | Satluj | M(o) | 0.89 | 5,652 |\n| 4441 | 0152P1604441 | 32.001 | 79.879 | Indus Upper | E(o) | 0.43 | 5,664 |\n| 4442 | 0153E0504442 | 31.942 | 77.428 | Beas | E(o) | 1.00 | 4,442 |\n| 4443 | 0153E0504443 | 31.917 | 77.422 | Beas | M(e) | 2.99 | 4,389 |\n| 4444 | 0153E0504444 | 31.916 | 77.419 | Beas | M(o) | 0.64 | 4,346 |\n| 4445 | 0153E0504445 | 31.844 | 77.462 | Beas | E(o) | 1.49 | 4,305 |\n| 4446 | 0153E0504446 | 31.842 | 77.461 | Beas | E(o) | 1.14 | 4,346 |\n| 4447 | 0153E0504447 | 31.838 | 77.475 | Beas | E(o) | 1.03 | 4,302 |\n| 4448 | 0153E0504448 | 31.837 | 77.471 | Beas | E(o) | 1.90 | 4,333 |\n| 4449 | 0153E0904449 | 31.973 | 77.735 | Beas | E(o) | 0.51 | 5,045 |\n| 4450 | 0153E0904450 | 31.973 | 77.700 | Beas | E(o) | 0.88 | 4,781 |\n| 4451 | 0153E0904451 | 31.949 | 77.712 | Beas | E(o) | 0.68 | 4,885 |\n| 4452 | 0153E0904452 | 31.943 | 77.587 | Beas | M(o) | 1.14 | 4,167 |\n| 4453 | 0153E0904453 | 31.915 | 77.526 | Beas | M(e) | 9.71 | 4,462 |\n| 4454 | 0153E0904454 | 31.902 | 77.536 | Beas | M(o) | 0.39 | 4,597 |\n| 4455 | 0153E0904455 | 31.902 | 77.535 | Beas | M(o) | 0.64 | 4,603 |\n| 4456 | 0153E0904456 | 31.899 | 77.538 | Beas | M(o) | 2.03 | 4,627 |\n| 4457 | 0153E0904457 | 31.898 | 77.526 | Beas | M(o) | 1.95 | 4,711 |\n| 4458 | 0153E0904458 | 31.898 | 77.533 | Beas | M(o) | 1.27 | 4,680 |\n| 4459 | 0153E0904459 | 31.886 | 77.537 | Beas | M(o) | 1.47 | 4,834 |\n| 4460 | 0153E0904460 | 31.881 | 77.544 | Beas | M(o) | 0.32 | 4,718 |\n| 4461 | 0153E0904461 | 31.852 | 77.640 | Beas | E(o) | 3.45 | 4,759 |\n| 4462 | 0153E0904462 | 31.851 | 77.649 | Beas | E(o) | 0.73 | 4,883 |\n| 4463 | 0153E0904463 | 31.846 | 77.592 | Beas | E(o) | 2.50 | 4,717 |\n| 4464 | 0153E0904464 | 31.845 | 77.638 | Beas | E(o) | 0.43 | 4,615 |\n| 4465 | 0153E0904465 | 31.843 | 77.561 | Beas | M(o) | 2.98 | 4,649 |\n| 4466 | 0153E0904466 | 31.784 | 77.662 | Beas | M(e) | 0.39 | 4,470 |\n| 4467 | 0153E1004467 | 31.745 | 77.635 | Beas | M(o) | 0.30 | 4,793 |\n| 4468 | 0153E1004468 | 31.740 | 77.555 | Beas | E(o) | 1.06 | 4,400 |\n| 4469 | 0153E1004469 | 31.734 | 77.556 | Beas | E(o) | 1.31 | 4,464 |\n| 4470 | 0153E1004470 | 31.734 | 77.564 | Beas | E(o) | 1.24 | 4,485 |\n| 4471 | 0153E1004471 | 31.733 | 77.561 | Beas | E(o) | 1.44 | 4,503 |\n| 4472 | 0153E1004472 | 31.729 | 77.662 | Beas | M(o) | 1.72 | 4,831 |\n| 4473 | 0153E1004473 | 31.726 | 77.668 | Beas | O | 0.26 | 4,636 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 10542, "line_end": 10689, "token_count_estimate": 1613, "basins": ["Indus"], "subbasins": ["Beas", "Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0152P1604439", "0152P1604440", "0152P1604441", "0153E0504442", "0153E0504443", "0153E0504444", "0153E0504445", "0153E0504446", "0153E0504447", "0153E0504448", "0153E0904449", "0153E0904450", "0153E0904451", "0153E0904452", "0153E0904453", "0153E0904454", "0153E0904455", "0153E0904456", "0153E0904457", "0153E0904458", "0153E0904459", "0153E0904460", "0153E0904461", "0153E0904462", "0153E0904463", "0153E0904464", "0153E0904465", "0153E0904466", "0153E1004467", "0153E1004468", "0153E1004469", "0153E1004470", "0153E1004471", "0153E1004472", "0153E1004473"]}}
{"id": "a0e8bffd28b43792", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4474 | 0153E1004474 | 31.702 | 77.686 | Satluj | E(o) | 1.59 | 4,785 |\n| 4475 | 0153E1004475 | 31.696 | 77.711 | Satluj | E(o) | 2.64 | 4,761 |\n| 4476 | 0153E1004476 | 31.679 | 77.672 | Satluj | M(o) | 0.39 | 4,678 |\n| 4477 | 0153E1004477 | 31.679 | 77.673 | Satluj | M(o) | 0.44 | 4,692 |\n| 4478 | 0153E1004478 | 31.677 | 77.675 | Satluj | E(o) | 0.94 | 4,623 |\n| 4479 | 0153E1004479 | 31.676 | 77.565 | Beas | E(o) | 1.02 | 4,264 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 10542, "line_end": 10689, "token_count_estimate": 369, "basins": ["Indus"], "subbasins": ["Beas", "Satluj"], "countries": [], "lake_ids": ["0153E1004474", "0153E1004475", "0153E1004476", "0153E1004477", "0153E1004478", "0153E1004479"]}}
{"id": "277725a9d590957c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 10690, "line_end": 10698, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e28f6d96824331b9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4480 | 0153E1004480 | 31.676 | 77.665 | Satluj | M(o) | 0.30 | 4,700 |\n| 4481 | 0153E1004481 | 31.673 | 77.663 | Satluj | M(o) | 2.22 | 4,709 |\n| 4482 | 0153E1004482 | 31.672 | 77.662 | Satluj | M(e) | 1.70 | 4,720 |\n| 4483 | 0153E1004483 | 31.670 | 77.599 | Beas | M(o) | 0.28 | 4,559 |\n| 4484 | 0153E1004484 | 31.668 | 77.597 | Beas | E(o) | 1.49 | 4,563 |\n| 4485 | 0153E1004485 | 31.668 | 77.590 | Beas | M(o) | 3.69 | 4,535 |\n| 4486 | 0153E1004486 | 31.667 | 77.664 | Satluj | E(o) | 0.32 | 4,755 |\n| 4487 | 0153E1004487 | 31.667 | 77.598 | Beas | M(o) | 0.25 | 4,582 |\n| 4488 | 0153E1004488 | 31.666 | 77.619 | Beas | M(e) | 4.02 | 4,603 |\n| 4489 | 0153E1004489 | 31.657 | 77.611 | Beas | M(e) | 0.43 | 4,651 |\n| 4490 | 0153E1004490 | 31.657 | 77.612 | Beas | M(o) | 0.73 | 4,668 |\n| 4491 | 0153E1304491 | 31.970 | 77.800 | Satluj | M(o) | 0.26 | 5,100 |\n| 4492 | 0153E1304492 | 31.953 | 77.782 | Satluj | M(e) | 0.62 | 5,091 |\n| 4493 | 0153E1304493 | 31.864 | 77.857 | Satluj | M(o) | 0.38 | 5,141 |\n| 4494 | 0153E1304494 | 31.849 | 77.789 | Beas | M(e) | 3.10 | 4,135 |\n| 4495 | 0153E1304495 | 31.849 | 77.791 | Beas | M(o) | 0.32 | 4,140 |\n| 4496 | 0153E1304496 | 31.847 | 77.791 | Beas | M(o) | 0.26 | 4,155 |\n| 4497 | 0153E1304497 | 31.845 | 77.792 | Beas | I(s) | 1.29 | 4,144 |\n| 4498 | 0153E1304498 | 31.842 | 77.793 | Beas | M(o) | 0.31 | 4,153 |\n| 4499 | 0153E1304499 | 31.838 | 77.794 | Beas | I(s) | 1.06 | 4,158 |\n| 4500 | 0153E1304500 | 31.837 | 77.791 | Beas | I(s) | 1.62 | 4,148 |\n| 4501 | 0153E1304501 | 31.834 | 77.790 | Beas | I(s) | 1.40 | 4,158 |\n| 4502 | 0153E1304502 | 31.834 | 77.793 | Beas | M(o) | 0.73 | 4,162 |\n| 4503 | 0153E1304503 | 31.830 | 77.790 | Beas | I(s) | 1.12 | 4,165 |\n| 4504 | 0153E1304504 | 31.817 | 77.851 | Satluj | I(s) | 0.29 | 5,114 |\n| 4505 | 0153E1304505 | 31.797 | 77.830 | Beas | E(o) | 0.74 | 5,153 |\n| 4506 | 0153E1304506 | 31.794 | 77.913 | Satluj | M(o) | 0.54 | 5,092 |\n| 4507 | 0153E1304507 | 31.787 | 77.982 | Satluj | I(s) | 0.41 | 4,956 |\n| 4508 | 0153E1304508 | 31.783 | 78.000 | Satluj | I(s) | 0.25 | 4,811 |\n| 4509 | 0153E1304509 | 31.760 | 77.925 | Satluj | I(s) | 0.67 | 5,042 |\n| 4510 | 0153E1404510 | 31.745 | 77.860 | Satluj | E(o) | 1.18 | 4,868 |\n| 4511 | 0153E1404511 | 31.731 | 77.937 | Satluj | E(o) | 0.58 | 4,659 |\n| 4512 | 0153E1404512 | 31.722 | 77.903 | Satluj | M(o) | 0.30 | 4,035 |\n| 4513 | 0153E1404513 | 31.721 | 77.901 | Satluj | M(o) | 0.68 | 4,057 |\n| 4514 | 0153E1404514 | 31.718 | 77.872 | Satluj | M(o) | 1.74 | 4,703 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10699, "line_end": 10846, "token_count_estimate": 1616, "basins": ["Indus"], "subbasins": ["Beas", "Satluj"], "countries": [], "lake_ids": ["0153E1004480", "0153E1004481", "0153E1004482", "0153E1004483", "0153E1004484", "0153E1004485", "0153E1004486", "0153E1004487", "0153E1004488", "0153E1004489", "0153E1004490", "0153E1304491", "0153E1304492", "0153E1304493", "0153E1304494", "0153E1304495", "0153E1304496", "0153E1304497", "0153E1304498", "0153E1304499", "0153E1304500", "0153E1304501", "0153E1304502", "0153E1304503", "0153E1304504", "0153E1304505", "0153E1304506", "0153E1304507", "0153E1304508", "0153E1304509", "0153E1404510", "0153E1404511", "0153E1404512", "0153E1404513", "0153E1404514"]}}
{"id": "85fc2e7934dc033d", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4515 | 0153E1404515 | 31.712 | 77.881 | Satluj | M(o) | 0.41 | 4,616 |\n| 4516 | 0153E1404516 | 31.707 | 77.945 | Satluj | M(o) | 0.49 | 3,980 |\n| 4517 | 0153E1404517 | 31.707 | 77.946 | Satluj | M(o) | 0.31 | 3,994 |\n| 4518 | 0153E1504518 | 31.481 | 77.959 | Satluj | E(o) | 1.59 | 3,812 |\n| 4519 | 0153I0104519 | 31.927 | 78.183 | Satluj | M(o) | 0.26 | 5,200 |\n| 4520 | 0153I0104520 | 31.924 | 78.183 | Satluj | I(s) | 0.44 | 5,291 |\n| 4521 | 0153I0104521 | 31.871 | 78.216 | Satluj | E(o) | 0.40 | 5,085 |\n| 4522 | 0153I0104522 | 31.867 | 78.116 | Satluj | E(o) | 1.59 | 5,266 |\n| 4523 | 0153I0104523 | 31.835 | 78.083 | Satluj | M(o) | 0.59 | 5,235 |\n| 4524 | 0153I0104524 | 31.778 | 78.102 | Satluj | E(o) | 0.36 | 4,710 |\n| 4525 | 0153I0104525 | 31.775 | 78.067 | Satluj | E(o) | 0.35 | 4,976 |\n| 4526 | 0153I0204526 | 31.749 | 78.117 | Satluj | E(o) | 0.56 | 4,948 |\n| 4527 | 0153I0204527 | 31.740 | 78.125 | Satluj | E(o) | 2.81 | 4,794 |\n| 4528 | 0153I0204528 | 31.737 | 78.126 | Satluj | E(o) | 0.34 | 4,849 |\n| 4529 | 0153I0204529 | 31.662 | 78.160 | Satluj | M(l) | 0.33 | 4,315 |\n| 4530 | 0153I0204530 | 31.661 | 78.168 | Satluj | M(e) | 23.20 | 4,255 |\n| 4531 | 0153I0204531 | 31.585 | 78.186 | Satluj | M(e) | 4.72 | 4,651 |\n| 4532 | 0153I0304532 | 31.428 | 78.078 | Satluj | M(l) | 0.37 | 4,295 |\n| 4533 | 0153I0304533 | 31.419 | 78.069 | Satluj | M(o) | 1.23 | 4,623 |\n| 4534 | 0153I0304534 | 31.408 | 78.027 | Satluj | M(e) | 3.20 | 4,421 |\n| 4535 | 0153I0304535 | 31.406 | 78.011 | Satluj | M(o) | 4.41 | 4,513 |\n| 4536 | 0153I0304536 | 31.402 | 78.008 | Satluj | E(o) | 0.78 | 4,640 |\n| 4537 | 0153I0304537 | 31.336 | 78.233 | Satluj | M(o) | 0.54 | 4,562 |\n| 4538 | 0153I0504538 | 31.972 | 78.461 | Satluj | M(o) | 5.70 | 5,256 |\n| 4539 | 0153I0504539 | 31.965 | 78.416 | Satluj | M(e) | 4.73 | 4,873 |\n| 4540 | 0153I0504540 | 31.945 | 78.464 | Satluj | E(o) | 0.41 | 5,223 |\n| 4541 | 0153I0504541 | 31.790 | 78.306 | Satluj | E(o) | 1.21 | 5,209 |\n| 4542 | 0153I0504542 | 31.780 | 78.294 | Satluj | M(o) | 0.54 | 4,965 |\n| 4543 | 0153I0604543 | 31.732 | 78.399 | Satluj | O | 1.97 | 4,816 |\n| 4544 | 0153I0604544 | 31.523 | 78.383 | Satluj | M(o) | 3.83 | 5,210 |\n| 4545 | 0153I0604545 | 31.502 | 78.433 | Satluj | I(s) | 0.51 | 4,656 |\n| 4546 | 0153I0704546 | 31.458 | 78.369 | Satluj | M(o) | 1.07 | 4,808 |\n| 4547 | 0153I0704547 | 31.438 | 78.409 | Satluj | E(o) | 1.55 | 5,095 |\n| 4548 | 0153I0704548 | 31.412 | 78.410 | Satluj | M(o) | 0.56 | 5,038 |\n| 4549 | 0153I0704549 | 31.404 | 78.425 | Satluj | M(o) | 0.41 | 5,126 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10699, "line_end": 10846, "token_count_estimate": 1650, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0153E1404515", "0153E1404516", "0153E1404517", "0153E1504518", "0153I0104519", "0153I0104520", "0153I0104521", "0153I0104522", "0153I0104523", "0153I0104524", "0153I0104525", "0153I0204526", "0153I0204527", "0153I0204528", "0153I0204529", "0153I0204530", "0153I0204531", "0153I0304532", "0153I0304533", "0153I0304534", "0153I0304535", "0153I0304536", "0153I0304537", "0153I0504538", "0153I0504539", "0153I0504540", "0153I0504541", "0153I0504542", "0153I0604543", "0153I0604544", "0153I0604545", "0153I0704546", "0153I0704547", "0153I0704548", "0153I0704549"]}}
{"id": "8dc2b0dc0fe9ab8f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4550 | 0153I0704550 | 31.404 | 78.420 | Satluj | E(o) | 1.90 | 5,023 |\n| 4551 | 0153I0704551 | 31.401 | 78.489 | Satluj | M(o) | 2.04 | 4,699 |\n| 4552 | 0153I0704552 | 31.339 | 78.254 | Satluj | M(o) | 11.46 | 4,676 |\n| 4553 | 0153I0704553 | 31.320 | 78.384 | Satluj | M(o) | 0.97 | 4,929 |\n| 4554 | 0153I0704554 | 31.315 | 78.330 | Satluj | M(o) | 0.31 | 4,399 |\n| 4555 | 0153I0704555 | 31.314 | 78.328 | Satluj | M(o) | 0.50 | 4,403 |\n| 4556 | 0153I0704556 | 31.312 | 78.330 | Satluj | M(o) | 0.72 | 4,411 |\n| 4557 | 0153I0704557 | 31.311 | 78.326 | Satluj | M(o) | 0.31 | 4,401 |\n| 4558 | 0153I0704558 | 31.306 | 78.326 | Satluj | M(o) | 0.47 | 4,411 |\n| 4559 | 0153I0904559 | 31.999 | 78.711 | Satluj | M(l) | 0.32 | 5,295 |\n| 4560 | 0153I0904560 | 31.991 | 78.721 | Satluj | M(l) | 0.50 | 5,437 |\n| 4561 | 0153I0904561 | 31.950 | 78.715 | Satluj | M(l) | 0.27 | 5,308 |\n| 4562 | 0153I0904562 | 31.944 | 78.718 | Satluj | M(l) | 0.29 | 5,443 |\n| 4563 | 0153I0904563 | 31.934 | 78.685 | Satluj | M(o) | 0.64 | 5,266 |\n| 4564 | 0153I0904564 | 31.931 | 78.693 | Satluj | M(o) | 0.27 | 5,307 |\n| 4565 | 0153I0904565 | 31.930 | 78.694 | Satluj | M(o) | 0.29 | 5,317 |\n| 4566 | 0153I0904566 | 31.929 | 78.691 | Satluj | M(o) | 0.32 | 5,307 |\n| 4567 | 0153I0904567 | 31.929 | 78.696 | Satluj | M(o) | 0.36 | 5,336 |\n| 4568 | 0153I0904568 | 31.928 | 78.694 | Satluj | M(o) | 0.28 | 5,319 |\n| 4569 | 0153I0904569 | 31.918 | 78.703 | Satluj | M(o) | 0.61 | 5,462 |\n| 4570 | 0153I0904570 | 31.912 | 78.701 | Satluj | E(c) | 3.50 | 5,566 |\n| 4571 | 0153I0904571 | 31.906 | 78.709 | Satluj | M(o) | 0.96 | 5,724 |\n| 4572 | 0153I0904572 | 31.901 | 78.719 | Satluj | M(o) | 0.57 | 5,583 |\n| 4573 | 0153I0904573 | 31.898 | 78.714 | Satluj | M(l) | 1.02 | 5,533 |\n| 4574 | 0153I0904574 | 31.897 | 78.701 | Satluj | I(s) | 0.39 | 5,397 |\n| 4575 | 0153I0904575 | 31.896 | 78.704 | Satluj | I(s) | 0.51 | 5,426 |\n| 4576 | 0153I0904576 | 31.891 | 78.724 | Satluj | I(s) | 0.37 | 5,678 |\n| 4577 | 0153I0904577 | 31.887 | 78.705 | Satluj | M(o) | 0.46 | 5,509 |\n| 4578 | 0153I1004578 | 31.709 | 78.741 | Satluj | M(o) | 1.89 | 5,551 |\n| 4579 | 0153I1004579 | 31.678 | 78.742 | Satluj | M(o) | 0.61 | 5,532 |\n| 4580 | 0153I1004580 | 31.657 | 78.747 | Satluj | M(o) | 0.35 | 5,467 |\n| 4581 | 0153I1004581 | 31.575 | 78.601 | Satluj | O | 0.43 | 4,858 |\n| 4582 | 0153I1004582 | 31.575 | 78.602 | Satluj | O | 0.77 | 4,861 |\n| 4583 | 0153I1004583 | 31.574 | 78.600 | Satluj | O | 0.29 | 4,858 |\n| 4584 | 0153I1004584 | 31.564 | 78.610 | Satluj | M(e) | 1.81 | 5,053 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 10699, "line_end": 10846, "token_count_estimate": 1648, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0153I0704550", "0153I0704551", "0153I0704552", "0153I0704553", "0153I0704554", "0153I0704555", "0153I0704556", "0153I0704557", "0153I0704558", "0153I0904559", "0153I0904560", "0153I0904561", "0153I0904562", "0153I0904563", "0153I0904564", "0153I0904565", "0153I0904566", "0153I0904567", "0153I0904568", "0153I0904569", "0153I0904570", "0153I0904571", "0153I0904572", "0153I0904573", "0153I0904574", "0153I0904575", "0153I0904576", "0153I0904577", "0153I1004578", "0153I1004579", "0153I1004580", "0153I1004581", "0153I1004582", "0153I1004583", "0153I1004584"]}}
{"id": "294b6b14e83b9720", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4585 | 0153I1004585 | 31.556 | 78.710 | Satluj | E(o) | 0.58 | 5,084 |\n| 4586 | 0153I1004586 | 31.545 | 78.738 | Satluj | M(o) | 0.65 | 5,303 |\n| 4587 | 0153I1004587 | 31.542 | 78.736 | Satluj | M(e) | 1.06 | 5,325 |\n| 4588 | 0153I1004588 | 31.531 | 78.717 | Satluj | M(o) | 0.99 | 5,155 |\n| 4589 | 0153I1004589 | 31.522 | 78.714 | Satluj | E(o) | 0.29 | 5,501 |\n| 4590 | 0153I1004590 | 31.519 | 78.735 | Satluj | M(o) | 3.00 | 5,121 |\n| 4591 | 0153I1004591 | 31.508 | 78.732 | Satluj | I(s) | 0.33 | 5,200 |\n| 4592 | 0153I1104592 | 31.486 | 78.732 | Satluj | M(o) | 0.25 | 5,256 |\n| 4593 | 0153I1104593 | 31.484 | 78.653 | Satluj | M(o) | 0.32 | 4,815 |\n| 4594 | 0153I1104594 | 31.474 | 78.675 | Satluj | I(s) | 0.28 | 5,082 |\n| 4595 | 0153I1104595 | 31.372 | 78.581 | Satluj | I(s) | 1.30 | 4,632 |\n| 4596 | 0153I1104596 | 31.369 | 78.581 | Satluj | I(s) | 0.27 | 4,672 |\n| 4597 | 0153I1104597 | 31.367 | 78.722 | Satluj | M(o) | 0.33 | 5,259 |\n| 4598 | 0153I1104598 | 31.363 | 78.724 | Satluj | M(o) | 0.43 | 5,216 |\n| 4599 | 0153I1104599 | 31.362 | 78.733 | Satluj | M(o) | 0.25 | 5,323 |\n| 4600 | 0153I1104600 | 31.361 | 78.583 | Satluj | I(s) | 0.27 | 4,748 |\n| 4601 | 0153I1104601 | 31.361 | 78.704 | Satluj | M(o) | 0.72 | 5,068 |\n| 4602 | 0153I1104602 | 31.360 | 78.668 | Satluj | I(s) | 0.32 | 4,863 |\n| 4603 | 0153I1104603 | 31.360 | 78.665 | Satluj | I(s) | 4.81 | 4,872 |\n| 4604 | 0153I1104604 | 31.359 | 78.733 | Satluj | M(o) | 0.87 | 5,316 |\n| 4605 | 0153I1104605 | 31.352 | 78.708 | Satluj | M(o) | 0.25 | 5,157 |\n| 4606 | 0153I1104606 | 31.350 | 78.731 | Satluj | M(o) | 0.55 | 5,302 |\n| 4607 | 0153I1104607 | 31.340 | 78.717 | Satluj | I(s) | 0.67 | 5,274 |\n| 4608 | 0153I1104608 | 31.336 | 78.718 | Satluj | M(o) | 0.38 | 5,243 |\n| 4609 | 0153I1104609 | 31.333 | 78.664 | Satluj | E(o) | 0.79 | 5,228 |\n| 4610 | 0153I1104610 | 31.326 | 78.702 | Satluj | I(s) | 0.57 | 5,321 |\n| 4611 | 0153I1104611 | 31.325 | 78.697 | Satluj | E(o) | 0.54 | 5,237 |\n| 4612 | 0153I1104612 | 31.318 | 78.574 | Satluj | M(o) | 0.28 | 5,302 |\n| 4613 | 0153I1204613 | 31.240 | 78.552 | Satluj | I(s) | 0.59 | 5,262 |\n| 4614 | 0153I1304614 | 32.000 | 78.910 | Satluj | O | 0.76 | 5,482 |\n| 4615 | 0153I1304615 | 31.993 | 78.845 | Satluj | M(e) | 20.90 | 5,613 |\n| 4616 | 0153I1304616 | 31.988 | 78.844 | Satluj | M(o) | 0.36 | 5,580 |\n| 4617 | 0153I1304617 | 31.981 | 78.838 | Satluj | M(e) | 3.53 | 5,513 |\n| 4618 | 0153I1304618 | 31.979 | 78.837 | Satluj | M(o) | 1.21 | 5,510 |\n| 4619 | 0153I1304619 | 31.978 | 78.756 | Satluj | M(l) | 0.35 | 5,676 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 10699, "line_end": 10846, "token_count_estimate": 1645, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0153I1004585", "0153I1004586", "0153I1004587", "0153I1004588", "0153I1004589", "0153I1004590", "0153I1004591", "0153I1104592", "0153I1104593", "0153I1104594", "0153I1104595", "0153I1104596", "0153I1104597", "0153I1104598", "0153I1104599", "0153I1104600", "0153I1104601", "0153I1104602", "0153I1104603", "0153I1104604", "0153I1104605", "0153I1104606", "0153I1104607", "0153I1104608", "0153I1104609", "0153I1104610", "0153I1104611", "0153I1104612", "0153I1204613", "0153I1304614", "0153I1304615", "0153I1304616", "0153I1304617", "0153I1304618", "0153I1304619"]}}
{"id": "12eb7ee8143b081e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4620 | 0153I1304620 | 31.977 | 78.838 | Satluj | M(o) | 1.24 | 5,499 |\n| 4621 | 0153I1304621 | 31.976 | 78.760 | Satluj | M(l) | 0.29 | 5,833 |\n| 4622 | 0153I1304622 | 31.975 | 78.843 | Satluj | O | 1.49 | 5,454 |\n| 4623 | 0153I1304623 | 31.971 | 78.798 | Satluj | E(o) | 0.33 | 5,746 |\n| 4624 | 0153I1304624 | 31.970 | 78.869 | Satluj | M(e) | 6.03 | 5,564 |\n| 4625 | 0153I1304625 | 31.964 | 78.812 | Satluj | M(e) | 3.94 | 5,615 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 10699, "line_end": 10846, "token_count_estimate": 369, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0153I1304620", "0153I1304621", "0153I1304622", "0153I1304623", "0153I1304624", "0153I1304625"]}}
{"id": "f880b09c2af9deae", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 10847, "line_end": 10858, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "77ef8384b84d530e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4626 | 0153I1304626 | 31.959 | 78.823 | Satluj | M(o) | 0.43 | 5,637 |\n| 4627 | 0153I1304627 | 31.957 | 78.867 | Satluj | E(o) | 3.95 | 5,680 |\n| 4628 | 0153I1304628 | 31.956 | 78.783 | Satluj | E(o) | 0.29 | 5,720 |\n| 4629 | 0153I1304629 | 31.936 | 78.810 | Satluj | I(s) | 1.09 | 5,262 |\n| 4630 | 0153I1304630 | 31.934 | 78.815 | Satluj | I(s) | 0.82 | 5,235 |\n| 4631 | 0153I1304631 | 31.933 | 78.818 | Satluj | I(s) | 0.44 | 5,232 |\n| 4632 | 0153I1304632 | 31.933 | 78.823 | Satluj | I(s) | 0.26 | 5,206 |\n| 4633 | 0153I1304633 | 31.933 | 78.821 | Satluj | I(s) | 0.35 | 5,218 |\n| 4634 | 0153I1304634 | 31.930 | 78.822 | Satluj | I(s) | 0.49 | 5,227 |\n| 4635 | 0153I1304635 | 31.930 | 78.782 | Satluj | I(s) | 0.85 | 5,581 |\n| 4636 | 0153I1304636 | 31.929 | 78.819 | Satluj | I(s) | 0.57 | 5,232 |\n| 4637 | 0153I1304637 | 31.929 | 78.835 | Satluj | E(o) | 0.31 | 5,055 |\n| 4638 | 0153I1304638 | 31.927 | 78.784 | Satluj | I(s) | 0.97 | 5,570 |\n| 4639 | 0153I1304639 | 31.919 | 78.784 | Satluj | M(e) | 13.44 | 5,351 |\n| 4640 | 0153I1304640 | 31.914 | 78.840 | Satluj | M(e) | 18.03 | 5,583 |\n| 4641 | 0153I1304641 | 31.908 | 78.802 | Satluj | M(o) | 1.19 | 5,421 |\n| 4642 | 0153I1304642 | 31.907 | 78.785 | Satluj | M(o) | 0.91 | 5,226 |\n| 4643 | 0153I1304643 | 31.905 | 78.786 | Satluj | M(o) | 0.33 | 5,230 |\n| 4644 | 0153I1304644 | 31.883 | 78.790 | Satluj | M(o) | 0.30 | 5,337 |\n| 4645 | 0153I1304645 | 31.882 | 78.794 | Satluj | M(o) | 0.57 | 5,348 |\n| 4646 | 0153I1304646 | 31.880 | 78.794 | Satluj | M(o) | 0.42 | 5,370 |\n| 4647 | 0153I1304647 | 31.876 | 78.801 | Satluj | M(o) | 0.55 | 5,417 |\n| 4648 | 0153I1404648 | 31.661 | 78.751 | Satluj | M(o) | 0.48 | 5,474 |\n| 4649 | 0153I1404649 | 31.658 | 78.762 | Satluj | M(o) | 0.69 | 5,415 |\n| 4650 | 0153I1404650 | 31.554 | 78.751 | Satluj | M(e) | 10.67 | 5,276 |\n| 4651 | 0153I1504651 | 31.484 | 78.766 | Satluj | M(o) | 0.31 | 5,212 |\n| 4652 | 0153I1504652 | 31.457 | 78.784 | Satluj | M(o) | 0.31 | 5,241 |\n| 4653 | 0153I1504653 | 31.450 | 78.793 | Satluj | M(o) | 7.61 | 5,268 |\n| 4654 | 0153I1504654 | 31.370 | 78.796 | Satluj | E(o) | 0.90 | 4,668 |\n| 4655 | 0153I1504655 | 31.368 | 78.757 | Satluj | M(o) | 0.26 | 5,323 |\n| 4656 | 0153I1504656 | 31.364 | 78.758 | Satluj | M(o) | 0.51 | 5,321 |\n| 4657 | 0153I1504657 | 31.362 | 78.763 | Satluj | M(o) | 0.26 | 5,279 |\n| 4658 | 0153I1504658 | 31.361 | 78.771 | Satluj | E(o) | 0.46 | 5,147 |\n| 4659 | 0153I1504659 | 31.353 | 78.758 | Satluj | E(c) | 3.69 | 5,349 |\n| 4660 | 0153I1504660 | 31.326 | 78.756 | Satluj | M(o) | 0.26 | 5,182 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 10859, "line_end": 11006, "token_count_estimate": 1663, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0153I1304626", "0153I1304627", "0153I1304628", "0153I1304629", "0153I1304630", "0153I1304631", "0153I1304632", "0153I1304633", "0153I1304634", "0153I1304635", "0153I1304636", "0153I1304637", "0153I1304638", "0153I1304639", "0153I1304640", "0153I1304641", "0153I1304642", "0153I1304643", "0153I1304644", "0153I1304645", "0153I1304646", "0153I1304647", "0153I1404648", "0153I1404649", "0153I1404650", "0153I1504651", "0153I1504652", "0153I1504653", "0153I1504654", "0153I1504655", "0153I1504656", "0153I1504657", "0153I1504658", "0153I1504659", "0153I1504660"]}}
{"id": "e1d24373cbb93675", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4661 | 0153I1504661 | 31.324 | 78.756 | Satluj | M(o) | 0.60 | 5,189 |\n| 4662 | 0153I1504662 | 31.322 | 78.758 | Satluj | M(o) | 0.63 | 5,209 |\n| 4663 | 0153I1504663 | 31.322 | 78.760 | Satluj | M(o) | 1.02 | 5,227 |\n| 4664 | 0153I1504664 | 31.322 | 78.757 | Satluj | M(o) | 0.83 | 5,212 |\n| 4665 | 0153I1504665 | 31.313 | 78.793 | Satluj | E(o) | 0.36 | 5,402 |\n| 4666 | 0153I1504666 | 31.306 | 78.878 | Satluj | E(o) | 1.52 | 5,227 |\n| 4667 | 0153I1504667 | 31.300 | 78.884 | Satluj | E(o) | 0.27 | 5,238 |\n| 4668 | 0153I1504668 | 31.299 | 78.799 | Satluj | E(o) | 0.27 | 5,474 |\n| 4669 | 0153I1504669 | 31.298 | 78.795 | Satluj | E(o) | 1.35 | 5,452 |\n| 4670 | 0153I1504670 | 31.295 | 78.804 | Satluj | M(o) | 0.27 | 5,396 |\n| 4671 | 0153I1504671 | 31.275 | 78.785 | Satluj | M(l) | 0.49 | 5,298 |\n| 4672 | 0153M0804672 | 31.233 | 79.306 | Satluj | E(o) | 3.87 | 5,077 |\n| 4673 | 0153M0804673 | 31.154 | 79.319 | Satluj | M(o) | 0.49 | 5,699 |\n| 4674 | 0153M0804674 | 31.153 | 79.341 | Satluj | M(e) | 3.43 | 5,481 |\n| 4675 | 0153M0804675 | 31.150 | 79.357 | Satluj | M(e) | 1.10 | 5,429 |\n| 4676 | 0153M0804676 | 31.150 | 79.362 | Satluj | M(o) | 0.30 | 5,467 |\n| 4677 | 0153M0804677 | 31.144 | 79.366 | Satluj | M(e) | 3.38 | 5,497 |\n| 4678 | 0153M0804678 | 31.131 | 79.491 | Satluj | E(c) | 1.37 | 5,842 |\n| 4679 | 0153M0804679 | 31.098 | 79.490 | Satluj | E(o) | 2.77 | 5,920 |\n| 4680 | 0153M1204680 | 31.197 | 79.511 | Satluj | M(o) | 0.35 | 5,689 |\n| 4681 | 0153M1204681 | 31.132 | 79.513 | Satluj | E(o) | 4.80 | 5,679 |\n| 4682 | 0153M1204682 | 31.127 | 79.526 | Satluj | E(o) | 2.12 | 5,789 |\n| 4683 | 0153M1204683 | 31.104 | 79.515 | Satluj | M(o) | 0.74 | 5,856 |\n| 4684 | 0153M1204684 | 31.102 | 79.507 | Satluj | E(o) | 1.41 | 5,928 |\n| 4685 | 0153M1204685 | 31.092 | 79.569 | Satluj | M(o) | 0.55 | 5,418 |\n| 4686 | 0153M1204686 | 31.089 | 79.513 | Satluj | M(lg) | 1.80 | 6,086 |\n| 4687 | 0153M1204687 | 31.077 | 79.649 | Satluj | E(o) | 1.45 | 5,652 |\n| 4688 | 0153M1204688 | 31.074 | 79.655 | Satluj | E(c) | 0.60 | 5,813 |\n| 4689 | 0153M1204689 | 31.072 | 79.652 | Satluj | E(o) | 1.08 | 5,756 |\n| 4690 | 0153M1204690 | 31.035 | 79.618 | Satluj | M(o) | 0.32 | 5,341 |\n| 4691 | 0153M1204691 | 31.031 | 79.608 | Satluj | M(l) | 0.95 | 5,390 |\n| 4692 | 0153M1204692 | 31.029 | 79.730 | Satluj | M(e) | 4.88 | 5,333 |\n| 4693 | 0153M1304693 | 31.997 | 79.870 | Satluj | E(o) | 0.25 | 5,662 |\n| 4694 | 0153M1304694 | 31.997 | 79.867 | Satluj | E(o) | 5.64 | 5,657 |\n| 4695 | 0153M1304695 | 31.996 | 79.872 | Satluj | E(o) | 1.58 | 5,661 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 10859, "line_end": 11006, "token_count_estimate": 1660, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0153I1504661", "0153I1504662", "0153I1504663", "0153I1504664", "0153I1504665", "0153I1504666", "0153I1504667", "0153I1504668", "0153I1504669", "0153I1504670", "0153I1504671", "0153M0804672", "0153M0804673", "0153M0804674", "0153M0804675", "0153M0804676", "0153M0804677", "0153M0804678", "0153M0804679", "0153M1204680", "0153M1204681", "0153M1204682", "0153M1204683", "0153M1204684", "0153M1204685", "0153M1204686", "0153M1204687", "0153M1204688", "0153M1204689", "0153M1204690", "0153M1204691", "0153M1204692", "0153M1304693", "0153M1304694", "0153M1304695"]}}
{"id": "26318ef1ab2c769b", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4696 | 0153M1304696 | 31.995 | 79.872 | Satluj | E(o) | 0.64 | 5,664 |\n| 4697 | 0153M1304697 | 31.992 | 79.846 | Satluj | E(o) | 2.00 | 5,646 |\n| 4698 | 0153M1304698 | 31.992 | 79.868 | Satluj | M(o) | 0.25 | 5,709 |\n| 4699 | 0153M1304699 | 31.988 | 79.836 | Satluj | E(o) | 2.10 | 5,641 |\n| 4700 | 0153M1304700 | 31.984 | 79.958 | Indus Upper | M(e) | 15.73 | 5,544 |\n| 4701 | 0153M1304701 | 31.977 | 79.872 | Satluj | E(o) | 6.99 | 5,612 |\n| 4702 | 0153M1304702 | 31.972 | 79.973 | Indus Upper | M(e) | 6.67 | 5,433 |\n| 4703 | 0153M1304703 | 31.970 | 79.882 | Satluj | M(o) | 2.58 | 5,582 |\n| 4704 | 0153M1304704 | 31.968 | 79.876 | Satluj | E(o) | 3.29 | 5,583 |\n| 4705 | 0153M1304705 | 31.967 | 79.890 | Satluj | M(o) | 2.21 | 5,616 |\n| 4706 | 0153M1304706 | 31.966 | 79.882 | Satluj | M(o) | 0.40 | 5,596 |\n| 4707 | 0153M1304707 | 31.964 | 79.899 | Satluj | E(c) | 4.75 | 5,739 |\n| 4708 | 0153M1304708 | 31.963 | 79.879 | Satluj | E(o) | 1.95 | 5,584 |\n| 4709 | 0153M1304709 | 31.961 | 79.937 | Indus Upper | M(o) | 1.04 | 5,651 |\n| 4710 | 0153M1304710 | 31.960 | 79.893 | Satluj | M(o) | 0.49 | 5,717 |\n| 4711 | 0153M1304711 | 31.959 | 79.936 | Indus Upper | M(e) | 4.10 | 5,653 |\n| 4712 | 0153M1304712 | 31.959 | 79.874 | Satluj | E(o) | 0.25 | 5,552 |\n| 4713 | 0153M1304713 | 31.959 | 79.895 | Satluj | M(o) | 2.99 | 5,699 |\n| 4714 | 0153M1304714 | 31.958 | 79.874 | Satluj | E(o) | 0.71 | 5,554 |\n| 4715 | 0153M1304715 | 31.956 | 79.924 | Indus Upper | M(e) | 1.80 | 5,708 |\n| 4716 | 0153M1304716 | 31.956 | 79.877 | Satluj | E(o) | 2.83 | 5,651 |\n| 4717 | 0153M1304717 | 31.955 | 79.891 | Satluj | M(o) | 0.28 | 5,678 |\n| 4718 | 0153M1304718 | 31.954 | 79.913 | Satluj | E(o) | 1.04 | 5,863 |\n| 4719 | 0153M1304719 | 31.950 | 79.856 | Satluj | E(o) | 1.45 | 5,457 |\n| 4720 | 0153M1304720 | 31.950 | 79.986 | Indus Upper | M(e) | 10.43 | 5,449 |\n| 4721 | 0153M1304721 | 31.940 | 79.876 | Satluj | E(o) | 0.59 | 5,528 |\n| 4722 | 0153M1304722 | 31.937 | 79.994 | Indus Upper | M(e) | 14.46 | 5,494 |\n| 4723 | 0153M1304723 | 31.935 | 79.890 | Satluj | E(o) | 0.32 | 5,643 |\n| 4724 | 0153M1304724 | 31.934 | 79.919 | Satluj | E(o) | 0.58 | 5,616 |\n| 4725 | 0153M1304725 | 31.932 | 79.882 | Satluj | E(c) | 2.25 | 5,630 |\n| 4726 | 0153M1304726 | 31.927 | 79.901 | Satluj | E(o) | 1.64 | 5,448 |\n| 4727 | 0153M1304727 | 31.925 | 79.865 | Satluj | E(o) | 31.43 | 5,356 |\n| 4728 | 0153M1304728 | 31.917 | 79.906 | Satluj | E(o) | 0.55 | 5,710 |\n| 4729 | 0153M1304729 | 31.917 | 79.919 | Indus Upper | E(o) | 0.45 | 5,744 |\n| 4730 | 0153M1304730 | 31.916 | 79.934 | Indus Upper | M(o) | 8.36 | 5,668 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 10859, "line_end": 11006, "token_count_estimate": 1660, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0153M1304696", "0153M1304697", "0153M1304698", "0153M1304699", "0153M1304700", "0153M1304701", "0153M1304702", "0153M1304703", "0153M1304704", "0153M1304705", "0153M1304706", "0153M1304707", "0153M1304708", "0153M1304709", "0153M1304710", "0153M1304711", "0153M1304712", "0153M1304713", "0153M1304714", "0153M1304715", "0153M1304716", "0153M1304717", "0153M1304718", "0153M1304719", "0153M1304720", "0153M1304721", "0153M1304722", "0153M1304723", "0153M1304724", "0153M1304725", "0153M1304726", "0153M1304727", "0153M1304728", "0153M1304729", "0153M1304730"]}}
{"id": "544730355d23d057", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4731 | 0153M1304731 | 31.913 | 79.938 | Indus Upper | M(o) | 1.43 | 5,661 |\n| 4732 | 0153M1304732 | 31.909 | 79.974 | Indus Upper | M(o) | 0.65 | 5,625 |\n| 4733 | 0153M1304733 | 31.909 | 79.979 | Indus Upper | E(o) | 0.31 | 5,589 |\n| 4734 | 0153M1304734 | 31.909 | 79.988 | Indus Upper | E(o) | 14.06 | 5,558 |\n| 4735 | 0153M1304735 | 31.909 | 79.981 | Indus Upper | E(o) | 0.99 | 5,586 |\n| 4736 | 0153M1304736 | 31.907 | 79.994 | Indus Upper | E(o) | 0.56 | 5,517 |\n| 4737 | 0153M1304737 | 31.906 | 79.995 | Indus Upper | E(o) | 0.75 | 5,507 |\n| 4738 | 0153M1304738 | 31.900 | 79.986 | Indus Upper | E(c) | 10.24 | 5,663 |\n| 4739 | 0153M1304739 | 31.898 | 79.978 | Indus Upper | M(o) | 0.29 | 5,761 |\n| 4740 | 0153M1304740 | 31.887 | 79.956 | Indus Upper | E(o) | 6.54 | 5,439 |\n| 4741 | 0153M1304741 | 31.878 | 79.984 | Indus Upper | E(o) | 27.77 | 5,514 |\n| 4742 | 0153M1604742 | 31.062 | 79.793 | Satluj | E(o) | 0.30 | 5,550 |\n| 4743 | 0153M1604743 | 31.040 | 79.753 | Satluj | M(e) | 3.85 | 5,547 |\n| 4744 | 0153N1304744 | 30.998 | 79.767 | Satluj | E(c) | 1.65 | 5,819 |\n| 4745 | 0153N1304745 | 30.991 | 79.904 | Satluj | E(o) | 0.42 | 4,625 |\n| 4746 | 0153N1304746 | 30.990 | 79.903 | Satluj | E(o) | 0.61 | 4,622 |\n| 4747 | 0153N1304747 | 30.989 | 79.947 | Satluj | E(o) | 0.66 | 4,543 |\n| 4748 | 0161B0304748 | 34.282 | 80.090 | Shyok | M(e) | 25.66 | 5,697 |\n| 4749 | 0161B0804749 | 34.217 | 80.313 | Shyok | O | 0.29 | 5,312 |\n| 4750 | 0161B1504750 | 34.426 | 80.796 | Shyok | M(lg) | 3.91 | 5,510 |\n| 4751 | 0161B1504751 | 34.407 | 80.801 | Shyok | M(lg) | 1.29 | 5,641 |\n| 4752 | 0161B1504752 | 34.328 | 80.810 | Shyok | M(lg) | 1.89 | 5,613 |\n| 4753 | 0161B1504753 | 34.325 | 80.833 | Shyok | M(lg) | 2.35 | 5,737 |\n| 4754 | 0161B1504754 | 34.325 | 80.829 | Shyok | M(lg) | 1.82 | 5,712 |\n| 4755 | 0161B1504755 | 34.324 | 80.840 | Shyok | M(lg) | 13.22 | 5,742 |\n| 4756 | 0161B1504756 | 34.320 | 80.815 | Shyok | M(lg) | 1.51 | 5,572 |\n| 4757 | 0161B1504757 | 34.316 | 80.858 | Shyok | I(d) | 232.34 | 5,709 |\n| 4758 | 0161B1504758 | 34.315 | 80.794 | Shyok | M(e) | 6.43 | 5,394 |\n| 4759 | 0161B1504759 | 34.311 | 80.884 | Shyok | M(lg) | 8.39 | 5,803 |\n| 4760 | 0161B1504760 | 34.307 | 80.982 | Shyok | M(e) | 42.60 | 5,729 |\n| 4761 | 0161B1504761 | 34.300 | 80.943 | Shyok | M(e) | 1.23 | 5,629 |\n| 4762 | 0161B1504762 | 34.292 | 80.824 | Shyok | M(e) | 1.00 | 5,660 |\n| 4763 | 0161B1504763 | 34.280 | 80.952 | Shyok | M(o) | 1.66 | 5,516 |\n| 4764 | 0161B1504764 | 34.279 | 80.929 | Shyok | M(o) | 0.35 | 5,647 |\n| 4765 | 0161B1504765 | 34.279 | 80.943 | Shyok | M(o) | 0.40 | 5,566 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 10859, "line_end": 11006, "token_count_estimate": 1650, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0153M1304731", "0153M1304732", "0153M1304733", "0153M1304734", "0153M1304735", "0153M1304736", "0153M1304737", "0153M1304738", "0153M1304739", "0153M1304740", "0153M1304741", "0153M1604742", "0153M1604743", "0153N1304744", "0153N1304745", "0153N1304746", "0153N1304747", "0161B0304748", "0161B0804749", "0161B1504750", "0161B1504751", "0161B1504752", "0161B1504753", "0161B1504754", "0161B1504755", "0161B1504756", "0161B1504757", "0161B1504758", "0161B1504759", "0161B1504760", "0161B1504761", "0161B1504762", "0161B1504763", "0161B1504764", "0161B1504765"]}}
{"id": "e2a60e5af1a9da36", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4766 | 0161B1504766 | 34.277 | 80.946 | Shyok | M(o) | 1.00 | 5,512 |\n| 4767 | 0161B1504767 | 34.269 | 80.953 | Shyok | M(o) | 0.26 | 5,463 |\n| 4768 | 0161C0104768 | 33.979 | 80.044 | Shyok | M(e) | 0.94 | 5,637 |\n| 4769 | 0161D0104769 | 32.994 | 80.211 | Shyok | M(o) | 0.39 | 5,792 |\n| 4770 | 0161D0104770 | 32.967 | 80.201 | Shyok | M(o) | 1.85 | 5,741 |\n| 4771 | 0161D0104771 | 32.965 | 80.202 | Shyok | M(o) | 1.75 | 5,742 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 10859, "line_end": 11006, "token_count_estimate": 373, "basins": ["Indus"], "subbasins": ["Shyok"], "countries": [], "lake_ids": ["0161B1504766", "0161B1504767", "0161C0104768", "0161D0104769", "0161D0104770", "0161D0104771"]}}
{"id": "68ee01e2f3005552", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 11007, "line_end": 11015, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e6a3b86cdd040d14", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4772 | 0161D0104772 | 32.957 | 80.218 | Shyok | E(o) | 0.41 | 5,893 |\n| 4773 | 0161D0104773 | 32.942 | 80.000 | Shyok | E(o) | 0.37 | 5,776 |\n| 4774 | 0161D0104774 | 32.920 | 80.039 | Shyok | M(o) | 0.67 | 5,619 |\n| 4775 | 0161D0104775 | 32.900 | 80.231 | Shyok | E(o) | 0.58 | 5,725 |\n| 4776 | 0161D0104776 | 32.897 | 80.223 | Shyok | I(s) | 0.42 | 5,681 |\n| 4777 | 0161D0104777 | 32.889 | 80.211 | Shyok | E(o) | 4.09 | 5,857 |\n| 4778 | 0161D0104778 | 32.888 | 80.222 | Shyok | I(s) | 0.83 | 5,890 |\n| 4779 | 0161D0104779 | 32.880 | 80.051 | Shyok | M(o) | 5.37 | 5,736 |\n| 4780 | 0161D0104780 | 32.872 | 80.126 | Shyok | E(c) | 1.32 | 5,631 |\n| 4781 | 0161D0104781 | 32.871 | 80.072 | Shyok | M(o) | 1.53 | 5,700 |\n| 4782 | 0161D0104782 | 32.862 | 80.116 | Shyok | M(o) | 1.13 | 5,688 |\n| 4783 | 0161D0104783 | 32.860 | 80.244 | Shyok | E(o) | 1.57 | 5,727 |\n| 4784 | 0161D0104784 | 32.860 | 80.106 | Shyok | M(o) | 2.16 | 5,651 |\n| 4785 | 0161D0104785 | 32.855 | 80.134 | Shyok | M(o) | 1.27 | 5,677 |\n| 4786 | 0161D0104786 | 32.854 | 80.149 | Shyok | M(o) | 0.31 | 5,616 |\n| 4787 | 0161D0104787 | 32.827 | 80.219 | Shyok | E(o) | 0.60 | 5,765 |\n| 4788 | 0161D0104788 | 32.825 | 80.220 | Shyok | E(o) | 0.55 | 5,766 |\n| 4789 | 0161D0304789 | 32.251 | 80.183 | Indus Upper | E(o) | 1.79 | 5,773 |\n| 4790 | 0161D0504790 | 32.949 | 80.385 | Shyok | M(o) | 0.34 | 5,698 |\n| 4791 | 0161D0504791 | 32.949 | 80.296 | Shyok | E(o) | 1.08 | 5,685 |\n| 4792 | 0161D0504792 | 32.911 | 80.362 | Shyok | E(c) | 1.11 | 5,632 |\n| 4793 | 0161D0504793 | 32.879 | 80.256 | Shyok | E(o) | 2.48 | 5,728 |\n| 4794 | 0161D0804794 | 32.076 | 80.374 | Indus Upper | O | 9.22 | 5,648 |\n| 4795 | 0161D0804795 | 32.057 | 80.358 | Indus Upper | E(o) | 0.29 | 5,858 |\n| 4796 | 0161D0804796 | 32.033 | 80.359 | Indus Upper | E(o) | 2.63 | 5,827 |\n| 4797 | 0161D0804797 | 32.028 | 80.368 | Indus Upper | E(o) | 0.35 | 5,830 |\n| 4798 | 0161D0804798 | 32.020 | 80.350 | Indus Upper | M(o) | 4.71 | 5,743 |\n| 4799 | 0161D0804799 | 32.011 | 80.370 | Indus Upper | E(o) | 2.48 | 5,661 |\n| 4800 | 0161D1204800 | 32.092 | 80.702 | Indus Upper | O | 0.80 | 5,111 |\n| 4801 | 0161D1504801 | 32.435 | 80.854 | Indus Upper | O | 1.47 | 4,454 |\n| 4802 | 0161D1504802 | 32.425 | 80.857 | Indus Upper | O | 0.51 | 4,454 |\n| 4803 | 0161D1504803 | 32.424 | 80.855 | Indus Upper | O | 0.98 | 4,455 |\n| 4804 | 0161D1504804 | 32.423 | 80.865 | Indus Upper | O | 58.84 | 4,452 |\n| 4805 | 0161D1504805 | 32.421 | 80.858 | Indus Upper | O | 0.44 | 4,451 |\n| 4806 | 0161D1504806 | 32.419 | 80.856 | Indus Upper | O | 3.84 | 4,454 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 11016, "line_end": 11163, "token_count_estimate": 1647, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0161D0104772", "0161D0104773", "0161D0104774", "0161D0104775", "0161D0104776", "0161D0104777", "0161D0104778", "0161D0104779", "0161D0104780", "0161D0104781", "0161D0104782", "0161D0104783", "0161D0104784", "0161D0104785", "0161D0104786", "0161D0104787", "0161D0104788", "0161D0304789", "0161D0504790", "0161D0504791", "0161D0504792", "0161D0504793", "0161D0804794", "0161D0804795", "0161D0804796", "0161D0804797", "0161D0804798", "0161D0804799", "0161D1204800", "0161D1504801", "0161D1504802", "0161D1504803", "0161D1504804", "0161D1504805", "0161D1504806"]}}
{"id": "09ae9ae8add492d6", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4807 | 0161D1504807 | 32.419 | 80.863 | Indus Upper | O | 1.89 | 4,453 |\n| 4808 | 0161F0304808 | 34.330 | 81.074 | Shyok | M(e) | 6.50 | 5,701 |\n| 4809 | 0161F0304809 | 34.319 | 81.165 | Shyok | E(o) | 1.13 | 5,273 |\n| 4810 | 0161F0304810 | 34.314 | 81.158 | Shyok | O | 3.76 | 5,271 |\n| 4811 | 0161F0304811 | 34.308 | 81.010 | Shyok | M(e) | 3.12 | 5,697 |\n| 4812 | 0161F0304812 | 34.304 | 81.140 | Shyok | O | 1.15 | 5,266 |\n| 4813 | 0161F0304813 | 34.299 | 81.202 | Shyok | O | 61.15 | 5,274 |\n| 4814 | 0161F0304814 | 34.290 | 81.155 | Shyok | O | 1.67 | 5,265 |\n| 4815 | 0161F0304815 | 34.283 | 81.109 | Shyok | O | 1.35 | 5,260 |\n| 4816 | 0161F0304816 | 34.281 | 81.130 | Shyok | O | 0.77 | 5,261 |\n| 4817 | 0161F0304817 | 34.268 | 81.131 | Shyok | M(o) | 1.43 | 5,439 |\n| 4818 | 0161F0304818 | 34.263 | 81.181 | Shyok | M(e) | 0.55 | 5,578 |\n| 4819 | 0161F0304819 | 34.262 | 81.132 | Shyok | M(e) | 5.61 | 5,489 |\n| 4820 | 0161F0304820 | 34.258 | 81.161 | Shyok | M(e) | 1.61 | 5,543 |\n| 4821 | 0161F0304821 | 34.256 | 81.192 | Shyok | M(o) | 0.72 | 5,627 |\n| 4822 | 0161F0304822 | 34.255 | 81.076 | Shyok | M(e) | 0.77 | 5,734 |\n| 4823 | 0161F0304823 | 34.255 | 81.108 | Shyok | M(o) | 0.62 | 5,512 |\n| 4824 | 0161F0304824 | 34.252 | 81.108 | Shyok | M(e) | 1.83 | 5,526 |\n| 4825 | 0161F0404825 | 34.213 | 81.187 | Shyok | E(o) | 2.62 | 5,669 |\n| 4826 | 0161F0404826 | 34.212 | 81.164 | Shyok | M(o) | 0.27 | 5,843 |\n| 4827 | 0161F0404827 | 34.211 | 81.187 | Shyok | M(o) | 0.53 | 5,676 |\n| 4828 | 0161F0404828 | 34.203 | 81.130 | Shyok | M(e) | 4.49 | 5,539 |\n| 4829 | 0161F0704829 | 34.341 | 81.257 | Shyok | E(o) | 51.89 | 5,298 |\n| 4830 | 0161F0704830 | 34.334 | 81.271 | Shyok | E(o) | 1.66 | 5,298 |\n| 4831 | 0161G1104831 | 33.447 | 81.673 | Shyok | E(o) | 0.56 | 5,885 |\n| 4832 | 0161H0304832 | 32.359 | 81.104 | Indus Upper | O | 2.90 | 4,509 |\n| 4833 | 0161H0304833 | 32.359 | 81.106 | Indus Upper | O | 0.69 | 4,513 |\n| 4834 | 0161H0304834 | 32.328 | 81.075 | Indus Upper | O | 0.93 | 4,653 |\n| 4835 | 0161H0804835 | 32.212 | 81.428 | Indus Upper | E(o) | 0.47 | 4,908 |\n| 4836 | 0161K0604836 | 33.694 | 82.326 | Shyok | O | 0.53 | 5,842 |\n| 4837 | 0162A0104837 | 31.945 | 80.007 | Indus Upper | M(o) | 0.86 | 5,402 |\n| 4838 | 0162A0104838 | 31.900 | 80.001 | Indus Upper | E(o) | 20.08 | 5,476 |\n| 4839 | 0162A0104839 | 31.881 | 79.999 | Indus Upper | E(o) | 13.93 | 5,511 |\n| 4840 | 0162A0204840 | 31.664 | 80.021 | Indus Upper | E(o) | 2.06 | 5,663 |\n| 4841 | 0162A0204841 | 31.648 | 80.031 | Indus Upper | E(o) | 2.06 | 5,641 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 11016, "line_end": 11163, "token_count_estimate": 1625, "basins": ["Indus"], "subbasins": ["Indus Upper", "Shyok"], "countries": [], "lake_ids": ["0161D1504807", "0161F0304808", "0161F0304809", "0161F0304810", "0161F0304811", "0161F0304812", "0161F0304813", "0161F0304814", "0161F0304815", "0161F0304816", "0161F0304817", "0161F0304818", "0161F0304819", "0161F0304820", "0161F0304821", "0161F0304822", "0161F0304823", "0161F0304824", "0161F0404825", "0161F0404826", "0161F0404827", "0161F0404828", "0161F0704829", "0161F0704830", "0161G1104831", "0161H0304832", "0161H0304833", "0161H0304834", "0161H0804835", "0161K0604836", "0162A0104837", "0162A0104838", "0162A0104839", "0162A0204840", "0162A0204841"]}}
{"id": "8a50a39bb94fe9bf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4842 | 0162A0504842 | 31.992 | 80.346 | Indus Upper | E(o) | 0.95 | 5,733 |\n| 4843 | 0162A0504843 | 31.990 | 80.346 | Indus Upper | E(o) | 1.50 | 5,733 |\n| 4844 | 0162A0504844 | 31.968 | 80.436 | Indus Upper | M(o) | 0.33 | 5,752 |\n| 4845 | 0162A0504845 | 31.948 | 80.446 | Indus Upper | M(o) | 0.32 | 5,766 |\n| 4846 | 0162A0504846 | 31.940 | 80.435 | Indus Upper | M(o) | 0.75 | 5,692 |\n| 4847 | 0162A0504847 | 31.931 | 80.456 | Indus Upper | M(e) | 0.48 | 5,821 |\n| 4848 | 0162A0504848 | 31.919 | 80.467 | Indus Upper | M(e) | 1.20 | 5,793 |\n| 4849 | 0162A0504849 | 31.916 | 80.449 | Indus Upper | M(o) | 1.19 | 5,754 |\n| 4850 | 0162A0504850 | 31.916 | 80.447 | Indus Upper | M(o) | 0.28 | 5,741 |\n| 4851 | 0162A0504851 | 31.914 | 80.482 | Indus Upper | E(c) | 0.79 | 5,766 |\n| 4852 | 0162A0504852 | 31.874 | 80.464 | Indus Upper | M(o) | 0.28 | 5,721 |\n| 4853 | 0162A0504853 | 31.873 | 80.486 | Indus Upper | E(o) | 2.40 | 5,634 |\n| 4854 | 0162A0504854 | 31.871 | 80.486 | Indus Upper | E(o) | 1.73 | 5,638 |\n| 4855 | 0162A0504855 | 31.869 | 80.488 | Indus Upper | E(o) | 1.70 | 5,635 |\n| 4856 | 0162A0504856 | 31.859 | 80.429 | Indus Upper | E(o) | 0.80 | 5,765 |\n| 4857 | 0162A0504857 | 31.849 | 80.494 | Indus Upper | E(o) | 0.30 | 5,710 |\n| 4858 | 0162A0504858 | 31.848 | 80.464 | Indus Upper | E(o) | 0.30 | 5,758 |\n| 4859 | 0162A0504859 | 31.845 | 80.480 | Indus Upper | M(o) | 1.12 | 5,750 |\n| 4860 | 0162A0604860 | 31.748 | 80.476 | Indus Upper | E(o) | 0.66 | 5,635 |\n| 4861 | 0162A0604861 | 31.742 | 80.485 | Indus Upper | E(o) | 0.26 | 5,759 |\n| 4862 | 0162A0904862 | 31.842 | 80.555 | Indus Upper | E(o) | 0.67 | 5,617 |\n| 4863 | 0162A0904863 | 31.838 | 80.548 | Indus Upper | M(o) | 7.25 | 5,700 |\n| 4864 | 0162A0904864 | 31.834 | 80.539 | Indus Upper | E(o) | 0.39 | 5,810 |\n| 4865 | 0162A0904865 | 31.832 | 80.536 | Indus Upper | E(o) | 0.64 | 5,792 |\n| 4866 | 0162A0904866 | 31.781 | 80.642 | Indus Upper | M(e) | 0.35 | 5,792 |\n| 4867 | 0162A0904867 | 31.780 | 80.637 | Indus Upper | M(e) | 0.38 | 5,836 |\n| 4868 | 0162A0904868 | 31.779 | 80.635 | Indus Upper | M(o) | 0.76 | 5,845 |\n| 4869 | 0162A0904869 | 31.778 | 80.570 | Indus Upper | M(o) | 0.53 | 5,623 |\n| 4870 | 0162A0904870 | 31.777 | 80.570 | Indus Upper | M(o) | 0.44 | 5,628 |\n| 4871 | 0162A0904871 | 31.774 | 80.537 | Indus Upper | M(e) | 1.71 | 5,587 |\n| 4872 | 0162A0904872 | 31.773 | 80.624 | Indus Upper | M(o) | 0.28 | 5,593 |\n| 4873 | 0162A0904873 | 31.772 | 80.626 | Indus Upper | E(o) | 1.89 | 5,593 |\n| 4874 | 0162A0904874 | 31.769 | 80.624 | Indus Upper | M(o) | 0.52 | 5,617 |\n| 4875 | 0162A0904875 | 31.768 | 80.511 | Indus Upper | M(o) | 0.60 | 5,575 |\n| 4876 | 0162A0904876 | 31.768 | 80.521 | Indus Upper | E(o) | 0.87 | 5,707 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 11016, "line_end": 11163, "token_count_estimate": 1681, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0162A0504842", "0162A0504843", "0162A0504844", "0162A0504845", "0162A0504846", "0162A0504847", "0162A0504848", "0162A0504849", "0162A0504850", "0162A0504851", "0162A0504852", "0162A0504853", "0162A0504854", "0162A0504855", "0162A0504856", "0162A0504857", "0162A0504858", "0162A0504859", "0162A0604860", "0162A0604861", "0162A0904862", "0162A0904863", "0162A0904864", "0162A0904865", "0162A0904866", "0162A0904867", "0162A0904868", "0162A0904869", "0162A0904870", "0162A0904871", "0162A0904872", "0162A0904873", "0162A0904874", "0162A0904875", "0162A0904876"]}}
{"id": "b43afb632c3de5a2", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4877 | 0162A0904877 | 31.752 | 80.633 | Indus Upper | E(o) | 1.85 | 5,777 |\n| 4878 | 0162A1004878 | 31.741 | 80.679 | Indus Upper | E(o) | 0.51 | 5,558 |\n| 4879 | 0162A1004879 | 31.739 | 80.686 | Indus Upper | E(o) | 0.64 | 5,669 |\n| 4880 | 0162A1004880 | 31.736 | 80.678 | Indus Upper | M(e) | 3.69 | 5,583 |\n| 4881 | 0162A1004881 | 31.735 | 80.610 | Indus Upper | E(o) | 0.35 | 5,754 |\n| 4882 | 0162A1004882 | 31.733 | 80.674 | Indus Upper | M(e) | 0.73 | 5,666 |\n| 4883 | 0162A1004883 | 31.725 | 80.680 | Indus Upper | M(e) | 0.38 | 5,661 |\n| 4884 | 0162A1004884 | 31.724 | 80.725 | Indus Upper | E(o) | 0.82 | 5,484 |\n| 4885 | 0162A1004885 | 31.713 | 80.721 | Indus Upper | M(o) | 0.96 | 5,543 |\n| 4886 | 0162A1004886 | 31.704 | 80.662 | Indus Upper | E(o) | 5.73 | 5,512 |\n| 4887 | 0162A1004887 | 31.695 | 80.615 | Indus Upper | M(e) | 1.73 | 5,565 |\n| 4888 | 0162A1004888 | 31.694 | 80.664 | Indus Upper | M(e) | 14.11 | 5,532 |\n| 4889 | 0162A1004889 | 31.671 | 80.592 | Indus Upper | E(o) | 1.19 | 5,699 |\n| 4890 | 0162A1004890 | 31.622 | 80.651 | Indus Upper | E(o) | 2.58 | 5,571 |\n| 4891 | 0162A1004891 | 31.618 | 80.732 | Indus Upper | M(e) | 2.97 | 5,562 |\n| 4892 | 0162A1004892 | 31.578 | 80.737 | Indus Upper | E(o) | 0.58 | 5,543 |\n| 4893 | 0162A1104893 | 31.369 | 80.685 | Indus Upper | E(c) | 2.39 | 5,432 |\n| 4894 | 0162A1404894 | 31.638 | 80.758 | Indus Upper | M(o) | 0.31 | 5,790 |\n| 4895 | 0162A1404895 | 31.600 | 80.779 | Indus Upper | E(o) | 1.87 | 5,600 |\n| 4896 | 0162A1404896 | 31.593 | 80.780 | Indus Upper | E(c) | 2.09 | 5,656 |\n| 4897 | 0162A1404897 | 31.586 | 80.809 | Indus Upper | E(o) | 0.42 | 5,569 |\n| 4898 | 0162A1404898 | 31.564 | 80.860 | Indus Upper | M(e) | 4.82 | 5,697 |\n| 4899 | 0162A1404899 | 31.559 | 80.833 | Indus Upper | M(o) | 0.80 | 5,628 |\n| 4900 | 0162A1404900 | 31.551 | 80.943 | Indus Upper | O | 7.89 | 5,137 |\n| 4901 | 0162A1404901 | 31.547 | 80.817 | Indus Upper | E(o) | 0.41 | 5,670 |\n| 4902 | 0162A1404902 | 31.546 | 80.817 | Indus Upper | M(o) | 1.23 | 5,661 |\n| 4903 | 0162A1404903 | 31.544 | 80.818 | Indus Upper | M(o) | 0.43 | 5,680 |\n| 4904 | 0162A1404904 | 31.540 | 80.831 | Indus Upper | E(o) | 6.95 | 5,657 |\n| 4905 | 0162A1404905 | 31.528 | 80.922 | Indus Upper | E(o) | 2.03 | 5,505 |\n| 4906 | 0162A1404906 | 31.517 | 80.791 | Indus Upper | M(e) | 5.11 | 5,658 |\n| 4907 | 0162A1404907 | 31.514 | 80.806 | Indus Upper | E(o) | 7.74 | 5,622 |\n| 4908 | 0162A1404908 | 31.514 | 80.794 | Indus Upper | M(e) | 4.06 | 5,676 |\n| 4909 | 0162A1404909 | 31.504 | 80.759 | Indus Upper | M(o) | 1.08 | 5,659 |\n| 4910 | 0162A1404910 | 31.502 | 80.892 | Indus Upper | E(o) | 0.27 | 5,582 |\n| 4911 | 0162A1504911 | 31.499 | 80.948 | Indus Upper | O | 19.60 | 5,238 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 11016, "line_end": 11163, "token_count_estimate": 1660, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0162A0904877", "0162A1004878", "0162A1004879", "0162A1004880", "0162A1004881", "0162A1004882", "0162A1004883", "0162A1004884", "0162A1004885", "0162A1004886", "0162A1004887", "0162A1004888", "0162A1004889", "0162A1004890", "0162A1004891", "0162A1004892", "0162A1104893", "0162A1404894", "0162A1404895", "0162A1404896", "0162A1404897", "0162A1404898", "0162A1404899", "0162A1404900", "0162A1404901", "0162A1404902", "0162A1404903", "0162A1404904", "0162A1404905", "0162A1404906", "0162A1404907", "0162A1404908", "0162A1404909", "0162A1404910", "0162A1504911"]}}
{"id": "6054ab2aff94018c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4912 | 0162A1504912 | 31.497 | 80.943 | Indus Upper | O | 1.24 | 5,237 |\n| 4913 | 0162A1504913 | 31.495 | 80.985 | Indus Upper | O | 24.74 | 5,386 |\n| 4914 | 0162A1504914 | 31.487 | 80.818 | Indus Upper | E(o) | 2.38 | 5,711 |\n| 4915 | 0162A1504915 | 31.484 | 80.928 | Indus Upper | O | 2.06 | 5,275 |\n| 4916 | 0162A1504916 | 31.483 | 80.765 | Indus Upper | E(o) | 0.40 | 5,682 |\n| 4917 | 0162A1504917 | 31.481 | 80.836 | Indus Upper | M(o) | 0.42 | 5,663 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 11016, "line_end": 11163, "token_count_estimate": 369, "basins": ["Indus"], "subbasins": ["Indus Upper"], "countries": [], "lake_ids": ["0162A1504912", "0162A1504913", "0162A1504914", "0162A1504915", "0162A1504916", "0162A1504917"]}}
{"id": "317bc3a4062bb075", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 11164, "line_end": 11175, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "686608c97f911a34", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4918 | 0162A1504918 | 31.479 | 80.935 | Indus Upper | E(o) | 0.60 | 5,431 |\n| 4919 | 0162A1504919 | 31.474 | 80.992 | Indus Upper | O | 26.90 | 5,408 |\n| 4920 | 0162A1504920 | 31.469 | 80.824 | Indus Upper | E(c) | 0.37 | 5,623 |\n| 4921 | 0162A1504921 | 31.417 | 80.765 | Indus Upper | E(o) | 0.34 | 5,351 |\n| 4922 | 0162A1504922 | 31.409 | 80.995 | Indus Upper | E(o) | 2.95 | 5,574 |\n| 4923 | 0162A1504923 | 31.384 | 80.980 | Indus Upper | E(o) | 0.32 | 5,466 |\n| 4924 | 0162A1504924 | 31.342 | 80.953 | Satluj | E(o) | 0.47 | 5,555 |\n| 4925 | 0162A1504925 | 31.342 | 80.900 | Satluj | E(o) | 0.69 | 5,535 |\n| 4926 | 0162A1504926 | 31.338 | 80.971 | Satluj | E(o) | 0.37 | 5,479 |\n| 4927 | 0162A1504927 | 31.319 | 80.955 | Satluj | E(o) | 1.14 | 5,516 |\n| 4928 | 0162A1504928 | 31.294 | 80.822 | Indus Upper | M(e) | 2.96 | 5,495 |\n| 4929 | 0162A1504929 | 31.264 | 80.839 | Satluj | E(o) | 0.27 | 5,594 |\n| 4930 | 0162A1504930 | 31.252 | 80.833 | Satluj | E(o) | 3.39 | 5,696 |\n| 4931 | 0162B0604931 | 30.608 | 80.295 | Satluj | M(o) | 1.19 | 4,957 |\n| 4932 | 0162B0604932 | 30.606 | 80.293 | Satluj | M(e) | 2.78 | 4,955 |\n| 4933 | 0162B0604933 | 30.605 | 80.416 | Satluj | M(o) | 0.33 | 5,456 |\n| 4934 | 0162B0604934 | 30.598 | 80.404 | Satluj | M(e) | 2.32 | 5,529 |\n| 4935 | 0162B0604935 | 30.572 | 80.339 | Satluj | E(o) | 0.91 | 5,606 |\n| 4936 | 0162B0604936 | 30.570 | 80.465 | Satluj | M(o) | 0.31 | 5,467 |\n| 4937 | 0162B0604937 | 30.556 | 80.476 | Satluj | M(e) | 0.72 | 5,315 |\n| 4938 | 0162B0604938 | 30.554 | 80.474 | Satluj | I(s) | 0.79 | 5,335 |\n| 4939 | 0162B0604939 | 30.554 | 80.450 | Satluj | M(o) | 2.91 | 5,362 |\n| 4940 | 0162B0604940 | 30.553 | 80.407 | Satluj | M(o) | 1.51 | 4,979 |\n| 4941 | 0162B0604941 | 30.553 | 80.472 | Satluj | I(s) | 1.22 | 5,316 |\n| 4942 | 0162B0604942 | 30.552 | 80.400 | Satluj | M(e) | 26.56 | 4,990 |\n| 4943 | 0162B0604943 | 30.551 | 80.471 | Satluj | I(s) | 1.56 | 5,327 |\n| 4944 | 0162B0604944 | 30.535 | 80.383 | Satluj | E(o) | 1.00 | 5,349 |\n| 4945 | 0162B1004945 | 30.565 | 80.612 | Satluj | M(o) | 0.26 | 5,551 |\n| 4946 | 0162B1004946 | 30.562 | 80.623 | Satluj | E(o) | 0.62 | 5,611 |\n| 4947 | 0162B1004947 | 30.556 | 80.617 | Satluj | M(o) | 1.22 | 5,727 |\n| 4948 | 0162B1004948 | 30.555 | 80.618 | Satluj | M(o) | 0.80 | 5,737 |\n| 4949 | 0162B1004949 | 30.545 | 80.599 | Satluj | M(e) | 5.76 | 5,497 |\n| 4950 | 0162B1004950 | 30.541 | 80.509 | Satluj | E(o) | 0.62 | 5,086 |\n| 4951 | 0162B1004951 | 30.539 | 80.618 | Satluj | M(o) | 0.84 | 5,546 |\n| 4952 | 0162B1104952 | 30.491 | 80.569 | Satluj | O | 0.31 | 5,092 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 11176, "line_end": 11323, "token_count_estimate": 1658, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162A1504918", "0162A1504919", "0162A1504920", "0162A1504921", "0162A1504922", "0162A1504923", "0162A1504924", "0162A1504925", "0162A1504926", "0162A1504927", "0162A1504928", "0162A1504929", "0162A1504930", "0162B0604931", "0162B0604932", "0162B0604933", "0162B0604934", "0162B0604935", "0162B0604936", "0162B0604937", "0162B0604938", "0162B0604939", "0162B0604940", "0162B0604941", "0162B0604942", "0162B0604943", "0162B0604944", "0162B1004945", "0162B1004946", "0162B1004947", "0162B1004948", "0162B1004949", "0162B1004950", "0162B1004951", "0162B1104952"]}}
{"id": "24b7bfe3b23b2a8a", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4953 | 0162B1104953 | 30.478 | 80.569 | Satluj | M(e) | 1.90 | 5,238 |\n| 4954 | 0162B1104954 | 30.477 | 80.592 | Satluj | M(e) | 12.63 | 5,263 |\n| 4955 | 0162B1104955 | 30.469 | 80.612 | Satluj | E(o) | 0.34 | 5,412 |\n| 4956 | 0162B1104956 | 30.463 | 80.640 | Satluj | E(o) | 0.71 | 5,357 |\n| 4957 | 0162B1104957 | 30.460 | 80.640 | Satluj | E(o) | 0.95 | 5,405 |\n| 4958 | 0162E0104958 | 31.985 | 81.063 | Indus Upper | O | 11.79 | 4,734 |\n| 4959 | 0162E0204959 | 31.687 | 81.063 | Indus Upper | E(o) | 0.96 | 5,367 |\n| 4960 | 0162E0304960 | 31.469 | 81.163 | Indus Upper | O | 1.23 | 5,340 |\n| 4961 | 0162E0304961 | 31.442 | 81.157 | Indus Upper | O | 13.01 | 5,240 |\n| 4962 | 0162E0304962 | 31.393 | 81.023 | Indus Upper | E(o) | 1.03 | 5,444 |\n| 4963 | 0162E0304963 | 31.332 | 81.179 | Indus Upper | E(o) | 1.13 | 5,397 |\n| 4964 | 0162E0304964 | 31.295 | 81.013 | Satluj | E(c) | 2.45 | 5,694 |\n| 4965 | 0162E0304965 | 31.293 | 81.033 | Satluj | O | 3.43 | 5,539 |\n| 4966 | 0162E0304966 | 31.290 | 81.049 | Satluj | E(o) | 0.39 | 5,572 |\n| 4967 | 0162E0304967 | 31.287 | 81.002 | Satluj | M(o) | 0.37 | 5,607 |\n| 4968 | 0162E0304968 | 31.287 | 81.048 | Satluj | E(o) | 0.47 | 5,638 |\n| 4969 | 0162E0304969 | 31.285 | 81.032 | Satluj | M(e) | 13.53 | 5,617 |\n| 4970 | 0162E0304970 | 31.285 | 81.176 | Indus Upper | E(o) | 18.10 | 5,171 |\n| 4971 | 0162E0304971 | 31.277 | 81.029 | Satluj | E(c) | 1.10 | 5,625 |\n| 4972 | 0162E0304972 | 31.277 | 81.165 | Indus Upper | E(o) | 6.08 | 5,189 |\n| 4973 | 0162E0304973 | 31.274 | 81.161 | Indus Upper | E(o) | 0.77 | 5,190 |\n| 4974 | 0162E0304974 | 31.273 | 81.085 | Satluj | M(o) | 1.62 | 5,590 |\n| 4975 | 0162E0304975 | 31.270 | 81.086 | Satluj | M(o) | 0.42 | 5,608 |\n| 4976 | 0162E0304976 | 31.270 | 81.109 | Satluj | M(o) | 3.12 | 5,593 |\n| 4977 | 0162E0304977 | 31.266 | 81.089 | Satluj | M(e) | 5.46 | 5,638 |\n| 4978 | 0162E0304978 | 31.264 | 81.116 | Satluj | E(o) | 1.71 | 5,707 |\n| 4979 | 0162E0304979 | 31.260 | 81.133 | Indus Upper | E(o) | 12.43 | 5,530 |\n| 4980 | 0162E0304980 | 31.260 | 81.117 | Indus Upper | E(o) | 3.79 | 5,700 |\n| 4981 | 0162E0304981 | 31.259 | 81.074 | Satluj | M(o) | 0.35 | 5,622 |\n| 4982 | 0162E0304982 | 31.259 | 81.148 | Indus Upper | E(o) | 3.25 | 5,421 |\n| 4983 | 0162E0304983 | 31.256 | 81.147 | Indus Upper | E(o) | 2.37 | 5,429 |\n| 4984 | 0162E0304984 | 31.250 | 81.151 | Indus Upper | E(o) | 4.65 | 5,493 |\n| 4985 | 0162E0404985 | 31.250 | 81.114 | Satluj | E(o) | 1.34 | 5,725 |\n| 4986 | 0162E0404986 | 31.246 | 81.142 | Satluj | E(o) | 8.04 | 5,475 |\n| 4987 | 0162E0404987 | 31.245 | 81.133 | Satluj | E(o) | 3.77 | 5,476 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 11176, "line_end": 11323, "token_count_estimate": 1644, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162B1104953", "0162B1104954", "0162B1104955", "0162B1104956", "0162B1104957", "0162E0104958", "0162E0204959", "0162E0304960", "0162E0304961", "0162E0304962", "0162E0304963", "0162E0304964", "0162E0304965", "0162E0304966", "0162E0304967", "0162E0304968", "0162E0304969", "0162E0304970", "0162E0304971", "0162E0304972", "0162E0304973", "0162E0304974", "0162E0304975", "0162E0304976", "0162E0304977", "0162E0304978", "0162E0304979", "0162E0304980", "0162E0304981", "0162E0304982", "0162E0304983", "0162E0304984", "0162E0404985", "0162E0404986", "0162E0404987"]}}
{"id": "5bbbf9d8d7ce781e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4988 | 0162E0404988 | 31.244 | 81.130 | Satluj | E(o) | 2.65 | 5,475 |\n| 4989 | 0162E0404989 | 31.244 | 81.103 | Satluj | E(o) | 3.49 | 5,628 |\n| 4990 | 0162E0404990 | 31.241 | 81.157 | Indus Upper | E(o) | 0.57 | 5,565 |\n| 4991 | 0162E0404991 | 31.240 | 81.083 | Satluj | M(e) | 4.22 | 5,487 |\n| 4992 | 0162E0404992 | 31.237 | 81.125 | Satluj | E(o) | 3.02 | 5,455 |\n| 4993 | 0162E0404993 | 31.234 | 81.138 | Satluj | M(e) | 10.92 | 5,562 |\n| 4994 | 0162E0404994 | 31.234 | 81.069 | Satluj | M(o) | 2.21 | 5,653 |\n| 4995 | 0162E0404995 | 31.231 | 81.145 | Satluj | E(o) | 1.45 | 5,587 |\n| 4996 | 0162E0404996 | 31.226 | 81.122 | Satluj | E(o) | 1.51 | 5,473 |\n| 4997 | 0162E0404997 | 31.220 | 81.129 | Satluj | E(o) | 0.54 | 5,604 |\n| 4998 | 0162E0404998 | 31.218 | 81.160 | Satluj | M(e) | 6.42 | 5,610 |\n| 4999 | 0162E0404999 | 31.210 | 81.173 | Indus Upper | E(o) | 0.82 | 5,591 |\n| 5000 | 0162E0405000 | 31.208 | 81.154 | Satluj | M(o) | 0.33 | 5,639 |\n| 5001 | 0162E0405001 | 31.207 | 81.019 | Satluj | M(o) | 2.17 | 5,523 |\n| 5002 | 0162E0405002 | 31.206 | 81.193 | Indus Upper | E(o) | 4.86 | 5,358 |\n| 5003 | 0162E0405003 | 31.204 | 81.148 | Satluj | M(o) | 0.70 | 5,494 |\n| 5004 | 0162E0405004 | 31.199 | 81.148 | Satluj | M(o) | 9.65 | 5,538 |\n| 5005 | 0162E0405005 | 31.195 | 81.138 | Satluj | M(e) | 4.75 | 5,564 |\n| 5006 | 0162E0405006 | 31.193 | 81.130 | Satluj | E(o) | 0.40 | 5,544 |\n| 5007 | 0162E0405007 | 31.189 | 81.179 | Indus Upper | E(o) | 1.57 | 5,713 |\n| 5008 | 0162E0405008 | 31.182 | 81.195 | Satluj | E(o) | 52.48 | 5,413 |\n| 5009 | 0162E0405009 | 31.181 | 81.235 | Indus Upper | E(o) | 0.62 | 5,718 |\n| 5010 | 0162E0405010 | 31.179 | 81.233 | Indus Upper | M(o) | 0.32 | 5,710 |\n| 5011 | 0162E0405011 | 31.179 | 81.194 | Satluj | E(o) | 0.48 | 5,413 |\n| 5012 | 0162E0405012 | 31.179 | 81.152 | Satluj | M(e) | 19.91 | 5,514 |\n| 5013 | 0162E0405013 | 31.178 | 81.229 | Indus Upper | M(o) | 1.00 | 5,683 |\n| 5014 | 0162E0405014 | 31.178 | 81.191 | Satluj | E(o) | 0.49 | 5,414 |\n| 5015 | 0162E0405015 | 31.178 | 81.224 | Indus Upper | M(e) | 3.29 | 5,633 |\n| 5016 | 0162E0405016 | 31.176 | 81.240 | Indus Upper | E(o) | 1.09 | 5,631 |\n| 5017 | 0162E0405017 | 31.175 | 81.207 | Satluj | E(o) | 0.56 | 5,429 |\n| 5018 | 0162E0405018 | 31.171 | 81.211 | Satluj | E(o) | 1.30 | 5,473 |\n| 5019 | 0162E0405019 | 31.169 | 81.141 | Satluj | E(o) | 0.49 | 5,551 |\n| 5020 | 0162E0405020 | 31.168 | 81.116 | Satluj | E(o) | 1.87 | 5,578 |\n| 5021 | 0162E0405021 | 31.166 | 81.200 | Satluj | E(o) | 0.34 | 5,596 |\n| 5022 | 0162E0405022 | 31.164 | 81.216 | Satluj | M(o) | 2.00 | 5,542 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 11176, "line_end": 11323, "token_count_estimate": 1643, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162E0404988", "0162E0404989", "0162E0404990", "0162E0404991", "0162E0404992", "0162E0404993", "0162E0404994", "0162E0404995", "0162E0404996", "0162E0404997", "0162E0404998", "0162E0404999", "0162E0405000", "0162E0405001", "0162E0405002", "0162E0405003", "0162E0405004", "0162E0405005", "0162E0405006", "0162E0405007", "0162E0405008", "0162E0405009", "0162E0405010", "0162E0405011", "0162E0405012", "0162E0405013", "0162E0405014", "0162E0405015", "0162E0405016", "0162E0405017", "0162E0405018", "0162E0405019", "0162E0405020", "0162E0405021", "0162E0405022"]}}
{"id": "f8784748dab546cd", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5023 | 0162E0405023 | 31.162 | 81.137 | Satluj | E(o) | 0.97 | 5,568 |\n| 5024 | 0162E0405024 | 31.161 | 81.226 | Satluj | E(o) | 1.25 | 5,686 |\n| 5025 | 0162E0405025 | 31.160 | 81.112 | Satluj | M(o) | 1.15 | 5,486 |\n| 5026 | 0162E0405026 | 31.156 | 81.206 | Satluj | E(o) | 0.49 | 5,835 |\n| 5027 | 0162E0405027 | 31.153 | 81.243 | Indus Upper | E(o) | 15.57 | 5,651 |\n| 5028 | 0162E0405028 | 31.149 | 81.225 | Satluj | M(o) | 1.86 | 5,665 |\n| 5029 | 0162E0405029 | 31.148 | 81.222 | Satluj | M(e) | 7.34 | 5,668 |\n| 5030 | 0162E0405030 | 31.146 | 81.233 | Satluj | E(o) | 0.46 | 5,604 |\n| 5031 | 0162E0405031 | 31.143 | 81.232 | Satluj | E(o) | 0.38 | 5,590 |\n| 5032 | 0162E0405032 | 31.138 | 81.182 | Satluj | M(o) | 0.96 | 5,662 |\n| 5033 | 0162E0405033 | 31.129 | 81.192 | Satluj | M(l) | 0.52 | 5,781 |\n| 5034 | 0162E0405034 | 31.129 | 81.228 | Satluj | M(e) | 4.30 | 5,492 |\n| 5035 | 0162E0405035 | 31.116 | 81.164 | Satluj | M(o) | 0.33 | 5,417 |\n| 5036 | 0162E0405036 | 31.111 | 81.222 | Satluj | M(o) | 1.17 | 5,650 |\n| 5037 | 0162E0405037 | 31.096 | 81.196 | Satluj | E(o) | 0.48 | 5,367 |\n| 5038 | 0162E0405038 | 31.074 | 81.207 | Satluj | E(c) | 0.61 | 5,716 |\n| 5039 | 0162E0405039 | 31.060 | 81.230 | Satluj | E(o) | 1.65 | 5,544 |\n| 5040 | 0162E0505040 | 31.834 | 81.372 | Indus Upper | E(o) | 0.54 | 5,121 |\n| 5041 | 0162E0505041 | 31.761 | 81.398 | Indus Upper | E(o) | 1.54 | 5,736 |\n| 5042 | 0162E0705042 | 31.401 | 81.264 | Indus Upper | E(o) | 0.37 | 5,600 |\n| 5043 | 0162E0705043 | 31.393 | 81.277 | Indus Upper | E(o) | 6.17 | 5,863 |\n| 5044 | 0162E0705044 | 31.364 | 81.281 | Indus Upper | E(o) | 1.31 | 5,728 |\n| 5045 | 0162E0705045 | 31.362 | 81.274 | Indus Upper | E(o) | 1.93 | 5,706 |\n| 5046 | 0162E0705046 | 31.360 | 81.268 | Indus Upper | E(o) | 1.62 | 5,846 |\n| 5047 | 0162E0705047 | 31.343 | 81.292 | Indus Upper | E(o) | 7.35 | 5,719 |\n| 5048 | 0162E0705048 | 31.321 | 81.336 | Indus Upper | M(o) | 4.66 | 5,608 |\n| 5049 | 0162E0705049 | 31.273 | 81.438 | Indus Upper | E(o) | 4.52 | 5,650 |\n| 5050 | 0162E0705050 | 31.270 | 81.437 | Indus Upper | E(o) | 1.03 | 5,657 |\n| 5051 | 0162E0705051 | 31.268 | 81.434 | Indus Upper | E(o) | 0.50 | 5,717 |\n| 5052 | 0162E0705052 | 31.264 | 81.431 | Indus Upper | M(e) | 3.30 | 5,751 |\n| 5053 | 0162E0705053 | 31.264 | 81.445 | Indus Upper | M(e) | 3.28 | 5,793 |\n| 5054 | 0162E0805054 | 31.241 | 81.399 | Indus Upper | E(o) | 3.40 | 5,596 |\n| 5055 | 0162E0805055 | 31.238 | 81.401 | Indus Upper | M(o) | 0.50 | 5,630 |\n| 5056 | 0162E0805056 | 31.237 | 81.402 | Indus Upper | E(o) | 1.92 | 5,629 |\n| 5057 | 0162E0805057 | 31.212 | 81.499 | Indus Upper | O | 0.31 | 5,202 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 11176, "line_end": 11323, "token_count_estimate": 1650, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162E0405023", "0162E0405024", "0162E0405025", "0162E0405026", "0162E0405027", "0162E0405028", "0162E0405029", "0162E0405030", "0162E0405031", "0162E0405032", "0162E0405033", "0162E0405034", "0162E0405035", "0162E0405036", "0162E0405037", "0162E0405038", "0162E0405039", "0162E0505040", "0162E0505041", "0162E0705042", "0162E0705043", "0162E0705044", "0162E0705045", "0162E0705046", "0162E0705047", "0162E0705048", "0162E0705049", "0162E0705050", "0162E0705051", "0162E0705052", "0162E0705053", "0162E0805054", "0162E0805055", "0162E0805056", "0162E0805057"]}}
{"id": "48f5f1f034bd30fb", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5058 | 0162E0805058 | 31.205 | 81.429 | Indus Upper | E(o) | 3.49 | 5,708 |\n| 5059 | 0162E0805059 | 31.202 | 81.432 | Indus Upper | E(o) | 0.92 | 5,713 |\n| 5060 | 0162E0805060 | 31.182 | 81.444 | Indus Upper | E(o) | 0.88 | 5,599 |\n| 5061 | 0162E0805061 | 31.173 | 81.322 | Satluj | E(o) | 0.56 | 5,555 |\n| 5062 | 0162E0805062 | 31.170 | 81.266 | Indus Upper | M(o) | 0.56 | 5,631 |\n| 5063 | 0162E0805063 | 31.169 | 81.267 | Indus Upper | M(e) | 1.54 | 5,677 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 11176, "line_end": 11323, "token_count_estimate": 374, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162E0805058", "0162E0805059", "0162E0805060", "0162E0805061", "0162E0805062", "0162E0805063"]}}
{"id": "f16ccba10525468e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 11324, "line_end": 11332, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2681b3d3c1fdda45", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5064 | 0162E0805064 | 31.162 | 81.258 | Indus Upper | E(o) | 1.20 | 5,654 |\n| 5065 | 0162E0805065 | 31.161 | 81.250 | Indus Upper | M(o) | 0.77 | 5,597 |\n| 5066 | 0162E0805066 | 31.149 | 81.441 | Indus Upper | E(o) | 2.06 | 5,667 |\n| 5067 | 0162E0805067 | 31.144 | 81.408 | Satluj | E(o) | 1.27 | 5,588 |\n| 5068 | 0162E0805068 | 31.140 | 81.259 | Satluj | M(o) | 1.26 | 5,748 |\n| 5069 | 0162E0805069 | 31.139 | 81.327 | Satluj | E(o) | 0.69 | 5,581 |\n| 5070 | 0162E0805070 | 31.138 | 81.383 | Satluj | E(c) | 8.09 | 5,591 |\n| 5071 | 0162E0805071 | 31.138 | 81.281 | Satluj | E(o) | 4.93 | 5,605 |\n| 5072 | 0162E0805072 | 31.137 | 81.424 | Satluj | E(o) | 4.86 | 5,612 |\n| 5073 | 0162E0805073 | 31.136 | 81.439 | Indus Upper | E(o) | 5.38 | 5,722 |\n| 5074 | 0162E0805074 | 31.135 | 81.484 | Indus Upper | E(c) | 0.80 | 5,628 |\n| 5075 | 0162E0805075 | 31.134 | 81.413 | Satluj | E(o) | 1.33 | 5,762 |\n| 5076 | 0162E0805076 | 31.128 | 81.394 | Satluj | M(o) | 0.59 | 5,698 |\n| 5077 | 0162E0805077 | 31.128 | 81.402 | Satluj | M(o) | 2.58 | 5,578 |\n| 5078 | 0162E0805078 | 31.127 | 81.485 | Indus Upper | E(o) | 0.88 | 5,694 |\n| 5079 | 0162E0805079 | 31.126 | 81.484 | Indus Upper | E(o) | 1.26 | 5,692 |\n| 5080 | 0162E0805080 | 31.126 | 81.367 | Satluj | M(o) | 0.39 | 5,620 |\n| 5081 | 0162E0805081 | 31.124 | 81.267 | Satluj | E(o) | 0.99 | 5,472 |\n| 5082 | 0162E0805082 | 31.123 | 81.263 | Satluj | E(o) | 0.97 | 5,535 |\n| 5083 | 0162E0805083 | 31.122 | 81.491 | Indus Upper | E(o) | 3.38 | 5,563 |\n| 5084 | 0162E0805084 | 31.117 | 81.259 | Satluj | E(o) | 3.73 | 5,710 |\n| 5085 | 0162E0805085 | 31.116 | 81.252 | Satluj | E(o) | 0.50 | 5,790 |\n| 5086 | 0162E0805086 | 31.114 | 81.435 | Satluj | M(e) | 3.85 | 5,524 |\n| 5087 | 0162E0805087 | 31.114 | 81.423 | Satluj | M(o) | 0.45 | 5,598 |\n| 5088 | 0162E0805088 | 31.110 | 81.476 | Satluj | O | 1.17 | 5,547 |\n| 5089 | 0162E0805089 | 31.105 | 81.472 | Satluj | O | 2.19 | 5,492 |\n| 5090 | 0162E0805090 | 31.104 | 81.413 | Satluj | M(o) | 1.23 | 5,494 |\n| 5091 | 0162E0805091 | 31.102 | 81.417 | Satluj | M(e) | 2.17 | 5,542 |\n| 5092 | 0162E0805092 | 31.102 | 81.469 | Satluj | O | 0.77 | 5,467 |\n| 5093 | 0162E0805093 | 31.099 | 81.469 | Satluj | O | 0.34 | 5,443 |\n| 5094 | 0162E0805094 | 31.096 | 81.319 | Satluj | O | 1.21 | 5,213 |\n| 5095 | 0162E0805095 | 31.093 | 81.372 | Satluj | E(o) | 0.60 | 5,556 |\n| 5096 | 0162E0805096 | 31.088 | 81.445 | Satluj | E(o) | 0.27 | 5,594 |\n| 5097 | 0162E0805097 | 31.086 | 81.331 | Satluj | O | 5.41 | 5,240 |\n| 5098 | 0162E0805098 | 31.084 | 81.426 | Satluj | E(o) | 0.93 | 5,636 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 11333, "line_end": 11480, "token_count_estimate": 1611, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162E0805064", "0162E0805065", "0162E0805066", "0162E0805067", "0162E0805068", "0162E0805069", "0162E0805070", "0162E0805071", "0162E0805072", "0162E0805073", "0162E0805074", "0162E0805075", "0162E0805076", "0162E0805077", "0162E0805078", "0162E0805079", "0162E0805080", "0162E0805081", "0162E0805082", "0162E0805083", "0162E0805084", "0162E0805085", "0162E0805086", "0162E0805087", "0162E0805088", "0162E0805089", "0162E0805090", "0162E0805091", "0162E0805092", "0162E0805093", "0162E0805094", "0162E0805095", "0162E0805096", "0162E0805097", "0162E0805098"]}}
{"id": "e15f136985265e3f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5099 | 0162E0805099 | 31.081 | 81.481 | Satluj | E(o) | 1.55 | 5,652 |\n| 5100 | 0162E0805100 | 31.080 | 81.425 | Satluj | E(o) | 0.65 | 5,614 |\n| 5101 | 0162E0805101 | 31.056 | 81.407 | Satluj | E(c) | 3.05 | 5,741 |\n| 5102 | 0162E0805102 | 31.039 | 81.483 | Satluj | M(o) | 0.26 | 5,522 |\n| 5103 | 0162E0805103 | 31.036 | 81.499 | Satluj | E(o) | 0.40 | 5,605 |\n| 5104 | 0162E0805104 | 31.023 | 81.486 | Satluj | M(lg) | 0.49 | 5,713 |\n| 5105 | 0162E0805105 | 31.014 | 81.458 | Satluj | M(o) | 0.38 | 5,466 |\n| 5106 | 0162E0805106 | 31.007 | 81.487 | Satluj | E(o) | 0.41 | 5,517 |\n| 5107 | 0162E0805107 | 31.006 | 81.489 | Satluj | E(o) | 0.88 | 5,507 |\n| 5108 | 1162E1105108 | 31.288 | 81.615 | Indus Upper | O | 8.68 | 5,220 |\n| 5109 | 1162E1105109 | 31.288 | 81.605 | Indus Upper | O | 0.75 | 5,225 |\n| 5110 | 1162E1105110 | 31.285 | 81.606 | Indus Upper | O | 0.82 | 5,229 |\n| 5111 | 1162E1105111 | 31.284 | 81.631 | Indus Upper | O | 25.89 | 5,259 |\n| 5112 | 1162E1105112 | 31.283 | 81.603 | Indus Upper | O | 0.90 | 5,227 |\n| 5113 | 1162E1105113 | 31.282 | 81.607 | Indus Upper | O | 4.63 | 5,226 |\n| 5114 | 1162E1105114 | 31.274 | 81.595 | Indus Upper | O | 156.89 | 5,229 |\n| 5115 | 1162E1105115 | 31.272 | 81.518 | Indus Upper | O | 28.77 | 5,172 |\n| 5116 | 1162E1105116 | 31.263 | 81.590 | Indus Upper | O | 0.81 | 5,231 |\n| 5117 | 1162E1205117 | 31.224 | 81.682 | Indus Upper | O | 9.59 | 5,343 |\n| 5118 | 1162E1205118 | 31.216 | 81.613 | Indus Upper | E(o) | 0.66 | 5,475 |\n| 5119 | 1162E1205119 | 31.207 | 81.602 | Indus Upper | E(o) | 8.14 | 5,599 |\n| 5120 | 1162E1205120 | 31.168 | 81.547 | Indus Upper | E(o) | 0.38 | 5,689 |\n| 5121 | 1162E1205121 | 31.168 | 81.547 | Indus Upper | E(o) | 0.67 | 5,687 |\n| 5122 | 1162E1205122 | 31.164 | 81.537 | Indus Upper | M(o) | 1.21 | 5,688 |\n| 5123 | 1162E1205123 | 31.161 | 81.522 | Indus Upper | M(o) | 0.47 | 5,803 |\n| 5124 | 1162E1205124 | 31.160 | 81.536 | Indus Upper | M(e) | 6.74 | 5,683 |\n| 5125 | 1162E1205125 | 31.146 | 81.513 | Indus Upper | E(c) | 1.29 | 5,637 |\n| 5126 | 1162E1205126 | 31.128 | 81.600 | Satluj | O | 0.26 | 5,441 |\n| 5127 | 1162E1205127 | 31.126 | 81.583 | Satluj | O | 0.35 | 5,381 |\n| 5128 | 1162E1205128 | 31.112 | 81.503 | Satluj | E(o) | 7.45 | 5,749 |\n| 5129 | 1162E1205129 | 31.111 | 81.525 | Indus Upper | E(o) | 2.48 | 5,778 |\n| 5130 | 1162E1205130 | 31.111 | 81.519 | Indus Upper | E(o) | 0.29 | 5,582 |\n| 5131 | 1162E1205131 | 31.103 | 81.546 | Satluj | M(e) | 2.39 | 5,558 |\n| 5132 | 1162E1205132 | 31.099 | 81.513 | Satluj | M(o) | 3.03 | 5,645 |\n| 5133 | 1162E1205133 | 31.098 | 81.554 | Satluj | E(o) | 1.82 | 5,491 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 11333, "line_end": 11480, "token_count_estimate": 1630, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["0162E0805099", "0162E0805100", "0162E0805101", "0162E0805102", "0162E0805103", "0162E0805104", "0162E0805105", "0162E0805106", "0162E0805107", "1162E1105108", "1162E1105109", "1162E1105110", "1162E1105111", "1162E1105112", "1162E1105113", "1162E1105114", "1162E1105115", "1162E1105116", "1162E1205117", "1162E1205118", "1162E1205119", "1162E1205120", "1162E1205121", "1162E1205122", "1162E1205123", "1162E1205124", "1162E1205125", "1162E1205126", "1162E1205127", "1162E1205128", "1162E1205129", "1162E1205130", "1162E1205131", "1162E1205132", "1162E1205133"]}}
{"id": "e4354362d0d40a2c", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5134 | 1162E1205134 | 31.095 | 81.531 | Satluj | E(o) | 1.03 | 5,575 |\n| 5135 | 1162E1205135 | 31.095 | 81.504 | Satluj | M(e) | 4.44 | 5,586 |\n| 5136 | 1162E1205136 | 31.094 | 81.561 | Satluj | E(c) | 1.20 | 5,559 |\n| 5137 | 1162E1205137 | 31.094 | 81.551 | Satluj | E(o) | 1.04 | 5,522 |\n| 5138 | 1162E1205138 | 31.093 | 81.530 | Satluj | M(o) | 1.68 | 5,578 |\n| 5139 | 1162E1205139 | 31.091 | 81.555 | Satluj | E(o) | 1.02 | 5,563 |\n| 5140 | 1162E1205140 | 31.090 | 81.606 | Satluj | E(o) | 0.25 | 5,684 |\n| 5141 | 1162E1205141 | 31.083 | 81.519 | Satluj | M(o) | 1.45 | 5,616 |\n| 5142 | 1162E1205142 | 31.079 | 81.548 | Satluj | E(c) | 1.81 | 5,604 |\n| 5143 | 1162E1205143 | 31.072 | 81.505 | Satluj | E(o) | 0.42 | 5,683 |\n| 5144 | 1162E1205144 | 31.071 | 81.723 | Satluj | E(o) | 7.73 | 5,606 |\n| 5145 | 1162E1205145 | 31.067 | 81.509 | Satluj | O | 0.55 | 5,582 |\n| 5146 | 1162E1205146 | 31.066 | 81.532 | Satluj | E(o) | 0.34 | 5,314 |\n| 5147 | 1162E1205147 | 31.064 | 81.504 | Satluj | E(o) | 0.88 | 5,635 |\n| 5148 | 1162E1205148 | 31.057 | 81.725 | Satluj | E(o) | 0.50 | 5,630 |\n| 5149 | 1162E1205149 | 31.051 | 81.515 | Satluj | M(o) | 1.48 | 5,498 |\n| 5150 | 1162E1205150 | 31.045 | 81.505 | Satluj | M(o) | 1.14 | 5,755 |\n| 5151 | 1162E1205151 | 31.039 | 81.514 | Satluj | M(o) | 1.25 | 5,501 |\n| 5152 | 1162E1205152 | 31.035 | 81.513 | Satluj | M(e) | 2.87 | 5,636 |\n| 5153 | 1162E1205153 | 31.032 | 81.519 | Satluj | E(o) | 0.52 | 5,550 |\n| 5154 | 1162E1205154 | 31.031 | 81.519 | Satluj | E(o) | 0.27 | 5,563 |\n| 5155 | 1162E1205155 | 31.026 | 81.667 | Satluj | E(c) | 1.46 | 5,558 |\n| 5156 | 0162F0705156 | 30.497 | 81.484 | Satluj | E(o) | 1.00 | 5,426 |\n| 5157 | 0162F0705157 | 30.494 | 81.359 | Satluj | I(s) | 0.46 | 5,693 |\n| 5158 | 0162F0705158 | 30.487 | 81.476 | Satluj | M(o) | 0.62 | 5,549 |\n| 5159 | 0162F0705159 | 30.482 | 81.475 | Satluj | M(e) | 2.23 | 5,578 |\n| 5160 | 0162F0705160 | 30.479 | 81.487 | Satluj | E(o) | 0.50 | 5,578 |\n| 5161 | 0162F0705161 | 30.477 | 81.401 | Satluj | M(o) | 0.60 | 5,754 |\n| 5162 | 0162F0705162 | 30.472 | 81.430 | Satluj | E(o) | 5.11 | 5,708 |\n| 5163 | 0162F0705163 | 30.471 | 81.434 | Satluj | M(o) | 1.20 | 5,689 |\n| 5164 | 0162F0705164 | 30.470 | 81.489 | Satluj | M(o) | 0.63 | 5,492 |\n| 5165 | 0162F0705165 | 30.466 | 81.485 | Satluj | M(o) | 0.25 | 5,522 |\n| 5166 | 0162F0705166 | 30.462 | 81.483 | Satluj | M(o) | 2.33 | 5,543 |\n| 5167 | 0162F0705167 | 30.460 | 81.413 | Satluj | M(o) | 2.59 | 5,557 |\n| 5168 | 0162F0705168 | 30.459 | 81.499 | Satluj | E(o) | 2.08 | 5,519 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 11333, "line_end": 11480, "token_count_estimate": 1638, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0162F0705156", "0162F0705157", "0162F0705158", "0162F0705159", "0162F0705160", "0162F0705161", "0162F0705162", "0162F0705163", "0162F0705164", "0162F0705165", "0162F0705166", "0162F0705167", "0162F0705168", "1162E1205134", "1162E1205135", "1162E1205136", "1162E1205137", "1162E1205138", "1162E1205139", "1162E1205140", "1162E1205141", "1162E1205142", "1162E1205143", "1162E1205144", "1162E1205145", "1162E1205146", "1162E1205147", "1162E1205148", "1162E1205149", "1162E1205150", "1162E1205151", "1162E1205152", "1162E1205153", "1162E1205154", "1162E1205155"]}}
{"id": "3a7f6b3d970e3082", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5169 | 0162F0705169 | 30.457 | 81.411 | Satluj | M(o) | 1.46 | 5,565 |\n| 5170 | 0162F0705170 | 30.455 | 81.493 | Satluj | E(o) | 1.47 | 5,624 |\n| 5171 | 0162F0705171 | 30.454 | 81.361 | Satluj | I(s) | 0.44 | 6,025 |\n| 5172 | 0162F0705172 | 30.454 | 81.496 | Satluj | E(o) | 0.39 | 5,634 |\n| 5173 | 0162F0705173 | 30.454 | 81.393 | Satluj | E(o) | 1.04 | 5,839 |\n| 5174 | 0162F0705174 | 30.451 | 81.377 | Satluj | E(o) | 3.06 | 5,794 |\n| 5175 | 0162F0705175 | 30.450 | 81.359 | Satluj | M(o) | 3.17 | 5,988 |\n| 5176 | 0162F0705176 | 30.449 | 81.433 | Satluj | E(o) | 7.40 | 5,623 |\n| 5177 | 0162F0705177 | 30.444 | 81.466 | Satluj | E(o) | 0.82 | 5,605 |\n| 5178 | 0162F0705178 | 30.438 | 81.457 | Satluj | E(o) | 3.21 | 5,486 |\n| 5179 | 0162F0705179 | 30.431 | 81.433 | Satluj | E(o) | 192.62 | 5,484 |\n| 5180 | 0162F0705180 | 30.430 | 81.463 | Satluj | M(o) | 13.70 | 5,572 |\n| 5181 | 0162F0705181 | 30.428 | 81.470 | Satluj | M(o) | 0.39 | 5,537 |\n| 5182 | 0162F0705182 | 30.428 | 81.471 | Satluj | M(o) | 0.41 | 5,535 |\n| 5183 | 0162F0705183 | 30.428 | 81.480 | Satluj | M(o) | 2.00 | 5,497 |\n| 5184 | 0162F0705184 | 30.427 | 81.466 | Satluj | M(o) | 2.02 | 5,544 |\n| 5185 | 0162F0705185 | 30.427 | 81.467 | Satluj | M(o) | 0.47 | 5,548 |\n| 5186 | 0162F0705186 | 30.426 | 81.476 | Satluj | M(o) | 1.18 | 5,514 |\n| 5187 | 0162F0705187 | 30.423 | 81.467 | Satluj | M(o) | 0.29 | 5,627 |\n| 5188 | 0162F0705188 | 30.417 | 81.472 | Satluj | M(o) | 0.26 | 5,581 |\n| 5189 | 0162F0705189 | 30.416 | 81.474 | Satluj | M(o) | 0.62 | 5,561 |\n| 5190 | 0162F0705190 | 30.416 | 81.468 | Satluj | M(e) | 10.02 | 5,603 |\n| 5191 | 0162F0705191 | 30.415 | 81.475 | Satluj | M(o) | 0.26 | 5,557 |\n| 5192 | 0162F0705192 | 30.414 | 81.476 | Satluj | M(o) | 0.76 | 5,565 |\n| 5193 | 0162F0705193 | 30.413 | 81.481 | Satluj | E(o) | 1.16 | 5,591 |\n| 5194 | 0162F0705194 | 30.413 | 81.468 | Satluj | M(o) | 0.32 | 5,618 |\n| 5195 | 0162F0705195 | 30.410 | 81.471 | Satluj | M(o) | 0.77 | 5,569 |\n| 5196 | 0162F0705196 | 30.409 | 81.488 | Satluj | E(o) | 2.59 | 5,629 |\n| 5197 | 0162F0705197 | 30.409 | 81.476 | Satluj | M(o) | 11.76 | 5,542 |\n| 5198 | 0162F0705198 | 30.409 | 81.479 | Satluj | E(o) | 0.41 | 5,600 |\n| 5199 | 0162F0705199 | 30.408 | 81.472 | Satluj | M(o) | 0.34 | 5,557 |\n| 5200 | 0162F0705200 | 30.403 | 81.476 | Satluj | E(o) | 3.61 | 5,520 |\n| 5201 | 0162F0705201 | 30.402 | 81.482 | Satluj | E(o) | 0.93 | 5,545 |\n| 5202 | 0162F0905202 | 30.949 | 81.668 | Satluj | E(o) | 2.20 | 4,958 |\n| 5203 | 1162F1005203 | 30.515 | 81.702 | Satluj | E(o) | 10.17 | 4,818 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 11333, "line_end": 11480, "token_count_estimate": 1651, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0162F0705169", "0162F0705170", "0162F0705171", "0162F0705172", "0162F0705173", "0162F0705174", "0162F0705175", "0162F0705176", "0162F0705177", "0162F0705178", "0162F0705179", "0162F0705180", "0162F0705181", "0162F0705182", "0162F0705183", "0162F0705184", "0162F0705185", "0162F0705186", "0162F0705187", "0162F0705188", "0162F0705189", "0162F0705190", "0162F0705191", "0162F0705192", "0162F0705193", "0162F0705194", "0162F0705195", "0162F0705196", "0162F0705197", "0162F0705198", "0162F0705199", "0162F0705200", "0162F0705201", "0162F0905202", "1162F1005203"]}}
{"id": "e739a7f2019dafdf", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5204 | 1162F1005204 | 30.507 | 81.696 | Satluj | E(o) | 0.67 | 4,817 |\n| 5205 | 1162F1005205 | 30.502 | 81.707 | Satluj | E(o) | 1.78 | 4,836 |\n| 5206 | 1162F1105206 | 30.497 | 81.695 | Satluj | E(o) | 1.65 | 4,824 |\n| 5207 | 1162F1105207 | 30.497 | 81.693 | Satluj | E(o) | 1.87 | 4,824 |\n| 5208 | 1162F1105208 | 30.495 | 81.701 | Satluj | E(o) | 1.00 | 4,836 |\n| 5209 | 1162F1105209 | 30.481 | 81.514 | Satluj | E(o) | 6.93 | 5,335 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 11333, "line_end": 11480, "token_count_estimate": 377, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["1162F1005204", "1162F1005205", "1162F1105206", "1162F1105207", "1162F1105208", "1162F1105209"]}}
{"id": "e48c44a01237772e", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 11481, "line_end": 11492, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "132c38259e44a3aa", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5210 | 1162F1105210 | 30.477 | 81.514 | Satluj | E(o) | 2.94 | 5,334 |\n| 5211 | 1162F1105211 | 30.455 | 81.698 | Satluj | E(o) | 2.21 | 5,185 |\n| 5212 | 1162F1105212 | 30.448 | 81.720 | Satluj | E(o) | 5.93 | 5,111 |\n| 5213 | 1162F1105213 | 30.448 | 81.676 | Satluj | E(o) | 7.29 | 5,212 |\n| 5214 | 1162F1105214 | 30.448 | 81.749 | Satluj | E(o) | 1.02 | 5,197 |\n| 5215 | 1162F1105215 | 30.441 | 81.713 | Satluj | E(o) | 2.50 | 5,133 |\n| 5216 | 1162F1105216 | 30.439 | 81.699 | Satluj | E(o) | 0.81 | 5,247 |\n| 5217 | 1162F1105217 | 30.431 | 81.697 | Satluj | E(o) | 3.93 | 5,299 |\n| 5218 | 1162F1105218 | 30.430 | 81.714 | Satluj | E(o) | 23.45 | 5,184 |\n| 5219 | 1162F1105219 | 30.429 | 81.741 | Satluj | O | 0.59 | 5,170 |\n| 5220 | 1162F1105220 | 30.428 | 81.722 | Satluj | E(o) | 2.51 | 5,216 |\n| 5221 | 1162F1105221 | 30.421 | 81.721 | Satluj | E(o) | 18.94 | 5,247 |\n| 5222 | 1162F1105222 | 30.409 | 81.746 | Satluj | E(o) | 7.57 | 5,275 |\n| 5223 | 1162F1105223 | 30.408 | 81.530 | Satluj | E(o) | 0.86 | 5,101 |\n| 5224 | 1162F1105224 | 30.406 | 81.533 | Satluj | E(o) | 0.55 | 5,109 |\n| 5225 | 1162F1105225 | 30.402 | 81.741 | Satluj | E(o) | 3.56 | 5,283 |\n| 5226 | 1162F1105226 | 30.401 | 81.542 | Satluj | E(o) | 1.87 | 5,112 |\n| 5227 | 1162F1105227 | 30.399 | 81.540 | Satluj | E(o) | 1.42 | 5,111 |\n| 5228 | 1162F1105228 | 30.393 | 81.533 | Satluj | E(o) | 17.49 | 5,128 |\n| 5229 | 1162F1105229 | 30.389 | 81.539 | Satluj | E(o) | 0.76 | 5,133 |\n| 5230 | 1162F1105230 | 30.386 | 81.536 | Satluj | E(o) | 7.18 | 5,130 |\n| 5231 | 1162F1105231 | 30.383 | 81.535 | Satluj | E(o) | 1.17 | 5,131 |\n| 5232 | 1162F1105232 | 30.380 | 81.532 | Satluj | E(o) | 2.48 | 5,132 |\n| 5233 | 1162F1105233 | 30.378 | 81.532 | Satluj | E(o) | 0.40 | 5,133 |\n| 5234 | 1162F1305234 | 30.989 | 81.762 | Satluj | E(o) | 1.58 | 5,550 |\n| 5235 | 1162F1305235 | 30.879 | 81.994 | Satluj | M(o) | 0.42 | 5,578 |\n| 5236 | 1162F1305236 | 30.879 | 81.995 | Satluj | M(o) | 0.54 | 5,601 |\n| 5237 | 1162F1305237 | 30.841 | 81.980 | Satluj | E(c) | 2.90 | 5,517 |\n| 5238 | 1162F1405238 | 30.571 | 81.928 | Satluj | O | 0.35 | 5,305 |\n| 5239 | 1162F1405239 | 30.549 | 81.990 | Satluj | M(o) | 0.39 | 5,575 |\n| 5240 | 1162F1405240 | 30.546 | 81.991 | Satluj | M(e) | 1.30 | 5,580 |\n| 5241 | 1162F1505241 | 30.476 | 81.930 | Satluj | O | 0.61 | 5,033 |\n| 5242 | 1162F1505242 | 30.465 | 81.936 | Satluj | O | 0.70 | 5,043 |\n| 5243 | 1162F1505243 | 30.463 | 81.993 | Satluj | O | 20.76 | 5,161 |\n| 5244 | 1162F1505244 | 30.459 | 81.771 | Satluj | O | 4.13 | 5,040 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 11493, "line_end": 11620, "token_count_estimate": 1650, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["1162F1105210", "1162F1105211", "1162F1105212", "1162F1105213", "1162F1105214", "1162F1105215", "1162F1105216", "1162F1105217", "1162F1105218", "1162F1105219", "1162F1105220", "1162F1105221", "1162F1105222", "1162F1105223", "1162F1105224", "1162F1105225", "1162F1105226", "1162F1105227", "1162F1105228", "1162F1105229", "1162F1105230", "1162F1105231", "1162F1105232", "1162F1105233", "1162F1305234", "1162F1305235", "1162F1305236", "1162F1305237", "1162F1405238", "1162F1405239", "1162F1405240", "1162F1505241", "1162F1505242", "1162F1505243", "1162F1505244"]}}
{"id": "f4c8610fd2b5c896", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5245 | 1162F1505245 | 30.453 | 81.993 | Satluj | O | 0.55 | 5,165 |\n| 5246 | 1162F1505246 | 30.452 | 81.998 | Satluj | O | 0.31 | 5,165 |\n| 5247 | 1162F1505247 | 30.450 | 81.993 | Satluj | O | 0.30 | 5,162 |\n| 5248 | 1162F1505248 | 30.448 | 81.994 | Satluj | O | 3.01 | 5,166 |\n| 5249 | 1162F1505249 | 30.446 | 81.999 | Satluj | O | 1.57 | 5,163 |\n| 5250 | 1162F1505250 | 30.438 | 81.821 | Satluj | E(o) | 4.02 | 5,384 |\n| 5251 | 1162F1505251 | 30.437 | 81.997 | Satluj | O | 0.86 | 5,163 |\n| 5252 | 1162F1505252 | 30.429 | 81.997 | Satluj | O | 0.41 | 5,168 |\n| 5253 | 1162F1505253 | 30.427 | 81.871 | Satluj | E(o) | 21.31 | 5,338 |\n| 5254 | 1162F1505254 | 30.423 | 81.798 | Satluj | E(o) | 0.92 | 5,466 |\n| 5255 | 1162F1505255 | 30.420 | 81.763 | Satluj | E(o) | 2.88 | 5,323 |\n| 5256 | 1162F1505256 | 30.419 | 81.868 | Satluj | E(o) | 14.11 | 5,356 |\n| 5257 | 1162F1505257 | 30.411 | 81.777 | Satluj | E(o) | 7.02 | 5,224 |\n| 5258 | 1162F1505258 | 30.410 | 81.846 | Satluj | E(o) | 0.50 | 5,802 |\n| 5259 | 1162F1505259 | 30.408 | 81.820 | Satluj | E(o) | 6.02 | 5,501 |\n| 5260 | 1162F1505260 | 30.408 | 81.766 | Satluj | E(o) | 0.96 | 5,389 |\n| 5261 | 1162F1505261 | 30.408 | 81.997 | Satluj | E(o) | 2.06 | 5,178 |\n| 5262 | 1162F1505262 | 30.406 | 81.998 | Satluj | E(o) | 0.50 | 5,182 |\n| 5263 | 1162F1505263 | 30.404 | 81.885 | Satluj | E(c) | 1.50 | 5,601 |\n| 5264 | 1162F1505264 | 30.404 | 81.774 | Satluj | E(o) | 0.57 | 5,291 |\n| 5265 | 1162F1505265 | 30.403 | 81.853 | Satluj | M(o) | 0.47 | 5,728 |\n| 5266 | 1162F1505266 | 30.401 | 81.775 | Satluj | E(o) | 0.79 | 5,293 |\n| 5267 | 1162F1505267 | 30.401 | 81.758 | Satluj | E(o) | 0.34 | 5,497 |\n| 5268 | 1162F1505268 | 30.401 | 81.784 | Satluj | E(o) | 4.43 | 5,328 |\n| 5269 | 1162F1505269 | 30.399 | 81.853 | Satluj | M(e) | 13.91 | 5,722 |\n| 5270 | 1162F1505270 | 30.399 | 81.775 | Satluj | E(o) | 2.59 | 5,296 |\n| 5271 | 1162F1505271 | 30.399 | 81.866 | Satluj | M(e) | 8.54 | 5,477 |\n| 5272 | 1162F1505272 | 30.399 | 81.777 | Satluj | E(o) | 0.59 | 5,296 |\n| 5273 | 1162F1505273 | 30.397 | 81.769 | Satluj | E(o) | 5.05 | 5,368 |\n| 5274 | 1162F1505274 | 30.397 | 81.792 | Satluj | E(o) | 3.22 | 5,415 |\n| 5275 | 1162F1505275 | 30.396 | 81.848 | Satluj | I(s) | 0.82 | 5,808 |\n| 5276 | 1162F1505276 | 30.395 | 81.785 | Satluj | E(o) | 0.40 | 5,356 |\n| 5277 | 1162F1505277 | 30.392 | 81.795 | Satluj | E(o) | 0.64 | 5,432 |\n| 5278 | 1162F1505278 | 30.392 | 81.964 | Satluj | M(e) | 20.83 | 5,675 |\n| 5279 | 1162F1505279 | 30.392 | 81.752 | Satluj | M(o) | 0.27 | 5,433 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 11493, "line_end": 11620, "token_count_estimate": 1660, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["1162F1505245", "1162F1505246", "1162F1505247", "1162F1505248", "1162F1505249", "1162F1505250", "1162F1505251", "1162F1505252", "1162F1505253", "1162F1505254", "1162F1505255", "1162F1505256", "1162F1505257", "1162F1505258", "1162F1505259", "1162F1505260", "1162F1505261", "1162F1505262", "1162F1505263", "1162F1505264", "1162F1505265", "1162F1505266", "1162F1505267", "1162F1505268", "1162F1505269", "1162F1505270", "1162F1505271", "1162F1505272", "1162F1505273", "1162F1505274", "1162F1505275", "1162F1505276", "1162F1505277", "1162F1505278", "1162F1505279"]}}
{"id": "c9c9330f4ded52ca", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5280 | 1162F1505280 | 30.392 | 81.838 | Satluj | E(o) | 1.33 | 5,563 |\n| 5281 | 1162F1505281 | 30.391 | 81.814 | Satluj | M(o) | 0.39 | 5,588 |\n| 5282 | 1162F1505282 | 30.391 | 81.917 | Satluj | M(o) | 0.29 | 5,584 |\n| 5283 | 1162F1505283 | 30.391 | 81.803 | Satluj | E(o) | 2.81 | 5,528 |\n| 5284 | 1162F1505284 | 30.391 | 81.989 | Satluj | E(o) | 4.18 | 5,268 |\n| 5285 | 1162F1505285 | 30.390 | 81.818 | Satluj | M(e) | 14.51 | 5,544 |\n| 5286 | 1162F1505286 | 30.390 | 81.895 | Satluj | M(e) | 10.27 | 5,441 |\n| 5287 | 1162F1505287 | 30.390 | 81.822 | Satluj | M(l) | 0.97 | 5,546 |\n| 5288 | 1162F1505288 | 30.390 | 81.932 | Satluj | M(o) | 0.68 | 5,244 |\n| 5289 | 1162F1505289 | 30.389 | 81.915 | Satluj | M(o) | 0.77 | 5,561 |\n| 5290 | 1162F1505290 | 30.388 | 81.848 | Satluj | M(o) | 0.29 | 5,639 |\n| 5291 | 1162F1505291 | 30.388 | 81.929 | Satluj | M(o) | 0.33 | 5,229 |\n| 5292 | 1162F1505292 | 30.388 | 81.844 | Satluj | M(o) | 0.38 | 5,668 |\n| 5293 | 1162F1505293 | 30.387 | 81.848 | Satluj | M(o) | 3.44 | 5,628 |\n| 5294 | 1162F1505294 | 30.386 | 81.928 | Satluj | M(o) | 0.30 | 5,231 |\n| 5295 | 1162F1505295 | 30.385 | 81.930 | Satluj | M(e) | 59.79 | 5,224 |\n| 5296 | 1162F1505296 | 30.385 | 81.841 | Satluj | M(e) | 12.42 | 5,571 |\n| 5297 | 1162F1505297 | 30.384 | 81.861 | Satluj | E(o) | 0.28 | 5,709 |\n| 5298 | 1162F1505298 | 30.383 | 81.825 | Satluj | M(o) | 0.49 | 5,598 |\n| 5299 | 1162F1505299 | 30.382 | 81.887 | Satluj | E(o) | 1.57 | 5,606 |\n| 5300 | 1162F1505300 | 30.381 | 81.830 | Satluj | M(o) | 3.02 | 5,498 |\n| 5301 | 1162F1505301 | 30.380 | 81.979 | Satluj | M(o) | 0.44 | 5,408 |\n| 5302 | 1162F1505302 | 30.379 | 81.842 | Satluj | M(o) | 2.89 | 5,668 |\n| 5303 | 1162F1505303 | 30.377 | 81.881 | Satluj | E(o) | 0.27 | 5,696 |\n| 5304 | 1162F1505304 | 30.376 | 81.878 | Satluj | M(lg) | 1.39 | 5,740 |\n| 5305 | 1162F1505305 | 30.370 | 81.914 | Satluj | M(o) | 0.70 | 5,536 |\n| 5306 | 1162F1505306 | 30.367 | 81.995 | Satluj | M(e) | 2.06 | 5,256 |\n| 5307 | 1162F1505307 | 30.352 | 81.978 | Satluj | M(o) | 7.23 | 5,470 |\n| 5308 | 1162F1505308 | 30.344 | 81.977 | Satluj | M(o) | 8.38 | 5,501 |\n| 5309 | 1162F1505309 | 30.343 | 81.982 | Satluj | M(lg) | 0.66 | 5,518 |\n| 5310 | 0162J0205310 | 30.596 | 82.072 | Satluj | E(o) | 8.96 | 5,495 |\n| 5311 | 0162J0205311 | 30.592 | 82.084 | Satluj | E(o) | 10.15 | 5,382 |\n| 5312 | 0162J0205312 | 30.585 | 82.116 | Satluj | E(o) | 1.40 | 5,422 |\n| 5313 | 0162J0205313 | 30.573 | 82.036 | Satluj | E(o) | 0.43 | 5,672 |\n| 5314 | 0162J0205314 | 30.571 | 82.124 | Satluj | E(o) | 0.81 | 5,488 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 11493, "line_end": 11620, "token_count_estimate": 1660, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0162J0205310", "0162J0205311", "0162J0205312", "0162J0205313", "0162J0205314", "1162F1505280", "1162F1505281", "1162F1505282", "1162F1505283", "1162F1505284", "1162F1505285", "1162F1505286", "1162F1505287", "1162F1505288", "1162F1505289", "1162F1505290", "1162F1505291", "1162F1505292", "1162F1505293", "1162F1505294", "1162F1505295", "1162F1505296", "1162F1505297", "1162F1505298", "1162F1505299", "1162F1505300", "1162F1505301", "1162F1505302", "1162F1505303", "1162F1505304", "1162F1505305", "1162F1505306", "1162F1505307", "1162F1505308", "1162F1505309"]}}
{"id": "f37a7e30a9d2d7d3", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 5315 | 0162J0205315 | 30.570 | 82.064 | Satluj | E(o) | 0.36 | 5,679 |\n| 5316 | 0162J0205316 | 30.557 | 82.035 | Satluj | E(o) | 0.91 | 5,447 |\n| 5317 | 0162J0205317 | 30.556 | 82.036 | Satluj | E(o) | 0.39 | 5,451 |\n| 5318 | 0162J0205318 | 30.547 | 82.016 | Satluj | E(o) | 0.88 | 5,653 |\n| 5319 | 0162J0205319 | 30.545 | 82.120 | Satluj | E(o) | 0.68 | 5,528 |\n| 5320 | 0162J0205320 | 30.543 | 82.156 | Satluj | E(o) | 1.79 | 5,442 |\n| 5321 | 0162J0205321 | 30.523 | 82.164 | Satluj | E(o) | 0.35 | 5,554 |\n| 5322 | 0162J0305322 | 30.456 | 82.006 | Satluj | E(o) | 0.48 | 5,170 |\n| 5323 | 0162J0305323 | 30.452 | 82.005 | Satluj | O | 2.01 | 5,164 |\n| 5324 | 0162J0305324 | 30.450 | 82.002 | Satluj | O | 0.36 | 5,168 |\n| 5325 | 0162J0305325 | 30.450 | 82.006 | Satluj | O | 0.89 | 5,164 |\n| 5326 | 0162J0305326 | 30.448 | 82.002 | Satluj | O | 1.15 | 5,163 |\n| 5327 | 0162J0305327 | 30.441 | 82.000 | Satluj | O | 0.55 | 5,161 |\n| 5328 | 0162J0305328 | 30.428 | 82.010 | Satluj | E(o) | 0.87 | 5,171 |\n| 5329 | 0162J0305329 | 30.418 | 82.004 | Satluj | O | 1.10 | 5,175 |\n| 5330 | 0162J0305330 | 30.417 | 82.005 | Satluj | O | 0.62 | 5,173 |\n| 5331 | 0162J0305331 | 30.408 | 82.005 | Satluj | O | 0.36 | 5,174 |\n| 5332 | 0162J0305332 | 30.380 | 82.021 | Satluj | M(o) | 1.08 | 5,543 |\n| 5333 | 0162J0305333 | 30.376 | 82.020 | Satluj | M(e) | 11.62 | 5,493 |\n| 5334 | 0162J0305334 | 30.374 | 82.016 | Satluj | M(e) | 3.80 | 5,502 |\n| 5335 | 0162J0605335 | 30.732 | 82.357 | Satluj | M(e) | 1.50 | 5,730 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 126, "line_start": 11493, "line_end": 11620, "token_count_estimate": 1017, "basins": ["Indus"], "subbasins": ["Satluj"], "countries": [], "lake_ids": ["0162J0205315", "0162J0205316", "0162J0205317", "0162J0205318", "0162J0205319", "0162J0205320", "0162J0205321", "0162J0305322", "0162J0305323", "0162J0305324", "0162J0305325", "0162J0305326", "0162J0305327", "0162J0305328", "0162J0305329", "0162J0305330", "0162J0305331", "0162J0305332", "0162J0305333", "0162J0305334", "0162J0605335"]}}
{"id": "6f90eb6665959eb9", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 11621, "line_end": 11627, "token_count_estimate": 62, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c65bfa69a8628f0f", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha\nType: text\n\nThere are 34 lakes having an area greater than 50 ha, which is just 0.63% of the total glacial lake count, but cover total of 20.26% of the total glacial lakes area. Spatial distribution of these very large sized lakes i.e. > 50 ha in area has been represented below in Figure 122 and details of these are given in Table 99, along with its area, type, geographic as well as hydrological location, and elevation at which they are situated. Among the 34 lakes, 22, 9 and 3 lakes are in the lake area range of 50-100 ha, 100-200 ha and greater than 200 ha respectively. Out of these 34 large lakes, maximum (13) are other glacial lakes and minimum (1 each) are other moraine dammed and glacial ice-dammed lakes. These very large sized lakes are situated within the elevation range of 3,486 to 5,709 m amsl, the largest one being in Gilgit subbasin at 4,286 m, followed by second largest in Shyok subbasin at 5,709 m.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha"], "chunk_type": "text", "line_start": 11629, "line_end": 11637, "token_count_estimate": 294, "basins": ["INDUS", "Indus"], "subbasins": ["Gilgit", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "fb67a12f58c4a598", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha\nType: table\nTable: Table 99: List of glacial lakes with area greater than 50 ha\n\n| S.No. | Glacial Lake ID No | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0143J1301449 | 34.845 | 74.809 | Indus Middle | E(c) | 50.44 | 3,992 |\n| 2 | 0161F0704829 | 34.341 | 81.257 | Shyok | E(o) | 51.89 | 5,298 |\n| 3 | 0143N0802065 | 34.094 | 75.498 | Jhelum | O | 52.27 | 3,575 |\n| 4 | 0162E0405009 | 31.182 | 81.195 | Satluj | E(o) | 52.48 | 5,413 |\n| 5 | 0143E0500769 | 35.949 | 73.289 | Gilgit | E(o) | 54.23 | 4,228 |\n| 6 | 0161D1504804 | 32.423 | 80.865 | Indus Upper | O | 58.84 | 4,452 |\n| 7 | 0152C1603156 | 33.159 | 76.984 | Indus Upper | M(e) | 59.78 | 4,479 |\n| 8 | 0162F1505296 | 30.385 | 81.930 | Satluj | M(e) | 59.79 | 5,224 |\n| 9 | 0143J0901397 | 34.920 | 74.521 | Jhelum | M(e) | 60.60 | 4,041 |\n| 10 | 0161F0304813 | 34.299 | 81.202 | Shyok | O | 61.15 | 5,274 |\n| 11 | 0152J0803836 | 34.233 | 78.426 | Shyok | O | 64.78 | 5,350 |\n| 12 | 0143N0201885 | 34.697 | 75.137 | Jhelum | E(o) | 64.95 | 4,103 |\n| 13 | 0152J1203868 | 34.151 | 78.553 | Shyok | E(o) | 65.02 | 5,566 |\n| 14 | 0143E0500774 | 35.945 | 73.365 | Gilgit | E(c) | 67.34 | 4,162 |\n| 15 | 0143K1001561 | 33.519 | 74.584 | Jhelum | E(o) | 70.83 | 3,934 |\n| 16 | 0143N0201897 | 34.666 | 75.179 | Jhelum | E(o) | 74.54 | 4,234 |\n| 17 | 0152H0203651 | 32.526 | 77.220 | Chenab | M(e) | 77.59 | 4,069 |\n| 18 | 0143N0401975 | 34.140 | 75.148 | Jhelum | E(c) | 82.93 | 3,780 |\n| 19 | 0143E0900941 | 35.865 | 73.746 | Gilgit | E(o) | 84.31 | 4,140 |\n| 20 | 0143J0101363 | 34.829 | 74.062 | Jhelum | E(c) | 93.90 | 3,681 |\n| 21 | 0152J0303811 | 34.457 | 78.136 | Shyok | M(o) | 95.68 | 5,295 |\n| 22 | 0143A0900591 | 35.944 | 72.595 | Gilgit | O | 96.35 | 3,761 |\n| 23 | 0142H1000208 | 36.644 | 73.646 | Gilgit | O | 105.07 | 3,821 |\n| 24 | 0143K1401588 | 33.512 | 74.769 | Jhelum | O | 128.54 | 3,486 |\n| 25 | 0152H1103771 | 32.499 | 77.547 | Chenab | M(e) | 128.69 | 4,150 |\n| 26 | 0143N0101839 | 34.991 | 75.236 | Indus Upper | O | 129.62 | 4,138 |\n| 27 | 0152K0703939 | 33.455 | 78.498 | Shyok | O | 147.89 | 5,308 |\n| 28 | 0162E1105115 | 31.274 | 81.595 | Indus Upper | O | 156.89 | 5,229 |\n| 29 | 0143J1501489 | 34.432 | 74.924 | Jhelum | E(c) | 161.04 | 3,571 |\n| 30 | 0152K0703940 | 33.427 | 78.488 | Shyok | O | 177.73 | 5,284 |\n| 31 | 0162F0705180 | 30.431 | 81.433 | Satluj | E(o) | 192.62 | 5,484 |\n| 32 | 0143A0900583 | 35.994 | 72.613 | Gilgit | O | 201.58 | 3,622 |\n| 33 | 0161B1504757 | 34.316 | 80.858 | Shyok | I(d) | 232.34 | 5,709 |\n| 34 | 0142H0900200 | 36.879 | 73.704 | Gilgit | O | 262.57 | 4,286 |", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha"], "chunk_type": "table", "table_caption": "Table 99: List of glacial lakes with area greater than 50 ha", "columns": ["S.No.", "Glacial Lake ID No", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 34, "line_start": 11638, "line_end": 11673, "token_count_estimate": 1570, "basins": ["Indus"], "subbasins": ["Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Satluj", "Shyok"], "countries": [], "lake_ids": ["0142H0900200", "0142H1000208", "0143A0900583", "0143A0900591", "0143E0500769", "0143E0500774", "0143E0900941", "0143J0101363", "0143J0901397", "0143J1301449", "0143J1501489", "0143K1001561", "0143K1401588", "0143N0101839", "0143N0201885", "0143N0201897", "0143N0401975", "0143N0802065", "0152C1603156", "0152H0203651", "0152H1103771", "0152J0303811", "0152J0803836", "0152J1203868", "0152K0703939", "0152K0703940", "0161B1504757", "0161D1504804", "0161F0304813", "0161F0704829", "0162E0405009", "0162E1105115", "0162F0705180", "0162F1505296"]}}
{"id": "daf23d0c136ea0bc", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha"], "chunk_type": "text", "line_start": 11674, "line_end": 11681, "token_count_estimate": 51, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "87789f2aa4b19383", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary\nType: text\n\n**Ablation:** The process that reduce the mass of the glacier (Cogley et al., 2011).\n\n**Ablation area/zone:** The part of the glacier where ablation exceeds accumulation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is negative. The extent of the ablation zone can vary strongly from year to year (Cogley et al., 2011).\n\n**Accumulation:** The process that add to the mass of the glacier (Cogley et al., 2011).\n\n**Accumulation area/zone:** The part of the glacier where accumulation exceeds ablation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is positive. The extent of the accumulation zone can vary strongly from year to year. The accumulation zone is not the same as the firn area (Cogley et al., 2011).\n\n**Altitude:** The vertical distance of a point above a datum, which is usually an estimate of mean sea level. Altitude and elevation are synonyms in common usage (Cogley et al., 2011).\n\n**Aspect:** The compass direction towards which a slope faces; measured clockwise in degrees from the North.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Avalanche:** A slide or flow of a mass of snow, firn or ice that becomes detached abruptly, often entraining additional material such as snow, debris and vegetation as it descends. The duration of an avalanche is typically seconds to minutes (Cogley et al., 2011).\n\n**Band:** One layer of multispectral image representing data values for a specific range of the electromagnetic spectrum of reflected light or heat.\n\n**Climate:** Climate is usually defined as the average weather or as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years. The relevant quantities are most often surface variables such as temperature, precipitation and wind (Pandey, 2019).\n\n**Climate change:** Climate change refers to a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. UNFCCC defines climate change as: 'a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods'. (Pandey, 2019).\n\n**Climate variability:** Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all spatial and temporal scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability) (Pandey, 2019).\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 11683, "line_end": 11825, "token_count_estimate": 852, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9dcab113f6194cc5", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary\nType: text\n\nthe composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods ' . ( Pandey , 2019 ) . * * Climate variability : * * Climate variability refers to variations in the mean state and other statistics ( such as standard deviations , the occurrence of extremes , etc . ) of the climate on all spatial and temporal scales beyond that of individual weather events . Variability may be due to natural internal processes within the climate system ( internal variability ) , or to variations in natural or anthropogenic external forcing ( external variability ) ( Pandey , 2019 ) . GLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Cryosphere:** The cryosphere is the part of the Earth system that contains ice, for example snow on the ground, glaciers, ice sheets, lake ice, river ice, sea ice, seasonally and perennially frozen ground (GCW 2016).\n\n**Database:** An organized, integrated collection of data related by a common fact or purpose.\n\n**Debris-covered glacier:** A glacier that is covered at its tongue with supra-glacial debris across its full width (Kirkbride, 2011). In the accumulation zone any deposited debris is buried by later snowfalls, but in the ablation zone debris remains at the surface and englacial debris is added to the surface layer from beneath as ice ablates away. The debris cover affects the rate of ablation, with very thin debris resulting in accelerated melt and debris thicker than a few tens of millimetres reducing the melting rate (Cogley et al., 2011).\n\n**Digital Elevation Model (DEM):** An array of numbers representing the elevation of part or all of the Earth’s surface as samples or averages at fixed spacing in two horizontal coordinate directions (Cogley et al., 2011).\n\n**Disaster:** A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNISDR 2017).\n\n**Disaster risk:** The potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity (UNISDR 2017).\n\n**Early warning system:** The set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare to act promptly and appropriately to reduce the possibility of harm or loss (Pandey, 2019).\n\n**Electromagnetic spectrum:** The spectrum of wavelengths of electromagnetic radiation.\n\n**Englacial:** Pertaining to the interior of the glacier, between the summer surface and the bed (Cogley et al., 2011).\n\n**Exposure:** The presence or situation of people, livelihoods, species or ecosystems, environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings, and other tangible human assets located in hazard-prone areas that could be adversely affected (UNISDR, 2017; Pandey, 2019).\n\n**Feature:** A real-world phenomenon, often used in cartography to name classes of elements shown on a map.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 11683, "line_end": 11825, "token_count_estimate": 871, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "580354dcefe68364", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary\nType: text\n\n* * Pertaining to the interior of the glacier , between the summer surface and the bed ( Cogley et al . , 2011 ) . * * Exposure : * * The presence or situation of people , livelihoods , species or ecosystems , environmental functions , services , and resources , infrastructure , or economic , social , or cultural assets in places and settings , and other tangible human assets located in hazard - prone areas that could be adversely affected ( UNISDR , 2017 ; Pandey , 2019 ) . * * Feature : * * A real - world phenomenon , often used in cartography to name classes of elements shown on a map .\n\n**Firn:** Snow (in which the pore space is at least partially interconnected, allowing air and water to circulate) that has survived at least one ablation season but has not been transformed to glacier ice (Cogley et al., 2011).\n\n**Flood:** The overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas not normally submerged. Floods include river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods and glacial lake outburst floods (Pandey, 2019).\n\n**Format:** The pattern into which data are systematically arranged for use on a computer.\n\n**Geographic Information System (GIS):** A set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world for a particular set of circumstances.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Glacial Lake Outburst Flood (GLOF):** Flood caused by the outburst of a glacial lake due to rapid accumulation of water in it, resulting to extreme damage in loss of lives and infrastructure in the downstream area.\n\n**Glacial Lake:** As a result of glacier thinning and retreating, melt water gets accumulated at terminal moraines or on it covered by glacier ice, is known as glacial lake.\n\n**Glacier Erosion Lake:** These are the water bodies formed in a depression after the glacier has retreated in a form of cirque or trough valley, might be isolated and far away from the present glaciated area, and mostly stable in nature.\n\n**Glacier:** A perennial mass of ice, and possibly firn and snow, originating on the land surface by their crystallization of snow or other forms of solid precipitation and showing evidence of past or present flow (Cogley et al., 2011).\n\n**Global Positioning System (GPS):** A GPS is a position-fixing system that uses the time taken for signals to travel from at least three GPS satellites in a known orbit to a receiver on the ground.\n\n**Hazard:** The potential occurrence of a natural or human-induced physical event or trend or physical impact that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources (Pandey, 2019).\n\n**Ice-dammed Lake:** An Ice-dammed Lake is produced on the side(s) of a glacier, when an advancing glacier happens to intercept a tributary/tributaries pouring into a main glacier valley.", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 11683, "line_end": 11825, "token_count_estimate": 847, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ff2fd54d116cb13", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary\nType: text\n\nGPS satellites in a known orbit to a receiver on the ground . * * Hazard : * * The potential occurrence of a natural or human - induced physical event or trend or physical impact that may cause loss of life , injury , or other health impacts , as well as damage and loss to property , infrastructure , livelihoods , service provision , ecosystems and environmental resources ( Pandey , 2019 ) . * * Ice - dammed Lake : * * An Ice - dammed Lake is produced on the side ( s ) of a glacier , when an advancing glacier happens to intercept a tributary / tributaries pouring into a main glacier valley .\n\n**Impacts:** The term impacts is used primarily to refer to the effects on natural and human systems of extreme weather and climate events and of climate change. Impacts generally refer to effects on lives, livelihoods, health, ecosystems, economies, societies, cultures, services and infrastructure due to the interaction of climate changes or hazardous climate events occurring within a specific time period and the vulnerability of an exposed society or system. Impacts are also referred to as consequences and outcomes. The impacts of climate change on geophysical systems, including floods, droughts and sea level rise, are a subset of impacts called physical impacts (Pandey, 2019).\n\n**Latitude:** Angle measured in a north-south direction from the Earth’s center to locations on the Earth’s surface.\n\n**Longitude:** Angle measured in an east-west direction from the Earth’s center to locations on the Earth’s surface.\n\n**Layer:** Usually represents a theme or a feature type within the database.\n\n**Map:** An abstract representation of the physical features of a portion of the Earth’s surface graphically displayed on a planar surface. Map display signs, symbols and spatial relationships among the features.\n\n**Melt water:** The liquid resulting from melting of ice, firn or snow (Cogley et al., 2011).\n\n**Moraine-dammed Lake:** In the retreating process of a glacier, ice tends to melt in the lowest part of the glacier surrounded by Lateral-moraines and End-moraines, and forms into a lake known as Moraine-dammed Lake or Proglacial Lake.\n\n**Pixel:** Smallest discrete element that makes up an image, generally represents either a small square or portion of the Earth’s surface, scanned by satellite or aircraft.\n\nGLACIAL LAKE ATLAS OF INDUS RIVER BASIN\n\n**Precipitation:** Liquid or solid products of the condensation of water vapour that fall from clouds or are deposited from the air onto the surface (Cogley et al., 2011).\n\n**Remote sensing:** The technique of obtaining data about the environment and surface of the earth from a distance, e.g. from an aircraft or satellite.\n\n**Resolution:** It is the accuracy at which a given map scale can depict the location and shape of geographic features.\n\n**Retreat:** Decrease of the length of a flow line (in case of glacier which is its terminus), measured from a fixed point. Advance is the opposite of retreat, that is, advance of the terminus (Cogley et al., 2011).", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 11683, "line_end": 11825, "token_count_estimate": 823, "basins": ["INDUS", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1462d1c426fdcd99", "text": "Document: GL Atlas Indus 12Nov2020 Final\nSection: Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary\nType: text\n\nal . , 2011 ) . * * Remote sensing : * * The technique of obtaining data about the environment and surface of the earth from a distance , e . g . from an aircraft or satellite . * * Resolution : * * It is the accuracy at which a given map scale can depict the location and shape of geographic features . * * Retreat : * * Decrease of the length of a flow line ( in case of glacier which is its terminus ) , measured from a fixed point . Advance is the opposite of retreat , that is , advance of the terminus ( Cogley et al . , 2011 ) .\n\n**Risk:** The potential for consequences where something of value is at stake and where the outcome is uncertain, recognizing the diversity of values. Risk is often represented as probability or likelihood of occurrence of hazardous events or trends multiplied by the impacts if these events or trends occur. In this report, the term risk is often used to refer to the potential, when the outcome is uncertain, for adverse consequences on lives, livelihoods, health, ecosystems and species, economic, social and cultural assets, services (including environmental services) and infrastructure (Pandey, 2019).\n\n**Scale:** The ratio or fraction between the distance on a map, chart or photograph and the corresponding distance on the surface of the Earth.\n\n**Slope:** A measure of change on surface value over distance, expressed in degrees or as a percentage.\n\n**Snow:** Solid precipitation in the form of ice crystals, chiefly in complex branched hexagonal form and often agglomerated into snowflakes; or an accumulation of the same on the Earth’s surface. It is also known as solid precipitation that has accumulated on the summer surface on a glacier and that transforms to firn at the end of the mass-balance year (Cogley et al., 2011).\n\n**Subglacial:** Pertaining to the glacier bed or to the material below the bed (Cogley et al., 2011).\n\n**Supra-glacial Lake:** Water bodies develop within the ice mass in any position of the glacier, but away from the terminal moraines are known as Supra-glacial lakes. Its basic characteristics are shifting, merging, and draining.\n\n**Terminus:** The lowest end of a glacier, also called glacier snout, glacier front or glacier toe (Cogley et al., 2011).\n\n**Tongue:** The lower, elongate part of a valley glacier or outlet glacier or a floating extension of a glacier or ice stream, laterally unconfined but markedly longer than wide (Cogley et al., 2011).\n\n**Topographic Map:** A map showing the features that describes the surface of a particular place or region. It contains contours indicating lines of equal surface elevation (relief), often referred to a topo maps.\n\n**Vulnerability:** The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt (Pandey, 2019).\n\nPrepared under: National Hydrology Project\n\nNational Remote Sensing Centre\nIndian Space Research Organisation\nDepartment of Space, Government of India\nHyderabad - 500 037", "metadata": {"source_file": "data/GL_Atlas_Indus_12Nov2020_Final_gemini.md", "document_title": "GL Atlas Indus 12Nov2020 Final", "section_path": "Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha > Annexure - IV: Glossary", "section_headings": ["Annexure - III: Glacial Lakes of Indus River basin greater than 50 ha", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 11683, "line_end": 11825, "token_count_estimate": 829, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "60cafd60a209ce16", "text": "Document: Glacial Lake Atlas Ganga Basin NRSC\nType: text\n\nNational Hydrology Project (NHP) was taken up by Department of Water Resources, River Development & Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti with the aim of improving the extent, quality and accessibility of water resources information and to strengthen the capacity of targeted water resources management institutions in India. National Remote Sensing Centre (NRSC), one of the premier centres of Indian Space Research Organisation (ISRO) is taking part in this initiative of DoWR, RD&GR as one of the Implementing Agencies under NHP.\n\nWater resources availability and the ability to derive its information using modern tools like remote sensing and GIS with high temporal and spatial coverage, will usher in a new era of efficient and equitable water management. In the era of satellites, with the availability of voluminous data, big data analytics, artificial intelligence and other information extraction methods provide access to rapid and reliable information on water resources even from inaccessible areas.\n\nThe might Himalayas on the northern boundary of the country stand high with large number of glaciers and glacial lakes. It is important to map the glacial lakes in detail due to the increasing incidents of flash floods in the Himalayas. The prime objective of detailed mapping of glacial lakes is to reduce the damage to the downstream areas from the devastation that is caused by Glacial Lake Outburst Floods (GLOF). Glacial Lake Atlas of Ganga River Basin containing a wealth of information about 4,707 glacial lakes is prepared to assess GLOF risk for the Ganga River Basin. The Atlas, written in a well-structured manner, would be of benefit to experienced professionals and subject experts.\n\nSince the Atlas will be accessible through the India-WRIS and BHUVAN portals, this would be a step forward in making available all the water related data and products from different Central and State Organisations available on one platform, ensuring ease of access to the end user.\n\nI compliment the initiative of NRSC team for successful completion of this important task under NHP, and look forward to the logical continuation of this effort in the form of GLOF risk assessment study and its outcome in the form of Disaster Risk Reduction (DRR) from GLOF in the Indian Himalayan Region.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "Glacial Lake Atlas Ganga Basin NRSC", "section_path": "", "section_headings": [], "chunk_type": "text", "line_start": 1, "line_end": 13, "token_count_estimate": 527, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0da4774c62991502", "text": "Document: MESSAGE\nSection: MESSAGE\nType: text\n\nSnow and glaciers play a crucial role in the interaction between atmospheric and land surface processes in high mountains areas like the Himalaya. Retreat and thinning of glaciers could lead to formation of glacial lakes with the accumulation of water near their snout. Breach of such lakes may result in Glacial Lake Outburst Floods (GLOF), a potential hazard for the people and infrastructure in the downstream reaches. Hence, identification and monitoring of glacial lakes is very critical for reducing the associated risk. Multispectral, multi-temporal data from the Indian Remote Sensing satellites enable systematic inventory and monitoring of glacial lakes in the Himalayan Region.\n\nNational Remote Sensing Centre (NRSC)/ISRO is one of the Implementing Agency under National Hydrology Project, funded by DoWR, RD&GR, Ministry of Jal Shakti, Govt. of India. Among the many work components which are being carried out by NRSC as part of NHP, the atlas of Glacial Lakes of Ganga River Basin is also generated. This atlas provides various information such as the type, hydrological & topographical characteristics etc., of glacial lakes of equal to more than 0.25 ha size in the Ganga River Basin. It will serve as a scientific source of information for technical & administrative management, supporting disaster risk reduction and climate change induced impact assessment.\n\nI appreciate the efforts of technical team of NRSC/ISRO, Hyderabad, for bringing out this atlas of Glacial Lakes in the Ganga River Basin. I am sure that the atlas will be of great use for researchers, professionals and administrative personnel, in strengthening the disaster risk reduction activities and climate change studies.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "MESSAGE", "section_headings": ["MESSAGE"], "chunk_type": "text", "line_start": 15, "line_end": 23, "token_count_estimate": 403, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "42f7884db82380f7", "text": "Document: MESSAGE\nSection: FOREWORD\nType: text\n\nSnow and glaciers provide major source of fresh water for major river systems originating from Himalayas thereby supporting the livelihood of millions of people, particularly in summer months. As the glacier retreats, glacial lakes form around the margins of glacier which store its melt water impounded by the moraines. These glacial lakes occasionally release large quantities of glacier melt water from associated moraine failure, resulting in devastating floods in the downstream reaches. Many such catastrophic floods occurred in the recent past in the Himalayas resulting in loss of lives and damaging critical infrastructure like hydro-power plants, bridges, etc. It is highly imperative to have the knowledge on location of glacial lakes and the consequences of related flood risk. Earth Observation satellite data is highly helpful in identifying and monitoring of glacial lakes, which by traditional ways is practically difficult due to their inhospitable and inaccessible location and highly rugged topography.\n\nThe Ganga River Basin atlas is brought out as part of an activity on “Glacial Lake Outburst Flood (GLOF) Risk Assessment of Glacial Lakes in the Himalayan Region of Indian River Basins”, taken up under the National Hydrology Project, funded by the Department of Water Resources, River Development and Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti, Government of India. Under this activity, glacial lakes of size greater than 0.25 ha are mapped using high resolution satellite data from the Resourcesat-2 LISS-IV MX. Using this geo-database, an atlas titled ‘Glacial Lake Atlas of Indus River Basin’ has already published and in continuation to that the present atlas titled ‘Glacial Lake Atlas of Ganga River Basin’ is prepared. This atlas is first of its kind depicting spatial distribution of glacial lakes of size greater than 0.25 ha in the Ganga River basin within India and transboundary hydrologic catchments. The details of glacial lakes are systematically documented at basin, subbasin, administrative, and transboundary region level, including lake type, area, and elevation distribution.\n\nThe Ganga River Basin Atlas forms as an authentic and recent reference data, and is useful in monitoring glacial lake dynamics, GLOF risk assessment, and long-term climate change impact analysis.\n\nI appreciate the study team for taking up this initiative to bring out an exclusive “Glacial Lake Atlas of Ganga River Basin”. I wish that this exhaustive atlas will be of immense value to Central/State Water Resources, Environmental, and Disaster Management Organisations, and as well as to professionals and academicians.\n\n**(Raj Kumar)**\n\nभारतीय अन्तरिक्ष अनुसंधान संगठन Indian Space Research Organisation", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "FOREWORD", "section_headings": ["FOREWORD"], "chunk_type": "text", "line_start": 25, "line_end": 38, "token_count_estimate": 632, "basins": ["Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8f56684f7ce43cfd", "text": "Document: MESSAGE\nSection: GLACIAL LAKE ATLAS OF GANGA RIVER BASIN > SUMMARY\nType: text\n\nNational Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Hyderabad as one of the Implementing Agency under the National Hydrology Project (NHP), is carrying out hydrological studies using satellite data and geo-spatial techniques. As part of this, detailed glacial lake inventory, prioritization for Glacial Lake Outburst Flood (GLOF) risk, and simulation of GLOF for selected lakes are taken up for entire catchment of Indian Himalayan Rivers covering Indus, Ganga, and Brahmaputra River basin. Under this activity, an updated inventory of glacial lakes using high resolution satellite data was prepared for the Indus River basin and published in December 2020 (NRSC-RSAA-WRG-WRAD-Nov2020-TR-0001702-V1.0), and currently an updated inventory of glacial lakes has been prepared for the Ganga River basin, and same for Brahmapurta River basin will be made soon. The present glacial lake atlas is based on the inventoried glacial lakes in part of Ganga River basin from its origin to foothills of Himalayas covering a catchment area of 2,47,110 Km².\n\nThe study portion of Ganga River basin covers part of India and transboundary region. Ganga River basin has been divided into 11 subbasins on the basis of confluence of major rivers contributing into the system viz., Yamuna joining the main river on the right, whereas rivers like Sarda, Ghaghara, Gandak, and Kosi joining on the left. Elevation in the river basin varies from the minimum 45 m to the maximum 8,848 m above mean sea level (amsl). In India, Ganga River basin extends in two states viz., Himachal Pradesh and Uttarakhand.\n\nIn the present study, glacial lakes with water spread area ≥ 0.25 ha have been mapped using Resourcesat-2 (RS-2) Linear Imaging Self Scanning Sensor-IV (LISS-IV) satellite data using visual interpretation techniques. Based on its process of lake formation, location, and type of damming material, glacial lakes are identified in nine out of ten different types, majorly grouped into four categories viz., Moraine-dammed, Ice-dammed, Glacier Erosion, and Other Glacial lakes.\n\nA total of 4,707 glacial lakes have been mapped in the Ganga River basin using a total of 105 high resolution multispectral RS-2 LISS-IV images, with a total lake water spread area of 20,685.12 ha. Each glacial lake has been given a 12 alpha-numeric unique glacial lake ID, along with several attributes that include hydrological, geometrical, geographical, and topographical characteristics. About 4,035 (85.72%) lakes are with < 5 ha lake area contributing to 23.13% of total lake area. The remaining lakes with ≥ 5 ha in size are 672 (14.28%) contributing to 76.87% of total lake area in the basin. There are only 58 glacial lakes in the Ganga River basin having an area of ≥ 50 ha. Other Glacial Erosion lake type are found to be the maximum with 1,744 (37.05%) occupying a total lake extent of 4,612.02 ha at 22.30% in the basin. More than half (i.e. 59.25%) of the lakes are situated in the very high altitude range of greater than 5,000 m amsl and dominated by Other Moraine Dammed lake type i.e. 48.97%.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "GLACIAL LAKE ATLAS OF GANGA RIVER BASIN > SUMMARY", "section_headings": ["GLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "SUMMARY"], "chunk_type": "text", "line_start": 54, "line_end": 72, "token_count_estimate": 841, "basins": ["Brahmaputra", "GANGA", "Ganga", "Indus"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Yamuna"], "countries": ["India"], "lake_ids": ["0001702"]}}
{"id": "3bf760b0a105d7f7", "text": "Document: MESSAGE\nSection: GLACIAL LAKE ATLAS OF GANGA RIVER BASIN > SUMMARY\nType: text\n\n87 % of total lake area in the basin . There are only 58 glacial lakes in the Ganga River basin having an area of ≥ 50 ha . Other Glacial Erosion lake type are found to be the maximum with 1 , 744 ( 37 . 05 % ) occupying a total lake extent of 4 , 612 . 02 ha at 22 . 30 % in the basin . More than half ( i . e . 59 . 25 % ) of the lakes are situated in the very high altitude range of greater than 5 , 000 m amsl and dominated by Other Moraine Dammed lake type i . e . 48 . 97 % .\n\nOut of 11 subbasins, only 6 subbasins contain glacial lakes, which are predominantly distributed in Kosi subbasin (51.77%) followed by Ghaghara subbasin (26.77%), with a total lake extent of 14,604.34 ha and 3,536.39 ha at 70.60% and 17.10% respectively in the entire basin. In terms of very large size lakes i.e. ≥ 50 ha, Kosi subbasin has majority i.e. 54 out of 58 large lakes within it. Minimum number of glacial lakes are present in Yamuna subbasin (0.76%) and then in Sarda subbasin (1.17%). Other Glacial Erosion lakes, which are dominant lake type in Ganga River basin are uniformly distributed in all subbasins, and found maximum in count in Kosi subbasin. However, Glacier Ice-dammed lake is only one in the entire Ganga River basin and is located in Gandak subbasin. Upper Ganga subbasin consists of higher number of Supra-glacial Lakes in the entire Ganga River basin, whereas Gandak subbasin contains higher number of Lateral Moraine Dammed Lake with Ice.\n\n***", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "GLACIAL LAKE ATLAS OF GANGA RIVER BASIN > SUMMARY", "section_headings": ["GLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "SUMMARY"], "chunk_type": "text", "line_start": 54, "line_end": 72, "token_count_estimate": 460, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "1587689bfaff92b0", "text": "Document: MESSAGE\nSection: GLACIAL LAKE ATLAS OF GANGA RIVER BASIN\nType: text\n\nA total of 369 (i.e. 7.84%) glacial lakes lies within Indian region covering 2.92% of the total lake area, whereas remaining 92.16% of lakes are located in transboundary region with a 97.08% of the total lake area.\n\nIn Indian region, majority of glacial lakes are of Other Moraine Dammed type (29.81%), followed by Supra-glacial (28.73%) and Other Glacial Erosion type (21.41%). Uttarakhand state shares 93.50% of lake count, followed by 6.50% in Himachal Pradesh, with a total lake area of 90.70% and 9.30% respectively. Majority of lakes in Uttarakhand and Himachal Pradesh are predominantly of lake area < 5 ha, but lying in high (4,001 - 5,000 m) and very high altitude range (> 5,000 m) respectively. Lakes in Himachal Pradesh are only situated above 4,000 m elevation.\n\nIn this atlas, map sheets (plates) are prepared in correspondence with the Survey of India (SOI) toposheet index (1:250,000 scale) which are 42 in number covering the entire Ganga River basin. Out of 42 plates, only 23 plates have glacial lakes and corresponding plates are incorporated in atlas. The map sheets are arranged in such a way that glacial lake map is on the right page and its corresponding satellite image is on the left page. At the end of the atlas, an annexure is provided containing list of all glacial lakes inventoried in the Ganga River basin with their unique glacial lake ID, latitude, longitude, subbasin, glacial lake type, area (ha), and elevation (m). Glacial Lake ID number of 12 alpha-numeric character has 3 characters with dark red colour depicting the corresponding toposheet number of the SOI of 1:250,000 scale.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "GLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "section_headings": ["GLACIAL LAKE ATLAS OF GANGA RIVER BASIN"], "chunk_type": "text", "line_start": 74, "line_end": 84, "token_count_estimate": 466, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a338bdd9c6b91fd6", "text": "Document: MESSAGE\nSection: 1. INTRODUCTION > 1.1 About Project\nType: text\n\nThe National Hydrology Project (NHP) sponsored by Department of Water Resources, River Development and Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti, Government of India (GOI) with financial aid from the World Bank. The objective of the project is to improve the extent and accessibility of water resources information and strengthen institutional capacity to enable improved water resources planning and management across India. The mission is to establish an effective and sound hydrologic database and Hydrological Information System (HIS), together with the development of consistent and scientifically based tools and design aids, to assist in the effective water resources planning and management of the implementing agencies.\n\nNHP is intended for setting up of a system for timely and reliable water resources data acquisition, storage, collation and management. It will also provide tools/systems for informed decision making through Decision Support Systems (DSS) for water resources assessment, flood management, reservoir operations, drought management, etc. NHP also seeks to build capacity of the State and Central sector organisations in water resources management through the use of Information Systems and adoption of State-of-the-art technologies like Remote Sensing. NHP will improve and expand hydrology data and information systems, strengthen water resources operation and planning systems, and enhance institutional capacity for water resources management. NHP will contribute to the GOI Digital India initiative by integrating water resources information across State and Central agencies.\n\nNational Remote Sensing Centre (NRSC), as one of the Implementing Agencies under NHP, is engaged with generation of geo-spatial products & services pertaining to water resources sector, generation of high resolution Digital Elevation Models (DEM), development of flood early warning systems, decision support system development for irrigation water management, modelling & dissemination of hydrological products to support water resources management and capacity building to NHP stakeholders. The satellite data based geo-spatial products & services, mainly encompassing the following:\n\n* Satellite Data/Geo-Spatial Data Hosting & Services through Bhuvan Web Portal\n* Water Resources Information Products & Services (Satellite/Model derived – Bhuvan/India- Water Resources Information System (India-WRIS)/National Water Informatics Centre (NWIC))\n* Customized Applications Development (Flood Forecasting, Irrigation Water Management)\n* Hydro-conditioned Digital Elevation Model (Satellite & Aerial)\n* Capacity Building (Customized Training & Hand Holding)\n\nAs part of various NHP technical studies carried out, NRSC has taken up “Glacial Lake Outburst Flood (GLOF) Risk Assessment of Glacial Lakes in the Himalayan Region of Indian River Basins”. In this activity, it was proposed to prepare an updated inventory of glacial lakes, prioritization and selection of critical glacial lakes based on certain characteristics (such as glacial lake, glacier, topography and others), GLOF modelling and flood inundation simulation for selected few lakes using high resolution Digital Elevation Model (DEM) for downstream of the lakes along their river reach, and to assess GLOF risk.\n\nAs a result of initial outcome of this activity, an updated inventory of glacial lakes in Indus and Ganga River basin was generated using multispectral (MX) high resolution satellite data of Resourcesat-2 (RS-2) Linear Imaging Self Scanning Sensor-IV (LISS-IV) for mapping lakes with size ≥ 0.25 ha. The geo-spatial database of glacial lakes\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "1. INTRODUCTION > 1.1 About Project", "section_headings": ["1. INTRODUCTION", "1.1 About Project"], "chunk_type": "text", "line_start": 88, "line_end": 116, "token_count_estimate": 826, "basins": ["GANGA", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "7318717a272a3e33", "text": "Document: MESSAGE\nSection: 1. INTRODUCTION > 1.1 About Project\nType: text\n\n) , GLOF modelling and flood inundation simulation for selected few lakes using high resolution Digital Elevation Model ( DEM ) for downstream of the lakes along their river reach , and to assess GLOF risk . As a result of initial outcome of this activity , an updated inventory of glacial lakes in Indus and Ganga River basin was generated using multispectral ( MX ) high resolution satellite data of Resourcesat - 2 ( RS - 2 ) Linear Imaging Self Scanning Sensor - IV ( LISS - IV ) for mapping lakes with size ≥ 0 . 25 ha . The geo - spatial database of glacial lakes * * * GLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nhas been used to prepare “Glacial Lake Atlas of Indus River Basin”, already published NRSC-RSAA-WRG-WRAD-Nov2020-TR-0001702-V1.0 (Rao et al., 2020) and presently the “Glacial Lake Atlas of Ganga River Basin”.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "1. INTRODUCTION > 1.1 About Project", "section_headings": ["1. INTRODUCTION", "1.1 About Project"], "chunk_type": "text", "line_start": 88, "line_end": 116, "token_count_estimate": 254, "basins": ["GANGA", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0001702"]}}
{"id": "0a8cf28de24e182e", "text": "Document: MESSAGE\nSection: 1. INTRODUCTION > 1.2 Glacial Lakes\nType: text\n\nIndian Himalayan Region (IHR) contains the world’s largest number of glaciers and snow outside the Polar Regions and are aptly called third pole of the world. Many studies undertaken globally showed that glaciers around the world have been retreating since the industrial revolution, which began around eighteenth century. As the glaciers are thinning and retreating, resulting in associated glacier melt water lakes are expanding in size and new lakes continue to form. The lakes receiving melt water from glaciers are generally known as glacial lakes. A glacial lake is defined as water mass existing in a sufficient amount and extending with a free surface in, under, beside, and/or in front of a glacier and originating from glacier activities and/or retreating processes of a glacier. As glaciers retreat, the formation of glacial lakes takes place behind moraine or ice ‘dam’. These damming materials are generally weak and can breach suddenly due to various triggering factors, leading to catastrophic floods. Such outburst floods are known as GLOF.\n\nGLOFs are characterized by extreme peak discharges, with an exceptional erosion/transport potential; therefore, they can turn into flow-type movements (Emmer, 2017). Failure of such lake happens due to many factors which include erosion process, increase in water pressure, merging of an avalanche/rock into lake, nature of the damming materials etc., and this may lead to a GLOF event which could be highly disastrous in nature and create long-term degradation in the valleys, both physically and socio-economically (Mool et al., 2001b). Accordingly, Emmer et al., (2016) showed an annual nonlinear increase in the number of scientific publications focusing on GLOFs recently. Hence, monitoring of glacier associated lakes is very useful in the IHR to identify critical glacial lakes, for which a detailed inventory of glacial lakes and its type is required. According to their position relative to the glacier and damming mechanism, these glacial lakes can be classified into several types (Panda et al., 2014).\n\nInventorying glacial lakes located in these remote mountain areas with rugged terrain and inclement weather by traditional means is very tedious and difficult, hence Remote Sensing (RS) data plays a greater role in generating information on glacial lakes (Kulkarni, 1991; Berither et al., 2007; Wagnon et al., 2007; Raj, 2010; Cogley et al., 2011; Pratap et al., 2016; Gupta et al., 2019, Guru et al., 2019). Satellites with high spatial, spectral and temporal resolution sensors are useful in deriving lake information with better accuracy and repeatedly.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "1. INTRODUCTION > 1.2 Glacial Lakes", "section_headings": ["1. INTRODUCTION", "1.2 Glacial Lakes"], "chunk_type": "text", "line_start": 118, "line_end": 124, "token_count_estimate": 660, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0aafe5c043bf2f1d", "text": "Document: MESSAGE\nSection: 1. INTRODUCTION > 1.3 Previous Studies\nType: text\n\nSeveral studies have been taken up in the past to assess the glacial lake distribution in the Hindu Kush Himalayas (HKH), covering parts of eight countries viz., Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan, and lies within five river basins of Amu Darya, Indus, Ganga, Brahmaputra, and Irrawaddy (Komori, 2007; Gardelle et al., 2011; Wang et al., 2011; Wang et al., 2012; Nie et al., 2013; Raj et al., 2013; Wang et al., 2013; Worni et al., 2013; Che et al., 2014; Bambari et al., 2015; Zhang et al., 2015; Nie et al., 2017; Rounce et al., 2017; Nagai et al., 2017; Gupta et al., 2019; Guru et al., 2019; Shugar et al., 2020). But only few glacial lake inventories are available in public domain, amongst which the first inventory was prepared by the International Centre for Integrated Mountain Development (ICIMOD), Nepal, for the entire HKH region (covering the entire IHR within it), using satellite data of the Land Observation Satellite (Landsat) Thematic Mapper (TM) of the United States Geological Survey (USGS) and the Indian Remote Sensing satellite (IRS-1D) Linear Imaging and\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSelf-scanning Sensor-III (LISS-III) during 1999-2005, along with topographic maps published between the 1950s and 1982 (Mool et al., 2001a; Mool et al., 2001b; Mool et al., 2003; Bhagat et al., 2004; Roohi et al., 2005; Sah et al., 2005; Wu et al., 2005, Ives et al., 2010). This inventory has been revised in 2018 using Landsat TM and Enhanced Thematic Mapper Plus (ETM+) data of years 2004-07 ± 3 (Maharjan et al., 2018). Both glacial lake inventories prepared by the ICIMOD, have mapped lakes with size > 0.3 ha.\n\nSecond inventory of glacial lakes and water bodies in the IHR (within India only) was carried out by the NRSC, Hyderabad in collaboration with the Central Water Commission (CWC), New Delhi (NRSC-RS&GISAA-WRG-CWC-Lakes-May2011-TR255). Glacial lakes and water bodies located in all three major basins of Indus, Ganga, and Brahmaputra, of size > 10 ha were mapped using Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data for the year 2009 (NRSC, 2011). Subsequently, monthly monitoring of these lakes (> 50 ha) was carried out using satellite data for the months of June to October during the years 2011 to 2015.\n\nThird latest glacial lake inventory is prepared by the Space Application Centre (SAC), Ahmedabad i.e. “National Wetland Atlas: High Altitude Lakes of India”, using IRS-P6 LISS-III, comprising high altitude lake information of the IHR, within Indian administrative region only (Panigrahy et al., 2012). In this atlas, wetlands of size > 2.25 ha were mapped as a polygons and less than that were mapped as a points, using satellite data for the period of 2006-08.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "1. INTRODUCTION > 1.3 Previous Studies", "section_headings": ["1. INTRODUCTION", "1.3 Previous Studies"], "chunk_type": "text", "line_start": 126, "line_end": 137, "token_count_estimate": 801, "basins": ["Brahmaputra", "GANGA", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "7e885a44fbdb8d9e", "text": "Document: MESSAGE\nSection: 1. INTRODUCTION > 1.4 Highlights of the Atlas\nType: text\n\n**The highlights of the present atlas:**\n* The present atlas is first of its kind depicting spatial distribution of glacial lakes of size ≥ 0.25 ha in Ganga River basin mapped using high resolution satellite data\n* The atlas provides the details of all the glacial lakes in entire catchment of Ganga River basin, both within Indian and transboundary region\n* The atlas contain details of area range-wise glacial lakes along with 9 categories of types. Further, the atlas present the distribution of glacial lakes in terms of area vs. type, elevation, area vs. elevation and type vs. elevation, at basin, subbasin, administrative and transboundary regions\n* The atlas also provides comprehensive list of all glacial lakes with unique ID considering hydrological, geometrical, geographical, topographical attribute information\n\n**The expected utility of the atlas:**\n* The atlas provides a comprehensive & systematic glacial lake database for Ganga River basin\n* In the context of climate change impact analysis, the atlas can be used as reference data for carrying out change analysis, both with respect to historical and future time periods\n* The atlas also provides authentic database for regular or periodic monitoring changes in spatial extent (expansion/shrinkage), and formation of new lakes\n* The atlas can also be used in conjunction with glacier information for their retreat and climate impact studies\n* The information on glacial lakes like their type, hydrological, topographical, and associated glaciers are useful in identifying the potential critical glacial lakes and consequent GLOF risk\n* Central and State Disaster Management Authorities can make use of the atlas for disaster mitigation planning and related programs\n* Can be used in Detailed Project Report (DPR) preparation for new hydropower/multi purpose project", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "1. INTRODUCTION > 1.4 Highlights of the Atlas", "section_headings": ["1. INTRODUCTION", "1.4 Highlights of the Atlas"], "chunk_type": "text", "line_start": 139, "line_end": 157, "token_count_estimate": 422, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f23c7d1e40575f61", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.1 Overview\nType: text\n\nThe IHR consist of three major river systems of Indus, Ganga, and Brahmaputra, stretches over four countries viz., India, China, Nepal and Bhutan, and on the basis of physiography it has been divided into four mountain regions viz., Eastern Himalayas, Central Himalayas, Western Himalayas, and the Karakoram Mountain range. The Ganga River basin is unique in the sense that it contains 9 of the 14 highest peaks in the world over 8,000 m in height, including Mt. Everest which is the high peak of Ganga River basin. The other peaks over 8,000 m in the basin are Kanchenjunga, Lhotse, Makalu, Cho Oyu, Dhaulagiri, Manaslu, Annapurna, and Shishapangma. The Ganga River basin extends over Central Himalayas in India, Nepal, Tibet (China), and Bangladesh.\n\nThe Ganga River originates as the Bhagirathi from the Gangotri group of glaciers in the Himalayas at an elevation of about 7,010 m amsl, in the Uttarkashi district of Uttarakhand, which has been joined by the Alaknanda at Devprayag, and the combined stream assumes the name 'Ganga' (origin and confluence has been shown in Figure 1). River flows through the highly terrain mountain region and debouches into the plains at Sukhi (near Rishikesh). It is joined by a large number of tributaries on both the banks in the course of its total run of about 2,525 Km before its outfall into the Bay of Bengal. The delta of the Ganga River is said to begin at the Farakka barrage in West Bengal, where the river divides into two arms namely the Padma which flows to Bangladesh and the Ganga which flows through West Bengal. Ganga River basin from its origin to the foothills of Himalayas with a catchment area of 2,47,110 Km² is considered in the present study, which extends from latitude 26.35° N to 31.46° N and from longitude 77.05° E to 88.95° E.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.1 Overview", "section_headings": ["2. STUDY AREA", "2.1 Overview"], "chunk_type": "text", "line_start": 161, "line_end": 165, "token_count_estimate": 489, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "7669c8c1da603a77", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: text\n\nMajor river flowing in the Ganga River basin is Yamuna, joining the main river on the right, whereas rivers like Sarda, Ghaghara, Gandak, and Kosi joining on the left. Considering hydrological setting of the aforesaid rivers, Ganga River basin is divided in 11 subbasins viz., Yamuna, Upper Ganga, Ramganga, Sarda, Ghaghara, Rapti, Gandak, Bhagmati, Kamla, Kosi, and Lower Ganga. Figure 2 shows the location of the study area with RS-2 LISS-IV satellite images. Table 1 shows the catchment area of each of the above subbasins.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "text", "line_start": 167, "line_end": 171, "token_count_estimate": 177, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "a393ad230a249102", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: table\nTable: Table 1: Details of subbasins of Ganga River basin\n\n| S. No. | Subbasin | Area (Km²) | Area (%) |\n| :--- | :--- | :--- | :--- |\n| 1 | Bhagmati | 7,635 | 3.09 |\n| 2 | Gandak | 36,465 | 14.76 |\n| 3 | Ghaghara | 53,072 | 21.48 |\n| 4 | Kamla | 6,106 | 2.47 |\n| 5 | Kosi | 59,709 | 24.16 |\n| 6 | Lower Ganga | 6,543 | 2.65 |\n| 7 | Ramganga | 11,455 | 4.64 |\n| 8 | Rapti | 11,423 | 4.62 |\n| 9 | Sarda | 17,326 | 7.01 |\n| 10 | Upper Ganga | 25,675 | 10.39 |\n| 11 | Yamuna | 11,701 | 4.73 |\n| | **Total** | **2,47,110** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "table", "table_caption": "Table 1: Details of subbasins of Ganga River basin", "columns": ["S. No.", "Subbasin", "Area (Km²)", "Area (%)"], "table_row_start": 1, "table_row_end": 12, "line_start": 172, "line_end": 185, "token_count_estimate": 309, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "18c97923bd7b2b65", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.3 Hydrology\nType: text\n\nAll the principal tributaries of the Ganga River system are fed by snow and glaciers in the upper parts of their mountainous catchments. The snow accumulation in their upper catchments usually starts from October to March months reaching peak in January/February. The river flows are at minimum during the winter months of December to March. When snow and glaciers start melting during summer months of April to June, river flows gradually increase and accelerated further by rains during July to September. The Ganga River basin carries average annual water potential of about 525 Billion Cubic Metre (BCM), of which total utilizable surface water resource in the basin is 250 BCM. The Ghaghara, Kosi, and Gandak combined carry almost half, and the Yamuna, Ramganga, and other major and minor tributaries combined constitute the remainder of the total supply of the system (Ganga Basin Report, 2014).\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.3 Hydrology", "section_headings": ["2. STUDY AREA", "2.3 Hydrology"], "chunk_type": "text", "line_start": 188, "line_end": 193, "token_count_estimate": 243, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "9d1e7fc8f0da5aae", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.4 Topography\nType: text\n\nThe study area mainly resides in the Central Himalayan region, which is also known as one of the main topographic division of the Indian subcontinent (Ganga Basin Report, 2014). The Central Himalayas comprises the Himalayan ranges including their numerous snow peaks rising above 7,000 m and each of these peaks is surrounded by snow fields and glaciers. All the tributaries are characterized by well regulated flows and assured supply of water throughout the year by these glaciers. The elevation of the study area ranges between 45 m and 8,848 m amsl, where glaciers and glacial lakes are mostly distributed in the higher altitude region. The mean elevation of the study area is 4,374 m amsl. Slope in the entire study area varies up to a maximum of 86.60°, while the mean slope in the Ganga River basin is 22.31°. Hypsometric curve is a graph which shows the proportion of land area that exist at various elevations by plotting relative area against relative height, as shown in Figure 3 for the study area.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.4 Topography", "section_headings": ["2. STUDY AREA", "2.4 Topography"], "chunk_type": "text", "line_start": 195, "line_end": 197, "token_count_estimate": 260, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "166f9612da56f31c", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.4 Topography\nType: figure\nFigure: Figure 3: Hypsometric curve of the study area\n\n**Figure 3: Hypsometric curve of the study area**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.4 Topography", "section_headings": ["2. STUDY AREA", "2.4 Topography"], "chunk_type": "figure", "figure_caption": "Figure 3: Hypsometric curve of the study area", "line_start": 198, "line_end": 198, "token_count_estimate": 52, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b559310c8b13b23", "text": "Document: MESSAGE\nSection: 2. STUDY AREA > 2.5 Climate\nType: text\n\nClimate over the Ganga River basin is mainly tropical and subtropical to temperate subhumid on the plains. The higher elevation zones in the Himalayan ranges especially in parts of Uttarakhand and Himachal Pradesh, experience lower temperatures than the other parts of the basin within India. Lowest annual precipitation < 500 mm is observed in the lowlands to a maximum of > 2,200 mm in upper region. The study area receives total average annual precipitation of approximately 1,200 mm. The average temperature in the basin ranges between 9°C to 40°C, where the minimum temperature is usually high mainly because of the lower plains of the basin.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "2. STUDY AREA > 2.5 Climate", "section_headings": ["2. STUDY AREA", "2.5 Climate"], "chunk_type": "text", "line_start": 201, "line_end": 214, "token_count_estimate": 199, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0dcb0ceca41303ce", "text": "Document: MESSAGE\nSection: 3. DATA USED\nType: text\n\nEarth observation satellites capture the data repeatedly in various spectral ranges and at different spatial and radiometric resolutions. For inventorying glacial lakes, high to medium resolution datasets are proved to be useful by many research studies (Bolch et al., 2010; Mergili et al., 2013; Wang et al., 2013; Zhang et al., 2015; Gupta et al., 2019, Guru et al., 2019). Data captured between September and December were mostly used because the presence of snow or cloud cover during this period is minimum. USGS satellite data of Landsat 5 and 7 (TM and ETM+) has been used widely for mapping glacial lakes due to free accessibility. Whereas, IRS satellite data from sensors of AWiFS, LISS-III, LISS-IV has also been used for such inventory.\n\nIn the present study, high resolution Resourcesat-2 LISS-IV satellite images with spatial resolution of 5.8 m covering a swath of 70 × 70 Km have been used for inventorying glacial lakes. Most of the images used for inventorying were of 2016-18 (84%) and remaining images pertain of previous years due to non-availability of cloud-free and snow-free images for the recent years. Majority of images were of September and December months (78%) due to less snow and cloud cover, and rest 22% images were of other months. Figure 4 shows the layout of the RS-2 LISS-IV scenes (path-wise) procured for the Ganga River basin along with its details in Table 2. The layout of satellite scenes is divided into paths (shown in separate colours) and rows (row numbers shown in the layout).\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 216, "line_end": 226, "token_count_estimate": 415, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d731469ae1df64c2", "text": "Document: MESSAGE\nSection: 3. DATA USED\nType: table\nTable: Table 2: Details of satellite scenes used for inventory\n\n| | Other Months | Sep - Dec | Total |\n|---|---|---|---|\n| **Prior to 2016** | 5 | 12 | 17 |\n| **2016-18** | 18 | 70 | 88 |\n| **Total** | **23** | **82** | **105** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "table", "table_caption": "Table 2: Details of satellite scenes used for inventory", "columns": ["", "Other Months", "Sep - Dec", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 227, "line_end": 231, "token_count_estimate": 115, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4edf1ac8fc349a6e", "text": "Document: MESSAGE\nSection: 3. DATA USED\nType: text\n\nDigital Elevation Model (DEM) of Cartosat satellite with 10 m spatial resolution has been used for topographic information and watershed boundary generation. Figure 5 shows elevation range map of the study area i.e. Ganga River basin. Other information like names of lakes and rivers has been gathered from digital toposheets available from University of Texas - Toposheet Library at 1:250,000 scale and Tibet Map Institute at 1:100,000 scale (U.S. Army Map Service 1955; Andre 2017).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 232, "line_end": 235, "token_count_estimate": 129, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "12175bf7a9cffb12", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation\nType: text\n\nThe spectral reflectance curve of water in the visible spectrum starts with a low in Blue region (0.4 to 0.5 μm), reaches peak in Green region (0.5 to 0.6 μm), decreases in Red region (0.6 to 0.7 μm) and probably the most distinctive characteristic is the energy absorption at Near InfraRed (NIR) wavelengths. Identifying and delineating water bodies with remote sensing data are carried out easily in near infrared wavelengths because of this absorption property in IR region. However, various physical conditions of water bodies (water depth, turbidity, chlorophyll content, etc.) manifest spectral changes. As a result of various conditions of lakes, the water in satellite images in False Colour Composite (FCC) ranges in appearance from light to dark blue to black. In the case of frozen lakes, it appears white.\n\nGlacial lake sizes are generally small, having circular, semi-circular, or elongated shapes with very fine texture and are generally associated with glaciers in high altitude areas. Certain types of glacial lakes, like erosion and cirque lakes are not necessarily associated with glaciers. Knowledge of the physical characteristics of the glacial lakes, and their associated features is always essential for the interpretation of the images.\n\nSatellite data interpretation can be done using visual image interpretation keys such as colour, size, tone, texture, pattern, association, shape, shadow, and orientation. A number of remote sensing methods had been developed for glacial lake detection and mapping or development of inventory (Kääb 2000; Mool et al., 2001a; Huggel et al., 2002; Huggel et al., 2006; Ives et al., 2010). Manual or automated lake mapping methods have certain difficulties in identifying the lakes, which are described in the following section. An attempt was made to study the accuracy of mapping of glacial lakes using multiple automated methods along with visual interpretation, the details of which are given in Annexure-1. From this study, it was concluded that visual interpretation method was best accurate method. Hence, in the present study glacial lakes and their different types are identified and mapped using RS-2 LISS-IV multispectral images using visual interpretation method.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation"], "chunk_type": "text", "line_start": 239, "line_end": 245, "token_count_estimate": 542, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53188b5f0c9512ab", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:\nType: text\n\nGlacial lake identification can be done either using visual interpretation or automatic mapping methods. The automatic mapping procedures have limitations due to varying terrain conditions like lakes situated in the shadow portions of mountains, presence of snow cover, cloud cover, and partly frozen lakes, etc. In the presence of snow cover on the glacier tongue or glacier’s ablation area where many Supra-glacial lakes may present, both methods have limitations and difficulties.\n\nAs lake water absorbs the incident radiation making it appear in darker tone and colour in the standard FCC of satellite data, similar response also prevails over shadow region of clouds or mountains on surface, which may lead to incorrect mapping. In fact, a mountain shadow covering a lake partly/completely within its vicinity, making it difficult to accurately map the lake boundary.\n\nMany lakes due to inclement terrain condition, can be under shadow of high peaks and will get missed in both ways of mapping. On the contrary, a lake can also present in white colour while it is in frozen form due to cold weather conditions over the area, then definitely it will not get classified when mapped with automatic algorithm. Whereas, frozen lakes can be identified and mapped using visual interpretation to some extent.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Difficulties in Lake Identification:"], "chunk_type": "text", "line_start": 247, "line_end": 253, "token_count_estimate": 317, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "284a52314394d7fc", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Challenges in Automatic Mapping:\nType: text\n\nIn the IHR, due to high and inclement terrain surface and due to near vertical acquisition of satellite images, some lakes get covered with shadows of mountains, which create problems in identifying glacial lakes. Also identification of lakes with high turbidity, partial ice covered lakes and the lakes in shadow areas are misclassified by automatic methods. Glacial lake mapping is always a semi-automatic approach because even after applying any of that methods, it should always be followed by the post processing i.e. correcting the errors using visual interpretation. Even in all cases, automatic mapping will never give the exact and accurate boundary of the lake, leading to necessary manual corrections.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Challenges in Automatic Mapping:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Challenges in Automatic Mapping:"], "chunk_type": "text", "line_start": 255, "line_end": 257, "token_count_estimate": 189, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1332545f7ba9f47e", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Reasoning for Visual Interpretation:\nType: text\n\nAlthough automatic mapping methods can speed up the detection of glacial lakes, but these methods could not be applied to the entire Himalayan region due to lot of variations in satellite scenes (seasons/years) and problems mentioned above. For example, if lakes are frozen or covered with snow or cloud and lies in a shadow area, they cannot be detected using these automatic methods. In such cases, the manual interpretation method will be helpful to map these lakes. Thus, any mapping of glacial lakes can be automated up to a certain extent only. So, visual image interpretation keys and technique will give accurate results and avoids misclassification. Therefore, in this present study, glacial lakes and its type identification, and its mapping for the entire Ganga River basin (within IHR) has been done manually using visual interpretation. High resolution satellite data available on Bhuvan/Google Earth has been used on need basis in finalizing various features of glacial lake database.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Reasoning for Visual Interpretation:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Reasoning for Visual Interpretation:"], "chunk_type": "text", "line_start": 259, "line_end": 261, "token_count_estimate": 249, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93fa46f63c0c0aba", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Limitations:\nType: text\n\nThe RS-2 LISS-IV MX data used for glacial lake database preparation sporadically covered with cloud and seasonal/permanent snow. Also, the Himalayan region being highly varying topography with steep slopes, the satellite data has hill shadows. Thus few glacial lakes would not have been mapped owing to the following constraints:\n\n* Presence of snow or cloud over the glacial lakes\n* Glacial lakes under frozen condition\n* Glacial lakes under mountain shadow", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Limitations:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Limitations:"], "chunk_type": "text", "line_start": 263, "line_end": 269, "token_count_estimate": 139, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6e9acfd2dd9fadf", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: text\n\nVarious researchers have proposed glacial lakes classification schemes based on dam type, process of lake formation, topographic feature, and geographical position (Hewitt 1982; Liu and Sharma 1988; Clague and Evans 2000; Mool et al., 2001a, 2001b). Lakes located on the glacier surface can be mapped using satellite data, but there are englacial and subglacial lakes that may also exist, but cannot be mapped from aerial/optical satellite images, requires ground based instrument (Yao et al., 2018). Majorly surface glacial lakes are classified in 4 classes and 10 subclasses, i.e. Moraine-dammed lake, Ice-dammed lake, Glacier Erosion lake (also known as Bed-rock lake), and Other Glacial lake. Two character symbol has been used for glacial lake classification, in\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nwhich first letter (uppercase) represents lake type and second letter (lowercase) within brackets represents lake subtype, for example, M(e) for End-moraine dammed lake. Details of types of lakes are given in Table 3 and their appearance in satellite images are shown in Figure 6.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "text", "line_start": 271, "line_end": 280, "token_count_estimate": 309, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "05b21446a527ca2c", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: table\nTable: Table 3: Glacial lake types and their identification keys\n\n| S.No. | Lake Type | Lake Subtype | Code | Identification Keys |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | Moraine-dammed Lake | End-moraine Dammed Lake | M(e) | Lake dammed by end (terminal) moraines |\n| 2 | Moraine-dammed Lake | Lateral Moraine Dammed Lake | M(l) | Lake dammed by lateral moraine(s) not in contact with glacial ice |\n| 3 | Moraine-dammed Lake | Lateral Moraine Dammed Lake (with Ice) | M(lg) | Lake dammed by lateral moraine(s) in contact with glacial ice |\n| 4 | Moraine-dammed Lake | Other Moraine Dammed Lake | M(o) | Lake dammed by other moraines |\n| 5 | Ice-dammed Lake | Supra-glacial Lake | I(s) | Pond or lake on the surface of a glacier |\n| 6 | Ice-dammed Lake | Glacier Ice-dammed Lake | I(d) | Lake dammed by glacier ice with no lateral moraines |\n| 7 | Glacier Erosion Lake | Cirque Erosion Lake | E(c) | A small pond occupying a cirque |\n| 8 | Glacier Erosion Lake | Glacier Trough Valley Erosion Lake | E(v) | Lakes formed in the glacier trough as a result of the glacier erosion process |\n| 9 | Glacier Erosion Lake | Other Glacial Erosion Lake | E(o) | Bodies of water occupying depressions formed by the glacial erosion process |\n| 10 | Other Glacial Lake | Other Glacial Lake | O | Lakes formed in a glaciated valley, and fed by glacial melt, but damming material not directly part of the glacial process |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "table", "table_caption": "Table 3: Glacial lake types and their identification keys", "columns": ["S.No.", "Lake Type", "Lake Subtype", "Code", "Identification Keys"], "table_row_start": 1, "table_row_end": 10, "line_start": 281, "line_end": 292, "token_count_estimate": 538, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68b8d4810e9045c4", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "text", "line_start": 293, "line_end": 297, "token_count_estimate": 39, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4cc36cbe3c6d4553", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: text\n\nA total of 22 attributes has been given to all mapped lake features in the geodatabase, which are broadly consisting information grouped in five different categories as shows in Table 4.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 299, "line_end": 303, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06c27dfa0500a976", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: table\nTable: Table 4: Details of glacial lake attributes\n\n| S.No. | Category | Attribute |\n| :--- | :--- | :--- |\n| 1 | Hydrological | Basin, subbasin, river, lake name |\n| 2 | Geometrical | Maximum length, mean width, surface area |\n| 3 | Geographical | Latitude, longitude, region, state, district, toposheet 250k, toposheet 50k |\n| 4 | Topographical | Elevation, aspect |\n| 5 | Lake Information | Feature types, glacial lake type, lake ID |\n| 6 | Data Source Information | Source of database, source of elevation, date of pass |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 4: Details of glacial lake attributes", "columns": ["S.No.", "Category", "Attribute"], "table_row_start": 1, "table_row_end": 6, "line_start": 304, "line_end": 311, "token_count_estimate": 203, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a7f0683774ecb217", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: text\n\nTypically, lake ID is given in 12 alpha-numeric character format like “0253N1300271”, where first two digits ‘02’ refers to Basin code which is Ganga (01-Indus and 03-Brahmaputra), next five characters ‘53N13’refers to the 1:250,000 (53N) and 1:50,000 (53N13) scale SOI Toposheet number, and the last five digits refers to the sequential number of each lake sorted from top left to bottom right. A typical example of the glacial lake database generated is given below in Table 5 along with fields and format.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 312, "line_end": 316, "token_count_estimate": 163, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0253N1300271", "53N13"]}}
{"id": "1a67bd0f703d7f23", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: table\nTable: Table 5: Typical example of glacial lake attribute database\n\n| S.No. | Database Fields | Type | Format / Unit | Lake Attribute |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | ID No | String | Text | 0253N1300271 |\n| 2 | Toposheet 250K | String | Text | 53N |\n| 3 | Toposheet 50K | String | Text | 53N13 |\n| 4 | Latitude* | Float | Decimal Degree | 30.901 |\n| 5 | Longitude* | Float | Decimal Degree | 79.754 |\n| 6 | Basin | String | Text | Ganga |\n| 7 | Subbasin | String | Text | Upper Ganga |\n| 8 | River | String | Text | Dhauliganga River |\n| 9 | Type (GL/WB) | String | Text | Glacial Lake |\n| 10 | Name | String | Text | Basudhara Tal |\n| 11 | Glacial Lake Type | String | Text | M(e): End-moraine Dammed Lake |\n| 12 | Surface Area | Float | ha | 22.035 |\n| 13 | Length | Float | Km | 0.791 |\n| 14 | Mean Width | Float | Km | 0.270 |\n| 15 | Elevation* | Integer | m (amsl) | 4677 |\n| 16 | Aspect | String | Text | SE |\n| 17 | Source of Database | String | Text | RS-2 LISS-IV |\n| 18 | Date of Pass | Date | DDMMYYYY | 20112016 |\n| 19 | Source of Elevation | String | Text | Cartosat DEM |\n| 20 | Region | String | Text | India |\n| 21 | State | String | Text | Uttarakhand |\n| 22 | District | String | Text | Chamoli |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 5: Typical example of glacial lake attribute database", "columns": ["S.No.", "Database Fields", "Type", "Format / Unit", "Lake Attribute"], "table_row_start": 1, "table_row_end": 22, "line_start": 317, "line_end": 340, "token_count_estimate": 591, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": ["India"], "lake_ids": ["0253N1300271", "20112016", "53N13"]}}
{"id": "96220eab57027a2f", "text": "Document: MESSAGE\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: text\n\n\\* Latitude, longitude, and elevation has been taken at the centroid of the lake\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 341, "line_end": 350, "token_count_estimate": 58, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c2a33a634a7a456", "text": "Document: MESSAGE\nSection: 5. RESULTS\nType: text\n\nThe mapped glacial lakes are analyzed for their distribution in terms of area, type, and elevation, at basin, subbasin, administrative and transboundary level. Area of mapped glacial lakes is ranging from a minimum of 0.25 ha to a maximum of 540.55 ha. Details of glacial lakes inventoried for the Ganga River basin is given in Annexure-II. The results are discussed in subsequent sections:", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS", "section_headings": ["5. RESULTS"], "chunk_type": "text", "line_start": 352, "line_end": 354, "token_count_estimate": 115, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "521313fe49736fd4", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics\nType: text\n\n**Area range-wise Distribution**\n\nA total of 4,707 glacial lakes (≥ 0.25 ha) were identified and mapped using RS-2 LISS-IV images for the entire Ganga River basin, with a total lake water spread area of 20,685.12 ha. Glacial lakes has been distributed in 6 different classes of area ranges viz., 0.25 - 0.5 ha, 0.5 - 1 ha, 1 - 5 ha, 5 - 10 ha, 10 - 50 ha and ≥ 50 ha, where area ranges indicate values ranging from the lower value (including) up to the upper value (excluding), for ex: 0.25 - 0.5 ha range include values ranging from 0.25 ha up to < 0.5 ha. Table 6 and Figure 7 shows the area range-wise distribution for the entire basin. About 4,035 (85.72%) lakes are with < 5 ha lake area contributing to 23.13% of total lake area. The remaining lakes with ≥ 5 ha in size are 672 (14.28%) contributing to 76.87% of total lake area in the basin. Details of lakes ≥ 50 ha is given in Annexure-III.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics"], "chunk_type": "text", "line_start": 356, "line_end": 362, "token_count_estimate": 281, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0cf0b6fa3f8d630b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics\nType: table\nTable: Table 6: Area range-wise distribution of Glacial Lakes (GL) in Ganga River basin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 1,279 | 452.30 | 2.19 |\n| 2 | 0.5 - 1 | 1,157 | 824.04 | 3.98 |\n| 3 | 1 - 5 | 1,599 | 3,508.94 | 16.96 |\n| 4 | 5 - 10 | 315 | 2,184.89 | 10.56 |\n| 5 | 10 - 50 | 299 | 5,938.21 | 28.71 |\n| 6 | ≥ 50 | 58 | 7,776.74 | 37.60 |\n| | **Total** | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 6: Area range-wise distribution of Glacial Lakes (GL) in Ganga River basin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 363, "line_end": 371, "token_count_estimate": 279, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7077c2c04deeca4b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lake in the entire Ganga River basin is given in Table 7 and Figure 8. Out of 10 types of lake described in the previous section, 9 types of lake are present in the basin except Glacier Trough Valley Erosion Lake (Ev). Amongst 9 types of glacial lake, Other Glacial Erosion lake is found to be the maximum with 1,744 (37.05%) occupying a total lake extent of 4,612.02 ha at 22.30% in the basin. Two other types of lake, namely, Other Moraine Dammed and Supra-glacial lakes are 1,740 (36.97%) and 617 (13.11%), extend over an area of 4,489.35 ha (21.70%) and 566.14 ha (2.74%) respectively.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics"], "chunk_type": "text", "line_start": 372, "line_end": 384, "token_count_estimate": 220, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5628ee11047394d3", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics\nType: table\nTable: Table 7: Type-wise distribution of GL in Ganga River basin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 260 | 8,591.78 | 41.53 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 100 | 568.12 | 2.75 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 9 | 5.48 | 0.03 |\n| 4 | M(o) | Other Moraine Dammed Lake | 1,740 | 4,489.35 | 21.70 |\n| 5 | I(s) | Supra-glacial Lake | 617 | 566.14 | 2.74 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 1 | 2.45 | 0.01 |\n| 7 | E(c) | Cirque Erosion Lake | 123 | 973.77 | 4.71 |\n| 8 | E(o) | Other Glacial Erosion Lake | 1,744 | 4,612.02 | 22.30 |\n| 9 | O | Other Glacial Lake | 113 | 876.01 | 4.23 |\n| | | **Total** | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 7: Type-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 385, "line_end": 396, "token_count_estimate": 431, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44a62af1c0ea01f1", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics"], "chunk_type": "text", "line_start": 397, "line_end": 404, "token_count_estimate": 36, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2baf8f037423ca45", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 8 and Figure 9. The lakes with < 5 ha in size (85.72%) are dominant with Other Moraine Dammed lake type (38.74%) followed by Other Glacial Erosion (38.64%) and Supra-glacial lake (14.94%). The lakes with ≥ 5 ha (14.28%) are dominated by Other Glacial Erosion lakes (27.53%) followed by End-moraine Dammed lake (27.23%) and Other Moraine Dammed lake (26.34%). All types of Moraine-dammed glacial lakes, which constitute about 44.81% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 406, "line_end": 410, "token_count_estimate": 187, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8abea46899d5e709", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 8: Area range-wise vs. Type-wise distribution of GL in Ganga River basin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 5 | 22 | 4 | 462 | 354 | 0 | 4 | 400 | 28 | 1,279 |\n| 2 | 0.5 - 1 | 12 | 24 | 4 | 484 | 166 | 0 | 5 | 433 | 29 | 1,157 |\n| 3 | 1 - 5 | 60 | 35 | 1 | 617 | 83 | 1 | 49 | 726 | 27 | 1,599 |\n| 4 | 5 - 10 | 41 | 6 | 0 | 106 | 6 | 0 | 34 | 115 | 7 | 315 |\n| 5 | 10 - 50 | 100 | 9 | 0 | 68 | 8 | 0 | 31 | 65 | 18 | 299 |\n| 6 | ≥ 50 | 42 | 4 | 0 | 3 | 0 | 0 | 0 | 5 | 4 | 58 |\n| | **Total** | **260** | **100** | **9** | **1,740** | **617** | **1** | **123** | **1,744** | **113** | **4,707** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 8: Area range-wise vs. Type-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 411, "line_end": 419, "token_count_estimate": 487, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b589c4e17d1cb932", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 420, "line_end": 428, "token_count_estimate": 46, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "451bf3e082eff034", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 9 and Figure 10 shows the distribution of the glacial lakes in the Ganga River basin as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 4,644 (98.66%) with total lake area of 20,451.70 ha (98.87%) and remaining 1.34% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 430, "line_end": 434, "token_count_estimate": 185, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7e3518a7f08dbabf", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 9: Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 1 | 9.87 | 0.05 |\n| 2 | 3,001 - 4,000 | 62 | 223.55 | 1.08 |\n| 3 | 4,001 - 5,000 | 1,855 | 7,374.89 | 35.65 |\n| 4 | > 5,000 | 2,789 | 13,076.81 | 63.22 |\n| | **Total** | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 9: Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 435, "line_end": 441, "token_count_estimate": 241, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed611da5c0db735e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 10 and Figure 11. It is noted that, about 59.25% of glacial lakes (2,789) are situated in very high altitude range i.e. > 5,000 m amsl, which also constitutes maximum total lake area within that range i.e 63.22%. However, very few glacial lakes (63) lies up to 4,000 m amsl, has maximum of its lakes with 0.25 - 0.5 ha lake area range. Figure 11 shows that maximum of lakes lying in very high altitude range is of size ranging 1 - 5 ha (i.e. 928), followed by lakes in high altitude range within in 1 - 5 ha (i.e. 659).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 442, "line_end": 451, "token_count_estimate": 237, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f23138f1e15bcf66", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 10: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000 - No. of lakes | Elevation Range (m) up to 3,000 - Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000 - No. of lakes | Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000 - No. of lakes | Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) > 5,000 - No. of lakes | Elevation Range (m) > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 21 | 6.90 | 486 | 173.33 | 772 | 272.07 | 1,279 | 452.30 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 17 | 12.44 | 426 | 305.22 | 714 | 506.38 | 1,157 | 824.04 |\n| 3 | 1 - 5 | 0 | 0.00 | 12 | 33.16 | 659 | 1,465.56 | 928 | 2,010.22 | 1,599 | 3,508.94 |\n| 4 | 5 - 10 | 1 | 9.87 | 5 | 34.15 | 137 | 967.00 | 172 | 1,173.87 | 315 | 2,184.89 |\n| 5 | 10 - 50 | 0 | 0.00 | 7 | 136.90 | 128 | 2,392.37 | 164 | 3,408.94 | 299 | 5,938.21 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 19 | 2,071.42 | 39 | 5,705.32 | 58 | 7,776.74 |\n| | **Total** | **1** | **9.87** | **62** | **223.55** | **1,855** | **7,374.90** | **2,789** | **13,076.80** | **4,707** | **20,685.12** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 10: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000 - No. of lakes", "Elevation Range (m) up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000 - No. of lakes", "Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000 - No. of lakes", "Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) > 5,000 - No. of lakes", "Elevation Range (m) > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 452, "line_end": 460, "token_count_estimate": 661, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c1e38f13c258cab6", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 11: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n**Figure 11: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 11: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "line_start": 462, "line_end": 462, "token_count_estimate": 84, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "07d0b428086b070f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per type-wise vs. elevation range-wise, given in Table 11 and Figure 12. The dominant lake type in the basin i.e. Other Glacial Erosion lake (37.05%) is predominantly located in the elevation range of 4,001 - 5,000 m (52.18%). The two other dominant lake types, namely, Other Moraine Dammed and Supra-glacial lake are mostly distributed in both > 5,000 m and 4,001 - 5,000 m elevation ranges. 75.82% of Moraine-dammed glacial lakes, which constitute 44.81% of the total lakes, lies in the very high altitude range of > 5,000 m amsl. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 13.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 463, "line_end": 475, "token_count_estimate": 244, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5bb7e7e0401d3981", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 11: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 3,001 - 4,000 | 3 | 1 | 0 | 8 | 14 | 0 | 1 | 23 | 12 | 62 |\n| 3 | 4,001 - 5,000 | 77 | 54 | 1 | 366 | 286 | 0 | 93 | 910 | 68 | 1,855 |\n| 4 | > 5,000 | 180 | 45 | 8 | 1,366 | 317 | 1 | 29 | 810 | 33 | 2,789 |\n| | **Total** | **260** | **100** | **9** | **1,740** | **617** | **1** | **123** | **1,744** | **113** | **4,707** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 11: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 476, "line_end": 482, "token_count_estimate": 461, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "97652fde720b159b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 12: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n**Figure 12: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 12: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "line_start": 484, "line_end": 484, "token_count_estimate": 82, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c1142f4bbdea1a70", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Gandak subbasin, glacial lakes has been distributed in all 6 different classes of area ranges. Table 12 and Figure 15 shows the area range-wise distribution of glacial lakes for the Gandak subbasin. About 551 (88.30%) lakes are with < 5 ha lake area contributing to 31.97% of total lake area. The remaining lakes with ≥ 5 ha in size are only 73 (11.70%) but contributing to 68.03% of total lake area in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 485, "line_end": 496, "token_count_estimate": 169, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "375c0bd848997456", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 12: Area range-wise distribution of GL in Gandak subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 189 | 66.76 | 3.50 |\n| 2 | 0.5 - 1 | 157 | 109.48 | 5.72 |\n| 3 | 1 - 5 | 205 | 435.25 | 22.75 |\n| 4 | 5 - 10 | 36 | 242.86 | 12.70 |\n| 5 | 10 - 50 | 35 | 628.84 | 32.87 |\n| 6 | ≥ 50 | 2 | 429.65 | 22.46 |\n| | **Total** | **624** | **1,912.84** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 12: Area range-wise distribution of GL in Gandak subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 497, "line_end": 505, "token_count_estimate": 263, "basins": ["Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "d1f1a310f2164b86", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lake in the Gandak subbasin is given in Table 13 and Figure 16. All 9 types of glacial lake are present in the Gandak subbasin, where Other Moraine Dammed lake is found to be the maximum with 312 (50.00%) occupying a total lake extent of 897.92 ha at 46.94% in the subbasin. Other Glacial Erosion lakes are second majority of lakes i.e. 125 (20.03%) and extend over an area of 259.63 ha (13.57%).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 506, "line_end": 518, "token_count_estimate": 172, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "a95066a27f1f5f9f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 13: Type-wise distribution of GL in Gandak subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 37 | 491.14 | 25.68 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 18 | 54.64 | 2.85 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 6 | 3.00 | 0.16 |\n| 4 | M(o) | Other Moraine Dammed Lake | 312 | 897.92 | 46.94 |\n| 5 | I(s) | Supra-glacial Lake | 101 | 80.91 | 4.23 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 1 | 2.45 | 0.13 |\n| 7 | E(c) | Cirque Erosion Lake | 4 | 31.79 | 1.66 |\n| 8 | E(o) | Other Glacial Erosion Lake | 125 | 259.63 | 13.57 |\n| 9 | O | Other Glacial Lake | 20 | 91.36 | 4.78 |\n| | | **Total** | **624** | **1,912.84** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 13: Type-wise distribution of GL in Gandak subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 519, "line_end": 530, "token_count_estimate": 414, "basins": ["Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "5ab6cb3e57821a7d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 14 and Figure 17. The lakes with < 5 ha in size (88.30%) are dominated by Other Moraine Dammed (51.91%) followed by Other Glacial Erosion type (20.33%). Lakes with ≥ 5 ha (11.70%) are dominated by Other Moraine Dammed (35.62%) followed by End-moraine Dammed type (30.14%). All types of Moraine-dammed lakes, which constitute about 59.78% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 536, "line_end": 540, "token_count_estimate": 160, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d551082653cf2cc9", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 14: Area range-wise vs. Type-wise distribution of GL in Gandak subbasin\n\n| S. No. | Lake Area Range (ha) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 4 | 4 | 85 | 59 | 0 | 0 | 35 | 1 | 189 |\n| 2 | 0.5 - 1 | 0 | 6 | 2 | 89 | 25 | 0 | 0 | 28 | 7 | 157 |\n| 3 | 1 - 5 | 14 | 6 | 0 | 112 | 15 | 1 | 2 | 49 | 6 | 205 |\n| 4 | 5 - 10 | 6 | 0 | 0 | 17 | 1 | 0 | 1 | 9 | 2 | 36 |\n| 5 | 10 - 50 | 15 | 2 | 0 | 8 | 1 | 0 | 1 | 4 | 4 | 35 |\n| 6 | ≥ 50 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 |\n| | **Total** | **37** | **18** | **6** | **312** | **101** | **1** | **4** | **125** | **20** | **624** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 14: Area range-wise vs. Type-wise distribution of GL in Gandak subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 541, "line_end": 549, "token_count_estimate": 524, "basins": ["Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "a3776b9168ad1165", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Gandak subbasin has been shown in Table 15 and Figure 18. Majority of glacial lakes are situated above 4,000 m elevation i.e. 601 (96.31%) with total lake area of 1,834.70 ha (95.91%) and remaining 3.69% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 552, "line_end": 556, "token_count_estimate": 116, "basins": ["Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "d279673fbff39870", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 15: Elevation range-wise distribution of GL in Gandak subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 1 | 9.87 | 0.52 |\n| 2 | 3,001 - 4,000 | 22 | 68.27 | 3.57 |\n| 3 | 4,001 - 5,000 | 220 | 1,060.56 | 55.44 |\n| 4 | > 5,000 | 381 | 774.14 | 40.47 |\n| | **Total** | **624** | **1,912.84** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 15: Elevation range-wise distribution of GL in Gandak subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 557, "line_end": 563, "token_count_estimate": 227, "basins": ["Ganga"], "subbasins": ["Gandak"], "countries": [], "lake_ids": []}}
{"id": "9fb63f53bab9abc7", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 564, "line_end": 573, "token_count_estimate": 59, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ceb3edaf652ef19f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\nThe Ghaghara subbasin is the second largest subbasin of the Ganga basin covering a total area of 53,072 Km² i.e. 21.48% of the total basin area (Figure 22). Karnal and Bheri are the two major tributaries of the subbasin and after their confluence the river assumes the name ‘Ghaghara’. Karnal River is the largest river draining into the river Ghaghara, which has many various tributaries viz., Humla, Mugu, Tila, and West Seti. The West Seti River rises near Api mountain peak and flows in south-easterly direction to join the Karnal. The Bheri River rises in the higher reaches of Dhaulagiri massif and drain into the mainstream in the lower hills of Nepal. A total of 1,260 glacial lakes has been mapped, covering a total area of 3,536.39 ha i.e. 0.06% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Ghaghara subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 18 and Figure 23 shows the area range-wise distribution of glacial lakes for the Ghaghara subbasin. About 1,090 (86.51%) lakes are with < 5 ha lake area contributing to 36.66% of total lake area. The remaining lakes with ≥ 5 ha in size are only 170 (13.49%) contributing to 63.34% of total lake area in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 575, "line_end": 588, "token_count_estimate": 393, "basins": ["GANGA", "Ganga"], "subbasins": ["Ghaghara"], "countries": ["Nepal"], "lake_ids": []}}
{"id": "b3918299a71e9ef2", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: table\nTable: Table 18: Area range-wise distribution of GL in Ghaghara subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 334 | 118.99 | 3.36 |\n| 2 | 0.5 - 1 | 321 | 232.49 | 6.57 |\n| 3 | 1 - 5 | 435 | 945.22 | 26.73 |\n| 4 | 5 - 10 | 84 | 594.54 | 16.81 |\n| 5 | 10 - 50 | 84 | 1,507.18 | 42.63 |\n| 6 | ≥ 50 | 2 | 137.97 | 3.90 |\n| | **Total** | **1,260** | **3,536.39** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "table", "table_caption": "Table 18: Area range-wise distribution of GL in Ghaghara subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 589, "line_end": 597, "token_count_estimate": 268, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "1ff436ddf2220386", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: figure\nFigure: Figure 23: Area range-wise distribution of GL in Ghaghara subbasin\n\n**Figure 23: Area range-wise distribution of GL in Ghaghara subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 23: Area range-wise distribution of GL in Ghaghara subbasin", "line_start": 599, "line_end": 599, "token_count_estimate": 73, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "8282d2847b92bb31", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lake in the Ghaghara subbasin is given in Table 19 and Figure 24. Out of 9 types of glacial lake, 8 types of lake are present in the Ghaghara subbasin, where Other Glacial Erosion lakes are found to be the maximum with 549 (43.57%) occupying a total lake extent of 1,284.57 ha at 36.32% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 485 (38.49%) and extend over a total area of 880.87 ha (24.91%). All Moraine-dammed lakes constitute about 45.71% of all lakes in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 600, "line_end": 612, "token_count_estimate": 208, "basins": ["GANGA", "Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "317024cc8b91063a", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: table\nTable: Table 19: Type-wise distribution of GL in Ghaghara subbasin\n\n| S.No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 61 | 710.41 | 20.10 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 27 | 158.60 | 4.48 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 3 | 2.48 | 0.07 |\n| 4 | M(o) | Other Moraine Dammed Lake | 485 | 880.87 | 24.91 |\n| 5 | I(s) | Supra-glacial Lake | 73 | 45.27 | 1.28 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 27 | 311.33 | 8.80 |\n| 8 | E(o) | Other Glacial Erosion Lake | 549 | 1,284.57 | 36.32 |\n| 9 | O | Other Glacial Lake | 35 | 142.86 | 4.04 |\n| | | **Total** | **1,260** | **3,536.39** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "table", "table_caption": "Table 19: Type-wise distribution of GL in Ghaghara subbasin", "columns": ["S.No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 613, "line_end": 624, "token_count_estimate": 420, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "24acdeebe1bced7c", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: figure\nFigure: Figure 24: Type-wise distribution of GL in Ghaghara subbasin\n\n**Figure 24: Type-wise distribution of GL in Ghaghara subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 24: Type-wise distribution of GL in Ghaghara subbasin", "line_start": 626, "line_end": 626, "token_count_estimate": 71, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "b5d3fa3827def37b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 20 and Figure 25. The lakes with < 5 ha in size (86.51%) are dominant with Other Glacial Erosion (44.59%) and Other Moraine Dammed lakes (41.19%). Lakes with ≥ 5 ha (13.49%) are also dominated by Other Glacial Erosion type (37.06%). All types of Glacier Erosion lake, which constitute about 45.71% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 627, "line_end": 638, "token_count_estimate": 171, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d81ac5bcf454e30d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: table\nTable: Table 20: Area range-wise vs. Type-wise distribution of GL in Ghaghara subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 7 | 0 | 133 | 43 | 0 | 1 | 139 | 9 | 334 |\n| 2 | 0.5 - 1 | 6 | 6 | 2 | 152 | 21 | 0 | 0 | 124 | 10 | 321 |\n| 3 | 1 - 5 | 14 | 7 | 1 | 164 | 9 | 0 | 8 | 223 | 9 | 435 |\n| 4 | 5 - 10 | 13 | 3 | 0 | 21 | 0 | 0 | 8 | 38 | 1 | 84 |\n| 5 | 10 - 50 | 25 | 3 | 0 | 15 | 0 | 0 | 10 | 25 | 6 | 84 |\n| 6 | ≥ 50 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| | **Total** | **61** | **27** | **3** | **485** | **73** | **0** | **27** | **549** | **35** | **1,260** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "table", "table_caption": "Table 20: Area range-wise vs. Type-wise distribution of GL in Ghaghara subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 639, "line_end": 647, "token_count_estimate": 455, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "98b9b07364557d19", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Ghaghara subbasin has been shown in Table 21 and Figure 26. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 1,252 (99.36%) with total lake area of 3,477.48 ha (98.33%) and remaining 0.64% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 648, "line_end": 661, "token_count_estimate": 149, "basins": ["GANGA", "Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "d1f928125208fb11", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: table\nTable: Table 21: Elevation range-wise distribution of GL in Ghaghara subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 8 | 58.91 | 1.67 |\n| 3 | 4,001 - 5,000 | 563 | 1,842.89 | 52.11 |\n| 4 | > 5,000 | 689 | 1,634.59 | 46.22 |\n| | **Total** | **1,260** | **3,536.39** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "table", "table_caption": "Table 21: Elevation range-wise distribution of GL in Ghaghara subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 662, "line_end": 668, "token_count_estimate": 230, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "b6dff8bf1bcdc2e3", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 22 and Figure 27. It is noted that, 54.68% of glacial lakes (689) are situated in very high altitude range i.e. > 5,000 m amsl, which constitutes a total lake area of 46.22%. However, majority of glacial lakes (614) of size < 5 ha lies above 5,000 m. 83.66% of lakes lying in high altitude range are < 5 ha, predominantly of size ranging 1 - 5 ha (i.e. 207), followed by lakes of size 0.25 - 0.5 ha (i.e. 148). It has been further noticed that, 54.12% of lakes ≥ 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 10 - 50 ha.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 669, "line_end": 682, "token_count_estimate": 263, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7eb02afce5fd7240", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: table\nTable: Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of Lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of Lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of Lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of Lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of Lakes | Total - Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 0 | 0.00 | 148 | 53.23 | 186 | 65.76 | 334 | 118.99 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 4 | 2.98 | 116 | 83.46 | 201 | 146.04 | 321 | 232.49 |\n| 3 | 1 - 5 | 0 | 0.00 | 1 | 1.68 | 207 | 465.99 | 227 | 477.55 | 435 | 945.22 |\n| 4 | 5 - 10 | 0 | 0.00 | 1 | 8.93 | 44 | 302.77 | 39 | 282.84 | 84 | 594.54 |\n| 5 | 10 - 50 | 0 | 0.00 | 2 | 45.31 | 47 | 875.12 | 35 | 586.75 | 84 | 1,507.18 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 1 | 62.33 | 1 | 75.65 | 2 | 137.97 |\n| | **Total** | **0** | **0.00** | **8** | **58.90** | **563** | **1,842.90** | **689** | **1,634.59** | **1,260** | **3,536.39** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "table", "table_caption": "Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of Lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of Lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of Lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of Lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of Lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 683, "line_end": 691, "token_count_estimate": 674, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "363a028fbb2c1f18", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: figure\nFigure: Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin\n\n**Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin", "line_start": 693, "line_end": 693, "token_count_estimate": 87, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "fc47c3d66c6f8b0e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 23 and Figure 28. The dominant lake type in the subbasin i.e. Other Glacial Erosion lake (549) with 43.57% is predominantly located in the elevation range of 4,001 - 5,000 m (59.02%). The other dominant lake type i.e. Other Moraine Dammed lake is distributed predominantly in > 5,000 m and 4,001 - 5,000 m elevation ranges with 72.99% and 26.80% respectively. Majority of all types of Moraine-dammed and Glacier Erosion lake lies above 4,000 m elevation at almost equal numbers i.e. 574 and 573 respectively. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 29.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 694, "line_end": 708, "token_count_estimate": 254, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "687e4bbf103aa794", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: table\nTable: Table 23: Type-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 3 | 8 |\n| 3 | 4,001 - 5,000 | 17 | 10 | 1 | 130 | 40 | 0 | 18 | 324 | 23 | 563 |\n| 4 | > 5,000 | 43 | 17 | 2 | 354 | 33 | 0 | 9 | 222 | 9 | 689 |\n| | **Total** | **61** | **27** | **3** | **485** | **73** | **0** | **27** | **549** | **35** | **1,260** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "table", "table_caption": "Table 23: Type-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 709, "line_end": 715, "token_count_estimate": 479, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "1e2dc91661958e93", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: figure\nFigure: Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin\n\n**Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Ghaghara subbasin", "line_start": 717, "line_end": 717, "token_count_estimate": 85, "basins": ["Ganga"], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": []}}
{"id": "0186e97a2b0f3239", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.2 Ghaghra Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.2 Ghaghra Subbasin"], "chunk_type": "text", "line_start": 718, "line_end": 730, "token_count_estimate": 60, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7ecf7c7aa460382e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: text\n\nThe Kosi subbasin is the largest subbasin of the Ganga River basin covering a total area of 59,709 Km² i.e. 24.16% of the total basin area (Figure 30). Kosi subbasin has so many high mountain peaks and Kosi River is joined by major tributaries like Sun Kosi, Tama Kosi, Dudh Kosi, Arun Kosi, and Tamur Kosi. The Arun Kosi River rises in the trans-Himalayan zone of Tibet, has cut a fantastic gorge across the main Himalayan range near the Everest massif. Yeru Chu and Bum Chu are the other two tributaries of Arun Kosi. The river Tamur Kosi which is the eastern tributary of the river Kosi, rises from the higher elevations of the western flank of the Kanchenjunga group of glaciers known as the Kumbhakaran Himal. A total of 2,437 glacial lakes has been mapped, covering a total area of 14,604.34 ha i.e. 0.24% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Kosi subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 24 and Figure 31 shows the area range-wise distribution of glacial lakes for the Kosi subbasin. About 2,039 (83.67%) lakes are with < 5 ha lake area contributing to 17.38% of total lake area. The remaining lakes with ≥ 5 ha in size are only 398 (16.33%) but contributing to 82.62% of total lake area in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "text", "line_start": 732, "line_end": 745, "token_count_estimate": 413, "basins": ["GANGA", "Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "2057bce0a5df8982", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: table\nTable: Table 24: Area range-wise distribution of GL in Kosi subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 606 | 215.04 | 1.47 |\n| 2 | 0.5 - 1 | 571 | 406.11 | 2.78 |\n| 3 | 1 - 5 | 862 | 1,917.98 | 13.13 |\n| 4 | 5 - 10 | 174 | 1,205.76 | 8.26 |\n| 5 | 10 - 50 | 170 | 3,650.33 | 25.00 |\n| 6 | ≥ 50 | 54 | 7,209.12 | 49.36 |\n| | **Total** | **2,437** | **14,604.34** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "table", "table_caption": "Table 24: Area range-wise distribution of GL in Kosi subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 746, "line_end": 754, "token_count_estimate": 271, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "15573811bdf3a345", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: figure\nFigure: Figure 31: Area range-wise distribution of GL in Kosi subbasin\n\n**Figure 31: Area range-wise distribution of GL in Kosi subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 31: Area range-wise distribution of GL in Kosi subbasin", "line_start": 756, "line_end": 756, "token_count_estimate": 70, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "bf83180fc80c72bd", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lake in the Kosi subbasin is given in Table 25 and Figure 32. Out of 9 types of glacial lake, 7 types of lake are present in the Kosi subbasin, where Other Glacial Erosion lake is found to be the maximum with 986 (40.46%) occupying a total lake extent of 2,956.50 ha at 20.24% in the subbasin. After that, Other Moraine Dammed and Supra-glacial lakes are in majority with 829 (34.02%) and 335 (13.75%) and extend over a total lake area of 2,577.28 ha (17.65%) and 360.17 ha (2.47%) respectively.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "text", "line_start": 757, "line_end": 769, "token_count_estimate": 206, "basins": ["GANGA", "Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "f78393376b3c3d3d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: table\nTable: Table 25: Type-wise distribution of GL in Kosi subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 138 | 7,243.03 | 49.60 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 39 | 320.10 | 2.19 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 829 | 2,577.28 | 17.65 |\n| 5 | I(s) | Supra-glacial Lake | 335 | 360.17 | 2.47 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 65 | 517.14 | 3.54 |\n| 8 | E(o) | Other Glacial Erosion Lake | 986 | 2,956.50 | 20.24 |\n| 9 | O | Other Glacial Lake | 45 | 630.12 | 4.31 |\n| | | **Total** | **2,437** | **14,604.34** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "table", "table_caption": "Table 25: Type-wise distribution of GL in Kosi subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 770, "line_end": 781, "token_count_estimate": 426, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "b9912b877934814c", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: figure\nFigure: Figure 32: Type-wise distribution of GL in Kosi subbasin\n\n**Figure 32: Type-wise distribution of GL in Kosi subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 32: Type-wise distribution of GL in Kosi subbasin", "line_start": 783, "line_end": 783, "token_count_estimate": 68, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "a348c85910794022", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 26 and Figure 33. The lakes with < 5 ha in size (83.67%) are dominant with Other Glacial Erosion (43.21%) and Other Moraine Dammed lakes (35.31%). Lakes with ≥ 5 ha (16.33%) are dominated by End-moraine Dammed type (28.89%). All types of Moraine-dammed lakes, which constitute about 41.28% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "text", "line_start": 784, "line_end": 795, "token_count_estimate": 173, "basins": ["GANGA", "Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "a3ca5c002af3655f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: table\nTable: Table 26: Area range-wise vs. Type-wise distribution of GL in Kosi subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 9 | 0 | 205 | 178 | 0 | 2 | 199 | 12 | 606 |\n| 2 | 0.5 - 1 | 5 | 7 | 0 | 200 | 94 | 0 | 0 | 257 | 8 | 571 |\n| 3 | 1 - 5 | 17 | 15 | 0 | 315 | 54 | 0 | 27 | 425 | 9 | 862 |\n| 4 | 5 - 10 | 21 | 1 | 0 | 62 | 4 | 0 | 17 | 65 | 4 | 174 |\n| 5 | 10 - 50 | 54 | 4 | 0 | 45 | 5 | 0 | 19 | 35 | 8 | 170 |\n| 6 | ≥ 50 | 40 | 3 | 0 | 2 | 0 | 0 | 0 | 5 | 4 | 54 |\n| | **Total** | **138** | **39** | **0** | **829** | **335** | **0** | **65** | **986** | **45** | **2,437** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "table", "table_caption": "Table 26: Area range-wise vs. Type-wise distribution of GL in Kosi subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 796, "line_end": 804, "token_count_estimate": 458, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "2e036c88c28065b2", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Kosi subbasin has been shown in Table 27 and Figure 34. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 2,416 (99.14%) with total lake area of 14,513.60 ha (99.38%) and remaining 0.86% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "text", "line_start": 805, "line_end": 817, "token_count_estimate": 147, "basins": ["GANGA", "Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "4db3b50054a95caf", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: table\nTable: Table 27: Elevation range-wise distribution of GL in Kosi subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 21 | 90.74 | 0.62 |\n| 3 | 4,001 - 5,000 | 856 | 4,049.65 | 27.73 |\n| 4 | > 5,000 | 1,560 | 10,463.95 | 71.65 |\n| | **Total** | **2,437** | **14,604.34** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "table", "table_caption": "Table 27: Elevation range-wise distribution of GL in Kosi subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 818, "line_end": 824, "token_count_estimate": 232, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "2b0fe829c5157b59", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.3 Kosi Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.3 Kosi Subbasin"], "chunk_type": "text", "line_start": 825, "line_end": 831, "token_count_estimate": 44, "basins": ["GANGA", "Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "1277574fdbdb9ce0", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 28 and Figure 35. It is noted that, 64.01% of glacial lakes (1,560) are situated in very high altitude range i.e. > 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 71.65%. However, 260 glacial lakes lies above 5,000 m, which are ≥ 5 ha in size. 83.33% of lakes lying in very high altitude range are < 5 ha, predominantly of size ranging 1 - 5 ha (i.e. 516), followed by lakes of size 0.25 - 0.5 ha (i.e. 411). It has been further noticed that, 65.33% of lakes ≥ 5 ha are lying within in very high altitude range i.e. > 5,000 m, majority of them falling in size range of 10 - 50 ha.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 833, "line_end": 837, "token_count_estimate": 251, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d3f54aa0ef10a244", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Kosi subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 4 | 1.22 | 191 | 68.94 | 411 | 144.89 | 606 | 215.04 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 3 | 2.10 | 195 | 142.19 | 373 | 261.82 | 571 | 406.11 |\n| 3 | 1 - 5 | 0 | 0.00 | 9 | 25.89 | 337 | 757.61 | 516 | 1,134.47 | 862 | 1,917.98 |\n| 4 | 5 - 10 | 0 | 0.00 | 2 | 13.45 | 65 | 469.40 | 107 | 722.91 | 174 | 1,205.76 |\n| 5 | 10 - 50 | 0 | 0.00 | 3 | 48.08 | 52 | 1,032.06 | 115 | 2,570.19 | 170 | 3,650.33 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 16 | 1,579.44 | 38 | 5,629.67 | 54 | 7,209.12 |\n| | **Total** | **0** | **0.00** | **21** | **90.74** | **856** | **4,049.65** | **1,560** | **10,463.95** | **2,437** | **14,604.34** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Kosi subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 838, "line_end": 846, "token_count_estimate": 660, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "871a1ea8be774522", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 847, "line_end": 856, "token_count_estimate": 48, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a488e2728120fbe3", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 29 and Figure 36. The dominant lake type in the subbasin i.e. Other Glacial Erosion lake (40.46%) is predominantly located in the elevation range of > 4,000 m (98.58%). The other dominant lake type, namely, Other Moraine Dammed and Supra-glacial lakes are distributed predominantly in > 5,000 m elevation range i.e. 83.47% and 68.06%. Majority of all types of Moraine-dammed (99.90%) and Glacier Erosion lake (98.57%) lies above 4,000 m elevation. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 37.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 858, "line_end": 862, "token_count_estimate": 217, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3dd25ef0c36ce5dc", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 29: Type-wise vs. Elevation range-wise distribution of GL in Kosi subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 1 | 4 | 0 | 1 | 14 | 1 | 21 |\n| 3 | 4,001 - 5,000 | 32 | 20 | 0 | 136 | 103 | 0 | 56 | 484 | 25 | 856 |\n| 4 | > 5,000 | 106 | 19 | 0 | 692 | 228 | 0 | 8 | 488 | 19 | 1,560 |\n| | **Total** | **138** | **39** | **0** | **829** | **335** | **0** | **65** | **986** | **45** | **2,437** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 29: Type-wise vs. Elevation range-wise distribution of GL in Kosi subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 863, "line_end": 869, "token_count_estimate": 460, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "4723d45ac7e7b79d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 870, "line_end": 877, "token_count_estimate": 61, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06e1e4ab0c5f4c5f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.4 Sarda Subbasin\nType: text\n\nThe Sarda subbasin is the fifth largest subbasin of the Ganga River basin covering a total area of 17,326 Km² i.e. 7.01% of the total basin area (Figure 38). Sarda River also known as Mahakali River, originates at Kalapani in the Himalayas at an elevation of 3,600 m amsl in the Pithoragarh district of Uttarakhand. It flows along Nepal’s western border with India. A total of 55 glacial lakes has been mapped, covering a total area of 118.84 ha i.e. 0.01% of the total area of the subbasin.\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.4 Sarda Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.4 Sarda Subbasin"], "chunk_type": "text", "line_start": 879, "line_end": 886, "token_count_estimate": 190, "basins": ["GANGA", "Ganga"], "subbasins": ["Sarda"], "countries": ["India", "Nepal"], "lake_ids": []}}
{"id": "3fbc270c4595c112", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nIn Sarda subbasin, glacial lakes have been distributed in 5 classes of area ranges except ≥ 50 ha range. Table 30 and Figure 39 shows the area range-wise distribution of glacial lakes for the Sarda subbasin. About 48 (87.28%) lakes are with < 5 ha lake area contributing to 44.97% of total lake area. The remaining lakes with ≥ 5 ha in size are only 7 (12.72%) contributing to 55.03% of total lake area in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 888, "line_end": 892, "token_count_estimate": 144, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "32d966f6c999b2b5", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 30: Area range-wise distribution of GL in Sarda subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 16 | 5.75 | 4.84 |\n| 2 | 0.5 - 1 | 13 | 8.54 | 7.19 |\n| 3 | 1 - 5 | 19 | 39.15 | 32.94 |\n| 4 | 5 - 10 | 4 | 26.38 | 22.20 |\n| 5 | 10 - 50 | 3 | 39.02 | 32.83 |\n| 6 | ≥ 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **55** | **118.84** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 30: Area range-wise distribution of GL in Sarda subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 893, "line_end": 901, "token_count_estimate": 253, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "6efcf2d8384d22ae", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 902, "line_end": 906, "token_count_estimate": 45, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2102314141c30422", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lake in the Sarda subbasin is given in Table 31 and Figure 40. Out of 9 types of glacial lake, 7 types of lake are present in the Sarda subbasin, where Other Moraine Dammed lake is found to be the maximum with 21 (38.18%) occupying a total lake extent of 24.24 ha at 20.40% in the subbasin. After that, Other Glacial Erosion and Supra-glacial lakes are in majority with 13 (23.64%) and 6 (10.91%), extend over a total lake area of 14.03 ha (11.81%) and 3.13 ha (2.63%) respectively.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 908, "line_end": 912, "token_count_estimate": 170, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "f33b7598c9bee35f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 31: Type-wise distribution of GL in Sarda subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 5 | 42.42 | 35.69 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 5 | 14.37 | 12.09 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 21 | 24.24 | 20.40 |\n| 5 | I(s) | Supra-glacial Lake | 6 | 3.13 | 2.63 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 4 | 20.38 | 17.15 |\n| 8 | E(o) | Other Glacial Erosion Lake | 13 | 14.03 | 11.81 |\n| 9 | O | Other Glacial Lake | 1 | 0.27 | 0.23 |\n| | | **Total** | **55** | **118.84** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 31: Type-wise distribution of GL in Sarda subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 913, "line_end": 924, "token_count_estimate": 404, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "64c319c0589624c3", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 925, "line_end": 928, "token_count_estimate": 42, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c06c31e8323b277b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 32 and Figure 41. The lakes with < 5 ha in size (87.28%) are dominant with Other Moraine Dammed (41.67%) and Other Glacial Erosion lakes (27.08%). Lakes with ≥ 5 ha (12.72%) are dominated by End-moraine Dammed type (42.86%). All types of Moraine-dammed lakes, which constitute about 56.36% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 930, "line_end": 934, "token_count_estimate": 147, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9d3551c5db520e8", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 32: Area range-wise vs. Type-wise distribution of GL in Sarda subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 6 | 4 | 0 | 0 | 4 | 1 | 16 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 8 | 2 | 0 | 0 | 3 | 0 | 13 |\n| 3 | 1 - 5 | 2 | 3 | 0 | 6 | 0 | 0 | 2 | 6 | 0 | 19 |\n| 4 | 5 - 10 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **5** | **5** | **0** | **21** | **6** | **0** | **4** | **13** | **1** | **55** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 32: Area range-wise vs. Type-wise distribution of GL in Sarda subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 935, "line_end": 943, "token_count_estimate": 450, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "cd1825d418d0466f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 944, "line_end": 946, "token_count_estimate": 45, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "419319ece0be0f9c", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Sarda subbasin has been shown in Table 33 and Figure 42. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 52 (94.55%) with total lake area of 117.92 ha (99.22%) and remaining 5.45% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 948, "line_end": 952, "token_count_estimate": 117, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "057230c052d1753c", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 33: Elevation range-wise distribution of GL in Sarda subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 3 | 0.92 | 0.78 |\n| 3 | 4,001 - 5,000 | 36 | 95.29 | 80.18 |\n| 4 | > 5,000 | 16 | 22.63 | 19.04 |\n| | **Total** | **55** | **118.84** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 33: Elevation range-wise distribution of GL in Sarda subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 953, "line_end": 959, "token_count_estimate": 221, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "b5fcd52f8bddd0c0", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 34 and Figure 43. It is noted that, 65.46% of glacial lakes (36) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes major share of total lake area within that range i.e. 80.18%. However, only 3 glacial lakes lies below 4,001 m, has all of its lakes 0.25 - 0. 5 ha in size and in medium altitude range only. 93.75% of lakes lying in very high altitude range are < 5 ha, predominantly of size ranges 0.25 - 0. 5 ha and 1 - 5 ha (i.e. 6 each), followed by lakes of size 0.5 - 1 ha (i.e. 3). It has been further noticed that, 85.71% of lakes ≥ 5 ha are lying within in high altitude range i.e. 4,001 - 5,000 m, equally in lake size ranges of 5 - 10 ha and 10 - 50 ha.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 960, "line_end": 970, "token_count_estimate": 304, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1a911c273e7d406", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 34: Area range-wise vs. Elevation range-wise distribution of GL in Sarda subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 (No. of lakes) | Elevation Range (m): up to 3,000 (Total Lake Area (ha)) | Elevation Range (m): 3,001 - 4,000 (No. of lakes) | Elevation Range (m): 3,001 - 4,000 (Total Lake Area (ha)) | Elevation Range (m): 4,001 - 5,000 (No. of lakes) | Elevation Range (m): 4,001 - 5,000 (Total Lake Area (ha)) | Elevation Range (m): > 5,000 (No. of lakes) | Elevation Range (m): > 5,000 (Total Lake Area (ha)) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 3 | 0.93 | 7 | 2.55 | 6 | 2.27 | 16 | 5.75 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 0 | 0.00 | 10 | 6.71 | 3 | 1.83 | 13 | 8.54 |\n| 3 | 1 - 5 | 0 | 0.00 | 0 | 0.00 | 13 | 25.90 | 6 | 13.25 | 19 | 39.15 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 3 | 21.10 | 1 | 5.28 | 4 | 26.38 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 3 | 39.02 | 0 | 0.00 | 3 | 39.02 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **3** | **0.93** | **36** | **95.28** | **16** | **22.63** | **55** | **118.84** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 34: Area range-wise vs. Elevation range-wise distribution of GL in Sarda subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 (No. of lakes)", "Elevation Range (m): up to 3,000 (Total Lake Area (ha))", "Elevation Range (m): 3,001 - 4,000 (No. of lakes)", "Elevation Range (m): 3,001 - 4,000 (Total Lake Area (ha))", "Elevation Range (m): 4,001 - 5,000 (No. of lakes)", "Elevation Range (m): 4,001 - 5,000 (Total Lake Area (ha))", "Elevation Range (m): > 5,000 (No. of lakes)", "Elevation Range (m): > 5,000 (Total Lake Area (ha))", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 971, "line_end": 979, "token_count_estimate": 618, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "00e74af845e2c9d7", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 35 and Figure 44. The dominant lake type in the subbasin i.e. Other Moraine Dammed lake (38.18%) is predominantly located in the elevation range of 4,001 - 5,000 m (61.90%). The other dominant lake type, namely, Other Glacial Erosion and Supra-glacial lakes are distributed predominantly in very high and high altitude elevation range, i.e. 53.85% and 100% respectively. Majority (93.55%) of all types of Moraine-dammed lakes lies above 4,000 m elevation. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 45.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 980, "line_end": 991, "token_count_estimate": 236, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30049cb2381316a7", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 35: Type-wise vs. Elevation range-wise distribution of GL in Sarda subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 3 |\n| 3 | 4,001 - 5,000 | 5 | 5 | 0 | 13 | 6 | 0 | 1 | 6 | 0 | 36 |\n| 4 | > 5,000 | 0 | 0 | 0 | 6 | 0 | 0 | 3 | 7 | 0 | 16 |\n| | **Total** | **5** | **5** | **0** | **21** | **6** | **0** | **4** | **13** | **1** | **55** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 35: Type-wise vs. Elevation range-wise distribution of GL in Sarda subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 992, "line_end": 998, "token_count_estimate": 448, "basins": ["Ganga"], "subbasins": ["Sarda"], "countries": [], "lake_ids": []}}
{"id": "9fc10206be351154", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 999, "line_end": 1008, "token_count_estimate": 59, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "78c9cf9a3c2fc4b9", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin\nType: text\n\nThe Upper Ganga subbasin is the fourth largest subbasin of the Ganga River basin covering a total area of 25,675 Km² i.e. 10.39% of the total basin area (Figure 46). Upper Ganga subbasin has two major tributaries viz., Bhagirathi and Alaknanda, which originates from Gangotri and Satopanth (north of the temple town of Badrinath) group of glaciers in upper regions of Uttarakhand. As these two rivers joins at Devprayag in Uttarakhand, river assumes the name ‘Ganga’. Alaknanda on the other hand has several major tributaries viz., Dhauli Ganga (originates from Kamet group of glaciers, confluence at Vishnuprayag), Nandakini (confluence at Nandprayag), Pindar (confluence at Karnaprayag), and Mandakini (originates from Chorabari group of glaciers near Kedarnath, confluence at Rudraprayag). A total of 295 lakes were mapped, covering a total area of 447.26 ha i.e. 0.01% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Upper Ganga subbasin, glacial lakes have been distributed in 5 area ranges except ≥ 50 ha range. Table 36 and Figure 47 shows the area range-wise distribution of glacial lakes for the Upper Ganga subbasin. About 274 (92.88%) lakes are with < 5 ha lake area contributing to 56.59% of total lake area. The remaining lakes with ≥ 5 ha in size are only 21 (7.12%) contributing to 43.41% of total lake area in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.5 Upper Ganga Subbasin"], "chunk_type": "text", "line_start": 1010, "line_end": 1023, "token_count_estimate": 450, "basins": ["GANGA", "Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "9de2e5a9270d4902", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin\nType: table\nTable: Table 36: Area range-wise distribution of GL in Upper Ganga subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 120 | 41.02 | 9.17 |\n| 2 | 0.5 - 1 | 84 | 59.14 | 13.22 |\n| 3 | 1 - 5 | 70 | 152.95 | 34.20 |\n| 4 | 5 - 10 | 16 | 107.08 | 23.94 |\n| 5 | 10 - 50 | 5 | 87.07 | 19.47 |\n| 6 | ≥ 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **295** | **447.26** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.5 Upper Ganga Subbasin"], "chunk_type": "table", "table_caption": "Table 36: Area range-wise distribution of GL in Upper Ganga subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1024, "line_end": 1032, "token_count_estimate": 263, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "f1a0301266d0cc92", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lake in the Upper Ganga subbasin is given in Table 37 and Figure 48. Out of 9 types of glacial lake, only 7 types of lake are present in the Upper Ganga subbasin, where Supra-glacial lake is found to be the maximum with 98 (33.22%) occupying a total lake area extent of 75.34 ha at 16.84% in the subbasin. Two other types of lake, namely, Other Moraine Dammed and Other Glacial Erosion lakes are 87 (29.49%) and 53 (17.97%) and extend over lake area of 105.41 ha (23.57%) and 66.82 ha (14.95%) respectively.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.5 Upper Ganga Subbasin"], "chunk_type": "text", "line_start": 1033, "line_end": 1045, "token_count_estimate": 209, "basins": ["GANGA", "Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "d9bbeb7e2b98c2db", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin\nType: table\nTable: Table 37: Type-wise distribution of GL in Upper Ganga subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 18 | 103.53 | 23.15 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 11 | 20.42 | 4.56 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 87 | 105.41 | 23.57 |\n| 5 | I(s) | Supra-glacial Lake | 98 | 75.34 | 16.84 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 20 | 66.39 | 14.84 |\n| 8 | E(o) | Other Glacial Erosion Lake | 53 | 66.82 | 14.95 |\n| 9 | O | Other Glacial Lake | 8 | 9.35 | 2.09 |\n| | | **Total** | **295** | **447.26** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.5 Upper Ganga Subbasin"], "chunk_type": "table", "table_caption": "Table 37: Type-wise distribution of GL in Upper Ganga subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 1046, "line_end": 1057, "token_count_estimate": 415, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "87545bd16fdb2084", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.5 Upper Ganga Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.5 Upper Ganga Subbasin"], "chunk_type": "text", "line_start": 1058, "line_end": 1063, "token_count_estimate": 46, "basins": ["GANGA", "Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "bbc31119f4c640c3", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 38 and Figure 49. The lakes with < 5 ha in size (92.88%) are dominant with Supra-glacial (34.67%) and Other Moraine Dammed type (29.93%). Lakes with ≥ 5 ha (7.12%) are equally dominated by Cirque Erosion and Other Moraine Dammed type, predominantly in the lake size range of 5 - 10 ha. All types of Moraine-dammed lakes, which constitutes about 39.32% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1065, "line_end": 1069, "token_count_estimate": 162, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe1a1d4afaedf85a", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 38: Area range-wise vs. Type-wise distribution of GL in Upper Ganga subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 1 | 0 | 31 | 66 | 0 | 1 | 17 | 3 | 120 |\n| 2 | 0.5 - 1 | 1 | 5 | 0 | 31 | 24 | 0 | 5 | 16 | 2 | 84 |\n| 3 | 1 - 5 | 12 | 4 | 0 | 20 | 5 | 0 | 9 | 17 | 3 | 70 |\n| 4 | 5 - 10 | 1 | 1 | 0 | 5 | 1 | 0 | 5 | 3 | 0 | 16 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 5 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **18** | **11** | **0** | **87** | **98** | **0** | **20** | **53** | **8** | **295** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 38: Area range-wise vs. Type-wise distribution of GL in Upper Ganga subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1070, "line_end": 1078, "token_count_estimate": 453, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "926ef94f9d924955", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 49: Area range-wise vs. Type-wise distribution of GL in Upper Ganga subbasin\n\nFigure 49: Area range-wise vs. Type-wise distribution of GL in Upper Ganga subbasin", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 49: Area range-wise vs. Type-wise distribution of GL in Upper Ganga subbasin", "line_start": 1080, "line_end": 1080, "token_count_estimate": 82, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "ab8ef647f2ad6d3b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1081, "line_end": 1088, "token_count_estimate": 46, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eeeac3d1b84e6f32", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Upper Ganga subbasin has been shown in Table 39 and Figure 50. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 291 (98.64%) with total lake area of 444.58 ha (99.40%) and remaining 1.36% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1090, "line_end": 1094, "token_count_estimate": 122, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "1908eb1635f966d9", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 39: Elevation range-wise distribution of GL in Upper Ganga subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 4 | 2.68 | 0.60 |\n| 3 | 4,001 - 5,000 | 148 | 263.09 | 58.82 |\n| 4 | > 5,000 | 143 | 181.49 | 40.58 |\n| | **Total** | **295** | **447.26** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 39: Elevation range-wise distribution of GL in Upper Ganga subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1095, "line_end": 1101, "token_count_estimate": 227, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "83a909c63aae0b8f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 50: Elevation range-wise distribution of GL in Upper Ganga subbasin\n\nFigure 50: Elevation range-wise distribution of GL in Upper Ganga subbasin", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 50: Elevation range-wise distribution of GL in Upper Ganga subbasin", "line_start": 1103, "line_end": 1103, "token_count_estimate": 73, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "740ce267b945a678", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 40 and Figure 51. It is noted that, 50.17% of glacial lakes (148) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 58.82%. However, 143 glacial lakes lies above 5,000 m, has majority of its lakes are < 5 ha i.e. 97.20%. Maximum lakes lying in high altitude range is of size ranging 0.25 - 0.5 ha (i.e. 65), followed by lakes of size 0.5 - 1 ha (i.e. 40). It has been further noticed that, 11.48% of lakes ≥ 5 ha are lying within in high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1104, "line_end": 1116, "token_count_estimate": 275, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5103e97dad7b6e32", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 40: Area range-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin\n\n| S. No. | Lake Area Range (ha) | up to 3,000: No. of lakes | up to 3,000: Total Lake Area (ha) | 3,001 - 4,000: No. of lakes | 3,001 - 4,000: Total Lake Area (ha) | 4,001 - 5,000: No. of lakes | 4,001 - 5,000: Total Lake Area (ha) | > 5,000: No. of lakes | > 5,000: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 2 | 0.83 | 65 | 22.49 | 53 | 17.69 | 120 | 41.02 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 1 | 0.74 | 40 | 27.88 | 43 | 30.52 | 84 | 59.14 |\n| 3 | 1 - 5 | 0 | 0.00 | 1 | 1.11 | 26 | 54.19 | 43 | 97.65 | 70 | 152.95 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 13 | 88.47 | 3 | 18.60 | 16 | 107.08 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 4 | 70.06 | 1 | 17.02 | 5 | 87.07 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **4** | **2.68** | **148** | **263.09** | **143** | **181.49** | **295** | **447.26** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 40: Area range-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "up to 3,000: No. of lakes", "up to 3,000: Total Lake Area (ha)", "3,001 - 4,000: No. of lakes", "3,001 - 4,000: Total Lake Area (ha)", "4,001 - 5,000: No. of lakes", "4,001 - 5,000: Total Lake Area (ha)", "> 5,000: No. of lakes", "> 5,000: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1117, "line_end": 1125, "token_count_estimate": 575, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "338d1cf34cc4630e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 51: Area range-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin\n\n**Figure 51: Area range-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 51: Area range-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin", "line_start": 1127, "line_end": 1127, "token_count_estimate": 88, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "a262558dc0ae073e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 41 and Figure 52. The dominant lake types in the subbasin i.e. Supra-glacial lake (33.22%) is predominantly located in the elevation range of 4,001 - 5,000 m (81.63%). The other dominant lake type, namely, Other Moraine Dammed and Other Glacial Erosion lakes are distributed predominantly in > 5,000 m and 4,001 - 5,000 m elevation range i.e. 81.61% and 56.60% respectively. All types of Moraine-dammed lakes, lie above 4,000 m elevation. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 53.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1128, "line_end": 1142, "token_count_estimate": 236, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "81e858ae2f8c280e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 41: Type-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 4 |\n| 3 | 4,001 - 5,000 | 3 | 7 | 0 | 16 | 80 | 0 | 11 | 30 | 1 | 148 |\n| 4 | > 5,000 | 15 | 4 | 0 | 71 | 18 | 0 | 9 | 22 | 4 | 143 |\n| | **Total** | **18** | **11** | **0** | **87** | **98** | **0** | **20** | **53** | **8** | **295** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 41: Type-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1143, "line_end": 1149, "token_count_estimate": 451, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "6a938655c040f49d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 52: Type-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin\n\n**Figure 52: Type-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 52: Type-wise vs. Elevation range-wise distribution of GL in Upper Ganga subbasin", "line_start": 1151, "line_end": 1151, "token_count_estimate": 86, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": []}}
{"id": "590237ed8aae821b", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**\n\n***\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1152, "line_end": 1161, "token_count_estimate": 63, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "daabe39ed82cb749", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.6 Yamuna Subbasin\nType: text\n\nThe Yamuna subbasin is the sixth largest subbasin of the Ganga River basin covering a total area of 11,701 Km² i.e. 4.73% of the total basin area (Figure 54). It is the largest tributary of Ganga River and drains the western most part of Ganga River basin. Tons and Giri rivers are the major tributaries of Yamuna River. The Tons River rises in the higher elevation beyond the valley of Har-ki-dun in Uttarakhand, and has Pabbar River as one of its tributary. The Giri River originates in Himachal Pradesh and drains the south-eastern corner of the state. A total of 36 glacial lakes has been mapped, covering a total area of 65.45 ha i.e. 0.01% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.2.6 Yamuna Subbasin", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.2.6 Yamuna Subbasin"], "chunk_type": "text", "line_start": 1163, "line_end": 1170, "token_count_estimate": 240, "basins": ["GANGA", "Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "3d963ef6bba81e42", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nIn Yamuna subbasin, glacial lakes have been distributed in 5 classes of area ranges except ≥ 50 ha area range. Table 42 and Figure 55 shows the area range-wise distribution of glacial lakes for the Yamuna subbasin. About 33 (91.67%) lakes are with < 5 ha lake area contributing to 48.00% of total lake area. The remaining lakes with ≥ 5 ha in size are only 3 (8.33%) contributing to 52.00% of total lake area in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1172, "line_end": 1176, "token_count_estimate": 146, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "2c083b92f86a6453", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 42: Area range-wise distribution of GL in Yamuna subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :---: | :---: | :---: |\n| 1 | 0.25 - 0.5 | 14 | 4.74 | 7.24 |\n| 2 | 0.5 - 1 | 11 | 8.28 | 12.65 |\n| 3 | 1 - 5 | 8 | 18.40 | 28.11 |\n| 4 | 5 - 10 | 1 | 8.26 | 12.63 |\n| 5 | 10 - 50 | 2 | 25.77 | 39.37 |\n| 6 | ≥ 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **36** | **65.45** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 42: Area range-wise distribution of GL in Yamuna subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1177, "line_end": 1185, "token_count_estimate": 266, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "a1007a9c0b25fe9e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1186, "line_end": 1192, "token_count_estimate": 44, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "688b35cef98ac5f2", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lake in the Yamuna subbasin is given in Table 43 and Figure 56. Out of 9 types of glacial lake, only 6 types of lake are present in the Yamuna subbasin, where Other Glacial Erosion lake is found to be the maximum with 18 (50.00%) occupying a total lake extent of 30.47 ha at 46.55% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 6 (16.67%) and extend over a total area of 3.63 ha 5.54%. in the subbasin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1194, "line_end": 1198, "token_count_estimate": 157, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "b7aad58cb7957290", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 43: Type-wise distribution of GL in Yamuna subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :---: | :--- | :---: | :---: | :---: |\n| 1 | M(e) | End-moraine Dammed Lake | 1 | 1.26 | 1.93 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 0 | 0.00 | 0.00 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 6 | 3.63 | 5.54 |\n| 5 | I(s) | Supra-glacial Lake | 4 | 1.32 | 2.02 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 3 | 26.74 | 40.85 |\n| 8 | E(o) | Other Glacial Erosion Lake | 18 | 30.47 | 46.55 |\n| 9 | O | Other Glacial Lake | 4 | 2.04 | 3.11 |\n| | | **Total** | **36** | **65.45** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 43: Type-wise distribution of GL in Yamuna subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 1199, "line_end": 1210, "token_count_estimate": 422, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "d3532c1a0a6ea186", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 44 and Figure 57. The lakes with < 5 ha in size (91.67%) are dominant with Other Glacial Erosion (51.52%) and Other Moraine Dammed lakes (18.18%). Lakes with ≥ 5 ha (8.33%) are also dominated by Cirque Erosion type (66.67%). All types of Glacier Erosion lakes, which constitute about 58.33% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1211, "line_end": 1220, "token_count_estimate": 156, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "148276117402758d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 44: Area range-wise vs. Type-wise distribution of GL in Yamuna subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 6 | 2 | 14 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 5 | 2 | 11 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 8 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| **Total** | | **1** | **0** | **0** | **6** | **4** | **0** | **3** | **18** | **4** | **36** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 44: Area range-wise vs. Type-wise distribution of GL in Yamuna subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1221, "line_end": 1229, "token_count_estimate": 447, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "c4d5cfb7e13de133", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Yamuna subbasin has been shown in Table 45 and Figure 58. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 32 (88.89%) with total lake area of 63.41 ha (96.89%) and remaining 11.11% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1230, "line_end": 1236, "token_count_estimate": 126, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "e938c522fe8c6df1", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 45: Elevation range-wise distribution of GL in Yamuna subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 4 | 2.04 | 3.11 |\n| 3 | 4,001 - 5,000 | 32 | 63.41 | 96.89 |\n| 4 | > 5,000 | 0 | 0.00 | 0.00 |\n| **Total** | | **36** | **65.45** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 45: Elevation range-wise distribution of GL in Yamuna subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1237, "line_end": 1243, "token_count_estimate": 217, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "bfea17c7343b2b49", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1244, "line_end": 1247, "token_count_estimate": 42, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dab96e7a95f6b881", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 46 and Figure 59. It is noted that, 88.89% of glacial lakes (32) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 96.89%. It has been further noticed that, 9.38% of lakes ≥ 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 10 - 50 ha. However, no glacial lakes lie up to 3,000 m and above 5,000 m elevation range. All lakes lying in medium altitude range are < 5 ha, distributed equally in size ranges 0.25 - 0.5 ha and 0.5 - 1 ha (i.e. 2 each).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1249, "line_end": 1253, "token_count_estimate": 235, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9741556dfdfef8ba", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 46: Area range-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin\n\n| S. No. | Lake Area Range (ha) | up to 3,000 m - No. of lakes | up to 3,000 m - Total Lake Area (ha) | 3,001 - 4,000 m - No. of lakes | 3,001 - 4,000 m - Total Lake Area (ha) | 4,001 - 5,000 m - No. of lakes | 4,001 - 5,000 m - Total Lake Area (ha) | > 5,000 m - No. of lakes | > 5,000 m - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 2 | 0.64 | 12 | 4.10 | 0 | 0.00 | 14 | 4.74 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 2 | 1.40 | 9 | 6.88 | 0 | 0.00 | 11 | 8.28 |\n| 3 | 1 - 5 | 0 | 0.00 | 0 | 0.00 | 8 | 18.40 | 0 | 0.00 | 8 | 18.40 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 1 | 8.26 | 0 | 0.00 | 1 | 8.26 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 2 | 25.77 | 0 | 0.00 | 2 | 25.77 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| Total | | 0 | 0.00 | 4 | 2.04 | 32 | 63.41 | 0 | 0.00 | 36 | 65.45 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 46: Area range-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "up to 3,000 m - No. of lakes", "up to 3,000 m - Total Lake Area (ha)", "3,001 - 4,000 m - No. of lakes", "3,001 - 4,000 m - Total Lake Area (ha)", "4,001 - 5,000 m - No. of lakes", "4,001 - 5,000 m - Total Lake Area (ha)", "> 5,000 m - No. of lakes", "> 5,000 m - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1254, "line_end": 1262, "token_count_estimate": 548, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "de69632fdecffcb4", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 59: Area range-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin\n\n**Figure 59: Area range-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 59: Area range-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin", "line_start": 1264, "line_end": 1264, "token_count_estimate": 87, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "a3206bf4748d99d7", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1265, "line_end": 1272, "token_count_estimate": 48, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f00ae422218a3744", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 47 and Figure 60. The dominant lake type in the subbasin i.e. Other Glacial Erosion lake (50.00%) is predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Supra-glacial lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 18.75% and 12.50% respectively. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 61.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1274, "line_end": 1278, "token_count_estimate": 182, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b2a74fdc439b4947", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 47: Type-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 |\n| 3 | 4,001 - 5,000 | 1 | 0 | 0 | 6 | 4 | 0 | 3 | 18 | 0 | 32 |\n| 4 | > 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 1 | 0 | 0 | 6 | 4 | 0 | 3 | 18 | 4 | 36 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 47: Type-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1279, "line_end": 1285, "token_count_estimate": 429, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "a7c6357284549507", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 60: Type-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin\n\n**Figure 60: Type-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 60: Type-wise vs. Elevation range-wise distribution of GL in Yamuna subbasin", "line_start": 1287, "line_end": 1287, "token_count_estimate": 85, "basins": ["Ganga"], "subbasins": ["Yamuna"], "countries": [], "lake_ids": []}}
{"id": "b093a53ed84f4308", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1288, "line_end": 1300, "token_count_estimate": 62, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cea8cc48b92a0446", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.3 Inter Comparison of Subbasins\nType: text\n\nGlacial lakes in all 6 subbasins of Ganga River basin are compared for number of glacial lakes, total lake area, lake types and their elevation ranges in the following sections.\n\n**Subbasin-wise Distribution**\n\nTable 48 and Figure 62 shows the subbasin-wise distribution of number of glacial lakes and their water spread area for the Ganga River basin. Lakes are predominantly distributed in Kosi (51.77%) followed by Ghaghara subbasin (26.77%), occupying a total lake extent of 14,604.34 ha and 3,536.39 ha at 70.60% and 17.10% respectively in the entire basin. However, minimum glacial lakes are present in Yamuna subbasin (0.76%) followed by Sarda subbasin (1.17%), covering a total lake extent of 0.32% and 0.57% respectively.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.3 Inter Comparison of Subbasins", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.3 Inter Comparison of Subbasins"], "chunk_type": "text", "line_start": 1302, "line_end": 1310, "token_count_estimate": 227, "basins": ["Ganga"], "subbasins": ["Ghaghara", "Kosi", "Sarda", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "75ed7f3a1a072ea8", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.3 Inter Comparison of Subbasins\nType: table\nTable: Table 48: Subbasin-wise distribution of GL in Ganga River basin\n\n| S. No. | Subbasin | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | Gandak | 624 | 1,912.84 | 9.25 |\n| 2 | Ghaghara | 1,260 | 3,536.39 | 17.10 |\n| 3 | Kosi | 2,437 | 14,604.34 | 70.60 |\n| 4 | Sarda | 55 | 118.84 | 0.57 |\n| 5 | Upper Ganga | 295 | 447.26 | 2.16 |\n| 6 | Yamuna | 36 | 65.45 | 0.32 |\n| | **Total** | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.3 Inter Comparison of Subbasins", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.3 Inter Comparison of Subbasins"], "chunk_type": "table", "table_caption": "Table 48: Subbasin-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Subbasin", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1311, "line_end": 1319, "token_count_estimate": 276, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "eb9d2f887d6de3a9", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.3 Inter Comparison of Subbasins\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.3 Inter Comparison of Subbasins", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.3 Inter Comparison of Subbasins"], "chunk_type": "text", "line_start": 1320, "line_end": 1327, "token_count_estimate": 45, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d97e3ff8e2dbb347", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution\nType: text\n\nGlacial lakes have been distributed in all subbasins for all 6 classes of area ranges. Table 49 and Figure 63 shows subbasin-area range-wise distribution of glacial lakes for the Ganga River basin. All subbasins contain glacial lakes in all area ranges except Sarda, Upper Ganga, and Yamuna subbasins, where lakes are not present in the area range of ≥ 50 ha. Kosi is the subbasin which has majority of lakes ≥ 50 ha i.e. 93.10%.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Area range-wise Distribution"], "chunk_type": "text", "line_start": 1329, "line_end": 1333, "token_count_estimate": 157, "basins": ["Ganga"], "subbasins": ["Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "1c283f2bf3d20960", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution\nType: table\nTable: Table 49: Subbasin-wise vs. Area range-wise distribution of GL in Ganga River basin\n\n| S. No. | Subbasin | Lake Area Range (ha): 0.25 - 0.5 No. of Lakes | Lake Area Range (ha): 0.25 - 0.5 Total Lake Area (ha) | Lake Area Range (ha): 0.5 - 1 No. of Lakes | Lake Area Range (ha): 0.5 - 1 Total Lake Area (ha) | Lake Area Range (ha): 1 - 5 No. of Lakes | Lake Area Range (ha): 1 - 5 Total Lake Area (ha) | Lake Area Range (ha): 5 - 10 No. of Lakes | Lake Area Range (ha): 5 - 10 Total Lake Area (ha) | Lake Area Range (ha): 10 - 50 No. of Lakes | Lake Area Range (ha): 10 - 50 Total Lake Area (ha) | Lake Area Range (ha): ≥ 50 No. of Lakes | Lake Area Range (ha): ≥ 50 Total Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Gandak | 189 | 66.76 | 157 | 109.47 | 205 | 435.25 | 36 | 242.86 | 35 | 628.84 | 2 | 429.65 |\n| 2 | Ghaghara | 334 | 118.99 | 321 | 232.49 | 435 | 945.22 | 84 | 594.54 | 84 | 1,507.18 | 2 | 137.97 |\n| 3 | Kosi | 606 | 215.04 | 571 | 406.11 | 862 | 1,917.98 | 174 | 1,205.76 | 170 | 3,650.33 | 54 | 7,209.12 |\n| 4 | Sarda | 16 | 5.75 | 13 | 8.54 | 19 | 39.15 | 4 | 26.38 | 3 | 39.02 | 0 | 0.00 |\n| 5 | Upper Ganga | 120 | 41.01 | 84 | 59.14 | 70 | 152.95 | 16 | 107.08 | 5 | 87.07 | 0 | 0.00 |\n| 6 | Yamuna | 14 | 4.74 | 11 | 8.28 | 8 | 18.40 | 1 | 8.26 | 2 | 25.77 | 0 | 0.00 |\n| | **Total** | **1,279** | **452.29** | **1,157** | **824.03** | **1,599** | **3,508.95** | **315** | **2,184.88** | **299** | **5,938.21** | **58** | **7,776.74** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 49: Subbasin-wise vs. Area range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Subbasin", "Lake Area Range (ha): 0.25 - 0.5 No. of Lakes", "Lake Area Range (ha): 0.25 - 0.5 Total Lake Area (ha)", "Lake Area Range (ha): 0.5 - 1 No. of Lakes", "Lake Area Range (ha): 0.5 - 1 Total Lake Area (ha)", "Lake Area Range (ha): 1 - 5 No. of Lakes", "Lake Area Range (ha): 1 - 5 Total Lake Area (ha)", "Lake Area Range (ha): 5 - 10 No. of Lakes", "Lake Area Range (ha): 5 - 10 Total Lake Area (ha)", "Lake Area Range (ha): 10 - 50 No. of Lakes", "Lake Area Range (ha): 10 - 50 Total Lake Area (ha)", "Lake Area Range (ha): ≥ 50 No. of Lakes", "Lake Area Range (ha): ≥ 50 Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1334, "line_end": 1342, "token_count_estimate": 773, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "4684310a57a451ba", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution\nType: figure\nFigure: Figure 63: Subbasin-wise vs. Area range-wise distribution of GL in Ganga River basin\n\n**Figure 63: Subbasin-wise vs. Area range-wise distribution of GL in Ganga River basin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Area range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 63: Subbasin-wise vs. Area range-wise distribution of GL in Ganga River basin", "line_start": 1344, "line_end": 1344, "token_count_estimate": 88, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "49d2bccbb7f45eee", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Area range-wise Distribution"], "chunk_type": "text", "line_start": 1345, "line_end": 1351, "token_count_estimate": 49, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3e9416a67c00a0bc", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution\nType: text\n\nGlacial lake distribution by subbasin vs. type-wise is given in Table 50 and Figure 64. It has been observed that, in descending order of total lake count, 4 types of lakes viz., Other Glacial Erosion, Other Moraine Dammed, Supra-glacial, and End-moraine Dammed lakes are distributed in all subbasins. The dominant lake type i.e. Other Glacial Erosion lake is found predominantly in Kosi (56.54%), Ghaghara (31.48%), Gandak (7.17%), and Upper Ganga (3.04%) respectively. Gandak subbasin consists higher number of Lateral Moraine Dammed lakes (with ice) i.e. 66.67%. Lateral Moraine Dammed lakes are present in all subbasins except Yamuna.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Type-wise Distribution"], "chunk_type": "text", "line_start": 1353, "line_end": 1357, "token_count_estimate": 218, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "5868f2d03a10af0c", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution\nType: table\nTable: Table 50: Subbasin-wise vs. Type-wise distribution of GL in Ganga River basin\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Gandak | 37 | 18 | 6 | 312 | 101 | 1 | 4 | 125 | 20 | 624 |\n| 2 | Ghaghara | 61 | 27 | 3 | 485 | 73 | 0 | 27 | 549 | 35 | 1,260 |\n| 3 | Kosi | 138 | 39 | 0 | 829 | 335 | 0 | 65 | 986 | 45 | 2,437 |\n| 4 | Sarda | 5 | 5 | 0 | 21 | 6 | 0 | 4 | 13 | 1 | 55 |\n| 5 | Upper Ganga | 18 | 11 | 0 | 87 | 98 | 0 | 20 | 53 | 8 | 295 |\n| 6 | Yamuna | 1 | 0 | 0 | 6 | 4 | 0 | 3 | 18 | 4 | 36 |\n| | **Total** | **260** | **100** | **9** | **1,740** | **617** | **1** | **123** | **1,744** | **113** | **4,707** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 50: Subbasin-wise vs. Type-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1358, "line_end": 1366, "token_count_estimate": 554, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "db08726bc761fd7e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution\nType: figure\nFigure: Figure 64: Subbasin-wise vs. Type-wise distribution of GL in Ganga River basin\n\n**Figure 64: Subbasin-wise vs. Type-wise distribution of GL in Ganga River basin**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 64: Subbasin-wise vs. Type-wise distribution of GL in Ganga River basin", "line_start": 1368, "line_end": 1368, "token_count_estimate": 84, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5c4273ace6c64a4", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Type-wise Distribution"], "chunk_type": "text", "line_start": 1369, "line_end": 1372, "token_count_estimate": 46, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ee08d3e9dccd06db", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per subbasin vs. elevation range-wise, given in Table 51 and Figure 65. Majority of glacial lakes are situated in all subbasins in very high altitude range i.e. > 5,000 m except in Yamuna subbasin. After that, majority of glacial lakes in all subbasins are located in high and medium altitude range i.e. 4,001 - 5,000 m and 3,001 - 4,000 m. Only one lake is located in Gandak subbasin in the elevation range up to 3,000 m.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1374, "line_end": 1378, "token_count_estimate": 169, "basins": ["Ganga"], "subbasins": ["Gandak", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "7b23f4cbb3b3dca6", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Elevation range-wise Distribution\nType: table\nTable: Table 51: Subbasin-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Subbasin | Elevation Range (m) - up to 3,000 - No. of Lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of Lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of Lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of Lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Gandak | 1 | 9.87 | 22 | 68.27 | 220 | 1,060.56 | 381 | 774.14 |\n| 2 | Ghaghara | 0 | 0.00 | 8 | 58.91 | 563 | 1,842.89 | 689 | 1,634.59 |\n| 3 | Kosi | 0 | 0.00 | 21 | 90.74 | 856 | 4,049.65 | 1,560 | 10,463.95 |\n| 4 | Sarda | 0 | 0.00 | 3 | 0.93 | 36 | 95.29 | 16 | 22.63 |\n| 5 | Upper Ganga | 0 | 0.00 | 4 | 2.68 | 148 | 263.09 | 143 | 181.49 |\n| 6 | Yamuna | 0 | 0.00 | 4 | 2.04 | 32 | 63.42 | 0 | 0.00 |\n| | **Total** | **1** | **9.87** | **62** | **223.57** | **1,855** | **7,374.90** | **2,789** | **13,076.80** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 51: Subbasin-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Subbasin", "Elevation Range (m) - up to 3,000 - No. of Lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of Lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of Lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of Lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1379, "line_end": 1387, "token_count_estimate": 575, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "bd0f236644cbc475", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Subbasin-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Subbasin-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1388, "line_end": 1396, "token_count_estimate": 50, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2efa32a0bc5e3acf", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.4 India Level Statistics\nType: text\n\nGanga River basin covers part of India and transboundary region, where in India it is covering a total area of 60,260 Km² i.e. 24.38% of the basin area. In India, basin area has been spread in three states viz., Himachal Pradesh, Uttarakhand, and West Bengal. Both states of Himachal Pradesh and Uttarakhand covers a total of 99.17% of the basin area lies within India and remaining 0.83% in West Bengal which does not contain any glacial lake. Amongst all 11 subbasins, only 4 subbasins viz., Yamuna, Upper Ganga, Ramganga, and Sarda (partly), has their source of origin and located within in the Indian region. Whereas, Ramganga subbasin does not contain any glacial lake. Remaining subbasins located entirely in transboundary region. A total of 369 glacial lakes lies within Indian region, covering a total area of 603.78 ha i.e. 0.01% of the total area of the Ganga River basin.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.4 India Level Statistics", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.4 India Level Statistics"], "chunk_type": "text", "line_start": 1398, "line_end": 1400, "token_count_estimate": 270, "basins": ["Ganga"], "subbasins": ["Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "b02b2d282451205e", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nIn Indian region, glacial lakes have been distributed in all 5 classes of area ranges except ≥ 50 ha. Table 52 and Figure 66 shows the area range-wise distribution of glacial lakes for the Indian region. About 339 (91.87%) lakes are with < 5 ha lake area contributing to 52.36% of total lake area. The remaining lakes with ≥ 5 ha in size are only 30 (8.13%) but contributing to 47.64% of total lake area in the region.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1402, "line_end": 1406, "token_count_estimate": 139, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4cb7c0b74bab4b0a", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 52: Area range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 145 | 49.60 | 8.21 |\n| 2 | 0.5 - 1 | 104 | 73.42 | 12.16 |\n| 3 | 1 - 5 | 90 | 193.14 | 31.99 |\n| 4 | 5 - 10 | 20 | 135.76 | 22.49 |\n| 5 | 10 - 50 | 10 | 151.86 | 25.15 |\n| 6 | ≥ 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **369** | **603.78** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 52: Area range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1407, "line_end": 1415, "token_count_estimate": 252, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a100fea938decadd", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Indian region has been shown in Table 55 and Figure 69. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 358 (97.02%) with total lake area of 598.15 ha (99.07%) and remaining 2.98% glacial lakes are below 4,001 m elevation.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1416, "line_end": 1426, "token_count_estimate": 140, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "52d684839df0d0e2", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 55: Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 11 | 5.63 | 0.93 |\n| 3 | 4,001 - 5,000 | 202 | 398.69 | 66.03 |\n| 4 | > 5,000 | 156 | 199.46 | 33.04 |\n| | **Total** | **369** | **603.78** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 55: Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1427, "line_end": 1433, "token_count_estimate": 217, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "379fd00935197a4f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: figure\nFigure: Figure 69: Elevation range-wise distribution of GL in India\n\nFigure 69: Elevation range-wise distribution of GL in India", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 69: Elevation range-wise distribution of GL in India", "line_start": 1435, "line_end": 1435, "token_count_estimate": 60, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8d67ab2cfff59a31", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 56 and Figure 70. It is noted that, 54.74% of glacial lakes (202) are situated in high altitude range i.e. 4,001 - 5,000 m, which also constitutes majority of total lake area within that range i.e. 66.03%. However, 11 glacial lakes lies below 4,001 m, has all of its lakes < 5 ha in size. 87.62% of lakes lying in high altitude range are < 5 ha, predominantly of size ranging 0.25 - 0.5 ha (i.e. 80), followed by lakes of size 0.5 - 1 ha (i.e. 55). It has been further noticed that, 12.38% of lakes ≥ 5 ha are lying within in the high altitude range only, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1436, "line_end": 1446, "token_count_estimate": 271, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b1b7268033b4eea4", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 56: Area range-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 No. of lakes | Elevation Range (m): up to 3,000 Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 No. of lakes | Elevation Range (m): 3,001 - 4,000 Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 No. of lakes | Elevation Range (m): 4,001 - 5,000 Total Lake Area (ha) | Elevation Range (m): > 5,000 No. of lakes | Elevation Range (m): > 5,000 Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 7 | 2.39 | 80 | 27.59 | 58 | 19.61 | 145 | 49.60 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 3 | 2.14 | 55 | 38.83 | 46 | 32.45 | 104 | 73.42 |\n| 3 | 1 - 5 | 0 | 0.00 | 1 | 1.11 | 42 | 85.54 | 47 | 106.49 | 90 | 193.14 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 16 | 111.88 | 4 | 23.89 | 20 | 135.76 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 9 | 134.85 | 1 | 17.02 | 10 | 151.86 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **11** | **5.64** | **202** | **398.69** | **156** | **199.46** | **369** | **603.78** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 56: Area range-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 No. of lakes", "Elevation Range (m): up to 3,000 Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 No. of lakes", "Elevation Range (m): 3,001 - 4,000 Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 No. of lakes", "Elevation Range (m): 4,001 - 5,000 Total Lake Area (ha)", "Elevation Range (m): > 5,000 No. of lakes", "Elevation Range (m): > 5,000 Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1447, "line_end": 1455, "token_count_estimate": 603, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e037b4f3fba94682", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: figure\nFigure: Figure 70: Area range-wise vs. Elevation range-wise distribution of GL in India\n\nFigure 70: Area range-wise vs. Elevation range-wise distribution of GL in India", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 70: Area range-wise vs. Elevation range-wise distribution of GL in India", "line_start": 1457, "line_end": 1457, "token_count_estimate": 72, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8a9626030f2515c7", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 57 and Figure 71. The dominant lake type in the region i.e. Other Moraine Dammed lake (29.81%) is predominantly located in the elevation range of > 5,000 m (69.09%). The other dominant lake type, namely, Supra-glacial and Other Glacial Erosion lakes are distributed predominantly in high altitude range i.e. 4,001 - 5,000 m elevation range, i.e. 83.02% and 63.29%. Majority i.e. 98.62% of all types of Moraine-dammed lakes lies above 4,000 m.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1458, "line_end": 1469, "token_count_estimate": 217, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06c05c08ac6838a5", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 57: Type-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 8 | 11 |\n| 3 | 4,001 - 5,000 | 9 | 7 | 0 | 32 | 88 | 0 | 15 | 50 | 1 | 202 |\n| 4 | > 5,000 | 15 | 4 | 0 | 76 | 18 | 0 | 11 | 28 | 4 | 156 |\n| | **Total** | **24** | **11** | **0** | **110** | **106** | **0** | **26** | **79** | **13** | **369** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 57: Type-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1470, "line_end": 1476, "token_count_estimate": 443, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "c46e6c48eb24cb6d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 81\n\n82 | National Remote Sensing Centre, ISRO, Hyderabad\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**5.5 Indian State’s Statistics**\n\nGlacial lakes located in 2 states of Indian region are compared for lake count, total lake area, lake types and their elevation ranges in the following sections.\n\n**State-wise Distribution**\n\nTable 58 and Figure 72 shows the State-wise distribution of glacial lakes of Indian region. Lakes are predominantly distributed in Uttarakhand (UK) with 345 (93.50%) occupying a total lake extent of 547.65 ha at 90.70% in the region. Himachal Pradesh contains only 24 glacial lakes (6.50%) extend over an area of 56.13 ha (9.30%).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1477, "line_end": 1495, "token_count_estimate": 216, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1d1d5b477f636d21", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 58: State-wise distribution of GL in India\n\n| S. No. | Code | State | No. of Lakes | Total Lake Area: (ha) | Total Lake Area: (%) |\n|---|---|---|---|---|---|\n| 1 | HP | Himachal Pradesh | 24 | 56.13 | 9.30 |\n| 2 | UK | Uttarakhand | 345 | 547.65 | 90.70 |\n| | | **Total** | **369** | **603.78** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 58: State-wise distribution of GL in India", "columns": ["S. No.", "Code", "State", "No. of Lakes", "Total Lake Area: (ha)", "Total Lake Area: (%)"], "table_row_start": 1, "table_row_end": 3, "line_start": 1496, "line_end": 1500, "token_count_estimate": 176, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5c191ab394e130a0", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**\n\n**State-Area range-wise Distribution**\n\nGlacial lakes have been distributed in both states for all 5 classes of area ranges except ≥ 50 ha. Table 59 and Figure 73 shows the State vs. area range-wise distribution of glacial lakes for the Indian region. It has been observed that, glacial lakes in Uttarakhand (UK) are predominantly < 5 ha (92.17%), majority of which are within 0.25 - 0.5 ha in size i.e. 40.29%, followed by lakes of 0.5 - 1 ha in size i.e. 27.83%. Not only in Uttarakhand (UK), but maximum number of lakes < 5 ha are (87.50%) located in Himachal Pradesh also.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1501, "line_end": 1510, "token_count_estimate": 209, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "57419508f305ba18", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 59: State-wise vs. Area range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | State: Himachal Pradesh - No. of lakes | State: Himachal Pradesh - Total Lake Area (ha) | State: Uttarakhand - No. of lakes | State: Uttarakhand - Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 6 | 1.93 | 139 | 47.66 | 145 | 49.60 |\n| 2 | 0.5 - 1 | 8 | 6.22 | 96 | 67.20 | 104 | 73.42 |\n| 3 | 1 - 5 | 7 | 13.95 | 83 | 179.19 | 90 | 193.14 |\n| 4 | 5 - 10 | 1 | 8.26 | 19 | 127.50 | 20 | 135.76 |\n| 5 | 10 - 50 | 2 | 25.77 | 8 | 126.10 | 10 | 151.86 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| **Total** | | **24** | **56.13** | **345** | **547.65** | **369** | **603.78** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 59: State-wise vs. Area range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "State: Himachal Pradesh - No. of lakes", "State: Himachal Pradesh - Total Lake Area (ha)", "State: Uttarakhand - No. of lakes", "State: Uttarakhand - Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1511, "line_end": 1519, "token_count_estimate": 398, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2b1c4991eb3226ac", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\n***\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**\n\n**State-Type-wise Distribution**\n\nGlacial lake distribution by State vs. type-wise is given in Table 60 and Figure 74. It has been observed that, Uttarakhand contains maximum number of all types of glacial lake in comparison with Himachal Pradesh, with majority of Supra-glacial i.e. 30.72%, followed by Other Moraine Dammed lakes i.e. 29.85%. All types of Moraine-dammed lakes in Uttarakhand are 138 with 40.00%.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1520, "line_end": 1530, "token_count_estimate": 159, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1025a49320c2e2b5", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 60: State-wise vs. Type-wise distribution of GL in India\n\n| S. No. | State | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Himachal Pradesh | 0 | 0 | 0 | 7 | 0 | 0 | 3 | 13 | 1 | 24 |\n| 2 | Uttarakhand | 24 | 11 | 0 | 103 | 106 | 0 | 23 | 66 | 12 | 345 |\n| **Total** | | **24** | **11** | **0** | **110** | **106** | **0** | **26** | **79** | **13** | **369** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 60: State-wise vs. Type-wise distribution of GL in India", "columns": ["S. No.", "State", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 1531, "line_end": 1535, "token_count_estimate": 351, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cc3a568de8db184d", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1536, "line_end": 1539, "token_count_estimate": 43, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "89dad21d59fd96c5", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lake in the districts of Himachal Pradesh is given in Table 63 and Figure 77. It has been observed that, Other Glacial Erosion lakes are maximum with 13 (54.17%) in the state, followed by Other Moraine Dammed lakes with 7 (29.17%). Shimla district contains maximum number of glacial lakes in comparison with Kinnaur district, with majority of Other Glacial Erosion lakes (62.50%).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1541, "line_end": 1545, "token_count_estimate": 135, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ed9646621a6e4eb", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 63: Type-wise distribution of GL in Districts of Himachal Pradesh\n\n| S.No. | District | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Kinnaur | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 3 | 1 | 8 |\n| 2 | Shimla | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 10 | 0 | 16 |\n| **Total** | | **0** | **0** | **0** | **7** | **0** | **0** | **3** | **13** | **1** | **24** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 63: Type-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S.No.", "District", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 1546, "line_end": 1550, "token_count_estimate": 348, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2870cdf1dd13c1ae", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 77: Type-wise distribution of GL in Districts of Himachal Pradesh\n\nFigure 77: Type-wise distribution of GL in Districts of Himachal Pradesh", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 77: Type-wise distribution of GL in Districts of Himachal Pradesh", "line_start": 1552, "line_end": 1552, "token_count_estimate": 67, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b174ff50d84543f7", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1553, "line_end": 1560, "token_count_estimate": 43, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ce45ad030e0bd57", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Himachal Pradesh has been shown in Table 64 and Figure 78. All glacial lakes in the districts of Himachal Pradesh are situated above 4,000 m elevation range i.e. 24 with total lake area of 56.13 ha. Shimla district contains all glacial lakes in the elevation range of 4,001 m - 5,000 m in comparison with Kinnaur district. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 79.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1562, "line_end": 1566, "token_count_estimate": 158, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e4b228aecd617783", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 64: Elevation range-wise distribution of GL in Districts of Himachal Pradesh\n\n| S.No. | Elevation Range (m) | Districts of Himachal Pradesh: Kinnaur: No. of Lakes | Districts of Himachal Pradesh: Kinnaur: Total Lake Area (ha) | Districts of Himachal Pradesh: Shimla: No. of Lakes | Districts of Himachal Pradesh: Shimla: Total Lake Area (ha) | Total: No. of Lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 2 | 3,001 - 4,000 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 3 | 4,001 - 5,000 | 3 | 3.24 | 16 | 49.46 | 19 | 52.70 |\n| 4 | > 5,000 | 5 | 3.43 | 0 | 0.00 | 5 | 3.43 |\n| **Total** | | **8** | **6.67** | **16** | **49.46** | **24** | **56.13** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 64: Elevation range-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S.No.", "Elevation Range (m)", "Districts of Himachal Pradesh: Kinnaur: No. of Lakes", "Districts of Himachal Pradesh: Kinnaur: Total Lake Area (ha)", "Districts of Himachal Pradesh: Shimla: No. of Lakes", "Districts of Himachal Pradesh: Shimla: Total Lake Area (ha)", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1567, "line_end": 1573, "token_count_estimate": 356, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8cbe06a61cd740c", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 78: Elevation range-wise distribution of GL in Districts of Himachal Pradesh\n\nFigure 78: Elevation range-wise distribution of GL in Districts of Himachal Pradesh", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 78: Elevation range-wise distribution of GL in Districts of Himachal Pradesh", "line_start": 1575, "line_end": 1575, "token_count_estimate": 73, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36102c9e3d46ac46", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.5.2 District Level Statistics of Uttarakhand\nType: text\n\nUttarakhand is the largest state covering area of Ganga River basin, contains glacial lakes in six districts viz., Bageshwar, Chamoli, Pithoragarh, Rudraprayag, Tehri Garhwal, and Uttarkashi. Amongst which, Chamoli has the majority of glacial lakes covering 46.14% of the total lake area in the state.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > 5.5.2 District Level Statistics of Uttarakhand", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "5.5.2 District Level Statistics of Uttarakhand"], "chunk_type": "text", "line_start": 1582, "line_end": 1584, "token_count_estimate": 118, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "87b5c66a6366c526", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nGlacial lakes has been distributed in 6 districts of Uttarakhand for 5 classes of area ranges except ≥ 50 ha area range, and area range-wise distribution for those has been shown in Table 65 and Figure 80. Glacial lakes in Chamoli district are found to be the maximum with 192 (55.65%) occupying a total lake extent of 252.71 ha at 46.14%. About 318 (92.17%) lakes are with < 5 ha lake area contributing to 53.69% of total lake area in the state. Whereas, remaining lakes in the state with ≥ 5 ha in size are only 7.83%, predominantly of 5 - 10 ha in size.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1586, "line_end": 1590, "token_count_estimate": 182, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "05f0e50798c8cd8f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: table\nTable: Table 65: Area range-wise distribution of GL in Districts of Uttarakhand\n\n| S. No. | District | 0.25 - 0.5: No. of Lakes | 0.25 - 0.5: Total Lake Area (ha) | 0.5 - 1: No. of Lakes | 0.5 - 1: Total Lake Area (ha) | 1 - 5: No. of Lakes | 1 - 5: Total Lake Area (ha) | 5 - 10: No. of Lakes | 5 - 10: Total Lake Area (ha) | 10 - 50: No. of Lakes | 10 - 50: Total Lake Area (ha) | Total: No. of Lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Bageshwar | 3 | 0.99 | 3 | 2.18 | 2 | 6.02 | 0 | 0.00 | 0 | 0.00 | 8 | 9.19 |\n| 2 | Chamoli | 92 | 31.17 | 53 | 37.13 | 36 | 75.72 | 7 | 47.18 | 4 | 61.51 | 192 | 252.71 |\n| 3 | Pithoragarh | 12 | 4.19 | 10 | 6.51 | 13 | 25.08 | 3 | 20.42 | 3 | 39.02 | 41 | 95.22 |\n| 4 | Rudraprayag | 2 | 0.68 | 1 | 0.55 | 6 | 14.68 | 2 | 15.15 | 0 | 0.00 | 11 | 31.06 |\n| 5 | Tehri Garhwal | 0 | 0.00 | 4 | 3.18 | 3 | 5.34 | 2 | 12.91 | 1 | 25.56 | 10 | 46.99 |\n| 6 | Uttarkashi | 30 | 10.63 | 25 | 17.65 | 23 | 52.36 | 5 | 31.84 | 0 | 0.00 | 83 | 112.48 |\n| | **Total** | **139** | **47.66** | **96** | **67.20** | **83** | **179.20** | **19** | **127.50** | **8** | **126.09** | **345** | **547.65** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 65: Area range-wise distribution of GL in Districts of Uttarakhand", "columns": ["S. No.", "District", "0.25 - 0.5: No. of Lakes", "0.25 - 0.5: Total Lake Area (ha)", "0.5 - 1: No. of Lakes", "0.5 - 1: Total Lake Area (ha)", "1 - 5: No. of Lakes", "1 - 5: Total Lake Area (ha)", "5 - 10: No. of Lakes", "5 - 10: Total Lake Area (ha)", "10 - 50: No. of Lakes", "10 - 50: Total Lake Area (ha)", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1591, "line_end": 1599, "token_count_estimate": 628, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc566e660e6f2e2f", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.1 Ganga Basin Level Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Ganga Basin Level Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1600, "line_end": 1603, "token_count_estimate": 43, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc2db9c1a7b8fbdf", "text": "Document: MESSAGE\nSection: 5. RESULTS > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lake in the districts of Uttarakhand is given in Table 66 and Figure 81. It has been observed that, only 7 types of glacial lake are distributed in the state, where Supra-glacial lakes are found to be the maximum with 106 (30.72%) in the state, followed by Other Moraine Dammed lakes with 103 (29.86%). Chamoli district contains maximum number of glacial lakes in comparison with other districts in the state, with majority of Supra-glacial lakes (38.02%), followed by Other Moraine Dammed lakes i.e. 32.29%.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Type-wise Distribution", "section_headings": ["5. RESULTS", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1605, "line_end": 1609, "token_count_estimate": 159, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62257c58240e0bdd", "text": "Document: MESSAGE\nSection: 5. RESULTS > Type-wise Distribution\nType: table\nTable: Table 66: Type-wise distribution of GL in Districts of Uttarakhand\n\n| S. No. | District | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Bageshwar | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 2 | 1 | 8 |\n| 2 | Chamoli | 8 | 4 | 0 | 62 | 73 | 0 | 11 | 30 | 4 | 192 |\n| 3 | Pithoragarh | 5 | 0 | 0 | 20 | 4 | 0 | 3 | 8 | 1 | 41 |\n| 4 | Rudraprayag | 0 | 0 | 0 | 0 | 2 | 0 | 4 | 5 | 0 | 11 |\n| 5 | Tehri Garhwal | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 5 | 1 | 10 |\n| 6 | Uttarkashi | 10 | 6 | 0 | 20 | 23 | 0 | 3 | 16 | 5 | 83 |\n| | **Total** | **24** | **11** | **0** | **103** | **106** | **0** | **23** | **66** | **12** | **345** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Type-wise Distribution", "section_headings": ["5. RESULTS", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 66: Type-wise distribution of GL in Districts of Uttarakhand", "columns": ["S. No.", "District", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1610, "line_end": 1618, "token_count_estimate": 428, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4f0c3c912694ce43", "text": "Document: MESSAGE\nSection: 5. RESULTS > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Type-wise Distribution", "section_headings": ["5. RESULTS", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1619, "line_end": 1625, "token_count_estimate": 34, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d60cfe615a85b560", "text": "Document: MESSAGE\nSection: 5. RESULTS > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Uttarakhand has been shown in Table 67 and Figure 82. Majority of glacial lakes (53.04%) are situated in high altitude i.e. 4,001 - 5,000 m elevation range with total lake area of 345.99 ha (63.18%). This is followed by glacial lakes in very high altitude elevation range with 43.77%. Chamoli district contains maximum number of glacial lakes above 4,000 m elevation in comparison with any other district in the state, with majority of them falling in very high altitude range i.e. > 5,000 m. Elevation range-type-wise spatial distribution of glacial lakes has been represented in Figure 83.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1627, "line_end": 1631, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5bec314929a204c", "text": "Document: MESSAGE\nSection: 5. RESULTS > Elevation range-wise Distribution\nType: table\nTable: Table 67: Elevation range-wise distribution of GL in Districts of Uttarakhand\n\n| S. No. | District | up to 3,000: No. of Lakes | up to 3,000: Total Lake Area (ha) | 3,001 - 4,000: No. of Lakes | 3,001 - 4,000: Total Lake Area (ha) | 4,001 - 5,000: No. of Lakes | 4,001 - 5,000: Total Lake Area (ha) | > 5,000: No. of Lakes | > 5,000: Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Bageshwar | 0 | 0.00 | 1 | 0.41 | 7 | 8.78 | 0 | 0.00 |\n| 2 | Chamoli | 0 | 0.00 | 1 | 0.74 | 85 | 121.99 | 106 | 129.99 |\n| 3 | Pithoragarh | 0 | 0.00 | 3 | 0.93 | 22 | 72.18 | 16 | 22.12 |\n| 4 | Rudraprayag | 0 | 0.00 | 0 | 0.00 | 11 | 31.05 | 0 | 0.00 |\n| 5 | Tehri Garhwal | 0 | 0.00 | 1 | 1.11 | 9 | 45.89 | 0 | 0.00 |\n| 6 | Uttarkashi | 0 | 0.00 | 5 | 2.45 | 49 | 66.10 | 29 | 43.92 |\n| | **Total** | **0** | **0.00** | **11** | **5.64** | **183** | **345.99** | **151** | **196.03** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 67: Elevation range-wise distribution of GL in Districts of Uttarakhand", "columns": ["S. No.", "District", "up to 3,000: No. of Lakes", "up to 3,000: Total Lake Area (ha)", "3,001 - 4,000: No. of Lakes", "3,001 - 4,000: Total Lake Area (ha)", "4,001 - 5,000: No. of Lakes", "4,001 - 5,000: Total Lake Area (ha)", "> 5,000: No. of Lakes", "> 5,000: Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1632, "line_end": 1640, "token_count_estimate": 473, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "55c6236681e0b80b", "text": "Document: MESSAGE\nSection: 5. RESULTS > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1641, "line_end": 1646, "token_count_estimate": 35, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c9dcbe7fe85dab23", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics\nType: text\n\nApart from India, Ganga River basin also covers part of transboundary region which has a total area of 1,86,848 Km² i.e. 75.62% of the total river basin area. This transboundary region covers majority part of it in Nepal and little in China (Tibetan region). Elevation in the transboundary region varies from minimum 45 m to maximum 8,848 m amsl. This region has upper part of Ghaghara, Rapti, Bhagmati, Kamla, Gandak, Lower Ganga, and Kosi subbasins, and part of Sarda subbasin. Subbasin region of Rapti, Bhagmati, Kamla, and Lower Ganga does not contain any glacial lake. A total of 4,338 glacial lakes lies within transboundary region, covering a total area of 20,081.34 ha i.e. 0.12% of the total area of the Ganga River basin under transboundary region.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1648, "line_end": 1650, "token_count_estimate": 236, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda"], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "970916ecd8c18cf6", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\nIn Transboundary region, glacial lakes have been distributed in all 6 classes of area ranges. Table 68 and Figure 84 shows the area range-wise distribution of glacial lakes for the transboundary region. About 3,696 (85.20%) lakes are with < 5 ha lake area contributing to 22.26% of total lake area. The remaining lakes with ≥ 5 ha in size are only 642 (14.80%) but contributing to 77.74% of total lake area in the region.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1652, "line_end": 1656, "token_count_estimate": 140, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "150347b9ad4ae4ec", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 68: Area range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1,134 | 402.70 | 2.01 |\n| 2 | 0.5 - 1 | 1,053 | 750.62 | 3.74 |\n| 3 | 1 - 5 | 1,509 | 3,315.81 | 16.51 |\n| 4 | 5 - 10 | 295 | 2,049.12 | 10.20 |\n| 5 | 10 - 50 | 289 | 5,786.35 | 28.81 |\n| 6 | ≥ 50 | 58 | 7,776.74 | 38.73 |\n| | **Total** | **4,338** | **20,081.34** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 68: Area range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1657, "line_end": 1665, "token_count_estimate": 273, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1572f586adc03482", "text": "Document: MESSAGE\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1666, "line_end": 1669, "token_count_estimate": 43, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0da8c121a4d63b63", "text": "Document: MESSAGE\nSection: 5. RESULTS > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lake in the transboundary region is given in Table 69 and Figure 85. All types of glacial lakes are present in the transboundary region, where Other Glacial Erosion lake is found to be the maximum with 1,665 (38.38%) occupying a total lake extent of 4,507.80 ha at 22.45% in the region. After that, Other Moraine Dammed and Supra-glacial lakes are in majority with 1,630 (37.57%) and 511 (11.78%) and extend over a total area of 4,357.83 ha (21.70%) and 487.57 ha (2.43%) respectively.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Type-wise Distribution", "section_headings": ["5. RESULTS", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1671, "line_end": 1675, "token_count_estimate": 163, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "165e6db4880131fc", "text": "Document: MESSAGE\nSection: 5. RESULTS > Type-wise Distribution\nType: table\nTable: Table 69: Type-wise distribution of GL in Transboundary region\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 236 | 8,444.57 | 42.05 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 89 | 547.70 | 2.73 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 9 | 5.48 | 0.03 |\n| 4 | M(o) | Other Moraine Dammed Lake | 1,630 | 4,357.83 | 21.70 |\n| 5 | I(s) | Supra-glacial Lake | 511 | 487.57 | 2.43 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 1 | 2.45 | 0.01 |\n| 7 | E(c) | Cirque Erosion Lake | 97 | 863.59 | 4.30 |\n| 8 | E(o) | Other Glacial Erosion Lake | 1,665 | 4,507.80 | 22.45 |\n| 9 | O | Other Glacial Lake | 100 | 864.35 | 4.30 |\n| | | **Total** | **4,338** | **20,081.34** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Type-wise Distribution", "section_headings": ["5. RESULTS", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 69: Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 1676, "line_end": 1687, "token_count_estimate": 416, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "67415885a0d1198a", "text": "Document: MESSAGE\nSection: 5. RESULTS > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Type-wise Distribution", "section_headings": ["5. RESULTS", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1688, "line_end": 1695, "token_count_estimate": 34, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74e5cfd4d541c277", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 70 and Figure 86. The lakes with < 5 ha in size (85.20%) are dominant with Other Glacial Erosion (40.15%) and Other Moraine Dammed lakes (39.48%). Lakes with ≥ 5 ha (14.80%) are also dominated by Other Glacial Erosion lakes (28.19%). All types of Moraine-dammed lakes, which constitute about 45.27%, are majorly with < 5 ha in water spread.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1697, "line_end": 1701, "token_count_estimate": 145, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aba494dd0f027065", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: table\nTable: Table 70: Area range-wise vs. Type-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 4 | 21 | 4 | 425 | 281 | 0 | 3 | 374 | 22 | 1,134 |\n| 2 | 0.5 - 1 | 11 | 19 | 4 | 443 | 141 | 0 | 0 | 410 | 25 | 1,053 |\n| 3 | 1 - 5 | 45 | 31 | 1 | 591 | 78 | 1 | 38 | 700 | 24 | 1,509 |\n| 4 | 5 - 10 | 40 | 5 | 0 | 100 | 5 | 0 | 26 | 112 | 7 | 295 |\n| 5 | 10 - 50 | 94 | 9 | 0 | 68 | 6 | 0 | 30 | 64 | 18 | 289 |\n| 6 | ≥ 50 | 42 | 4 | 0 | 3 | 0 | 0 | 0 | 5 | 4 | 58 |\n| | **Total** | **236** | **89** | **9** | **1,630** | **511** | **1** | **97** | **1,665** | **100** | **4,338** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 70: Area range-wise vs. Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1702, "line_end": 1710, "token_count_estimate": 456, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "77061557a6a648d5", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the transboundary region has been shown in Table 71 and Figure 87. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 4,286 (98.80%) with total lake area of 19,853.55 ha, contributing 98.87% of lake area.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1711, "line_end": 1723, "token_count_estimate": 125, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c89f8fc99bf6987", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: table\nTable: Table 71: Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 1 | 9.87 | 0.05 |\n| 2 | 3,001 - 4,000 | 51 | 217.92 | 1.08 |\n| 3 | 4,001 - 5,000 | 1,653 | 6,976.20 | 34.74 |\n| 4 | > 5,000 | 2,633 | 12,877.35 | 64.13 |\n| | **Total** | **4,338** | **20,081.34** | **100.00** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 71: Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1724, "line_end": 1730, "token_count_estimate": 222, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "66e01b13728f8043", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 72 and Figure 88. It is noted that, 60.70% of glacial lakes (2,633) are situated in very high altitude range i.e. > 5,000 m, which also constitutes majority of total lake area within that range i.e. 64.13%. However, 52 glacial lakes lies below 4,001 m, has 75.00% of its lakes < 5 ha in size. 85.95% of lakes lying in very high altitude range are < 5 ha, majorly of size ranging 1 - 5 ha (i.e. 881), followed by lakes of size 0.25 - 0.5 ha (i.e. 714). It has been further noticed that, 57.63% of lakes ≥ 5 ha are lying within in the very high altitude range, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1731, "line_end": 1745, "token_count_estimate": 264, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d3125a7a1678abc", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: table\nTable: Table 72: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 (No. of lakes) | Elevation Range (m): up to 3,000 (Total Lake Area ha) | Elevation Range (m): 3,001 - 4,000 (No. of lakes) | Elevation Range (m): 3,001 - 4,000 (Total Lake Area ha) | Elevation Range (m): 4,001 - 5,000 (No. of lakes) | Elevation Range (m): 4,001 - 5,000 (Total Lake Area ha) | Elevation Range (m): > 5,000 (No. of lakes) | Elevation Range (m): > 5,000 (Total Lake Area ha) | Total (No. of lakes) | Total (Lake Area ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 14 | 4.51 | 406 | 145.73 | 714 | 252.46 | 1,134 | 402.70 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 14 | 10.30 | 371 | 266.39 | 668 | 473.93 | 1,053 | 750.62 |\n| 3 | 1 - 5 | 0 | 0.00 | 11 | 32.06 | 617 | 1,380.02 | 881 | 1,903.73 | 1,509 | 3,315.81 |\n| 4 | 5 - 10 | 1 | 9.87 | 5 | 34.15 | 121 | 855.12 | 168 | 1,149.98 | 295 | 2,049.12 |\n| 5 | 10 - 50 | 0 | 0.00 | 7 | 136.90 | 119 | 2,257.52 | 163 | 3,391.93 | 289 | 5,786.35 |\n| 6 | ≥ 50 | 0 | 0.00 | 0 | 0.00 | 19 | 2,071.42 | 39 | 5,705.32 | 58 | 7,776.74 |\n| | **Total** | **1** | **9.87** | **51** | **217.92** | **1,653** | **6,976.20** | **2,633** | **12,877.35** | **4,338** | **20,081.34** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 72: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 (No. of lakes)", "Elevation Range (m): up to 3,000 (Total Lake Area ha)", "Elevation Range (m): 3,001 - 4,000 (No. of lakes)", "Elevation Range (m): 3,001 - 4,000 (Total Lake Area ha)", "Elevation Range (m): 4,001 - 5,000 (No. of lakes)", "Elevation Range (m): 4,001 - 5,000 (Total Lake Area ha)", "Elevation Range (m): > 5,000 (No. of lakes)", "Elevation Range (m): > 5,000 (Total Lake Area ha)", "Total (No. of lakes)", "Total (Lake Area ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1746, "line_end": 1754, "token_count_estimate": 655, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "794653847973acc6", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 73 and Figure 89. The dominant lake type in the subbasin i.e. Other Glacial Erosion lake (38.38%) is predominantly located in the elevation range 4,001 - 5,000 m (52.03%). The other dominant lake type, namely, Other Moraine Dammed and Supra-glacial lakes are also majorly distributed in very high altitude range > 5,000 m elevation range, i.e. 48.99% and 11.36%. Majority i.e. 76.58% of all types of Moraine-dammed lake lies in > 5,000 m.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1755, "line_end": 1764, "token_count_estimate": 207, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7ca72712cd4122b3", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: table\nTable: Table 73: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 3,001 - 4,000 | 3 | 1 | 0 | 6 | 14 | 0 | 1 | 22 | 4 | 51 |\n| 3 | 4,001 - 5,000 | 68 | 47 | 1 | 334 | 198 | 0 | 78 | 860 | 67 | 1,653 |\n| 4 | > 5,000 | 165 | 41 | 8 | 1,290 | 299 | 1 | 18 | 782 | 29 | 2,633 |\n| | **Total** | **236** | **89** | **9** | **1,630** | **511** | **1** | **97** | **1,665** | **100** | **4,338** |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 73: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1765, "line_end": 1771, "token_count_estimate": 403, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71c5634e11ccf895", "text": "Document: MESSAGE\nSection: 5. RESULTS > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "5. RESULTS > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1772, "line_end": 1778, "token_count_estimate": 36, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "94007e7e07dad24b", "text": "Document: MESSAGE\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 90 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Ganga River basin. A total of 42 toposheets covered the entire study area, of which 23 toposheets contain glacial lakes.\n\nFigure 90 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Ganga River basin. A total of 42 toposheets covered the entire study area, of which 23 toposheets contain glacial lakes.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 90 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Ganga River basin. A total of 42 toposheets covered the entire study area, of which 23 toposheets contain glacial lakes.", "line_start": 1781, "line_end": 1781, "token_count_estimate": 142, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9451adffc58f416b", "text": "Document: MESSAGE\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1782, "line_end": 1790, "token_count_estimate": 34, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "265ce7d0ae0e7e6c", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nState: Himachal Pradesh, Uttarakhand\nMap 1\nPlate No: 53I\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 1792, "line_end": 1800, "token_count_estimate": 73, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc3683df94f30d44", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 7 | 3 | 18 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 8 | 2 | 16 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 7 | 1 | 11 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 2 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 1 | 0 | 0 | 13 | 4 | 0 | 3 | 0 | 23 | 6 | 50 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1801, "line_end": 1809, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3e9fa56d811ba99f", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF GANGA BASIN**\n\n**State: Himachal Pradesh, Uttarakhand**\n**Map 2**\n**Plate No: 53I**\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n\n**Location Map**\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 1810, "line_end": 1831, "token_count_estimate": 170, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f720f334b87c93a6", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 5 | 2.5 |\n| 3 | 4,001 - 5,000 | 36 | 76.6 |\n| 4 | > 5,000 | 9 | 5.5 |\n| | Total | 50 | 84.6 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1832, "line_end": 1838, "token_count_estimate": 156, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "156eb82aac6e26b2", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Subbasins**\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF GANGA BASIN**\n\n**State: Uttarakhand**\n**Map 3**\n**Plate No: 53J**\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 1839, "line_end": 1902, "token_count_estimate": 303, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "38260dfa8c61bf95", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 2 | 0.5 - 1 | 1 | 1 | 0 | 4 | 0 | 0 | 1 | 0 | 2 | 0 | 9 |\n| 3 | 1 - 5 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 6 | 1 | 10 |\n| 4 | 5 - 10 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 4 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 2 | 3 | 0 | 5 | 2 | 0 | 2 | 0 | 11 | 1 | 26 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1903, "line_end": 1911, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9940dfd6c04bdf7f", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nState: Uttarakhand\nMap 6\nPlate No: 53M\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 1912, "line_end": 1936, "token_count_estimate": 163, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b4a4a113ec48486b", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 56 | 78.9 |\n| | Total | 56 | 78.9 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1937, "line_end": 1943, "token_count_estimate": 156, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0917db89cfa88ec8", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nGanga Basin\nIndia\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nState: Uttarakhand\nMap 7\nPlate No: 53N\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 1944, "line_end": 2015, "token_count_estimate": 307, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "ab0cb6617760d5d3", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 1 | 0 | 17 | 65 | 0 | 1 | 0 | 13 | 1 | 99 |\n| 2 | 0.5 - 1 | 0 | 3 | 0 | 17 | 23 | 0 | 2 | 0 | 8 | 0 | 53 |\n| 3 | 1 - 5 | 4 | 3 | 0 | 9 | 4 | 0 | 6 | 0 | 6 | 0 | 32 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 1 | 0 | 5 | 0 | 0 | 0 | 9 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 4 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 7 | 7 | 0 | 46 | 95 | 0 | 14 | 0 | 27 | 1 | 197 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2016, "line_end": 2024, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6795735869b5a4eb", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nState: Uttarakhand Map 8 Plate No: 53N\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2025, "line_end": 2046, "token_count_estimate": 148, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a8527c591a990e3", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 2 | 1.2 |\n| 3 | 4,001 - 5,000 | 118 | 168.2 |\n| 4 | > 5,000 | 77 | 93.8 |\n| | Total | 197 | 263.1 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2047, "line_end": 2053, "token_count_estimate": 157, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a2c319e7a4f9d72", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nState: Uttarakhand Map 9 Plate No: 62B\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2054, "line_end": 2083, "token_count_estimate": 195, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "75913592d904c4c3", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 8 | 3 | 0 | 0 | 0 | 5 | 1 | 18 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 8 | 1 | 0 | 0 | 0 | 3 | 0 | 12 |\n| 3 | 1 - 5 | 2 | 2 | 0 | 10 | 0 | 0 | 2 | 0 | 6 | 0 | 22 |\n| 4 | 5 - 10 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 4 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 6 | 4 | 0 | 27 | 4 | 0 | 4 | 0 | 15 | 1 | 61 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2084, "line_end": 2092, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c9a36acf14161783", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nState: Uttarakhand\n\nMap 10\n\nPlate No: 62B\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2093, "line_end": 2116, "token_count_estimate": 147, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "176c1cfaa07c7261", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 3 | 0.9 |\n| 3 | 4,001 - 5,000 | 28 | 85.5 |\n| 4 | > 5,000 | 30 | 96.9 |\n| | Total | 61 | 183.4 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2117, "line_end": 2123, "token_count_estimate": 158, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a65ec3136e982b23", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nState: Uttarakhand\n\nMap 11\n\nPlate No: 62C\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2124, "line_end": 2159, "token_count_estimate": 196, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5d703b7c28c6f364", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 3 | 1 - 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 6 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2160, "line_end": 2168, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9063a74c37e16d57", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nState: Uttarakhand\nMap 12\nPlate No: 62C\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2169, "line_end": 2191, "token_count_estimate": 137, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8b2ccd49bcc83fc3", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 6 | 9.2 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 6 | 9.2 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2192, "line_end": 2198, "token_count_estimate": 156, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f208ee0a3152d575", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nState: Uttarakhand\nMap 13\nPlate No: 62F\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2199, "line_end": 2232, "token_count_estimate": 196, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9dbad59c25e3292f", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 4 | 0 | 37 | 16 | 0 | 0 | 0 | 27 | 4 | 88 |\n| 2 | 0.5 - 1 | 3 | 3 | 1 | 42 | 11 | 0 | 0 | 0 | 21 | 2 | 83 |\n| 3 | 1 - 5 | 10 | 7 | 1 | 50 | 7 | 0 | 2 | 0 | 30 | 7 | 114 |\n| 4 | 5 - 10 | 9 | 0 | 0 | 9 | 0 | 0 | 2 | 0 | 2 | 0 | 22 |\n| 5 | 10 - 50 | 15 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 5 | 5 | 31 |\n| 6 | ≥ 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| Total | | 38 | 14 | 2 | 144 | 34 | 0 | 4 | 0 | 85 | 18 | 339 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2233, "line_end": 2241, "token_count_estimate": 508, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae20e96da0fe690d", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**\n**GLACIAL LAKES IN PART OF GANGA BASIN**\n\n**Transboundary Region**\n**Map 22**\n**Plate No: 62L**\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types**\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\n**Location Map**\nGanga Basin\nIndia\nSubbasins: Bhagmati, Gandak, Ghaghara, Kamla, Kosi, Lower Ganga, Ramganga, Rapti, Sarda, Upper Ganga, Yamuna\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2242, "line_end": 2278, "token_count_estimate": 264, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "6e18a97d326d9f29", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 14 | 40.6 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 14 | 40.6 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2279, "line_end": 2285, "token_count_estimate": 156, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "685cae7f625a1b76", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\n**GLACIAL LAKE ATLAS OF GANGA RIVER BASIN**\n**SATELLITE IMAGE OF PART OF GANGA BASIN**\n\n**Transboundary Region**\n**Map 23**\n**Plate No: 62O**\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2286, "line_end": 2331, "token_count_estimate": 260, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9cd8c389516914e3", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 3 | 23 | 7 | 0 | 0 | 0 | 25 | 3 | 61 |\n| 2 | 0.5 - 1 | 0 | 0 | 1 | 30 | 3 | 0 | 0 | 0 | 27 | 2 | 63 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 30 | 3 | 0 | 1 | 0 | 58 | 1 | 94 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 7 | 0 | 11 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 7 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 4 | 0 | 4 | 89 | 13 | 0 | 1 | 0 | 118 | 7 | 236 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2332, "line_end": 2340, "token_count_estimate": 508, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9d44f3b1c08c8dd2", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 24\nPlate No: 62O\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2341, "line_end": 2394, "token_count_estimate": 243, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "e4d75fe1e73f4973", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 10 | 31.9 |\n| 4 | > 5,000 | 226 | 440.8 |\n| | Total | 236 | 472.7 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2395, "line_end": 2401, "token_count_estimate": 160, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "968d4ec93e9fb06c", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 25\nPlate No: 62P\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2402, "line_end": 2449, "token_count_estimate": 227, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "604d1f3061abe8ff", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 1 | 25 | 4 | 0 | 0 | 0 | 9 | 1 | 41 |\n| 2 | 0.5 - 1 | 0 | 2 | 1 | 33 | 6 | 0 | 0 | 0 | 7 | 0 | 49 |\n| 3 | 1 - 5 | 4 | 3 | 0 | 44 | 0 | 0 | 0 | 0 | 18 | 0 | 69 |\n| 4 | 5 - 10 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |\n| 5 | 10 - 50 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| | Total | 12 | 7 | 2 | 104 | 10 | 0 | 0 | 0 | 35 | 1 | 171 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2450, "line_end": 2458, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b2efe8e55aa12721", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 26\nPlate No: 62P\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2459, "line_end": 2508, "token_count_estimate": 242, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "e0e4e05d1e3beaf4", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 44 | 471.3 |\n| 4 | > 5,000 | 127 | 230.7 |\n| | Total | 171 | 702.0 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2509, "line_end": 2515, "token_count_estimate": 157, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df8338db6f99d618", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 27\nPlate No: 71D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2516, "line_end": 2562, "token_count_estimate": 232, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0267e522b4c35581", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Types of Glacial Lakes Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 18 | 19 | 0 | 0 | 0 | 14 | 0 | 52 |\n| 2 | 0.5 - 1 | 0 | 2 | 0 | 24 | 6 | 0 | 0 | 0 | 6 | 0 | 38 |\n| 3 | 1 - 5 | 5 | 3 | 0 | 26 | 5 | 1 | 1 | 0 | 9 | 0 | 50 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| 5 | 10 - 50 | 4 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 9 |\n| 6 | ≥ 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| | Total | 12 | 8 | 0 | 69 | 30 | 1 | 2 | 0 | 32 | 0 | 154 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2563, "line_end": 2571, "token_count_estimate": 578, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "742e1f5e4f910a48", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 28\nPlate No: 71D\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2572, "line_end": 2599, "token_count_estimate": 158, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c5693a189f0dec22", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 9.9 |\n| 2 | 3,001 - 4,000 | 20 | 57.3 |\n| 3 | 4,001 - 5,000 | 70 | 250.1 |\n| 4 | > 5,000 | 63 | 91.7 |\n| Total | | 154 | 409.0 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2600, "line_end": 2606, "token_count_estimate": 160, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62c79715786be3ad", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Subbasins**\n*   Bhagmati\n*   Gandak\n*   Ghaghara\n*   Kamla\n*   Kosi\n*   Lower Ganga\n*   Ramganga\n*   Rapti\n*   Sarda\n*   Upper Ganga\n*   Yamuna\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 29\nPlate No: 71H\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2607, "line_end": 2667, "token_count_estimate": 314, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "b67db9b710ef9668", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 6 | 0 | 74 | 53 | 0 | 0 | 0 | 34 | 4 | 172 |\n| 2 | 0.5 - 1 | 2 | 3 | 0 | 60 | 30 | 0 | 0 | 0 | 36 | 9 | 140 |\n| 3 | 1 - 5 | 4 | 2 | 0 | 74 | 14 | 0 | 1 | 0 | 64 | 10 | 169 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 21 | 1 | 0 | 1 | 0 | 10 | 2 | 40 |\n| 5 | 10 - 50 | 10 | 0 | 0 | 15 | 2 | 0 | 1 | 0 | 5 | 4 | 37 |\n| 6 | ≥ 50 | 10 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |\n| Total | | 32 | 11 | 0 | 245 | 100 | 0 | 3 | 0 | 149 | 29 | 569 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2668, "line_end": 2676, "token_count_estimate": 508, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e87ea5dac4ef7176", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 30\nPlate No: 71H\n\n0 5 10 20 Km\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2677, "line_end": 2734, "token_count_estimate": 247, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "1a3d6a60f3fd4b42", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 2 | 10.9 |\n| 3 | 4,001 - 5,000 | 210 | 730.8 |\n| 4 | > 5,000 | 357 | 3,600.1 |\n| | Total | 569 | 4,341.9 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2735, "line_end": 2741, "token_count_estimate": 165, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f2c466270bded327", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 31\nPlate No: 71K\n\n0 5 10 20 Km\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2742, "line_end": 2788, "token_count_estimate": 231, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "336cbf5b474858dc", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | ≥ 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 6 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2789, "line_end": 2797, "token_count_estimate": 588, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d04553988a2e7ac", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 32\nPlate No: 71K\n\nKosi\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2798, "line_end": 2853, "token_count_estimate": 244, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "84ebb672029b977e", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 6 | 9.7 |\n| | Total | 6 | 9.7 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2854, "line_end": 2860, "token_count_estimate": 156, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eb8dbc900ae77fcf", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 33\nPlate No: 71L\n\nKosi\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2861, "line_end": 2909, "token_count_estimate": 234, "basins": ["GANGA"], "subbasins": ["Kosi"], "countries": ["India"], "lake_ids": []}}
{"id": "b343a3d416d780ea", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 106 | 75 | 0 | 0 | 0 | 38 | 1 | 221 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 99 | 34 | 0 | 0 | 0 | 31 | 1 | 166 |\n| 3 | 1 - 5 | 9 | 4 | 0 | 126 | 16 | 0 | 0 | 0 | 24 | 0 | 179 |\n| 4 | 5 - 10 | 6 | 0 | 0 | 20 | 2 | 0 | 0 | 0 | 2 | 0 | 30 |\n| 5 | 10 - 50 | 15 | 1 | 0 | 9 | 2 | 0 | 0 | 0 | 2 | 0 | 29 |\n| 6 | ≥ 50 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10 |\n| | Total | 38 | 8 | 0 | 360 | 129 | 0 | 0 | 0 | 98 | 2 | 635 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2910, "line_end": 2918, "token_count_estimate": 589, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f059b096c310348", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 34\nPlate No: 71L\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2919, "line_end": 2965, "token_count_estimate": 237, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "7b3afcdd289e0636", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 69 | 221.6 |\n| 4 | > 5,000 | 566 | 2,269.2 |\n| | Total | 635 | 2,490.8 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2966, "line_end": 2972, "token_count_estimate": 171, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8a69efc03e34b50a", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 35\nPlate No: 71P\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 2973, "line_end": 3018, "token_count_estimate": 226, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "ba72e1b218da235e", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake End-moraine Dammed Lake | Moraine Dammed Lake Lateral Moraine Dammed Lake | Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake Other Moraine Dammed Lake | Ice Dammed Lake Supra-glacial Lake | Ice Dammed Lake Glacier Ice-dammed Lake | Erosion Lake Cirque Erosion Lake | Erosion Lake Glacier Trough Valley Erosion Lake | Erosion Lake Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 43 | 6 | 57 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 15 | 0 | 0 | 0 | 0 | 81 | 2 | 99 |\n| 3 | 1 - 5 | 2 | 1 | 0 | 28 | 0 | 0 | 4 | 0 | 130 | 5 | 170 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 10 | 0 | 0 | 1 | 0 | 24 | 3 | 40 |\n| 5 | 10 - 50 | 18 | 0 | 0 | 9 | 0 | 0 | 1 | 0 | 19 | 5 | 52 |\n| 6 | ≥ 50 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 13 |\n| | Total | 28 | 2 | 0 | 70 | 0 | 0 | 6 | 0 | 301 | 24 | 431 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake End-moraine Dammed Lake", "Moraine Dammed Lake Lateral Moraine Dammed Lake", "Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake Other Moraine Dammed Lake", "Ice Dammed Lake Supra-glacial Lake", "Ice Dammed Lake Glacier Ice-dammed Lake", "Erosion Lake Cirque Erosion Lake", "Erosion Lake Glacier Trough Valley Erosion Lake", "Erosion Lake Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3019, "line_end": 3027, "token_count_estimate": 525, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc65412abbbc3fbc", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 38\nPlate No: 72E\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3028, "line_end": 3075, "token_count_estimate": 236, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "084b8ff1a80de4bc", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 1 | 6.1 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 1 | 6.1 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3076, "line_end": 3082, "token_count_estimate": 154, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "04501696e51d9530", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 39\nPlate No: 72I\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3083, "line_end": 3123, "token_count_estimate": 227, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "888a2dd0d2a0c387", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 3 | 0 | 44 | 63 | 0 | 0 | 0 | 31 | 1 | 143 |\n| 2 | 0.5 - 1 | 2 | 2 | 0 | 36 | 31 | 0 | 0 | 0 | 30 | 0 | 101 |\n| 3 | 1 - 5 | 3 | 5 | 0 | 76 | 19 | 0 | 0 | 0 | 45 | 0 | 148 |\n| 4 | 5 - 10 | 4 | 1 | 0 | 15 | 2 | 0 | 1 | 0 | 8 | 1 | 32 |\n| 5 | 10 - 50 | 6 | 2 | 0 | 14 | 2 | 0 | 1 | 0 | 2 | 0 | 27 |\n| 6 | ≥ 50 | 5 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |\n| Total | | 21 | 15 | 0 | 186 | 117 | 0 | 2 | 0 | 116 | 2 | 459 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3124, "line_end": 3132, "token_count_estimate": 508, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73229805181f4efa", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 40\nPlate No: 72I\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3133, "line_end": 3155, "token_count_estimate": 147, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51adb62999b96a8b", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 245 | 1,074.5 |\n| 4 | > 5,000 | 214 | 1,091.7 |\n| Total | | 459 | 2,166.2 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3156, "line_end": 3162, "token_count_estimate": 164, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a226f89e0630c97", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nTransboundary Region\nMap 41\nPlate No: 72M\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3163, "line_end": 3193, "token_count_estimate": 168, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f11ae68ab00211ed", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 9 | 11 | 0 | 2 | 0 | 62 | 0 | 85 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 21 | 7 | 0 | 0 | 0 | 83 | 0 | 112 |\n| 3 | 1 - 5 | 2 | 3 | 0 | 51 | 3 | 0 | 22 | 0 | 150 | 0 | 231 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 7 | 0 | 0 | 14 | 0 | 26 | 0 | 50 |\n| 5 | 10 - 50 | 6 | 1 | 0 | 2 | 0 | 0 | 16 | 0 | 7 | 2 | 34 |\n| 6 | ≥ 50 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |\n| Total | | 17 | 6 | 0 | 90 | 21 | 0 | 54 | 0 | 328 | 2 | 518 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3194, "line_end": 3202, "token_count_estimate": 509, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "441c669cccdbdf43", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKES IN PART OF GANGA BASIN\n\nTransboundary Region\nMap 42\nPlate No: 72M\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nGanga Basin\nIndia\n\nSubbasins\nBhagmati\nGandak\nGhaghara\nKamla\nKosi\nLower Ganga\nRamganga\nRapti\nSarda\nUpper Ganga\nYamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3203, "line_end": 3252, "token_count_estimate": 226, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "966e6e5aceece4b4", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 20 | 89.5 |\n| 3 | 4,001 - 5,000 | 354 | 1,500.1 |\n| 4 | > 5,000 | 144 | 634.1 |\n| | Total | 518 | 2,223.8 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3253, "line_end": 3259, "token_count_estimate": 164, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "833c9a37215f365f", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF GANGA BASIN\n\nState: Sikkim\nMap 43\nPlate No: 77D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3260, "line_end": 3307, "token_count_estimate": 228, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b1c4d5aa4ca740a5", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 3 | 1 | 8 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 4 | 3 | 11 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 3 | 1 | 0 | 1 | 0 | 15 | 0 | 20 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |\n| 6 | ≥ 50 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |\n| | Total | 9 | 0 | 0 | 9 | 2 | 0 | 1 | 0 | 24 | 5 | 50 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3308, "line_end": 3316, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0d3b1b4603ffd21b", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF GANGA BASIN**\n\nState: Sikkim\nMap 44\nPlate No: 77D\n\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3317, "line_end": 3341, "token_count_estimate": 153, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "23971870f8c3b83d", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 5 | 104.3 |\n| 4 | > 5,000 | 45 | 333.1 |\n| Total | | 50 | 437.3 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3342, "line_end": 3348, "token_count_estimate": 157, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab6fc2882de2bfbd", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF GANGA BASIN**\n\nState: Sikkim, West Bengal\nMap 45\nPlate No: 78A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3349, "line_end": 3378, "token_count_estimate": 176, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "66c164e73fdf76c9", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 8 | 6 | 0 | 0 | 0 | 2 | 0 | 17 |\n| 2 | 0.5 - 1 | 0 | 2 | 0 | 6 | 5 | 0 | 0 | 0 | 5 | 0 | 18 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 6 | 8 | 0 | 0 | 0 | 11 | 0 | 26 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |\n| 6 | ≥ 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| Total | | 7 | 3 | 0 | 25 | 19 | 0 | 1 | 0 | 18 | 0 | 73 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3379, "line_end": 3387, "token_count_estimate": 507, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa4d7008889d1d13", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF GANGA BASIN**\n\nState: Sikkim, West Bengal\nMap 46\nPlate No: 78A\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\nGanga Basin\nIndia\n\nSubbasins:\n* Bhagmati\n* Gandak\n* Ghaghara\n* Kamla\n* Kosi\n* Lower Ganga\n* Ramganga\n* Rapti\n* Sarda\n* Upper Ganga\n* Yamuna\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3388, "line_end": 3431, "token_count_estimate": 256, "basins": ["GANGA", "Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Upper Ganga", "Yamuna"], "countries": ["India"], "lake_ids": []}}
{"id": "141f21404182f30a", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 22 | 30.9 |\n| 4 | > 5,000 | 51 | 388.8 |\n| Total | | 73 | 419.6 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3432, "line_end": 3438, "token_count_estimate": 157, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d70e6f49d61e18cb", "text": "Document: MESSAGE\nSection: SATELLITE IMAGE OF PART OF GANGA BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "SATELLITE IMAGE OF PART OF GANGA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF GANGA BASIN"], "chunk_type": "text", "line_start": 3439, "line_end": 3482, "token_count_estimate": 201, "basins": ["GANGA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5b84ee7ec063d5f2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Overview:\nType: text\n\nThere are several automatic and semi-automatic glacial lake mapping method reported in the literature, but no method produce good and accurate results of mapping. Kääb et al. (2002), attempted the automatic classification of glacial lakes using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, but the algorithm was not robust enough to be applied to other images except ASTER images. Using LANDSAT images, Huggel et al. (2002) suggested the Normalized Difference Water Index (NDWI) according to theory low water reflectance in the NIR band and high reflectance in blue band but glacial lakes get misclassified as shadow area using this method.\n\nWangchuk et al. (2019), delineated glacial lakes using Sentinel-1 SAR images, a semi-automated approach, based on a radar signal intensity threshold between water and non-water feature classes followed by post-processing including elevations, slopes, vegetation and size thresholds, but drawback still persist as lakes which are severely affected by the wind and waves that increase the roughness and thus the backscatter would neither be identified correctly, partially or at all, due to the use of a single threshold. Hence, to ensure correct classifications of lakes, visual inspection of images and quality control is required for final accurate results.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Overview:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Overview:"], "chunk_type": "text", "line_start": 3486, "line_end": 3490, "token_count_estimate": 339, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fff1d6c767fb9718", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping methods:\nType: text\n\nThe NDWI, which provides an automatic way to detect water bodies including glacial lakes was adopted by many researchers for inventorying purpose. It is a ratio combining two different spectral bands that enhance water spectral signals by contrasting the reflectance between different wavelengths and removing a large portion of noise components in different wavelengths, can be expressed as:\n\nNDWI = (Green Band - NIR Band) / (Green Band + NIR Band)\n\nOther than NDWI, two more pixel-based classification techniques i.e. supervised (by giving homogeneous signature sites) and unsupervised (by giving certain number of feature classes to classify based on spectral behavior) classification techniques can also be applied. Object-based classification using eCognition software can also be done using various factors like by giving threshold values and suitable membership functions, by including indices like NDWI, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Glacier Index (NDGI), and by using layers such as slope and NIR band.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping methods:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping methods:"], "chunk_type": "text", "line_start": 3492, "line_end": 3498, "token_count_estimate": 282, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d74c5e0b75df8b1", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nA study was attempted using RS-2 LISS-IV data to compare the mapping accuracy of lakes using 4 automated methods (NDWI, Supervised, Unsupervised and Object based) with visual interpretation method. All four automatic mapping methods along with visual interpretation technique were used in an area which has deep water bodies and snow covered glacial lakes along with shadowed region (upper mountainous parts of Teesta basin). Using NDWI method, most of lakes got classified, but it also classify shadow areas as water pixels due to the similar spectral reflectance conditions. Even if the threshold value of NDWI is changed in such a way that all water pixels\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\nin a lake should get classified, many deep water bodies and shadowed portions having same spectral reflectance values will get misclassified as water pixels or in some glacial lakes water pixels are missing.\n\nUnsupervised classification technique misclassifies not only shadows as lakes, but also some part of glaciers that are in retreating condition and having similar spectral reflectance values of lakes (light blue in colour). In supervised classification output, with good amount of signature sites, cloud/mountain shadows are classified as water pixels. Overall, using pixel-based classification methods, it is difficult to distinguish between deep water bodies and shadows as they have same spectral reflectance values. Pixel-based classified output of all three methods along with the total area of lakes in the study area is shown in Figure 91.\n\nUsing object-based classification method, along with various layers like slope (as the glacial lakes are located at higher elevation) and NIR band, results misclassification of shadows, though it is less in comparison to the pixel-based classified output, but at many locations water pixels are not classified. Also, if we compare the areas of lakes that is being classified using automatic method that with the area of manually mapped lakes, automatic mapped lakes has huge difference and extent of misclassification, which need to be corrected again using visual interpretation method. Figure 92 shows the comparison of the glacial lake extents of object-based classification and manual mapping using visual interpretation keys.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Annexure - II: List of Glacial Lakes**\n\nEach lake is given a unique ID, formatted in 12 alpha-numeric character. First two digit of ID refers to the basin code, next five character refers to the SOI 250K and 50K Toposheet No., and the last five digit refers to the sequential number of each lake sorted from top left to bottom right. For example:\n\n0253I0300001\n\n02 | 53I03 | 00001", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 3500, "line_end": 3529, "token_count_estimate": 673, "basins": ["GANGA"], "subbasins": ["Teesta"], "countries": [], "lake_ids": ["00001", "0253I0300001", "53I03"]}}
{"id": "c5bb81b26fd2c6fc", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| Basin Code | SOI 250K Toposheet No. | SOI 50K Toposheet No. | Lake No. |\n|---|---|---|---|\n| 02 | 53I | 03 | 00001 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["Basin Code", "SOI 250K Toposheet No.", "SOI 50K Toposheet No.", "Lake No."], "table_row_start": 1, "table_row_end": 1, "line_start": 3530, "line_end": 3532, "token_count_estimate": 97, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00001"]}}
{"id": "a097f50518b491a8", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nTable 74 shows the list of all glacial lakes mapped in the Ganga River basin along with few important attributes.", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 3533, "line_end": 3537, "token_count_estimate": 61, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "728f833d35aff66a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable: Table 74: List of Glacial Lakes of the Ganga River Basin with few important attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0253I0300001 | 31.398 | 78.089 | Yamuna | E(o) | 3.22 | 4,673 | 51 | 0253J0900051 | 30.981 | 78.519 | Yamuna | M(o) | 0.89 | 4,807 |\n| 2 | 0253I0300002 | 31.345 | 78.142 | Yamuna | E(o) | 1.22 | 4,523 | 52 | 0253J0900052 | 30.979 | 78.522 | Yamuna | M(o) | 0.32 | 4,796 |\n| 3 | 0253I0300003 | 31.329 | 78.185 | Yamuna | E(c) | 3.37 | 4,437 | 53 | 0253J0900053 | 30.978 | 78.520 | Yamuna | M(o) | 0.62 | 4,847 |\n| 4 | 0253I0300004 | 31.323 | 78.182 | Yamuna | E(c) | 8.26 | 4,493 | 54 | 0253J1300054 | 30.919 | 78.962 | Upper Ganga | I(s) | 2.08 | 4,664 |\n| 5 | 0253I0300005 | 31.320 | 78.165 | Yamuna | E(o) | 2.67 | 4,427 | 55 | 0253J1300055 | 30.912 | 78.958 | Upper Ganga | M(l) | 6.00 | 4,707 |\n| 6 | 0253I0300006 | 31.319 | 78.242 | Yamuna | E(o) | 0.37 | 4,438 | 56 | 0253J1300056 | 30.911 | 78.771 | Upper Ganga | E(c) | 3.15 | 4,822 |\n| 7 | 0253I0400007 | 31.238 | 78.241 | Yamuna | E(o) | 0.35 | 4,331 | 57 | 0253J1300057 | 30.892 | 78.819 | Upper Ganga | M(l) | 3.26 | 4,643 |\n| 8 | 0253I0400008 | 31.231 | 78.211 | Yamuna | E(o) | 10.66 | 4,264 | 58 | 0253J1300058 | 30.888 | 78.828 | Upper Ganga | M(o) | 0.92 | 4,704 |\n| 9 | 0253I0700009 | 31.300 | 78.279 | Yamuna | M(o) | 0.28 | 4,414 | 59 | 0253J1300059 | 30.888 | 78.835 | Upper Ganga | M(e) | 0.70 | 4,769 |\n| 10 | 0253I0700010 | 31.284 | 78.302 | Yamuna | E(o) | 0.35 | 4,981 | 60 | 0253J1300060 | 30.881 | 78.981 | Upper Ganga | I(s) | 0.32 | 4,896 |\n| 11 | 0253I0700011 | 31.275 | 78.283 | Yamuna | E(o) | 0.33 | 4,557 | 61 | 0253J1300061 | 30.835 | 78.947 | Upper Ganga | E(o) | 0.77 | 4,948 |\n| 12 | 0253I0700012 | 31.274 | 78.312 | Yamuna | E(o) | 0.70 | 4,862 | 62 | 0253J1300062 | 30.789 | 78.958 | Upper Ganga | O | 1.11 | 3,707 |\n| 13 | 0253I0700013 | 31.273 | 78.315 | Yamuna | E(o) | 0.40 | 4,836 | 63 | 0253J1300063 | 30.751 | 78.985 | Upper Ganga | M(o) | 0.84 | 4,742 |\n| 14 | 0253I0700014 | 31.269 | 78.339 | Yamuna | M(e) | 1.26 | 4,885 | 64 | 0253J1300064 | 30.751 | 78.988 | Upper Ganga | M(l) | 0.99 | 4,770 |\n| 15 | 0253I0700015 | 31.265 | 78.305 | Yamuna | M(o) | 0.58 | 4,680 | 65 | 0253J1300065 | 30.751 | 78.970 | Upper Ganga | E(o) | 2.35 | 4,521 |\n| 16 | 0253I0700016 | 31.264 | 78.253 | Yamuna | E(o) | 0.98 | 4,396 | 66 | 0253J1400066 | 30.748 | 78.977 | Upper Ganga | E(o) | 6.08 | 4,560 |\n| 17 | 0253I0700017 | 31.262 | 78.272 | Yamuna | E(o) | 0.76 | 4,800 | 67 | 0253J1400067 | 30.747 | 78.847 | Upper Ganga | E(o) | 1.65 | 4,545 |\n| 18 | 0253I0700018 | 31.260 | 78.255 | Yamuna | E(c) | 15.10 | 4,403 | 68 | 0253J1400068 | 30.746 | 78.987 | Upper Ganga | M(e) | 25.56 | 4,734 |\n| 19 | 0253I0800019 | 31.242 | 78.254 | Yamuna | M(o) | 0.95 | 4,586 | 69 | 0253J1400069 | 30.745 | 78.844 | Upper Ganga | E(o) | 0.56 | 4,589 |\n| 20 | 0253I0800020 | 31.228 | 78.347 | Yamuna | E(o) | 2.07 | 4,557 | 70 | 0253J1400070 | 30.733 | 78.799 | Upper Ganga | E(o) | 1.11 | 4,491 |\n| 21 | 0253I0800021 | 31.210 | 78.402 | Yamuna | I(s) | 0.28 | 4,252 | 71 | 0253J1400071 | 30.731 | 78.934 | Upper Ganga | E(c) | 0.58 | 4,343 |\n| 22 | 0253I0800022 | 31.209 | 78.405 | Yamuna | I(s) | 0.33 | 4,263 | 72 | 0253J1400072 | 30.726 | 78.808 | Upper Ganga | E(o) | 7.08 | 4,620 |\n| 23 | 0253I0800023 | 31.209 | 78.297 | Yamuna | E(o) | 0.69 | 4,301 | 73 | 0253J1400073 | 30.724 | 78.997 | Upper Ganga | E(o) | 1.41 | 4,697 |\n| 24 | 0253I0800024 | 31.201 | 78.299 | Yamuna | E(o) | 0.38 | 4,537 | 74 | 0253J1400074 | 30.722 | 78.816 | Upper Ganga | E(o) | 1.88 | 4,545 |\n| 25 | 0253I0800025 | 31.185 | 78.348 | Yamuna | O | 0.80 | 3,760 | 75 | 0253J1400075 | 30.722 | 78.995 | Upper Ganga | E(o) | 4.43 | 4,559 |\n| 26 | 0253I0800026 | 31.185 | 78.343 | Yamuna | O | 0.28 | 3,734 | 76 | 0253J1400076 | 30.719 | 78.814 | Upper Ganga | E(o) | 6.83 | 4,569 |\n| 27 | 0253I0800027 | 31.184 | 78.344 | Yamuna | O | 0.36 | 3,745 | 77 | 0253M0300077 | 31.379 | 79.014 | Upper Ganga | M(e) | 4.00 | 5,380 |\n| 28 | 0253I0800028 | 31.179 | 78.406 | Yamuna | E(o) | 0.72 | 4,799 | 78 | 0253M0300078 | 31.298 | 79.011 | Upper Ganga | M(o) | 3.25 | 5,331 |\n| 29 | 0253I0800029 | 31.081 | 78.458 | Yamuna | O | 0.59 | 3,599 | 79 | 0253M0300079 | 31.267 | 79.226 | Upper Ganga | M(o) | 2.10 | 5,469 |\n| 30 | 0253I0800030 | 31.078 | 78.380 | Yamuna | E(o) | 3.31 | 4,367 | 80 | 0253M0400080 | 31.225 | 79.155 | Upper Ganga | M(e) | 1.27 | 5,510 |\n| 31 | 0253I0800031 | 31.011 | 78.487 | Yamuna | E(o) | 1.27 | 4,698 | 81 | 0253M0400081 | 31.224 | 79.167 | Upper Ganga | E(c) | 0.99 | 5,533 |\n| 32 | 0253I1200032 | 31.174 | 78.628 | Upper Ganga | M(o) | 5.64 | 4,510 | 82 | 0253M0400082 | 31.221 | 79.152 | Upper Ganga | M(o) | 0.28 | 5,513 |\n| 33 | 0253I1200033 | 31.163 | 78.649 | Upper Ganga | E(o) | 0.39 | 4,689 | 83 | 0253M0400083 | 31.211 | 79.166 | Upper Ganga | E(o) | 1.64 | 5,523 |\n| 34 | 0253I1200034 | 31.152 | 78.526 | Yamuna | I(s) | 0.41 | 4,326 | 84 | 0253M0400084 | 31.195 | 79.165 | Upper Ganga | M(o) | 0.44 | 5,460 |\n| 35 | 0253I1200035 | 31.151 | 78.522 | Yamuna | I(s) | 0.30 | 4,286 | 85 | 0253M0400085 | 31.191 | 79.150 | Upper Ganga | M(e) | 7.89 | 5,365 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": "Table 74: List of Glacial Lakes of the Ganga River Basin with few important attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 3538, "line_end": 3589, "token_count_estimate": 3095, "basins": ["Ganga"], "subbasins": ["Upper Ganga", "Yamuna"], "countries": [], "lake_ids": ["0253I0300001", "0253I0300002", "0253I0300003", "0253I0300004", "0253I0300005", "0253I0300006", "0253I0400007", "0253I0400008", "0253I0700009", "0253I0700010", "0253I0700011", "0253I0700012", "0253I0700013", "0253I0700014", "0253I0700015", "0253I0700016", "0253I0700017", "0253I0700018", "0253I0800019", "0253I0800020", "0253I0800021", "0253I0800022", "0253I0800023", "0253I0800024", "0253I0800025", "0253I0800026", "0253I0800027", "0253I0800028", "0253I0800029", "0253I0800030", "0253I0800031", "0253I1200032", "0253I1200033", "0253I1200034", "0253I1200035", "0253J0900051", "0253J0900052", "0253J0900053", "0253J1300054", "0253J1300055", "0253J1300056", "0253J1300057", "0253J1300058", "0253J1300059", "0253J1300060", "0253J1300061", "0253J1300062", "0253J1300063", "0253J1300064", "0253J1300065"]}}
{"id": "aa2afb6bebc5494c", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable: Table 74: List of Glacial Lakes of the Ganga River Basin with few important attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 36 | 0253I1200036 | 31.144 | 78.687 | Upper Ganga | O | 0.42 | 3,849 | 86 | 0253M0400086 | 31.189 | 79.146 | Upper Ganga | M(e) | 1.73 | 5,355 |\n| 37 | 0253I1500037 | 31.299 | 78.867 | Upper Ganga | M(o) | 1.14 | 5,270 | 87 | 0253M0400087 | 31.179 | 79.000 | Upper Ganga | M(o) | 0.27 | 5,219 |\n| 38 | 0253I1500038 | 31.280 | 78.896 | Upper Ganga | M(o) | 0.35 | 5,265 | 88 | 0253M0400088 | 31.178 | 79.154 | Upper Ganga | E(c) | 3.12 | 5,560 |\n| 39 | 0253I1500039 | 31.279 | 78.833 | Upper Ganga | M(o) | 0.25 | 5,238 | 89 | 0253M0400089 | 31.175 | 79.000 | Upper Ganga | M(o) | 0.35 | 5,146 |\n| 40 | 0253I1500040 | 31.271 | 78.827 | Upper Ganga | E(o) | 0.50 | 5,283 | 90 | 0253M0400090 | 31.042 | 79.242 | Upper Ganga | M(o) | 0.29 | 5,360 |\n| 41 | 0253I1500041 | 31.270 | 78.939 | Upper Ganga | M(o) | 5.23 | 4,695 | 91 | 0253M0400091 | 31.039 | 79.237 | Upper Ganga | M(o) | 0.48 | 5,423 |\n| 42 | 0253I1500042 | 31.259 | 78.911 | Upper Ganga | M(o) | 0.51 | 5,164 | 92 | 0253M0800092 | 31.168 | 79.273 | Upper Ganga | M(o) | 0.63 | 5,433 |\n| 43 | 0253I1500043 | 31.253 | 78.938 | Upper Ganga | M(o) | 0.28 | 5,117 | 93 | 0253M0800093 | 31.152 | 79.267 | Upper Ganga | M(e) | 4.59 | 5,427 |\n| 44 | 0253I1600044 | 31.233 | 78.857 | Upper Ganga | M(o) | 0.83 | 5,259 | 94 | 0253M0800094 | 31.142 | 79.260 | Upper Ganga | M(e) | 1.71 | 5,488 |\n| 45 | 0253I1600045 | 31.203 | 78.957 | Upper Ganga | M(o) | 0.93 | 5,262 | 95 | 0253M0800095 | 31.138 | 79.309 | Upper Ganga | M(e) | 1.08 | 5,671 |\n| 46 | 0253I1600046 | 31.199 | 78.851 | Upper Ganga | E(o) | 1.16 | 4,607 | 96 | 0253M0800096 | 31.129 | 79.328 | Upper Ganga | M(l) | 0.69 | 5,715 |\n| 47 | 0253I1600047 | 31.183 | 78.794 | Upper Ganga | E(o) | 0.55 | 4,599 | 97 | 0253M0800097 | 31.128 | 79.307 | Upper Ganga | M(o) | 0.54 | 5,648 |\n| 48 | 0253I1600048 | 31.178 | 78.967 | Upper Ganga | E(o) | 0.70 | 5,288 | 98 | 0253M0800098 | 31.064 | 79.287 | Upper Ganga | M(e) | 1.04 | 5,390 |\n| 49 | 0253I1600049 | 31.177 | 78.872 | Upper Ganga | O | 1.17 | 4,446 | 99 | 0253M0800099 | 31.062 | 79.415 | Upper Ganga | M(o) | 0.69 | 5,549 |\n| 50 | 0253I1600050 | 31.104 | 78.897 | Upper Ganga | M(o) | 0.92 | 4,974 | 100 | 0253M0800100 | 31.060 | 79.410 | Upper Ganga | M(o) | 3.28 | 5,525 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": "Table 74: List of Glacial Lakes of the Ganga River Basin with few important attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 50, "line_start": 3538, "line_end": 3589, "token_count_estimate": 1453, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253I1200036", "0253I1500037", "0253I1500038", "0253I1500039", "0253I1500040", "0253I1500041", "0253I1500042", "0253I1500043", "0253I1600044", "0253I1600045", "0253I1600046", "0253I1600047", "0253I1600048", "0253I1600049", "0253I1600050", "0253M0400086", "0253M0400087", "0253M0400088", "0253M0400089", "0253M0400090", "0253M0400091", "0253M0800092", "0253M0800093", "0253M0800094", "0253M0800095", "0253M0800096", "0253M0800097", "0253M0800098", "0253M0800099", "0253M0800100"]}}
{"id": "d918239c5c29ac9f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 3590, "line_end": 3598, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85fdae666562ccc6", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 101 | 0253M0800101 | 31.060 | 79.414 | Upper Ganga | M(o) | 3.17 | 5,519 | 174 | 0253N0500174 | 30.968 | 79.467 | Upper Ganga | I(s) | 0.41 | 5,589 |\n| 102 | 0253M0800102 | 31.059 | 79.396 | Upper Ganga | E(c) | 2.28 | 5,634 | 175 | 0253N0500175 | 30.967 | 79.362 | Upper Ganga | M(e) | 3.08 | 5,477 |\n| 103 | 0253M0800103 | 31.054 | 79.407 | Upper Ganga | M(o) | 2.10 | 5,479 | 176 | 0253N0500176 | 30.965 | 79.493 | Upper Ganga | M(o) | 0.40 | 5,413 |\n| 104 | 0253M0800104 | 31.053 | 79.412 | Upper Ganga | M(e) | 1.58 | 5,537 | 177 | 0253N0500177 | 30.964 | 79.386 | Upper Ganga | M(o) | 1.28 | 5,323 |\n| 105 | 0253M0800105 | 31.047 | 79.400 | Upper Ganga | O | 4.37 | 5,440 | 178 | 0253N0500178 | 30.964 | 79.461 | Upper Ganga | M(o) | 0.96 | 5,441 |\n| 106 | 0253M0800106 | 31.046 | 79.403 | Upper Ganga | O | 0.93 | 5,436 | 179 | 0253N0500179 | 30.962 | 79.462 | Upper Ganga | M(o) | 0.59 | 5,443 |\n| 107 | 0253M0800107 | 31.043 | 79.397 | Upper Ganga | O | 0.27 | 5,427 | 180 | 0253N0500180 | 30.954 | 79.359 | Upper Ganga | M(o) | 0.38 | 5,571 |\n| 108 | 0253M0800108 | 31.042 | 79.393 | Upper Ganga | O | 0.67 | 5,401 | 181 | 0253N0500181 | 30.951 | 79.310 | Upper Ganga | M(o) | 0.73 | 5,638 |\n| 109 | 0253M0800109 | 31.033 | 79.274 | Upper Ganga | M(o) | 0.39 | 5,262 | 182 | 0253N0500182 | 30.950 | 79.309 | Upper Ganga | M(o) | 0.92 | 5,622 |\n| 110 | 0253M0800110 | 31.029 | 79.375 | Upper Ganga | E(o) | 0.39 | 5,425 | 183 | 0253N0500183 | 30.948 | 79.341 | Upper Ganga | M(e) | 2.20 | 5,579 |\n| 111 | 0253M0800111 | 31.026 | 79.360 | Upper Ganga | M(o) | 0.58 | 5,465 | 184 | 0253N0500184 | 30.946 | 79.314 | Upper Ganga | M(o) | 0.56 | 5,610 |\n| 112 | 0253M0800112 | 31.025 | 79.350 | Upper Ganga | M(o) | 0.90 | 5,490 | 185 | 0253N0500185 | 30.945 | 79.490 | Upper Ganga | I(s) | 0.82 | 5,088 |\n| 113 | 0253M0800113 | 31.025 | 79.363 | Upper Ganga | M(o) | 1.54 | 5,473 | 186 | 0253N0500186 | 30.939 | 79.482 | Upper Ganga | I(s) | 0.35 | 4,988 |\n| 114 | 0253M0800114 | 31.024 | 79.352 | Upper Ganga | M(o) | 0.26 | 5,490 | 187 | 0253N0500187 | 30.939 | 79.372 | Upper Ganga | M(o) | 0.40 | 5,511 |\n| 115 | 0253M0800115 | 31.024 | 79.349 | Upper Ganga | M(o) | 0.25 | 5,480 | 188 | 0253N0500188 | 30.937 | 79.367 | Upper Ganga | M(l) | 0.67 | 5,554 |\n| 116 | 0253M0800116 | 31.024 | 79.356 | Upper Ganga | M(o) | 1.04 | 5,433 | 189 | 0253N0500189 | 30.935 | 79.416 | Upper Ganga | E(c) | 1.60 | 5,296 |\n| 117 | 0253M0800117 | 31.023 | 79.361 | Upper Ganga | M(o) | 3.34 | 5,463 | 190 | 0253N0500190 | 30.934 | 79.493 | Upper Ganga | I(s) | 0.42 | 4,964 |\n| 118 | 0253M0800118 | 31.023 | 79.350 | Upper Ganga | M(o) | 0.76 | 5,469 | 191 | 0253N0500191 | 30.926 | 79.407 | Upper Ganga | E(o) | 0.90 | 5,439 |\n| 119 | 0253M0800119 | 31.023 | 79.344 | Upper Ganga | E(o) | 0.34 | 5,462 | 192 | 0253N0500192 | 30.924 | 79.310 | Upper Ganga | M(o) | 0.92 | 5,561 |\n| 120 | 0253M0800120 | 31.022 | 79.350 | Upper Ganga | M(o) | 0.37 | 5,455 | 193 | 0253N0500193 | 30.923 | 79.309 | Upper Ganga | M(o) | 0.59 | 5,574 |\n| 121 | 0253M0800121 | 31.017 | 79.428 | Upper Ganga | E(o) | 0.67 | 5,687 | 194 | 0253N0500194 | 30.923 | 79.463 | Upper Ganga | M(o) | 0.27 | 4,745 |\n| 122 | 0253M0800122 | 31.016 | 79.369 | Upper Ganga | E(o) | 0.60 | 5,385 | 195 | 0253N0500195 | 30.922 | 79.310 | Upper Ganga | M(o) | 0.68 | 5,560 |\n| 123 | 0253M0800123 | 31.016 | 79.453 | Upper Ganga | M(o) | 0.98 | 5,689 | 196 | 0253N0500196 | 30.920 | 79.314 | Upper Ganga | M(o) | 1.97 | 5,514 |\n| 124 | 0253M0800124 | 31.015 | 79.363 | Upper Ganga | E(o) | 0.89 | 5,392 | 197 | 0253N0500197 | 30.890 | 79.304 | Upper Ganga | E(c) | 2.31 | 5,350 |\n| 125 | 0253M0800125 | 31.015 | 79.367 | Upper Ganga | E(o) | 0.35 | 5,391 | 198 | 0253N0500198 | 30.811 | 79.313 | Upper Ganga | E(o) | 0.34 | 5,245 |\n| 126 | 0253M0800126 | 31.014 | 79.449 | Upper Ganga | E(o) | 2.14 | 5,703 | 199 | 0253N0500199 | 30.806 | 79.315 | Upper Ganga | E(o) | 0.87 | 5,220 |\n| 127 | 0253M0800127 | 31.012 | 79.432 | Upper Ganga | E(o) | 1.12 | 5,626 | 200 | 0253N0500200 | 30.805 | 79.318 | Upper Ganga | E(o) | 0.53 | 5,220 |\n| 128 | 0253M0800128 | 31.007 | 79.443 | Upper Ganga | M(o) | 0.63 | 5,586 | 201 | 0253N0500201 | 30.805 | 79.295 | Upper Ganga | I(s) | 0.34 | 4,697 |\n| 129 | 0253M0800129 | 31.005 | 79.406 | Upper Ganga | M(o) | 1.98 | 5,529 | 202 | 0253N0500202 | 30.796 | 79.298 | Upper Ganga | I(s) | 0.32 | 4,645 |\n| 130 | 0253M0800130 | 31.004 | 79.446 | Upper Ganga | E(c) | 0.52 | 5,583 | 203 | 0253N0500203 | 30.791 | 79.361 | Upper Ganga | I(s) | 0.28 | 4,273 |\n| 131 | 0253M0800131 | 31.004 | 79.405 | Upper Ganga | M(o) | 0.45 | 5,534 | 204 | 0253N0500204 | 30.790 | 79.368 | Upper Ganga | I(s) | 0.64 | 4,228 |\n| 132 | 0253M0800132 | 31.001 | 79.274 | Upper Ganga | I(s) | 0.68 | 5,403 | 205 | 0253N0500205 | 30.767 | 79.397 | Upper Ganga | I(s) | 0.34 | 4,033 |\n| 133 | 0253N0100133 | 30.913 | 79.092 | Upper Ganga | I(s) | 0.37 | 4,241 | 206 | 0253N0500206 | 30.762 | 79.398 | Upper Ganga | I(s) | 0.53 | 4,028 |\n| 134 | 0253N0100134 | 30.912 | 79.189 | Upper Ganga | M(o) | 0.68 | 5,204 | 207 | 0253N0500207 | 30.754 | 79.368 | Upper Ganga | I(s) | 0.25 | 4,274 |\n| 135 | 0253N0100135 | 30.910 | 79.095 | Upper Ganga | I(s) | 0.36 | 4,257 | 208 | 0253N0500208 | 30.754 | 79.363 | Upper Ganga | I(s) | 0.25 | 4,292 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 3599, "line_end": 3673, "token_count_estimate": 3203, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253M0800101", "0253M0800102", "0253M0800103", "0253M0800104", "0253M0800105", "0253M0800106", "0253M0800107", "0253M0800108", "0253M0800109", "0253M0800110", "0253M0800111", "0253M0800112", "0253M0800113", "0253M0800114", "0253M0800115", "0253M0800116", "0253M0800117", "0253M0800118", "0253M0800119", "0253M0800120", "0253M0800121", "0253M0800122", "0253M0800123", "0253M0800124", "0253M0800125", "0253M0800126", "0253M0800127", "0253M0800128", "0253M0800129", "0253M0800130", "0253M0800131", "0253M0800132", "0253N0100133", "0253N0100134", "0253N0100135", "0253N0500174", "0253N0500175", "0253N0500176", "0253N0500177", "0253N0500178", "0253N0500179", "0253N0500180", "0253N0500181", "0253N0500182", "0253N0500183", "0253N0500184", "0253N0500185", "0253N0500186", "0253N0500187", "0253N0500188"]}}
{"id": "284493f0b4dc49e9", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 136 | 0253N0100136 | 30.907 | 79.172 | Upper Ganga | I(s) | 0.76 | 4,906 | 209 | 0253N0500209 | 30.750 | 79.361 | Upper Ganga | I(s) | 0.30 | 4,319 |\n| 137 | 0253N0100137 | 30.894 | 79.096 | Upper Ganga | I(s) | 0.49 | 4,369 | 210 | 0253N0600210 | 30.746 | 79.351 | Upper Ganga | I(s) | 0.27 | 4,388 |\n| 138 | 0253N0100138 | 30.888 | 79.098 | Upper Ganga | I(s) | 0.76 | 4,403 | 211 | 0253N0600211 | 30.744 | 79.337 | Upper Ganga | I(s) | 0.25 | 4,465 |\n| 139 | 0253N0100139 | 30.881 | 79.098 | Upper Ganga | I(s) | 0.27 | 4,427 | 212 | 0253N0600212 | 30.744 | 79.357 | Upper Ganga | M(l) | 0.91 | 4,342 |\n| 140 | 0253N0100140 | 30.868 | 79.099 | Upper Ganga | I(s) | 0.30 | 4,483 | 213 | 0253N0600213 | 30.655 | 79.298 | Upper Ganga | E(o) | 1.65 | 4,813 |\n| 141 | 0253N0100141 | 30.858 | 79.110 | Upper Ganga | I(s) | 0.35 | 4,534 | 214 | 0253N0600214 | 30.610 | 79.455 | Upper Ganga | E(o) | 0.49 | 4,677 |\n| 142 | 0253N0100142 | 30.856 | 79.110 | Upper Ganga | I(s) | 0.27 | 4,542 | 215 | 0253N0600215 | 30.607 | 79.458 | Upper Ganga | E(o) | 1.38 | 4,609 |\n| 143 | 0253N0100143 | 30.847 | 79.114 | Upper Ganga | I(s) | 2.24 | 4,610 | 216 | 0253N0600216 | 30.607 | 79.448 | Upper Ganga | E(o) | 0.26 | 4,475 |\n| 144 | 0253N0100144 | 30.834 | 79.114 | Upper Ganga | I(s) | 0.71 | 4,639 | 217 | 0253N0600217 | 30.604 | 79.325 | Upper Ganga | E(c) | 6.56 | 4,371 |\n| 145 | 0253N0100145 | 30.826 | 79.126 | Upper Ganga | I(s) | 0.53 | 4,679 | 218 | 0253N0900218 | 30.970 | 79.747 | Upper Ganga | M(o) | 0.29 | 5,450 |\n| 146 | 0253N0100146 | 30.817 | 79.128 | Upper Ganga | I(s) | 0.35 | 4,706 | 219 | 0253N0900219 | 30.945 | 79.709 | Upper Ganga | I(s) | 0.31 | 5,093 |\n| 147 | 0253N0100147 | 30.808 | 79.150 | Upper Ganga | I(s) | 0.46 | 4,784 | 220 | 0253N0900220 | 30.937 | 79.690 | Upper Ganga | M(o) | 0.30 | 5,399 |\n| 148 | 0253N0100148 | 30.807 | 79.123 | Upper Ganga | M(l) | 3.49 | 4,731 | 221 | 0253N0900221 | 30.937 | 79.689 | Upper Ganga | M(o) | 0.62 | 5,389 |\n| 149 | 0253N0100149 | 30.806 | 79.154 | Upper Ganga | I(s) | 0.78 | 4,790 | 222 | 0253N0900222 | 30.935 | 79.504 | Upper Ganga | I(s) | 0.28 | 5,063 |\n| 150 | 0253N0100150 | 30.804 | 79.126 | Upper Ganga | E(o) | 0.44 | 4,733 | 223 | 0253N0900223 | 30.933 | 79.684 | Upper Ganga | I(s) | 0.47 | 5,208 |\n| 151 | 0253N0100151 | 30.804 | 79.157 | Upper Ganga | I(s) | 0.60 | 4,803 | 224 | 0253N0900224 | 30.929 | 79.723 | Upper Ganga | I(s) | 0.28 | 4,885 |\n| 152 | 0253N0100152 | 30.758 | 79.059 | Upper Ganga | I(s) | 0.36 | 4,037 | 225 | 0253N0900225 | 30.928 | 79.713 | Upper Ganga | I(s) | 0.37 | 4,970 |\n| 153 | 0253N0100153 | 30.758 | 79.057 | Upper Ganga | I(s) | 0.32 | 4,051 | 226 | 0253N0900226 | 30.923 | 79.739 | Upper Ganga | I(s) | 0.49 | 4,808 |\n| 154 | 0253N0200154 | 30.742 | 79.010 | Upper Ganga | E(c) | 3.03 | 4,761 | 227 | 0253N0900227 | 30.918 | 79.740 | Upper Ganga | I(s) | 0.55 | 4,760 |\n| 155 | 0253N0200155 | 30.734 | 79.034 | Upper Ganga | E(o) | 1.06 | 4,537 | 228 | 0253N0900228 | 30.916 | 79.541 | Upper Ganga | M(o) | 2.71 | 5,385 |\n| 156 | 0253N0200156 | 30.726 | 79.038 | Upper Ganga | E(c) | 8.59 | 4,221 | 229 | 0253N0900229 | 30.910 | 79.746 | Upper Ganga | I(s) | 6.08 | 4,717 |\n| 157 | 0253N0200157 | 30.722 | 79.127 | Upper Ganga | E(o) | 3.10 | 4,467 | 230 | 0253N0900230 | 30.909 | 79.539 | Upper Ganga | M(o) | 5.11 | 5,349 |\n| 158 | 0253N0200158 | 30.660 | 79.249 | Upper Ganga | E(c) | 0.55 | 4,479 | 231 | 0253N0900231 | 30.905 | 79.736 | Upper Ganga | I(s) | 1.72 | 4,713 |\n| 159 | 0253N0500159 | 30.995 | 79.292 | Upper Ganga | M(l) | 0.65 | 5,553 | 232 | 0253N0900232 | 30.904 | 79.747 | Upper Ganga | I(s) | 11.05 | 4,683 |\n| 160 | 0253N0500160 | 30.995 | 79.410 | Upper Ganga | M(e) | 0.28 | 5,457 | 233 | 0253N0900233 | 30.903 | 79.732 | Upper Ganga | I(s) | 0.41 | 4,733 |\n| 161 | 0253N0500161 | 30.995 | 79.409 | Upper Ganga | M(o) | 0.46 | 5,461 | 234 | 0253N0900234 | 30.903 | 79.674 | Upper Ganga | M(o) | 1.14 | 5,441 |\n| 162 | 0253N0500162 | 30.994 | 79.354 | Upper Ganga | M(o) | 1.81 | 5,402 | 235 | 0253N0900235 | 30.901 | 79.746 | Upper Ganga | I(s) | 11.41 | 4,689 |\n| 163 | 0253N0500163 | 30.993 | 79.356 | Upper Ganga | M(o) | 0.75 | 5,365 | 236 | 0253N0900236 | 30.898 | 79.748 | Upper Ganga | M(o) | 0.98 | 4,697 |\n| 164 | 0253N0500164 | 30.992 | 79.303 | Upper Ganga | M(l) | 1.94 | 5,578 | 237 | 0253N0900237 | 30.895 | 79.718 | Upper Ganga | I(s) | 0.33 | 4,842 |\n| 165 | 0253N0500165 | 30.991 | 79.359 | Upper Ganga | M(o) | 3.29 | 5,342 | 238 | 0253N0900238 | 30.891 | 79.528 | Upper Ganga | M(o) | 2.05 | 5,398 |\n| 166 | 0253N0500166 | 30.984 | 79.350 | Upper Ganga | E(o) | 0.44 | 5,639 | 239 | 0253N0900239 | 30.891 | 79.704 | Upper Ganga | I(s) | 0.86 | 4,913 |\n| 167 | 0253N0500167 | 30.982 | 79.341 | Upper Ganga | M(o) | 0.30 | 5,612 | 240 | 0253N0900240 | 30.890 | 79.713 | Upper Ganga | I(s) | 0.77 | 4,874 |\n| 168 | 0253N0500168 | 30.982 | 79.368 | Upper Ganga | M(o) | 0.56 | 5,269 | 241 | 0253N0900241 | 30.888 | 79.737 | Upper Ganga | M(o) | 0.26 | 4,948 |\n| 169 | 0253N0500169 | 30.981 | 79.488 | Upper Ganga | M(o) | 5.60 | 5,656 | 242 | 0253N0900242 | 30.886 | 79.525 | Upper Ganga | E(c) | 1.06 | 5,359 |\n| 170 | 0253N0500170 | 30.981 | 79.367 | Upper Ganga | M(o) | 0.25 | 5,267 | 243 | 0253N0900243 | 30.884 | 79.669 | Upper Ganga | I(s) | 0.60 | 5,136 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 3599, "line_end": 3673, "token_count_estimate": 3238, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253N0100136", "0253N0100137", "0253N0100138", "0253N0100139", "0253N0100140", "0253N0100141", "0253N0100142", "0253N0100143", "0253N0100144", "0253N0100145", "0253N0100146", "0253N0100147", "0253N0100148", "0253N0100149", "0253N0100150", "0253N0100151", "0253N0100152", "0253N0100153", "0253N0200154", "0253N0200155", "0253N0200156", "0253N0200157", "0253N0200158", "0253N0500159", "0253N0500160", "0253N0500161", "0253N0500162", "0253N0500163", "0253N0500164", "0253N0500165", "0253N0500166", "0253N0500167", "0253N0500168", "0253N0500169", "0253N0500170", "0253N0500209", "0253N0600210", "0253N0600211", "0253N0600212", "0253N0600213", "0253N0600214", "0253N0600215", "0253N0600216", "0253N0600217", "0253N0900218", "0253N0900219", "0253N0900220", "0253N0900221", "0253N0900222", "0253N0900223"]}}
{"id": "4923724e1b2641d6", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 171 | 0253N0500171 | 30.978 | 79.364 | Upper Ganga | I(s) | 0.25 | 5,269 | 244 | 0253N0900244 | 30.881 | 79.532 | Upper Ganga | I(s) | 0.30 | 5,016 |\n| 172 | 0253N0500172 | 30.976 | 79.482 | Upper Ganga | M(o) | 0.57 | 5,618 | 245 | 0253N0900245 | 30.875 | 79.547 | Upper Ganga | M(o) | 0.25 | 5,503 |\n| 173 | 0253N0500173 | 30.976 | 79.460 | Upper Ganga | M(e) | 17.02 | 5,537 | 246 | 0253N0900246 | 30.875 | 79.550 | Upper Ganga | E(o) | 0.37 | 5,472 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 3599, "line_end": 3673, "token_count_estimate": 416, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253N0500171", "0253N0500172", "0253N0500173", "0253N0900244", "0253N0900245", "0253N0900246"]}}
{"id": "ea61147a82ab8d99", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 3674, "line_end": 3681, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d75470d5e9d4250", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 247 | 0253N0900247 | 30.870 | 79.736 | Upper Ganga | I(s) | 1.08 | 4,992 |\n| 248 | 0253N0900248 | 30.870 | 79.733 | Upper Ganga | I(s) | 0.25 | 5,027 |\n| 249 | 0253N0900249 | 30.869 | 79.552 | Upper Ganga | E(o) | 0.47 | 5,332 |\n| 250 | 0253N0900250 | 30.868 | 79.514 | Upper Ganga | I(s) | 0.58 | 4,878 |\n| 251 | 0253N0900251 | 30.840 | 79.525 | Upper Ganga | I(s) | 0.48 | 4,455 |\n| 252 | 0253N0900252 | 30.838 | 79.526 | Upper Ganga | I(s) | 1.63 | 4,468 |\n| 253 | 0253N0900253 | 30.800 | 79.707 | Upper Ganga | I(s) | 0.44 | 4,275 |\n| 254 | 0253N0900254 | 30.794 | 79.712 | Upper Ganga | I(s) | 0.40 | 4,282 |\n| 255 | 0253N0900255 | 30.761 | 79.580 | Upper Ganga | I(s) | 0.50 | 4,375 |\n| 256 | 0253N1000256 | 30.746 | 79.742 | Upper Ganga | I(s) | 0.29 | 4,556 |\n| 257 | 0253N1000257 | 30.746 | 79.745 | Upper Ganga | I(s) | 0.32 | 4,545 |\n| 258 | 0253N1000258 | 30.743 | 79.738 | Upper Ganga | I(s) | 0.50 | 4,586 |\n| 259 | 0253N1000259 | 30.730 | 79.574 | Upper Ganga | E(c) | 0.47 | 4,533 |\n| 260 | 0253N1000260 | 30.699 | 79.618 | Upper Ganga | E(c) | 9.74 | 4,138 |\n| 261 | 0253N1000261 | 30.639 | 79.695 | Upper Ganga | M(l) | 1.38 | 4,246 |\n| 262 | 0253N1000262 | 30.629 | 79.665 | Upper Ganga | E(c) | 7.52 | 4,361 |\n| 263 | 0253N1000263 | 30.584 | 79.700 | Upper Ganga | E(o) | 0.56 | 4,686 |\n| 264 | 0253N1100264 | 30.334 | 79.678 | Upper Ganga | E(o) | 0.51 | 4,448 |\n| 265 | 0253N1100265 | 30.333 | 79.677 | Upper Ganga | E(o) | 0.39 | 4,466 |\n| 266 | 0253N1100266 | 30.329 | 79.678 | Upper Ganga | E(o) | 1.09 | 4,493 |\n| 267 | 0253N1200267 | 30.177 | 79.589 | Upper Ganga | E(o) | 0.74 | 3,231 |\n| 268 | 0253N1300268 | 30.988 | 79.751 | Upper Ganga | E(c) | 1.02 | 5,626 |\n| 269 | 0253N1300269 | 30.978 | 79.751 | Upper Ganga | E(o) | 0.29 | 5,538 |\n| 270 | 0253N1300270 | 30.908 | 79.825 | Upper Ganga | E(c) | 8.11 | 4,898 |\n| 271 | 0253N1300271 | 30.901 | 79.754 | Upper Ganga | M(e) | 22.04 | 4,677 |\n| 272 | 0253N1300272 | 30.899 | 79.751 | Upper Ganga | M(o) | 0.71 | 4,685 |\n| 273 | 0253N1300273 | 30.898 | 79.754 | Upper Ganga | M(o) | 1.69 | 4,680 |\n| 274 | 0253N1300274 | 30.897 | 79.757 | Upper Ganga | M(o) | 0.76 | 4,681 |\n| 275 | 0253N1300275 | 30.897 | 79.756 | Upper Ganga | M(o) | 0.26 | 4,684 |\n| 276 | 0253N1300276 | 30.879 | 79.944 | Upper Ganga | E(o) | 0.46 | 5,209 |\n| 277 | 0253N1300277 | 30.864 | 79.975 | Upper Ganga | E(o) | 0.29 | 4,664 |\n| 278 | 0253N1300278 | 30.863 | 79.980 | Upper Ganga | E(o) | 0.42 | 4,704 |\n| 279 | 0253N1300279 | 30.837 | 79.905 | Upper Ganga | E(c) | 0.52 | 5,501 |\n| 280 | 0253N1300280 | 30.835 | 79.920 | Upper Ganga | M(o) | 0.26 | 5,183 |\n| 281 | 0253N1300281 | 30.830 | 79.894 | Upper Ganga | M(e) | 4.83 | 5,189 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 3682, "line_end": 3756, "token_count_estimate": 1669, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253N0900247", "0253N0900248", "0253N0900249", "0253N0900250", "0253N0900251", "0253N0900252", "0253N0900253", "0253N0900254", "0253N0900255", "0253N1000256", "0253N1000257", "0253N1000258", "0253N1000259", "0253N1000260", "0253N1000261", "0253N1000262", "0253N1000263", "0253N1100264", "0253N1100265", "0253N1100266", "0253N1200267", "0253N1300268", "0253N1300269", "0253N1300270", "0253N1300271", "0253N1300272", "0253N1300273", "0253N1300274", "0253N1300275", "0253N1300276", "0253N1300277", "0253N1300278", "0253N1300279", "0253N1300280", "0253N1300281"]}}
{"id": "e25653d5e297754a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 282 | 0253N1300282 | 30.826 | 79.761 | Upper Ganga | M(o) | 0.86 | 4,915 |\n| 283 | 0253N1300283 | 30.826 | 79.920 | Upper Ganga | M(o) | 0.33 | 5,291 |\n| 284 | 0253N1300284 | 30.825 | 79.918 | Upper Ganga | M(o) | 0.31 | 5,297 |\n| 285 | 0253N1300285 | 30.823 | 79.772 | Upper Ganga | M(o) | 0.31 | 4,865 |\n| 286 | 0253N1300286 | 30.814 | 79.926 | Upper Ganga | M(o) | 5.02 | 4,944 |\n| 287 | 0253N1300287 | 30.811 | 79.921 | Upper Ganga | M(e) | 2.94 | 5,001 |\n| 288 | 0253N1300288 | 30.810 | 79.912 | Upper Ganga | E(o) | 1.16 | 5,125 |\n| 289 | 0253N1400289 | 30.750 | 79.754 | Upper Ganga | I(s) | 0.27 | 4,473 |\n| 290 | 0253N1400290 | 30.747 | 79.768 | Upper Ganga | I(s) | 0.30 | 4,368 |\n| 291 | 0253N1400291 | 30.701 | 79.782 | Upper Ganga | I(s) | 0.55 | 4,146 |\n| 292 | 0253N1400292 | 30.695 | 79.773 | Upper Ganga | I(s) | 0.84 | 4,232 |\n| 293 | 0253N1400293 | 30.567 | 79.893 | Upper Ganga | I(s) | 0.25 | 4,483 |\n| 294 | 0253N1400294 | 30.559 | 79.946 | Upper Ganga | M(l) | 0.43 | 4,698 |\n| 295 | 0253N1400295 | 30.557 | 79.893 | Upper Ganga | I(s) | 0.45 | 4,576 |\n| 296 | 0253N1400296 | 30.555 | 79.947 | Upper Ganga | I(s) | 0.42 | 4,684 |\n| 297 | 0253N1400297 | 30.551 | 79.950 | Upper Ganga | I(s) | 0.42 | 4,724 |\n| 298 | 0253N1400298 | 30.550 | 79.950 | Upper Ganga | I(s) | 0.35 | 4,714 |\n| 299 | 0253N1400299 | 30.541 | 79.938 | Upper Ganga | M(o) | 0.45 | 4,916 |\n| 300 | 0253N1400300 | 30.518 | 79.919 | Upper Ganga | I(s) | 0.28 | 5,367 |\n| 301 | 0253N1400301 | 30.501 | 79.988 | Upper Ganga | I(s) | 0.27 | 5,164 |\n| 302 | 0253N1500302 | 30.493 | 79.871 | Upper Ganga | I(s) | 0.35 | 5,420 |\n| 303 | 0253N1500303 | 30.485 | 79.875 | Upper Ganga | I(s) | 0.41 | 5,315 |\n| 304 | 0253N1500304 | 30.484 | 79.991 | Upper Ganga | I(s) | 0.29 | 5,068 |\n| 305 | 0253N1500305 | 30.483 | 79.932 | Upper Ganga | I(s) | 0.31 | 5,348 |\n| 306 | 0253N1500306 | 30.481 | 79.874 | Upper Ganga | M(o) | 3.35 | 5,288 |\n| 307 | 0253N1500307 | 30.480 | 79.987 | Upper Ganga | I(s) | 0.26 | 5,022 |\n| 308 | 0253N1500308 | 30.480 | 79.984 | Upper Ganga | I(s) | 0.27 | 5,008 |\n| 309 | 0253N1500309 | 30.476 | 79.986 | Upper Ganga | I(s) | 0.40 | 4,995 |\n| 310 | 0253N1500310 | 30.473 | 79.982 | Upper Ganga | I(s) | 0.27 | 4,954 |\n| 311 | 0253N1500311 | 30.472 | 79.986 | Upper Ganga | I(s) | 0.25 | 4,958 |\n| 312 | 0253N1500312 | 30.445 | 79.975 | Upper Ganga | I(s) | 0.27 | 4,631 |\n| 313 | 0253N1500313 | 30.435 | 79.961 | Upper Ganga | I(s) | 0.28 | 4,490 |\n| 314 | 0253N1500314 | 30.435 | 79.964 | Upper Ganga | I(s) | 0.27 | 4,500 |\n| 315 | 0253N1500315 | 30.424 | 79.976 | Upper Ganga | I(s) | 0.54 | 4,575 |\n| 316 | 0253N1500316 | 30.419 | 79.979 | Upper Ganga | I(s) | 0.69 | 4,613 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 3682, "line_end": 3756, "token_count_estimate": 1669, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253N1300282", "0253N1300283", "0253N1300284", "0253N1300285", "0253N1300286", "0253N1300287", "0253N1300288", "0253N1400289", "0253N1400290", "0253N1400291", "0253N1400292", "0253N1400293", "0253N1400294", "0253N1400295", "0253N1400296", "0253N1400297", "0253N1400298", "0253N1400299", "0253N1400300", "0253N1400301", "0253N1500302", "0253N1500303", "0253N1500304", "0253N1500305", "0253N1500306", "0253N1500307", "0253N1500308", "0253N1500309", "0253N1500310", "0253N1500311", "0253N1500312", "0253N1500313", "0253N1500314", "0253N1500315", "0253N1500316"]}}
{"id": "b4deb15161cdee1b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 317 | 0253N1500317 | 30.417 | 79.976 | Upper Ganga | I(s) | 0.26 | 4,618 |\n| 318 | 0253N1500318 | 30.365 | 79.766 | Upper Ganga | I(s) | 0.63 | 4,685 |\n| 319 | 0253N1500319 | 30.349 | 79.914 | Upper Ganga | E(o) | 0.35 | 5,104 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 3682, "line_end": 3756, "token_count_estimate": 232, "basins": ["Ganga"], "subbasins": ["Upper Ganga"], "countries": [], "lake_ids": ["0253N1500317", "0253N1500318", "0253N1500319"]}}
{"id": "ff005c259e944129", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 320 | 0253N1500320 | 30.347 | 79.933 | Upper Ganga | I(s) | 0.67 | 4,621 |\n| 321 | 0253N1500321 | 30.318 | 79.915 | Upper Ganga | I(s) | 0.42 | 4,832 |\n| 322 | 0253N1500322 | 30.312 | 79.928 | Upper Ganga | E(o) | 0.58 | 4,942 |\n| 323 | 0253N1500323 | 30.266 | 79.836 | Upper Ganga | I(s) | 0.26 | 4,736 |\n| 324 | 0253N1500324 | 30.266 | 79.838 | Upper Ganga | I(s) | 0.64 | 4,721 |\n| 325 | 0253N1500325 | 30.263 | 79.837 | Upper Ganga | I(s) | 0.32 | 4,743 |\n| 326 | 0253N1500326 | 30.262 | 79.854 | Upper Ganga | I(s) | 0.77 | 4,629 |\n| 327 | 0253N1600327 | 30.238 | 79.999 | Upper Ganga | O | 0.41 | 3,701 |\n| 328 | 0253N1600328 | 30.201 | 79.888 | Upper Ganga | E(o) | 0.76 | 4,278 |\n| 329 | 0253N1600329 | 30.184 | 79.876 | Upper Ganga | E(c) | 4.67 | 4,462 |\n| 330 | 0262B0200330 | 30.584 | 80.165 | Upper Ganga | M(o) | 3.29 | 5,078 |\n| 331 | 0262B0200331 | 30.565 | 80.179 | Sarda | M(e) | 17.81 | 4,872 |\n| 332 | 0262B0300332 | 30.484 | 80.156 | Sarda | M(o) | 0.54 | 5,061 |\n| 333 | 0262B0300333 | 30.381 | 80.239 | Sarda | M(o) | 0.25 | 3,975 |\n| 334 | 0262B0300334 | 30.380 | 80.237 | Sarda | M(o) | 0.39 | 3,954 |\n| 335 | 0262B0400335 | 30.219 | 80.070 | Upper Ganga | E(o) | 1.35 | 4,469 |\n| 336 | 0262B0400336 | 30.210 | 80.136 | Sarda | E(c) | 9.66 | 4,340 |\n| 337 | 0262B0400337 | 30.181 | 80.158 | Sarda | E(o) | 0.52 | 4,571 |\n| 338 | 0262B0600338 | 30.545 | 80.256 | Sarda | E(o) | 0.35 | 5,262 |\n| 339 | 0262B0600339 | 30.541 | 80.283 | Sarda | E(o) | 0.72 | 5,176 |\n| 340 | 0262B0600340 | 30.529 | 80.283 | Sarda | M(o) | 0.39 | 5,262 |\n| 341 | 0262B0600341 | 30.509 | 80.411 | Sarda | M(o) | 0.57 | 5,110 |\n| 342 | 0262B0700342 | 30.491 | 80.375 | Sarda | E(c) | 5.28 | 5,078 |\n| 343 | 0262B0700343 | 30.475 | 80.333 | Sarda | I(s) | 0.28 | 4,580 |\n| 344 | 0262B0700344 | 30.474 | 80.327 | Sarda | I(s) | 0.26 | 4,640 |\n| 345 | 0262B0700345 | 30.469 | 80.315 | Sarda | I(s) | 0.46 | 4,717 |\n| 346 | 0262B0700346 | 30.466 | 80.309 | Sarda | I(s) | 0.91 | 4,769 |\n| 347 | 0262B0700347 | 30.458 | 80.369 | Sarda | M(o) | 0.54 | 4,406 |\n| 348 | 0262B0700348 | 30.448 | 80.427 | Sarda | M(o) | 0.45 | 5,105 |\n| 349 | 0262B0700349 | 30.446 | 80.387 | Sarda | M(e) | 10.00 | 4,306 |\n| 350 | 0262B0700350 | 30.400 | 80.436 | Sarda | O | 0.28 | 3,887 |\n| 351 | 0262B0700351 | 30.276 | 80.452 | Sarda | M(o) | 0.74 | 4,257 |\n| 352 | 0262B1100352 | 30.456 | 80.516 | Sarda | M(o) | 3.05 | 5,249 |\n| 353 | 0262B1100353 | 30.453 | 80.720 | Ghaghara | M(o) | 1.02 | 5,558 |\n| 354 | 0262B1100354 | 30.452 | 80.722 | Ghaghara | M(o) | 0.31 | 5,536 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 3758, "line_end": 3832, "token_count_estimate": 1648, "basins": ["Ganga"], "subbasins": ["Ghaghara", "Sarda", "Upper Ganga"], "countries": [], "lake_ids": ["0253N1500320", "0253N1500321", "0253N1500322", "0253N1500323", "0253N1500324", "0253N1500325", "0253N1500326", "0253N1600327", "0253N1600328", "0253N1600329", "0262B0200330", "0262B0200331", "0262B0300332", "0262B0300333", "0262B0300334", "0262B0400335", "0262B0400336", "0262B0400337", "0262B0600338", "0262B0600339", "0262B0600340", "0262B0600341", "0262B0700342", "0262B0700343", "0262B0700344", "0262B0700345", "0262B0700346", "0262B0700347", "0262B0700348", "0262B0700349", "0262B0700350", "0262B0700351", "0262B1100352", "0262B1100353", "0262B1100354"]}}
{"id": "c784c1d1e64ca0c5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 355 | 0262B1100355 | 30.417 | 80.682 | Sarda | E(c) | 2.11 | 5,396 |\n| 356 | 0262B1100356 | 30.409 | 80.510 | Sarda | M(o) | 0.70 | 4,725 |\n| 357 | 0262B1100357 | 30.408 | 80.511 | Sarda | M(o) | 2.18 | 4,712 |\n| 358 | 0262B1100358 | 30.392 | 80.532 | Sarda | M(e) | 11.21 | 4,753 |\n| 359 | 0262B1100359 | 30.388 | 80.525 | Sarda | M(o) | 1.16 | 4,735 |\n| 360 | 0262B1100360 | 30.382 | 80.576 | Sarda | E(o) | 2.61 | 5,385 |\n| 361 | 0262B1100361 | 30.381 | 80.592 | Sarda | M(o) | 0.49 | 5,145 |\n| 362 | 0262B1100362 | 30.373 | 80.603 | Sarda | M(o) | 1.19 | 4,822 |\n| 363 | 0262B1100363 | 30.357 | 80.628 | Sarda | M(o) | 0.66 | 4,828 |\n| 364 | 0262B1100364 | 30.354 | 80.655 | Sarda | M(o) | 5.48 | 4,504 |\n| 365 | 0262B1100365 | 30.336 | 80.644 | Sarda | M(o) | 2.19 | 4,608 |\n| 366 | 0262B1100366 | 30.324 | 80.590 | Sarda | M(e) | 1.13 | 4,345 |\n| 367 | 0262B1100367 | 30.267 | 80.591 | Sarda | M(o) | 1.77 | 4,995 |\n| 368 | 0262B1100368 | 30.264 | 80.713 | Sarda | M(e) | 2.27 | 4,525 |\n| 369 | 0262B1200369 | 30.108 | 80.511 | Sarda | E(o) | 1.07 | 4,451 |\n| 370 | 0262B1500370 | 30.412 | 80.763 | Ghaghara | M(o) | 4.17 | 5,557 |\n| 371 | 0262B1500371 | 30.405 | 80.762 | Ghaghara | M(o) | 3.25 | 5,445 |\n| 372 | 0262B1500372 | 30.402 | 80.784 | Ghaghara | M(e) | 43.35 | 5,088 |\n| 373 | 0262B1500373 | 30.398 | 80.841 | Ghaghara | M(o) | 0.39 | 5,391 |\n| 374 | 0262B1500374 | 30.366 | 80.892 | Ghaghara | E(o) | 0.92 | 5,624 |\n| 375 | 0262B1500375 | 30.359 | 80.912 | Ghaghara | E(o) | 0.46 | 5,507 |\n| 376 | 0262B1500376 | 30.359 | 80.915 | Ghaghara | M(o) | 0.97 | 5,533 |\n| 377 | 0262B1500377 | 30.350 | 80.877 | Ghaghara | E(o) | 2.86 | 5,329 |\n| 378 | 0262B1500378 | 30.349 | 80.888 | Ghaghara | E(o) | 0.43 | 5,262 |\n| 379 | 0262B1500379 | 30.347 | 80.886 | Ghaghara | E(o) | 12.47 | 5,253 |\n| 380 | 0262B1500380 | 30.335 | 80.760 | Sarda | E(o) | 0.30 | 5,070 |\n| 381 | 0262B1500381 | 30.334 | 80.850 | Ghaghara | M(o) | 0.41 | 5,299 |\n| 382 | 0262B1500382 | 30.301 | 80.904 | Sarda | E(o) | 1.08 | 5,356 |\n| 383 | 0262B1500383 | 30.298 | 80.899 | Sarda | E(c) | 3.33 | 5,156 |\n| 384 | 0262B1500384 | 30.283 | 80.852 | Sarda | E(o) | 0.29 | 5,314 |\n| 385 | 0262B1500385 | 30.280 | 80.854 | Sarda | E(o) | 1.07 | 5,244 |\n| 386 | 0262B1600386 | 30.055 | 80.984 | Sarda | M(o) | 0.63 | 4,173 |\n| 387 | 0262B1600387 | 30.053 | 80.883 | Sarda | M(l) | 3.11 | 4,342 |\n| 388 | 0262B1600388 | 30.049 | 80.887 | Sarda | M(l) | 2.01 | 4,414 |\n| 389 | 0262B1600389 | 30.041 | 80.878 | Sarda | M(l) | 5.96 | 4,430 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 3758, "line_end": 3832, "token_count_estimate": 1621, "basins": [], "subbasins": ["Ghaghara", "Sarda"], "countries": [], "lake_ids": ["0262B1100355", "0262B1100356", "0262B1100357", "0262B1100358", "0262B1100359", "0262B1100360", "0262B1100361", "0262B1100362", "0262B1100363", "0262B1100364", "0262B1100365", "0262B1100366", "0262B1100367", "0262B1100368", "0262B1200369", "0262B1500370", "0262B1500371", "0262B1500372", "0262B1500373", "0262B1500374", "0262B1500375", "0262B1500376", "0262B1500377", "0262B1500378", "0262B1500379", "0262B1500380", "0262B1500381", "0262B1500382", "0262B1500383", "0262B1500384", "0262B1500385", "0262B1600386", "0262B1600387", "0262B1600388", "0262B1600389"]}}
{"id": "b10929df6829064e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 390 | 0262B1600390 | 30.035 | 80.883 | Sarda | M(l) | 0.29 | 4,457 |\n| 391 | 0262C1300391 | 29.974 | 80.904 | Sarda | M(l) | 2.99 | 4,188 |\n| 392 | 0262C1300392 | 29.962 | 80.975 | Sarda | M(o) | 0.51 | 4,174 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 3758, "line_end": 3832, "token_count_estimate": 222, "basins": [], "subbasins": ["Sarda"], "countries": [], "lake_ids": ["0262B1600390", "0262C1300391", "0262C1300392"]}}
{"id": "4e8ad1bfce6d7464", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 3833, "line_end": 3841, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5075f4ac5cf9367f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 393 | 0262C1300393 | 29.955 | 80.969 | Sarda | M(o) | 0.36 | 4,152 |\n| 394 | 0262C1300394 | 29.921 | 80.812 | Sarda | E(o) | 0.47 | 4,854 |\n| 395 | 0262C1300395 | 29.864 | 80.958 | Sarda | E(o) | 1.66 | 4,809 |\n| 396 | 0262C1300396 | 29.862 | 80.955 | Sarda | E(o) | 3.18 | 4,724 |\n| 397 | 0262F0300397 | 30.373 | 81.238 | Ghaghara | E(o) | 0.29 | 5,484 |\n| 398 | 0262F0300398 | 30.369 | 81.241 | Ghaghara | M(o) | 0.64 | 5,416 |\n| 399 | 0262F0300399 | 30.368 | 81.239 | Ghaghara | M(o) | 0.35 | 5,423 |\n| 400 | 0262F0300400 | 30.291 | 81.099 | Ghaghara | E(o) | 0.78 | 4,134 |\n| 401 | 0262F0300401 | 30.288 | 81.122 | Ghaghara | E(o) | 1.04 | 4,058 |\n| 402 | 0262F0400402 | 30.189 | 81.017 | Sarda | E(o) | 0.71 | 4,934 |\n| 403 | 0262F0400403 | 30.147 | 81.099 | Ghaghara | M(e) | 1.92 | 5,211 |\n| 404 | 0262F0400404 | 30.139 | 81.101 | Ghaghara | M(e) | 0.52 | 5,020 |\n| 405 | 0262F0400405 | 30.077 | 81.051 | Sarda | I(s) | 0.43 | 4,280 |\n| 406 | 0262F0400406 | 30.077 | 81.207 | Ghaghara | O | 0.33 | 4,304 |\n| 407 | 0262F0400407 | 30.074 | 81.236 | Ghaghara | E(o) | 0.85 | 5,297 |\n| 408 | 0262F0400408 | 30.068 | 81.067 | Sarda | I(s) | 0.79 | 4,452 |\n| 409 | 0262F0400409 | 30.066 | 81.212 | Ghaghara | O | 0.26 | 4,498 |\n| 410 | 0262F0400410 | 30.063 | 81.214 | Ghaghara | O | 0.84 | 4,511 |\n| 411 | 0262F0400411 | 30.063 | 81.217 | Ghaghara | O | 1.82 | 4,523 |\n| 412 | 0262F0400412 | 30.061 | 81.218 | Ghaghara | O | 0.44 | 4,525 |\n| 413 | 0262F0400413 | 30.056 | 81.196 | Ghaghara | I(s) | 0.85 | 4,474 |\n| 414 | 0262F0400414 | 30.048 | 81.185 | Ghaghara | I(s) | 0.97 | 4,609 |\n| 415 | 0262F0400415 | 30.043 | 81.178 | Ghaghara | I(s) | 0.25 | 4,665 |\n| 416 | 0262F0400416 | 30.042 | 81.243 | Ghaghara | M(o) | 0.92 | 5,017 |\n| 417 | 0262F0400417 | 30.042 | 81.182 | Ghaghara | I(s) | 0.27 | 4,641 |\n| 418 | 0262F0400418 | 30.040 | 81.182 | Ghaghara | I(s) | 0.26 | 4,656 |\n| 419 | 0262F0400419 | 30.039 | 81.177 | Ghaghara | I(s) | 1.26 | 4,668 |\n| 420 | 0262F0400420 | 30.038 | 81.179 | Ghaghara | I(s) | 0.66 | 4,670 |\n| 421 | 0262F0400421 | 30.038 | 81.181 | Ghaghara | I(s) | 0.44 | 4,667 |\n| 422 | 0262F0400422 | 30.034 | 81.149 | Ghaghara | I(s) | 0.26 | 4,912 |\n| 423 | 0262F0400423 | 30.034 | 81.175 | Ghaghara | I(s) | 0.75 | 4,702 |\n| 424 | 0262F0400424 | 30.033 | 81.146 | Ghaghara | I(s) | 0.59 | 4,915 |\n| 425 | 0262F0400425 | 30.032 | 81.175 | Ghaghara | I(s) | 0.34 | 4,723 |\n| 426 | 0262F0400426 | 30.032 | 81.137 | Ghaghara | I(s) | 0.38 | 4,971 |\n| 427 | 0262F0400427 | 30.031 | 81.172 | Ghaghara | I(s) | 0.44 | 4,721 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 3842, "line_end": 3916, "token_count_estimate": 1636, "basins": [], "subbasins": ["Ghaghara", "Sarda"], "countries": [], "lake_ids": ["0262C1300393", "0262C1300394", "0262C1300395", "0262C1300396", "0262F0300397", "0262F0300398", "0262F0300399", "0262F0300400", "0262F0300401", "0262F0400402", "0262F0400403", "0262F0400404", "0262F0400405", "0262F0400406", "0262F0400407", "0262F0400408", "0262F0400409", "0262F0400410", "0262F0400411", "0262F0400412", "0262F0400413", "0262F0400414", "0262F0400415", "0262F0400416", "0262F0400417", "0262F0400418", "0262F0400419", "0262F0400420", "0262F0400421", "0262F0400422", "0262F0400423", "0262F0400424", "0262F0400425", "0262F0400426", "0262F0400427"]}}
{"id": "54260507c820b6bd", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 428 | 0262F0400428 | 30.030 | 81.168 | Ghaghara | I(s) | 0.35 | 4,779 |\n| 429 | 0262F0400429 | 30.024 | 81.092 | Ghaghara | I(s) | 0.58 | 4,827 |\n| 430 | 0262F0400430 | 30.021 | 81.094 | Ghaghara | I(s) | 0.50 | 4,813 |\n| 431 | 0262F0400431 | 30.021 | 81.085 | Ghaghara | M(l) | 0.49 | 4,821 |\n| 432 | 0262F0400432 | 30.020 | 81.080 | Ghaghara | M(o) | 0.29 | 4,817 |\n| 433 | 0262F0400433 | 30.007 | 81.065 | Ghaghara | M(o) | 0.70 | 4,902 |\n| 434 | 0262F0400434 | 30.001 | 81.065 | Ghaghara | M(o) | 3.29 | 4,749 |\n| 435 | 0262F0700435 | 30.461 | 81.336 | Ghaghara | M(o) | 0.94 | 6,014 |\n| 436 | 0262F0700436 | 30.431 | 81.311 | Ghaghara | I(s) | 0.27 | 5,859 |\n| 437 | 0262F0700437 | 30.430 | 81.314 | Ghaghara | I(s) | 1.11 | 5,852 |\n| 438 | 0262F0700438 | 30.430 | 81.324 | Ghaghara | M(o) | 0.60 | 5,931 |\n| 439 | 0262F0700439 | 30.429 | 81.317 | Ghaghara | I(s) | 0.96 | 5,852 |\n| 440 | 0262F0700440 | 30.426 | 81.367 | Ghaghara | E(o) | 0.96 | 5,779 |\n| 441 | 0262F0700441 | 30.424 | 81.330 | Ghaghara | M(o) | 12.68 | 5,807 |\n| 442 | 0262F0700442 | 30.424 | 81.371 | Ghaghara | E(o) | 7.07 | 5,745 |\n| 443 | 0262F0700443 | 30.423 | 81.367 | Ghaghara | E(o) | 0.86 | 5,791 |\n| 444 | 0262F0700444 | 30.421 | 81.318 | Ghaghara | I(s) | 0.26 | 5,773 |\n| 445 | 0262F0700445 | 30.420 | 81.365 | Ghaghara | E(o) | 0.78 | 5,793 |\n| 446 | 0262F0700446 | 30.419 | 81.354 | Ghaghara | E(c) | 2.47 | 5,790 |\n| 447 | 0262F0700447 | 30.417 | 81.427 | Ghaghara | O | 17.88 | 5,496 |\n| 448 | 0262F0700448 | 30.415 | 81.326 | Ghaghara | I(s) | 0.25 | 5,717 |\n| 449 | 0262F0700449 | 30.413 | 81.322 | Ghaghara | I(s) | 0.55 | 5,701 |\n| 450 | 0262F0700450 | 30.412 | 81.323 | Ghaghara | I(s) | 0.46 | 5,679 |\n| 451 | 0262F0700451 | 30.411 | 81.323 | Ghaghara | I(s) | 0.56 | 5,685 |\n| 452 | 0262F0700452 | 30.407 | 81.423 | Ghaghara | O | 2.27 | 5,478 |\n| 453 | 0262F0700453 | 30.405 | 81.427 | Ghaghara | O | 11.18 | 5,472 |\n| 454 | 0262F0700454 | 30.404 | 81.452 | Ghaghara | O | 3.49 | 5,696 |\n| 455 | 0262F0700455 | 30.403 | 81.339 | Ghaghara | E(o) | 4.06 | 5,583 |\n| 456 | 0262F0700456 | 30.402 | 81.363 | Ghaghara | E(o) | 6.95 | 5,590 |\n| 457 | 0262F0700457 | 30.400 | 81.363 | Ghaghara | E(o) | 0.86 | 5,593 |\n| 458 | 0262F0700458 | 30.399 | 81.293 | Ghaghara | M(o) | 3.07 | 5,511 |\n| 459 | 0262F0700459 | 30.390 | 81.313 | Ghaghara | I(s) | 0.25 | 5,434 |\n| 460 | 0262F0700460 | 30.390 | 81.325 | Ghaghara | E(o) | 0.71 | 5,460 |\n| 461 | 0262F0700461 | 30.378 | 81.421 | Ghaghara | E(o) | 25.56 | 5,659 |\n| 462 | 0262F0700462 | 30.373 | 81.292 | Ghaghara | M(o) | 0.88 | 5,507 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 3842, "line_end": 3916, "token_count_estimate": 1634, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F0400428", "0262F0400429", "0262F0400430", "0262F0400431", "0262F0400432", "0262F0400433", "0262F0400434", "0262F0700435", "0262F0700436", "0262F0700437", "0262F0700438", "0262F0700439", "0262F0700440", "0262F0700441", "0262F0700442", "0262F0700443", "0262F0700444", "0262F0700445", "0262F0700446", "0262F0700447", "0262F0700448", "0262F0700449", "0262F0700450", "0262F0700451", "0262F0700452", "0262F0700453", "0262F0700454", "0262F0700455", "0262F0700456", "0262F0700457", "0262F0700458", "0262F0700459", "0262F0700460", "0262F0700461", "0262F0700462"]}}
{"id": "2607cd554c7f938a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 463 | 0262F0700463 | 30.366 | 81.360 | Ghaghara | E(o) | 0.40 | 5,844 |\n| 464 | 0262F0700464 | 30.364 | 81.376 | Ghaghara | E(o) | 10.33 | 5,756 |\n| 465 | 0262F0700465 | 30.363 | 81.251 | Ghaghara | M(o) | 0.81 | 5,413 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 3842, "line_end": 3916, "token_count_estimate": 232, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F0700463", "0262F0700464", "0262F0700465"]}}
{"id": "f3d7c2a0f61aef3b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 466 | 0262F0700466 | 30.363 | 81.364 | Ghaghara | M(o) | 0.93 | 5,724 |\n| 467 | 0262F0700467 | 30.362 | 81.386 | Ghaghara | E(o) | 4.75 | 5,616 |\n| 468 | 0262F0700468 | 30.360 | 81.415 | Ghaghara | E(o) | 0.66 | 5,708 |\n| 469 | 0262F0700469 | 30.357 | 81.440 | Ghaghara | M(e) | 2.86 | 5,507 |\n| 470 | 0262F0700470 | 30.356 | 81.428 | Ghaghara | O | 0.37 | 5,722 |\n| 471 | 0262F0700471 | 30.354 | 81.334 | Ghaghara | E(o) | 0.57 | 5,855 |\n| 472 | 0262F0700472 | 30.353 | 81.351 | Ghaghara | M(o) | 1.77 | 5,837 |\n| 473 | 0262F0700473 | 30.347 | 81.332 | Ghaghara | I(s) | 1.45 | 5,757 |\n| 474 | 0262F0700474 | 30.343 | 81.389 | Ghaghara | E(o) | 1.76 | 5,765 |\n| 475 | 0262F0700475 | 30.343 | 81.413 | Ghaghara | E(o) | 30.39 | 5,734 |\n| 476 | 0262F0700476 | 30.343 | 81.462 | Ghaghara | M(o) | 0.99 | 5,466 |\n| 477 | 0262F0700477 | 30.342 | 81.467 | Ghaghara | M(o) | 0.44 | 5,442 |\n| 478 | 0262F0700478 | 30.342 | 81.458 | Ghaghara | M(o) | 1.96 | 5,469 |\n| 479 | 0262F0700479 | 30.340 | 81.465 | Ghaghara | M(e) | 1.80 | 5,439 |\n| 480 | 0262F0700480 | 30.339 | 81.462 | Ghaghara | M(o) | 0.26 | 5,465 |\n| 481 | 0262F0700481 | 30.338 | 81.399 | Ghaghara | E(o) | 1.85 | 5,718 |\n| 482 | 0262F0700482 | 30.329 | 81.418 | Ghaghara | I(s) | 2.06 | 5,705 |\n| 483 | 0262F0700483 | 30.324 | 81.406 | Ghaghara | M(o) | 1.02 | 5,731 |\n| 484 | 0262F0700484 | 30.323 | 81.486 | Ghaghara | M(o) | 1.38 | 5,313 |\n| 485 | 0262F0700485 | 30.322 | 81.376 | Ghaghara | M(e) | 13.96 | 5,542 |\n| 486 | 0262F0700486 | 30.321 | 81.358 | Ghaghara | M(o) | 0.25 | 5,436 |\n| 487 | 0262F0700487 | 30.320 | 81.450 | Ghaghara | M(e) | 3.15 | 5,486 |\n| 488 | 0262F0700488 | 30.319 | 81.414 | Ghaghara | M(o) | 1.24 | 5,621 |\n| 489 | 0262F0700489 | 30.319 | 81.445 | Ghaghara | M(o) | 12.20 | 5,497 |\n| 490 | 0262F0700490 | 30.318 | 81.423 | Ghaghara | M(o) | 0.28 | 5,567 |\n| 491 | 0262F0700491 | 30.317 | 81.448 | Ghaghara | M(e) | 5.13 | 5,477 |\n| 492 | 0262F0700492 | 30.315 | 81.360 | Ghaghara | M(e) | 4.15 | 5,476 |\n| 493 | 0262F0700493 | 30.314 | 81.399 | Ghaghara | M(e) | 9.47 | 5,631 |\n| 494 | 0262F0700494 | 30.312 | 81.409 | Ghaghara | M(e) | 13.27 | 5,634 |\n| 495 | 0262F0700495 | 30.311 | 81.459 | Ghaghara | M(o) | 0.84 | 5,382 |\n| 496 | 0262F0700496 | 30.311 | 81.426 | Ghaghara | M(o) | 0.38 | 5,537 |\n| 497 | 0262F0700497 | 30.304 | 81.401 | Ghaghara | M(o) | 0.44 | 5,580 |\n| 498 | 0262F0700498 | 30.304 | 81.402 | Ghaghara | M(o) | 0.92 | 5,588 |\n| 499 | 0262F0700499 | 30.303 | 81.391 | Ghaghara | M(o) | 4.80 | 5,521 |\n| 500 | 0262F0700500 | 30.302 | 81.399 | Ghaghara | M(e) | 11.68 | 5,580 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 3918, "line_end": 3992, "token_count_estimate": 1652, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F0700466", "0262F0700467", "0262F0700468", "0262F0700469", "0262F0700470", "0262F0700471", "0262F0700472", "0262F0700473", "0262F0700474", "0262F0700475", "0262F0700476", "0262F0700477", "0262F0700478", "0262F0700479", "0262F0700480", "0262F0700481", "0262F0700482", "0262F0700483", "0262F0700484", "0262F0700485", "0262F0700486", "0262F0700487", "0262F0700488", "0262F0700489", "0262F0700490", "0262F0700491", "0262F0700492", "0262F0700493", "0262F0700494", "0262F0700495", "0262F0700496", "0262F0700497", "0262F0700498", "0262F0700499", "0262F0700500"]}}
{"id": "f2ea4dd81529b678", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 501 | 0262F0700501 | 30.302 | 81.387 | Ghaghara | M(o) | 0.90 | 5,512 |\n| 502 | 0262F0700502 | 30.301 | 81.496 | Ghaghara | E(o) | 0.81 | 5,121 |\n| 503 | 0262F0700503 | 30.300 | 81.499 | Ghaghara | E(o) | 0.39 | 5,130 |\n| 504 | 0262F0700504 | 30.297 | 81.388 | Ghaghara | M(e) | 26.15 | 5,516 |\n| 505 | 0262F0700505 | 30.296 | 81.453 | Ghaghara | M(o) | 3.65 | 4,988 |\n| 506 | 0262F0700506 | 30.296 | 81.493 | Ghaghara | E(o) | 0.58 | 5,174 |\n| 507 | 0262F0700507 | 30.295 | 81.449 | Ghaghara | M(o) | 4.72 | 4,985 |\n| 508 | 0262F0700508 | 30.294 | 81.375 | Ghaghara | M(e) | 8.72 | 5,428 |\n| 509 | 0262F0700509 | 30.290 | 81.364 | Ghaghara | M(e) | 7.57 | 5,427 |\n| 510 | 0262F0700510 | 30.289 | 81.500 | Ghaghara | E(o) | 3.68 | 5,038 |\n| 511 | 0262F0700511 | 30.285 | 81.474 | Ghaghara | M(o) | 0.53 | 5,309 |\n| 512 | 0262F0700512 | 30.285 | 81.475 | Ghaghara | M(o) | 1.19 | 5,310 |\n| 513 | 0262F0700513 | 30.281 | 81.482 | Ghaghara | E(o) | 0.25 | 5,255 |\n| 514 | 0262F0700514 | 30.280 | 81.457 | Ghaghara | M(o) | 0.99 | 5,381 |\n| 515 | 0262F0700515 | 30.279 | 81.349 | Ghaghara | M(o) | 0.85 | 5,446 |\n| 516 | 0262F0700516 | 30.277 | 81.457 | Ghaghara | M(lg) | 0.56 | 5,433 |\n| 517 | 0262F0700517 | 30.272 | 81.483 | Ghaghara | M(o) | 0.56 | 5,208 |\n| 518 | 0262F0700518 | 30.270 | 81.481 | Ghaghara | M(o) | 0.41 | 5,265 |\n| 519 | 0262F0700519 | 30.268 | 81.438 | Ghaghara | I(s) | 3.08 | 5,273 |\n| 520 | 0262F0700520 | 30.266 | 81.349 | Ghaghara | M(e) | 20.37 | 5,207 |\n| 521 | 0262F0800521 | 30.242 | 81.470 | Ghaghara | M(o) | 0.28 | 5,356 |\n| 522 | 0262F0800522 | 30.241 | 81.332 | Ghaghara | M(e) | 8.11 | 5,395 |\n| 523 | 0262F0800523 | 30.233 | 81.350 | Ghaghara | M(e) | 26.79 | 5,297 |\n| 524 | 0262F0800524 | 30.230 | 81.416 | Ghaghara | M(l) | 0.62 | 5,180 |\n| 525 | 0262F0800525 | 30.228 | 81.415 | Ghaghara | M(o) | 1.25 | 5,108 |\n| 526 | 0262F0800526 | 30.225 | 81.416 | Ghaghara | M(e) | 0.78 | 5,062 |\n| 527 | 0262F0800527 | 30.221 | 81.406 | Ghaghara | M(o) | 0.78 | 5,392 |\n| 528 | 0262F0800528 | 30.219 | 81.336 | Ghaghara | M(e) | 3.52 | 5,279 |\n| 529 | 0262F0800529 | 30.213 | 81.383 | Ghaghara | M(l) | 4.39 | 5,428 |\n| 530 | 0262F0800530 | 30.213 | 81.373 | Ghaghara | M(o) | 0.53 | 5,519 |\n| 531 | 0262F0800531 | 30.212 | 81.371 | Ghaghara | M(o) | 0.87 | 5,522 |\n| 532 | 0262F0800532 | 30.211 | 81.361 | Ghaghara | M(o) | 1.84 | 5,568 |\n| 533 | 0262F0800533 | 30.113 | 81.351 | Ghaghara | E(o) | 0.87 | 4,886 |\n| 534 | 0262F0800534 | 30.080 | 81.293 | Ghaghara | M(o) | 1.23 | 5,081 |\n| 535 | 0262F0800535 | 30.034 | 81.334 | Ghaghara | M(o) | 0.25 | 4,968 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 3918, "line_end": 3992, "token_count_estimate": 1659, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F0700501", "0262F0700502", "0262F0700503", "0262F0700504", "0262F0700505", "0262F0700506", "0262F0700507", "0262F0700508", "0262F0700509", "0262F0700510", "0262F0700511", "0262F0700512", "0262F0700513", "0262F0700514", "0262F0700515", "0262F0700516", "0262F0700517", "0262F0700518", "0262F0700519", "0262F0700520", "0262F0800521", "0262F0800522", "0262F0800523", "0262F0800524", "0262F0800525", "0262F0800526", "0262F0800527", "0262F0800528", "0262F0800529", "0262F0800530", "0262F0800531", "0262F0800532", "0262F0800533", "0262F0800534", "0262F0800535"]}}
{"id": "793c6ee3a4dbf3db", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 536 | 0262F0800536 | 30.033 | 81.335 | Ghaghara | M(o) | 0.65 | 4,967 |\n| 537 | 0262F0800537 | 30.030 | 81.296 | Ghaghara | M(o) | 0.42 | 5,123 |\n| 538 | 0262F0800538 | 30.021 | 81.367 | Ghaghara | M(o) | 1.22 | 5,010 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 3918, "line_end": 3992, "token_count_estimate": 231, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F0800536", "0262F0800537", "0262F0800538"]}}
{"id": "4319398310feb690", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 3993, "line_end": 4002, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "950ad0b780912c9b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 539 | 0262F0800539 | 30.021 | 81.315 | Ghaghara | M(o) | 1.49 | 5,099 |\n| 540 | 0262F0800540 | 30.020 | 81.317 | Ghaghara | M(e) | 0.61 | 5,125 |\n| 541 | 0262F0800541 | 30.019 | 81.423 | Ghaghara | M(o) | 0.63 | 5,266 |\n| 542 | 0262F0800542 | 30.011 | 81.388 | Ghaghara | E(c) | 8.53 | 5,254 |\n| 543 | 0262F1100543 | 30.254 | 81.689 | Ghaghara | O | 27.78 | 4,264 |\n| 544 | 0262F1200544 | 30.433 | 80.682 | Ghaghara | M(o) | 0.61 | 5,373 |\n| 545 | 0262F1200545 | 30.406 | 81.721 | Ghaghara | E(o) | 0.45 | 5,329 |\n| 546 | 0262F1200546 | 30.388 | 81.566 | Ghaghara | O | 1.16 | 5,100 |\n| 547 | 0262F1200547 | 30.384 | 81.569 | Ghaghara | O | 1.60 | 5,099 |\n| 548 | 0262F1200548 | 30.378 | 81.574 | Ghaghara | O | 22.49 | 5,097 |\n| 549 | 0262F1200549 | 30.333 | 81.512 | Ghaghara | E(o) | 3.07 | 5,218 |\n| 550 | 0262F1200550 | 30.331 | 81.513 | Ghaghara | E(o) | 1.17 | 5,220 |\n| 551 | 0262F1200551 | 30.328 | 81.512 | Ghaghara | E(o) | 12.27 | 5,221 |\n| 552 | 0262F1200552 | 30.326 | 81.516 | Ghaghara | E(o) | 0.68 | 5,221 |\n| 553 | 0262F1200553 | 30.309 | 81.640 | Ghaghara | E(o) | 4.99 | 5,147 |\n| 554 | 0262F1200554 | 30.309 | 81.655 | Ghaghara | E(o) | 1.35 | 5,037 |\n| 555 | 0262F1200555 | 30.306 | 81.639 | Ghaghara | E(o) | 0.45 | 5,190 |\n| 556 | 0262F1200556 | 30.294 | 81.602 | Ghaghara | E(o) | 0.84 | 5,157 |\n| 557 | 0262F1200557 | 30.289 | 81.614 | Ghaghara | M(o) | 6.22 | 5,073 |\n| 558 | 0262F1200558 | 30.287 | 81.624 | Ghaghara | M(o) | 0.49 | 5,159 |\n| 559 | 0262F1200559 | 30.286 | 81.565 | Ghaghara | E(o) | 3.44 | 5,155 |\n| 560 | 0262F1200560 | 30.284 | 81.574 | Ghaghara | E(o) | 14.32 | 5,077 |\n| 561 | 0262F1200561 | 30.284 | 81.623 | Ghaghara | M(o) | 0.66 | 5,165 |\n| 562 | 0262F1200562 | 30.280 | 81.591 | Ghaghara | M(o) | 1.70 | 5,057 |\n| 563 | 0262F1200563 | 30.277 | 81.518 | Ghaghara | M(o) | 2.12 | 5,298 |\n| 564 | 0262F1200564 | 30.276 | 81.595 | Ghaghara | M(o) | 0.32 | 5,146 |\n| 565 | 0262F1200565 | 30.274 | 81.569 | Ghaghara | M(o) | 7.82 | 5,167 |\n| 566 | 0262F1200566 | 30.274 | 81.543 | Ghaghara | E(o) | 4.04 | 5,142 |\n| 567 | 0262F1200567 | 30.273 | 81.538 | Ghaghara | E(o) | 0.37 | 5,199 |\n| 568 | 0262F1200568 | 30.272 | 81.524 | Ghaghara | M(o) | 0.42 | 5,332 |\n| 569 | 0262F1200569 | 30.271 | 81.536 | Ghaghara | M(o) | 4.20 | 5,211 |\n| 570 | 0262F1200570 | 30.270 | 81.509 | Ghaghara | M(o) | 0.39 | 5,253 |\n| 571 | 0262F1200571 | 30.269 | 81.508 | Ghaghara | M(o) | 0.31 | 5,254 |\n| 572 | 0262F1200572 | 30.268 | 81.528 | Ghaghara | M(o) | 0.25 | 5,371 |\n| 573 | 0262F1200573 | 30.265 | 81.557 | Ghaghara | E(c) | 3.09 | 5,295 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4003, "line_end": 4077, "token_count_estimate": 1643, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F0800539", "0262F0800540", "0262F0800541", "0262F0800542", "0262F1100543", "0262F1200544", "0262F1200545", "0262F1200546", "0262F1200547", "0262F1200548", "0262F1200549", "0262F1200550", "0262F1200551", "0262F1200552", "0262F1200553", "0262F1200554", "0262F1200555", "0262F1200556", "0262F1200557", "0262F1200558", "0262F1200559", "0262F1200560", "0262F1200561", "0262F1200562", "0262F1200563", "0262F1200564", "0262F1200565", "0262F1200566", "0262F1200567", "0262F1200568", "0262F1200569", "0262F1200570", "0262F1200571", "0262F1200572", "0262F1200573"]}}
{"id": "f505dda076426a6b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 574 | 0262F1200574 | 30.207 | 81.593 | Ghaghara | E(o) | 0.81 | 5,139 |\n| 575 | 0262F1200575 | 30.202 | 81.557 | Ghaghara | M(o) | 0.46 | 5,136 |\n| 576 | 0262F1200576 | 30.185 | 81.748 | Ghaghara | M(o) | 0.52 | 5,092 |\n| 577 | 0262F1200577 | 30.180 | 81.734 | Ghaghara | M(o) | 0.28 | 5,142 |\n| 578 | 0262F1200578 | 30.176 | 81.652 | Ghaghara | M(o) | 0.38 | 5,240 |\n| 579 | 0262F1200579 | 30.174 | 81.653 | Ghaghara | M(o) | 0.97 | 5,229 |\n| 580 | 0262F1200580 | 30.173 | 81.728 | Ghaghara | E(o) | 4.20 | 4,983 |\n| 581 | 0262F1200581 | 30.167 | 81.744 | Ghaghara | E(o) | 1.04 | 5,081 |\n| 582 | 0262F1200582 | 30.166 | 81.724 | Ghaghara | E(o) | 0.34 | 4,853 |\n| 583 | 0262F1200583 | 30.164 | 81.665 | Ghaghara | M(o) | 7.93 | 4,902 |\n| 584 | 0262F1200584 | 30.163 | 81.659 | Ghaghara | M(o) | 1.89 | 4,957 |\n| 585 | 0262F1200585 | 30.162 | 81.748 | Ghaghara | M(o) | 16.11 | 5,046 |\n| 586 | 0262F1200586 | 30.162 | 81.658 | Ghaghara | M(o) | 1.45 | 4,980 |\n| 587 | 0262F1200587 | 30.157 | 81.665 | Ghaghara | M(o) | 0.26 | 5,081 |\n| 588 | 0262F1200588 | 30.154 | 81.644 | Ghaghara | M(o) | 9.21 | 4,915 |\n| 589 | 0262F1200589 | 30.147 | 81.721 | Ghaghara | O | 2.50 | 4,555 |\n| 590 | 0262F1200590 | 30.141 | 81.693 | Ghaghara | E(o) | 2.96 | 4,605 |\n| 591 | 0262F1200591 | 30.140 | 81.685 | Ghaghara | M(o) | 21.34 | 4,655 |\n| 592 | 0262F1200592 | 30.134 | 81.649 | Ghaghara | E(o) | 0.29 | 4,660 |\n| 593 | 0262F1200593 | 30.134 | 81.624 | Ghaghara | E(o) | 1.42 | 4,710 |\n| 594 | 0262F1200594 | 30.123 | 81.701 | Ghaghara | E(o) | 2.07 | 4,746 |\n| 595 | 0262F1200595 | 30.123 | 81.663 | Ghaghara | M(o) | 0.96 | 4,902 |\n| 596 | 0262F1200596 | 30.118 | 81.663 | Ghaghara | M(o) | 2.82 | 4,800 |\n| 597 | 0262F1200597 | 30.111 | 81.680 | Ghaghara | E(o) | 2.73 | 4,259 |\n| 598 | 0262F1200598 | 30.105 | 81.740 | Ghaghara | E(o) | 0.62 | 4,772 |\n| 599 | 0262F1200599 | 30.104 | 81.737 | Ghaghara | E(o) | 0.43 | 4,725 |\n| 600 | 0262F1200600 | 30.006 | 81.510 | Ghaghara | M(o) | 0.44 | 5,228 |\n| 601 | 0262F1200601 | 30.003 | 81.554 | Ghaghara | M(o) | 1.11 | 4,897 |\n| 602 | 0262F1500602 | 30.371 | 81.838 | Ghaghara | E(o) | 0.27 | 5,707 |\n| 603 | 0262F1500603 | 30.369 | 81.860 | Ghaghara | M(o) | 1.31 | 5,650 |\n| 604 | 0262F1500604 | 30.367 | 81.850 | Ghaghara | M(o) | 0.82 | 5,764 |\n| 605 | 0262F1500605 | 30.365 | 81.867 | Ghaghara | M(e) | 1.69 | 5,591 |\n| 606 | 0262F1500606 | 30.352 | 81.897 | Ghaghara | E(o) | 1.11 | 5,712 |\n| 607 | 0262F1500607 | 30.350 | 81.948 | Ghaghara | M(o) | 1.16 | 5,558 |\n| 608 | 0262F1500608 | 30.349 | 81.906 | Ghaghara | E(o) | 1.37 | 5,520 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4003, "line_end": 4077, "token_count_estimate": 1624, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1200574", "0262F1200575", "0262F1200576", "0262F1200577", "0262F1200578", "0262F1200579", "0262F1200580", "0262F1200581", "0262F1200582", "0262F1200583", "0262F1200584", "0262F1200585", "0262F1200586", "0262F1200587", "0262F1200588", "0262F1200589", "0262F1200590", "0262F1200591", "0262F1200592", "0262F1200593", "0262F1200594", "0262F1200595", "0262F1200596", "0262F1200597", "0262F1200598", "0262F1200599", "0262F1200600", "0262F1200601", "0262F1500602", "0262F1500603", "0262F1500604", "0262F1500605", "0262F1500606", "0262F1500607", "0262F1500608"]}}
{"id": "b4c3dcb95282efd0", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 609 | 0262F1500609 | 30.348 | 81.874 | Ghaghara | M(o) | 7.77 | 5,442 |\n| 610 | 0262F1500610 | 30.346 | 81.885 | Ghaghara | M(l) | 0.66 | 5,395 |\n| 611 | 0262F1500611 | 30.346 | 81.880 | Ghaghara | M(o) | 0.26 | 5,414 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4003, "line_end": 4077, "token_count_estimate": 231, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1500609", "0262F1500610", "0262F1500611"]}}
{"id": "683c961473d6496c", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 612 | 0262F1500612 | 30.346 | 81.861 | Ghaghara | M(e) | 15.56 | 5,431 |\n| 613 | 0262F1500613 | 30.346 | 81.876 | Ghaghara | M(o) | 0.39 | 5,448 |\n| 614 | 0262F1500614 | 30.345 | 81.831 | Ghaghara | M(o) | 10.48 | 5,520 |\n| 615 | 0262F1500615 | 30.345 | 81.846 | Ghaghara | M(e) | 2.53 | 5,507 |\n| 616 | 0262F1500616 | 30.344 | 81.841 | Ghaghara | E(o) | 0.72 | 5,491 |\n| 617 | 0262F1500617 | 30.344 | 81.892 | Ghaghara | M(e) | 2.88 | 5,287 |\n| 618 | 0262F1500618 | 30.338 | 81.857 | Ghaghara | M(l) | 1.49 | 5,609 |\n| 619 | 0262F1500619 | 30.338 | 81.913 | Ghaghara | M(o) | 28.20 | 5,135 |\n| 620 | 0262F1500620 | 30.336 | 81.920 | Ghaghara | M(e) | 10.09 | 5,130 |\n| 621 | 0262F1500621 | 30.333 | 81.871 | Ghaghara | E(o) | 0.41 | 5,682 |\n| 622 | 0262F1500622 | 30.332 | 81.868 | Ghaghara | M(l) | 0.31 | 5,651 |\n| 623 | 0262F1500623 | 30.330 | 81.964 | Ghaghara | M(o) | 0.43 | 5,358 |\n| 624 | 0262F1500624 | 30.329 | 81.833 | Ghaghara | M(o) | 2.77 | 5,614 |\n| 625 | 0262F1500625 | 30.327 | 81.815 | Ghaghara | M(o) | 0.54 | 5,544 |\n| 626 | 0262F1500626 | 30.327 | 81.823 | Ghaghara | M(o) | 0.56 | 5,621 |\n| 627 | 0262F1500627 | 30.326 | 81.818 | Ghaghara | M(o) | 3.28 | 5,522 |\n| 628 | 0262F1500628 | 30.326 | 81.836 | Ghaghara | M(o) | 1.15 | 5,577 |\n| 629 | 0262F1500629 | 30.325 | 81.873 | Ghaghara | E(o) | 3.73 | 5,632 |\n| 630 | 0262F1500630 | 30.325 | 81.813 | Ghaghara | M(o) | 2.37 | 5,514 |\n| 631 | 0262F1500631 | 30.324 | 81.833 | Ghaghara | M(e) | 11.65 | 5,571 |\n| 632 | 0262F1500632 | 30.323 | 81.914 | Ghaghara | M(o) | 0.72 | 5,414 |\n| 633 | 0262F1500633 | 30.323 | 81.811 | Ghaghara | M(o) | 0.92 | 5,503 |\n| 634 | 0262F1500634 | 30.320 | 81.975 | Ghaghara | M(l) | 0.69 | 5,377 |\n| 635 | 0262F1500635 | 30.317 | 81.922 | Ghaghara | M(o) | 1.25 | 5,432 |\n| 636 | 0262F1500636 | 30.314 | 81.902 | Ghaghara | M(o) | 0.43 | 5,347 |\n| 637 | 0262F1500637 | 30.313 | 81.876 | Ghaghara | M(e) | 10.69 | 5,546 |\n| 638 | 0262F1500638 | 30.307 | 81.866 | Ghaghara | M(e) | 10.94 | 5,492 |\n| 639 | 0262F1500639 | 30.302 | 81.872 | Ghaghara | M(e) | 7.17 | 5,542 |\n| 640 | 0262F1500640 | 30.302 | 81.865 | Ghaghara | M(o) | 1.98 | 5,566 |\n| 641 | 0262F1500641 | 30.301 | 81.843 | Ghaghara | M(e) | 9.27 | 5,350 |\n| 642 | 0262F1500642 | 30.301 | 81.886 | Ghaghara | E(o) | 0.56 | 5,605 |\n| 643 | 0262F1500643 | 30.299 | 81.889 | Ghaghara | E(o) | 0.28 | 5,591 |\n| 644 | 0262F1500644 | 30.299 | 81.828 | Ghaghara | M(o) | 0.26 | 5,328 |\n| 645 | 0262F1500645 | 30.298 | 81.985 | Ghaghara | E(o) | 1.21 | 5,339 |\n| 646 | 0262F1500646 | 30.291 | 81.892 | Ghaghara | E(o) | 0.36 | 5,480 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4079, "line_end": 4153, "token_count_estimate": 1640, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1500612", "0262F1500613", "0262F1500614", "0262F1500615", "0262F1500616", "0262F1500617", "0262F1500618", "0262F1500619", "0262F1500620", "0262F1500621", "0262F1500622", "0262F1500623", "0262F1500624", "0262F1500625", "0262F1500626", "0262F1500627", "0262F1500628", "0262F1500629", "0262F1500630", "0262F1500631", "0262F1500632", "0262F1500633", "0262F1500634", "0262F1500635", "0262F1500636", "0262F1500637", "0262F1500638", "0262F1500639", "0262F1500640", "0262F1500641", "0262F1500642", "0262F1500643", "0262F1500644", "0262F1500645", "0262F1500646"]}}
{"id": "9e0808b039020f3f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 647 | 0262F1500647 | 30.290 | 81.810 | Ghaghara | M(o) | 7.18 | 5,325 |\n| 648 | 0262F1500648 | 30.289 | 81.838 | Ghaghara | M(l) | 2.17 | 5,486 |\n| 649 | 0262F1500649 | 30.287 | 81.822 | Ghaghara | M(o) | 1.88 | 5,659 |\n| 650 | 0262F1500650 | 30.286 | 81.785 | Ghaghara | E(o) | 2.04 | 5,363 |\n| 651 | 0262F1500651 | 30.284 | 81.873 | Ghaghara | E(o) | 3.93 | 5,620 |\n| 652 | 0262F1500652 | 30.282 | 81.870 | Ghaghara | E(o) | 0.92 | 5,591 |\n| 653 | 0262F1500653 | 30.281 | 81.857 | Ghaghara | I(s) | 2.72 | 5,605 |\n| 654 | 0262F1500654 | 30.281 | 81.866 | Ghaghara | E(o) | 0.25 | 5,576 |\n| 655 | 0262F1500655 | 30.281 | 81.870 | Ghaghara | E(o) | 0.41 | 5,575 |\n| 656 | 0262F1500656 | 30.280 | 81.869 | Ghaghara | E(o) | 1.28 | 5,571 |\n| 657 | 0262F1500657 | 30.278 | 81.880 | Ghaghara | M(o) | 1.01 | 5,545 |\n| 658 | 0262F1500658 | 30.277 | 81.877 | Ghaghara | M(o) | 5.46 | 5,526 |\n| 659 | 0262F1500659 | 30.268 | 81.872 | Ghaghara | M(e) | 8.03 | 5,417 |\n| 660 | 0262F1500660 | 30.265 | 81.920 | Ghaghara | M(o) | 2.29 | 5,080 |\n| 661 | 0262F1500661 | 30.258 | 81.904 | Ghaghara | M(o) | 0.32 | 5,437 |\n| 662 | 0262F1500662 | 30.254 | 81.914 | Ghaghara | M(o) | 0.76 | 5,274 |\n| 663 | 0262F1500663 | 30.253 | 81.884 | Ghaghara | M(l) | 1.69 | 5,568 |\n| 664 | 0262F1500664 | 30.251 | 81.878 | Ghaghara | I(s) | 1.05 | 5,409 |\n| 665 | 0262F1600665 | 30.249 | 81.939 | Ghaghara | E(o) | 1.35 | 5,036 |\n| 666 | 0262F1600666 | 30.249 | 81.880 | Ghaghara | I(s) | 0.80 | 5,429 |\n| 667 | 0262F1600667 | 30.243 | 81.896 | Ghaghara | M(o) | 0.26 | 5,441 |\n| 668 | 0262F1600668 | 30.242 | 81.901 | Ghaghara | M(o) | 2.78 | 5,406 |\n| 669 | 0262F1600669 | 30.242 | 81.903 | Ghaghara | M(o) | 0.72 | 5,408 |\n| 670 | 0262F1600670 | 30.240 | 81.898 | Ghaghara | M(o) | 1.22 | 5,419 |\n| 671 | 0262F1600671 | 30.240 | 81.997 | Ghaghara | E(o) | 1.48 | 5,045 |\n| 672 | 0262F1600672 | 30.237 | 81.915 | Ghaghara | E(o) | 0.49 | 5,335 |\n| 673 | 0262F1600673 | 30.233 | 81.906 | Ghaghara | M(o) | 0.66 | 5,353 |\n| 674 | 0262F1600674 | 30.222 | 81.777 | Ghaghara | M(e) | 9.09 | 5,356 |\n| 675 | 0262F1600675 | 30.222 | 81.805 | Ghaghara | M(o) | 0.44 | 5,388 |\n| 676 | 0262F1600676 | 30.218 | 81.883 | Ghaghara | M(o) | 1.75 | 5,384 |\n| 677 | 0262F1600677 | 30.216 | 81.802 | Ghaghara | M(e) | 19.31 | 5,343 |\n| 678 | 0262F1600678 | 30.214 | 81.758 | Ghaghara | M(e) | 12.12 | 5,368 |\n| 679 | 0262F1600679 | 30.204 | 81.878 | Ghaghara | M(e) | 12.66 | 5,518 |\n| 680 | 0262F1600680 | 30.202 | 81.947 | Ghaghara | E(o) | 0.29 | 5,301 |\n| 681 | 0262F1600681 | 30.201 | 81.918 | Ghaghara | M(o) | 1.58 | 5,173 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4079, "line_end": 4153, "token_count_estimate": 1638, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1500647", "0262F1500648", "0262F1500649", "0262F1500650", "0262F1500651", "0262F1500652", "0262F1500653", "0262F1500654", "0262F1500655", "0262F1500656", "0262F1500657", "0262F1500658", "0262F1500659", "0262F1500660", "0262F1500661", "0262F1500662", "0262F1500663", "0262F1500664", "0262F1600665", "0262F1600666", "0262F1600667", "0262F1600668", "0262F1600669", "0262F1600670", "0262F1600671", "0262F1600672", "0262F1600673", "0262F1600674", "0262F1600675", "0262F1600676", "0262F1600677", "0262F1600678", "0262F1600679", "0262F1600680", "0262F1600681"]}}
{"id": "94d5b788bb583c0b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 682 | 0262F1600682 | 30.201 | 81.930 | Ghaghara | M(o) | 0.40 | 5,128 |\n| 683 | 0262F1600683 | 30.197 | 81.863 | Ghaghara | M(l) | 1.79 | 5,172 |\n| 684 | 0262F1600684 | 30.196 | 81.826 | Ghaghara | M(o) | 0.84 | 5,086 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4079, "line_end": 4153, "token_count_estimate": 230, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1600682", "0262F1600683", "0262F1600684"]}}
{"id": "41665f0599d8ff6f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 4154, "line_end": 4160, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7217458b7a80c83", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 685 | 0262F1600685 | 30.191 | 81.788 | Ghaghara | M(o) | 0.44 | 5,451 |\n| 686 | 0262F1600686 | 30.190 | 81.786 | Ghaghara | M(o) | 1.15 | 5,442 |\n| 687 | 0262F1600687 | 30.189 | 81.792 | Ghaghara | M(o) | 1.03 | 5,423 |\n| 688 | 0262F1600688 | 30.189 | 81.800 | Ghaghara | E(o) | 0.39 | 5,363 |\n| 689 | 0262F1600689 | 30.188 | 81.824 | Ghaghara | M(lg) | 1.13 | 5,345 |\n| 690 | 0262F1600690 | 30.183 | 81.823 | Ghaghara | M(l) | 0.33 | 5,398 |\n| 691 | 0262F1600691 | 30.182 | 81.804 | Ghaghara | M(o) | 0.95 | 5,278 |\n| 692 | 0262F1600692 | 30.181 | 81.820 | Ghaghara | M(o) | 0.25 | 5,356 |\n| 693 | 0262F1600693 | 30.179 | 81.873 | Ghaghara | M(l) | 1.45 | 5,354 |\n| 694 | 0262F1600694 | 30.178 | 81.881 | Ghaghara | M(o) | 0.63 | 5,450 |\n| 695 | 0262F1600695 | 30.174 | 81.875 | Ghaghara | M(l) | 0.39 | 5,355 |\n| 696 | 0262F1600696 | 30.172 | 81.879 | Ghaghara | M(l) | 1.91 | 5,336 |\n| 697 | 0262F1600697 | 30.167 | 81.952 | Ghaghara | M(o) | 2.62 | 4,933 |\n| 698 | 0262F1600698 | 30.160 | 81.768 | Ghaghara | E(o) | 0.25 | 5,242 |\n| 699 | 0262F1600699 | 30.157 | 81.773 | Ghaghara | M(o) | 1.39 | 5,188 |\n| 700 | 0262F1600700 | 30.156 | 81.773 | Ghaghara | E(o) | 0.26 | 5,200 |\n| 701 | 0262F1600701 | 30.142 | 81.958 | Ghaghara | E(o) | 0.25 | 5,218 |\n| 702 | 0262F1600702 | 30.141 | 81.952 | Ghaghara | E(o) | 0.48 | 5,294 |\n| 703 | 0262F1600703 | 30.140 | 81.998 | Ghaghara | M(o) | 5.10 | 4,773 |\n| 704 | 0262F1600704 | 30.140 | 81.981 | Ghaghara | M(o) | 1.93 | 4,975 |\n| 705 | 0262F1600705 | 30.139 | 81.773 | Ghaghara | M(o) | 0.69 | 5,111 |\n| 706 | 0262F1600706 | 30.139 | 81.789 | Ghaghara | M(o) | 1.79 | 5,169 |\n| 707 | 0262F1600707 | 30.131 | 81.793 | Ghaghara | M(o) | 2.22 | 5,156 |\n| 708 | 0262F1600708 | 30.129 | 81.781 | Ghaghara | M(e) | 75.65 | 5,015 |\n| 709 | 0262F1600709 | 30.126 | 81.787 | Ghaghara | E(o) | 0.49 | 5,029 |\n| 710 | 0262F1600710 | 30.122 | 81.771 | Ghaghara | E(o) | 0.39 | 4,867 |\n| 711 | 0262F1600711 | 30.121 | 81.768 | Ghaghara | E(o) | 1.36 | 4,892 |\n| 712 | 0262F1600712 | 30.119 | 81.873 | Ghaghara | M(o) | 1.27 | 5,097 |\n| 713 | 0262F1600713 | 30.119 | 81.876 | Ghaghara | E(c) | 6.33 | 5,037 |\n| 714 | 0262F1600714 | 30.112 | 81.814 | Ghaghara | M(o) | 2.12 | 4,907 |\n| 715 | 0262F1600715 | 30.107 | 81.837 | Ghaghara | M(o) | 0.77 | 4,607 |\n| 716 | 0262F1600716 | 30.107 | 81.833 | Ghaghara | M(o) | 0.55 | 4,677 |\n| 717 | 0262F1600717 | 30.105 | 81.805 | Ghaghara | M(o) | 1.18 | 4,959 |\n| 718 | 0262F1600718 | 30.104 | 81.829 | Ghaghara | M(o) | 0.25 | 4,706 |\n| 719 | 0262F1600719 | 30.102 | 81.827 | Ghaghara | M(o) | 0.56 | 4,691 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4161, "line_end": 4235, "token_count_estimate": 1641, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1600685", "0262F1600686", "0262F1600687", "0262F1600688", "0262F1600689", "0262F1600690", "0262F1600691", "0262F1600692", "0262F1600693", "0262F1600694", "0262F1600695", "0262F1600696", "0262F1600697", "0262F1600698", "0262F1600699", "0262F1600700", "0262F1600701", "0262F1600702", "0262F1600703", "0262F1600704", "0262F1600705", "0262F1600706", "0262F1600707", "0262F1600708", "0262F1600709", "0262F1600710", "0262F1600711", "0262F1600712", "0262F1600713", "0262F1600714", "0262F1600715", "0262F1600716", "0262F1600717", "0262F1600718", "0262F1600719"]}}
{"id": "fc7da14190e1e8c6", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 720 | 0262F1600720 | 30.100 | 81.811 | Ghaghara | M(o) | 0.40 | 4,902 |\n| 721 | 0262F1600721 | 30.098 | 81.826 | Ghaghara | M(e) | 3.27 | 4,769 |\n| 722 | 0262F1600722 | 30.097 | 81.796 | Ghaghara | M(o) | 2.50 | 5,080 |\n| 723 | 0262F1600723 | 30.090 | 81.815 | Ghaghara | M(o) | 5.38 | 4,821 |\n| 724 | 0262F1600724 | 30.089 | 81.810 | Ghaghara | E(o) | 0.41 | 4,857 |\n| 725 | 0262F1600725 | 30.085 | 81.829 | Ghaghara | M(o) | 1.11 | 4,744 |\n| 726 | 0262F1600726 | 30.070 | 81.802 | Ghaghara | E(o) | 0.60 | 4,308 |\n| 727 | 0262F1600727 | 30.064 | 81.967 | Ghaghara | M(o) | 0.50 | 5,031 |\n| 728 | 0262F1600728 | 30.059 | 81.955 | Ghaghara | O | 11.43 | 4,440 |\n| 729 | 0262F1600729 | 30.057 | 81.941 | Ghaghara | M(e) | 13.87 | 4,564 |\n| 730 | 0262F1600730 | 30.049 | 81.942 | Ghaghara | E(o) | 2.32 | 4,633 |\n| 731 | 0262F1600731 | 30.034 | 81.886 | Ghaghara | M(o) | 0.37 | 4,746 |\n| 732 | 0262F1600732 | 30.030 | 81.856 | Ghaghara | E(o) | 1.49 | 4,394 |\n| 733 | 0262F1600733 | 30.027 | 81.867 | Ghaghara | E(o) | 0.43 | 4,146 |\n| 734 | 0262F1600734 | 30.024 | 81.935 | Ghaghara | O | 0.91 | 4,210 |\n| 735 | 0262F1600735 | 30.019 | 81.936 | Ghaghara | O | 2.66 | 4,161 |\n| 736 | 0262G0100736 | 29.996 | 81.078 | Ghaghara | I(s) | 1.26 | 4,629 |\n| 737 | 0262G0100737 | 29.990 | 81.059 | Ghaghara | I(s) | 0.82 | 4,784 |\n| 738 | 0262G0100738 | 29.980 | 81.113 | Ghaghara | M(e) | 2.01 | 5,125 |\n| 739 | 0262G0100739 | 29.975 | 81.173 | Ghaghara | E(c) | 3.79 | 4,337 |\n| 740 | 0262G0100740 | 29.931 | 81.035 | Ghaghara | M(l) | 9.22 | 4,905 |\n| 741 | 0262G0100741 | 29.931 | 81.038 | Ghaghara | M(o) | 0.64 | 4,922 |\n| 742 | 0262G0100742 | 29.929 | 81.040 | Ghaghara | M(l) | 0.68 | 4,872 |\n| 743 | 0262G0100743 | 29.926 | 81.038 | Ghaghara | I(s) | 0.36 | 4,841 |\n| 744 | 0262G0100744 | 29.921 | 81.031 | Ghaghara | M(e) | 18.61 | 4,821 |\n| 745 | 0262G0100745 | 29.920 | 81.075 | Ghaghara | I(s) | 0.60 | 4,419 |\n| 746 | 0262G0100746 | 29.920 | 81.056 | Ghaghara | M(o) | 1.00 | 4,604 |\n| 747 | 0262G0100747 | 29.918 | 81.075 | Ghaghara | I(s) | 0.83 | 4,422 |\n| 748 | 0262G0100748 | 29.856 | 81.190 | Ghaghara | E(o) | 0.94 | 3,598 |\n| 749 | 0262G0100749 | 29.783 | 81.153 | Ghaghara | O | 0.56 | 3,859 |\n| 750 | 0262G0500750 | 29.990 | 81.357 | Ghaghara | E(c) | 1.12 | 5,128 |\n| 751 | 0262G0500751 | 29.987 | 81.480 | Ghaghara | E(o) | 0.37 | 5,096 |\n| 752 | 0262G0500752 | 29.982 | 81.474 | Ghaghara | E(o) | 3.11 | 4,951 |\n| 753 | 0262G0500753 | 29.958 | 81.484 | Ghaghara | I(s) | 0.42 | 4,412 |\n| 754 | 0262G0500754 | 29.943 | 81.480 | Ghaghara | I(s) | 0.25 | 4,719 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4161, "line_end": 4235, "token_count_estimate": 1628, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262F1600720", "0262F1600721", "0262F1600722", "0262F1600723", "0262F1600724", "0262F1600725", "0262F1600726", "0262F1600727", "0262F1600728", "0262F1600729", "0262F1600730", "0262F1600731", "0262F1600732", "0262F1600733", "0262F1600734", "0262F1600735", "0262G0100736", "0262G0100737", "0262G0100738", "0262G0100739", "0262G0100740", "0262G0100741", "0262G0100742", "0262G0100743", "0262G0100744", "0262G0100745", "0262G0100746", "0262G0100747", "0262G0100748", "0262G0100749", "0262G0500750", "0262G0500751", "0262G0500752", "0262G0500753", "0262G0500754"]}}
{"id": "de6249ca7f6eb6a4", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 755 | 0262G0600755 | 29.694 | 81.483 | Ghaghara | E(o) | 0.55 | 4,468 |\n| 756 | 0262G0900756 | 29.964 | 81.704 | Ghaghara | O | 0.68 | 4,368 |\n| 757 | 0262G0900757 | 29.951 | 81.610 | Ghaghara | E(o) | 0.61 | 4,797 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4161, "line_end": 4235, "token_count_estimate": 227, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262G0600755", "0262G0900756", "0262G0900757"]}}
{"id": "5be829ba04b6aa08", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 758 | 0262G0900758 | 29.948 | 81.604 | Ghaghara | M(o) | 3.08 | 4,660 |\n| 759 | 0262G0900759 | 29.946 | 81.620 | Ghaghara | E(o) | 1.03 | 4,695 |\n| 760 | 0262G0900760 | 29.941 | 81.621 | Ghaghara | E(o) | 0.34 | 4,743 |\n| 761 | 0262G0900761 | 29.941 | 81.681 | Ghaghara | E(o) | 0.27 | 4,752 |\n| 762 | 0262G0900762 | 29.936 | 81.674 | Ghaghara | E(o) | 11.23 | 4,569 |\n| 763 | 0262G0900763 | 29.924 | 81.574 | Ghaghara | E(o) | 3.86 | 4,570 |\n| 764 | 0262G0900764 | 29.920 | 81.618 | Ghaghara | E(o) | 0.34 | 4,548 |\n| 765 | 0262G0900765 | 29.919 | 81.625 | Ghaghara | E(o) | 0.64 | 4,485 |\n| 766 | 0262G0900766 | 29.919 | 81.739 | Ghaghara | M(o) | 1.78 | 4,650 |\n| 767 | 0262G0900767 | 29.917 | 81.601 | Ghaghara | E(o) | 2.51 | 4,503 |\n| 768 | 0262G0900768 | 29.917 | 81.730 | Ghaghara | M(o) | 0.25 | 4,896 |\n| 769 | 0262G0900769 | 29.916 | 81.684 | Ghaghara | E(o) | 0.35 | 4,752 |\n| 770 | 0262G0900770 | 29.913 | 81.747 | Ghaghara | M(o) | 0.66 | 4,788 |\n| 771 | 0262G0900771 | 29.910 | 81.723 | Ghaghara | M(o) | 0.61 | 4,909 |\n| 772 | 0262G0900772 | 29.909 | 81.721 | Ghaghara | E(o) | 0.42 | 4,900 |\n| 773 | 0262G0900773 | 29.905 | 81.701 | Ghaghara | E(o) | 1.16 | 4,633 |\n| 774 | 0262G0900774 | 29.902 | 81.724 | Ghaghara | E(o) | 0.31 | 4,753 |\n| 775 | 0262G0900775 | 29.898 | 81.747 | Ghaghara | E(o) | 12.90 | 4,692 |\n| 776 | 0262G0900776 | 29.898 | 81.578 | Ghaghara | M(e) | 28.51 | 3,581 |\n| 777 | 0262G0900777 | 29.892 | 81.735 | Ghaghara | E(o) | 5.50 | 4,529 |\n| 778 | 0262G0900778 | 29.862 | 81.585 | Ghaghara | M(o) | 2.05 | 4,665 |\n| 779 | 0262G0900779 | 29.860 | 81.641 | Ghaghara | E(o) | 5.61 | 4,596 |\n| 780 | 0262G0900780 | 29.856 | 81.620 | Ghaghara | E(o) | 0.59 | 4,378 |\n| 781 | 0262G0900781 | 29.856 | 81.517 | Ghaghara | M(o) | 0.86 | 4,081 |\n| 782 | 0262G0900782 | 29.855 | 81.516 | Ghaghara | M(o) | 0.29 | 4,076 |\n| 783 | 0262G0900783 | 29.855 | 81.624 | Ghaghara | E(o) | 1.17 | 4,921 |\n| 784 | 0262G0900784 | 29.854 | 81.618 | Ghaghara | E(o) | 1.32 | 4,123 |\n| 785 | 0262G0900785 | 29.853 | 81.515 | Ghaghara | M(o) | 1.30 | 4,053 |\n| 786 | 0262G0900786 | 29.851 | 81.623 | Ghaghara | E(o) | 1.72 | 4,583 |\n| 787 | 0262G0900787 | 29.851 | 81.619 | Ghaghara | E(o) | 1.57 | 4,482 |\n| 788 | 0262G0900788 | 29.850 | 81.511 | Ghaghara | M(o) | 1.68 | 3,998 |\n| 789 | 0262G0900789 | 29.844 | 81.553 | Ghaghara | M(o) | 1.41 | 4,983 |\n| 790 | 0262G0900790 | 29.838 | 81.551 | Ghaghara | M(o) | 0.80 | 4,998 |\n| 791 | 0262G0900791 | 29.838 | 81.547 | Ghaghara | M(o) | 0.25 | 4,952 |\n| 792 | 0262G0900792 | 29.836 | 81.552 | Ghaghara | M(o) | 2.82 | 4,994 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4237, "line_end": 4311, "token_count_estimate": 1626, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262G0900758", "0262G0900759", "0262G0900760", "0262G0900761", "0262G0900762", "0262G0900763", "0262G0900764", "0262G0900765", "0262G0900766", "0262G0900767", "0262G0900768", "0262G0900769", "0262G0900770", "0262G0900771", "0262G0900772", "0262G0900773", "0262G0900774", "0262G0900775", "0262G0900776", "0262G0900777", "0262G0900778", "0262G0900779", "0262G0900780", "0262G0900781", "0262G0900782", "0262G0900783", "0262G0900784", "0262G0900785", "0262G0900786", "0262G0900787", "0262G0900788", "0262G0900789", "0262G0900790", "0262G0900791", "0262G0900792"]}}
{"id": "fa8d816ce00cad05", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 793 | 0262G0900793 | 29.828 | 81.529 | Ghaghara | E(c) | 0.33 | 4,907 |\n| 794 | 0262G0900794 | 29.828 | 81.550 | Ghaghara | E(o) | 0.56 | 4,878 |\n| 795 | 0262G0900795 | 29.800 | 81.525 | Ghaghara | M(o) | 1.40 | 4,890 |\n| 796 | 0262G0900796 | 29.798 | 81.527 | Ghaghara | M(o) | 0.93 | 4,904 |\n| 797 | 0262G0900797 | 29.778 | 81.641 | Ghaghara | E(o) | 4.38 | 4,589 |\n| 798 | 0262G0900798 | 29.777 | 81.605 | Ghaghara | E(o) | 1.30 | 4,522 |\n| 799 | 0262G0900799 | 29.773 | 81.527 | Ghaghara | M(e) | 49.99 | 4,576 |\n| 800 | 0262G0900800 | 29.773 | 81.583 | Ghaghara | E(o) | 3.40 | 4,583 |\n| 801 | 0262G0900801 | 29.768 | 81.539 | Ghaghara | E(o) | 0.83 | 4,868 |\n| 802 | 0262G0900802 | 29.765 | 81.537 | Ghaghara | M(o) | 1.02 | 4,835 |\n| 803 | 0262G0900803 | 29.760 | 81.580 | Ghaghara | E(o) | 2.68 | 4,276 |\n| 804 | 0262G0900804 | 29.760 | 81.540 | Ghaghara | E(o) | 0.41 | 4,893 |\n| 805 | 0262G0900805 | 29.755 | 81.573 | Ghaghara | E(o) | 0.56 | 5,049 |\n| 806 | 0262G0900806 | 29.755 | 81.564 | Ghaghara | I(s) | 1.72 | 4,870 |\n| 807 | 0262G0900807 | 29.754 | 81.571 | Ghaghara | E(o) | 0.90 | 5,023 |\n| 808 | 0262G0900808 | 29.753 | 81.575 | Ghaghara | E(o) | 0.27 | 5,043 |\n| 809 | 0262G1000809 | 29.746 | 81.560 | Ghaghara | E(o) | 0.40 | 5,167 |\n| 810 | 0262G1000810 | 29.745 | 81.565 | Ghaghara | E(o) | 1.73 | 5,049 |\n| 811 | 0262G1000811 | 29.744 | 81.564 | Ghaghara | M(o) | 0.49 | 4,985 |\n| 812 | 0262G1000812 | 29.743 | 81.544 | Ghaghara | M(o) | 5.63 | 5,152 |\n| 813 | 0262G1000813 | 29.741 | 81.570 | Ghaghara | M(o) | 5.91 | 5,072 |\n| 814 | 0262G1000814 | 29.702 | 81.529 | Ghaghara | M(o) | 0.83 | 5,079 |\n| 815 | 0262G1000815 | 29.699 | 81.537 | Ghaghara | E(o) | 0.92 | 5,039 |\n| 816 | 0262G1000816 | 29.648 | 81.556 | Ghaghara | E(c) | 11.64 | 4,551 |\n| 817 | 0262G1000817 | 29.640 | 81.551 | Ghaghara | E(o) | 2.24 | 4,472 |\n| 818 | 0262G1000818 | 29.621 | 81.548 | Ghaghara | E(c) | 5.24 | 4,403 |\n| 819 | 0262G1000819 | 29.610 | 81.544 | Ghaghara | E(c) | 13.28 | 4,467 |\n| 820 | 0262G1300820 | 29.922 | 81.763 | Ghaghara | E(o) | 0.40 | 4,312 |\n| 821 | 0262G1300821 | 29.909 | 81.776 | Ghaghara | E(o) | 1.15 | 4,955 |\n| 822 | 0262G1300822 | 29.904 | 81.793 | Ghaghara | E(o) | 0.39 | 4,921 |\n| 823 | 0262G1300823 | 29.903 | 81.975 | Ghaghara | E(o) | 3.06 | 4,261 |\n| 824 | 0262G1300824 | 29.902 | 81.979 | Ghaghara | E(o) | 3.38 | 4,286 |\n| 825 | 0262G1300825 | 29.901 | 81.988 | Ghaghara | E(o) | 0.43 | 4,411 |\n| 826 | 0262G1300826 | 29.900 | 81.793 | Ghaghara | E(o) | 0.73 | 4,852 |\n| 827 | 0262G1300827 | 29.899 | 81.988 | Ghaghara | E(c) | 3.47 | 4,393 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4237, "line_end": 4311, "token_count_estimate": 1631, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262G0900793", "0262G0900794", "0262G0900795", "0262G0900796", "0262G0900797", "0262G0900798", "0262G0900799", "0262G0900800", "0262G0900801", "0262G0900802", "0262G0900803", "0262G0900804", "0262G0900805", "0262G0900806", "0262G0900807", "0262G0900808", "0262G1000809", "0262G1000810", "0262G1000811", "0262G1000812", "0262G1000813", "0262G1000814", "0262G1000815", "0262G1000816", "0262G1000817", "0262G1000818", "0262G1000819", "0262G1300820", "0262G1300821", "0262G1300822", "0262G1300823", "0262G1300824", "0262G1300825", "0262G1300826", "0262G1300827"]}}
{"id": "6d6c001d4d46e15d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 828 | 0262J0300828 | 30.354 | 82.088 | Ghaghara | M(o) | 5.05 | 5,345 |\n| 829 | 0262J0300829 | 30.350 | 82.088 | Ghaghara | M(o) | 8.06 | 5,317 |\n| 830 | 0262J0300830 | 30.349 | 82.091 | Ghaghara | M(o) | 0.41 | 5,325 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4237, "line_end": 4311, "token_count_estimate": 232, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262J0300828", "0262J0300829", "0262J0300830"]}}
{"id": "cfc4d23bc2ef65e0", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 4312, "line_end": 4319, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f43c374852dfbddc", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 831 | 0262J0300831 | 30.346 | 82.084 | Ghaghara | M(o) | 5.24 | 5,380 |\n| 832 | 0262J0300832 | 30.346 | 82.087 | Ghaghara | M(o) | 0.66 | 5,392 |\n| 833 | 0262J0300833 | 30.345 | 82.093 | Ghaghara | M(o) | 0.44 | 5,261 |\n| 834 | 0262J0300834 | 30.334 | 82.073 | Ghaghara | M(o) | 0.33 | 5,390 |\n| 835 | 0262J0300835 | 30.316 | 82.017 | Ghaghara | M(o) | 0.84 | 5,341 |\n| 836 | 0262J0300836 | 30.315 | 82.012 | Ghaghara | M(o) | 3.66 | 5,345 |\n| 837 | 0262J0300837 | 30.312 | 82.041 | Ghaghara | E(o) | 0.47 | 5,399 |\n| 838 | 0262J0300838 | 30.311 | 82.040 | Ghaghara | E(o) | 0.44 | 5,395 |\n| 839 | 0262J0300839 | 30.303 | 82.047 | Ghaghara | E(o) | 0.77 | 5,465 |\n| 840 | 0262J0300840 | 30.301 | 82.105 | Ghaghara | M(o) | 0.33 | 5,337 |\n| 841 | 0262J0300841 | 30.290 | 82.051 | Ghaghara | M(o) | 0.41 | 5,206 |\n| 842 | 0262J0300842 | 30.289 | 82.047 | Ghaghara | M(o) | 1.64 | 5,267 |\n| 843 | 0262J0300843 | 30.286 | 82.047 | Ghaghara | M(o) | 1.08 | 5,289 |\n| 844 | 0262J0300844 | 30.277 | 82.039 | Ghaghara | E(o) | 0.64 | 5,234 |\n| 845 | 0262J0300845 | 30.264 | 82.097 | Ghaghara | M(o) | 0.25 | 5,330 |\n| 846 | 0262J0300846 | 30.263 | 82.099 | Ghaghara | M(o) | 3.74 | 5,328 |\n| 847 | 0262J0300847 | 30.262 | 82.002 | Ghaghara | E(o) | 2.72 | 5,237 |\n| 848 | 0262J0400848 | 30.244 | 82.091 | Ghaghara | M(o) | 0.53 | 5,300 |\n| 849 | 0262J0400849 | 30.239 | 82.001 | Ghaghara | E(o) | 0.48 | 5,084 |\n| 850 | 0262J0400850 | 30.218 | 82.105 | Ghaghara | M(o) | 0.77 | 5,464 |\n| 851 | 0262J0400851 | 30.216 | 82.091 | Ghaghara | M(o) | 0.26 | 5,289 |\n| 852 | 0262J0400852 | 30.215 | 82.086 | Ghaghara | M(o) | 2.91 | 5,164 |\n| 853 | 0262J0400853 | 30.215 | 82.110 | Ghaghara | M(o) | 2.99 | 5,396 |\n| 854 | 0262J0400854 | 30.213 | 82.095 | Ghaghara | M(o) | 1.08 | 5,327 |\n| 855 | 0262J0400855 | 30.203 | 82.105 | Ghaghara | M(o) | 3.90 | 5,359 |\n| 856 | 0262J0400856 | 30.203 | 82.098 | Ghaghara | M(o) | 1.99 | 5,417 |\n| 857 | 0262J0400857 | 30.196 | 82.118 | Ghaghara | M(e) | 9.42 | 5,152 |\n| 858 | 0262J0400858 | 30.196 | 82.115 | Ghaghara | I(s) | 0.26 | 5,188 |\n| 859 | 0262J0400859 | 30.195 | 82.102 | Ghaghara | M(o) | 1.49 | 5,314 |\n| 860 | 0262J0400860 | 30.186 | 82.095 | Ghaghara | M(o) | 2.34 | 4,850 |\n| 861 | 0262J0400861 | 30.171 | 82.062 | Ghaghara | E(o) | 2.43 | 5,155 |\n| 862 | 0262J0400862 | 30.167 | 82.063 | Ghaghara | E(o) | 1.41 | 5,109 |\n| 863 | 0262J0400863 | 30.165 | 82.170 | Ghaghara | M(l) | 7.51 | 5,196 |\n| 864 | 0262J0400864 | 30.162 | 82.100 | Ghaghara | E(o) | 0.60 | 5,198 |\n| 865 | 0262J0400865 | 30.149 | 82.161 | Ghaghara | M(l) | 16.65 | 4,994 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4320, "line_end": 4394, "token_count_estimate": 1682, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262J0300831", "0262J0300832", "0262J0300833", "0262J0300834", "0262J0300835", "0262J0300836", "0262J0300837", "0262J0300838", "0262J0300839", "0262J0300840", "0262J0300841", "0262J0300842", "0262J0300843", "0262J0300844", "0262J0300845", "0262J0300846", "0262J0300847", "0262J0400848", "0262J0400849", "0262J0400850", "0262J0400851", "0262J0400852", "0262J0400853", "0262J0400854", "0262J0400855", "0262J0400856", "0262J0400857", "0262J0400858", "0262J0400859", "0262J0400860", "0262J0400861", "0262J0400862", "0262J0400863", "0262J0400864", "0262J0400865"]}}
{"id": "883bb6d13e194fa2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 866 | 0262J0400866 | 30.144 | 82.175 | Ghaghara | I(s) | 0.26 | 5,080 |\n| 867 | 0262J0400867 | 30.144 | 82.169 | Ghaghara | I(s) | 0.28 | 5,043 |\n| 868 | 0262J0400868 | 30.143 | 82.162 | Ghaghara | I(s) | 0.80 | 5,004 |\n| 869 | 0262J0400869 | 30.140 | 82.156 | Ghaghara | I(s) | 0.38 | 4,963 |\n| 870 | 0262J0400870 | 30.139 | 82.154 | Ghaghara | I(s) | 0.38 | 4,957 |\n| 871 | 0262J0400871 | 30.139 | 82.153 | Ghaghara | I(s) | 0.25 | 4,942 |\n| 872 | 0262J0400872 | 30.138 | 82.140 | Ghaghara | I(s) | 0.25 | 4,867 |\n| 873 | 0262J0400873 | 30.138 | 82.116 | Ghaghara | M(o) | 0.46 | 4,906 |\n| 874 | 0262J0400874 | 30.135 | 82.134 | Ghaghara | I(s) | 0.25 | 4,917 |\n| 875 | 0262J0400875 | 30.106 | 82.151 | Ghaghara | M(l) | 0.26 | 5,159 |\n| 876 | 0262J0400876 | 30.090 | 82.121 | Ghaghara | M(o) | 0.28 | 5,176 |\n| 877 | 0262J0400877 | 30.089 | 82.123 | Ghaghara | M(o) | 1.52 | 5,187 |\n| 878 | 0262J0400878 | 30.079 | 82.119 | Ghaghara | M(e) | 1.38 | 4,913 |\n| 879 | 0262J0400879 | 30.074 | 82.136 | Ghaghara | M(o) | 0.34 | 5,134 |\n| 880 | 0262J0400880 | 30.067 | 82.127 | Ghaghara | M(l) | 62.33 | 4,829 |\n| 881 | 0262J0400881 | 30.065 | 82.120 | Ghaghara | M(o) | 0.27 | 5,073 |\n| 882 | 0262J0400882 | 30.060 | 82.250 | Ghaghara | I(s) | 0.35 | 4,968 |\n| 883 | 0262J0400883 | 30.059 | 82.132 | Ghaghara | M(e) | 0.84 | 4,802 |\n| 884 | 0262J0400884 | 30.057 | 82.136 | Ghaghara | M(o) | 0.25 | 4,817 |\n| 885 | 0262J0400885 | 30.057 | 82.143 | Ghaghara | M(o) | 3.30 | 4,846 |\n| 886 | 0262J0400886 | 30.054 | 82.239 | Ghaghara | E(o) | 0.25 | 5,240 |\n| 887 | 0262J0400887 | 30.046 | 82.167 | Ghaghara | M(o) | 2.60 | 5,067 |\n| 888 | 0262J0400888 | 30.040 | 82.162 | Ghaghara | M(o) | 0.38 | 4,902 |\n| 889 | 0262J0400889 | 30.039 | 82.099 | Ghaghara | E(o) | 0.64 | 5,156 |\n| 890 | 0262J0400890 | 30.036 | 82.218 | Ghaghara | M(o) | 0.71 | 5,026 |\n| 891 | 0262J0400891 | 30.034 | 82.167 | Ghaghara | M(o) | 1.30 | 5,074 |\n| 892 | 0262J0400892 | 30.033 | 82.088 | Ghaghara | M(o) | 0.82 | 5,085 |\n| 893 | 0262J0400893 | 30.032 | 82.127 | Ghaghara | M(o) | 1.59 | 5,043 |\n| 894 | 0262J0400894 | 30.023 | 82.164 | Ghaghara | M(o) | 2.78 | 5,001 |\n| 895 | 0262J0400895 | 30.023 | 82.122 | Ghaghara | M(o) | 1.30 | 5,023 |\n| 896 | 0262J0400896 | 30.021 | 82.170 | Ghaghara | M(o) | 0.58 | 5,024 |\n| 897 | 0262J0400897 | 30.002 | 82.037 | Ghaghara | M(o) | 0.68 | 5,013 |\n| 898 | 0262J0800898 | 30.038 | 82.268 | Ghaghara | M(l) | 0.42 | 4,732 |\n| 899 | 0262J0800899 | 30.032 | 82.284 | Ghaghara | M(o) | 1.59 | 5,036 |\n| 900 | 0262J0800900 | 30.031 | 82.275 | Ghaghara | E(o) | 0.31 | 4,824 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4320, "line_end": 4394, "token_count_estimate": 1661, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262J0400866", "0262J0400867", "0262J0400868", "0262J0400869", "0262J0400870", "0262J0400871", "0262J0400872", "0262J0400873", "0262J0400874", "0262J0400875", "0262J0400876", "0262J0400877", "0262J0400878", "0262J0400879", "0262J0400880", "0262J0400881", "0262J0400882", "0262J0400883", "0262J0400884", "0262J0400885", "0262J0400886", "0262J0400887", "0262J0400888", "0262J0400889", "0262J0400890", "0262J0400891", "0262J0400892", "0262J0400893", "0262J0400894", "0262J0400895", "0262J0400896", "0262J0400897", "0262J0800898", "0262J0800899", "0262J0800900"]}}
{"id": "e806589a0a135524", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 901 | 0262J0800901 | 30.024 | 82.270 | Ghaghara | E(o) | 2.79 | 4,662 |\n| 902 | 0262J0800902 | 30.010 | 82.300 | Ghaghara | E(c) | 8.96 | 5,099 |\n| 903 | 0262J0800903 | 30.003 | 82.300 | Ghaghara | E(o) | 2.91 | 4,921 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4320, "line_end": 4394, "token_count_estimate": 229, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262J0800901", "0262J0800902", "0262J0800903"]}}
{"id": "305bbd44a06c1fa2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 904 | 0262K0100904 | 29.994 | 82.045 | Ghaghara | M(o) | 2.57 | 4,724 |\n| 905 | 0262K0100905 | 29.993 | 82.197 | Ghaghara | M(l) | 24.67 | 4,379 |\n| 906 | 0262K0100906 | 29.991 | 82.056 | Ghaghara | E(o) | 0.43 | 4,863 |\n| 907 | 0262K0100907 | 29.977 | 82.209 | Ghaghara | M(o) | 1.54 | 4,937 |\n| 908 | 0262K0100908 | 29.972 | 82.059 | Ghaghara | M(o) | 0.35 | 4,781 |\n| 909 | 0262K0100909 | 29.971 | 82.250 | Ghaghara | E(c) | 17.27 | 4,881 |\n| 910 | 0262K0100910 | 29.968 | 82.216 | Ghaghara | E(o) | 0.55 | 4,867 |\n| 911 | 0262K0100911 | 29.961 | 82.084 | Ghaghara | M(o) | 2.52 | 4,750 |\n| 912 | 0262K0100912 | 29.935 | 82.208 | Ghaghara | E(c) | 12.01 | 4,562 |\n| 913 | 0262K0100913 | 29.931 | 82.159 | Ghaghara | M(o) | 0.79 | 4,966 |\n| 914 | 0262K0100914 | 29.931 | 82.219 | Ghaghara | M(o) | 0.42 | 5,053 |\n| 915 | 0262K0100915 | 29.929 | 82.166 | Ghaghara | M(o) | 0.88 | 4,729 |\n| 916 | 0262K0100916 | 29.929 | 82.207 | Ghaghara | E(c) | 32.97 | 4,550 |\n| 917 | 0262K0100917 | 29.928 | 82.235 | Ghaghara | O | 12.27 | 4,236 |\n| 918 | 0262K0100918 | 29.923 | 82.202 | Ghaghara | M(o) | 0.32 | 4,544 |\n| 919 | 0262K0100919 | 29.915 | 82.215 | Ghaghara | E(o) | 1.44 | 4,840 |\n| 920 | 0262K0100920 | 29.912 | 82.215 | Ghaghara | M(o) | 5.79 | 4,842 |\n| 921 | 0262K0100921 | 29.905 | 82.230 | Ghaghara | M(lg) | 0.78 | 4,971 |\n| 922 | 0262K0100922 | 29.902 | 82.006 | Ghaghara | O | 1.93 | 4,293 |\n| 923 | 0262K0100923 | 29.899 | 82.006 | Ghaghara | E(o) | 0.60 | 4,305 |\n| 924 | 0262K0100924 | 29.898 | 82.007 | Ghaghara | E(o) | 0.48 | 4,322 |\n| 925 | 0262K0100925 | 29.897 | 82.249 | Ghaghara | E(c) | 5.71 | 4,464 |\n| 926 | 0262K0100926 | 29.896 | 82.009 | Ghaghara | E(o) | 3.28 | 4,314 |\n| 927 | 0262K0100927 | 29.894 | 82.032 | Ghaghara | E(o) | 0.63 | 4,659 |\n| 928 | 0262K0100928 | 29.891 | 82.034 | Ghaghara | E(o) | 1.33 | 4,546 |\n| 929 | 0262K0100929 | 29.889 | 82.044 | Ghaghara | E(o) | 1.62 | 4,465 |\n| 930 | 0262K0100930 | 29.887 | 82.234 | Ghaghara | E(o) | 7.36 | 4,524 |\n| 931 | 0262K0100931 | 29.885 | 82.046 | Ghaghara | E(o) | 3.88 | 4,416 |\n| 932 | 0262K0100932 | 29.885 | 82.049 | Ghaghara | E(o) | 3.20 | 4,408 |\n| 933 | 0262K0100933 | 29.884 | 82.034 | Ghaghara | E(o) | 1.02 | 4,530 |\n| 934 | 0262K0100934 | 29.884 | 82.064 | Ghaghara | E(o) | 0.83 | 4,448 |\n| 935 | 0262K0100935 | 29.884 | 82.027 | Ghaghara | E(o) | 0.39 | 4,674 |\n| 936 | 0262K0100936 | 29.882 | 82.037 | Ghaghara | E(o) | 8.29 | 4,521 |\n| 937 | 0262K0100937 | 29.881 | 82.062 | Ghaghara | E(o) | 0.41 | 4,488 |\n| 938 | 0262K0100938 | 29.879 | 82.061 | Ghaghara | E(o) | 1.48 | 4,493 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4396, "line_end": 4470, "token_count_estimate": 1625, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0100904", "0262K0100905", "0262K0100906", "0262K0100907", "0262K0100908", "0262K0100909", "0262K0100910", "0262K0100911", "0262K0100912", "0262K0100913", "0262K0100914", "0262K0100915", "0262K0100916", "0262K0100917", "0262K0100918", "0262K0100919", "0262K0100920", "0262K0100921", "0262K0100922", "0262K0100923", "0262K0100924", "0262K0100925", "0262K0100926", "0262K0100927", "0262K0100928", "0262K0100929", "0262K0100930", "0262K0100931", "0262K0100932", "0262K0100933", "0262K0100934", "0262K0100935", "0262K0100936", "0262K0100937", "0262K0100938"]}}
{"id": "2cf38bb73e8b62ce", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 939 | 0262K0100939 | 29.879 | 82.058 | Ghaghara | E(o) | 0.35 | 4,523 |\n| 940 | 0262K0100940 | 29.868 | 82.079 | Ghaghara | E(o) | 0.95 | 4,361 |\n| 941 | 0262K0200941 | 29.743 | 82.228 | Ghaghara | M(o) | 0.76 | 4,637 |\n| 942 | 0262K0200942 | 29.709 | 82.243 | Ghaghara | E(o) | 0.91 | 4,815 |\n| 943 | 0262K0200943 | 29.707 | 82.173 | Ghaghara | M(o) | 0.44 | 4,348 |\n| 944 | 0262K0200944 | 29.706 | 82.229 | Ghaghara | M(o) | 0.55 | 5,003 |\n| 945 | 0262K0200945 | 29.702 | 82.245 | Ghaghara | M(o) | 10.53 | 4,671 |\n| 946 | 0262K0200946 | 29.693 | 82.240 | Ghaghara | M(o) | 2.82 | 4,690 |\n| 947 | 0262K0200947 | 29.692 | 82.177 | Ghaghara | E(o) | 7.45 | 4,383 |\n| 948 | 0262K0200948 | 29.691 | 82.172 | Ghaghara | E(o) | 1.06 | 4,327 |\n| 949 | 0262K0200949 | 29.687 | 82.177 | Ghaghara | E(o) | 4.16 | 4,491 |\n| 950 | 0262K0200950 | 29.683 | 82.197 | Ghaghara | E(o) | 1.18 | 4,647 |\n| 951 | 0262K0200951 | 29.680 | 82.245 | Ghaghara | I(s) | 0.32 | 5,066 |\n| 952 | 0262K0200952 | 29.679 | 82.206 | Ghaghara | E(o) | 9.09 | 4,608 |\n| 953 | 0262K0200953 | 29.675 | 82.193 | Ghaghara | M(o) | 15.17 | 4,223 |\n| 954 | 0262K0200954 | 29.675 | 82.222 | Ghaghara | M(o) | 0.26 | 4,749 |\n| 955 | 0262K0200955 | 29.673 | 82.190 | Ghaghara | E(o) | 3.31 | 4,233 |\n| 956 | 0262K0200956 | 29.670 | 82.229 | Ghaghara | E(o) | 5.62 | 4,757 |\n| 957 | 0262K0200957 | 29.669 | 82.222 | Ghaghara | E(o) | 11.34 | 4,577 |\n| 958 | 0262K0200958 | 29.668 | 82.194 | Ghaghara | E(o) | 12.67 | 4,379 |\n| 959 | 0262K0200959 | 29.666 | 82.203 | Ghaghara | E(c) | 19.44 | 4,388 |\n| 960 | 0262K0200960 | 29.665 | 82.244 | Ghaghara | E(o) | 2.64 | 4,720 |\n| 961 | 0262K0200961 | 29.665 | 82.240 | Ghaghara | E(o) | 1.20 | 4,779 |\n| 962 | 0262K0200962 | 29.665 | 82.248 | Ghaghara | E(o) | 2.04 | 4,703 |\n| 963 | 0262K0200963 | 29.664 | 82.215 | Ghaghara | E(o) | 3.58 | 4,629 |\n| 964 | 0262K0200964 | 29.663 | 82.195 | Ghaghara | M(o) | 4.59 | 4,377 |\n| 965 | 0262K0200965 | 29.662 | 82.190 | Ghaghara | E(o) | 0.41 | 4,425 |\n| 966 | 0262K0200966 | 29.661 | 82.192 | Ghaghara | E(o) | 0.53 | 4,429 |\n| 967 | 0262K0200967 | 29.660 | 82.216 | Ghaghara | E(o) | 1.63 | 4,665 |\n| 968 | 0262K0200968 | 29.660 | 82.207 | Ghaghara | E(o) | 1.55 | 4,593 |\n| 969 | 0262K0200969 | 29.660 | 82.222 | Ghaghara | E(o) | 2.92 | 4,622 |\n| 970 | 0262K0200970 | 29.658 | 82.239 | Ghaghara | E(o) | 0.74 | 4,689 |\n| 971 | 0262K0200971 | 29.655 | 82.245 | Ghaghara | E(o) | 0.57 | 4,530 |\n| 972 | 0262K0200972 | 29.654 | 82.223 | Ghaghara | E(o) | 13.57 | 4,492 |\n| 973 | 0262K0200973 | 29.651 | 82.207 | Ghaghara | E(o) | 0.50 | 4,424 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4396, "line_end": 4470, "token_count_estimate": 1613, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0100939", "0262K0100940", "0262K0200941", "0262K0200942", "0262K0200943", "0262K0200944", "0262K0200945", "0262K0200946", "0262K0200947", "0262K0200948", "0262K0200949", "0262K0200950", "0262K0200951", "0262K0200952", "0262K0200953", "0262K0200954", "0262K0200955", "0262K0200956", "0262K0200957", "0262K0200958", "0262K0200959", "0262K0200960", "0262K0200961", "0262K0200962", "0262K0200963", "0262K0200964", "0262K0200965", "0262K0200966", "0262K0200967", "0262K0200968", "0262K0200969", "0262K0200970", "0262K0200971", "0262K0200972", "0262K0200973"]}}
{"id": "24a10bd0f63e484f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 974 | 0262K0200974 | 29.651 | 82.221 | Ghaghara | E(o) | 0.90 | 4,486 |\n| 975 | 0262K0200975 | 29.651 | 82.212 | Ghaghara | E(o) | 1.85 | 4,530 |\n| 976 | 0262K0200976 | 29.648 | 82.202 | Ghaghara | E(o) | 2.94 | 4,266 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4396, "line_end": 4470, "token_count_estimate": 226, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0200974", "0262K0200975", "0262K0200976"]}}
{"id": "56f179c0b662324a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 4471, "line_end": 4479, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a2ffaef54551933e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 977 | 0262K0200977 | 29.647 | 82.226 | Ghaghara | E(o) | 3.53 | 4,351 |\n| 978 | 0262K0400978 | 29.147 | 82.160 | Ghaghara | E(o) | 0.66 | 3,907 |\n| 979 | 0262K0400979 | 29.121 | 82.222 | Ghaghara | E(o) | 0.32 | 4,341 |\n| 980 | 0262K0400980 | 29.119 | 82.233 | Ghaghara | E(o) | 0.56 | 4,235 |\n| 981 | 0262K0400981 | 29.116 | 82.227 | Ghaghara | E(o) | 0.81 | 4,277 |\n| 982 | 0262K0400982 | 29.116 | 82.236 | Ghaghara | E(o) | 10.84 | 4,270 |\n| 983 | 0262K0400983 | 29.112 | 82.230 | Ghaghara | E(o) | 5.20 | 4,369 |\n| 984 | 0262K0400984 | 29.111 | 82.246 | Ghaghara | E(o) | 2.97 | 4,398 |\n| 985 | 0262K0400985 | 29.110 | 82.219 | Ghaghara | E(o) | 2.37 | 4,350 |\n| 986 | 0262K0400986 | 29.110 | 82.227 | Ghaghara | E(o) | 0.81 | 4,390 |\n| 987 | 0262K0400987 | 29.106 | 82.237 | Ghaghara | E(o) | 8.79 | 4,512 |\n| 988 | 0262K0400988 | 29.103 | 82.199 | Ghaghara | E(o) | 1.57 | 4,353 |\n| 989 | 0262K0400989 | 29.103 | 82.227 | Ghaghara | E(o) | 0.26 | 4,487 |\n| 990 | 0262K0400990 | 29.101 | 82.228 | Ghaghara | E(o) | 0.41 | 4,452 |\n| 991 | 0262K0400991 | 29.099 | 82.250 | Ghaghara | E(o) | 0.59 | 4,388 |\n| 992 | 0262K0400992 | 29.097 | 82.202 | Ghaghara | E(o) | 3.55 | 4,332 |\n| 993 | 0262K0400993 | 29.084 | 82.181 | Ghaghara | E(o) | 0.44 | 4,339 |\n| 994 | 0262K0400994 | 29.084 | 82.186 | Ghaghara | E(o) | 11.01 | 4,242 |\n| 995 | 0262K0400995 | 29.069 | 82.182 | Ghaghara | E(o) | 1.05 | 4,358 |\n| 996 | 0262K0500996 | 29.998 | 82.278 | Ghaghara | M(o) | 1.18 | 4,999 |\n| 997 | 0262K0500997 | 29.991 | 82.316 | Ghaghara | M(o) | 1.09 | 5,065 |\n| 998 | 0262K0500998 | 29.979 | 82.395 | Ghaghara | E(o) | 3.55 | 5,218 |\n| 999 | 0262K0500999 | 29.976 | 82.392 | Ghaghara | M(o) | 0.84 | 5,157 |\n| 1000 | 0262K0501000 | 29.968 | 82.434 | Ghaghara | E(o) | 0.62 | 4,895 |\n| 1001 | 0262K0501001 | 29.956 | 82.400 | Ghaghara | M(o) | 0.33 | 5,304 |\n| 1002 | 0262K0501002 | 29.955 | 82.321 | Ghaghara | E(o) | 0.89 | 4,772 |\n| 1003 | 0262K0501003 | 29.950 | 82.461 | Ghaghara | M(o) | 0.60 | 5,123 |\n| 1004 | 0262K0501004 | 29.948 | 82.301 | Ghaghara | M(o) | 0.65 | 4,885 |\n| 1005 | 0262K0501005 | 29.933 | 82.484 | Ghaghara | M(o) | 1.06 | 4,858 |\n| 1006 | 0262K0501006 | 29.928 | 82.259 | Ghaghara | M(o) | 1.10 | 4,887 |\n| 1007 | 0262K0501007 | 29.924 | 82.443 | Ghaghara | E(o) | 0.57 | 4,486 |\n| 1008 | 0262K0501008 | 29.922 | 82.445 | Ghaghara | E(o) | 6.56 | 4,483 |\n| 1009 | 0262K0501009 | 29.898 | 82.474 | Ghaghara | E(o) | 3.34 | 4,907 |\n| 1010 | 0262K0501010 | 29.893 | 82.463 | Ghaghara | M(o) | 0.47 | 4,968 |\n| 1011 | 0262K0501011 | 29.893 | 82.436 | Ghaghara | M(o) | 0.87 | 5,031 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4480, "line_end": 4554, "token_count_estimate": 1631, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0200977", "0262K0400978", "0262K0400979", "0262K0400980", "0262K0400981", "0262K0400982", "0262K0400983", "0262K0400984", "0262K0400985", "0262K0400986", "0262K0400987", "0262K0400988", "0262K0400989", "0262K0400990", "0262K0400991", "0262K0400992", "0262K0400993", "0262K0400994", "0262K0400995", "0262K0500996", "0262K0500997", "0262K0500998", "0262K0500999", "0262K0501000", "0262K0501001", "0262K0501002", "0262K0501003", "0262K0501004", "0262K0501005", "0262K0501006", "0262K0501007", "0262K0501008", "0262K0501009", "0262K0501010", "0262K0501011"]}}
{"id": "eb34956c62c54fea", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1012 | 0262K0501012 | 29.892 | 82.336 | Ghaghara | E(o) | 1.66 | 4,543 |\n| 1013 | 0262K0501013 | 29.880 | 82.389 | Ghaghara | M(o) | 0.93 | 5,010 |\n| 1014 | 0262K0501014 | 29.880 | 82.447 | Ghaghara | E(o) | 0.36 | 5,246 |\n| 1015 | 0262K0501015 | 29.878 | 82.383 | Ghaghara | M(o) | 12.59 | 4,967 |\n| 1016 | 0262K0501016 | 29.876 | 82.389 | Ghaghara | M(o) | 0.59 | 5,032 |\n| 1017 | 0262K0501017 | 29.875 | 82.451 | Ghaghara | E(o) | 0.65 | 5,275 |\n| 1018 | 0262K0501018 | 29.867 | 82.441 | Ghaghara | M(o) | 0.39 | 5,124 |\n| 1019 | 0262K0501019 | 29.865 | 82.423 | Ghaghara | M(o) | 0.37 | 5,319 |\n| 1020 | 0262K0501020 | 29.865 | 82.411 | Ghaghara | M(o) | 0.85 | 5,113 |\n| 1021 | 0262K0501021 | 29.864 | 82.384 | Ghaghara | M(o) | 0.56 | 5,086 |\n| 1022 | 0262K0501022 | 29.862 | 82.383 | Ghaghara | M(o) | 3.25 | 5,111 |\n| 1023 | 0262K0501023 | 29.862 | 82.337 | Ghaghara | M(o) | 0.42 | 5,179 |\n| 1024 | 0262K0501024 | 29.862 | 82.385 | Ghaghara | M(o) | 0.26 | 5,094 |\n| 1025 | 0262K0501025 | 29.861 | 82.439 | Ghaghara | M(o) | 0.92 | 5,242 |\n| 1026 | 0262K0501026 | 29.860 | 82.486 | Ghaghara | E(o) | 0.26 | 4,915 |\n| 1027 | 0262K0501027 | 29.859 | 82.333 | Ghaghara | E(c) | 8.76 | 5,057 |\n| 1028 | 0262K0501028 | 29.859 | 82.483 | Ghaghara | E(o) | 1.40 | 4,904 |\n| 1029 | 0262K0501029 | 29.855 | 82.483 | Ghaghara | M(o) | 0.35 | 4,970 |\n| 1030 | 0262K0501030 | 29.854 | 82.339 | Ghaghara | M(o) | 4.97 | 4,965 |\n| 1031 | 0262K0501031 | 29.853 | 82.377 | Ghaghara | E(o) | 2.21 | 5,014 |\n| 1032 | 0262K0501032 | 29.850 | 82.355 | Ghaghara | E(c) | 4.46 | 5,028 |\n| 1033 | 0262K0501033 | 29.848 | 82.447 | Ghaghara | M(o) | 0.51 | 5,076 |\n| 1034 | 0262K0501034 | 29.847 | 82.475 | Ghaghara | E(o) | 0.46 | 5,031 |\n| 1035 | 0262K0501035 | 29.846 | 82.449 | Ghaghara | E(o) | 0.59 | 5,056 |\n| 1036 | 0262K0501036 | 29.846 | 82.464 | Ghaghara | E(o) | 1.21 | 4,959 |\n| 1037 | 0262K0501037 | 29.843 | 82.481 | Ghaghara | E(o) | 0.82 | 5,008 |\n| 1038 | 0262K0501038 | 29.843 | 82.439 | Ghaghara | M(o) | 0.70 | 5,056 |\n| 1039 | 0262K0501039 | 29.843 | 82.476 | Ghaghara | E(o) | 5.95 | 4,936 |\n| 1040 | 0262K0501040 | 29.842 | 82.472 | Ghaghara | E(o) | 3.50 | 4,892 |\n| 1041 | 0262K0501041 | 29.841 | 82.352 | Ghaghara | M(o) | 0.38 | 5,041 |\n| 1042 | 0262K0501042 | 29.840 | 82.350 | Ghaghara | M(o) | 1.04 | 5,038 |\n| 1043 | 0262K0501043 | 29.839 | 82.355 | Ghaghara | E(o) | 1.73 | 4,977 |\n| 1044 | 0262K0501044 | 29.838 | 82.475 | Ghaghara | E(o) | 1.98 | 4,909 |\n| 1045 | 0262K0501045 | 29.837 | 82.349 | Ghaghara | E(o) | 2.07 | 4,996 |\n| 1046 | 0262K0501046 | 29.828 | 82.466 | Ghaghara | E(o) | 2.26 | 4,511 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4480, "line_end": 4554, "token_count_estimate": 1640, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0501012", "0262K0501013", "0262K0501014", "0262K0501015", "0262K0501016", "0262K0501017", "0262K0501018", "0262K0501019", "0262K0501020", "0262K0501021", "0262K0501022", "0262K0501023", "0262K0501024", "0262K0501025", "0262K0501026", "0262K0501027", "0262K0501028", "0262K0501029", "0262K0501030", "0262K0501031", "0262K0501032", "0262K0501033", "0262K0501034", "0262K0501035", "0262K0501036", "0262K0501037", "0262K0501038", "0262K0501039", "0262K0501040", "0262K0501041", "0262K0501042", "0262K0501043", "0262K0501044", "0262K0501045", "0262K0501046"]}}
{"id": "174d16d9eb7b1816", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1047 | 0262K0501047 | 29.825 | 82.433 | Ghaghara | M(o) | 0.51 | 5,168 |\n| 1048 | 0262K0501048 | 29.819 | 82.453 | Ghaghara | M(o) | 0.88 | 5,110 |\n| 1049 | 0262K0501049 | 29.816 | 82.377 | Ghaghara | M(e) | 0.94 | 4,958 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4480, "line_end": 4554, "token_count_estimate": 230, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0501047", "0262K0501048", "0262K0501049"]}}
{"id": "55af1eae8fcb0a01", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1050 | 0262K0501050 | 29.816 | 82.450 | Ghaghara | E(o) | 0.27 | 5,196 |\n| 1051 | 0262K0501051 | 29.815 | 82.448 | Ghaghara | M(o) | 1.17 | 5,192 |\n| 1052 | 0262K0501052 | 29.811 | 82.440 | Ghaghara | M(o) | 0.27 | 5,279 |\n| 1053 | 0262K0501053 | 29.808 | 82.447 | Ghaghara | M(o) | 1.51 | 5,222 |\n| 1054 | 0262K0501054 | 29.806 | 82.432 | Ghaghara | M(o) | 0.56 | 5,061 |\n| 1055 | 0262K0501055 | 29.802 | 82.414 | Ghaghara | M(o) | 0.35 | 5,156 |\n| 1056 | 0262K0501056 | 29.801 | 82.450 | Ghaghara | M(o) | 0.92 | 5,205 |\n| 1057 | 0262K0501057 | 29.801 | 82.269 | Ghaghara | E(o) | 5.15 | 4,882 |\n| 1058 | 0262K0501058 | 29.798 | 82.416 | Ghaghara | M(o) | 2.99 | 5,107 |\n| 1059 | 0262K0501059 | 29.798 | 82.450 | Ghaghara | M(o) | 0.25 | 5,189 |\n| 1060 | 0262K0501060 | 29.793 | 82.365 | Ghaghara | M(o) | 1.14 | 5,065 |\n| 1061 | 0262K0501061 | 29.792 | 82.438 | Ghaghara | E(o) | 0.31 | 4,862 |\n| 1062 | 0262K0501062 | 29.790 | 82.460 | Ghaghara | M(o) | 1.95 | 5,224 |\n| 1063 | 0262K0501063 | 29.789 | 82.436 | Ghaghara | E(o) | 0.56 | 4,854 |\n| 1064 | 0262K0501064 | 29.787 | 82.369 | Ghaghara | M(o) | 0.57 | 4,886 |\n| 1065 | 0262K0501065 | 29.784 | 82.393 | Ghaghara | M(o) | 0.56 | 5,175 |\n| 1066 | 0262K0501066 | 29.778 | 82.390 | Ghaghara | M(o) | 0.96 | 4,895 |\n| 1067 | 0262K0501067 | 29.776 | 82.449 | Ghaghara | E(o) | 7.82 | 4,949 |\n| 1068 | 0262K0501068 | 29.774 | 82.452 | Ghaghara | E(o) | 2.40 | 4,948 |\n| 1069 | 0262K0501069 | 29.769 | 82.356 | Ghaghara | M(o) | 0.27 | 4,922 |\n| 1070 | 0262K0501070 | 29.763 | 82.472 | Ghaghara | M(o) | 1.62 | 4,763 |\n| 1071 | 0262K0501071 | 29.761 | 82.405 | Ghaghara | M(o) | 0.70 | 5,048 |\n| 1072 | 0262K0501072 | 29.757 | 82.453 | Ghaghara | E(o) | 0.46 | 4,480 |\n| 1073 | 0262K0501073 | 29.754 | 82.415 | Ghaghara | E(c) | 42.40 | 4,692 |\n| 1074 | 0262K0501074 | 29.754 | 82.437 | Ghaghara | E(o) | 3.26 | 4,165 |\n| 1075 | 0262K0501075 | 29.752 | 82.410 | Ghaghara | E(o) | 0.29 | 4,697 |\n| 1076 | 0262K0601076 | 29.740 | 82.369 | Ghaghara | M(e) | 0.34 | 4,857 |\n| 1077 | 0262K0601077 | 29.740 | 82.468 | Ghaghara | E(o) | 5.93 | 4,976 |\n| 1078 | 0262K0601078 | 29.733 | 82.465 | Ghaghara | E(o) | 0.42 | 4,670 |\n| 1079 | 0262K0601079 | 29.733 | 82.458 | Ghaghara | E(o) | 5.01 | 4,578 |\n| 1080 | 0262K0601080 | 29.729 | 82.353 | Ghaghara | E(o) | 1.95 | 4,613 |\n| 1081 | 0262K0601081 | 29.727 | 82.470 | Ghaghara | E(o) | 3.81 | 4,792 |\n| 1082 | 0262K0601082 | 29.722 | 82.465 | Ghaghara | E(o) | 2.13 | 4,674 |\n| 1083 | 0262K0601083 | 29.718 | 82.364 | Ghaghara | E(o) | 6.83 | 4,505 |\n| 1084 | 0262K0601084 | 29.715 | 82.366 | Ghaghara | M(o) | 0.35 | 4,751 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4556, "line_end": 4630, "token_count_estimate": 1627, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0501050", "0262K0501051", "0262K0501052", "0262K0501053", "0262K0501054", "0262K0501055", "0262K0501056", "0262K0501057", "0262K0501058", "0262K0501059", "0262K0501060", "0262K0501061", "0262K0501062", "0262K0501063", "0262K0501064", "0262K0501065", "0262K0501066", "0262K0501067", "0262K0501068", "0262K0501069", "0262K0501070", "0262K0501071", "0262K0501072", "0262K0501073", "0262K0501074", "0262K0501075", "0262K0601076", "0262K0601077", "0262K0601078", "0262K0601079", "0262K0601080", "0262K0601081", "0262K0601082", "0262K0601083", "0262K0601084"]}}
{"id": "88cecbc90b83cbc2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1085 | 0262K0601085 | 29.714 | 82.250 | Ghaghara | M(l) | 11.54 | 4,651 |\n| 1086 | 0262K0601086 | 29.714 | 82.365 | Ghaghara | M(o) | 0.74 | 4,799 |\n| 1087 | 0262K0601087 | 29.705 | 82.343 | Ghaghara | E(o) | 4.77 | 4,563 |\n| 1088 | 0262K0601088 | 29.704 | 82.339 | Ghaghara | E(o) | 6.99 | 4,560 |\n| 1089 | 0262K0601089 | 29.703 | 82.355 | Ghaghara | E(o) | 0.58 | 4,860 |\n| 1090 | 0262K0601090 | 29.703 | 82.336 | Ghaghara | E(o) | 4.38 | 4,567 |\n| 1091 | 0262K0601091 | 29.703 | 82.478 | Ghaghara | E(o) | 1.46 | 4,778 |\n| 1092 | 0262K0601092 | 29.702 | 82.332 | Ghaghara | E(o) | 1.37 | 4,551 |\n| 1093 | 0262K0601093 | 29.700 | 82.327 | Ghaghara | E(c) | 9.09 | 4,399 |\n| 1094 | 0262K0601094 | 29.698 | 82.373 | Ghaghara | M(o) | 13.27 | 4,842 |\n| 1095 | 0262K0601095 | 29.693 | 82.376 | Ghaghara | E(o) | 2.23 | 4,780 |\n| 1096 | 0262K0601096 | 29.687 | 82.419 | Ghaghara | M(o) | 2.41 | 4,636 |\n| 1097 | 0262K0601097 | 29.674 | 82.409 | Ghaghara | M(o) | 3.56 | 4,953 |\n| 1098 | 0262K0601098 | 29.669 | 82.417 | Ghaghara | E(o) | 2.09 | 4,795 |\n| 1099 | 0262K0601099 | 29.667 | 82.408 | Ghaghara | E(o) | 3.57 | 4,801 |\n| 1100 | 0262K0601100 | 29.667 | 82.265 | Ghaghara | O | 0.37 | 4,331 |\n| 1101 | 0262K0601101 | 29.665 | 82.416 | Ghaghara | E(c) | 3.71 | 4,638 |\n| 1102 | 0262K0601102 | 29.662 | 82.259 | Ghaghara | E(o) | 7.18 | 4,499 |\n| 1103 | 0262K0601103 | 29.662 | 82.411 | Ghaghara | E(o) | 1.85 | 4,789 |\n| 1104 | 0262K0601104 | 29.662 | 82.354 | Ghaghara | E(o) | 1.74 | 4,408 |\n| 1105 | 0262K0601105 | 29.661 | 82.275 | Ghaghara | E(o) | 0.70 | 4,579 |\n| 1106 | 0262K0601106 | 29.659 | 82.277 | Ghaghara | E(o) | 1.36 | 4,582 |\n| 1107 | 0262K0601107 | 29.658 | 82.443 | Ghaghara | E(o) | 0.60 | 4,659 |\n| 1108 | 0262K0601108 | 29.656 | 82.368 | Ghaghara | E(o) | 0.83 | 4,354 |\n| 1109 | 0262K0601109 | 29.655 | 82.354 | Ghaghara | E(o) | 1.23 | 4,395 |\n| 1110 | 0262K0601110 | 29.654 | 82.257 | Ghaghara | E(o) | 7.42 | 4,478 |\n| 1111 | 0262K0601111 | 29.653 | 82.366 | Ghaghara | E(o) | 5.04 | 4,366 |\n| 1112 | 0262K0601112 | 29.653 | 82.286 | Ghaghara | E(o) | 1.50 | 4,236 |\n| 1113 | 0262K0601113 | 29.650 | 82.262 | Ghaghara | E(o) | 2.13 | 4,375 |\n| 1114 | 0262K0601114 | 29.650 | 82.284 | Ghaghara | E(o) | 1.43 | 4,317 |\n| 1115 | 0262K0601115 | 29.649 | 82.368 | Ghaghara | E(o) | 3.13 | 4,469 |\n| 1116 | 0262K0601116 | 29.648 | 82.399 | Ghaghara | E(o) | 1.09 | 4,580 |\n| 1117 | 0262K0601117 | 29.647 | 82.258 | Ghaghara | E(o) | 4.49 | 4,213 |\n| 1118 | 0262K0601118 | 29.637 | 82.356 | Ghaghara | E(o) | 1.50 | 4,273 |\n| 1119 | 0262K0601119 | 29.628 | 82.357 | Ghaghara | E(o) | 0.26 | 4,494 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4556, "line_end": 4630, "token_count_estimate": 1610, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0601085", "0262K0601086", "0262K0601087", "0262K0601088", "0262K0601089", "0262K0601090", "0262K0601091", "0262K0601092", "0262K0601093", "0262K0601094", "0262K0601095", "0262K0601096", "0262K0601097", "0262K0601098", "0262K0601099", "0262K0601100", "0262K0601101", "0262K0601102", "0262K0601103", "0262K0601104", "0262K0601105", "0262K0601106", "0262K0601107", "0262K0601108", "0262K0601109", "0262K0601110", "0262K0601111", "0262K0601112", "0262K0601113", "0262K0601114", "0262K0601115", "0262K0601116", "0262K0601117", "0262K0601118", "0262K0601119"]}}
{"id": "3bbc7c085941ae27", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1120 | 0262K0601120 | 29.524 | 82.445 | Ghaghara | E(o) | 0.68 | 4,597 |\n| 1121 | 0262K0601121 | 29.518 | 82.310 | Ghaghara | M(o) | 0.84 | 4,478 |\n| 1122 | 0262K0601122 | 29.514 | 82.308 | Ghaghara | E(o) | 3.35 | 4,409 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4556, "line_end": 4630, "token_count_estimate": 229, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0601120", "0262K0601121", "0262K0601122"]}}
{"id": "1ddb15dd9d8abc54", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 4631, "line_end": 4638, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "85269d0309b17114", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1123 | 0262K0601123 | 29.513 | 82.312 | Ghaghara | E(o) | 2.07 | 4,482 |\n| 1124 | 0262K0601124 | 29.505 | 82.328 | Ghaghara | E(o) | 3.14 | 4,727 |\n| 1125 | 0262K0601125 | 29.505 | 82.455 | Ghaghara | M(o) | 0.34 | 5,195 |\n| 1126 | 0262K0601126 | 29.500 | 82.304 | Ghaghara | E(o) | 2.27 | 4,512 |\n| 1127 | 0262K0701127 | 29.500 | 82.314 | Ghaghara | E(o) | 0.28 | 4,533 |\n| 1128 | 0262K0701128 | 29.498 | 82.313 | Ghaghara | E(o) | 1.91 | 4,539 |\n| 1129 | 0262K0701129 | 29.497 | 82.308 | Ghaghara | E(o) | 0.42 | 4,598 |\n| 1130 | 0262K0701130 | 29.496 | 82.316 | Ghaghara | E(o) | 0.31 | 4,476 |\n| 1131 | 0262K0701131 | 29.495 | 82.338 | Ghaghara | E(o) | 1.11 | 4,516 |\n| 1132 | 0262K0701132 | 29.491 | 82.380 | Ghaghara | E(o) | 1.59 | 4,679 |\n| 1133 | 0262K0701133 | 29.491 | 82.390 | Ghaghara | E(o) | 6.01 | 4,764 |\n| 1134 | 0262K0701134 | 29.489 | 82.337 | Ghaghara | E(o) | 1.69 | 4,316 |\n| 1135 | 0262K0701135 | 29.486 | 82.491 | Ghaghara | E(o) | 4.14 | 4,980 |\n| 1136 | 0262K0701136 | 29.486 | 82.350 | Ghaghara | E(o) | 8.39 | 4,732 |\n| 1137 | 0262K0701137 | 29.485 | 82.353 | Ghaghara | E(o) | 0.25 | 4,715 |\n| 1138 | 0262K0701138 | 29.482 | 82.463 | Ghaghara | M(o) | 0.37 | 4,701 |\n| 1139 | 0262K0701139 | 29.476 | 82.374 | Ghaghara | E(o) | 6.07 | 4,441 |\n| 1140 | 0262K0701140 | 29.475 | 82.350 | Ghaghara | E(o) | 1.06 | 4,374 |\n| 1141 | 0262K0701141 | 29.475 | 82.386 | Ghaghara | O | 0.80 | 4,267 |\n| 1142 | 0262K0701142 | 29.471 | 82.439 | Ghaghara | M(o) | 0.27 | 5,085 |\n| 1143 | 0262K0701143 | 29.470 | 82.308 | Ghaghara | E(o) | 2.47 | 4,377 |\n| 1144 | 0262K0701144 | 29.469 | 82.311 | Ghaghara | E(o) | 1.21 | 4,362 |\n| 1145 | 0262K0701145 | 29.467 | 82.398 | Ghaghara | E(o) | 5.37 | 4,421 |\n| 1146 | 0262K0701146 | 29.463 | 82.380 | Ghaghara | O | 8.93 | 3,978 |\n| 1147 | 0262K0701147 | 29.459 | 82.458 | Ghaghara | M(o) | 0.26 | 5,093 |\n| 1148 | 0262K0701148 | 29.459 | 82.394 | Ghaghara | M(o) | 7.42 | 4,466 |\n| 1149 | 0262K0701149 | 29.457 | 82.308 | Ghaghara | E(o) | 0.25 | 4,340 |\n| 1150 | 0262K0701150 | 29.453 | 82.303 | Ghaghara | E(o) | 5.25 | 4,420 |\n| 1151 | 0262K0701151 | 29.452 | 82.305 | Ghaghara | E(o) | 0.67 | 4,414 |\n| 1152 | 0262K0701152 | 29.450 | 82.277 | Ghaghara | E(o) | 0.43 | 4,248 |\n| 1153 | 0262K0701153 | 29.449 | 82.274 | Ghaghara | E(o) | 0.85 | 4,294 |\n| 1154 | 0262K0701154 | 29.448 | 82.271 | Ghaghara | E(o) | 0.34 | 4,328 |\n| 1155 | 0262K0701155 | 29.437 | 82.387 | Ghaghara | M(o) | 1.16 | 4,817 |\n| 1156 | 0262K0701156 | 29.436 | 82.272 | Ghaghara | E(o) | 0.64 | 4,259 |\n| 1157 | 0262K0701157 | 29.432 | 82.402 | Ghaghara | E(o) | 3.70 | 4,529 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4639, "line_end": 4713, "token_count_estimate": 1612, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0601123", "0262K0601124", "0262K0601125", "0262K0601126", "0262K0701127", "0262K0701128", "0262K0701129", "0262K0701130", "0262K0701131", "0262K0701132", "0262K0701133", "0262K0701134", "0262K0701135", "0262K0701136", "0262K0701137", "0262K0701138", "0262K0701139", "0262K0701140", "0262K0701141", "0262K0701142", "0262K0701143", "0262K0701144", "0262K0701145", "0262K0701146", "0262K0701147", "0262K0701148", "0262K0701149", "0262K0701150", "0262K0701151", "0262K0701152", "0262K0701153", "0262K0701154", "0262K0701155", "0262K0701156", "0262K0701157"]}}
{"id": "a8a904f7cf926683", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1158 | 0262K0701158 | 29.432 | 82.361 | Ghaghara | E(o) | 27.07 | 4,398 |\n| 1159 | 0262K0701159 | 29.428 | 82.400 | Ghaghara | E(o) | 1.96 | 4,572 |\n| 1160 | 0262K0701160 | 29.427 | 82.360 | Ghaghara | E(o) | 0.37 | 4,468 |\n| 1161 | 0262K0701161 | 29.421 | 82.457 | Ghaghara | E(o) | 0.97 | 4,677 |\n| 1162 | 0262K0701162 | 29.417 | 82.456 | Ghaghara | E(o) | 2.15 | 4,612 |\n| 1163 | 0262K0701163 | 29.414 | 82.414 | Ghaghara | E(o) | 0.29 | 4,034 |\n| 1164 | 0262K0701164 | 29.408 | 82.398 | Ghaghara | O | 0.82 | 3,979 |\n| 1165 | 0262K0701165 | 29.408 | 82.430 | Ghaghara | E(c) | 19.81 | 4,415 |\n| 1166 | 0262K0701166 | 29.405 | 82.441 | Ghaghara | E(o) | 1.07 | 4,763 |\n| 1167 | 0262K0701167 | 29.391 | 82.394 | Ghaghara | E(o) | 16.79 | 3,954 |\n| 1168 | 0262K0701168 | 29.389 | 82.383 | Ghaghara | E(o) | 0.59 | 4,303 |\n| 1169 | 0262K0701169 | 29.387 | 82.416 | Ghaghara | E(o) | 13.31 | 4,447 |\n| 1170 | 0262K0701170 | 29.386 | 82.466 | Ghaghara | E(o) | 3.77 | 4,610 |\n| 1171 | 0262K0701171 | 29.386 | 82.472 | Ghaghara | E(o) | 0.53 | 4,473 |\n| 1172 | 0262K0701172 | 29.384 | 82.424 | Ghaghara | E(c) | 46.79 | 4,434 |\n| 1173 | 0262K0701173 | 29.382 | 82.410 | Ghaghara | E(o) | 10.19 | 4,287 |\n| 1174 | 0262K0701174 | 29.382 | 82.397 | Ghaghara | M(o) | 4.75 | 4,372 |\n| 1175 | 0262K0701175 | 29.379 | 82.385 | Ghaghara | E(o) | 1.53 | 4,396 |\n| 1176 | 0262K0701176 | 29.373 | 82.473 | Ghaghara | E(o) | 0.74 | 4,556 |\n| 1177 | 0262K0701177 | 29.372 | 82.473 | Ghaghara | E(o) | 0.30 | 4,564 |\n| 1178 | 0262K0701178 | 29.372 | 82.393 | Ghaghara | E(o) | 0.51 | 4,248 |\n| 1179 | 0262K0701179 | 29.319 | 82.482 | Ghaghara | E(o) | 1.83 | 4,546 |\n| 1180 | 0262K0701180 | 29.310 | 82.495 | Ghaghara | E(o) | 0.40 | 4,592 |\n| 1181 | 0262K0701181 | 29.309 | 82.474 | Ghaghara | E(o) | 15.73 | 4,081 |\n| 1182 | 0262K0701182 | 29.288 | 82.491 | Ghaghara | E(o) | 0.45 | 4,733 |\n| 1183 | 0262K0801183 | 29.208 | 82.482 | Ghaghara | E(c) | 5.12 | 4,264 |\n| 1184 | 0262K0801184 | 29.128 | 82.270 | Ghaghara | O | 0.85 | 4,465 |\n| 1185 | 0262K0801185 | 29.125 | 82.299 | Ghaghara | E(o) | 1.19 | 4,246 |\n| 1186 | 0262K0801186 | 29.121 | 82.298 | Ghaghara | E(o) | 1.22 | 4,303 |\n| 1187 | 0262K0801187 | 29.119 | 82.256 | Ghaghara | E(o) | 13.37 | 4,430 |\n| 1188 | 0262K0801188 | 29.115 | 82.332 | Ghaghara | E(o) | 0.47 | 4,138 |\n| 1189 | 0262K0801189 | 29.111 | 82.259 | Ghaghara | E(o) | 9.93 | 4,478 |\n| 1190 | 0262K0801190 | 29.109 | 82.326 | Ghaghara | E(o) | 0.87 | 4,191 |\n| 1191 | 0262K0801191 | 29.108 | 82.329 | Ghaghara | E(o) | 0.89 | 4,161 |\n| 1192 | 0262K0801192 | 29.107 | 82.268 | Ghaghara | E(o) | 2.25 | 4,399 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4639, "line_end": 4713, "token_count_estimate": 1621, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0701158", "0262K0701159", "0262K0701160", "0262K0701161", "0262K0701162", "0262K0701163", "0262K0701164", "0262K0701165", "0262K0701166", "0262K0701167", "0262K0701168", "0262K0701169", "0262K0701170", "0262K0701171", "0262K0701172", "0262K0701173", "0262K0701174", "0262K0701175", "0262K0701176", "0262K0701177", "0262K0701178", "0262K0701179", "0262K0701180", "0262K0701181", "0262K0701182", "0262K0801183", "0262K0801184", "0262K0801185", "0262K0801186", "0262K0801187", "0262K0801188", "0262K0801189", "0262K0801190", "0262K0801191", "0262K0801192"]}}
{"id": "3398f9d7bc038980", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1193 | 0262K0801193 | 29.102 | 82.250 | Ghaghara | E(o) | 0.97 | 4,407 |\n| 1194 | 0262K0801194 | 29.097 | 82.272 | Ghaghara | E(o) | 0.40 | 4,532 |\n| 1195 | 0262K0801195 | 29.094 | 82.255 | Ghaghara | E(o) | 2.51 | 4,411 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4639, "line_end": 4713, "token_count_estimate": 228, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0801193", "0262K0801194", "0262K0801195"]}}
{"id": "079489510ae0f6f8", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1196 | 0262K0801196 | 29.094 | 82.263 | Ghaghara | E(o) | 0.42 | 4,474 |\n| 1197 | 0262K0801197 | 29.085 | 82.272 | Ghaghara | E(o) | 2.51 | 4,428 |\n| 1198 | 0262K0801198 | 29.084 | 82.262 | Ghaghara | E(o) | 2.13 | 4,333 |\n| 1199 | 0262K0801199 | 29.068 | 82.265 | Ghaghara | E(o) | 0.52 | 4,181 |\n| 1200 | 0262K0801200 | 29.062 | 82.263 | Ghaghara | E(o) | 0.44 | 4,116 |\n| 1201 | 0262K0901201 | 29.965 | 82.531 | Ghaghara | E(o) | 0.33 | 4,905 |\n| 1202 | 0262K0901202 | 29.964 | 82.533 | Ghaghara | E(o) | 2.56 | 4,910 |\n| 1203 | 0262K0901203 | 29.959 | 82.529 | Ghaghara | E(o) | 0.48 | 4,932 |\n| 1204 | 0262K0901204 | 29.955 | 82.545 | Ghaghara | E(o) | 1.42 | 5,066 |\n| 1205 | 0262K0901205 | 29.955 | 82.539 | Ghaghara | E(o) | 0.30 | 4,984 |\n| 1206 | 0262K0901206 | 29.954 | 82.534 | Ghaghara | E(o) | 0.43 | 4,841 |\n| 1207 | 0262K0901207 | 29.953 | 82.535 | Ghaghara | E(o) | 0.32 | 4,837 |\n| 1208 | 0262K0901208 | 29.952 | 82.556 | Ghaghara | M(o) | 0.92 | 5,180 |\n| 1209 | 0262K0901209 | 29.952 | 82.554 | Ghaghara | E(o) | 0.49 | 5,182 |\n| 1210 | 0262K0901210 | 29.949 | 82.534 | Ghaghara | E(o) | 1.31 | 4,826 |\n| 1211 | 0262K0901211 | 29.947 | 82.559 | Ghaghara | M(o) | 0.57 | 5,316 |\n| 1212 | 0262K0901212 | 29.946 | 82.550 | Ghaghara | E(o) | 0.72 | 5,036 |\n| 1213 | 0262K0901213 | 29.945 | 82.550 | Ghaghara | E(o) | 0.67 | 5,020 |\n| 1214 | 0262K0901214 | 29.945 | 82.536 | Ghaghara | E(o) | 0.85 | 4,787 |\n| 1215 | 0262K0901215 | 29.944 | 82.548 | Ghaghara | E(o) | 0.87 | 5,013 |\n| 1216 | 0262K0901216 | 29.938 | 82.534 | Ghaghara | E(o) | 0.46 | 4,646 |\n| 1217 | 0262K0901217 | 29.905 | 82.518 | Ghaghara | E(o) | 8.93 | 4,812 |\n| 1218 | 0262K0901218 | 29.903 | 82.530 | Ghaghara | M(o) | 0.79 | 5,042 |\n| 1219 | 0262K0901219 | 29.902 | 82.570 | Ghaghara | E(o) | 0.48 | 4,831 |\n| 1220 | 0262K0901220 | 29.894 | 82.568 | Ghaghara | E(o) | 0.37 | 4,674 |\n| 1221 | 0262K0901221 | 29.893 | 82.537 | Ghaghara | E(o) | 1.21 | 4,842 |\n| 1222 | 0262K0901222 | 29.893 | 82.514 | Ghaghara | E(o) | 11.14 | 5,031 |\n| 1223 | 0262K0901223 | 29.892 | 82.533 | Ghaghara | E(o) | 0.64 | 4,956 |\n| 1224 | 0262K0901224 | 29.889 | 82.507 | Ghaghara | E(o) | 4.23 | 4,832 |\n| 1225 | 0262K0901225 | 29.885 | 82.588 | Ghaghara | M(o) | 0.50 | 5,167 |\n| 1226 | 0262K0901226 | 29.884 | 82.527 | Ghaghara | E(o) | 0.28 | 4,896 |\n| 1227 | 0262K0901227 | 29.883 | 82.608 | Ghaghara | E(o) | 0.28 | 5,406 |\n| 1228 | 0262K0901228 | 29.879 | 82.523 | Ghaghara | E(o) | 0.94 | 4,932 |\n| 1229 | 0262K0901229 | 29.878 | 82.574 | Ghaghara | M(o) | 0.47 | 5,030 |\n| 1230 | 0262K0901230 | 29.877 | 82.518 | Ghaghara | E(o) | 0.51 | 4,831 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4715, "line_end": 4789, "token_count_estimate": 1650, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0801196", "0262K0801197", "0262K0801198", "0262K0801199", "0262K0801200", "0262K0901201", "0262K0901202", "0262K0901203", "0262K0901204", "0262K0901205", "0262K0901206", "0262K0901207", "0262K0901208", "0262K0901209", "0262K0901210", "0262K0901211", "0262K0901212", "0262K0901213", "0262K0901214", "0262K0901215", "0262K0901216", "0262K0901217", "0262K0901218", "0262K0901219", "0262K0901220", "0262K0901221", "0262K0901222", "0262K0901223", "0262K0901224", "0262K0901225", "0262K0901226", "0262K0901227", "0262K0901228", "0262K0901229", "0262K0901230"]}}
{"id": "b2bf7e5cdb86f1e1", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1231 | 0262K0901231 | 29.876 | 82.521 | Ghaghara | E(o) | 0.27 | 4,819 |\n| 1232 | 0262K0901232 | 29.875 | 82.597 | Ghaghara | E(o) | 2.88 | 5,190 |\n| 1233 | 0262K0901233 | 29.871 | 82.596 | Ghaghara | E(o) | 0.69 | 5,148 |\n| 1234 | 0262K0901234 | 29.861 | 82.574 | Ghaghara | E(o) | 0.56 | 5,318 |\n| 1235 | 0262K0901235 | 29.859 | 82.586 | Ghaghara | E(o) | 0.93 | 5,117 |\n| 1236 | 0262K0901236 | 29.856 | 82.563 | Ghaghara | E(o) | 6.28 | 4,863 |\n| 1237 | 0262K0901237 | 29.853 | 82.604 | Ghaghara | I(s) | 0.49 | 4,821 |\n| 1238 | 0262K0901238 | 29.853 | 82.577 | Ghaghara | E(o) | 0.49 | 5,347 |\n| 1239 | 0262K0901239 | 29.850 | 82.594 | Ghaghara | E(o) | 0.36 | 4,960 |\n| 1240 | 0262K0901240 | 29.849 | 82.575 | Ghaghara | E(o) | 2.60 | 5,230 |\n| 1241 | 0262K0901241 | 29.848 | 82.580 | Ghaghara | E(o) | 0.53 | 5,142 |\n| 1242 | 0262K0901242 | 29.832 | 82.697 | Ghaghara | M(o) | 1.10 | 5,283 |\n| 1243 | 0262K0901243 | 29.831 | 82.705 | Ghaghara | M(o) | 0.48 | 5,197 |\n| 1244 | 0262K0901244 | 29.829 | 82.672 | Ghaghara | M(o) | 0.84 | 5,243 |\n| 1245 | 0262K0901245 | 29.828 | 82.674 | Ghaghara | M(o) | 0.57 | 5,143 |\n| 1246 | 0262K0901246 | 29.822 | 82.676 | Ghaghara | E(o) | 2.68 | 5,103 |\n| 1247 | 0262K0901247 | 29.822 | 82.712 | Ghaghara | M(o) | 19.68 | 5,037 |\n| 1248 | 0262K0901248 | 29.812 | 82.702 | Ghaghara | M(o) | 0.58 | 5,100 |\n| 1249 | 0262K0901249 | 29.812 | 82.701 | Ghaghara | M(o) | 1.05 | 5,058 |\n| 1250 | 0262K0901250 | 29.809 | 82.664 | Ghaghara | M(o) | 4.11 | 4,766 |\n| 1251 | 0262K0901251 | 29.807 | 82.664 | Ghaghara | M(o) | 0.44 | 4,738 |\n| 1252 | 0262K0901252 | 29.807 | 82.634 | Ghaghara | M(o) | 0.67 | 5,081 |\n| 1253 | 0262K0901253 | 29.806 | 82.594 | Ghaghara | E(o) | 0.45 | 5,046 |\n| 1254 | 0262K0901254 | 29.803 | 82.674 | Ghaghara | M(o) | 2.66 | 4,702 |\n| 1255 | 0262K0901255 | 29.800 | 82.677 | Ghaghara | M(o) | 0.28 | 4,743 |\n| 1256 | 0262K0901256 | 29.800 | 82.602 | Ghaghara | E(o) | 0.92 | 4,943 |\n| 1257 | 0262K0901257 | 29.800 | 82.677 | Ghaghara | M(o) | 0.46 | 4,740 |\n| 1258 | 0262K0901258 | 29.798 | 82.671 | Ghaghara | M(e) | 13.70 | 4,654 |\n| 1259 | 0262K0901259 | 29.771 | 82.640 | Ghaghara | E(o) | 1.93 | 4,827 |\n| 1260 | 0262K0901260 | 29.767 | 82.630 | Ghaghara | M(o) | 0.66 | 4,774 |\n| 1261 | 0262K0901261 | 29.766 | 82.630 | Ghaghara | M(o) | 1.90 | 4,787 |\n| 1262 | 0262K0901262 | 29.763 | 82.574 | Ghaghara | M(o) | 0.66 | 4,634 |\n| 1263 | 0262K0901263 | 29.760 | 82.601 | Ghaghara | M(o) | 0.31 | 4,810 |\n| 1264 | 0262K0901264 | 29.758 | 82.591 | Ghaghara | M(o) | 0.69 | 4,938 |\n| 1265 | 0262K0901265 | 29.755 | 82.664 | Ghaghara | M(o) | 1.36 | 5,439 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4715, "line_end": 4789, "token_count_estimate": 1644, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0901231", "0262K0901232", "0262K0901233", "0262K0901234", "0262K0901235", "0262K0901236", "0262K0901237", "0262K0901238", "0262K0901239", "0262K0901240", "0262K0901241", "0262K0901242", "0262K0901243", "0262K0901244", "0262K0901245", "0262K0901246", "0262K0901247", "0262K0901248", "0262K0901249", "0262K0901250", "0262K0901251", "0262K0901252", "0262K0901253", "0262K0901254", "0262K0901255", "0262K0901256", "0262K0901257", "0262K0901258", "0262K0901259", "0262K0901260", "0262K0901261", "0262K0901262", "0262K0901263", "0262K0901264", "0262K0901265"]}}
{"id": "d0bb346b80bb25f5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1266 | 0262K0901266 | 29.754 | 82.645 | Ghaghara | M(o) | 0.38 | 5,269 |\n| 1267 | 0262K0901267 | 29.751 | 82.687 | Ghaghara | M(o) | 1.61 | 5,393 |\n| 1268 | 0262K0901268 | 29.750 | 82.683 | Ghaghara | M(o) | 1.17 | 5,375 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4715, "line_end": 4789, "token_count_estimate": 227, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K0901266", "0262K0901267", "0262K0901268"]}}
{"id": "93d1b836ade53a91", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 4790, "line_end": 4794, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e42da686b7b6e774", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1269 | 0262K1001269 | 29.750 | 82.662 | Ghaghara | M(o) | 0.49 | 5,368 |\n| 1270 | 0262K1001270 | 29.748 | 82.655 | Ghaghara | E(o) | 0.53 | 5,148 |\n| 1271 | 0262K1001271 | 29.747 | 82.685 | Ghaghara | E(o) | 0.56 | 5,355 |\n| 1272 | 0262K1001272 | 29.745 | 82.619 | Ghaghara | M(o) | 1.38 | 5,214 |\n| 1273 | 0262K1001273 | 29.745 | 82.741 | Ghaghara | I(s) | 0.31 | 5,225 |\n| 1274 | 0262K1001274 | 29.744 | 82.617 | Ghaghara | M(o) | 0.48 | 5,202 |\n| 1275 | 0262K1001275 | 29.743 | 82.647 | Ghaghara | M(o) | 7.56 | 5,138 |\n| 1276 | 0262K1001276 | 29.743 | 82.705 | Ghaghara | M(o) | 11.27 | 5,346 |\n| 1277 | 0262K1001277 | 29.740 | 82.740 | Ghaghara | I(s) | 0.95 | 5,205 |\n| 1278 | 0262K1001278 | 29.736 | 82.641 | Ghaghara | M(o) | 0.64 | 5,261 |\n| 1279 | 0262K1001279 | 29.736 | 82.685 | Ghaghara | M(o) | 1.01 | 5,695 |\n| 1280 | 0262K1001280 | 29.732 | 82.642 | Ghaghara | M(o) | 0.48 | 5,237 |\n| 1281 | 0262K1001281 | 29.731 | 82.637 | Ghaghara | M(o) | 6.05 | 5,075 |\n| 1282 | 0262K1001282 | 29.723 | 82.644 | Ghaghara | M(o) | 18.86 | 4,978 |\n| 1283 | 0262K1001283 | 29.722 | 82.697 | Ghaghara | M(o) | 0.48 | 5,472 |\n| 1284 | 0262K1001284 | 29.701 | 82.576 | Ghaghara | E(o) | 3.38 | 4,830 |\n| 1285 | 0262K1001285 | 29.700 | 82.603 | Ghaghara | E(o) | 2.27 | 4,933 |\n| 1286 | 0262K1001286 | 29.696 | 82.561 | Ghaghara | E(o) | 11.37 | 4,830 |\n| 1287 | 0262K1001287 | 29.694 | 82.609 | Ghaghara | E(o) | 2.80 | 4,931 |\n| 1288 | 0262K1001288 | 29.683 | 82.616 | Ghaghara | E(o) | 2.38 | 4,854 |\n| 1289 | 0262K1001289 | 29.665 | 82.647 | Ghaghara | E(o) | 1.28 | 5,115 |\n| 1290 | 0262K1001290 | 29.663 | 82.641 | Ghaghara | M(o) | 0.25 | 5,162 |\n| 1291 | 0262K1001291 | 29.644 | 82.643 | Ghaghara | M(o) | 0.53 | 5,263 |\n| 1292 | 0262K1001292 | 29.644 | 82.744 | Ghaghara | M(o) | 0.59 | 4,783 |\n| 1293 | 0262K1001293 | 29.644 | 82.642 | Ghaghara | M(o) | 0.74 | 5,260 |\n| 1294 | 0262K1001294 | 29.637 | 82.649 | Ghaghara | E(o) | 0.27 | 5,133 |\n| 1295 | 0262K1001295 | 29.630 | 82.611 | Ghaghara | M(o) | 0.54 | 5,094 |\n| 1296 | 0262K1001296 | 29.607 | 82.577 | Ghaghara | M(o) | 0.36 | 5,415 |\n| 1297 | 0262K1101297 | 29.450 | 82.654 | Ghaghara | E(o) | 1.38 | 5,544 |\n| 1298 | 0262K1101298 | 29.444 | 82.623 | Ghaghara | I(s) | 0.42 | 4,799 |\n| 1299 | 0262K1101299 | 29.440 | 82.620 | Ghaghara | I(s) | 0.25 | 4,845 |\n| 1300 | 0262K1101300 | 29.438 | 82.577 | Ghaghara | I(s) | 0.33 | 5,189 |\n| 1301 | 0262K1101301 | 29.431 | 82.561 | Ghaghara | I(s) | 0.48 | 5,039 |\n| 1302 | 0262K1101302 | 29.430 | 82.572 | Ghaghara | I(s) | 0.64 | 5,078 |\n| 1303 | 0262K1101303 | 29.428 | 82.671 | Ghaghara | E(o) | 1.58 | 4,327 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4795, "line_end": 4869, "token_count_estimate": 1630, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K1001269", "0262K1001270", "0262K1001271", "0262K1001272", "0262K1001273", "0262K1001274", "0262K1001275", "0262K1001276", "0262K1001277", "0262K1001278", "0262K1001279", "0262K1001280", "0262K1001281", "0262K1001282", "0262K1001283", "0262K1001284", "0262K1001285", "0262K1001286", "0262K1001287", "0262K1001288", "0262K1001289", "0262K1001290", "0262K1001291", "0262K1001292", "0262K1001293", "0262K1001294", "0262K1001295", "0262K1001296", "0262K1101297", "0262K1101298", "0262K1101299", "0262K1101300", "0262K1101301", "0262K1101302", "0262K1101303"]}}
{"id": "27b0662b970396d5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1304 | 0262K1101304 | 29.423 | 82.719 | Ghaghara | M(o) | 0.40 | 5,373 |\n| 1305 | 0262K1101305 | 29.419 | 82.650 | Ghaghara | E(o) | 0.46 | 5,083 |\n| 1306 | 0262K1101306 | 29.414 | 82.642 | Ghaghara | M(o) | 0.77 | 5,118 |\n| 1307 | 0262K1101307 | 29.413 | 82.533 | Ghaghara | E(o) | 0.67 | 4,915 |\n| 1308 | 0262K1101308 | 29.396 | 82.582 | Ghaghara | M(o) | 0.37 | 4,891 |\n| 1309 | 0262K1101309 | 29.372 | 82.584 | Ghaghara | M(e) | 0.65 | 4,697 |\n| 1310 | 0262K1101310 | 29.371 | 82.583 | Ghaghara | M(o) | 0.39 | 4,700 |\n| 1311 | 0262K1101311 | 29.368 | 82.693 | Ghaghara | I(s) | 0.25 | 4,214 |\n| 1312 | 0262K1101312 | 29.360 | 82.560 | Ghaghara | E(o) | 1.34 | 4,996 |\n| 1313 | 0262K1101313 | 29.354 | 82.739 | Ghaghara | M(e) | 2.49 | 4,952 |\n| 1314 | 0262K1101314 | 29.353 | 82.718 | Ghaghara | M(o) | 0.33 | 4,488 |\n| 1315 | 0262K1101315 | 29.346 | 82.744 | Ghaghara | I(s) | 0.30 | 4,767 |\n| 1316 | 0262K1101316 | 29.332 | 82.505 | Ghaghara | E(o) | 0.26 | 4,667 |\n| 1317 | 0262K1101317 | 29.317 | 82.547 | Ghaghara | I(s) | 0.60 | 4,320 |\n| 1318 | 0262K1101318 | 29.307 | 82.506 | Ghaghara | M(e) | 0.45 | 4,533 |\n| 1319 | 0262K1101319 | 29.304 | 82.509 | Ghaghara | I(s) | 0.56 | 4,600 |\n| 1320 | 0262K1101320 | 29.297 | 82.705 | Ghaghara | M(e) | 10.03 | 5,003 |\n| 1321 | 0262K1101321 | 29.270 | 82.590 | Ghaghara | M(e) | 10.04 | 4,335 |\n| 1322 | 0262K1101322 | 29.260 | 82.508 | Ghaghara | E(o) | 4.48 | 4,402 |\n| 1323 | 0262K1101323 | 29.257 | 82.549 | Ghaghara | M(o) | 0.63 | 5,092 |\n| 1324 | 0262K1201324 | 29.249 | 82.564 | Ghaghara | M(o) | 29.00 | 4,647 |\n| 1325 | 0262K1201325 | 29.245 | 82.548 | Ghaghara | M(o) | 0.25 | 4,995 |\n| 1326 | 0262K1201326 | 29.243 | 82.723 | Ghaghara | E(o) | 0.29 | 4,600 |\n| 1327 | 0262K1201327 | 29.241 | 82.527 | Ghaghara | M(o) | 0.62 | 5,005 |\n| 1328 | 0262K1201328 | 29.225 | 82.706 | Ghaghara | E(o) | 0.25 | 5,197 |\n| 1329 | 0262K1201329 | 29.216 | 82.563 | Ghaghara | M(o) | 2.45 | 5,002 |\n| 1330 | 0262K1201330 | 29.215 | 82.724 | Ghaghara | E(o) | 1.12 | 5,111 |\n| 1331 | 0262K1201331 | 29.215 | 82.503 | Ghaghara | E(o) | 1.91 | 4,491 |\n| 1332 | 0262K1201332 | 29.212 | 82.681 | Ghaghara | E(o) | 0.29 | 5,114 |\n| 1333 | 0262K1201333 | 29.194 | 82.546 | Ghaghara | E(o) | 35.80 | 4,693 |\n| 1334 | 0262K1201334 | 29.185 | 82.563 | Ghaghara | E(o) | 16.36 | 4,597 |\n| 1335 | 0262K1201335 | 29.180 | 82.517 | Ghaghara | E(o) | 6.87 | 4,475 |\n| 1336 | 0262K1201336 | 29.113 | 82.716 | Ghaghara | M(o) | 1.07 | 4,779 |\n| 1337 | 0262K1401337 | 29.735 | 82.752 | Ghaghara | M(o) | 0.40 | 5,404 |\n| 1338 | 0262K1401338 | 29.734 | 82.751 | Ghaghara | M(o) | 0.60 | 5,299 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4795, "line_end": 4869, "token_count_estimate": 1647, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K1101304", "0262K1101305", "0262K1101306", "0262K1101307", "0262K1101308", "0262K1101309", "0262K1101310", "0262K1101311", "0262K1101312", "0262K1101313", "0262K1101314", "0262K1101315", "0262K1101316", "0262K1101317", "0262K1101318", "0262K1101319", "0262K1101320", "0262K1101321", "0262K1101322", "0262K1101323", "0262K1201324", "0262K1201325", "0262K1201326", "0262K1201327", "0262K1201328", "0262K1201329", "0262K1201330", "0262K1201331", "0262K1201332", "0262K1201333", "0262K1201334", "0262K1201335", "0262K1201336", "0262K1401337", "0262K1401338"]}}
{"id": "002529436c6036ed", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1339 | 0262K1401339 | 29.718 | 82.770 | Ghaghara | M(o) | 1.37 | 5,374 |\n| 1340 | 0262K1401340 | 29.718 | 82.755 | Ghaghara | M(o) | 0.53 | 5,271 |\n| 1341 | 0262K1401341 | 29.693 | 82.937 | Ghaghara | M(o) | 2.50 | 5,501 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4795, "line_end": 4869, "token_count_estimate": 229, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K1401339", "0262K1401340", "0262K1401341"]}}
{"id": "6d74dc0a86513012", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1342 | 0262K1401342 | 29.689 | 82.939 | Ghaghara | M(o) | 8.30 | 5,503 |\n| 1343 | 0262K1401343 | 29.688 | 82.918 | Ghaghara | M(o) | 0.74 | 5,299 |\n| 1344 | 0262K1401344 | 29.683 | 82.925 | Ghaghara | E(o) | 0.61 | 5,349 |\n| 1345 | 0262K1401345 | 29.681 | 82.810 | Ghaghara | M(o) | 0.96 | 5,022 |\n| 1346 | 0262K1401346 | 29.680 | 82.889 | Ghaghara | I(s) | 0.27 | 5,288 |\n| 1347 | 0262K1401347 | 29.680 | 82.900 | Ghaghara | I(s) | 0.26 | 5,238 |\n| 1348 | 0262K1401348 | 29.678 | 82.818 | Ghaghara | M(o) | 0.39 | 5,372 |\n| 1349 | 0262K1401349 | 29.677 | 82.839 | Ghaghara | M(o) | 2.54 | 5,121 |\n| 1350 | 0262K1401350 | 29.673 | 82.822 | Ghaghara | M(o) | 0.83 | 5,286 |\n| 1351 | 0262K1401351 | 29.673 | 82.900 | Ghaghara | I(s) | 0.43 | 5,199 |\n| 1352 | 0262K1401352 | 29.672 | 82.841 | Ghaghara | E(o) | 0.59 | 5,019 |\n| 1353 | 0262K1401353 | 29.665 | 82.944 | Ghaghara | E(o) | 0.60 | 5,542 |\n| 1354 | 0262K1401354 | 29.663 | 82.872 | Ghaghara | M(o) | 4.57 | 5,040 |\n| 1355 | 0262K1401355 | 29.650 | 82.994 | Ghaghara | E(o) | 0.31 | 5,322 |\n| 1356 | 0262K1401356 | 29.646 | 82.954 | Ghaghara | E(o) | 0.90 | 5,095 |\n| 1357 | 0262K1401357 | 29.638 | 82.789 | Ghaghara | M(o) | 0.83 | 5,196 |\n| 1358 | 0262K1401358 | 29.635 | 82.802 | Ghaghara | M(o) | 0.70 | 5,466 |\n| 1359 | 0262K1401359 | 29.632 | 82.787 | Ghaghara | M(o) | 3.89 | 5,123 |\n| 1360 | 0262K1401360 | 29.631 | 82.800 | Ghaghara | M(o) | 1.17 | 5,195 |\n| 1361 | 0262K1401361 | 29.624 | 82.763 | Ghaghara | E(o) | 0.27 | 4,954 |\n| 1362 | 0262K1501362 | 29.397 | 82.770 | Ghaghara | M(o) | 0.35 | 5,154 |\n| 1363 | 0262K1501363 | 29.355 | 82.813 | Ghaghara | M(o) | 2.92 | 4,799 |\n| 1364 | 0262K1501364 | 29.345 | 82.864 | Ghaghara | M(o) | 0.26 | 5,284 |\n| 1365 | 0262K1501365 | 29.345 | 82.909 | Ghaghara | O | 0.54 | 4,963 |\n| 1366 | 0262K1501366 | 29.335 | 82.797 | Ghaghara | M(e) | 7.66 | 4,689 |\n| 1367 | 0262K1501367 | 29.321 | 82.762 | Ghaghara | M(o) | 0.64 | 4,899 |\n| 1368 | 0262K1501368 | 29.320 | 82.834 | Ghaghara | E(o) | 1.27 | 5,094 |\n| 1369 | 0262K1501369 | 29.317 | 82.761 | Ghaghara | M(l) | 0.65 | 4,813 |\n| 1370 | 0262K1501370 | 29.312 | 82.843 | Ghaghara | M(e) | 1.21 | 4,893 |\n| 1371 | 0262K1501371 | 29.309 | 82.933 | Ghaghara | E(o) | 0.73 | 4,977 |\n| 1372 | 0262K1501372 | 29.301 | 82.770 | Ghaghara | I(s) | 0.39 | 4,657 |\n| 1373 | 0262K1501373 | 29.301 | 82.816 | Ghaghara | M(o) | 0.75 | 4,559 |\n| 1374 | 0262K1501374 | 29.301 | 82.779 | Ghaghara | E(o) | 0.33 | 4,824 |\n| 1375 | 0262K1501375 | 29.293 | 82.767 | Ghaghara | I(s) | 0.25 | 4,548 |\n| 1376 | 0262K1501376 | 29.254 | 82.807 | Ghaghara | E(o) | 2.56 | 4,253 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4871, "line_end": 4945, "token_count_estimate": 1638, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K1401342", "0262K1401343", "0262K1401344", "0262K1401345", "0262K1401346", "0262K1401347", "0262K1401348", "0262K1401349", "0262K1401350", "0262K1401351", "0262K1401352", "0262K1401353", "0262K1401354", "0262K1401355", "0262K1401356", "0262K1401357", "0262K1401358", "0262K1401359", "0262K1401360", "0262K1401361", "0262K1501362", "0262K1501363", "0262K1501364", "0262K1501365", "0262K1501366", "0262K1501367", "0262K1501368", "0262K1501369", "0262K1501370", "0262K1501371", "0262K1501372", "0262K1501373", "0262K1501374", "0262K1501375", "0262K1501376"]}}
{"id": "c1a0881e51873b0d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1377 | 0262K1601377 | 29.248 | 82.827 | Ghaghara | E(o) | 1.12 | 4,509 |\n| 1378 | 0262K1601378 | 29.247 | 82.814 | Ghaghara | E(o) | 3.46 | 4,190 |\n| 1379 | 0262K1601379 | 29.246 | 82.816 | Ghaghara | E(o) | 0.34 | 4,229 |\n| 1380 | 0262K1601380 | 29.240 | 82.957 | Ghaghara | E(o) | 0.49 | 4,806 |\n| 1381 | 0262K1601381 | 29.238 | 82.982 | Ghaghara | E(o) | 0.29 | 4,719 |\n| 1382 | 0262K1601382 | 29.234 | 82.829 | Ghaghara | E(o) | 9.01 | 4,117 |\n| 1383 | 0262K1601383 | 29.229 | 82.842 | Ghaghara | E(o) | 1.26 | 4,208 |\n| 1384 | 0262K1601384 | 29.209 | 82.862 | Ghaghara | E(o) | 0.35 | 4,726 |\n| 1385 | 0262K1601385 | 29.161 | 82.751 | Ghaghara | M(o) | 0.49 | 4,901 |\n| 1386 | 0262K1601386 | 29.154 | 82.775 | Ghaghara | M(o) | 0.84 | 5,012 |\n| 1387 | 0262K1601387 | 29.145 | 82.779 | Ghaghara | M(o) | 0.46 | 5,173 |\n| 1388 | 0262K1601388 | 29.141 | 82.782 | Ghaghara | M(o) | 0.94 | 4,992 |\n| 1389 | 0262K1601389 | 29.139 | 82.785 | Ghaghara | M(o) | 1.61 | 4,979 |\n| 1390 | 0262K1601390 | 29.079 | 82.979 | Ghaghara | M(o) | 1.18 | 5,053 |\n| 1391 | 0262L0101391 | 28.978 | 82.081 | Ghaghara | E(o) | 1.37 | 4,240 |\n| 1392 | 0262L0101392 | 28.977 | 82.088 | Ghaghara | M(o) | 1.79 | 4,205 |\n| 1393 | 0262L0901393 | 28.892 | 82.697 | Ghaghara | M(o) | 1.17 | 4,719 |\n| 1394 | 0262L0901394 | 28.862 | 82.674 | Ghaghara | E(o) | 0.29 | 4,522 |\n| 1395 | 0262L0901395 | 28.856 | 82.668 | Ghaghara | M(o) | 1.09 | 4,670 |\n| 1396 | 0262L1301396 | 28.984 | 82.959 | Ghaghara | E(o) | 0.81 | 4,695 |\n| 1397 | 0262L1301397 | 28.846 | 82.819 | Ghaghara | E(o) | 0.85 | 4,549 |\n| 1398 | 0262L1301398 | 28.828 | 82.861 | Ghaghara | E(o) | 1.09 | 4,676 |\n| 1399 | 0262L1301399 | 28.828 | 82.862 | Ghaghara | M(o) | 0.33 | 4,710 |\n| 1400 | 0262L1301400 | 28.819 | 82.867 | Ghaghara | E(o) | 18.36 | 4,429 |\n| 1401 | 0262L1301401 | 28.811 | 82.769 | Ghaghara | M(o) | 0.45 | 4,868 |\n| 1402 | 0262L1301402 | 28.790 | 82.981 | Ghaghara | E(c) | 10.60 | 4,796 |\n| 1403 | 0262L1301403 | 28.784 | 82.977 | Ghaghara | E(o) | 1.12 | 4,755 |\n| 1404 | 0262L1301404 | 28.772 | 82.963 | Ghaghara | E(o) | 1.33 | 4,688 |\n| 1405 | 0262O0201405 | 29.662 | 83.011 | Ghaghara | M(o) | 0.53 | 5,497 |\n| 1406 | 0262O0201406 | 29.647 | 83.033 | Ghaghara | E(o) | 0.30 | 5,469 |\n| 1407 | 0262O0201407 | 29.639 | 83.034 | Ghaghara | E(o) | 0.71 | 5,521 |\n| 1408 | 0262O0201408 | 29.613 | 83.128 | Ghaghara | E(o) | 2.33 | 5,270 |\n| 1409 | 0262O0201409 | 29.611 | 83.211 | Ghaghara | E(o) | 7.05 | 5,245 |\n| 1410 | 0262O0201410 | 29.608 | 83.099 | Ghaghara | E(o) | 0.34 | 5,335 |\n| 1411 | 0262O0201411 | 29.604 | 83.067 | Ghaghara | E(o) | 1.25 | 5,468 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4871, "line_end": 4945, "token_count_estimate": 1627, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262K1601377", "0262K1601378", "0262K1601379", "0262K1601380", "0262K1601381", "0262K1601382", "0262K1601383", "0262K1601384", "0262K1601385", "0262K1601386", "0262K1601387", "0262K1601388", "0262K1601389", "0262K1601390", "0262L0101391", "0262L0101392", "0262L0901393", "0262L0901394", "0262L0901395", "0262L1301396", "0262L1301397", "0262L1301398", "0262L1301399", "0262L1301400", "0262L1301401", "0262L1301402", "0262L1301403", "0262L1301404", "0262O0201405", "0262O0201406", "0262O0201407", "0262O0201408", "0262O0201409", "0262O0201410", "0262O0201411"]}}
{"id": "6f01665fd9966d15", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1412 | 0262O0201412 | 29.602 | 83.209 | Ghaghara | E(o) | 6.21 | 5,232 |\n| 1413 | 0262O0201413 | 29.602 | 83.191 | Ghaghara | E(o) | 0.29 | 5,483 |\n| 1414 | 0262O0201414 | 29.597 | 83.051 | Ghaghara | M(o) | 0.44 | 5,351 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4871, "line_end": 4945, "token_count_estimate": 228, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262O0201412", "0262O0201413", "0262O0201414"]}}
{"id": "fee5b64d2e73430e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 4946, "line_end": 4953, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a49e5f383d656c07", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1415 | 0262O0201415 | 29.593 | 83.051 | Ghaghara | M(o) | 0.25 | 5,550 |\n| 1416 | 0262O0201416 | 29.586 | 83.184 | Ghaghara | E(o) | 1.09 | 5,330 |\n| 1417 | 0262O0201417 | 29.571 | 83.189 | Ghaghara | E(o) | 5.68 | 5,222 |\n| 1418 | 0262O0201418 | 29.554 | 83.244 | Ghaghara | E(o) | 0.89 | 5,262 |\n| 1419 | 0262O0201419 | 29.542 | 83.210 | Ghaghara | E(o) | 0.71 | 4,789 |\n| 1420 | 0262O0401420 | 29.237 | 83.110 | Ghaghara | E(o) | 1.05 | 5,010 |\n| 1421 | 0262O0401421 | 29.201 | 83.231 | Ghaghara | O | 0.64 | 4,802 |\n| 1422 | 0262O0401422 | 29.201 | 83.227 | Ghaghara | O | 0.41 | 4,843 |\n| 1423 | 0262O0401423 | 29.176 | 83.249 | Ghaghara | O | 0.66 | 4,993 |\n| 1424 | 0262O0401424 | 29.173 | 83.222 | Ghaghara | E(o) | 0.50 | 5,050 |\n| 1425 | 0262O0401425 | 29.172 | 83.012 | Ghaghara | M(o) | 3.70 | 5,191 |\n| 1426 | 0262O0401426 | 29.167 | 83.247 | Ghaghara | E(o) | 2.80 | 5,071 |\n| 1427 | 0262O0401427 | 29.129 | 83.081 | Ghaghara | M(o) | 0.98 | 5,387 |\n| 1428 | 0262O0401428 | 29.122 | 83.036 | Ghaghara | M(o) | 0.77 | 5,080 |\n| 1429 | 0262O0401429 | 29.114 | 83.021 | Ghaghara | E(o) | 1.12 | 4,881 |\n| 1430 | 0262O0401430 | 29.109 | 83.070 | Ghaghara | M(o) | 4.27 | 5,277 |\n| 1431 | 0262O0401431 | 29.108 | 83.026 | Ghaghara | M(o) | 12.95 | 4,884 |\n| 1432 | 0262O0401432 | 29.079 | 83.039 | Ghaghara | E(o) | 3.03 | 4,967 |\n| 1433 | 0262O0401433 | 29.071 | 83.030 | Ghaghara | E(o) | 0.46 | 5,216 |\n| 1434 | 0262O0401434 | 29.070 | 83.036 | Ghaghara | M(o) | 0.73 | 5,096 |\n| 1435 | 0262O0401435 | 29.051 | 83.036 | Ghaghara | M(o) | 0.84 | 5,105 |\n| 1436 | 0262O0401436 | 29.020 | 83.029 | Ghaghara | E(o) | 1.48 | 5,223 |\n| 1437 | 0262O0401437 | 29.016 | 83.027 | Ghaghara | E(c) | 4.97 | 5,034 |\n| 1438 | 0262O0401438 | 29.008 | 83.178 | Ghaghara | E(o) | 0.25 | 5,149 |\n| 1439 | 0262O0401439 | 29.006 | 83.232 | Ghaghara | E(o) | 1.75 | 5,368 |\n| 1440 | 0262O0401440 | 29.005 | 83.208 | Ghaghara | M(o) | 0.32 | 5,352 |\n| 1441 | 0262O0401441 | 29.002 | 83.182 | Ghaghara | M(o) | 9.04 | 4,992 |\n| 1442 | 0262O0401442 | 29.000 | 83.185 | Ghaghara | M(o) | 0.51 | 5,018 |\n| 1443 | 0262O0601443 | 29.570 | 83.270 | Ghaghara | E(o) | 2.64 | 5,345 |\n| 1444 | 0262O0601444 | 29.568 | 83.264 | Ghaghara | E(o) | 3.09 | 5,281 |\n| 1445 | 0262O0601445 | 29.559 | 83.270 | Ghaghara | E(o) | 1.31 | 5,441 |\n| 1446 | 0262O0601446 | 29.541 | 83.250 | Ghaghara | E(o) | 8.78 | 5,433 |\n| 1447 | 0262O0601447 | 29.525 | 83.261 | Ghaghara | E(o) | 2.39 | 5,389 |\n| 1448 | 0262O0601448 | 29.511 | 83.260 | Ghaghara | E(o) | 0.25 | 5,332 |\n| 1449 | 0262O0601449 | 29.510 | 83.253 | Ghaghara | E(o) | 0.26 | 5,208 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4954, "line_end": 5028, "token_count_estimate": 1632, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262O0201415", "0262O0201416", "0262O0201417", "0262O0201418", "0262O0201419", "0262O0401420", "0262O0401421", "0262O0401422", "0262O0401423", "0262O0401424", "0262O0401425", "0262O0401426", "0262O0401427", "0262O0401428", "0262O0401429", "0262O0401430", "0262O0401431", "0262O0401432", "0262O0401433", "0262O0401434", "0262O0401435", "0262O0401436", "0262O0401437", "0262O0401438", "0262O0401439", "0262O0401440", "0262O0401441", "0262O0401442", "0262O0601443", "0262O0601444", "0262O0601445", "0262O0601446", "0262O0601447", "0262O0601448", "0262O0601449"]}}
{"id": "a5c7b5602972d506", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1450 | 0262O0701450 | 29.496 | 83.288 | Ghaghara | E(o) | 1.78 | 5,344 |\n| 1451 | 0262O0701451 | 29.494 | 83.313 | Ghaghara | E(o) | 0.31 | 5,292 |\n| 1452 | 0262O0701452 | 29.492 | 83.338 | Ghaghara | E(o) | 1.13 | 5,392 |\n| 1453 | 0262O0701453 | 29.477 | 83.302 | Ghaghara | E(o) | 0.37 | 5,096 |\n| 1454 | 0262O0701454 | 29.434 | 83.339 | Ghaghara | E(o) | 1.30 | 5,352 |\n| 1455 | 0262O0701455 | 29.409 | 83.403 | Ghaghara | E(o) | 0.77 | 5,373 |\n| 1456 | 0262O0701456 | 29.405 | 83.405 | Ghaghara | E(o) | 0.86 | 5,322 |\n| 1457 | 0262O0701457 | 29.380 | 83.386 | Ghaghara | E(o) | 0.60 | 5,161 |\n| 1458 | 0262O0701458 | 29.378 | 83.387 | Ghaghara | E(o) | 0.37 | 5,170 |\n| 1459 | 0262O0701459 | 29.377 | 83.390 | Ghaghara | E(o) | 11.85 | 5,175 |\n| 1460 | 0262O0701460 | 29.372 | 83.394 | Ghaghara | E(o) | 0.97 | 5,259 |\n| 1461 | 0262O0701461 | 29.367 | 83.402 | Ghaghara | E(o) | 1.08 | 5,326 |\n| 1462 | 0262O0701462 | 29.366 | 83.404 | Ghaghara | M(o) | 0.28 | 5,335 |\n| 1463 | 0262O0701463 | 29.362 | 83.395 | Ghaghara | M(o) | 0.27 | 5,473 |\n| 1464 | 0262O0701464 | 29.323 | 83.255 | Ghaghara | O | 0.41 | 4,720 |\n| 1465 | 0262O0701465 | 29.312 | 83.437 | Ghaghara | E(o) | 0.54 | 5,534 |\n| 1466 | 0262O0701466 | 29.312 | 83.424 | Ghaghara | E(o) | 0.61 | 5,343 |\n| 1467 | 0262O0701467 | 29.292 | 83.444 | Ghaghara | E(o) | 2.26 | 5,500 |\n| 1468 | 0262O0701468 | 29.259 | 83.495 | Ghaghara | M(o) | 0.90 | 5,624 |\n| 1469 | 0262O0701469 | 29.255 | 83.493 | Ghaghara | M(o) | 2.18 | 5,576 |\n| 1470 | 0262O0701470 | 29.253 | 83.492 | Ghaghara | M(o) | 1.02 | 5,554 |\n| 1471 | 0262O0701471 | 29.252 | 83.315 | Ghaghara | E(o) | 3.40 | 5,241 |\n| 1472 | 0262O0701472 | 29.251 | 83.321 | Ghaghara | E(o) | 0.40 | 5,256 |\n| 1473 | 0262O0801473 | 29.247 | 83.464 | Ghaghara | E(o) | 0.78 | 5,458 |\n| 1474 | 0262O0801474 | 29.246 | 83.328 | Ghaghara | M(o) | 0.94 | 5,315 |\n| 1475 | 0262O0801475 | 29.246 | 83.337 | Ghaghara | M(o) | 0.37 | 5,469 |\n| 1476 | 0262O0801476 | 29.240 | 83.334 | Ghaghara | I(s) | 0.71 | 5,582 |\n| 1477 | 0262O0801477 | 29.233 | 83.342 | Ghaghara | M(o) | 1.86 | 5,445 |\n| 1478 | 0262O0801478 | 29.226 | 83.354 | Ghaghara | M(o) | 0.89 | 5,365 |\n| 1479 | 0262O0801479 | 29.226 | 83.458 | Ghaghara | E(o) | 1.48 | 5,508 |\n| 1480 | 0262O0801480 | 29.222 | 83.333 | Ghaghara | E(o) | 2.64 | 5,278 |\n| 1481 | 0262O0801481 | 29.215 | 83.457 | Ghaghara | E(o) | 2.28 | 5,601 |\n| 1482 | 0262O0801482 | 29.210 | 83.436 | Ghaghara | M(o) | 1.27 | 5,376 |\n| 1483 | 0262O0801483 | 29.210 | 83.445 | Ghaghara | M(o) | 0.51 | 5,600 |\n| 1484 | 0262O0801484 | 29.206 | 83.498 | Ghaghara | M(o) | 0.97 | 5,507 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4954, "line_end": 5028, "token_count_estimate": 1640, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262O0701450", "0262O0701451", "0262O0701452", "0262O0701453", "0262O0701454", "0262O0701455", "0262O0701456", "0262O0701457", "0262O0701458", "0262O0701459", "0262O0701460", "0262O0701461", "0262O0701462", "0262O0701463", "0262O0701464", "0262O0701465", "0262O0701466", "0262O0701467", "0262O0701468", "0262O0701469", "0262O0701470", "0262O0701471", "0262O0701472", "0262O0801473", "0262O0801474", "0262O0801475", "0262O0801476", "0262O0801477", "0262O0801478", "0262O0801479", "0262O0801480", "0262O0801481", "0262O0801482", "0262O0801483", "0262O0801484"]}}
{"id": "49ebd99a033be62b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1485 | 0262O0801485 | 29.200 | 83.489 | Ghaghara | E(o) | 6.06 | 5,522 |\n| 1486 | 0262O0801486 | 29.199 | 83.427 | Ghaghara | E(o) | 0.42 | 5,247 |\n| 1487 | 0262O0801487 | 29.196 | 83.433 | Ghaghara | M(o) | 0.84 | 5,304 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 4954, "line_end": 5028, "token_count_estimate": 227, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262O0801485", "0262O0801486", "0262O0801487"]}}
{"id": "d616e8088d18143e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1488 | 0262O0801488 | 29.196 | 83.435 | Ghaghara | M(o) | 0.39 | 5,312 |\n| 1489 | 0262O0801489 | 29.194 | 83.438 | Ghaghara | M(o) | 1.02 | 5,324 |\n| 1490 | 0262O0801490 | 29.190 | 83.478 | Ghaghara | E(o) | 1.84 | 5,484 |\n| 1491 | 0262O0801491 | 29.175 | 83.486 | Ghaghara | E(o) | 0.34 | 5,341 |\n| 1492 | 0262O0801492 | 29.174 | 83.485 | Ghaghara | E(o) | 0.57 | 5,336 |\n| 1493 | 0262O0801493 | 29.162 | 83.491 | Ghaghara | E(o) | 3.84 | 5,480 |\n| 1494 | 0262O0801494 | 29.121 | 83.308 | Ghaghara | E(o) | 0.44 | 5,251 |\n| 1495 | 0262O0801495 | 29.111 | 83.318 | Ghaghara | M(o) | 1.11 | 5,342 |\n| 1496 | 0262O0801496 | 29.110 | 83.317 | Ghaghara | M(o) | 0.27 | 5,355 |\n| 1497 | 0262O0801497 | 29.067 | 83.383 | Ghaghara | E(o) | 1.80 | 5,246 |\n| 1498 | 0262O0801498 | 29.045 | 83.286 | Ghaghara | E(o) | 0.25 | 5,197 |\n| 1499 | 0262O0801499 | 29.040 | 83.265 | Ghaghara | M(o) | 1.31 | 5,237 |\n| 1500 | 0262O0801500 | 29.037 | 83.281 | Ghaghara | E(o) | 1.85 | 5,234 |\n| 1501 | 0262O0801501 | 29.018 | 83.258 | Ghaghara | M(o) | 0.98 | 5,235 |\n| 1502 | 0262O0801502 | 29.016 | 83.255 | Ghaghara | M(o) | 0.86 | 5,315 |\n| 1503 | 0262O0801503 | 29.014 | 83.255 | Ghaghara | M(o) | 0.41 | 5,324 |\n| 1504 | 0262O0801504 | 29.014 | 83.287 | Ghaghara | M(o) | 0.60 | 5,247 |\n| 1505 | 0262O0801505 | 29.012 | 83.277 | Ghaghara | M(o) | 0.92 | 5,457 |\n| 1506 | 0262O0801506 | 29.004 | 83.280 | Ghaghara | M(o) | 1.01 | 5,339 |\n| 1507 | 0262O1101507 | 29.251 | 83.510 | Ghaghara | E(o) | 0.35 | 5,664 |\n| 1508 | 0262O1201508 | 29.232 | 83.503 | Ghaghara | E(o) | 2.61 | 5,413 |\n| 1509 | 0262O1201509 | 29.230 | 83.502 | Ghaghara | E(o) | 2.70 | 5,408 |\n| 1510 | 0262O1201510 | 29.229 | 83.719 | Gandak | E(o) | 0.75 | 5,406 |\n| 1511 | 0262O1201511 | 29.227 | 83.717 | Gandak | E(o) | 0.92 | 5,397 |\n| 1512 | 0262O1201512 | 29.218 | 83.702 | Gandak | M(o) | 42.49 | 5,426 |\n| 1513 | 0262O1201513 | 29.217 | 83.503 | Ghaghara | E(o) | 0.63 | 5,421 |\n| 1514 | 0262O1201514 | 29.201 | 83.684 | Gandak | M(e) | 22.46 | 5,482 |\n| 1515 | 0262O1201515 | 29.201 | 83.515 | Ghaghara | E(o) | 1.86 | 5,519 |\n| 1516 | 0262O1201516 | 29.199 | 83.564 | Ghaghara | E(o) | 3.34 | 5,558 |\n| 1517 | 0262O1201517 | 29.198 | 83.744 | Gandak | M(o) | 0.70 | 5,790 |\n| 1518 | 0262O1201518 | 29.198 | 83.665 | Gandak | E(o) | 3.99 | 5,696 |\n| 1519 | 0262O1201519 | 29.195 | 83.735 | Gandak | M(o) | 1.66 | 5,842 |\n| 1520 | 0262O1201520 | 29.194 | 83.556 | Ghaghara | E(o) | 0.74 | 5,485 |\n| 1521 | 0262O1201521 | 29.193 | 83.668 | Gandak | E(o) | 1.17 | 5,682 |\n| 1522 | 0262O1201522 | 29.193 | 83.506 | Ghaghara | E(o) | 6.82 | 5,553 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5030, "line_end": 5104, "token_count_estimate": 1643, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262O0801488", "0262O0801489", "0262O0801490", "0262O0801491", "0262O0801492", "0262O0801493", "0262O0801494", "0262O0801495", "0262O0801496", "0262O0801497", "0262O0801498", "0262O0801499", "0262O0801500", "0262O0801501", "0262O0801502", "0262O0801503", "0262O0801504", "0262O0801505", "0262O0801506", "0262O1101507", "0262O1201508", "0262O1201509", "0262O1201510", "0262O1201511", "0262O1201512", "0262O1201513", "0262O1201514", "0262O1201515", "0262O1201516", "0262O1201517", "0262O1201518", "0262O1201519", "0262O1201520", "0262O1201521", "0262O1201522"]}}
{"id": "bc83aba77d655561", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1523 | 0262O1201523 | 29.193 | 83.522 | Ghaghara | E(o) | 1.73 | 5,415 |\n| 1524 | 0262O1201524 | 29.192 | 83.580 | Ghaghara | E(o) | 2.99 | 5,535 |\n| 1525 | 0262O1201525 | 29.188 | 83.562 | Ghaghara | E(o) | 1.54 | 5,445 |\n| 1526 | 0262O1201526 | 29.188 | 83.510 | Ghaghara | E(o) | 0.41 | 5,467 |\n| 1527 | 0262O1201527 | 29.187 | 83.745 | Gandak | M(o) | 0.49 | 5,683 |\n| 1528 | 0262O1201528 | 29.185 | 83.512 | Ghaghara | E(o) | 1.93 | 5,450 |\n| 1529 | 0262O1201529 | 29.184 | 83.507 | Ghaghara | E(o) | 0.55 | 5,472 |\n| 1530 | 0262O1201530 | 29.183 | 83.705 | Gandak | M(e) | 2.33 | 5,704 |\n| 1531 | 0262O1201531 | 29.182 | 83.512 | Ghaghara | E(o) | 0.40 | 5,452 |\n| 1532 | 0262O1201532 | 29.180 | 83.707 | Gandak | M(o) | 0.65 | 5,684 |\n| 1533 | 0262O1201533 | 29.175 | 83.748 | Gandak | O | 17.07 | 5,360 |\n| 1534 | 0262O1201534 | 29.173 | 83.711 | Gandak | E(o) | 7.83 | 5,642 |\n| 1535 | 0262O1201535 | 29.173 | 83.508 | Ghaghara | M(o) | 4.09 | 5,548 |\n| 1536 | 0262O1201536 | 29.170 | 83.552 | Ghaghara | E(o) | 1.31 | 5,302 |\n| 1537 | 0262O1201537 | 29.169 | 83.694 | Gandak | E(o) | 0.30 | 5,675 |\n| 1538 | 0262O1201538 | 29.168 | 83.563 | Ghaghara | E(o) | 3.52 | 5,477 |\n| 1539 | 0262O1201539 | 29.164 | 83.555 | Ghaghara | E(o) | 1.02 | 5,384 |\n| 1540 | 0262O1201540 | 29.162 | 83.661 | Ghaghara | E(o) | 2.58 | 5,746 |\n| 1541 | 0262O1201541 | 29.161 | 83.664 | Gandak | E(o) | 0.47 | 5,739 |\n| 1542 | 0262O1201542 | 29.160 | 83.624 | Ghaghara | E(o) | 1.65 | 5,592 |\n| 1543 | 0262O1201543 | 29.158 | 83.549 | Ghaghara | E(o) | 2.98 | 5,430 |\n| 1544 | 0262O1201544 | 29.152 | 83.677 | Gandak | M(o) | 0.50 | 5,666 |\n| 1545 | 0262O1201545 | 29.149 | 83.546 | Ghaghara | E(o) | 3.89 | 5,447 |\n| 1546 | 0262O1201546 | 29.143 | 83.721 | Gandak | E(o) | 0.40 | 5,811 |\n| 1547 | 0262O1201547 | 29.140 | 83.525 | Ghaghara | E(o) | 1.34 | 5,391 |\n| 1548 | 0262O1201548 | 29.131 | 83.734 | Gandak | E(o) | 1.35 | 5,697 |\n| 1549 | 0262O1201549 | 29.123 | 83.563 | Ghaghara | E(o) | 0.58 | 5,446 |\n| 1550 | 0262O1201550 | 29.119 | 83.640 | Ghaghara | M(o) | 1.18 | 5,593 |\n| 1551 | 0262O1201551 | 29.117 | 83.738 | Gandak | M(o) | 11.61 | 5,517 |\n| 1552 | 0262O1201552 | 29.115 | 83.631 | Ghaghara | E(o) | 0.26 | 5,597 |\n| 1553 | 0262O1201553 | 29.113 | 83.749 | Gandak | E(o) | 2.27 | 5,456 |\n| 1554 | 0262O1201554 | 29.109 | 83.675 | Gandak | M(o) | 0.57 | 5,690 |\n| 1555 | 0262O1201555 | 29.106 | 83.672 | Gandak | M(o) | 1.19 | 5,653 |\n| 1556 | 0262O1201556 | 29.104 | 83.638 | Gandak | E(o) | 2.20 | 5,690 |\n| 1557 | 0262O1201557 | 29.103 | 83.675 | Gandak | M(o) | 0.40 | 5,678 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5030, "line_end": 5104, "token_count_estimate": 1596, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262O1201523", "0262O1201524", "0262O1201525", "0262O1201526", "0262O1201527", "0262O1201528", "0262O1201529", "0262O1201530", "0262O1201531", "0262O1201532", "0262O1201533", "0262O1201534", "0262O1201535", "0262O1201536", "0262O1201537", "0262O1201538", "0262O1201539", "0262O1201540", "0262O1201541", "0262O1201542", "0262O1201543", "0262O1201544", "0262O1201545", "0262O1201546", "0262O1201547", "0262O1201548", "0262O1201549", "0262O1201550", "0262O1201551", "0262O1201552", "0262O1201553", "0262O1201554", "0262O1201555", "0262O1201556", "0262O1201557"]}}
{"id": "2d8c42296eb5f31a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1558 | 0262O1201558 | 29.102 | 83.630 | Gandak | M(o) | 6.47 | 5,763 |\n| 1559 | 0262O1201559 | 29.100 | 83.627 | Gandak | M(o) | 1.08 | 5,754 |\n| 1560 | 0262O1201560 | 29.099 | 83.585 | Ghaghara | E(o) | 0.73 | 5,315 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5030, "line_end": 5104, "token_count_estimate": 226, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262O1201558", "0262O1201559", "0262O1201560"]}}
{"id": "6ef410924210f6fc", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 5105, "line_end": 5115, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cef247317715f01a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1561 | 0262O1201561 | 29.098 | 83.586 | Ghaghara | E(o) | 0.54 | 5,336 |\n| 1562 | 0262O1201562 | 29.094 | 83.607 | Ghaghara | E(o) | 1.68 | 5,581 |\n| 1563 | 0262O1201563 | 29.093 | 83.531 | Ghaghara | O | 2.89 | 4,954 |\n| 1564 | 0262O1201564 | 29.093 | 83.637 | Gandak | E(o) | 2.17 | 5,599 |\n| 1565 | 0262O1201565 | 29.092 | 83.633 | Gandak | E(o) | 1.07 | 5,598 |\n| 1566 | 0262O1201566 | 29.092 | 83.627 | Gandak | E(o) | 0.67 | 5,620 |\n| 1567 | 0262O1201567 | 29.090 | 83.714 | Gandak | M(o) | 0.34 | 5,660 |\n| 1568 | 0262O1201568 | 29.090 | 83.634 | Gandak | E(o) | 2.06 | 5,589 |\n| 1569 | 0262O1201569 | 29.088 | 83.628 | Gandak | M(o) | 0.91 | 5,678 |\n| 1570 | 0262O1201570 | 29.087 | 83.599 | Ghaghara | E(o) | 0.56 | 5,457 |\n| 1571 | 0262O1201571 | 29.085 | 83.617 | Ghaghara | M(o) | 0.46 | 5,556 |\n| 1572 | 0262O1201572 | 29.085 | 83.615 | Ghaghara | M(o) | 0.30 | 5,553 |\n| 1573 | 0262O1201573 | 29.085 | 83.748 | Gandak | M(o) | 1.79 | 5,754 |\n| 1574 | 0262O1201574 | 29.080 | 83.655 | Gandak | E(o) | 2.99 | 5,437 |\n| 1575 | 0262O1201575 | 29.077 | 83.669 | Gandak | E(o) | 1.93 | 5,375 |\n| 1576 | 0262O1201576 | 29.077 | 83.609 | Ghaghara | O | 0.32 | 5,484 |\n| 1577 | 0262O1201577 | 29.076 | 83.644 | Gandak | E(o) | 1.49 | 5,515 |\n| 1578 | 0262O1201578 | 29.076 | 83.652 | Gandak | M(o) | 2.63 | 5,480 |\n| 1579 | 0262O1201579 | 29.076 | 83.648 | Gandak | E(o) | 0.59 | 5,483 |\n| 1580 | 0262O1201580 | 29.076 | 83.637 | Gandak | M(o) | 0.52 | 5,583 |\n| 1581 | 0262O1201581 | 29.075 | 83.654 | Gandak | M(o) | 1.55 | 5,466 |\n| 1582 | 0262O1201582 | 29.072 | 83.645 | Gandak | M(o) | 3.91 | 5,527 |\n| 1583 | 0262O1201583 | 29.069 | 83.649 | Gandak | E(o) | 1.18 | 5,572 |\n| 1584 | 0262O1201584 | 29.063 | 83.661 | Gandak | E(o) | 0.55 | 5,644 |\n| 1585 | 0262O1201585 | 29.051 | 83.605 | Ghaghara | M(o) | 3.06 | 5,552 |\n| 1586 | 0262O1201586 | 29.046 | 83.603 | Ghaghara | M(o) | 0.30 | 5,564 |\n| 1587 | 0262O1201587 | 29.046 | 83.674 | Gandak | M(e) | 12.14 | 5,431 |\n| 1588 | 0262O1201588 | 29.041 | 83.598 | Ghaghara | E(o) | 0.65 | 5,422 |\n| 1589 | 0262O1201589 | 29.039 | 83.669 | Gandak | M(o) | 7.88 | 5,480 |\n| 1590 | 0262O1201590 | 29.038 | 83.664 | Gandak | M(o) | 0.54 | 5,508 |\n| 1591 | 0262O1201591 | 29.030 | 83.582 | Ghaghara | M(o) | 0.59 | 5,567 |\n| 1592 | 0262O1201592 | 29.024 | 83.632 | Gandak | M(o) | 3.05 | 5,644 |\n| 1593 | 0262O1201593 | 29.022 | 83.643 | Gandak | M(o) | 0.52 | 5,665 |\n| 1594 | 0262O1201594 | 29.022 | 83.639 | Gandak | M(o) | 1.06 | 5,610 |\n| 1595 | 0262O1201595 | 29.022 | 83.620 | Gandak | M(o) | 1.03 | 5,676 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5116, "line_end": 5190, "token_count_estimate": 1598, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262O1201561", "0262O1201562", "0262O1201563", "0262O1201564", "0262O1201565", "0262O1201566", "0262O1201567", "0262O1201568", "0262O1201569", "0262O1201570", "0262O1201571", "0262O1201572", "0262O1201573", "0262O1201574", "0262O1201575", "0262O1201576", "0262O1201577", "0262O1201578", "0262O1201579", "0262O1201580", "0262O1201581", "0262O1201582", "0262O1201583", "0262O1201584", "0262O1201585", "0262O1201586", "0262O1201587", "0262O1201588", "0262O1201589", "0262O1201590", "0262O1201591", "0262O1201592", "0262O1201593", "0262O1201594", "0262O1201595"]}}
{"id": "fc635867cee425ef", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1596 | 0262O1201596 | 29.022 | 83.683 | Gandak | I(s) | 1.22 | 5,870 |\n| 1597 | 0262O1201597 | 29.019 | 83.634 | Gandak | M(o) | 0.43 | 5,557 |\n| 1598 | 0262O1201598 | 29.018 | 83.604 | Ghaghara | E(o) | 0.76 | 5,520 |\n| 1599 | 0262O1201599 | 29.017 | 83.635 | Gandak | M(o) | 4.11 | 5,551 |\n| 1600 | 0262O1201600 | 29.015 | 83.668 | Gandak | M(o) | 0.50 | 5,709 |\n| 1601 | 0262O1201601 | 29.014 | 83.672 | Gandak | I(s) | 0.41 | 5,691 |\n| 1602 | 0262O1201602 | 29.009 | 83.633 | Gandak | M(o) | 0.25 | 5,552 |\n| 1603 | 0262O1201603 | 29.008 | 83.649 | Gandak | E(o) | 0.33 | 5,595 |\n| 1604 | 0262O1201604 | 29.008 | 83.634 | Gandak | M(o) | 0.44 | 5,556 |\n| 1605 | 0262O1201605 | 29.007 | 83.503 | Ghaghara | M(o) | 1.01 | 5,426 |\n| 1606 | 0262O1201606 | 29.005 | 83.689 | Gandak | E(o) | 1.63 | 5,555 |\n| 1607 | 0262O1201607 | 29.005 | 83.553 | Ghaghara | M(o) | 0.81 | 5,405 |\n| 1608 | 0262O1201608 | 29.003 | 83.718 | Gandak | M(o) | 0.65 | 5,632 |\n| 1609 | 0262O1201609 | 29.002 | 83.610 | Ghaghara | M(o) | 0.53 | 5,584 |\n| 1610 | 0262O1201610 | 29.001 | 83.571 | Ghaghara | M(o) | 0.38 | 5,372 |\n| 1611 | 0262O1501611 | 29.330 | 83.819 | Gandak | I(s) | 0.34 | 6,056 |\n| 1612 | 0262O1501612 | 29.324 | 83.817 | Gandak | I(s) | 0.43 | 5,999 |\n| 1613 | 0262O1501613 | 29.323 | 83.816 | Gandak | I(s) | 0.28 | 5,998 |\n| 1614 | 0262O1501614 | 29.317 | 83.848 | Gandak | E(o) | 0.26 | 5,560 |\n| 1615 | 0262O1501615 | 29.310 | 83.832 | Gandak | M(o) | 0.41 | 5,755 |\n| 1616 | 0262O1501616 | 29.308 | 83.867 | Gandak | I(s) | 0.73 | 5,971 |\n| 1617 | 0262O1501617 | 29.308 | 83.816 | Gandak | I(s) | 1.36 | 5,988 |\n| 1618 | 0262O1501618 | 29.300 | 83.819 | Gandak | I(s) | 2.13 | 5,989 |\n| 1619 | 0262O1501619 | 29.300 | 83.863 | Gandak | M(o) | 0.85 | 5,894 |\n| 1620 | 0262O1501620 | 29.290 | 83.818 | Gandak | M(lg) | 0.37 | 5,924 |\n| 1621 | 0262O1501621 | 29.288 | 83.820 | Gandak | M(lg) | 0.28 | 5,897 |\n| 1622 | 0262O1501622 | 29.283 | 83.806 | Gandak | M(lg) | 0.38 | 6,033 |\n| 1623 | 0262O1501623 | 29.282 | 83.853 | Gandak | M(o) | 0.25 | 5,583 |\n| 1624 | 0262O1501624 | 29.281 | 83.800 | Gandak | I(s) | 0.46 | 6,044 |\n| 1625 | 0262O1501625 | 29.279 | 83.798 | Gandak | I(s) | 0.42 | 6,003 |\n| 1626 | 0262O1501626 | 29.258 | 83.815 | Gandak | M(o) | 0.25 | 5,843 |\n| 1627 | 0262O1601627 | 29.246 | 83.804 | Gandak | M(lg) | 0.83 | 5,844 |\n| 1628 | 0262O1601628 | 29.246 | 83.796 | Gandak | E(o) | 1.79 | 5,867 |\n| 1629 | 0262O1601629 | 29.245 | 83.797 | Gandak | E(o) | 0.99 | 5,864 |\n| 1630 | 0262O1601630 | 29.243 | 83.802 | Gandak | I(s) | 0.92 | 5,901 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5116, "line_end": 5190, "token_count_estimate": 1594, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262O1201596", "0262O1201597", "0262O1201598", "0262O1201599", "0262O1201600", "0262O1201601", "0262O1201602", "0262O1201603", "0262O1201604", "0262O1201605", "0262O1201606", "0262O1201607", "0262O1201608", "0262O1201609", "0262O1201610", "0262O1501611", "0262O1501612", "0262O1501613", "0262O1501614", "0262O1501615", "0262O1501616", "0262O1501617", "0262O1501618", "0262O1501619", "0262O1501620", "0262O1501621", "0262O1501622", "0262O1501623", "0262O1501624", "0262O1501625", "0262O1501626", "0262O1601627", "0262O1601628", "0262O1601629", "0262O1601630"]}}
{"id": "863569adfd22521a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1631 | 0262O1601631 | 29.227 | 83.807 | Gandak | I(s) | 0.36 | 5,897 |\n| 1632 | 0262O1601632 | 29.205 | 83.848 | Gandak | M(o) | 1.89 | 5,424 |\n| 1633 | 0262O1601633 | 29.178 | 83.851 | Gandak | M(o) | 0.38 | 5,655 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5116, "line_end": 5190, "token_count_estimate": 224, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0262O1601631", "0262O1601632", "0262O1601633"]}}
{"id": "4a3401efe8817386", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1634 | 0262O1601634 | 29.169 | 83.765 | Gandak | M(o) | 1.75 | 5,540 |\n| 1635 | 0262O1601635 | 29.147 | 83.803 | Gandak | M(o) | 2.17 | 5,666 |\n| 1636 | 0262O1601636 | 29.137 | 83.790 | Gandak | M(o) | 3.07 | 5,900 |\n| 1637 | 0262O1601637 | 29.118 | 83.779 | Gandak | M(o) | 1.98 | 5,651 |\n| 1638 | 0262O1601638 | 29.115 | 83.786 | Gandak | M(e) | 6.00 | 5,592 |\n| 1639 | 0262O1601639 | 29.110 | 83.757 | Gandak | E(o) | 2.31 | 5,411 |\n| 1640 | 0262O1601640 | 29.056 | 83.780 | Gandak | M(o) | 0.66 | 5,592 |\n| 1641 | 0262O1601641 | 29.006 | 83.759 | Gandak | E(o) | 0.91 | 5,633 |\n| 1642 | 0262P0101642 | 28.996 | 83.195 | Ghaghara | I(s) | 0.65 | 5,290 |\n| 1643 | 0262P0101643 | 28.993 | 83.173 | Ghaghara | M(e) | 15.98 | 4,821 |\n| 1644 | 0262P0101644 | 28.992 | 83.216 | Ghaghara | E(o) | 2.45 | 5,199 |\n| 1645 | 0262P0101645 | 28.992 | 83.201 | Ghaghara | M(o) | 1.99 | 5,142 |\n| 1646 | 0262P0101646 | 28.968 | 83.208 | Ghaghara | M(e) | 8.34 | 5,253 |\n| 1647 | 0262P0101647 | 28.967 | 83.210 | Ghaghara | M(o) | 1.54 | 5,271 |\n| 1648 | 0262P0101648 | 28.965 | 83.209 | Ghaghara | M(o) | 0.26 | 5,251 |\n| 1649 | 0262P0101649 | 28.964 | 83.179 | Ghaghara | M(o) | 0.73 | 5,089 |\n| 1650 | 0262P0101650 | 28.962 | 83.207 | Ghaghara | M(o) | 0.87 | 5,078 |\n| 1651 | 0262P0101651 | 28.959 | 83.187 | Ghaghara | M(e) | 27.62 | 5,071 |\n| 1652 | 0262P0101652 | 28.818 | 83.074 | Ghaghara | E(o) | 1.57 | 4,867 |\n| 1653 | 0262P0101653 | 28.817 | 83.109 | Ghaghara | M(o) | 0.72 | 5,198 |\n| 1654 | 0262P0101654 | 28.815 | 83.109 | Ghaghara | M(o) | 0.57 | 5,182 |\n| 1655 | 0262P0101655 | 28.805 | 83.124 | Ghaghara | M(o) | 0.44 | 4,865 |\n| 1656 | 0262P0101656 | 28.804 | 83.122 | Ghaghara | M(o) | 0.49 | 4,890 |\n| 1657 | 0262P0101657 | 28.803 | 83.068 | Ghaghara | M(o) | 2.43 | 4,910 |\n| 1658 | 0262P0101658 | 28.802 | 83.102 | Ghaghara | M(o) | 0.70 | 5,268 |\n| 1659 | 0262P0101659 | 28.802 | 83.063 | Ghaghara | M(l) | 0.29 | 4,952 |\n| 1660 | 0262P0101660 | 28.798 | 83.186 | Ghaghara | M(e) | 6.30 | 5,301 |\n| 1661 | 0262P0101661 | 28.794 | 83.151 | Ghaghara | I(s) | 0.37 | 5,027 |\n| 1662 | 0262P0101662 | 28.793 | 83.153 | Ghaghara | I(s) | 0.94 | 5,050 |\n| 1663 | 0262P0101663 | 28.790 | 83.041 | Ghaghara | M(o) | 0.97 | 5,132 |\n| 1664 | 0262P0101664 | 28.790 | 83.015 | Ghaghara | M(o) | 1.24 | 5,015 |\n| 1665 | 0262P0101665 | 28.789 | 83.096 | Ghaghara | M(o) | 0.25 | 5,413 |\n| 1666 | 0262P0101666 | 28.787 | 83.180 | Ghaghara | M(o) | 1.70 | 5,426 |\n| 1667 | 0262P0101667 | 28.780 | 83.042 | Ghaghara | M(o) | 1.39 | 4,902 |\n| 1668 | 0262P0101668 | 28.778 | 83.133 | Ghaghara | M(o) | 0.44 | 5,499 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5192, "line_end": 5266, "token_count_estimate": 1632, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262O1601634", "0262O1601635", "0262O1601636", "0262O1601637", "0262O1601638", "0262O1601639", "0262O1601640", "0262O1601641", "0262P0101642", "0262P0101643", "0262P0101644", "0262P0101645", "0262P0101646", "0262P0101647", "0262P0101648", "0262P0101649", "0262P0101650", "0262P0101651", "0262P0101652", "0262P0101653", "0262P0101654", "0262P0101655", "0262P0101656", "0262P0101657", "0262P0101658", "0262P0101659", "0262P0101660", "0262P0101661", "0262P0101662", "0262P0101663", "0262P0101664", "0262P0101665", "0262P0101666", "0262P0101667", "0262P0101668"]}}
{"id": "19521b8c66314eb9", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1669 | 0262P0101669 | 28.778 | 83.046 | Ghaghara | M(o) | 5.96 | 4,945 |\n| 1670 | 0262P0101670 | 28.777 | 83.127 | Ghaghara | M(o) | 0.70 | 5,528 |\n| 1671 | 0262P0101671 | 28.776 | 83.131 | Ghaghara | M(o) | 0.70 | 5,521 |\n| 1672 | 0262P0101672 | 28.773 | 83.041 | Ghaghara | M(o) | 0.91 | 4,997 |\n| 1673 | 0262P0101673 | 28.771 | 83.032 | Ghaghara | M(o) | 1.10 | 4,925 |\n| 1674 | 0262P0101674 | 28.769 | 83.036 | Ghaghara | M(o) | 2.78 | 5,022 |\n| 1675 | 0262P0101675 | 28.769 | 83.121 | Ghaghara | M(o) | 0.38 | 5,578 |\n| 1676 | 0262P0101676 | 28.764 | 83.016 | Ghaghara | E(o) | 0.59 | 4,872 |\n| 1677 | 0262P0101677 | 28.760 | 83.059 | Ghaghara | E(o) | 1.75 | 5,047 |\n| 1678 | 0262P0101678 | 28.758 | 83.032 | Ghaghara | E(o) | 0.28 | 5,059 |\n| 1679 | 0262P0101679 | 28.756 | 83.039 | Ghaghara | M(o) | 0.90 | 5,034 |\n| 1680 | 0262P0101680 | 28.756 | 83.071 | Ghaghara | E(o) | 0.46 | 4,785 |\n| 1681 | 0262P0101681 | 28.752 | 83.069 | Ghaghara | E(o) | 0.42 | 4,793 |\n| 1682 | 0262P0201682 | 28.749 | 83.069 | Ghaghara | E(o) | 1.40 | 4,767 |\n| 1683 | 0262P0201683 | 28.738 | 83.045 | Ghaghara | E(o) | 2.75 | 4,760 |\n| 1684 | 0262P0201684 | 28.733 | 83.032 | Ghaghara | E(o) | 0.28 | 4,610 |\n| 1685 | 0262P0201685 | 28.727 | 83.121 | Ghaghara | M(o) | 0.69 | 5,158 |\n| 1686 | 0262P0201686 | 28.723 | 83.095 | Ghaghara | M(o) | 0.49 | 4,761 |\n| 1687 | 0262P0201687 | 28.723 | 83.118 | Ghaghara | M(o) | 0.36 | 5,190 |\n| 1688 | 0262P0201688 | 28.722 | 83.093 | Ghaghara | M(o) | 3.25 | 4,759 |\n| 1689 | 0262P0201689 | 28.720 | 83.137 | Ghaghara | M(l) | 0.73 | 5,058 |\n| 1690 | 0262P0201690 | 28.719 | 83.130 | Ghaghara | I(s) | 0.42 | 5,074 |\n| 1691 | 0262P0201691 | 28.718 | 83.126 | Ghaghara | E(o) | 1.28 | 5,058 |\n| 1692 | 0262P0201692 | 28.718 | 83.112 | Ghaghara | M(o) | 0.26 | 5,159 |\n| 1693 | 0262P0201693 | 28.716 | 83.111 | Ghaghara | M(o) | 1.48 | 5,064 |\n| 1694 | 0262P0201694 | 28.707 | 83.108 | Ghaghara | E(o) | 0.60 | 4,820 |\n| 1695 | 0262P0201695 | 28.707 | 83.163 | Ghaghara | M(o) | 1.33 | 5,012 |\n| 1696 | 0262P0201696 | 28.707 | 83.106 | Ghaghara | E(o) | 1.73 | 4,811 |\n| 1697 | 0262P0201697 | 28.703 | 83.161 | Ghaghara | E(o) | 0.92 | 5,120 |\n| 1698 | 0262P0201698 | 28.703 | 83.105 | Ghaghara | E(o) | 1.62 | 4,724 |\n| 1699 | 0262P0201699 | 28.701 | 83.131 | Ghaghara | E(o) | 0.55 | 4,704 |\n| 1700 | 0262P0201700 | 28.681 | 83.208 | Ghaghara | M(o) | 2.13 | 4,815 |\n| 1701 | 0262P0201701 | 28.670 | 83.126 | Ghaghara | E(o) | 0.46 | 4,884 |\n| 1702 | 0262P0201702 | 28.667 | 83.137 | Ghaghara | E(o) | 0.56 | 4,697 |\n| 1703 | 0262P0201703 | 28.665 | 83.097 | Ghaghara | E(o) | 1.71 | 4,444 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5192, "line_end": 5266, "token_count_estimate": 1635, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262P0101669", "0262P0101670", "0262P0101671", "0262P0101672", "0262P0101673", "0262P0101674", "0262P0101675", "0262P0101676", "0262P0101677", "0262P0101678", "0262P0101679", "0262P0101680", "0262P0101681", "0262P0201682", "0262P0201683", "0262P0201684", "0262P0201685", "0262P0201686", "0262P0201687", "0262P0201688", "0262P0201689", "0262P0201690", "0262P0201691", "0262P0201692", "0262P0201693", "0262P0201694", "0262P0201695", "0262P0201696", "0262P0201697", "0262P0201698", "0262P0201699", "0262P0201700", "0262P0201701", "0262P0201702", "0262P0201703"]}}
{"id": "075621f2ce161561", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1704 | 0262P0201704 | 28.661 | 83.146 | Ghaghara | E(o) | 0.32 | 4,475 |\n| 1705 | 0262P0201705 | 28.658 | 83.107 | Ghaghara | E(o) | 3.67 | 4,480 |\n| 1706 | 0262P0201706 | 28.652 | 83.187 | Ghaghara | E(o) | 0.91 | 4,325 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5192, "line_end": 5266, "token_count_estimate": 226, "basins": [], "subbasins": ["Ghaghara"], "countries": [], "lake_ids": ["0262P0201704", "0262P0201705", "0262P0201706"]}}
{"id": "1e6e3bc05be8ec88", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 5267, "line_end": 5277, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "864561239cf2bc36", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1707 | 0262P0501707 | 28.999 | 83.287 | Ghaghara | M(o) | 4.75 | 5,429 |\n| 1708 | 0262P0501708 | 28.981 | 83.298 | Ghaghara | M(o) | 0.52 | 5,357 |\n| 1709 | 0262P0501709 | 28.980 | 83.300 | Ghaghara | M(o) | 1.16 | 5,360 |\n| 1710 | 0262P0501710 | 28.979 | 83.286 | Ghaghara | M(o) | 0.28 | 5,469 |\n| 1711 | 0262P0501711 | 28.964 | 83.277 | Ghaghara | E(o) | 0.35 | 5,286 |\n| 1712 | 0262P0501712 | 28.929 | 83.495 | Ghaghara | M(o) | 0.49 | 5,432 |\n| 1713 | 0262P0501713 | 28.928 | 83.429 | Ghaghara | M(o) | 1.27 | 5,432 |\n| 1714 | 0262P0501714 | 28.926 | 83.428 | Ghaghara | M(o) | 0.26 | 5,384 |\n| 1715 | 0262P0501715 | 28.912 | 83.463 | Ghaghara | M(o) | 0.55 | 5,393 |\n| 1716 | 0262P0501716 | 28.908 | 83.461 | Ghaghara | M(o) | 0.83 | 5,402 |\n| 1717 | 0262P0501717 | 28.906 | 83.463 | Ghaghara | M(o) | 1.09 | 5,401 |\n| 1718 | 0262P0501718 | 28.905 | 83.463 | Ghaghara | E(o) | 2.47 | 5,400 |\n| 1719 | 0262P0501719 | 28.896 | 83.466 | Ghaghara | M(o) | 1.71 | 5,569 |\n| 1720 | 0262P0501720 | 28.893 | 83.466 | Ghaghara | M(o) | 1.02 | 5,566 |\n| 1721 | 0262P0501721 | 28.893 | 83.337 | Ghaghara | O | 0.39 | 4,542 |\n| 1722 | 0262P0501722 | 28.889 | 83.492 | Ghaghara | M(o) | 0.41 | 5,399 |\n| 1723 | 0262P0501723 | 28.875 | 83.449 | Ghaghara | M(o) | 0.61 | 5,243 |\n| 1724 | 0262P0501724 | 28.874 | 83.448 | Ghaghara | E(o) | 0.32 | 5,246 |\n| 1725 | 0262P0501725 | 28.871 | 83.493 | Ghaghara | M(o) | 2.10 | 5,532 |\n| 1726 | 0262P0501726 | 28.870 | 83.493 | Ghaghara | M(o) | 0.37 | 5,535 |\n| 1727 | 0262P0501727 | 28.867 | 83.490 | Ghaghara | M(l) | 5.27 | 5,521 |\n| 1728 | 0262P0501728 | 28.858 | 83.489 | Ghaghara | M(o) | 0.58 | 5,399 |\n| 1729 | 0262P0501729 | 28.858 | 83.473 | Ghaghara | M(o) | 1.27 | 5,177 |\n| 1730 | 0262P0501730 | 28.855 | 83.417 | Ghaghara | M(o) | 4.26 | 5,293 |\n| 1731 | 0262P0501731 | 28.833 | 83.471 | Ghaghara | M(o) | 1.05 | 5,444 |\n| 1732 | 0262P0501732 | 28.832 | 83.473 | Ghaghara | M(o) | 0.84 | 5,475 |\n| 1733 | 0262P0501733 | 28.831 | 83.470 | Ghaghara | M(o) | 0.80 | 5,452 |\n| 1734 | 0262P0501734 | 28.787 | 83.330 | Ghaghara | M(e) | 43.57 | 4,445 |\n| 1735 | 0262P0601735 | 28.713 | 83.415 | Gandak | M(lg) | 0.48 | 5,084 |\n| 1736 | 0262P0601736 | 28.699 | 83.328 | Gandak | M(o) | 0.41 | 5,044 |\n| 1737 | 0262P0901737 | 29.000 | 83.610 | Ghaghara | M(o) | 0.42 | 5,580 |\n| 1738 | 0262P0901738 | 28.999 | 83.747 | Gandak | M(o) | 0.64 | 5,730 |\n| 1739 | 0262P0901739 | 28.998 | 83.668 | Gandak | E(o) | 1.88 | 5,461 |\n| 1740 | 0262P0901740 | 28.986 | 83.714 | Gandak | E(o) | 1.98 | 5,620 |\n| 1741 | 0262P0901741 | 28.984 | 83.730 | Gandak | M(o) | 1.74 | 5,584 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5278, "line_end": 5425, "token_count_estimate": 1628, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262P0501707", "0262P0501708", "0262P0501709", "0262P0501710", "0262P0501711", "0262P0501712", "0262P0501713", "0262P0501714", "0262P0501715", "0262P0501716", "0262P0501717", "0262P0501718", "0262P0501719", "0262P0501720", "0262P0501721", "0262P0501722", "0262P0501723", "0262P0501724", "0262P0501725", "0262P0501726", "0262P0501727", "0262P0501728", "0262P0501729", "0262P0501730", "0262P0501731", "0262P0501732", "0262P0501733", "0262P0501734", "0262P0601735", "0262P0601736", "0262P0901737", "0262P0901738", "0262P0901739", "0262P0901740", "0262P0901741"]}}
{"id": "87f49a9be9fe6c12", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1742 | 0262P0901742 | 28.984 | 83.708 | Gandak | M(o) | 0.50 | 5,576 |\n| 1743 | 0262P0901743 | 28.982 | 83.727 | Gandak | M(o) | 1.59 | 5,566 |\n| 1744 | 0262P0901744 | 28.978 | 83.627 | Gandak | M(o) | 0.41 | 5,541 |\n| 1745 | 0262P0901745 | 28.974 | 83.740 | Gandak | M(o) | 2.01 | 5,717 |\n| 1746 | 0262P0901746 | 28.973 | 83.743 | Gandak | M(o) | 1.22 | 5,695 |\n| 1747 | 0262P0901747 | 28.973 | 83.744 | Gandak | M(o) | 0.27 | 5,685 |\n| 1748 | 0262P0901748 | 28.970 | 83.640 | Gandak | M(e) | 3.05 | 5,516 |\n| 1749 | 0262P0901749 | 28.969 | 83.606 | Gandak | M(e) | 1.18 | 5,496 |\n| 1750 | 0262P0901750 | 28.967 | 83.605 | Gandak | M(e) | 2.90 | 5,496 |\n| 1751 | 0262P0901751 | 28.962 | 83.574 | Ghaghara | M(o) | 0.95 | 5,465 |\n| 1752 | 0262P0901752 | 28.962 | 83.633 | Gandak | M(o) | 2.15 | 5,540 |\n| 1753 | 0262P0901753 | 28.958 | 83.733 | Gandak | M(o) | 1.46 | 5,858 |\n| 1754 | 0262P0901754 | 28.958 | 83.575 | Ghaghara | M(o) | 1.59 | 5,534 |\n| 1755 | 0262P0901755 | 28.954 | 83.737 | Gandak | M(o) | 2.98 | 5,649 |\n| 1756 | 0262P0901756 | 28.950 | 83.717 | Gandak | M(o) | 0.70 | 5,739 |\n| 1757 | 0262P0901757 | 28.942 | 83.598 | Gandak | M(o) | 2.75 | 5,483 |\n| 1758 | 0262P0901758 | 28.904 | 83.533 | Gandak | M(o) | 0.68 | 5,634 |\n| 1759 | 0262P0901759 | 28.886 | 83.527 | Gandak | M(e) | 30.92 | 5,578 |\n| 1760 | 0262P0901760 | 28.854 | 83.550 | Gandak | M(o) | 1.14 | 5,617 |\n| 1761 | 0262P0901761 | 28.854 | 83.530 | Gandak | M(o) | 1.73 | 5,543 |\n| 1762 | 0262P0901762 | 28.845 | 83.541 | Gandak | M(o) | 0.56 | 5,427 |\n| 1763 | 0262P0901763 | 28.833 | 83.627 | Gandak | M(o) | 2.63 | 5,502 |\n| 1764 | 0262P1001764 | 28.634 | 83.560 | Gandak | E(o) | 2.17 | 4,277 |\n| 1765 | 0262P1301765 | 28.996 | 83.755 | Gandak | M(e) | 2.43 | 5,553 |\n| 1766 | 0262P1301766 | 28.855 | 83.915 | Gandak | E(o) | 0.53 | 5,055 |\n| 1767 | 0262P1301767 | 28.843 | 83.941 | Gandak | M(o) | 1.62 | 5,635 |\n| 1768 | 0262P1301768 | 28.840 | 83.964 | Gandak | M(o) | 0.87 | 5,777 |\n| 1769 | 0262P1301769 | 28.833 | 83.961 | Gandak | M(o) | 0.86 | 5,976 |\n| 1770 | 0262P1301770 | 28.829 | 83.965 | Gandak | I(s) | 0.65 | 5,760 |\n| 1771 | 0262P1301771 | 28.824 | 83.999 | Gandak | M(lg) | 0.65 | 5,482 |\n| 1772 | 0262P1301772 | 28.813 | 83.964 | Gandak | M(o) | 0.88 | 5,444 |\n| 1773 | 0262P1301773 | 28.807 | 83.976 | Gandak | M(o) | 0.52 | 5,391 |\n| 1774 | 0262P1301774 | 28.802 | 83.960 | Gandak | M(o) | 1.28 | 5,460 |\n| 1775 | 0262P1301775 | 28.800 | 83.960 | Gandak | M(o) | 0.93 | 5,467 |\n| 1776 | 0262P1301776 | 28.799 | 83.978 | Gandak | M(o) | 1.16 | 5,327 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5278, "line_end": 5425, "token_count_estimate": 1595, "basins": [], "subbasins": ["Gandak", "Ghaghara"], "countries": [], "lake_ids": ["0262P0901742", "0262P0901743", "0262P0901744", "0262P0901745", "0262P0901746", "0262P0901747", "0262P0901748", "0262P0901749", "0262P0901750", "0262P0901751", "0262P0901752", "0262P0901753", "0262P0901754", "0262P0901755", "0262P0901756", "0262P0901757", "0262P0901758", "0262P0901759", "0262P0901760", "0262P0901761", "0262P0901762", "0262P0901763", "0262P1001764", "0262P1301765", "0262P1301766", "0262P1301767", "0262P1301768", "0262P1301769", "0262P1301770", "0262P1301771", "0262P1301772", "0262P1301773", "0262P1301774", "0262P1301775", "0262P1301776"]}}
{"id": "670bcd94942b5d4b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1777 | 0262P1301777 | 28.799 | 83.962 | Gandak | M(o) | 0.25 | 5,458 |\n| 1778 | 0262P1301778 | 28.796 | 83.981 | Gandak | M(o) | 0.26 | 5,313 |\n| 1779 | 0262P1301779 | 28.794 | 83.983 | Gandak | M(o) | 2.76 | 5,282 |\n| 1780 | 0262P1301780 | 28.784 | 83.995 | Gandak | M(o) | 0.72 | 4,939 |\n| 1781 | 0262P1301781 | 28.775 | 83.907 | Gandak | I(s) | 0.68 | 5,731 |\n| 1782 | 0262P1301782 | 28.770 | 83.910 | Gandak | I(s) | 0.32 | 5,652 |\n| 1783 | 0262P1301783 | 28.768 | 83.897 | Gandak | M(o) | 0.27 | 5,717 |\n| 1784 | 0262P1301784 | 28.752 | 83.935 | Gandak | M(o) | 0.57 | 5,385 |\n| 1785 | 0262P1301785 | 28.752 | 83.929 | Gandak | M(o) | 1.15 | 5,210 |\n| 1786 | 0262P1301786 | 28.751 | 83.928 | Gandak | M(o) | 0.26 | 5,214 |\n| 1787 | 0262P1401787 | 28.749 | 83.931 | Gandak | M(o) | 0.61 | 5,197 |\n| 1788 | 0262P1401788 | 28.744 | 83.997 | Gandak | M(o) | 1.58 | 4,906 |\n| 1789 | 0262P1401789 | 28.728 | 83.891 | Gandak | M(l) | 1.07 | 5,158 |\n| 1790 | 0262P1401790 | 28.726 | 83.890 | Gandak | M(e) | 14.99 | 5,103 |\n| 1791 | 0262P1401791 | 28.725 | 83.903 | Gandak | E(o) | 1.99 | 5,114 |\n| 1792 | 0262P1401792 | 28.723 | 83.840 | Gandak | E(o) | 0.35 | 5,396 |\n| 1793 | 0262P1401793 | 28.722 | 83.891 | Gandak | M(o) | 1.54 | 5,092 |\n| 1794 | 0262P1401794 | 28.721 | 83.892 | Gandak | M(o) | 0.33 | 5,074 |\n| 1795 | 0262P1401795 | 28.718 | 83.857 | Gandak | M(o) | 0.72 | 5,489 |\n| 1796 | 0262P1401796 | 28.716 | 83.913 | Gandak | E(o) | 1.77 | 5,053 |\n| 1797 | 0262P1401797 | 28.712 | 83.920 | Gandak | E(o) | 10.97 | 4,986 |\n| 1798 | 0262P1401798 | 28.710 | 83.896 | Gandak | E(o) | 2.51 | 5,017 |\n| 1799 | 0262P1401799 | 28.707 | 83.822 | Gandak | M(l) | 1.44 | 5,119 |\n| 1800 | 0262P1401800 | 28.705 | 83.870 | Gandak | M(o) | 0.29 | 5,296 |\n| 1801 | 0262P1401801 | 28.701 | 83.837 | Gandak | M(o) | 1.30 | 4,989 |\n| 1802 | 0262P1401802 | 28.697 | 83.879 | Gandak | M(o) | 3.91 | 5,168 |\n| 1803 | 0262P1401803 | 28.691 | 83.852 | Gandak | M(o) | 340.21 | 4,910 |\n| 1804 | 0262P1401804 | 28.672 | 83.866 | Gandak | M(l) | 0.63 | 4,971 |\n| 1805 | 0262P1401805 | 28.672 | 83.864 | Gandak | M(l) | 2.58 | 4,955 |\n| 1806 | 0262P1401806 | 28.671 | 83.859 | Gandak | M(o) | 1.38 | 4,993 |\n| 1807 | 0262P1401807 | 28.669 | 83.867 | Gandak | I(s) | 0.56 | 5,015 |\n| 1808 | 0262P1401808 | 28.641 | 83.788 | Gandak | M(e) | 8.33 | 4,070 |\n| 1809 | 0262P1401809 | 28.548 | 83.863 | Gandak | I(s) | 0.33 | 4,177 |\n| 1810 | 0262P1401810 | 28.534 | 83.870 | Gandak | I(s) | 0.98 | 4,033 |\n| 1811 | 0262P1501811 | 28.469 | 83.773 | Gandak | E(o) | 1.67 | 4,378 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5278, "line_end": 5425, "token_count_estimate": 1597, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0262P1301777", "0262P1301778", "0262P1301779", "0262P1301780", "0262P1301781", "0262P1301782", "0262P1301783", "0262P1301784", "0262P1301785", "0262P1301786", "0262P1401787", "0262P1401788", "0262P1401789", "0262P1401790", "0262P1401791", "0262P1401792", "0262P1401793", "0262P1401794", "0262P1401795", "0262P1401796", "0262P1401797", "0262P1401798", "0262P1401799", "0262P1401800", "0262P1401801", "0262P1401802", "0262P1401803", "0262P1401804", "0262P1401805", "0262P1401806", "0262P1401807", "0262P1401808", "0262P1401809", "0262P1401810", "0262P1501811"]}}
{"id": "cd17de6cb092b50f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1812 | 0271D0101812 | 28.930 | 84.206 | Gandak | I(s) | 1.45 | 5,416 |\n| 1813 | 0271D0101813 | 28.879 | 84.225 | Gandak | M(o) | 0.51 | 5,392 |\n| 1814 | 0271D0101814 | 28.873 | 84.102 | Gandak | M(o) | 0.28 | 5,554 |\n| 1815 | 0271D0101815 | 28.863 | 84.238 | Gandak | I(s) | 0.27 | 5,108 |\n| 1816 | 0271D0101816 | 28.853 | 84.066 | Gandak | M(o) | 0.37 | 5,616 |\n| 1817 | 0271D0101817 | 28.851 | 84.064 | Gandak | M(o) | 0.54 | 5,578 |\n| 1818 | 0271D0101818 | 28.851 | 84.061 | Gandak | M(o) | 0.45 | 5,500 |\n| 1819 | 0271D0101819 | 28.850 | 84.060 | Gandak | M(o) | 0.27 | 5,502 |\n| 1820 | 0271D0101820 | 28.848 | 84.179 | Gandak | M(o) | 0.68 | 5,642 |\n| 1821 | 0271D0101821 | 28.844 | 84.134 | Gandak | I(s) | 0.47 | 5,378 |\n| 1822 | 0271D0101822 | 28.841 | 84.203 | Gandak | M(o) | 0.47 | 5,510 |\n| 1823 | 0271D0101823 | 28.830 | 84.223 | Gandak | M(o) | 1.18 | 5,612 |\n| 1824 | 0271D0101824 | 28.830 | 84.145 | Gandak | M(o) | 2.31 | 5,479 |\n| 1825 | 0271D0101825 | 28.826 | 84.150 | Gandak | M(e) | 10.74 | 5,406 |\n| 1826 | 0271D0101826 | 28.826 | 84.054 | Gandak | M(o) | 0.85 | 5,413 |\n| 1827 | 0271D0101827 | 28.825 | 84.057 | Gandak | E(o) | 0.26 | 5,396 |\n| 1828 | 0271D0101828 | 28.825 | 84.035 | Gandak | I(s) | 0.35 | 5,377 |\n| 1829 | 0271D0101829 | 28.820 | 84.125 | Gandak | M(o) | 3.76 | 5,311 |\n| 1830 | 0271D0101830 | 28.820 | 84.248 | Gandak | M(o) | 0.43 | 5,599 |\n| 1831 | 0271D0101831 | 28.779 | 84.084 | Gandak | M(o) | 0.55 | 5,304 |\n| 1832 | 0271D0101832 | 28.771 | 84.118 | Gandak | E(o) | 1.14 | 5,467 |\n| 1833 | 0271D0101833 | 28.761 | 84.069 | Gandak | M(o) | 4.98 | 5,108 |\n| 1834 | 0271D0101834 | 28.751 | 84.083 | Gandak | M(l) | 2.70 | 5,145 |\n| 1835 | 0271D0201835 | 28.716 | 84.076 | Gandak | M(o) | 0.34 | 5,264 |\n| 1836 | 0271D0201836 | 28.701 | 84.124 | Gandak | M(o) | 1.12 | 5,286 |\n| 1837 | 0271D0201837 | 28.679 | 84.061 | Gandak | E(o) | 3.43 | 4,622 |\n| 1838 | 0271D0201838 | 28.663 | 84.017 | Gandak | M(o) | 5.30 | 3,512 |\n| 1839 | 0271D0201839 | 28.638 | 84.016 | Gandak | M(o) | 2.39 | 4,649 |\n| 1840 | 0271D0201840 | 28.603 | 84.093 | Gandak | E(o) | 0.85 | 4,946 |\n| 1841 | 0271D0201841 | 28.600 | 84.092 | Gandak | E(o) | 0.34 | 4,960 |\n| 1842 | 0271D0201842 | 28.583 | 84.084 | Gandak | M(e) | 1.65 | 4,864 |\n| 1843 | 0271D0201843 | 28.548 | 84.042 | Gandak | M(o) | 0.86 | 4,625 |\n| 1844 | 0271D0301844 | 28.447 | 84.117 | Gandak | E(o) | 9.87 | 2,450 |\n| 1845 | 0271D0301845 | 28.444 | 84.231 | Gandak | M(o) | 0.56 | 4,668 |\n| 1846 | 0271D0301846 | 28.434 | 84.206 | Gandak | E(o) | 1.36 | 4,175 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5278, "line_end": 5425, "token_count_estimate": 1591, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271D0101812", "0271D0101813", "0271D0101814", "0271D0101815", "0271D0101816", "0271D0101817", "0271D0101818", "0271D0101819", "0271D0101820", "0271D0101821", "0271D0101822", "0271D0101823", "0271D0101824", "0271D0101825", "0271D0101826", "0271D0101827", "0271D0101828", "0271D0101829", "0271D0101830", "0271D0101831", "0271D0101832", "0271D0101833", "0271D0101834", "0271D0201835", "0271D0201836", "0271D0201837", "0271D0201838", "0271D0201839", "0271D0201840", "0271D0201841", "0271D0201842", "0271D0201843", "0271D0301844", "0271D0301845", "0271D0301846"]}}
{"id": "dfe353334740abac", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1847 | 0271D0301847 | 28.427 | 84.200 | Gandak | E(o) | 0.66 | 4,046 |\n| 1848 | 0271D0301848 | 28.412 | 84.232 | Gandak | E(o) | 0.85 | 4,350 |\n| 1849 | 0271D0301849 | 28.408 | 84.218 | Gandak | E(o) | 4.01 | 4,124 |\n| 1850 | 0271D0501850 | 28.854 | 84.375 | Gandak | I(d) | 2.45 | 5,827 |\n| 1851 | 0271D0501851 | 28.824 | 84.418 | Gandak | M(o) | 1.14 | 5,870 |\n| 1852 | 0271D0501852 | 28.817 | 84.333 | Gandak | M(e) | 3.99 | 5,275 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 5278, "line_end": 5425, "token_count_estimate": 354, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271D0301847", "0271D0301848", "0271D0301849", "0271D0501850", "0271D0501851", "0271D0501852"]}}
{"id": "bb7a599a578c05de", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 5426, "line_end": 5434, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aa5fc8da64c278fd", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1853 | 0271D0501853 | 28.784 | 84.355 | Gandak | M(o) | 0.41 | 5,270 |\n| 1854 | 0271D0501854 | 28.772 | 84.363 | Gandak | E(o) | 0.30 | 5,262 |\n| 1855 | 0271D0601855 | 28.693 | 84.432 | Gandak | I(s) | 0.32 | 4,411 |\n| 1856 | 0271D0601856 | 28.688 | 84.428 | Gandak | I(s) | 1.22 | 4,398 |\n| 1857 | 0271D0601857 | 28.688 | 84.438 | Gandak | E(o) | 0.74 | 4,327 |\n| 1858 | 0271D0601858 | 28.686 | 84.489 | Gandak | E(o) | 0.36 | 4,705 |\n| 1859 | 0271D0601859 | 28.685 | 84.430 | Gandak | I(s) | 0.25 | 4,384 |\n| 1860 | 0271D0601860 | 28.683 | 84.432 | Gandak | I(s) | 0.56 | 4,344 |\n| 1861 | 0271D0601861 | 28.680 | 84.436 | Gandak | I(s) | 0.68 | 4,344 |\n| 1862 | 0271D0601862 | 28.665 | 84.462 | Gandak | M(l) | 0.52 | 4,142 |\n| 1863 | 0271D0601863 | 28.663 | 84.472 | Gandak | M(l) | 24.93 | 4,088 |\n| 1864 | 0271D0601864 | 28.662 | 84.459 | Gandak | E(o) | 0.30 | 4,143 |\n| 1865 | 0271D0601865 | 28.662 | 84.357 | Gandak | I(s) | 0.26 | 4,183 |\n| 1866 | 0271D0601866 | 28.657 | 84.458 | Gandak | M(l) | 10.57 | 4,039 |\n| 1867 | 0271D0601867 | 28.648 | 84.364 | Gandak | M(l) | 0.40 | 4,088 |\n| 1868 | 0271D0601868 | 28.645 | 84.375 | Gandak | I(s) | 0.25 | 3,941 |\n| 1869 | 0271D0601869 | 28.645 | 84.272 | Gandak | M(o) | 1.08 | 5,182 |\n| 1870 | 0271D0601870 | 28.645 | 84.263 | Gandak | M(o) | 1.43 | 4,969 |\n| 1871 | 0271D0601871 | 28.644 | 84.269 | Gandak | M(o) | 0.67 | 5,132 |\n| 1872 | 0271D0601872 | 28.632 | 84.470 | Gandak | M(o) | 0.94 | 3,695 |\n| 1873 | 0271D0601873 | 28.627 | 84.291 | Gandak | M(e) | 4.48 | 4,775 |\n| 1874 | 0271D0601874 | 28.622 | 84.305 | Gandak | M(o) | 0.57 | 4,851 |\n| 1875 | 0271D0601875 | 28.620 | 84.315 | Gandak | M(o) | 0.49 | 5,021 |\n| 1876 | 0271D0601876 | 28.616 | 84.320 | Gandak | M(o) | 2.75 | 5,195 |\n| 1877 | 0271D0601877 | 28.613 | 84.317 | Gandak | M(o) | 1.20 | 5,210 |\n| 1878 | 0271D0601878 | 28.539 | 84.471 | Gandak | M(o) | 0.33 | 4,750 |\n| 1879 | 0271D0601879 | 28.538 | 84.481 | Gandak | M(o) | 0.80 | 5,054 |\n| 1880 | 0271D0701880 | 28.497 | 84.256 | Gandak | M(e) | 11.38 | 4,987 |\n| 1881 | 0271D0701881 | 28.492 | 84.276 | Gandak | M(o) | 0.64 | 4,988 |\n| 1882 | 0271D0701882 | 28.488 | 84.486 | Gandak | M(e) | 89.44 | 4,038 |\n| 1883 | 0271D0701883 | 28.462 | 84.262 | Gandak | M(e) | 1.76 | 4,589 |\n| 1884 | 0271D0701884 | 28.459 | 84.259 | Gandak | M(e) | 6.36 | 4,545 |\n| 1885 | 0271D0701885 | 28.459 | 84.477 | Gandak | E(o) | 1.40 | 4,690 |\n| 1886 | 0271D0701886 | 28.457 | 84.259 | Gandak | M(o) | 0.78 | 4,549 |\n| 1887 | 0271D0701887 | 28.457 | 84.252 | Gandak | M(o) | 1.30 | 4,579 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5435, "line_end": 5582, "token_count_estimate": 1562, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271D0501853", "0271D0501854", "0271D0601855", "0271D0601856", "0271D0601857", "0271D0601858", "0271D0601859", "0271D0601860", "0271D0601861", "0271D0601862", "0271D0601863", "0271D0601864", "0271D0601865", "0271D0601866", "0271D0601867", "0271D0601868", "0271D0601869", "0271D0601870", "0271D0601871", "0271D0601872", "0271D0601873", "0271D0601874", "0271D0601875", "0271D0601876", "0271D0601877", "0271D0601878", "0271D0601879", "0271D0701880", "0271D0701881", "0271D0701882", "0271D0701883", "0271D0701884", "0271D0701885", "0271D0701886", "0271D0701887"]}}
{"id": "691b2bff7009123f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1888 | 0271D0701888 | 28.453 | 84.254 | Gandak | M(l) | 4.84 | 4,571 |\n| 1889 | 0271D0701889 | 28.441 | 84.290 | Gandak | E(o) | 1.51 | 4,025 |\n| 1890 | 0271D0901890 | 28.842 | 84.702 | Gandak | M(o) | 0.33 | 5,511 |\n| 1891 | 0271D0901891 | 28.834 | 84.700 | Gandak | M(o) | 1.06 | 5,392 |\n| 1892 | 0271D0901892 | 28.789 | 84.682 | Gandak | M(o) | 0.82 | 5,650 |\n| 1893 | 0271D1001893 | 28.747 | 84.600 | Gandak | M(e) | 8.45 | 5,005 |\n| 1894 | 0271D1001894 | 28.715 | 84.568 | Gandak | M(o) | 0.29 | 4,802 |\n| 1895 | 0271D1001895 | 28.707 | 84.579 | Gandak | M(o) | 0.72 | 4,751 |\n| 1896 | 0271D1001896 | 28.706 | 84.598 | Gandak | I(s) | 1.13 | 4,582 |\n| 1897 | 0271D1001897 | 28.705 | 84.598 | Gandak | I(s) | 1.25 | 4,589 |\n| 1898 | 0271D1001898 | 28.704 | 84.613 | Gandak | I(s) | 0.30 | 4,494 |\n| 1899 | 0271D1001899 | 28.701 | 84.607 | Gandak | I(s) | 0.56 | 4,548 |\n| 1900 | 0271D1001900 | 28.698 | 84.625 | Gandak | M(o) | 0.28 | 4,474 |\n| 1901 | 0271D1001901 | 28.677 | 84.532 | Gandak | M(o) | 0.42 | 5,167 |\n| 1902 | 0271D1001902 | 28.666 | 84.528 | Gandak | M(o) | 1.00 | 5,067 |\n| 1903 | 0271D1001903 | 28.664 | 84.558 | Gandak | M(l) | 1.10 | 4,736 |\n| 1904 | 0271D1001904 | 28.662 | 84.527 | Gandak | I(s) | 2.43 | 5,072 |\n| 1905 | 0271D1001905 | 28.661 | 84.546 | Gandak | I(s) | 0.95 | 4,893 |\n| 1906 | 0271D1001906 | 28.652 | 84.730 | Gandak | E(o) | 0.31 | 5,299 |\n| 1907 | 0271D1001907 | 28.638 | 84.730 | Gandak | M(o) | 2.07 | 5,398 |\n| 1908 | 0271D1001908 | 28.636 | 84.733 | Gandak | M(o) | 0.42 | 5,402 |\n| 1909 | 0271D1001909 | 28.596 | 84.629 | Gandak | M(e) | 22.25 | 3,632 |\n| 1910 | 0271D1001910 | 28.561 | 84.651 | Gandak | M(l) | 0.77 | 3,927 |\n| 1911 | 0271D1001911 | 28.559 | 84.653 | Gandak | M(o) | 0.89 | 3,881 |\n| 1912 | 0271D1001912 | 28.558 | 84.651 | Gandak | I(s) | 0.36 | 3,898 |\n| 1913 | 0271D1001913 | 28.555 | 84.639 | Gandak | E(o) | 0.38 | 4,033 |\n| 1914 | 0271D1001914 | 28.552 | 84.647 | Gandak | I(s) | 0.27 | 3,934 |\n| 1915 | 0271D1001915 | 28.545 | 84.637 | Gandak | I(s) | 0.27 | 3,999 |\n| 1916 | 0271D1001916 | 28.543 | 84.643 | Gandak | M(o) | 0.43 | 4,044 |\n| 1917 | 0271D1001917 | 28.542 | 84.633 | Gandak | I(s) | 0.25 | 4,015 |\n| 1918 | 0271D1001918 | 28.507 | 84.687 | Gandak | I(s) | 0.26 | 3,719 |\n| 1919 | 0271D1001919 | 28.507 | 84.686 | Gandak | I(s) | 0.28 | 3,725 |\n| 1920 | 0271D1101920 | 28.497 | 84.675 | Gandak | I(s) | 0.47 | 3,826 |\n| 1921 | 0271D1101921 | 28.494 | 84.733 | Gandak | M(o) | 1.96 | 4,721 |\n| 1922 | 0271D1101922 | 28.426 | 84.746 | Gandak | I(s) | 0.45 | 3,473 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5435, "line_end": 5582, "token_count_estimate": 1547, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271D0701888", "0271D0701889", "0271D0901890", "0271D0901891", "0271D0901892", "0271D1001893", "0271D1001894", "0271D1001895", "0271D1001896", "0271D1001897", "0271D1001898", "0271D1001899", "0271D1001900", "0271D1001901", "0271D1001902", "0271D1001903", "0271D1001904", "0271D1001905", "0271D1001906", "0271D1001907", "0271D1001908", "0271D1001909", "0271D1001910", "0271D1001911", "0271D1001912", "0271D1001913", "0271D1001914", "0271D1001915", "0271D1001916", "0271D1001917", "0271D1001918", "0271D1001919", "0271D1101920", "0271D1101921", "0271D1101922"]}}
{"id": "31eeb1519b45959d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1923 | 0271D1101923 | 28.398 | 84.729 | Gandak | M(o) | 0.80 | 3,795 |\n| 1924 | 0271D1101924 | 28.397 | 84.726 | Gandak | E(o) | 0.27 | 3,796 |\n| 1925 | 0271D1101925 | 28.386 | 84.720 | Gandak | I(s) | 0.41 | 3,949 |\n| 1926 | 0271D1101926 | 28.383 | 84.581 | Gandak | E(c) | 1.21 | 4,589 |\n| 1927 | 0271D1101927 | 28.378 | 84.580 | Gandak | E(o) | 0.45 | 4,483 |\n| 1928 | 0271D1101928 | 28.375 | 84.580 | Gandak | E(o) | 1.22 | 4,458 |\n| 1929 | 0271D1101929 | 28.372 | 84.579 | Gandak | E(o) | 11.03 | 4,430 |\n| 1930 | 0271D1101930 | 28.361 | 84.736 | Gandak | M(o) | 0.56 | 4,895 |\n| 1931 | 0271D1101931 | 28.349 | 84.633 | Gandak | E(o) | 0.48 | 4,283 |\n| 1932 | 0271D1101932 | 28.336 | 84.717 | Gandak | E(o) | 3.58 | 4,106 |\n| 1933 | 0271D1101933 | 28.335 | 84.678 | Gandak | M(o) | 0.90 | 4,845 |\n| 1934 | 0271D1301934 | 28.925 | 84.989 | Gandak | M(o) | 0.60 | 5,573 |\n| 1935 | 0271D1301935 | 28.921 | 84.990 | Gandak | E(o) | 0.32 | 5,508 |\n| 1936 | 0271D1301936 | 28.869 | 84.801 | Gandak | E(o) | 0.30 | 5,559 |\n| 1937 | 0271D1301937 | 28.860 | 84.783 | Gandak | M(o) | 1.83 | 5,352 |\n| 1938 | 0271D1301938 | 28.835 | 84.797 | Gandak | M(o) | 1.91 | 5,197 |\n| 1939 | 0271D1301939 | 28.830 | 84.843 | Gandak | M(o) | 2.31 | 5,425 |\n| 1940 | 0271D1301940 | 28.826 | 84.851 | Gandak | M(o) | 3.61 | 5,333 |\n| 1941 | 0271D1301941 | 28.824 | 84.859 | Gandak | M(o) | 0.26 | 5,475 |\n| 1942 | 0271D1301942 | 28.821 | 84.875 | Gandak | M(o) | 1.03 | 5,217 |\n| 1943 | 0271D1301943 | 28.820 | 84.871 | Gandak | M(o) | 1.06 | 5,294 |\n| 1944 | 0271D1301944 | 28.772 | 84.899 | Gandak | M(o) | 0.60 | 5,262 |\n| 1945 | 0271D1401945 | 28.670 | 84.981 | Gandak | E(o) | 0.26 | 4,151 |\n| 1946 | 0271D1401946 | 28.642 | 84.778 | Gandak | M(o) | 0.76 | 5,034 |\n| 1947 | 0271D1401947 | 28.640 | 84.789 | Gandak | M(o) | 3.54 | 5,181 |\n| 1948 | 0271D1401948 | 28.634 | 84.752 | Gandak | M(o) | 1.84 | 4,795 |\n| 1949 | 0271D1401949 | 28.631 | 84.994 | Gandak | M(o) | 0.31 | 5,111 |\n| 1950 | 0271D1401950 | 28.621 | 84.787 | Gandak | M(o) | 1.33 | 5,161 |\n| 1951 | 0271D1401951 | 28.621 | 84.792 | Gandak | M(o) | 2.32 | 5,126 |\n| 1952 | 0271D1401952 | 28.617 | 84.912 | Gandak | M(e) | 10.08 | 4,683 |\n| 1953 | 0271D1401953 | 28.613 | 84.922 | Gandak | M(e) | 3.57 | 4,863 |\n| 1954 | 0271D1401954 | 28.607 | 84.913 | Gandak | I(s) | 0.60 | 4,892 |\n| 1955 | 0271D1401955 | 28.606 | 84.967 | Gandak | I(s) | 0.30 | 4,761 |\n| 1956 | 0271D1401956 | 28.605 | 84.910 | Gandak | I(s) | 0.28 | 4,916 |\n| 1957 | 0271D1401957 | 28.601 | 84.787 | Gandak | M(o) | 0.52 | 5,148 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5435, "line_end": 5582, "token_count_estimate": 1551, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271D1101923", "0271D1101924", "0271D1101925", "0271D1101926", "0271D1101927", "0271D1101928", "0271D1101929", "0271D1101930", "0271D1101931", "0271D1101932", "0271D1101933", "0271D1301934", "0271D1301935", "0271D1301936", "0271D1301937", "0271D1301938", "0271D1301939", "0271D1301940", "0271D1301941", "0271D1301942", "0271D1301943", "0271D1301944", "0271D1401945", "0271D1401946", "0271D1401947", "0271D1401948", "0271D1401949", "0271D1401950", "0271D1401951", "0271D1401952", "0271D1401953", "0271D1401954", "0271D1401955", "0271D1401956", "0271D1401957"]}}
{"id": "18185c741b2df382", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1958 | 0271D1401958 | 28.600 | 84.874 | Gandak | M(o) | 0.88 | 4,872 |\n| 1959 | 0271D1401959 | 28.505 | 84.802 | Gandak | E(o) | 21.26 | 3,684 |\n| 1960 | 0271D1501960 | 28.489 | 84.799 | Gandak | E(o) | 1.89 | 4,375 |\n| 1961 | 0271D1501961 | 28.424 | 84.755 | Gandak | I(s) | 0.59 | 3,426 |\n| 1962 | 0271D1501962 | 28.380 | 84.780 | Gandak | E(c) | 16.80 | 4,656 |\n| 1963 | 0271D1501963 | 28.341 | 84.757 | Gandak | E(o) | 0.63 | 3,958 |\n| 1964 | 0271D1501964 | 28.329 | 84.806 | Gandak | E(o) | 0.28 | 4,234 |\n| 1965 | 0271D1501965 | 28.308 | 84.755 | Gandak | E(o) | 0.60 | 3,860 |\n| 1966 | 0271H0101966 | 28.985 | 85.169 | Gandak | E(o) | 0.83 | 5,563 |\n| 1967 | 0271H0101967 | 28.979 | 85.161 | Gandak | E(o) | 3.28 | 5,432 |\n| 1968 | 0271H0101968 | 28.973 | 85.163 | Gandak | E(o) | 0.51 | 5,358 |\n| 1969 | 0271H0101969 | 28.970 | 85.055 | Gandak | E(o) | 0.90 | 5,615 |\n| 1970 | 0271H0101970 | 28.968 | 85.163 | Gandak | E(o) | 1.52 | 5,310 |\n| 1971 | 0271H0101971 | 28.968 | 85.007 | Gandak | E(o) | 0.42 | 5,749 |\n| 1972 | 0271H0101972 | 28.961 | 85.084 | Gandak | E(o) | 17.03 | 5,388 |\n| 1973 | 0271H0101973 | 28.961 | 85.040 | Gandak | E(o) | 8.10 | 5,584 |\n| 1974 | 0271H0101974 | 28.959 | 85.004 | Gandak | E(o) | 1.11 | 5,585 |\n| 1975 | 0271H0101975 | 28.959 | 85.089 | Gandak | E(o) | 3.84 | 5,370 |\n| 1976 | 0271H0101976 | 28.956 | 85.009 | Gandak | E(o) | 0.88 | 5,566 |\n| 1977 | 0271H0101977 | 28.955 | 85.081 | Gandak | M(o) | 0.93 | 5,505 |\n| 1978 | 0271H0101978 | 28.954 | 85.083 | Gandak | M(o) | 1.92 | 5,521 |\n| 1979 | 0271H0101979 | 28.954 | 85.014 | Gandak | E(o) | 0.48 | 5,507 |\n| 1980 | 0271H0101980 | 28.948 | 85.094 | Gandak | E(o) | 0.29 | 5,589 |\n| 1981 | 0271H0101981 | 28.945 | 85.045 | Gandak | E(o) | 6.64 | 5,411 |\n| 1982 | 0271H0101982 | 28.937 | 85.044 | Gandak | E(o) | 0.60 | 5,408 |\n| 1983 | 0271H0101983 | 28.936 | 85.043 | Gandak | E(o) | 0.95 | 5,405 |\n| 1984 | 0271H0101984 | 28.935 | 85.048 | Gandak | E(o) | 0.40 | 5,502 |\n| 1985 | 0271H0101985 | 28.935 | 85.033 | Gandak | E(o) | 4.39 | 5,312 |\n| 1986 | 0271H0101986 | 28.931 | 85.001 | Gandak | M(o) | 1.80 | 5,476 |\n| 1987 | 0271H0101987 | 28.931 | 85.086 | Gandak | M(o) | 0.52 | 5,422 |\n| 1988 | 0271H0101988 | 28.927 | 85.085 | Gandak | M(o) | 1.63 | 5,482 |\n| 1989 | 0271H0101989 | 28.896 | 85.074 | Gandak | E(o) | 5.36 | 5,229 |\n| 1990 | 0271H0101990 | 28.794 | 85.037 | Gandak | M(o) | 3.30 | 5,388 |\n| 1991 | 0271H0101991 | 28.790 | 85.050 | Gandak | M(o) | 5.48 | 5,223 |\n| 1992 | 0271H0101992 | 28.787 | 85.047 | Gandak | M(o) | 1.86 | 5,267 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5435, "line_end": 5582, "token_count_estimate": 1533, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271D1401958", "0271D1401959", "0271D1501960", "0271D1501961", "0271D1501962", "0271D1501963", "0271D1501964", "0271D1501965", "0271H0101966", "0271H0101967", "0271H0101968", "0271H0101969", "0271H0101970", "0271H0101971", "0271H0101972", "0271H0101973", "0271H0101974", "0271H0101975", "0271H0101976", "0271H0101977", "0271H0101978", "0271H0101979", "0271H0101980", "0271H0101981", "0271H0101982", "0271H0101983", "0271H0101984", "0271H0101985", "0271H0101986", "0271H0101987", "0271H0101988", "0271H0101989", "0271H0101990", "0271H0101991", "0271H0101992"]}}
{"id": "0251ad3f8f57f523", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1993 | 0271H0101993 | 28.781 | 85.083 | Gandak | M(o) | 1.15 | 5,251 |\n| 1994 | 0271H0101994 | 28.778 | 85.081 | Gandak | M(o) | 0.37 | 5,334 |\n| 1995 | 0271H0101995 | 28.777 | 85.127 | Gandak | M(o) | 0.79 | 5,495 |\n| 1996 | 0271H0101996 | 28.776 | 85.122 | Gandak | M(o) | 5.68 | 5,445 |\n| 1997 | 0271H0101997 | 28.776 | 85.080 | Gandak | M(o) | 0.88 | 5,385 |\n| 1998 | 0271H0101998 | 28.775 | 85.150 | Gandak | M(o) | 0.38 | 5,514 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 5435, "line_end": 5582, "token_count_estimate": 340, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["0271H0101993", "0271H0101994", "0271H0101995", "0271H0101996", "0271H0101997", "0271H0101998"]}}
{"id": "0d4e46f26b3f7564", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 5583, "line_end": 5594, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6610318408c4f3d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2291 | 0271H1102291 | 28.267 | 85.680 | Gandak | E(o) | 0.28 | 5,216 |\n| 2292 | 0271H1102292 | 28.263 | 85.697 | Gandak | E(o) | 0.27 | 5,279 |\n| 2293 | 0271H1102293 | 28.262 | 85.675 | Gandak | E(o) | 0.76 | 5,102 |\n| 2294 | 0271H1102294 | 28.261 | 85.681 | Gandak | M(o) | 0.35 | 5,148 |\n| 2295 | 0271H1102295 | 28.259 | 85.696 | Gandak | E(o) | 0.51 | 5,285 |\n| 2296 | 0271H1202296 | 28.245 | 85.715 | Gandak | I(s) | 1.80 | 4,641 |\n| 2297 | 0271H1202297 | 28.242 | 85.704 | Gandak | I(s) | 0.28 | 4,601 |\n| 2298 | 0271H1202298 | 28.241 | 85.708 | Gandak | I(s) | 0.59 | 4,598 |\n| 2299 | 0271H1202299 | 28.236 | 85.639 | Gandak | I(s) | 0.30 | 4,593 |\n| 2300 | 0271H1202300 | 28.236 | 85.611 | Gandak | M(o) | 0.64 | 5,170 |\n| 2301 | 0271H1202301 | 28.235 | 85.700 | Gandak | I(s) | 0.32 | 4,533 |\n| 2302 | 0271H1202302 | 28.233 | 85.611 | Gandak | M(o) | 2.93 | 5,083 |\n| 2303 | 0271H1202303 | 28.231 | 85.617 | Gandak | M(o) | 0.34 | 5,196 |\n| 2304 | 0271H1202304 | 28.230 | 85.645 | Gandak | I(s) | 0.37 | 4,506 |\n| 2305 | 0271H1202305 | 28.229 | 85.621 | Gandak | M(o) | 0.29 | 5,195 |\n| 2306 | 0271H1202306 | 28.219 | 85.562 | Gandak | M(e) | 4.48 | 3,985 |\n| 2307 | 0271H1202307 | 28.189 | 85.723 | Gandak | I(s) | 0.28 | 4,764 |\n| 2308 | 0271H1202308 | 28.188 | 85.749 | Gandak | I(s) | 0.26 | 4,969 |\n| 2309 | 0271H1202309 | 28.186 | 85.744 | Gandak | I(s) | 0.42 | 4,936 |\n| 2310 | 0271H1202310 | 28.183 | 85.704 | Gandak | M(l) | 0.44 | 4,725 |\n| 2311 | 0271H1202311 | 28.180 | 85.695 | Gandak | M(o) | 0.66 | 4,828 |\n| 2312 | 0271H1202312 | 28.179 | 85.698 | Gandak | M(o) | 5.74 | 4,748 |\n| 2313 | 0271H1202313 | 28.178 | 85.694 | Gandak | M(o) | 0.26 | 4,850 |\n| 2314 | 0271H1202314 | 28.173 | 85.512 | Gandak | M(o) | 0.68 | 5,078 |\n| 2315 | 0271H1202315 | 28.173 | 85.524 | Gandak | M(o) | 0.57 | 4,965 |\n| 2316 | 0271H1202316 | 28.171 | 85.601 | Gandak | M(o) | 0.81 | 5,037 |\n| 2317 | 0271H1202317 | 28.168 | 85.520 | Gandak | M(o) | 0.33 | 5,143 |\n| 2318 | 0271H1202318 | 28.163 | 85.630 | Gandak | M(o) | 13.05 | 4,986 |\n| 2319 | 0271H1202319 | 28.159 | 85.748 | Kosi | M(o) | 0.81 | 4,961 |\n| 2320 | 0271H1202320 | 28.154 | 85.739 | Kosi | M(e) | 0.70 | 4,777 |\n| 2321 | 0271H1202321 | 28.153 | 85.741 | Kosi | M(l) | 0.33 | 4,739 |\n| 2322 | 0271H1202322 | 28.140 | 85.526 | Kosi | M(o) | 0.43 | 5,003 |\n| 2323 | 0271H1202323 | 28.136 | 85.557 | Kosi | M(o) | 0.50 | 4,869 |\n| 2324 | 0271H1202324 | 28.118 | 85.625 | Kosi | M(o) | 2.10 | 4,889 |\n| 2325 | 0271H1202325 | 28.114 | 85.635 | Kosi | E(o) | 1.38 | 4,784 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5595, "line_end": 5669, "token_count_estimate": 1593, "basins": [], "subbasins": ["Gandak", "Kosi"], "countries": [], "lake_ids": ["0271H1102291", "0271H1102292", "0271H1102293", "0271H1102294", "0271H1102295", "0271H1202296", "0271H1202297", "0271H1202298", "0271H1202299", "0271H1202300", "0271H1202301", "0271H1202302", "0271H1202303", "0271H1202304", "0271H1202305", "0271H1202306", "0271H1202307", "0271H1202308", "0271H1202309", "0271H1202310", "0271H1202311", "0271H1202312", "0271H1202313", "0271H1202314", "0271H1202315", "0271H1202316", "0271H1202317", "0271H1202318", "0271H1202319", "0271H1202320", "0271H1202321", "0271H1202322", "0271H1202323", "0271H1202324", "0271H1202325"]}}
{"id": "961172d80f7e322d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2326 | 0271H1202326 | 28.113 | 85.729 | Kosi | M(o) | 0.33 | 4,248 |\n| 2327 | 0271H1202327 | 28.106 | 85.536 | Kosi | E(o) | 0.65 | 4,310 |\n| 2328 | 0271H1202328 | 28.105 | 85.620 | Kosi | M(o) | 1.46 | 4,800 |\n| 2329 | 0271H1202329 | 28.103 | 85.614 | Kosi | M(o) | 1.32 | 4,591 |\n| 2330 | 0271H1202330 | 28.082 | 85.507 | Kosi | E(o) | 0.87 | 4,456 |\n| 2331 | 0271H1202331 | 28.042 | 85.717 | Kosi | O | 2.66 | 4,064 |\n| 2332 | 0271H1202332 | 28.042 | 85.714 | Kosi | O | 0.25 | 4,050 |\n| 2333 | 0271H1202333 | 28.041 | 85.715 | Kosi | O | 0.30 | 4,048 |\n| 2334 | 0271H1202334 | 28.040 | 85.717 | Kosi | O | 0.87 | 4,052 |\n| 2335 | 0271H1302335 | 28.947 | 85.784 | Kosi | E(o) | 0.74 | 5,648 |\n| 2336 | 0271H1302336 | 28.931 | 85.773 | Kosi | E(o) | 1.29 | 5,597 |\n| 2337 | 0271H1502337 | 28.486 | 85.949 | Kosi | E(o) | 0.36 | 5,244 |\n| 2338 | 0271H1502338 | 28.483 | 85.856 | Kosi | E(o) | 0.74 | 5,612 |\n| 2339 | 0271H1502339 | 28.475 | 85.826 | Kosi | M(o) | 0.78 | 5,737 |\n| 2340 | 0271H1502340 | 28.474 | 85.828 | Kosi | M(o) | 0.66 | 5,732 |\n| 2341 | 0271H1502341 | 28.473 | 85.825 | Kosi | M(o) | 0.32 | 5,750 |\n| 2342 | 0271H1502342 | 28.468 | 85.848 | Kosi | E(o) | 1.61 | 5,705 |\n| 2343 | 0271H1502343 | 28.468 | 85.790 | Kosi | M(l) | 0.32 | 5,522 |\n| 2344 | 0271H1502344 | 28.467 | 85.788 | Kosi | M(o) | 0.64 | 5,511 |\n| 2345 | 0271H1502345 | 28.464 | 85.819 | Kosi | M(o) | 1.14 | 5,853 |\n| 2346 | 0271H1502346 | 28.452 | 85.783 | Kosi | M(o) | 0.33 | 5,552 |\n| 2347 | 0271H1502347 | 28.450 | 85.783 | Kosi | M(o) | 0.54 | 5,568 |\n| 2348 | 0271H1502348 | 28.446 | 85.794 | Kosi | M(o) | 1.64 | 5,796 |\n| 2349 | 0271H1502349 | 28.442 | 85.780 | Kosi | M(e) | 48.25 | 5,577 |\n| 2350 | 0271H1502350 | 28.441 | 85.751 | Kosi | E(o) | 0.37 | 5,865 |\n| 2351 | 0271H1502351 | 28.435 | 85.770 | Kosi | E(o) | 0.60 | 5,788 |\n| 2352 | 0271H1502352 | 28.431 | 85.821 | Kosi | E(o) | 0.37 | 5,726 |\n| 2353 | 0271H1502353 | 28.426 | 85.779 | Kosi | I(s) | 0.91 | 5,645 |\n| 2354 | 0271H1502354 | 28.413 | 85.856 | Kosi | M(o) | 3.63 | 5,581 |\n| 2355 | 0271H1502355 | 28.403 | 85.883 | Kosi | I(s) | 0.65 | 5,332 |\n| 2356 | 0271H1502356 | 28.399 | 85.864 | Kosi | I(s) | 0.86 | 5,432 |\n| 2357 | 0271H1502357 | 28.399 | 85.867 | Kosi | I(s) | 1.27 | 5,425 |\n| 2358 | 0271H1502358 | 28.399 | 85.872 | Kosi | I(s) | 0.26 | 5,412 |\n| 2359 | 0271H1502359 | 28.399 | 85.871 | Kosi | I(s) | 0.36 | 5,418 |\n| 2360 | 0271H1502360 | 28.398 | 85.866 | Kosi | I(s) | 0.75 | 5,422 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5595, "line_end": 5669, "token_count_estimate": 1585, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1202326", "0271H1202327", "0271H1202328", "0271H1202329", "0271H1202330", "0271H1202331", "0271H1202332", "0271H1202333", "0271H1202334", "0271H1302335", "0271H1302336", "0271H1502337", "0271H1502338", "0271H1502339", "0271H1502340", "0271H1502341", "0271H1502342", "0271H1502343", "0271H1502344", "0271H1502345", "0271H1502346", "0271H1502347", "0271H1502348", "0271H1502349", "0271H1502350", "0271H1502351", "0271H1502352", "0271H1502353", "0271H1502354", "0271H1502355", "0271H1502356", "0271H1502357", "0271H1502358", "0271H1502359", "0271H1502360"]}}
{"id": "cef1e0efece082cd", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2361 | 0271H1502361 | 28.397 | 85.814 | Kosi | I(s) | 0.35 | 5,744 |\n| 2362 | 0271H1502362 | 28.397 | 85.863 | Kosi | I(s) | 0.25 | 5,430 |\n| 2363 | 0271H1502363 | 28.396 | 85.814 | Kosi | I(s) | 0.40 | 5,741 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5595, "line_end": 5669, "token_count_estimate": 225, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1502361", "0271H1502362", "0271H1502363"]}}
{"id": "b956466affad3411", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2364 | 0271H1502364 | 28.395 | 85.810 | Kosi | I(s) | 0.42 | 5,764 |\n| 2365 | 0271H1502365 | 28.393 | 85.836 | Kosi | I(s) | 0.28 | 5,597 |\n| 2366 | 0271H1502366 | 28.393 | 85.821 | Kosi | I(s) | 0.35 | 5,706 |\n| 2367 | 0271H1502367 | 28.393 | 85.857 | Kosi | I(s) | 0.75 | 5,445 |\n| 2368 | 0271H1502368 | 28.392 | 85.834 | Kosi | I(s) | 0.27 | 5,609 |\n| 2369 | 0271H1502369 | 28.392 | 85.823 | Kosi | I(s) | 0.26 | 5,689 |\n| 2370 | 0271H1502370 | 28.392 | 85.838 | Kosi | I(s) | 0.48 | 5,590 |\n| 2371 | 0271H1502371 | 28.391 | 85.838 | Kosi | I(s) | 0.71 | 5,575 |\n| 2372 | 0271H1502372 | 28.391 | 85.841 | Kosi | I(s) | 0.68 | 5,565 |\n| 2373 | 0271H1502373 | 28.390 | 85.852 | Kosi | I(s) | 0.26 | 5,477 |\n| 2374 | 0271H1502374 | 28.390 | 85.885 | Kosi | E(o) | 1.34 | 5,412 |\n| 2375 | 0271H1502375 | 28.389 | 85.856 | Kosi | M(l) | 3.75 | 5,407 |\n| 2376 | 0271H1502376 | 28.388 | 85.858 | Kosi | E(o) | 0.78 | 5,406 |\n| 2377 | 0271H1502377 | 28.388 | 85.873 | Kosi | M(o) | 1.31 | 5,514 |\n| 2378 | 0271H1502378 | 28.385 | 85.863 | Kosi | M(o) | 1.32 | 5,500 |\n| 2379 | 0271H1502379 | 28.384 | 85.794 | Kosi | I(s) | 0.71 | 6,055 |\n| 2380 | 0271H1502380 | 28.382 | 85.792 | Kosi | I(s) | 0.67 | 5,939 |\n| 2381 | 0271H1502381 | 28.381 | 85.865 | Kosi | M(o) | 0.41 | 5,542 |\n| 2382 | 0271H1502382 | 28.380 | 85.870 | Kosi | M(o) | 0.56 | 5,537 |\n| 2383 | 0271H1502383 | 28.380 | 85.865 | Kosi | M(o) | 0.25 | 5,552 |\n| 2384 | 0271H1502384 | 28.380 | 85.866 | Kosi | M(o) | 0.33 | 5,543 |\n| 2385 | 0271H1502385 | 28.375 | 85.896 | Kosi | E(o) | 0.26 | 5,244 |\n| 2386 | 0271H1502386 | 28.375 | 85.888 | Kosi | E(o) | 0.50 | 5,269 |\n| 2387 | 0271H1502387 | 28.371 | 85.890 | Kosi | E(o) | 27.07 | 5,242 |\n| 2388 | 0271H1502388 | 28.369 | 85.890 | Kosi | E(o) | 1.58 | 5,242 |\n| 2389 | 0271H1502389 | 28.368 | 85.893 | Kosi | E(o) | 1.72 | 5,246 |\n| 2390 | 0271H1502390 | 28.366 | 85.911 | Kosi | M(o) | 0.72 | 5,078 |\n| 2391 | 0271H1502391 | 28.360 | 85.871 | Kosi | M(e) | 463.78 | 5,212 |\n| 2392 | 0271H1502392 | 28.360 | 85.902 | Kosi | M(o) | 1.24 | 5,182 |\n| 2393 | 0271H1502393 | 28.357 | 85.914 | Kosi | E(o) | 0.75 | 5,185 |\n| 2394 | 0271H1502394 | 28.354 | 85.910 | Kosi | E(o) | 1.59 | 5,196 |\n| 2395 | 0271H1502395 | 28.347 | 85.859 | Kosi | M(o) | 0.31 | 5,660 |\n| 2396 | 0271H1502396 | 28.344 | 85.861 | Kosi | M(o) | 0.39 | 5,443 |\n| 2397 | 0271H1502397 | 28.343 | 85.849 | Kosi | M(o) | 0.31 | 5,263 |\n| 2398 | 0271H1502398 | 28.341 | 85.879 | Kosi | E(o) | 1.61 | 5,178 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5671, "line_end": 5745, "token_count_estimate": 1588, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1502364", "0271H1502365", "0271H1502366", "0271H1502367", "0271H1502368", "0271H1502369", "0271H1502370", "0271H1502371", "0271H1502372", "0271H1502373", "0271H1502374", "0271H1502375", "0271H1502376", "0271H1502377", "0271H1502378", "0271H1502379", "0271H1502380", "0271H1502381", "0271H1502382", "0271H1502383", "0271H1502384", "0271H1502385", "0271H1502386", "0271H1502387", "0271H1502388", "0271H1502389", "0271H1502390", "0271H1502391", "0271H1502392", "0271H1502393", "0271H1502394", "0271H1502395", "0271H1502396", "0271H1502397", "0271H1502398"]}}
{"id": "9fdd1873314cea62", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2399 | 0271H1502399 | 28.338 | 85.879 | Kosi | E(o) | 1.26 | 5,172 |\n| 2400 | 0271H1502400 | 28.338 | 85.871 | Kosi | E(o) | 1.09 | 5,183 |\n| 2401 | 0271H1502401 | 28.333 | 85.913 | Kosi | E(o) | 2.38 | 5,325 |\n| 2402 | 0271H1502402 | 28.329 | 85.869 | Kosi | M(o) | 213.52 | 5,167 |\n| 2403 | 0271H1502403 | 28.323 | 85.908 | Kosi | M(o) | 14.56 | 5,387 |\n| 2404 | 0271H1502404 | 28.323 | 85.924 | Kosi | M(o) | 8.80 | 5,324 |\n| 2405 | 0271H1502405 | 28.322 | 85.838 | Kosi | M(e) | 540.35 | 5,067 |\n| 2406 | 0271H1502406 | 28.321 | 85.930 | Kosi | M(o) | 11.18 | 5,298 |\n| 2407 | 0271H1502407 | 28.320 | 85.909 | Kosi | M(o) | 0.28 | 5,467 |\n| 2408 | 0271H1502408 | 28.320 | 85.935 | Kosi | M(o) | 0.29 | 5,357 |\n| 2409 | 0271H1502409 | 28.320 | 85.903 | Kosi | M(o) | 5.20 | 5,436 |\n| 2410 | 0271H1502410 | 28.317 | 85.930 | Kosi | M(o) | 0.41 | 5,377 |\n| 2411 | 0271H1502411 | 28.316 | 85.951 | Kosi | M(o) | 6.03 | 5,230 |\n| 2412 | 0271H1502412 | 28.315 | 85.761 | Kosi | M(l) | 0.30 | 5,564 |\n| 2413 | 0271H1502413 | 28.315 | 85.985 | Kosi | E(o) | 0.25 | 5,075 |\n| 2414 | 0271H1502414 | 28.314 | 85.965 | Kosi | E(o) | 5.60 | 5,124 |\n| 2415 | 0271H1502415 | 28.314 | 85.771 | Kosi | M(o) | 1.95 | 5,612 |\n| 2416 | 0271H1502416 | 28.313 | 85.948 | Kosi | M(o) | 25.06 | 5,227 |\n| 2417 | 0271H1502417 | 28.311 | 85.775 | Kosi | M(o) | 0.83 | 5,603 |\n| 2418 | 0271H1502418 | 28.310 | 85.771 | Kosi | M(o) | 1.75 | 5,595 |\n| 2419 | 0271H1502419 | 28.309 | 85.871 | Kosi | E(o) | 1.79 | 5,281 |\n| 2420 | 0271H1502420 | 28.309 | 85.768 | Kosi | M(o) | 0.41 | 5,595 |\n| 2421 | 0271H1502421 | 28.309 | 85.770 | Kosi | M(o) | 0.71 | 5,596 |\n| 2422 | 0271H1502422 | 28.308 | 85.773 | Kosi | M(o) | 0.57 | 5,616 |\n| 2423 | 0271H1502423 | 28.308 | 85.759 | Kosi | I(s) | 1.06 | 5,489 |\n| 2424 | 0271H1502424 | 28.307 | 85.768 | Kosi | M(o) | 0.65 | 5,574 |\n| 2425 | 0271H1502425 | 28.305 | 85.767 | Kosi | M(o) | 1.95 | 5,574 |\n| 2426 | 0271H1502426 | 28.303 | 85.902 | Kosi | M(o) | 0.93 | 5,441 |\n| 2427 | 0271H1502427 | 28.302 | 85.875 | Kosi | E(o) | 1.44 | 5,273 |\n| 2428 | 0271H1502428 | 28.297 | 85.819 | Kosi | E(c) | 28.10 | 5,080 |\n| 2429 | 0271H1502429 | 28.294 | 85.868 | Kosi | E(o) | 0.30 | 5,213 |\n| 2430 | 0271H1502430 | 28.293 | 85.830 | Kosi | M(o) | 28.50 | 5,023 |\n| 2431 | 0271H1502431 | 28.289 | 85.787 | Kosi | M(o) | 0.66 | 5,448 |\n| 2432 | 0271H1502432 | 28.289 | 85.871 | Kosi | E(o) | 0.34 | 5,191 |\n| 2433 | 0271H1502433 | 28.288 | 85.894 | Kosi | E(o) | 1.00 | 5,245 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5671, "line_end": 5745, "token_count_estimate": 1605, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1502399", "0271H1502400", "0271H1502401", "0271H1502402", "0271H1502403", "0271H1502404", "0271H1502405", "0271H1502406", "0271H1502407", "0271H1502408", "0271H1502409", "0271H1502410", "0271H1502411", "0271H1502412", "0271H1502413", "0271H1502414", "0271H1502415", "0271H1502416", "0271H1502417", "0271H1502418", "0271H1502419", "0271H1502420", "0271H1502421", "0271H1502422", "0271H1502423", "0271H1502424", "0271H1502425", "0271H1502426", "0271H1502427", "0271H1502428", "0271H1502429", "0271H1502430", "0271H1502431", "0271H1502432", "0271H1502433"]}}
{"id": "33c0ac774a05f1f3", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2434 | 0271H1502434 | 28.287 | 85.784 | Kosi | M(o) | 6.18 | 5,426 |\n| 2435 | 0271H1502435 | 28.287 | 85.927 | Kosi | E(o) | 3.94 | 5,143 |\n| 2436 | 0271H1502436 | 28.286 | 85.827 | Kosi | M(o) | 7.15 | 5,043 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5671, "line_end": 5745, "token_count_estimate": 226, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1502434", "0271H1502435", "0271H1502436"]}}
{"id": "269ec05069f8e582", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 5746, "line_end": 5752, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "65236b482971e015", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2437 | 0271H1502437 | 28.286 | 85.929 | Kosi | E(o) | 0.25 | 5,144 |\n| 2438 | 0271H1502438 | 28.285 | 85.930 | Kosi | E(o) | 0.66 | 5,141 |\n| 2439 | 0271H1502439 | 28.285 | 85.839 | Kosi | M(o) | 6.50 | 4,973 |\n| 2440 | 0271H1502440 | 28.284 | 85.972 | Kosi | M(o) | 0.98 | 5,251 |\n| 2441 | 0271H1502441 | 28.281 | 85.934 | Kosi | E(o) | 1.04 | 5,123 |\n| 2442 | 0271H1502442 | 28.281 | 85.760 | Kosi | I(s) | 0.62 | 5,249 |\n| 2443 | 0271H1502443 | 28.281 | 85.757 | Kosi | I(s) | 1.06 | 5,229 |\n| 2444 | 0271H1502444 | 28.279 | 85.751 | Kosi | M(o) | 3.52 | 5,313 |\n| 2445 | 0271H1502445 | 28.277 | 85.870 | Kosi | E(o) | 1.24 | 5,196 |\n| 2446 | 0271H1502446 | 28.277 | 85.926 | Kosi | E(o) | 1.06 | 5,278 |\n| 2447 | 0271H1502447 | 28.276 | 85.840 | Kosi | O | 1.60 | 4,966 |\n| 2448 | 0271H1502448 | 28.275 | 85.802 | Kosi | M(o) | 0.88 | 5,457 |\n| 2449 | 0271H1502449 | 28.275 | 85.798 | Kosi | M(o) | 1.92 | 5,379 |\n| 2450 | 0271H1502450 | 28.272 | 85.931 | Kosi | E(o) | 4.37 | 5,302 |\n| 2451 | 0271H1502451 | 28.271 | 85.783 | Kosi | M(e) | 7.46 | 5,321 |\n| 2452 | 0271H1502452 | 28.270 | 85.761 | Kosi | I(s) | 0.47 | 5,221 |\n| 2453 | 0271H1502453 | 28.270 | 85.806 | Kosi | M(o) | 1.46 | 5,387 |\n| 2454 | 0271H1502454 | 28.268 | 85.897 | Kosi | E(o) | 4.04 | 5,252 |\n| 2455 | 0271H1502455 | 28.267 | 85.872 | Kosi | E(o) | 0.97 | 5,295 |\n| 2456 | 0271H1502456 | 28.265 | 85.797 | Kosi | E(o) | 4.12 | 5,273 |\n| 2457 | 0271H1502457 | 28.264 | 85.760 | Kosi | I(s) | 0.61 | 5,211 |\n| 2458 | 0271H1502458 | 28.264 | 85.770 | Kosi | I(s) | 0.34 | 5,152 |\n| 2459 | 0271H1502459 | 28.261 | 85.878 | Kosi | E(o) | 2.62 | 5,213 |\n| 2460 | 0271H1502460 | 28.261 | 85.915 | Kosi | O | 15.97 | 5,106 |\n| 2461 | 0271H1502461 | 28.259 | 85.781 | Kosi | I(s) | 0.71 | 5,045 |\n| 2462 | 0271H1502462 | 28.259 | 85.837 | Kosi | E(o) | 1.88 | 4,976 |\n| 2463 | 0271H1502463 | 28.258 | 85.782 | Kosi | I(s) | 0.55 | 5,038 |\n| 2464 | 0271H1502464 | 28.258 | 85.918 | Kosi | O | 1.51 | 5,107 |\n| 2465 | 0271H1502465 | 28.257 | 85.784 | Kosi | I(s) | 2.30 | 5,028 |\n| 2466 | 0271H1502466 | 28.255 | 85.917 | Kosi | O | 3.92 | 5,106 |\n| 2467 | 0271H1502467 | 28.254 | 85.788 | Kosi | I(s) | 1.40 | 5,019 |\n| 2468 | 0271H1502468 | 28.253 | 85.790 | Kosi | I(s) | 0.68 | 5,009 |\n| 2469 | 0271H1602469 | 28.248 | 85.943 | Kosi | E(o) | 0.30 | 5,291 |\n| 2470 | 0271H1602470 | 28.247 | 85.883 | Kosi | E(o) | 1.04 | 5,122 |\n| 2471 | 0271H1602471 | 28.245 | 85.826 | Kosi | M(o) | 0.42 | 5,068 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5753, "line_end": 5827, "token_count_estimate": 1607, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1502437", "0271H1502438", "0271H1502439", "0271H1502440", "0271H1502441", "0271H1502442", "0271H1502443", "0271H1502444", "0271H1502445", "0271H1502446", "0271H1502447", "0271H1502448", "0271H1502449", "0271H1502450", "0271H1502451", "0271H1502452", "0271H1502453", "0271H1502454", "0271H1502455", "0271H1502456", "0271H1502457", "0271H1502458", "0271H1502459", "0271H1502460", "0271H1502461", "0271H1502462", "0271H1502463", "0271H1502464", "0271H1502465", "0271H1502466", "0271H1502467", "0271H1502468", "0271H1602469", "0271H1602470", "0271H1602471"]}}
{"id": "50f33bb0abc08f7d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2472 | 0271H1602472 | 28.244 | 85.817 | Kosi | E(o) | 3.29 | 5,089 |\n| 2473 | 0271H1602473 | 28.242 | 85.922 | Kosi | E(o) | 0.97 | 5,175 |\n| 2474 | 0271H1602474 | 28.242 | 85.774 | Kosi | I(s) | 0.42 | 5,096 |\n| 2475 | 0271H1602475 | 28.241 | 85.766 | Kosi | I(s) | 1.41 | 5,143 |\n| 2476 | 0271H1602476 | 28.241 | 85.768 | Kosi | I(s) | 0.26 | 5,124 |\n| 2477 | 0271H1602477 | 28.241 | 85.830 | Kosi | E(o) | 0.76 | 5,002 |\n| 2478 | 0271H1602478 | 28.240 | 85.880 | Kosi | M(o) | 1.47 | 5,193 |\n| 2479 | 0271H1602479 | 28.240 | 85.778 | Kosi | I(s) | 4.41 | 5,085 |\n| 2480 | 0271H1602480 | 28.239 | 85.773 | Kosi | I(s) | 0.32 | 5,107 |\n| 2481 | 0271H1602481 | 28.239 | 85.818 | Kosi | E(o) | 4.05 | 5,032 |\n| 2482 | 0271H1602482 | 28.238 | 85.831 | Kosi | E(o) | 0.58 | 4,999 |\n| 2483 | 0271H1602483 | 28.237 | 85.775 | Kosi | I(s) | 0.68 | 5,091 |\n| 2484 | 0271H1602484 | 28.235 | 85.778 | Kosi | I(s) | 0.48 | 5,083 |\n| 2485 | 0271H1602485 | 28.235 | 85.922 | Kosi | E(o) | 1.14 | 5,316 |\n| 2486 | 0271H1602486 | 28.235 | 85.776 | Kosi | I(s) | 0.26 | 5,093 |\n| 2487 | 0271H1602487 | 28.226 | 85.801 | Kosi | M(o) | 0.25 | 4,841 |\n| 2488 | 0271H1602488 | 28.226 | 85.800 | Kosi | M(o) | 0.25 | 4,847 |\n| 2489 | 0271H1602489 | 28.224 | 85.804 | Kosi | M(o) | 4.64 | 4,745 |\n| 2490 | 0271H1602490 | 28.223 | 85.808 | Kosi | I(s) | 0.44 | 4,784 |\n| 2491 | 0271H1602491 | 28.221 | 85.808 | Kosi | I(s) | 0.39 | 4,794 |\n| 2492 | 0271H1602492 | 28.220 | 85.812 | Kosi | I(s) | 0.42 | 4,786 |\n| 2493 | 0271H1602493 | 28.211 | 85.847 | Kosi | M(e) | 61.34 | 4,374 |\n| 2494 | 0271H1602494 | 28.194 | 85.871 | Kosi | M(o) | 7.38 | 4,617 |\n| 2495 | 0271H1602495 | 28.193 | 85.879 | Kosi | E(o) | 3.52 | 4,623 |\n| 2496 | 0271H1602496 | 28.182 | 85.923 | Kosi | E(o) | 47.08 | 4,355 |\n| 2497 | 0271H1602497 | 28.171 | 85.891 | Kosi | M(e) | 0.78 | 4,425 |\n| 2498 | 0271H1602498 | 28.169 | 85.890 | Kosi | I(s) | 0.94 | 4,452 |\n| 2499 | 0271H1602499 | 28.168 | 85.866 | Kosi | M(o) | 3.90 | 4,686 |\n| 2500 | 0271H1602500 | 28.155 | 85.911 | Kosi | M(e) | 7.95 | 4,486 |\n| 2501 | 0271H1602501 | 28.151 | 85.905 | Kosi | I(s) | 12.60 | 4,495 |\n| 2502 | 0271H1602502 | 28.139 | 85.827 | Kosi | M(o) | 2.29 | 4,763 |\n| 2503 | 0271H1602503 | 28.139 | 85.919 | Kosi | M(o) | 10.15 | 4,865 |\n| 2504 | 0271H1602504 | 28.137 | 85.901 | Kosi | M(o) | 0.36 | 4,806 |\n| 2505 | 0271H1602505 | 28.137 | 85.788 | Kosi | M(o) | 2.54 | 4,574 |\n| 2506 | 0271H1602506 | 28.133 | 85.897 | Kosi | M(o) | 0.65 | 4,849 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5753, "line_end": 5827, "token_count_estimate": 1603, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1602472", "0271H1602473", "0271H1602474", "0271H1602475", "0271H1602476", "0271H1602477", "0271H1602478", "0271H1602479", "0271H1602480", "0271H1602481", "0271H1602482", "0271H1602483", "0271H1602484", "0271H1602485", "0271H1602486", "0271H1602487", "0271H1602488", "0271H1602489", "0271H1602490", "0271H1602491", "0271H1602492", "0271H1602493", "0271H1602494", "0271H1602495", "0271H1602496", "0271H1602497", "0271H1602498", "0271H1602499", "0271H1602500", "0271H1602501", "0271H1602502", "0271H1602503", "0271H1602504", "0271H1602505", "0271H1602506"]}}
{"id": "54b4a1d768cff040", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2507 | 0271H1602507 | 28.129 | 85.837 | Kosi | M(o) | 1.85 | 4,485 |\n| 2508 | 0271H1602508 | 28.128 | 85.898 | Kosi | M(o) | 0.34 | 4,885 |\n| 2509 | 0271H1602509 | 28.119 | 85.769 | Kosi | E(o) | 0.49 | 4,764 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5753, "line_end": 5827, "token_count_estimate": 223, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1602507", "0271H1602508", "0271H1602509"]}}
{"id": "410618e64312376f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2510 | 0271H1602510 | 28.113 | 85.767 | Kosi | E(o) | 0.26 | 4,719 |\n| 2511 | 0271H1602511 | 28.079 | 85.815 | Kosi | E(o) | 3.62 | 4,262 |\n| 2512 | 0271H1602512 | 28.073 | 85.854 | Kosi | E(o) | 0.46 | 4,452 |\n| 2513 | 0271H1602513 | 28.071 | 85.802 | Kosi | E(o) | 0.65 | 4,348 |\n| 2514 | 0271H1602514 | 28.066 | 85.944 | Kosi | E(o) | 5.68 | 4,529 |\n| 2515 | 0271H1602515 | 28.062 | 85.814 | Kosi | E(o) | 1.26 | 4,346 |\n| 2516 | 0271H1602516 | 28.061 | 85.936 | Kosi | E(o) | 1.58 | 4,643 |\n| 2517 | 0271H1602517 | 28.061 | 85.838 | Kosi | E(o) | 2.92 | 4,630 |\n| 2518 | 0271H1602518 | 28.055 | 85.828 | Kosi | E(o) | 0.91 | 4,521 |\n| 2519 | 0271H1602519 | 28.054 | 85.828 | Kosi | E(o) | 0.37 | 4,528 |\n| 2520 | 0271H1602520 | 28.053 | 85.837 | Kosi | E(o) | 3.02 | 4,383 |\n| 2521 | 0271H1602521 | 28.051 | 85.825 | Kosi | E(o) | 7.29 | 4,253 |\n| 2522 | 0271H1602522 | 28.049 | 85.848 | Kosi | E(o) | 1.17 | 4,258 |\n| 2523 | 0271H1602523 | 28.047 | 85.839 | Kosi | E(o) | 0.39 | 4,493 |\n| 2524 | 0271H1602524 | 28.046 | 85.927 | Kosi | M(o) | 0.56 | 4,661 |\n| 2525 | 0271H1602525 | 28.045 | 85.840 | Kosi | E(o) | 2.46 | 4,479 |\n| 2526 | 0271H1602526 | 28.037 | 85.944 | Kosi | O | 0.39 | 4,198 |\n| 2527 | 0271H1602527 | 28.034 | 85.879 | Kosi | E(o) | 2.13 | 4,253 |\n| 2528 | 0271H1602528 | 28.032 | 85.882 | Kosi | E(o) | 0.74 | 4,270 |\n| 2529 | 0271H1602529 | 28.032 | 85.944 | Kosi | O | 0.88 | 4,370 |\n| 2530 | 0271H1602530 | 28.027 | 85.891 | Kosi | E(o) | 0.51 | 4,418 |\n| 2531 | 0271H1602531 | 28.021 | 85.885 | Kosi | E(o) | 0.53 | 4,404 |\n| 2532 | 0271H1602532 | 28.020 | 85.869 | Kosi | E(o) | 1.23 | 4,201 |\n| 2533 | 0271H1602533 | 28.017 | 85.869 | Kosi | E(o) | 1.96 | 4,198 |\n| 2534 | 0271H1602534 | 28.007 | 85.866 | Kosi | E(o) | 1.41 | 4,001 |\n| 2535 | 0271K0802535 | 29.126 | 86.370 | Kosi | E(o) | 1.03 | 5,463 |\n| 2536 | 0271K0802536 | 29.116 | 86.352 | Kosi | E(o) | 1.53 | 5,322 |\n| 2537 | 0271K0802537 | 29.112 | 86.410 | Kosi | E(o) | 0.50 | 5,403 |\n| 2538 | 0271K0802538 | 29.104 | 86.379 | Kosi | E(o) | 0.35 | 5,280 |\n| 2539 | 0271K0802539 | 29.090 | 86.421 | Kosi | E(o) | 4.36 | 5,287 |\n| 2540 | 0271K0802540 | 29.079 | 86.422 | Kosi | E(o) | 1.95 | 5,231 |\n| 2541 | 0271L0102541 | 28.960 | 86.066 | Kosi | E(o) | 0.63 | 5,655 |\n| 2542 | 0271L0102542 | 28.953 | 86.075 | Kosi | E(o) | 1.90 | 5,443 |\n| 2543 | 0271L0102543 | 28.940 | 86.075 | Kosi | E(o) | 1.73 | 5,589 |\n| 2544 | 0271L0102544 | 28.938 | 86.074 | Kosi | E(o) | 0.38 | 5,602 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5829, "line_end": 5903, "token_count_estimate": 1608, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1602510", "0271H1602511", "0271H1602512", "0271H1602513", "0271H1602514", "0271H1602515", "0271H1602516", "0271H1602517", "0271H1602518", "0271H1602519", "0271H1602520", "0271H1602521", "0271H1602522", "0271H1602523", "0271H1602524", "0271H1602525", "0271H1602526", "0271H1602527", "0271H1602528", "0271H1602529", "0271H1602530", "0271H1602531", "0271H1602532", "0271H1602533", "0271H1602534", "0271K0802535", "0271K0802536", "0271K0802537", "0271K0802538", "0271K0802539", "0271K0802540", "0271L0102541", "0271L0102542", "0271L0102543", "0271L0102544"]}}
{"id": "0813a3b8747bf3c5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2545 | 0271L0102545 | 28.936 | 86.064 | Kosi | E(o) | 0.75 | 5,584 |\n| 2546 | 0271L0102546 | 28.899 | 86.011 | Kosi | E(o) | 1.76 | 5,628 |\n| 2547 | 0271L0102547 | 28.898 | 85.982 | Kosi | E(o) | 1.14 | 5,499 |\n| 2548 | 0271L0302548 | 28.364 | 86.249 | Kosi | M(o) | 0.49 | 5,679 |\n| 2549 | 0271L0302549 | 28.351 | 86.179 | Kosi | M(o) | 1.94 | 5,365 |\n| 2550 | 0271L0302550 | 28.349 | 86.182 | Kosi | M(o) | 1.63 | 5,365 |\n| 2551 | 0271L0302551 | 28.347 | 86.225 | Kosi | M(e) | 55.90 | 5,348 |\n| 2552 | 0271L0302552 | 28.345 | 86.185 | Kosi | M(o) | 0.60 | 5,415 |\n| 2553 | 0271L0302553 | 28.341 | 86.187 | Kosi | M(o) | 0.25 | 5,433 |\n| 2554 | 0271L0302554 | 28.340 | 86.191 | Kosi | M(o) | 0.43 | 5,430 |\n| 2555 | 0271L0302555 | 28.335 | 86.192 | Kosi | M(e) | 55.00 | 5,422 |\n| 2556 | 0271L0302556 | 28.321 | 86.158 | Kosi | M(o) | 22.44 | 5,549 |\n| 2557 | 0271L0302557 | 28.303 | 86.157 | Kosi | M(e) | 59.05 | 5,307 |\n| 2558 | 0271L0302558 | 28.301 | 86.153 | Kosi | E(o) | 0.27 | 5,393 |\n| 2559 | 0271L0302559 | 28.295 | 86.164 | Kosi | M(o) | 0.34 | 5,372 |\n| 2560 | 0271L0302560 | 28.295 | 86.151 | Kosi | M(e) | 16.51 | 5,346 |\n| 2561 | 0271L0302561 | 28.294 | 86.131 | Kosi | M(e) | 23.99 | 5,244 |\n| 2562 | 0271L0302562 | 28.292 | 86.158 | Kosi | M(o) | 0.69 | 5,410 |\n| 2563 | 0271L0302563 | 28.288 | 86.128 | Kosi | M(o) | 0.25 | 5,300 |\n| 2564 | 0271L0302564 | 28.286 | 86.132 | Kosi | M(o) | 0.34 | 5,275 |\n| 2565 | 0271L0302565 | 28.278 | 86.236 | Kosi | M(o) | 1.94 | 5,424 |\n| 2566 | 0271L0302566 | 28.276 | 86.178 | Kosi | M(o) | 0.78 | 5,436 |\n| 2567 | 0271L0302567 | 28.274 | 86.215 | Kosi | M(o) | 0.37 | 5,422 |\n| 2568 | 0271L0302568 | 28.273 | 86.156 | Kosi | M(o) | 2.86 | 5,417 |\n| 2569 | 0271L0302569 | 28.273 | 86.103 | Kosi | M(o) | 1.25 | 5,463 |\n| 2570 | 0271L0302570 | 28.272 | 86.180 | Kosi | M(o) | 6.71 | 5,404 |\n| 2571 | 0271L0302571 | 28.272 | 86.121 | Kosi | E(o) | 0.25 | 5,665 |\n| 2572 | 0271L0302572 | 28.270 | 86.170 | Kosi | M(o) | 0.61 | 5,532 |\n| 2573 | 0271L0302573 | 28.270 | 86.127 | Kosi | M(e) | 1.75 | 5,548 |\n| 2574 | 0271L0302574 | 28.270 | 86.187 | Kosi | M(e) | 12.64 | 5,335 |\n| 2575 | 0271L0302575 | 28.269 | 86.127 | Kosi | M(o) | 1.91 | 5,552 |\n| 2576 | 0271L0302576 | 28.268 | 86.197 | Kosi | M(o) | 0.93 | 5,454 |\n| 2577 | 0271L0302577 | 28.267 | 86.130 | Kosi | M(o) | 1.24 | 5,525 |\n| 2578 | 0271L0302578 | 28.266 | 86.217 | Kosi | M(o) | 2.97 | 5,410 |\n| 2579 | 0271L0302579 | 28.265 | 86.222 | Kosi | M(o) | 1.45 | 5,374 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5829, "line_end": 5903, "token_count_estimate": 1600, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0102545", "0271L0102546", "0271L0102547", "0271L0302548", "0271L0302549", "0271L0302550", "0271L0302551", "0271L0302552", "0271L0302553", "0271L0302554", "0271L0302555", "0271L0302556", "0271L0302557", "0271L0302558", "0271L0302559", "0271L0302560", "0271L0302561", "0271L0302562", "0271L0302563", "0271L0302564", "0271L0302565", "0271L0302566", "0271L0302567", "0271L0302568", "0271L0302569", "0271L0302570", "0271L0302571", "0271L0302572", "0271L0302573", "0271L0302574", "0271L0302575", "0271L0302576", "0271L0302577", "0271L0302578", "0271L0302579"]}}
{"id": "624d36c1556a8c6d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2580 | 0271L0302580 | 28.262 | 86.217 | Kosi | M(o) | 1.98 | 5,395 |\n| 2581 | 0271L0302581 | 28.262 | 86.213 | Kosi | M(o) | 0.42 | 5,541 |\n| 2582 | 0271L0302582 | 28.260 | 86.211 | Kosi | M(o) | 0.35 | 5,534 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5829, "line_end": 5903, "token_count_estimate": 223, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0302580", "0271L0302581", "0271L0302582"]}}
{"id": "ff1b40584e3b1503", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 5904, "line_end": 5911, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d75c26cd48a87559", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2583 | 0271L0302583 | 28.260 | 86.219 | Kosi | M(o) | 0.27 | 5,423 |\n| 2584 | 0271L0302584 | 28.260 | 86.213 | Kosi | M(o) | 1.93 | 5,501 |\n| 2585 | 0271L0302585 | 28.260 | 86.250 | Kosi | M(o) | 0.39 | 5,549 |\n| 2586 | 0271L0302586 | 28.259 | 86.234 | Kosi | I(s) | 0.36 | 5,174 |\n| 2587 | 0271L0302587 | 28.259 | 86.102 | Kosi | E(o) | 1.03 | 5,216 |\n| 2588 | 0271L0302588 | 28.257 | 86.151 | Kosi | M(o) | 2.22 | 5,359 |\n| 2589 | 0271L0302589 | 28.257 | 86.247 | Kosi | M(o) | 0.49 | 5,625 |\n| 2590 | 0271L0302590 | 28.256 | 86.231 | Kosi | I(s) | 0.67 | 5,167 |\n| 2591 | 0271L0302591 | 28.255 | 86.147 | Kosi | M(o) | 3.41 | 5,380 |\n| 2592 | 0271L0302592 | 28.255 | 86.216 | Kosi | M(o) | 1.36 | 5,470 |\n| 2593 | 0271L0302593 | 28.254 | 86.223 | Kosi | M(o) | 0.85 | 5,378 |\n| 2594 | 0271L0302594 | 28.254 | 86.141 | Kosi | M(o) | 5.36 | 5,394 |\n| 2595 | 0271L0302595 | 28.254 | 86.117 | Kosi | M(o) | 1.07 | 5,304 |\n| 2596 | 0271L0302596 | 28.253 | 86.103 | Kosi | M(e) | 14.98 | 5,189 |\n| 2597 | 0271L0302597 | 28.253 | 86.115 | Kosi | M(o) | 0.49 | 5,295 |\n| 2598 | 0271L0302598 | 28.253 | 86.236 | Kosi | I(s) | 0.42 | 5,141 |\n| 2599 | 0271L0302599 | 28.252 | 86.218 | Kosi | M(o) | 9.08 | 5,438 |\n| 2600 | 0271L0402600 | 28.249 | 86.150 | Kosi | M(o) | 13.16 | 5,331 |\n| 2601 | 0271L0402601 | 28.247 | 86.235 | Kosi | I(s) | 0.42 | 5,116 |\n| 2602 | 0271L0402602 | 28.247 | 86.088 | Kosi | M(o) | 1.52 | 5,593 |\n| 2603 | 0271L0402603 | 28.244 | 86.126 | Kosi | E(o) | 0.31 | 5,446 |\n| 2604 | 0271L0402604 | 28.244 | 86.227 | Kosi | M(e) | 2.91 | 5,281 |\n| 2605 | 0271L0402605 | 28.243 | 86.157 | Kosi | M(o) | 2.84 | 5,531 |\n| 2606 | 0271L0402606 | 28.243 | 86.196 | Kosi | M(e) | 17.69 | 5,338 |\n| 2607 | 0271L0402607 | 28.241 | 86.112 | Kosi | M(o) | 0.57 | 5,518 |\n| 2608 | 0271L0402608 | 28.241 | 86.128 | Kosi | M(o) | 0.76 | 5,401 |\n| 2609 | 0271L0402609 | 28.240 | 86.222 | Kosi | M(o) | 0.67 | 5,453 |\n| 2610 | 0271L0402610 | 28.239 | 86.158 | Kosi | M(o) | 1.05 | 5,460 |\n| 2611 | 0271L0402611 | 28.239 | 86.242 | Kosi | I(s) | 2.12 | 5,070 |\n| 2612 | 0271L0402612 | 28.238 | 86.195 | Kosi | M(o) | 0.53 | 5,501 |\n| 2613 | 0271L0402613 | 28.238 | 86.113 | Kosi | M(o) | 0.33 | 5,444 |\n| 2614 | 0271L0402614 | 28.237 | 86.112 | Kosi | M(o) | 0.40 | 5,474 |\n| 2615 | 0271L0402615 | 28.237 | 86.227 | Kosi | M(o) | 1.22 | 5,425 |\n| 2616 | 0271L0402616 | 28.236 | 86.115 | Kosi | M(o) | 0.53 | 5,419 |\n| 2617 | 0271L0402617 | 28.235 | 86.111 | Kosi | M(o) | 0.62 | 5,535 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5912, "line_end": 5986, "token_count_estimate": 1614, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0302583", "0271L0302584", "0271L0302585", "0271L0302586", "0271L0302587", "0271L0302588", "0271L0302589", "0271L0302590", "0271L0302591", "0271L0302592", "0271L0302593", "0271L0302594", "0271L0302595", "0271L0302596", "0271L0302597", "0271L0302598", "0271L0302599", "0271L0402600", "0271L0402601", "0271L0402602", "0271L0402603", "0271L0402604", "0271L0402605", "0271L0402606", "0271L0402607", "0271L0402608", "0271L0402609", "0271L0402610", "0271L0402611", "0271L0402612", "0271L0402613", "0271L0402614", "0271L0402615", "0271L0402616", "0271L0402617"]}}
{"id": "bf07c7ab656fd824", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2618 | 0271L0402618 | 28.233 | 86.116 | Kosi | M(o) | 0.28 | 5,420 |\n| 2619 | 0271L0402619 | 28.233 | 86.156 | Kosi | M(o) | 1.22 | 5,444 |\n| 2620 | 0271L0402620 | 28.231 | 86.148 | Kosi | M(o) | 1.80 | 5,450 |\n| 2621 | 0271L0402621 | 28.231 | 86.118 | Kosi | M(o) | 0.29 | 5,411 |\n| 2622 | 0271L0402622 | 28.230 | 86.146 | Kosi | M(o) | 1.72 | 5,450 |\n| 2623 | 0271L0402623 | 28.229 | 86.222 | Kosi | M(o) | 0.25 | 5,491 |\n| 2624 | 0271L0402624 | 28.228 | 86.220 | Kosi | M(o) | 4.19 | 5,508 |\n| 2625 | 0271L0402625 | 28.228 | 86.225 | Kosi | E(o) | 0.29 | 5,443 |\n| 2626 | 0271L0402626 | 28.228 | 86.204 | Kosi | M(o) | 5.14 | 5,446 |\n| 2627 | 0271L0402627 | 28.225 | 86.227 | Kosi | E(o) | 0.31 | 5,397 |\n| 2628 | 0271L0402628 | 28.225 | 86.149 | Kosi | M(o) | 0.85 | 5,404 |\n| 2629 | 0271L0402629 | 28.224 | 86.206 | Kosi | M(o) | 4.42 | 5,395 |\n| 2630 | 0271L0402630 | 28.223 | 86.235 | Kosi | E(o) | 0.32 | 5,245 |\n| 2631 | 0271L0402631 | 28.221 | 86.086 | Kosi | M(o) | 8.58 | 5,170 |\n| 2632 | 0271L0402632 | 28.216 | 86.235 | Kosi | M(o) | 0.35 | 5,328 |\n| 2633 | 0271L0402633 | 28.215 | 86.164 | Kosi | M(o) | 0.78 | 5,484 |\n| 2634 | 0271L0402634 | 28.214 | 86.071 | Kosi | M(o) | 0.36 | 5,201 |\n| 2635 | 0271L0402635 | 28.213 | 86.095 | Kosi | E(o) | 0.29 | 5,394 |\n| 2636 | 0271L0402636 | 28.213 | 86.227 | Kosi | M(o) | 0.64 | 5,436 |\n| 2637 | 0271L0402637 | 28.211 | 86.213 | Kosi | M(o) | 5.96 | 5,380 |\n| 2638 | 0271L0402638 | 28.210 | 86.164 | Kosi | E(o) | 0.30 | 5,440 |\n| 2639 | 0271L0402639 | 28.209 | 86.058 | Kosi | E(o) | 3.34 | 4,977 |\n| 2640 | 0271L0402640 | 28.208 | 86.078 | Kosi | E(o) | 0.58 | 5,338 |\n| 2641 | 0271L0402641 | 28.208 | 86.086 | Kosi | E(o) | 0.80 | 5,232 |\n| 2642 | 0271L0402642 | 28.207 | 86.217 | Kosi | M(o) | 0.53 | 5,454 |\n| 2643 | 0271L0402643 | 28.207 | 86.227 | Kosi | I(s) | 0.51 | 5,663 |\n| 2644 | 0271L0402644 | 28.206 | 86.239 | Kosi | M(o) | 1.51 | 5,453 |\n| 2645 | 0271L0402645 | 28.203 | 86.078 | Kosi | E(o) | 0.31 | 5,282 |\n| 2646 | 0271L0402646 | 28.202 | 86.241 | Kosi | M(o) | 0.59 | 5,365 |\n| 2647 | 0271L0402647 | 28.202 | 86.221 | Kosi | M(o) | 0.47 | 5,516 |\n| 2648 | 0271L0402648 | 28.201 | 86.163 | Kosi | M(o) | 2.61 | 5,101 |\n| 2649 | 0271L0402649 | 28.201 | 86.240 | Kosi | M(o) | 1.67 | 5,364 |\n| 2650 | 0271L0402650 | 28.200 | 86.219 | Kosi | M(o) | 0.65 | 5,473 |\n| 2651 | 0271L0402651 | 28.199 | 86.223 | Kosi | M(l) | 0.42 | 5,413 |\n| 2652 | 0271L0402652 | 28.197 | 86.183 | Kosi | M(o) | 1.69 | 5,200 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5912, "line_end": 5986, "token_count_estimate": 1617, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0402618", "0271L0402619", "0271L0402620", "0271L0402621", "0271L0402622", "0271L0402623", "0271L0402624", "0271L0402625", "0271L0402626", "0271L0402627", "0271L0402628", "0271L0402629", "0271L0402630", "0271L0402631", "0271L0402632", "0271L0402633", "0271L0402634", "0271L0402635", "0271L0402636", "0271L0402637", "0271L0402638", "0271L0402639", "0271L0402640", "0271L0402641", "0271L0402642", "0271L0402643", "0271L0402644", "0271L0402645", "0271L0402646", "0271L0402647", "0271L0402648", "0271L0402649", "0271L0402650", "0271L0402651", "0271L0402652"]}}
{"id": "a1b02f699b50c50e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2653 | 0271L0402653 | 28.196 | 86.071 | Kosi | E(o) | 0.32 | 5,685 |\n| 2654 | 0271L0402654 | 28.196 | 86.133 | Kosi | M(o) | 3.26 | 5,340 |\n| 2655 | 0271L0402655 | 28.194 | 86.220 | Kosi | M(o) | 4.69 | 5,234 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5912, "line_end": 5986, "token_count_estimate": 228, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0402653", "0271L0402654", "0271L0402655"]}}
{"id": "a19cd998b7543e82", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2656 | 0271L0402656 | 28.193 | 86.245 | Kosi | M(o) | 0.91 | 5,363 |\n| 2657 | 0271L0402657 | 28.193 | 86.160 | Kosi | E(o) | 0.82 | 4,910 |\n| 2658 | 0271L0402658 | 28.190 | 86.134 | Kosi | M(o) | 1.10 | 5,215 |\n| 2659 | 0271L0402659 | 28.189 | 86.133 | Kosi | M(o) | 0.51 | 5,219 |\n| 2660 | 0271L0402660 | 28.188 | 86.249 | Kosi | M(o) | 1.54 | 5,283 |\n| 2661 | 0271L0402661 | 28.187 | 86.090 | Kosi | M(o) | 0.40 | 5,330 |\n| 2662 | 0271L0402662 | 28.185 | 86.129 | Kosi | M(o) | 0.68 | 5,122 |\n| 2663 | 0271L0402663 | 28.185 | 86.247 | Kosi | M(o) | 0.61 | 5,266 |\n| 2664 | 0271L0402664 | 28.185 | 86.186 | Kosi | M(o) | 3.26 | 5,238 |\n| 2665 | 0271L0402665 | 28.183 | 86.226 | Kosi | M(o) | 12.34 | 5,223 |\n| 2666 | 0271L0402666 | 28.181 | 86.249 | Kosi | M(o) | 4.42 | 5,244 |\n| 2667 | 0271L0402667 | 28.179 | 86.073 | Kosi | E(o) | 0.83 | 5,168 |\n| 2668 | 0271L0402668 | 28.178 | 86.170 | Kosi | M(o) | 0.72 | 5,092 |\n| 2669 | 0271L0402669 | 28.178 | 86.195 | Kosi | M(o) | 0.59 | 5,220 |\n| 2670 | 0271L0402670 | 28.176 | 86.095 | Kosi | E(o) | 2.58 | 5,065 |\n| 2671 | 0271L0402671 | 28.170 | 86.060 | Kosi | M(o) | 4.43 | 5,169 |\n| 2672 | 0271L0402672 | 28.169 | 86.180 | Kosi | M(o) | 1.07 | 5,383 |\n| 2673 | 0271L0402673 | 28.166 | 86.184 | Kosi | M(o) | 0.52 | 5,292 |\n| 2674 | 0271L0402674 | 28.163 | 86.067 | Kosi | M(e) | 1.64 | 5,160 |\n| 2675 | 0271L0402675 | 28.160 | 86.076 | Kosi | M(o) | 1.31 | 5,237 |\n| 2676 | 0271L0402676 | 28.156 | 86.110 | Kosi | M(o) | 0.41 | 5,170 |\n| 2677 | 0271L0402677 | 28.139 | 86.098 | Kosi | M(o) | 0.69 | 5,345 |\n| 2678 | 0271L0402678 | 28.136 | 86.096 | Kosi | M(o) | 2.59 | 5,307 |\n| 2679 | 0271L0402679 | 28.135 | 86.169 | Kosi | E(o) | 0.93 | 4,894 |\n| 2680 | 0271L0402680 | 28.135 | 86.100 | Kosi | M(o) | 0.80 | 5,245 |\n| 2681 | 0271L0402681 | 28.132 | 86.098 | Kosi | M(o) | 0.86 | 5,270 |\n| 2682 | 0271L0402682 | 28.131 | 86.096 | Kosi | M(o) | 0.28 | 5,354 |\n| 2683 | 0271L0402683 | 28.127 | 86.083 | Kosi | M(o) | 0.76 | 5,336 |\n| 2684 | 0271L0402684 | 28.126 | 86.105 | Kosi | M(o) | 5.75 | 5,195 |\n| 2685 | 0271L0402685 | 28.125 | 86.051 | Kosi | E(o) | 2.04 | 4,587 |\n| 2686 | 0271L0402686 | 28.124 | 86.116 | Kosi | M(o) | 0.25 | 5,154 |\n| 2687 | 0271L0402687 | 28.123 | 86.125 | Kosi | M(o) | 0.51 | 5,137 |\n| 2688 | 0271L0402688 | 28.120 | 86.096 | Kosi | M(o) | 0.49 | 5,288 |\n| 2689 | 0271L0402689 | 28.118 | 86.116 | Kosi | M(o) | 0.37 | 5,087 |\n| 2690 | 0271L0402690 | 28.118 | 86.119 | Kosi | M(o) | 5.81 | 5,024 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5988, "line_end": 6062, "token_count_estimate": 1615, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0402656", "0271L0402657", "0271L0402658", "0271L0402659", "0271L0402660", "0271L0402661", "0271L0402662", "0271L0402663", "0271L0402664", "0271L0402665", "0271L0402666", "0271L0402667", "0271L0402668", "0271L0402669", "0271L0402670", "0271L0402671", "0271L0402672", "0271L0402673", "0271L0402674", "0271L0402675", "0271L0402676", "0271L0402677", "0271L0402678", "0271L0402679", "0271L0402680", "0271L0402681", "0271L0402682", "0271L0402683", "0271L0402684", "0271L0402685", "0271L0402686", "0271L0402687", "0271L0402688", "0271L0402689", "0271L0402690"]}}
{"id": "6d5f435cc3b21a37", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2691 | 0271L0402691 | 28.117 | 86.115 | Kosi | M(o) | 0.81 | 5,095 |\n| 2692 | 0271L0402692 | 28.114 | 86.116 | Kosi | M(o) | 0.43 | 5,123 |\n| 2693 | 0271L0402693 | 28.096 | 86.195 | Kosi | M(o) | 1.62 | 4,875 |\n| 2694 | 0271L0402694 | 28.095 | 86.193 | Kosi | M(o) | 6.83 | 4,879 |\n| 2695 | 0271L0402695 | 28.090 | 86.238 | Kosi | M(o) | 2.78 | 5,161 |\n| 2696 | 0271L0402696 | 28.088 | 86.195 | Kosi | M(o) | 0.45 | 5,038 |\n| 2697 | 0271L0402697 | 28.087 | 86.196 | Kosi | M(o) | 0.32 | 5,036 |\n| 2698 | 0271L0402698 | 28.079 | 86.250 | Kosi | M(o) | 1.93 | 4,883 |\n| 2699 | 0271L0402699 | 28.070 | 86.057 | Kosi | M(o) | 0.29 | 4,638 |\n| 2700 | 0271L0402700 | 28.069 | 86.059 | Kosi | M(o) | 0.29 | 4,644 |\n| 2701 | 0271L0402701 | 28.069 | 86.027 | Kosi | E(o) | 3.78 | 4,493 |\n| 2702 | 0271L0402702 | 28.067 | 86.066 | Kosi | M(o) | 32.50 | 4,630 |\n| 2703 | 0271L0402703 | 28.065 | 86.024 | Kosi | E(o) | 0.31 | 4,561 |\n| 2704 | 0271L0402704 | 28.056 | 86.207 | Kosi | M(o) | 0.52 | 5,210 |\n| 2705 | 0271L0402705 | 28.047 | 86.036 | Kosi | M(o) | 0.64 | 4,803 |\n| 2706 | 0271L0402706 | 28.037 | 86.073 | Kosi | M(o) | 0.67 | 4,894 |\n| 2707 | 0271L0402707 | 28.036 | 86.077 | Kosi | M(o) | 0.52 | 4,988 |\n| 2708 | 0271L0402708 | 28.032 | 86.073 | Kosi | M(o) | 2.32 | 5,005 |\n| 2709 | 0271L0402709 | 28.024 | 86.094 | Kosi | E(o) | 0.62 | 4,963 |\n| 2710 | 0271L0402710 | 28.023 | 86.099 | Kosi | M(o) | 1.47 | 4,945 |\n| 2711 | 0271L0402711 | 28.015 | 86.049 | Kosi | E(o) | 0.92 | 4,627 |\n| 2712 | 0271L0402712 | 28.015 | 86.036 | Kosi | E(o) | 0.74 | 4,271 |\n| 2713 | 0271L0402713 | 28.009 | 86.035 | Kosi | E(o) | 1.91 | 4,434 |\n| 2714 | 0271L0402714 | 28.002 | 86.037 | Kosi | E(o) | 0.36 | 4,579 |\n| 2715 | 0271L0402715 | 28.002 | 86.078 | Kosi | M(o) | 0.81 | 4,731 |\n| 2716 | 0271L0502716 | 28.789 | 86.497 | Kosi | E(o) | 2.43 | 5,479 |\n| 2717 | 0271L0602717 | 28.712 | 87.481 | Kosi | E(o) | 5.85 | 5,239 |\n| 2718 | 0271L0602718 | 28.693 | 87.387 | Kosi | E(o) | 1.07 | 5,376 |\n| 2719 | 0271L0602719 | 28.688 | 87.415 | Kosi | E(o) | 0.41 | 5,504 |\n| 2720 | 0271L0602720 | 28.680 | 87.377 | Kosi | E(o) | 8.67 | 5,197 |\n| 2721 | 0271L0602721 | 28.676 | 87.491 | Kosi | O | 0.62 | 4,900 |\n| 2722 | 0271L0702722 | 28.424 | 86.308 | Kosi | E(o) | 0.59 | 5,711 |\n| 2723 | 0271L0702723 | 28.402 | 86.382 | Kosi | M(o) | 0.25 | 5,507 |\n| 2724 | 0271L0702724 | 28.401 | 86.386 | Kosi | M(o) | 0.27 | 5,501 |\n| 2725 | 0271L0702725 | 28.401 | 86.384 | Kosi | M(o) | 0.32 | 5,507 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5988, "line_end": 6062, "token_count_estimate": 1629, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0402691", "0271L0402692", "0271L0402693", "0271L0402694", "0271L0402695", "0271L0402696", "0271L0402697", "0271L0402698", "0271L0402699", "0271L0402700", "0271L0402701", "0271L0402702", "0271L0402703", "0271L0402704", "0271L0402705", "0271L0402706", "0271L0402707", "0271L0402708", "0271L0402709", "0271L0402710", "0271L0402711", "0271L0402712", "0271L0402713", "0271L0402714", "0271L0402715", "0271L0502716", "0271L0602717", "0271L0602718", "0271L0602719", "0271L0602720", "0271L0602721", "0271L0702722", "0271L0702723", "0271L0702724", "0271L0702725"]}}
{"id": "a59786c3bba1a990", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2726 | 0271L0702726 | 28.398 | 86.487 | Kosi | M(o) | 1.14 | 5,625 |\n| 2727 | 0271L0702727 | 28.397 | 86.440 | Kosi | M(o) | 1.69 | 5,539 |\n| 2728 | 0271L0702728 | 28.397 | 86.321 | Kosi | M(o) | 2.07 | 5,351 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 5988, "line_end": 6062, "token_count_estimate": 221, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0702726", "0271L0702727", "0271L0702728"]}}
{"id": "732fc7156e4f011c", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6063, "line_end": 6069, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "00eb2c21d9d4f29e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2729 | 0271L0702729 | 28.394 | 86.482 | Kosi | M(o) | 0.36 | 5,626 |\n| 2730 | 0271L0702730 | 28.393 | 86.451 | Kosi | M(e) | 5.24 | 5,624 |\n| 2731 | 0271L0702731 | 28.392 | 86.440 | Kosi | M(e) | 4.92 | 5,557 |\n| 2732 | 0271L0702732 | 28.392 | 86.415 | Kosi | M(e) | 20.17 | 5,502 |\n| 2733 | 0271L0702733 | 28.392 | 86.276 | Kosi | E(o) | 0.72 | 5,874 |\n| 2734 | 0271L0702734 | 28.381 | 86.384 | Kosi | M(e) | 13.92 | 5,551 |\n| 2735 | 0271L0702735 | 28.378 | 86.488 | Kosi | M(o) | 1.99 | 5,730 |\n| 2736 | 0271L0702736 | 28.374 | 86.305 | Kosi | M(e) | 391.50 | 5,346 |\n| 2737 | 0271L0702737 | 28.374 | 86.259 | Kosi | M(e) | 27.54 | 5,544 |\n| 2738 | 0271L0702738 | 28.370 | 86.412 | Kosi | M(o) | 0.97 | 5,800 |\n| 2739 | 0271L0702739 | 28.366 | 86.482 | Kosi | E(o) | 1.46 | 5,795 |\n| 2740 | 0271L0702740 | 28.365 | 86.491 | Kosi | M(o) | 0.65 | 5,621 |\n| 2741 | 0271L0702741 | 28.363 | 86.487 | Kosi | M(e) | 4.97 | 5,615 |\n| 2742 | 0271L0702742 | 28.359 | 86.479 | Kosi | M(o) | 0.64 | 5,772 |\n| 2743 | 0271L0702743 | 28.358 | 86.475 | Kosi | E(o) | 0.90 | 5,859 |\n| 2744 | 0271L0702744 | 28.356 | 86.260 | Kosi | M(o) | 2.62 | 5,893 |\n| 2745 | 0271L0702745 | 28.348 | 86.493 | Kosi | M(e) | 34.51 | 5,524 |\n| 2746 | 0271L0702746 | 28.331 | 86.250 | Kosi | M(o) | 0.43 | 5,747 |\n| 2747 | 0271L0702747 | 28.320 | 86.467 | Kosi | M(l) | 0.59 | 5,655 |\n| 2748 | 0271L0702748 | 28.318 | 86.479 | Kosi | M(o) | 0.34 | 5,551 |\n| 2749 | 0271L0702749 | 28.314 | 86.509 | Kosi | M(o) | 0.82 | 5,411 |\n| 2750 | 0271L0702750 | 28.286 | 86.514 | Kosi | I(s) | 4.71 | 5,158 |\n| 2751 | 0271L0702751 | 28.285 | 86.496 | Kosi | M(o) | 0.90 | 5,178 |\n| 2752 | 0271L0702752 | 28.285 | 86.494 | Kosi | M(o) | 0.84 | 5,162 |\n| 2753 | 0271L0702753 | 28.284 | 86.487 | Kosi | I(s) | 1.78 | 5,210 |\n| 2754 | 0271L0702754 | 28.284 | 86.266 | Kosi | M(o) | 2.29 | 5,439 |\n| 2755 | 0271L0702755 | 28.283 | 86.313 | Kosi | M(o) | 1.76 | 5,399 |\n| 2756 | 0271L0702756 | 28.281 | 86.289 | Kosi | M(o) | 0.72 | 5,438 |\n| 2757 | 0271L0702757 | 28.279 | 86.295 | Kosi | M(o) | 0.81 | 5,372 |\n| 2758 | 0271L0702758 | 28.276 | 86.310 | Kosi | I(s) | 0.44 | 5,191 |\n| 2759 | 0271L0702759 | 28.276 | 86.296 | Kosi | E(o) | 0.45 | 5,402 |\n| 2760 | 0271L0702760 | 28.276 | 86.379 | Kosi | I(s) | 0.25 | 5,434 |\n| 2761 | 0271L0702761 | 28.275 | 86.304 | Kosi | I(s) | 0.43 | 5,191 |\n| 2762 | 0271L0702762 | 28.273 | 86.385 | Kosi | I(s) | 0.45 | 5,405 |\n| 2763 | 0271L0702763 | 28.271 | 86.323 | Kosi | M(o) | 0.67 | 5,318 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6070, "line_end": 6144, "token_count_estimate": 1608, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0702729", "0271L0702730", "0271L0702731", "0271L0702732", "0271L0702733", "0271L0702734", "0271L0702735", "0271L0702736", "0271L0702737", "0271L0702738", "0271L0702739", "0271L0702740", "0271L0702741", "0271L0702742", "0271L0702743", "0271L0702744", "0271L0702745", "0271L0702746", "0271L0702747", "0271L0702748", "0271L0702749", "0271L0702750", "0271L0702751", "0271L0702752", "0271L0702753", "0271L0702754", "0271L0702755", "0271L0702756", "0271L0702757", "0271L0702758", "0271L0702759", "0271L0702760", "0271L0702761", "0271L0702762", "0271L0702763"]}}
{"id": "e3aecaa65a96b242", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2764 | 0271L0702764 | 28.270 | 86.387 | Kosi | I(s) | 0.30 | 5,396 |\n| 2765 | 0271L0702765 | 28.270 | 86.388 | Kosi | I(s) | 0.26 | 5,381 |\n| 2766 | 0271L0702766 | 28.269 | 86.408 | Kosi | E(o) | 1.57 | 5,454 |\n| 2767 | 0271L0702767 | 28.269 | 86.384 | Kosi | I(s) | 0.38 | 5,385 |\n| 2768 | 0271L0702768 | 28.267 | 86.315 | Kosi | E(o) | 0.31 | 5,350 |\n| 2769 | 0271L0702769 | 28.266 | 86.384 | Kosi | I(s) | 0.59 | 5,379 |\n| 2770 | 0271L0702770 | 28.266 | 86.387 | Kosi | I(s) | 0.26 | 5,368 |\n| 2771 | 0271L0702771 | 28.265 | 86.413 | Kosi | M(o) | 1.14 | 5,448 |\n| 2772 | 0271L0702772 | 28.265 | 86.389 | Kosi | I(s) | 0.35 | 5,368 |\n| 2773 | 0271L0702773 | 28.264 | 86.379 | Kosi | I(s) | 0.62 | 5,371 |\n| 2774 | 0271L0702774 | 28.264 | 86.296 | Kosi | I(s) | 0.25 | 5,069 |\n| 2775 | 0271L0702775 | 28.264 | 86.463 | Kosi | O | 0.25 | 5,341 |\n| 2776 | 0271L0702776 | 28.263 | 86.410 | Kosi | E(o) | 0.36 | 5,448 |\n| 2777 | 0271L0702777 | 28.262 | 86.385 | Kosi | I(s) | 1.32 | 5,358 |\n| 2778 | 0271L0702778 | 28.262 | 86.262 | Kosi | M(o) | 8.72 | 5,350 |\n| 2779 | 0271L0702779 | 28.258 | 86.393 | Kosi | I(s) | 0.42 | 5,314 |\n| 2780 | 0271L0702780 | 28.257 | 86.293 | Kosi | I(s) | 0.46 | 5,045 |\n| 2781 | 0271L0702781 | 28.256 | 86.311 | Kosi | I(s) | 0.65 | 5,623 |\n| 2782 | 0271L0702782 | 28.255 | 86.382 | Kosi | M(o) | 0.66 | 5,370 |\n| 2783 | 0271L0702783 | 28.255 | 86.259 | Kosi | M(o) | 0.88 | 5,452 |\n| 2784 | 0271L0702784 | 28.253 | 86.290 | Kosi | I(s) | 0.26 | 5,010 |\n| 2785 | 0271L0702785 | 28.253 | 86.264 | Kosi | M(o) | 0.72 | 5,378 |\n| 2786 | 0271L0702786 | 28.252 | 86.254 | Kosi | M(o) | 1.09 | 5,569 |\n| 2787 | 0271L0702787 | 28.252 | 86.313 | Kosi | M(o) | 0.26 | 5,537 |\n| 2788 | 0271L0702788 | 28.199 | 86.582 | Kosi | M(e) | 134.64 | 5,094 |\n| 2789 | 0271L0802789 | 28.250 | 86.395 | Kosi | I(s) | 0.78 | 5,270 |\n| 2790 | 0271L0802790 | 28.249 | 86.288 | Kosi | I(s) | 0.26 | 4,983 |\n| 2791 | 0271L0802791 | 28.247 | 86.393 | Kosi | I(s) | 0.27 | 5,236 |\n| 2792 | 0271L0802792 | 28.247 | 86.257 | Kosi | M(o) | 0.88 | 5,474 |\n| 2793 | 0271L0802793 | 28.247 | 86.260 | Kosi | M(o) | 0.54 | 5,395 |\n| 2794 | 0271L0802794 | 28.247 | 86.317 | Kosi | M(o) | 3.31 | 5,466 |\n| 2795 | 0271L0802795 | 28.247 | 86.287 | Kosi | I(s) | 0.32 | 4,963 |\n| 2796 | 0271L0802796 | 28.247 | 86.262 | Kosi | M(o) | 0.43 | 5,392 |\n| 2797 | 0271L0802797 | 28.246 | 86.395 | Kosi | I(s) | 0.28 | 5,240 |\n| 2798 | 0271L0802798 | 28.246 | 86.308 | Kosi | M(o) | 0.94 | 5,554 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6070, "line_end": 6144, "token_count_estimate": 1603, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0702764", "0271L0702765", "0271L0702766", "0271L0702767", "0271L0702768", "0271L0702769", "0271L0702770", "0271L0702771", "0271L0702772", "0271L0702773", "0271L0702774", "0271L0702775", "0271L0702776", "0271L0702777", "0271L0702778", "0271L0702779", "0271L0702780", "0271L0702781", "0271L0702782", "0271L0702783", "0271L0702784", "0271L0702785", "0271L0702786", "0271L0702787", "0271L0702788", "0271L0802789", "0271L0802790", "0271L0802791", "0271L0802792", "0271L0802793", "0271L0802794", "0271L0802795", "0271L0802796", "0271L0802797", "0271L0802798"]}}
{"id": "55ddbc7b50021840", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2799 | 0271L0802799 | 28.245 | 86.289 | Kosi | I(s) | 0.27 | 4,967 |\n| 2800 | 0271L0802800 | 28.245 | 86.321 | Kosi | M(o) | 21.56 | 5,361 |\n| 2801 | 0271L0802801 | 28.245 | 86.394 | Kosi | I(s) | 1.05 | 5,228 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 6070, "line_end": 6144, "token_count_estimate": 226, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802799", "0271L0802800", "0271L0802801"]}}
{"id": "1f55e0c52aabe420", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2802 | 0271L0802802 | 28.244 | 86.395 | Kosi | I(s) | 0.86 | 5,217 |\n| 2803 | 0271L0802803 | 28.244 | 86.436 | Kosi | M(o) | 0.61 | 5,509 |\n| 2804 | 0271L0802804 | 28.243 | 86.291 | Kosi | I(s) | 1.11 | 4,940 |\n| 2805 | 0271L0802805 | 28.240 | 86.291 | Kosi | I(s) | 0.38 | 4,931 |\n| 2806 | 0271L0802806 | 28.240 | 86.365 | Kosi | M(e) | 24.72 | 5,327 |\n| 2807 | 0271L0802807 | 28.238 | 86.395 | Kosi | I(s) | 0.86 | 5,183 |\n| 2808 | 0271L0802808 | 28.238 | 86.371 | Kosi | M(o) | 3.04 | 5,324 |\n| 2809 | 0271L0802809 | 28.237 | 86.339 | Kosi | M(o) | 0.52 | 5,572 |\n| 2810 | 0271L0802810 | 28.236 | 86.393 | Kosi | I(s) | 0.49 | 5,171 |\n| 2811 | 0271L0802811 | 28.236 | 86.356 | Kosi | M(o) | 8.56 | 5,399 |\n| 2812 | 0271L0802812 | 28.235 | 86.288 | Kosi | I(s) | 0.72 | 4,902 |\n| 2813 | 0271L0802813 | 28.233 | 86.331 | Kosi | M(o) | 3.07 | 5,359 |\n| 2814 | 0271L0802814 | 28.232 | 86.412 | Kosi | M(e) | 8.60 | 5,363 |\n| 2815 | 0271L0802815 | 28.232 | 86.323 | Kosi | M(o) | 0.90 | 5,289 |\n| 2816 | 0271L0802816 | 28.231 | 86.433 | Kosi | M(o) | 0.81 | 5,497 |\n| 2817 | 0271L0802817 | 28.231 | 86.316 | Kosi | M(o) | 0.66 | 5,255 |\n| 2818 | 0271L0802818 | 28.231 | 86.315 | Kosi | M(o) | 0.25 | 5,251 |\n| 2819 | 0271L0802819 | 28.230 | 86.436 | Kosi | M(o) | 3.50 | 5,465 |\n| 2820 | 0271L0802820 | 28.230 | 86.313 | Kosi | M(o) | 0.33 | 5,239 |\n| 2821 | 0271L0802821 | 28.230 | 86.312 | Kosi | M(o) | 0.86 | 5,233 |\n| 2822 | 0271L0802822 | 28.229 | 86.320 | Kosi | M(o) | 8.20 | 5,272 |\n| 2823 | 0271L0802823 | 28.228 | 86.391 | Kosi | I(s) | 1.53 | 5,107 |\n| 2824 | 0271L0802824 | 28.227 | 86.420 | Kosi | M(o) | 2.40 | 5,414 |\n| 2825 | 0271L0802825 | 28.226 | 86.391 | Kosi | I(s) | 0.56 | 5,092 |\n| 2826 | 0271L0802826 | 28.224 | 86.431 | Kosi | E(o) | 0.25 | 5,540 |\n| 2827 | 0271L0802827 | 28.224 | 86.394 | Kosi | I(s) | 1.66 | 5,083 |\n| 2828 | 0271L0802828 | 28.223 | 86.453 | Kosi | E(o) | 0.34 | 5,258 |\n| 2829 | 0271L0802829 | 28.221 | 86.394 | Kosi | I(s) | 0.26 | 5,069 |\n| 2830 | 0271L0802830 | 28.220 | 86.283 | Kosi | M(o) | 0.25 | 4,840 |\n| 2831 | 0271L0802831 | 28.220 | 86.441 | Kosi | M(o) | 0.39 | 5,374 |\n| 2832 | 0271L0802832 | 28.219 | 86.437 | Kosi | M(o) | 0.59 | 5,412 |\n| 2833 | 0271L0802833 | 28.219 | 86.303 | Kosi | M(o) | 0.78 | 5,257 |\n| 2834 | 0271L0802834 | 28.218 | 86.280 | Kosi | M(o) | 5.44 | 4,828 |\n| 2835 | 0271L0802835 | 28.217 | 86.302 | Kosi | M(o) | 1.32 | 5,277 |\n| 2836 | 0271L0802836 | 28.217 | 86.305 | Kosi | M(o) | 1.86 | 5,264 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6146, "line_end": 6220, "token_count_estimate": 1643, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802802", "0271L0802803", "0271L0802804", "0271L0802805", "0271L0802806", "0271L0802807", "0271L0802808", "0271L0802809", "0271L0802810", "0271L0802811", "0271L0802812", "0271L0802813", "0271L0802814", "0271L0802815", "0271L0802816", "0271L0802817", "0271L0802818", "0271L0802819", "0271L0802820", "0271L0802821", "0271L0802822", "0271L0802823", "0271L0802824", "0271L0802825", "0271L0802826", "0271L0802827", "0271L0802828", "0271L0802829", "0271L0802830", "0271L0802831", "0271L0802832", "0271L0802833", "0271L0802834", "0271L0802835", "0271L0802836"]}}
{"id": "797ff2ec566331f1", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2837 | 0271L0802837 | 28.216 | 86.396 | Kosi | I(s) | 0.96 | 5,034 |\n| 2838 | 0271L0802838 | 28.216 | 86.414 | Kosi | E(o) | 0.70 | 5,312 |\n| 2839 | 0271L0802839 | 28.216 | 86.359 | Kosi | M(o) | 0.65 | 5,620 |\n| 2840 | 0271L0802840 | 28.215 | 86.276 | Kosi | M(o) | 0.28 | 4,819 |\n| 2841 | 0271L0802841 | 28.214 | 86.277 | Kosi | M(o) | 0.45 | 4,819 |\n| 2842 | 0271L0802842 | 28.214 | 86.275 | Kosi | M(o) | 0.48 | 4,815 |\n| 2843 | 0271L0802843 | 28.213 | 86.274 | Kosi | M(o) | 0.46 | 4,804 |\n| 2844 | 0271L0802844 | 28.211 | 86.273 | Kosi | M(o) | 0.42 | 4,801 |\n| 2845 | 0271L0802845 | 28.210 | 86.426 | Kosi | E(o) | 0.95 | 5,285 |\n| 2846 | 0271L0802846 | 28.210 | 86.272 | Kosi | M(o) | 0.34 | 4,795 |\n| 2847 | 0271L0802847 | 28.208 | 86.402 | Kosi | M(o) | 0.28 | 4,993 |\n| 2848 | 0271L0802848 | 28.203 | 86.298 | Kosi | M(o) | 0.26 | 5,528 |\n| 2849 | 0271L0802849 | 28.203 | 86.250 | Kosi | M(o) | 0.37 | 5,171 |\n| 2850 | 0271L0802850 | 28.202 | 86.309 | Kosi | M(e) | 6.57 | 5,430 |\n| 2851 | 0271L0802851 | 28.200 | 86.361 | Kosi | M(o) | 0.27 | 5,563 |\n| 2852 | 0271L0802852 | 28.199 | 86.357 | Kosi | M(o) | 0.63 | 5,541 |\n| 2853 | 0271L0802853 | 28.194 | 86.343 | Kosi | M(o) | 0.94 | 5,421 |\n| 2854 | 0271L0802854 | 28.194 | 86.314 | Kosi | M(e) | 27.79 | 5,245 |\n| 2855 | 0271L0802855 | 28.193 | 86.361 | Kosi | M(e) | 3.22 | 5,463 |\n| 2856 | 0271L0802856 | 28.193 | 86.351 | Kosi | M(e) | 19.87 | 5,339 |\n| 2857 | 0271L0802857 | 28.192 | 86.326 | Kosi | M(o) | 1.99 | 5,401 |\n| 2858 | 0271L0802858 | 28.191 | 86.342 | Kosi | M(o) | 0.37 | 5,583 |\n| 2859 | 0271L0802859 | 28.189 | 86.323 | Kosi | M(o) | 1.06 | 5,357 |\n| 2860 | 0271L0802860 | 28.189 | 86.369 | Kosi | M(o) | 1.58 | 5,457 |\n| 2861 | 0271L0802861 | 28.187 | 86.356 | Kosi | M(o) | 0.26 | 5,324 |\n| 2862 | 0271L0802862 | 28.186 | 86.348 | Kosi | M(o) | 0.94 | 5,439 |\n| 2863 | 0271L0802863 | 28.186 | 86.343 | Kosi | M(o) | 2.57 | 5,487 |\n| 2864 | 0271L0802864 | 28.182 | 86.347 | Kosi | M(o) | 2.25 | 5,452 |\n| 2865 | 0271L0802865 | 28.181 | 86.343 | Kosi | M(l) | 2.48 | 5,441 |\n| 2866 | 0271L0802866 | 28.178 | 86.322 | Kosi | M(o) | 3.80 | 5,226 |\n| 2867 | 0271L0802867 | 28.178 | 86.408 | Kosi | E(o) | 0.55 | 5,309 |\n| 2868 | 0271L0802868 | 28.178 | 86.390 | Kosi | E(o) | 0.30 | 5,192 |\n| 2869 | 0271L0802869 | 28.177 | 86.352 | Kosi | M(o) | 0.44 | 5,253 |\n| 2870 | 0271L0802870 | 28.176 | 86.357 | Kosi | M(o) | 1.25 | 5,237 |\n| 2871 | 0271L0802871 | 28.173 | 86.394 | Kosi | E(o) | 0.82 | 5,097 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6146, "line_end": 6220, "token_count_estimate": 1626, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802837", "0271L0802838", "0271L0802839", "0271L0802840", "0271L0802841", "0271L0802842", "0271L0802843", "0271L0802844", "0271L0802845", "0271L0802846", "0271L0802847", "0271L0802848", "0271L0802849", "0271L0802850", "0271L0802851", "0271L0802852", "0271L0802853", "0271L0802854", "0271L0802855", "0271L0802856", "0271L0802857", "0271L0802858", "0271L0802859", "0271L0802860", "0271L0802861", "0271L0802862", "0271L0802863", "0271L0802864", "0271L0802865", "0271L0802866", "0271L0802867", "0271L0802868", "0271L0802869", "0271L0802870", "0271L0802871"]}}
{"id": "9b030c680f91ac31", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2872 | 0271L0802872 | 28.170 | 86.308 | Kosi | M(o) | 2.41 | 5,011 |\n| 2873 | 0271L0802873 | 28.169 | 86.469 | Kosi | E(o) | 0.36 | 4,858 |\n| 2874 | 0271L0802874 | 28.166 | 86.283 | Kosi | M(o) | 0.94 | 5,008 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 73, "line_start": 6146, "line_end": 6220, "token_count_estimate": 226, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802872", "0271L0802873", "0271L0802874"]}}
{"id": "e9d899864005230d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6221, "line_end": 6228, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9ec516ed10b32076", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2875 | 0271L0802875 | 28.165 | 86.358 | Kosi | M(o) | 1.67 | 5,331 |\n| 2876 | 0271L0802876 | 28.164 | 86.304 | Kosi | M(o) | 0.51 | 5,303 |\n| 2877 | 0271L0802877 | 28.164 | 86.298 | Kosi | M(o) | 0.26 | 5,120 |\n| 2878 | 0271L0802878 | 28.162 | 86.347 | Kosi | E(o) | 0.28 | 5,517 |\n| 2879 | 0271L0802879 | 28.161 | 86.294 | Kosi | M(o) | 0.44 | 5,110 |\n| 2880 | 0271L0802880 | 28.156 | 86.338 | Kosi | M(o) | 7.57 | 5,284 |\n| 2881 | 0271L0802881 | 28.156 | 86.350 | Kosi | E(o) | 1.36 | 5,381 |\n| 2882 | 0271L0802882 | 28.154 | 86.360 | Kosi | E(o) | 0.52 | 5,589 |\n| 2883 | 0271L0802883 | 28.154 | 86.351 | Kosi | E(o) | 3.37 | 5,382 |\n| 2884 | 0271L0802884 | 28.152 | 86.363 | Kosi | E(o) | 1.12 | 5,425 |\n| 2885 | 0271L0802885 | 28.152 | 86.330 | Kosi | M(o) | 6.72 | 5,154 |\n| 2886 | 0271L0802886 | 28.152 | 86.370 | Kosi | M(o) | 1.08 | 5,321 |\n| 2887 | 0271L0802887 | 28.150 | 86.313 | Kosi | M(o) | 1.06 | 5,044 |\n| 2888 | 0271L0802888 | 28.148 | 86.388 | Kosi | E(o) | 0.27 | 5,225 |\n| 2889 | 0271L0802889 | 28.144 | 86.303 | Kosi | M(o) | 1.57 | 5,210 |\n| 2890 | 0271L0802890 | 28.140 | 86.284 | Kosi | M(o) | 0.25 | 5,105 |\n| 2891 | 0271L0802891 | 28.138 | 86.388 | Kosi | E(o) | 0.42 | 4,889 |\n| 2892 | 0271L0802892 | 28.136 | 86.264 | Kosi | M(o) | 3.38 | 5,250 |\n| 2893 | 0271L0802893 | 28.128 | 86.337 | Kosi | E(o) | 0.70 | 5,310 |\n| 2894 | 0271L0802894 | 28.124 | 86.288 | Kosi | E(o) | 1.25 | 5,024 |\n| 2895 | 0271L0802895 | 28.123 | 86.258 | Kosi | M(o) | 0.36 | 5,259 |\n| 2896 | 0271L0802896 | 28.121 | 86.332 | Kosi | E(o) | 1.04 | 5,208 |\n| 2897 | 0271L0802897 | 28.121 | 86.269 | Kosi | M(o) | 0.25 | 5,220 |\n| 2898 | 0271L0802898 | 28.120 | 86.328 | Kosi | M(o) | 1.99 | 5,266 |\n| 2899 | 0271L0802899 | 28.119 | 86.279 | Kosi | E(o) | 0.65 | 4,935 |\n| 2900 | 0271L0802900 | 28.111 | 86.323 | Kosi | E(o) | 0.38 | 5,318 |\n| 2901 | 0271L0802901 | 28.108 | 86.498 | Kosi | M(o) | 0.59 | 4,788 |\n| 2902 | 0271L0802902 | 28.096 | 86.254 | Kosi | M(o) | 0.60 | 5,218 |\n| 2903 | 0271L0802903 | 28.092 | 86.319 | Kosi | M(o) | 0.33 | 5,302 |\n| 2904 | 0271L0802904 | 28.092 | 86.257 | Kosi | M(o) | 7.14 | 5,063 |\n| 2905 | 0271L0802905 | 28.090 | 86.433 | Kosi | M(o) | 0.56 | 4,984 |\n| 2906 | 0271L0802906 | 28.083 | 86.470 | Kosi | M(o) | 7.43 | 5,022 |\n| 2907 | 0271L0802907 | 28.083 | 86.481 | Kosi | M(o) | 0.48 | 5,202 |\n| 2908 | 0271L0802908 | 28.083 | 86.457 | Kosi | M(o) | 0.27 | 5,221 |\n| 2909 | 0271L0802909 | 28.082 | 86.498 | Kosi | M(o) | 0.32 | 5,245 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6229, "line_end": 6376, "token_count_estimate": 1615, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802875", "0271L0802876", "0271L0802877", "0271L0802878", "0271L0802879", "0271L0802880", "0271L0802881", "0271L0802882", "0271L0802883", "0271L0802884", "0271L0802885", "0271L0802886", "0271L0802887", "0271L0802888", "0271L0802889", "0271L0802890", "0271L0802891", "0271L0802892", "0271L0802893", "0271L0802894", "0271L0802895", "0271L0802896", "0271L0802897", "0271L0802898", "0271L0802899", "0271L0802900", "0271L0802901", "0271L0802902", "0271L0802903", "0271L0802904", "0271L0802905", "0271L0802906", "0271L0802907", "0271L0802908", "0271L0802909"]}}
{"id": "636ca208189e3ef3", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2910 | 0271L0802910 | 28.079 | 86.253 | Kosi | M(o) | 2.83 | 4,901 |\n| 2911 | 0271L0802911 | 28.077 | 86.489 | Kosi | I(s) | 0.38 | 5,451 |\n| 2912 | 0271L0802912 | 28.076 | 86.438 | Kosi | E(o) | 2.13 | 5,089 |\n| 2913 | 0271L0802913 | 28.075 | 86.479 | Kosi | M(o) | 0.86 | 5,056 |\n| 2914 | 0271L0802914 | 28.074 | 86.467 | Kosi | M(o) | 1.28 | 5,328 |\n| 2915 | 0271L0802915 | 28.068 | 86.476 | Kosi | M(o) | 0.33 | 5,190 |\n| 2916 | 0271L0802916 | 28.068 | 86.450 | Kosi | M(o) | 0.60 | 5,201 |\n| 2917 | 0271L0802917 | 28.067 | 86.461 | Kosi | M(o) | 0.63 | 5,279 |\n| 2918 | 0271L0802918 | 28.065 | 86.473 | Kosi | M(o) | 1.59 | 5,226 |\n| 2919 | 0271L0802919 | 28.064 | 86.456 | Kosi | M(e) | 1.77 | 5,204 |\n| 2920 | 0271L0802920 | 28.059 | 86.499 | Kosi | I(s) | 0.44 | 5,640 |\n| 2921 | 0271L0802921 | 28.056 | 86.263 | Kosi | E(o) | 2.57 | 4,570 |\n| 2922 | 0271L0802922 | 28.053 | 86.491 | Kosi | M(o) | 1.85 | 5,345 |\n| 2923 | 0271L0802923 | 28.051 | 86.491 | Kosi | M(o) | 0.81 | 5,353 |\n| 2924 | 0271L0802924 | 28.051 | 86.460 | Kosi | E(o) | 0.76 | 5,175 |\n| 2925 | 0271L0802925 | 28.050 | 86.493 | Kosi | M(o) | 2.73 | 5,317 |\n| 2926 | 0271L0802926 | 28.049 | 86.484 | Kosi | M(o) | 0.64 | 5,249 |\n| 2927 | 0271L0802927 | 28.048 | 86.490 | Kosi | M(o) | 0.25 | 5,318 |\n| 2928 | 0271L0802928 | 28.047 | 86.481 | Kosi | M(o) | 0.29 | 5,238 |\n| 2929 | 0271L0802929 | 28.041 | 86.498 | Kosi | E(o) | 0.87 | 5,232 |\n| 2930 | 0271L0802930 | 28.037 | 86.408 | Kosi | M(o) | 3.23 | 4,925 |\n| 2931 | 0271L0802931 | 28.036 | 86.481 | Kosi | M(l) | 3.62 | 4,990 |\n| 2932 | 0271L0802932 | 28.033 | 86.500 | Kosi | M(e) | 60.86 | 5,057 |\n| 2933 | 0271L0802933 | 28.028 | 86.364 | Kosi | E(o) | 0.41 | 4,882 |\n| 2934 | 0271L0802934 | 28.027 | 86.390 | Kosi | M(o) | 0.47 | 5,402 |\n| 2935 | 0271L0802935 | 28.026 | 86.418 | Kosi | I(s) | 0.26 | 5,349 |\n| 2936 | 0271L0802936 | 28.023 | 86.391 | Kosi | M(o) | 1.35 | 5,301 |\n| 2937 | 0271L0802937 | 28.021 | 86.389 | Kosi | M(o) | 0.33 | 5,328 |\n| 2938 | 0271L0802938 | 28.019 | 86.393 | Kosi | M(o) | 0.75 | 5,290 |\n| 2939 | 0271L0802939 | 28.016 | 86.498 | Kosi | M(o) | 0.33 | 5,210 |\n| 2940 | 0271L0802940 | 28.014 | 86.475 | Kosi | M(o) | 1.19 | 5,352 |\n| 2941 | 0271L0802941 | 28.011 | 86.412 | Kosi | M(o) | 1.03 | 5,172 |\n| 2942 | 0271L0802942 | 28.006 | 86.481 | Kosi | M(o) | 1.53 | 5,320 |\n| 2943 | 0271L0802943 | 28.005 | 86.479 | Kosi | M(o) | 0.38 | 5,349 |\n| 2944 | 0271L0802944 | 28.003 | 86.445 | Kosi | M(o) | 2.17 | 5,101 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6229, "line_end": 6376, "token_count_estimate": 1660, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802910", "0271L0802911", "0271L0802912", "0271L0802913", "0271L0802914", "0271L0802915", "0271L0802916", "0271L0802917", "0271L0802918", "0271L0802919", "0271L0802920", "0271L0802921", "0271L0802922", "0271L0802923", "0271L0802924", "0271L0802925", "0271L0802926", "0271L0802927", "0271L0802928", "0271L0802929", "0271L0802930", "0271L0802931", "0271L0802932", "0271L0802933", "0271L0802934", "0271L0802935", "0271L0802936", "0271L0802937", "0271L0802938", "0271L0802939", "0271L0802940", "0271L0802941", "0271L0802942", "0271L0802943", "0271L0802944"]}}
{"id": "c3220a6a378ec26e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2945 | 0271L0802945 | 28.001 | 86.425 | Kosi | E(o) | 4.13 | 4,935 |\n| 2946 | 0271L0802946 | 28.000 | 86.435 | Kosi | M(o) | 1.31 | 5,007 |\n| 2947 | 0271L0902947 | 28.887 | 86.514 | Kosi | E(o) | 98.23 | 5,098 |\n| 2948 | 0271L0902948 | 28.840 | 86.575 | Kosi | E(o) | 26.39 | 5,465 |\n| 2949 | 0271L0902949 | 28.815 | 86.529 | Kosi | E(o) | 32.37 | 5,326 |\n| 2950 | 0271L0902950 | 28.799 | 86.508 | Kosi | E(o) | 0.62 | 5,570 |\n| 2951 | 0271L1102951 | 28.321 | 86.513 | Kosi | M(o) | 0.57 | 5,321 |\n| 2952 | 0271L1102952 | 28.321 | 86.511 | Kosi | M(o) | 10.75 | 5,321 |\n| 2953 | 0271L1102953 | 28.319 | 86.505 | Kosi | M(o) | 1.34 | 5,330 |\n| 2954 | 0271L1102954 | 28.317 | 86.505 | Kosi | M(o) | 1.35 | 5,320 |\n| 2955 | 0271L1102955 | 28.317 | 86.502 | Kosi | M(o) | 1.28 | 5,334 |\n| 2956 | 0271L1102956 | 28.311 | 86.515 | Kosi | M(o) | 1.55 | 5,512 |\n| 2957 | 0271L1102957 | 28.311 | 86.522 | Kosi | M(o) | 4.09 | 5,490 |\n| 2958 | 0271L1102958 | 28.311 | 86.518 | Kosi | M(o) | 1.59 | 5,498 |\n| 2959 | 0271L1102959 | 28.303 | 86.500 | Kosi | E(o) | 0.91 | 5,467 |\n| 2960 | 0271L1102960 | 28.297 | 86.483 | Kosi | E(o) | 0.74 | 5,528 |\n| 2961 | 0271L1102961 | 28.290 | 86.669 | Kosi | E(o) | 0.39 | 5,878 |\n| 2962 | 0271L1102962 | 28.284 | 86.510 | Kosi | I(s) | 2.02 | 5,166 |\n| 2963 | 0271L1102963 | 28.283 | 86.502 | Kosi | I(s) | 22.63 | 5,157 |\n| 2964 | 0271L1102964 | 28.280 | 86.481 | Kosi | M(o) | 0.47 | 5,241 |\n| 2965 | 0271L1102965 | 28.277 | 86.671 | Kosi | M(o) | 0.93 | 5,822 |\n| 2966 | 0271L1102966 | 28.276 | 86.665 | Kosi | M(o) | 0.58 | 5,862 |\n| 2967 | 0271L1102967 | 28.273 | 86.676 | Kosi | M(o) | 1.71 | 5,744 |\n| 2968 | 0271L1102968 | 28.267 | 86.502 | Kosi | M(o) | 2.63 | 5,557 |\n| 2969 | 0271L1102969 | 28.261 | 86.693 | Kosi | E(o) | 0.40 | 5,745 |\n| 2970 | 0271L1102970 | 28.259 | 86.540 | Kosi | M(o) | 0.74 | 5,543 |\n| 2971 | 0271L1102971 | 28.258 | 86.516 | Kosi | E(o) | 0.70 | 5,856 |\n| 2972 | 0271L1102972 | 28.258 | 86.520 | Kosi | E(o) | 0.33 | 5,842 |\n| 2973 | 0271L1102973 | 28.256 | 86.515 | Kosi | E(o) | 0.55 | 5,842 |\n| 2974 | 0271L1102974 | 28.256 | 86.517 | Kosi | E(o) | 0.40 | 5,823 |\n| 2975 | 0271L1102975 | 28.255 | 86.530 | Kosi | M(o) | 1.06 | 5,581 |\n| 2976 | 0271L1102976 | 28.255 | 86.518 | Kosi | E(o) | 0.40 | 5,815 |\n| 2977 | 0271L1102977 | 28.250 | 86.536 | Kosi | E(o) | 0.54 | 5,694 |\n| 2978 | 0271L1202978 | 28.277 | 86.676 | Kosi | M(o) | 1.58 | 5,773 |\n| 2979 | 0271L1202979 | 28.274 | 86.688 | Kosi | M(o) | 6.00 | 5,613 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6229, "line_end": 6376, "token_count_estimate": 1598, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L0802945", "0271L0802946", "0271L0902947", "0271L0902948", "0271L0902949", "0271L0902950", "0271L1102951", "0271L1102952", "0271L1102953", "0271L1102954", "0271L1102955", "0271L1102956", "0271L1102957", "0271L1102958", "0271L1102959", "0271L1102960", "0271L1102961", "0271L1102962", "0271L1102963", "0271L1102964", "0271L1102965", "0271L1102966", "0271L1102967", "0271L1102968", "0271L1102969", "0271L1102970", "0271L1102971", "0271L1102972", "0271L1102973", "0271L1102974", "0271L1102975", "0271L1102976", "0271L1102977", "0271L1202978", "0271L1202979"]}}
{"id": "b5954d7c9530cb09", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2980 | 0271L1202980 | 28.257 | 86.652 | Kosi | E(o) | 1.86 | 5,743 |\n| 2981 | 0271L1202981 | 28.247 | 86.662 | Kosi | M(o) | 0.51 | 5,754 |\n| 2982 | 0271L1202982 | 28.245 | 86.509 | Kosi | M(o) | 0.68 | 5,593 |\n| 2983 | 0271L1202983 | 28.242 | 86.658 | Kosi | M(o) | 1.34 | 5,690 |\n| 2984 | 0271L1202984 | 28.235 | 86.749 | Kosi | M(o) | 0.53 | 5,781 |\n| 2985 | 0271L1202985 | 28.234 | 86.667 | Kosi | M(e) | 9.87 | 5,671 |\n| 2986 | 0271L1202986 | 28.227 | 86.669 | Kosi | E(o) | 0.34 | 5,902 |\n| 2987 | 0271L1202987 | 28.221 | 86.507 | Kosi | M(o) | 0.58 | 5,504 |\n| 2988 | 0271L1202988 | 28.221 | 86.503 | Kosi | M(o) | 4.47 | 5,483 |\n| 2989 | 0271L1202989 | 28.216 | 86.588 | Kosi | M(o) | 0.32 | 5,108 |\n| 2990 | 0271L1202990 | 28.215 | 86.587 | Kosi | M(o) | 0.58 | 5,106 |\n| 2991 | 0271L1202991 | 28.215 | 86.586 | Kosi | M(o) | 0.50 | 5,104 |\n| 2992 | 0271L1202992 | 28.214 | 86.616 | Kosi | M(o) | 0.42 | 5,482 |\n| 2993 | 0271L1202993 | 28.214 | 86.624 | Kosi | M(o) | 0.72 | 5,542 |\n| 2994 | 0271L1202994 | 28.214 | 86.584 | Kosi | M(o) | 0.31 | 5,101 |\n| 2995 | 0271L1202995 | 28.213 | 86.624 | Kosi | M(o) | 1.01 | 5,545 |\n| 2996 | 0271L1202996 | 28.213 | 86.620 | Kosi | M(o) | 0.48 | 5,526 |\n| 2997 | 0271L1202997 | 28.213 | 86.615 | Kosi | M(o) | 0.32 | 5,481 |\n| 2998 | 0271L1202998 | 28.212 | 86.585 | Kosi | M(o) | 2.16 | 5,099 |\n| 2999 | 0271L1202999 | 28.212 | 86.617 | Kosi | M(o) | 0.38 | 5,504 |\n| 3000 | 0271L1203000 | 28.212 | 86.626 | Kosi | M(o) | 1.04 | 5,551 |\n| 3001 | 0271L1203001 | 28.211 | 86.583 | Kosi | M(o) | 0.37 | 5,096 |\n| 3002 | 0271L1203002 | 28.211 | 86.585 | Kosi | M(o) | 0.29 | 5,100 |\n| 3003 | 0271L1203003 | 28.211 | 86.743 | Kosi | M(o) | 1.29 | 5,874 |\n| 3004 | 0271L1203004 | 28.210 | 86.621 | Kosi | M(o) | 0.66 | 5,533 |\n| 3005 | 0271L1203005 | 28.210 | 86.654 | Kosi | M(o) | 1.13 | 5,718 |\n| 3006 | 0271L1203006 | 28.207 | 86.629 | Kosi | M(e) | 27.78 | 5,539 |\n| 3007 | 0271L1203007 | 28.204 | 86.585 | Kosi | M(o) | 0.34 | 5,099 |\n| 3008 | 0271L1203008 | 28.204 | 86.748 | Kosi | M(o) | 5.62 | 5,734 |\n| 3009 | 0271L1203009 | 28.202 | 86.622 | Kosi | M(o) | 4.76 | 5,600 |\n| 3010 | 0271L1203010 | 28.202 | 86.549 | Kosi | M(o) | 6.19 | 5,338 |\n| 3011 | 0271L1203011 | 28.194 | 86.544 | Kosi | M(o) | 0.66 | 5,502 |\n| 3012 | 0271L1203012 | 28.187 | 86.604 | Kosi | M(o) | 0.58 | 5,537 |\n| 3013 | 0271L1203013 | 28.185 | 86.532 | Kosi | M(e) | 67.68 | 5,025 |\n| 3014 | 0271L1203014 | 28.181 | 86.530 | Kosi | M(o) | 0.74 | 5,162 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6229, "line_end": 6376, "token_count_estimate": 1593, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1202980", "0271L1202981", "0271L1202982", "0271L1202983", "0271L1202984", "0271L1202985", "0271L1202986", "0271L1202987", "0271L1202988", "0271L1202989", "0271L1202990", "0271L1202991", "0271L1202992", "0271L1202993", "0271L1202994", "0271L1202995", "0271L1202996", "0271L1202997", "0271L1202998", "0271L1202999", "0271L1203000", "0271L1203001", "0271L1203002", "0271L1203003", "0271L1203004", "0271L1203005", "0271L1203006", "0271L1203007", "0271L1203008", "0271L1203009", "0271L1203010", "0271L1203011", "0271L1203012", "0271L1203013", "0271L1203014"]}}
{"id": "dd133631fd925e8a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3015 | 0271L1203015 | 28.181 | 86.581 | Kosi | M(o) | 0.31 | 5,241 |\n| 3016 | 0271L1203016 | 28.180 | 86.509 | Kosi | E(o) | 0.30 | 4,915 |\n| 3017 | 0271L1203017 | 28.178 | 86.589 | Kosi | M(o) | 0.31 | 5,182 |\n| 3018 | 0271L1203018 | 28.173 | 86.586 | Kosi | M(o) | 0.33 | 5,231 |\n| 3019 | 0271L1203019 | 28.172 | 86.518 | Kosi | M(o) | 4.13 | 5,191 |\n| 3020 | 0271L1203020 | 28.170 | 86.624 | Kosi | M(o) | 3.75 | 5,769 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6229, "line_end": 6376, "token_count_estimate": 354, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1203015", "0271L1203016", "0271L1203017", "0271L1203018", "0271L1203019", "0271L1203020"]}}
{"id": "b810cc106a68fe23", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6377, "line_end": 6383, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46dee78579057cda", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3021 | 0271L1203021 | 28.162 | 86.613 | Kosi | I(s) | 0.35 | 5,547 |\n| 3022 | 0271L1203022 | 28.161 | 86.619 | Kosi | I(s) | 0.73 | 5,581 |\n| 3023 | 0271L1203023 | 28.155 | 86.637 | Kosi | I(s) | 0.30 | 5,745 |\n| 3024 | 0271L1203024 | 28.151 | 86.535 | Kosi | M(o) | 18.48 | 5,220 |\n| 3025 | 0271L1203025 | 28.150 | 86.724 | Kosi | I(s) | 0.45 | 5,988 |\n| 3026 | 0271L1203026 | 28.149 | 86.742 | Kosi | I(s) | 0.46 | 5,773 |\n| 3027 | 0271L1203027 | 28.148 | 86.613 | Kosi | I(s) | 0.29 | 5,781 |\n| 3028 | 0271L1203028 | 28.148 | 86.615 | Kosi | M(o) | 0.35 | 5,748 |\n| 3029 | 0271L1203029 | 28.141 | 86.553 | Kosi | M(o) | 1.27 | 5,473 |\n| 3030 | 0271L1203030 | 28.139 | 86.554 | Kosi | M(o) | 0.38 | 5,434 |\n| 3031 | 0271L1203031 | 28.139 | 86.594 | Kosi | M(o) | 0.27 | 5,538 |\n| 3032 | 0271L1203032 | 28.135 | 86.531 | Kosi | M(e) | 97.85 | 4,984 |\n| 3033 | 0271L1203033 | 28.134 | 86.556 | Kosi | M(o) | 0.25 | 5,426 |\n| 3034 | 0271L1203034 | 28.133 | 86.548 | Kosi | M(o) | 3.67 | 5,237 |\n| 3035 | 0271L1203035 | 28.132 | 86.551 | Kosi | E(o) | 0.33 | 5,286 |\n| 3036 | 0271L1203036 | 28.126 | 86.531 | Kosi | E(o) | 0.30 | 5,355 |\n| 3037 | 0271L1203037 | 28.125 | 86.529 | Kosi | M(o) | 0.44 | 5,357 |\n| 3038 | 0271L1203038 | 28.106 | 86.531 | Kosi | M(e) | 4.57 | 5,266 |\n| 3039 | 0271L1203039 | 28.099 | 86.511 | Kosi | I(s) | 0.91 | 4,850 |\n| 3040 | 0271L1203040 | 28.098 | 86.514 | Kosi | I(s) | 0.34 | 4,858 |\n| 3041 | 0271L1203041 | 28.097 | 86.547 | Kosi | I(s) | 0.29 | 5,111 |\n| 3042 | 0271L1203042 | 28.093 | 86.518 | Kosi | I(s) | 0.75 | 4,885 |\n| 3043 | 0271L1203043 | 28.091 | 86.523 | Kosi | I(s) | 0.26 | 4,912 |\n| 3044 | 0271L1203044 | 28.089 | 86.547 | Kosi | I(s) | 0.30 | 5,063 |\n| 3045 | 0271L1203045 | 28.085 | 86.506 | Kosi | M(o) | 0.33 | 5,136 |\n| 3046 | 0271L1203046 | 28.084 | 86.545 | Kosi | M(o) | 0.43 | 5,228 |\n| 3047 | 0271L1203047 | 28.084 | 86.543 | Kosi | M(o) | 0.49 | 5,222 |\n| 3048 | 0271L1203048 | 28.083 | 86.503 | Kosi | M(o) | 1.16 | 5,167 |\n| 3049 | 0271L1203049 | 28.083 | 86.514 | Kosi | M(o) | 0.62 | 5,195 |\n| 3050 | 0271L1203050 | 28.078 | 86.581 | Kosi | I(s) | 0.26 | 5,375 |\n| 3051 | 0271L1203051 | 28.073 | 86.520 | Kosi | M(e) | 23.11 | 5,216 |\n| 3052 | 0271L1203052 | 28.070 | 86.585 | Kosi | I(s) | 0.36 | 5,315 |\n| 3053 | 0271L1203053 | 28.070 | 86.590 | Kosi | I(s) | 0.26 | 5,308 |\n| 3054 | 0271L1203054 | 28.067 | 86.583 | Kosi | I(s) | 0.50 | 5,323 |\n| 3055 | 0271L1203055 | 28.067 | 86.516 | Kosi | M(o) | 0.26 | 5,289 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6384, "line_end": 6531, "token_count_estimate": 1602, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1203021", "0271L1203022", "0271L1203023", "0271L1203024", "0271L1203025", "0271L1203026", "0271L1203027", "0271L1203028", "0271L1203029", "0271L1203030", "0271L1203031", "0271L1203032", "0271L1203033", "0271L1203034", "0271L1203035", "0271L1203036", "0271L1203037", "0271L1203038", "0271L1203039", "0271L1203040", "0271L1203041", "0271L1203042", "0271L1203043", "0271L1203044", "0271L1203045", "0271L1203046", "0271L1203047", "0271L1203048", "0271L1203049", "0271L1203050", "0271L1203051", "0271L1203052", "0271L1203053", "0271L1203054", "0271L1203055"]}}
{"id": "6ef0043d7b48634f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3056 | 0271L1203056 | 28.066 | 86.585 | Kosi | I(s) | 0.33 | 5,297 |\n| 3057 | 0271L1203057 | 28.066 | 86.585 | Kosi | I(s) | 0.34 | 5,289 |\n| 3058 | 0271L1203058 | 28.063 | 86.520 | Kosi | M(o) | 2.79 | 5,255 |\n| 3059 | 0271L1203059 | 28.062 | 86.525 | Kosi | M(o) | 0.25 | 5,226 |\n| 3060 | 0271L1203060 | 28.060 | 86.591 | Kosi | I(s) | 0.44 | 5,236 |\n| 3061 | 0271L1203061 | 28.053 | 86.624 | Kosi | I(s) | 0.51 | 5,386 |\n| 3062 | 0271L1203062 | 28.053 | 86.624 | Kosi | I(s) | 0.30 | 5,381 |\n| 3063 | 0271L1203063 | 28.050 | 86.582 | Kosi | I(s) | 0.73 | 5,213 |\n| 3064 | 0271L1203064 | 28.049 | 86.588 | Kosi | I(s) | 1.52 | 5,156 |\n| 3065 | 0271L1203065 | 28.048 | 86.594 | Kosi | I(s) | 0.65 | 5,172 |\n| 3066 | 0271L1203066 | 28.048 | 86.679 | Kosi | I(s) | 0.39 | 5,204 |\n| 3067 | 0271L1203067 | 28.048 | 86.504 | Kosi | M(e) | 9.00 | 5,341 |\n| 3068 | 0271L1203068 | 28.047 | 86.586 | Kosi | I(s) | 0.25 | 5,163 |\n| 3069 | 0271L1203069 | 28.046 | 86.587 | Kosi | I(s) | 0.38 | 5,158 |\n| 3070 | 0271L1203070 | 28.046 | 86.625 | Kosi | I(s) | 0.28 | 5,314 |\n| 3071 | 0271L1203071 | 28.045 | 86.592 | Kosi | I(s) | 0.50 | 5,137 |\n| 3072 | 0271L1203072 | 28.044 | 86.514 | Kosi | M(l) | 57.94 | 5,241 |\n| 3073 | 0271L1203073 | 28.044 | 86.709 | Kosi | M(o) | 0.89 | 5,330 |\n| 3074 | 0271L1203074 | 28.044 | 86.519 | Kosi | M(o) | 1.10 | 5,262 |\n| 3075 | 0271L1203075 | 28.044 | 86.681 | Kosi | I(s) | 0.25 | 5,186 |\n| 3076 | 0271L1203076 | 28.044 | 86.627 | Kosi | I(s) | 1.26 | 5,285 |\n| 3077 | 0271L1203077 | 28.042 | 86.518 | Kosi | M(l) | 2.92 | 5,256 |\n| 3078 | 0271L1203078 | 28.041 | 86.706 | Kosi | M(o) | 2.75 | 5,357 |\n| 3079 | 0271L1203079 | 28.040 | 86.673 | Kosi | M(o) | 2.38 | 5,221 |\n| 3080 | 0271L1203080 | 28.039 | 86.508 | Kosi | M(o) | 0.92 | 5,221 |\n| 3081 | 0271L1203081 | 28.039 | 86.588 | Kosi | I(s) | 0.45 | 5,125 |\n| 3082 | 0271L1203082 | 28.038 | 86.710 | Kosi | M(e) | 15.44 | 5,363 |\n| 3083 | 0271L1203083 | 28.037 | 86.651 | Kosi | M(o) | 0.59 | 5,437 |\n| 3084 | 0271L1203084 | 28.036 | 86.689 | Kosi | I(s) | 0.60 | 5,121 |\n| 3085 | 0271L1203085 | 28.036 | 86.610 | Kosi | M(o) | 1.73 | 5,335 |\n| 3086 | 0271L1203086 | 28.036 | 86.602 | Kosi | E(o) | 0.67 | 5,543 |\n| 3087 | 0271L1203087 | 28.036 | 86.651 | Kosi | M(o) | 0.26 | 5,430 |\n| 3088 | 0271L1203088 | 28.035 | 86.508 | Kosi | I(s) | 0.64 | 5,101 |\n| 3089 | 0271L1203089 | 28.034 | 86.671 | Kosi | E(o) | 1.65 | 5,400 |\n| 3090 | 0271L1203090 | 28.034 | 86.580 | Kosi | I(s) | 0.64 | 5,054 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6384, "line_end": 6531, "token_count_estimate": 1618, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1203056", "0271L1203057", "0271L1203058", "0271L1203059", "0271L1203060", "0271L1203061", "0271L1203062", "0271L1203063", "0271L1203064", "0271L1203065", "0271L1203066", "0271L1203067", "0271L1203068", "0271L1203069", "0271L1203070", "0271L1203071", "0271L1203072", "0271L1203073", "0271L1203074", "0271L1203075", "0271L1203076", "0271L1203077", "0271L1203078", "0271L1203079", "0271L1203080", "0271L1203081", "0271L1203082", "0271L1203083", "0271L1203084", "0271L1203085", "0271L1203086", "0271L1203087", "0271L1203088", "0271L1203089", "0271L1203090"]}}
{"id": "512140eb21bce383", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3091 | 0271L1203091 | 28.031 | 86.623 | Kosi | I(s) | 0.43 | 5,177 |\n| 3092 | 0271L1203092 | 28.031 | 86.724 | Kosi | I(s) | 0.49 | 5,078 |\n| 3093 | 0271L1203093 | 28.031 | 86.647 | Kosi | M(o) | 1.97 | 5,324 |\n| 3094 | 0271L1203094 | 28.030 | 86.553 | Kosi | I(s) | 0.25 | 5,179 |\n| 3095 | 0271L1203095 | 28.029 | 86.689 | Kosi | I(s) | 0.43 | 5,079 |\n| 3096 | 0271L1203096 | 28.029 | 86.657 | Kosi | I(s) | 0.35 | 5,506 |\n| 3097 | 0271L1203097 | 28.029 | 86.670 | Kosi | M(o) | 2.86 | 5,219 |\n| 3098 | 0271L1203098 | 28.029 | 86.582 | Kosi | I(s) | 0.41 | 5,054 |\n| 3099 | 0271L1203099 | 28.028 | 86.736 | Kosi | I(s) | 0.33 | 5,234 |\n| 3100 | 0271L1203100 | 28.028 | 86.559 | Kosi | I(s) | 0.42 | 5,135 |\n| 3101 | 0271L1203101 | 28.028 | 86.723 | Kosi | I(s) | 0.25 | 5,056 |\n| 3102 | 0271L1203102 | 28.028 | 86.685 | Kosi | M(o) | 2.09 | 5,161 |\n| 3103 | 0271L1203103 | 28.028 | 86.687 | Kosi | M(o) | 1.46 | 5,153 |\n| 3104 | 0271L1203104 | 28.027 | 86.557 | Kosi | I(s) | 0.72 | 5,141 |\n| 3105 | 0271L1203105 | 28.026 | 86.682 | Kosi | M(o) | 18.71 | 5,149 |\n| 3106 | 0271L1203106 | 28.026 | 86.609 | Kosi | E(o) | 0.27 | 5,348 |\n| 3107 | 0271L1203107 | 28.025 | 86.676 | Kosi | M(o) | 0.27 | 5,200 |\n| 3108 | 0271L1203108 | 28.025 | 86.559 | Kosi | I(s) | 0.25 | 5,122 |\n| 3109 | 0271L1203109 | 28.024 | 86.671 | Kosi | M(o) | 2.57 | 5,336 |\n| 3110 | 0271L1203110 | 28.023 | 86.579 | Kosi | I(s) | 0.52 | 5,026 |\n| 3111 | 0271L1203111 | 28.023 | 86.608 | Kosi | M(o) | 1.07 | 5,419 |\n| 3112 | 0271L1203112 | 28.022 | 86.600 | Kosi | M(o) | 0.49 | 5,268 |\n| 3113 | 0271L1203113 | 28.022 | 86.565 | Kosi | I(s) | 0.30 | 5,097 |\n| 3114 | 0271L1203114 | 28.021 | 86.708 | Kosi | I(s) | 1.08 | 4,995 |\n| 3115 | 0271L1203115 | 28.021 | 86.579 | Kosi | I(s) | 0.49 | 5,015 |\n| 3116 | 0271L1203116 | 28.021 | 86.548 | Kosi | M(o) | 0.72 | 5,183 |\n| 3117 | 0271L1203117 | 28.021 | 86.697 | Kosi | I(s) | 0.35 | 4,985 |\n| 3118 | 0271L1203118 | 28.020 | 86.658 | Kosi | E(o) | 0.39 | 5,458 |\n| 3119 | 0271L1203119 | 28.020 | 86.550 | Kosi | M(o) | 0.27 | 5,173 |\n| 3120 | 0271L1203120 | 28.019 | 86.552 | Kosi | M(o) | 0.37 | 5,161 |\n| 3121 | 0271L1203121 | 28.019 | 86.733 | Kosi | M(o) | 3.45 | 5,224 |\n| 3122 | 0271L1203122 | 28.019 | 86.697 | Kosi | I(s) | 0.35 | 4,971 |\n| 3123 | 0271L1203123 | 28.017 | 86.721 | Kosi | M(o) | 19.81 | 5,066 |\n| 3124 | 0271L1203124 | 28.017 | 86.581 | Kosi | I(s) | 1.25 | 4,999 |\n| 3125 | 0271L1203125 | 28.016 | 86.729 | Kosi | M(o) | 0.26 | 5,188 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6384, "line_end": 6531, "token_count_estimate": 1623, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1203091", "0271L1203092", "0271L1203093", "0271L1203094", "0271L1203095", "0271L1203096", "0271L1203097", "0271L1203098", "0271L1203099", "0271L1203100", "0271L1203101", "0271L1203102", "0271L1203103", "0271L1203104", "0271L1203105", "0271L1203106", "0271L1203107", "0271L1203108", "0271L1203109", "0271L1203110", "0271L1203111", "0271L1203112", "0271L1203113", "0271L1203114", "0271L1203115", "0271L1203116", "0271L1203117", "0271L1203118", "0271L1203119", "0271L1203120", "0271L1203121", "0271L1203122", "0271L1203123", "0271L1203124", "0271L1203125"]}}
{"id": "2476e94fdb74d65d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3126 | 0271L1203126 | 28.016 | 86.675 | Kosi | E(o) | 0.61 | 5,208 |\n| 3127 | 0271L1203127 | 28.016 | 86.643 | Kosi | M(o) | 3.42 | 5,109 |\n| 3128 | 0271L1203128 | 28.016 | 86.724 | Kosi | M(o) | 1.08 | 5,103 |\n| 3129 | 0271L1203129 | 28.015 | 86.503 | Kosi | M(o) | 1.92 | 5,320 |\n| 3130 | 0271L1203130 | 28.015 | 86.699 | Kosi | I(s) | 0.55 | 4,975 |\n| 3131 | 0271L1203131 | 28.014 | 86.705 | Kosi | I(s) | 0.94 | 4,957 |\n| 3132 | 0271L1203132 | 28.010 | 86.544 | Kosi | I(s) | 0.27 | 5,277 |\n| 3133 | 0271L1203133 | 28.009 | 86.560 | Kosi | M(o) | 0.59 | 5,209 |\n| 3134 | 0271L1203134 | 28.009 | 86.695 | Kosi | I(s) | 1.12 | 4,922 |\n| 3135 | 0271L1203135 | 28.008 | 86.628 | Kosi | I(s) | 0.58 | 5,021 |\n| 3136 | 0271L1203136 | 28.008 | 86.566 | Kosi | M(o) | 0.52 | 5,176 |\n| 3137 | 0271L1203137 | 28.007 | 86.641 | Kosi | M(o) | 3.81 | 5,157 |\n| 3138 | 0271L1203138 | 28.007 | 86.592 | Kosi | I(s) | 0.37 | 4,943 |\n| 3139 | 0271L1203139 | 28.006 | 86.682 | Kosi | M(l) | 17.13 | 4,947 |\n| 3140 | 0271L1203140 | 28.005 | 86.627 | Kosi | I(s) | 0.30 | 5,021 |\n| 3141 | 0271L1203141 | 28.005 | 86.632 | Kosi | M(l) | 2.49 | 5,062 |\n| 3142 | 0271L1203142 | 28.005 | 86.625 | Kosi | I(s) | 0.55 | 5,013 |\n| 3143 | 0271L1203143 | 28.004 | 86.598 | Kosi | I(s) | 0.29 | 4,918 |\n| 3144 | 0271L1203144 | 28.003 | 86.694 | Kosi | I(s) | 0.47 | 4,919 |\n| 3145 | 0271L1203145 | 28.003 | 86.693 | Kosi | I(s) | 0.35 | 4,919 |\n| 3146 | 0271L1203146 | 28.001 | 86.624 | Kosi | I(s) | 0.99 | 4,982 |\n| 3147 | 0271L1203147 | 28.000 | 86.692 | Kosi | I(s) | 1.22 | 4,909 |\n| 3148 | 0271L1603148 | 28.195 | 86.802 | Kosi | E(o) | 0.61 | 5,588 |\n| 3149 | 0271L1603149 | 28.190 | 86.774 | Kosi | M(o) | 0.52 | 5,490 |\n| 3150 | 0271L1603150 | 28.185 | 86.805 | Kosi | M(e) | 2.64 | 5,530 |\n| 3151 | 0271L1603151 | 28.182 | 86.758 | Kosi | I(s) | 0.63 | 5,526 |\n| 3152 | 0271L1603152 | 28.180 | 86.770 | Kosi | E(o) | 0.40 | 5,681 |\n| 3153 | 0271L1603153 | 28.178 | 86.775 | Kosi | M(o) | 0.41 | 5,855 |\n| 3154 | 0271L1603154 | 28.166 | 86.807 | Kosi | M(e) | 5.21 | 5,663 |\n| 3155 | 0271L1603155 | 28.145 | 86.968 | Kosi | M(o) | 1.97 | 5,729 |\n| 3156 | 0271L1603156 | 28.144 | 86.834 | Kosi | M(o) | 0.49 | 5,410 |\n| 3157 | 0271L1603157 | 28.144 | 86.834 | Kosi | I(s) | 1.89 | 5,408 |\n| 3158 | 0271L1603158 | 28.141 | 86.831 | Kosi | I(s) | 6.11 | 5,414 |\n| 3159 | 0271L1603159 | 28.132 | 86.851 | Kosi | M(o) | 3.45 | 5,155 |\n| 3160 | 0271L1603160 | 28.130 | 86.855 | Kosi | I(s) | 0.35 | 5,207 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6384, "line_end": 6531, "token_count_estimate": 1586, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1203126", "0271L1203127", "0271L1203128", "0271L1203129", "0271L1203130", "0271L1203131", "0271L1203132", "0271L1203133", "0271L1203134", "0271L1203135", "0271L1203136", "0271L1203137", "0271L1203138", "0271L1203139", "0271L1203140", "0271L1203141", "0271L1203142", "0271L1203143", "0271L1203144", "0271L1203145", "0271L1203146", "0271L1203147", "0271L1603148", "0271L1603149", "0271L1603150", "0271L1603151", "0271L1603152", "0271L1603153", "0271L1603154", "0271L1603155", "0271L1603156", "0271L1603157", "0271L1603158", "0271L1603159", "0271L1603160"]}}
{"id": "5e12dada94770c99", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3161 | 0271L1603161 | 28.130 | 86.856 | Kosi | I(s) | 0.39 | 5,214 |\n| 3162 | 0271L1603162 | 28.129 | 86.858 | Kosi | I(s) | 0.36 | 5,218 |\n| 3163 | 0271L1603163 | 28.118 | 86.862 | Kosi | I(s) | 6.09 | 5,224 |\n| 3164 | 0271L1603164 | 28.112 | 86.863 | Kosi | I(s) | 39.40 | 5,218 |\n| 3165 | 0271L1603165 | 28.108 | 86.861 | Kosi | I(s) | 0.58 | 5,233 |\n| 3166 | 0271L1603166 | 28.098 | 86.890 | Kosi | M(o) | 0.48 | 5,544 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6384, "line_end": 6531, "token_count_estimate": 354, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271L1603161", "0271L1603162", "0271L1603163", "0271L1603164", "0271L1603165", "0271L1603166"]}}
{"id": "63469424dfb7bd35", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6532, "line_end": 6537, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c50595a7a536b81a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3459 | 0271P1003459 | 28.749 | 87.650 | Kosi | E(o) | 0.83 | 5,662 |\n| 3460 | 0271P1003460 | 28.748 | 87.605 | Kosi | E(o) | 3.72 | 5,620 |\n| 3461 | 0271P1003461 | 28.747 | 87.625 | Kosi | E(o) | 17.28 | 5,545 |\n| 3462 | 0271P1003462 | 28.747 | 87.545 | Kosi | O | 0.39 | 5,262 |\n| 3463 | 0271P1003463 | 28.744 | 87.593 | Kosi | E(o) | 4.27 | 5,527 |\n| 3464 | 0271P1003464 | 28.740 | 87.628 | Kosi | E(o) | 0.73 | 5,521 |\n| 3465 | 0271P1003465 | 28.738 | 87.544 | Kosi | O | 2.07 | 5,245 |\n| 3466 | 0271P1003466 | 28.730 | 87.549 | Kosi | E(o) | 1.49 | 5,269 |\n| 3467 | 0271P1003467 | 28.728 | 87.645 | Kosi | E(o) | 0.97 | 5,401 |\n| 3468 | 0271P1003468 | 28.727 | 87.583 | Kosi | E(o) | 0.36 | 5,531 |\n| 3469 | 0271P1003469 | 28.725 | 87.565 | Kosi | E(o) | 5.06 | 5,392 |\n| 3470 | 0271P1003470 | 28.722 | 87.626 | Kosi | E(o) | 0.55 | 5,674 |\n| 3471 | 0271P1003471 | 28.721 | 87.579 | Kosi | E(o) | 0.43 | 5,531 |\n| 3472 | 0271P1003472 | 28.717 | 87.605 | Kosi | E(o) | 2.14 | 5,412 |\n| 3473 | 0271P1003473 | 28.717 | 87.596 | Kosi | E(o) | 1.81 | 5,626 |\n| 3474 | 0271P1003474 | 28.712 | 87.548 | Kosi | O | 41.20 | 5,188 |\n| 3475 | 0271P1003475 | 28.694 | 87.534 | Kosi | E(o) | 60.74 | 5,158 |\n| 3476 | 0271P1003476 | 28.607 | 88.606 | Kosi | O | 0.97 | 4,695 |\n| 3477 | 0271P1003477 | 28.607 | 88.605 | Kosi | O | 0.60 | 4,694 |\n| 3478 | 0271P1003478 | 28.588 | 88.653 | Kosi | O | 0.40 | 5,014 |\n| 3479 | 0271P1103479 | 28.365 | 87.689 | Kosi | E(o) | 2.80 | 5,455 |\n| 3480 | 0271P1103480 | 28.351 | 87.684 | Kosi | E(o) | 0.54 | 5,512 |\n| 3481 | 0271P1103481 | 28.350 | 87.682 | Kosi | E(o) | 2.29 | 5,514 |\n| 3482 | 0271P1103482 | 28.343 | 87.677 | Kosi | E(o) | 0.38 | 5,454 |\n| 3483 | 0271P1103483 | 28.342 | 87.674 | Kosi | E(o) | 4.68 | 5,457 |\n| 3484 | 0271P1103484 | 28.328 | 87.679 | Kosi | E(o) | 0.65 | 5,508 |\n| 3485 | 0271P1103485 | 28.321 | 87.671 | Kosi | E(o) | 0.28 | 5,451 |\n| 3486 | 0271P1103486 | 28.298 | 87.550 | Kosi | E(o) | 0.75 | 5,555 |\n| 3487 | 0271P1103487 | 28.294 | 87.670 | Kosi | E(o) | 0.41 | 5,753 |\n| 3488 | 0271P1103488 | 28.292 | 87.661 | Kosi | E(o) | 1.99 | 5,612 |\n| 3489 | 0271P1103489 | 28.285 | 87.565 | Kosi | E(o) | 0.74 | 5,528 |\n| 3490 | 0271P1103490 | 28.281 | 87.662 | Kosi | M(o) | 0.27 | 5,715 |\n| 3491 | 0271P1103491 | 28.280 | 87.661 | Kosi | M(o) | 0.35 | 5,717 |\n| 3492 | 0271P1103492 | 28.280 | 87.663 | Kosi | M(o) | 0.28 | 5,718 |\n| 3493 | 0271P1103493 | 28.279 | 87.670 | Kosi | M(o) | 2.49 | 5,632 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6538, "line_end": 6685, "token_count_estimate": 1574, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271P1003459", "0271P1003460", "0271P1003461", "0271P1003462", "0271P1003463", "0271P1003464", "0271P1003465", "0271P1003466", "0271P1003467", "0271P1003468", "0271P1003469", "0271P1003470", "0271P1003471", "0271P1003472", "0271P1003473", "0271P1003474", "0271P1003475", "0271P1003476", "0271P1003477", "0271P1003478", "0271P1103479", "0271P1103480", "0271P1103481", "0271P1103482", "0271P1103483", "0271P1103484", "0271P1103485", "0271P1103486", "0271P1103487", "0271P1103488", "0271P1103489", "0271P1103490", "0271P1103491", "0271P1103492", "0271P1103493"]}}
{"id": "f0ea4eb5a8a7b5a0", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3494 | 0271P1103494 | 28.278 | 87.661 | Kosi | M(o) | 3.76 | 5,765 |\n| 3495 | 0271P1103495 | 28.277 | 87.588 | Kosi | M(o) | 5.35 | 5,516 |\n| 3496 | 0271P1103496 | 28.271 | 87.640 | Kosi | M(o) | 1.72 | 5,714 |\n| 3497 | 0271P1103497 | 28.268 | 87.634 | Kosi | M(o) | 10.04 | 5,674 |\n| 3498 | 0271P1103498 | 28.262 | 87.588 | Kosi | E(o) | 0.75 | 5,855 |\n| 3499 | 0271P1103499 | 28.255 | 87.648 | Kosi | M(o) | 1.53 | 5,730 |\n| 3500 | 0271P1103500 | 28.252 | 87.655 | Kosi | M(o) | 2.14 | 5,633 |\n| 3501 | 0271P1103501 | 28.250 | 87.621 | Kosi | E(o) | 7.44 | 5,840 |\n| 3502 | 0271P1203502 | 28.249 | 87.527 | Kosi | E(o) | 0.25 | 5,192 |\n| 3503 | 0271P1203503 | 28.249 | 87.658 | Kosi | M(o) | 1.32 | 5,588 |\n| 3504 | 0271P1203504 | 28.248 | 87.599 | Kosi | E(o) | 8.90 | 5,731 |\n| 3505 | 0271P1203505 | 28.247 | 87.527 | Kosi | E(o) | 4.71 | 5,186 |\n| 3506 | 0271P1203506 | 28.242 | 87.547 | Kosi | E(o) | 1.88 | 5,167 |\n| 3507 | 0271P1203507 | 28.239 | 87.659 | Kosi | M(o) | 0.84 | 5,636 |\n| 3508 | 0271P1203508 | 28.237 | 87.616 | Kosi | M(o) | 0.74 | 5,707 |\n| 3509 | 0271P1203509 | 28.237 | 87.619 | Kosi | M(o) | 1.06 | 5,707 |\n| 3510 | 0271P1203510 | 28.236 | 87.501 | Kosi | M(o) | 20.48 | 5,225 |\n| 3511 | 0271P1203511 | 28.236 | 87.659 | Kosi | M(o) | 3.59 | 5,594 |\n| 3512 | 0271P1203512 | 28.234 | 87.604 | Kosi | M(o) | 0.63 | 5,606 |\n| 3513 | 0271P1203513 | 28.234 | 87.607 | Kosi | M(o) | 3.02 | 5,618 |\n| 3514 | 0271P1203514 | 28.230 | 87.591 | Kosi | M(e) | 78.90 | 5,410 |\n| 3515 | 0271P1203515 | 28.229 | 87.558 | Kosi | M(e) | 6.80 | 5,340 |\n| 3516 | 0271P1203516 | 28.228 | 87.578 | Kosi | M(e) | 21.94 | 5,247 |\n| 3517 | 0271P1203517 | 28.224 | 87.582 | Kosi | M(o) | 2.09 | 5,309 |\n| 3518 | 0271P1203518 | 28.222 | 87.574 | Kosi | M(o) | 6.13 | 5,394 |\n| 3519 | 0271P1203519 | 28.219 | 87.534 | Kosi | E(o) | 0.97 | 5,317 |\n| 3520 | 0271P1203520 | 28.211 | 87.533 | Kosi | E(o) | 5.54 | 5,222 |\n| 3521 | 0271P1203521 | 28.206 | 87.560 | Kosi | M(e) | 15.95 | 5,337 |\n| 3522 | 0271P1203522 | 28.202 | 87.504 | Kosi | O | 1.12 | 4,795 |\n| 3523 | 0271P1203523 | 28.200 | 87.505 | Kosi | O | 1.49 | 4,804 |\n| 3524 | 0271P1203524 | 28.195 | 87.641 | Kosi | M(e) | 47.42 | 5,352 |\n| 3525 | 0271P1203525 | 28.188 | 87.505 | Kosi | E(o) | 3.85 | 5,143 |\n| 3526 | 0271P1203526 | 28.180 | 87.508 | Kosi | E(o) | 0.85 | 5,219 |\n| 3527 | 0271P1203527 | 28.178 | 87.563 | Kosi | M(e) | 104.19 | 5,011 |\n| 3528 | 0271P1203528 | 28.177 | 87.507 | Kosi | E(o) | 0.93 | 5,248 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6538, "line_end": 6685, "token_count_estimate": 1591, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271P1103494", "0271P1103495", "0271P1103496", "0271P1103497", "0271P1103498", "0271P1103499", "0271P1103500", "0271P1103501", "0271P1203502", "0271P1203503", "0271P1203504", "0271P1203505", "0271P1203506", "0271P1203507", "0271P1203508", "0271P1203509", "0271P1203510", "0271P1203511", "0271P1203512", "0271P1203513", "0271P1203514", "0271P1203515", "0271P1203516", "0271P1203517", "0271P1203518", "0271P1203519", "0271P1203520", "0271P1203521", "0271P1203522", "0271P1203523", "0271P1203524", "0271P1203525", "0271P1203526", "0271P1203527", "0271P1203528"]}}
{"id": "983b05f5ae2d3bf1", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3529 | 0271P1203529 | 28.174 | 87.530 | Kosi | E(o) | 3.83 | 5,198 |\n| 3530 | 0271P1203530 | 28.173 | 87.562 | Kosi | M(o) | 2.33 | 5,113 |\n| 3531 | 0271P1203531 | 28.168 | 87.503 | Kosi | E(o) | 1.08 | 5,312 |\n| 3532 | 0271P1203532 | 28.167 | 87.623 | Kosi | M(e) | 20.87 | 5,375 |\n| 3533 | 0271P1203533 | 28.167 | 87.554 | Kosi | E(o) | 1.11 | 5,171 |\n| 3534 | 0271P1203534 | 28.164 | 87.578 | Kosi | M(e) | 20.22 | 5,171 |\n| 3535 | 0271P1203535 | 28.161 | 87.517 | Kosi | E(o) | 1.39 | 5,173 |\n| 3536 | 0271P1203536 | 28.161 | 87.532 | Kosi | E(o) | 7.74 | 5,079 |\n| 3537 | 0271P1203537 | 28.160 | 87.605 | Kosi | E(o) | 0.80 | 5,310 |\n| 3538 | 0271P1203538 | 28.156 | 87.502 | Kosi | E(o) | 0.29 | 5,224 |\n| 3539 | 0271P1203539 | 28.156 | 87.567 | Kosi | E(o) | 0.49 | 5,153 |\n| 3540 | 0271P1203540 | 28.155 | 87.612 | Kosi | M(o) | 13.82 | 5,241 |\n| 3541 | 0271P1203541 | 28.154 | 87.617 | Kosi | M(o) | 5.47 | 5,259 |\n| 3542 | 0271P1203542 | 28.154 | 87.564 | Kosi | E(o) | 2.53 | 5,188 |\n| 3543 | 0271P1203543 | 28.150 | 87.563 | Kosi | E(o) | 2.32 | 5,250 |\n| 3544 | 0271P1203544 | 28.142 | 87.552 | Kosi | M(o) | 0.52 | 5,093 |\n| 3545 | 0271P1203545 | 28.141 | 87.588 | Kosi | E(o) | 6.93 | 5,151 |\n| 3546 | 0271P1203546 | 28.140 | 87.541 | Kosi | E(o) | 1.39 | 4,968 |\n| 3547 | 0271P1203547 | 28.138 | 87.658 | Kosi | E(o) | 0.28 | 5,056 |\n| 3548 | 0271P1203548 | 28.138 | 87.653 | Kosi | E(o) | 0.35 | 5,058 |\n| 3549 | 0271P1203549 | 28.138 | 87.650 | Kosi | E(o) | 5.99 | 5,059 |\n| 3550 | 0271P1203550 | 28.137 | 87.655 | Kosi | E(o) | 0.28 | 5,059 |\n| 3551 | 0271P1203551 | 28.135 | 87.656 | Kosi | E(o) | 0.77 | 5,052 |\n| 3552 | 0271P1203552 | 28.135 | 87.653 | Kosi | E(o) | 0.52 | 5,056 |\n| 3553 | 0271P1203553 | 28.134 | 87.629 | Kosi | E(o) | 1.16 | 5,726 |\n| 3554 | 0271P1203554 | 28.133 | 87.621 | Kosi | E(o) | 6.65 | 5,468 |\n| 3555 | 0271P1203555 | 28.131 | 87.607 | Kosi | E(o) | 2.12 | 5,391 |\n| 3556 | 0271P1203556 | 28.131 | 87.599 | Kosi | M(o) | 14.18 | 5,261 |\n| 3557 | 0271P1203557 | 28.129 | 87.561 | Kosi | E(o) | 0.34 | 5,296 |\n| 3558 | 0271P1203558 | 28.125 | 87.556 | Kosi | E(o) | 7.84 | 5,069 |\n| 3559 | 0271P1203559 | 28.118 | 87.615 | Kosi | M(e) | 35.67 | 5,052 |\n| 3560 | 0271P1203560 | 28.116 | 87.586 | Kosi | M(e) | 12.43 | 5,050 |\n| 3561 | 0271P1203561 | 28.114 | 87.655 | Kosi | M(e) | 146.34 | 4,954 |\n| 3562 | 0271P1203562 | 28.107 | 87.584 | Kosi | M(e) | 15.91 | 4,968 |\n| 3563 | 0271P1203563 | 28.103 | 87.569 | Kosi | E(o) | 3.13 | 4,904 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6538, "line_end": 6685, "token_count_estimate": 1587, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271P1203529", "0271P1203530", "0271P1203531", "0271P1203532", "0271P1203533", "0271P1203534", "0271P1203535", "0271P1203536", "0271P1203537", "0271P1203538", "0271P1203539", "0271P1203540", "0271P1203541", "0271P1203542", "0271P1203543", "0271P1203544", "0271P1203545", "0271P1203546", "0271P1203547", "0271P1203548", "0271P1203549", "0271P1203550", "0271P1203551", "0271P1203552", "0271P1203553", "0271P1203554", "0271P1203555", "0271P1203556", "0271P1203557", "0271P1203558", "0271P1203559", "0271P1203560", "0271P1203561", "0271P1203562", "0271P1203563"]}}
{"id": "a29886b397208f19", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3564 | 0271P1203564 | 28.103 | 87.627 | Kosi | E(o) | 0.94 | 5,447 |\n| 3565 | 0271P1203565 | 28.103 | 87.619 | Kosi | E(o) | 0.53 | 5,487 |\n| 3566 | 0271P1203566 | 28.093 | 87.637 | Kosi | M(e) | 72.47 | 5,178 |\n| 3567 | 0271P1203567 | 28.091 | 87.548 | Kosi | E(o) | 3.26 | 5,080 |\n| 3568 | 0271P1203568 | 28.082 | 87.572 | Kosi | E(o) | 0.57 | 5,179 |\n| 3569 | 0271P1203569 | 28.078 | 87.558 | Kosi | E(o) | 1.77 | 5,057 |\n| 3570 | 0271P1203570 | 28.072 | 87.578 | Kosi | M(l) | 3.21 | 4,813 |\n| 3571 | 0271P1203571 | 28.072 | 87.629 | Kosi | E(o) | 2.90 | 5,504 |\n| 3572 | 0271P1203572 | 28.070 | 87.632 | Kosi | E(o) | 1.68 | 5,482 |\n| 3573 | 0271P1203573 | 28.068 | 87.501 | Kosi | E(o) | 0.66 | 4,739 |\n| 3574 | 0271P1203574 | 28.064 | 87.628 | Kosi | E(o) | 3.87 | 5,360 |\n| 3575 | 0271P1203575 | 28.060 | 87.502 | Kosi | E(o) | 1.41 | 4,932 |\n| 3576 | 0271P1203576 | 28.060 | 87.530 | Kosi | E(o) | 1.35 | 4,843 |\n| 3577 | 0271P1203577 | 28.057 | 87.622 | Kosi | M(o) | 1.09 | 5,143 |\n| 3578 | 0271P1203578 | 28.052 | 87.548 | Kosi | E(o) | 0.29 | 5,081 |\n| 3579 | 0271P1203579 | 28.052 | 87.627 | Kosi | M(e) | 18.40 | 5,066 |\n| 3580 | 0271P1203580 | 28.051 | 87.504 | Kosi | E(o) | 1.00 | 4,962 |\n| 3581 | 0271P1203581 | 28.049 | 87.621 | Kosi | M(o) | 0.40 | 5,231 |\n| 3582 | 0271P1203582 | 28.048 | 87.581 | Kosi | M(o) | 7.25 | 4,716 |\n| 3583 | 0271P1203583 | 28.046 | 87.517 | Kosi | E(o) | 1.12 | 4,922 |\n| 3584 | 0271P1203584 | 28.045 | 87.536 | Kosi | E(o) | 1.33 | 4,889 |\n| 3585 | 0271P1203585 | 28.044 | 87.626 | Kosi | M(o) | 3.17 | 5,206 |\n| 3586 | 0271P1203586 | 28.020 | 87.584 | Kosi | M(e) | 2.56 | 4,783 |\n| 3587 | 0271P1203587 | 28.019 | 87.501 | Kosi | E(o) | 1.28 | 4,752 |\n| 3588 | 0271P1203588 | 28.018 | 87.572 | Kosi | E(o) | 0.45 | 4,961 |\n| 3589 | 0271P1203589 | 28.014 | 87.607 | Kosi | E(o) | 1.54 | 5,206 |\n| 3590 | 0271P1203590 | 28.013 | 87.526 | Kosi | E(o) | 0.64 | 4,686 |\n| 3591 | 0271P1203591 | 28.013 | 87.611 | Kosi | E(c) | 2.19 | 5,076 |\n| 3592 | 0271P1203592 | 28.013 | 87.607 | Kosi | E(o) | 0.54 | 5,204 |\n| 3593 | 0271P1203593 | 28.012 | 87.510 | Kosi | E(o) | 1.47 | 4,898 |\n| 3594 | 0271P1203594 | 28.010 | 87.520 | Kosi | E(o) | 1.73 | 4,730 |\n| 3595 | 0271P1203595 | 28.008 | 87.516 | Kosi | E(o) | 0.77 | 4,760 |\n| 3596 | 0271P1303596 | 28.752 | 87.937 | Kosi | E(o) | 0.55 | 5,598 |\n| 3597 | 0271P1303597 | 27.961 | 87.928 | Kosi | M(o) | 2.17 | 5,103 |\n| 3598 | 0271P1603598 | 28.078 | 87.854 | Kosi | E(o) | 3.49 | 5,546 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6538, "line_end": 6685, "token_count_estimate": 1603, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271P1203564", "0271P1203565", "0271P1203566", "0271P1203567", "0271P1203568", "0271P1203569", "0271P1203570", "0271P1203571", "0271P1203572", "0271P1203573", "0271P1203574", "0271P1203575", "0271P1203576", "0271P1203577", "0271P1203578", "0271P1203579", "0271P1203580", "0271P1203581", "0271P1203582", "0271P1203583", "0271P1203584", "0271P1203585", "0271P1203586", "0271P1203587", "0271P1203588", "0271P1203589", "0271P1203590", "0271P1203591", "0271P1203592", "0271P1203593", "0271P1203594", "0271P1203595", "0271P1303596", "0271P1303597", "0271P1603598"]}}
{"id": "3cda2e241c0d621a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3599 | 0271P1603599 | 28.036 | 87.856 | Kosi | M(o) | 0.45 | 5,759 |\n| 3600 | 0271P1603600 | 28.035 | 87.858 | Kosi | M(o) | 2.10 | 5,751 |\n| 3601 | 0271P1603601 | 28.032 | 87.870 | Kosi | M(o) | 1.50 | 5,655 |\n| 3602 | 0271P1603602 | 28.030 | 87.869 | Kosi | M(o) | 1.20 | 5,675 |\n| 3603 | 0271P1603603 | 28.027 | 87.870 | Kosi | M(o) | 0.97 | 5,686 |\n| 3604 | 0271P1603604 | 28.026 | 87.865 | Kosi | M(o) | 1.00 | 5,717 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6538, "line_end": 6685, "token_count_estimate": 355, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271P1603599", "0271P1603600", "0271P1603601", "0271P1603602", "0271P1603603", "0271P1603604"]}}
{"id": "909b39a316d96d07", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6686, "line_end": 6692, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "645dbf8a5fdb7cd5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3605 | 0271P1603605 | 28.021 | 87.895 | Kosi | M(o) | 7.54 | 5,685 |\n| 3606 | 0271P1603606 | 28.018 | 87.828 | Kosi | E(o) | 1.26 | 5,700 |\n| 3607 | 0272E1303607 | 27.990 | 85.879 | Kosi | E(o) | 6.13 | 4,114 |\n| 3608 | 0272I0103608 | 27.997 | 86.047 | Kosi | E(o) | 2.15 | 4,367 |\n| 3609 | 0272I0103609 | 27.995 | 86.071 | Kosi | M(o) | 0.47 | 4,526 |\n| 3610 | 0272I0103610 | 27.994 | 86.033 | Kosi | E(o) | 2.25 | 4,270 |\n| 3611 | 0272I0103611 | 27.980 | 86.091 | Kosi | E(o) | 0.55 | 4,774 |\n| 3612 | 0272I0103612 | 27.947 | 86.096 | Kosi | M(o) | 0.32 | 4,876 |\n| 3613 | 0272I0103613 | 27.934 | 86.118 | Kosi | E(o) | 1.05 | 4,769 |\n| 3614 | 0272I0103614 | 27.931 | 86.131 | Kosi | E(o) | 3.28 | 4,923 |\n| 3615 | 0272I0103615 | 27.918 | 86.042 | Kosi | E(o) | 0.71 | 4,129 |\n| 3616 | 0272I0103616 | 27.916 | 86.071 | Kosi | E(o) | 0.29 | 4,229 |\n| 3617 | 0272I0103617 | 27.914 | 86.130 | Kosi | E(o) | 1.98 | 4,365 |\n| 3618 | 0272I0103618 | 27.911 | 86.062 | Kosi | E(o) | 0.25 | 4,231 |\n| 3619 | 0272I0103619 | 27.909 | 86.154 | Kosi | E(o) | 1.98 | 4,562 |\n| 3620 | 0272I0103620 | 27.894 | 86.045 | Kosi | E(o) | 0.47 | 4,020 |\n| 3621 | 0272I0103621 | 27.870 | 86.167 | Kosi | E(o) | 0.56 | 4,149 |\n| 3622 | 0272I0503622 | 27.998 | 86.426 | Kosi | E(o) | 0.50 | 4,935 |\n| 3623 | 0272I0503623 | 27.996 | 86.424 | Kosi | E(o) | 5.68 | 4,928 |\n| 3624 | 0272I0503624 | 27.995 | 86.339 | Kosi | M(o) | 1.67 | 4,685 |\n| 3625 | 0272I0503625 | 27.993 | 86.397 | Kosi | M(l) | 2.65 | 4,631 |\n| 3626 | 0272I0503626 | 27.991 | 86.391 | Kosi | I(s) | 0.32 | 4,630 |\n| 3627 | 0272I0503627 | 27.990 | 86.395 | Kosi | I(s) | 0.43 | 4,641 |\n| 3628 | 0272I0503628 | 27.990 | 86.392 | Kosi | I(s) | 0.29 | 4,633 |\n| 3629 | 0272I0503629 | 27.987 | 86.492 | Kosi | M(o) | 1.13 | 5,571 |\n| 3630 | 0272I0503630 | 27.986 | 86.281 | Kosi | E(o) | 9.81 | 4,688 |\n| 3631 | 0272I0503631 | 27.956 | 86.458 | Kosi | I(s) | 1.98 | 5,076 |\n| 3632 | 0272I0503632 | 27.950 | 86.417 | Kosi | M(e) | 0.78 | 4,977 |\n| 3633 | 0272I0503633 | 27.948 | 86.384 | Kosi | M(o) | 1.50 | 4,794 |\n| 3634 | 0272I0503634 | 27.947 | 86.446 | Kosi | M(e) | 156.76 | 5,046 |\n| 3635 | 0272I0503635 | 27.938 | 86.432 | Kosi | M(o) | 4.19 | 5,046 |\n| 3636 | 0272I0503636 | 27.936 | 86.433 | Kosi | M(o) | 0.60 | 5,049 |\n| 3637 | 0272I0503637 | 27.932 | 86.422 | Kosi | M(o) | 1.58 | 4,979 |\n| 3638 | 0272I0503638 | 27.929 | 86.433 | Kosi | M(o) | 31.99 | 5,025 |\n| 3639 | 0272I0503639 | 27.929 | 86.446 | Kosi | M(o) | 6.53 | 5,195 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6693, "line_end": 6840, "token_count_estimate": 1612, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271P1603605", "0271P1603606", "0272E1303607", "0272I0103608", "0272I0103609", "0272I0103610", "0272I0103611", "0272I0103612", "0272I0103613", "0272I0103614", "0272I0103615", "0272I0103616", "0272I0103617", "0272I0103618", "0272I0103619", "0272I0103620", "0272I0103621", "0272I0503622", "0272I0503623", "0272I0503624", "0272I0503625", "0272I0503626", "0272I0503627", "0272I0503628", "0272I0503629", "0272I0503630", "0272I0503631", "0272I0503632", "0272I0503633", "0272I0503634", "0272I0503635", "0272I0503636", "0272I0503637", "0272I0503638", "0272I0503639"]}}
{"id": "b460af0167dcd2c7", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3640 | 0272I0503640 | 27.928 | 86.410 | Kosi | E(o) | 5.02 | 5,059 |\n| 3641 | 0272I0503641 | 27.928 | 86.432 | Kosi | M(o) | 0.35 | 5,031 |\n| 3642 | 0272I0503642 | 27.927 | 86.420 | Kosi | M(o) | 15.72 | 5,020 |\n| 3643 | 0272I0503643 | 27.917 | 86.465 | Kosi | M(o) | 17.56 | 5,259 |\n| 3644 | 0272I0503644 | 27.916 | 86.477 | Kosi | M(o) | 14.08 | 5,110 |\n| 3645 | 0272I0503645 | 27.891 | 86.465 | Kosi | M(l) | 2.99 | 4,698 |\n| 3646 | 0272I0503646 | 27.874 | 86.357 | Kosi | E(o) | 1.35 | 5,109 |\n| 3647 | 0272I0503647 | 27.869 | 86.355 | Kosi | E(o) | 4.01 | 4,841 |\n| 3648 | 0272I0503648 | 27.861 | 86.476 | Kosi | M(e) | 158.40 | 4,550 |\n| 3649 | 0272I0503649 | 27.855 | 86.395 | Kosi | E(o) | 0.73 | 5,135 |\n| 3650 | 0272I0503650 | 27.854 | 86.423 | Kosi | M(o) | 0.46 | 5,075 |\n| 3651 | 0272I0503651 | 27.853 | 86.398 | Kosi | E(o) | 1.08 | 5,065 |\n| 3652 | 0272I0503652 | 27.850 | 86.356 | Kosi | E(o) | 11.18 | 4,424 |\n| 3653 | 0272I0503653 | 27.845 | 86.433 | Kosi | M(o) | 7.81 | 5,118 |\n| 3654 | 0272I0503654 | 27.845 | 86.463 | Kosi | M(o) | 9.65 | 4,951 |\n| 3655 | 0272I0503655 | 27.845 | 86.410 | Kosi | M(o) | 3.04 | 4,909 |\n| 3656 | 0272I0503656 | 27.841 | 86.330 | Kosi | O | 0.28 | 4,281 |\n| 3657 | 0272I0503657 | 27.835 | 86.482 | Kosi | M(o) | 2.31 | 4,844 |\n| 3658 | 0272I0503658 | 27.830 | 86.494 | Kosi | M(o) | 0.61 | 5,073 |\n| 3659 | 0272I0503659 | 27.809 | 86.448 | Kosi | M(o) | 0.27 | 4,908 |\n| 3660 | 0272I0503660 | 27.805 | 86.458 | Kosi | M(l) | 0.30 | 4,887 |\n| 3661 | 0272I0503661 | 27.803 | 86.459 | Kosi | M(l) | 0.51 | 4,886 |\n| 3662 | 0272I0503662 | 27.802 | 86.471 | Kosi | M(o) | 0.30 | 4,970 |\n| 3663 | 0272I0503663 | 27.802 | 86.427 | Kosi | M(o) | 0.51 | 5,002 |\n| 3664 | 0272I0503664 | 27.801 | 86.470 | Kosi | E(o) | 1.55 | 4,955 |\n| 3665 | 0272I0503665 | 27.799 | 86.395 | Kosi | E(o) | 1.83 | 4,802 |\n| 3666 | 0272I0503666 | 27.798 | 86.478 | Kosi | M(o) | 4.51 | 4,963 |\n| 3667 | 0272I0503667 | 27.794 | 86.424 | Kosi | M(o) | 1.98 | 4,999 |\n| 3668 | 0272I0503668 | 27.784 | 86.400 | Kosi | E(o) | 1.17 | 4,870 |\n| 3669 | 0272I0503669 | 27.782 | 86.431 | Kosi | E(o) | 0.43 | 5,076 |\n| 3670 | 0272I0503670 | 27.782 | 86.389 | Kosi | E(o) | 3.03 | 4,756 |\n| 3671 | 0272I0503671 | 27.779 | 86.383 | Kosi | E(o) | 2.67 | 4,839 |\n| 3672 | 0272I0503672 | 27.778 | 86.392 | Kosi | M(o) | 1.17 | 4,836 |\n| 3673 | 0272I0503673 | 27.777 | 86.391 | Kosi | M(o) | 2.18 | 4,841 |\n| 3674 | 0272I0603674 | 27.750 | 86.419 | Kosi | E(o) | 3.26 | 4,834 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6693, "line_end": 6840, "token_count_estimate": 1608, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I0503640", "0272I0503641", "0272I0503642", "0272I0503643", "0272I0503644", "0272I0503645", "0272I0503646", "0272I0503647", "0272I0503648", "0272I0503649", "0272I0503650", "0272I0503651", "0272I0503652", "0272I0503653", "0272I0503654", "0272I0503655", "0272I0503656", "0272I0503657", "0272I0503658", "0272I0503659", "0272I0503660", "0272I0503661", "0272I0503662", "0272I0503663", "0272I0503664", "0272I0503665", "0272I0503666", "0272I0503667", "0272I0503668", "0272I0503669", "0272I0503670", "0272I0503671", "0272I0503672", "0272I0503673", "0272I0603674"]}}
{"id": "b2ba73917ec38ed3", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3675 | 0272I0603675 | 27.748 | 86.422 | Kosi | E(o) | 0.25 | 4,931 |\n| 3676 | 0272I0603676 | 27.746 | 86.495 | Kosi | E(o) | 1.10 | 4,440 |\n| 3677 | 0272I0603677 | 27.731 | 86.421 | Kosi | E(o) | 4.29 | 4,505 |\n| 3678 | 0272I0603678 | 27.728 | 86.404 | Kosi | E(o) | 3.05 | 4,413 |\n| 3679 | 0272I0603679 | 27.725 | 86.418 | Kosi | E(o) | 1.37 | 4,225 |\n| 3680 | 0272I0603680 | 27.670 | 86.387 | Kosi | E(o) | 2.22 | 4,031 |\n| 3681 | 0272I0903681 | 28.000 | 86.598 | Kosi | I(s) | 0.53 | 4,907 |\n| 3682 | 0272I0903682 | 28.000 | 86.622 | Kosi | I(s) | 1.22 | 4,966 |\n| 3683 | 0272I0903683 | 27.999 | 86.600 | Kosi | I(s) | 0.70 | 4,888 |\n| 3684 | 0272I0903684 | 27.999 | 86.693 | Kosi | I(s) | 0.68 | 4,900 |\n| 3685 | 0272I0903685 | 27.998 | 86.603 | Kosi | I(s) | 0.33 | 4,887 |\n| 3686 | 0272I0903686 | 27.998 | 86.620 | Kosi | I(s) | 0.85 | 4,968 |\n| 3687 | 0272I0903687 | 27.996 | 86.663 | Kosi | M(o) | 2.28 | 5,264 |\n| 3688 | 0272I0903688 | 27.996 | 86.695 | Kosi | I(s) | 0.32 | 4,894 |\n| 3689 | 0272I0903689 | 27.995 | 86.663 | Kosi | M(o) | 0.43 | 5,274 |\n| 3690 | 0272I0903690 | 27.995 | 86.693 | Kosi | I(s) | 0.26 | 4,881 |\n| 3691 | 0272I0903691 | 27.995 | 86.530 | Kosi | M(o) | 0.51 | 5,277 |\n| 3692 | 0272I0903692 | 27.994 | 86.576 | Kosi | M(o) | 0.43 | 5,227 |\n| 3693 | 0272I0903693 | 27.993 | 86.602 | Kosi | I(s) | 0.57 | 4,859 |\n| 3694 | 0272I0903694 | 27.993 | 86.722 | Kosi | I(s) | 0.55 | 5,116 |\n| 3695 | 0272I0903695 | 27.993 | 86.616 | Kosi | I(s) | 1.83 | 4,938 |\n| 3696 | 0272I0903696 | 27.993 | 86.695 | Kosi | I(s) | 0.45 | 4,879 |\n| 3697 | 0272I0903697 | 27.993 | 86.734 | Kosi | I(s) | 0.87 | 5,170 |\n| 3698 | 0272I0903698 | 27.992 | 86.725 | Kosi | I(s) | 0.35 | 5,129 |\n| 3699 | 0272I0903699 | 27.992 | 86.731 | Kosi | I(s) | 0.35 | 5,167 |\n| 3700 | 0272I0903700 | 27.992 | 86.725 | Kosi | I(s) | 0.26 | 5,127 |\n| 3701 | 0272I0903701 | 27.992 | 86.652 | Kosi | M(o) | 2.04 | 5,261 |\n| 3702 | 0272I0903702 | 27.991 | 86.691 | Kosi | I(s) | 0.85 | 4,861 |\n| 3703 | 0272I0903703 | 27.991 | 86.743 | Kosi | I(s) | 0.87 | 5,209 |\n| 3704 | 0272I0903704 | 27.990 | 86.588 | Kosi | E(o) | 2.52 | 5,242 |\n| 3705 | 0272I0903705 | 27.990 | 86.691 | Kosi | I(s) | 0.74 | 4,867 |\n| 3706 | 0272I0903706 | 27.990 | 86.617 | Kosi | M(o) | 0.25 | 4,959 |\n| 3707 | 0272I0903707 | 27.989 | 86.649 | Kosi | M(o) | 5.10 | 5,204 |\n| 3708 | 0272I0903708 | 27.988 | 86.609 | Kosi | M(o) | 3.06 | 4,824 |\n| 3709 | 0272I0903709 | 27.988 | 86.644 | Kosi | M(o) | 1.19 | 5,374 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6693, "line_end": 6840, "token_count_estimate": 1602, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I0603675", "0272I0603676", "0272I0603677", "0272I0603678", "0272I0603679", "0272I0603680", "0272I0903681", "0272I0903682", "0272I0903683", "0272I0903684", "0272I0903685", "0272I0903686", "0272I0903687", "0272I0903688", "0272I0903689", "0272I0903690", "0272I0903691", "0272I0903692", "0272I0903693", "0272I0903694", "0272I0903695", "0272I0903696", "0272I0903697", "0272I0903698", "0272I0903699", "0272I0903700", "0272I0903701", "0272I0903702", "0272I0903703", "0272I0903704", "0272I0903705", "0272I0903706", "0272I0903707", "0272I0903708", "0272I0903709"]}}
{"id": "0d39fda2dc5b84dc", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3710 | 0272I0903710 | 27.986 | 86.606 | Kosi | I(s) | 0.25 | 4,826 |\n| 3711 | 0272I0903711 | 27.986 | 86.554 | Kosi | I(s) | 0.41 | 5,080 |\n| 3712 | 0272I0903712 | 27.984 | 86.607 | Kosi | I(s) | 0.58 | 4,815 |\n| 3713 | 0272I0903713 | 27.984 | 86.672 | Kosi | E(o) | 0.25 | 5,163 |\n| 3714 | 0272I0903714 | 27.984 | 86.567 | Kosi | M(o) | 0.45 | 5,257 |\n| 3715 | 0272I0903715 | 27.981 | 86.556 | Kosi | I(s) | 0.29 | 5,063 |\n| 3716 | 0272I0903716 | 27.981 | 86.699 | Kosi | I(s) | 0.43 | 4,834 |\n| 3717 | 0272I0903717 | 27.981 | 86.609 | Kosi | M(o) | 3.07 | 4,805 |\n| 3718 | 0272I0903718 | 27.980 | 86.539 | Kosi | M(o) | 1.06 | 5,250 |\n| 3719 | 0272I0903719 | 27.979 | 86.733 | Kosi | M(o) | 1.58 | 5,421 |\n| 3720 | 0272I0903720 | 27.978 | 86.704 | Kosi | M(o) | 1.66 | 4,839 |\n| 3721 | 0272I0903721 | 27.977 | 86.547 | Kosi | M(o) | 0.29 | 5,108 |\n| 3722 | 0272I0903722 | 27.977 | 86.609 | Kosi | M(o) | 0.57 | 4,793 |\n| 3723 | 0272I0903723 | 27.977 | 86.607 | Kosi | M(o) | 2.03 | 4,796 |\n| 3724 | 0272I0903724 | 27.976 | 86.543 | Kosi | M(o) | 0.84 | 5,159 |\n| 3725 | 0272I0903725 | 27.975 | 86.681 | Kosi | M(l) | 57.83 | 4,834 |\n| 3726 | 0272I0903726 | 27.975 | 86.590 | Kosi | E(o) | 0.50 | 5,296 |\n| 3727 | 0272I0903727 | 27.975 | 86.737 | Kosi | M(o) | 2.81 | 5,353 |\n| 3728 | 0272I0903728 | 27.973 | 86.563 | Kosi | M(o) | 0.83 | 5,027 |\n| 3729 | 0272I0903729 | 27.972 | 86.557 | Kosi | E(o) | 0.98 | 5,077 |\n| 3730 | 0272I0903730 | 27.971 | 86.635 | Kosi | M(o) | 1.69 | 5,061 |\n| 3731 | 0272I0903731 | 27.970 | 86.588 | Kosi | M(o) | 1.53 | 5,200 |\n| 3732 | 0272I0903732 | 27.970 | 86.654 | Kosi | M(o) | 1.97 | 5,116 |\n| 3733 | 0272I0903733 | 27.969 | 86.746 | Kosi | M(o) | 0.45 | 5,164 |\n| 3734 | 0272I0903734 | 27.967 | 86.732 | Kosi | E(o) | 0.56 | 5,325 |\n| 3735 | 0272I0903735 | 27.965 | 86.677 | Kosi | E(o) | 0.82 | 5,151 |\n| 3736 | 0272I0903736 | 27.961 | 86.696 | Kosi | I(s) | 2.03 | 4,745 |\n| 3737 | 0272I0903737 | 27.961 | 86.700 | Kosi | I(s) | 0.43 | 4,774 |\n| 3738 | 0272I0903738 | 27.961 | 86.663 | Kosi | M(o) | 2.06 | 5,164 |\n| 3739 | 0272I0903739 | 27.958 | 86.661 | Kosi | M(o) | 4.84 | 5,155 |\n| 3740 | 0272I0903740 | 27.955 | 86.567 | Kosi | I(s) | 1.06 | 4,867 |\n| 3741 | 0272I0903741 | 27.954 | 86.697 | Kosi | I(s) | 0.35 | 4,741 |\n| 3742 | 0272I0903742 | 27.954 | 86.699 | Kosi | I(s) | 0.67 | 4,727 |\n| 3743 | 0272I0903743 | 27.954 | 86.701 | Kosi | I(s) | 0.46 | 4,737 |\n| 3744 | 0272I0903744 | 27.953 | 86.649 | Kosi | M(o) | 0.90 | 5,354 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6693, "line_end": 6840, "token_count_estimate": 1607, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I0903710", "0272I0903711", "0272I0903712", "0272I0903713", "0272I0903714", "0272I0903715", "0272I0903716", "0272I0903717", "0272I0903718", "0272I0903719", "0272I0903720", "0272I0903721", "0272I0903722", "0272I0903723", "0272I0903724", "0272I0903725", "0272I0903726", "0272I0903727", "0272I0903728", "0272I0903729", "0272I0903730", "0272I0903731", "0272I0903732", "0272I0903733", "0272I0903734", "0272I0903735", "0272I0903736", "0272I0903737", "0272I0903738", "0272I0903739", "0272I0903740", "0272I0903741", "0272I0903742", "0272I0903743", "0272I0903744"]}}
{"id": "9a63e8cf5273f284", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3745 | 0272I0903745 | 27.953 | 86.705 | Kosi | I(s) | 0.74 | 4,738 |\n| 3746 | 0272I0903746 | 27.952 | 86.565 | Kosi | I(s) | 1.98 | 4,871 |\n| 3747 | 0272I0903747 | 27.952 | 86.553 | Kosi | I(s) | 0.25 | 4,955 |\n| 3748 | 0272I0903748 | 27.951 | 86.700 | Kosi | I(s) | 0.71 | 4,741 |\n| 3749 | 0272I0903749 | 27.951 | 86.690 | Kosi | M(l) | 42.11 | 4,741 |\n| 3750 | 0272I0903750 | 27.951 | 86.709 | Kosi | I(s) | 0.66 | 4,724 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6693, "line_end": 6840, "token_count_estimate": 358, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I0903745", "0272I0903746", "0272I0903747", "0272I0903748", "0272I0903749", "0272I0903750"]}}
{"id": "81d3ba69817b991e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6841, "line_end": 6850, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e337e081de90d7f6", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3751 | 0272I0903751 | 27.950 | 86.572 | Kosi | I(s) | 0.57 | 4,867 |\n| 3752 | 0272I0903752 | 27.946 | 86.651 | Kosi | M(o) | 6.81 | 5,132 |\n| 3753 | 0272I0903753 | 27.946 | 86.670 | Kosi | M(o) | 0.66 | 5,163 |\n| 3754 | 0272I0903754 | 27.945 | 86.704 | Kosi | I(s) | 0.31 | 4,716 |\n| 3755 | 0272I0903755 | 27.944 | 86.670 | Kosi | M(o) | 0.55 | 5,199 |\n| 3756 | 0272I0903756 | 27.944 | 86.549 | Kosi | M(o) | 5.50 | 5,192 |\n| 3757 | 0272I0903757 | 27.943 | 86.554 | Kosi | M(o) | 1.39 | 5,134 |\n| 3758 | 0272I0903758 | 27.943 | 86.710 | Kosi | I(s) | 8.26 | 4,724 |\n| 3759 | 0272I0903759 | 27.941 | 86.699 | Kosi | M(l) | 18.00 | 4,709 |\n| 3760 | 0272I0903760 | 27.941 | 86.714 | Kosi | I(s) | 1.89 | 4,711 |\n| 3761 | 0272I0903761 | 27.940 | 86.708 | Kosi | I(s) | 2.46 | 4,712 |\n| 3762 | 0272I0903762 | 27.938 | 86.711 | Kosi | I(s) | 9.83 | 4,706 |\n| 3763 | 0272I0903763 | 27.937 | 86.715 | Kosi | M(o) | 0.35 | 4,706 |\n| 3764 | 0272I0903764 | 27.936 | 86.713 | Kosi | I(s) | 1.36 | 4,705 |\n| 3765 | 0272I0903765 | 27.934 | 86.712 | Kosi | M(o) | 0.49 | 4,711 |\n| 3766 | 0272I0903766 | 27.934 | 86.706 | Kosi | E(o) | 0.74 | 4,669 |\n| 3767 | 0272I0903767 | 27.934 | 86.648 | Kosi | E(o) | 2.14 | 4,900 |\n| 3768 | 0272I0903768 | 27.933 | 86.746 | Kosi | M(e) | 7.56 | 4,948 |\n| 3769 | 0272I0903769 | 27.932 | 86.647 | Kosi | E(o) | 1.06 | 4,897 |\n| 3770 | 0272I0903770 | 27.932 | 86.713 | Kosi | M(o) | 0.71 | 4,697 |\n| 3771 | 0272I0903771 | 27.930 | 86.715 | Kosi | M(o) | 0.67 | 4,692 |\n| 3772 | 0272I0903772 | 27.928 | 86.714 | Kosi | M(o) | 0.25 | 4,690 |\n| 3773 | 0272I0903773 | 27.921 | 86.675 | Kosi | M(o) | 2.72 | 5,259 |\n| 3774 | 0272I0903774 | 27.920 | 86.745 | Kosi | M(o) | 2.48 | 4,864 |\n| 3775 | 0272I0903775 | 27.909 | 86.580 | Kosi | M(o) | 1.73 | 5,098 |\n| 3776 | 0272I0903776 | 27.908 | 86.585 | Kosi | M(o) | 0.59 | 5,254 |\n| 3777 | 0272I0903777 | 27.907 | 86.587 | Kosi | E(o) | 0.36 | 5,224 |\n| 3778 | 0272I0903778 | 27.907 | 86.588 | Kosi | E(o) | 0.25 | 5,215 |\n| 3779 | 0272I0903779 | 27.905 | 86.581 | Kosi | M(o) | 2.14 | 5,037 |\n| 3780 | 0272I0903780 | 27.901 | 86.576 | Kosi | M(o) | 2.76 | 5,160 |\n| 3781 | 0272I0903781 | 27.880 | 86.568 | Kosi | M(o) | 0.50 | 5,071 |\n| 3782 | 0272I0903782 | 27.880 | 86.608 | Kosi | E(o) | 0.37 | 4,763 |\n| 3783 | 0272I0903783 | 27.874 | 86.586 | Kosi | M(e) | 40.18 | 4,368 |\n| 3784 | 0272I0903784 | 27.873 | 86.685 | Kosi | E(o) | 1.77 | 4,919 |\n| 3785 | 0272I0903785 | 27.857 | 86.500 | Kosi | M(l) | 2.47 | 5,335 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6851, "line_end": 6998, "token_count_estimate": 1610, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I0903751", "0272I0903752", "0272I0903753", "0272I0903754", "0272I0903755", "0272I0903756", "0272I0903757", "0272I0903758", "0272I0903759", "0272I0903760", "0272I0903761", "0272I0903762", "0272I0903763", "0272I0903764", "0272I0903765", "0272I0903766", "0272I0903767", "0272I0903768", "0272I0903769", "0272I0903770", "0272I0903771", "0272I0903772", "0272I0903773", "0272I0903774", "0272I0903775", "0272I0903776", "0272I0903777", "0272I0903778", "0272I0903779", "0272I0903780", "0272I0903781", "0272I0903782", "0272I0903783", "0272I0903784", "0272I0903785"]}}
{"id": "4c4402e1f1e7c71e", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3786 | 0272I0903786 | 27.840 | 86.568 | Kosi | M(o) | 0.39 | 5,082 |\n| 3787 | 0272I0903787 | 27.840 | 86.630 | Kosi | E(o) | 1.15 | 4,467 |\n| 3788 | 0272I0903788 | 27.836 | 86.585 | Kosi | M(o) | 1.61 | 5,136 |\n| 3789 | 0272I0903789 | 27.833 | 86.565 | Kosi | M(o) | 2.69 | 4,943 |\n| 3790 | 0272I0903790 | 27.828 | 86.573 | Kosi | M(o) | 3.97 | 4,754 |\n| 3791 | 0272I0903791 | 27.826 | 86.569 | Kosi | M(l) | 4.76 | 4,788 |\n| 3792 | 0272I0903792 | 27.823 | 86.571 | Kosi | M(e) | 3.79 | 4,768 |\n| 3793 | 0272I0903793 | 27.795 | 86.617 | Kosi | M(o) | 1.75 | 5,182 |\n| 3794 | 0272I0903794 | 27.791 | 86.621 | Kosi | M(o) | 46.73 | 5,157 |\n| 3795 | 0272I0903795 | 27.789 | 86.613 | Kosi | M(o) | 0.57 | 5,199 |\n| 3796 | 0272I0903796 | 27.788 | 86.632 | Kosi | M(o) | 4.68 | 5,210 |\n| 3797 | 0272I0903797 | 27.786 | 86.632 | Kosi | M(o) | 2.47 | 5,095 |\n| 3798 | 0272I0903798 | 27.783 | 86.628 | Kosi | M(o) | 1.62 | 4,981 |\n| 3799 | 0272I0903799 | 27.782 | 86.627 | Kosi | M(o) | 0.52 | 4,979 |\n| 3800 | 0272I0903800 | 27.781 | 86.589 | Kosi | M(o) | 1.11 | 5,054 |\n| 3801 | 0272I0903801 | 27.779 | 86.612 | Kosi | M(e) | 117.31 | 4,831 |\n| 3802 | 0272I0903802 | 27.778 | 86.643 | Kosi | M(o) | 29.27 | 5,163 |\n| 3803 | 0272I0903803 | 27.777 | 86.681 | Kosi | E(o) | 0.54 | 4,528 |\n| 3804 | 0272I0903804 | 27.772 | 86.645 | Kosi | M(o) | 1.69 | 5,189 |\n| 3805 | 0272I0903805 | 27.750 | 86.523 | Kosi | M(e) | 2.47 | 4,111 |\n| 3806 | 0272I1003806 | 27.728 | 86.569 | Kosi | M(o) | 2.95 | 5,002 |\n| 3807 | 0272I1003807 | 27.723 | 86.602 | Kosi | M(o) | 0.89 | 4,955 |\n| 3808 | 0272I1003808 | 27.715 | 86.537 | Kosi | E(o) | 0.37 | 4,881 |\n| 3809 | 0272I1003809 | 27.713 | 86.542 | Kosi | E(o) | 8.99 | 4,940 |\n| 3810 | 0272I1003810 | 27.712 | 86.571 | Kosi | E(o) | 1.16 | 4,900 |\n| 3811 | 0272I1003811 | 27.711 | 86.599 | Kosi | M(l) | 7.73 | 4,607 |\n| 3812 | 0272I1003812 | 27.711 | 86.565 | Kosi | M(o) | 0.66 | 4,973 |\n| 3813 | 0272I1003813 | 27.710 | 86.590 | Kosi | M(o) | 3.26 | 4,638 |\n| 3814 | 0272I1003814 | 27.709 | 86.563 | Kosi | M(o) | 2.57 | 4,870 |\n| 3815 | 0272I1003815 | 27.708 | 86.597 | Kosi | M(o) | 0.73 | 4,546 |\n| 3816 | 0272I1003816 | 27.707 | 86.585 | Kosi | M(o) | 0.61 | 4,742 |\n| 3817 | 0272I1003817 | 27.703 | 86.559 | Kosi | E(o) | 8.24 | 4,803 |\n| 3818 | 0272I1003818 | 27.703 | 86.605 | Kosi | E(o) | 1.15 | 4,637 |\n| 3819 | 0272I1003819 | 27.701 | 86.572 | Kosi | E(o) | 3.98 | 4,718 |\n| 3820 | 0272I1003820 | 27.700 | 86.609 | Kosi | E(o) | 0.50 | 4,725 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6851, "line_end": 6998, "token_count_estimate": 1593, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I0903786", "0272I0903787", "0272I0903788", "0272I0903789", "0272I0903790", "0272I0903791", "0272I0903792", "0272I0903793", "0272I0903794", "0272I0903795", "0272I0903796", "0272I0903797", "0272I0903798", "0272I0903799", "0272I0903800", "0272I0903801", "0272I0903802", "0272I0903803", "0272I0903804", "0272I0903805", "0272I1003806", "0272I1003807", "0272I1003808", "0272I1003809", "0272I1003810", "0272I1003811", "0272I1003812", "0272I1003813", "0272I1003814", "0272I1003815", "0272I1003816", "0272I1003817", "0272I1003818", "0272I1003819", "0272I1003820"]}}
{"id": "e240e1fc98231d09", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3821 | 0272I1003821 | 27.700 | 86.608 | Kosi | E(o) | 0.91 | 4,739 |\n| 3822 | 0272I1003822 | 27.699 | 86.526 | Kosi | E(o) | 6.05 | 4,391 |\n| 3823 | 0272I1003823 | 27.697 | 86.560 | Kosi | E(o) | 0.47 | 4,866 |\n| 3824 | 0272I1003824 | 27.697 | 86.577 | Kosi | E(o) | 0.31 | 4,653 |\n| 3825 | 0272I1003825 | 27.697 | 86.592 | Kosi | M(o) | 0.57 | 4,419 |\n| 3826 | 0272I1003826 | 27.696 | 86.551 | Kosi | E(o) | 0.26 | 4,819 |\n| 3827 | 0272I1003827 | 27.696 | 86.576 | Kosi | E(o) | 0.30 | 4,666 |\n| 3828 | 0272I1003828 | 27.695 | 86.574 | Kosi | E(o) | 0.52 | 4,680 |\n| 3829 | 0272I1003829 | 27.695 | 86.571 | Kosi | E(o) | 0.78 | 4,687 |\n| 3830 | 0272I1003830 | 27.693 | 86.549 | Kosi | E(o) | 2.10 | 4,704 |\n| 3831 | 0272I1003831 | 27.691 | 86.605 | Kosi | E(o) | 0.78 | 4,688 |\n| 3832 | 0272I1003832 | 27.690 | 86.532 | Kosi | E(o) | 0.60 | 4,504 |\n| 3833 | 0272I1003833 | 27.687 | 86.550 | Kosi | E(o) | 0.66 | 4,669 |\n| 3834 | 0272I1003834 | 27.686 | 86.554 | Kosi | E(o) | 1.48 | 4,604 |\n| 3835 | 0272I1003835 | 27.686 | 86.580 | Kosi | E(o) | 0.30 | 4,626 |\n| 3836 | 0272I1003836 | 27.685 | 86.570 | Kosi | E(o) | 1.42 | 4,736 |\n| 3837 | 0272I1003837 | 27.685 | 86.543 | Kosi | E(o) | 0.83 | 4,611 |\n| 3838 | 0272I1003838 | 27.685 | 86.627 | Kosi | E(o) | 0.68 | 4,542 |\n| 3839 | 0272I1003839 | 27.682 | 86.601 | Kosi | E(o) | 1.26 | 4,528 |\n| 3840 | 0272I1003840 | 27.680 | 86.543 | Kosi | E(o) | 2.09 | 4,492 |\n| 3841 | 0272I1003841 | 27.679 | 86.574 | Kosi | M(o) | 0.87 | 4,566 |\n| 3842 | 0272I1003842 | 27.677 | 86.545 | Kosi | E(o) | 3.02 | 4,462 |\n| 3843 | 0272I1003843 | 27.676 | 86.564 | Kosi | E(o) | 0.39 | 4,521 |\n| 3844 | 0272I1003844 | 27.675 | 86.564 | Kosi | E(o) | 0.48 | 4,530 |\n| 3845 | 0272I1003845 | 27.674 | 86.609 | Kosi | E(o) | 7.28 | 4,396 |\n| 3846 | 0272I1003846 | 27.672 | 86.542 | Kosi | E(o) | 0.38 | 4,532 |\n| 3847 | 0272I1003847 | 27.672 | 86.534 | Kosi | E(o) | 1.41 | 4,434 |\n| 3848 | 0272I1003848 | 27.672 | 86.541 | Kosi | E(o) | 0.60 | 4,537 |\n| 3849 | 0272I1003849 | 27.669 | 86.605 | Kosi | E(o) | 0.84 | 4,260 |\n| 3850 | 0272I1003850 | 27.668 | 86.518 | Kosi | E(o) | 0.38 | 4,148 |\n| 3851 | 0272I1003851 | 27.667 | 86.547 | Kosi | E(o) | 1.11 | 4,324 |\n| 3852 | 0272I1003852 | 27.666 | 86.527 | Kosi | E(o) | 2.53 | 4,339 |\n| 3853 | 0272I1003853 | 27.666 | 86.565 | Kosi | E(o) | 0.44 | 4,423 |\n| 3854 | 0272I1003854 | 27.664 | 86.615 | Kosi | E(o) | 0.40 | 4,448 |\n| 3855 | 0272I1003855 | 27.664 | 86.544 | Kosi | E(o) | 0.54 | 4,356 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6851, "line_end": 6998, "token_count_estimate": 1580, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1003821", "0272I1003822", "0272I1003823", "0272I1003824", "0272I1003825", "0272I1003826", "0272I1003827", "0272I1003828", "0272I1003829", "0272I1003830", "0272I1003831", "0272I1003832", "0272I1003833", "0272I1003834", "0272I1003835", "0272I1003836", "0272I1003837", "0272I1003838", "0272I1003839", "0272I1003840", "0272I1003841", "0272I1003842", "0272I1003843", "0272I1003844", "0272I1003845", "0272I1003846", "0272I1003847", "0272I1003848", "0272I1003849", "0272I1003850", "0272I1003851", "0272I1003852", "0272I1003853", "0272I1003854", "0272I1003855"]}}
{"id": "ffcbb23d41ba8356", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3856 | 0272I1003856 | 27.663 | 86.538 | Kosi | E(o) | 1.16 | 4,335 |\n| 3857 | 0272I1003857 | 27.655 | 86.614 | Kosi | E(o) | 0.96 | 4,228 |\n| 3858 | 0272I1003858 | 27.654 | 86.519 | Kosi | E(o) | 0.62 | 4,201 |\n| 3859 | 0272I1003859 | 27.654 | 86.567 | Kosi | E(o) | 0.28 | 4,213 |\n| 3860 | 0272I1003860 | 27.650 | 86.580 | Kosi | E(o) | 1.12 | 4,248 |\n| 3861 | 0272I1003861 | 27.648 | 86.521 | Kosi | E(o) | 1.24 | 4,308 |\n| 3862 | 0272I1303862 | 28.000 | 86.995 | Kosi | I(s) | 0.97 | 5,369 |\n| 3863 | 0272I1303863 | 27.999 | 86.998 | Kosi | I(s) | 0.47 | 5,333 |\n| 3864 | 0272I1303864 | 27.997 | 86.835 | Kosi | M(o) | 11.55 | 5,321 |\n| 3865 | 0272I1303865 | 27.996 | 86.823 | Kosi | M(o) | 0.35 | 5,400 |\n| 3866 | 0272I1303866 | 27.996 | 86.820 | Kosi | M(o) | 1.15 | 5,356 |\n| 3867 | 0272I1303867 | 27.993 | 86.838 | Kosi | M(o) | 1.68 | 5,298 |\n| 3868 | 0272I1303868 | 27.993 | 86.844 | Kosi | I(s) | 0.48 | 5,232 |\n| 3869 | 0272I1303869 | 27.993 | 86.996 | Kosi | I(s) | 0.64 | 5,340 |\n| 3870 | 0272I1303870 | 27.992 | 86.762 | Kosi | I(s) | 0.35 | 5,334 |\n| 3871 | 0272I1303871 | 27.984 | 86.844 | Kosi | I(s) | 0.29 | 5,183 |\n| 3872 | 0272I1303872 | 27.984 | 86.845 | Kosi | I(s) | 0.27 | 5,183 |\n| 3873 | 0272I1303873 | 27.984 | 86.988 | Kosi | I(s) | 1.65 | 5,403 |\n| 3874 | 0272I1303874 | 27.982 | 86.782 | Kosi | M(o) | 1.15 | 5,360 |\n| 3875 | 0272I1303875 | 27.982 | 86.785 | Kosi | I(s) | 0.55 | 5,372 |\n| 3876 | 0272I1303876 | 27.979 | 86.822 | Kosi | I(s) | 0.52 | 5,170 |\n| 3877 | 0272I1303877 | 27.979 | 86.820 | Kosi | I(s) | 0.33 | 5,165 |\n| 3878 | 0272I1303878 | 27.978 | 86.818 | Kosi | I(s) | 0.37 | 5,185 |\n| 3879 | 0272I1303879 | 27.976 | 86.811 | Kosi | I(s) | 0.53 | 5,184 |\n| 3880 | 0272I1303880 | 27.976 | 86.810 | Kosi | I(s) | 0.34 | 5,194 |\n| 3881 | 0272I1303881 | 27.975 | 86.804 | Kosi | I(s) | 24.84 | 5,183 |\n| 3882 | 0272I1303882 | 27.974 | 86.794 | Kosi | I(s) | 1.24 | 5,211 |\n| 3883 | 0272I1303883 | 27.969 | 86.828 | Kosi | I(s) | 0.42 | 5,029 |\n| 3884 | 0272I1303884 | 27.966 | 86.829 | Kosi | I(s) | 0.61 | 5,024 |\n| 3885 | 0272I1303885 | 27.965 | 86.807 | Kosi | M(o) | 0.60 | 5,211 |\n| 3886 | 0272I1303886 | 27.963 | 86.812 | Kosi | E(o) | 1.57 | 5,068 |\n| 3887 | 0272I1303887 | 27.962 | 86.774 | Kosi | M(o) | 3.37 | 5,100 |\n| 3888 | 0272I1303888 | 27.961 | 86.828 | Kosi | I(s) | 0.49 | 4,989 |\n| 3889 | 0272I1303889 | 27.960 | 86.831 | Kosi | M(l) | 1.97 | 5,047 |\n| 3890 | 0272I1303890 | 27.956 | 86.806 | Kosi | M(o) | 1.26 | 4,994 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6851, "line_end": 6998, "token_count_estimate": 1612, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1003856", "0272I1003857", "0272I1003858", "0272I1003859", "0272I1003860", "0272I1003861", "0272I1303862", "0272I1303863", "0272I1303864", "0272I1303865", "0272I1303866", "0272I1303867", "0272I1303868", "0272I1303869", "0272I1303870", "0272I1303871", "0272I1303872", "0272I1303873", "0272I1303874", "0272I1303875", "0272I1303876", "0272I1303877", "0272I1303878", "0272I1303879", "0272I1303880", "0272I1303881", "0272I1303882", "0272I1303883", "0272I1303884", "0272I1303885", "0272I1303886", "0272I1303887", "0272I1303888", "0272I1303889", "0272I1303890"]}}
{"id": "4c389361fe40e6b2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3891 | 0272I1303891 | 27.956 | 86.822 | Kosi | I(s) | 0.31 | 4,954 |\n| 3892 | 0272I1303892 | 27.955 | 86.823 | Kosi | I(s) | 0.72 | 4,956 |\n| 3893 | 0272I1303893 | 27.954 | 86.825 | Kosi | I(s) | 0.34 | 4,945 |\n| 3894 | 0272I1303894 | 27.954 | 86.778 | Kosi | M(l) | 0.25 | 5,046 |\n| 3895 | 0272I1303895 | 27.953 | 86.769 | Kosi | E(o) | 0.82 | 5,048 |\n| 3896 | 0272I1303896 | 27.953 | 86.811 | Kosi | M(o) | 0.32 | 5,009 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 6851, "line_end": 6998, "token_count_estimate": 357, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1303891", "0272I1303892", "0272I1303893", "0272I1303894", "0272I1303895", "0272I1303896"]}}
{"id": "edc56758ab0c4a5d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 6999, "line_end": 7005, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "10ad9f719a3145dc", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3897 | 0272I1303897 | 27.951 | 86.777 | Kosi | M(e) | 3.67 | 5,010 |\n| 3898 | 0272I1303898 | 27.950 | 86.782 | Kosi | M(o) | 3.65 | 5,028 |\n| 3899 | 0272I1303899 | 27.950 | 86.855 | Kosi | M(o) | 0.99 | 5,434 |\n| 3900 | 0272I1303900 | 27.950 | 86.822 | Kosi | I(s) | 2.94 | 4,936 |\n| 3901 | 0272I1303901 | 27.949 | 86.858 | Kosi | M(o) | 0.96 | 5,444 |\n| 3902 | 0272I1303902 | 27.946 | 86.804 | Kosi | E(o) | 0.48 | 4,952 |\n| 3903 | 0272I1303903 | 27.945 | 86.818 | Kosi | I(s) | 1.54 | 4,945 |\n| 3904 | 0272I1303904 | 27.944 | 86.824 | Kosi | M(o) | 1.68 | 4,929 |\n| 3905 | 0272I1303905 | 27.943 | 86.864 | Kosi | I(s) | 0.28 | 5,267 |\n| 3906 | 0272I1303906 | 27.943 | 86.771 | Kosi | E(o) | 0.39 | 4,839 |\n| 3907 | 0272I1303907 | 27.942 | 86.816 | Kosi | I(s) | 1.39 | 4,936 |\n| 3908 | 0272I1303908 | 27.942 | 86.813 | Kosi | I(s) | 0.26 | 4,940 |\n| 3909 | 0272I1303909 | 27.941 | 86.801 | Kosi | E(o) | 0.57 | 4,882 |\n| 3910 | 0272I1303910 | 27.939 | 86.815 | Kosi | I(s) | 2.89 | 4,939 |\n| 3911 | 0272I1303911 | 27.938 | 86.847 | Kosi | M(o) | 0.91 | 5,351 |\n| 3912 | 0272I1303912 | 27.936 | 86.813 | Kosi | M(o) | 0.37 | 4,908 |\n| 3913 | 0272I1303913 | 27.935 | 86.811 | Kosi | M(o) | 0.33 | 4,919 |\n| 3914 | 0272I1303914 | 27.934 | 86.890 | Kosi | I(s) | 0.27 | 5,133 |\n| 3915 | 0272I1303915 | 27.933 | 86.891 | Kosi | I(s) | 0.27 | 5,130 |\n| 3916 | 0272I1303916 | 27.933 | 86.887 | Kosi | I(s) | 0.27 | 5,136 |\n| 3917 | 0272I1303917 | 27.933 | 86.809 | Kosi | M(o) | 0.42 | 4,906 |\n| 3918 | 0272I1303918 | 27.933 | 86.840 | Kosi | E(o) | 0.31 | 5,461 |\n| 3919 | 0272I1303919 | 27.932 | 86.844 | Kosi | M(o) | 0.48 | 5,466 |\n| 3920 | 0272I1303920 | 27.931 | 86.840 | Kosi | M(o) | 0.33 | 5,430 |\n| 3921 | 0272I1303921 | 27.930 | 86.952 | Kosi | I(s) | 0.33 | 5,321 |\n| 3922 | 0272I1303922 | 27.930 | 86.840 | Kosi | M(o) | 0.46 | 5,426 |\n| 3923 | 0272I1303923 | 27.929 | 86.921 | Kosi | I(s) | 0.50 | 5,152 |\n| 3924 | 0272I1303924 | 27.929 | 86.872 | Kosi | M(l) | 0.25 | 5,190 |\n| 3925 | 0272I1303925 | 27.929 | 86.858 | Kosi | E(o) | 1.54 | 5,216 |\n| 3926 | 0272I1303926 | 27.929 | 86.838 | Kosi | M(o) | 3.22 | 5,444 |\n| 3927 | 0272I1303927 | 27.927 | 86.915 | Kosi | I(s) | 0.81 | 5,131 |\n| 3928 | 0272I1303928 | 27.926 | 86.920 | Kosi | I(s) | 0.35 | 5,138 |\n| 3929 | 0272I1303929 | 27.926 | 86.838 | Kosi | M(o) | 0.34 | 5,498 |\n| 3930 | 0272I1303930 | 27.925 | 86.917 | Kosi | I(s) | 0.25 | 5,124 |\n| 3931 | 0272I1303931 | 27.924 | 86.921 | Kosi | I(s) | 0.26 | 5,130 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7006, "line_end": 7153, "token_count_estimate": 1609, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1303897", "0272I1303898", "0272I1303899", "0272I1303900", "0272I1303901", "0272I1303902", "0272I1303903", "0272I1303904", "0272I1303905", "0272I1303906", "0272I1303907", "0272I1303908", "0272I1303909", "0272I1303910", "0272I1303911", "0272I1303912", "0272I1303913", "0272I1303914", "0272I1303915", "0272I1303916", "0272I1303917", "0272I1303918", "0272I1303919", "0272I1303920", "0272I1303921", "0272I1303922", "0272I1303923", "0272I1303924", "0272I1303925", "0272I1303926", "0272I1303927", "0272I1303928", "0272I1303929", "0272I1303930", "0272I1303931"]}}
{"id": "7c80536da185164f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3932 | 0272I1303932 | 27.924 | 86.786 | Kosi | M(l) | 54.85 | 4,512 |\n| 3933 | 0272I1303933 | 27.923 | 86.912 | Kosi | I(s) | 0.93 | 5,096 |\n| 3934 | 0272I1303934 | 27.923 | 86.889 | Kosi | I(s) | 0.27 | 5,035 |\n| 3935 | 0272I1303935 | 27.919 | 86.908 | Kosi | I(s) | 0.56 | 5,069 |\n| 3936 | 0272I1303936 | 27.915 | 86.866 | Kosi | M(o) | 0.83 | 4,998 |\n| 3937 | 0272I1303937 | 27.914 | 86.867 | Kosi | I(s) | 1.13 | 4,994 |\n| 3938 | 0272I1303938 | 27.914 | 86.883 | Kosi | M(o) | 0.37 | 4,974 |\n| 3939 | 0272I1303939 | 27.913 | 86.910 | Kosi | I(s) | 0.41 | 5,028 |\n| 3940 | 0272I1303940 | 27.913 | 86.950 | Kosi | I(s) | 0.52 | 5,174 |\n| 3941 | 0272I1303941 | 27.912 | 86.952 | Kosi | I(s) | 0.25 | 5,170 |\n| 3942 | 0272I1303942 | 27.911 | 86.900 | Kosi | I(s) | 1.07 | 5,006 |\n| 3943 | 0272I1303943 | 27.908 | 86.905 | Kosi | I(s) | 0.46 | 4,999 |\n| 3944 | 0272I1303944 | 27.907 | 86.947 | Kosi | I(s) | 0.26 | 5,131 |\n| 3945 | 0272I1303945 | 27.906 | 86.902 | Kosi | I(s) | 0.34 | 4,989 |\n| 3946 | 0272I1303946 | 27.904 | 86.902 | Kosi | I(s) | 0.25 | 4,995 |\n| 3947 | 0272I1303947 | 27.901 | 86.944 | Kosi | I(s) | 0.28 | 5,084 |\n| 3948 | 0272I1303948 | 27.901 | 86.912 | Kosi | M(o) | 0.57 | 5,008 |\n| 3949 | 0272I1303949 | 27.900 | 86.909 | Kosi | M(o) | 3.28 | 5,011 |\n| 3950 | 0272I1303950 | 27.898 | 86.925 | Kosi | M(e) | 139.77 | 5,003 |\n| 3951 | 0272I1303951 | 27.897 | 86.797 | Kosi | M(o) | 1.28 | 5,064 |\n| 3952 | 0272I1303952 | 27.894 | 86.913 | Kosi | E(c) | 11.28 | 4,986 |\n| 3953 | 0272I1303953 | 27.891 | 86.935 | Kosi | I(s) | 0.58 | 5,074 |\n| 3954 | 0272I1303954 | 27.891 | 86.793 | Kosi | M(o) | 3.64 | 5,143 |\n| 3955 | 0272I1303955 | 27.890 | 86.768 | Kosi | I(s) | 0.40 | 5,057 |\n| 3956 | 0272I1303956 | 27.889 | 86.879 | Kosi | I(s) | 0.37 | 4,875 |\n| 3957 | 0272I1303957 | 27.889 | 86.841 | Kosi | M(o) | 0.46 | 4,660 |\n| 3958 | 0272I1303958 | 27.889 | 86.929 | Kosi | E(o) | 0.59 | 5,224 |\n| 3959 | 0272I1303959 | 27.887 | 86.781 | Kosi | M(o) | 0.57 | 5,199 |\n| 3960 | 0272I1303960 | 27.887 | 86.935 | Kosi | I(s) | 0.34 | 5,110 |\n| 3961 | 0272I1303961 | 27.887 | 86.844 | Kosi | M(o) | 4.44 | 4,675 |\n| 3962 | 0272I1303962 | 27.887 | 86.897 | Kosi | M(o) | 5.35 | 5,141 |\n| 3963 | 0272I1303963 | 27.885 | 86.894 | Kosi | M(o) | 0.74 | 5,109 |\n| 3964 | 0272I1303964 | 27.885 | 86.781 | Kosi | M(o) | 0.51 | 5,175 |\n| 3965 | 0272I1303965 | 27.885 | 86.757 | Kosi | E(o) | 0.46 | 5,124 |\n| 3966 | 0272I1303966 | 27.884 | 86.891 | Kosi | M(o) | 1.38 | 5,046 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7006, "line_end": 7153, "token_count_estimate": 1604, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1303932", "0272I1303933", "0272I1303934", "0272I1303935", "0272I1303936", "0272I1303937", "0272I1303938", "0272I1303939", "0272I1303940", "0272I1303941", "0272I1303942", "0272I1303943", "0272I1303944", "0272I1303945", "0272I1303946", "0272I1303947", "0272I1303948", "0272I1303949", "0272I1303950", "0272I1303951", "0272I1303952", "0272I1303953", "0272I1303954", "0272I1303955", "0272I1303956", "0272I1303957", "0272I1303958", "0272I1303959", "0272I1303960", "0272I1303961", "0272I1303962", "0272I1303963", "0272I1303964", "0272I1303965", "0272I1303966"]}}
{"id": "30f7d8816dac2ee8", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 3967 | 0272I1303967 | 27.880 | 86.854 | Kosi | I(s) | 0.56 | 4,807 |\n| 3968 | 0272I1303968 | 27.879 | 86.892 | Kosi | M(o) | 0.50 | 5,131 |\n| 3969 | 0272I1303969 | 27.879 | 86.934 | Kosi | I(s) | 0.34 | 5,217 |\n| 3970 | 0272I1303970 | 27.878 | 86.881 | Kosi | I(s) | 0.25 | 4,969 |\n| 3971 | 0272I1303971 | 27.876 | 86.880 | Kosi | I(s) | 0.98 | 5,004 |\n| 3972 | 0272I1303972 | 27.873 | 86.878 | Kosi | I(s) | 0.58 | 5,017 |\n| 3973 | 0272I1303973 | 27.871 | 86.881 | Kosi | I(s) | 0.36 | 5,025 |\n| 3974 | 0272I1303974 | 27.868 | 86.877 | Kosi | I(s) | 0.27 | 5,081 |\n| 3975 | 0272I1303975 | 27.860 | 86.926 | Kosi | M(o) | 2.92 | 5,471 |\n| 3976 | 0272I1303976 | 27.858 | 86.926 | Kosi | M(o) | 0.38 | 5,468 |\n| 3977 | 0272I1303977 | 27.857 | 86.937 | Kosi | M(o) | 21.45 | 5,473 |\n| 3978 | 0272I1303978 | 27.857 | 86.918 | Kosi | M(o) | 7.38 | 5,418 |\n| 3979 | 0272I1303979 | 27.850 | 86.928 | Kosi | M(o) | 48.86 | 5,411 |\n| 3980 | 0272I1303980 | 27.848 | 86.857 | Kosi | M(o) | 1.37 | 5,337 |\n| 3981 | 0272I1303981 | 27.840 | 86.951 | Kosi | M(o) | 6.94 | 5,430 |\n| 3982 | 0272I1303982 | 27.838 | 86.875 | Kosi | M(o) | 3.63 | 5,329 |\n| 3983 | 0272I1303983 | 27.837 | 86.935 | Kosi | M(e) | 29.39 | 5,209 |\n| 3984 | 0272I1303984 | 27.836 | 86.958 | Kosi | M(o) | 4.01 | 5,554 |\n| 3985 | 0272I1303985 | 27.834 | 86.909 | Kosi | M(o) | 1.32 | 5,400 |\n| 3986 | 0272I1303986 | 27.832 | 86.917 | Kosi | M(o) | 33.07 | 5,360 |\n| 3987 | 0272I1303987 | 27.828 | 86.914 | Kosi | M(o) | 5.55 | 5,358 |\n| 3988 | 0272I1303988 | 27.825 | 86.933 | Kosi | M(o) | 1.91 | 5,182 |\n| 3989 | 0272I1303989 | 27.824 | 86.927 | Kosi | M(o) | 7.09 | 5,240 |\n| 3990 | 0272I1303990 | 27.824 | 86.933 | Kosi | M(o) | 0.45 | 5,187 |\n| 3991 | 0272I1303991 | 27.822 | 86.934 | Kosi | M(o) | 2.95 | 5,184 |\n| 3992 | 0272I1303992 | 27.821 | 86.910 | Kosi | M(o) | 2.15 | 5,471 |\n| 3993 | 0272I1303993 | 27.821 | 86.960 | Kosi | M(o) | 4.22 | 5,450 |\n| 3994 | 0272I1303994 | 27.819 | 86.936 | Kosi | M(o) | 0.25 | 5,180 |\n| 3995 | 0272I1303995 | 27.818 | 86.955 | Kosi | M(e) | 7.16 | 5,440 |\n| 3996 | 0272I1303996 | 27.817 | 86.937 | Kosi | M(o) | 2.63 | 5,178 |\n| 3997 | 0272I1303997 | 27.817 | 86.935 | Kosi | M(o) | 0.36 | 5,186 |\n| 3998 | 0272I1303998 | 27.816 | 86.953 | Kosi | M(o) | 0.34 | 5,432 |\n| 3999 | 0272I1303999 | 27.811 | 86.844 | Kosi | M(o) | 6.37 | 5,541 |\n| 4000 | 0272I1304000 | 27.805 | 86.974 | Kosi | M(e) | 16.77 | 5,516 |\n| 4001 | 0272I1304001 | 27.804 | 86.918 | Kosi | M(o) | 0.31 | 5,404 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7006, "line_end": 7153, "token_count_estimate": 1601, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1303967", "0272I1303968", "0272I1303969", "0272I1303970", "0272I1303971", "0272I1303972", "0272I1303973", "0272I1303974", "0272I1303975", "0272I1303976", "0272I1303977", "0272I1303978", "0272I1303979", "0272I1303980", "0272I1303981", "0272I1303982", "0272I1303983", "0272I1303984", "0272I1303985", "0272I1303986", "0272I1303987", "0272I1303988", "0272I1303989", "0272I1303990", "0272I1303991", "0272I1303992", "0272I1303993", "0272I1303994", "0272I1303995", "0272I1303996", "0272I1303997", "0272I1303998", "0272I1303999", "0272I1304000", "0272I1304001"]}}
{"id": "5a8288d453f8707b", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4002 | 0272I1304002 | 27.803 | 86.984 | Kosi | M(o) | 1.09 | 5,591 |\n| 4003 | 0272I1304003 | 27.802 | 86.980 | Kosi | M(o) | 0.34 | 5,563 |\n| 4004 | 0272I1304004 | 27.799 | 86.966 | Kosi | M(e) | 21.36 | 5,396 |\n| 4005 | 0272I1304005 | 27.796 | 86.942 | Kosi | O | 6.92 | 5,053 |\n| 4006 | 0272I1304006 | 27.795 | 86.877 | Kosi | M(o) | 1.67 | 5,317 |\n| 4007 | 0272I1304007 | 27.794 | 86.911 | Kosi | M(o) | 18.87 | 5,274 |\n| 4008 | 0272I1304008 | 27.793 | 86.838 | Kosi | M(o) | 22.89 | 5,351 |\n| 4009 | 0272I1304009 | 27.793 | 86.957 | Kosi | M(o) | 0.28 | 5,355 |\n| 4010 | 0272I1304010 | 27.790 | 86.910 | Kosi | I(s) | 0.28 | 5,265 |\n| 4011 | 0272I1304011 | 27.790 | 86.837 | Kosi | M(o) | 0.75 | 5,317 |\n| 4012 | 0272I1304012 | 27.789 | 86.849 | Kosi | M(o) | 0.68 | 5,416 |\n| 4013 | 0272I1304013 | 27.789 | 86.877 | Kosi | M(o) | 0.41 | 5,111 |\n| 4014 | 0272I1304014 | 27.783 | 86.957 | Kosi | M(e) | 87.28 | 5,198 |\n| 4015 | 0272I1304015 | 27.778 | 86.867 | Kosi | I(s) | 3.68 | 4,962 |\n| 4016 | 0272I1304016 | 27.775 | 86.866 | Kosi | I(s) | 0.44 | 4,966 |\n| 4017 | 0272I1304017 | 27.775 | 86.797 | Kosi | I(s) | 0.27 | 4,691 |\n| 4018 | 0272I1304018 | 27.774 | 86.868 | Kosi | I(s) | 0.49 | 4,962 |\n| 4019 | 0272I1304019 | 27.773 | 86.867 | Kosi | I(s) | 0.39 | 4,969 |\n| 4020 | 0272I1304020 | 27.772 | 86.869 | Kosi | I(s) | 0.43 | 4,937 |\n| 4021 | 0272I1304021 | 27.771 | 86.869 | Kosi | I(s) | 0.26 | 4,944 |\n| 4022 | 0272I1304022 | 27.771 | 86.871 | Kosi | I(s) | 1.02 | 4,946 |\n| 4023 | 0272I1304023 | 27.768 | 86.926 | Kosi | M(o) | 0.57 | 5,150 |\n| 4024 | 0272I1304024 | 27.766 | 86.871 | Kosi | I(s) | 13.68 | 4,931 |\n| 4025 | 0272I1304025 | 27.766 | 86.799 | Kosi | M(o) | 0.25 | 4,588 |\n| 4026 | 0272I1304026 | 27.764 | 86.876 | Kosi | M(l) | 0.58 | 5,029 |\n| 4027 | 0272I1304027 | 27.762 | 86.870 | Kosi | M(o) | 0.31 | 4,938 |\n| 4028 | 0272I1304028 | 27.760 | 86.863 | Kosi | M(e) | 6.75 | 5,119 |\n| 4029 | 0272I1304029 | 27.760 | 86.922 | Kosi | M(o) | 1.15 | 5,314 |\n| 4030 | 0272I1304030 | 27.759 | 86.875 | Kosi | M(o) | 4.84 | 4,921 |\n| 4031 | 0272I1304031 | 27.757 | 86.888 | Kosi | M(o) | 7.01 | 5,104 |\n| 4032 | 0272I1304032 | 27.755 | 86.958 | Kosi | M(o) | 86.50 | 4,927 |\n| 4033 | 0272I1304033 | 27.754 | 86.871 | Kosi | M(o) | 0.32 | 4,885 |\n| 4034 | 0272I1304034 | 27.754 | 86.943 | Kosi | M(o) | 0.50 | 4,941 |\n| 4035 | 0272I1404035 | 27.747 | 86.870 | Kosi | E(o) | 1.42 | 4,869 |\n| 4036 | 0272I1404036 | 27.743 | 86.929 | Kosi | E(o) | 0.92 | 4,812 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7006, "line_end": 7153, "token_count_estimate": 1587, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1304002", "0272I1304003", "0272I1304004", "0272I1304005", "0272I1304006", "0272I1304007", "0272I1304008", "0272I1304009", "0272I1304010", "0272I1304011", "0272I1304012", "0272I1304013", "0272I1304014", "0272I1304015", "0272I1304016", "0272I1304017", "0272I1304018", "0272I1304019", "0272I1304020", "0272I1304021", "0272I1304022", "0272I1304023", "0272I1304024", "0272I1304025", "0272I1304026", "0272I1304027", "0272I1304028", "0272I1304029", "0272I1304030", "0272I1304031", "0272I1304032", "0272I1304033", "0272I1304034", "0272I1404035", "0272I1404036"]}}
{"id": "4e56e1db654b8195", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4037 | 0272I1404037 | 27.743 | 86.844 | Kosi | M(e) | 25.73 | 4,362 |\n| 4038 | 0272I1404038 | 27.742 | 86.886 | Kosi | M(o) | 0.30 | 5,169 |\n| 4039 | 0272I1404039 | 27.742 | 86.875 | Kosi | E(o) | 0.34 | 4,780 |\n| 4040 | 0272I1404040 | 27.739 | 86.822 | Kosi | M(o) | 0.31 | 4,715 |\n| 4041 | 0272I1404041 | 27.737 | 86.872 | Kosi | M(e) | 0.52 | 4,729 |\n| 4042 | 0272I1404042 | 27.737 | 86.821 | Kosi | M(e) | 0.46 | 4,629 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7006, "line_end": 7153, "token_count_estimate": 352, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1404037", "0272I1404038", "0272I1404039", "0272I1404040", "0272I1404041", "0272I1404042"]}}
{"id": "a54ce03ce02aa3e2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 7154, "line_end": 7161, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f2493eb1296e3b5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4043 | 0272I1404043 | 27.736 | 86.876 | Kosi | M(o) | 1.16 | 4,735 |\n| 4044 | 0272I1404044 | 27.735 | 86.850 | Kosi | M(o) | 0.26 | 4,512 |\n| 4045 | 0272I1404045 | 27.730 | 86.904 | Kosi | E(o) | 0.31 | 5,129 |\n| 4046 | 0272I1404046 | 27.725 | 86.900 | Kosi | M(o) | 3.02 | 5,204 |\n| 4047 | 0272I1404047 | 27.721 | 86.907 | Kosi | M(o) | 1.51 | 5,017 |\n| 4048 | 0272I1404048 | 27.719 | 86.910 | Kosi | M(o) | 14.93 | 4,989 |\n| 4049 | 0272I1404049 | 27.714 | 86.916 | Kosi | M(o) | 6.46 | 5,100 |\n| 4050 | 0272I1404050 | 27.711 | 86.928 | Kosi | E(o) | 0.33 | 4,734 |\n| 4051 | 0272I1404051 | 27.711 | 86.977 | Kosi | M(e) | 6.30 | 4,643 |\n| 4052 | 0272I1404052 | 27.696 | 86.792 | Kosi | M(o) | 12.95 | 4,951 |\n| 4053 | 0272I1404053 | 27.694 | 86.858 | Kosi | M(o) | 0.61 | 4,945 |\n| 4054 | 0272I1404054 | 27.693 | 86.921 | Kosi | M(o) | 1.15 | 5,022 |\n| 4055 | 0272I1404055 | 27.687 | 86.858 | Kosi | M(e) | 31.80 | 4,764 |\n| 4056 | 0272I1404056 | 27.681 | 86.853 | Kosi | E(o) | 11.85 | 4,694 |\n| 4057 | 0272I1404057 | 27.679 | 86.794 | Kosi | M(o) | 1.25 | 4,981 |\n| 4058 | 0272I1404058 | 27.677 | 86.853 | Kosi | E(o) | 0.36 | 4,771 |\n| 4059 | 0272I1404059 | 27.648 | 86.854 | Kosi | M(o) | 5.08 | 4,350 |\n| 4060 | 0272I1404060 | 27.648 | 86.850 | Kosi | M(o) | 0.40 | 4,350 |\n| 4061 | 0272I1404061 | 27.637 | 86.983 | Kosi | E(c) | 7.04 | 4,702 |\n| 4062 | 0272I1404062 | 27.626 | 86.862 | Kosi | E(o) | 1.00 | 4,860 |\n| 4063 | 0272I1404063 | 27.624 | 86.849 | Kosi | E(o) | 1.28 | 4,526 |\n| 4064 | 0272I1404064 | 27.611 | 86.847 | Kosi | E(o) | 0.92 | 4,331 |\n| 4065 | 0272I1404065 | 27.610 | 86.844 | Kosi | E(o) | 2.03 | 4,276 |\n| 4066 | 0272I1404066 | 27.609 | 86.849 | Kosi | E(o) | 9.46 | 4,331 |\n| 4067 | 0272M0104067 | 27.999 | 87.122 | Kosi | E(o) | 2.00 | 4,875 |\n| 4068 | 0272M0104068 | 27.998 | 87.006 | Kosi | I(s) | 0.70 | 5,310 |\n| 4069 | 0272M0104069 | 27.995 | 87.182 | Kosi | E(o) | 0.35 | 4,418 |\n| 4070 | 0272M0104070 | 27.994 | 87.189 | Kosi | E(o) | 0.76 | 4,551 |\n| 4071 | 0272M0104071 | 27.993 | 87.196 | Kosi | E(o) | 0.52 | 4,532 |\n| 4072 | 0272M0104072 | 27.993 | 87.132 | Kosi | E(o) | 1.21 | 4,734 |\n| 4073 | 0272M0104073 | 27.993 | 87.124 | Kosi | E(o) | 3.77 | 4,769 |\n| 4074 | 0272M0104074 | 27.992 | 87.182 | Kosi | E(o) | 1.31 | 4,415 |\n| 4075 | 0272M0104075 | 27.992 | 87.056 | Kosi | M(l) | 0.32 | 5,106 |\n| 4076 | 0272M0104076 | 27.992 | 87.015 | Kosi | I(s) | 0.45 | 5,264 |\n| 4077 | 0272M0104077 | 27.991 | 87.002 | Kosi | I(s) | 0.34 | 5,319 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7162, "line_end": 7309, "token_count_estimate": 1597, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272I1404043", "0272I1404044", "0272I1404045", "0272I1404046", "0272I1404047", "0272I1404048", "0272I1404049", "0272I1404050", "0272I1404051", "0272I1404052", "0272I1404053", "0272I1404054", "0272I1404055", "0272I1404056", "0272I1404057", "0272I1404058", "0272I1404059", "0272I1404060", "0272I1404061", "0272I1404062", "0272I1404063", "0272I1404064", "0272I1404065", "0272I1404066", "0272M0104067", "0272M0104068", "0272M0104069", "0272M0104070", "0272M0104071", "0272M0104072", "0272M0104073", "0272M0104074", "0272M0104075", "0272M0104076", "0272M0104077"]}}
{"id": "32f180b9fa1a5c9d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4078 | 0272M0104078 | 27.989 | 87.204 | Kosi | E(o) | 7.74 | 4,314 |\n| 4079 | 0272M0104079 | 27.988 | 87.243 | Kosi | E(o) | 0.96 | 4,454 |\n| 4080 | 0272M0104080 | 27.987 | 87.247 | Kosi | E(o) | 4.16 | 4,563 |\n| 4081 | 0272M0104081 | 27.987 | 87.051 | Kosi | I(s) | 0.52 | 5,069 |\n| 4082 | 0272M0104082 | 27.987 | 87.041 | Kosi | I(s) | 0.89 | 5,124 |\n| 4083 | 0272M0104083 | 27.986 | 87.026 | Kosi | I(s) | 0.39 | 5,207 |\n| 4084 | 0272M0104084 | 27.986 | 87.070 | Kosi | I(s) | 0.92 | 4,951 |\n| 4085 | 0272M0104085 | 27.985 | 87.082 | Kosi | I(s) | 1.42 | 4,884 |\n| 4086 | 0272M0104086 | 27.985 | 87.052 | Kosi | I(s) | 0.40 | 5,084 |\n| 4087 | 0272M0104087 | 27.978 | 87.026 | Kosi | E(o) | 2.00 | 5,354 |\n| 4088 | 0272M0104088 | 27.978 | 87.121 | Kosi | M(l) | 0.82 | 4,573 |\n| 4089 | 0272M0104089 | 27.965 | 87.235 | Kosi | E(o) | 1.67 | 4,227 |\n| 4090 | 0272M0104090 | 27.964 | 87.242 | Kosi | E(o) | 2.63 | 4,409 |\n| 4091 | 0272M0104091 | 27.964 | 87.055 | Kosi | E(o) | 1.01 | 5,684 |\n| 4092 | 0272M0104092 | 27.961 | 87.244 | Kosi | E(o) | 1.69 | 4,468 |\n| 4093 | 0272M0104093 | 27.959 | 87.247 | Kosi | E(c) | 12.02 | 4,457 |\n| 4094 | 0272M0104094 | 27.956 | 87.235 | Kosi | E(o) | 0.26 | 4,534 |\n| 4095 | 0272M0104095 | 27.954 | 87.234 | Kosi | E(o) | 0.49 | 4,503 |\n| 4096 | 0272M0104096 | 27.949 | 87.238 | Kosi | E(o) | 1.64 | 4,440 |\n| 4097 | 0272M0104097 | 27.949 | 87.193 | Kosi | I(s) | 0.36 | 3,987 |\n| 4098 | 0272M0104098 | 27.948 | 87.189 | Kosi | I(s) | 1.11 | 4,011 |\n| 4099 | 0272M0104099 | 27.948 | 87.200 | Kosi | I(s) | 0.27 | 3,913 |\n| 4100 | 0272M0104100 | 27.946 | 87.194 | Kosi | I(s) | 0.26 | 3,965 |\n| 4101 | 0272M0104101 | 27.944 | 87.174 | Kosi | I(s) | 0.32 | 4,087 |\n| 4102 | 0272M0104102 | 27.943 | 87.211 | Kosi | I(s) | 0.59 | 3,786 |\n| 4103 | 0272M0104103 | 27.942 | 87.170 | Kosi | I(s) | 0.36 | 4,118 |\n| 4104 | 0272M0104104 | 27.942 | 87.176 | Kosi | I(s) | 0.29 | 4,100 |\n| 4105 | 0272M0104105 | 27.941 | 87.168 | Kosi | I(s) | 0.35 | 4,125 |\n| 4106 | 0272M0104106 | 27.906 | 87.188 | Kosi | E(c) | 33.14 | 4,490 |\n| 4107 | 0272M0104107 | 27.877 | 87.222 | Kosi | E(c) | 1.98 | 4,589 |\n| 4108 | 0272M0104108 | 27.876 | 87.227 | Kosi | E(o) | 1.25 | 4,584 |\n| 4109 | 0272M0104109 | 27.874 | 87.033 | Kosi | I(s) | 0.68 | 5,202 |\n| 4110 | 0272M0104110 | 27.872 | 87.225 | Kosi | E(c) | 2.40 | 4,466 |\n| 4111 | 0272M0104111 | 27.871 | 87.228 | Kosi | E(c) | 2.42 | 4,465 |\n| 4112 | 0272M0104112 | 27.854 | 87.079 | Kosi | M(o) | 0.61 | 5,214 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7162, "line_end": 7309, "token_count_estimate": 1597, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0104078", "0272M0104079", "0272M0104080", "0272M0104081", "0272M0104082", "0272M0104083", "0272M0104084", "0272M0104085", "0272M0104086", "0272M0104087", "0272M0104088", "0272M0104089", "0272M0104090", "0272M0104091", "0272M0104092", "0272M0104093", "0272M0104094", "0272M0104095", "0272M0104096", "0272M0104097", "0272M0104098", "0272M0104099", "0272M0104100", "0272M0104101", "0272M0104102", "0272M0104103", "0272M0104104", "0272M0104105", "0272M0104106", "0272M0104107", "0272M0104108", "0272M0104109", "0272M0104110", "0272M0104111", "0272M0104112"]}}
{"id": "5e9b4450b5606e19", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4113 | 0272M0104113 | 27.844 | 87.081 | Kosi | M(e) | 41.15 | 4,847 |\n| 4114 | 0272M0104114 | 27.840 | 87.201 | Kosi | M(o) | 7.41 | 3,991 |\n| 4115 | 0272M0104115 | 27.835 | 87.106 | Kosi | M(o) | 0.27 | 5,364 |\n| 4116 | 0272M0104116 | 27.834 | 87.100 | Kosi | M(o) | 2.81 | 5,314 |\n| 4117 | 0272M0104117 | 27.832 | 87.103 | Kosi | M(o) | 3.25 | 5,302 |\n| 4118 | 0272M0104118 | 27.829 | 87.095 | Kosi | M(o) | 12.06 | 5,225 |\n| 4119 | 0272M0104119 | 27.829 | 87.065 | Kosi | M(o) | 5.55 | 4,821 |\n| 4120 | 0272M0104120 | 27.828 | 87.068 | Kosi | M(o) | 2.00 | 4,818 |\n| 4121 | 0272M0104121 | 27.826 | 87.070 | Kosi | M(o) | 0.60 | 4,806 |\n| 4122 | 0272M0104122 | 27.813 | 87.139 | Kosi | M(e) | 9.27 | 4,790 |\n| 4123 | 0272M0104123 | 27.808 | 87.147 | Kosi | M(o) | 4.99 | 4,974 |\n| 4124 | 0272M0104124 | 27.804 | 87.131 | Kosi | M(o) | 1.11 | 4,619 |\n| 4125 | 0272M0104125 | 27.798 | 87.092 | Kosi | M(e) | 182.16 | 4,543 |\n| 4126 | 0272M0104126 | 27.795 | 87.114 | Kosi | M(o) | 1.19 | 4,521 |\n| 4127 | 0272M0104127 | 27.760 | 87.237 | Kosi | E(c) | 1.18 | 4,176 |\n| 4128 | 0272M0204128 | 27.716 | 87.065 | Kosi | E(o) | 3.01 | 3,993 |\n| 4129 | 0272M0204129 | 27.707 | 87.236 | Kosi | E(c) | 0.43 | 4,103 |\n| 4130 | 0272M0204130 | 27.701 | 87.087 | Kosi | E(o) | 0.63 | 4,392 |\n| 4131 | 0272M0204131 | 27.698 | 87.069 | Kosi | E(o) | 6.03 | 3,772 |\n| 4132 | 0272M0204132 | 27.691 | 87.206 | Kosi | E(c) | 11.49 | 4,088 |\n| 4133 | 0272M0204133 | 27.647 | 87.069 | Kosi | E(o) | 0.80 | 4,166 |\n| 4134 | 0272M0204134 | 27.645 | 87.050 | Kosi | E(c) | 2.36 | 4,369 |\n| 4135 | 0272M0204135 | 27.629 | 87.051 | Kosi | E(o) | 1.61 | 4,185 |\n| 4136 | 0272M0204136 | 27.625 | 87.055 | Kosi | E(o) | 0.69 | 4,293 |\n| 4137 | 0272M0504137 | 28.000 | 87.470 | Kosi | E(o) | 1.27 | 4,791 |\n| 4138 | 0272M0504138 | 28.000 | 87.256 | Kosi | E(o) | 0.78 | 4,737 |\n| 4139 | 0272M0504139 | 27.999 | 87.491 | Kosi | E(o) | 0.85 | 4,787 |\n| 4140 | 0272M0504140 | 27.997 | 87.477 | Kosi | E(c) | 7.95 | 4,416 |\n| 4141 | 0272M0504141 | 27.997 | 87.466 | Kosi | E(o) | 1.04 | 4,559 |\n| 4142 | 0272M0504142 | 27.995 | 87.255 | Kosi | E(o) | 2.54 | 4,705 |\n| 4143 | 0272M0504143 | 27.994 | 87.466 | Kosi | E(o) | 0.63 | 4,592 |\n| 4144 | 0272M0504144 | 27.993 | 87.305 | Kosi | E(c) | 1.05 | 4,767 |\n| 4145 | 0272M0504145 | 27.990 | 87.282 | Kosi | E(o) | 0.69 | 4,851 |\n| 4146 | 0272M0504146 | 27.988 | 87.252 | Kosi | E(o) | 0.52 | 4,612 |\n| 4147 | 0272M0504147 | 27.987 | 87.305 | Kosi | E(o) | 0.97 | 4,555 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7162, "line_end": 7309, "token_count_estimate": 1593, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0104113", "0272M0104114", "0272M0104115", "0272M0104116", "0272M0104117", "0272M0104118", "0272M0104119", "0272M0104120", "0272M0104121", "0272M0104122", "0272M0104123", "0272M0104124", "0272M0104125", "0272M0104126", "0272M0104127", "0272M0204128", "0272M0204129", "0272M0204130", "0272M0204131", "0272M0204132", "0272M0204133", "0272M0204134", "0272M0204135", "0272M0204136", "0272M0504137", "0272M0504138", "0272M0504139", "0272M0504140", "0272M0504141", "0272M0504142", "0272M0504143", "0272M0504144", "0272M0504145", "0272M0504146", "0272M0504147"]}}
{"id": "6a6311327f1fae79", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4148 | 0272M0504148 | 27.985 | 87.268 | Kosi | E(o) | 0.99 | 4,826 |\n| 4149 | 0272M0504149 | 27.983 | 87.303 | Kosi | E(o) | 0.99 | 4,526 |\n| 4150 | 0272M0504150 | 27.983 | 87.345 | Kosi | E(o) | 26.18 | 3,728 |\n| 4151 | 0272M0504151 | 27.982 | 87.266 | Kosi | E(o) | 7.68 | 4,782 |\n| 4152 | 0272M0504152 | 27.982 | 87.258 | Kosi | E(o) | 9.79 | 4,718 |\n| 4153 | 0272M0504153 | 27.981 | 87.303 | Kosi | E(o) | 0.53 | 4,520 |\n| 4154 | 0272M0504154 | 27.975 | 87.270 | Kosi | E(o) | 1.19 | 4,866 |\n| 4155 | 0272M0504155 | 27.974 | 87.280 | Kosi | E(c) | 9.08 | 4,795 |\n| 4156 | 0272M0504156 | 27.973 | 87.267 | Kosi | E(o) | 0.39 | 4,840 |\n| 4157 | 0272M0504157 | 27.972 | 87.348 | Kosi | E(o) | 3.84 | 3,963 |\n| 4158 | 0272M0504158 | 27.969 | 87.255 | Kosi | E(o) | 3.01 | 4,677 |\n| 4159 | 0272M0504159 | 27.968 | 87.277 | Kosi | E(o) | 0.38 | 4,711 |\n| 4160 | 0272M0504160 | 27.966 | 87.268 | Kosi | E(o) | 1.18 | 4,727 |\n| 4161 | 0272M0504161 | 27.965 | 87.257 | Kosi | E(c) | 6.38 | 4,649 |\n| 4162 | 0272M0504162 | 27.965 | 87.481 | Kosi | E(o) | 0.97 | 4,340 |\n| 4163 | 0272M0504163 | 27.963 | 87.266 | Kosi | E(o) | 0.27 | 4,728 |\n| 4164 | 0272M0504164 | 27.963 | 87.274 | Kosi | E(o) | 0.42 | 4,652 |\n| 4165 | 0272M0504165 | 27.963 | 87.485 | Kosi | E(o) | 0.55 | 4,384 |\n| 4166 | 0272M0504166 | 27.963 | 87.486 | Kosi | E(o) | 0.27 | 4,380 |\n| 4167 | 0272M0504167 | 27.961 | 87.257 | Kosi | E(o) | 1.97 | 4,647 |\n| 4168 | 0272M0504168 | 27.961 | 87.267 | Kosi | E(o) | 0.61 | 4,666 |\n| 4169 | 0272M0504169 | 27.960 | 87.485 | Kosi | E(o) | 1.04 | 4,432 |\n| 4170 | 0272M0504170 | 27.958 | 87.254 | Kosi | E(o) | 2.38 | 4,553 |\n| 4171 | 0272M0504171 | 27.956 | 87.287 | Kosi | E(o) | 0.92 | 4,809 |\n| 4172 | 0272M0504172 | 27.955 | 87.286 | Kosi | E(o) | 0.99 | 4,809 |\n| 4173 | 0272M0504173 | 27.954 | 87.488 | Kosi | E(o) | 1.36 | 4,487 |\n| 4174 | 0272M0504174 | 27.953 | 87.491 | Kosi | E(c) | 2.95 | 4,512 |\n| 4175 | 0272M0504175 | 27.948 | 87.349 | Kosi | E(c) | 10.13 | 3,991 |\n| 4176 | 0272M0504176 | 27.947 | 87.261 | Kosi | E(c) | 6.70 | 4,364 |\n| 4177 | 0272M0504177 | 27.945 | 87.305 | Kosi | E(o) | 1.61 | 4,809 |\n| 4178 | 0272M0504178 | 27.945 | 87.302 | Kosi | E(o) | 0.68 | 4,795 |\n| 4179 | 0272M0504179 | 27.944 | 87.301 | Kosi | E(o) | 0.37 | 4,806 |\n| 4180 | 0272M0504180 | 27.943 | 87.289 | Kosi | E(o) | 2.11 | 4,573 |\n| 4181 | 0272M0504181 | 27.938 | 87.303 | Kosi | E(c) | 9.19 | 4,592 |\n| 4182 | 0272M0504182 | 27.932 | 87.293 | Kosi | E(o) | 12.13 | 4,373 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7162, "line_end": 7309, "token_count_estimate": 1600, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0504148", "0272M0504149", "0272M0504150", "0272M0504151", "0272M0504152", "0272M0504153", "0272M0504154", "0272M0504155", "0272M0504156", "0272M0504157", "0272M0504158", "0272M0504159", "0272M0504160", "0272M0504161", "0272M0504162", "0272M0504163", "0272M0504164", "0272M0504165", "0272M0504166", "0272M0504167", "0272M0504168", "0272M0504169", "0272M0504170", "0272M0504171", "0272M0504172", "0272M0504173", "0272M0504174", "0272M0504175", "0272M0504176", "0272M0504177", "0272M0504178", "0272M0504179", "0272M0504180", "0272M0504181", "0272M0504182"]}}
{"id": "f570d519a2802e88", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4183 | 0272M0504183 | 27.932 | 87.454 | Kosi | E(o) | 2.30 | 4,131 |\n| 4184 | 0272M0504184 | 27.932 | 87.432 | Kosi | E(o) | 4.61 | 4,051 |\n| 4185 | 0272M0504185 | 27.929 | 87.443 | Kosi | E(o) | 1.84 | 4,211 |\n| 4186 | 0272M0504186 | 27.929 | 87.451 | Kosi | E(o) | 1.58 | 3,937 |\n| 4187 | 0272M0504187 | 27.929 | 87.302 | Kosi | E(o) | 0.43 | 4,542 |\n| 4188 | 0272M0504188 | 27.929 | 87.304 | Kosi | E(o) | 1.02 | 4,544 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7162, "line_end": 7309, "token_count_estimate": 358, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0504183", "0272M0504184", "0272M0504185", "0272M0504186", "0272M0504187", "0272M0504188"]}}
{"id": "c738222409578390", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 7310, "line_end": 7316, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e8ed17a0a3987774", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4189 | 0272M0504189 | 27.925 | 87.316 | Kosi | E(o) | 0.88 | 4,564 |\n| 4190 | 0272M0504190 | 27.924 | 87.321 | Kosi | E(o) | 4.27 | 4,447 |\n| 4191 | 0272M0504191 | 27.922 | 87.300 | Kosi | E(c) | 3.25 | 4,560 |\n| 4192 | 0272M0504192 | 27.918 | 87.306 | Kosi | E(c) | 11.15 | 4,416 |\n| 4193 | 0272M0504193 | 27.913 | 87.305 | Kosi | E(o) | 3.68 | 4,359 |\n| 4194 | 0272M0504194 | 27.912 | 87.299 | Kosi | E(c) | 7.13 | 4,274 |\n| 4195 | 0272M0504195 | 27.899 | 87.494 | Kosi | E(o) | 1.74 | 4,398 |\n| 4196 | 0272M0504196 | 27.854 | 87.283 | Kosi | E(c) | 5.79 | 4,533 |\n| 4197 | 0272M0504197 | 27.848 | 87.308 | Kosi | E(o) | 0.71 | 4,255 |\n| 4198 | 0272M0504198 | 27.846 | 87.311 | Kosi | E(o) | 0.85 | 4,169 |\n| 4199 | 0272M0504199 | 27.842 | 87.291 | Kosi | E(c) | 8.36 | 4,455 |\n| 4200 | 0272M0504200 | 27.838 | 87.310 | Kosi | E(o) | 0.28 | 4,117 |\n| 4201 | 0272M0504201 | 27.838 | 87.301 | Kosi | E(o) | 4.37 | 4,180 |\n| 4202 | 0272M0504202 | 27.837 | 87.341 | Kosi | E(c) | 19.17 | 4,243 |\n| 4203 | 0272M0504203 | 27.836 | 87.305 | Kosi | E(o) | 0.82 | 4,201 |\n| 4204 | 0272M0504204 | 27.824 | 87.332 | Kosi | E(o) | 6.06 | 4,152 |\n| 4205 | 0272M0504205 | 27.824 | 87.361 | Kosi | E(o) | 4.84 | 3,907 |\n| 4206 | 0272M0504206 | 27.822 | 87.356 | Kosi | E(o) | 3.91 | 3,973 |\n| 4207 | 0272M0504207 | 27.822 | 87.327 | Kosi | E(o) | 0.69 | 4,089 |\n| 4208 | 0272M0504208 | 27.821 | 87.314 | Kosi | E(o) | 0.59 | 4,068 |\n| 4209 | 0272M0504209 | 27.820 | 87.316 | Kosi | E(o) | 1.63 | 4,072 |\n| 4210 | 0272M0504210 | 27.820 | 87.320 | Kosi | E(o) | 0.75 | 4,167 |\n| 4211 | 0272M0504211 | 27.817 | 87.330 | Kosi | E(o) | 0.64 | 4,007 |\n| 4212 | 0272M0504212 | 27.814 | 87.333 | Kosi | E(o) | 4.54 | 3,954 |\n| 4213 | 0272M0504213 | 27.812 | 87.309 | Kosi | E(o) | 1.85 | 3,893 |\n| 4214 | 0272M0504214 | 27.806 | 87.307 | Kosi | E(o) | 0.33 | 3,842 |\n| 4215 | 0272M0604215 | 27.587 | 87.486 | Kosi | E(o) | 2.07 | 4,282 |\n| 4216 | 0272M0604216 | 27.563 | 87.487 | Kosi | E(c) | 2.53 | 4,205 |\n| 4217 | 0272M0604217 | 27.529 | 87.479 | Kosi | E(o) | 0.76 | 4,327 |\n| 4218 | 0272M0604218 | 27.529 | 87.463 | Kosi | E(o) | 0.91 | 3,998 |\n| 4219 | 0272M0604219 | 27.527 | 87.478 | Kosi | E(o) | 6.69 | 4,345 |\n| 4220 | 0272M0604220 | 27.526 | 87.481 | Kosi | E(o) | 1.19 | 4,314 |\n| 4221 | 0272M0604221 | 27.526 | 87.468 | Kosi | E(o) | 1.28 | 4,214 |\n| 4222 | 0272M0604222 | 27.523 | 87.477 | Kosi | E(o) | 1.23 | 4,385 |\n| 4223 | 0272M0604223 | 27.522 | 87.469 | Kosi | E(o) | 3.76 | 4,254 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7317, "line_end": 7464, "token_count_estimate": 1607, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0504189", "0272M0504190", "0272M0504191", "0272M0504192", "0272M0504193", "0272M0504194", "0272M0504195", "0272M0504196", "0272M0504197", "0272M0504198", "0272M0504199", "0272M0504200", "0272M0504201", "0272M0504202", "0272M0504203", "0272M0504204", "0272M0504205", "0272M0504206", "0272M0504207", "0272M0504208", "0272M0504209", "0272M0504210", "0272M0504211", "0272M0504212", "0272M0504213", "0272M0504214", "0272M0604215", "0272M0604216", "0272M0604217", "0272M0604218", "0272M0604219", "0272M0604220", "0272M0604221", "0272M0604222", "0272M0604223"]}}
{"id": "cf8fee69ab27f223", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4224 | 0272M0604224 | 27.520 | 87.484 | Kosi | E(o) | 0.43 | 4,294 |\n| 4225 | 0272M0604225 | 27.520 | 87.463 | Kosi | E(o) | 0.86 | 4,308 |\n| 4226 | 0272M0604226 | 27.519 | 87.487 | Kosi | E(o) | 0.58 | 4,239 |\n| 4227 | 0272M0604227 | 27.518 | 87.471 | Kosi | E(o) | 0.77 | 4,321 |\n| 4228 | 0272M0604228 | 27.517 | 87.453 | Kosi | E(o) | 1.62 | 4,306 |\n| 4229 | 0272M0604229 | 27.517 | 87.464 | Kosi | E(c) | 4.31 | 4,316 |\n| 4230 | 0272M0604230 | 27.515 | 87.471 | Kosi | E(o) | 1.20 | 4,342 |\n| 4231 | 0272M0604231 | 27.504 | 87.456 | Kosi | E(c) | 2.55 | 4,149 |\n| 4232 | 0272M0904232 | 27.999 | 87.504 | Kosi | E(o) | 4.02 | 4,640 |\n| 4233 | 0272M0904233 | 27.997 | 87.522 | Kosi | E(c) | 12.38 | 4,720 |\n| 4234 | 0272M0904234 | 27.996 | 87.597 | Kosi | E(o) | 1.60 | 5,079 |\n| 4235 | 0272M0904235 | 27.992 | 87.586 | Kosi | E(o) | 0.89 | 4,844 |\n| 4236 | 0272M0904236 | 27.991 | 87.518 | Kosi | E(o) | 5.40 | 4,669 |\n| 4237 | 0272M0904237 | 27.990 | 87.585 | Kosi | E(o) | 1.99 | 4,849 |\n| 4238 | 0272M0904238 | 27.990 | 87.574 | Kosi | E(o) | 6.88 | 4,819 |\n| 4239 | 0272M0904239 | 27.987 | 87.589 | Kosi | E(o) | 1.19 | 4,961 |\n| 4240 | 0272M0904240 | 27.986 | 87.583 | Kosi | E(o) | 12.33 | 4,826 |\n| 4241 | 0272M0904241 | 27.986 | 87.578 | Kosi | E(o) | 0.41 | 4,823 |\n| 4242 | 0272M0904242 | 27.986 | 87.559 | Kosi | E(c) | 7.07 | 4,898 |\n| 4243 | 0272M0904243 | 27.984 | 87.577 | Kosi | E(o) | 7.61 | 4,817 |\n| 4244 | 0272M0904244 | 27.980 | 87.588 | Kosi | E(c) | 0.35 | 4,832 |\n| 4245 | 0272M0904245 | 27.979 | 87.557 | Kosi | E(c) | 11.03 | 4,767 |\n| 4246 | 0272M0904246 | 27.969 | 87.542 | Kosi | E(o) | 1.05 | 4,726 |\n| 4247 | 0272M0904247 | 27.959 | 87.523 | Kosi | E(o) | 5.21 | 4,693 |\n| 4248 | 0272M0904248 | 27.958 | 87.527 | Kosi | E(o) | 2.09 | 4,698 |\n| 4249 | 0272M0904249 | 27.957 | 87.735 | Kosi | E(o) | 0.68 | 5,184 |\n| 4250 | 0272M0904250 | 27.956 | 87.502 | Kosi | E(o) | 0.90 | 4,693 |\n| 4251 | 0272M0904251 | 27.933 | 87.587 | Kosi | E(o) | 0.34 | 5,216 |\n| 4252 | 0272M0904252 | 27.931 | 87.534 | Kosi | E(o) | 1.19 | 4,448 |\n| 4253 | 0272M0904253 | 27.925 | 87.711 | Kosi | M(o) | 0.51 | 5,505 |\n| 4254 | 0272M0904254 | 27.918 | 87.725 | Kosi | M(o) | 4.39 | 5,294 |\n| 4255 | 0272M0904255 | 27.916 | 87.722 | Kosi | M(o) | 1.90 | 5,315 |\n| 4256 | 0272M0904256 | 27.916 | 87.703 | Kosi | E(o) | 4.19 | 5,382 |\n| 4257 | 0272M0904257 | 27.915 | 87.728 | Kosi | M(o) | 0.82 | 5,328 |\n| 4258 | 0272M0904258 | 27.915 | 87.534 | Kosi | E(o) | 1.09 | 3,957 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7317, "line_end": 7464, "token_count_estimate": 1606, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0604224", "0272M0604225", "0272M0604226", "0272M0604227", "0272M0604228", "0272M0604229", "0272M0604230", "0272M0604231", "0272M0904232", "0272M0904233", "0272M0904234", "0272M0904235", "0272M0904236", "0272M0904237", "0272M0904238", "0272M0904239", "0272M0904240", "0272M0904241", "0272M0904242", "0272M0904243", "0272M0904244", "0272M0904245", "0272M0904246", "0272M0904247", "0272M0904248", "0272M0904249", "0272M0904250", "0272M0904251", "0272M0904252", "0272M0904253", "0272M0904254", "0272M0904255", "0272M0904256", "0272M0904257", "0272M0904258"]}}
{"id": "eff20923ebee56f7", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4259 | 0272M0904259 | 27.900 | 87.699 | Kosi | M(e) | 11.27 | 5,226 |\n| 4260 | 0272M0904260 | 27.898 | 87.509 | Kosi | E(o) | 1.12 | 4,588 |\n| 4261 | 0272M0904261 | 27.897 | 87.501 | Kosi | E(o) | 4.63 | 4,436 |\n| 4262 | 0272M0904262 | 27.888 | 87.693 | Kosi | M(o) | 3.37 | 5,143 |\n| 4263 | 0272M0904263 | 27.888 | 87.722 | Kosi | M(o) | 2.34 | 5,471 |\n| 4264 | 0272M0904264 | 27.886 | 87.702 | Kosi | M(o) | 8.37 | 5,174 |\n| 4265 | 0272M0904265 | 27.879 | 87.575 | Kosi | E(o) | 3.35 | 4,624 |\n| 4266 | 0272M0904266 | 27.876 | 87.592 | Kosi | E(o) | 0.32 | 5,087 |\n| 4267 | 0272M0904267 | 27.864 | 87.737 | Kosi | E(c) | 13.98 | 5,337 |\n| 4268 | 0272M0904268 | 27.863 | 87.655 | Kosi | E(o) | 1.40 | 5,304 |\n| 4269 | 0272M0904269 | 27.860 | 87.542 | Kosi | E(o) | 2.18 | 4,660 |\n| 4270 | 0272M0904270 | 27.853 | 87.602 | Kosi | M(o) | 3.92 | 4,925 |\n| 4271 | 0272M0904271 | 27.850 | 87.721 | Kosi | M(o) | 0.26 | 5,253 |\n| 4272 | 0272M0904272 | 27.850 | 87.724 | Kosi | M(o) | 0.82 | 5,257 |\n| 4273 | 0272M0904273 | 27.850 | 87.723 | Kosi | M(o) | 0.75 | 5,256 |\n| 4274 | 0272M0904274 | 27.850 | 87.729 | Kosi | M(o) | 1.36 | 5,302 |\n| 4275 | 0272M0904275 | 27.850 | 87.720 | Kosi | M(o) | 0.49 | 5,253 |\n| 4276 | 0272M0904276 | 27.849 | 87.716 | Kosi | M(o) | 2.31 | 5,250 |\n| 4277 | 0272M0904277 | 27.844 | 87.664 | Kosi | M(o) | 3.36 | 5,160 |\n| 4278 | 0272M0904278 | 27.842 | 87.716 | Kosi | M(o) | 0.41 | 5,312 |\n| 4279 | 0272M0904279 | 27.842 | 87.728 | Kosi | E(o) | 8.98 | 5,422 |\n| 4280 | 0272M0904280 | 27.841 | 87.717 | Kosi | M(o) | 0.85 | 5,321 |\n| 4281 | 0272M0904281 | 27.838 | 87.719 | Kosi | M(o) | 0.84 | 5,288 |\n| 4282 | 0272M0904282 | 27.836 | 87.605 | Kosi | E(c) | 8.76 | 4,876 |\n| 4283 | 0272M0904283 | 27.836 | 87.722 | Kosi | M(o) | 0.39 | 5,308 |\n| 4284 | 0272M0904284 | 27.833 | 87.621 | Kosi | M(o) | 2.01 | 5,082 |\n| 4285 | 0272M0904285 | 27.832 | 87.661 | Kosi | M(o) | 1.26 | 5,271 |\n| 4286 | 0272M0904286 | 27.831 | 87.659 | Kosi | M(o) | 2.37 | 5,191 |\n| 4287 | 0272M0904287 | 27.831 | 87.611 | Kosi | M(o) | 1.96 | 5,124 |\n| 4288 | 0272M0904288 | 27.828 | 87.624 | Kosi | M(o) | 1.85 | 5,113 |\n| 4289 | 0272M0904289 | 27.826 | 87.629 | Kosi | E(o) | 0.62 | 5,247 |\n| 4290 | 0272M0904290 | 27.822 | 87.739 | Kosi | E(o) | 7.05 | 5,191 |\n| 4291 | 0272M0904291 | 27.820 | 87.654 | Kosi | E(o) | 0.29 | 5,130 |\n| 4292 | 0272M0904292 | 27.820 | 87.672 | Kosi | M(o) | 2.41 | 5,162 |\n| 4293 | 0272M0904293 | 27.818 | 87.657 | Kosi | E(o) | 0.26 | 5,187 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7317, "line_end": 7464, "token_count_estimate": 1600, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0904259", "0272M0904260", "0272M0904261", "0272M0904262", "0272M0904263", "0272M0904264", "0272M0904265", "0272M0904266", "0272M0904267", "0272M0904268", "0272M0904269", "0272M0904270", "0272M0904271", "0272M0904272", "0272M0904273", "0272M0904274", "0272M0904275", "0272M0904276", "0272M0904277", "0272M0904278", "0272M0904279", "0272M0904280", "0272M0904281", "0272M0904282", "0272M0904283", "0272M0904284", "0272M0904285", "0272M0904286", "0272M0904287", "0272M0904288", "0272M0904289", "0272M0904290", "0272M0904291", "0272M0904292", "0272M0904293"]}}
{"id": "fcf7a86219773eba", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4294 | 0272M0904294 | 27.817 | 87.654 | Kosi | E(o) | 0.37 | 5,108 |\n| 4295 | 0272M0904295 | 27.817 | 87.676 | Kosi | E(o) | 0.54 | 5,022 |\n| 4296 | 0272M0904296 | 27.816 | 87.749 | Kosi | M(e) | 17.17 | 4,903 |\n| 4297 | 0272M0904297 | 27.814 | 87.632 | Kosi | M(o) | 1.37 | 5,111 |\n| 4298 | 0272M0904298 | 27.813 | 87.636 | Kosi | E(o) | 1.41 | 5,052 |\n| 4299 | 0272M0904299 | 27.811 | 87.578 | Kosi | E(o) | 1.00 | 4,816 |\n| 4300 | 0272M0904300 | 27.809 | 87.701 | Kosi | M(o) | 2.91 | 5,175 |\n| 4301 | 0272M0904301 | 27.808 | 87.681 | Kosi | E(o) | 0.39 | 5,201 |\n| 4302 | 0272M0904302 | 27.808 | 87.633 | Kosi | M(o) | 0.68 | 5,068 |\n| 4303 | 0272M0904303 | 27.808 | 87.631 | Kosi | M(o) | 0.67 | 5,068 |\n| 4304 | 0272M0904304 | 27.806 | 87.709 | Kosi | E(o) | 0.46 | 4,944 |\n| 4305 | 0272M0904305 | 27.805 | 87.749 | Kosi | M(o) | 1.16 | 4,714 |\n| 4306 | 0272M0904306 | 27.803 | 87.679 | Kosi | E(o) | 1.71 | 4,942 |\n| 4307 | 0272M0904307 | 27.802 | 87.747 | Kosi | M(o) | 0.26 | 4,693 |\n| 4308 | 0272M0904308 | 27.799 | 87.663 | Kosi | E(o) | 1.06 | 5,085 |\n| 4309 | 0272M0904309 | 27.799 | 87.720 | Kosi | E(o) | 5.07 | 5,027 |\n| 4310 | 0272M0904310 | 27.797 | 87.631 | Kosi | E(o) | 9.59 | 4,940 |\n| 4311 | 0272M0904311 | 27.794 | 87.663 | Kosi | E(o) | 0.39 | 5,055 |\n| 4312 | 0272M0904312 | 27.794 | 87.631 | Kosi | E(o) | 0.30 | 4,928 |\n| 4313 | 0272M0904313 | 27.792 | 87.584 | Kosi | E(o) | 0.67 | 4,906 |\n| 4314 | 0272M0904314 | 27.791 | 87.587 | Kosi | E(o) | 3.30 | 4,837 |\n| 4315 | 0272M0904315 | 27.789 | 87.683 | Kosi | E(o) | 0.42 | 4,804 |\n| 4316 | 0272M0904316 | 27.785 | 87.679 | Kosi | E(o) | 0.77 | 5,011 |\n| 4317 | 0272M0904317 | 27.785 | 87.584 | Kosi | E(o) | 2.84 | 4,767 |\n| 4318 | 0272M0904318 | 27.783 | 87.662 | Kosi | M(o) | 1.24 | 5,036 |\n| 4319 | 0272M0904319 | 27.781 | 87.546 | Kosi | E(o) | 0.39 | 4,577 |\n| 4320 | 0272M0904320 | 27.781 | 87.661 | Kosi | M(o) | 3.97 | 5,007 |\n| 4321 | 0272M0904321 | 27.779 | 87.685 | Kosi | E(o) | 1.21 | 4,855 |\n| 4322 | 0272M0904322 | 27.777 | 87.577 | Kosi | E(o) | 8.23 | 4,622 |\n| 4323 | 0272M0904323 | 27.770 | 87.658 | Kosi | M(o) | 3.03 | 5,014 |\n| 4324 | 0272M0904324 | 27.770 | 87.675 | Kosi | E(o) | 0.37 | 5,282 |\n| 4325 | 0272M0904325 | 27.767 | 87.636 | Kosi | E(o) | 2.52 | 5,024 |\n| 4326 | 0272M0904326 | 27.765 | 87.689 | Kosi | E(o) | 0.44 | 5,048 |\n| 4327 | 0272M0904327 | 27.759 | 87.691 | Kosi | E(o) | 1.11 | 5,005 |\n| 4328 | 0272M0904328 | 27.758 | 87.652 | Kosi | M(o) | 0.61 | 5,185 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7317, "line_end": 7464, "token_count_estimate": 1601, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0904294", "0272M0904295", "0272M0904296", "0272M0904297", "0272M0904298", "0272M0904299", "0272M0904300", "0272M0904301", "0272M0904302", "0272M0904303", "0272M0904304", "0272M0904305", "0272M0904306", "0272M0904307", "0272M0904308", "0272M0904309", "0272M0904310", "0272M0904311", "0272M0904312", "0272M0904313", "0272M0904314", "0272M0904315", "0272M0904316", "0272M0904317", "0272M0904318", "0272M0904319", "0272M0904320", "0272M0904321", "0272M0904322", "0272M0904323", "0272M0904324", "0272M0904325", "0272M0904326", "0272M0904327", "0272M0904328"]}}
{"id": "855b6e068dc4b06d", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4329 | 0272M0904329 | 27.758 | 87.650 | Kosi | M(o) | 1.39 | 5,145 |\n| 4330 | 0272M0904330 | 27.755 | 87.651 | Kosi | M(o) | 0.71 | 5,110 |\n| 4331 | 0272M0904331 | 27.754 | 87.647 | Kosi | M(o) | 0.51 | 5,093 |\n| 4332 | 0272M0904332 | 27.754 | 87.693 | Kosi | E(o) | 0.75 | 5,000 |\n| 4333 | 0272M0904333 | 27.753 | 87.648 | Kosi | M(o) | 0.70 | 5,095 |\n| 4334 | 0272M1004334 | 27.747 | 87.649 | Kosi | M(o) | 4.72 | 5,006 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7317, "line_end": 7464, "token_count_estimate": 354, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M0904329", "0272M0904330", "0272M0904331", "0272M0904332", "0272M0904333", "0272M1004334"]}}
{"id": "a480ded931a50c65", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 7465, "line_end": 7469, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "152e8f8065726863", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4335 | 0272M1004335 | 27.743 | 87.605 | Kosi | E(o) | 0.84 | 4,814 |\n| 4336 | 0272M1004336 | 27.739 | 87.673 | Kosi | E(o) | 1.78 | 4,842 |\n| 4337 | 0272M1004337 | 27.737 | 87.713 | Kosi | E(o) | 5.52 | 4,764 |\n| 4338 | 0272M1004338 | 27.736 | 87.667 | Kosi | E(o) | 0.33 | 4,930 |\n| 4339 | 0272M1004339 | 27.736 | 87.703 | Kosi | E(o) | 0.43 | 4,804 |\n| 4340 | 0272M1004340 | 27.735 | 87.662 | Kosi | E(o) | 0.42 | 4,988 |\n| 4341 | 0272M1004341 | 27.732 | 87.624 | Kosi | M(o) | 1.27 | 4,980 |\n| 4342 | 0272M1004342 | 27.731 | 87.623 | Kosi | M(o) | 1.83 | 4,983 |\n| 4343 | 0272M1004343 | 27.730 | 87.654 | Kosi | E(o) | 0.75 | 4,929 |\n| 4344 | 0272M1004344 | 27.730 | 87.726 | Kosi | E(o) | 0.43 | 4,642 |\n| 4345 | 0272M1004345 | 27.729 | 87.718 | Kosi | E(o) | 0.30 | 4,687 |\n| 4346 | 0272M1004346 | 27.729 | 87.674 | Kosi | E(o) | 1.76 | 4,877 |\n| 4347 | 0272M1004347 | 27.729 | 87.677 | Kosi | E(o) | 3.18 | 4,829 |\n| 4348 | 0272M1004348 | 27.729 | 87.632 | Kosi | M(o) | 3.14 | 4,943 |\n| 4349 | 0272M1004349 | 27.728 | 87.670 | Kosi | E(o) | 0.95 | 4,934 |\n| 4350 | 0272M1004350 | 27.728 | 87.596 | Kosi | E(o) | 2.39 | 4,855 |\n| 4351 | 0272M1004351 | 27.728 | 87.719 | Kosi | E(o) | 0.54 | 4,713 |\n| 4352 | 0272M1004352 | 27.727 | 87.717 | Kosi | E(o) | 0.78 | 4,753 |\n| 4353 | 0272M1004353 | 27.726 | 87.720 | Kosi | E(o) | 0.41 | 4,719 |\n| 4354 | 0272M1004354 | 27.725 | 87.619 | Kosi | M(o) | 7.54 | 5,048 |\n| 4355 | 0272M1004355 | 27.721 | 87.555 | Kosi | E(o) | 0.91 | 4,583 |\n| 4356 | 0272M1004356 | 27.719 | 87.563 | Kosi | E(o) | 3.20 | 4,805 |\n| 4357 | 0272M1004357 | 27.718 | 87.648 | Kosi | E(o) | 0.66 | 5,088 |\n| 4358 | 0272M1004358 | 27.709 | 87.693 | Kosi | E(o) | 2.93 | 4,753 |\n| 4359 | 0272M1004359 | 27.706 | 87.691 | Kosi | E(c) | 2.65 | 4,749 |\n| 4360 | 0272M1004360 | 27.705 | 87.596 | Kosi | E(o) | 2.57 | 4,773 |\n| 4361 | 0272M1004361 | 27.705 | 87.599 | Kosi | E(o) | 2.64 | 4,813 |\n| 4362 | 0272M1004362 | 27.703 | 87.652 | Kosi | E(o) | 1.55 | 4,824 |\n| 4363 | 0272M1004363 | 27.702 | 87.656 | Kosi | E(o) | 4.21 | 4,740 |\n| 4364 | 0272M1004364 | 27.695 | 87.695 | Kosi | E(o) | 0.95 | 4,768 |\n| 4365 | 0272M1004365 | 27.694 | 87.681 | Kosi | E(o) | 0.27 | 4,720 |\n| 4366 | 0272M1004366 | 27.694 | 87.693 | Kosi | E(o) | 1.60 | 4,760 |\n| 4367 | 0272M1004367 | 27.693 | 87.624 | Kosi | E(o) | 0.40 | 5,129 |\n| 4368 | 0272M1004368 | 27.690 | 87.692 | Kosi | E(o) | 5.18 | 4,651 |\n| 4369 | 0272M1004369 | 27.688 | 87.541 | Kosi | E(o) | 2.52 | 4,620 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7470, "line_end": 7617, "token_count_estimate": 1602, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1004335", "0272M1004336", "0272M1004337", "0272M1004338", "0272M1004339", "0272M1004340", "0272M1004341", "0272M1004342", "0272M1004343", "0272M1004344", "0272M1004345", "0272M1004346", "0272M1004347", "0272M1004348", "0272M1004349", "0272M1004350", "0272M1004351", "0272M1004352", "0272M1004353", "0272M1004354", "0272M1004355", "0272M1004356", "0272M1004357", "0272M1004358", "0272M1004359", "0272M1004360", "0272M1004361", "0272M1004362", "0272M1004363", "0272M1004364", "0272M1004365", "0272M1004366", "0272M1004367", "0272M1004368", "0272M1004369"]}}
{"id": "39da6de85e0aa368", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4370 | 0272M1004370 | 27.687 | 87.690 | Kosi | E(o) | 1.36 | 4,608 |\n| 4371 | 0272M1004371 | 27.684 | 87.688 | Kosi | E(o) | 1.94 | 4,584 |\n| 4372 | 0272M1004372 | 27.683 | 87.722 | Kosi | E(o) | 0.64 | 4,713 |\n| 4373 | 0272M1004373 | 27.681 | 87.583 | Kosi | E(c) | 7.04 | 4,766 |\n| 4374 | 0272M1004374 | 27.681 | 87.694 | Kosi | E(c) | 13.30 | 4,633 |\n| 4375 | 0272M1004375 | 27.681 | 87.517 | Kosi | E(c) | 10.10 | 4,472 |\n| 4376 | 0272M1004376 | 27.680 | 87.603 | Kosi | M(o) | 2.54 | 4,862 |\n| 4377 | 0272M1004377 | 27.679 | 87.712 | Kosi | E(o) | 3.06 | 4,912 |\n| 4378 | 0272M1004378 | 27.676 | 87.734 | Kosi | E(o) | 2.77 | 4,447 |\n| 4379 | 0272M1004379 | 27.675 | 87.633 | Kosi | E(o) | 4.66 | 4,923 |\n| 4380 | 0272M1004380 | 27.675 | 87.516 | Kosi | E(c) | 5.83 | 4,642 |\n| 4381 | 0272M1004381 | 27.674 | 87.625 | Kosi | M(o) | 4.20 | 4,910 |\n| 4382 | 0272M1004382 | 27.674 | 87.621 | Kosi | M(o) | 2.04 | 4,939 |\n| 4383 | 0272M1004383 | 27.674 | 87.555 | Kosi | E(o) | 5.09 | 4,664 |\n| 4384 | 0272M1004384 | 27.673 | 87.694 | Kosi | E(o) | 1.60 | 4,521 |\n| 4385 | 0272M1004385 | 27.673 | 87.728 | Kosi | E(o) | 4.35 | 4,693 |\n| 4386 | 0272M1004386 | 27.672 | 87.637 | Kosi | E(o) | 0.78 | 4,923 |\n| 4387 | 0272M1004387 | 27.672 | 87.629 | Kosi | M(o) | 0.81 | 4,862 |\n| 4388 | 0272M1004388 | 27.672 | 87.620 | Kosi | M(o) | 1.45 | 5,017 |\n| 4389 | 0272M1004389 | 27.671 | 87.519 | Kosi | E(o) | 1.29 | 4,734 |\n| 4390 | 0272M1004390 | 27.671 | 87.660 | Kosi | E(o) | 0.75 | 4,666 |\n| 4391 | 0272M1004391 | 27.670 | 87.518 | Kosi | E(o) | 0.32 | 4,746 |\n| 4392 | 0272M1004392 | 27.669 | 87.662 | Kosi | E(o) | 0.30 | 4,663 |\n| 4393 | 0272M1004393 | 27.667 | 87.702 | Kosi | E(o) | 7.45 | 4,589 |\n| 4394 | 0272M1004394 | 27.667 | 87.707 | Kosi | E(o) | 3.71 | 4,590 |\n| 4395 | 0272M1004395 | 27.666 | 87.519 | Kosi | E(c) | 6.72 | 4,815 |\n| 4396 | 0272M1004396 | 27.666 | 87.586 | Kosi | E(o) | 2.33 | 4,560 |\n| 4397 | 0272M1004397 | 27.664 | 87.665 | Kosi | E(o) | 3.70 | 4,563 |\n| 4398 | 0272M1004398 | 27.662 | 87.733 | Kosi | E(o) | 0.89 | 4,669 |\n| 4399 | 0272M1004399 | 27.662 | 87.629 | Kosi | E(c) | 3.61 | 4,902 |\n| 4400 | 0272M1004400 | 27.662 | 87.711 | Kosi | E(o) | 0.88 | 4,656 |\n| 4401 | 0272M1004401 | 27.662 | 87.701 | Kosi | E(o) | 1.48 | 4,436 |\n| 4402 | 0272M1004402 | 27.661 | 87.723 | Kosi | E(o) | 2.46 | 4,826 |\n| 4403 | 0272M1004403 | 27.661 | 87.730 | Kosi | E(o) | 2.47 | 4,688 |\n| 4404 | 0272M1004404 | 27.660 | 87.727 | Kosi | E(o) | 0.26 | 4,705 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7470, "line_end": 7617, "token_count_estimate": 1575, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1004370", "0272M1004371", "0272M1004372", "0272M1004373", "0272M1004374", "0272M1004375", "0272M1004376", "0272M1004377", "0272M1004378", "0272M1004379", "0272M1004380", "0272M1004381", "0272M1004382", "0272M1004383", "0272M1004384", "0272M1004385", "0272M1004386", "0272M1004387", "0272M1004388", "0272M1004389", "0272M1004390", "0272M1004391", "0272M1004392", "0272M1004393", "0272M1004394", "0272M1004395", "0272M1004396", "0272M1004397", "0272M1004398", "0272M1004399", "0272M1004400", "0272M1004401", "0272M1004402", "0272M1004403", "0272M1004404"]}}
{"id": "47e617af2ebcffb2", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4405 | 0272M1004405 | 27.660 | 87.657 | Kosi | E(o) | 0.49 | 4,492 |\n| 4406 | 0272M1004406 | 27.659 | 87.614 | Kosi | E(o) | 2.75 | 4,727 |\n| 4407 | 0272M1004407 | 27.657 | 87.726 | Kosi | E(o) | 2.29 | 4,727 |\n| 4408 | 0272M1004408 | 27.657 | 87.709 | Kosi | E(o) | 1.26 | 4,611 |\n| 4409 | 0272M1004409 | 27.656 | 87.711 | Kosi | E(o) | 0.51 | 4,622 |\n| 4410 | 0272M1004410 | 27.655 | 87.622 | Kosi | E(o) | 1.12 | 4,689 |\n| 4411 | 0272M1004411 | 27.655 | 87.553 | Kosi | E(o) | 5.70 | 4,429 |\n| 4412 | 0272M1004412 | 27.653 | 87.610 | Kosi | E(o) | 1.13 | 4,631 |\n| 4413 | 0272M1004413 | 27.652 | 87.698 | Kosi | E(o) | 0.85 | 4,456 |\n| 4414 | 0272M1004414 | 27.652 | 87.723 | Kosi | E(o) | 0.76 | 4,760 |\n| 4415 | 0272M1004415 | 27.652 | 87.709 | Kosi | E(o) | 5.97 | 4,478 |\n| 4416 | 0272M1004416 | 27.651 | 87.703 | Kosi | E(o) | 14.36 | 4,460 |\n| 4417 | 0272M1004417 | 27.651 | 87.518 | Kosi | E(o) | 3.32 | 4,473 |\n| 4418 | 0272M1004418 | 27.650 | 87.612 | Kosi | E(o) | 0.98 | 4,599 |\n| 4419 | 0272M1004419 | 27.649 | 87.731 | Kosi | E(o) | 0.89 | 4,712 |\n| 4420 | 0272M1004420 | 27.648 | 87.737 | Kosi | E(o) | 0.36 | 4,583 |\n| 4421 | 0272M1004421 | 27.648 | 87.560 | Kosi | E(o) | 1.68 | 4,223 |\n| 4422 | 0272M1004422 | 27.648 | 87.612 | Kosi | E(o) | 0.95 | 4,592 |\n| 4423 | 0272M1004423 | 27.646 | 87.623 | Kosi | E(o) | 9.59 | 4,439 |\n| 4424 | 0272M1004424 | 27.645 | 87.620 | Kosi | E(o) | 2.57 | 4,441 |\n| 4425 | 0272M1004425 | 27.645 | 87.573 | Kosi | O | 11.78 | 3,860 |\n| 4426 | 0272M1004426 | 27.641 | 87.615 | Kosi | E(o) | 1.47 | 4,595 |\n| 4427 | 0272M1004427 | 27.640 | 87.553 | Kosi | E(o) | 1.77 | 4,609 |\n| 4428 | 0272M1004428 | 27.640 | 87.618 | Kosi | E(o) | 5.14 | 4,566 |\n| 4429 | 0272M1004429 | 27.639 | 87.633 | Kosi | E(c) | 16.45 | 4,289 |\n| 4430 | 0272M1004430 | 27.639 | 87.732 | Kosi | E(o) | 3.43 | 4,578 |\n| 4431 | 0272M1004431 | 27.639 | 87.615 | Kosi | E(o) | 1.45 | 4,573 |\n| 4432 | 0272M1004432 | 27.638 | 87.716 | Kosi | E(o) | 2.51 | 4,537 |\n| 4433 | 0272M1004433 | 27.636 | 87.624 | Kosi | E(o) | 5.92 | 4,425 |\n| 4434 | 0272M1004434 | 27.634 | 87.715 | Kosi | E(o) | 1.78 | 4,496 |\n| 4435 | 0272M1004435 | 27.632 | 87.540 | Kosi | E(c) | 1.34 | 4,778 |\n| 4436 | 0272M1004436 | 27.632 | 87.614 | Kosi | E(o) | 6.34 | 4,435 |\n| 4437 | 0272M1004437 | 27.630 | 87.543 | Kosi | E(o) | 3.36 | 4,713 |\n| 4438 | 0272M1004438 | 27.630 | 87.696 | Kosi | E(c) | 14.11 | 4,179 |\n| 4439 | 0272M1004439 | 27.629 | 87.615 | Kosi | E(o) | 2.78 | 4,422 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7470, "line_end": 7617, "token_count_estimate": 1564, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1004405", "0272M1004406", "0272M1004407", "0272M1004408", "0272M1004409", "0272M1004410", "0272M1004411", "0272M1004412", "0272M1004413", "0272M1004414", "0272M1004415", "0272M1004416", "0272M1004417", "0272M1004418", "0272M1004419", "0272M1004420", "0272M1004421", "0272M1004422", "0272M1004423", "0272M1004424", "0272M1004425", "0272M1004426", "0272M1004427", "0272M1004428", "0272M1004429", "0272M1004430", "0272M1004431", "0272M1004432", "0272M1004433", "0272M1004434", "0272M1004435", "0272M1004436", "0272M1004437", "0272M1004438", "0272M1004439"]}}
{"id": "49547594dc1c7827", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4440 | 0272M1004440 | 27.629 | 87.706 | Kosi | E(o) | 13.01 | 4,319 |\n| 4441 | 0272M1004441 | 27.626 | 87.740 | Kosi | E(o) | 3.17 | 4,345 |\n| 4442 | 0272M1004442 | 27.625 | 87.548 | Kosi | E(o) | 1.78 | 4,462 |\n| 4443 | 0272M1004443 | 27.623 | 87.697 | Kosi | E(o) | 0.81 | 4,311 |\n| 4444 | 0272M1004444 | 27.621 | 87.720 | Kosi | E(o) | 0.30 | 4,641 |\n| 4445 | 0272M1004445 | 27.620 | 87.712 | Kosi | E(o) | 12.63 | 4,454 |\n| 4446 | 0272M1004446 | 27.619 | 87.736 | Kosi | E(o) | 4.00 | 4,457 |\n| 4447 | 0272M1004447 | 27.617 | 87.543 | Kosi | E(c) | 10.93 | 4,471 |\n| 4448 | 0272M1004448 | 27.616 | 87.561 | Kosi | E(c) | 1.66 | 4,385 |\n| 4449 | 0272M1004449 | 27.612 | 87.550 | Kosi | E(c) | 4.43 | 4,547 |\n| 4450 | 0272M1004450 | 27.609 | 87.708 | Kosi | E(o) | 4.13 | 4,268 |\n| 4451 | 0272M1004451 | 27.603 | 87.535 | Kosi | E(o) | 1.41 | 4,367 |\n| 4452 | 0272M1004452 | 27.602 | 87.522 | Kosi | E(c) | 4.53 | 4,250 |\n| 4453 | 0272M1004453 | 27.601 | 87.699 | Kosi | E(o) | 0.60 | 3,993 |\n| 4454 | 0272M1004454 | 27.600 | 87.550 | Kosi | E(c) | 3.26 | 4,322 |\n| 4455 | 0272M1004455 | 27.596 | 87.536 | Kosi | E(o) | 0.91 | 4,406 |\n| 4456 | 0272M1004456 | 27.594 | 87.516 | Kosi | E(o) | 1.55 | 4,314 |\n| 4457 | 0272M1004457 | 27.594 | 87.561 | Kosi | E(c) | 3.68 | 4,244 |\n| 4458 | 0272M1004458 | 27.592 | 87.555 | Kosi | E(o) | 0.54 | 4,439 |\n| 4459 | 0272M1004459 | 27.590 | 87.531 | Kosi | E(o) | 1.69 | 4,381 |\n| 4460 | 0272M1004460 | 27.590 | 87.564 | Kosi | E(o) | 1.15 | 4,153 |\n| 4461 | 0272M1004461 | 27.588 | 87.513 | Kosi | E(o) | 0.98 | 4,148 |\n| 4462 | 0272M1004462 | 27.588 | 87.523 | Kosi | E(o) | 0.86 | 4,480 |\n| 4463 | 0272M1004463 | 27.587 | 87.534 | Kosi | E(o) | 0.37 | 4,324 |\n| 4464 | 0272M1004464 | 27.586 | 87.507 | Kosi | E(o) | 3.90 | 4,296 |\n| 4465 | 0272M1004465 | 27.582 | 87.502 | Kosi | E(o) | 0.36 | 4,438 |\n| 4466 | 0272M1004466 | 27.581 | 87.532 | Kosi | E(c) | 3.74 | 4,272 |\n| 4467 | 0272M1004467 | 27.576 | 87.534 | Kosi | E(o) | 1.09 | 4,260 |\n| 4468 | 0272M1004468 | 27.573 | 87.536 | Kosi | E(o) | 0.34 | 4,122 |\n| 4469 | 0272M1304469 | 27.989 | 87.837 | Kosi | E(o) | 1.65 | 5,799 |\n| 4470 | 0272M1304470 | 27.988 | 87.869 | Kosi | M(e) | 7.00 | 5,656 |\n| 4471 | 0272M1304471 | 27.980 | 87.833 | Kosi | M(o) | 2.19 | 5,654 |\n| 4472 | 0272M1304472 | 27.977 | 87.845 | Kosi | I(s) | 0.80 | 5,823 |\n| 4473 | 0272M1304473 | 27.976 | 87.832 | Kosi | M(o) | 1.36 | 5,619 |\n| 4474 | 0272M1304474 | 27.975 | 87.851 | Kosi | M(o) | 2.62 | 5,837 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7470, "line_end": 7617, "token_count_estimate": 1598, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1004440", "0272M1004441", "0272M1004442", "0272M1004443", "0272M1004444", "0272M1004445", "0272M1004446", "0272M1004447", "0272M1004448", "0272M1004449", "0272M1004450", "0272M1004451", "0272M1004452", "0272M1004453", "0272M1004454", "0272M1004455", "0272M1004456", "0272M1004457", "0272M1004458", "0272M1004459", "0272M1004460", "0272M1004461", "0272M1004462", "0272M1004463", "0272M1004464", "0272M1004465", "0272M1004466", "0272M1004467", "0272M1004468", "0272M1304469", "0272M1304470", "0272M1304471", "0272M1304472", "0272M1304473", "0272M1304474"]}}
{"id": "e70886070c3abc56", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4475 | 0272M1304475 | 27.972 | 87.861 | Kosi | E(o) | 0.36 | 5,803 |\n| 4476 | 0272M1304476 | 27.972 | 87.845 | Kosi | M(o) | 6.26 | 5,776 |\n| 4477 | 0272M1304477 | 27.969 | 87.868 | Kosi | M(o) | 4.00 | 5,646 |\n| 4478 | 0272M1304478 | 27.969 | 87.884 | Kosi | M(l) | 17.90 | 5,597 |\n| 4479 | 0272M1304479 | 27.968 | 87.891 | Kosi | M(l) | 1.18 | 5,582 |\n| 4480 | 0272M1304480 | 27.966 | 87.760 | Kosi | E(o) | 1.11 | 5,445 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7470, "line_end": 7617, "token_count_estimate": 350, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1304475", "0272M1304476", "0272M1304477", "0272M1304478", "0272M1304479", "0272M1304480"]}}
{"id": "4111ace82046f53a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 7618, "line_end": 7625, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31e8066cce570c7f", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4481 | 0272M1304481 | 27.965 | 87.871 | Kosi | M(o) | 2.67 | 5,707 |\n| 4482 | 0272M1304482 | 27.964 | 87.814 | Kosi | M(e) | 40.61 | 5,249 |\n| 4483 | 0272M1304483 | 27.956 | 87.765 | Kosi | E(o) | 0.61 | 5,168 |\n| 4484 | 0272M1304484 | 27.952 | 87.908 | Kosi | M(e) | 64.79 | 5,165 |\n| 4485 | 0272M1304485 | 27.951 | 87.985 | Kosi | M(o) | 4.31 | 5,484 |\n| 4486 | 0272M1304486 | 27.950 | 87.930 | Kosi | M(e) | 83.66 | 5,106 |\n| 4487 | 0272M1304487 | 27.948 | 87.806 | Kosi | M(o) | 0.50 | 5,553 |\n| 4488 | 0272M1304488 | 27.946 | 87.981 | Kosi | M(o) | 5.96 | 5,556 |\n| 4489 | 0272M1304489 | 27.945 | 87.789 | Kosi | M(o) | 2.07 | 5,384 |\n| 4490 | 0272M1304490 | 27.944 | 87.894 | Kosi | M(o) | 0.55 | 5,493 |\n| 4491 | 0272M1304491 | 27.943 | 87.897 | Kosi | M(o) | 2.66 | 5,493 |\n| 4492 | 0272M1304492 | 27.937 | 87.836 | Kosi | E(o) | 2.29 | 5,370 |\n| 4493 | 0272M1304493 | 27.937 | 87.832 | Kosi | E(o) | 0.93 | 5,448 |\n| 4494 | 0272M1304494 | 27.931 | 87.806 | Kosi | M(o) | 0.72 | 5,430 |\n| 4495 | 0272M1304495 | 27.928 | 88.002 | Kosi | M(e) | 113.22 | 5,348 |\n| 4496 | 0272M1304496 | 27.926 | 87.771 | Kosi | M(e) | 97.66 | 4,913 |\n| 4497 | 0272M1304497 | 27.926 | 87.991 | Kosi | E(c) | 5.60 | 5,602 |\n| 4498 | 0272M1304498 | 27.923 | 87.751 | Kosi | I(s) | 1.08 | 5,083 |\n| 4499 | 0272M1304499 | 27.916 | 87.811 | Kosi | E(o) | 1.01 | 5,400 |\n| 4500 | 0272M1304500 | 27.913 | 87.849 | Kosi | E(o) | 0.44 | 5,455 |\n| 4501 | 0272M1304501 | 27.912 | 87.849 | Kosi | E(o) | 1.76 | 5,458 |\n| 4502 | 0272M1304502 | 27.911 | 87.816 | Kosi | M(e) | 2.44 | 5,174 |\n| 4503 | 0272M1304503 | 27.906 | 87.919 | Kosi | E(o) | 2.97 | 5,458 |\n| 4504 | 0272M1304504 | 27.904 | 87.916 | Kosi | E(o) | 2.91 | 5,463 |\n| 4505 | 0272M1304505 | 27.903 | 87.850 | Kosi | E(o) | 1.16 | 5,251 |\n| 4506 | 0272M1304506 | 27.886 | 87.860 | Kosi | E(o) | 0.29 | 5,405 |\n| 4507 | 0272M1304507 | 27.881 | 87.805 | Kosi | M(e) | 34.32 | 4,690 |\n| 4508 | 0272M1304508 | 27.878 | 87.806 | Kosi | E(o) | 0.69 | 4,728 |\n| 4509 | 0272M1304509 | 27.878 | 87.854 | Kosi | E(o) | 1.19 | 5,212 |\n| 4510 | 0272M1304510 | 27.876 | 87.856 | Kosi | E(o) | 1.86 | 5,214 |\n| 4511 | 0272M1304511 | 27.873 | 87.803 | Kosi | E(o) | 0.73 | 4,944 |\n| 4512 | 0272M1304512 | 27.873 | 87.801 | Kosi | E(o) | 0.57 | 4,969 |\n| 4513 | 0272M1304513 | 27.872 | 87.889 | Kosi | E(o) | 0.41 | 5,292 |\n| 4514 | 0272M1304514 | 27.871 | 87.892 | Kosi | E(o) | 0.38 | 5,144 |\n| 4515 | 0272M1304515 | 27.869 | 87.866 | Kosi | M(e) | 68.12 | 4,910 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7626, "line_end": 7773, "token_count_estimate": 1618, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1304481", "0272M1304482", "0272M1304483", "0272M1304484", "0272M1304485", "0272M1304486", "0272M1304487", "0272M1304488", "0272M1304489", "0272M1304490", "0272M1304491", "0272M1304492", "0272M1304493", "0272M1304494", "0272M1304495", "0272M1304496", "0272M1304497", "0272M1304498", "0272M1304499", "0272M1304500", "0272M1304501", "0272M1304502", "0272M1304503", "0272M1304504", "0272M1304505", "0272M1304506", "0272M1304507", "0272M1304508", "0272M1304509", "0272M1304510", "0272M1304511", "0272M1304512", "0272M1304513", "0272M1304514", "0272M1304515"]}}
{"id": "c30d2e85f5e7dd96", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4516 | 0272M1304516 | 27.868 | 87.930 | Kosi | E(o) | 0.57 | 5,444 |\n| 4517 | 0272M1304517 | 27.865 | 87.933 | Kosi | E(o) | 0.90 | 5,306 |\n| 4518 | 0272M1304518 | 27.863 | 87.807 | Kosi | E(o) | 0.62 | 5,066 |\n| 4519 | 0272M1304519 | 27.858 | 87.929 | Kosi | E(o) | 0.49 | 5,309 |\n| 4520 | 0272M1304520 | 27.855 | 87.753 | Kosi | M(o) | 4.86 | 5,358 |\n| 4521 | 0272M1304521 | 27.853 | 87.790 | Kosi | M(o) | 6.12 | 5,274 |\n| 4522 | 0272M1304522 | 27.851 | 87.807 | Kosi | E(o) | 0.27 | 5,117 |\n| 4523 | 0272M1304523 | 27.849 | 87.831 | Kosi | O | 33.50 | 4,437 |\n| 4524 | 0272M1304524 | 27.847 | 87.970 | Kosi | M(o) | 2.39 | 5,304 |\n| 4525 | 0272M1304525 | 27.846 | 87.962 | Kosi | M(e) | 8.19 | 5,205 |\n| 4526 | 0272M1304526 | 27.844 | 87.875 | Kosi | E(c) | 4.82 | 5,281 |\n| 4527 | 0272M1304527 | 27.841 | 87.795 | Kosi | M(o) | 0.74 | 4,854 |\n| 4528 | 0272M1304528 | 27.838 | 87.936 | Kosi | M(o) | 1.19 | 4,712 |\n| 4529 | 0272M1304529 | 27.838 | 87.929 | Kosi | M(o) | 1.48 | 4,708 |\n| 4530 | 0272M1304530 | 27.833 | 87.923 | Kosi | M(o) | 0.64 | 4,767 |\n| 4531 | 0272M1304531 | 27.826 | 87.799 | Kosi | E(o) | 2.76 | 5,122 |\n| 4532 | 0272M1304532 | 27.809 | 87.891 | Kosi | M(o) | 0.70 | 4,905 |\n| 4533 | 0272M1304533 | 27.806 | 87.819 | Kosi | M(o) | 1.12 | 5,163 |\n| 4534 | 0272M1304534 | 27.796 | 87.947 | Kosi | M(o) | 0.28 | 5,397 |\n| 4535 | 0272M1304535 | 27.793 | 87.974 | Kosi | M(e) | 22.32 | 5,183 |\n| 4536 | 0272M1304536 | 27.791 | 87.929 | Kosi | M(o) | 0.59 | 5,082 |\n| 4537 | 0272M1304537 | 27.790 | 87.934 | Kosi | M(o) | 14.10 | 4,938 |\n| 4538 | 0272M1304538 | 27.784 | 87.775 | Kosi | M(l) | 1.11 | 4,936 |\n| 4539 | 0272M1304539 | 27.781 | 87.945 | Kosi | M(e) | 3.82 | 4,862 |\n| 4540 | 0272M1304540 | 27.777 | 87.800 | Kosi | M(o) | 0.44 | 4,996 |\n| 4541 | 0272M1304541 | 27.769 | 87.968 | Kosi | M(o) | 1.59 | 5,221 |\n| 4542 | 0272M1304542 | 27.765 | 87.791 | Kosi | E(o) | 0.59 | 4,831 |\n| 4543 | 0272M1304543 | 27.762 | 87.794 | Kosi | E(o) | 1.28 | 4,906 |\n| 4544 | 0272M1304544 | 27.757 | 87.777 | Kosi | E(c) | 25.46 | 4,708 |\n| 4545 | 0272M1304545 | 27.755 | 87.812 | Kosi | E(o) | 0.50 | 4,842 |\n| 4546 | 0272M1304546 | 27.754 | 87.766 | Kosi | E(o) | 1.03 | 4,809 |\n| 4547 | 0272M1404547 | 27.749 | 87.784 | Kosi | E(o) | 0.60 | 4,936 |\n| 4548 | 0272M1404548 | 27.749 | 87.787 | Kosi | E(o) | 0.72 | 4,964 |\n| 4549 | 0272M1404549 | 27.747 | 87.754 | Kosi | E(o) | 0.38 | 4,699 |\n| 4550 | 0272M1404550 | 27.747 | 87.799 | Kosi | E(o) | 0.44 | 4,650 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7626, "line_end": 7773, "token_count_estimate": 1596, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1304516", "0272M1304517", "0272M1304518", "0272M1304519", "0272M1304520", "0272M1304521", "0272M1304522", "0272M1304523", "0272M1304524", "0272M1304525", "0272M1304526", "0272M1304527", "0272M1304528", "0272M1304529", "0272M1304530", "0272M1304531", "0272M1304532", "0272M1304533", "0272M1304534", "0272M1304535", "0272M1304536", "0272M1304537", "0272M1304538", "0272M1304539", "0272M1304540", "0272M1304541", "0272M1304542", "0272M1304543", "0272M1304544", "0272M1304545", "0272M1304546", "0272M1404547", "0272M1404548", "0272M1404549", "0272M1404550"]}}
{"id": "401c0bc553478171", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4551 | 0272M1404551 | 27.745 | 87.782 | Kosi | E(c) | 19.69 | 4,775 |\n| 4552 | 0272M1404552 | 27.743 | 87.784 | Kosi | E(o) | 1.40 | 4,788 |\n| 4553 | 0272M1404553 | 27.738 | 87.778 | Kosi | E(o) | 22.06 | 4,705 |\n| 4554 | 0272M1404554 | 27.738 | 87.943 | Kosi | E(o) | 0.35 | 5,126 |\n| 4555 | 0272M1404555 | 27.737 | 87.941 | Kosi | E(o) | 5.41 | 5,055 |\n| 4556 | 0272M1404556 | 27.735 | 87.773 | Kosi | E(o) | 3.09 | 4,672 |\n| 4557 | 0272M1404557 | 27.735 | 87.811 | Kosi | E(o) | 3.11 | 4,802 |\n| 4558 | 0272M1404558 | 27.722 | 87.928 | Kosi | M(o) | 3.83 | 5,040 |\n| 4559 | 0272M1404559 | 27.717 | 87.790 | Kosi | E(o) | 4.54 | 4,481 |\n| 4560 | 0272M1404560 | 27.714 | 87.755 | Kosi | E(o) | 2.01 | 4,401 |\n| 4561 | 0272M1404561 | 27.705 | 87.895 | Kosi | E(o) | 0.45 | 4,635 |\n| 4562 | 0272M1404562 | 27.695 | 87.893 | Kosi | E(o) | 2.72 | 4,563 |\n| 4563 | 0272M1404563 | 27.681 | 87.881 | Kosi | E(o) | 1.52 | 4,715 |\n| 4564 | 0272M1404564 | 27.680 | 87.879 | Kosi | E(o) | 0.91 | 4,716 |\n| 4565 | 0272M1404565 | 27.679 | 87.908 | Kosi | E(o) | 0.43 | 4,980 |\n| 4566 | 0272M1404566 | 27.674 | 87.883 | Kosi | E(o) | 1.03 | 4,813 |\n| 4567 | 0272M1404567 | 27.672 | 87.886 | Kosi | E(o) | 0.45 | 4,660 |\n| 4568 | 0272M1404568 | 27.672 | 87.883 | Kosi | E(o) | 0.78 | 4,666 |\n| 4569 | 0272M1404569 | 27.657 | 87.865 | Kosi | E(o) | 2.39 | 4,300 |\n| 4570 | 0272M1404570 | 27.653 | 87.890 | Kosi | E(o) | 1.59 | 4,695 |\n| 4571 | 0272M1404571 | 27.647 | 87.981 | Kosi | M(o) | 2.22 | 4,903 |\n| 4572 | 0272M1404572 | 27.641 | 87.836 | Kosi | E(o) | 0.81 | 4,196 |\n| 4573 | 0272M1404573 | 27.638 | 87.986 | Kosi | M(l) | 1.36 | 4,533 |\n| 4574 | 0272M1404574 | 27.596 | 87.967 | Kosi | E(o) | 0.45 | 5,018 |\n| 4575 | 0272M1404575 | 27.595 | 87.972 | Kosi | E(o) | 2.51 | 4,936 |\n| 4576 | 0272M1404576 | 27.592 | 87.972 | Kosi | E(o) | 0.64 | 4,929 |\n| 4577 | 0272M1404577 | 27.591 | 87.977 | Kosi | E(c) | 2.76 | 4,977 |\n| 4578 | 0272M1404578 | 27.570 | 87.952 | Kosi | E(o) | 1.09 | 4,319 |\n| 4579 | 0272M1404579 | 27.537 | 87.971 | Kosi | E(o) | 0.69 | 4,387 |\n| 4580 | 0272M1404580 | 27.523 | 87.993 | Kosi | E(o) | 1.29 | 4,389 |\n| 4581 | 0272M1404581 | 27.514 | 87.959 | Kosi | E(o) | 0.36 | 4,272 |\n| 4582 | 0272M1404582 | 27.514 | 87.990 | Kosi | E(o) | 0.25 | 4,136 |\n| 4583 | 0272M1404583 | 27.511 | 87.989 | Kosi | E(o) | 0.41 | 4,001 |\n| 4584 | 0272M1404584 | 27.510 | 87.958 | Kosi | E(o) | 1.15 | 4,193 |\n| 4585 | 0277D0204585 | 28.746 | 88.132 | Kosi | E(o) | 6.28 | 5,583 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 7626, "line_end": 7773, "token_count_estimate": 1593, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0272M1404551", "0272M1404552", "0272M1404553", "0272M1404554", "0272M1404555", "0272M1404556", "0272M1404557", "0272M1404558", "0272M1404559", "0272M1404560", "0272M1404561", "0272M1404562", "0272M1404563", "0272M1404564", "0272M1404565", "0272M1404566", "0272M1404567", "0272M1404568", "0272M1404569", "0272M1404570", "0272M1404571", "0272M1404572", "0272M1404573", "0272M1404574", "0272M1404575", "0272M1404576", "0272M1404577", "0272M1404578", "0272M1404579", "0272M1404580", "0272M1404581", "0272M1404582", "0272M1404583", "0272M1404584", "0277D0204585"]}}
{"id": "02987e7f371cbd1c", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4586 | 0277D0204586 | 28.739 | 88.137 | Kosi | E(o) | 1.14 | 5,494 |\n| 4587 | 0277D0204587 | 28.736 | 88.107 | Kosi | E(o) | 0.26 | 5,629 |\n| 4588 | 0277D0204588 | 28.734 | 88.123 | Kosi | E(o) | 0.49 | 5,582 |\n| 4589 | 0277D0204589 | 28.731 | 88.123 | Kosi | E(o) | 0.56 | 5,571 |\n| 4590 | 0277D0204590 | 28.730 | 88.140 | Kosi | E(o) | 0.58 | 5,431 |\n| 4591 | 0277D0204591 | 28.729 | 88.135 | Kosi | E(o) | 2.85 | 5,520 |\n| 4592 | 0277D0204592 | 28.724 | 88.129 | Kosi | E(o) | 1.84 | 5,472 |\n| 4593 | 0277D0204593 | 28.723 | 88.135 | Kosi | E(o) | 2.23 | 5,385 |\n| 4594 | 0277D0204594 | 28.717 | 88.125 | Kosi | E(o) | 2.83 | 5,490 |\n| 4595 | 0277D0204595 | 28.715 | 88.149 | Kosi | E(c) | 1.47 | 5,445 |\n| 4596 | 0277D0204596 | 28.707 | 88.242 | Kosi | E(o) | 0.75 | 5,419 |\n| 4597 | 0277D0204597 | 28.687 | 88.231 | Kosi | E(o) | 1.35 | 5,485 |\n| 4598 | 0277D0204598 | 28.685 | 88.239 | Kosi | E(o) | 1.23 | 5,381 |\n| 4599 | 0277D0404599 | 28.038 | 88.023 | Kosi | I(s) | 1.02 | 5,485 |\n| 4600 | 0277D0404600 | 28.036 | 88.023 | Kosi | I(s) | 0.35 | 5,498 |\n| 4601 | 0277D0404601 | 28.011 | 88.240 | Kosi | M(o) | 0.29 | 5,271 |\n| 4602 | 0277D0404602 | 28.004 | 88.241 | Kosi | M(e) | 41.80 | 5,269 |\n| 4603 | 0277D0604603 | 28.660 | 88.280 | Kosi | E(o) | 1.44 | 5,533 |\n| 4604 | 0277D0604604 | 28.653 | 88.314 | Kosi | E(o) | 1.94 | 5,517 |\n| 4605 | 0277D0604605 | 28.650 | 88.393 | Kosi | E(o) | 0.75 | 5,639 |\n| 4606 | 0277D0604606 | 28.649 | 88.333 | Kosi | E(o) | 2.90 | 5,434 |\n| 4607 | 0277D0604607 | 28.639 | 88.407 | Kosi | E(o) | 1.44 | 5,577 |\n| 4608 | 0277D0604608 | 28.633 | 88.283 | Kosi | E(o) | 1.44 | 5,406 |\n| 4609 | 0277D0704609 | 28.726 | 88.124 | Kosi | E(o) | 0.48 | 5,530 |\n| 4610 | 0277D0804610 | 28.118 | 88.378 | Kosi | O | 0.81 | 4,777 |\n| 4611 | 0277D0804611 | 28.118 | 88.381 | Kosi | O | 0.48 | 4,776 |\n| 4612 | 0277D0804612 | 28.109 | 88.393 | Kosi | O | 0.81 | 4,782 |\n| 4613 | 0277D0804613 | 28.109 | 88.396 | Kosi | O | 0.50 | 4,778 |\n| 4614 | 0277D0804614 | 28.054 | 88.427 | Kosi | O | 101.66 | 4,888 |\n| 4615 | 0277D0804615 | 28.036 | 88.381 | Kosi | M(o) | 1.01 | 5,515 |\n| 4616 | 0277D0804616 | 28.034 | 88.382 | Kosi | M(o) | 0.77 | 5,515 |\n| 4617 | 0277D0804617 | 28.033 | 88.461 | Kosi | E(o) | 13.87 | 5,156 |\n| 4618 | 0277D0804618 | 28.032 | 88.377 | Kosi | M(o) | 0.53 | 5,561 |\n| 4619 | 0277D0804619 | 28.032 | 88.378 | Kosi | M(o) | 0.46 | 5,553 |\n| 4620 | 0277D0804620 | 28.030 | 88.440 | Kosi | E(o) | 1.78 | 5,017 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 7626, "line_end": 7773, "token_count_estimate": 1591, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0277D0204586", "0277D0204587", "0277D0204588", "0277D0204589", "0277D0204590", "0277D0204591", "0277D0204592", "0277D0204593", "0277D0204594", "0277D0204595", "0277D0204596", "0277D0204597", "0277D0204598", "0277D0404599", "0277D0404600", "0277D0404601", "0277D0404602", "0277D0604603", "0277D0604604", "0277D0604605", "0277D0604606", "0277D0604607", "0277D0604608", "0277D0704609", "0277D0804610", "0277D0804611", "0277D0804612", "0277D0804613", "0277D0804614", "0277D0804615", "0277D0804616", "0277D0804617", "0277D0804618", "0277D0804619", "0277D0804620"]}}
{"id": "aee82f70cf68a370", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 4621 | 0277D0804621 | 28.026 | 88.377 | Kosi | M(o) | 2.38 | 5,617 |\n| 4622 | 0277D0804622 | 28.022 | 88.355 | Kosi | M(e) | 56.29 | 5,195 |\n| 4623 | 0277D0804623 | 28.018 | 88.255 | Kosi | M(o) | 0.55 | 5,256 |\n| 4624 | 0277D0804624 | 28.017 | 88.288 | Kosi | M(e) | 50.43 | 5,268 |\n| 4625 | 0277D0804625 | 28.014 | 88.436 | Kosi | M(o) | 0.42 | 5,361 |\n| 4626 | 0277D0804626 | 28.010 | 88.373 | Kosi | M(o) | 3.13 | 5,509 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 146, "line_start": 7626, "line_end": 7773, "token_count_estimate": 361, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0277D0804621", "0277D0804622", "0277D0804623", "0277D0804624", "0277D0804625", "0277D0804626"]}}
{"id": "9a73c21153bd28a5", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 7774, "line_end": 7779, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6882e483278db7b4", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 4627 | 0277D0804627 | 28.009 | 88.259 | Kosi | M(e) | 59.70 | 5,256 |\n| 4628 | 0277D0804628 | 28.005 | 88.320 | Kosi | M(e) | 38.58 | 5,104 |\n| 4629 | 0277D0804629 | 28.001 | 88.327 | Kosi | M(e) | 6.51 | 5,299 |\n| 4630 | 0277D0804630 | 28.000 | 88.388 | Kosi | E(o) | 1.28 | 5,507 |\n| 4631 | 0277D1004631 | 28.554 | 88.718 | Kosi | E(o) | 2.39 | 5,502 |\n| 4632 | 0277D1204632 | 28.065 | 88.543 | Kosi | M(e) | 7.31 | 5,712 |\n| 4633 | 0277D1204633 | 28.057 | 88.517 | Kosi | M(e) | 7.03 | 5,561 |\n| 4634 | 0277D1204634 | 28.056 | 88.520 | Kosi | M(e) | 0.94 | 5,571 |\n| 4635 | 0278A0104635 | 27.994 | 88.211 | Kosi | M(o) | 5.08 | 5,233 |\n| 4636 | 0278A0104636 | 27.988 | 88.221 | Kosi | M(e) | 11.94 | 5,372 |\n| 4637 | 0278A0104637 | 27.946 | 88.075 | Kosi | M(e) | 148.59 | 5,488 |\n| 4638 | 0278A0104638 | 27.943 | 88.041 | Kosi | M(o) | 10.32 | 5,821 |\n| 4639 | 0278A0104639 | 27.933 | 88.066 | Kosi | M(e) | 83.35 | 5,563 |\n| 4640 | 0278A0104640 | 27.928 | 88.019 | Kosi | M(e) | 13.25 | 5,692 |\n| 4641 | 0278A0104641 | 27.879 | 88.019 | Kosi | M(o) | 0.75 | 5,645 |\n| 4642 | 0278A0104642 | 27.873 | 88.054 | Kosi | M(o) | 0.67 | 5,658 |\n| 4643 | 0278A0104643 | 27.861 | 88.051 | Kosi | M(o) | 0.93 | 5,521 |\n| 4644 | 0278A0104644 | 27.860 | 88.054 | Kosi | M(o) | 5.79 | 5,491 |\n| 4645 | 0278A0104645 | 27.851 | 88.029 | Kosi | I(s) | 0.81 | 5,168 |\n| 4646 | 0278A0104646 | 27.847 | 88.031 | Kosi | I(s) | 2.14 | 5,139 |\n| 4647 | 0278A0104647 | 27.845 | 88.102 | Kosi | M(o) | 0.32 | 5,751 |\n| 4648 | 0278A0104648 | 27.839 | 88.026 | Kosi | E(o) | 1.37 | 5,395 |\n| 4649 | 0278A0104649 | 27.839 | 88.037 | Kosi | I(s) | 1.07 | 5,102 |\n| 4650 | 0278A0104650 | 27.838 | 88.040 | Kosi | I(s) | 1.53 | 5,101 |\n| 4651 | 0278A0104651 | 27.835 | 88.038 | Kosi | I(s) | 0.43 | 5,110 |\n| 4652 | 0278A0104652 | 27.835 | 88.037 | Kosi | I(s) | 0.52 | 5,104 |\n| 4653 | 0278A0104653 | 27.835 | 88.078 | Kosi | M(e) | 16.48 | 5,604 |\n| 4654 | 0278A0104654 | 27.835 | 88.014 | Kosi | E(o) | 0.71 | 5,457 |\n| 4655 | 0278A0104655 | 27.834 | 88.067 | Kosi | M(e) | 2.94 | 5,601 |\n| 4656 | 0278A0104656 | 27.834 | 88.040 | Kosi | I(s) | 0.46 | 5,094 |\n| 4657 | 0278A0104657 | 27.832 | 88.068 | Kosi | M(o) | 0.81 | 5,590 |\n| 4658 | 0278A0104658 | 27.830 | 88.070 | Kosi | M(o) | 0.52 | 5,580 |\n| 4659 | 0278A0104659 | 27.830 | 88.069 | Kosi | M(o) | 0.43 | 5,583 |\n| 4660 | 0278A0104660 | 27.829 | 88.040 | Kosi | I(s) | 0.47 | 5,084 |\n| 4661 | 0278A0104661 | 27.817 | 88.131 | Kosi | E(o) | 0.40 | 5,589 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7780, "line_end": 7862, "token_count_estimate": 1640, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0277D0804627", "0277D0804628", "0277D0804629", "0277D0804630", "0277D1004631", "0277D1204632", "0277D1204633", "0277D1204634", "0278A0104635", "0278A0104636", "0278A0104637", "0278A0104638", "0278A0104639", "0278A0104640", "0278A0104641", "0278A0104642", "0278A0104643", "0278A0104644", "0278A0104645", "0278A0104646", "0278A0104647", "0278A0104648", "0278A0104649", "0278A0104650", "0278A0104651", "0278A0104652", "0278A0104653", "0278A0104654", "0278A0104655", "0278A0104656", "0278A0104657", "0278A0104658", "0278A0104659", "0278A0104660", "0278A0104661"]}}
{"id": "1f9d041e14184557", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 4662 | 0278A0104662 | 27.804 | 88.138 | Kosi | I(s) | 2.33 | 5,300 |\n| 4663 | 0278A0104663 | 27.801 | 88.107 | Kosi | M(o) | 6.38 | 5,495 |\n| 4664 | 0278A0104664 | 27.797 | 88.007 | Kosi | M(o) | 1.17 | 5,106 |\n| 4665 | 0278A0104665 | 27.796 | 88.105 | Kosi | M(o) | 2.46 | 5,373 |\n| 4666 | 0278A0104666 | 27.793 | 88.000 | Kosi | E(o) | 2.04 | 5,114 |\n| 4667 | 0278A0104667 | 27.786 | 88.142 | Kosi | I(s) | 1.30 | 5,180 |\n| 4668 | 0278A0104668 | 27.785 | 88.141 | Kosi | I(s) | 0.48 | 5,179 |\n| 4669 | 0278A0104669 | 27.774 | 88.018 | Kosi | M(o) | 0.90 | 4,596 |\n| 4670 | 0278A0104670 | 27.771 | 88.019 | Kosi | M(o) | 1.04 | 4,596 |\n| 4671 | 0278A0104671 | 27.770 | 88.016 | Kosi | M(o) | 0.26 | 4,592 |\n| 4672 | 0278A0204672 | 27.640 | 88.070 | Kosi | I(s) | 1.24 | 4,865 |\n| 4673 | 0278A0204673 | 27.633 | 88.069 | Kosi | I(s) | 0.36 | 4,813 |\n| 4674 | 0278A0204674 | 27.630 | 88.072 | Kosi | I(s) | 0.78 | 4,810 |\n| 4675 | 0278A0204675 | 27.614 | 88.068 | Kosi | I(s) | 1.11 | 4,702 |\n| 4676 | 0278A0204676 | 27.612 | 88.062 | Kosi | I(s) | 0.56 | 4,661 |\n| 4677 | 0278A0204677 | 27.605 | 88.044 | Kosi | E(o) | 0.74 | 4,922 |\n| 4678 | 0278A0204678 | 27.593 | 88.026 | Kosi | M(l) | 0.52 | 4,476 |\n| 4679 | 0278A0204679 | 27.593 | 88.029 | Kosi | M(l) | 0.57 | 4,475 |\n| 4680 | 0278A0204680 | 27.592 | 88.012 | Kosi | M(l) | 0.42 | 4,433 |\n| 4681 | 0278A0204681 | 27.586 | 88.041 | Kosi | I(s) | 1.15 | 4,483 |\n| 4682 | 0278A0204682 | 27.582 | 88.056 | Kosi | M(o) | 0.34 | 4,661 |\n| 4683 | 0278A0204683 | 27.563 | 88.043 | Kosi | E(o) | 0.63 | 5,110 |\n| 4684 | 0278A0204684 | 27.560 | 88.034 | Kosi | E(o) | 3.07 | 5,058 |\n| 4685 | 0278A0204685 | 27.559 | 88.038 | Kosi | E(o) | 1.19 | 5,131 |\n| 4686 | 0278A0204686 | 27.558 | 88.035 | Kosi | E(o) | 0.35 | 5,117 |\n| 4687 | 0278A0204687 | 27.549 | 88.037 | Kosi | E(o) | 1.07 | 5,096 |\n| 4688 | 0278A0204688 | 27.549 | 88.057 | Kosi | M(o) | 0.35 | 5,238 |\n| 4689 | 0278A0204689 | 27.548 | 88.027 | Kosi | E(o) | 1.08 | 4,964 |\n| 4690 | 0278A0204690 | 27.547 | 88.003 | Kosi | E(o) | 3.31 | 4,614 |\n| 4691 | 0278A0204691 | 27.545 | 88.050 | Kosi | M(o) | 25.72 | 5,020 |\n| 4692 | 0278A0204692 | 27.541 | 88.053 | Kosi | M(o) | 0.49 | 5,099 |\n| 4693 | 0278A0204693 | 27.541 | 88.045 | Kosi | E(o) | 1.56 | 4,934 |\n| 4694 | 0278A0204694 | 27.530 | 88.054 | Kosi | E(o) | 1.02 | 5,104 |\n| 4695 | 0278A0304695 | 27.490 | 88.038 | Kosi | E(o) | 2.80 | 4,444 |\n| 4696 | 0278A0304696 | 27.475 | 88.035 | Kosi | E(o) | 2.68 | 4,369 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 7780, "line_end": 7862, "token_count_estimate": 1629, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0278A0104662", "0278A0104663", "0278A0104664", "0278A0104665", "0278A0104666", "0278A0104667", "0278A0104668", "0278A0104669", "0278A0104670", "0278A0104671", "0278A0204672", "0278A0204673", "0278A0204674", "0278A0204675", "0278A0204676", "0278A0204677", "0278A0204678", "0278A0204679", "0278A0204680", "0278A0204681", "0278A0204682", "0278A0204683", "0278A0204684", "0278A0204685", "0278A0204686", "0278A0204687", "0278A0204688", "0278A0204689", "0278A0204690", "0278A0204691", "0278A0204692", "0278A0204693", "0278A0204694", "0278A0304695", "0278A0304696"]}}
{"id": "1ff526f959d5414a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 4697 | 0278A0304697 | 27.474 | 88.040 | Kosi | E(o) | 0.65 | 4,452 |\n| 4698 | 0278A0304698 | 27.438 | 88.058 | Kosi | E(c) | 8.13 | 4,323 |\n| 4699 | 0278A0304699 | 27.437 | 88.049 | Kosi | E(o) | 0.65 | 4,221 |\n| 4700 | 0278A0504700 | 27.997 | 88.382 | Kosi | M(o) | 1.84 | 5,516 |\n| 4701 | 0278A0504701 | 27.996 | 88.316 | Kosi | M(o) | 2.44 | 5,204 |\n| 4702 | 0278A0504702 | 27.994 | 88.402 | Kosi | M(e) | 18.97 | 5,171 |\n| 4703 | 0278A0504703 | 27.994 | 88.321 | Kosi | M(o) | 0.47 | 5,241 |\n| 4704 | 0278A0504704 | 27.994 | 88.322 | Kosi | M(o) | 0.46 | 5,245 |\n| 4705 | 0278A0504705 | 27.992 | 88.317 | Kosi | M(o) | 1.61 | 5,277 |\n| 4706 | 0278A0504706 | 27.988 | 88.306 | Kosi | I(s) | 0.53 | 5,529 |\n| 4707 | 0278A0504707 | 27.986 | 88.317 | Kosi | I(s) | 0.31 | 5,389 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 81, "line_start": 7780, "line_end": 7862, "token_count_estimate": 594, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0278A0304697", "0278A0304698", "0278A0304699", "0278A0504700", "0278A0504701", "0278A0504702", "0278A0504703", "0278A0504704", "0278A0504705", "0278A0504706", "0278A0504707"]}}
{"id": "67c5a91d46175c9a", "text": "Document: MESSAGE\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes > Mapping results:", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "Mapping results:"], "chunk_type": "text", "line_start": 7863, "line_end": 7867, "token_count_estimate": 48, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9825bf4c3216822a", "text": "Document: MESSAGE\nSection: Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha\nType: text\n\nThere are 58 lakes having an area ≥ 50 ha, which is just 1.23% of the total glacial lake count, but covers total of 37.59% of the total glacial lakes area. Spatial distribution of these very large sized lakes i.e. ≥ 50 ha in area has been represented below in Figure 93 and details of these are given in Table 75, along with its area, type, geographic as well as hydrological location, and elevation at which they are situated. Among these 58 lakes, 33, 18 and 7 lakes are in the lake area range of < 100 ha, 100 - 250 ha and ≥ 250 ha respectively. Out of these 58 large lakes, majority (49) are moraine-dammed glacial lakes and few are glacier erosion lakes (5) and other glacial lakes (4). These very large sized lakes are situated within the elevation range of 4,038 to 5,589 m amsl, the largest one being in Kosi subbasin at 5,352 m, followed by second largest also in Kosi subbasin at 5,067 m.\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha"], "chunk_type": "text", "line_start": 7869, "line_end": 7878, "token_count_estimate": 296, "basins": ["GANGA", "Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "1fcfe666462143d5", "text": "Document: MESSAGE\nSection: Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha\nType: table\nTable: Table 75: List of glacial lakes with area ≥ 50 ha\n\n| S.No. | Glacial Lake ID No | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0277D0804624 | 28.017 | 88.288 | Kosi | M(e) | 50.43 | 5,268 |\n| 2 | 0271H0602059 | 28.644 | 85.491 | Kosi | M(e) | 50.98 | 4,985 |\n| 3 | 0271H1102183 | 28.485 | 85.736 | Kosi | M(e) | 51.99 | 5,335 |\n| 4 | 0272I1303932 | 27.924 | 86.786 | Kosi | M(l) | 54.85 | 4,512 |\n| 5 | 0271L0302555 | 28.335 | 86.192 | Kosi | M(e) | 55.00 | 5,422 |\n| 6 | 0271L0302551 | 28.347 | 86.225 | Kosi | M(e) | 55.90 | 5,348 |\n| 7 | 0277D0804622 | 28.022 | 88.355 | Kosi | M(e) | 56.29 | 5,195 |\n| 8 | 0272I0903725 | 27.975 | 86.681 | Kosi | M(l) | 57.83 | 4,834 |\n| 9 | 0271L1203072 | 28.044 | 86.514 | Kosi | M(l) | 57.94 | 5,241 |\n| 10 | 0271L0302557 | 28.303 | 86.157 | Kosi | M(e) | 59.05 | 5,307 |\n| 11 | 0277D0804627 | 28.009 | 88.259 | Kosi | M(e) | 59.70 | 5,256 |\n| 12 | 0271P1003475 | 28.694 | 87.534 | Kosi | E(o) | 60.74 | 5,158 |\n| 13 | 0271L0802932 | 28.033 | 86.500 | Kosi | M(e) | 60.86 | 5,057 |\n| 14 | 0271H1602493 | 28.211 | 85.847 | Kosi | M(e) | 61.34 | 4,374 |\n| 15 | 0262J0400880 | 30.067 | 82.127 | Ghaghara | M(l) | 62.33 | 4,829 |\n| 16 | 0272M1304484 | 27.952 | 87.908 | Kosi | M(e) | 64.79 | 5,165 |\n| 17 | 0271P0403183 | 28.205 | 87.052 | Kosi | O | 65.11 | 4,980 |\n| 18 | 0271L1203013 | 28.185 | 86.532 | Kosi | M(e) | 67.68 | 5,025 |\n| 19 | 0272M1304515 | 27.869 | 87.866 | Kosi | M(e) | 68.12 | 4,910 |\n| 20 | 0271P1203566 | 28.093 | 87.637 | Kosi | M(e) | 72.47 | 5,178 |\n| 21 | 0262F1600708 | 30.129 | 81.781 | Ghaghara | M(e) | 75.65 | 5,015 |\n| 22 | 0271P1203514 | 28.230 | 87.591 | Kosi | M(e) | 78.90 | 5,410 |\n| 23 | 0271P0403232 | 28.068 | 87.047 | Kosi | M(e) | 78.93 | 5,589 |\n| 24 | 0271P0903420 | 28.858 | 86.519 | Kosi | E(o) | 81.24 | 5,254 |\n| 25 | 0278A0104639 | 27.933 | 88.066 | Kosi | M(e) | 83.35 | 5,563 |\n| 26 | 0272M1304486 | 27.950 | 87.930 | Kosi | M(e) | 83.66 | 5,106 |\n| 27 | 0272I1304032 | 27.755 | 86.958 | Kosi | M(o) | 86.50 | 4,927 |\n| 28 | 0272I1304014 | 27.783 | 86.957 | Kosi | M(e) | 87.28 | 5,198 |\n| 29 | 0271D0701882 | 28.488 | 84.486 | Gandak | M(e) | 89.44 | 4,038 |\n| 30 | 0271P0403201 | 28.152 | 87.158 | Kosi | O | 94.74 | 5,141 |\n| 31 | 0272M1304496 | 27.926 | 87.771 | Kosi | M(e) | 97.66 | 4,913 |\n| 32 | 0271L1203032 | 28.135 | 86.531 | Kosi | M(e) | 97.85 | 4,984 |\n| 33 | 0271L0902947 | 28.887 | 86.514 | Kosi | E(o) | 98.23 | 5,098 |\n| 34 | 0271P0703342 | 28.393 | 86.379 | Kosi | M(e) | 100.11 | 5,482 |\n| 35 | 0277D0804614 | 28.054 | 88.427 | Kosi | O | 101.66 | 4,888 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha"], "chunk_type": "table", "table_caption": "Table 75: List of glacial lakes with area ≥ 50 ha", "columns": ["S.No.", "Glacial Lake ID No", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 7879, "line_end": 7920, "token_count_estimate": 1610, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi"], "countries": [], "lake_ids": ["0262F1600708", "0262J0400880", "0271D0701882", "0271H0602059", "0271H1102183", "0271H1602493", "0271L0302551", "0271L0302555", "0271L0302557", "0271L0802932", "0271L0902947", "0271L1203013", "0271L1203032", "0271L1203072", "0271P0403183", "0271P0403201", "0271P0403232", "0271P0703342", "0271P0903420", "0271P1003475", "0271P1203514", "0271P1203566", "0272I0903725", "0272I1303932", "0272I1304014", "0272I1304032", "0272M1304484", "0272M1304486", "0272M1304496", "0272M1304515", "0277D0804614", "0277D0804622", "0277D0804624", "0277D0804627", "0278A0104639"]}}
{"id": "e0bbd43e33d05ef9", "text": "Document: MESSAGE\nSection: Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha\nType: table\nTable: Table 75: List of glacial lakes with area ≥ 50 ha\n\n| S.No. | Glacial Lake ID No | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 36 | 0271P0403182 | 28.208 | 87.101 | Kosi | E(o) | 101.93 | 4,852 |\n| 37 | 0271H1002154 | 28.616 | 85.527 | Kosi | M(e) | 103.58 | 5,113 |\n| 38 | 0271P1203527 | 28.178 | 87.563 | Kosi | M(e) | 104.19 | 5,011 |\n| 39 | 0272M1304495 | 27.928 | 88.002 | Kosi | M(e) | 113.22 | 5,348 |\n| 40 | 0272I0903801 | 27.779 | 86.612 | Kosi | M(e) | 117.31 | 4,831 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha"], "chunk_type": "table", "table_caption": "Table 75: List of glacial lakes with area ≥ 50 ha", "columns": ["S.No.", "Glacial Lake ID No", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 40, "line_start": 7879, "line_end": 7920, "token_count_estimate": 328, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0271H1002154", "0271P0403182", "0271P1203527", "0272I0903801", "0272M1304495"]}}
{"id": "3936df21e516d7c1", "text": "Document: MESSAGE\nSection: Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha"], "chunk_type": "text", "line_start": 7921, "line_end": 7927, "token_count_estimate": 48, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1361e6a3be0f9a66", "text": "Document: MESSAGE\nSection: Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 41 | 0271H1002153 | 28.623 | 85.510 | Kosi | M(e) | 118.78 | 5,127 |\n| 42 | 0271H1002162 | 28.562 | 85.602 | Kosi | M(e) | 129.19 | 5,361 |\n| 43 | 0271P0803345 | 28.213 | 87.470 | Kosi | O | 131.39 | 4,781 |\n| 44 | 0271L0702788 | 28.199 | 86.582 | Kosi | M(e) | 134.64 | 5,094 |\n| 45 | 0272I1303950 | 27.898 | 86.925 | Kosi | M(e) | 139.77 | 5,003 |\n| 46 | 0271P1203561 | 28.114 | 87.655 | Kosi | M(e) | 146.34 | 4,954 |\n| 47 | 0278A0104637 | 27.946 | 88.075 | Kosi | M(e) | 148.59 | 5,488 |\n| 48 | 0272I0503634 | 27.947 | 86.446 | Kosi | M(e) | 156.76 | 5,046 |\n| 49 | 0272I0503648 | 27.861 | 86.476 | Kosi | M(e) | 158.40 | 4,550 |\n| 50 | 0272M0104125 | 27.798 | 87.092 | Kosi | M(e) | 182.16 | 4,543 |\n| 51 | 0271H1502402 | 28.329 | 85.869 | Kosi | M(o) | 213.52 | 5,167 |\n| 52 | 0271P0903422 | 28.832 | 86.522 | Kosi | E(o) | 281.32 | 5,319 |\n| 53 | 0262P1401803 | 28.691 | 83.852 | Gandak | M(o) | 340.21 | 4,910 |\n| 54 | 0271L0702736 | 28.374 | 86.305 | Kosi | M(e) | 391.50 | 5,346 |\n| 55 | 0271H1502391 | 28.360 | 85.871 | Kosi | M(e) | 463.78 | 5,212 |\n| 56 | 0271H1002177 | 28.494 | 85.636 | Kosi | M(e) | 490.68 | 5,278 |\n| 57 | 0271H1502405 | 28.322 | 85.838 | Kosi | M(e) | 540.35 | 5,067 |\n| 58 | 0271H1002165 | 28.532 | 85.609 | Kosi | M(e) | 540.55 | 5,352 |", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 18, "line_start": 7928, "line_end": 7947, "token_count_estimate": 873, "basins": ["Ganga"], "subbasins": ["Gandak", "Kosi"], "countries": [], "lake_ids": ["0262P1401803", "0271H1002153", "0271H1002162", "0271H1002165", "0271H1002177", "0271H1502391", "0271H1502402", "0271H1502405", "0271L0702736", "0271L0702788", "0271P0803345", "0271P0903422", "0271P1203561", "0272I0503634", "0272I0503648", "0272I1303950", "0272M0104125", "0278A0104637"]}}
{"id": "349d691cce543530", "text": "Document: MESSAGE\nSection: Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha", "section_headings": ["Annexure - III: Glacial Lakes of Ganga River basin with area ≥ 50 ha"], "chunk_type": "text", "line_start": 7948, "line_end": 7954, "token_count_estimate": 47, "basins": ["GANGA", "Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8a31316d81f4de0", "text": "Document: MESSAGE\nSection: Annexure - IV: Glossary\nType: text\n\n**Ablation:** The process that reduce the mass of the glacier (Cogley et al., 2011).\n\n**Ablation area/zone:** The part of the glacier where ablation exceeds accumulation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is negative. The extent of the ablation zone can vary strongly from year to year (Cogley et al., 2011).\n\n**Accumulation:** The process that add to the mass of the glacier (Cogley et al., 2011).\n\n**Accumulation area/zone:** The part of the glacier where accumulation exceeds ablation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is positive. The extent of the accumulation zone can vary strongly from year to year. The accumulation zone is not the same as the firn area (Cogley et al., 2011).\n\n**Altitude:** The vertical distance of a point above a datum, which is usually an estimate of mean sea level. Altitude and elevation are synonyms in common usage (Cogley et al., 2011).\n\n**Aspect:** The compass direction towards which a slope faces; measured clockwise in degrees from the North.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Avalanche:** A slide or flow of a mass of snow, firn or ice that becomes detached abruptly, often entraining additional material such as snow, debris and vegetation as it descends. The duration of an avalanche is typically seconds to minutes (Cogley et al., 2011).\n\n**Band:** One layer of multispectral image representing data values for a specific range of the electromagnetic spectrum of reflected light or heat.\n\n**Climate:** Climate is usually defined as the average weather or as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years. The relevant quantities are most often surface variables such as temperature, precipitation and wind (Pandey, 2019).\n\n**Climate change:** Climate change refers to a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. UNFCCC defines climate change as: ‘a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods’. (Pandey, 2019).\n\n**Climate variability:** Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all spatial and temporal scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability) (Pandey, 2019).\n\n**Cryosphere:** The cryosphere is the part of the Earth system that contains ice, for example snow on the ground, glaciers, ice sheets, lake ice, river ice, sea ice, seasonally and perennially frozen ground (GCW 2016).\n\n**Database:** An organized, integrated collection of data related by a common fact or purpose.\n\n***", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - IV: Glossary", "section_headings": ["Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 7956, "line_end": 8037, "token_count_estimate": 860, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74f29f65ec1c0f06", "text": "Document: MESSAGE\nSection: Annexure - IV: Glossary\nType: text\n\nand temporal scales beyond that of individual weather events . Variability may be due to natural internal processes within the climate system ( internal variability ) , or to variations in natural or anthropogenic external forcing ( external variability ) ( Pandey , 2019 ) . * * Cryosphere : * * The cryosphere is the part of the Earth system that contains ice , for example snow on the ground , glaciers , ice sheets , lake ice , river ice , sea ice , seasonally and perennially frozen ground ( GCW 2016 ) . * * Database : * * An organized , integrated collection of data related by a common fact or purpose . * * *\n\nGLACIAL LAKE ATLAS OF GANGA RIVER BASIN\n\n**Debris-covered glacier:** A glacier that is covered at its tongue with supra-glacial debris across its full width (Kirkbride, 2011). In the accumulation zone any deposited debris is buried by later snowfalls, but in the ablation zone debris remains at the surface and englacial debris is added to the surface layer from beneath as ice ablates away. The debris cover affects the rate of ablation, with very thin debris resulting in accelerated melt and debris thicker than a few tens of millimetres reducing the melting rate (Cogley et al., 2011).\n\n**Digital Elevation Model (DEM):** An array of numbers representing the elevation of part or all of the Earth’s surface as samples or averages at fixed spacing in two horizontal coordinate directions (Cogley et al., 2011).\n\n**Disaster:** A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNISDR 2017).\n\n**Disaster risk:** The potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity (UNISDR 2017).\n\n**Early warning system:** The set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare to act promptly and appropriately to reduce the possibility of harm or loss (Pandey, 2019).\n\n**Electromagnetic spectrum:** The spectrum of wavelengths of electromagnetic radiation.\n\n**Englacial:** Pertaining to the interior of the glacier, between the summer surface and the bed (Cogley et al., 2011).\n\n**Exposure:** The presence or situation of people, livelihoods, species, ecosystems, environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings, and other tangible human assets located in hazard-prone areas that could be adversely affected (UNISDR, 2017; Pandey, 2019).\n\n**Feature:** A real-world phenomenon, often used in cartography to name classes of elements shown on a map.\n\n**Firn:** Snow (in which the pore space is at least partially interconnected, allowing air and water to circulate) that has survived at least one ablation season but has not been transformed to glacier ice (Cogley et al., 2011).", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - IV: Glossary", "section_headings": ["Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 7956, "line_end": 8037, "token_count_estimate": 833, "basins": ["GANGA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3852fc2295994f63", "text": "Document: MESSAGE\nSection: Annexure - IV: Glossary\nType: text\n\nresources , infrastructure , or economic , social , or cultural assets in places and settings , and other tangible human assets located in hazard - prone areas that could be adversely affected ( UNISDR , 2017 ; Pandey , 2019 ) . * * Feature : * * A real - world phenomenon , often used in cartography to name classes of elements shown on a map . * * Firn : * * Snow ( in which the pore space is at least partially interconnected , allowing air and water to circulate ) that has survived at least one ablation season but has not been transformed to glacier ice ( Cogley et al . , 2011 ) .\n\n**Flood:** The overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas not normally submerged. Floods include river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods and glacial lake outburst floods (Pandey, 2019).\n\n**Format:** The pattern into which data are systematically arranged for use on a computer.\n\n**Geographic Information System (GIS):** A set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world for a particular set of circumstances.\n\n**Glacial Lake Outburst Flood (GLOF):** Flood caused by the outburst of a glacial lake due to rapid accumulation of water in it, resulting to extreme damage in loss of lives and infrastructure in the downstream area.\n\n**Glacial Lake:** As a result of glacier thinning and retreating, melt water gets accumulated at terminal moraines or on it covered by glacier ice, is known as glacial lake.\n\n**Glacier Erosion Lake:** These are the water bodies formed in a depression after the glacier has retreated in a form of cirque or trough valley, might be isolated and far away from the present glaciated area, and mostly stable in nature.\n\nPrepared under: National Hydrology Project\n\nNational Remote Sensing Centre\nIndian Space Research Organisation\nDepartment of Space, Government of India\nHyderabad - 500 037\n\nNHP\nइसरो isro", "metadata": {"source_file": "data/Glacial_Lake_Atlas_Ganga_Basin_NRSC_gemini.md", "document_title": "MESSAGE", "section_path": "Annexure - IV: Glossary", "section_headings": ["Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 7956, "line_end": 8037, "token_count_estimate": 536, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "84a07539f45577af", "text": "Document: IHR GlacialLake Atlas (1)\nSection: SUMMARY\nType: text\n\nNational Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Hyderabad as one of the Implementing Agency under the National Hydrology Project (NHP), is carrying out hydrological studies using satellite data and geospatial techniques. As part of this, detailed glacial lake inventory, prioritization for Glacial Lake Outburst Flood (GLOF) risk, and simulation of GLOF for selected lakes are taken up for entire catchment of Indian Himalayan Rivers covering Indus, Ganga, and Brahmaputra River Basins. Under this activity, an updated inventory of glacial lakes using high resolution satellite data was prepared for the Indus, Ganga and Brahmaputra River basins and published in December 2020 (NRSC-RSAA-WRG-WRAD-Nov2020-TR-0001702-V1.0), June 2021 (NRSC-RSAA-WRG-WRAD-Mar2021-TR-0001818-V1.0), and July 2022 (NRSC-RSAA-WRG-WRAD-May2022-TR-0002026-V1.0) respectively, and currently an updated inventory of glacial lakes has been prepared for the Indian Himalayan River basins. The present glacial lake atlas is based on the inventoried glacial lakes in part of Indus, Ganga and Brahmaputra River basins from its origin to foothills of Himalayas covering a total catchment area of 9,89,784 Km².\n\nThe study portion of all the three river basins covers part of India and transboundary region. Elevation in the river basin varies from the minimum 45 m to the maximum 8,848 m above mean sea level (amsl). In India, the study area falls in four states viz., Himachal Pradesh, Uttarakhand, Sikkim and Arunachal Pradesh and 2 Union-Territories namely Jammu & Kashmir and Ladakh.\n\nIn the present study, glacial lakes with water spread area ≥ 0.25 ha have been mapped using Resourcesat-2 (RS-2) Linear Imaging Self Scanning Sensor-IV (LISS-IV) satellite data using visual interpretation techniques. Based on its process of lake formation, location, and type of damming material, glacial lakes are identified in all ten different types, majorly grouped into four categories viz., Moraine-dammed, Ice-dammed, Glacier Erosion, and Other glacial lakes.\n\nA total of 28,043 glacial lakes have been mapped in the Indian Himalayan River basins using a total of 397 high resolution multispectral RS-2 LISS-IV images, with a total lake water spread area of 1,31,070.90 ha. Each glacial lake has been given a 12 alpha-numeric unique glacial lake ID, along with several attributes that include hydrological, geometrical, geographical, and topographical characteristics. About 23,167 (82.61%) lakes are with < 5 ha lake area contributing to 23.72% of total lake area. The remaining lakes with > 5 ha in size are 4,876 (17.39%) contributing to 76.28% of total lake area in the Indian Himalayan River Basins. There are only 299 glacial lakes in the Indian Himalayan River basins having an area of greater than 50 ha. Other Glacial Erosion Lake type are found to be the maximum with 16,106 (57.43%) occupying a total lake extent of 61,228.37 ha at 46.71% in the basin. Majority (i.e. 93.08%) of the lakes are situated above the high altitude range of greater than 4,000 m amsl and dominated by Other Glacial Erosion lake type i.e., 57.32%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "SUMMARY", "section_headings": ["SUMMARY"], "chunk_type": "text", "line_start": 4, "line_end": 34, "token_count_estimate": 851, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": ["0001702", "0001818", "0002026"]}}
{"id": "07f8f15dd67917b3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: SUMMARY\nType: text\n\ntotal lake area in the Indian Himalayan River Basins . There are only 299 glacial lakes in the Indian Himalayan River basins having an area of greater than 50 ha . Other Glacial Erosion Lake type are found to be the maximum with 16 , 106 ( 57 . 43 % ) occupying a total lake extent of 61 , 228 . 37 ha at 46 . 71 % in the basin . Majority ( i . e . 93 . 08 % ) of the lakes are situated above the high altitude range of greater than 4 , 000 m amsl and dominated by Other Glacial Erosion lake type i . e . , 57 . 32 % .\n\nGlacial lakes are predominantly distributed in Brahmaputra basin (64.19%) followed by Indus basin (19.02%) and Ganga basin (16.78%), with a total lake extent of 92,990.74 ha, 17,395.03 ha and 20,685.13 ha at 70.95%, 13.27% and 15.78% respectively in the entire basins. In terms of very large size lakes i.e. ≥ 50 ha, Brahmaputra basin has majority i.e. 207 out of 299 large lakes within it. Other Glacial Erosion lakes, which are dominant lake type in Indian Himalayan River basins are distributed in all basins, and found maximum in count in Brahmaputra basin (73.55%) and minimum in Ganga basin (10.83%). However, Glacier Ice-dammed lakes are only five in\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nthe entire Indian Himalayan River basins and is located two each in Brahmaputra & Indus and one in Ganga basin. Indus basin consists of higher number of Supra-Glacial Lakes in the entire Indian Himalayan River basins, whereas Brahmaputra basin contains highest number of Other Moraine Dammed lakes.\n\nA total of 7,570 (i.e. 26.99%) glacial lakes lies within Indian region covering 23.00% of the total lake area, whereas remaining 73.01% of lakes are located in transboundary region with a 77.00% of the total lake area. In Indian region, majority of glacial lakes are of Other Glacial Erosion Lake type (56.64%), followed by Other Moraine Dammed lakes (12.85%). Arunachal Pradesh, Sikkim, Himachal Pradesh, and Uttarakhand share 28.90%, 9.68%, 7.09%, and 4.58% of lake count with a total area of 41.43%, 10.84%, 3.26%, and 1.86% respectively. A total of 24 (i.e., 41.38%) glacial lakes with lake area > 50 ha are situated in the Union-Territories of Jammu & Kashmir and Ladakh. Sikkim state has 288 (20.67%) number of glacial lakes lying above 5,000 m elevation.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "SUMMARY", "section_headings": ["SUMMARY"], "chunk_type": "text", "line_start": 4, "line_end": 34, "token_count_estimate": 688, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5b5f4837b80eaff", "text": "Document: IHR GlacialLake Atlas (1)\nSection: SUMMARY\nType: text\n\nArunachal Pradesh , Sikkim , Himachal Pradesh , and Uttarakhand share 28 . 90 % , 9 . 68 % , 7 . 09 % , and 4 . 58 % of lake count with a total area of 41 . 43 % , 10 . 84 % , 3 . 26 % , and 1 . 86 % respectively . A total of 24 ( i . e . , 41 . 38 % ) glacial lakes with lake area > 50 ha are situated in the Union - Territories of Jammu & Kashmir and Ladakh . Sikkim state has 288 ( 20 . 67 % ) number of glacial lakes lying above 5 , 000 m elevation .\n\nIn this atlas, map sheets (plates) are prepared on the basis of 2° X 2° grids which are 42 in number covering the entire Indian Himalayan River basins. Out of 42 plates, only 39 plates have glacial lakes and corresponding plates are incorporated in atlas. The map sheets are arranged in such a way that glacial lake map is on the right page and its corresponding satellite image is on the left page. At the end of the atlas, an annexure is provided containing list of glacial lakes of size ≥ 10 ha inventoried in the Indian Himalayan River basins with their unique glacial lake ID, latitude, longitude, subbasin, glacial lake type, area (ha), and elevation (m). Glacial Lake ID number of 12 alpha-numeric character has 3 characters bold with dark red colour depicting the corresponding toposheet number of the SOI of 1:250,000 scale.\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "SUMMARY", "section_headings": ["SUMMARY"], "chunk_type": "text", "line_start": 4, "line_end": 34, "token_count_estimate": 418, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8fd3f822421a182", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 1. INTRODUCTION > 1.1 About Project\nType: text\n\nThe National Hydrology Project (NHP) sponsored by Department of Water Resources, River Development and Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti, Government of India (GOI) with financial aid from the World Bank. The objective of the project is to improve the extent and accessibility of water resources information and strengthen institutional capacity to enable improved water resources planning and management across India. The mission is to establish an effective and sound hydrologic database and Hydrological Information System (HIS), together with the development of consistent and scientifically based tools and design aids, to assist in the effective water resources planning and management of the implementing agencies.\n\nNHP is intended for setting up of a system for timely and reliable water resources data acquisition, storage, collation and management. It will also provide tools/systems for informed decision making through Decision Support Systems (DSS) for water resources assessment, flood management, reservoir operations, drought management, etc. NHP also seeks to build capacity of the State and Central sector organisations in water resources management through the use of Information Systems and adoption of State-of-the-art technologies like Remote Sensing. NHP will improve and expand hydrology data and information systems, strengthen water resources operation and planning systems, and enhance institutional capacity for water resources management. NHP will contribute to the GOI Digital India initiative by integrating water resources information across State and Central agencies.\n\nNational Remote Sensing Centre (NRSC), as one of the Implementing Agency under NHP, is engaged with generation of geo-spatial products & services pertaining to water resources sector, generation of high-resolution Digital Elevation Models (DEM), development of flood early warning systems, decision support system development for irrigation water management, modelling & dissemination of hydrological products to support water resources management and capacity building to NHP stakeholders. The satellite data based geo-spatial products & services, mainly encompassing the following:\n\n* Satellite Data/Geo-Spatial Data Hosting & Services through Bhuvan Web Portal\n* Water Resources Information Products & Services (Satellite/Model derived – Bhuvan/India- Water Resources Information System (India-WRIS)/National Water Informatics Centre (NWIC))\n* Customized Applications Development (Flood Forecasting, Irrigation Water Management)\n* Hydro-conditioned Digital Elevation Model (Satellite & Aerial)\n* Capacity Building (Customized Training & Hand Holding)\n\nAs part of various NHP technical studies carried out, NRSC has taken up “Glacial Lake Outburst Flood (GLOF) Risk Assessment of Glacial Lakes in the Himalayan Region of Indian River Basins”. In this activity, it was proposed to prepare an updated inventory of glacial lakes, prioritization and selection of critical glacial lakes based on certain characteristics (such as glacial lake, glacier, topography and others), GLOF modelling and flood inundation simulation for selected few lakes using high resolution Digital Elevation Model (DEM) for downstream of the lakes along their river reach, and to assess GLOF risk.\n\nAs a result of initial outcome of this activity, an updated inventory of glacial lakes in Indus, Ganga and Brahmaputra", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "1. INTRODUCTION > 1.1 About Project", "section_headings": ["1. INTRODUCTION", "1.1 About Project"], "chunk_type": "text", "line_start": 38, "line_end": 57, "token_count_estimate": 755, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cbf748eed79d5137", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.1 Overview\nType: text\n\nThe IHR consists of three major river systems of Indus, Ganga, and Brahmaputra stretches over five countries viz., India, China, Nepal, Pakistan and Bhutan, and on the basis of physiography it has been divided into four mountain regions viz., Eastern Himalayas, Central Himalayas, Western Himalayas, and the Karakoram Mountain range. The Indian Himalayan River Basins cover a total drainage area of 9,89,784 Sq.Km. In India it has a share of about 38.78% which is 3,83,880 Sq.Km cutting across the states of Arunachal Pradesh, Assam, Himachal Pradesh, Uttarakhand, West Bengal, Meghalaya, Nagaland and Sikkim and union territories of Jammu & Kashmir, and Ladakh. Figure 1 shows the origin of the Ganga, and the Brahmaputra river of the Indian Himalayan River Basins.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.1 Overview", "section_headings": ["2. STUDY AREA", "2.1 Overview"], "chunk_type": "text", "line_start": 61, "line_end": 63, "token_count_estimate": 233, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "405b9f722055b469", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.1 Overview > Brahmaputra Basin\nType: text\n\nTopographically River Brahmaputra is unique in terms of its diverse environment as it has cold plateau in Tibet, rainy Himalayan region, alluvial lands of Assam and the large deltaic plains of Bangladesh. The Brahmaputra River also known as Yarlung Tsangpo (in Tibet) originates in the glacier mass from the Kailash ranges of Himalayas at an altitude of 5,150 m south of the lake ’Konggyu Tsho’. The higher elevation zones in the basin causes snow fall mostly over the northern region. The river flows through a length of 2,900 Km out of which 916 Km in the Indian Territory and joins finally in the Bay of Bengal. The catchment area receives number of tributaries at its north and south banks especially in Indian region. The catchment receives heavy rainfall with significant spatial variability. The land use/cover of the basin consist extensive forest cover, plantations, crop fields and swampy water lands, the northern part of the basin (outside India) covers mostly snow. There are number of hydraulic structures constructed across the tributaries in terms of weirs, barrages for the purpose of Irrigation and Dams for the hydro power utilization. The Brahmaputra River basin from its origin to foothills of Himalayas with a catchment area of 3,99,833 Sq.Km which extends from latitude 26.70° N to 32.70° N and from longitude 82.00° E to 97.77° E.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.1 Overview > Brahmaputra Basin", "section_headings": ["2. STUDY AREA", "2.1 Overview", "Brahmaputra Basin"], "chunk_type": "text", "line_start": 65, "line_end": 67, "token_count_estimate": 377, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cbdff0afaee4187f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.1 Overview > Ganga Basin\nType: text\n\nThe Ganga River basin is unique in the sense that it contains 9 of the 14 highest peaks in the world over 8,000 m in height, including Mt. Everest which is the high peak of Ganga River basin. The other peaks over 8,000 m in the basin are Kanchenjunga, Lhotse, Makalu, Cho Oyu, Dhaulagiri, Manaslu, Annapurna, and Shishapangma. The Ganga River basin extends over Central Himalayas in India, Nepal, Tibet (China), and Bangladesh.\n\nThe Ganga River originates as the Bhagirathi from the Gangotri group of glaciers in the Himalayas at an elevation of about 7,010 m amsl, in the Uttarkashi district of Uttarakhand, which has been joined by the Alaknanda at Devprayag, and the combined stream assumes the name ‘Ganga’. River flows through the highly terrain mountain region and debouches into the plains at Sukhi (near Rishikesh). It is joined by a large number of tributaries on both the banks in the course of its total run of about 2,525 km before its outfall into the Bay of Bengal. The delta of the Ganga River is said to begin at the Farakka barrage in West Bengal, where the river divides into two arms namely the Padma which flows to Bangladesh and the Ganga which flows through West Bengal. Ganga River basin from its origin to the foothills of Himalayas with a catchment area of 2,47,110 Km² which extends from latitude 26.35° N to 31.46° N and from longitude 77.05° E to 88.95° E.\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.1 Overview > Ganga Basin", "section_headings": ["2. STUDY AREA", "2.1 Overview", "Ganga Basin"], "chunk_type": "text", "line_start": 69, "line_end": 76, "token_count_estimate": 417, "basins": ["Ganga"], "subbasins": [], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "940d8cf1074166e3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > Indus Basin\nType: text\n\nThe Indus River basin is unique in the sense that it contains seven of the world’s highest peaks after Mt. Everest, among these are K2 (8,557 m), Nanga Parbat (8,100 m) and Rakaposhi (7,800 m). The Indus basin extends over Western Himalayas and Karakoram Mountain range in India, China, and Nepal.\n\nIndus River originates from Bokhar Chu (glacier) in northern slopes of Mt. Kailash (6,714 m) from the Tibet Autonomous Region of China (Husain, 2012; CWC, 2019). The Indus River follows a long and straight course in Ladakh region (flowing in northwest direction in India), running between the Ladakh and the Zaskar ranges. The gradient of the river is also gentle here. It forms a spectacular gorge near Gilgit (at Bunzi, north of Nanga Parbat Glacier) in Jammu & Kashmir. Downstream, the river passes by the Nanga Parbat Glacier, crossing the Himalayas, it turns into south-west and enters Pakistan near Chillar in the Dardistan region. Finally, the Indus River drains into the Arabian Sea near the port city of Karachi, Pakistan after forming a huge delta. It has a total length of 2,880 Km, of which 709 Km lies in India. The Indus River basin from its origin to foothills of Himalayas has a catchment area of 3,42,841 Km² which extends from latitude 30.32° N to 37.09° N and from longitude 72.50° E to 82.45° E.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > Indus Basin", "section_headings": ["2. STUDY AREA", "Indus Basin"], "chunk_type": "text", "line_start": 78, "line_end": 82, "token_count_estimate": 392, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": ["China", "India", "Nepal"], "lake_ids": []}}
{"id": "584300a16e8160c3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.2 Hydrological Divide > Brahmaputra Basin\nType: text\n\nThe entire Brahmaputra River basin is sub divided into three reaches namely upper, middle and lower. In the upper reach the river is fed by glaciers. In the lower and middle reaches, it is joined by number of tributaries. The principal tributaries of the Brahmaputra River joining from right are the Lohit, the Dibang, the Subansiri, the Jiabharali, the Dhansiri, the Manas, the Torsa, the Sankosh and the Teesta whereas the Burhidihing, the Desang, the Dikhow, the Dhansiri and the Kopili joins it from left.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide > Brahmaputra Basin", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide", "Brahmaputra Basin"], "chunk_type": "text", "line_start": 86, "line_end": 88, "token_count_estimate": 176, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Lohit", "Manas", "Subansiri", "Teesta"], "countries": [], "lake_ids": []}}
{"id": "fc685073f1a265a9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.2 Hydrological Divide > Ganga Basin\nType: text\n\nMajor rivers flowing in the Ganga River basin is Yamuna, joining the main river on the right, whereas rivers like Sarda, Ghaghara, Gandak, and Kosi joining on the left.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide > Ganga Basin", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide", "Ganga Basin"], "chunk_type": "text", "line_start": 90, "line_end": 92, "token_count_estimate": 83, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Sarda", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "3e196b84a281b68b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > Indus Basin\nType: text\n\nMajor rivers flowing in the Indus River basin are Satluj, Beas, Ravi, Chenab, Jhelum, and Zaskar joining the main river on the left, whereas rivers like Sengee Tsangpo, Shyok, and Gilgit joining on the right.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > Indus Basin", "section_headings": ["2. STUDY AREA", "Indus Basin"], "chunk_type": "text", "line_start": 94, "line_end": 96, "token_count_estimate": 92, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Gilgit", "Jhelum", "Ravi", "Satluj", "Shyok"], "countries": [], "lake_ids": []}}
{"id": "9a648e5bb49a71ec", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > Indus Basin\nType: figure\nFigure: Figure 2 shows the location of the study area with Resourcesat-2 (RS-2) Linear Imaging Self Scanner (LISS-IV) satellite images. Table 1 shows the catchment area of each of the above basins.\n\nFigure 2 shows the location of the study area with Resourcesat-2 (RS-2) Linear Imaging Self Scanner (LISS-IV) satellite images. Table 1 shows the catchment area of each of the above basins.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > Indus Basin", "section_headings": ["2. STUDY AREA", "Indus Basin"], "chunk_type": "figure", "figure_caption": "Figure 2 shows the location of the study area with Resourcesat-2 (RS-2) Linear Imaging Self Scanner (LISS-IV) satellite images. Table 1 shows the catchment area of each of the above basins.", "line_start": 97, "line_end": 97, "token_count_estimate": 130, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c71c8dad10459b16", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > Indus Basin\nType: table\nTable: Table 1: Basin Details of Indian Himalayan River Basins\n\n| S. No. | Basin | Area (Km²) | Area (%) |\n| :--- | :--- | :--- | :--- |\n| 1 | Brahmaputra | 3,99,833 | 40.40 |\n| 2 | Ganga | 2,47,110 | 24.96 |\n| 3 | Indus | 3,42,841 | 34.64 |\n| | **Total** | **9,89,784** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > Indus Basin", "section_headings": ["2. STUDY AREA", "Indus Basin"], "chunk_type": "table", "table_caption": "Table 1: Basin Details of Indian Himalayan River Basins", "columns": ["S. No.", "Basin", "Area (Km²)", "Area (%)"], "table_row_start": 1, "table_row_end": 4, "line_start": 101, "line_end": 106, "token_count_estimate": 165, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "195c9efbe22e56a6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.3 Hydrology > Brahmaputra Basin\nType: text\n\nThe Upper reach of the River Brahmaputra flows through 1,625 Km from the source of origination point to the Indo-China border through Tibetan plateau and enters India at Kobo, Arunachal Pradesh, Upper reach of the river is mostly fed by snow and glaciers. In the middle reach between India and Bangladesh border it flows through a length of 916 Km where it has numerous riverine islands because of its low gradient. The entire lower reach falls within the Bangladesh flows about a length of 337 Km and drains into Bay of Bengal. The river flows are at low in winter season, gradually increases in summer season (due to melting of snow and glaciers in upper reaches) and reaches peak in monsoon season. The average water resources potential (In India) of the basin is 537.24 BCM out of which utilizable surface water resource is 24 BCM (Brahmaputra Basin Report, 2014).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.3 Hydrology > Brahmaputra Basin", "section_headings": ["2. STUDY AREA", "2.3 Hydrology", "Brahmaputra Basin"], "chunk_type": "text", "line_start": 111, "line_end": 114, "token_count_estimate": 243, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "64a1a7dc965181d1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.3 Hydrology > Ganga Basin\nType: text\n\nAll the principal tributaries of the Ganga River system are fed by snow and glaciers in the upper parts of their mountainous catchments. The snow accumulation in their upper catchments usually starts from October to March months reaching peak in January/February. The river flows are at minimum during the winter months of December to March. When snow and glaciers start melting during summer months of April to June, river flows gradually increase and accelerated further by rainy season of July to September. The Ganga River basin carries average annual water potential of about 525 Billion Cubic Metre (BCM), of which total utilizable surface water resource in the basin is 250 BCM. The Ghaghara, Kosi, and Gandak combined carry almost half, and the Yamuna, Ramganga, and other major and minor tributaries combined constitute the remainder of the total supply of the system (Ganga Basin Report, 2014).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.3 Hydrology > Ganga Basin", "section_headings": ["2. STUDY AREA", "2.3 Hydrology", "Ganga Basin"], "chunk_type": "text", "line_start": 116, "line_end": 118, "token_count_estimate": 241, "basins": ["Ganga"], "subbasins": ["Gandak", "Ghaghara", "Kosi", "Yamuna"], "countries": [], "lake_ids": []}}
{"id": "eebfa919c064788d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.3 Hydrology > Indus Basin\nType: text\n\nAll the principal tributaries of the Indus River system are fed by snow and glaciers in the upper parts of their mountainous catchments. The snow accumulation in their upper catchments usually starts from October to March months reaching peak in January/February. The river flows are at minimum during the winter months of December to March. When snow and glaciers start melting during summer months of April to June, river flows gradually increase and accelerated further by rainy season of July to September. Annually, the upper Indus carries about 110 Billion Cubic Metre (BCM) slightly less than half the total supply of water in the Indus River system. The Jhelum and Chenab combined carry roughly one-fourth, and the Ravi, Beas, and Satluj combined constitute the remainder of the total supply of the system.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.3 Hydrology > Indus Basin", "section_headings": ["2. STUDY AREA", "2.3 Hydrology", "Indus Basin"], "chunk_type": "text", "line_start": 120, "line_end": 122, "token_count_estimate": 224, "basins": ["Indus"], "subbasins": ["Beas", "Chenab", "Jhelum", "Ravi", "Satluj"], "countries": [], "lake_ids": []}}
{"id": "8b5ac69fcea51c7f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.4 Topography > Brahmaputra Basin\nType: text\n\nThe Brahmaputra basin mainly resides in the middle and eastern Himalayan region, which is also known as one of the main topographic divisions of the Indian subcontinent. The Himalayas comprises the Himalayan ranges including their numerous snow peaks and each of these peaks is surrounded by snow fields and glaciers. The elevation ranges between 450 m and 8,352 m amsl, where glaciers and glacial lakes are mostly distributed in the higher altitude region. The mean elevation of the study area is about 4,048 m amsl.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.4 Topography > Brahmaputra Basin", "section_headings": ["2. STUDY AREA", "2.4 Topography", "Brahmaputra Basin"], "chunk_type": "text", "line_start": 126, "line_end": 128, "token_count_estimate": 157, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ad1eb4f050d4711", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.4 Topography > Ganga Basin\nType: text\n\nThe Ganga basin mainly resides in the Central Himalayan region, which is also known as one of the main topographic divisions of the Indian subcontinent (Ganga Basin Report, 2014). The Central Himalayas comprises the Himalayan ranges including their numerous snow peaks rising above 7,000 m and each of these peaks is surrounded by snow fields and glaciers. All the tributaries are characterized by well regulated flows and assured supply of water throughout the year by these glaciers. The elevation ranges between 45 m and 8,848 m amsl, where glaciers and glacial lakes are mostly distributed in the higher altitude region. The mean elevation of the study area is 4,374 m amsl. Slope in the Ganga basin varies up to a maximum of 86.60°, while the mean slope in the Ganga River basin is 22.31°.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.4 Topography > Ganga Basin", "section_headings": ["2. STUDY AREA", "2.4 Topography", "Ganga Basin"], "chunk_type": "text", "line_start": 130, "line_end": 132, "token_count_estimate": 228, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f0e60b324c63c718", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.4 Topography > Indus Basin\nType: text\n\nThe elevation of the Indus basin ranges between 153 m and 8,557 m above mean sea level (amsl), where glaciers and glacial lakes are mostly distributed in the higher altitude region. The mean elevation of the study area is 4,355 m amsl. The transverse glaciers present in the basin at different locations and occasional landslides may also dam the river resulting in formation of temporary lakes. Downwards, the Indus crosses the Central Himalayan Range through a huge synclinal gorge. The Indus makes several deep gorges, amongst which the deepest of all is at Gilgit, which is 5,200 m in height amsl. Slope in the entire study area varies up to a maximum of 87.76°, while the mean slope in the Indus River basin is 22.23°.\n\nHypsometric curve shown in figure 3 depicts the proportion of land area that exists at various elevations by plotting relative area against relative height for the Indian Himalayan River basins.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.4 Topography > Indus Basin", "section_headings": ["2. STUDY AREA", "2.4 Topography", "Indus Basin"], "chunk_type": "text", "line_start": 134, "line_end": 138, "token_count_estimate": 260, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": [], "lake_ids": []}}
{"id": "5c0385472e4ab040", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.5 Climate > Brahmaputra Basin\nType: text\n\nThe climate of the Brahmaputra River basin varies from the harsh cold, and dry conditions found in Tibet to the generally hot and humid conditions prevailing in Assam state. Tibetan winters are severely cold, with average\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\ntemperatures below 0°C, while summers are mild and sunny. The upper river valley lies in the rain shadow of the Himalayas, and precipitation there is relatively light (Lhasa receives about 400 mm annually). Climate over the Brahmaputra River basin mainly comprises of four seasons in a year namely winter, summer, and monsoon and post monsoon. The winter season begins in December and continues to the end of February. From March onwards, the hot weather starts and continues up to the last week of May. The monsoon begins in the last week of May or in early June and the basin receives heavy rainfall spatially distributed over the basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.5 Climate > Brahmaputra Basin", "section_headings": ["2. STUDY AREA", "2.5 Climate", "Brahmaputra Basin"], "chunk_type": "text", "line_start": 142, "line_end": 149, "token_count_estimate": 257, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1ae2dd50450832d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.5 Climate > Ganga Basin\nType: text\n\nClimate over the Ganga River basin is mainly tropical and subtropical to temperate subhumid on the plains. The higher elevation zones in the Himalayan ranges especially in parts of Uttarakhand and Himachal Pradesh, experience lower temperatures than the other parts of the basin within India. Lowest annual precipitation < 500 mm is observed in the lowlands to a maximum of > 2,200 mm in upper region. The study area receives total average annual precipitation of approximately 1,200 mm. The average temperature in the basin ranges between 9°C to 40°C, where the minimum temperature is usually high mainly because of the lower plains of the basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.5 Climate > Ganga Basin", "section_headings": ["2. STUDY AREA", "2.5 Climate", "Ganga Basin"], "chunk_type": "text", "line_start": 151, "line_end": 153, "token_count_estimate": 182, "basins": ["Ganga"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cd10b15b11ed4825", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 2. STUDY AREA > 2.5 Climate > Indus Basin\nType: text\n\nClimate over the Indus River basin varies from subtropical arid and semiarid to temperate subhumid on the plains of Sindh and Punjab provinces to alpine in the mountainous highlands of the north. Annual precipitation ranges between 100 and 500 mm in the lowlands to a maximum of 2,000 mm on mountain slopes. In the lower plains, December to February is the cold season and mean monthly temperatures vary from 14°C to 20°C. Mean monthly temperatures during March to June vary from 42°C to 44°C. Whereas, in the upper plains mean temperature ranges from 23°C to 49°C during summer and from 2°C to 23°C during winter.\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "2. STUDY AREA > 2.5 Climate > Indus Basin", "section_headings": ["2. STUDY AREA", "2.5 Climate", "Indus Basin"], "chunk_type": "text", "line_start": 155, "line_end": 164, "token_count_estimate": 211, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa61a03c69a4681c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 3. DATA USED\nType: text\n\nEarth observation satellites capture the data repeatedly in various spectral ranges and at different spatial and radiometric resolutions. For inventorying glacial lakes, high to medium resolution datasets are proved to be useful by many research studies (Bolch et al., 2010; Mergili et al., 2013; Wang et al., 2013; Zhang et al., 2015; Gupta et al., 2019, Guru et al., 2019). Data captured between September and December were mostly used because the presence of snow or cloud cover during this period is minimum. USGS satellite data of Landsat 5 and 7 (TM and ETM+) has been used widely for mapping glacial lakes due to free accessibility. Whereas, IRS satellite data from sensors of AWiFS, LISS-III, LISS-IV has also been used for such inventory.\n\nIn the present study, high resolution Resourcesat-2 LISS-IV satellite images with spatial resolution of 5.8 m covering a swath of 70 × 70 Km have been used for inventorying glacial lakes. Maximum of the images used for inventorying were of 2016-2021 (71.54%) and remaining images procured were of previous years due to non-availability of cloud-free and snow-free images for the recent years. Majority of images were of September and December months (76.32%) due to less snow and cloud cover, and rest 23.68% images of other months. Figure 4 shows the layout of the RS-2 LISS-IV scenes (path-wise) procured for the Indian Himalayan River basins along with its details in Table 2. The layout of satellite scenes is divided into paths (shown in separate colours) and rows (row numbers shown in the layout).\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 166, "line_end": 182, "token_count_estimate": 431, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f25389b0edfc98c2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 3. DATA USED\nType: table\nTable: Table 2: Details of satellite scenes used for inventory\n\n| | Other Months | Sep - Dec | Total |\n| :--- | :---: | :---: | :---: |\n| Prior to 2016 | 33 | 80 | 113 |\n| 2016-21 | 61 | 223 | 284 |\n| **Total** | **94** | **303** | **397** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "table", "table_caption": "Table 2: Details of satellite scenes used for inventory", "columns": ["", "Other Months", "Sep - Dec", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 183, "line_end": 187, "token_count_estimate": 129, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "40c3616f345e7276", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 3. DATA USED\nType: text\n\nDigital Elevation Model (DEM) of Cartosat satellite with 10 m spatial resolution has been used for topographic information and watershed boundary generation. Figure 5 shows elevation range map of the study area i.e. Indian Himalayan River basins. Other information like names of lakes and rivers has been gathered from digital toposheets available from University of Texas - Toposheet Library at 1:250,000 scale and Tibet Map Institute at 1:100,000 scale (U.S. Army Map Service 1955; Andre 2017).\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 188, "line_end": 198, "token_count_estimate": 155, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "000df80870e58df3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation\nType: text\n\nThe spectral reflectance curve of water in the visible spectrum starts with a low in Blue region (0.4 to 0.5 μm), reaches peak in Green region (0.5 to 0.6 μm), decreases in Red region (0.6 to 0.7 μm) and probably the most distinctive characteristic is the energy absorption at Near InfraRed (NIR) wavelengths. Identifying and delineating water bodies with remote sensing data are carried out easily in near infrared wavelengths because of this absorption property in IR region. However, various physical conditions of water bodies (water depth, turbidity, chlorophyll content, etc.) manifest spectral changes. As a result of various conditions of lakes, the water in satellite images in False Colour Composite (FCC) ranges in appearance from light to dark blue to black. In the case of frozen lakes, it appears white.\n\nGlacial lake sizes are generally small, having circular, semi-circular, or elongated shapes with very fine texture and are generally associated with glaciers in high altitude areas. Certain types of glacial lakes, like erosion and cirque lakes are not necessarily associated with glaciers. Knowledge of the physical characteristics of the glacial lakes, and their associated features is always essential for the interpretation of the images.\n\nSatellite data interpretation can be done using visual image interpretation keys such as colour, size, tone, texture, pattern, association, shape, shadow, and orientation. A number of remote sensing methods had been developed for glacial lake detection and mapping or development of inventory (Kääb 2000; Mool et al., 2001a; Huggel et al., 2002; Huggel et al., 2006; Ives et al., 2010). Manual or automated lake mapping methods have certain difficulties in identifying the lakes, which are described in the following section. An attempt was made to study the accuracy of mapping of glacial lakes using multiple automated methods along with visual interpretation, the details of which are given in Annexure-1. From this study, it was concluded that visual interpretation method was best accurate method. Hence, in the present study glacial lakes and their different types are identified and mapped using RS-2 LISS-IV multispectral images using visual interpretation method.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation"], "chunk_type": "text", "line_start": 202, "line_end": 208, "token_count_estimate": 548, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9acede6d7013bf3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:\nType: text\n\nGlacial lake identification can be done either using visual interpretation or automatic mapping methods. The automatic mapping procedures have limitations due to varying terrain conditions like lakes situated in the shadow portions of mountains, presence of snow cover, cloud cover, and partly frozen lakes, etc. In the presence of snow cover on the glacier tongue or glacier’s ablation area where many Supra-glacial lakes may be present, both methods have limitations and difficulties.\n\nAs lake water absorbs the incident radiation making it appear in darker tone and colour in the standard FCC of satellite data, similar response also prevails over shadow region of clouds or mountains on surface, which may lead to incorrect mapping. In fact, a mountain shadow covering a lake partly/completely within its vicinity, making it difficult to accurately map the lake boundary.\n\nMany lakes due to inclement terrain condition, can be under shadow of high peaks and will get missed in both ways of mapping. On the contrary, a lake can also present in white colour while it is in frozen form due to cold\n\nweather conditions over the area, then definitely it will not get classified while automatic mapping. Whereas, frozen lakes can be identified and mapped using visual interpretation to some extent.\n\n**Challenges in Automatic Mapping:**\n\nIn the IHR, due to high and inclement terrain surface and due to near vertical acquisition of satellite images, some lakes get covered with shadows of mountains, which create problems in identifying glacial lakes. Also, identification of lakes with high turbidity, partial ice-covered lakes and the lakes in shadow areas are misclassified by automatic methods. Glacial lake mapping is always a semi-automatic approach because even after applying any of those methods, it should always be followed by the post processing i.e. correcting the errors using visual interpretation. Even in all cases, automatic mapping will never give the exact and accurate boundary of the lake, leading to necessary manual corrections.\n\n**Reasoning for Visual Interpretation:**\n\nAlthough automatic mapping methods can speed up the detection of glacial lakes, but these methods could not be applied to the entire Himalayan region due to lot of variations in satellite scenes (seasons/years) and problems mentioned above. For example, if lakes are frozen or covered with snow or cloud and lies in a shadow area, they cannot be detected using these automatic methods. In such cases, the manual interpretation method will be helpful to map these lakes. Thus, any mapping of glacial lakes can be automated up to a certain extent only. So, visual image interpretation keys and technique will give accurate results and avoids misclassification. Therefore, in this present study, glacial lakes and its type identification, and its mapping for the entire Indian Himalayan River Basins has been done manually using visual interpretation. High resolution satellite data available on Bhuvan/Google Earth has been used on need basis in finalizing various features of glacial lake database.\n\n**Limitations:**\n\nThe RS-2 LISS-IV MX data used for glacial lake database preparation sporadically covered with cloud and seasonal/permanent snow. Also, the Himalayan region being highly varying topography with steep slopes, the satellite data has hill shadows. Thus, few glacial lakes would not have been mapped owing to the following constraints:\n* Presence of snow or cloud over the glacial lakes\n* Glacial lakes under frozen condition\n* Glacial lakes under mountain shadow", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Difficulties in Lake Identification:"], "chunk_type": "text", "line_start": 210, "line_end": 234, "token_count_estimate": 839, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54f097d431ff0853", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: text\n\nVarious researchers have proposed glacial lakes classification schemes based on dam type, process of lake formation, topographic feature, and geographical position (Hewitt 1982; Liu and Sharma 1988; Clague and Evans 2000; Mool et al., 2001a, 2001b). Lakes located on the glacier surface can be mapped using satellite data, but there are englacial and subglacial lakes that may also exist, but cannot be mapped from aerial/optical satellite images, requires ground-based instrument (Yao et al., 2018). Majorly surface glacial lakes are classified in 4 classes and 10 subclasses, i.e. Moraine-dammed lake, Ice-dammed lake, Glacier Erosion lake (also known as Bed-rock lake), and Other Glacial lake. Two-character symbol has been used for glacial lake classification, in which first letter (uppercase) represents lake type and second letter (lowercase) within brackets represents lake subtype, for example, M(e) for End-moraine dammed lake. Details of types of lakes are given in Table 3 and their appearance in satellite images are shown in Figure 6.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "text", "line_start": 236, "line_end": 240, "token_count_estimate": 305, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e50ad0d009f9be19", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: table\nTable: Table 3: Glacial lake types and their identification keys\n\n| S. No. | Lake Type | Lake Subtype | Code | Identification Keys |\n|---|---|---|---|---|\n| 1 | Moraine-dammed Lake | End-moraine Dammed Lake | M(e) | Lake dammed by end (terminal) moraines |\n| 2 | Moraine-dammed Lake | Lateral Moraine Dammed Lake | M(l) | Lake dammed by lateral moraine(s) not in contact with glacial ice |\n| 3 | Moraine-dammed Lake | Lateral Moraine Dammed Lake (with Ice) | M(lg) | Lake dammed by lateral moraine(s) in contact with glacial ice |\n| 4 | Moraine-dammed Lake | Other Moraine Dammed Lake | M(o) | Lake dammed by other moraines |\n| 5 | Ice-dammed Lake | Supra-glacial Lake | I(s) | Pond or lake on the surface of a glacier |\n| 6 | Ice-dammed Lake | Glacier Ice-dammed Lake | I(d) | Lake dammed by glacier ice with no lateral moraines |\n| 7 | Glacier Erosion Lake | Cirque Erosion Lake | E(c) | A small pond occupying a cirque |\n| 8 | Glacier Erosion Lake | Glacier Trough Valley Erosion Lake | E(v) | Lakes formed in the glacier trough as a result of the glacier erosion process |\n| 9 | Glacier Erosion Lake | Other Glacial Erosion Lake | E(o) | Bodies of water occupying depressions formed by the glacial erosion process |\n| 10 | Other Glacial Lake | Other Glacial Lake | O | Lakes formed in a glaciated valley, and fed by glacial melt, but damming material not directly part of the glacial process |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "table", "table_caption": "Table 3: Glacial lake types and their identification keys", "columns": ["S. No.", "Lake Type", "Lake Subtype", "Code", "Identification Keys"], "table_row_start": 1, "table_row_end": 10, "line_start": 241, "line_end": 252, "token_count_estimate": 534, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b86bd67bad4bae5c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4.3 Lake Attribute Information\nType: text\n\nA total of 22 attributes has been given to all mapped lake features in the geodatabase, which are broadly consisting information grouped in five different categories as shows in Table 4.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4.3 Lake Attribute Information", "section_headings": ["4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 256, "line_end": 260, "token_count_estimate": 61, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1c194b94d463971a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4.3 Lake Attribute Information\nType: table\nTable: Table 4: Details of glacial lake attributes\n\n| S.No. | Category | Attribute |\n|---|---|---|\n| 1 | Hydrological | Basin, subbasin, river, lake name |\n| 2 | Geometrical | Maximum length, mean width, surface area |\n| 3 | Geographical | Latitude, longitude, region, state, district, toposheet 250k, toposheet 50k |\n| 4 | Topographical | Elevation, aspect |\n| 5 | Lake Information | Feature types, glacial lake type, lake ID |\n| 6 | Data Source Information | Source of database, source of elevation, date of pass |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4.3 Lake Attribute Information", "section_headings": ["4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 4: Details of glacial lake attributes", "columns": ["S.No.", "Category", "Attribute"], "table_row_start": 1, "table_row_end": 6, "line_start": 261, "line_end": 268, "token_count_estimate": 196, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "068a0612efc1bbda", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4.3 Lake Attribute Information\nType: text\n\nTypically, lake ID is given in 12 alpha-numeric character format like “0378A1303118”, where first two digits ‘03’ refers to Basin code which is Brahmaputra (01-Indus and 02- Ganga), next five characters ‘78A13’ refers to the 1:250,000 (78A) and 1:50,000 (78A13) scale SOI Toposheet number, and the last five digits refers to the sequential number of each lake sorted from top left to bottom right. A typical example of the glacial lake database generated is given below in Table 5 along with fields and format.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4.3 Lake Attribute Information", "section_headings": ["4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 269, "line_end": 273, "token_count_estimate": 160, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0378A1303118", "78A13"]}}
{"id": "3283363ca922bcc3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4.3 Lake Attribute Information\nType: table\nTable: Table 5: Typical example of glacial lake attribute database\n\n| S. No. | Database Fields | Type | Format / Unit | Lake Attribute |\n|---|---|---|---|---|\n| 1 | ID No | String | Text | 0378A1303118 |\n| 2 | Toposheet 250K | String | Text | 78A |\n| 3 | Toposheet 50K | String | Text | 78A13 |\n| 4 | Latitude* | Float | Decimal Degree | 27.990 |\n| 5 | Longitude* | Float | Decimal Degree | 88.816 |\n| 6 | Basin | String | Text | Brahmaputra |\n| 7 | Subbasin | String | Text | Teesta |\n| 8 | River | String | Text | Teesta River |\n| 9 | Type (GL/WB) | String | Text | Glacial Lake |\n| 10 | Name | String | Text | Khangchung Tso |\n| 11 | Glacial Lake Type | String | Text | M(e): End-moraine Dammed Lake |\n| 12 | Surface Area | Float | ha | 174.29 |\n| 13 | Length | Float | Km | 3.13 |\n| 14 | Mean Width | Float | Km | 0.75 |\n| 15 | Elevation | Integer | m (amsl) | 5303 |\n| 16 | Aspect | String | Text | NW |\n| 17 | Source of Database | String | Text | RS-2 LISS-IV |\n| 18 | Date of Pass | Date | DDMMYYYY | 17112016 |\n| 19 | Source of Elevation | String | Text | Cartosat DEM |\n| 20 | Region | String | Text | India |\n| 21 | State | String | Text | Sikkim |\n| 22 | District | String | Text | North Sikkim |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4.3 Lake Attribute Information", "section_headings": ["4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 5: Typical example of glacial lake attribute database", "columns": ["S. No.", "Database Fields", "Type", "Format / Unit", "Lake Attribute"], "table_row_start": 1, "table_row_end": 22, "line_start": 274, "line_end": 297, "token_count_estimate": 581, "basins": ["Brahmaputra"], "subbasins": ["Teesta"], "countries": ["India"], "lake_ids": ["0378A1303118", "17112016", "78A13"]}}
{"id": "902fc8e241ce4d08", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 4.3 Lake Attribute Information\nType: text\n\n\\* Latitude and longitude has been taken at the centroid of the lake", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "4.3 Lake Attribute Information", "section_headings": ["4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 298, "line_end": 300, "token_count_estimate": 39, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abb5ee51c925edab", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS\nType: text\n\nThe mapped glacial lakes are analyzed for their distribution in terms of area, type, and elevation, at catchment, basin, administrative and transboundary level. Area of mapped glacial lakes is ranging from a minimum of 0.25 ha to a maximum of 2,658.49 ha in Brahmaputra basin, 540.55 ha in Ganga basin, and 262.56 ha in Indus basin. Details of glacial lakes of size ≥ 10 ha inventoried for the Himalayan River catchment is given in Annexure-II. The results are discussed in subsequent sections:", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS", "section_headings": ["5. RESULTS"], "chunk_type": "text", "line_start": 302, "line_end": 304, "token_count_estimate": 154, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dac30aca347e5224", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-wise Distribution\nType: text\n\nA total of 28,043 glacial lakes (≥ 0.25 ha) were identified and mapped using RS-2 LISS-IV images for the entire Himalayan River catchment, with a total lake water spread area of 1,31,070.90 ha. Table 6 and Figure 7 shows the area range-wise distribution of glacial lakes for the entire catchment. About 23,167 (82.61%) lakes are with < 5 ha lake area contributing to 23.72% of total lake area. The remaining lakes with > 5 ha in size are 4,876 (17.39%) contributing to 76.28% of total lake area in the basin. Details of lakes ≥ 50 ha is given in Annexure-III.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 308, "line_end": 312, "token_count_estimate": 190, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30f8a27787200b85", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-wise Distribution\nType: table\nTable: Table 6: Area range-wise distribution of Glacial Lakes (GL) in Indian Himalayan River Basins\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 6,284 | 2,244.06 | 1.71 |\n| 2 | 0.5 - 1 | 6,252 | 4,473.30 | 3.41 |\n| 3 | 1 - 5 | 10,631 | 24,370.94 | 18.59 |\n| 4 | 5 - 10 | 2,445 | 17,180.97 | 13.11 |\n| 5 | 10 - 50 | 2,132 | 41,281.30 | 31.50 |\n| 6 | > 50 | 299 | 41,520.33 | 31.68 |\n| | **Total** | **28,043** | **1,31,070.90** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 6: Area range-wise distribution of Glacial Lakes (GL) in Indian Himalayan River Basins", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 313, "line_end": 321, "token_count_estimate": 299, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "904438bca535c3aa", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 322, "line_end": 327, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cfaec3a3d3692b6d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the entire Indian Himalayan River Basins is given in Table 7 and Figure 8. Out of 10 types of lakes described above, all types of lakes are present in the catchment. Out of 10 types of glacial lakes, Other Glacial Erosion lakes are found to be the maximum with 16,106 (57.43%) occupying a total lake extent of 61,228.37 ha at 46.71% in the Indian Himalayan River basins. Two other types of lake, namely, Other Moraine Dammed and Other Glacial lakes are 5,664 (20.20%) and 1,798 (6.41%), extend over an area of 15,159.27 ha (11.57%) and 15,331.89 ha (11.70%) respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 329, "line_end": 333, "token_count_estimate": 211, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9ee9816cd14d5870", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-wise Distribution\nType: table\nTable: Table 7: Type-wise distribution of GL in Indian Himalayan River basins\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 1,058 | 21,479.58 | 16.39 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 363 | 1,145.38 | 0.87 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 56 | 118.33 | 0.09 |\n| 4 | M(o) | Other Moraine Dammed Lake | 5,664 | 15,159.27 | 11.57 |\n| 5 | I(s) | Supra-glacial Lake | 1,698 | 1,438.32 | 1.10 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 5 | 239.48 | 0.18 |\n| 7 | E(c) | Cirque Erosion Lake | 1,286 | 11,467.10 | 8.75 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 9 | 3,463.16 | 2.64 |\n| 9 | E(o) | Other Glacial Erosion Lake | 16,106 | 61,228.37 | 46.71 |\n| 10 | O | Other Glacial Lake | 1,798 | 15,331.89 | 11.70 |\n| | | **Total** | **28,043** | **1,31,070.90** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 7: Type-wise distribution of GL in Indian Himalayan River basins", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 334, "line_end": 346, "token_count_estimate": 486, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93d416dc6ad6d4d5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 347, "line_end": 353, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "319a5692b7919666", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 8 and Figure 9. The lakes with < 5 ha in size (82.61%) are dominant with Other Glacial Erosion Lake type (58.25%) followed by Other Moraine Dammed (21.78%) and Other Glacial Lake (6.46%). The lakes with > 5 ha (17.39%) are dominated by Other Glacial Erosion lakes (53.57%) followed by Cirque Erosion Lake (13.97%) and Other Moraine Dammed Lake (12.69%). All types of Moraine-dammed glacial lakes, which constitute about 25.46% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 355, "line_end": 359, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb92a2a8dd262639", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 8: Area range-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 60 | 102 | 18 | 1,513 | 939 | 0 | 24 | 0 | 3,155 | 473 | 6,284 |\n| 2 | 0.5 - 1 | 68 | 99 | 10 | 1,488 | 504 | 1 | 65 | 0 | 3,597 | 420 | 6,252 |\n| 3 | 1 - 5 | 353 | 123 | 23 | 2,044 | 223 | 3 | 516 | 1 | 6,742 | 603 | 10,631 |\n| 4 | 5 - 10 | 179 | 15 | 3 | 369 | 20 | 0 | 331 | 0 | 1,422 | 106 | 2,445 |\n| 5 | 10 - 50 | 301 | 19 | 2 | 231 | 12 | 0 | 337 | 1 | 1,089 | 140 | 2,132 |\n| 6 | > 50 | 97 | 5 | 0 | 19 | 0 | 1 | 13 | 7 | 101 | 56 | 299 |\n| | **Total** | **1,058** | **363** | **56** | **5,664** | **1,698** | **5** | **1,286** | **9** | **16,106** | **1,798** | **28,043** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 8: Area range-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 360, "line_end": 368, "token_count_estimate": 529, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4d2610f8a1d6eb18", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Elevation range-wise Distribution**\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 9 and Figure 10 shows the distribution of the glacial lakes in the Himalayan River Catchment as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 26,103 (93.08%) with total lake area of 1,15,750.40 ha (88.31%) and remaining 6.92% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 369, "line_end": 378, "token_count_estimate": 220, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc5040534afa5ee2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 9: Elevation range-wise distribution of GL in Indian Himalayan River Basins\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 33 | 209.78 | 0.16 |\n| 2 | 3,001 - 4,000 | 1,907 | 15,110.70 | 11.53 |\n| 3 | 4,001 - 5,000 | 14,322 | 68,201.49 | 52.03 |\n| 4 | > 5,000 | 11,781 | 47,548.91 | 36.28 |\n| | **Total** | **28,043** | **1,31,070.90** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 9: Elevation range-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 379, "line_end": 385, "token_count_estimate": 251, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91ec31ddaeb1215c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 10 and Figure 11. It is noted that, about 42.01% of glacial lakes (11,781) are situated in very high-altitude range i.e. > 5,000 m amsl, which also constitutes total lake area within that range i.e. 36.28%. However, very few glacial lakes (33) lie below 3,000 m amsl, has maximum of its lakes with 0.25 - 5 ha lake area range. Figure 11 shows that maximum of lakes lying in very high-altitude range is of size ranging 1 - 5 ha (i.e. 4,333), followed by lakes in high altitude range within in 0.25 – 0.5 ha (i.e. 2,978).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 386, "line_end": 396, "token_count_estimate": 253, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "317faf322c0392e1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 10: Area range vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000: No. of lakes | Elevation Range (m) up to 3,000: Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000: No. of lakes | Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000: No. of lakes | Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha) | Elevation Range (m) > 5,000: No. of lakes | Elevation Range (m) > 5,000: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 10 | 3.48 | 338 | 122.49 | 2,958 | 1,061.35 | 2,978 | 1,056.75 | 6,284 | 2,244.06 |\n| 2 | 0.5 - 1 | 5 | 3.26 | 282 | 200.37 | 3,032 | 2,178.56 | 2,933 | 2,091.14 | 6,252 | 4,473.30 |\n| 3 | 1 - 5 | 8 | 22.25 | 703 | 1,729.26 | 5,587 | 12,977.12 | 4,333 | 9,642.23 | 10,631 | 24,370.94 |\n| 4 | 5 - 10 | 2 | 15.64 | 263 | 1,885.54 | 1,378 | 9,713.04 | 802 | 5,566.77 | 2,445 | 17,180.97 |\n| 5 | 10 - 50 | 8 | 165.17 | 286 | 5,875.04 | 1,215 | 23,137.02 | 623 | 12,104.00 | 2,132 | 41,281.30 |\n| 6 | > 50 | 0 | 0.00 | 35 | 5,297.95 | 152 | 19,134.18 | 112 | 17,087.95 | 299 | 41,520.33 |\n| | **Total** | **33** | **209.78** | **1,907** | **15,110.70** | **14,322** | **68,201.49** | **11,781** | **47,548.91** | **28,043** | **1,31,070.90** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 10: Area range vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000: No. of lakes", "Elevation Range (m) up to 3,000: Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000: No. of lakes", "Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000: No. of lakes", "Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha)", "Elevation Range (m) > 5,000: No. of lakes", "Elevation Range (m) > 5,000: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 397, "line_end": 405, "token_count_estimate": 726, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4850abcf9fa725e6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 406, "line_end": 409, "token_count_estimate": 56, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f4bd78d5f4ee9b07", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per type-wise vs. elevation range-wise, given in Table 11 and Figure 12. The dominant lake type in the Indian Himalayan River basin i.e., Other Glacial Erosion lakes (57.43%) are predominantly located in the elevation range of 4,001 - 5,000 m (60.03%). The two other dominant lake types, namely, Other Moraine Dammed and Other Glacial lakes are mostly distributed in both 4,001 - 5,000 m and > 5,000 m elevation ranges. 70.12% of Moraine-dammed glacial lakes, which constitute 25.46% of the total lakes, lies in the very high-altitude range of > 5,000 m amsl. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 13.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 411, "line_end": 415, "token_count_estimate": 234, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "79aea4ba62d74951", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 11: Type-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins\n\n| S.No. | Elevation Range (m) | Types of Glacial Lake M(e) | Types of Glacial Lake M(l) | Types of Glacial Lake M(lg) | Types of Glacial Lake M(o) | Types of Glacial Lake I(s) | Types of Glacial Lake I(d) | Types of Glacial Lake E(c) | Types of Glacial Lake E(v) | Types of Glacial Lake E(o) | Types of Glacial Lake O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 2 | 1 | 0 | 10 | 11 | 0 | 0 | 0 | 5 | 4 | 33 |\n| 2 | 3,001 - 4,000 | 34 | 35 | 0 | 75 | 270 | 0 | 207 | 3 | 1,138 | 145 | 1,907 |\n| 3 | 4,001 - 5,000 | 326 | 207 | 15 | 1,429 | 841 | 0 | 905 | 5 | 9,668 | 926 | 14,322 |\n| 4 | > 5,000 | 696 | 120 | 41 | 4,150 | 576 | 5 | 174 | 1 | 5,295 | 723 | 11,781 |\n| | **Total** | **1,058** | **363** | **56** | **5,664** | **1,698** | **5** | **1,286** | **9** | **16,106** | **1,798** | **28,043** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 11: Type-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S.No.", "Elevation Range (m)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 416, "line_end": 422, "token_count_estimate": 515, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d84dcefc1eb015fc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.1 Indian Himalayan River Basins Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Indian Himalayan River Basins Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 423, "line_end": 436, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1c4731fde7db838", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > 5.2.1 Brahmaputra Basin\nType: text\n\nTopographically River Brahmaputra is unique in terms of its diverse environment as it has cold plateau in Tibet, rainy Himalayan region, alluvial lands of Assam and the large deltaic plains of Bangladesh. The Brahmaputra River also known as Yarlung Tsangpo (in Tibet) originates in the glacier mass from the Kailash ranges of Himalayas at an altitude of 5,150 m south of the lake 'Konggyu Tsho'. The river flows through a length of 2,900 Km out of which 916 Km in the Indian Territory and joins finally in the Bay of Bengal. The catchment area receives number of tributaries at its north and south banks especially in Indian region. The Brahmaputra River basin from its origin to foothills of Himalayas with a catchment area of 3,99,833 Sq.Km is considered in the present study, which extends from latitude 26.70º N to 32.70º N and from longitude 82.00º E to 97.77º E. A total of 18,001 glacial lakes has been mapped, covering a total area of 92,990.74 ha (Figure 14).\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > 5.2.1 Brahmaputra Basin", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "5.2.1 Brahmaputra Basin"], "chunk_type": "text", "line_start": 440, "line_end": 444, "token_count_estimate": 314, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "46f3611cc7ac842d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nA total of 18,001 glacial lakes (≥ 0.25 ha) were identified and mapped using RS-2 LISS-IV images for the entire Brahmaputra River basin, with a total lake water spread area of 92,990.74 ha. Table 12 and Figure 15 shows the area range-wise distribution of glacial lakes for the entire basin. About 14,499 (80.55%) lakes are with < 5 ha lake area contributing to 21.92% of total lake area. The remaining lakes with > 5 ha in size are 3,502 (19.45%) contributing to 78.08% of total lake area in the basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 446, "line_end": 450, "token_count_estimate": 175, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "76a44d24b1d23aa4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 12: Area range-wise distribution of Glacial Lakes (GL) in Brahmaputra River basin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3,627 | 1,294.83 | 1.39 |\n| 2 | 0.5 - 1 | 3,856 | 2,771.84 | 2.98 |\n| 3 | 1 - 5 | 7,016 | 16,315.71 | 17.55 |\n| 4 | 5 - 10 | 1,749 | 12,348.79 | 13.28 |\n| 5 | 10 - 50 | 1,546 | 30,040.25 | 32.31 |\n| 6 | > 50 | 207 | 30,219.32 | 32.50 |\n| | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 12: Area range-wise distribution of Glacial Lakes (GL) in Brahmaputra River basin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 451, "line_end": 459, "token_count_estimate": 292, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "20071b8c85793da5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 460, "line_end": 465, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "83082db6d96be464", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the entire Brahmaputra River basin is given in Table 13 and Figure 16. Out of 10 types of lakes described above, all types of lakes are present in the basin. Out of 10 types of glacial lakes, Other Glacial Erosion lakes are found to be the maximum with 11,846 (65.81%) occupying a total lake extent of 48,368.91 ha at 52.01% in the basin. Two other types of lake, namely, Other Moraine Dammed and Other Glacial lakes are 3,019 (16.77%) and 1,481 (8.23%), extend over an area of 9,457.52 ha (10.17%) and 12,049.76 ha (12.96%) respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 467, "line_end": 471, "token_count_estimate": 203, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8a4fe70ccf81eb4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 13: Type-wise distribution of GL in Brahmaputra River basin\n\n| S.No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 391 | 10,620.51 | 11.42 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 38 | 259.67 | 0.28 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 2 | 0.93 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 3,019 | 9,457.52 | 10.17 |\n| 5 | I(s) | Supra-glacial Lake | 272 | 263.13 | 0.28 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 2 | 2.50 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 943 | 8,521.90 | 9.16 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 7 | 3,445.91 | 3.71 |\n| 9 | E(o) | Other Glacial Erosion Lake | 11,846 | 48,368.91 | 52.01 |\n| 10 | O | Other Glacial Lake | 1,481 | 12,049.76 | 12.96 |\n| | | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 13: Type-wise distribution of GL in Brahmaputra River basin", "columns": ["S.No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 472, "line_end": 484, "token_count_estimate": 475, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "59be0fc62eb65b58", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 485, "line_end": 487, "token_count_estimate": 52, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "67b287e6dba3c2b7", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 14 and Figure 17. The lakes with < 5 ha in size (80.55%) are dominant with Other Glacial Erosion Lake type (67.58%) followed by Other Moraine Dammed (18.02%) and Other Glacial Lake (8.66%). The lakes with > 5 ha (19.45%) are dominated by Other Glacial Erosion lakes (58.45%) followed by Cirque Erosion Lake (14.85%) and Other Moraine Dammed Lake (11.62%). All types of Moraine-dammed glacial lakes, which constitute about 18.99% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 489, "line_end": 493, "token_count_estimate": 187, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "25b4a0c8ff2cf08d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 14: Area range-wise vs. Type-wise distribution of GL in Brahmaputra River basin\n\n| S.No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 6 | 6 | 1 | 718 | 153 | 0 | 13 | 0 | 2,324 | 406 | 3,627 |\n| 2 | 0.5 - 1 | 12 | 6 | 1 | 743 | 75 | 1 | 46 | 0 | 2,636 | 336 | 3,856 |\n| 3 | 1 - 5 | 93 | 17 | 0 | 1,151 | 38 | 1 | 364 | 0 | 4,839 | 513 | 7,016 |\n| 4 | 5 - 10 | 78 | 3 | 0 | 236 | 4 | 0 | 250 | 0 | 1,092 | 86 | 1,749 |\n| 5 | 10 - 50 | 152 | 5 | 0 | 156 | 2 | 0 | 262 | 0 | 868 | 101 | 1,546 |\n| 6 | > 50 | 50 | 1 | 0 | 15 | 0 | 0 | 8 | 7 | 87 | 39 | 207 |\n| | **Total** | **391** | **38** | **2** | **3,019** | **272** | **2** | **943** | **7** | **11,846** | **1,481** | **18,001** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 14: Area range-wise vs. Type-wise distribution of GL in Brahmaputra River basin", "columns": ["S.No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 494, "line_end": 502, "token_count_estimate": 516, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "997629b68d189ef3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 503, "line_end": 506, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "086b4c734fc9b03d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 15 and Figure 18 shows the distribution of the glacial lakes in the Brahmaputra basin as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 16,802 (93.34%) with total lake area of 81,290.89 ha (87.41%) and remaining 6.66% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 508, "line_end": 512, "token_count_estimate": 192, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c25b4bb31e00fde5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 15: Elevation range-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 11 | 152.21 | 0.16 |\n| 2 | 3,001 - 4,000 | 1,188 | 11,547.28 | 12.42 |\n| 3 | 4,001 - 5,000 | 9,670 | 52,525.25 | 56.48 |\n| 4 | > 5,000 | 7,132 | 28,765.99 | 30.93 |\n| | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 15: Elevation range-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 513, "line_end": 519, "token_count_estimate": 245, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "47b2cc914a841e1c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 520, "line_end": 526, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0e0080087a883b78", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 16 and Figure 19. It is noted that, about 39.62% of glacial lakes (7,132) are situated in very high-altitude range i.e. > 5,000 m amsl, which also constitutes total lake area within that range i.e. 30.93%. However, very few glacial lakes (11) lie below 3,000 m amsl, has maximum of its lakes with 10 - 50 ha lake area range. Figure 19 shows that maximum of lakes lying in very high-altitude range is of size ranging 1 - 5 ha (i.e. 2,728), followed by lakes in high altitude range within in 1 - 5 ha (i.e. 3,772).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 528, "line_end": 532, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "79e8bc3750541688", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 16: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Lake Area Range (ha) | Elevation Range: up to 3,000 (m) - No. of lakes | Elevation Range: up to 3,000 (m) - Total Lake Area (ha) | Elevation Range: 3,001 - 4,000 (m) - No. of lakes | Elevation Range: 3,001 - 4,000 (m) - Total Lake Area (ha) | Elevation Range: 4,001 - 5,000 (m) - No. of lakes | Elevation Range: 4,001 - 5,000 (m) - Total Lake Area (ha) | Elevation Range: > 5,000 (m) - No. of lakes | Elevation Range: > 5,000 (m) - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 98 | 36.60 | 1,824 | 655.23 | 1,705 | 603.00 | 3,627 | 1,294.83 |\n| 2 | 0.5 - 1 | 1 | 0.64 | 124 | 90.74 | 1,966 | 1,417.19 | 1,765 | 1,263.30 | 3,856 | 2,771.84 |\n| 3 | 1 - 5 | 3 | 10.07 | 513 | 1,299.91 | 3,772 | 8,855.14 | 2,728 | 6,150.52 | 7,016 | 16,315.71 |\n| 4 | 5 - 10 | 1 | 5.77 | 219 | 1,576.08 | 1,035 | 7,306.45 | 494 | 3,460.51 | 1,749 | 12,348.79 |\n| 5 | 10 - 50 | 6 | 135.75 | 209 | 4,288.89 | 952 | 18,351.30 | 379 | 7,264.24 | 1,546 | 30,040.25 |\n| 6 | > 50 | 0 | 0.00 | 25 | 4,255.00 | 121 | 15,939.70 | 61 | 10,024.36 | 207 | 30,219.32 |\n| **Total** | | **11** | **152.21** | **1,188** | **11,547.28** | **9,670** | **52,525.25** | **7,132** | **28,765.99** | **18,001** | **92,990.74** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 16: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range: up to 3,000 (m) - No. of lakes", "Elevation Range: up to 3,000 (m) - Total Lake Area (ha)", "Elevation Range: 3,001 - 4,000 (m) - No. of lakes", "Elevation Range: 3,001 - 4,000 (m) - Total Lake Area (ha)", "Elevation Range: 4,001 - 5,000 (m) - No. of lakes", "Elevation Range: 4,001 - 5,000 (m) - Total Lake Area (ha)", "Elevation Range: > 5,000 (m) - No. of lakes", "Elevation Range: > 5,000 (m) - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 533, "line_end": 541, "token_count_estimate": 719, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "69d0544640699c16", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 542, "line_end": 548, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db511f601cf64070", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > 5.2.2 Ganga Basin\nType: text\n\nThe Ganga River originates as the Bhagirathi from the Gangotri group of glaciers in the Himalayas at an elevation of about 7,010 m amsl, in the Uttarkashi district of Uttarakhand, which has been joined by the Alaknanda at Devprayag, and the combined stream assumes the name ‘Ganga’. River flows through the highly terrain mountain region and debouches into the plains at Sukhi (near Rishikesh). It is joined by a large number of tributaries on both the banks in the course of its total run of about 2,525 km before its outfall into the Bay of Bengal. The delta of the Ganga River is said to begin at the Farakka barrage in West Bengal, where the river divides into two arms namely the Padma which flows to Bangladesh and the Ganga which flows through West Bengal. Ganga River basin from its origin to the foothills of Himalayas with a catchment area of 2,47,110 Km² is considered in the present study, which extends from latitude 26.35° N to 31.46° N and from longitude 77.05° E to 88.95° E (Figure 22).\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > 5.2.2 Ganga Basin", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "5.2.2 Ganga Basin"], "chunk_type": "text", "line_start": 550, "line_end": 558, "token_count_estimate": 316, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b71a3df8afb28fb6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nA total of 4,707 glacial lakes ≥ 0.25 ha were identified and mapped using RS-2 LISS-IV images for the entire Ganga River basin, with a total lake water spread area of 20,685.12 ha. Table 18 and Figure 23 shows the area range-wise distribution of glacial lakes for the entire basin. About 4,035 (85.72%) lakes are with < 5 ha lake area contributing to 23.13% of total lake area. The remaining lakes with > 5 ha in size are 672 (14.28%) contributing to 76.87% of total lake area in the basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 560, "line_end": 564, "token_count_estimate": 171, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9da52fa0e0a15f25", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 18: Area range-wise distribution of Glacial Lakes (GL) in Ganga River basin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1,279 | 452.30 | 2.19 |\n| 2 | 0.5 - 1 | 1,157 | 824.04 | 3.98 |\n| 3 | 1 - 5 | 1,599 | 3,508.94 | 16.96 |\n| 4 | 5 - 10 | 315 | 2,184.89 | 10.56 |\n| 5 | 10 - 50 | 299 | 5,938.21 | 28.71 |\n| 6 | > 50 | 58 | 7,776.74 | 37.60 |\n| | **Total** | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 18: Area range-wise distribution of Glacial Lakes (GL) in Ganga River basin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 565, "line_end": 573, "token_count_estimate": 281, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "616493b6b951eb06", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the entire Ganga River basin is given in Table 19 and Figure 24. Out of 10 types of lakes described above, 9 types of lakes are present in the basin except Glacier Trough Valley Erosion Lake. Out of 9 types of glacial lakes, Other Glacial Erosion lakes are found to be the maximum with 1,744 (37.05%) occupying a total lake extent of 4,612.02 ha at 22.30% in the basin. Two other types of lake, namely, Other Moraine Dammed and Supra-glacial lake are 1,740 (36.97%) and 617 (13.11%), extend over an area of 4,489.35 ha (21.70%) and 566.14 ha (2.74%) respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 574, "line_end": 586, "token_count_estimate": 234, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d2a4f9828ce6b52b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 19: Type-wise distribution of GL in Ganga River basin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 260 | 8,591.78 | 41.53 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 100 | 568.12 | 2.75 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 9 | 5.48 | 0.03 |\n| 4 | M(o) | Other Moraine Dammed Lake | 1,740 | 4,489.35 | 21.70 |\n| 5 | I(s) | Supra-glacial Lake | 617 | 566.14 | 2.74 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 1 | 2.45 | 0.01 |\n| 7 | E(c) | Cirque Erosion Lake | 123 | 973.77 | 4.71 |\n| 8 | E(o) | Other Glacial Erosion Lake | 1,744 | 4,612.02 | 22.30 |\n| 9 | O | Other Glacial Lake | 113 | 876.01 | 4.23 |\n| | | **Total** | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 19: Type-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 10, "line_start": 587, "line_end": 598, "token_count_estimate": 444, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06efa9508f20b81a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 20 and Figure 25. The lakes with < 5 ha in size (85.72%) are dominant with Other Moraine Dammed Lake type (38.74%) followed by Other Glacial Erosion (38.64%) and Supra-glacial lake (14.94%). The lakes with > 5 ha (14.28%) are dominated by Other Glacial Erosion lakes (27.53%) followed by End Moraine Dammed Lake (27.23%) and Other Moraine Dammed Lake (26.34%). All types of Moraine-dammed glacial lakes, which constitute about 44.81% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 599, "line_end": 611, "token_count_estimate": 219, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7f3acdb67481ddc5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 20: Area range-wise vs. Type-wise distribution of GL in Ganga River basin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 5 | 22 | 4 | 462 | 354 | 0 | 4 | 400 | 28 | 1,279 |\n| 2 | 0.5 - 1 | 12 | 24 | 4 | 484 | 166 | 0 | 5 | 433 | 29 | 1,157 |\n| 3 | 1 - 5 | 60 | 35 | 1 | 617 | 83 | 1 | 49 | 726 | 27 | 1,599 |\n| 4 | 5 - 10 | 41 | 6 | 0 | 106 | 6 | 0 | 34 | 115 | 7 | 315 |\n| 5 | 10 - 50 | 100 | 9 | 0 | 68 | 8 | 0 | 31 | 65 | 18 | 299 |\n| 6 | > 50 | 42 | 4 | 0 | 3 | 0 | 0 | 0 | 5 | 4 | 58 |\n| | **Total** | **260** | **100** | **9** | **1,740** | **617** | **1** | **123** | **1,744** | **113** | **4,707** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 20: Area range-wise vs. Type-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 612, "line_end": 620, "token_count_estimate": 490, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "74cc4e787d8e3f63", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 621, "line_end": 626, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "257d4c9d02f744ff", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 21 and Figure 26 shows the distribution of the glacial lakes in the Ganga basin as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 4,644 (98.66%) with total lake area of 20,451.70 ha (98.87%) and remaining 1.34% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 628, "line_end": 632, "token_count_estimate": 189, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aeb7a3fa11f7f79a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 21: Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 1 | 9.87 | 0.05 |\n| 2 | 3,001 - 4,000 | 62 | 223.55 | 1.08 |\n| 3 | 4,001 - 5,000 | 1,855 | 7,374.89 | 35.65 |\n| 4 | > 5,000 | 2,789 | 13,076.81 | 63.22 |\n| **Total** | | **4,707** | **20,685.12** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 21: Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 633, "line_end": 639, "token_count_estimate": 237, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "423a0f80e276a87f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 640, "line_end": 642, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "95cb515ec53781f9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 22 and Figure 27. It is noted that, about 59.25% of glacial lakes (2,789) are situated in very high-altitude range i.e. > 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 63.22%. However, very few glacial lakes (63) lie up to 4,000 m amsl, has maximum of its lakes with 0.25 - 0.5 ha lake area range. Figure 27 shows that maximum of lakes lying in very high-altitude range is of size ranging 1 - 5 ha (i.e. 928), followed by lakes in high altitude range within in 1 - 5 ha (i.e. 659).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 644, "line_end": 648, "token_count_estimate": 221, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2221e4d61cb42b5f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Lake Area Range (ha) | Elevation Range up to 3,000 (m) - No. of Lakes | Elevation Range up to 3,000 (m) - Total Lake Area (ha) | Elevation Range 3,001 - 4,000 (m) - No. of Lakes | Elevation Range 3,001 - 4,000 (m) - Total Lake Area (ha) | Elevation Range 4,001 - 5,000 (m) - No. of Lakes | Elevation Range 4,001 - 5,000 (m) - Total Lake Area (ha) | Elevation Range > 5,000 (m) - No. of Lakes | Elevation Range > 5,000 (m) - Total Lake Area (ha) | Total - No. of Lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 21 | 6.90 | 486 | 173.33 | 772 | 272.07 | 1,279 | 452.30 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 17 | 12.44 | 426 | 305.22 | 714 | 506.38 | 1,157 | 824.04 |\n| 3 | 1 - 5 | 0 | 0.00 | 12 | 33.16 | 659 | 1,465.56 | 928 | 2,010.22 | 1,599 | 3,508.94 |\n| 4 | 5 - 10 | 1 | 9.87 | 5 | 34.15 | 137 | 967.00 | 172 | 1,173.87 | 315 | 2,184.89 |\n| 5 | 10 - 50 | 0 | 0.00 | 7 | 136.90 | 128 | 2,392.37 | 164 | 3,408.94 | 299 | 5,938.21 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 19 | 2,071.42 | 39 | 5,705.32 | 58 | 7,776.74 |\n| **Total** | | **1** | **9.87** | **62** | **223.55** | **1,855** | **7,374.90** | **2,789** | **13,076.80** | **4,707** | **20,685.12** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range up to 3,000 (m) - No. of Lakes", "Elevation Range up to 3,000 (m) - Total Lake Area (ha)", "Elevation Range 3,001 - 4,000 (m) - No. of Lakes", "Elevation Range 3,001 - 4,000 (m) - Total Lake Area (ha)", "Elevation Range 4,001 - 5,000 (m) - No. of Lakes", "Elevation Range 4,001 - 5,000 (m) - Total Lake Area (ha)", "Elevation Range > 5,000 (m) - No. of Lakes", "Elevation Range > 5,000 (m) - Total Lake Area (ha)", "Total - No. of Lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 649, "line_end": 657, "token_count_estimate": 669, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "87fcfad080381838", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 658, "line_end": 661, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3598e28fb6930621", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per type-wise vs. elevation range-wise, given in Table 23 and Figure 28. The dominant lake type in the Ganga River basin i.e., Other Glacial Erosion lakes (37.05%) are predominantly located in the elevation range of 4,001 - 5,000 m (52.18%). The two other dominant lake types, namely, Other Moraine Dammed and Supra-glacial lake are mostly distributed in both 4,001 - 5,000 m and > 5,000 m elevation ranges. 75.82% of Moraine-dammed glacial lakes, which constitute 44.81% of the total lakes, lies in the very high-altitude range of > 5,000 m amsl. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 29.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 663, "line_end": 667, "token_count_estimate": 230, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "65c8963c9993d435", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 23: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 3,001 - 4,000 | 3 | 1 | 0 | 8 | 14 | 0 | 1 | 23 | 12 | 62 |\n| 3 | 4,001 - 5,000 | 77 | 54 | 1 | 366 | 286 | 0 | 93 | 910 | 68 | 1,855 |\n| 4 | > 5,000 | 180 | 45 | 8 | 1,366 | 317 | 1 | 29 | 810 | 33 | 2,789 |\n| | **Total** | **260** | **100** | **9** | **1,740** | **617** | **1** | **123** | **1,744** | **113** | **4,707** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 23: Type-wise vs. Elevation range-wise distribution of GL in Ganga River basin", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 668, "line_end": 674, "token_count_estimate": 398, "basins": ["Ganga"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8c168f3f9bcad2a1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > 5.2.3 Indus Basin\nType: text\n\nIndus River originates from Bokhar Chu (glacier) in northern slopes of Mt. Kailash (6,714 m) from the Tibet Autonomous Region of China (Husain, 2012; CWC, 2019). The Indus River follows a long and straight course in Ladakh region (flowing in northwest direction in India), running between the Ladakh and the Zaskar ranges. The gradient of the river is also gentle here. It forms a spectacular gorge near Gilgit (at Bunzi, north of Nanga Parbat Glacier) in Jammu & Kashmir. Downstream, the river passes by the Nanga Parbat Glacier, crossing the Himalayas, it turns into south-west and enters Pakistan near Chillar in the Dardistan region. Finally, the Indus River drains into the Arabian Sea near the port city of Karachi, Pakistan after forming a huge delta. It has a total length of 2,880 Km, of which 709 Km lies in India. The present study area extends from latitude 30.32° N to 37.09° N and from longitude 72.50° E to 82.45° E (Figure 30).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > 5.2.3 Indus Basin", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "5.2.3 Indus Basin"], "chunk_type": "text", "line_start": 678, "line_end": 680, "token_count_estimate": 298, "basins": ["Indus"], "subbasins": ["Gilgit"], "countries": ["China", "India"], "lake_ids": []}}
{"id": "33e7f41d53ac4335", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nA total of 5,335 glacial lakes (≥ 0.25 ha) were identified and mapped using RS-2 LISS-IV images for the entire Indus River basin, with a total lake water spread area of 17,395.03 ha. Table 24 and Figure 31 shows the area range-wise distribution of glacial lakes for the entire basin. About 4,633 (86.84%) lakes are with < 5 ha lake area contributing to 34.04% of total lake area. The remaining lakes with > 5 ha in size are 702 (13.16%) contributing to 65.96% of total lake area in the basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 682, "line_end": 686, "token_count_estimate": 173, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9824ece03cc3016f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 24: Area range-wise distribution of Glacial Lakes (GL) in Indus River basin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 1,378 | 496.93 | 2.86 |\n| 2 | 0.5 - 1 | 1,239 | 877.42 | 5.04 |\n| 3 | 1 - 5 | 2,016 | 4,546.28 | 26.14 |\n| 4 | 5 - 10 | 381 | 2,647.29 | 15.22 |\n| 5 | 10 - 50 | 287 | 5,302.84 | 30.48 |\n| 6 | > 50 | 34 | 3,524.27 | 20.26 |\n| | **Total** | **5,335** | **17,395.03** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 24: Area range-wise distribution of Glacial Lakes (GL) in Indus River basin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 687, "line_end": 695, "token_count_estimate": 296, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "667a17b95ebc3b6a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 696, "line_end": 699, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a39f50866a0528fe", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the entire Indus River basin is given in Table 25 and Figure 32. Out of 10 types of glacial lakes, Other Glacial Erosion lakes are found to be the maximum with 2,516 (47.16%) occupying a total lake extent of 8,247.44 ha at 47.42% in the basin. Two other types of lake, namely, Other Moraine Dammed and Supra-glacial lake are 905 (16.96%) and 809 (15.16%), extend over an area of 1,212.40 ha (6.97%) and 609.05 ha (3.50%) respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 701, "line_end": 705, "token_count_estimate": 177, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72e8fb01929b8e2a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 25: Type-wise distribution of GL in Indus River basin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 407 | 2,267.29 | 13.03 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 225 | 317.59 | 1.83 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 45 | 111.92 | 0.64 |\n| 4 | M(o) | Other Moraine Dammed Lake | 905 | 1,212.40 | 6.97 |\n| 5 | I(s) | Supra-glacial Lake | 809 | 609.05 | 3.50 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 2 | 234.53 | 1.35 |\n| 7 | E(c) | Cirque Erosion Lake | 220 | 1,971.43 | 11.33 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 2 | 17.25 | 0.10 |\n| 9 | E(o) | Other Glacial Erosion Lake | 2,516 | 8,247.44 | 47.42 |\n| 10 | O | Other Glacial Lake | 204 | 2,406.12 | 13.83 |\n| | | **Total** | **5,335** | **17,395.03** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 25: Type-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 706, "line_end": 718, "token_count_estimate": 468, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4aabd813707989ac", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 719, "line_end": 725, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "761b0ae5965b4fae", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 26 and Figure 33. The lakes with < 5 ha in size (86.84%) are dominant with Other Glacial Erosion Lake type (46.10%) followed by Other Moraine Dammed (18.80%) and Supra-glacial lake (17.20%). The lakes with > 5 ha (13.16%) are also dominated by Other Glacial Erosion lakes (54.10%) with remaining 9 types together contributing to 45.90%. All types of Moraine-dammed glacial lakes, which constitute about 29.64% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 727, "line_end": 731, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "382007ba0e273aa2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 26: Area range-wise vs. Type-wise distribution of GL in Indus River basin\n\n| S. No. | Lake Area Range (ha) | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 49 | 74 | 13 | 333 | 432 | 0 | 7 | 0 | 431 | 39 | 1,378 |\n| 2 | 0.5 - 1 | 44 | 69 | 5 | 261 | 263 | 0 | 14 | 0 | 528 | 55 | 1,239 |\n| 3 | 1 - 5 | 200 | 71 | 22 | 276 | 102 | 1 | 103 | 1 | 1,177 | 63 | 2,016 |\n| 4 | 5 - 10 | 60 | 6 | 3 | 27 | 10 | 0 | 47 | 0 | 215 | 13 | 381 |\n| 5 | 10 - 50 | 49 | 5 | 2 | 7 | 2 | 0 | 44 | 1 | 156 | 21 | 287 |\n| 6 | > 50 | 5 | 0 | 0 | 1 | 0 | 1 | 5 | 0 | 9 | 13 | 34 |\n| | **Total** | **407** | **225** | **45** | **905** | **809** | **2** | **220** | **2** | **2,516** | **204** | **5,335** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 26: Area range-wise vs. Type-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 732, "line_end": 740, "token_count_estimate": 586, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "463b804181c8f726", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 741, "line_end": 746, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2170d17890dd4c24", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 27 and Figure 34 shows the distribution of the glacial lakes in the Indus basin as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 4,657 (87.29% of the total lake count) with total lake area of 14,007.46 ha (80.53%) and remaining 12.71% glacial lakes are below 4,000 m elevation", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 748, "line_end": 752, "token_count_estimate": 193, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3608a091054e863", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 27: Elevation range-wise distribution of GL in Indus River basin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 21 | 47.70 | 0.27 |\n| 2 | 3,001 - 4,000 | 657 | 3,339.87 | 19.20 |\n| 3 | 4,001 - 5,000 | 2,797 | 8,301.35 | 47.73 |\n| 4 | > 5,000 | 1,860 | 5,706.11 | 32.80 |\n| | **Total** | **5,335** | **17,395.03** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 27: Elevation range-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 753, "line_end": 759, "token_count_estimate": 241, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a55bc915b6d0f8a3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 760, "line_end": 766, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf7899cebf46f1f4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 28 and Figure 35. It is noted that, more than 50% of glacial lakes (2,797) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 47.73%. However, very few glacial lakes (only 21) lies up to 3,000 m amsl, has maximum of its lakes with 0.25 - 0.5 ha lake area range. Figure 35 shows that maximum of lakes lying-in high-altitude range is of size ranging 1 - 5 ha (i.e. 1,156), followed by lakes in very high altitude range within in 1 - 5 ha (i.e. 677).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 768, "line_end": 772, "token_count_estimate": 227, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd8ac9529327fde9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Indus River basin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 (No. of lakes) | Elevation Range (m): up to 3,000 (Total Lake Area ha) | Elevation Range (m): 3,001 - 4,000 (No. of lakes) | Elevation Range (m): 3,001 - 4,000 (Total Lake Area ha) | Elevation Range (m): 4,001 - 5,000 (No. of lakes) | Elevation Range (m): 4,001 - 5,000 (Total Lake Area ha) | Elevation Range (m): > 5,000 (No. of lakes) | Elevation Range (m): > 5,000 (Total Lake Area ha) | Total (No. of lakes) | Total (Lake Area ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 10 | 3.48 | 219 | 78.99 | 648 | 232.79 | 501 | 181.68 | 1,378 | 496.93 |\n| 2 | 0.5 - 1 | 4 | 2.62 | 141 | 97.19 | 640 | 456.15 | 454 | 321.46 | 1,239 | 877.42 |\n| 3 | 1 - 5 | 5 | 12.18 | 178 | 396.19 | 1,156 | 2,656.42 | 677 | 1,481.49 | 2,016 | 4,546.28 |\n| 4 | 5 - 10 | 0 | 0.00 | 39 | 275.31 | 206 | 1,439.59 | 136 | 932.39 | 381 | 2,647.29 |\n| 5 | 10 - 50 | 2 | 29.42 | 70 | 1,449.25 | 135 | 2,393.35 | 80 | 1,430.82 | 287 | 5,302.84 |\n| 6 | > 50 | 0 | 0.00 | 10 | 1,042.95 | 12 | 1,123.06 | 12 | 1,358.27 | 34 | 3,524.27 |\n| | **Total** | **21** | **47.70** | **657** | **3,339.87** | **2,797** | **8,301.35** | **1,860** | **5,706.11** | **5,335** | **17,395.03** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 (No. of lakes)", "Elevation Range (m): up to 3,000 (Total Lake Area ha)", "Elevation Range (m): 3,001 - 4,000 (No. of lakes)", "Elevation Range (m): 3,001 - 4,000 (Total Lake Area ha)", "Elevation Range (m): 4,001 - 5,000 (No. of lakes)", "Elevation Range (m): 4,001 - 5,000 (Total Lake Area ha)", "Elevation Range (m): > 5,000 (No. of lakes)", "Elevation Range (m): > 5,000 (Total Lake Area ha)", "Total (No. of lakes)", "Total (Lake Area ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 773, "line_end": 781, "token_count_estimate": 684, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b17470c6fe5c6a9d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 782, "line_end": 787, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8d8c29e8873950a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per type-wise vs. elevation range-wise, given in Table 29 and Figure 36. The dominant lake type in the basin i.e., Other Glacial Erosion lakes (i.e. 47.16% in number and 47.41% in extent) are predominantly located in the elevation range of 4,001 - 5,000 m (64.67%). The two other dominant lake types, namely, Other Moraine Dammed and Supra-glacial lake are distributed in both 4,001 - 5,000 m and > 5,000 m elevation ranges. 56.26% of Moraine-dammed glacial lakes, which constitute 29.65% of the total lakes, lies in the very high-altitude range of > 5,000 m amsl. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 37.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 789, "line_end": 793, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6577cab6d852256", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 29: Type-wise vs. Elevation range-wise distribution of GL in Indus River basin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake M(e) | Types of Glacial Lake M(l) | Types of Glacial Lake M(lg) | Types of Glacial Lake M(o) | Types of Glacial Lake I(s) | Types of Glacial Lake I(d) | Types of Glacial Lake E(c) | Types of Glacial Lake E(v) | Types of Glacial Lake E(o) | Types of Glacial Lake O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 2 | 1 | 0 | 6 | 11 | 0 | 0 | 0 | 0 | 1 | 21 |\n| 2 | 3,001 - 4,000 | 20 | 34 | 0 | 41 | 240 | 0 | 48 | 1 | 246 | 27 | 657 |\n| 3 | 4,001 - 5,000 | 102 | 128 | 13 | 345 | 396 | 0 | 125 | 1 | 1,627 | 60 | 2,797 |\n| 4 | > 5,000 | 283 | 62 | 32 | 513 | 162 | 2 | 47 | 0 | 643 | 116 | 1,860 |\n| **Total** | | **407** | **225** | **45** | **905** | **809** | **2** | **220** | **2** | **2,516** | **204** | **5,335** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 29: Type-wise vs. Elevation range-wise distribution of GL in Indus River basin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 794, "line_end": 800, "token_count_estimate": 497, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f3832a2875e57c80", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.2 Basin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Basin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 801, "line_end": 812, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0babcddad7f91bb9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins\nType: text\n\nGlacial lakes in all the three basins namely Brahmaputra, Ganga, and Indus River basin are compared for number of glacial lakes, total lake area, lake types and their elevation ranges in the following sections.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins"], "chunk_type": "text", "line_start": 814, "line_end": 816, "token_count_estimate": 81, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "02068dec17d38a8e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution\nType: text\n\nTable 30 shows the basin-wise distribution of number of glacial lakes and their water spread area for the Indian Himalayan River Basins. Figure 38 shows the basin-wise distribution of number of glacial lakes for the Indian Himalayan River Basins. Lakes are predominantly distributed in Brahmaputra basin (64.19%) followed by Indus basin (19.02%), occupying a total lake extent of 92,990.74 ha and 17,395.03 ha respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-wise Distribution"], "chunk_type": "text", "line_start": 818, "line_end": 822, "token_count_estimate": 142, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "424ccc85df362483", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution\nType: table\nTable: Table 30: Basin-wise distribution of GL in Indian Himlayan River Basins\n\n| S. No. | Basin | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | Brahmaputra | 18,001 | 92,990.74 | 70.95 |\n| 2 | Ganga | 4,707 | 20,685.12 | 15.78 |\n| 3 | Indus | 5,335 | 17,395.03 | 13.27 |\n| | **Total** | **28,043** | **1,31,070.90** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-wise Distribution"], "chunk_type": "table", "table_caption": "Table 30: Basin-wise distribution of GL in Indian Himlayan River Basins", "columns": ["S. No.", "Basin", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 4, "line_start": 823, "line_end": 828, "token_count_estimate": 210, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ec7d61cf8173f85", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution\nType: figure\nFigure: Figure 38: Basin-wise distribution of GL in Indian Himlayan River Basins\n\n**Figure 38: Basin-wise distribution of GL in Indian Himlayan River Basins**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 38: Basin-wise distribution of GL in Indian Himlayan River Basins", "line_start": 830, "line_end": 830, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f68186854af4789d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-wise Distribution"], "chunk_type": "text", "line_start": 831, "line_end": 836, "token_count_estimate": 52, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce6dec34f6e9f949", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-Area range-wise Distribution\nType: text\n\nGlacial lakes have been distributed in all basins for 6 classes of area ranges. Table 31 and Figure 39 shows basin-area range-wise distribution of glacial lakes for the Indian Himalayan River Basins. All basins contain glacial lakes in all area ranges. Majority of lakes > 50 ha i.e. 207 are found to be situated in Brahmaputra basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-Area range-wise Distribution"], "chunk_type": "text", "line_start": 838, "line_end": 842, "token_count_estimate": 128, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "678d57f0eb956336", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-Area range-wise Distribution\nType: table\nTable: Table 31: Basin-wise vs. Area range-wise distribution of GL in Indian Himalayan River Basins\n\n| S. No. | Basin | 0.25 - 0.5 No. of lakes | 0.25 - 0.5 Total Lake Area (ha) | 0.5 - 1 No. of lakes | 0.5 - 1 Total Lake Area (ha) | 1 - 5 No. of lakes | 1 - 5 Total Lake Area (ha) | 5 - 10 No. of lakes | 5 - 10 Total Lake Area (ha) | 10 - 50 No. of lakes | 10 - 50 Total Lake Area (ha) | > 50 No. of lakes | > 50 Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Brahmaputra | 3,627 | 1,294.83 | 3,856 | 2,771.84 | 7,016 | 16,315.71 | 1,749 | 12,348.79 | 1,546 | 30,040.25 | 207 | 30,219.32 |\n| 2 | Ganga | 1,279 | 452.30 | 1,157 | 824.04 | 1,599 | 3,508.94 | 315 | 2,184.89 | 299 | 5,938.21 | 58 | 7,776.74 |\n| 3 | Indus | 1,378 | 496.93 | 1,239 | 877.42 | 2,016 | 4,546.28 | 381 | 2,647.29 | 287 | 5,302.84 | 34 | 3,524.27 |\n| | **Total** | **6,284** | **2,244.06** | **6,252** | **4,473.30** | **10,631** | **24,370.94** | **2,445** | **17,180.97** | **2,132** | **41,281.30** | **299** | **41,520.33** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 31: Basin-wise vs. Area range-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S. No.", "Basin", "0.25 - 0.5 No. of lakes", "0.25 - 0.5 Total Lake Area (ha)", "0.5 - 1 No. of lakes", "0.5 - 1 Total Lake Area (ha)", "1 - 5 No. of lakes", "1 - 5 Total Lake Area (ha)", "5 - 10 No. of lakes", "5 - 10 Total Lake Area (ha)", "10 - 50 No. of lakes", "10 - 50 Total Lake Area (ha)", "> 50 No. of lakes", "> 50 Lake Area (ha)"], "table_row_start": 1, "table_row_end": 4, "line_start": 843, "line_end": 848, "token_count_estimate": 547, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "254eaa4f860eba54", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > Basin-Area range-wise Distribution\nType: figure\nFigure: Figure 39: Basin-wise vs. Area range-wise distribution of GL in Indian Himalayan River Basins\n\n**Figure 39: Basin-wise vs. Area range-wise distribution of GL in Indian Himalayan River Basins**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > Basin-Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "Basin-Area range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 39: Basin-wise vs. Area range-wise distribution of GL in Indian Himalayan River Basins", "line_start": 850, "line_end": 850, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b989202e430caf47", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: text\n\n**Basin-Type-wise Distribution**\n\nGlacial lake distribution by basin vs. type-wise is given in Table 32 and Figure 40. It has been observed that, in descending order of total lake count, 3 types of lakes viz., Other Glacial Erosion, Other Moraine Dammed, and Other glacial lakes are distributed in all basins, and their total is found predominantly in Brahmaputra (58.28%), Indus (12.92%), and Ganga (12.82%) basin respectively. Indus basin consists higher number of Supra-glacial lakes i.e. 47.64%. Glacier Trough Valley Erosion Lake are not present in Ganga basin.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "text", "line_start": 857, "line_end": 863, "token_count_estimate": 205, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ec2634bddb67018b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: table\nTable: Table 32: Basin-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins\n\n| S. No. | Basin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Brahmaputra | 391 | 38 | 2 | 3,019 | 272 | 2 | 943 | 7 | 11,846 | 1,481 | 18,001 |\n| 2 | Ganga | 260 | 100 | 9 | 1,740 | 617 | 1 | 123 | 0 | 1,744 | 113 | 4,707 |\n| 3 | Indus | 407 | 225 | 45 | 905 | 809 | 2 | 220 | 2 | 2,516 | 204 | 5,335 |\n| | **Total** | **1,058** | **363** | **56** | **5,664** | **1,698** | **5** | **1,286** | **9** | **16,106** | **1,798** | **28,043** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "table", "table_caption": "Table 32: Basin-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S. No.", "Basin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 4, "line_start": 864, "line_end": 869, "token_count_estimate": 473, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e2d16890d2d95f62", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: figure\nFigure: Figure 40: Basin-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins\n\n**Figure 40: Basin-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "figure", "figure_caption": "Figure 40: Basin-wise vs. Type-wise distribution of GL in Indian Himalayan River Basins", "line_start": 871, "line_end": 871, "token_count_estimate": 98, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "61db7de54ef136b2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: text\n\n52 | National Remote Sensing Centre, ISRO, Hyderabad\n\n***", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "text", "line_start": 872, "line_end": 876, "token_count_estimate": 60, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "37bdf6af96eec2da", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: text\n\n**Basin-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per basin vs. elevation-range wise, given in Table 33 and Figure 41. Majority of glacial lakes are situated in all basins in high altitude range i.e. 4,001 - 5,000 m. After that, majority of glacial lakes in all basins are located in very high and medium altitude range i.e. > 5,000 m and 3,001 - 4,000 m. Only 33 lakes are located in all the basins in the elevation range < 3,000 m.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "text", "line_start": 878, "line_end": 884, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2886513fdeabbfe1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: table\nTable: Table 33: Basin-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins\n\n| S. No. | Basin | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Brahmaputra | 11 | 152.21 | 1,188 | 11,547.28 | 9,670 | 52,525.25 | 7,132 | 28,765.99 |\n| 2 | Ganga | 1 | 9.87 | 62 | 223.55 | 1,855 | 7,374.89 | 2,789 | 13,076.81 |\n| 3 | Indus | 21 | 47.70 | 657 | 3,339.87 | 2,797 | 8,301.35 | 1,860 | 5,706.11 |\n| | **Total** | **33** | **209.78** | **1,907** | **15,110.70** | **14,322** | **68,201.49** | **11,781** | **47,548.91** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "table", "table_caption": "Table 33: Basin-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins", "columns": ["S. No.", "Basin", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 4, "line_start": 885, "line_end": 890, "token_count_estimate": 478, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e7b12e1e4f41d773", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: figure\nFigure: Figure 41: Basin-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins\n\n**Figure 41: Basin-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "figure", "figure_caption": "Figure 41: Basin-wise vs. Elevation range-wise distribution of GL in Indian Himalayan River Basins", "line_start": 892, "line_end": 892, "token_count_estimate": 102, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9ab8009febe58bd", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: text\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 53\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.3 Inter Comparison of Basins > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Basins", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "text", "line_start": 893, "line_end": 898, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a012aaa21a30b1b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics\nType: text\n\nIndian Himalayan River Basins covers part of India and transboundary region, where in India it is covering a total area of 3,83,880 Km² i.e. 38.78% of the catchment area. In India, catchment area has been spread in four states viz., Arunachal Pradesh, Sikkim, Himachal Pradesh, Uttarakhand and two union territories (UT’s) viz., Jammu & Kashmir and Ladakh respectively. A total of 7,570 glacial lakes lies within Indian region, covering a total area of 30,148.53 ha.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics", "section_headings": ["5. RESULTS", "5.4 India Level Statistics"], "chunk_type": "text", "line_start": 900, "line_end": 902, "token_count_estimate": 157, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9e1d504b72cd4a38", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Area Range-wise Distribution\nType: text\n\nIn Indian region, glacial lakes have been distributed in all 6 classes of area ranges. Table 34 and Figure 42 shows the area range-wise distribution of glacial lakes for the Indian region. About 6,204 (81.96%) lakes are with < 5 ha lake area contributing to 27.95% of total lake area. The remaining lakes with > 5 ha in size are 1,366 (18.04%) but contributing to 72.05% of total lake area in the region.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Area Range-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Area Range-wise Distribution"], "chunk_type": "text", "line_start": 904, "line_end": 908, "token_count_estimate": 137, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "076bd9a26feb953d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Area Range-wise Distribution\nType: table\nTable: Table 34: Area range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1,736 | 620.24 | 2.06 |\n| 2 | 0.5 - 1 | 1,606 | 1,143.71 | 3.79 |\n| 3 | 1 - 5 | 2,862 | 6,662.04 | 22.10 |\n| 4 | 5 - 10 | 712 | 5,026.33 | 16.67 |\n| 5 | 10 - 50 | 596 | 11,428.48 | 37.91 |\n| 6 | > 50 | 58 | 5,267.70 | 17.47 |\n| | **Total** | **7,570** | **30,148.53** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Area Range-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Area Range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 34: Area range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 909, "line_end": 917, "token_count_estimate": 276, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "de4ea38ec28a9238", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Area Range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Area Range-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Area Range-wise Distribution"], "chunk_type": "text", "line_start": 918, "line_end": 920, "token_count_estimate": 51, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8f2d635b0322e9cc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Indian region is given in Table 35 and Figure 43. All 10 different types of glacial lakes are present in the Indian region, where Other Glacial Erosion lakes are found to be the maximum with 4,288 (56.64%) occupying a total lake extent of 16,240.17 ha at 53.87% in the region. After that, Other Moraine Dammed Lakes and Supra-glacial Lake are in majority with 973 (12.85%) and 939 (12.40%) and extend over a total area of 2,194.51 ha (7.28%) and 700.78 ha (2.32%) respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 922, "line_end": 926, "token_count_estimate": 176, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "045c4f7da880293a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: table\nTable: Table 35: Type-wise distribution of GL in India\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 344 | 3,135.38 | 10.40 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 236 | 356.05 | 1.18 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 30 | 65.88 | 0.22 |\n| 4 | M(o) | Other Moraine Dammed Lake | 973 | 2,194.51 | 7.28 |\n| 5 | I(s) | Supra-glacial Lake | 939 | 700.78 | 2.32 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 1 | 2.19 | 0.01 |\n| 7 | E(c) | Cirque Erosion Lake | 539 | 4,951.41 | 16.42 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 1 | 3.90 | 0.01 |\n| 9 | E(o) | Other Glacial Erosion Lake | 4,288 | 16,240.17 | 53.87 |\n| 10 | O | Other Glacial Lake | 219 | 2,498.26 | 8.29 |\n| | | **Total** | **7,570** | **30,148.53** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 35: Type-wise distribution of GL in India", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 927, "line_end": 939, "token_count_estimate": 463, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "bdd914b0060eb1d4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 36 and Figure 44. The lakes with < 5 ha in size (81.95%) are dominant with Other Glacial Erosion Lakes (56.09%) and Supra-glacial Lakes (14.87%). Lakes with > 5 ha (18.05%) are dominated by Other Glacial Erosion Lakes (61.05%). All types of Moraine-dammed lakes, which constitute about 20.91% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 940, "line_end": 949, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2b3af1b63f6dd597", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: table\nTable: Table 36: Area range-wise vs. Type-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 44 | 73 | 11 | 317 | 537 | 0 | 8 | 0 | 699 | 47 | 1,736 |\n| 2 | 0.5 - 1 | 36 | 71 | 4 | 286 | 278 | 0 | 23 | 0 | 858 | 50 | 1,606 |\n| 3 | 1 - 5 | 152 | 79 | 12 | 290 | 108 | 1 | 224 | 1 | 1,923 | 72 | 2,862 |\n| 4 | 5 - 10 | 47 | 7 | 2 | 47 | 12 | 0 | 146 | 0 | 439 | 12 | 712 |\n| 5 | 10 - 50 | 52 | 6 | 1 | 28 | 4 | 0 | 130 | 0 | 350 | 25 | 596 |\n| 6 | > 50 | 13 | 0 | 0 | 5 | 0 | 0 | 8 | 0 | 19 | 13 | 58 |\n| | **Total** | **344** | **236** | **30** | **973** | **939** | **1** | **539** | **1** | **4,288** | **219** | **7,570** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 36: Area range-wise vs. Type-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 950, "line_end": 958, "token_count_estimate": 602, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "951099dad6e12b7d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 44: Area range-wise vs. Type-wise distribution of GL in India\n\n**Figure 44: Area range-wise vs. Type-wise distribution of GL in India**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 44: Area range-wise vs. Type-wise distribution of GL in India", "line_start": 960, "line_end": 960, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "69a9d71be8390ccc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: text\n\n56 | National Remote Sensing Centre, ISRO, Hyderabad\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Indian region has been shown in Table 37 and Figure 45. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 6,113 (80.75%) with total lake area of 20,960.88 ha (69.52%) and remaining 19.25% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 961, "line_end": 973, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9fe18a47f60f2ff6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: table\nTable: Table 37: Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 25 | 92.43 | 0.31 |\n| 2 | 3,001 - 4,000 | 1,432 | 9,095.22 | 30.17 |\n| 3 | 4,001 - 5,000 | 4,720 | 16,247.11 | 53.89 |\n| 4 | > 5,000 | 1,393 | 4,713.77 | 15.64 |\n| | **Total** | **7,570** | **30,148.53** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 37: Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 974, "line_end": 980, "token_count_estimate": 244, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "76f6394c17e0a351", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 45: Elevation range-wise distribution of GL in India\n\n**Figure 45: Elevation range-wise distribution of GL in India**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 45: Elevation range-wise distribution of GL in India", "line_start": 982, "line_end": 982, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2171969565cab069", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: text\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 57\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 38 and Figure 46. It is noted that, 62.35% of glacial lakes (4,720) are situated in high altitude range i.e. 4,001 - 5,000 m, which also constitutes majority of total lake area within that range i.e. 53.89%. However, 25 glacial lakes lie below 3,000 m. 1,231 number of glacial lakes (88.37%) lying in very high altitude range are < 5 ha, predominantly of size ranging 1 – 5 ha (i.e. 451), followed by lakes of size 0.25 – 0.5 ha (i.e. 425).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 983, "line_end": 996, "token_count_estimate": 240, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "beaebe24930254a5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: table\nTable: Table 38: Area range-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | Elevation up to 3,000 m (No. of lakes) | Elevation up to 3,000 m (Total Lake Area (ha)) | Elevation 3,001 - 4,000 m (No. of lakes) | Elevation 3,001 - 4,000 m (Total Lake Area (ha)) | Elevation 4,001 - 5,000 m (No. of lakes) | Elevation 4,001 - 5,000 m (Total Lake Area (ha)) | Elevation > 5,000 m (No. of lakes) | Elevation > 5,000 m (Total Lake Area (ha)) | Total (No. of lakes) | Total (Lake Area (ha)) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 10 | 3.48 | 272 | 98.56 | 1,029 | 367.40 | 425 | 150.80 | 1,736 | 620.24 |\n| 2 | 0.5 - 1 | 4 | 2.62 | 221 | 157.64 | 1,026 | 734.48 | 355 | 248.97 | 1,606 | 1,143.71 |\n| 3 | 1 - 5 | 6 | 16.89 | 514 | 1,239.00 | 1,891 | 4,411.74 | 451 | 994.41 | 2,862 | 6,662.04 |\n| 4 | 5 - 10 | 0 | 0.00 | 188 | 1,354.05 | 431 | 3,018.56 | 93 | 653.74 | 712 | 5,026.33 |\n| 5 | 10 - 50 | 5 | 69.44 | 216 | 4,388.03 | 322 | 5,937.65 | 53 | 1,033.38 | 596 | 11,428.48 |\n| 6 | > 50 | 0 | 0.00 | 21 | 1,857.95 | 21 | 1,777.28 | 16 | 1,632.48 | 58 | 5,267.70 |\n| | **Total** | **25** | **92.43** | **1,432** | **9,095.22** | **4,720** | **16,247.11** | **1,393** | **4,713.77** | **7,570** | **30,148.53** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 38: Area range-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation up to 3,000 m (No. of lakes)", "Elevation up to 3,000 m (Total Lake Area (ha))", "Elevation 3,001 - 4,000 m (No. of lakes)", "Elevation 3,001 - 4,000 m (Total Lake Area (ha))", "Elevation 4,001 - 5,000 m (No. of lakes)", "Elevation 4,001 - 5,000 m (Total Lake Area (ha))", "Elevation > 5,000 m (No. of lakes)", "Elevation > 5,000 m (Total Lake Area (ha))", "Total (No. of lakes)", "Total (Lake Area (ha))"], "table_row_start": 1, "table_row_end": 7, "line_start": 997, "line_end": 1005, "token_count_estimate": 672, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "84aa65ce52f0b03f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 46: Area range-wise vs. Elevation range-wise distribution of GL in India\n\n**Figure 46: Area range-wise vs. Elevation range-wise distribution of GL in India**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 46: Area range-wise vs. Elevation range-wise distribution of GL in India", "line_start": 1007, "line_end": 1007, "token_count_estimate": 78, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "96d611bc7465ac09", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 39 and Figure 47. The dominant lake type in the region i.e., Other Glacial Erosion Lakes (56.64%) are predominantly located in the elevation range of 4,001 - 5,000 m (71.53%). The other dominant lake type, namely, Supra Glacial Lakes and Other Moraine Dammed lakes are distributed predominantly in altitude range of 4,001 - 5,000 m. In very high altitude range, Other Moraine Dammed lakes are distributed predominantly 487 (50.05%). Majority i.e. 93.18% of all types of Moraine-dammed lakes lies above 4,000 m.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1008, "line_end": 1023, "token_count_estimate": 237, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "16f73179f0c57405", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: table\nTable: Table 39: Type-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 2 | 1 | 0 | 6 | 11 | 0 | 0 | 0 | 1 | 4 | 25 |\n| 2 | 3,001 - 4,000 | 22 | 34 | 0 | 43 | 241 | 0 | 160 | 1 | 835 | 96 | 1,432 |\n| 3 | 4,001 - 5,000 | 120 | 140 | 13 | 437 | 522 | 0 | 346 | 0 | 3,067 | 75 | 4,720 |\n| 4 | > 5,000 | 200 | 61 | 17 | 487 | 165 | 1 | 33 | 0 | 385 | 44 | 1,393 |\n| | **Total** | **344** | **236** | **30** | **973** | **939** | **1** | **539** | **1** | **4,288** | **219** | **7,570** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 39: Type-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1024, "line_end": 1030, "token_count_estimate": 418, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "68c083f1583f68a6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 47: Type-wise vs. Elevation range-wise distribution of GL in India\n\n**Figure 47: Type-wise vs. Elevation range-wise distribution of GL in India**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 47: Type-wise vs. Elevation range-wise distribution of GL in India", "line_start": 1032, "line_end": 1032, "token_count_estimate": 76, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "642c3948397a5fec", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.4 India Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.4 India Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1033, "line_end": 1040, "token_count_estimate": 50, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "4cac297842b74a4a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.5 Indian State’s and UT’s Statistics\nType: text\n\nGlacial lakes located in 4 states and 2 Union-Territories of Indian regions are compared for lake count, total lake area, lake types and their elevation ranges in the following sections.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.5 Indian State’s and UT’s Statistics", "section_headings": ["5. RESULTS", "5.5 Indian State’s and UT’s Statistics"], "chunk_type": "text", "line_start": 1042, "line_end": 1044, "token_count_estimate": 75, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "75d8a097c80d3a6f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-wise Distribution\nType: text\n\nTable 40 and Figure 48 shows the State/UT-wise distribution of glacial lakes of Indian region. Ladakh (UT) contains maximum of 3,219 glacial lakes (42.52%) extend over an area of 9,965.34 ha (33.05%). Total Lakes in Arunachal Pradesh are 2,188 (28.90%) occupying a total lake extent of 12,490.77 ha at 41.43% in the region.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-wise Distribution", "section_headings": ["5. RESULTS", "5.5 Indian State’s and UT’s Statistics", "State/UT-wise Distribution"], "chunk_type": "text", "line_start": 1046, "line_end": 1050, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "63550a7bd83c8095", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-wise Distribution\nType: table\nTable: Table 40: State/UT-wise distribution of GL in India\n\n| S.No. | Code | State/UT | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | AP | Arunachal Pradesh | 2,188 | 12,490.77 | 41.43 |\n| 2 | SK | Sikkim | 733 | 3,267.28 | 10.84 |\n| 3 | HP | Himachal Pradesh | 537 | 983.74 | 3.26 |\n| 4 | UK | Uttarakhand | 347 | 560.61 | 1.86 |\n| 5 | JK | Jammu & Kashmir (UT) | 546 | 2,880.79 | 9.56 |\n| 6 | LA | Ladakh (UT) | 3,219 | 9,965.34 | 33.05 |\n| | | **Total** | **7,570** | **30,148.53** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-wise Distribution", "section_headings": ["5. RESULTS", "5.5 Indian State’s and UT’s Statistics", "State/UT-wise Distribution"], "chunk_type": "table", "table_caption": "Table 40: State/UT-wise distribution of GL in India", "columns": ["S.No.", "Code", "State/UT", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1051, "line_end": 1059, "token_count_estimate": 311, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2cf3077fc029b1bc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-wise Distribution", "section_headings": ["5. RESULTS", "5.5 Indian State’s and UT’s Statistics", "State/UT-wise Distribution"], "chunk_type": "text", "line_start": 1060, "line_end": 1065, "token_count_estimate": 57, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8a9fea09f5d2d763", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-Area range-Type-wise Distribution\nType: text\n\nGlacial lakes have been distributed in all states and UTs for all classes of area range. Table 41 and Figure 49 shows the State/UT-area range-wise distribution of glacial lakes for the Indian region. It has been observed that, glacial lakes in Arunachal Pradesh (AP) are predominantly < 5 ha (69.70%), majority of which are within 1 - 5 ha in size i.e. 60.85%, followed by lakes of 0.5 - 1 ha in size i.e. 22.62%. Not only in Arunachal Pradesh (AP), maximum number of lakes < 5 ha are (84.31%) located in Sikkim.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.5 Indian State’s and UT’s Statistics", "State/UT-Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1067, "line_end": 1071, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2c1d305a397641a2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-Area range-Type-wise Distribution\nType: table\nTable: Table 41: State/UT-wise vs. Area range-wise distribution of GL in India\n\n| S.No. | Lake Area Range (ha) | Arunachal Pradesh - No. of lakes | Arunachal Pradesh - Total Lake Area (ha) | Sikkim - No. of lakes | Sikkim - Total Lake Area (ha) | Himachal Pradesh - No. of lakes | Himachal Pradesh - Total Lake Area (ha) | Uttarakhand - No. of lakes | Uttarakhand - Total Lake Area (ha) | Jammu & Kashmir (UT) - No. of lakes | Jammu & Kashmir (UT) - Total Lake Area (ha) | Ladakh (UT) - No. of lakes | Ladakh (UT) - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 252 | 92.35 | 206 | 69.95 | 208 | 73.3 | 140 | 48 | 123 | 45.05 | 807 | 291.60 |\n| 2 | 0.5 - 1 | 345 | 250.31 | 164 | 119.43 | 135 | 92.83 | 96 | 67.2 | 125 | 88.08 | 741 | 525.86 |\n| 3 | 1 - 5 | 928 | 2,303.41 | 248 | 562.68 | 168 | 334.67 | 83 | 179.19 | 179 | 423.41 | 1,256 | 2,858.67 |\n| 4 | 5 - 10 | 341 | 2,418.08 | 50 | 374.34 | 16 | 119.00 | 19 | 127.5 | 49 | 358.24 | 237 | 1,629.17 |\n| 5 | 10 - 50 | 301 | 5,900.86 | 54 | 1,057.19 | 8 | 157.64 | 9 | 138.73 | 63 | 1,315.91 | 161 | 2,858.15 |\n| 6 | > 50 | 21 | 1,525.76 | 11 | 1,083.69 | 2 | 206.28 | 0 | 0.00 | 7 | 650.10 | 17 | 1,801.88 |\n| | **Total** | **2,188** | **12,490.77** | **733** | **3,267.28** | **537** | **983.74** | **347** | **560.61** | **546** | **2,880.79** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "5. RESULTS > 5.5 Indian State’s and UT’s Statistics > State/UT-Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.5 Indian State’s and UT’s Statistics", "State/UT-Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 41: State/UT-wise vs. Area range-wise distribution of GL in India", "columns": ["S.No.", "Lake Area Range (ha)", "Arunachal Pradesh - No. of lakes", "Arunachal Pradesh - Total Lake Area (ha)", "Sikkim - No. of lakes", "Sikkim - Total Lake Area (ha)", "Himachal Pradesh - No. of lakes", "Himachal Pradesh - Total Lake Area (ha)", "Uttarakhand - No. of lakes", "Uttarakhand - Total Lake Area (ha)", "Jammu & Kashmir (UT) - No. of lakes", "Jammu & Kashmir (UT) - Total Lake Area (ha)", "Ladakh (UT) - No. of lakes", "Ladakh (UT) - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1072, "line_end": 1080, "token_count_estimate": 725, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a94ce9f011dd803b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Type-wise Distribution\nType: text\n\nGlacial lake distribution by State/UT vs. type-wise is given in Table 42 and Figure 50. It has been observed that, Arunachal Pradesh and Sikkim contains same types of glacial lakes except Supra-Glacial Lake in Sikkim, with majority of Other Glacial Erosion Lakes i.e. 78.93%, followed by Cirque Erosion Lakes i.e. 12.89%. All types of moraine dammed lakes in Sikkim are 184 with 25.10%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Type-wise Distribution"], "chunk_type": "text", "line_start": 1088, "line_end": 1092, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "406cea19401599ae", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Type-wise Distribution\nType: table\nTable: Table 42: State/UT-wise vs. Type-wise distribution of GL in India\n\n| S. No. | State/UT | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(c) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Arunachal Pradesh | 7 | 1 | 0 | 83 | 0 | 0 | 282 | 0 | 1,727 | 88 | 2,188 |\n| 2 | Sikkim | 31 | 5 | 0 | 148 | 72 | 0 | 46 | 0 | 423 | 8 | 733 |\n| 3 | Himachal Pradesh | 46 | 28 | 1 | 216 | 65 | 0 | 6 | 0 | 167 | 8 | 537 |\n| 4 | Uttarakhand | 25 | 11 | 0 | 103 | 106 | 0 | 23 | 0 | 67 | 12 | 347 |\n| 5 | Jammu & Kashmir (UT) | 20 | 9 | 0 | 69 | 54 | 0 | 66 | 0 | 319 | 9 | 546 |\n| 6 | Ladakh (UT) | 215 | 182 | 29 | 354 | 642 | 1 | 116 | 1 | 1,585 | 94 | 3,219 |\n| | **Total** | **344** | **236** | **30** | **973** | **939** | **1** | **539** | **1** | **4,288** | **219** | **7,570** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 42: State/UT-wise vs. Type-wise distribution of GL in India", "columns": ["S. No.", "State/UT", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1093, "line_end": 1101, "token_count_estimate": 516, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "56793b2a3c0d30f6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per State/UT vs. elevation-range wise, given in Table 43 and Figure 51. It has been observed that, majority of glacial lakes (62.35%) are located in high altitude range i.e. 4,001 - 5,000 m in all the states. This is followed by medium altitude range i.e. 3,001 - 4,000 m in all states and UTs (18.92%).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1104, "line_end": 1108, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2120c712e0b2759d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution\nType: table\nTable: Table 43: State/UT-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | Arunachal Pradesh - No. of lakes | Arunachal Pradesh - Total Lake Area (ha) | Sikkim - No. of lakes | Sikkim - Total Lake Area (ha) | Himachal Pradesh - No. of lakes | Himachal Pradesh - Total Lake Area (ha) | Uttarakhand - No. of lakes | Uttarakhand - Total Lake Area (ha) | Jammu & Kashmir (UT) - No. of lakes | Jammu & Kashmir (UT) - Total Lake Area (ha) | Ladakh (UT) - No. of lakes | Ladakh (UT) - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 4 | 44.73 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 21 | 47.70 |\n| 2 | 3,001 - 4,000 | 739 | 5,610.27 | 25 | 139.44 | 22 | 45.85 | 11 | 5.64 | 277 | 2,087.45 | 358 | 1,206.57 |\n| 3 | 4,001 - 5,000 | 1,363 | 6,610.21 | 420 | 1,170.71 | 318 | 711.08 | 183 | 345.98 | 268 | 792.42 | 2,168 | 6,616.71 |\n| 4 | > 5,000 | 82 | 225.56 | 288 | 1,957.14 | 197 | 226.81 | 153 | 208.99 | 1 | 0.93 | 672 | 2,094.36 |\n| | **Total** | **2,188** | **12,490.77** | **733** | **3,267.28** | **537** | **983.74** | **347** | **560.61** | **546** | **2,880.79** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 43: State/UT-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "Arunachal Pradesh - No. of lakes", "Arunachal Pradesh - Total Lake Area (ha)", "Sikkim - No. of lakes", "Sikkim - Total Lake Area (ha)", "Himachal Pradesh - No. of lakes", "Himachal Pradesh - Total Lake Area (ha)", "Uttarakhand - No. of lakes", "Uttarakhand - Total Lake Area (ha)", "Jammu & Kashmir (UT) - No. of lakes", "Jammu & Kashmir (UT) - Total Lake Area (ha)", "Ladakh (UT) - No. of lakes", "Ladakh (UT) - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1109, "line_end": 1115, "token_count_estimate": 621, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "22499e53a27bd04a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1116, "line_end": 1119, "token_count_estimate": 65, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ca064aa1837015c3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh\nType: text\n\nArunachal Pradesh is the largest state covering area of Brahmaputra River basin, contains glacial lakes only in fourteen districts. Amongst which, Dibang Valley covers the majority of the total lake area.\n\n**Area range-wise Distribution**\n\nGlacial lakes have been distributed in 14 districts for 6 classes of area ranges, and area range-wise distribution for 14 districts has been shown in Table 44 and Figure 52. Glacial lakes in Dibang Valley district are found to be the maximum with 669 (30.58%) occupying a total lake extent of 5746.19 ha at 46.00%. About 1,525 (69.70%) lakes are with < 5 ha lake area contributing to 21.18% of total lake area in Arunachal Pradesh.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.1 District Level Statistics of Arunachal Pradesh"], "chunk_type": "text", "line_start": 1121, "line_end": 1129, "token_count_estimate": 233, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "7bc5444233c50edb", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh\nType: table\nTable: Table 44: Area range-wise distribution of GL in Districts of Arunachal Pradesh\n\n| S. No. | District | 0.25 - 0.5: No. of lakes | 0.25 - 0.5: Total Lake Area (ha) | 0.5 - 1: No. of lakes | 0.5 - 1: Total Lake Area (ha) | 1 - 5: No. of lakes | 1 - 5: Total Lake Area (ha) | 5 - 10: No. of lakes | 5 - 10: Total Lake Area (ha) | 10 - 50: No. of lakes | 10 - 50: Total Lake Area (ha) | > 50: No. of lakes | > 50: Total Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Anjaw | 52 | 18.60 | 75 | 56.11 | 193 | 451.54 | 65 | 471.47 | 61 | 1,149.56 | 3 | 172.29 |\n| 2 | Changlang | 4 | 1.59 | 0 | 0.00 | 1 | 1.93 | 2 | 15.25 | 2 | 25.18 | 0 | 0.00 |\n| 3 | Dibang Valley | 30 | 11.97 | 76 | 55.56 | 282 | 747.80 | 124 | 891.21 | 145 | 3,042.37 | 12 | 997.29 |\n| 4 | East Kameng | 5 | 1.78 | 11 | 8.02 | 28 | 71.67 | 12 | 86.32 | 7 | 150.02 | 0 | 0.00 |\n| 5 | Kra Daadi | 1 | 0.25 | 0 | 0.00 | 1 | 1.19 | 2 | 17.19 | 0 | 0.00 | 0 | 0.00 |\n| 6 | Kurung Kumey | 9 | 3.31 | 20 | 15.55 | 33 | 73.39 | 7 | 52.67 | 5 | 86.20 | 1 | 55.58 |\n| 7 | Lohit | 0 | 0.00 | 0 | 0.00 | 2 | 7.52 | 0 | 0.00 | 1 | 12.55 | 0 | 0.00 |\n| 8 | Lower Dibang Valley | 0 | 0.00 | 0 | 0.00 | 3 | 13.81 | 2 | 14.26 | 1 | 16.14 | 0 | 0.00 |\n| 9 | Siang | 2 | 0.69 | 2 | 1.65 | 6 | 17.88 | 2 | 14.06 | 1 | 18.47 | 0 | 0.00 |\n| 10 | Tawang | 95 | 34.09 | 77 | 54.43 | 196 | 466.16 | 53 | 362.67 | 18 | 317.63 | 4 | 249.94 |\n| 11 | Upper Siang | 1 | 0.48 | 9 | 6.29 | 36 | 91.11 | 20 | 141.17 | 21 | 383.11 | 0 | 0.00 |\n| 12 | Upper Subansiri | 9 | 3.33 | 21 | 14.02 | 64 | 172.45 | 34 | 230.52 | 26 | 490.47 | 0 | 0.00 |\n| 13 | West Kameng | 42 | 15.47 | 45 | 32.11 | 63 | 142.52 | 14 | 91.60 | 9 | 141.81 | 0 | 0.00 |\n| 14 | West Siang | 2 | 0.78 | 9 | 6.58 | 20 | 44.44 | 4 | 29.69 | 4 | 67.35 | 1 | 50.66 |\n| | **Total** | **252** | **92.34** | **345** | **250.32** | **928** | **2,303.41** | **341** | **2,418.08** | **301** | **5,900.86** | **21** | **1,525.76** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.1 District Level Statistics of Arunachal Pradesh"], "chunk_type": "table", "table_caption": "Table 44: Area range-wise distribution of GL in Districts of Arunachal Pradesh", "columns": ["S. No.", "District", "0.25 - 0.5: No. of lakes", "0.25 - 0.5: Total Lake Area (ha)", "0.5 - 1: No. of lakes", "0.5 - 1: Total Lake Area (ha)", "1 - 5: No. of lakes", "1 - 5: Total Lake Area (ha)", "5 - 10: No. of lakes", "5 - 10: Total Lake Area (ha)", "10 - 50: No. of lakes", "10 - 50: Total Lake Area (ha)", "> 50: No. of lakes", "> 50: Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1130, "line_end": 1146, "token_count_estimate": 1150, "basins": [], "subbasins": ["Dibang", "Lohit", "Subansiri"], "countries": [], "lake_ids": []}}
{"id": "a1ae6038cd5fd705", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the districts of Arunachal Pradesh is given in Table 45 and Figure 53. It has been observed that, Other Glacial Erosion lakes are maximum with 489 (73.09%) in the Dibang Valley district, followed by Cirque Erosion Lake with 117 (17.49%). Total Glacial Erosion Lakes are 2,009 (91.82%) out of total lakes in the Arunachal Pradesh", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.1 District Level Statistics of Arunachal Pradesh"], "chunk_type": "text", "line_start": 1147, "line_end": 1155, "token_count_estimate": 183, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "d5dc9765162dce17", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh\nType: table\nTable: Table 45: Type-wise distribution of GL in Districts of Arunachal Pradesh\n\n| S. No. | District | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total: No. of Lakes | Total: Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Anjaw | 0 | 0 | 0 | 3 | 0 | 0 | 28 | 0 | 393 | 25 | 449 | 2,319.57 |\n| 2 | Changlang | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 6 | 1 | 9 | 43.95 |\n| 3 | Dibang Valley | 4 | 0 | 0 | 29 | 0 | 0 | 117 | 0 | 489 | 30 | 669 | 5,746.20 |\n| 4 | East Kameng | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 59 | 0 | 63 | 317.81 |\n| 5 | Kra Daadi | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 4 | 18.63 |\n| 6 | Kurung Kumey | 2 | 0 | 0 | 9 | 0 | 0 | 14 | 0 | 50 | 0 | 75 | 286.70 |\n| 7 | Lohit | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 20.07 |\n| 8 | Lower Dibang Valley | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 4 | 0 | 6 | 44.21 |\n| 9 | Siang | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 11 | 0 | 13 | 52.75 |\n| 10 | Tawang | 0 | 0 | 0 | 14 | 0 | 0 | 50 | 0 | 365 | 14 | 443 | 1,484.92 |\n| 11 | Upper Siang | 0 | 0 | 0 | 1 | 0 | 0 | 22 | 0 | 62 | 2 | 87 | 622.16 |\n| 12 | Upper Subansiri | 1 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 125 | 7 | 154 | 910.79 |\n| 13 | West Kameng | 0 | 1 | 0 | 27 | 0 | 0 | 9 | 0 | 132 | 4 | 173 | 423.51 |\n| 14 | West Siang | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 27 | 4 | 40 | 199.50 |\n| | **Total** | **7** | **1** | **0** | **83** | **0** | **0** | **282** | **0** | **1,727** | **88** | **2,188** | **12,490.77** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.1 District Level Statistics of Arunachal Pradesh"], "chunk_type": "table", "table_caption": "Table 45: Type-wise distribution of GL in Districts of Arunachal Pradesh", "columns": ["S. No.", "District", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1156, "line_end": 1172, "token_count_estimate": 1062, "basins": [], "subbasins": ["Dibang", "Lohit", "Subansiri"], "countries": [], "lake_ids": []}}
{"id": "5f34bbb6f0327dbc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.1 District Level Statistics of Arunachal Pradesh", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.1 District Level Statistics of Arunachal Pradesh"], "chunk_type": "text", "line_start": 1173, "line_end": 1176, "token_count_estimate": 77, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ab716112373ac3f3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Arunachal Pradesh has been shown in Table 46 and Figure 54. Majority of glacial lakes are situated in the elevation range of 4,001 – 5,000 i.e. 1,363 with total lake area of 6,610.22 ha. Tawang district contains maximum number of glacial lakes in the elevation range of > 5,000 m i.e. 54. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 55.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1178, "line_end": 1182, "token_count_estimate": 177, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "965151e9c1c0ba1d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: table\nTable: Table 46: Elevation range-wise distribution of GL in Districts of Arunachal Pradesh\n\n| S. No. | District | up to 3,000 - No. of lakes | up to 3,000 - Total Lake Area (ha) | 3,001 - 4,000 - No. of lakes | 3,001 - 4,000 - Total Lake Area (ha) | 4,001 - 5,000 - No. of lakes | 4,001 - 5,000 - Total Lake Area (ha) | > 5,000 - No. of lakes | > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anjaw | 0 | 0.00 | 174 | 1,049.58 | 275 | 1,269.99 | 0 | 0.00 |\n| 2 | Changlang | 0 | 0.00 | 9 | 43.95 | 0 | 0.00 | 0 | 0.00 |\n| 3 | Dibang Valley | 2 | 27.47 | 345 | 3,151.43 | 322 | 2,567.30 | 0 | 0.00 |\n| 4 | East Kameng | 0 | 0.00 | 12 | 86.34 | 51 | 231.47 | 0 | 0.00 |\n| 5 | Kra Daadi | 0 | 0.00 | 3 | 11.43 | 1 | 7.20 | 0 | 0.00 |\n| 6 | Kurung Kumey | 0 | 0.00 | 10 | 28.21 | 65 | 258.49 | 0 | 0.00 |\n| 7 | Lohit | 2 | 17.27 | 1 | 2.80 | 0 | 0.00 | 0 | 0.00 |\n| 8 | Lower Dibang Valley | 0 | 0.00 | 5 | 40.00 | 1 | 4.21 | 0 | 0.00 |\n| 9 | Siang | 0 | 0.00 | 9 | 27.28 | 4 | 25.47 | 0 | 0.00 |\n| 10 | Tawang | 0 | 0.00 | 14 | 69.60 | 373 | 1,254.93 | 56 | 160.39 |\n| 11 | Upper Siang | 0 | 0.00 | 63 | 495.71 | 24 | 126.45 | 0 | 0.00 |\n| 12 | Upper Subansiri | 0 | 0.00 | 63 | 454.02 | 91 | 456.77 | 0 | 0.00 |\n| 13 | West Kameng | 0 | 0.00 | 3 | 6.39 | 144 | 351.94 | 26 | 65.16 |\n| 14 | West Siang | 0 | 0.00 | 28 | 143.50 | 12 | 56.00 | 0 | 0.00 |\n| | **Total** | **4** | **45.74** | **739** | **5,610.24** | **1,363** | **6,610.22** | **82** | **226.55** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 46: Elevation range-wise distribution of GL in Districts of Arunachal Pradesh", "columns": ["S. No.", "District", "up to 3,000 - No. of lakes", "up to 3,000 - Total Lake Area (ha)", "3,001 - 4,000 - No. of lakes", "3,001 - 4,000 - Total Lake Area (ha)", "4,001 - 5,000 - No. of lakes", "4,001 - 5,000 - Total Lake Area (ha)", "> 5,000 - No. of lakes", "> 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1183, "line_end": 1199, "token_count_estimate": 837, "basins": [], "subbasins": ["Dibang", "Lohit", "Subansiri"], "countries": [], "lake_ids": []}}
{"id": "69c80bd8ddce5d72", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1200, "line_end": 1209, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d64ffc6d5eace49", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: text\n\nSikkim is the second largest state covering area of Brahmaputra River basin, contains glacial lakes in four districts viz., North Sikkim, South Sikkim, West Sikkim, and East Sikkim. Amongst which, North Sikkim has the majority of glacial lakes covering 88.89% of the total lake area in Sikkim.\n\n**Area range-wise Distribution**\n\nGlacial lakes have been distributed in 4 districts of Sikkim for 6 classes of area ranges, and area range-wise distribution for those has been shown in Table 47 and Figure 56. Glacial lakes in North Sikkim district are found to be the maximum with 589 (80.35%) occupying a total lake extent of 2,904.39 ha at 88.89%. About 618 (84.31%) lakes are with < 5 ha lake area contributing to 23.02% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 15.69%, predominantly of 5 - 50 ha in size.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "text", "line_start": 1211, "line_end": 1219, "token_count_estimate": 300, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1021ddef5804e458", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: table\nTable: Table 47: Area range-wise distribution of GL in Districts of Sikkim\n\n| S. No. | District | 0.25 - 0.5 No. of lakes | 0.25 - 0.5 Total Lake Area (ha) | 0.5 - 1 No. of lakes | 0.5 - 1 Total Lake Area (ha) | 1 - 5 No. of lakes | 1 - 5 Total Lake Area (ha) | 5 - 10 No. of lakes | 5 - 10 Total Lake Area (ha) | 10 - 50 No. of lakes | 10 - 50 Total Lake Area (ha) | > 50 No. of lakes | > 50 Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | North Sikkim | 165 | 56.86 | 126 | 90.55 | 200 | 462.75 | 40 | 301.17 | 47 | 909.39 | 11 | 1,083.67 |\n| 2 | South Sikkim | 1 | 0.44 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 3 | West Sikkim | 18 | 5.85 | 18 | 14.22 | 17 | 34.61 | 3 | 22.22 | 3 | 60.83 | 0 | 0.00 |\n| 4 | East Sikkim | 22 | 6.80 | 20 | 14.66 | 31 | 65.32 | 7 | 50.96 | 4 | 86.98 | 0 | 0.00 |\n| | **Total** | **206** | **69.95** | **164** | **119.43** | **248** | **562.68** | **50** | **374.35** | **54** | **1,057.20** | **11** | **1,083.67** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "table", "table_caption": "Table 47: Area range-wise distribution of GL in Districts of Sikkim", "columns": ["S. No.", "District", "0.25 - 0.5 No. of lakes", "0.25 - 0.5 Total Lake Area (ha)", "0.5 - 1 No. of lakes", "0.5 - 1 Total Lake Area (ha)", "1 - 5 No. of lakes", "1 - 5 Total Lake Area (ha)", "5 - 10 No. of lakes", "5 - 10 Total Lake Area (ha)", "10 - 50 No. of lakes", "10 - 50 Total Lake Area (ha)", "> 50 No. of lakes", "> 50 Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1220, "line_end": 1226, "token_count_estimate": 552, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6d50528dedeb026", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: text\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the districts of Sikkim is given in Table 48 and Figure 57. It has been observed that, only 7 types of glacial lakes are distributed in Sikkim, where Other Glacial Erosion Lakes are found to be the maximum with 423 (57.70%) in Sikkim, followed by Other Moraine Dammed lakes with 148 (20.19%). North Sikkim district contains maximum number of glacial lakes in comparison with other districts in Sikkim, with majority of Other Glacial Erosion Lakes (51.78%), followed by Other Moraine Dammed lakes i.e. 23.93%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "text", "line_start": 1227, "line_end": 1233, "token_count_estimate": 217, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "92a768780f32a018", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: table\nTable: Table 48: Type-wise distribution of GL in Districts of Sikkim\n\n| S. No. | District | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total No. of Lakes | Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | North Sikkim | 30 | 2 | 0 | 141 | 68 | 0 | 36 | 0 | 305 | 7 | 589 | 2,904.39 |\n| 2 | South Sikkim | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0.44 |\n| 3 | West Sikkim | 1 | 3 | 0 | 7 | 4 | 0 | 2 | 0 | 42 | 0 | 59 | 137.73 |\n| 4 | East Sikkim | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 75 | 1 | 84 | 224.72 |\n| | **Total** | **31** | **5** | **0** | **148** | **72** | **0** | **46** | **0** | **423** | **8** | **733** | **3,267.28** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "table", "table_caption": "Table 48: Type-wise distribution of GL in Districts of Sikkim", "columns": ["S. No.", "District", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total No. of Lakes", "Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1234, "line_end": 1240, "token_count_estimate": 464, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fc95b2d161ef14a1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the districts of Sikkim has been shown in Table 49 and Figure 58. Majority of glacial lakes (57.29%) are situated in high altitude i.e. 4,001 - 5,000 m elevation range with total lake area of 1,170.72 ha (35.83%). This is followed by glacial lakes in very high-altitude elevation range with 39.29%. North Sikkim district contains maximum number of glacial lakes above 4,000 m elevation in comparison with any other district in Sikkim, with majority of them falling in high altitude range i.e. 4,001 - 5,000 m. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 59.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "text", "line_start": 1241, "line_end": 1250, "token_count_estimate": 262, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3826b4cdd9dd86f5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: table\nTable: Table 49: Elevation range-wise distribution of GL in Districts of Sikkim\n\n| S. No. | District | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | North Sikkim | 0 | 0.00 | 10 | 33.76 | 293 | 914.45 | 286 | 1,956.18 |\n| 2 | South Sikkim | 0 | 0.00 | 0 | 0.00 | 1 | 0.44 | 0 | 0.00 |\n| 3 | West Sikkim | 0 | 0.00 | 0 | 0.00 | 57 | 136.79 | 2 | 0.94 |\n| 4 | East Sikkim | 0 | 0.00 | 15 | 105.68 | 69 | 119.04 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **25** | **139.44** | **420** | **1,170.72** | **288** | **1,957.12** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "table", "table_caption": "Table 49: Elevation range-wise distribution of GL in Districts of Sikkim", "columns": ["S. No.", "District", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1251, "line_end": 1257, "token_count_estimate": 518, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d18c977c41b9aeb2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "text", "line_start": 1258, "line_end": 1273, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71caba45b08a20bc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh\nType: text\n\nHimachal Pradesh state has a total 12 districts but only 6 districts has glacial lakes in it viz., Chamba, Kangra, Kinnaur, Kullu, Lahul & Spiti, and Shimla. Amongst which, Lahul & Spiti has the majority of glacial lakes covering 47.80% of the total lake area in Himachal Pradesh.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh"], "chunk_type": "text", "line_start": 1275, "line_end": 1277, "token_count_estimate": 143, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d8f082959f937bb3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution\nType: text\n\nGlacial lakes have been distributed in 6 districts of Himachal Pradesh for all 6 classes of area ranges, and area range-wise distribution for those has been shown in Table 50 and Figure 60. Glacial lakes in Lahul & Spiti district are found to be the maximum with 185 (34.45%) occupying a total lake extent of 470.18 ha at 47.8%. About 511 (95.16%) lakes are with < 5 ha lake area contributing to 50.91% of total lake area in the district. Whereas, remaining lakes in Himachal Pradesh with > 5 ha in size are only 4.84%, majorly of 5 - 10 ha in size.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1279, "line_end": 1283, "token_count_estimate": 218, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "32217e5ea40669c4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution\nType: table\nTable: Table 50: Area range-wise distribution of GL in Districts of Himachal Pradesh\n\n| S. No. | District | Lake Area Range (ha): 0.25 - 0.5 (No. of lakes) | Lake Area Range (ha): 0.25 - 0.5 (Total Lake Area ha) | Lake Area Range (ha): 0.5 - 1 (No. of lakes) | Lake Area Range (ha): 0.5 - 1 (Total Lake Area ha) | Lake Area Range (ha): 1 - 5 (No. of lakes) | Lake Area Range (ha): 1 - 5 (Total Lake Area ha) | Lake Area Range (ha): 5 - 10 (No. of lakes) | Lake Area Range (ha): 5 - 10 (Total Lake Area ha) | Lake Area Range (ha): 10 - 50 (No. of lakes) | Lake Area Range (ha): 10 - 50 (Total Lake Area ha) | Lake Area Range (ha): > 50 (No. of lakes) | Lake Area Range (ha): > 50 (Total Lake Area ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Chamba | 21 | 7.71 | 14 | 10.4 | 29 | 60.41 | 1 | 9.7 | 1 | 25.65 | 0 | 0.00 |\n| 2 | Kangra | 14 | 4.82 | 11 | 7.43 | 13 | 25.05 | 1 | 8.15 | 0 | 0.00 | 0 | 0.00 |\n| 3 | Kinnaur | 57 | 19.33 | 38 | 25.46 | 28 | 59.39 | 2 | 13.31 | 3 | 45.32 | 0 | 0.00 |\n| 4 | Kullu | 25 | 9.23 | 24 | 16.27 | 41 | 79.27 | 3 | 25.84 | 0 | 0.00 | 0 | 0.00 |\n| 5 | Lahul & Spiti | 82 | 29.08 | 42 | 28.28 | 49 | 91.92 | 8 | 53.73 | 2 | 60.89 | 2 | 206.28 |\n| 6 | Shimla | 9 | 3.13 | 6 | 4.99 | 8 | 18.63 | 1 | 8.26 | 2 | 25.77 | 0 | 0.00 |\n| | **Total** | **208** | **73.30** | **135** | **92.83** | **168** | **334.67** | **16** | **118.99** | **8** | **157.63** | **2** | **206.28** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 50: Area range-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S. No.", "District", "Lake Area Range (ha): 0.25 - 0.5 (No. of lakes)", "Lake Area Range (ha): 0.25 - 0.5 (Total Lake Area ha)", "Lake Area Range (ha): 0.5 - 1 (No. of lakes)", "Lake Area Range (ha): 0.5 - 1 (Total Lake Area ha)", "Lake Area Range (ha): 1 - 5 (No. of lakes)", "Lake Area Range (ha): 1 - 5 (Total Lake Area ha)", "Lake Area Range (ha): 5 - 10 (No. of lakes)", "Lake Area Range (ha): 5 - 10 (Total Lake Area ha)", "Lake Area Range (ha): 10 - 50 (No. of lakes)", "Lake Area Range (ha): 10 - 50 (Total Lake Area ha)", "Lake Area Range (ha): > 50 (No. of lakes)", "Lake Area Range (ha): > 50 (Total Lake Area ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1284, "line_end": 1292, "token_count_estimate": 737, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc6b569ed9e39445", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1293, "line_end": 1299, "token_count_estimate": 86, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cc98547a40570ca7", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the districts of Himachal Pradesh is given in Table 51 and Figure 61. It has been observed that, only 8 types of glacial lakes are distributed in Himachal Pradesh, where Other Moraine Dammed lakes are found to be the maximum with 216 (40.22%) lakes, followed by Other Glacial Erosion lakes with 167 (31.10%). Lahul & Spiti district contains maximum number of glacial lakes in comparison with other districts in Himachal Pradesh, with majority of Other Moraine Dammed lakes (35.14%), followed by Supra-glacial and Other Glacial Erosion lakes in equal proportion i.e. 22.70%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1301, "line_end": 1305, "token_count_estimate": 223, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d92cb463ed422919", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution\nType: table\nTable: Table 51: Type-wise distribution of GL in Districts of Himachal Pradesh\n\n| S. No. | District | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total: No. of Lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Chamba | 8 | 2 | 0 | 19 | 3 | 0 | 0 | 0 | 34 | 0 | 66 | 113.88 |\n| 2 | Kangra | 2 | 3 | 0 | 20 | 2 | 0 | 0 | 0 | 12 | 0 | 39 | 45.45 |\n| 3 | Kinnaur | 6 | 10 | 0 | 67 | 13 | 0 | 2 | 0 | 25 | 5 | 128 | 162.82 |\n| 4 | Kullu | 8 | 0 | 0 | 38 | 5 | 0 | 1 | 0 | 39 | 2 | 93 | 130.61 |\n| 5 | Lahul & Spiti | 21 | 13 | 1 | 65 | 42 | 0 | 0 | 0 | 42 | 1 | 185 | 470.18 |\n| 6 | Shimla | 1 | 0 | 0 | 7 | 0 | 0 | 3 | 0 | 15 | 0 | 26 | 60.78 |\n| | **Total** | **46** | **28** | **1** | **216** | **65** | **0** | **6** | **0** | **167** | **8** | **537** | **983.74** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 51: Type-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S. No.", "District", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1306, "line_end": 1314, "token_count_estimate": 639, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cebb9bc3c22dd279", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.3 District Level Statistics of Himachal Pradesh > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.3 District Level Statistics of Himachal Pradesh", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1315, "line_end": 1318, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eec8db8040b643a8", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Himachal Pradesh has been shown in Table 52 and Figure 62. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 515 (95.90%) with total lake area of 937.88 ha (95.34%), with majority of them situated in high altitude range i.e. 4,001 - 5,000 m. Whereas, remaining 4.10% glacial lakes are below 4,000 m elevation, all situated in medium altitude range. Lahul & Spiti district contains maximum number of glacial lakes above 4,000 m elevation in comparison with any other district in the state, with majority of them falling in very high altitude range i.e. > 5,000 m i.e. 54.31%. No lake is present in low altitude range in the state. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 63.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1320, "line_end": 1324, "token_count_estimate": 275, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30746f39c103c4f8", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: table\nTable: Table 52: Elevation range-wise distribution of GL in Districts of Himachal Pradesh\n\n| S. No. | District | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Chamba | 0 | 0.00 | 9 | 39.47 | 50 | 69.39 | 7 | 5.02 |\n| 2 | Kangra | 0 | 0.00 | 0 | 0.00 | 39 | 45.45 | 0 | 0.00 |\n| 3 | Kinnaur | 0 | 0.00 | 3 | 2.39 | 44 | 81.11 | 81 | 79.32 |\n| 4 | Kullu | 0 | 0.00 | 3 | 1.23 | 88 | 128.14 | 2 | 1.25 |\n| 5 | Lahul & Spiti | 0 | 0.00 | 7 | 2.76 | 71 | 326.21 | 107 | 141.21 |\n| 6 | Shimla | 0 | 0.00 | 0 | 0.00 | 26 | 60.78 | 0 | 0.00 |\n| | **Total** | **0** | **0** | **22** | **45.85** | **318** | **711.08** | **197** | **226.80** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 52: Elevation range-wise distribution of GL in Districts of Himachal Pradesh", "columns": ["S. No.", "District", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1325, "line_end": 1333, "token_count_estimate": 558, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "073559decb2bfe77", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1334, "line_end": 1345, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "77b830cdccdf96f5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand\nType: text\n\nUttarakhand state contains glacial lakes in six districts viz., Bageshwar, Chamoli, Pithoragarh, Rudraprayag, Tehri Garhwal, and Uttarkashi. Amongst which, Chamoli has the majority of glacial lakes covering 45.08% of the total lake area in the state.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand"], "chunk_type": "text", "line_start": 1347, "line_end": 1349, "token_count_estimate": 131, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9e549c922cde2f2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Area range-wise Distribution\nType: text\n\nGlacial lakes have been distributed in 6 districts of Uttarakhand for 5 classes of area ranges except >50 ha area range, and area range-wise distribution for those has been shown in Table 53 and Figure 64. Glacial lakes in Chamoli district are found to be the maximum with 192 (55.34%) occupying a total lake extent of 252.71 ha at 45.08%. About 319 (91.93%) lakes are with < 5 ha lake area contributing to 52.51% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 8.07%, predominantly of 5 - 10 ha in size.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1351, "line_end": 1355, "token_count_estimate": 217, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "791a3b958daa55d1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Area range-wise Distribution\nType: table\nTable: Table 53: Area range-wise distribution of GL in Districts of Uttarakhand\n\n| S. No. | District | 0.25 - 0.5: No. of lakes | 0.25 - 0.5: Total Lake Area (ha) | 0.5 - 1: No. of lakes | 0.5 - 1: Total Lake Area (ha) | 1 - 5: No. of lakes | 1 - 5: Total Lake Area (ha) | 5 - 10: No. of lakes | 5 - 10: Total Lake Area (ha) | 10 - 50: No. of lakes | 10 - 50: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Bageshwar | 3 | 0.99 | 3 | 2.18 | 2 | 6.02 | 0 | 0.00 | 0 | 0.00 | 8 | 9.19 |\n| 2 | Chamoli | 92 | 31.17 | 53 | 37.13 | 36 | 75.72 | 7 | 47.18 | 4 | 61.51 | 192 | 252.72 |\n| 3 | Pithoragarh | 13 | 4.53 | 10 | 6.51 | 13 | 25.08 | 3 | 20.42 | 4 | 51.65 | 43 | 108.18 |\n| 4 | Rudraprayag | 2 | 0.68 | 1 | 0.55 | 6 | 14.68 | 2 | 15.15 | 0 | 0.00 | 11 | 31.05 |\n| 5 | Tehri Garhwal | 0 | 0.00 | 4 | 3.18 | 3 | 5.34 | 2 | 12.91 | 1 | 25.56 | 10 | 46.99 |\n| 6 | Uttarkashi | 30 | 10.53 | 25 | 17.65 | 23 | 52.36 | 5 | 31.84 | 0 | 0.00 | 83 | 112.47 |\n| **Total** | | **140** | **47.99** | **96** | **67.20** | **83** | **179.20** | **19** | **127.50** | **9** | **138.72** | **347** | **560.60** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 53: Area range-wise distribution of GL in Districts of Uttarakhand", "columns": ["S. No.", "District", "0.25 - 0.5: No. of lakes", "0.25 - 0.5: Total Lake Area (ha)", "0.5 - 1: No. of lakes", "0.5 - 1: Total Lake Area (ha)", "1 - 5: No. of lakes", "1 - 5: Total Lake Area (ha)", "5 - 10: No. of lakes", "5 - 10: Total Lake Area (ha)", "10 - 50: No. of lakes", "10 - 50: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1356, "line_end": 1364, "token_count_estimate": 665, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7bd0d63e4e571ec8", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1365, "line_end": 1367, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b2e084867a5d3afd", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the districts of Uttarakhand is given in Table 54 and Figure 65. It has been observed that, only 7 types of glacial lakes are distributed in the state, where Supra-glacial lakes are found to be the maximum with 106 (30.55%) in the state, followed by Other Moraine Dammed lakes with 103 (29.68%). Chamoli district contains maximum number of glacial lakes in comparison with other districts in the state, with majority of Supra-glacial lakes (38.02%), followed by Other Moraine Dammed lakes i.e. 32.29%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1369, "line_end": 1373, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "15d8ca36e0fd9af6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Type-wise Distribution\nType: table\nTable: Table 54: Type-wise distribution of GL in Districts of Uttarakhand\n\n| S. No. | District | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total: No. of Lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Bageshwar | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 2 | 1 | 8 | 9.19 |\n| 2 | Chamoli | 8 | 4 | 0 | 62 | 73 | 0 | 11 | 0 | 30 | 4 | 192 | 252.71 |\n| 3 | Pithoragarh | 6 | 0 | 0 | 20 | 4 | 0 | 3 | 0 | 9 | 1 | 43 | 108.19 |\n| 4 | Rudraprayag | 0 | 0 | 0 | 0 | 2 | 0 | 4 | 0 | 5 | 0 | 11 | 31.06 |\n| 5 | Tehri Garhwal | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 5 | 1 | 10 | 46.99 |\n| 6 | Uttarkashi | 10 | 6 | 0 | 20 | 23 | 0 | 3 | 0 | 16 | 5 | 83 | 112.47 |\n| **Total** | | **25** | **11** | **0** | **103** | **106** | **0** | **23** | **0** | **67** | **12** | **347** | **560.61** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 54: Type-wise distribution of GL in Districts of Uttarakhand", "columns": ["S. No.", "District", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1374, "line_end": 1382, "token_count_estimate": 557, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a98f3ea45d7b4bda", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.4 District Level Statistics of Uttarkhand > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.4 District Level Statistics of Uttarkhand", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1383, "line_end": 1386, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "907f6e04de3b5cc0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Uttarakhand has been shown in Table 55 and Figure 66. Majority of glacial lakes (52.74%) are situated in high altitude i.e. 4,001 - 5,000 m elevation range with total lake area of 345.99 ha (61.72%). This is followed by glacial lakes in very high altitude elevation range with 44.09%. Chamoli district contains maximum number of glacial lakes above 4,000 m elevation in comparison with any other district in the state, with majority of them falling in very high altitude range i.e. > 5,000 m. Elevation range-type-wise distribution of glacial lakes in Uttarakhand has been represented in Figure 67.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1388, "line_end": 1392, "token_count_estimate": 229, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b33560bd263286ce", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: table\nTable: Table 55: Elevation range-wise distribution of GL in Districts of Uttarakhand\n\n| S. No. | District | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | Bageshwar | 0 | 0.00 | 1 | 0.41 | 7 | 8.78 | 0 | 0.00 |\n| 2 | Chamoli | 0 | 0.00 | 1 | 0.74 | 85 | 121.99 | 106 | 129.99 |\n| 3 | Pithoragarh | 0 | 0.00 | 3 | 0.93 | 22 | 72.18 | 18 | 35.08 |\n| 4 | Rudraprayag | 0 | 0.00 | 0 | 0.00 | 11 | 31.05 | 0 | 0.00 |\n| 5 | Tehri Garhwal | 0 | 0.00 | 1 | 1.11 | 9 | 45.89 | 0 | 0.00 |\n| 6 | Uttarkashi | 0 | 0.00 | 5 | 2.45 | 49 | 66.1 | 29 | 43.92 |\n| | **Total** | **0** | **0.00** | **11** | **5.64** | **183** | **345.99** | **153** | **208.99** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 55: Elevation range-wise distribution of GL in Districts of Uttarakhand", "columns": ["S. No.", "District", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1393, "line_end": 1401, "token_count_estimate": 574, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "27e6290ddb5c7ee0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\n78 | National Remote Sensing Centre, ISRO, Hyderabad\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 79\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**5.5.4 District Level Statistics of Ladakh (UT)**\n\nLadakh (UT) is the largest union territory of all in terms of area, divided in two districts viz., Kargil and Leh. Amongst which, Leh covers the majority of the total area.\n\n**Area range-wise Distribution**\n\nGlacial lakes have been distributed in both districts for all 6 classes of area ranges, and area range-wise distribution for both districts has been shown in Table 56 and Figure 68. Glacial lakes in Leh district are found to be the maximum with 2,912 (90.46%) occupying a total lake extent of 9,014.22 ha at 90.45%. About 2,547 (87.46%) lakes are with < 5 ha lake area contributing to 36.64% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 12.54%, predominantly of 5 - 10 ha in size.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1402, "line_end": 1421, "token_count_estimate": 338, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "813ad24511a57b66", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: table\nTable: Table 56: Area range-wise distribution of GL in Districts of Ladakh (UT)\n\n| S.No. | Lake Area Range (ha) | District: Kargil (No. of lakes) | District: Kargil (Total Lake Area (ha)) | District: Leh (No. of lakes) | District: Leh (Total Lake Area (ha)) | District: Total (No. of lakes) | District: Total (Total Lake Area (ha)) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 59 | 20.99 | 748 | 270.61 | 807 | 291.60 |\n| 2 | 0.5 - 1 | 59 | 43.17 | 682 | 482.69 | 741 | 525.86 |\n| 3 | 1 - 5 | 139 | 308.73 | 1,117 | 2,549.94 | 1,256 | 2,858.67 |\n| 4 | 5 - 10 | 30 | 209.11 | 207 | 1,420.06 | 237 | 1,629.17 |\n| 5 | 10 - 50 | 19 | 309.33 | 142 | 2,548.82 | 161 | 2,858.15 |\n| 6 | > 50 | 1 | 59.78 | 16 | 1,742.10 | 17 | 1,801.88 |\n| | **Total** | **307** | **951.12** | **2,912** | **9,014.22** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 56: Area range-wise distribution of GL in Districts of Ladakh (UT)", "columns": ["S.No.", "Lake Area Range (ha)", "District: Kargil (No. of lakes)", "District: Kargil (Total Lake Area (ha))", "District: Leh (No. of lakes)", "District: Leh (Total Lake Area (ha))", "District: Total (No. of lakes)", "District: Total (Total Lake Area (ha))"], "table_row_start": 1, "table_row_end": 7, "line_start": 1422, "line_end": 1430, "token_count_estimate": 462, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "98d769db6a01035f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the districts of Ladakh (UT) is given in Table 57 and Figure 69. It has been observed that, Other Glacial Erosion lakes are maximum with 1,585 (49.24%) in the UT, followed by Supra-glacial lakes with 642 (19.94%). Leh district contains maximum number of all types of glacial lakes in comparison with Kargil district in the UT, with majority of Other Glacial Erosion lakes (87.63%), followed by Supra-glacial lakes i.e. 38.92%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1431, "line_end": 1443, "token_count_estimate": 211, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7761bf273ab8122a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: table\nTable: Table 57: Type-wise distribution of GL in Districts of Ladakh (UT)\n\n| S.No. | District | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total: No. of Lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Kargil | 16 | 6 | 1 | 52 | 25 | 0 | 8 | 0 | 196 | 3 | 307 | 951.12 |\n| 2 | Leh | 199 | 176 | 28 | 302 | 617 | 1 | 108 | 1 | 1,389 | 91 | 2,912 | 9,014.22 |\n| | **Total** | **215** | **182** | **29** | **354** | **642** | **1** | **116** | **1** | **1,585** | **94** | **3,219** | **9,965.34** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 57: Type-wise distribution of GL in Districts of Ladakh (UT)", "columns": ["S.No.", "District", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 3, "line_start": 1444, "line_end": 1448, "token_count_estimate": 453, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb1b378d52a05730", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1449, "line_end": 1455, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4fa217099303ff9f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.6 District Level Statistics of Jammu & Kashmir (UT)\nType: text\n\nJammu & Kashmir (UT) is the second largest union territory of all in terms of area, has total 22 districts but only 15 districts have glacial lakes.\n\n**Area range-wise Distribution**\n\nGlacial lakes have been distributed in all 15 districts of J&K (UT) for all 6 classes of area ranges, and area range-wise distribution for those has been shown in Table 59 and Figure 72. Glacial lakes in Kishtwar district are found to be the maximum with 197 (36.08%) occupying a total lake extent of 392.64 ha at 13.63%. About 179 (90.86%) lakes are with < 5 ha lake area contributing to 46.57% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 9.14%, predominantly of 5 - 10 ha in size.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.6 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.6 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "text", "line_start": 1457, "line_end": 1465, "token_count_estimate": 259, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "43ab9702a18b510c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.6 District Level Statistics of Jammu & Kashmir (UT)\nType: table\nTable: Table 59: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n| S. No. | District | 0.25 - 0.5 ha (No. of lakes) | 0.25 - 0.5 ha (Total Lake Area) | 0.5 - 1 ha (No. of lakes) | 0.5 - 1 ha (Total Lake Area) | 1 - 5 ha (No. of lakes) | 1 - 5 ha (Total Lake Area) | 5 - 10 ha (No. of lakes) | 5 - 10 ha (Total Lake Area) | 10 - 50 ha (No. of lakes) | 10 - 50 ha (Total Lake Area) | > 50 ha (No. of lakes) | > 50 ha (Total Lake Area) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anantnag | 11 | 4.30 | 5 | 3.54 | 15 | 40.89 | 7 | 50.78 | 12 | 200.10 | 2 | 135.20 |\n| 2 | Badgam | 2 | 0.86 | 2 | 1.47 | 8 | 20.30 | 7 | 57.63 | 6 | 127.53 | 0 | 0.00 |\n| 3 | Bandipore | 6 | 2.44 | 10 | 7.31 | 29 | 73.84 | 7 | 50.27 | 12 | 322.77 | 0 | 0.00 |\n| 4 | Baramula | 4 | 1.64 | 2 | 1.12 | 2 | 3.67 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 5 | Doda | 2 | 0.61 | 3 | 1.78 | 6 | 8.35 | 2 | 19.20 | 0 | 0.00 | 0 | 0.00 |\n| 6 | Ganderbal | 8 | 3.03 | 9 | 6.70 | 20 | 42.83 | 2 | 13.67 | 5 | 86.00 | 1 | 161.04 |\n| 7 | Kishtwar | 70 | 24.81 | 55 | 37.26 | 54 | 120.81 | 13 | 92.22 | 5 | 117.54 | 0 | 0.00 |\n| 8 | Kulgam | 5 | 2.09 | 3 | 2.30 | 6 | 18.18 | 2 | 12.59 | 10 | 188.03 | 2 | 199.37 |\n| 9 | Kupwara | 0 | 0.00 | 1 | 0.68 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 10 | Muzaffarabad | 10 | 3.29 | 24 | 17.66 | 25 | 59.61 | 5 | 35.24 | 8 | 168.94 | 2 | 154.50 |\n| 11 | Punch | 2 | 0.79 | 9 | 6.79 | 6 | 13.41 | 2 | 12.56 | 3 | 46.65 | 0 | 0.00 |\n| 12 | Rajauri | 2 | 0.83 | 0 | 0.00 | 5 | 12.64 | 2 | 14.08 | 1 | 14.88 | 0 | 0.00 |\n| 13 | Reasi | 1 | 0.36 | 1 | 0.75 | 2 | 4.13 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 14 | Srinagar | 0 | 0.00 | 1 | 0.70 | 0 | 0.00 | 0 | 0.00 | 1 | 43.47 | 0 | 0.00 |\n| 15 | Udhampur | 0 | 0.00 | 0 | 0.00 | 1 | 4.73 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **123** | **45.05** | **125** | **88.08** | **179** | **423.41** | **49** | **358.24** | **63** | **1,315.91** | **7** | **650.10** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.6 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.6 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "table", "table_caption": "Table 59: Area range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "columns": ["S. No.", "District", "0.25 - 0.5 ha (No. of lakes)", "0.25 - 0.5 ha (Total Lake Area)", "0.5 - 1 ha (No. of lakes)", "0.5 - 1 ha (Total Lake Area)", "1 - 5 ha (No. of lakes)", "1 - 5 ha (Total Lake Area)", "5 - 10 ha (No. of lakes)", "5 - 10 ha (Total Lake Area)", "10 - 50 ha (No. of lakes)", "10 - 50 ha (Total Lake Area)", "> 50 ha (No. of lakes)", "> 50 ha (Total Lake Area)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1466, "line_end": 1483, "token_count_estimate": 1154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f45ccd91a7c3929", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.6 District Level Statistics of Jammu & Kashmir (UT)\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > 5.5.6 District Level Statistics of Jammu & Kashmir (UT)", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "5.5.6 District Level Statistics of Jammu & Kashmir (UT)"], "chunk_type": "text", "line_start": 1484, "line_end": 1488, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e3fecfb30312f214", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the districts of J&K (UT) is given in Table 60 and Figure 73. It has been observed that, only 7 types of glacial lakes are distributed in the UT, where Other Glacial Erosion lakes are found to be the maximum with 319 (58.42%) in the UT, followed by Other Moraine Dammed lakes with 69 (12.64%). Kishtwar district contains maximum number of all types of glacial lakes in comparison with other districts in the UT, with majority of Other Glacial Erosion lakes (35.03%), followed by Supra-glacial lakes i.e. 23.86%.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1490, "line_end": 1494, "token_count_estimate": 204, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed63dc2f1a3afee0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Type-wise Distribution\nType: table\nTable: Table 60: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n| S. No. | District | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total: No. of Lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anantnag | 1 | 1 | 0 | 3 | 0 | 0 | 4 | 0 | 42 | 1 | 52 | 434.82 |\n| 2 | Badgam | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 23 | 0 | 25 | 207.80 |\n| 3 | Bandipore | 0 | 0 | 0 | 11 | 0 | 0 | 13 | 0 | 39 | 1 | 64 | 456.63 |\n| 4 | Baramula | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 0 | 8 | 6.43 |\n| 5 | Doda | 1 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 6 | 0 | 13 | 29.94 |\n| 6 | Ganderbal | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 39 | 0 | 45 | 313.26 |\n| 7 | Kishtwar | 15 | 8 | 0 | 40 | 47 | 0 | 14 | 0 | 69 | 4 | 197 | 392.64 |\n| 8 | Kulgam | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 24 | 1 | 28 | 422.55 |\n| 9 | Kupwara | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0.68 |\n| 10 | Muzaffarabad | 2 | 0 | 0 | 12 | 6 | 0 | 13 | 0 | 39 | 2 | 74 | 439.24 |\n| 11 | Punch | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 20 | 0 | 22 | 80.21 |\n| 12 | Rajauri | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 0 | 10 | 42.43 |\n| 13 | Reasi | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 4 | 5.25 |\n| 14 | Srinagar | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 44.17 |\n| 15 | Udhampur | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4.73 |\n| | **Total** | **20** | **9** | **0** | **69** | **54** | **0** | **66** | **0** | **319** | **9** | **546** | **2,880.79** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 60: Type-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "columns": ["S. No.", "District", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total: No. of Lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1495, "line_end": 1512, "token_count_estimate": 1055, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abcbee2dcb911be5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Type-wise Distribution\nType: text\n\n***", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1513, "line_end": 1515, "token_count_estimate": 54, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "53d41ed9e8e70133", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of J&K (UT) has been shown in Table 61 and Figure 74. Majority of glacial lakes are situated below 4,000 m elevation range i.e. 277 (50.73%) with total lake area of 2,087.45 ha (72.46%), all in medium altitude range only. Whereas, remaining 49.27% glacial lakes are above 4,000 m elevation, majorly in high altitude range. Kishtwar district contains maximum number of glacial lakes above and below 4,000 m elevation in comparison with any other district in the UT, with majority of them falling in high altitude range i.e. 4,001 - 5,000 m i.e. 70.01%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 75.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1517, "line_end": 1521, "token_count_estimate": 249, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae9b3b86b15635c9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: table\nTable: Table 61: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\n| S. No. | District | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anantnag | 0 | 0.00 | 35 | 368.53 | 17 | 66.29 | 0 | 0.00 |\n| 2 | Badgam | 0 | 0.00 | 11 | 123.49 | 14 | 84.32 | 0 | 0.00 |\n| 3 | Bandipore | 0 | 0.00 | 46 | 413.64 | 18 | 42.99 | 0 | 0.00 |\n| 4 | Baramula | 0 | 0.00 | 7 | 6.02 | 1 | 0.41 | 0 | 0.00 |\n| 5 | Doda | 0 | 0.00 | 5 | 13.99 | 8 | 15.95 | 0 | 0.00 |\n| 6 | Ganderbal | 0 | 0.00 | 27 | 278.90 | 18 | 34.37 | 0 | 0.00 |\n| 7 | Kishtwar | 0 | 0.00 | 58 | 83.66 | 138 | 308.04 | 1 | 0.93 |\n| 8 | Kulgam | 0 | 0.00 | 23 | 365.56 | 5 | 56.98 | 0 | 0.00 |\n| 9 | Kupwara | 0 | 0.00 | 1 | 0.68 | 0 | 0.00 | 0 | 0.00 |\n| 10 | Muzaffarabad | 0 | 0.00 | 30 | 262.87 | 44 | 176.37 | 0 | 0.00 |\n| 11 | Punch | 0 | 0.00 | 18 | 76.19 | 4 | 4.02 | 0 | 0.00 |\n| 12 | Rajauri | 0 | 0.00 | 9 | 39.77 | 1 | 2.67 | 0 | 0.00 |\n| 13 | Reasi | 0 | 0.00 | 4 | 5.25 | 0 | 0.00 | 0 | 0.00 |\n| 14 | Srinagar | 0 | 0.00 | 2 | 44.17 | 0 | 0.00 | 0 | 0.00 |\n| 15 | Udhampur | 0 | 0.00 | 1 | 4.73 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **277** | **2,087.45** | **268** | **792.42** | **1** | **0.93** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 61: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "columns": ["S. No.", "District", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1522, "line_end": 1539, "token_count_estimate": 904, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "331dfdd1c8bfc4d6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1540, "line_end": 1543, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1703c297d0a8baa4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: figure\nFigure: Figure 74: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)\n\nFigure 74: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 74: Elevation range-wise distribution of GL in Districts of Jammu & Kashmir (UT)", "line_start": 1544, "line_end": 1544, "token_count_estimate": 104, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1735a618a363f0a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1545, "line_end": 1551, "token_count_estimate": 74, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f9331e5a99c29e67", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: figure\nFigure: Figure 75: Elevation range-Type-wise spatial distribution of GL in Jammu & Kashmir (UT)\n\nFigure 75: Elevation range-Type-wise spatial distribution of GL in Jammu & Kashmir (UT)", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 75: Elevation range-Type-wise spatial distribution of GL in Jammu & Kashmir (UT)", "line_start": 1552, "line_end": 1552, "token_count_estimate": 108, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b4bb876f64ccfb88", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > State/UT-Elevation range-wise Distribution > Elevation range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "State/UT-Elevation range-wise Distribution", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1553, "line_end": 1558, "token_count_estimate": 73, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "219eb483e38d8558", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics\nType: text\n\nTransboundary area of Indian Himalayan River Basins has a total area of 6,05,904 Km² i.e. 61.22%. This transboundary region covers majority part of it in China, Nepal and Bhutan. A total of 20,473 glacial lakes lies within transboundary region, covering a total area of 1,00,922.36 ha i.e. 16.66% of the total area of the Indian Himalayan River Basins under transboundary region.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 1560, "line_end": 1562, "token_count_estimate": 149, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "Nepal"], "lake_ids": []}}
{"id": "3d3fcedfcd83bd7b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\nIn Transboundary region, glacial lakes have been distributed in all 6 classes of area ranges. Table 62 and Figure 76 shows the area range-wise distribution of glacial lakes for the Transboundary region. About 16,963 (82.86%) lakes are with < 5 ha lake area contributing to 22.46% of total lake area. The remaining lakes with > 5 ha in size are only 3,510 (17.14%) but contributing to 77.54% of total lake area in the region.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1564, "line_end": 1568, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d754eb66ba816b05", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 62: Area range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 4,548 | 1,623.80 | 1.61 |\n| 2 | 0.5 - 1 | 4,646 | 3,329.63 | 3.30 |\n| 3 | 1 - 5 | 7,769 | 17,708.90 | 17.55 |\n| 4 | 5 - 10 | 1,733 | 12,154.62 | 12.04 |\n| 5 | 10 - 50 | 1,536 | 29,852.91 | 29.58 |\n| 6 | > 50 | 241 | 36,252.50 | 35.92 |\n| | **Total** | **20,473** | **1,00,922.36** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 62: Area range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1569, "line_end": 1577, "token_count_estimate": 299, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1b5d843e82f44af6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Transboundary region is given in Table 63 and Figure 77. All types of glacial lakes are present in the Transboundary region, where Other Glacial Erosion lakes are found to be the maximum with 11,818 (57.72%) occupying a total lake extent of 44,988.30 ha at 44.58% in the region. After that, Other Moraine Dammed and Other Glacial lakes are in majority with 4,691 (22.91%) and 1,579 (7.71%) and extend over a total area of 12,964.75 ha (12.85%) and 12,833.52 ha (12.72%) respectively.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1580, "line_end": 1584, "token_count_estimate": 199, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91b885a865fba5ef", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-wise Distribution\nType: table\nTable: Table 63: Type-wise distribution of GL in Transboundary region\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 714 | 18,344.22 | 18.18 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 127 | 789.33 | 0.78 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 26 | 52.45 | 0.05 |\n| 4 | M(o) | Other Moraine Dammed Lake | 4,691 | 12,964.75 | 12.85 |\n| 5 | I(s) | Supra-glacial Lake | 759 | 737.54 | 0.73 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 4 | 237.29 | 0.24 |\n| 7 | E(c) | Cirque Erosion Lake | 747 | 6,515.70 | 6.46 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 8 | 3,459.26 | 3.43 |\n| 9 | E(o) | Other Glacial Erosion Lake | 11,818 | 44,988.30 | 44.58 |\n| 10 | O | Other Glacial Lake | 1,579 | 12,833.52 | 12.72 |\n| | | **Total** | **20,473** | **1,00,922.36** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 63: Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1585, "line_end": 1597, "token_count_estimate": 495, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d80c00247221f395", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 64 and Figure 78. The lakes with < 5 ha in size (82.86%) are dominant with Other Glacial Erosion (59.03%) and Other Moraine Dammed lakes (24.48%). Lakes with > 5 ha (17.14%) are also dominated by Other Glacial Erosion lakes (51.40%). All types of Moraine-dammed lakes, which constitute about 27.15%, are majorly with < 5 ha in water spread.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1601, "line_end": 1605, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6246d4a6f1941a5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 64: Area range-wise vs. Type-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 16 | 29 | 7 | 1,196 | 402 | 0 | 16 | 0 | 2,456 | 426 | 4,548 |\n| 2 | 0.5 - 1 | 32 | 28 | 6 | 1,202 | 226 | 1 | 42 | 0 | 2,739 | 370 | 4,646 |\n| 3 | 1 - 5 | 201 | 44 | 11 | 1,754 | 115 | 2 | 292 | 0 | 4,819 | 531 | 7,769 |\n| 4 | 5 - 10 | 132 | 8 | 1 | 322 | 8 | 0 | 185 | 0 | 983 | 94 | 1,733 |\n| 5 | 10 - 50 | 249 | 13 | 1 | 203 | 8 | 0 | 207 | 1 | 739 | 115 | 1,536 |\n| 6 | > 50 | 84 | 5 | 0 | 14 | 0 | 1 | 5 | 7 | 82 | 43 | 241 |\n| **Total** | | **714** | **127** | **26** | **4,691** | **759** | **4** | **747** | **8** | **11,818** | **1,579** | **20,473** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area range-Type-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 64: Area range-wise vs. Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1606, "line_end": 1614, "token_count_estimate": 532, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c8ea185561a3aba3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Elevation range-wise distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Transboundary region has been shown in Table 65 and Figure 79. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 19,990 (97.64%) with total lake area of 94,789.30 ha, contributing 93.92% of lake area.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Elevation range-wise distribution"], "chunk_type": "text", "line_start": 1617, "line_end": 1621, "token_count_estimate": 127, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7c115eb3f1a7d689", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Elevation range-wise distribution\nType: table\nTable: Table 65: Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation range (m) | No. of lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 8 | 117.28 | 0.12 |\n| 2 | 3,001 - 4,000 | 475 | 6,015.78 | 5.96 |\n| 3 | 4,001 - 5,000 | 9,602 | 51,953.95 | 51.48 |\n| 4 | > 5,000 | 10,388 | 42,835.35 | 42.44 |\n| **Total** | | **20,473** | **1,00,922.36** | **100.00** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Elevation range-wise distribution"], "chunk_type": "table", "table_caption": "Table 65: Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation range (m)", "No. of lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1622, "line_end": 1628, "token_count_estimate": 258, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06946a81979e8204", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Elevation range-wise distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Elevation range-wise distribution"], "chunk_type": "text", "line_start": 1629, "line_end": 1632, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4c56da98f0c163ea", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 66 and Figure 80. It is noted that, 46.90% of glacial lakes (9,602) are situated in high altitude range i.e. 4,001 - 5,000 m, which also constitutes majority of total lake area within that range i.e. 51.48%. However, 8 glacial lakes lies below 3,000 m, has 37.50% of its lakes < 5 ha in size. 86.76% of lakes lying in very high-altitude range are < 5 ha, majorly of size ranging 1 - 5 ha (i.e. 3,882), followed by lakes of size 0.5 - 1 ha (i.e. 2,578). It has been further noticed that, 39.17% of lakes > 5 ha are lying within in the very high altitude range, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area Elevation-range-wise Distribution"], "chunk_type": "text", "line_start": 1634, "line_end": 1638, "token_count_estimate": 269, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ac96376bfa3b657", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution\nType: table\nTable: Table 66: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) | Total: No. of lakes | Total: Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 66 | 23.93 | 1,929 | 693.92 | 2,553 | 905.95 | 4,548 | 1,623.80 |\n| 2 | 0.5 - 1 | 1 | 0.62 | 61 | 42.87 | 2,006 | 1,443.66 | 2,578 | 1,842.47 | 4,646 | 3,329.63 |\n| 3 | 1 - 5 | 2 | 5.34 | 189 | 490.16 | 3,696 | 8,565.48 | 3,882 | 8,647.91 | 7,769 | 17,708.90 |\n| 4 | 5 - 10 | 2 | 15.62 | 75 | 531.29 | 947 | 6,694.58 | 709 | 4,913.13 | 1,733 | 12,154.62 |\n| 5 | 10 - 50 | 3 | 95.70 | 70 | 1,486.91 | 893 | 17,199.67 | 570 | 11,070.63 | 1,536 | 29,852.91 |\n| 6 | > 50 | 0 | 0.00 | 14 | 3,440.62 | 131 | 17,356.62 | 96 | 15,455.26 | 241 | 36,252.50 |\n| Total | | 8 | 117.28 | 475 | 6,015.78 | 9,602 | 51,953.95 | 10,388 | 42,835.36 | 20,473 | 1,00,922.36 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area Elevation-range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 66: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)", "Total: No. of lakes", "Total: Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1639, "line_end": 1647, "token_count_estimate": 698, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c24166c4068e96c6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution\nType: figure\nFigure: Figure 80: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n**Figure 80: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area Elevation-range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 80: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region", "line_start": 1649, "line_end": 1649, "token_count_estimate": 105, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae4f91d96ba93d24", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Area Elevation-range-wise Distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Area Elevation-range-wise Distribution"], "chunk_type": "text", "line_start": 1650, "line_end": 1657, "token_count_estimate": 72, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93766c6b79c89366", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 67 and Figure 81. The dominant lake type in the Transboundary i.e., Other Glacial Erosion lakes (57.72%) are predominantly located in the elevation range 4,001 - 5,000 m (55.86%). The other dominant lake type, namely, Other Moraine Dammed and Other Glacial Lakes are also majorly distributed in very high-altitude range > 5,000 m and 4001 – 5,000 m elevation range, i.e. 78.08% and 53.89% respectively. Majority i.e. 76.33% of all types of Moraine-dammed lakes lies in > 5,000 m.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Type-Elevation range-wise distribution"], "chunk_type": "text", "line_start": 1659, "line_end": 1663, "token_count_estimate": 224, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8bc4f44b81b49108", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution\nType: table\nTable: Table 67: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation Range | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 8 |\n| 2 | 3,001 - 4,000 | 12 | 1 | 0 | 32 | 29 | 0 | 47 | 2 | 303 | 49 | 475 |\n| 3 | 4,001 - 5,000 | 206 | 67 | 2 | 992 | 319 | 0 | 559 | 5 | 6,601 | 851 | 9,602 |\n| 4 | > 5,000 | 496 | 59 | 24 | 3,663 | 411 | 4 | 141 | 1 | 4,910 | 679 | 10,388 |\n| Total | | 713 | 127 | 26 | 4,691 | 759 | 4 | 747 | 8 | 11,818 | 1,579 | 20,473 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Type-Elevation range-wise distribution"], "chunk_type": "table", "table_caption": "Table 67: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation Range", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1664, "line_end": 1670, "token_count_estimate": 505, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4b020e86670de6b5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution\nType: figure\nFigure: Figure 81: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n**Figure 81: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Type-Elevation range-wise distribution"], "chunk_type": "figure", "figure_caption": "Figure 81: Type-wise vs. Elevation range-wise distribution of GL in Transboundary region", "line_start": 1672, "line_end": 1672, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8981c815085765a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nEnd Moraine Glacial Lakes at the snout of Glacier in Kosi Subbasin, located in Ganga Basin as seen in FCC satellite image\n\nSatellite: Resourcesat-2\nSensor: LISS-IV MX\nDate of Image: 26.11.2016\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS > 5.6 Transboundary Region Statistics > Type-Elevation range-wise distribution", "section_headings": ["GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "5.6 Transboundary Region Statistics", "Type-Elevation range-wise distribution"], "chunk_type": "text", "line_start": 1673, "line_end": 1691, "token_count_estimate": 145, "basins": ["Ganga"], "subbasins": ["Kosi"], "countries": [], "lake_ids": []}}
{"id": "b385640143ab66d4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 82 shows the layout map representing 2° X 2° grid overlaid on satellite image acquisition year layer covering the Indian Himalayan River basins. A total of 42 grids covered the entire study area, of which 39 grids contain glacial lakes.\n\nFigure 82 shows the layout map representing 2° X 2° grid overlaid on satellite image acquisition year layer covering the Indian Himalayan River basins. A total of 42 grids covered the entire study area, of which 39 grids contain glacial lakes.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 82 shows the layout map representing 2° X 2° grid overlaid on satellite image acquisition year layer covering the Indian Himalayan River basins. A total of 42 grids covered the entire study area, of which 39 grids contain glacial lakes.", "line_start": 1694, "line_end": 1694, "token_count_estimate": 146, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d4980e9503a1fb93", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 82: Layout of 2° X 2° grid and year of satellite data used\n\nFigure 82: Layout of 2° X 2° grid and year of satellite data used", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 82: Layout of 2° X 2° grid and year of satellite data used", "line_start": 1696, "line_end": 1696, "token_count_estimate": 68, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f743b7c6a5a6f13c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nJammu & Kashmir, Ladakh\nMap 7\nPlate No: 4\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 18)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1697, "line_end": 1714, "token_count_estimate": 113, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a970b74fb1a27e00", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 17 | 2 | 6 | 105 | 0 | 0 | 0 | 69 | 2 | 204 |\n| 2 | 0.5 - 1 | 3 | 26 | 1 | 20 | 80 | 0 | 3 | 0 | 137 | 1 | 271 |\n| 3 | 1 - 5 | 10 | 26 | 2 | 30 | 36 | 0 | 30 | 0 | 348 | 5 | 487 |\n| 4 | 5 - 10 | 3 | 5 | 0 | 3 | 7 | 0 | 19 | 0 | 64 | 0 | 101 |\n| 5 | 10 - 50 | 3 | 4 | 0 | 1 | 2 | 0 | 16 | 0 | 51 | 1 | 78 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 5 |\n| | Total | 22 | 78 | 5 | 60 | 230 | 0 | 70 | 0 | 671 | 10 | 1146 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1715, "line_end": 1723, "token_count_estimate": 512, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7463cdf32e29218b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nJammu & Kashmir, Ladakh\nMap 8\nPlate No: 4\n\nData Source: Resourcesat-2 LISS-IV\n\nGL Area Range: <1 ha, 1 - 10 ha, 10 - 50 ha, > 50 ha\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\nSoI 250k: 42L, 42P, 43I, 43M, 52A, 43J, 43N, 52B\n\n**Basin**\nIndus\nGanga\nBrahmaputra\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1724, "line_end": 1766, "token_count_estimate": 274, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8bc3d6c3b4cd7679", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 10 | 9.6 |\n| 2 | 3,001 - 4,000 | 224 | 1,075.5 |\n| 3 | 4,001 - 5,000 | 901 | 2,864.5 |\n| 4 | > 5,000 | 11 | 17.6 |\n| | Total | 1146 | 3,967.1 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1767, "line_end": 1773, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dc9560221c64009a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nLadakh, Transboundary\nMap 9\nPlate No: 5\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 14)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1774, "line_end": 1816, "token_count_estimate": 246, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "3d3db0e206ac1d4b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 11 | 24 | 7 | 19 | 124 | 0 | 0 | 0 | 17 | 4 | 206 |\n| 2 | 0.5 - 1 | 11 | 20 | 3 | 27 | 91 | 0 | 0 | 0 | 19 | 7 | 178 |\n| 3 | 1 - 5 | 38 | 28 | 9 | 18 | 34 | 0 | 3 | 0 | 42 | 6 | 178 |\n| 4 | 5 - 10 | 8 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 10 | 1 | 23 |\n| 5 | 10 - 50 | 6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 12 |\n| 6 | > 50 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| | Total | 74 | 74 | 22 | 65 | 250 | 0 | 3 | 0 | 91 | 19 | 598 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1817, "line_end": 1825, "token_count_estimate": 599, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa40f4e40ca80837", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nLadakh, Transboundary\nMap 10\nPlate No: 5\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nIndian Himalayan Catchment Region\nIndia\n\nSoI 250k\n\nBasin\nIndus\nGanga\nBrahmaputra\n\n52A\n52B\n52E\n52F\n52J\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1826, "line_end": 1881, "token_count_estimate": 242, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e4e2150150fb832d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 46 | 25.7 |\n| 3 | 4,001 - 5,000 | 261 | 353.6 |\n| 4 | > 5,000 | 291 | 669.6 |\n| | Total | 598 | 1,048.9 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1882, "line_end": 1888, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c36d9ee5df57087", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nLadakh, Transboundary\n\nMap 11\n\nPlate No: 6\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1889, "line_end": 1936, "token_count_estimate": 239, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "6839ce0627b01023", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake (End-moraine Dammed Lake) | Types of Glacial Lakes: Moraine Dammed Lake (Lateral Moraine Dammed Lake) | Types of Glacial Lakes: Moraine Dammed Lake (Lateral Moraine Dammed Lake with Ice) | Types of Glacial Lakes: Moraine Dammed Lake (Other Moraine Dammed Lake) | Types of Glacial Lakes: Ice Dammed Lake (Supra-glacial Lake) | Types of Glacial Lakes: Ice Dammed Lake (Glacier Ice-dammed Lake) | Types of Glacial Lakes: Erosion Lake (Cirque Erosion Lake) | Types of Glacial Lakes: Erosion Lake (Glacier Trough Valley Erosion Lake) | Types of Glacial Lakes: Erosion Lake (Other Glacial Erosion Lake) | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | | | 0 | 1 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake (End-moraine Dammed Lake)", "Types of Glacial Lakes: Moraine Dammed Lake (Lateral Moraine Dammed Lake)", "Types of Glacial Lakes: Moraine Dammed Lake (Lateral Moraine Dammed Lake with Ice)", "Types of Glacial Lakes: Moraine Dammed Lake (Other Moraine Dammed Lake)", "Types of Glacial Lakes: Ice Dammed Lake (Supra-glacial Lake)", "Types of Glacial Lakes: Ice Dammed Lake (Glacier Ice-dammed Lake)", "Types of Glacial Lakes: Erosion Lake (Cirque Erosion Lake)", "Types of Glacial Lakes: Erosion Lake (Glacier Trough Valley Erosion Lake)", "Types of Glacial Lakes: Erosion Lake (Other Glacial Erosion Lake)", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1937, "line_end": 1945, "token_count_estimate": 604, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9a5ca0cb94f0c68c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nLadakh, Transboundary\n\nMap 12\n\nPlate No: 6\n\nData Source: Resourcesat-2 LISS-IV\n\nGL Area Range: <1 ha, 1 - 10 ha, 10 - 50 ha, > 50 ha\n\nDistribution of Glacial Lake Types\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\nLocation Map\nIndian Himalayan Catchment Region\n\nBasin\n*   Indus\n*   Ganga\n*   Brahmaputra\n\nSoI 250k\n52J, 52N, 61B\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 1946, "line_end": 1992, "token_count_estimate": 251, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9b0705e966dcc52f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 2 | 7.0 |\n| Total | | 2 | 7.0 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1993, "line_end": 1999, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1fbd1c74654e92b4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nTransboundary Map 13 Plate No: 7\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2000, "line_end": 2042, "token_count_estimate": 245, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "7b32b19f714fd14c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 1 - 5 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2043, "line_end": 2051, "token_count_estimate": 508, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1d0c8dfb772981c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nTransboundary Map 14 Plate No: 7\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\nDistribution of Glacial Lake Types\n\nLocation Map\nIndian Himalayan Catchment Region\nIndia\n\nBasin\nIndus\nGanga\nBrahmaputra\n\nSoI 250k\n61B\n61F\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2052, "line_end": 2091, "token_count_estimate": 215, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "286434d49c1048f8", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 2 | 5.2 |\n| | Total | 2 | 5.2 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2092, "line_end": 2098, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aca9461216b486d0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nMap 17\n\nHimachal Pradesh, Jammu & Kashmir, Ladakh\n\nPlate No: 9\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 17)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2099, "line_end": 2143, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "bd78391044eb9eef", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 7 | 0 | 24 | 37 | 0 | 1 | 0 | 48 | 0 | 118 |\n| 2 | 0.5 - 1 | 3 | 6 | 0 | 16 | 18 | 0 | 2 | 0 | 39 | 1 | 85 |\n| 3 | 1 - 5 | 12 | 2 | 0 | 25 | 2 | 0 | 11 | 0 | 75 | 2 | 129 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 2 | 1 | 0 | 9 | 0 | 14 | 0 | 30 |\n| 5 | 10 - 50 | 5 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 20 | 1 | 34 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 4 |\n| Total | | 25 | 15 | 0 | 67 | 58 | 0 | 32 | 0 | 197 | 6 | 400 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2144, "line_end": 2152, "token_count_estimate": 508, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "407b3a5bca008a65", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nMap 18\n\nHimachal Pradesh, Jammu & Kashmir, Ladakh\n\nPlate No: 9\n\nData Source: Resourcesat-2 LISS-IV\n\nGL Area Range <1 ha  1 - 10 ha  10 - 50 ha  > 50 ha\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2153, "line_end": 2182, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e2dec3890d6d6a71", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 156 | 978.7 |\n| 3 | 4,001 - 5,000 | 235 | 632.0 |\n| 4 | > 5,000 | 9 | 6.5 |\n| Total | | 400 | 1,617.2 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2183, "line_end": 2189, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ab72fa2ababd08a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nHimachal Pradesh, Jammu & Kashmir, Ladakh, Transboundary | Map 19 | Plate No: 10\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 19)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2190, "line_end": 2216, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0268a4489250ca59", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 11 | 6 | 3 | 52 | 39 | 0 | 0 | 0 | 45 | 9 | **165** |\n| 2 | 0.5 - 1 | 11 | 6 | 0 | 43 | 17 | 0 | 1 | 0 | 29 | 7 | **114** |\n| 3 | 1 - 5 | 35 | 5 | 0 | 44 | 6 | 0 | 4 | 0 | 50 | 7 | **151** |\n| 4 | 5 - 10 | 15 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 12 | 1 | **34** |\n| 5 | 10 - 50 | 4 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 5 | 3 | **15** |\n| 6 | > 50 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | **6** |\n| **Total** | | **79** | **17** | **3** | **146** | **62** | **0** | **7** | **0** | **141** | **30** | **485** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2217, "line_end": 2225, "token_count_estimate": 570, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1cc3aa718862a51f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nHimachal Pradesh, Jammu & Kashmir, Ladakh, Transboundary | Map 20 | Plate No: 10\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha | 1 - 10 ha | 10 - 50 ha | > 50 ha\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\n\n**SoI 250k**\n52B | 52F | 52J\n52C | 52G | 52K\n52D | 52H | 52L\n\n**Basin**\nIndus\nGanga\nBrahmaputra\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2226, "line_end": 2269, "token_count_estimate": 295, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cbc8284632c35b5e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 8 | 9.3 |\n| 3 | 4,001 - 5,000 | 130 | 489.1 |\n| 4 | > 5,000 | 347 | 1,177.7 |\n| | **Total** | **485** | **1,676.1** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2270, "line_end": 2276, "token_count_estimate": 180, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9e214a2833fa29f4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n\\> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Map 21**\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nLadakh, Transboundary\nPlate No: 11\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 17)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2277, "line_end": 2319, "token_count_estimate": 256, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "d5298a5c27201e4e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 1 | 32 | 9 | 0 | 1 | 0 | 10 | 3 | 56 |\n| 2 | 0.5 - 1 | 6 | 0 | 0 | 24 | 14 | 0 | 0 | 0 | 17 | 4 | 65 |\n| 3 | 1 - 5 | 23 | 0 | 0 | 29 | 2 | 1 | 8 | 0 | 45 | 4 | 112 |\n| 4 | 5 - 10 | 7 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 8 | 0 | 21 |\n| 5 | 10 - 50 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 8 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| Total | | 42 | 0 | 1 | 91 | 25 | 1 | 9 | 0 | 82 | 12 | 263 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2320, "line_end": 2328, "token_count_estimate": 599, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "57c114502f383dc2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Map 22**\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nLadakh, Transboundary\nPlate No: 11\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\nDistribution of Glacial Lake Types\nLocation Map\nIndian Himalayan Catchment Region\nIndia\n\nBasin\nIndus\nGanga\nBrahmaputra\n\nSoI 250k\n52J 52N 61B\n52K 52O 61C\n52L 52P 61D\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2329, "line_end": 2373, "token_count_estimate": 241, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "13acb0669390753f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 263 | 694.8 |\n| Total | | 263 | 694.8 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2374, "line_end": 2380, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c1ba7357976493d5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nUttarakhand, Transboundary\nMap 31\nPlate No: 16\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 14)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2381, "line_end": 2425, "token_count_estimate": 246, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "339bd2bc376c3199", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 0 | 1 | 54 | 7 | 0 | 1 | 0 | 57 | 80 | 203 |\n| 2 | 0.5 - 1 | 1 | 2 | 0 | 36 | 4 | 0 | 2 | 0 | 64 | 83 | 192 |\n| 3 | 1 - 5 | 27 | 0 | 1 | 46 | 1 | 0 | 13 | 0 | 100 | 84 | 272 |\n| 4 | 5 - 10 | 7 | 0 | 0 | 8 | 0 | 0 | 2 | 0 | 24 | 10 | 51 |\n| 5 | 10 - 50 | 14 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 13 | 20 | 50 |\n| 6 | > 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 9 |\n| | **Total** | **54** | **2** | **2** | **147** | **12** | **0** | **18** | **0** | **261** | **281** | **777** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2426, "line_end": 2434, "token_count_estimate": 535, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "773875b6bd9f7a27", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nUttarakhand, Transboundary\nMap 32\nPlate No: 16\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha, 1 - 10 ha, 10 - 50 ha, > 50 ha\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\n* Indian Himalayan Catchment Region\n* India\n* **Basin:** Indus, Ganga, Brahmaputra\n* **SoI 250k:** 61D, 61H, 62A, 62E, 62B, 62F, 62J\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2435, "line_end": 2474, "token_count_estimate": 285, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0e8c4e40495a4300", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 67 | 1,243.7 |\n| 4 | > 5,000 | 710 | 2,865.1 |\n| | **Total** | **777** | **4,108.8** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2475, "line_end": 2481, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1c034b331894d8e0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\n**DISCLAIMER:** The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nTransboundary\nMap 39\nPlate No: 20\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2482, "line_end": 2526, "token_count_estimate": 263, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "7dd64009c3f9b747", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 11 | 1 | 0 | 0 | 0 | 45 | 1 | 58 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 14 | 2 | 0 | 0 | 0 | 39 | 4 | 59 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 27 | 0 | 0 | 1 | 0 | 59 | 3 | 90 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 8 | 2 | 12 |\n| 5 | 10 - 50 | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 7 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| Total | | 1 | 1 | 0 | 56 | 3 | 0 | 1 | 0 | 154 | 11 | 227 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2527, "line_end": 2535, "token_count_estimate": 508, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d630121543ed7454", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nTransboundary\nMap 40\nPlate No: 20\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range <1 ha  1 - 10 ha  10 - 50 ha  > 50 ha\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\n\n**Indian Himalayan Catchment Region**\nIndia\nSoI 250k\n77J\n77N\n82A\n82B\n\n**Basin**\nIndus\nGanga\nBrahmaputra\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2536, "line_end": 2584, "token_count_estimate": 252, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "a3ed0f898554340f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 9 | 17.1 |\n| 4 | > 5,000 | 218 | 505.6 |\n| Total | | 227 | 522.7 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2585, "line_end": 2591, "token_count_estimate": 160, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc0210de37403bd4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nTransboundary\nMap 41\nPlate No: 21\n\nLhasa Tsangpo\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 10)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2592, "line_end": 2635, "token_count_estimate": 259, "basins": [], "subbasins": ["Lhasa Tsangpo", "Lower Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "fe6c7340a9634f32", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 16 | 2 | 0 | 0 | 0 | 40 | 94 | 152 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 28 | 3 | 0 | 1 | 0 | 54 | 72 | 159 |\n| 3 | 1 - 5 | 5 | 2 | 0 | 68 | 1 | 0 | 0 | 0 | 120 | 118 | 314 |\n| 4 | 5 - 10 | 7 | 0 | 0 | 11 | 0 | 0 | 1 | 0 | 21 | 26 | 66 |\n| 5 | 10 - 50 | 10 | 1 | 0 | 6 | 0 | 0 | 0 | 0 | 20 | 13 | 50 |\n| 6 | > 50 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 12 | 8 | 24 |\n| | Total | 25 | 4 | 0 | 130 | 6 | 0 | 2 | 0 | 267 | 331 | 765 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2636, "line_end": 2644, "token_count_estimate": 512, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b583d523bb93c44a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nTransboundary\nMap 42\nPlate No: 21\n\nLhasa Tsangpo\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha | 1 - 10 ha | 10 - 50 ha | > 50 ha\n\nDistribution of Glacial Lake Types\n\nLocation Map\nIndian Himalayan Catchment Region\nIndia\nBasin: Indus, Ganga, Brahmaputra\nSoI 250k\n82A, 82E, 82B, 82F, 82J\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2645, "line_end": 2683, "token_count_estimate": 247, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Lhasa Tsangpo", "Lower Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "2bab173bad8d5efc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 4 | 546.7 |\n| 3 | 4,001 - 5,000 | 420 | 4,749.0 |\n| 4 | > 5,000 | 341 | 1,946.0 |\n| | Total | 765 | 7,241.7 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2684, "line_end": 2690, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a04ccfa4e3f1f984", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nTransboundary\nMap 43\nPlate No: 22\n\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2691, "line_end": 2736, "token_count_estimate": 252, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "016b88735d073888", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 19 | 5 | 0 | 0 | 0 | 51 | 3 | 79 |\n| 2 | 0.5 - 1 | 0 | 2 | 0 | 17 | 2 | 0 | 1 | 0 | 31 | 2 | 55 |\n| 3 | 1 - 5 | 2 | 0 | 0 | 21 | 1 | 0 | 1 | 0 | 72 | 2 | 99 |\n| 4 | 5 - 10 | 1 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 21 | 1 | 27 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 16 | 1 | 23 |\n| 6 | > 50 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| | Total | 9 | 4 | 0 | 63 | 8 | 0 | 2 | 0 | 192 | 9 | 287 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2737, "line_end": 2745, "token_count_estimate": 599, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8cfaafc59aec544e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nTransboundary\nMap 44\nPlate No: 22\n\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha | 1 - 10 ha | 10 - 50 ha | > 50 ha\n\nDistribution of Glacial Lake Types\n\nLocation Map\nIndian Himalayan Catchment Region\nIndia\nSoI 250k\n\nBasin\nIndus\nGanga\nBrahmaputra\n82J 82N 91B\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2746, "line_end": 2787, "token_count_estimate": 232, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Lower Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "007657ad410faa7f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 36.1 |\n| 2 | 3,001 - 4,000 | 9 | 238.1 |\n| 3 | 4,001 - 5,000 | 161 | 751.0 |\n| 4 | > 5,000 | 116 | 288.4 |\n| | Total | 287 | 1,313.6 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2788, "line_end": 2794, "token_count_estimate": 163, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f88adc76bfb82b32", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nMoraine Dammed Lake | Ice Dammed Lake | Erosion Lake | Other Glacial Lake\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\nUttarakhand\nMap 45\nPlate No: 24\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 13)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2795, "line_end": 2840, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0995b9625969ba6e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 5 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 5 | 0 | 9 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 4 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 1 | 6 | 0 | 2 | 0 | 9 | 1 | 19 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2841, "line_end": 2849, "token_count_estimate": 509, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "679a6d1f7980bb89", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\nUttarakhand\nMap 46\nPlate No: 24\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2850, "line_end": 2872, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "831063b9d07e07f0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 2 | 1.2 |\n| 3 | 4,001 - 5,000 | 17 | 24.4 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 19 | 25.6 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2873, "line_end": 2879, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "40e5887c9e6c2746", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\nUttarakhand, Transboundary\nMap 47\nPlate No: 25\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 16)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2880, "line_end": 2908, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "921403cf9c6537b2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 8 | 0 | 96 | 25 | 0 | 1 | 0 | 87 | 29 | 248 |\n| 2 | 0.5 - 1 | 5 | 4 | 3 | 98 | 15 | 0 | 0 | 0 | 76 | 19 | 220 |\n| 3 | 1 - 5 | 13 | 11 | 1 | 116 | 8 | 0 | 7 | 0 | 145 | 23 | 324 |\n| 4 | 5 - 10 | 10 | 4 | 0 | 20 | 0 | 0 | 8 | 0 | 28 | 4 | 74 |\n| 5 | 10 - 50 | 24 | 3 | 0 | 10 | 0 | 0 | 9 | 0 | 19 | 6 | 71 |\n| 6 | > 50 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 7 |\n| Total | | 58 | 31 | 4 | 340 | 48 | 0 | 25 | 0 | 355 | 83 | 944 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2909, "line_end": 2917, "token_count_estimate": 512, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "59ec8799e6d2f11b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\nUttarakhand, Transboundary\nMap 48\nPlate No: 25\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha, 1 - 10 ha, 10 - 50 ha, > 50 ha\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2918, "line_end": 2944, "token_count_estimate": 190, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bdd5d5dae494c375", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 8 | 58.9 |\n| 3 | 4,001 - 5,000 | 511 | 2,121.5 |\n| 4 | > 5,000 | 425 | 1,316.0 |\n| | Total | 944 | 3,496.4 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2945, "line_end": 2951, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df2f2e9d2f914cc8", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m): up to 3,000, 3,001 - 4,000, 4,001 - 5,000, > 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\nTransboundary | Map 49 | Plate No: 26\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 18)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2952, "line_end": 2988, "token_count_estimate": 252, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b2b1f6f2337b42df", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 3 | 4 | 133 | 34 | 0 | 2 | 0 | 107 | 36 | 319 |\n| 2 | 0.5 - 1 | 1 | 4 | 2 | 142 | 15 | 0 | 0 | 0 | 93 | 26 | 283 |\n| 3 | 1 - 5 | 11 | 6 | 0 | 167 | 6 | 1 | 3 | 0 | 179 | 42 | 415 |\n| 4 | 5 - 10 | 8 | 1 | 0 | 20 | 0 | 0 | 0 | 0 | 21 | 2 | 52 |\n| 5 | 10 - 50 | 19 | 2 | 0 | 10 | 0 | 0 | 1 | 0 | 11 | 6 | 49 |\n| 6 | > 50 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 7 | 13 |\n| | Total | 43 | 16 | 6 | 473 | 55 | 1 | 6 | 0 | 412 | 119 | 1131 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2989, "line_end": 2997, "token_count_estimate": 514, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8febe77827afd897", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\nTransboundary | Map 50 | Plate No: 26\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha | 1 - 10 ha | 10 - 50 ha | > 50 ha\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2998, "line_end": 3021, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5e5ea47775a60280", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 9.9 |\n| 2 | 3,001 - 4,000 | 3 | 6.5 |\n| 3 | 4,001 - 5,000 | 238 | 2,114.5 |\n| 4 | > 5,000 | 889 | 2,541.8 |\n| | Total | 1131 | 4,672.7 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3022, "line_end": 3028, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2700eae0a88af85e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nTransboundary | Map 51 | Plate No: 27\n--- | --- | ---\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 16)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3029, "line_end": 3071, "token_count_estimate": 256, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8c0bee42b918601d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 3 | 0 | 108 | 41 | 0 | 0 | 0 | 74 | 10 | 237 |\n| 2 | 0.5 - 1 | 1 | 4 | 1 | 99 | 20 | 0 | 4 | 0 | 98 | 8 | 235 |\n| 3 | 1 - 5 | 10 | 3 | 0 | 159 | 6 | 0 | 3 | 0 | 123 | 16 | 320 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 22 | 1 | 0 | 2 | 0 | 16 | 4 | 49 |\n| 5 | 10 - 50 | 17 | 0 | 0 | 11 | 1 | 0 | 1 | 0 | 13 | 1 | 44 |\n| 6 | > 50 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 15 |\n| | **Total** | **44** | **10** | **1** | **399** | **69** | **0** | **10** | **0** | **327** | **40** | **900** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3072, "line_end": 3080, "token_count_estimate": 535, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bdf14e7c95c89608", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nTransboundary | Map 52 | Plate No: 27\n--- | --- | ---\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\n**Distribution of Glacial Lake Types**\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\nSoI 250k: 71B, 71F, 71C, 71G, 71K, 71D, 71H, 71L\nBasin: Indus, Ganga, Brahmaputra\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3081, "line_end": 3114, "token_count_estimate": 266, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0c7a21a04e869de5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 16 | 56.1 |\n| 3 | 4,001 - 5,000 | 104 | 380.0 |\n| 4 | > 5,000 | 780 | 4,741.4 |\n| | **Total** | **900** | **5,177.5** |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3115, "line_end": 3121, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62c1bad09889de2d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nTransboundary\nMap 53\nPlate No: 28\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 17)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3122, "line_end": 3168, "token_count_estimate": 255, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2ddedacaeb9b9c4e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | End-moraine Dammed Lake | Lateral Moraine Dammed Lake | Lateral Moraine Dammed Lake with Ice | Other Moraine Dammed Lake | Supra-glacial Lake | Glacier Ice-dammed Lake | Cirque Erosion Lake | Glacier Trough Valley Erosion Lake | Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 74 | 6 | 93 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 19 | 0 | 0 | 0 | 0 | 88 | 8 | 115 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 26 | 0 | 0 | 4 | 0 | 139 | 10 | 179 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 27 | 3 | 37 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 18 | 6 | 28 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 3 |\n| | Total | 2 | 0 | 0 | 66 | 0 | 0 | 5 | 0 | 347 | 35 | 455 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "End-moraine Dammed Lake", "Lateral Moraine Dammed Lake", "Lateral Moraine Dammed Lake with Ice", "Other Moraine Dammed Lake", "Supra-glacial Lake", "Glacier Ice-dammed Lake", "Cirque Erosion Lake", "Glacier Trough Valley Erosion Lake", "Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3169, "line_end": 3177, "token_count_estimate": 464, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "685d025d748a1f94", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nTransboundary\nMap 54\nPlate No: 28\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3178, "line_end": 3200, "token_count_estimate": 158, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a51971e432771924", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 18 | 229.9 |\n| 4 | > 5,000 | 437 | 1,434.7 |\n| | Total | 455 | 1,664.5 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3201, "line_end": 3207, "token_count_estimate": 164, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fa7e3114a71ca169", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nIndian Himalayan Catchment Region\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nTransboundary Map 55 Plate No: 29\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 18)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3208, "line_end": 3235, "token_count_estimate": 184, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "23b7ab616d322071", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 28 | 1 | 0 | 2 | 0 | 108 | 9 | 148 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 46 | 1 | 0 | 2 | 0 | 149 | 6 | 205 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 67 | 0 | 0 | 2 | 0 | 197 | 7 | 277 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 10 | 0 | 0 | 2 | 0 | 26 | 2 | 44 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 19 | 2 | 28 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 |\n| | Total | 13 | 0 | 0 | 154 | 2 | 0 | 8 | 0 | 501 | 27 | 705 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3236, "line_end": 3244, "token_count_estimate": 511, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b74ade9c49e27ade", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nTransboundary Map 56 Plate No: 29\n\nData Source: Resourcesat-2 LISS-IV\n\nGL Area Range\n<1 ha\n1 - 10 ha\n10 - 50 ha\n> 50 ha\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3245, "line_end": 3275, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae5e887759972b60", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 2 | 5.2 |\n| 3 | 4,001 - 5,000 | 16 | 125.1 |\n| 4 | > 5,000 | 687 | 1,873.2 |\n| | Total | 705 | 2,003.5 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3276, "line_end": 3282, "token_count_estimate": 163, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d1cc8f151b943204", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nIndian Himalayan Catchment Region\nIndia\n\n**SoI 250k**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3283, "line_end": 3288, "token_count_estimate": 39, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "077da22c85bae442", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| | | |\n|---|---|---|\n| 77B | 77F | 77J |\n| 77C | 77G | 77K |\n| 77D | 77H | 77L |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["", "", ""], "table_row_start": 1, "table_row_end": 3, "line_start": 3289, "line_end": 3293, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad88d55ab1851e56", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Basin**\nIndus\nGanga\nBrahmaputra\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n**GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS**\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nTransboundary\n**Map 57**\nPlate No: 30\n\nLhasa Tsangpo\nLower Yarlung Tsangpo\nSubansiri\nManas\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 18)\n0 10 20 30 40 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3294, "line_end": 3353, "token_count_estimate": 307, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Lhasa Tsangpo", "Lower Yarlung Tsangpo", "Manas", "Subansiri", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "9e1154fab6fd4104", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 33 | 3 | 0 | 1 | 0 | 112 | 25 | 174 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 34 | 1 | 0 | 4 | 0 | 179 | 15 | 234 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 47 | 0 | 0 | 16 | 0 | 331 | 38 | 436 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 6 | 0 | 0 | 4 | 0 | 58 | 7 | 78 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 44 | 12 | 63 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 5 |\n| | Total | 12 | 0 | 0 | 122 | 4 | 0 | 27 | 0 | 727 | 98 | 990 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3354, "line_end": 3362, "token_count_estimate": 512, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e43034326dd169b9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\n**GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS**\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nTransboundary\n**Map 58**\nPlate No: 30\n\nLhasa Tsangpo\nLower Yarlung Tsangpo\nSubansiri\nManas\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha, 1 - 10 ha, 10 - 50 ha, > 50 ha\n0 10 20 30 40 Km\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\nSoI 250k\n\n77J, 77N, 82B\n77K, 77O, 82C\n77L, 77P, 82D\n\nBasin:\nIndus\nGanga\nBrahmaputra\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3363, "line_end": 3421, "token_count_estimate": 301, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Lhasa Tsangpo", "Lower Yarlung Tsangpo", "Manas", "Subansiri", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "66f736a2185d5874", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 1 | 0.5 |\n| 3 | 4,001 - 5,000 | 128 | 1,007.0 |\n| 4 | > 5,000 | 861 | 2,372.6 |\n| | Total | 990 | 3,380.1 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3422, "line_end": 3428, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "95c7b0407d823502", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nArunchal Pradesh, Transboundary\nMap 59\nPlate No: 31\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 19)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3429, "line_end": 3469, "token_count_estimate": 243, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "fd59fbd9b463f8c6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 1 | 1 | 69 | 1 | 0 | 3 | 0 | 562 | 31 | 671 |\n| 2 | 0.5 - 1 | 6 | 0 | 0 | 74 | 1 | 0 | 5 | 0 | 627 | 33 | 746 |\n| 3 | 1 - 5 | 8 | 0 | 0 | 134 | 1 | 0 | 57 | 0 | 1293 | 48 | 1541 |\n| 4 | 5 - 10 | 13 | 0 | 0 | 21 | 0 | 0 | 43 | 0 | 332 | 19 | 428 |\n| 5 | 10 - 50 | 10 | 0 | 0 | 17 | 0 | 0 | 33 | 0 | 264 | 18 | 342 |\n| 6 | > 50 | 4 | 0 | 0 | 1 | 0 | 0 | 2 | 3 | 21 | 4 | 35 |\n| | Total | 44 | 1 | 1 | 316 | 3 | 0 | 143 | 3 | 3099 | 153 | 3763 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3470, "line_end": 3478, "token_count_estimate": 520, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e84f1df7857f6bc9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nArunchal Pradesh, Transboundary\nMap 60\nPlate No: 31\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range <1 ha  1 - 10 ha  10 - 50 ha  > 50 ha\n\nDistribution of Glacial Lake Types\n\nLocation Map\nIndian Himalayan Catchment Region\nIndia\n\nSoI 250k", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3479, "line_end": 3512, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f9923ac7128b2240", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| | | |\n|---|---|---|\n| 82B | 82F | 82J |\n| 82C | 82G | 82K |\n| 82D | 82H | 82L |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["", "", ""], "table_row_start": 1, "table_row_end": 3, "line_start": 3513, "line_end": 3517, "token_count_estimate": 84, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "187e878732bf8d5b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nBasin\nIndus\nGanga\nBrahmaputra\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3518, "line_end": 3525, "token_count_estimate": 44, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "25454888733ad12d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 5.8 |\n| 2 | 3,001 - 4,000 | 113 | 3,947.0 |\n| 3 | 4,001 - 5,000 | 1274 | 7,089.3 |\n| 4 | > 5,000 | 660 | 1,395.7 |\n| | Total | 2048 | 12,437.7 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3526, "line_end": 3532, "token_count_estimate": 173, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8acce370c10e951c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nArunchal Pradesh, Transboundary\nMap 61\nPlate No: 32\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 19)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3533, "line_end": 3576, "token_count_estimate": 241, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "69ef2e840af9a49a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 1 | 0 | 24 | 7 | 0 | 1 | 0 | 117 | 11 | 162 |\n| 2 | 0.5 - 1 | 1 | 1 | 0 | 45 | 4 | 0 | 4 | 0 | 229 | 10 | 294 |\n| 3 | 1 - 5 | 9 | 0 | 0 | 64 | 0 | 0 | 61 | 0 | 542 | 22 | 698 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 26 | 0 | 0 | 65 | 0 | 170 | 3 | 266 |\n| 5 | 10 - 50 | 11 | 0 | 0 | 20 | 0 | 0 | 91 | 0 | 167 | 11 | 300 |\n| 6 | > 50 | 1 | 0 | 0 | 5 | 0 | 0 | 2 | 4 | 12 | 1 | 25 |\n| | Total | 25 | 2 | 0 | 184 | 11 | 0 | 224 | 4 | 1237 | 58 | 1745 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3577, "line_end": 3585, "token_count_estimate": 515, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cce623c59dfe5e8d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nArunchal Pradesh, Transboundary\nMap 62\nPlate No: 32\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3586, "line_end": 3616, "token_count_estimate": 200, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a71df06778e696c9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 4 | 52.2 |\n| 2 | 3,001 - 4,000 | 625 | 4,937.9 |\n| 3 | 4,001 - 5,000 | 1063 | 7,302.8 |\n| 4 | > 5,000 | 53 | 85.2 |\n| | Total | 1745 | 12,378.1 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3617, "line_end": 3623, "token_count_estimate": 170, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f092bdbea99dc392", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\n**Basin**\nIndus\nGanga\nBrahmaputra", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3624, "line_end": 3644, "token_count_estimate": 98, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be4100c4ff3b1f26", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| SoI 250k | | |\n|---|---|---|\n| 82J | 82N | 91B |\n| 82K | 82O | 91C |\n| 82L | 82P | 91D |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["SoI 250k", "", ""], "table_row_start": 1, "table_row_end": 3, "line_start": 3645, "line_end": 3649, "token_count_estimate": 88, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e634437211dbc45a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nArunchal Pradesh, Transboundary Map 63 Plate No: 33\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 10)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3650, "line_end": 3676, "token_count_estimate": 184, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "bc53d2f2ae45d795", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 1 | 0 | 89 | 5 | 0 | 0 | 0 | 250 | 46 | 393 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 88 | 2 | 0 | 1 | 0 | 264 | 31 | 387 |\n| 3 | 1 - 5 | 23 | 3 | 0 | 121 | 4 | 0 | 21 | 0 | 413 | 36 | 621 |\n| 4 | 5 - 10 | 9 | 0 | 0 | 30 | 0 | 0 | 9 | 0 | 100 | 3 | 151 |\n| 5 | 10 - 50 | 15 | 0 | 0 | 18 | 0 | 0 | 21 | 0 | 74 | 6 | 134 |\n| 6 | > 50 | 2 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 7 | 3 | 15 |\n| Total | | 52 | 5 | 0 | 348 | 11 | 0 | 52 | 0 | 1108 | 125 | 1701 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3677, "line_end": 3685, "token_count_estimate": 516, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "905d9416f5483ab4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nArunchal Pradesh, Transboundary Map 64 Plate No: 33\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range <1 ha 1 - 10 ha 10 - 50 ha > 50 ha\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3686, "line_end": 3711, "token_count_estimate": 172, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "04a8104c3c0f514a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 3 | 40.8 |\n| 2 | 3,001 - 4,000 | 87 | 2,903.9 |\n| 3 | 4,001 - 5,000 | 1356 | 5,182.5 |\n| 4 | > 5,000 | 255 | 551.3 |\n| | Total | 1701 | 8,678.5 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3712, "line_end": 3718, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af156854cfc9889a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\nTransboundary Map 65 Plate No: 36\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 18)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3719, "line_end": 3742, "token_count_estimate": 179, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f58b12f8c0b23ee6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 5 | 0 | 107 | 56 | 0 | 0 | 0 | 51 | 6 | 225 |\n| 2 | 0.5 - 1 | 3 | 2 | 0 | 104 | 29 | 0 | 0 | 0 | 49 | 9 | 196 |\n| 3 | 1 - 5 | 8 | 6 | 0 | 122 | 21 | 0 | 1 | 0 | 76 | 10 | 244 |\n| 4 | 5 - 10 | 6 | 0 | 0 | 32 | 0 | 0 | 0 | 0 | 8 | 2 | 48 |\n| 5 | 10 - 50 | 11 | 0 | 0 | 17 | 2 | 0 | 1 | 0 | 2 | 4 | 37 |\n| 6 | > 50 | 8 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |\n| Total | | 36 | 14 | 0 | 383 | 108 | 0 | 2 | 0 | 186 | 31 | 760 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3743, "line_end": 3751, "token_count_estimate": 511, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f79b007c91f89711", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\nTransboundary Map 66 Plate No: 36\n\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3752, "line_end": 3770, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e2814018f048bcc9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 3 | 5.7 |\n| 3 | 4,001 - 5,000 | 256 | 809.6 |\n| 4 | > 5,000 | 501 | 2,415.7 |\n| | Total | 760 | 3,231.0 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3771, "line_end": 3777, "token_count_estimate": 165, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f78781326c9be50", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\nSikkim, Transboundary\nMap 69\nPlate No: 38\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 14)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3778, "line_end": 3804, "token_count_estimate": 183, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "ee1ea2d88e546a56", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 101 | 56 | 0 | 0 | 0 | 339 | 9 | 505 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 119 | 36 | 0 | 12 | 0 | 339 | 21 | 528 |\n| 3 | 1 - 5 | 10 | 5 | 0 | 187 | 18 | 0 | 71 | 0 | 579 | 28 | 898 |\n| 4 | 5 - 10 | 12 | 1 | 0 | 43 | 3 | 0 | 47 | 0 | 100 | 2 | 208 |\n| 5 | 10 - 50 | 43 | 2 | 0 | 41 | 1 | 0 | 51 | 0 | 73 | 2 | 213 |\n| 6 | > 50 | 18 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 10 | 1 | 33 |\n| | Total | 83 | 9 | 0 | 494 | 114 | 0 | 182 | 0 | 1440 | 63 | 2385 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3805, "line_end": 3813, "token_count_estimate": 517, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ef4e34d67741307", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\nSikkim, Transboundary\nMap 70\nPlate No: 38\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3814, "line_end": 3834, "token_count_estimate": 156, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2922afae87cf236f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 26 | 129.4 |\n| 3 | 4,001 - 5,000 | 1397 | 7,484.4 |\n| 4 | > 5,000 | 625 | 3,808.7 |\n| | Total | 2048 | 11,422.4 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3835, "line_end": 3841, "token_count_estimate": 168, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "600c77ae61c1dac2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nArunchal Pradesh, Transboundary\nMap 71\nPlate No: 39\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 14)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3842, "line_end": 3870, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0c0c0a3a70c9f4fa", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 79 | 20 | 0 | 3 | 0 | 394 | 9 | 505 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 57 | 5 | 0 | 7 | 0 | 364 | 9 | 442 |\n| 3 | 1 - 5 | 10 | 2 | 0 | 97 | 2 | 0 | 101 | 0 | 671 | 15 | 898 |\n| 4 | 5 - 10 | 8 | 0 | 0 | 22 | 0 | 0 | 57 | 0 | 135 | 2 | 224 |\n| 5 | 10 - 50 | 17 | 1 | 0 | 18 | 1 | 0 | 45 | 0 | 99 | 2 | 183 |\n| 6 | > 50 | 6 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 9 | 3 | 22 |\n| | Total | 41 | 3 | 0 | 275 | 28 | 0 | 215 | 0 | 1672 | 40 | 2274 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3871, "line_end": 3879, "token_count_estimate": 606, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7c6b2a54d8fa85f1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nArunchal Pradesh, Transboundary\nMap 72\nPlate No: 39\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3880, "line_end": 3905, "token_count_estimate": 184, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8436e7e8c61eba6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 44 | 201.4 |\n| 3 | 4,001 - 5,000 | 1421 | 6,816.5 |\n| 4 | > 5,000 | 583 | 1,864.2 |\n| | Total | 2048 | 8,882.1 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3906, "line_end": 3912, "token_count_estimate": 168, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db855b2ce2c8778f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n**GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS**\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\nArunchal Pradesh, Transboundary\nMap 73\nPlate No: 40\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 11)\n0 10 20 30 40 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3913, "line_end": 3956, "token_count_estimate": 253, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f2e4d9b3a8870d09", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 11 | 2 | 0 | 1 | 0 | 17 | 0 | 31 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 12 | 2 | 1 | 2 | 0 | 28 | 1 | 46 |\n| 3 | 1 - 5 | 7 | 0 | 0 | 17 | 1 | 1 | 10 | 0 | 54 | 1 | 91 |\n| 4 | 5 - 10 | 11 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 19 | 0 | 37 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 10 | 1 | 20 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 4 |\n| | Total | 23 | 0 | 0 | 43 | 5 | 2 | 23 | 0 | 130 | 3 | 229 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3957, "line_end": 3965, "token_count_estimate": 509, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "64e15c9a6284a384", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\n**GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS**\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\nArunchal Pradesh, Transboundary\nMap 74\nPlate No: 40\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range: <1 ha | 1 - 10 ha | 10 - 50 ha | > 50 ha\n0 10 20 30 40 Km\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\nBasin\nIndus\nGanga\nBrahmaputra\n\nSoI 250k\n82D | 82H | 82L\n83A | 83E | 83I\n83B | 83F\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 3966, "line_end": 4011, "token_count_estimate": 284, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b6fefd249dddf816", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 29 | 187.4 |\n| 3 | 4,001 - 5,000 | 142 | 793.6 |\n| 4 | > 5,000 | 58 | 182.7 |\n| | Total | 229 | 1,163.8 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4012, "line_end": 4018, "token_count_estimate": 165, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62181e3e8a958b79", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\n**DISCLAIMER:**\nThe Administrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Map 75**\n\n**SATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION**\n\nArunchal Pradesh | Plate No: 41\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 4019, "line_end": 4062, "token_count_estimate": 256, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b8a7f4fb236a1318", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 3 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 1 | 8 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4063, "line_end": 4071, "token_count_estimate": 508, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed6cafdaf25b1a38", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Map 76**\n\n**GLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS**\n\nArunchal Pradesh | Plate No: 41\n\nData Source: Resourcesat-2 LISS-IV\n\nGL Area Range: <1 ha | 1 - 10 ha | 10 - 50 ha | > 50 ha\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake | Ice Dammed Lake | Erosion Lake | Other Glacial Lake\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 4072, "line_end": 4105, "token_count_estimate": 244, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e1c4326bda8c9f87", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 2 | 17.3 |\n| 2 | 3,001 - 4,000 | 5 | 20.7 |\n| 3 | 4,001 - 5,000 | 1 | 8.1 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 8 | 46.1 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4106, "line_end": 4112, "token_count_estimate": 159, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "93dbe6f197dc864d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**SoI 250k**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 4113, "line_end": 4114, "token_count_estimate": 31, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b0518f7e4e7752d0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| | | |\n|---|---|---|\n| 82L | 82P | 91D |\n| 83I | 83M | 92A |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["", "", ""], "table_row_start": 1, "table_row_end": 2, "line_start": 4115, "line_end": 4118, "token_count_estimate": 70, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7470754268e0025c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Basin**\nIndus\nGanga\nBrahmaputra\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nSATELLITE IMAGE OF PART OF HIMALAYAN CATCHMENT REGION\n\nArunchal Pradesh, Transboundary\nMap 77\nPlate No: 42\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 4119, "line_end": 4167, "token_count_estimate": 259, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b78a4b31610009da", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | End-moraine Dammed Lake | Lateral Moraine Dammed Lake | Lateral Moraine Dammed Lake with Ice | Other Moraine Dammed Lake | Supra-glacial Lake | Glacier Ice-dammed Lake | Cirque Erosion Lake | Glacier Trough Valley Erosion Lake | Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54 | 8 | 62 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 79 | 6 | 86 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 1 | 0 | 0 | 8 | 0 | 187 | 14 | 210 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 11 | 0 | 54 | 1 | 67 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 2 | 0 | 0 | 12 | 0 | 54 | 0 | 68 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 |\n| | Total | 0 | 0 | 0 | 5 | 0 | 0 | 31 | 0 | 431 | 29 | 496 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "End-moraine Dammed Lake", "Lateral Moraine Dammed Lake", "Lateral Moraine Dammed Lake with Ice", "Other Moraine Dammed Lake", "Supra-glacial Lake", "Glacier Ice-dammed Lake", "Cirque Erosion Lake", "Glacier Trough Valley Erosion Lake", "Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4168, "line_end": 4176, "token_count_estimate": 464, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "df9053c58b655e43", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKES IN PART OF INDIAN HIMALAYAN RIVER BASINS\n\nArunchal Pradesh, Transboundary\nMap 78\nPlate No: 42\n\nData Source: Resourcesat-2 LISS-IV\nGL Area Range • <1 ha • 1 - 10 ha • 10 - 50 ha • > 50 ha\n\n**Distribution of Glacial Lake Types**\n• Moraine Dammed Lake • Ice Dammed Lake • Erosion Lake • Other Glacial Lake\n\n**Location Map**\nIndian Himalayan Catchment Region\nIndia\n\nSoI 250k\n91D 91H\n92A 92E\n\n**Basin**\nIndus\nGanga\nBrahmaputra\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 4177, "line_end": 4220, "token_count_estimate": 260, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8b619b7ba80b2040", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 178 | 1,071.7 |\n| 3 | 4,001 - 5,000 | 318 | 1,422.1 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 496 | 2,493.8 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4221, "line_end": 4227, "token_count_estimate": 166, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51d717d13df2972d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\n**National Remote Sensing Centre, ISRO**\nDepartment of Space, Government of India\nUnder:\n**National Hydrolgy Project**\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 4228, "line_end": 4268, "token_count_estimate": 203, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b8ba3ca9c675bbdd", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: text\n\n**Overview:**\n\nThere are several automatic and semi-automatic glacial lake mapping method reported in the literature, but no method produce good and accurate results of mapping. Kääb et al. (2002), attempted the automatic classification of glacial lakes using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, but the algorithm was not robust enough to be applied to other images except ASTER images. Using LANDSAT images, Huggel et al. (2002) suggested the Normalized Difference Water Index (NDWI) according to theory low water reflectance in the NIR band and high reflectance in blue band but glacial lakes get misclassified as shadow area using this method.\n\nWangchuk et al. (2019), delineated glacial lakes using Sentinel-1 SAR images, a semi-automated approach, based on a radar signal intensity threshold between water and non-water feature classes followed by post-processing including elevations, slopes, vegetation and size thresholds, but drawback still persist as lakes which are severely affected by the wind and waves that increase the roughness and thus the backscatter would neither be identified correctly, partially or at all, due to the use of a single threshold. Hence, to ensure correct classifications of lakes, visual inspection of images and quality control is required for final accurate results.\n\n**Mapping methods:**\n\nThe NDWI, which provides an automatic way to detect water bodies including glacial lakes was adopted by many researchers for inventorying purpose. It is a ratio combining two different spectral bands that enhance water spectral signals by contrasting the reflectance between different wavelengths and removing a large portion of noise components in different wavelengths, can be expressed as:\n\nNDWI = (Green Band - NIR Band) / (Green Band + NIR Band)\n\nOther than NDWI, two more pixel-based classification techniques i.e. supervised (by giving homogeneous signature sites) and unsupervised (by giving certain number of feature classes to classify based on spectral behavior) classification techniques can also be applied. Object-based classification using eCognition software can also be done using various factors like by giving threshold values and suitable membership functions, by including indices like NDWI, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Glacier Index (NDGI), and by using layers such as slope and NIR band.\n\n**Mapping results:**\n\nA study was attempted using RS-2 LISS-IV data to compare the mapping accuracy of lakes using 4 automated methods (NDWI, Supervised, Unsupervised and Object based) with visual interpretation method. All four automatic mapping methods along with visual interpretation technique were used in an area which has deep water bodies and snow covered glacial lakes along with shadowed region (upper mountainous parts of Teesta basin). Using NDWI method, most of lakes got classified, but it also classify shadow areas as water pixels due to the similar spectral reflectance conditions. Even if the threshold value of NDWI is changed in such a way that all water pixels\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\nin a lake should get classified, many deep water bodies and shadowed portions having same spectral reflectance values will get misclassified as water pixels or in some glacial lakes water pixels are missing.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 4424, "line_end": 4453, "token_count_estimate": 823, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "e3a0decf8f1ddc5d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: text\n\nalong with visual interpretation technique were used in an area which has deep water bodies and snow covered glacial lakes along with shadowed region ( upper mountainous parts of Teesta basin ) . Using NDWI method , most of lakes got classified , but it also classify shadow areas as water pixels due to the similar spectral reflectance conditions . Even if the threshold value of NDWI is changed in such a way that all water pixels GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS in a lake should get classified , many deep water bodies and shadowed portions having same spectral reflectance values will get misclassified as water pixels or in some glacial lakes water pixels are missing .\n\nUnsupervised classification technique misclassifies not only shadows as lakes, but also some part of glaciers that are in retreating condition and having similar spectral reflectance values of lakes (light blue in colour). In supervised classification output, with good amount of signature sites, cloud/mountain shadows are classified as water pixels. Overall, using pixel-based classification methods, it is difficult to distinguish between deep water bodies and shadows as they have same spectral reflectance values. Pixel-based classified output of all three methods along with the total area of lakes in the study area is shown in Figure 83.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 4424, "line_end": 4453, "token_count_estimate": 345, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "e78a8adb929c502b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: figure\nFigure: Figure 83: Pixel-based classified output, (a): NDWI, (b): Supervised, (c): Unsupervised\n\nFigure 83: Pixel-based classified output, (a): NDWI, (b): Supervised, (c): Unsupervised", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "figure", "figure_caption": "Figure 83: Pixel-based classified output, (a): NDWI, (b): Supervised, (c): Unsupervised", "line_start": 4454, "line_end": 4454, "token_count_estimate": 100, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7e41352402cbd748", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: text\n\nUsing object-based classification method, along with various layers like slope (as the glacial lakes are located at higher elevation) and NIR band, results misclassification of shadows, though it is less in comparison to the pixel-based classified output, but at many locations water pixels are not classified. Also, if we compare the areas of lakes that is being classified using automatic method that with the area of manually mapped lakes, automatic mapped lakes has huge difference and extent of misclassification, which need to be corrected again using visual interpretation method. Figure 84 shows the comparison of the glacial lake extents of object-based classification and manual mapping using visual interpretation keys.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 4455, "line_end": 4457, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a3507c81c2a0918", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: figure\nFigure: Figure 84: (d): Object-based classified output, (e) manual mapping output\n\nFigure 84: (d): Object-based classified output, (e) manual mapping output", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "figure", "figure_caption": "Figure 84: (d): Object-based classified output, (e) manual mapping output", "line_start": 4458, "line_end": 4458, "token_count_estimate": 80, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "005ac6d4ef6a310d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - I: Automatic Identification and Mapping of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - I: Automatic Identification and Mapping of Glacial Lakes", "section_headings": ["Annexure - I: Automatic Identification and Mapping of Glacial Lakes"], "chunk_type": "text", "line_start": 4459, "line_end": 4465, "token_count_estimate": 53, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "78e1367b4445ff5e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nEach lake is given a unique ID, formatted in 12 alpha-numeric character. First two digit of ID refers to the basin code, next five characters refers to the SOI 250k and 50K Toposheet No., and the last five digit refers to the sequential number of each lake sorted from top left to bottom right. For example:", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 4467, "line_end": 4469, "token_count_estimate": 110, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5df46372810c9b4b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| | 0378A0115656 | | |\n| :---: | :---: | :---: | :---: |\n| 03 | 78A01 | | 15656 |\n| | 78A | 01 | |\n| Basin Code | SOI 250K Toposheet No. | SOI 50K Toposheet No. | Lake No. |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["", "0378A0115656", "", ""], "table_row_start": 1, "table_row_end": 3, "line_start": 4470, "line_end": 4474, "token_count_estimate": 133, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["0378A0115656", "15656", "78A01"]}}
{"id": "1e76be4804af37ac", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nTable 68 shows the details of 2,431 glacial lakes (≥10 ha) mapped in the Indian Himalayan River Basins along with few important attributes.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 4475, "line_end": 4479, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af7f63c47ad50764", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable: Table 68: List of glacial lakes (≥10 ha) of the Indian Himlayan River Basins with few attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 01 42D15 00009 | 36.412 | 72.901 | Gilgit | M(e) | 11.23 | 4,256 | 51 | 01 43E09 00912 | 35.904 | 73.568 | Gilgit | E(o) | 18.03 | 4,109 |\n| 2 | 01 42H03 00089 | 36.263 | 73.048 | Gilgit | O | 21.68 | 4,618 | 52 | 01 43E09 00919 | 35.889 | 73.692 | Gilgit | E(o) | 11.02 | 3,995 |\n| 3 | 01 42H06 00150 | 36.642 | 73.407 | Gilgit | M(e) | 14.06 | 2,748 | 53 | 01 43E09 00928 | 35.880 | 73.577 | Gilgit | E(c) | 30.86 | 4,059 |\n| 4 | 01 42H08 00166 | 36.131 | 73.384 | Gilgit | E(o) | 23.35 | 4,503 | 54 | 01 43E09 00929 | 35.877 | 73.615 | Gilgit | E(o) | 14.58 | 3,993 |\n| 5 | 01 42H08 00171 | 36.116 | 73.423 | Gilgit | E(o) | 21.76 | 4,437 | 55 | 01 43E09 00941 | 35.865 | 73.746 | Gilgit | E(o) | 84.31 | 4,140 |\n| 6 | 01 42H08 00173 | 36.112 | 73.449 | Gilgit | E(o) | 11.22 | 4,427 | 56 | 01 43E09 00961 | 35.783 | 73.507 | Indus Middle | E(o) | 12.26 | 3,918 |\n| 7 | 01 42H08 00174 | 36.108 | 73.465 | Gilgit | E(o) | 21.58 | 4,488 | 57 | 01 43E09 00962 | 35.782 | 73.572 | Indus Middle | E(o) | 22.07 | 4,082 |\n| 8 | 01 42H08 00179 | 36.086 | 73.461 | Gilgit | E(o) | 13.98 | 4,577 | 58 | 01 43E13 00983 | 35.972 | 73.911 | Gilgit | E(c) | 29.25 | 4,426 |\n| 9 | 01 42H08 00185 | 36.024 | 73.328 | Gilgit | E(o) | 10.65 | 4,559 | 59 | 01 43E13 01005 | 35.939 | 73.985 | Gilgit | E(o) | 19.57 | 4,258 |\n| 10 | 01 42H08 00198 | 36.000 | 73.312 | Gilgit | E(o) | 13.08 | 4,615 | 60 | 01 43E13 01012 | 35.917 | 73.945 | Gilgit | E(o) | 12.08 | 4,178 |\n| 11 | 01 42H09 00200 | 36.879 | 73.704 | Gilgit | O | 262.57 | 4,286 | 61 | 01 43E13 01013 | 35.916 | 73.992 | Gilgit | E(o) | 11.42 | 4,312 |\n| 12 | 01 42H10 00208 | 36.644 | 73.646 | Gilgit | O | 105.07 | 3,821 | 62 | 01 43E13 01022 | 35.906 | 73.922 | Gilgit | E(o) | 15.98 | 4,223 |\n| 13 | 01 42H11 00220 | 36.431 | 73.566 | Gilgit | E(o) | 12.25 | 4,030 | 63 | 01 43E13 01036 | 35.882 | 73.763 | Gilgit | E(o) | 40.94 | 4,104 |\n| 14 | 01 42H11 00230 | 36.351 | 73.514 | Gilgit | E(o) | 17.25 | 4,420 | 64 | 01 43E13 01045 | 35.868 | 73.929 | Gilgit | E(o) | 17.55 | 4,064 |\n| 15 | 01 42H12 00249 | 36.154 | 73.616 | Gilgit | E(o) | 13.83 | 4,545 | 65 | 01 43E13 01053 | 35.858 | 73.869 | Gilgit | E(o) | 15.26 | 4,211 |\n| 16 | 01 42H12 00250 | 36.145 | 73.639 | Gilgit | E(o) | 17.96 | 4,484 | 66 | 01 43E13 01054 | 35.856 | 73.775 | Indus Middle | E(o) | 28.28 | 3,962 |\n| 17 | 01 42H12 00253 | 36.129 | 73.505 | Gilgit | E(o) | 23.11 | 4,482 | 67 | 01 43E13 01057 | 35.852 | 73.877 | Gilgit | E(o) | 19.43 | 4,166 |\n| 18 | 01 42H12 00262 | 36.096 | 73.668 | Gilgit | E(c) | 10.48 | 4,423 | 68 | 01 43E16 01082 | 35.247 | 73.799 | Indus Middle | E(o) | 17.06 | 3,988 |\n| 19 | 01 42H12 00268 | 36.080 | 73.646 | Gilgit | O | 28.87 | 4,344 | 69 | 01 43E16 01096 | 35.184 | 73.958 | Indus Middle | E(o) | 10.49 | 4,120 |\n| 20 | 01 42H12 00295 | 36.039 | 73.592 | Gilgit | E(o) | 18.87 | 4,274 | 70 | 01 43F13 01107 | 34.830 | 73.985 | Jhelum | E(o) | 20.67 | 3,873 |\n| 21 | 01 42H12 00301 | 36.030 | 73.714 | Gilgit | E(o) | 10.47 | 4,320 | 71 | 01 43I01 01122 | 35.962 | 74.019 | Gilgit | E(o) | 15.37 | 4,285 |\n| 22 | 01 42H12 00327 | 36.011 | 73.558 | Gilgit | E(o) | 24.48 | 4,232 | 72 | 01 43I01 01141 | 35.817 | 74.083 | Gilgit | E(c) | 16.71 | 4,269 |\n| 23 | 01 42H15 00357 | 36.265 | 73.955 | Gilgit | O | 27.89 | 3,706 | 73 | 01 43I01 01158 | 35.779 | 74.205 | Gilgit | E(o) | 12.36 | 4,304 |\n| 24 | 01 42L04 00386 | 36.239 | 74.084 | Gilgit | E(o) | 20.57 | 3,453 | 74 | 01 43I01 01159 | 35.773 | 74.196 | Gilgit | E(c) | 11.01 | 4,368 |\n| 25 | 01 42P04 00481 | 36.137 | 75.136 | Gilgit | I(s) | 24.88 | 3,697 | 75 | 01 43I02 01186 | 35.707 | 74.243 | Indus Middle | E(o) | 15.89 | 4,318 |\n| 26 | 01 42P04 00492 | 36.126 | 75.140 | Gilgit | I(s) | 12.99 | 3,725 | 76 | 01 43I02 01192 | 35.686 | 74.230 | Indus Middle | E(o) | 14.12 | 4,336 |\n| 27 | 01 42P11 00571 | 36.440 | 75.686 | Gilgit | E(o) | 28.09 | 4,709 | 77 | 01 43I04 01224 | 35.073 | 74.177 | Jhelum | E(c) | 11.97 | 4,042 |\n| 28 | 01 43A09 00583 | 35.994 | 72.613 | Gilgit | O | 201.58 | 3,622 | 78 | 01 43I05 01239 | 35.821 | 74.294 | Gilgit | E(c) | 12.04 | 4,313 |\n| 29 | 01 43A09 00591 | 35.944 | 72.595 | Gilgit | O | 96.35 | 3,761 | 79 | 01 43I06 01249 | 35.722 | 74.256 | Indus Middle | E(o) | 10.77 | 4,254 |\n| 30 | 01 43A13 00645 | 35.869 | 72.940 | Gilgit | E(o) | 20.01 | 4,378 | 80 | 01 43I11 01289 | 35.400 | 74.676 | Indus Middle | E(o) | 13.97 | 3,386 |\n| 31 | 01 43A13 00649 | 35.855 | 72.980 | Gilgit | E(v) | 13.35 | 4,325 | 81 | 01 43I15 01319 | 35.332 | 74.941 | Indus Middle | E(o) | 11.74 | 4,427 |\n| 32 | 01 43E01 00682 | 35.920 | 73.064 | Gilgit | O | 21.34 | 4,303 | 82 | 01 43I15 01320 | 35.330 | 74.786 | Indus Middle | M(l) | 15.07 | 3,475 |\n| 33 | 01 43E01 00689 | 35.906 | 73.155 | Gilgit | E(o) | 24.24 | 4,328 | 83 | 01 43I15 01322 | 35.315 | 74.937 | Indus Middle | M(e) | 20.13 | 4,197 |\n| 34 | 01 43E01 00708 | 35.871 | 73.073 | Gilgit | E(o) | 15.95 | 4,617 | 84 | 01 43I16 01333 | 35.082 | 74.961 | Indus Middle | E(o) | 30.64 | 4,317 |\n| 35 | 01 43E01 00726 | 35.829 | 73.228 | Indus Middle | E(o) | 12.38 | 4,195 | 85 | 01 43I16 01334 | 35.075 | 74.958 | Indus Middle | E(o) | 10.45 | 4,413 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 68: List of glacial lakes (≥10 ha) of the Indian Himlayan River Basins with few attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4480, "line_end": 4531, "token_count_estimate": 3079, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["00009", "00089", "00150", "00166", "00171", "00173", "00174", "00179", "00185", "00198", "00200", "00208", "00220", "00230", "00249", "00250", "00253", "00262", "00268", "00295", "00301", "00327", "00357", "00386", "00481", "00492", "00571", "00583", "00591", "00645", "00649", "00682", "00689", "00708", "00726", "00912", "00919", "00928", "00929", "00941", "00961", "00962", "00983", "01005", "01012", "01013", "01022", "01036", "01045", "01053"]}}
{"id": "d3793a20a111eceb", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable: Table 68: List of glacial lakes (≥10 ha) of the Indian Himlayan River Basins with few attributes\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) | S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 36 | 01 43E01 00728 | 35.825 | 73.211 | Indus Middle | E(c) | 26.44 | 4,271 | 86 | 01 43J01 01345 | 34.962 | 74.100 | Jhelum | E(o) | 29.65 | 3,603 |\n| 37 | 01 43E02 00735 | 35.730 | 73.216 | Indus Middle | E(o) | 26.36 | 3,970 | 87 | 01 43J01 01350 | 34.948 | 74.138 | Jhelum | E(c) | 20.03 | 4,011 |\n| 38 | 01 43E05 00742 | 35.987 | 73.313 | Gilgit | E(o) | 10.23 | 4,583 | 88 | 01 43J01 01352 | 34.926 | 74.155 | Jhelum | E(c) | 21.64 | 3,926 |\n| 39 | 01 43E05 00757 | 35.962 | 73.397 | Gilgit | E(o) | 18.84 | 4,349 | 89 | 01 43J01 01353 | 34.902 | 74.048 | Jhelum | E(c) | 21.58 | 3,896 |\n| 40 | 01 43E05 00769 | 35.949 | 73.289 | Gilgit | E(o) | 54.23 | 4,228 | 90 | 01 43J01 01360 | 34.858 | 74.077 | Jhelum | E(c) | 20.51 | 3,680 |\n| 41 | 01 43E05 00774 | 35.945 | 73.365 | Gilgit | E(c) | 67.34 | 4,162 | 91 | 01 43J01 01363 | 34.829 | 74.062 | Jhelum | E(c) | 93.90 | 3,681 |\n| 42 | 01 43E05 00781 | 35.936 | 73.269 | Indus Middle | E(o) | 11.45 | 4,362 | 92 | 01 43J05 01374 | 34.876 | 74.477 | Jhelum | E(c) | 12.31 | 3,592 |\n| 43 | 01 43E05 00790 | 35.925 | 73.274 | Indus Middle | E(o) | 11.46 | 4,227 | 93 | 01 43J09 01397 | 34.920 | 74.521 | Jhelum | M(e) | 60.60 | 4,041 |\n| 44 | 01 43E05 00791 | 35.925 | 73.342 | Indus Middle | E(o) | 11.69 | 4,396 | 94 | 01 43J09 01404 | 34.908 | 74.680 | Indus Middle | E(c) | 23.48 | 3,864 |\n| 45 | 01 43E05 00795 | 35.918 | 73.371 | Gilgit | E(o) | 12.25 | 4,502 | 95 | 01 43J09 01414 | 34.849 | 74.523 | Jhelum | E(c) | 22.54 | 4,016 |\n| 46 | 01 43E05 00837 | 35.817 | 73.297 | Indus Middle | O | 15.36 | 2,981 | 96 | 01 43J09 01417 | 34.840 | 74.676 | Jhelum | E(c) | 10.66 | 3,868 |\n| 47 | 01 43E06 00854 | 35.739 | 73.256 | Indus Middle | E(c) | 18.92 | 4,016 | 97 | 01 43J13 01448 | 34.846 | 74.787 | Indus Middle | E(o) | 14.18 | 3,883 |\n| 48 | 01 43E06 00855 | 35.675 | 73.344 | Indus Middle | E(c) | 25.12 | 3,428 | 98 | 01 43J13 01449 | 34.845 | 74.809 | Indus Middle | E(c) | 50.44 | 3,992 |\n| 49 | 01 43E06 00856 | 35.652 | 73.357 | Indus Middle | E(o) | 19.01 | 3,735 | 99 | 01 43J13 01450 | 34.840 | 74.761 | Indus Middle | E(c) | 25.43 | 3,979 |\n| 50 | 01 43E06 00858 | 35.643 | 73.352 | Indus Middle | E(o) | 13.95 | 3,747 | 100 | 01 43J14 01457 | 34.548 | 74.819 | Jhelum | E(c) | 31.47 | 3,872 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": "Table 68: List of glacial lakes (≥10 ha) of the Indian Himlayan River Basins with few attributes", "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)", "S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 50, "line_start": 4480, "line_end": 4531, "token_count_estimate": 1439, "basins": ["Indus"], "subbasins": ["Gilgit", "Indus Middle", "Jhelum"], "countries": [], "lake_ids": ["00728", "00735", "00742", "00757", "00769", "00774", "00781", "00790", "00791", "00795", "00837", "00854", "00855", "00856", "00858", "01345", "01350", "01352", "01353", "01360", "01363", "01374", "01397", "01404", "01414", "01417", "01448", "01449", "01450", "01457", "43E01", "43E02", "43E05", "43E06", "43J01", "43J05", "43J09", "43J13", "43J14"]}}
{"id": "73455e2e37f7cf29", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 4532, "line_end": 4541, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3010840d5a0e6256", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 101 | 01 43J14 01458 | 34.537 | 74.828 | Jhelum | E(c) | 30.51 | 3,922 |\n| 102 | 01 43J15 01462 | 34.493 | 74.921 | Jhelum | E(o) | 38.39 | 3,881 |\n| 103 | 01 43J15 01464 | 34.489 | 74.906 | Jhelum | E(c) | 11.89 | 4,097 |\n| 104 | 01 43J15 01467 | 34.470 | 74.913 | Jhelum | E(o) | 10.44 | 3,852 |\n| 105 | 01 43J15 01473 | 34.457 | 74.985 | Jhelum | M(o) | 10.33 | 3,748 |\n| 106 | 01 43J15 01480 | 34.452 | 74.932 | Jhelum | E(o) | 12.30 | 3,889 |\n| 107 | 01 43J15 01485 | 34.448 | 74.909 | Jhelum | E(c) | 15.48 | 3,979 |\n| 108 | 01 43J15 01486 | 34.444 | 74.892 | Jhelum | E(c) | 33.95 | 3,846 |\n| 109 | 01 43J15 01489 | 34.432 | 74.924 | Jhelum | E(c) | 161.04 | 3,571 |\n| 110 | 01 43J15 01493 | 34.418 | 74.936 | Jhelum | E(o) | 36.65 | 3,503 |\n| 111 | 01 43J15 01499 | 34.392 | 74.874 | Jhelum | E(o) | 27.31 | 3,556 |\n| 112 | 01 43J15 01500 | 34.385 | 74.944 | Jhelum | E(c) | 11.22 | 3,722 |\n| 113 | 01 43J15 01501 | 34.380 | 74.876 | Jhelum | E(o) | 11.58 | 3,738 |\n| 114 | 01 43K05 01515 | 33.866 | 74.417 | Jhelum | E(c) | 13.62 | 3,968 |\n| 115 | 01 43K05 01525 | 33.841 | 74.428 | Jhelum | E(o) | 45.46 | 3,954 |\n| 116 | 01 43K05 01527 | 33.829 | 74.435 | Jhelum | E(o) | 15.18 | 4,043 |\n| 117 | 01 43K05 01528 | 33.820 | 74.451 | Jhelum | E(c) | 29.43 | 3,997 |\n| 118 | 01 43K10 01537 | 33.740 | 74.526 | Jhelum | E(o) | 10.16 | 4,018 |\n| 119 | 01 43K10 01538 | 33.735 | 74.523 | Jhelum | E(o) | 13.69 | 4,064 |\n| 120 | 01 43K10 01544 | 33.559 | 74.526 | Jhelum | E(o) | 22.97 | 3,851 |\n| 121 | 01 43K10 01546 | 33.550 | 74.543 | Jhelum | E(o) | 16.04 | 3,912 |\n| 122 | 01 43K10 01550 | 33.540 | 74.523 | Jhelum | E(o) | 10.32 | 3,812 |\n| 123 | 01 43K10 01551 | 33.540 | 74.565 | Jhelum | E(o) | 21.73 | 3,967 |\n| 124 | 01 43K10 01554 | 33.536 | 74.555 | Jhelum | E(o) | 31.85 | 4,006 |\n| 125 | 01 43K10 01558 | 33.530 | 74.574 | Jhelum | E(o) | 12.27 | 3,955 |\n| 126 | 01 43K10 01559 | 33.528 | 74.561 | Jhelum | E(c) | 11.15 | 4,096 |\n| 127 | 01 43K10 01560 | 33.522 | 74.605 | Jhelum | E(o) | 12.33 | 3,844 |\n| 128 | 01 43K10 01561 | 33.519 | 74.584 | Jhelum | E(o) | 70.83 | 3,934 |\n| 129 | 01 43K10 01565 | 33.510 | 74.564 | Jhelum | E(o) | 25.15 | 3,909 |\n| 130 | 01 43K10 01567 | 33.509 | 74.625 | Jhelum | E(o) | 33.70 | 3,937 |\n| 131 | 01 43K10 01568 | 33.505 | 74.594 | Jhelum | E(c) | 11.35 | 4,089 |\n| 132 | 01 43K11 01571 | 33.496 | 74.565 | Jhelum | E(o) | 11.18 | 3,812 |\n| 133 | 01 43K11 01582 | 33.444 | 74.614 | Chenab | E(c) | 14.88 | 3,580 |\n| 134 | 01 43K14 01588 | 33.512 | 74.769 | Jhelum | O | 128.54 | 3,486 |\n| 135 | 01 43K14 01591 | 33.503 | 74.850 | Jhelum | E(o) | 14.64 | 3,623 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4542, "line_end": 4691, "token_count_estimate": 1591, "basins": [], "subbasins": ["Chenab", "Jhelum"], "countries": [], "lake_ids": ["01458", "01462", "01464", "01467", "01473", "01480", "01485", "01486", "01489", "01493", "01499", "01500", "01501", "01515", "01525", "01527", "01528", "01537", "01538", "01544", "01546", "01550", "01551", "01554", "01558", "01559", "01560", "01561", "01565", "01567", "01568", "01571", "01582", "01588", "01591", "43J14", "43J15", "43K05", "43K10", "43K11", "43K14"]}}
{"id": "e17a443042b47f75", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 136 | 01 43M03 01632 | 35.264 | 75.194 | Indus Middle | E(o) | 19.03 | 4,551 |\n| 137 | 01 43M04 01638 | 35.231 | 75.182 | Indus Middle | E(o) | 11.26 | 4,547 |\n| 138 | 01 43M04 01664 | 35.119 | 75.228 | Indus Upper | E(o) | 17.81 | 4,586 |\n| 139 | 01 43M04 01679 | 35.049 | 75.198 | Indus Middle | E(c) | 12.71 | 4,398 |\n| 140 | 01 43M04 01682 | 35.016 | 75.001 | Indus Middle | E(o) | 10.87 | 3,931 |\n| 141 | 01 43M08 01717 | 35.182 | 75.422 | Indus Middle | E(o) | 14.33 | 4,530 |\n| 142 | 01 43M08 01728 | 35.143 | 75.261 | Indus Upper | E(o) | 11.84 | 4,659 |\n| 143 | 01 43M08 01730 | 35.132 | 75.321 | Indus Upper | E(o) | 19.58 | 4,526 |\n| 144 | 01 43M12 01758 | 35.172 | 75.521 | Indus Upper | E(o) | 10.30 | 4,551 |\n| 145 | 01 43M12 01776 | 35.096 | 75.579 | Indus Middle | E(o) | 10.98 | 4,375 |\n| 146 | 01 43M12 01789 | 35.027 | 75.725 | Indus Upper | E(o) | 12.92 | 4,647 |\n| 147 | 01 43N01 01839 | 34.991 | 75.236 | Indus Upper | O | 129.62 | 4,138 |\n| 148 | 01 43N01 01842 | 34.972 | 75.051 | Indus Middle | E(c) | 14.29 | 4,265 |\n| 149 | 01 43N01 01852 | 34.922 | 75.250 | Indus Upper | E(o) | 14.22 | 4,152 |\n| 150 | 01 43N01 01854 | 34.920 | 75.177 | Indus Upper | E(o) | 14.54 | 4,257 |\n| 151 | 01 43N01 01856 | 34.919 | 75.189 | Indus Upper | E(o) | 14.34 | 4,225 |\n| 152 | 01 43N02 01885 | 34.697 | 75.137 | Jhelum | E(o) | 64.95 | 4,103 |\n| 153 | 01 43N02 01889 | 34.684 | 75.140 | Jhelum | E(o) | 21.41 | 4,133 |\n| 154 | 01 43N02 01897 | 34.666 | 75.179 | Jhelum | E(o) | 74.54 | 4,234 |\n| 155 | 01 43N03 01929 | 34.422 | 75.058 | Jhelum | E(c) | 40.12 | 3,570 |\n| 156 | 01 43N03 01932 | 34.397 | 75.103 | Jhelum | E(c) | 29.55 | 3,812 |\n| 157 | 01 43N03 01937 | 34.388 | 75.119 | Jhelum | E(c) | 47.25 | 3,663 |\n| 158 | 01 43N04 01966 | 34.207 | 75.147 | Jhelum | E(o) | 10.35 | 3,699 |\n| 159 | 01 43N04 01967 | 34.203 | 75.205 | Jhelum | E(o) | 15.92 | 3,682 |\n| 160 | 01 43N04 01973 | 34.156 | 75.113 | Jhelum | E(o) | 10.27 | 3,941 |\n| 161 | 01 43N04 01974 | 34.144 | 75.110 | Jhelum | E(o) | 43.47 | 3,810 |\n| 162 | 01 43N04 01975 | 34.140 | 75.148 | Jhelum | E(c) | 82.93 | 3,780 |\n| 163 | 01 43N04 01978 | 34.093 | 75.161 | Jhelum | E(o) | 17.80 | 3,919 |\n| 164 | 01 43N04 01979 | 34.063 | 75.216 | Jhelum | E(o) | 13.36 | 3,691 |\n| 165 | 01 43N05 01997 | 34.825 | 75.382 | Indus Upper | E(c) | 22.94 | 4,315 |\n| 166 | 01 43N05 02010 | 34.775 | 75.480 | Indus Upper | E(o) | 10.80 | 4,626 |\n| 167 | 01 43N08 02045 | 34.234 | 75.275 | Jhelum | E(o) | 18.23 | 3,830 |\n| 168 | 01 43N08 02049 | 34.193 | 75.321 | Jhelum | E(c) | 10.81 | 3,734 |\n| 169 | 01 43N08 02050 | 34.184 | 75.373 | Jhelum | E(c) | 16.80 | 4,276 |\n| 170 | 01 43N08 02055 | 34.139 | 75.377 | Jhelum | E(o) | 33.45 | 3,709 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4542, "line_end": 4691, "token_count_estimate": 1608, "basins": ["Indus"], "subbasins": ["Indus Middle", "Indus Upper", "Jhelum"], "countries": [], "lake_ids": ["01632", "01638", "01664", "01679", "01682", "01717", "01728", "01730", "01758", "01776", "01789", "01839", "01842", "01852", "01854", "01856", "01885", "01889", "01897", "01929", "01932", "01937", "01966", "01967", "01973", "01974", "01975", "01978", "01979", "01997", "02010", "02045", "02049", "02050", "02055", "43M03", "43M04", "43M08", "43M12", "43N01", "43N02", "43N03", "43N04", "43N05", "43N08"]}}
{"id": "7d19cb470d632752", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 171 | 01 43N08 02065 | 34.094 | 75.498 | Jhelum | O | 52.27 | 3,575 |\n| 172 | 01 43N08 02066 | 34.067 | 75.475 | Jhelum | E(o) | 17.85 | 3,724 |\n| 173 | 01 43N09 02074 | 34.987 | 75.553 | Indus Upper | E(o) | 17.86 | 4,269 |\n| 174 | 01 43N09 02078 | 34.965 | 75.545 | Indus Upper | E(o) | 13.70 | 4,412 |\n| 175 | 01 43N09 02089 | 34.947 | 75.592 | Indus Upper | E(o) | 24.89 | 4,558 |\n| 176 | 01 43N09 02115 | 34.896 | 75.617 | Indus Upper | E(o) | 11.97 | 4,547 |\n| 177 | 01 43N09 02117 | 34.893 | 75.713 | Indus Upper | E(o) | 19.39 | 4,589 |\n| 178 | 01 43N09 02124 | 34.883 | 75.680 | Indus Upper | E(o) | 13.31 | 4,627 |\n| 179 | 01 43N09 02126 | 34.873 | 75.716 | Indus Upper | E(o) | 15.83 | 4,664 |\n| 180 | 01 43N09 02149 | 34.808 | 75.545 | Indus Upper | E(o) | 12.86 | 4,687 |\n| 181 | 01 43N11 02173 | 34.491 | 75.649 | Indus Upper | E(o) | 15.37 | 4,521 |\n| 182 | 01 43N13 02194 | 34.947 | 75.763 | Indus Upper | E(o) | 25.14 | 4,778 |\n| 183 | 01 43N13 02207 | 34.888 | 75.892 | Indus Upper | E(o) | 10.29 | 4,882 |\n| 184 | 01 43N13 02212 | 34.878 | 75.871 | Indus Upper | E(o) | 18.96 | 4,687 |\n| 185 | 01 43N13 02218 | 34.859 | 75.942 | Indus Upper | E(o) | 13.73 | 4,796 |\n| 186 | 01 43N16 02259 | 34.040 | 75.844 | Indus Upper | M(e) | 25.26 | 4,094 |\n| 187 | 01 43O05 02269 | 33.953 | 75.378 | Jhelum | E(o) | 15.37 | 3,644 |\n| 188 | 01 43O05 02270 | 33.952 | 75.411 | Chenab | E(c) | 26.79 | 4,082 |\n| 189 | 01 43O05 02273 | 33.929 | 75.389 | Jhelum | E(c) | 19.34 | 4,048 |\n| 190 | 01 43O05 02294 | 33.754 | 75.470 | Jhelum | E(o) | 10.88 | 3,921 |\n| 191 | 01 43O06 02296 | 33.619 | 75.537 | Chenab | E(c) | 10.85 | 3,852 |\n| 192 | 01 52A01 02354 | 35.954 | 76.030 | Indus Middle | M(l) | 10.62 | 4,276 |\n| 193 | 01 52A04 02407 | 35.171 | 76.205 | Shyok | O | 30.72 | 3,301 |\n| 194 | 01 52A04 02408 | 35.097 | 76.234 | Shyok | E(o) | 22.96 | 4,512 |\n| 195 | 01 52A06 02465 | 35.730 | 76.410 | Indus Middle | M(l) | 13.28 | 4,161 |\n| 196 | 01 52A06 02485 | 35.720 | 76.375 | Indus Middle | M(l) | 12.14 | 4,140 |\n| 197 | 01 52A08 02503 | 35.092 | 76.252 | Shyok | M(e) | 24.01 | 4,533 |\n| 198 | 01 52A08 02509 | 35.074 | 76.293 | Shyok | E(c) | 12.08 | 4,599 |\n| 199 | 01 52A08 02513 | 35.056 | 76.261 | Indus Upper | E(o) | 16.89 | 4,881 |\n| 200 | 01 52A08 02526 | 35.011 | 76.320 | Indus Upper | E(o) | 15.36 | 4,753 |\n| 201 | 01 52B03 02731 | 34.351 | 76.075 | Indus Upper | M(e) | 10.63 | 4,435 |\n| 202 | 01 52B05 02771 | 34.854 | 76.383 | Indus Upper | E(o) | 13.61 | 4,688 |\n| 203 | 01 52B05 02772 | 34.854 | 76.368 | Indus Upper | E(o) | 13.27 | 4,821 |\n| 204 | 01 52B05 02780 | 34.833 | 76.352 | Indus Upper | E(o) | 10.34 | 4,818 |\n| 205 | 01 52B05 02784 | 34.827 | 76.456 | Shyok | E(o) | 19.82 | 4,648 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 4542, "line_end": 4691, "token_count_estimate": 1622, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Middle", "Indus Upper", "Jhelum", "Shyok"], "countries": [], "lake_ids": ["02065", "02066", "02074", "02078", "02089", "02115", "02117", "02124", "02126", "02149", "02173", "02194", "02207", "02212", "02218", "02259", "02269", "02270", "02273", "02294", "02296", "02354", "02407", "02408", "02465", "02485", "02503", "02509", "02513", "02526", "02731", "02771", "02772", "02780", "02784", "43N08", "43N09", "43N11", "43N13", "43N16", "43O05", "43O06", "52A01", "52A04", "52A06", "52A08", "52B03", "52B05"]}}
{"id": "3bbce36374f3fc66", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 206 | 01 52B05 02787 | 34.818 | 76.409 | Indus Upper | E(o) | 10.20 | 4,816 |\n| 207 | 01 52B05 02808 | 34.772 | 76.500 | Indus Upper | E(o) | 10.53 | 4,761 |\n| 208 | 01 52B05 02816 | 34.762 | 76.469 | Indus Upper | E(o) | 17.67 | 4,895 |\n| 209 | 01 52B09 02844 | 34.813 | 76.517 | Shyok | E(o) | 20.60 | 4,771 |\n| 210 | 01 52B09 02845 | 34.811 | 76.502 | Shyok | E(o) | 12.91 | 4,762 |\n| 211 | 01 52B09 02855 | 34.765 | 76.711 | Shyok | E(o) | 10.06 | 5,123 |\n| 212 | 01 52B12 02878 | 34.051 | 76.718 | Indus Upper | M(e) | 15.80 | 5,093 |\n| 213 | 01 52B12 02881 | 34.005 | 76.721 | Indus Upper | M(e) | 18.32 | 5,050 |\n| 214 | 01 52B14 02898 | 34.671 | 76.758 | Shyok | E(o) | 29.88 | 4,965 |\n| 215 | 01 52B16 02938 | 34.006 | 76.788 | Indus Upper | M(e) | 14.15 | 5,126 |\n| 216 | 01 52C01 02941 | 33.945 | 76.230 | Indus Upper | M(e) | 49.66 | 4,357 |\n| 217 | 01 52C01 02942 | 33.942 | 76.019 | Chenab | M(e) | 24.05 | 4,197 |\n| 218 | 01 52C01 02950 | 33.868 | 76.121 | Chenab | M(e) | 39.44 | 4,071 |\n| 219 | 01 52C05 03044 | 33.844 | 76.375 | Indus Upper | M(e) | 18.49 | 4,116 |\n| 220 | 01 52C06 03061 | 33.507 | 76.450 | Chenab | O | 16.41 | 4,219 |\n| 221 | 01 52C16 03156 | 33.159 | 76.984 | Indus Upper | M(e) | 59.78 | 4,479 |\n| 222 | 01 52D07 03191 | 32.336 | 76.332 | Ravi | E(o) | 25.65 | 3,971 |\n| 223 | 01 52E11 03389 | 35.476 | 77.513 | Shyok | M(lg) | 21.93 | 5,342 |\n| 224 | 01 52E12 03425 | 35.032 | 77.700 | Shyok | M(l) | 18.02 | 5,161 |\n| 225 | 01 52E16 03441 | 35.060 | 77.856 | Shyok | O | 32.78 | 4,679 |\n| 226 | 01 52F09 03545 | 34.905 | 77.616 | Shyok | M(e) | 14.51 | 5,125 |\n| 227 | 01 52F15 03582 | 34.398 | 77.983 | Shyok | M(e) | 28.02 | 5,344 |\n| 228 | 01 52G10 03609 | 33.556 | 77.664 | Indus Upper | O | 10.15 | 5,222 |\n| 229 | 01 52G13 03615 | 33.999 | 77.979 | Shyok | O | 43.72 | 4,991 |\n| 230 | 01 52H02 03651 | 32.526 | 77.220 | Chenab | M(e) | 77.59 | 4,069 |\n| 231 | 01 52H11 03771 | 32.499 | 77.547 | Chenab | M(e) | 128.69 | 4,150 |\n| 232 | 01 52H11 03772 | 32.483 | 77.615 | Chenab | E(o) | 47.30 | 4,276 |\n| 233 | 01 52J02 03794 | 34.521 | 78.090 | Shyok | M(e) | 10.62 | 5,270 |\n| 234 | 01 52J02 03796 | 34.520 | 78.101 | Shyok | M(e) | 10.28 | 5,265 |\n| 235 | 01 52J03 03811 | 34.457 | 78.136 | Shyok | M(o) | 95.68 | 5,295 |\n| 236 | 01 52J03 03815 | 34.446 | 78.143 | Shyok | M(e) | 20.53 | 5,311 |\n| 237 | 01 52J03 03821 | 34.401 | 78.079 | Shyok | M(e) | 20.39 | 5,307 |\n| 238 | 01 52J03 03822 | 34.390 | 78.089 | Shyok | E(o) | 11.16 | 5,226 |\n| 239 | 01 52J08 03836 | 34.233 | 78.426 | Shyok | O | 64.78 | 5,350 |\n| 240 | 01 52J12 03865 | 34.200 | 78.516 | Shyok | E(o) | 24.44 | 5,386 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 4542, "line_end": 4691, "token_count_estimate": 1612, "basins": ["Indus"], "subbasins": ["Chenab", "Indus Upper", "Ravi", "Shyok"], "countries": [], "lake_ids": ["02787", "02808", "02816", "02844", "02845", "02855", "02878", "02881", "02898", "02938", "02941", "02942", "02950", "03044", "03061", "03156", "03191", "03389", "03425", "03441", "03545", "03582", "03609", "03615", "03651", "03771", "03772", "03794", "03796", "03811", "03815", "03821", "03822", "03836", "03865", "52B05", "52B09", "52B12", "52B14", "52B16", "52C01", "52C05", "52C06", "52C16", "52D07", "52E11", "52E12", "52E16", "52F09", "52F15"]}}
{"id": "40230c412cfbbb15", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 241 | 01 52J12 03868 | 34.151 | 78.553 | Shyok | E(o) | 65.02 | 5,566 |\n| 242 | 01 52K03 03890 | 33.281 | 78.230 | Indus Upper | E(o) | 34.70 | 5,646 |\n| 243 | 01 52K04 03898 | 33.137 | 78.197 | Indus Upper | M(o) | 10.30 | 5,733 |\n| 244 | 01 52K07 03937 | 33.496 | 78.498 | Shyok | E(o) | 16.54 | 5,428 |\n| 245 | 01 52K07 03939 | 33.455 | 78.498 | Shyok | O | 147.89 | 5,308 |\n| 246 | 01 52K07 03940 | 33.427 | 78.488 | Shyok | O | 177.73 | 5,284 |\n| 247 | 01 52K08 03964 | 33.055 | 78.470 | Satluj | M(o) | 36.49 | 5,745 |\n| 248 | 01 52K10 03984 | 33.558 | 78.506 | Shyok | E(c) | 25.33 | 5,665 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 4542, "line_end": 4691, "token_count_estimate": 438, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["03868", "03890", "03898", "03937", "03939", "03940", "03964", "03984", "52J12", "52K03", "52K04", "52K07", "52K08", "52K10"]}}
{"id": "cd0013801d7d4633", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 4692, "line_end": 4698, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b772085dbf02b4f6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 249 | 01 52K10 03986 | 33.517 | 78.519 | Shyok | E(o) | 13.66 | 5,404 |\n| 250 | 01 52K11 03987 | 33.474 | 78.502 | Shyok | O | 15.10 | 5,312 |\n| 251 | 01 52L07 04043 | 32.363 | 78.272 | Satluj | M(e) | 13.59 | 5,357 |\n| 252 | 01 52L10 04049 | 32.736 | 78.726 | Indus Upper | M(e) | 34.79 | 5,626 |\n| 253 | 01 52L15 04075 | 32.492 | 78.852 | Indus Upper | M(e) | 11.12 | 5,706 |\n| 254 | 01 52L15 04080 | 32.441 | 78.925 | Indus Upper | M(e) | 10.65 | 5,680 |\n| 255 | 01 52L15 04092 | 32.409 | 78.900 | Indus Upper | M(e) | 30.44 | 5,468 |\n| 256 | 01 52L15 04109 | 32.320 | 78.979 | Satluj | M(o) | 10.51 | 5,498 |\n| 257 | 01 52L16 04178 | 32.029 | 78.845 | Satluj | M(e) | 15.62 | 5,621 |\n| 258 | 01 52P04 04252 | 32.210 | 79.044 | Satluj | O | 21.55 | 5,272 |\n| 259 | 01 52P07 04308 | 32.377 | 79.393 | Satluj | O | 24.86 | 5,448 |\n| 260 | 01 52P11 04322 | 32.399 | 79.622 | Indus Upper | E(o) | 23.72 | 5,058 |\n| 261 | 01 52P11 04326 | 32.389 | 79.659 | Indus Upper | M(e) | 12.30 | 5,720 |\n| 262 | 01 52P11 04368 | 32.298 | 79.662 | Indus Upper | E(o) | 17.15 | 5,690 |\n| 263 | 01 53I02 04530 | 31.661 | 78.168 | Satluj | M(e) | 23.20 | 4,255 |\n| 264 | 01 53I07 04552 | 31.339 | 78.254 | Satluj | M(o) | 11.46 | 4,676 |\n| 265 | 01 53I13 04615 | 31.993 | 78.845 | Satluj | M(e) | 20.90 | 5,613 |\n| 266 | 01 53I13 04639 | 31.919 | 78.784 | Satluj | M(e) | 13.44 | 5,351 |\n| 267 | 01 53I13 04640 | 31.914 | 78.840 | Satluj | M(e) | 18.03 | 5,583 |\n| 268 | 01 53I14 04650 | 31.554 | 78.751 | Satluj | M(e) | 10.67 | 5,276 |\n| 269 | 01 53M13 04700 | 31.984 | 79.958 | Indus Upper | M(e) | 15.73 | 5,544 |\n| 270 | 01 53M13 04720 | 31.950 | 79.986 | Indus Upper | M(e) | 10.43 | 5,449 |\n| 271 | 01 53M13 04722 | 31.937 | 79.994 | Indus Upper | M(e) | 14.46 | 5,494 |\n| 272 | 01 53M13 04727 | 31.925 | 79.865 | Satluj | E(o) | 31.43 | 5,356 |\n| 273 | 01 53M13 04734 | 31.909 | 79.988 | Indus Upper | E(o) | 14.06 | 5,558 |\n| 274 | 01 53M13 04738 | 31.900 | 79.986 | Indus Upper | E(c) | 10.24 | 5,663 |\n| 275 | 01 53M13 04741 | 31.878 | 79.984 | Indus Upper | E(o) | 27.77 | 5,514 |\n| 276 | 01 61B03 04748 | 34.282 | 80.090 | Shyok | M(e) | 25.66 | 5,697 |\n| 277 | 01 61B15 04755 | 34.324 | 80.840 | Shyok | M(lg) | 13.22 | 5,742 |\n| 278 | 01 61B15 04757 | 34.316 | 80.858 | Shyok | I(d) | 232.34 | 5,709 |\n| 279 | 01 61B15 04760 | 34.307 | 80.982 | Shyok | M(e) | 42.60 | 5,729 |\n| 280 | 01 61D15 04804 | 32.423 | 80.865 | Indus Upper | O | 58.84 | 4,452 |\n| 281 | 01 61F03 04813 | 34.299 | 81.202 | Shyok | O | 61.15 | 5,274 |\n| 282 | 01 61F07 04829 | 34.341 | 81.257 | Shyok | E(o) | 51.89 | 5,298 |\n| 283 | 01 62A01 04838 | 31.900 | 80.001 | Indus Upper | E(o) | 20.08 | 5,476 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4699, "line_end": 4848, "token_count_estimate": 1626, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj", "Shyok"], "countries": [], "lake_ids": ["03986", "03987", "04043", "04049", "04075", "04080", "04092", "04109", "04178", "04252", "04308", "04322", "04326", "04368", "04530", "04552", "04615", "04639", "04640", "04650", "04700", "04720", "04722", "04727", "04734", "04738", "04741", "04748", "04755", "04757", "04760", "04804", "04813", "04829", "04838", "52K10", "52K11", "52L07", "52L10", "52L15", "52L16", "52P04", "52P07", "52P11", "53I02", "53I07", "53I13", "53I14", "53M13", "61B03"]}}
{"id": "a2a8b54db79e08ca", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 284 | 01 62A01 04839 | 31.881 | 79.999 | Indus Upper | E(o) | 13.93 | 5,511 |\n| 285 | 01 62A10 04888 | 31.694 | 80.664 | Indus Upper | M(e) | 14.11 | 5,532 |\n| 286 | 01 62A15 04911 | 31.499 | 80.948 | Indus Upper | O | 19.60 | 5,238 |\n| 287 | 01 62A15 04913 | 31.495 | 80.985 | Indus Upper | O | 24.74 | 5,386 |\n| 288 | 01 62A15 04919 | 31.474 | 80.992 | Indus Upper | O | 26.90 | 5,408 |\n| 289 | 01 62B06 04942 | 30.552 | 80.400 | Satluj | M(e) | 26.56 | 4,990 |\n| 290 | 01 62B11 04954 | 30.477 | 80.592 | Satluj | M(e) | 12.63 | 5,263 |\n| 291 | 01 62E01 04958 | 31.985 | 81.063 | Indus Upper | O | 11.79 | 4,734 |\n| 292 | 01 62E03 04961 | 31.442 | 81.157 | Indus Upper | O | 13.01 | 5,240 |\n| 293 | 01 62E03 04969 | 31.285 | 81.032 | Satluj | M(e) | 13.53 | 5,617 |\n| 294 | 01 62E03 04970 | 31.285 | 81.176 | Indus Upper | E(o) | 18.10 | 5,171 |\n| 295 | 01 62E03 04979 | 31.260 | 81.133 | Indus Upper | E(o) | 12.43 | 5,530 |\n| 296 | 01 62E04 04993 | 31.234 | 81.138 | Satluj | M(e) | 10.92 | 5,562 |\n| 297 | 01 62E04 05008 | 31.182 | 81.195 | Satluj | E(o) | 52.48 | 5,413 |\n| 298 | 01 62E04 05012 | 31.179 | 81.152 | Satluj | M(e) | 19.91 | 5,514 |\n| 299 | 01 62E04 05027 | 31.153 | 81.243 | Indus Upper | E(o) | 15.57 | 5,651 |\n| 300 | 01 62E11 05111 | 31.284 | 81.631 | Indus Upper | O | 25.89 | 5,259 |\n| 301 | 01 62E11 05114 | 31.274 | 81.595 | Indus Upper | O | 156.89 | 5,229 |\n| 302 | 01 62E11 05115 | 31.272 | 81.518 | Indus Upper | O | 28.77 | 5,172 |\n| 303 | 01 62F07 05179 | 30.431 | 81.433 | Satluj | E(o) | 192.62 | 5,484 |\n| 304 | 01 62F07 05180 | 30.430 | 81.463 | Satluj | M(o) | 13.70 | 5,572 |\n| 305 | 01 62F07 05190 | 30.416 | 81.468 | Satluj | M(e) | 10.02 | 5,603 |\n| 306 | 01 62F07 05197 | 30.409 | 81.476 | Satluj | M(o) | 11.76 | 5,542 |\n| 307 | 01 62F10 05203 | 30.515 | 81.702 | Satluj | E(o) | 10.17 | 4,818 |\n| 308 | 01 62F11 05218 | 30.430 | 81.714 | Satluj | E(o) | 23.45 | 5,184 |\n| 309 | 01 62F11 05221 | 30.421 | 81.721 | Satluj | E(o) | 18.94 | 5,247 |\n| 310 | 01 62F11 05228 | 30.393 | 81.533 | Satluj | E(o) | 17.49 | 5,128 |\n| 311 | 01 62F15 05243 | 30.463 | 81.993 | Satluj | O | 20.76 | 5,161 |\n| 312 | 01 62F15 05253 | 30.427 | 81.871 | Satluj | E(o) | 21.31 | 5,338 |\n| 313 | 01 62F15 05256 | 30.419 | 81.868 | Satluj | E(o) | 14.11 | 5,356 |\n| 314 | 01 62F15 05269 | 30.399 | 81.853 | Satluj | M(e) | 13.91 | 5,722 |\n| 315 | 01 62F15 05278 | 30.392 | 81.964 | Satluj | M(e) | 20.83 | 5,675 |\n| 316 | 01 62F15 05285 | 30.390 | 81.818 | Satluj | M(e) | 14.51 | 5,544 |\n| 317 | 01 62F15 05286 | 30.390 | 81.895 | Satluj | M(e) | 10.27 | 5,441 |\n| 318 | 01 62F15 05295 | 30.385 | 81.930 | Satluj | M(e) | 59.79 | 5,224 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4699, "line_end": 4848, "token_count_estimate": 1621, "basins": ["Indus"], "subbasins": ["Indus Upper", "Satluj"], "countries": [], "lake_ids": ["04839", "04888", "04911", "04913", "04919", "04942", "04954", "04958", "04961", "04969", "04970", "04979", "04993", "05008", "05012", "05027", "05111", "05114", "05115", "05179", "05180", "05190", "05197", "05203", "05218", "05221", "05228", "05243", "05253", "05256", "05269", "05278", "05285", "05286", "05295", "62A01", "62A10", "62A15", "62B06", "62B11", "62E01", "62E03", "62E04", "62E11", "62F07", "62F10", "62F11", "62F15"]}}
{"id": "3f05521a25995e30", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 319 | 01 62F15 05296 | 30.385 | 81.841 | Satluj | M(e) | 12.42 | 5,571 |\n| 320 | 01 62J02 05311 | 30.592 | 82.084 | Satluj | E(o) | 10.15 | 5,382 |\n| 321 | 01 62J03 05333 | 30.376 | 82.020 | Satluj | M(e) | 11.62 | 5,493 |\n| 322 | 02 53I04 00008 | 31.231 | 78.211 | Yamuna | E(o) | 10.66 | 4,264 |\n| 323 | 02 53I07 00018 | 31.260 | 78.255 | Yamuna | E(c) | 15.10 | 4,403 |\n| 324 | 02 53J14 00068 | 30.746 | 78.987 | Bhagirathi | M(e) | 25.56 | 4,734 |\n| 325 | 02 53N05 00173 | 30.976 | 79.460 | Alaknanda | M(e) | 17.02 | 5,537 |\n| 326 | 02 53N09 00232 | 30.904 | 79.747 | Alaknanda | I(s) | 11.05 | 4,683 |\n| 327 | 02 53N09 00235 | 30.901 | 79.746 | Alaknanda | I(s) | 11.41 | 4,689 |\n| 328 | 02 53N13 00271 | 30.901 | 79.754 | Alaknanda | M(e) | 22.04 | 4,677 |\n| 329 | 02 62B02 00331 | 30.565 | 80.179 | Sarda | M(e) | 17.81 | 4,872 |\n| 330 | 02 62B07 00349 | 30.446 | 80.387 | Sarda | M(e) | 10.00 | 4,306 |\n| 331 | 02 62B11 00358 | 30.392 | 80.532 | Sarda | M(e) | 11.21 | 4,753 |\n| 332 | 02 62B15 00372 | 30.402 | 80.784 | Humla | M(e) | 43.35 | 5,088 |\n| 333 | 02 62B15 00379 | 30.347 | 80.886 | Humla | E(o) | 12.47 | 5,253 |\n| 334 | 02 62F07 00441 | 30.424 | 81.330 | Humla | M(o) | 12.68 | 5,807 |\n| 335 | 02 62F07 00447 | 30.417 | 81.427 | Humla | O | 17.88 | 5,496 |\n| 336 | 02 62F07 00453 | 30.405 | 81.427 | Humla | O | 11.18 | 5,472 |\n| 337 | 02 62F07 00461 | 30.378 | 81.421 | Humla | E(o) | 25.56 | 5,659 |\n| 338 | 02 62F07 00464 | 30.364 | 81.376 | Humla | E(o) | 10.33 | 5,756 |\n| 339 | 02 62F07 00475 | 30.343 | 81.413 | Humla | E(o) | 30.39 | 5,734 |\n| 340 | 02 62F07 00485 | 30.322 | 81.376 | Humla | M(e) | 13.96 | 5,542 |\n| 341 | 02 62F07 00489 | 30.319 | 81.445 | Humla | M(o) | 12.20 | 5,497 |\n| 342 | 02 62F07 00494 | 30.312 | 81.409 | Humla | M(e) | 13.27 | 5,634 |\n| 343 | 02 62F07 00500 | 30.302 | 81.399 | Humla | M(e) | 11.68 | 5,580 |\n| 344 | 02 62F07 00504 | 30.297 | 81.388 | Humla | M(e) | 26.15 | 5,516 |\n| 345 | 02 62F07 00520 | 30.266 | 81.349 | Humla | M(e) | 20.37 | 5,207 |\n| 346 | 02 62F08 00523 | 30.233 | 81.350 | Humla | M(e) | 26.79 | 5,297 |\n| 347 | 02 62F11 00543 | 30.254 | 81.689 | Humla | O | 27.78 | 4,264 |\n| 348 | 02 62F12 00548 | 30.378 | 81.574 | Humla | O | 22.49 | 5,097 |\n| 349 | 02 62F12 00551 | 30.328 | 81.512 | Humla | E(o) | 12.27 | 5,221 |\n| 350 | 02 62F12 00560 | 30.284 | 81.574 | Humla | E(o) | 14.32 | 5,077 |\n| 351 | 02 62F12 00585 | 30.162 | 81.748 | Humla | M(o) | 16.11 | 5,046 |\n| 352 | 02 62F12 00591 | 30.140 | 81.685 | Humla | M(o) | 21.34 | 4,655 |\n| 353 | 02 62F15 00612 | 30.346 | 81.861 | Humla | M(e) | 15.56 | 5,431 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 4699, "line_end": 4848, "token_count_estimate": 1618, "basins": [], "subbasins": ["Sarda", "Satluj", "Yamuna"], "countries": [], "lake_ids": ["00008", "00018", "00068", "00173", "00232", "00235", "00271", "00331", "00349", "00358", "00372", "00379", "00441", "00447", "00453", "00461", "00464", "00475", "00485", "00489", "00494", "00500", "00504", "00520", "00523", "00543", "00548", "00551", "00560", "00585", "00591", "00612", "05296", "05311", "05333", "53I04", "53I07", "53J14", "53N05", "53N09", "53N13", "62B02", "62B07", "62B11", "62B15", "62F07", "62F08", "62F11", "62F12", "62F15"]}}
{"id": "e8e310ebb7e5bbca", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 354 | 02 62F15 00614 | 30.345 | 81.831 | Humla | M(o) | 10.48 | 5,520 |\n| 355 | 02 62F15 00619 | 30.338 | 81.913 | Humla | M(o) | 28.20 | 5,135 |\n| 356 | 02 62F15 00620 | 30.336 | 81.920 | Humla | M(e) | 10.09 | 5,130 |\n| 357 | 02 62F15 00631 | 30.324 | 81.833 | Humla | M(e) | 11.65 | 5,571 |\n| 358 | 02 62F15 00637 | 30.313 | 81.876 | Humla | M(e) | 10.69 | 5,546 |\n| 359 | 02 62F15 00638 | 30.307 | 81.866 | Humla | M(e) | 10.94 | 5,492 |\n| 360 | 02 62F16 00677 | 30.216 | 81.802 | Humla | M(e) | 19.31 | 5,343 |\n| 361 | 02 62F16 00678 | 30.214 | 81.758 | Humla | M(e) | 12.12 | 5,368 |\n| 362 | 02 62F16 00679 | 30.204 | 81.878 | Humla | M(e) | 12.66 | 5,518 |\n| 363 | 02 62F16 00708 | 30.129 | 81.781 | Humla | M(e) | 75.65 | 5,015 |\n| 364 | 02 62F16 00728 | 30.059 | 81.955 | Humla | O | 11.43 | 4,440 |\n| 365 | 02 62F16 00729 | 30.057 | 81.941 | Humla | M(e) | 13.87 | 4,564 |\n| 366 | 02 62G01 00744 | 29.921 | 81.031 | West Seti | M(e) | 18.61 | 4,821 |\n| 367 | 02 62G09 00762 | 29.936 | 81.674 | Kawari | E(o) | 11.23 | 4,569 |\n| 368 | 02 62G09 00775 | 29.898 | 81.747 | Kawari | E(o) | 12.90 | 4,692 |\n| 369 | 02 62G09 00776 | 29.898 | 81.578 | Kawari | M(e) | 28.51 | 3,581 |\n| 370 | 02 62G09 00799 | 29.773 | 81.527 | West Seti | M(e) | 49.99 | 4,576 |\n| 371 | 02 62G10 00816 | 29.648 | 81.556 | Karnal | E(c) | 11.64 | 4,551 |\n| 372 | 02 62G10 00819 | 29.610 | 81.544 | Karnal | E(c) | 13.28 | 4,467 |\n| 373 | 02 62J04 00865 | 30.149 | 82.161 | Humla | M(l) | 16.65 | 4,994 |\n| 374 | 02 62J04 00880 | 30.067 | 82.127 | Humla | M(l) | 62.33 | 4,829 |\n| 375 | 02 62K01 00905 | 29.993 | 82.197 | Humla | M(l) | 24.67 | 4,379 |\n| 376 | 02 62K01 00909 | 29.971 | 82.250 | Humla | E(c) | 17.27 | 4,881 |\n| 377 | 02 62K01 00912 | 29.935 | 82.208 | Humla | E(c) | 12.01 | 4,562 |\n| 378 | 02 62K01 00916 | 29.929 | 82.207 | Humla | E(c) | 32.97 | 4,550 |\n| 379 | 02 62K01 00917 | 29.928 | 82.235 | Humla | O | 12.27 | 4,236 |\n| 380 | 02 62K02 00945 | 29.702 | 82.245 | Humla | M(o) | 10.53 | 4,671 |\n| 381 | 02 62K02 00953 | 29.675 | 82.193 | Humla | M(o) | 15.17 | 4,223 |\n| 382 | 02 62K02 00957 | 29.669 | 82.222 | Humla | E(o) | 11.34 | 4,577 |\n| 383 | 02 62K02 00958 | 29.668 | 82.194 | Humla | E(o) | 12.67 | 4,379 |\n| 384 | 02 62K02 00959 | 29.666 | 82.203 | Humla | E(c) | 19.44 | 4,388 |\n| 385 | 02 62K02 00972 | 29.654 | 82.223 | Mugu | E(o) | 13.57 | 4,492 |\n| 386 | 02 62K04 00982 | 29.116 | 82.236 | Tila | E(o) | 10.84 | 4,270 |\n| 387 | 02 62K04 00994 | 29.084 | 82.186 | Tila | E(o) | 11.01 | 4,242 |\n| 388 | 02 62K05 01015 | 29.878 | 82.383 | Humla | M(o) | 12.59 | 4,967 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 4699, "line_end": 4848, "token_count_estimate": 1598, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00614", "00619", "00620", "00631", "00637", "00638", "00677", "00678", "00679", "00708", "00728", "00729", "00744", "00762", "00775", "00776", "00799", "00816", "00819", "00865", "00880", "00905", "00909", "00912", "00916", "00917", "00945", "00953", "00957", "00958", "00959", "00972", "00982", "00994", "01015", "62F15", "62F16", "62G01", "62G09", "62G10", "62J04", "62K01", "62K02", "62K04", "62K05"]}}
{"id": "4bb77994a0dfcd4d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 389 | 02 62K05 01073 | 29.754 | 82.415 | Mugu | E(c) | 42.40 | 4,692 |\n| 390 | 02 62K06 01085 | 29.714 | 82.250 | Humla | M(l) | 11.54 | 4,651 |\n| 391 | 02 62K06 01094 | 29.698 | 82.373 | Mugu | M(o) | 13.27 | 4,842 |\n| 392 | 02 62K07 01158 | 29.432 | 82.361 | Tila | E(o) | 27.07 | 4,398 |\n| 393 | 02 62K07 01165 | 29.408 | 82.430 | Tila | E(c) | 19.81 | 4,415 |\n| 394 | 02 62K07 01167 | 29.391 | 82.394 | Tila | E(o) | 16.79 | 3,954 |\n| 395 | 02 62K07 01169 | 29.387 | 82.416 | Tila | E(o) | 13.31 | 4,447 |\n| 396 | 02 62K07 01172 | 29.384 | 82.424 | Tila | E(c) | 46.79 | 4,434 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 4699, "line_end": 4848, "token_count_estimate": 428, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["01073", "01085", "01094", "01158", "01165", "01167", "01169", "01172", "62K05", "62K06", "62K07"]}}
{"id": "9e3b8661ee9918a7", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 4849, "line_end": 4854, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb74f94960c78f5b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 397 | 02 62K07 01173 | 29.382 | 82.410 | Tila | E(o) | 10.19 | 4,287 |\n| 398 | 02 62K07 01181 | 29.309 | 82.474 | Tila | E(o) | 15.73 | 4,081 |\n| 399 | 02 62K08 01187 | 29.119 | 82.256 | Tila | E(o) | 13.37 | 4,430 |\n| 400 | 02 62K09 01222 | 29.893 | 82.514 | Mugu | E(o) | 11.14 | 5,031 |\n| 401 | 02 62K09 01247 | 29.822 | 82.712 | Mugu | M(o) | 19.68 | 5,037 |\n| 402 | 02 62K09 01258 | 29.798 | 82.671 | Mugu | M(e) | 13.70 | 4,654 |\n| 403 | 02 62K10 01276 | 29.743 | 82.705 | Mugu | M(o) | 11.27 | 5,346 |\n| 404 | 02 62K10 01282 | 29.723 | 82.644 | Mugu | M(o) | 18.86 | 4,978 |\n| 405 | 02 62K10 01286 | 29.696 | 82.561 | Mugu | E(o) | 11.37 | 4,830 |\n| 406 | 02 62K11 01320 | 29.297 | 82.705 | Bheri | M(e) | 10.03 | 5,003 |\n| 407 | 02 62K11 01321 | 29.270 | 82.590 | Bheri | M(e) | 10.04 | 4,335 |\n| 408 | 02 62K12 01324 | 29.249 | 82.564 | Bheri | M(o) | 29.00 | 4,647 |\n| 409 | 02 62K12 01333 | 29.194 | 82.546 | Tila | E(o) | 35.80 | 4,693 |\n| 410 | 02 62K12 01334 | 29.185 | 82.563 | Tila | E(o) | 16.36 | 4,597 |\n| 411 | 02 62L13 01400 | 28.819 | 82.867 | Bheri | E(o) | 18.36 | 4,429 |\n| 412 | 02 62L13 01402 | 28.790 | 82.981 | Bheri | E(c) | 10.60 | 4,796 |\n| 413 | 02 62O04 01431 | 29.108 | 83.026 | Bheri | M(o) | 12.95 | 4,884 |\n| 414 | 02 62O07 01459 | 29.377 | 83.390 | Mugu | E(o) | 11.85 | 5,175 |\n| 415 | 02 62O12 01512 | 29.218 | 83.702 | Kali Gandak | M(o) | 42.49 | 5,426 |\n| 416 | 02 62O12 01514 | 29.201 | 83.684 | Kali Gandak | M(e) | 22.46 | 5,482 |\n| 417 | 02 62O12 01533 | 29.175 | 83.748 | Kali Gandak | O | 17.07 | 5,360 |\n| 418 | 02 62O12 01551 | 29.117 | 83.738 | Kali Gandak | M(o) | 11.61 | 5,517 |\n| 419 | 02 62O12 01587 | 29.046 | 83.674 | Kali Gandak | M(e) | 12.14 | 5,431 |\n| 420 | 02 62P01 01643 | 28.993 | 83.173 | Bheri | M(e) | 15.98 | 4,821 |\n| 421 | 02 62P01 01651 | 28.959 | 83.187 | Bheri | M(e) | 27.62 | 5,071 |\n| 422 | 02 62P05 01734 | 28.787 | 83.330 | Bheri | M(e) | 43.57 | 4,445 |\n| 423 | 02 62P09 01759 | 28.886 | 83.527 | Kali Gandak | M(e) | 30.92 | 5,578 |\n| 424 | 02 62P14 01790 | 28.726 | 83.890 | Marsyangdi | M(e) | 14.99 | 5,103 |\n| 425 | 02 62P14 01797 | 28.712 | 83.920 | Marsyangdi | E(o) | 10.97 | 4,986 |\n| 426 | 02 62P14 01803 | 28.691 | 83.852 | Marsyangdi | M(o) | 340.21 | 4,910 |\n| 427 | 02 71D01 01825 | 28.826 | 84.150 | Marsyangdi | M(e) | 10.74 | 5,406 |\n| 428 | 02 71D06 01863 | 28.663 | 84.472 | Marsyangdi | M(l) | 24.93 | 4,088 |\n| 429 | 02 71D06 01866 | 28.657 | 84.458 | Marsyangdi | M(l) | 10.57 | 4,039 |\n| 430 | 02 71D07 01880 | 28.497 | 84.256 | Marsyangdi | M(e) | 11.38 | 4,987 |\n| 431 | 02 71D07 01882 | 28.488 | 84.486 | Marsyangdi | M(e) | 89.44 | 4,038 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 4855, "line_end": 5004, "token_count_estimate": 1612, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["01173", "01181", "01187", "01222", "01247", "01258", "01276", "01282", "01286", "01320", "01321", "01324", "01333", "01334", "01400", "01402", "01431", "01459", "01512", "01514", "01533", "01551", "01587", "01643", "01651", "01734", "01759", "01790", "01797", "01803", "01825", "01863", "01866", "01880", "01882", "62K07", "62K08", "62K09", "62K10", "62K11", "62K12", "62L13", "62O04", "62O07", "62O12", "62P01", "62P05", "62P09", "62P14", "71D01"]}}
{"id": "6881e2ba7c87263c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 432 | 02 71D10 01909 | 28.596 | 84.629 | Budhi Gandak | M(e) | 22.25 | 3,632 |\n| 433 | 02 71D11 01929 | 28.372 | 84.579 | Marsyangdi | E(o) | 11.03 | 4,430 |\n| 434 | 02 71D14 01952 | 28.617 | 84.912 | Budhi Gandak | M(e) | 10.08 | 4,683 |\n| 435 | 02 71D14 01959 | 28.505 | 84.802 | Budhi Gandak | E(o) | 21.26 | 3,684 |\n| 436 | 02 71D15 01962 | 28.380 | 84.780 | Budhi Gandak | E(c) | 16.80 | 4,656 |\n| 437 | 02 71H01 01972 | 28.961 | 85.084 | Trisuli | E(o) | 17.03 | 5,388 |\n| 438 | 02 71H02 02020 | 28.585 | 85.022 | Budhi Gandak | M(o) | 11.99 | 5,047 |\n| 439 | 02 71H03 02038 | 28.292 | 85.170 | Trisuli | M(e) | 20.33 | 4,724 |\n| 440 | 02 71H06 02053 | 28.676 | 85.410 | Trisuli | M(e) | 21.04 | 5,447 |\n| 441 | 02 71H06 02056 | 28.662 | 85.475 | Bum Chu | M(e) | 20.42 | 5,217 |\n| 442 | 02 71H06 02059 | 28.644 | 85.491 | Bum Chu | M(e) | 50.98 | 4,985 |\n| 443 | 02 71H06 02060 | 28.642 | 85.474 | Bum Chu | M(e) | 33.30 | 5,135 |\n| 444 | 02 71H06 02072 | 28.568 | 85.457 | Trisuli | M(e) | 23.60 | 4,495 |\n| 445 | 02 71H06 02075 | 28.566 | 85.464 | Trisuli | M(e) | 16.35 | 4,428 |\n| 446 | 02 71H06 02078 | 28.559 | 85.333 | Trisuli | I(s) | 12.60 | 4,973 |\n| 447 | 02 71H06 02097 | 28.508 | 85.494 | Trisuli | M(e) | 26.42 | 4,798 |\n| 448 | 02 71H08 02119 | 28.124 | 85.469 | Trisuli | M(o) | 14.08 | 4,787 |\n| 449 | 02 71H08 02127 | 28.083 | 85.413 | Trisuli | O | 12.59 | 4,393 |\n| 450 | 02 71H08 02130 | 28.080 | 85.407 | Trisuli | O | 15.16 | 4,327 |\n| 451 | 02 71H08 02141 | 28.061 | 85.389 | Trisuli | O | 16.50 | 4,205 |\n| 452 | 02 71H10 02153 | 28.623 | 85.510 | Bum Chu | M(e) | 118.78 | 5,127 |\n| 453 | 02 71H10 02154 | 28.616 | 85.527 | Bum Chu | M(e) | 103.58 | 5,113 |\n| 454 | 02 71H10 02158 | 28.580 | 85.596 | Bum Chu | M(o) | 26.15 | 5,436 |\n| 455 | 02 71H10 02159 | 28.575 | 85.583 | Bum Chu | M(o) | 22.13 | 5,438 |\n| 456 | 02 71H10 02160 | 28.573 | 85.659 | Bum Chu | E(o) | 10.62 | 5,238 |\n| 457 | 02 71H10 02162 | 28.562 | 85.602 | Bum Chu | M(e) | 129.19 | 5,361 |\n| 458 | 02 71H10 02164 | 28.548 | 85.616 | Bum Chu | E(o) | 21.83 | 5,443 |\n| 459 | 02 71H10 02165 | 28.532 | 85.609 | Bum Chu | M(e) | 540.55 | 5,352 |\n| 460 | 02 71H10 02166 | 28.527 | 85.638 | Bum Chu | M(o) | 31.01 | 5,299 |\n| 461 | 02 71H10 02177 | 28.494 | 85.636 | Bum Chu | M(e) | 490.68 | 5,278 |\n| 462 | 02 71H11 02183 | 28.485 | 85.736 | Bum Chu | M(e) | 51.99 | 5,335 |\n| 463 | 02 71H11 02190 | 28.468 | 85.519 | Trisuli | M(e) | 43.67 | 4,445 |\n| 464 | 02 71H11 02194 | 28.460 | 85.684 | Bum Chu | M(o) | 18.49 | 5,705 |\n| 465 | 02 71H11 02209 | 28.426 | 85.564 | Trisuli | M(e) | 29.39 | 4,873 |\n| 466 | 02 71H11 02224 | 28.405 | 85.588 | Trisuli | M(o) | 10.51 | 5,251 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 4855, "line_end": 5004, "token_count_estimate": 1643, "basins": [], "subbasins": ["Gandak"], "countries": [], "lake_ids": ["01909", "01929", "01952", "01959", "01962", "01972", "02020", "02038", "02053", "02056", "02059", "02060", "02072", "02075", "02078", "02097", "02119", "02127", "02130", "02141", "02153", "02154", "02158", "02159", "02160", "02162", "02164", "02165", "02166", "02177", "02183", "02190", "02194", "02209", "02224", "71D10", "71D11", "71D14", "71D15", "71H01", "71H02", "71H03", "71H06", "71H08", "71H10", "71H11"]}}
{"id": "44a8178b77420126", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 467 | 02 71H11 02228 | 28.404 | 85.605 | Trisuli | M(o) | 12.01 | 5,265 |\n| 468 | 02 71H11 02252 | 28.358 | 85.538 | Trisuli | M(o) | 10.83 | 4,390 |\n| 469 | 02 71H12 02318 | 28.163 | 85.630 | Trisuli | M(o) | 13.05 | 4,986 |\n| 470 | 02 71H15 02349 | 28.442 | 85.780 | Bum Chu | M(e) | 48.25 | 5,577 |\n| 471 | 02 71H15 02387 | 28.371 | 85.890 | Sun Kosi | E(o) | 27.07 | 5,242 |\n| 472 | 02 71H15 02391 | 28.360 | 85.871 | Sun Kosi | M(e) | 463.78 | 5,212 |\n| 473 | 02 71H15 02402 | 28.329 | 85.869 | Sun Kosi | M(o) | 213.52 | 5,167 |\n| 474 | 02 71H15 02403 | 28.323 | 85.908 | Sun Kosi | M(o) | 14.56 | 5,387 |\n| 475 | 02 71H15 02405 | 28.322 | 85.838 | Sun Kosi | M(e) | 540.35 | 5,067 |\n| 476 | 02 71H15 02406 | 28.321 | 85.930 | Sun Kosi | M(o) | 11.18 | 5,298 |\n| 477 | 02 71H15 02416 | 28.313 | 85.948 | Sun Kosi | M(o) | 25.06 | 5,227 |\n| 478 | 02 71H15 02428 | 28.297 | 85.819 | Sun Kosi | E(c) | 28.10 | 5,080 |\n| 479 | 02 71H15 02430 | 28.293 | 85.830 | Sun Kosi | M(o) | 28.50 | 5,023 |\n| 480 | 02 71H15 02460 | 28.261 | 85.915 | Sun Kosi | O | 15.97 | 5,106 |\n| 481 | 02 71H16 02493 | 28.211 | 85.847 | Sun Kosi | M(e) | 61.34 | 4,374 |\n| 482 | 02 71H16 02496 | 28.182 | 85.923 | Sun Kosi | E(o) | 47.08 | 4,355 |\n| 483 | 02 71H16 02501 | 28.151 | 85.905 | Sun Kosi | I(s) | 12.60 | 4,495 |\n| 484 | 02 71H16 02503 | 28.139 | 85.919 | Sun Kosi | M(o) | 10.15 | 4,865 |\n| 485 | 02 71L03 02551 | 28.347 | 86.225 | Sun Kosi | M(e) | 55.90 | 5,348 |\n| 486 | 02 71L03 02555 | 28.335 | 86.192 | Sun Kosi | M(e) | 55.00 | 5,422 |\n| 487 | 02 71L03 02556 | 28.321 | 86.158 | Sun Kosi | M(o) | 22.44 | 5,549 |\n| 488 | 02 71L03 02557 | 28.303 | 86.157 | Sun Kosi | M(e) | 59.05 | 5,307 |\n| 489 | 02 71L03 02560 | 28.295 | 86.151 | Sun Kosi | M(e) | 16.51 | 5,346 |\n| 490 | 02 71L03 02561 | 28.294 | 86.131 | Sun Kosi | M(e) | 23.99 | 5,244 |\n| 491 | 02 71L03 02574 | 28.270 | 86.187 | Tama Kosi | M(e) | 12.64 | 5,335 |\n| 492 | 02 71L03 02596 | 28.253 | 86.103 | Sun Kosi | M(e) | 14.98 | 5,189 |\n| 493 | 02 71L04 02600 | 28.249 | 86.150 | Tama Kosi | M(o) | 13.16 | 5,331 |\n| 494 | 02 71L04 02606 | 28.243 | 86.196 | Tama Kosi | M(e) | 17.69 | 5,338 |\n| 495 | 02 71L04 02665 | 28.183 | 86.226 | Tama Kosi | M(o) | 12.34 | 5,223 |\n| 496 | 02 71L04 02702 | 28.067 | 86.066 | Sun Kosi | M(o) | 32.50 | 4,630 |\n| 497 | 02 71L07 02732 | 28.392 | 86.415 | Bum Chu | M(e) | 20.17 | 5,502 |\n| 498 | 02 71L07 02734 | 28.381 | 86.384 | Bum Chu | M(e) | 13.92 | 5,551 |\n| 499 | 02 71L07 02736 | 28.374 | 86.305 | Bum Chu | M(e) | 391.50 | 5,346 |\n| 500 | 02 71L07 02737 | 28.374 | 86.259 | Sun Kosi | M(e) | 27.54 | 5,544 |\n| 501 | 02 71L07 02745 | 28.348 | 86.493 | Bum Chu | M(e) | 34.51 | 5,524 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 4855, "line_end": 5004, "token_count_estimate": 1642, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["02228", "02252", "02318", "02349", "02387", "02391", "02402", "02403", "02405", "02406", "02416", "02428", "02430", "02460", "02493", "02496", "02501", "02503", "02551", "02555", "02556", "02557", "02560", "02561", "02574", "02596", "02600", "02606", "02665", "02702", "02732", "02734", "02736", "02737", "02745", "71H11", "71H12", "71H15", "71H16", "71L03", "71L04", "71L07"]}}
{"id": "94e6205cb0cb3e17", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 502 | 02 71L07 02788 | 28.199 | 86.582 | Bum Chu | M(e) | 134.64 | 5,094 |\n| 503 | 02 71L08 02800 | 28.245 | 86.321 | Tama Kosi | M(o) | 21.56 | 5,361 |\n| 504 | 02 71L08 02806 | 28.240 | 86.365 | Tama Kosi | M(e) | 24.72 | 5,327 |\n| 505 | 02 71L08 02854 | 28.194 | 86.314 | Tama Kosi | M(e) | 27.79 | 5,245 |\n| 506 | 02 71L08 02856 | 28.193 | 86.351 | Tama Kosi | M(e) | 19.87 | 5,339 |\n| 507 | 02 71L08 02932 | 28.033 | 86.500 | Tama Kosi | M(e) | 60.86 | 5,057 |\n| 508 | 02 71L09 02947 | 28.887 | 86.514 | Bum Chu | E(o) | 98.23 | 5,098 |\n| 509 | 02 71L09 02948 | 28.840 | 86.575 | Bum Chu | E(o) | 26.39 | 5,465 |\n| 510 | 02 71L09 02949 | 28.815 | 86.529 | Bum Chu | E(o) | 32.37 | 5,326 |\n| 511 | 02 71L11 02952 | 28.321 | 86.511 | Bum Chu | M(o) | 10.75 | 5,321 |\n| 512 | 02 71L11 02963 | 28.283 | 86.502 | Bum Chu | I(s) | 22.63 | 5,157 |\n| 513 | 02 71L12 03006 | 28.207 | 86.629 | Bum Chu | M(e) | 27.78 | 5,539 |\n| 514 | 02 71L12 03013 | 28.185 | 86.532 | Tama Kosi | M(e) | 67.68 | 5,025 |\n| 515 | 02 71L12 03024 | 28.151 | 86.535 | Tama Kosi | M(o) | 18.48 | 5,220 |\n| 516 | 02 71L12 03032 | 28.135 | 86.531 | Tama Kosi | M(e) | 97.85 | 4,984 |\n| 517 | 02 71L12 03051 | 28.073 | 86.520 | Tama Kosi | M(e) | 23.11 | 5,216 |\n| 518 | 02 71L12 03072 | 28.044 | 86.514 | Tama Kosi | M(l) | 57.94 | 5,241 |\n| 519 | 02 71L12 03082 | 28.038 | 86.710 | Dudh Kosi | M(e) | 15.44 | 5,363 |\n| 520 | 02 71L12 03105 | 28.026 | 86.682 | Dudh Kosi | M(o) | 18.71 | 5,149 |\n| 521 | 02 71L12 03123 | 28.017 | 86.721 | Dudh Kosi | M(o) | 19.81 | 5,066 |\n| 522 | 02 71L12 03139 | 28.006 | 86.682 | Dudh Kosi | M(l) | 17.13 | 4,947 |\n| 523 | 02 71L16 03164 | 28.112 | 86.863 | Arun Kosi | I(s) | 39.40 | 5,218 |\n| 524 | 02 71P03 03176 | 28.274 | 87.150 | Arun Kosi | E(o) | 11.91 | 5,012 |\n| 525 | 02 71P03 03177 | 28.270 | 87.246 | Arun Kosi | E(o) | 15.31 | 5,166 |\n| 526 | 02 71P04 03181 | 28.226 | 87.053 | Arun Kosi | M(e) | 17.19 | 5,050 |\n| 527 | 02 71P04 03182 | 28.208 | 87.101 | Arun Kosi | E(o) | 101.93 | 4,852 |\n| 528 | 02 71P04 03183 | 28.205 | 87.052 | Arun Kosi | O | 65.11 | 4,980 |\n| 529 | 02 71P04 03196 | 28.166 | 87.122 | Arun Kosi | E(o) | 12.23 | 5,219 |\n| 530 | 02 71P04 03197 | 28.159 | 87.143 | Arun Kosi | E(o) | 18.93 | 5,154 |\n| 531 | 02 71P04 03201 | 28.152 | 87.158 | Arun Kosi | O | 94.74 | 5,141 |\n| 532 | 02 71P04 03202 | 28.152 | 87.174 | Arun Kosi | O | 10.97 | 5,075 |\n| 533 | 02 71P04 03208 | 28.142 | 87.105 | Arun Kosi | M(o) | 16.53 | 5,472 |\n| 534 | 02 71P04 03209 | 28.142 | 87.112 | Arun Kosi | M(o) | 25.06 | 5,470 |\n| 535 | 02 71P04 03214 | 28.130 | 87.082 | Arun Kosi | M(e) | 19.29 | 5,518 |\n| 536 | 02 71P04 03222 | 28.111 | 87.065 | Arun Kosi | M(o) | 31.03 | 5,468 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 4855, "line_end": 5004, "token_count_estimate": 1657, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["02788", "02800", "02806", "02854", "02856", "02932", "02947", "02948", "02949", "02952", "02963", "03006", "03013", "03024", "03032", "03051", "03072", "03082", "03105", "03123", "03139", "03164", "03176", "03177", "03181", "03182", "03183", "03196", "03197", "03201", "03202", "03208", "03209", "03214", "03222", "71L07", "71L08", "71L09", "71L11", "71L12", "71L16", "71P03", "71P04"]}}
{"id": "da27fbcfa67d79c3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 537 | 02 71P04 03231 | 28.069 | 87.134 | Arun Kosi | M(e) | 25.49 | 4,879 |\n| 538 | 02 71P04 03232 | 28.068 | 87.047 | Arun Kosi | M(e) | 78.93 | 5,589 |\n| 539 | 02 71P04 03233 | 28.065 | 87.193 | Arun Kosi | M(o) | 10.78 | 4,837 |\n| 540 | 02 71P04 03235 | 28.039 | 87.167 | Arun Kosi | M(e) | 24.07 | 4,739 |\n| 541 | 02 71P04 03237 | 28.032 | 87.190 | Arun Kosi | E(c) | 27.29 | 5,013 |\n| 542 | 02 71P04 03245 | 28.007 | 87.028 | Arun Kosi | M(e) | 14.23 | 5,509 |\n| 543 | 02 71P04 03246 | 28.005 | 87.142 | Arun Kosi | M(e) | 43.70 | 4,450 |\n| 544 | 02 71P05 03264 | 28.775 | 87.437 | Bum Chu | E(o) | 21.80 | 5,553 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 4855, "line_end": 5004, "token_count_estimate": 450, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["03231", "03232", "03233", "03235", "03237", "03245", "03246", "03264", "71P04", "71P05"]}}
{"id": "1ad3da3d91f0f87b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5005, "line_end": 5009, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c17b052c73a491e2", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 545 | 02 71P05 03275 | 28.760 | 87.417 | Bum Chu | E(o) | 15.68 | 5,458 |\n| 546 | 02 71P05 03278 | 28.754 | 87.427 | Bum Chu | E(o) | 13.58 | 5,511 |\n| 547 | 02 71P05 03279 | 28.750 | 87.455 | Bum Chu | E(o) | 31.67 | 5,680 |\n| 548 | 02 71P06 03290 | 28.742 | 87.382 | Bum Chu | E(o) | 41.57 | 5,501 |\n| 549 | 02 71P06 03293 | 28.737 | 87.479 | Bum Chu | E(o) | 23.55 | 5,415 |\n| 550 | 02 71P06 03299 | 28.735 | 87.440 | Bum Chu | E(o) | 23.96 | 5,577 |\n| 551 | 02 71P06 03305 | 28.734 | 87.406 | Bum Chu | E(o) | 18.44 | 5,600 |\n| 552 | 02 71P06 03311 | 28.728 | 87.478 | Bum Chu | E(o) | 24.81 | 5,384 |\n| 553 | 02 71P07 03342 | 28.393 | 86.379 | Bum Chu | M(e) | 100.11 | 5,482 |\n| 554 | 02 71P08 03345 | 28.213 | 87.470 | Arun Kosi | O | 131.39 | 4,781 |\n| 555 | 02 71P08 03350 | 28.172 | 87.479 | Arun Kosi | M(e) | 23.91 | 5,290 |\n| 556 | 02 71P08 03352 | 28.165 | 87.418 | Arun Kosi | O | 10.93 | 4,818 |\n| 557 | 02 71P08 03353 | 28.161 | 87.487 | Arun Kosi | M(e) | 10.29 | 5,298 |\n| 558 | 02 71P08 03355 | 28.160 | 87.443 | Arun Kosi | O | 32.33 | 4,854 |\n| 559 | 02 71P08 03360 | 28.148 | 87.469 | Arun Kosi | M(o) | 39.58 | 5,106 |\n| 560 | 02 71P08 03365 | 28.137 | 87.428 | Arun Kosi | M(e) | 25.85 | 5,191 |\n| 561 | 02 71P08 03384 | 28.064 | 87.354 | Arun Kosi | E(o) | 13.08 | 4,190 |\n| 562 | 02 71P08 03407 | 28.023 | 87.270 | Arun Kosi | E(o) | 10.23 | 4,636 |\n| 563 | 02 71P08 03412 | 28.016 | 87.259 | Arun Kosi | E(o) | 33.07 | 4,672 |\n| 564 | 02 71P08 03413 | 28.012 | 87.297 | Arun Kosi | E(o) | 12.13 | 4,416 |\n| 565 | 02 71P09 03420 | 28.858 | 86.519 | Bum Chu | E(o) | 81.24 | 5,254 |\n| 566 | 02 71P09 03421 | 28.841 | 87.509 | Bum Chu | E(o) | 25.20 | 5,486 |\n| 567 | 02 71P09 03422 | 28.832 | 86.522 | Bum Chu | E(o) | 281.32 | 5,319 |\n| 568 | 02 71P09 03438 | 28.773 | 87.553 | Yeru Chu | O | 25.64 | 5,319 |\n| 569 | 02 71P10 03461 | 28.747 | 87.625 | Yeru Chu | E(o) | 17.28 | 5,545 |\n| 570 | 02 71P10 03474 | 28.712 | 87.548 | Yeru Chu | O | 41.20 | 5,188 |\n| 571 | 02 71P10 03475 | 28.694 | 87.534 | Bum Chu | E(o) | 60.74 | 5,158 |\n| 572 | 02 71P11 03497 | 28.268 | 87.634 | Arun Kosi | M(o) | 10.04 | 5,674 |\n| 573 | 02 71P12 03510 | 28.236 | 87.501 | Arun Kosi | M(o) | 20.48 | 5,225 |\n| 574 | 02 71P12 03514 | 28.230 | 87.591 | Arun Kosi | M(e) | 78.90 | 5,410 |\n| 575 | 02 71P12 03516 | 28.228 | 87.578 | Arun Kosi | M(e) | 21.94 | 5,247 |\n| 576 | 02 71P12 03521 | 28.206 | 87.560 | Arun Kosi | M(e) | 15.95 | 5,337 |\n| 577 | 02 71P12 03524 | 28.195 | 87.641 | Yeru Chu | M(e) | 47.42 | 5,352 |\n| 578 | 02 71P12 03527 | 28.178 | 87.563 | Arun Kosi | M(e) | 104.19 | 5,011 |\n| 579 | 02 71P12 03532 | 28.167 | 87.623 | Yeru Chu | M(e) | 20.87 | 5,375 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5010, "line_end": 5159, "token_count_estimate": 1646, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["03275", "03278", "03279", "03290", "03293", "03299", "03305", "03311", "03342", "03345", "03350", "03352", "03353", "03355", "03360", "03365", "03384", "03407", "03412", "03413", "03420", "03421", "03422", "03438", "03461", "03474", "03475", "03497", "03510", "03514", "03516", "03521", "03524", "03527", "03532", "71P05", "71P06", "71P07", "71P08", "71P09", "71P10", "71P11", "71P12"]}}
{"id": "cad19a843347a4a1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 580 | 02 71P12 03534 | 28.164 | 87.578 | Arun Kosi | M(e) | 20.22 | 5,171 |\n| 581 | 02 71P12 03540 | 28.155 | 87.612 | Arun Kosi | M(o) | 13.82 | 5,241 |\n| 582 | 02 71P12 03556 | 28.131 | 87.599 | Arun Kosi | M(o) | 14.18 | 5,261 |\n| 583 | 02 71P12 03559 | 28.118 | 87.615 | Arun Kosi | M(e) | 35.67 | 5,052 |\n| 584 | 02 71P12 03560 | 28.116 | 87.586 | Arun Kosi | M(e) | 12.43 | 5,050 |\n| 585 | 02 71P12 03561 | 28.114 | 87.655 | Arun Kosi | M(e) | 146.34 | 4,954 |\n| 586 | 02 71P12 03562 | 28.107 | 87.584 | Arun Kosi | M(e) | 15.91 | 4,968 |\n| 587 | 02 71P12 03566 | 28.093 | 87.637 | Arun Kosi | M(e) | 72.47 | 5,178 |\n| 588 | 02 71P12 03579 | 28.052 | 87.627 | Arun Kosi | M(e) | 18.40 | 5,066 |\n| 589 | 02 72I05 03634 | 27.947 | 86.446 | Tama Kosi | M(e) | 156.76 | 5,046 |\n| 590 | 02 72I05 03638 | 27.929 | 86.433 | Tama Kosi | M(o) | 31.99 | 5,025 |\n| 591 | 02 72I05 03642 | 27.927 | 86.420 | Tama Kosi | M(o) | 15.72 | 5,020 |\n| 592 | 02 72I05 03643 | 27.917 | 86.465 | Tama Kosi | M(o) | 17.56 | 5,259 |\n| 593 | 02 72I05 03644 | 27.916 | 86.477 | Tama Kosi | M(o) | 14.08 | 5,110 |\n| 594 | 02 72I05 03648 | 27.861 | 86.476 | Tama Kosi | M(e) | 158.40 | 4,550 |\n| 595 | 02 72I05 03652 | 27.850 | 86.356 | Tama Kosi | E(o) | 11.18 | 4,424 |\n| 596 | 02 72I09 03725 | 27.975 | 86.681 | Dudh Kosi | M(l) | 57.83 | 4,834 |\n| 597 | 02 72I09 03749 | 27.951 | 86.690 | Dudh Kosi | M(l) | 42.11 | 4,741 |\n| 598 | 02 72I09 03759 | 27.941 | 86.699 | Dudh Kosi | M(l) | 18.00 | 4,709 |\n| 599 | 02 72I09 03783 | 27.874 | 86.586 | Dudh Kosi | M(e) | 40.18 | 4,368 |\n| 600 | 02 72I09 03794 | 27.791 | 86.621 | Dudh Kosi | M(o) | 46.73 | 5,157 |\n| 601 | 02 72I09 03801 | 27.779 | 86.612 | Dudh Kosi | M(e) | 117.31 | 4,831 |\n| 602 | 02 72I09 03802 | 27.778 | 86.643 | Dudh Kosi | M(o) | 29.27 | 5,163 |\n| 603 | 02 72I13 03864 | 27.997 | 86.835 | Dudh Kosi | M(o) | 11.55 | 5,321 |\n| 604 | 02 72I13 03881 | 27.975 | 86.804 | Dudh Kosi | I(s) | 24.84 | 5,183 |\n| 605 | 02 72I13 03932 | 27.924 | 86.786 | Dudh Kosi | M(l) | 54.85 | 4,512 |\n| 606 | 02 72I13 03950 | 27.898 | 86.925 | Dudh Kosi | M(e) | 139.77 | 5,003 |\n| 607 | 02 72I13 03952 | 27.894 | 86.913 | Dudh Kosi | E(c) | 11.28 | 4,986 |\n| 608 | 02 72I13 03977 | 27.857 | 86.937 | Dudh Kosi | M(o) | 21.45 | 5,473 |\n| 609 | 02 72I13 03979 | 27.850 | 86.928 | Dudh Kosi | M(o) | 48.86 | 5,411 |\n| 610 | 02 72I13 03983 | 27.837 | 86.935 | Dudh Kosi | M(e) | 29.39 | 5,209 |\n| 611 | 02 72I13 03986 | 27.832 | 86.917 | Dudh Kosi | M(o) | 33.07 | 5,360 |\n| 612 | 02 72I13 04000 | 27.805 | 86.974 | Dudh Kosi | M(e) | 16.77 | 5,516 |\n| 613 | 02 72I13 04004 | 27.799 | 86.966 | Dudh Kosi | M(e) | 21.36 | 5,396 |\n| 614 | 02 72I13 04007 | 27.794 | 86.911 | Dudh Kosi | M(o) | 18.87 | 5,274 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5010, "line_end": 5159, "token_count_estimate": 1673, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["03534", "03540", "03556", "03559", "03560", "03561", "03562", "03566", "03579", "03634", "03638", "03642", "03643", "03644", "03648", "03652", "03725", "03749", "03759", "03783", "03794", "03801", "03802", "03864", "03881", "03932", "03950", "03952", "03977", "03979", "03983", "03986", "04000", "04004", "04007", "71P12", "72I05", "72I09", "72I13"]}}
{"id": "614830d9de5a947c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 615 | 02 72I13 04008 | 27.793 | 86.838 | Dudh Kosi | M(o) | 22.89 | 5,351 |\n| 616 | 02 72I13 04014 | 27.783 | 86.957 | Dudh Kosi | M(e) | 87.28 | 5,198 |\n| 617 | 02 72I13 04024 | 27.766 | 86.871 | Dudh Kosi | I(s) | 13.68 | 4,931 |\n| 618 | 02 72I13 04032 | 27.755 | 86.958 | Dudh Kosi | M(o) | 86.50 | 4,927 |\n| 619 | 02 72I14 04037 | 27.743 | 86.844 | Dudh Kosi | M(e) | 25.73 | 4,362 |\n| 620 | 02 72I14 04048 | 27.719 | 86.910 | Dudh Kosi | M(o) | 14.93 | 4,989 |\n| 621 | 02 72I14 04052 | 27.696 | 86.792 | Dudh Kosi | M(o) | 12.95 | 4,951 |\n| 622 | 02 72I14 04055 | 27.687 | 86.858 | Dudh Kosi | M(e) | 31.80 | 4,764 |\n| 623 | 02 72I14 04056 | 27.681 | 86.853 | Dudh Kosi | E(o) | 11.85 | 4,694 |\n| 624 | 02 72M01 04093 | 27.959 | 87.247 | Arun Kosi | E(c) | 12.02 | 4,457 |\n| 625 | 02 72M01 04106 | 27.906 | 87.188 | Arun Kosi | E(c) | 33.14 | 4,490 |\n| 626 | 02 72M01 04113 | 27.844 | 87.081 | Arun Kosi | M(e) | 41.15 | 4,847 |\n| 627 | 02 72M01 04118 | 27.829 | 87.095 | Arun Kosi | M(o) | 12.06 | 5,225 |\n| 628 | 02 72M01 04125 | 27.798 | 87.092 | Arun Kosi | M(e) | 182.16 | 4,543 |\n| 629 | 02 72M02 04132 | 27.691 | 87.206 | Arun Kosi | E(c) | 11.49 | 4,088 |\n| 630 | 02 72M05 04150 | 27.983 | 87.345 | Arun Kosi | E(o) | 26.18 | 3,728 |\n| 631 | 02 72M05 04175 | 27.948 | 87.349 | Arun Kosi | E(c) | 10.13 | 3,991 |\n| 632 | 02 72M05 04182 | 27.932 | 87.293 | Arun Kosi | E(o) | 12.13 | 4,373 |\n| 633 | 02 72M05 04192 | 27.918 | 87.306 | Arun Kosi | E(c) | 11.15 | 4,416 |\n| 634 | 02 72M05 04202 | 27.837 | 87.341 | Arun Kosi | E(c) | 19.17 | 4,243 |\n| 635 | 02 72M09 04233 | 27.997 | 87.522 | Arun Kosi | E(c) | 12.38 | 4,720 |\n| 636 | 02 72M09 04240 | 27.986 | 87.583 | Arun Kosi | E(o) | 12.33 | 4,826 |\n| 637 | 02 72M09 04245 | 27.979 | 87.557 | Arun Kosi | E(c) | 11.03 | 4,767 |\n| 638 | 02 72M09 04259 | 27.900 | 87.699 | Arun Kosi | M(e) | 11.27 | 5,226 |\n| 639 | 02 72M09 04267 | 27.864 | 87.737 | Arun Kosi | E(c) | 13.98 | 5,337 |\n| 640 | 02 72M09 04296 | 27.816 | 87.749 | Tamur Kosi | M(e) | 17.17 | 4,903 |\n| 641 | 02 72M10 04374 | 27.681 | 87.694 | Tamur Kosi | E(c) | 13.30 | 4,633 |\n| 642 | 02 72M10 04375 | 27.681 | 87.517 | Arun Kosi | E(c) | 10.10 | 4,472 |\n| 643 | 02 72M10 04416 | 27.651 | 87.703 | Tamur Kosi | E(o) | 14.36 | 4,460 |\n| 644 | 02 72M10 04425 | 27.645 | 87.573 | Tamur Kosi | O | 11.78 | 3,860 |\n| 645 | 02 72M10 04429 | 27.639 | 87.633 | Tamur Kosi | E(c) | 16.45 | 4,289 |\n| 646 | 02 72M10 04438 | 27.630 | 87.696 | Tamur Kosi | E(c) | 14.11 | 4,179 |\n| 647 | 02 72M10 04440 | 27.629 | 87.706 | Tamur Kosi | E(o) | 13.01 | 4,319 |\n| 648 | 02 72M10 04445 | 27.620 | 87.712 | Tamur Kosi | E(o) | 12.63 | 4,454 |\n| 649 | 02 72M10 04447 | 27.617 | 87.543 | Tamur Kosi | E(c) | 10.93 | 4,471 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5010, "line_end": 5159, "token_count_estimate": 1672, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["04008", "04014", "04024", "04032", "04037", "04048", "04052", "04055", "04056", "04093", "04106", "04113", "04118", "04125", "04132", "04150", "04175", "04182", "04192", "04202", "04233", "04240", "04245", "04259", "04267", "04296", "04374", "04375", "04416", "04425", "04429", "04438", "04440", "04445", "04447", "72I13", "72I14", "72M01", "72M02", "72M05", "72M09", "72M10"]}}
{"id": "577cc3cb6774db9e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 650 | 02 72M13 04478 | 27.969 | 87.884 | Yeru Chu | M(l) | 17.90 | 5,597 |\n| 651 | 02 72M13 04482 | 27.964 | 87.814 | Arun Kosi | M(e) | 40.61 | 5,249 |\n| 652 | 02 72M13 04484 | 27.952 | 87.908 | Yeru Chu | M(e) | 64.79 | 5,165 |\n| 653 | 02 72M13 04486 | 27.950 | 87.930 | Yeru Chu | M(e) | 83.66 | 5,106 |\n| 654 | 02 72M13 04495 | 27.928 | 88.002 | Yeru Chu | M(e) | 113.22 | 5,348 |\n| 655 | 02 72M13 04496 | 27.926 | 87.771 | Arun Kosi | M(e) | 97.66 | 4,913 |\n| 656 | 02 72M13 04507 | 27.881 | 87.805 | Tamur Kosi | M(e) | 34.32 | 4,690 |\n| 657 | 02 72M13 04515 | 27.869 | 87.866 | Tamur Kosi | M(e) | 68.12 | 4,910 |\n| 658 | 02 72M13 04523 | 27.849 | 87.831 | Tamur Kosi | O | 33.50 | 4,437 |\n| 659 | 02 72M13 04535 | 27.793 | 87.974 | Tamur Kosi | M(e) | 22.32 | 5,183 |\n| 660 | 02 72M13 04537 | 27.790 | 87.934 | Tamur Kosi | M(o) | 14.10 | 4,938 |\n| 661 | 02 72M13 04544 | 27.757 | 87.777 | Tamur Kosi | E(c) | 25.46 | 4,708 |\n| 662 | 02 72M14 04551 | 27.745 | 87.782 | Tamur Kosi | E(c) | 19.69 | 4,775 |\n| 663 | 02 72M14 04553 | 27.738 | 87.778 | Tamur Kosi | E(o) | 22.06 | 4,705 |\n| 664 | 02 77D04 04602 | 28.004 | 88.241 | Yeru Chu | M(e) | 41.80 | 5,269 |\n| 665 | 02 77D08 04614 | 28.054 | 88.427 | Yeru Chu | O | 101.66 | 4,888 |\n| 666 | 02 77D08 04617 | 28.033 | 88.461 | Yeru Chu | E(o) | 13.87 | 5,156 |\n| 667 | 02 77D08 04622 | 28.022 | 88.355 | Yeru Chu | M(e) | 56.29 | 5,195 |\n| 668 | 02 77D08 04624 | 28.017 | 88.288 | Yeru Chu | M(e) | 50.43 | 5,268 |\n| 669 | 02 77D08 04627 | 28.009 | 88.259 | Yeru Chu | M(e) | 59.70 | 5,256 |\n| 670 | 02 77D08 04628 | 28.005 | 88.320 | Yeru Chu | M(e) | 38.58 | 5,104 |\n| 671 | 02 78A01 04636 | 27.988 | 88.221 | Yeru Chu | M(e) | 11.94 | 5,372 |\n| 672 | 02 78A01 04637 | 27.946 | 88.075 | Yeru Chu | M(e) | 148.59 | 5,488 |\n| 673 | 02 78A01 04638 | 27.943 | 88.041 | Yeru Chu | M(o) | 10.32 | 5,821 |\n| 674 | 02 78A01 04639 | 27.933 | 88.066 | Yeru Chu | M(e) | 83.35 | 5,563 |\n| 675 | 02 78A01 04640 | 27.928 | 88.019 | Yeru Chu | M(e) | 13.25 | 5,692 |\n| 676 | 02 78A01 04653 | 27.835 | 88.078 | Tamur Kosi | M(e) | 16.48 | 5,604 |\n| 677 | 02 78A02 04691 | 27.545 | 88.050 | Tamur Kosi | M(o) | 25.72 | 5,020 |\n| 678 | 02 78A05 04702 | 27.994 | 88.402 | Yeru Chu | M(e) | 18.97 | 5,171 |\n| 679 | 03 62J03 00022 | 30.478 | 82.173 | Upper YarlungTsangpo | O | 31.29 | 5,250 |\n| 680 | 03 62J03 00044 | 30.468 | 82.060 | Upper YarlungTsangpo | O | 378.80 | 5,180 |\n| 681 | 03 62J03 00049 | 30.461 | 82.108 | Upper YarlungTsangpo | O | 16.73 | 5,177 |\n| 682 | 03 62J03 00085 | 30.431 | 82.181 | Upper YarlungTsangpo | O | 32.23 | 5,000 |\n| 683 | 03 62J03 00123 | 30.413 | 82.171 | Upper YarlungTsangpo | O | 37.71 | 5,002 |\n| 684 | 03 62J03 00151 | 30.398 | 82.192 | Upper YarlungTsangpo | E(o) | 86.53 | 5,203 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5010, "line_end": 5159, "token_count_estimate": 1667, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["00022", "00044", "00049", "00085", "00123", "00151", "04478", "04482", "04484", "04486", "04495", "04496", "04507", "04515", "04523", "04535", "04537", "04544", "04551", "04553", "04602", "04614", "04617", "04622", "04624", "04627", "04628", "04636", "04637", "04638", "04639", "04640", "04653", "04691", "04702", "62J03", "72M13", "72M14", "77D04", "77D08", "78A01", "78A02", "78A05"]}}
{"id": "ea284a457577cab0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 685 | 03 62J03 00161 | 30.366 | 82.156 | Upper YarlungTsangpo | E(o) | 12.73 | 5,450 |\n| 686 | 03 62J03 00163 | 30.362 | 82.055 | Upper YarlungTsangpo | M(e) | 56.83 | 5,283 |\n| 687 | 03 62J03 00175 | 30.352 | 82.238 | Upper YarlungTsangpo | O | 29.81 | 5,106 |\n| 688 | 03 62J03 00177 | 30.340 | 82.141 | Upper YarlungTsangpo | M(e) | 16.12 | 5,580 |\n| 689 | 03 62J03 00180 | 30.310 | 82.200 | Upper YarlungTsangpo | M(e) | 19.08 | 5,530 |\n| 690 | 03 62J03 00188 | 30.282 | 82.169 | Upper YarlungTsangpo | M(e) | 16.58 | 5,339 |\n| 691 | 03 62J03 00201 | 30.255 | 82.209 | Upper YarlungTsangpo | M(e) | 128.70 | 5,057 |\n| 692 | 03 62J03 00207 | 30.221 | 82.232 | Upper YarlungTsangpo | M(e) | 44.31 | 5,582 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 5010, "line_end": 5159, "token_count_estimate": 479, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00161", "00163", "00175", "00177", "00180", "00188", "00201", "00207", "62J03"]}}
{"id": "db9029b9d626322e", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5160, "line_end": 5164, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "80bcea32002be9c0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1285 | 03 78I09 07447 | 27.939 | 90.535 | Manas | E(c) | 59.94 | 5,036 |\n| 1286 | 03 78I09 07468 | 27.925 | 90.555 | Manas | E(o) | 29.18 | 4,774 |\n| 1287 | 03 78I09 07470 | 27.921 | 90.503 | Manas | E(o) | 13.38 | 5,082 |\n| 1288 | 03 78I09 07471 | 27.919 | 90.537 | Manas | E(c) | 38.13 | 4,935 |\n| 1289 | 03 78I09 07474 | 27.915 | 90.559 | Manas | M(o) | 11.08 | 4,939 |\n| 1290 | 03 78I09 07487 | 27.895 | 90.552 | Manas | E(c) | 24.13 | 4,977 |\n| 1291 | 03 78I09 07488 | 27.895 | 90.598 | Manas | E(o) | 12.32 | 4,640 |\n| 1292 | 03 78I09 07499 | 27.884 | 90.712 | Manas | E(o) | 12.81 | 4,875 |\n| 1293 | 03 78I09 07500 | 27.881 | 90.566 | Manas | E(o) | 18.83 | 4,666 |\n| 1294 | 03 78I09 07503 | 27.878 | 90.571 | Manas | E(o) | 23.37 | 4,664 |\n| 1295 | 03 78I09 07504 | 27.876 | 90.581 | Manas | E(o) | 16.89 | 4,599 |\n| 1296 | 03 78I09 07510 | 27.861 | 90.591 | Manas | E(o) | 74.98 | 4,770 |\n| 1297 | 03 78I09 07514 | 27.857 | 90.740 | Manas | E(o) | 11.88 | 4,406 |\n| 1298 | 03 78I09 07528 | 27.844 | 90.618 | Manas | E(o) | 12.54 | 4,767 |\n| 1299 | 03 78I09 07537 | 27.827 | 90.701 | Manas | E(o) | 10.37 | 4,363 |\n| 1300 | 03 78I09 07553 | 27.805 | 90.577 | Manas | E(c) | 10.11 | 4,704 |\n| 1301 | 03 78I09 07554 | 27.803 | 90.585 | Manas | E(o) | 10.15 | 4,647 |\n| 1302 | 03 78I09 07557 | 27.798 | 90.620 | Manas | E(c) | 18.31 | 4,574 |\n| 1303 | 03 78I09 07559 | 27.796 | 90.634 | Manas | E(c) | 40.52 | 4,462 |\n| 1304 | 03 78I09 07565 | 27.787 | 90.646 | Manas | E(o) | 26.59 | 4,550 |\n| 1305 | 03 78I09 07590 | 27.765 | 90.625 | Manas | E(o) | 10.80 | 4,338 |\n| 1306 | 03 78I09 07592 | 27.764 | 90.636 | Manas | E(o) | 12.42 | 4,614 |\n| 1307 | 03 78I09 07601 | 27.757 | 90.624 | Manas | E(c) | 12.45 | 4,410 |\n| 1308 | 03 78I13 07685 | 27.942 | 90.756 | Manas | M(o) | 16.41 | 5,031 |\n| 1309 | 03 78I13 07694 | 27.907 | 90.812 | Manas | M(o) | 35.67 | 4,804 |\n| 1310 | 03 78I13 07697 | 27.897 | 90.816 | Manas | E(o) | 44.54 | 4,712 |\n| 1311 | 03 78I13 07703 | 27.886 | 90.830 | Manas | E(o) | 16.34 | 4,845 |\n| 1312 | 03 78I13 07706 | 27.880 | 90.791 | Manas | E(o) | 10.66 | 4,807 |\n| 1313 | 03 78I13 07709 | 27.873 | 90.803 | Manas | E(o) | 27.12 | 4,453 |\n| 1314 | 03 78I13 07712 | 27.867 | 90.816 | Manas | E(o) | 53.98 | 4,135 |\n| 1315 | 03 78I13 07719 | 27.841 | 90.831 | Manas | E(o) | 18.14 | 4,568 |\n| 1316 | 03 78I13 07723 | 27.830 | 90.811 | Manas | E(o) | 18.02 | 4,367 |\n| 1317 | 03 78I13 07726 | 27.821 | 90.810 | Manas | E(c) | 14.45 | 4,613 |\n| 1318 | 03 78I13 07729 | 27.810 | 90.864 | Manas | E(o) | 22.79 | 4,112 |\n| 1319 | 03 78I13 07736 | 27.790 | 90.784 | Manas | E(o) | 10.15 | 4,263 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5165, "line_end": 5314, "token_count_estimate": 1605, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": ["07447", "07468", "07470", "07471", "07474", "07487", "07488", "07499", "07500", "07503", "07504", "07510", "07514", "07528", "07537", "07553", "07554", "07557", "07559", "07565", "07590", "07592", "07601", "07685", "07694", "07697", "07703", "07706", "07709", "07712", "07719", "07723", "07726", "07729", "07736", "78I09", "78I13"]}}
{"id": "399615abd449c6e4", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1320 | 03 78I13 07738 | 27.784 | 90.811 | Manas | E(c) | 10.09 | 4,126 |\n| 1321 | 03 78I13 07759 | 27.757 | 90.807 | Manas | E(c) | 18.25 | 4,181 |\n| 1322 | 03 78I14 07764 | 27.748 | 90.821 | Manas | E(c) | 11.59 | 4,438 |\n| 1323 | 03 78I14 07780 | 27.707 | 90.960 | Manas | E(o) | 11.57 | 3,952 |\n| 1324 | 03 77P08 07817 | 28.037 | 91.452 | Manas | M(o) | 56.24 | 4,737 |\n| 1325 | 03 77P12 07829 | 28.164 | 91.511 | Manas | M(e) | 11.98 | 5,221 |\n| 1326 | 03 77P12 07834 | 28.143 | 91.664 | Manas | M(e) | 19.31 | 5,139 |\n| 1327 | 03 77P12 07845 | 28.024 | 91.597 | Manas | M(e) | 36.52 | 4,804 |\n| 1328 | 03 77P12 07847 | 28.014 | 91.676 | Manas | E(o) | 14.26 | 4,422 |\n| 1329 | 03 77P15 07853 | 28.355 | 91.960 | Manas | E(o) | 20.15 | 4,774 |\n| 1330 | 03 77P16 07886 | 28.102 | 91.942 | Manas | E(o) | 117.73 | 4,705 |\n| 1331 | 03 77P16 07892 | 28.059 | 91.939 | Manas | E(o) | 240.03 | 4,631 |\n| 1332 | 03 78M05 07903 | 27.916 | 91.450 | Manas | E(c) | 13.44 | 4,422 |\n| 1333 | 03 78M05 07915 | 27.879 | 91.449 | Manas | E(c) | 10.23 | 4,229 |\n| 1334 | 03 78M05 07923 | 27.871 | 91.459 | Manas | E(c) | 12.59 | 3,832 |\n| 1335 | 03 78M05 07928 | 27.860 | 91.495 | Manas | E(o) | 10.51 | 3,913 |\n| 1336 | 03 78M09 07942 | 27.996 | 91.511 | Manas | M(o) | 12.13 | 5,319 |\n| 1337 | 03 78M09 07958 | 27.912 | 91.577 | Manas | E(o) | 13.64 | 4,628 |\n| 1338 | 03 78M09 07965 | 27.901 | 91.620 | Manas | E(o) | 14.82 | 4,938 |\n| 1339 | 03 78M09 07966 | 27.898 | 91.535 | Manas | E(c) | 27.53 | 4,430 |\n| 1340 | 03 78M09 07972 | 27.893 | 91.649 | Manas | E(c) | 24.45 | 4,819 |\n| 1341 | 03 78M09 07982 | 27.888 | 91.596 | Manas | E(c) | 16.69 | 4,626 |\n| 1342 | 03 78M09 07990 | 27.878 | 91.517 | Manas | E(c) | 32.71 | 4,248 |\n| 1343 | 03 78M09 07993 | 27.877 | 91.717 | Manas | E(o) | 18.45 | 4,533 |\n| 1344 | 03 78M09 07994 | 27.877 | 91.634 | Manas | E(o) | 44.48 | 4,480 |\n| 1345 | 03 78M09 08006 | 27.869 | 91.671 | Manas | E(o) | 45.27 | 4,054 |\n| 1346 | 03 78M09 08010 | 27.861 | 91.629 | Manas | E(o) | 10.38 | 4,581 |\n| 1347 | 03 78M09 08017 | 27.854 | 91.500 | Manas | E(c) | 31.88 | 4,049 |\n| 1348 | 03 78M09 08020 | 27.851 | 91.604 | Manas | E(c) | 31.40 | 4,509 |\n| 1349 | 03 78M09 08021 | 27.847 | 91.583 | Manas | E(c) | 49.42 | 4,656 |\n| 1350 | 03 78M09 08027 | 27.841 | 91.565 | Manas | E(o) | 16.27 | 4,558 |\n| 1351 | 03 78M09 08030 | 27.838 | 91.605 | Manas | O | 66.35 | 4,125 |\n| 1352 | 03 78M09 08032 | 27.834 | 91.553 | Manas | E(c) | 67.23 | 4,521 |\n| 1353 | 03 78M09 08034 | 27.824 | 91.559 | Manas | E(c) | 10.93 | 4,674 |\n| 1354 | 03 78M09 08035 | 27.815 | 91.595 | Manas | E(c) | 14.20 | 4,678 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5165, "line_end": 5314, "token_count_estimate": 1604, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": ["07738", "07759", "07764", "07780", "07817", "07829", "07834", "07845", "07847", "07853", "07886", "07892", "07903", "07915", "07923", "07928", "07942", "07958", "07965", "07966", "07972", "07982", "07990", "07993", "07994", "08006", "08010", "08017", "08020", "08021", "08027", "08030", "08032", "08034", "08035", "77P08", "77P12", "77P15", "77P16", "78I13", "78I14", "78M05", "78M09"]}}
{"id": "727f29471f4d64b9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1355 | 03 78M09 08040 | 27.811 | 91.501 | Manas | E(c) | 17.12 | 4,213 |\n| 1356 | 03 78M09 08042 | 27.809 | 91.549 | Manas | E(c) | 19.04 | 4,297 |\n| 1357 | 03 78M09 08044 | 27.806 | 91.590 | Manas | E(o) | 13.80 | 4,406 |\n| 1358 | 03 78M09 08045 | 27.802 | 91.643 | Manas | E(c) | 12.24 | 4,504 |\n| 1359 | 03 78M09 08046 | 27.801 | 91.579 | Manas | E(o) | 19.76 | 4,617 |\n| 1360 | 03 78M09 08047 | 27.798 | 91.593 | Manas | E(c) | 16.43 | 4,066 |\n| 1361 | 03 78M09 08055 | 27.757 | 91.650 | Manas | E(o) | 16.67 | 4,152 |\n| 1362 | 03 78M09 08057 | 27.756 | 91.529 | Manas | E(o) | 15.81 | 3,868 |\n| 1363 | 03 78M10 08072 | 27.702 | 91.634 | Manas | E(o) | 16.42 | 4,319 |\n| 1364 | 03 78M13 08075 | 27.992 | 91.952 | Manas | O | 40.89 | 4,354 |\n| 1365 | 03 78M13 08095 | 27.901 | 91.896 | Manas | O | 217.48 | 4,452 |\n| 1366 | 03 78M13 08106 | 27.874 | 91.833 | Manas | E(o) | 10.26 | 4,340 |\n| 1367 | 03 78M13 08127 | 27.849 | 91.948 | Manas | E(o) | 19.90 | 4,630 |\n| 1368 | 03 78M13 08130 | 27.841 | 91.892 | Manas | O | 145.65 | 4,638 |\n| 1369 | 03 78M13 08161 | 27.796 | 91.925 | Manas | E(o) | 22.77 | 4,499 |\n| 1370 | 03 78M13 08162 | 27.795 | 91.838 | Manas | E(c) | 19.81 | 4,667 |\n| 1371 | 03 78M13 08172 | 27.785 | 91.948 | Manas | E(o) | 13.08 | 4,484 |\n| 1372 | 03 78M14 08191 | 27.746 | 91.870 | Manas | E(o) | 44.61 | 4,345 |\n| 1373 | 03 78M14 08209 | 27.734 | 91.871 | Manas | E(o) | 12.54 | 4,456 |\n| 1374 | 03 78M14 08244 | 27.698 | 91.903 | Manas | E(o) | 13.14 | 4,047 |\n| 1375 | 03 78M14 08247 | 27.697 | 91.873 | Manas | E(o) | 13.11 | 4,467 |\n| 1376 | 03 78M14 08256 | 27.693 | 91.857 | Manas | E(o) | 17.65 | 4,283 |\n| 1377 | 03 78M14 08289 | 27.673 | 91.875 | Manas | E(o) | 33.23 | 4,222 |\n| 1378 | 03 78M14 08320 | 27.625 | 91.803 | Manas | E(c) | 10.79 | 4,329 |\n| 1379 | 03 78M14 08324 | 27.620 | 91.779 | Manas | E(o) | 11.38 | 3,978 |\n| 1380 | 03 82D04 08346 | 28.239 | 92.033 | Manas | E(o) | 12.05 | 5,130 |\n| 1381 | 03 82D04 08348 | 28.230 | 92.042 | Manas | E(o) | 15.97 | 5,107 |\n| 1382 | 03 82D04 08349 | 28.228 | 92.023 | Manas | E(o) | 11.52 | 5,115 |\n| 1383 | 03 82D04 08350 | 28.223 | 92.043 | Manas | E(o) | 15.06 | 5,099 |\n| 1384 | 03 82D04 08354 | 28.193 | 92.039 | Manas | E(o) | 25.64 | 5,036 |\n| 1385 | 03 82D04 08362 | 28.068 | 92.147 | Manas | E(o) | 16.73 | 4,973 |\n| 1386 | 03 83A01 08421 | 27.851 | 92.161 | Manas | E(o) | 16.55 | 4,824 |\n| 1387 | 03 83A01 08435 | 27.789 | 92.048 | Manas | M(o) | 11.07 | 4,952 |\n| 1388 | 03 83A01 08449 | 27.753 | 92.041 | Manas | E(c) | 10.23 | 4,084 |\n| 1389 | 03 83A02 08517 | 27.588 | 92.222 | Manas | E(o) | 13.08 | 4,455 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5165, "line_end": 5314, "token_count_estimate": 1603, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": ["08040", "08042", "08044", "08045", "08046", "08047", "08055", "08057", "08072", "08075", "08095", "08106", "08127", "08130", "08161", "08162", "08172", "08191", "08209", "08244", "08247", "08256", "08289", "08320", "08324", "08346", "08348", "08349", "08350", "08354", "08362", "08421", "08435", "08449", "08517", "78M09", "78M10", "78M13", "78M14", "82D04", "83A01", "83A02"]}}
{"id": "2934474e679b12a9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1390 | 03 83A02 08537 | 27.566 | 92.216 | Manas | E(o) | 19.59 | 4,348 |\n| 1391 | 03 83A02 08539 | 27.565 | 92.065 | Manas | E(o) | 14.28 | 4,326 |\n| 1392 | 03 83A02 08572 | 27.519 | 92.033 | Manas | E(o) | 60.79 | 4,274 |\n| 1393 | 03 83A02 08576 | 27.514 | 92.048 | Manas | E(o) | 14.07 | 4,088 |\n| 1394 | 03 83A02 08580 | 27.509 | 92.101 | Manas | O | 18.91 | 4,148 |\n| 1395 | 03 83A02 08584 | 27.505 | 92.045 | Manas | E(o) | 10.80 | 4,170 |\n| 1396 | 03 83A05 08626 | 27.774 | 92.315 | Manas | E(o) | 13.13 | 4,870 |\n| 1397 | 03 83A05 08628 | 27.771 | 92.435 | Manas | M(o) | 55.56 | 5,179 |\n| 1398 | 03 83A05 08631 | 27.763 | 92.425 | Manas | M(o) | 19.37 | 5,094 |\n| 1399 | 03 83A05 08637 | 27.756 | 92.401 | Manas | E(o) | 13.46 | 4,967 |\n| 1400 | 03 83A06 08676 | 27.715 | 92.386 | Manas | E(o) | 12.28 | 5,117 |\n| 1401 | 03 82E04 08784 | 31.030 | 93.163 | Lower Yarlung Tsangpo | O | 13.30 | 5,195 |\n| 1402 | 03 82F02 08814 | 30.621 | 93.181 | Lower Yarlung Tsangpo | O | 689.79 | 4,499 |\n| 1403 | 03 82F02 08824 | 30.535 | 93.058 | Lower Yarlung Tsangpo | E(o) | 86.74 | 4,817 |\n| 1404 | 03 82F06 08849 | 30.527 | 93.419 | Lower Yarlung Tsangpo | M(o) | 31.64 | 4,758 |\n| 1405 | 03 82F06 08851 | 30.521 | 93.445 | Lower Yarlung Tsangpo | M(o) | 114.42 | 4,780 |\n| 1406 | 03 82F07 08854 | 30.493 | 93.357 | Lower Yarlung Tsangpo | M(e) | 22.25 | 4,702 |\n| 1407 | 03 82F07 08860 | 30.463 | 93.390 | Lower Yarlung Tsangpo | M(e) | 12.41 | 4,788 |\n| 1408 | 03 82F07 08862 | 30.425 | 93.492 | Lower Yarlung Tsangpo | E(o) | 32.69 | 4,496 |\n| 1409 | 03 82F09 08884 | 30.803 | 93.683 | Lower Yarlung Tsangpo | M(l) | 11.10 | 5,130 |\n| 1410 | 03 82F10 08895 | 30.532 | 93.516 | Lower Yarlung Tsangpo | M(e) | 36.56 | 4,778 |\n| 1411 | 03 82F11 08900 | 30.474 | 93.535 | Lower Yarlung Tsangpo | M(o) | 24.84 | 5,015 |\n| 1412 | 03 82F11 08905 | 30.461 | 93.509 | Lower Yarlung Tsangpo | E(o) | 12.12 | 4,785 |\n| 1413 | 03 82F11 08913 | 30.441 | 93.630 | Lower Yarlung Tsangpo | M(o) | 12.45 | 4,685 |\n| 1414 | 03 82F11 08921 | 30.355 | 93.632 | Lower Yarlung Tsangpo | M(e) | 54.25 | 4,442 |\n| 1415 | 03 82F11 08922 | 30.354 | 93.529 | Lower Yarlung Tsangpo | M(e) | 11.42 | 4,750 |\n| 1416 | 03 82F11 08923 | 30.348 | 93.507 | Lower Yarlung Tsangpo | M(e) | 43.13 | 4,537 |\n| 1417 | 03 82F13 08926 | 30.883 | 93.831 | Lower Yarlung Tsangpo | M(e) | 13.43 | 4,801 |\n| 1418 | 03 82F14 08949 | 30.659 | 93.894 | Lower Yarlung Tsangpo | M(e) | 25.13 | 4,791 |\n| 1419 | 03 82F15 08965 | 30.489 | 93.823 | Lower Yarlung Tsangpo | E(o) | 10.41 | 4,933 |\n| 1420 | 03 82J01 08974 | 30.831 | 94.000 | Lower Yarlung Tsangpo | M(e) | 35.39 | 4,760 |\n| 1421 | 03 82J01 08980 | 30.789 | 94.117 | Lower Yarlung Tsangpo | E(o) | 12.40 | 4,656 |\n| 1422 | 03 82J02 08987 | 30.732 | 94.211 | Lower Yarlung Tsangpo | M(e) | 10.54 | 4,550 |\n| 1423 | 03 82J02 08993 | 30.706 | 94.118 | Lower Yarlung Tsangpo | E(o) | 17.53 | 4,741 |\n| 1424 | 03 82J03 09007 | 30.262 | 94.134 | Lower Yarlung Tsangpo | M(e) | 12.60 | 4,161 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5165, "line_end": 5314, "token_count_estimate": 1732, "basins": [], "subbasins": ["Lower Yarlung Tsangpo", "Manas"], "countries": [], "lake_ids": ["08537", "08539", "08572", "08576", "08580", "08584", "08626", "08628", "08631", "08637", "08676", "08784", "08814", "08824", "08849", "08851", "08854", "08860", "08862", "08884", "08895", "08900", "08905", "08913", "08921", "08922", "08923", "08926", "08949", "08965", "08974", "08980", "08987", "08993", "09007", "82E04", "82F02", "82F06", "82F07", "82F09", "82F10", "82F11", "82F13", "82F14", "82F15", "82J01", "82J02", "82J03", "83A02", "83A05"]}}
{"id": "869c3f30794d716c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1425 | 03 82J05 09013 | 30.849 | 94.344 | Lower Yarlung Tsangpo | M(o) | 22.22 | 5,015 |\n| 1426 | 03 82J05 09014 | 30.842 | 94.353 | Lower Yarlung Tsangpo | M(o) | 11.70 | 4,973 |\n| 1427 | 03 82J06 09024 | 30.683 | 94.323 | Lower Yarlung Tsangpo | M(e) | 24.02 | 4,121 |\n| 1428 | 03 82J06 09026 | 30.654 | 94.492 | Lower Yarlung Tsangpo | M(e) | 530.64 | 3,942 |\n| 1429 | 03 82J06 09027 | 30.626 | 94.444 | Lower Yarlung Tsangpo | M(e) | 66.04 | 4,095 |\n| 1430 | 03 82J07 09034 | 30.487 | 94.266 | Lower Yarlung Tsangpo | E(o) | 25.30 | 4,456 |\n| 1431 | 03 82J07 09037 | 30.431 | 94.455 | Lower Yarlung Tsangpo | M(o) | 13.03 | 3,902 |\n| 1432 | 03 82J08 09048 | 30.181 | 94.288 | Lower Yarlung Tsangpo | M(o) | 16.21 | 4,363 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 5165, "line_end": 5314, "token_count_estimate": 488, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["09013", "09014", "09024", "09026", "09027", "09034", "09037", "09048", "82J05", "82J06", "82J07", "82J08"]}}
{"id": "08bf74da4d5f80ae", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5315, "line_end": 5321, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d349345db436cfa", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1433 | 03 82J08 09049 | 30.176 | 94.282 | Lower Yarlung Tsangpo | M(o) | 13.91 | 4,607 |\n| 1434 | 03 82J11 09055 | 30.452 | 94.603 | Lower Yarlung Tsangpo | M(e) | 181.75 | 3,998 |\n| 1435 | 03 82J11 09059 | 30.393 | 94.653 | Lower Yarlung Tsangpo | E(o) | 18.96 | 4,007 |\n| 1436 | 03 82J11 09060 | 30.352 | 94.732 | Lower Yarlung Tsangpo | M(o) | 36.07 | 2,698 |\n| 1437 | 03 82J14 09067 | 30.537 | 94.759 | Lower Yarlung Tsangpo | M(e) | 51.12 | 3,631 |\n| 1438 | 03 82N03 09084 | 30.482 | 95.032 | Lower Yarlung Tsangpo | M(e) | 14.85 | 4,335 |\n| 1439 | 03 82N03 09087 | 30.395 | 95.082 | Lower Yarlung Tsangpo | M(e) | 12.22 | 4,151 |\n| 1440 | 03 82B08 09097 | 30.093 | 92.485 | Lower Yarlung Tsangpo | E(o) | 11.49 | 5,158 |\n| 1441 | 03 82B08 09101 | 30.072 | 92.457 | Lower Yarlung Tsangpo | E(o) | 10.98 | 5,096 |\n| 1442 | 03 82B08 09108 | 30.049 | 92.443 | Lower Yarlung Tsangpo | E(o) | 52.80 | 4,993 |\n| 1443 | 03 82B11 09122 | 30.327 | 92.720 | Lower Yarlung Tsangpo | E(o) | 14.10 | 5,213 |\n| 1444 | 03 82B12 09143 | 30.089 | 92.533 | Lower Yarlung Tsangpo | E(o) | 11.30 | 5,084 |\n| 1445 | 03 82B12 09183 | 30.006 | 92.742 | Lower Yarlung Tsangpo | E(o) | 14.10 | 5,108 |\n| 1446 | 03 82B15 09215 | 30.297 | 92.925 | Lower Yarlung Tsangpo | E(o) | 29.42 | 4,831 |\n| 1447 | 03 82B16 09228 | 30.229 | 92.968 | Lower Yarlung Tsangpo | E(o) | 10.60 | 4,851 |\n| 1448 | 03 82B16 09232 | 30.219 | 92.941 | Lower Yarlung Tsangpo | E(o) | 12.89 | 5,208 |\n| 1449 | 03 82B16 09246 | 30.197 | 92.976 | Lower Yarlung Tsangpo | E(o) | 24.87 | 4,995 |\n| 1450 | 03 82C01 09280 | 29.918 | 92.180 | Lower Yarlung Tsangpo | E(o) | 37.73 | 5,161 |\n| 1451 | 03 82C01 09281 | 29.905 | 92.192 | Lower Yarlung Tsangpo | E(o) | 25.14 | 4,992 |\n| 1452 | 03 82C05 09326 | 29.816 | 92.399 | Lower Yarlung Tsangpo | E(o) | 13.49 | 5,068 |\n| 1453 | 03 82C06 09339 | 29.601 | 92.490 | Lower Yarlung Tsangpo | E(o) | 15.90 | 4,951 |\n| 1454 | 03 82C10 09386 | 29.590 | 92.716 | Lower Yarlung Tsangpo | E(o) | 13.21 | 5,381 |\n| 1455 | 03 82C10 09413 | 29.539 | 92.636 | Lower Yarlung Tsangpo | M(e) | 14.07 | 5,179 |\n| 1456 | 03 82C13 09433 | 29.777 | 92.925 | Lower Yarlung Tsangpo | E(o) | 10.33 | 5,093 |\n| 1457 | 03 82C14 09443 | 29.750 | 92.778 | Lower Yarlung Tsangpo | M(e) | 14.19 | 5,210 |\n| 1458 | 03 82F03 09503 | 30.296 | 93.216 | Lower Yarlung Tsangpo | M(o) | 12.34 | 4,942 |\n| 1459 | 03 82F04 09548 | 30.028 | 93.197 | Lower Yarlung Tsangpo | E(o) | 32.59 | 5,014 |\n| 1460 | 03 82F07 09574 | 30.327 | 93.351 | Lower Yarlung Tsangpo | E(o) | 23.62 | 4,549 |\n| 1461 | 03 82F07 09576 | 30.319 | 93.343 | Lower Yarlung Tsangpo | M(e) | 47.25 | 4,613 |\n| 1462 | 03 82F07 09585 | 30.287 | 93.480 | Lower Yarlung Tsangpo | E(o) | 16.83 | 4,544 |\n| 1463 | 03 82F07 09589 | 30.267 | 93.457 | Lower Yarlung Tsangpo | M(o) | 70.57 | 4,076 |\n| 1464 | 03 82F08 09609 | 30.167 | 93.480 | Lower Yarlung Tsangpo | E(o) | 14.34 | 4,873 |\n| 1465 | 03 82F08 09625 | 30.116 | 93.274 | Lower Yarlung Tsangpo | E(o) | 32.70 | 4,630 |\n| 1466 | 03 82F08 09627 | 30.099 | 93.272 | Lower Yarlung Tsangpo | M(o) | 33.54 | 4,704 |\n| 1467 | 03 82F08 09638 | 30.079 | 93.359 | Lower Yarlung Tsangpo | E(o) | 10.08 | 4,950 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5322, "line_end": 5471, "token_count_estimate": 1812, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["09049", "09055", "09059", "09060", "09067", "09084", "09087", "09097", "09101", "09108", "09122", "09143", "09183", "09215", "09228", "09232", "09246", "09280", "09281", "09326", "09339", "09386", "09413", "09433", "09443", "09503", "09548", "09574", "09576", "09585", "09589", "09609", "09625", "09627", "09638", "82B08", "82B11", "82B12", "82B15", "82B16", "82C01", "82C05", "82C06", "82C10", "82C13", "82C14", "82F03", "82F04", "82F07", "82F08"]}}
{"id": "40be2044a5784f19", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1468 | 03 82F08 09647 | 30.072 | 93.357 | Lower Yarlung Tsangpo | M(o) | 11.84 | 5,065 |\n| 1469 | 03 82F08 09651 | 30.065 | 93.334 | Lower Yarlung Tsangpo | E(o) | 10.38 | 4,888 |\n| 1470 | 03 82F08 09654 | 30.059 | 93.277 | Lower Yarlung Tsangpo | E(o) | 17.78 | 4,928 |\n| 1471 | 03 82F08 09663 | 30.025 | 93.406 | Lower Yarlung Tsangpo | E(o) | 21.26 | 4,772 |\n| 1472 | 03 82F08 09672 | 30.008 | 93.437 | Lower Yarlung Tsangpo | E(o) | 40.99 | 4,696 |\n| 1473 | 03 82F12 09686 | 30.249 | 93.614 | Lower Yarlung Tsangpo | E(o) | 11.57 | 4,482 |\n| 1474 | 03 82F12 09688 | 30.242 | 93.638 | Lower Yarlung Tsangpo | M(e) | 108.96 | 4,181 |\n| 1475 | 03 82F12 09690 | 30.233 | 93.577 | Lower Yarlung Tsangpo | E(o) | 11.77 | 4,337 |\n| 1476 | 03 82F12 09691 | 30.228 | 93.636 | Lower Yarlung Tsangpo | E(o) | 10.00 | 4,171 |\n| 1477 | 03 82F12 09699 | 30.208 | 93.512 | Lower Yarlung Tsangpo | M(o) | 12.21 | 4,227 |\n| 1478 | 03 82F12 09706 | 30.172 | 93.718 | Lower Yarlung Tsangpo | M(e) | 13.41 | 4,601 |\n| 1479 | 03 82F12 09707 | 30.163 | 93.729 | Lower Yarlung Tsangpo | E(o) | 13.86 | 4,178 |\n| 1480 | 03 82F12 09710 | 30.153 | 93.527 | Lower Yarlung Tsangpo | M(e) | 15.56 | 4,847 |\n| 1481 | 03 82F12 09724 | 30.112 | 93.501 | Lower Yarlung Tsangpo | E(o) | 16.31 | 4,516 |\n| 1482 | 03 82F15 09750 | 30.261 | 93.764 | Lower Yarlung Tsangpo | M(e) | 64.75 | 4,019 |\n| 1483 | 03 82F16 09751 | 30.249 | 93.989 | Lower Yarlung Tsangpo | E(o) | 23.37 | 4,320 |\n| 1484 | 03 82F16 09753 | 30.200 | 93.798 | Lower Yarlung Tsangpo | M(e) | 20.39 | 4,195 |\n| 1485 | 03 82F16 09767 | 30.088 | 93.755 | Lower Yarlung Tsangpo | E(c) | 21.21 | 4,840 |\n| 1486 | 03 82F16 09768 | 30.075 | 93.974 | Lower Yarlung Tsangpo | E(c) | 15.80 | 4,615 |\n| 1487 | 03 82F16 09771 | 30.059 | 93.781 | Lower Yarlung Tsangpo | O | 20.24 | 3,751 |\n| 1488 | 03 82F16 09776 | 30.020 | 93.967 | Lower Yarlung Tsangpo | E(v) | 2,658.4 | 3,475 |\n| 1489 | 03 82G01 09827 | 29.809 | 93.018 | Lower Yarlung Tsangpo | M(o) | 11.17 | 5,360 |\n| 1490 | 03 82G01 09854 | 29.764 | 93.115 | Lower Yarlung Tsangpo | E(c) | 23.92 | 5,076 |\n| 1491 | 03 82G01 09862 | 29.758 | 93.123 | Lower Yarlung Tsangpo | E(o) | 10.51 | 5,099 |\n| 1492 | 03 82G02 09867 | 29.747 | 93.149 | Lower Yarlung Tsangpo | E(o) | 15.14 | 4,943 |\n| 1493 | 03 82G02 09880 | 29.693 | 93.068 | Lower Yarlung Tsangpo | E(o) | 16.51 | 4,939 |\n| 1494 | 03 82G02 09942 | 29.580 | 93.199 | Lower Yarlung Tsangpo | E(o) | 16.98 | 5,180 |\n| 1495 | 03 82G02 09954 | 29.532 | 93.236 | Lower Yarlung Tsangpo | E(o) | 11.28 | 5,018 |\n| 1496 | 03 82G02 09959 | 29.520 | 93.177 | Lower Yarlung Tsangpo | E(o) | 13.09 | 5,210 |\n| 1497 | 03 82G05 09974 | 30.000 | 93.385 | Lower Yarlung Tsangpo | E(c) | 20.57 | 4,873 |\n| 1498 | 03 82G05 10004 | 29.948 | 93.250 | Lower Yarlung Tsangpo | E(o) | 10.39 | 5,069 |\n| 1499 | 03 82G06 10036 | 29.732 | 93.498 | Lower Yarlung Tsangpo | E(o) | 30.64 | 4,476 |\n| 1500 | 03 82G06 10041 | 29.716 | 93.427 | Lower Yarlung Tsangpo | E(o) | 12.70 | 5,001 |\n| 1501 | 03 82G06 10054 | 29.658 | 93.275 | Lower Yarlung Tsangpo | E(o) | 13.27 | 4,967 |\n| 1502 | 03 82G06 10055 | 29.650 | 93.337 | Lower Yarlung Tsangpo | M(e) | 26.37 | 4,738 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5322, "line_end": 5471, "token_count_estimate": 1792, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["09647", "09651", "09654", "09663", "09672", "09686", "09688", "09690", "09691", "09699", "09706", "09707", "09710", "09724", "09750", "09751", "09753", "09767", "09768", "09771", "09776", "09827", "09854", "09862", "09867", "09880", "09942", "09954", "09959", "09974", "10004", "10036", "10041", "10054", "10055", "82F08", "82F12", "82F15", "82F16", "82G01", "82G02", "82G05", "82G06"]}}
{"id": "59bd639689c97d71", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1503 | 03 82G06 10057 | 29.648 | 93.253 | Lower Yarlung Tsangpo | M(o) | 11.73 | 5,137 |\n| 1504 | 03 82G06 10072 | 29.603 | 93.427 | Lower Yarlung Tsangpo | E(o) | 11.69 | 4,512 |\n| 1505 | 03 82G06 10075 | 29.601 | 93.465 | Lower Yarlung Tsangpo | E(o) | 32.35 | 4,604 |\n| 1506 | 03 82G06 10090 | 29.555 | 93.269 | Lower Yarlung Tsangpo | E(o) | 13.97 | 4,784 |\n| 1507 | 03 82G06 10094 | 29.541 | 93.345 | Lower Yarlung Tsangpo | E(o) | 100.03 | 4,631 |\n| 1508 | 03 82G10 10174 | 29.644 | 93.548 | Lower Yarlung Tsangpo | E(o) | 19.85 | 4,738 |\n| 1509 | 03 82G10 10183 | 29.630 | 93.561 | Lower Yarlung Tsangpo | E(o) | 44.35 | 4,563 |\n| 1510 | 03 82G10 10195 | 29.606 | 93.588 | Lower Yarlung Tsangpo | E(o) | 17.97 | 4,734 |\n| 1511 | 03 82G10 10196 | 29.605 | 93.528 | Lower Yarlung Tsangpo | E(o) | 20.77 | 4,995 |\n| 1512 | 03 82G10 10200 | 29.598 | 93.524 | Lower Yarlung Tsangpo | E(o) | 12.48 | 4,945 |\n| 1513 | 03 82G10 10203 | 29.594 | 93.731 | Lower Yarlung Tsangpo | E(o) | 34.03 | 4,549 |\n| 1514 | 03 82G10 10208 | 29.585 | 93.746 | Lower Yarlung Tsangpo | E(c) | 10.59 | 4,699 |\n| 1515 | 03 82G10 10246 | 29.520 | 93.673 | Lower Yarlung Tsangpo | E(o) | 30.15 | 4,549 |\n| 1516 | 03 82G10 10253 | 29.513 | 93.620 | Lower Yarlung Tsangpo | O | 79.97 | 4,362 |\n| 1517 | 03 82G10 10255 | 29.512 | 93.714 | Lower Yarlung Tsangpo | E(o) | 39.25 | 4,344 |\n| 1518 | 03 82G11 10262 | 29.477 | 93.631 | Lower Yarlung Tsangpo | E(o) | 82.79 | 4,369 |\n| 1519 | 03 82G11 10269 | 29.458 | 93.621 | Lower Yarlung Tsangpo | E(o) | 15.28 | 4,856 |\n| 1520 | 03 82G11 10273 | 29.452 | 93.620 | Lower Yarlung Tsangpo | E(o) | 11.10 | 4,894 |\n| 1521 | 03 82G11 10276 | 29.443 | 93.715 | Lower Yarlung Tsangpo | E(o) | 14.82 | 4,414 |\n| 1522 | 03 82G13 10286 | 29.950 | 93.972 | Lower Yarlung Tsangpo | E(o) | 21.53 | 4,239 |\n| 1523 | 03 82G13 10291 | 29.924 | 93.889 | Lower Yarlung Tsangpo | E(o) | 11.86 | 4,453 |\n| 1524 | 03 82G13 10300 | 29.907 | 93.897 | Lower Yarlung Tsangpo | E(o) | 13.27 | 4,492 |\n| 1525 | 03 82G13 10312 | 29.891 | 93.993 | Lower Yarlung Tsangpo | E(c) | 16.50 | 4,552 |\n| 1526 | 03 82G13 10313 | 29.878 | 93.839 | Lower Yarlung Tsangpo | E(o) | 18.71 | 4,592 |\n| 1527 | 03 82G14 10346 | 29.657 | 93.752 | Lower Yarlung Tsangpo | E(c) | 15.21 | 4,490 |\n| 1528 | 03 82G14 10370 | 29.621 | 93.779 | Lower Yarlung Tsangpo | E(o) | 15.45 | 4,409 |\n| 1529 | 03 82G14 10373 | 29.613 | 93.910 | Lower Yarlung Tsangpo | E(o) | 11.21 | 4,622 |\n| 1530 | 03 82G14 10392 | 29.589 | 93.929 | Lower Yarlung Tsangpo | E(o) | 18.93 | 4,669 |\n| 1531 | 03 82G14 10393 | 29.587 | 93.897 | Lower Yarlung Tsangpo | E(o) | 33.63 | 4,786 |\n| 1532 | 03 82G14 10405 | 29.568 | 93.902 | Lower Yarlung Tsangpo | E(o) | 11.96 | 4,610 |\n| 1533 | 03 82G14 10415 | 29.558 | 93.879 | Lower Yarlung Tsangpo | O | 11.57 | 4,479 |\n| 1534 | 03 82G14 10423 | 29.545 | 93.910 | Lower Yarlung Tsangpo | E(c) | 21.21 | 4,840 |\n| 1535 | 03 82G14 10427 | 29.542 | 93.830 | Lower Yarlung Tsangpo | E(o) | 56.22 | 4,419 |\n| 1536 | 03 82G14 10443 | 29.524 | 93.981 | Lower Yarlung Tsangpo | E(o) | 11.62 | 4,276 |\n| 1537 | 03 82G14 10448 | 29.506 | 93.911 | Lower Yarlung Tsangpo | E(o) | 40.89 | 4,555 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5322, "line_end": 5471, "token_count_estimate": 1749, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["10057", "10072", "10075", "10090", "10094", "10174", "10183", "10195", "10196", "10200", "10203", "10208", "10246", "10253", "10255", "10262", "10269", "10273", "10276", "10286", "10291", "10300", "10312", "10313", "10346", "10370", "10373", "10392", "10393", "10405", "10415", "10423", "10427", "10443", "10448", "82G06", "82G10", "82G11", "82G13", "82G14"]}}
{"id": "24f474034ffec88d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1538 | 03 82G14 10450 | 29.506 | 93.806 | Lower Yarlung Tsangpo | E(o) | 21.65 | 4,504 |\n| 1539 | 03 82G14 10453 | 29.504 | 93.888 | Lower Yarlung Tsangpo | E(o) | 17.72 | 4,619 |\n| 1540 | 03 82G14 10455 | 29.502 | 93.937 | Lower Yarlung Tsangpo | E(o) | 56.29 | 4,444 |\n| 1541 | 03 82G15 10459 | 29.491 | 93.963 | Lower Yarlung Tsangpo | E(c) | 13.03 | 4,704 |\n| 1542 | 03 82J04 10481 | 30.132 | 94.123 | Lower Yarlung Tsangpo | E(o) | 12.34 | 4,318 |\n| 1543 | 03 82J04 10483 | 30.126 | 94.090 | Lower Yarlung Tsangpo | E(o) | 276.68 | 3,802 |\n| 1544 | 03 82J04 10484 | 30.115 | 94.188 | Lower Yarlung Tsangpo | M(e) | 94.40 | 3,905 |\n| 1545 | 03 82J04 10486 | 30.064 | 94.189 | Lower Yarlung Tsangpo | E(o) | 35.47 | 4,616 |\n| 1546 | 03 82J04 10489 | 30.046 | 94.157 | Lower Yarlung Tsangpo | E(o) | 107.17 | 4,294 |\n| 1547 | 03 82J04 10493 | 30.019 | 94.123 | Lower Yarlung Tsangpo | E(o) | 11.82 | 4,356 |\n| 1548 | 03 82J08 10499 | 30.099 | 94.270 | Lower Yarlung Tsangpo | M(e) | 68.10 | 3,924 |\n| 1549 | 03 82K01 10514 | 29.948 | 94.161 | Lower Yarlung Tsangpo | O | 19.33 | 4,052 |\n| 1550 | 03 82K01 10520 | 29.941 | 94.093 | Lower Yarlung Tsangpo | O | 16.70 | 4,059 |\n| 1551 | 03 82K01 10544 | 29.896 | 94.209 | Lower Yarlung Tsangpo | E(o) | 28.27 | 4,444 |\n| 1552 | 03 82K01 10558 | 29.881 | 94.207 | Lower Yarlung Tsangpo | E(o) | 13.62 | 4,448 |\n| 1553 | 03 82K01 10568 | 29.856 | 94.155 | Lower Yarlung Tsangpo | E(o) | 10.11 | 4,320 |\n| 1554 | 03 82K01 10584 | 29.834 | 94.160 | Lower Yarlung Tsangpo | E(o) | 12.74 | 4,390 |\n| 1555 | 03 82K01 10585 | 29.833 | 94.135 | Lower Yarlung Tsangpo | E(o) | 16.20 | 4,723 |\n| 1556 | 03 82K01 10590 | 29.817 | 94.133 | Lower Yarlung Tsangpo | E(c) | 45.22 | 4,558 |\n| 1557 | 03 82K02 10599 | 29.658 | 94.212 | Lower Yarlung Tsangpo | E(o) | 16.43 | 4,583 |\n| 1558 | 03 82K02 10605 | 29.645 | 94.185 | Lower Yarlung Tsangpo | E(o) | 11.91 | 4,503 |\n| 1559 | 03 82K02 10608 | 29.636 | 94.169 | Lower Yarlung Tsangpo | E(o) | 12.66 | 4,808 |\n| 1560 | 03 82K02 10609 | 29.632 | 94.099 | Lower Yarlung Tsangpo | E(o) | 17.46 | 4,711 |\n| 1561 | 03 82K02 10613 | 29.622 | 94.163 | Lower Yarlung Tsangpo | E(o) | 12.48 | 4,716 |\n| 1562 | 03 82K02 10616 | 29.611 | 94.154 | Lower Yarlung Tsangpo | E(o) | 25.65 | 4,690 |\n| 1563 | 03 82K02 10620 | 29.601 | 94.076 | Lower Yarlung Tsangpo | E(o) | 20.80 | 4,730 |\n| 1564 | 03 82K02 10638 | 29.578 | 94.113 | Lower Yarlung Tsangpo | E(o) | 14.46 | 4,509 |\n| 1565 | 03 82K02 10643 | 29.573 | 94.139 | Lower Yarlung Tsangpo | E(o) | 12.26 | 4,673 |\n| 1566 | 03 82K02 10655 | 29.556 | 94.242 | Lower Yarlung Tsangpo | E(o) | 17.57 | 4,598 |\n| 1567 | 03 82K02 10657 | 29.555 | 94.087 | Lower Yarlung Tsangpo | E(o) | 12.53 | 4,751 |\n| 1568 | 03 82K02 10659 | 29.551 | 94.111 | Lower Yarlung Tsangpo | E(o) | 12.32 | 4,578 |\n| 1569 | 03 82K02 10660 | 29.550 | 94.120 | Lower Yarlung Tsangpo | E(o) | 24.97 | 4,579 |\n| 1570 | 03 82K02 10661 | 29.545 | 94.067 | Lower Yarlung Tsangpo | E(o) | 50.77 | 4,299 |\n| 1571 | 03 82K02 10664 | 29.543 | 94.086 | Lower Yarlung Tsangpo | E(o) | 14.75 | 4,631 |\n| 1572 | 03 82K02 10670 | 29.538 | 94.113 | Lower Yarlung Tsangpo | E(o) | 36.72 | 4,520 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5322, "line_end": 5471, "token_count_estimate": 1753, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["10450", "10453", "10455", "10459", "10481", "10483", "10484", "10486", "10489", "10493", "10499", "10514", "10520", "10544", "10558", "10568", "10584", "10585", "10590", "10599", "10605", "10608", "10609", "10613", "10616", "10620", "10638", "10643", "10655", "10657", "10659", "10660", "10661", "10664", "10670", "82G14", "82G15", "82J04", "82J08", "82K01", "82K02"]}}
{"id": "368fd96e5bd06e03", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1573 | 03 82K02 10673 | 29.537 | 94.034 | Lower Yarlung Tsangpo | E(o) | 25.24 | 4,509 |\n| 1574 | 03 82K02 10684 | 29.529 | 94.076 | Lower Yarlung Tsangpo | E(c) | 13.47 | 4,691 |\n| 1575 | 03 82K02 10685 | 29.529 | 94.001 | Lower Yarlung Tsangpo | E(o) | 31.13 | 4,262 |\n| 1576 | 03 82K02 10688 | 29.526 | 94.057 | Lower Yarlung Tsangpo | E(o) | 78.00 | 4,533 |\n| 1577 | 03 82K02 10693 | 29.522 | 94.098 | Lower Yarlung Tsangpo | E(o) | 11.80 | 4,520 |\n| 1578 | 03 82K02 10696 | 29.518 | 94.121 | Lower Yarlung Tsangpo | E(o) | 118.34 | 4,501 |\n| 1579 | 03 82K02 10701 | 29.507 | 94.028 | Lower Yarlung Tsangpo | E(o) | 11.22 | 4,737 |\n| 1580 | 03 82K02 10703 | 29.505 | 94.133 | Lower Yarlung Tsangpo | E(o) | 101.69 | 4,577 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 5322, "line_end": 5471, "token_count_estimate": 471, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["10673", "10684", "10685", "10688", "10693", "10696", "10701", "10703", "82K02"]}}
{"id": "0aca633982884160", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5472, "line_end": 5481, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0f6d28141fde7e88", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1581 | 03 82K03 10706 | 29.499 | 94.192 | Lower Yarlung Tsangpo | E(o) | 10.99 | 4,490 |\n| 1582 | 03 82K03 10707 | 29.499 | 94.155 | Lower Yarlung Tsangpo | E(o) | 13.64 | 4,720 |\n| 1583 | 03 82K03 10711 | 29.493 | 94.178 | Lower Yarlung Tsangpo | E(o) | 15.99 | 4,512 |\n| 1584 | 03 82K03 10715 | 29.491 | 94.131 | Lower Yarlung Tsangpo | E(o) | 12.02 | 4,608 |\n| 1585 | 03 82K03 10726 | 29.475 | 94.151 | Lower Yarlung Tsangpo | E(o) | 14.43 | 4,632 |\n| 1586 | 03 82K03 10727 | 29.475 | 94.194 | Lower Yarlung Tsangpo | E(o) | 14.33 | 4,420 |\n| 1587 | 03 82K03 10730 | 29.472 | 94.236 | Lower Yarlung Tsangpo | E(o) | 50.80 | 4,509 |\n| 1588 | 03 82K03 10731 | 29.472 | 94.213 | Lower Yarlung Tsangpo | E(o) | 16.17 | 4,432 |\n| 1589 | 03 82K03 10732 | 29.470 | 94.162 | Lower Yarlung Tsangpo | E(o) | 12.19 | 4,602 |\n| 1590 | 03 82K03 10753 | 29.447 | 94.168 | Lower Yarlung Tsangpo | E(o) | 10.83 | 4,730 |\n| 1591 | 03 82K03 10763 | 29.426 | 94.186 | Lower Yarlung Tsangpo | E(o) | 10.18 | 4,487 |\n| 1592 | 03 82K03 10764 | 29.424 | 94.197 | Lower Yarlung Tsangpo | E(o) | 18.25 | 4,316 |\n| 1593 | 03 82K05 10780 | 29.965 | 94.251 | Lower Yarlung Tsangpo | E(o) | 19.39 | 4,289 |\n| 1594 | 03 82K05 10788 | 29.915 | 94.280 | Lower Yarlung Tsangpo | E(c) | 183.14 | 4,385 |\n| 1595 | 03 82K05 10792 | 29.906 | 94.365 | Lower Yarlung Tsangpo | E(o) | 11.51 | 4,391 |\n| 1596 | 03 82K05 10796 | 29.897 | 94.294 | Lower Yarlung Tsangpo | E(o) | 13.12 | 4,465 |\n| 1597 | 03 82K05 10800 | 29.894 | 94.284 | Lower Yarlung Tsangpo | E(o) | 24.56 | 4,288 |\n| 1598 | 03 82K05 10804 | 29.886 | 94.266 | Lower Yarlung Tsangpo | E(o) | 26.51 | 4,265 |\n| 1599 | 03 82K05 10806 | 29.881 | 94.377 | Lower Yarlung Tsangpo | O | 12.37 | 4,575 |\n| 1600 | 03 82K05 10842 | 29.829 | 94.363 | Lower Yarlung Tsangpo | E(o) | 15.32 | 4,612 |\n| 1601 | 03 82K05 10843 | 29.829 | 94.282 | Lower Yarlung Tsangpo | E(o) | 14.73 | 4,403 |\n| 1602 | 03 82K05 10845 | 29.828 | 94.462 | Lower Yarlung Tsangpo | E(o) | 57.27 | 4,133 |\n| 1603 | 03 82K05 10848 | 29.819 | 94.358 | Lower Yarlung Tsangpo | E(o) | 24.21 | 4,439 |\n| 1604 | 03 82K05 10852 | 29.813 | 94.433 | Lower Yarlung Tsangpo | O | 228.43 | 4,083 |\n| 1605 | 03 82K05 10862 | 29.780 | 94.448 | Lower Yarlung Tsangpo | E(o) | 23.84 | 4,412 |\n| 1606 | 03 82K05 10872 | 29.768 | 94.465 | Lower Yarlung Tsangpo | E(o) | 13.38 | 4,463 |\n| 1607 | 03 82K05 10873 | 29.767 | 94.482 | Lower Yarlung Tsangpo | E(o) | 18.00 | 4,417 |\n| 1608 | 03 82K06 10880 | 29.744 | 94.461 | Lower Yarlung Tsangpo | E(c) | 16.75 | 4,534 |\n| 1609 | 03 82K06 10883 | 29.732 | 94.483 | Lower Yarlung Tsangpo | E(o) | 37.20 | 4,552 |\n| 1610 | 03 82K06 10889 | 29.682 | 94.494 | Lower Yarlung Tsangpo | E(o) | 17.55 | 4,529 |\n| 1611 | 03 82K06 10899 | 29.574 | 94.276 | Lower Yarlung Tsangpo | M(o) | 10.49 | 4,609 |\n| 1612 | 03 82K06 10916 | 29.528 | 94.343 | Lower Yarlung Tsangpo | E(o) | 14.61 | 4,312 |\n| 1613 | 03 82K09 10959 | 29.849 | 94.519 | Lower Yarlung Tsangpo | E(o) | 17.70 | 4,594 |\n| 1614 | 03 82K09 10962 | 29.808 | 94.501 | Lower Yarlung Tsangpo | O | 55.81 | 4,305 |\n| 1615 | 03 82K09 10964 | 29.785 | 94.516 | Lower Yarlung Tsangpo | E(o) | 27.91 | 4,497 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5482, "line_end": 5557, "token_count_estimate": 1746, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["10706", "10707", "10711", "10715", "10726", "10727", "10730", "10731", "10732", "10753", "10763", "10764", "10780", "10788", "10792", "10796", "10800", "10804", "10806", "10842", "10843", "10845", "10848", "10852", "10862", "10872", "10873", "10880", "10883", "10889", "10899", "10916", "10959", "10962", "10964", "82K03", "82K05", "82K06", "82K09"]}}
{"id": "4ad80db8d39efb7f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1616 | 03 82K09 10965 | 29.782 | 94.527 | Lower Yarlung Tsangpo | E(o) | 18.97 | 4,585 |\n| 1617 | 03 77O11 10998 | 29.462 | 91.646 | Lower Yarlung Tsangpo | E(o) | 10.39 | 5,296 |\n| 1618 | 03 77O15 11036 | 29.430 | 91.971 | Lower Yarlung Tsangpo | E(o) | 20.31 | 5,197 |\n| 1619 | 03 77P13 11055 | 28.802 | 91.937 | Lower Yarlung Tsangpo | M(o) | 11.76 | 5,207 |\n| 1620 | 03 82C02 11078 | 29.536 | 92.020 | Lower Yarlung Tsangpo | E(o) | 18.57 | 5,130 |\n| 1621 | 03 82C06 11110 | 29.560 | 92.451 | Lower Yarlung Tsangpo | E(o) | 20.94 | 4,921 |\n| 1622 | 03 82C06 11112 | 29.547 | 92.479 | Lower Yarlung Tsangpo | E(o) | 15.93 | 5,077 |\n| 1623 | 03 82C06 11123 | 29.526 | 92.491 | Lower Yarlung Tsangpo | E(o) | 11.23 | 5,279 |\n| 1624 | 03 82C06 11135 | 29.510 | 92.459 | Lower Yarlung Tsangpo | E(o) | 23.75 | 5,077 |\n| 1625 | 03 82C06 11136 | 29.505 | 92.464 | Lower Yarlung Tsangpo | E(o) | 12.37 | 5,103 |\n| 1626 | 03 82C06 11137 | 29.503 | 92.470 | Lower Yarlung Tsangpo | E(o) | 11.62 | 5,161 |\n| 1627 | 03 82C07 11154 | 29.355 | 92.453 | Lower Yarlung Tsangpo | M(e) | 12.87 | 5,072 |\n| 1628 | 03 82C07 11161 | 29.334 | 92.373 | Lower Yarlung Tsangpo | O | 12.30 | 5,065 |\n| 1629 | 03 82C07 11165 | 29.331 | 92.431 | Lower Yarlung Tsangpo | M(o) | 20.13 | 5,217 |\n| 1630 | 03 82C07 11184 | 29.316 | 92.434 | Lower Yarlung Tsangpo | E(o) | 12.78 | 4,911 |\n| 1631 | 03 82C07 11192 | 29.287 | 92.453 | Lower Yarlung Tsangpo | E(c) | 23.36 | 4,890 |\n| 1632 | 03 82C08 11206 | 29.195 | 92.443 | Lower Yarlung Tsangpo | E(o) | 22.46 | 4,984 |\n| 1633 | 03 82C10 11217 | 29.541 | 92.687 | Lower Yarlung Tsangpo | E(o) | 15.98 | 5,120 |\n| 1634 | 03 82C10 11220 | 29.532 | 92.521 | Lower Yarlung Tsangpo | E(o) | 22.44 | 5,249 |\n| 1635 | 03 82C10 11226 | 29.524 | 92.511 | Lower Yarlung Tsangpo | E(o) | 15.08 | 5,266 |\n| 1636 | 03 82C10 11232 | 29.517 | 92.739 | Lower Yarlung Tsangpo | E(o) | 24.01 | 4,930 |\n| 1637 | 03 82C10 11233 | 29.517 | 92.681 | Lower Yarlung Tsangpo | E(o) | 11.55 | 5,276 |\n| 1638 | 03 82C11 11247 | 29.488 | 92.644 | Lower Yarlung Tsangpo | M(e) | 12.89 | 5,225 |\n| 1639 | 03 82C11 11267 | 29.390 | 92.549 | Lower Yarlung Tsangpo | E(o) | 33.54 | 4,947 |\n| 1640 | 03 82C11 11311 | 29.300 | 92.651 | Lower Yarlung Tsangpo | E(c) | 22.22 | 5,149 |\n| 1641 | 03 82C11 11319 | 29.283 | 92.656 | Lower Yarlung Tsangpo | E(o) | 15.24 | 5,050 |\n| 1642 | 03 82C12 11338 | 29.226 | 92.625 | Lower Yarlung Tsangpo | E(o) | 16.34 | 5,115 |\n| 1643 | 03 82C14 11365 | 29.666 | 92.800 | Lower Yarlung Tsangpo | E(o) | 36.70 | 5,126 |\n| 1644 | 03 82C14 11367 | 29.656 | 92.783 | Lower Yarlung Tsangpo | E(o) | 14.97 | 5,123 |\n| 1645 | 03 82C14 11368 | 29.656 | 92.835 | Lower Yarlung Tsangpo | E(o) | 20.35 | 4,997 |\n| 1646 | 03 82C14 11382 | 29.581 | 92.791 | Lower Yarlung Tsangpo | E(o) | 15.11 | 4,884 |\n| 1647 | 03 82C15 11441 | 29.276 | 92.802 | Lower Yarlung Tsangpo | E(o) | 15.87 | 5,207 |\n| 1648 | 03 82C16 11454 | 29.250 | 92.996 | Lower Yarlung Tsangpo | E(o) | 27.64 | 4,810 |\n| 1649 | 03 82C16 11476 | 29.202 | 92.984 | Lower Yarlung Tsangpo | E(o) | 12.02 | 5,013 |\n| 1650 | 03 82D02 11538 | 28.705 | 92.111 | Lower Yarlung Tsangpo | O | 38.58 | 4,780 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5482, "line_end": 5557, "token_count_estimate": 1762, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["10965", "10998", "11036", "11055", "11078", "11110", "11112", "11123", "11135", "11136", "11137", "11154", "11161", "11165", "11184", "11192", "11206", "11217", "11220", "11226", "11232", "11233", "11247", "11267", "11311", "11319", "11338", "11365", "11367", "11368", "11382", "11441", "11454", "11476", "11538", "77O11", "77O15", "77P13", "82C02", "82C06", "82C07", "82C08", "82C10", "82C11", "82C12", "82C14", "82C15", "82C16", "82D02", "82K09"]}}
{"id": "b8a31c0f13e63945", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1651 | 03 82D02 11550 | 28.648 | 92.160 | Lower Yarlung Tsangpo | E(o) | 19.85 | 4,976 |\n| 1652 | 03 82D05 11555 | 28.976 | 92.253 | Lower Yarlung Tsangpo | O | 14.68 | 4,824 |\n| 1653 | 03 82G03 11641 | 29.437 | 93.173 | Lower Yarlung Tsangpo | E(o) | 16.23 | 5,034 |\n| 1654 | 03 82G03 11683 | 29.310 | 93.093 | Lower Yarlung Tsangpo | E(o) | 14.42 | 4,819 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 74, "line_start": 5482, "line_end": 5557, "token_count_estimate": 278, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["11550", "11555", "11641", "11683", "82D02", "82D05", "82G03"]}}
{"id": "15d73f2b110b41e0", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1655 | 03 82G03 11705 | 29.284 | 93.117 | Lower Yarlung Tsangpo | E(o) | 13.20 | 4,765 |\n| 1656 | 03 82G04 11725 | 29.246 | 93.043 | Lower Yarlung Tsangpo | E(o) | 12.13 | 5,086 |\n| 1657 | 03 82G04 11732 | 29.238 | 93.038 | Lower Yarlung Tsangpo | E(o) | 13.89 | 4,976 |\n| 1658 | 03 82G04 11768 | 29.157 | 93.069 | Lower Yarlung Tsangpo | E(o) | 15.68 | 4,461 |\n| 1659 | 03 82G06 11780 | 29.555 | 93.454 | Lower Yarlung Tsangpo | E(o) | 13.05 | 4,911 |\n| 1660 | 03 82G06 11787 | 29.533 | 93.390 | Lower Yarlung Tsangpo | E(o) | 26.45 | 4,799 |\n| 1661 | 03 82G06 11788 | 29.525 | 93.470 | Lower Yarlung Tsangpo | E(o) | 46.03 | 4,686 |\n| 1662 | 03 82G06 11793 | 29.514 | 93.367 | Lower Yarlung Tsangpo | E(o) | 19.73 | 4,869 |\n| 1663 | 03 82G06 11802 | 29.503 | 93.497 | Lower Yarlung Tsangpo | E(o) | 10.36 | 4,822 |\n| 1664 | 03 82G07 11803 | 29.494 | 93.303 | Lower Yarlung Tsangpo | E(o) | 11.32 | 4,971 |\n| 1665 | 03 82G07 11808 | 29.485 | 93.299 | Lower Yarlung Tsangpo | E(o) | 24.69 | 4,947 |\n| 1666 | 03 82G07 11810 | 29.476 | 93.299 | Lower Yarlung Tsangpo | E(o) | 20.93 | 4,846 |\n| 1667 | 03 82G07 11825 | 29.444 | 93.412 | Lower Yarlung Tsangpo | E(o) | 10.74 | 4,797 |\n| 1668 | 03 82G07 11830 | 29.439 | 93.254 | Lower Yarlung Tsangpo | E(o) | 14.15 | 4,755 |\n| 1669 | 03 82G07 11835 | 29.421 | 93.291 | Lower Yarlung Tsangpo | E(o) | 44.79 | 4,644 |\n| 1670 | 03 82G07 11839 | 29.399 | 93.319 | Lower Yarlung Tsangpo | E(o) | 24.37 | 4,757 |\n| 1671 | 03 82G07 11856 | 29.322 | 93.265 | Lower Yarlung Tsangpo | E(o) | 26.11 | 4,951 |\n| 1672 | 03 82G07 11875 | 29.296 | 93.424 | Lower Yarlung Tsangpo | E(o) | 15.29 | 4,648 |\n| 1673 | 03 82G07 11896 | 29.270 | 93.285 | Lower Yarlung Tsangpo | E(o) | 10.41 | 5,066 |\n| 1674 | 03 82G08 11911 | 29.240 | 93.276 | Lower Yarlung Tsangpo | E(o) | 58.59 | 4,914 |\n| 1675 | 03 82G08 11922 | 29.220 | 93.306 | Lower Yarlung Tsangpo | E(o) | 35.12 | 4,887 |\n| 1676 | 03 82G08 11939 | 29.197 | 93.346 | Lower Yarlung Tsangpo | E(o) | 10.94 | 4,841 |\n| 1677 | 03 82G08 11940 | 29.196 | 93.327 | Lower Yarlung Tsangpo | E(c) | 17.01 | 4,728 |\n| 1678 | 03 82G08 11942 | 29.190 | 93.275 | Lower Yarlung Tsangpo | E(o) | 11.02 | 5,021 |\n| 1679 | 03 82G08 11946 | 29.180 | 93.272 | Lower Yarlung Tsangpo | E(o) | 17.38 | 4,932 |\n| 1680 | 03 82G08 11949 | 29.178 | 93.298 | Lower Yarlung Tsangpo | M(o) | 11.66 | 4,915 |\n| 1681 | 03 82G08 11960 | 29.125 | 93.326 | Lower Yarlung Tsangpo | E(o) | 10.42 | 4,886 |\n| 1682 | 03 82G11 11984 | 29.497 | 93.502 | Lower Yarlung Tsangpo | E(o) | 15.32 | 4,702 |\n| 1683 | 03 82G11 11988 | 29.466 | 93.590 | Lower Yarlung Tsangpo | E(o) | 30.38 | 4,615 |\n| 1684 | 03 82G11 11989 | 29.462 | 93.556 | Lower Yarlung Tsangpo | E(o) | 21.52 | 4,719 |\n| 1685 | 03 82G11 12002 | 29.419 | 93.739 | Lower Yarlung Tsangpo | E(o) | 14.71 | 4,594 |\n| 1686 | 03 82G11 12004 | 29.417 | 93.691 | Lower Yarlung Tsangpo | E(o) | 38.87 | 4,557 |\n| 1687 | 03 82G11 12006 | 29.405 | 93.708 | Lower Yarlung Tsangpo | E(o) | 74.32 | 4,505 |\n| 1688 | 03 82G11 12007 | 29.404 | 93.650 | Lower Yarlung Tsangpo | E(o) | 31.08 | 4,624 |\n| 1689 | 03 82G11 12015 | 29.396 | 93.682 | Lower Yarlung Tsangpo | E(o) | 15.84 | 4,888 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5559, "line_end": 5634, "token_count_estimate": 1761, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["11705", "11725", "11732", "11768", "11780", "11787", "11788", "11793", "11802", "11803", "11808", "11810", "11825", "11830", "11835", "11839", "11856", "11875", "11896", "11911", "11922", "11939", "11940", "11942", "11946", "11949", "11960", "11984", "11988", "11989", "12002", "12004", "12006", "12007", "12015", "82G03", "82G04", "82G06", "82G07", "82G08", "82G11"]}}
{"id": "8ce06c4b639ddef1", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1690 | 03 82G11 12019 | 29.389 | 93.610 | Lower Yarlung Tsangpo | E(o) | 15.44 | 4,772 |\n| 1691 | 03 82G11 12023 | 29.383 | 93.640 | Lower Yarlung Tsangpo | E(o) | 39.57 | 4,717 |\n| 1692 | 03 82G11 12030 | 29.370 | 93.694 | Lower Yarlung Tsangpo | E(o) | 48.66 | 4,725 |\n| 1693 | 03 82G11 12043 | 29.331 | 93.721 | Lower Yarlung Tsangpo | E(o) | 49.58 | 4,610 |\n| 1694 | 03 82G11 12045 | 29.329 | 93.749 | Lower Yarlung Tsangpo | E(o) | 24.59 | 4,528 |\n| 1695 | 03 82G11 12046 | 29.328 | 93.677 | Lower Yarlung Tsangpo | E(o) | 33.31 | 4,628 |\n| 1696 | 03 82G11 12053 | 29.312 | 93.627 | Lower Yarlung Tsangpo | E(c) | 16.09 | 4,835 |\n| 1697 | 03 82G11 12065 | 29.287 | 93.736 | Lower Yarlung Tsangpo | E(o) | 59.16 | 4,562 |\n| 1698 | 03 82G11 12066 | 29.287 | 93.701 | Lower Yarlung Tsangpo | E(o) | 18.25 | 4,643 |\n| 1699 | 03 82G12 12093 | 29.056 | 93.694 | Lower Yarlung Tsangpo | E(o) | 16.82 | 4,576 |\n| 1700 | 03 82G12 12102 | 29.046 | 93.741 | Lower Yarlung Tsangpo | O | 11.84 | 4,201 |\n| 1701 | 03 82G12 12110 | 29.037 | 93.735 | Lower Yarlung Tsangpo | O | 22.43 | 4,202 |\n| 1702 | 03 82G12 12113 | 29.034 | 93.602 | Lower Yarlung Tsangpo | E(o) | 21.73 | 4,602 |\n| 1703 | 03 82G12 12124 | 29.025 | 93.567 | Lower Yarlung Tsangpo | O | 10.65 | 4,553 |\n| 1704 | 03 82G12 12126 | 29.024 | 93.731 | Lower Yarlung Tsangpo | O | 11.04 | 4,214 |\n| 1705 | 03 82G12 12139 | 29.017 | 93.713 | Lower Yarlung Tsangpo | E(o) | 25.14 | 4,411 |\n| 1706 | 03 82G12 12160 | 29.003 | 93.566 | Lower Yarlung Tsangpo | E(o) | 14.37 | 4,824 |\n| 1707 | 03 82G12 12161 | 29.003 | 93.668 | Lower Yarlung Tsangpo | E(o) | 17.11 | 4,558 |\n| 1708 | 03 82G14 12167 | 29.507 | 93.840 | Lower Yarlung Tsangpo | M(o) | 24.29 | 4,449 |\n| 1709 | 03 82G15 12175 | 29.488 | 93.826 | Lower Yarlung Tsangpo | E(o) | 13.43 | 4,741 |\n| 1710 | 03 82G15 12186 | 29.474 | 93.864 | Lower Yarlung Tsangpo | E(o) | 13.98 | 4,422 |\n| 1711 | 03 82G15 12196 | 29.465 | 93.996 | Lower Yarlung Tsangpo | E(o) | 20.29 | 4,471 |\n| 1712 | 03 82G15 12204 | 29.454 | 93.978 | Lower Yarlung Tsangpo | E(o) | 16.10 | 4,670 |\n| 1713 | 03 82G15 12214 | 29.420 | 93.864 | Lower Yarlung Tsangpo | E(o) | 11.53 | 4,611 |\n| 1714 | 03 82G15 12221 | 29.414 | 93.783 | Lower Yarlung Tsangpo | E(o) | 11.71 | 4,291 |\n| 1715 | 03 82G15 12229 | 29.404 | 93.775 | Lower Yarlung Tsangpo | E(o) | 11.71 | 4,666 |\n| 1716 | 03 82G15 12250 | 29.359 | 93.823 | Lower Yarlung Tsangpo | E(c) | 21.53 | 4,518 |\n| 1717 | 03 82G15 12260 | 29.341 | 93.788 | Lower Yarlung Tsangpo | E(o) | 27.07 | 4,497 |\n| 1718 | 03 82G15 12262 | 29.337 | 93.758 | Lower Yarlung Tsangpo | E(o) | 14.33 | 4,540 |\n| 1719 | 03 82G15 12268 | 29.311 | 93.806 | Lower Yarlung Tsangpo | E(o) | 10.51 | 4,623 |\n| 1720 | 03 82G15 12274 | 29.305 | 93.835 | Lower Yarlung Tsangpo | E(o) | 13.90 | 4,614 |\n| 1721 | 03 82G16 12324 | 29.038 | 93.762 | Lower Yarlung Tsangpo | E(o) | 21.96 | 4,242 |\n| 1722 | 03 82G16 12326 | 29.035 | 93.836 | Lower Yarlung Tsangpo | O | 67.89 | 4,116 |\n| 1723 | 03 82G16 12334 | 29.029 | 93.982 | Lower Yarlung Tsangpo | E(o) | 31.69 | 4,072 |\n| 1724 | 03 82G16 12338 | 29.021 | 93.815 | Lower Yarlung Tsangpo | E(o) | 12.30 | 4,729 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5559, "line_end": 5634, "token_count_estimate": 1742, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["12019", "12023", "12030", "12043", "12045", "12046", "12053", "12065", "12066", "12093", "12102", "12110", "12113", "12124", "12126", "12139", "12160", "12161", "12167", "12175", "12186", "12196", "12204", "12214", "12221", "12229", "12250", "12260", "12262", "12268", "12274", "12324", "12326", "12334", "12338", "82G11", "82G12", "82G14", "82G15", "82G16"]}}
{"id": "b9c96d7a0674bb3d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1725 | 03 82G16 12339 | 29.021 | 93.756 | Lower Yarlung Tsangpo | E(c) | 15.06 | 4,696 |\n| 1726 | 03 82H01 12359 | 28.871 | 93.224 | Lower Yarlung Tsangpo | M(o) | 21.82 | 4,801 |\n| 1727 | 03 82H01 12365 | 28.773 | 93.172 | Lower Yarlung Tsangpo | O | 11.23 | 4,522 |\n| 1728 | 03 82H01 12368 | 28.767 | 93.073 | Lower Yarlung Tsangpo | O | 16.39 | 4,149 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 74, "line_start": 5559, "line_end": 5634, "token_count_estimate": 275, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["12339", "12359", "12365", "12368", "82G16", "82H01"]}}
{"id": "b7f174e238e43367", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5635, "line_end": 5643, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cca038a0555b5286", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1729 | 03 82H01 12371 | 28.757 | 93.079 | Lower Yarlung Tsangpo | E(o) | 22.39 | 4,161 |\n| 1730 | 03 82H09 12465 | 28.771 | 93.631 | Lower Yarlung Tsangpo | E(c) | 17.84 | 4,688 |\n| 1731 | 03 82H10 12487 | 28.672 | 93.723 | Lower Yarlung Tsangpo | E(o) | 12.09 | 4,361 |\n| 1732 | 03 82H13 12506 | 28.856 | 94.000 | Lower Yarlung Tsangpo | E(o) | 73.89 | 3,868 |\n| 1733 | 03 82H13 12512 | 28.847 | 93.784 | Lower Yarlung Tsangpo | E(o) | 24.48 | 4,057 |\n| 1734 | 03 82H13 12515 | 28.841 | 93.752 | Lower Yarlung Tsangpo | E(o) | 31.25 | 4,120 |\n| 1735 | 03 82H13 12520 | 28.790 | 93.912 | Lower Yarlung Tsangpo | E(o) | 17.23 | 4,226 |\n| 1736 | 03 82H13 12524 | 28.785 | 93.923 | Lower Yarlung Tsangpo | E(o) | 13.50 | 4,147 |\n| 1737 | 03 82K03 12559 | 29.487 | 94.099 | Lower Yarlung Tsangpo | E(o) | 10.43 | 4,733 |\n| 1738 | 03 82K03 12564 | 29.484 | 94.034 | Lower Yarlung Tsangpo | E(o) | 22.22 | 4,470 |\n| 1739 | 03 82K03 12567 | 29.481 | 94.118 | Lower Yarlung Tsangpo | E(o) | 19.23 | 4,601 |\n| 1740 | 03 82K03 12582 | 29.467 | 94.131 | Lower Yarlung Tsangpo | E(o) | 11.02 | 4,487 |\n| 1741 | 03 82K03 12583 | 29.465 | 94.103 | Lower Yarlung Tsangpo | E(o) | 11.99 | 4,516 |\n| 1742 | 03 82K03 12586 | 29.464 | 94.045 | Lower Yarlung Tsangpo | E(o) | 40.19 | 4,509 |\n| 1743 | 03 82K03 12591 | 29.457 | 94.052 | Lower Yarlung Tsangpo | E(c) | 13.35 | 4,692 |\n| 1744 | 03 82K03 12595 | 29.454 | 94.015 | Lower Yarlung Tsangpo | E(o) | 16.67 | 4,391 |\n| 1745 | 03 82K03 12596 | 29.454 | 94.006 | Lower Yarlung Tsangpo | E(o) | 20.79 | 4,391 |\n| 1746 | 03 82K03 12599 | 29.450 | 94.155 | Lower Yarlung Tsangpo | E(c) | 16.57 | 4,610 |\n| 1747 | 03 82K03 12619 | 29.417 | 94.150 | Lower Yarlung Tsangpo | E(o) | 11.51 | 4,438 |\n| 1748 | 03 82K03 12630 | 29.402 | 94.199 | Lower Yarlung Tsangpo | E(o) | 14.59 | 4,616 |\n| 1749 | 03 82K03 12631 | 29.401 | 94.208 | Lower Yarlung Tsangpo | E(o) | 10.07 | 4,485 |\n| 1750 | 03 82K03 12641 | 29.396 | 94.066 | Lower Yarlung Tsangpo | E(o) | 16.68 | 4,523 |\n| 1751 | 03 82K03 12658 | 29.375 | 94.019 | Lower Yarlung Tsangpo | E(o) | 19.85 | 4,418 |\n| 1752 | 03 82K03 12668 | 29.361 | 94.147 | Lower Yarlung Tsangpo | E(o) | 16.26 | 4,551 |\n| 1753 | 03 82K03 12691 | 29.343 | 94.161 | Lower Yarlung Tsangpo | E(o) | 11.01 | 4,564 |\n| 1754 | 03 82K03 12699 | 29.321 | 94.119 | Lower Yarlung Tsangpo | E(c) | 12.58 | 4,553 |\n| 1755 | 03 82K03 12709 | 29.296 | 94.202 | Lower Yarlung Tsangpo | O | 42.94 | 3,931 |\n| 1756 | 03 82K03 12710 | 29.291 | 94.146 | Lower Yarlung Tsangpo | E(o) | 15.72 | 4,468 |\n| 1757 | 03 82K03 12718 | 29.271 | 94.167 | Lower Yarlung Tsangpo | E(o) | 10.00 | 4,523 |\n| 1758 | 03 82K04 12729 | 29.101 | 94.124 | Lower Yarlung Tsangpo | E(c) | 45.28 | 4,562 |\n| 1759 | 03 82K04 12738 | 29.054 | 94.002 | Lower Yarlung Tsangpo | M(e) | 13.14 | 4,966 |\n| 1760 | 03 82K04 12739 | 29.052 | 94.097 | Lower Yarlung Tsangpo | E(o) | 24.12 | 4,338 |\n| 1761 | 03 82K04 12743 | 29.031 | 94.055 | Lower Yarlung Tsangpo | E(o) | 18.78 | 4,240 |\n| 1762 | 03 82K04 12746 | 29.025 | 94.066 | Lower Yarlung Tsangpo | M(o) | 11.70 | 4,289 |\n| 1763 | 03 82K04 12747 | 29.017 | 94.039 | Lower Yarlung Tsangpo | E(c) | 19.69 | 4,107 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5644, "line_end": 5719, "token_count_estimate": 1745, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["12371", "12465", "12487", "12506", "12512", "12515", "12520", "12524", "12559", "12564", "12567", "12582", "12583", "12586", "12591", "12595", "12596", "12599", "12619", "12630", "12631", "12641", "12658", "12668", "12691", "12699", "12709", "12710", "12718", "12729", "12738", "12739", "12743", "12746", "12747", "82H01", "82H09", "82H10", "82H13", "82K03", "82K04"]}}
{"id": "2b79b858dc8a312b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1764 | 03 82K07 12752 | 29.397 | 94.304 | Lower Yarlung Tsangpo | E(o) | 11.24 | 4,321 |\n| 1765 | 03 82K08 12780 | 29.077 | 94.271 | Lower Yarlung Tsangpo | O | 17.08 | 3,682 |\n| 1766 | 03 82K08 12781 | 29.075 | 94.296 | Lower Yarlung Tsangpo | E(o) | 11.64 | 4,377 |\n| 1767 | 03 82K08 12782 | 29.069 | 94.300 | Lower Yarlung Tsangpo | M(o) | 12.91 | 4,435 |\n| 1768 | 03 82K11 12795 | 29.311 | 94.611 | Lower Yarlung Tsangpo | E(c) | 20.77 | 4,253 |\n| 1769 | 03 82K11 12796 | 29.308 | 94.637 | Lower Yarlung Tsangpo | E(c) | 15.03 | 4,142 |\n| 1770 | 03 82K14 12810 | 29.545 | 94.965 | Lower Yarlung Tsangpo | E(c) | 91.95 | 4,300 |\n| 1771 | 03 82K15 12816 | 29.374 | 94.892 | Lower Yarlung Tsangpo | M(e) | 27.56 | 4,455 |\n| 1772 | 03 82L01 12818 | 28.995 | 94.006 | Lower Yarlung Tsangpo | M(o) | 13.40 | 4,578 |\n| 1773 | 03 82L01 12825 | 28.906 | 94.004 | Lower Yarlung Tsangpo | E(o) | 12.97 | 4,423 |\n| 1774 | 03 82L01 12826 | 28.888 | 94.030 | Lower Yarlung Tsangpo | E(o) | 16.96 | 4,104 |\n| 1775 | 03 82L01 12827 | 28.880 | 94.040 | Lower Yarlung Tsangpo | E(o) | 14.86 | 4,130 |\n| 1776 | 03 82L01 12829 | 28.870 | 94.018 | Lower Yarlung Tsangpo | E(o) | 11.77 | 4,303 |\n| 1777 | 03 82L05 12837 | 28.986 | 94.270 | Lower Yarlung Tsangpo | E(o) | 50.66 | 3,478 |\n| 1778 | 03 77P13 12844 | 28.776 | 91.981 | Subansiri | M(e) | 16.44 | 5,013 |\n| 1779 | 03 82D04 12864 | 28.046 | 92.133 | Subansiri | E(o) | 12.58 | 5,113 |\n| 1780 | 03 82D12 12912 | 28.206 | 92.742 | Subansiri | M(e) | 10.79 | 5,176 |\n| 1781 | 03 82D12 12915 | 28.185 | 92.696 | Subansiri | M(e) | 27.82 | 5,162 |\n| 1782 | 03 82D12 12968 | 28.001 | 92.641 | Subansiri | M(e) | 10.94 | 5,269 |\n| 1783 | 03 82D16 13006 | 28.216 | 92.768 | Subansiri | M(e) | 13.27 | 5,164 |\n| 1784 | 03 82H02 13015 | 28.721 | 93.217 | Subansiri | E(o) | 17.01 | 4,474 |\n| 1785 | 03 82H02 13034 | 28.658 | 93.144 | Subansiri | E(o) | 11.84 | 4,518 |\n| 1786 | 03 82H03 13095 | 28.342 | 93.092 | Subansiri | E(o) | 62.44 | 4,251 |\n| 1787 | 03 82H03 13097 | 28.320 | 93.047 | Subansiri | E(o) | 72.68 | 4,255 |\n| 1788 | 03 82H06 13111 | 28.650 | 93.462 | Subansiri | E(o) | 16.59 | 4,046 |\n| 1789 | 03 82H06 13124 | 28.541 | 93.380 | Subansiri | E(o) | 16.25 | 4,307 |\n| 1790 | 03 82H06 13135 | 28.524 | 93.394 | Subansiri | E(o) | 12.17 | 4,249 |\n| 1791 | 03 82H06 13138 | 28.520 | 93.377 | Subansiri | E(o) | 13.61 | 4,247 |\n| 1792 | 03 82H07 13154 | 28.313 | 93.391 | Subansiri | E(o) | 20.03 | 4,066 |\n| 1793 | 03 82H07 13156 | 28.305 | 93.437 | Subansiri | O | 35.07 | 3,952 |\n| 1794 | 03 82H07 13157 | 28.297 | 93.419 | Subansiri | E(o) | 20.77 | 3,841 |\n| 1795 | 03 82H10 13161 | 28.668 | 93.734 | Subansiri | E(c) | 19.78 | 4,016 |\n| 1796 | 03 82H10 13168 | 28.594 | 93.726 | Subansiri | E(c) | 25.13 | 4,208 |\n| 1797 | 03 82H10 13171 | 28.585 | 93.743 | Subansiri | E(o) | 11.14 | 3,758 |\n| 1798 | 03 82H10 13172 | 28.585 | 93.712 | Subansiri | E(o) | 14.16 | 3,873 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5644, "line_end": 5719, "token_count_estimate": 1672, "basins": [], "subbasins": ["Lower Yarlung Tsangpo", "Subansiri"], "countries": [], "lake_ids": ["12752", "12780", "12781", "12782", "12795", "12796", "12810", "12816", "12818", "12825", "12826", "12827", "12829", "12837", "12844", "12864", "12912", "12915", "12968", "13006", "13015", "13034", "13095", "13097", "13111", "13124", "13135", "13138", "13154", "13156", "13157", "13161", "13168", "13171", "13172", "77P13", "82D04", "82D12", "82D16", "82H02", "82H03", "82H06", "82H07", "82H10", "82K07", "82K08", "82K11", "82K14", "82K15", "82L01"]}}
{"id": "1f7baf162a09ac69", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1799 | 03 82H10 13180 | 28.576 | 93.578 | Subansiri | E(o) | 36.88 | 3,842 |\n| 1800 | 03 82H10 13183 | 28.568 | 93.563 | Subansiri | E(o) | 16.13 | 4,074 |\n| 1801 | 03 82H11 13195 | 28.489 | 93.555 | Subansiri | E(o) | 13.67 | 4,647 |\n| 1802 | 03 82H11 13197 | 28.483 | 93.603 | Subansiri | E(o) | 12.34 | 3,987 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 74, "line_start": 5644, "line_end": 5719, "token_count_estimate": 264, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": ["13180", "13183", "13195", "13197", "82H10", "82H11"]}}
{"id": "ced1c0ecb3012b0c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1803 | 03 82H11 13203 | 28.459 | 93.605 | Subansiri | E(o) | 11.86 | 3,957 |\n| 1804 | 03 82H11 13204 | 28.458 | 93.579 | Subansiri | E(o) | 47.52 | 3,922 |\n| 1805 | 03 82H14 13211 | 28.616 | 93.820 | Subansiri | E(o) | 46.32 | 3,590 |\n| 1806 | 03 82H14 13212 | 28.597 | 93.805 | Subansiri | E(o) | 12.29 | 4,163 |\n| 1807 | 03 82H14 13216 | 28.587 | 93.839 | Subansiri | E(o) | 11.27 | 4,296 |\n| 1808 | 03 82H14 13218 | 28.583 | 93.812 | Subansiri | E(o) | 11.42 | 3,799 |\n| 1809 | 03 82H14 13226 | 28.549 | 93.884 | Subansiri | E(o) | 15.78 | 4,070 |\n| 1810 | 03 82H15 13236 | 28.270 | 93.781 | Subansiri | E(o) | 12.78 | 3,537 |\n| 1811 | 03 83A05 13281 | 27.831 | 92.345 | Subansiri | M(e) | 22.14 | 5,032 |\n| 1812 | 03 83A09 13285 | 27.980 | 92.651 | Subansiri | M(e) | 52.21 | 4,988 |\n| 1813 | 03 82D16 13317 | 28.117 | 92.969 | Subansiri | M(o) | 18.06 | 4,214 |\n| 1814 | 03 82D16 13318 | 28.116 | 92.951 | Subansiri | E(c) | 55.58 | 4,648 |\n| 1815 | 03 82D16 13323 | 28.106 | 92.970 | Subansiri | M(o) | 12.93 | 4,340 |\n| 1816 | 03 82D16 13331 | 28.087 | 92.937 | Subansiri | E(o) | 21.48 | 4,328 |\n| 1817 | 03 82D16 13337 | 28.062 | 92.884 | Subansiri | E(o) | 23.64 | 4,365 |\n| 1818 | 03 82H03 13344 | 28.339 | 93.122 | Subansiri | E(c) | 14.38 | 4,086 |\n| 1819 | 03 82H07 13364 | 28.294 | 93.355 | Subansiri | E(c) | 10.09 | 3,597 |\n| 1820 | 03 83A06 13468 | 27.728 | 92.436 | Jia Bharali | M(l) | 13.80 | 4,995 |\n| 1821 | 03 83A06 13506 | 27.637 | 92.407 | Jia Bharali | E(o) | 21.73 | 4,325 |\n| 1822 | 03 83A06 13534 | 27.602 | 92.385 | Jia Bharali | E(o) | 10.47 | 4,227 |\n| 1823 | 03 83A09 13546 | 27.835 | 92.685 | Jia Bharali | E(o) | 18.24 | 3,951 |\n| 1824 | 03 83A09 13551 | 27.825 | 92.647 | Jia Bharali | E(o) | 49.71 | 4,609 |\n| 1825 | 03 83A10 13559 | 27.742 | 92.515 | Jia Bharali | E(o) | 13.05 | 3,863 |\n| 1826 | 03 83A13 13581 | 27.920 | 92.793 | Jia Bharali | E(o) | 10.67 | 4,291 |\n| 1827 | 03 83A13 13589 | 27.904 | 92.810 | Jia Bharali | E(c) | 12.25 | 4,306 |\n| 1828 | 03 83A13 13593 | 27.901 | 92.786 | Jia Bharali | E(o) | 10.15 | 4,248 |\n| 1829 | 03 83A13 13602 | 27.877 | 92.800 | Jia Bharali | E(o) | 35.96 | 3,943 |\n| 1830 | 03 82J08 13613 | 30.248 | 94.356 | Lower Yarlung Tsangpo | E(c) | 14.76 | 4,853 |\n| 1831 | 03 82J08 13614 | 30.219 | 94.336 | Lower Yarlung Tsangpo | M(o) | 11.64 | 4,565 |\n| 1832 | 03 82J08 13615 | 30.214 | 94.383 | Lower Yarlung Tsangpo | E(o) | 26.99 | 4,715 |\n| 1833 | 03 82J08 13620 | 30.175 | 94.408 | Lower Yarlung Tsangpo | E(o) | 28.00 | 3,566 |\n| 1834 | 03 82J08 13621 | 30.174 | 94.346 | Lower Yarlung Tsangpo | E(v) | 180.80 | 3,654 |\n| 1835 | 03 82J08 13625 | 30.142 | 94.301 | Lower Yarlung Tsangpo | M(o) | 11.45 | 4,405 |\n| 1836 | 03 82J08 13633 | 30.098 | 94.483 | Lower Yarlung Tsangpo | O | 15.50 | 3,289 |\n| 1837 | 03 82J08 13634 | 30.073 | 94.464 | Lower Yarlung Tsangpo | E(o) | 90.19 | 4,110 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5721, "line_end": 5796, "token_count_estimate": 1671, "basins": [], "subbasins": ["Jia Bharali", "Lower Yarlung Tsangpo", "Subansiri"], "countries": [], "lake_ids": ["13203", "13204", "13211", "13212", "13216", "13218", "13226", "13236", "13281", "13285", "13317", "13318", "13323", "13331", "13337", "13344", "13364", "13468", "13506", "13534", "13546", "13551", "13559", "13581", "13589", "13593", "13602", "13613", "13614", "13615", "13620", "13621", "13625", "13633", "13634", "82D16", "82H03", "82H07", "82H11", "82H14", "82H15", "82J08", "83A05", "83A06", "83A09", "83A10", "83A13"]}}
{"id": "7657447f09ff1214", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1838 | 03 82J08 13639 | 30.053 | 94.358 | Lower Yarlung Tsangpo | E(o) | 25.92 | 4,730 |\n| 1839 | 03 82J08 13644 | 30.028 | 94.396 | Lower Yarlung Tsangpo | E(o) | 11.01 | 4,664 |\n| 1840 | 03 82J08 13653 | 30.017 | 94.444 | Lower Yarlung Tsangpo | E(c) | 11.69 | 4,609 |\n| 1841 | 03 82J08 13655 | 30.013 | 94.472 | Lower Yarlung Tsangpo | E(c) | 66.38 | 4,327 |\n| 1842 | 03 82J08 13657 | 30.005 | 94.384 | Lower Yarlung Tsangpo | E(o) | 59.09 | 4,020 |\n| 1843 | 03 82K05 13668 | 29.985 | 94.342 | Lower Yarlung Tsangpo | E(o) | 10.51 | 4,407 |\n| 1844 | 03 82K05 13670 | 29.984 | 94.328 | Lower Yarlung Tsangpo | E(o) | 11.45 | 4,515 |\n| 1845 | 03 82K05 13679 | 29.959 | 94.292 | Lower Yarlung Tsangpo | E(v) | 134.02 | 4,282 |\n| 1846 | 03 82K05 13681 | 29.954 | 94.320 | Lower Yarlung Tsangpo | O | 10.03 | 4,232 |\n| 1847 | 03 82K05 13682 | 29.954 | 94.486 | Lower Yarlung Tsangpo | E(o) | 10.30 | 4,550 |\n| 1848 | 03 82K05 13686 | 29.947 | 94.358 | Lower Yarlung Tsangpo | E(o) | 109.97 | 4,148 |\n| 1849 | 03 82K05 13690 | 29.942 | 94.287 | Lower Yarlung Tsangpo | E(o) | 27.11 | 4,296 |\n| 1850 | 03 82K05 13696 | 29.932 | 94.388 | Lower Yarlung Tsangpo | E(o) | 13.30 | 4,238 |\n| 1851 | 03 82K05 13707 | 29.913 | 94.499 | Lower Yarlung Tsangpo | E(o) | 34.40 | 4,443 |\n| 1852 | 03 82K05 13714 | 29.896 | 94.461 | Lower Yarlung Tsangpo | E(o) | 85.25 | 4,346 |\n| 1853 | 03 82K05 13716 | 29.884 | 94.446 | Lower Yarlung Tsangpo | E(o) | 16.18 | 4,632 |\n| 1854 | 03 82K09 13737 | 29.943 | 94.717 | Lower Yarlung Tsangpo | E(o) | 10.75 | 4,554 |\n| 1855 | 03 82K09 13740 | 29.941 | 94.613 | Lower Yarlung Tsangpo | E(o) | 10.19 | 4,546 |\n| 1856 | 03 82K09 13741 | 29.941 | 94.589 | Lower Yarlung Tsangpo | E(o) | 48.08 | 4,509 |\n| 1857 | 03 82K09 13747 | 29.933 | 94.720 | Lower Yarlung Tsangpo | E(c) | 23.31 | 4,372 |\n| 1858 | 03 82K09 13772 | 29.910 | 94.711 | Lower Yarlung Tsangpo | E(o) | 10.29 | 4,411 |\n| 1859 | 03 82K09 13777 | 29.902 | 94.561 | Lower Yarlung Tsangpo | E(o) | 16.26 | 4,495 |\n| 1860 | 03 82K09 13778 | 29.902 | 94.528 | Lower Yarlung Tsangpo | E(o) | 10.55 | 4,591 |\n| 1861 | 03 82K09 13779 | 29.900 | 94.533 | Lower Yarlung Tsangpo | E(o) | 15.42 | 4,615 |\n| 1862 | 03 82K09 13781 | 29.899 | 94.701 | Lower Yarlung Tsangpo | E(c) | 21.07 | 4,500 |\n| 1863 | 03 82K09 13789 | 29.892 | 94.727 | Lower Yarlung Tsangpo | E(o) | 13.60 | 4,288 |\n| 1864 | 03 82K09 13791 | 29.890 | 94.569 | Lower Yarlung Tsangpo | E(v) | 158.02 | 4,149 |\n| 1865 | 03 82K09 13795 | 29.885 | 94.646 | Lower Yarlung Tsangpo | E(o) | 27.76 | 4,308 |\n| 1866 | 03 82K09 13797 | 29.882 | 94.582 | Lower Yarlung Tsangpo | E(o) | 18.69 | 4,418 |\n| 1867 | 03 82K09 13798 | 29.879 | 94.541 | Lower Yarlung Tsangpo | E(o) | 37.67 | 4,266 |\n| 1868 | 03 82K09 13800 | 29.877 | 94.630 | Lower Yarlung Tsangpo | E(o) | 22.63 | 4,490 |\n| 1869 | 03 82K09 13802 | 29.875 | 94.616 | Lower Yarlung Tsangpo | E(o) | 15.79 | 4,588 |\n| 1870 | 03 82K09 13806 | 29.869 | 94.687 | Lower Yarlung Tsangpo | E(o) | 24.37 | 4,378 |\n| 1871 | 03 82K09 13824 | 29.861 | 94.531 | Lower Yarlung Tsangpo | E(o) | 17.89 | 4,592 |\n| 1872 | 03 82K09 13842 | 29.852 | 94.583 | Lower Yarlung Tsangpo | E(o) | 13.48 | 4,688 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5721, "line_end": 5796, "token_count_estimate": 1749, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["13639", "13644", "13653", "13655", "13657", "13668", "13670", "13679", "13681", "13682", "13686", "13690", "13696", "13707", "13714", "13716", "13737", "13740", "13741", "13747", "13772", "13777", "13778", "13779", "13781", "13789", "13791", "13795", "13797", "13798", "13800", "13802", "13806", "13824", "13842", "82J08", "82K05", "82K09"]}}
{"id": "cc1b2da2dd89a990", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1873 | 03 82K09 13855 | 29.829 | 94.633 | Lower Yarlung Tsangpo | E(v) | 60.20 | 4,231 |\n| 1874 | 03 82K09 13861 | 29.825 | 94.550 | Lower Yarlung Tsangpo | O | 12.85 | 4,275 |\n| 1875 | 03 82K09 13863 | 29.824 | 94.599 | Lower Yarlung Tsangpo | E(o) | 11.98 | 4,564 |\n| 1876 | 03 82K09 13869 | 29.815 | 94.572 | Lower Yarlung Tsangpo | E(o) | 13.32 | 4,400 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 74, "line_start": 5721, "line_end": 5796, "token_count_estimate": 278, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["13855", "13861", "13863", "13869", "82K09"]}}
{"id": "81154861d0d7264b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5797, "line_end": 5804, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "66383c89d5bf0f10", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1877 | 03 82K09 13880 | 29.796 | 94.577 | Lower Yarlung Tsangpo | E(o) | 29.63 | 4,351 |\n| 1878 | 03 82K09 13886 | 29.779 | 94.601 | Lower Yarlung Tsangpo | E(v) | 184.22 | 4,146 |\n| 1879 | 03 82K09 13888 | 29.777 | 94.560 | Lower Yarlung Tsangpo | E(o) | 32.49 | 4,161 |\n| 1880 | 03 82K09 13889 | 29.776 | 94.573 | Lower Yarlung Tsangpo | E(o) | 47.72 | 4,161 |\n| 1881 | 03 82H13 13922 | 28.763 | 93.987 | Dihang | E(c) | 20.22 | 3,957 |\n| 1882 | 03 82H14 13926 | 28.742 | 93.915 | Dihang | E(o) | 10.10 | 4,150 |\n| 1883 | 03 82H14 13937 | 28.728 | 93.795 | Dihang | E(c) | 10.98 | 4,205 |\n| 1884 | 03 82H14 13941 | 28.707 | 93.792 | Dihang | E(o) | 17.92 | 3,793 |\n| 1885 | 03 82H14 13964 | 28.606 | 93.845 | Dihang | E(c) | 12.39 | 4,022 |\n| 1886 | 03 82H14 13975 | 28.562 | 93.887 | Dihang | E(c) | 13.03 | 3,938 |\n| 1887 | 03 82L01 13989 | 28.757 | 94.153 | Dihang | E(o) | 13.33 | 3,875 |\n| 1888 | 03 82L01 13993 | 28.754 | 94.134 | Dihang | E(o) | 10.91 | 4,057 |\n| 1889 | 03 82L01 13994 | 28.753 | 94.001 | Dihang | E(c) | 22.89 | 4,019 |\n| 1890 | 03 82L05 14004 | 28.963 | 94.372 | Dihang | E(c) | 18.95 | 4,145 |\n| 1891 | 03 82L05 14005 | 28.953 | 94.383 | Dihang | E(o) | 19.27 | 3,858 |\n| 1892 | 03 82L05 14019 | 28.837 | 94.451 | Dihang | E(o) | 18.47 | 4,136 |\n| 1893 | 03 82N02 14031 | 30.603 | 95.182 | Lower Yarlung Tsangpo | M(e) | 110.12 | 4,278 |\n| 1894 | 03 82N03 14046 | 30.269 | 95.217 | Lower Yarlung Tsangpo | E(o) | 12.18 | 3,938 |\n| 1895 | 03 82N06 14063 | 30.571 | 95.252 | Lower Yarlung Tsangpo | M(e) | 34.62 | 4,520 |\n| 1896 | 03 82N06 14076 | 30.531 | 95.487 | Lower Yarlung Tsangpo | E(o) | 10.31 | 4,783 |\n| 1897 | 03 82N07 14096 | 30.420 | 95.296 | Lower Yarlung Tsangpo | E(o) | 22.11 | 4,404 |\n| 1898 | 03 82N07 14098 | 30.392 | 95.419 | Lower Yarlung Tsangpo | E(o) | 10.82 | 4,317 |\n| 1899 | 03 82N10 14116 | 30.591 | 95.551 | Lower Yarlung Tsangpo | E(o) | 36.70 | 5,043 |\n| 1900 | 03 82N10 14121 | 30.576 | 95.576 | Lower Yarlung Tsangpo | E(o) | 15.48 | 4,924 |\n| 1901 | 03 82N10 14125 | 30.564 | 95.644 | Lower Yarlung Tsangpo | E(o) | 18.17 | 5,073 |\n| 1902 | 03 82N10 14150 | 30.541 | 95.703 | Lower Yarlung Tsangpo | E(o) | 15.73 | 5,053 |\n| 1903 | 03 82N10 14154 | 30.537 | 95.548 | Lower Yarlung Tsangpo | E(o) | 13.31 | 4,811 |\n| 1904 | 03 82N10 14159 | 30.533 | 95.565 | Lower Yarlung Tsangpo | E(o) | 10.34 | 4,918 |\n| 1905 | 03 82N10 14161 | 30.527 | 95.693 | Lower Yarlung Tsangpo | E(o) | 12.41 | 4,988 |\n| 1906 | 03 82N10 14171 | 30.522 | 95.701 | Lower Yarlung Tsangpo | M(o) | 13.86 | 4,991 |\n| 1907 | 03 82N10 14176 | 30.514 | 95.669 | Lower Yarlung Tsangpo | E(o) | 16.25 | 4,761 |\n| 1908 | 03 82N10 14193 | 30.473 | 95.575 | Lower Yarlung Tsangpo | E(o) | 56.47 | 4,866 |\n| 1909 | 03 82N11 14200 | 30.496 | 95.650 | Lower Yarlung Tsangpo | E(o) | 11.52 | 4,954 |\n| 1910 | 03 82N11 14210 | 30.470 | 95.588 | Lower Yarlung Tsangpo | E(o) | 13.82 | 4,876 |\n| 1911 | 03 82N11 14235 | 30.436 | 95.602 | Lower Yarlung Tsangpo | E(o) | 33.96 | 5,002 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5805, "line_end": 5954, "token_count_estimate": 1682, "basins": [], "subbasins": ["Dihang", "Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["13880", "13886", "13888", "13889", "13922", "13926", "13937", "13941", "13964", "13975", "13989", "13993", "13994", "14004", "14005", "14019", "14031", "14046", "14063", "14076", "14096", "14098", "14116", "14121", "14125", "14150", "14154", "14159", "14161", "14171", "14176", "14193", "14200", "14210", "14235", "82H13", "82H14", "82K09", "82L01", "82L05", "82N02", "82N03", "82N06", "82N07", "82N10", "82N11"]}}
{"id": "9b6bb22a84ac2358", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1912 | 03 82N11 14266 | 30.391 | 95.657 | Lower Yarlung Tsangpo | E(o) | 18.30 | 4,734 |\n| 1913 | 03 82N11 14270 | 30.386 | 95.745 | Lower Yarlung Tsangpo | O | 10.71 | 4,257 |\n| 1914 | 03 82N11 14272 | 30.381 | 95.654 | Lower Yarlung Tsangpo | M(o) | 25.10 | 4,751 |\n| 1915 | 03 82N11 14287 | 30.326 | 95.611 | Lower Yarlung Tsangpo | M(o) | 17.32 | 4,916 |\n| 1916 | 03 82N11 14296 | 30.300 | 95.548 | Lower Yarlung Tsangpo | M(o) | 13.14 | 4,419 |\n| 1917 | 03 82N11 14300 | 30.269 | 95.606 | Lower Yarlung Tsangpo | M(e) | 38.89 | 4,459 |\n| 1918 | 03 82N11 14301 | 30.251 | 95.604 | Lower Yarlung Tsangpo | M(o) | 134.45 | 4,442 |\n| 1919 | 03 82N12 14303 | 30.238 | 95.603 | Lower Yarlung Tsangpo | M(o) | 15.95 | 4,394 |\n| 1920 | 03 82N12 14304 | 30.229 | 95.592 | Lower Yarlung Tsangpo | M(o) | 25.71 | 4,354 |\n| 1921 | 03 82N12 14305 | 30.223 | 95.543 | Lower Yarlung Tsangpo | M(e) | 12.71 | 4,173 |\n| 1922 | 03 82N12 14308 | 30.221 | 95.584 | Lower Yarlung Tsangpo | M(e) | 85.49 | 4,342 |\n| 1923 | 03 82N15 14337 | 30.341 | 95.856 | Lower Yarlung Tsangpo | M(o) | 18.58 | 4,851 |\n| 1924 | 03 82N15 14358 | 30.288 | 95.871 | Lower Yarlung Tsangpo | E(o) | 11.60 | 4,724 |\n| 1925 | 03 82N16 14379 | 30.180 | 95.857 | Lower Yarlung Tsangpo | E(o) | 21.83 | 4,442 |\n| 1926 | 03 82N16 14390 | 30.008 | 95.917 | Lower Yarlung Tsangpo | M(o) | 12.77 | 4,670 |\n| 1927 | 03 82O09 14396 | 29.805 | 95.642 | Lower Yarlung Tsangpo | M(e) | 24.48 | 4,131 |\n| 1928 | 03 82O09 14401 | 29.796 | 95.512 | Lower Yarlung Tsangpo | E(o) | 29.47 | 4,133 |\n| 1929 | 03 82O13 14404 | 29.992 | 95.866 | Lower Yarlung Tsangpo | M(e) | 46.26 | 4,312 |\n| 1930 | 03 82O13 14407 | 29.982 | 95.905 | Lower Yarlung Tsangpo | E(o) | 19.77 | 4,189 |\n| 1931 | 03 82O13 14420 | 29.905 | 95.909 | Lower Yarlung Tsangpo | E(o) | 13.26 | 4,168 |\n| 1932 | 03 82O13 14426 | 29.870 | 95.982 | Lower Yarlung Tsangpo | M(o) | 11.87 | 4,707 |\n| 1933 | 03 91C02 14526 | 29.598 | 96.141 | Lower Yarlung Tsangpo | M(o) | 65.09 | 4,025 |\n| 1934 | 03 91C02 14528 | 29.579 | 96.161 | Lower Yarlung Tsangpo | M(o) | 13.00 | 3,874 |\n| 1935 | 03 91C05 14535 | 29.907 | 96.257 | Lower Yarlung Tsangpo | E(o) | 19.83 | 4,578 |\n| 1936 | 03 91C05 14536 | 29.904 | 96.399 | Lower Yarlung Tsangpo | E(o) | 10.21 | 4,892 |\n| 1937 | 03 91C05 14537 | 29.893 | 96.380 | Lower Yarlung Tsangpo | E(o) | 22.74 | 4,681 |\n| 1938 | 03 91C05 14541 | 29.883 | 96.391 | Lower Yarlung Tsangpo | M(o) | 27.39 | 4,691 |\n| 1939 | 03 91C05 14547 | 29.874 | 96.325 | Lower Yarlung Tsangpo | M(e) | 19.41 | 4,117 |\n| 1940 | 03 91C05 14548 | 29.867 | 96.370 | Lower Yarlung Tsangpo | M(o) | 15.47 | 4,775 |\n| 1941 | 03 91C05 14550 | 29.823 | 96.351 | Lower Yarlung Tsangpo | M(o) | 101.06 | 4,870 |\n| 1942 | 03 91C05 14568 | 29.762 | 96.375 | Lower Yarlung Tsangpo | E(o) | 12.04 | 4,810 |\n| 1943 | 03 91C06 14592 | 29.706 | 96.308 | Lower Yarlung Tsangpo | E(c) | 23.92 | 4,845 |\n| 1944 | 03 91C06 14612 | 29.626 | 96.470 | Lower Yarlung Tsangpo | E(o) | 12.62 | 4,364 |\n| 1945 | 03 91C06 14616 | 29.571 | 96.375 | Lower Yarlung Tsangpo | E(c) | 27.01 | 4,279 |\n| 1946 | 03 91C07 14620 | 29.465 | 96.500 | Lower Yarlung Tsangpo | M(e) | 32.33 | 3,818 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5805, "line_end": 5954, "token_count_estimate": 1739, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["14266", "14270", "14272", "14287", "14296", "14300", "14301", "14303", "14304", "14305", "14308", "14337", "14358", "14379", "14390", "14396", "14401", "14404", "14407", "14420", "14426", "14526", "14528", "14535", "14536", "14537", "14541", "14547", "14548", "14550", "14568", "14592", "14612", "14616", "14620", "82N11", "82N12", "82N15", "82N16", "82O09", "82O13", "91C02", "91C05", "91C06", "91C07"]}}
{"id": "33326ac99c0a1667", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1947 | 03 91C10 14628 | 29.664 | 96.553 | Lower Yarlung Tsangpo | M(e) | 21.41 | 4,696 |\n| 1948 | 03 82K12 14643 | 29.177 | 94.526 | Dihang | E(o) | 14.84 | 4,258 |\n| 1949 | 03 82K14 14646 | 29.504 | 94.969 | Dihang | E(c) | 15.54 | 4,451 |\n| 1950 | 03 82K15 14652 | 29.330 | 94.816 | Dihang | E(c) | 27.40 | 4,151 |\n| 1951 | 03 82K15 14654 | 29.316 | 94.922 | Dihang | E(o) | 31.30 | 3,657 |\n| 1952 | 03 82K15 14659 | 29.256 | 94.770 | Dihang | O | 12.67 | 3,294 |\n| 1953 | 03 82K16 14663 | 29.226 | 94.783 | Dihang | E(c) | 13.12 | 3,683 |\n| 1954 | 03 82K16 14664 | 29.222 | 94.780 | Dihang | E(c) | 19.59 | 3,754 |\n| 1955 | 03 82L05 14665 | 28.978 | 94.398 | Dihang | E(o) | 24.66 | 3,111 |\n| 1956 | 03 82O03 14676 | 29.483 | 95.038 | Dihang | E(o) | 40.06 | 3,897 |\n| 1957 | 03 82O03 14679 | 29.478 | 95.074 | Dihang | E(o) | 40.76 | 3,474 |\n| 1958 | 03 82O03 14682 | 29.254 | 95.227 | Dihang | E(o) | 20.72 | 1,441 |\n| 1959 | 03 82O04 14687 | 29.069 | 95.242 | Dihang | E(o) | 18.51 | 3,823 |\n| 1960 | 03 82O04 14692 | 29.058 | 95.237 | Dihang | E(c) | 10.02 | 3,743 |\n| 1961 | 03 82O08 14707 | 29.188 | 95.453 | Dihang | E(c) | 15.04 | 3,866 |\n| 1962 | 03 82O08 14708 | 29.183 | 95.451 | Dihang | E(c) | 10.73 | 3,922 |\n| 1963 | 03 82O08 14709 | 29.180 | 95.486 | Dihang | E(o) | 93.81 | 3,533 |\n| 1964 | 03 82O08 14712 | 29.163 | 95.491 | Dihang | E(o) | 34.65 | 3,544 |\n| 1965 | 03 82O08 14715 | 29.161 | 95.464 | Dihang | E(o) | 19.76 | 3,826 |\n| 1966 | 03 82O08 14728 | 29.144 | 95.493 | Dihang | E(o) | 17.69 | 3,673 |\n| 1967 | 03 82O08 14730 | 29.140 | 95.405 | Dihang | E(o) | 20.64 | 3,765 |\n| 1968 | 03 82O08 14738 | 29.062 | 95.263 | Dihang | E(o) | 48.24 | 3,668 |\n| 1969 | 03 82O09 14741 | 29.763 | 95.546 | Dihang | E(c) | 47.44 | 3,474 |\n| 1970 | 03 82O10 14747 | 29.723 | 95.528 | Dihang | E(o) | 13.44 | 3,065 |\n| 1971 | 03 82O10 14765 | 29.633 | 95.613 | Dihang | E(c) | 39.40 | 4,320 |\n| 1972 | 03 82O10 14773 | 29.599 | 95.740 | Dihang | E(o) | 14.79 | 4,010 |\n| 1973 | 03 82O10 14774 | 29.596 | 95.691 | Dihang | E(c) | 11.35 | 4,314 |\n| 1974 | 03 82O10 14778 | 29.557 | 95.679 | Dihang | E(o) | 10.64 | 4,199 |\n| 1975 | 03 82O10 14781 | 29.513 | 95.693 | Dihang | E(c) | 37.79 | 3,922 |\n| 1976 | 03 82O11 14793 | 29.377 | 95.630 | Dihang | E(o) | 33.66 | 3,750 |\n| 1977 | 03 82O11 14797 | 29.374 | 95.558 | Dihang | E(o) | 12.79 | 4,049 |\n| 1978 | 03 82O11 14798 | 29.374 | 95.718 | Dihang | E(o) | 12.83 | 4,046 |\n| 1979 | 03 82O11 14799 | 29.372 | 95.712 | Dihang | E(o) | 13.60 | 4,042 |\n| 1980 | 03 82O11 14803 | 29.367 | 95.696 | Dihang | E(c) | 12.43 | 4,282 |\n| 1981 | 03 82O11 14806 | 29.363 | 95.598 | Dihang | E(o) | 15.57 | 4,150 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5805, "line_end": 5954, "token_count_estimate": 1553, "basins": [], "subbasins": ["Dihang", "Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["14628", "14643", "14646", "14652", "14654", "14659", "14663", "14664", "14665", "14676", "14679", "14682", "14687", "14692", "14707", "14708", "14709", "14712", "14715", "14728", "14730", "14738", "14741", "14747", "14765", "14773", "14774", "14778", "14781", "14793", "14797", "14798", "14799", "14803", "14806", "82K12", "82K14", "82K15", "82K16", "82L05", "82O03", "82O04", "82O08", "82O09", "82O10", "82O11", "91C10"]}}
{"id": "82d4d822ab3a7785", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 1982 | 03 82O11 14807 | 29.359 | 95.583 | Dihang | E(o) | 33.71 | 4,193 |\n| 1983 | 03 82O11 14808 | 29.359 | 95.656 | Dihang | E(o) | 13.11 | 3,951 |\n| 1984 | 03 82O11 14811 | 29.356 | 95.664 | Dihang | E(c) | 16.40 | 4,091 |\n| 1985 | 03 82O11 14818 | 29.347 | 95.732 | Dihang | E(c) | 11.27 | 4,140 |\n| 1986 | 03 82O11 14819 | 29.347 | 95.695 | Dihang | E(c) | 11.54 | 4,101 |\n| 1987 | 03 82O11 14823 | 29.336 | 95.741 | Dihang | E(c) | 11.33 | 4,111 |\n| 1988 | 03 82O11 14831 | 29.324 | 95.590 | Dihang | E(o) | 25.10 | 4,016 |\n| 1989 | 03 82O11 14834 | 29.304 | 95.640 | Dihang | E(v) | 70.17 | 3,322 |\n| 1990 | 03 82O11 14835 | 29.304 | 95.725 | Dihang | E(o) | 12.17 | 3,952 |\n| 1991 | 03 82O11 14845 | 29.283 | 95.740 | Dihang | E(o) | 20.05 | 4,168 |\n| 1992 | 03 82O11 14846 | 29.273 | 95.739 | Dihang | E(c) | 11.19 | 4,023 |\n| 1993 | 03 82O12 14856 | 29.237 | 95.659 | Dihang | E(o) | 12.70 | 4,016 |\n| 1994 | 03 82O12 14857 | 29.234 | 95.596 | Dihang | E(o) | 14.68 | 3,750 |\n| 1995 | 03 82O12 14858 | 29.233 | 95.654 | Dihang | E(c) | 17.08 | 3,915 |\n| 1996 | 03 82O12 14869 | 29.202 | 95.545 | Dihang | E(c) | 21.83 | 3,838 |\n| 1997 | 03 82O12 14870 | 29.200 | 95.505 | Dihang | E(o) | 24.28 | 4,104 |\n| 1998 | 03 82O12 14873 | 29.195 | 95.592 | Dihang | E(c) | 43.26 | 3,708 |\n| 1999 | 03 82O14 14884 | 29.539 | 95.795 | Dihang | E(c) | 22.16 | 3,895 |\n| 2000 | 03 82O14 14885 | 29.535 | 95.804 | Dihang | E(c) | 20.81 | 4,065 |\n| 2001 | 03 82G08 11928 | 29.206 | 93.316 | Lower Yarlung Tsangpo | E(o) | 6.75 | 4,650 |\n| 2002 | 03 82G08 11935 | 29.200 | 93.278 | Lower Yarlung Tsangpo | E(o) | 5.29 | 4,870 |\n| 2001 | 03 82O15 14899 | 29.387 | 95.987 | Dihang | M(o) | 42.62 | 4,251 |\n| 2002 | 03 82O15 14901 | 29.375 | 95.951 | Dihang | M(o) | 17.15 | 4,296 |\n| 2003 | 03 82O15 14903 | 29.371 | 95.873 | Dihang | E(o) | 103.75 | 4,344 |\n| 2004 | 03 82O15 14906 | 29.354 | 95.927 | Dihang | M(o) | 37.97 | 4,357 |\n| 2005 | 03 82O15 14907 | 29.352 | 95.902 | Dihang | M(o) | 20.65 | 4,212 |\n| 2006 | 03 82O15 14908 | 29.352 | 95.915 | Dihang | M(o) | 46.14 | 4,357 |\n| 2007 | 03 82O15 14909 | 29.335 | 95.871 | Dihang | E(o) | 27.26 | 4,025 |\n| 2008 | 03 82O15 14911 | 29.327 | 95.847 | Dihang | E(o) | 27.90 | 4,290 |\n| 2009 | 03 82O15 14913 | 29.323 | 95.857 | Dihang | E(o) | 15.01 | 4,169 |\n| 2010 | 03 82P02 14923 | 28.720 | 95.160 | Dihang | E(c) | 21.86 | 3,640 |\n| 2011 | 03 82P05 14924 | 28.964 | 95.252 | Dihang | E(o) | 19.91 | 3,717 |\n| 2012 | 03 82P05 14928 | 28.943 | 95.298 | Dihang | E(o) | 11.19 | 3,780 |\n| 2013 | 03 82P05 14929 | 28.922 | 95.323 | Dihang | E(o) | 16.42 | 3,841 |\n| 2014 | 03 82P05 14934 | 28.899 | 95.313 | Dihang | E(o) | 20.34 | 3,788 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5805, "line_end": 5954, "token_count_estimate": 1567, "basins": [], "subbasins": ["Dihang", "Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["11928", "11935", "14807", "14808", "14811", "14818", "14819", "14823", "14831", "14834", "14835", "14845", "14846", "14856", "14857", "14858", "14869", "14870", "14873", "14884", "14885", "14899", "14901", "14903", "14906", "14907", "14908", "14909", "14911", "14913", "14923", "14924", "14928", "14929", "14934", "82G08", "82O11", "82O12", "82O14", "82O15", "82P02", "82P05"]}}
{"id": "541bd059d2d59de3", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2015 | 03 82P05 14937 | 28.876 | 95.316 | Dihang | E(o) | 14.98 | 3,847 |\n| 2016 | 03 82O08 14954 | 29.176 | 95.447 | Dibang | E(o) | 14.14 | 3,906 |\n| 2017 | 03 82O08 14955 | 29.167 | 95.450 | Dibang | E(o) | 21.38 | 3,945 |\n| 2018 | 03 82O08 14963 | 29.141 | 95.437 | Dibang | O | 21.42 | 3,322 |\n| 2019 | 03 82O08 14974 | 29.129 | 95.439 | Dibang | E(o) | 53.88 | 3,284 |\n| 2020 | 03 82O08 14977 | 29.120 | 95.397 | Dibang | E(o) | 11.15 | 3,914 |\n| 2021 | 03 82O08 14979 | 29.117 | 95.480 | Dibang | E(c) | 12.98 | 3,706 |\n| 2022 | 03 82O08 14991 | 29.085 | 95.438 | Dibang | E(o) | 20.76 | 3,706 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 5805, "line_end": 5954, "token_count_estimate": 422, "basins": [], "subbasins": ["Dibang", "Dihang"], "countries": [], "lake_ids": ["14937", "14954", "14955", "14963", "14974", "14977", "14979", "14991", "82O08", "82P05"]}}
{"id": "ec9193d21a06a59c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 5955, "line_end": 5961, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bdada99bd7b8786c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2023 | 03 82O08 14998 | 29.074 | 95.427 | Dibang | E(c) | 11.42 | 3,670 |\n| 2024 | 03 82O08 15000 | 29.072 | 95.405 | Dibang | E(o) | 19.08 | 3,864 |\n| 2025 | 03 82O12 15012 | 29.208 | 95.742 | Dibang | E(o) | 10.44 | 3,913 |\n| 2026 | 03 82O12 15018 | 29.176 | 95.617 | Dibang | O | 41.23 | 3,063 |\n| 2027 | 03 82O12 15020 | 29.172 | 95.736 | Dibang | E(o) | 10.14 | 3,652 |\n| 2028 | 03 82O12 15024 | 29.166 | 95.674 | Dibang | E(o) | 18.91 | 3,771 |\n| 2029 | 03 82O12 15049 | 29.092 | 95.566 | Dibang | E(o) | 29.05 | 3,247 |\n| 2030 | 03 82O12 15052 | 29.084 | 95.501 | Dibang | E(o) | 26.85 | 3,480 |\n| 2031 | 03 82O15 15072 | 29.294 | 95.887 | Dibang | E(c) | 28.29 | 3,517 |\n| 2032 | 03 82O15 15078 | 29.270 | 95.925 | Dibang | O | 16.04 | 2,605 |\n| 2033 | 03 82O16 15082 | 29.251 | 95.957 | Dibang | E(o) | 10.41 | 3,738 |\n| 2034 | 03 82O16 15084 | 29.250 | 95.993 | Dibang | E(o) | 29.10 | 4,204 |\n| 2035 | 03 82O16 15087 | 29.239 | 95.980 | Dibang | E(o) | 16.96 | 4,155 |\n| 2036 | 03 82O16 15096 | 29.232 | 95.981 | Dibang | E(o) | 30.93 | 4,167 |\n| 2037 | 03 82O16 15098 | 29.229 | 95.962 | Dibang | E(o) | 14.29 | 4,328 |\n| 2038 | 03 82O16 15103 | 29.219 | 95.749 | Dibang | E(o) | 17.46 | 3,951 |\n| 2039 | 03 82O16 15105 | 29.202 | 95.797 | Dibang | E(o) | 15.02 | 4,054 |\n| 2040 | 03 82O16 15108 | 29.173 | 95.826 | Dibang | E(o) | 14.92 | 3,790 |\n| 2041 | 03 82O16 15113 | 29.153 | 95.772 | Dibang | E(o) | 10.39 | 3,864 |\n| 2042 | 03 82O16 15124 | 29.074 | 95.946 | Dibang | E(o) | 12.93 | 4,165 |\n| 2043 | 03 82O16 15126 | 29.019 | 95.910 | Dibang | E(c) | 17.62 | 3,671 |\n| 2044 | 03 82O16 15129 | 29.011 | 95.885 | Dibang | E(o) | 55.15 | 3,778 |\n| 2045 | 03 82P05 15133 | 28.977 | 95.263 | Dibang | E(c) | 10.42 | 3,769 |\n| 2046 | 03 82P05 15143 | 28.917 | 95.340 | Dibang | E(o) | 10.79 | 3,901 |\n| 2047 | 03 82P05 15145 | 28.911 | 95.334 | Dibang | E(o) | 12.17 | 3,789 |\n| 2048 | 03 82P05 15151 | 28.875 | 95.377 | Dibang | E(c) | 11.85 | 3,504 |\n| 2049 | 03 82P05 15152 | 28.875 | 95.334 | Dibang | E(o) | 20.54 | 3,756 |\n| 2050 | 03 82P05 15153 | 28.873 | 95.350 | Dibang | E(o) | 22.84 | 3,647 |\n| 2051 | 03 82P05 15157 | 28.863 | 95.340 | Dibang | E(o) | 10.28 | 3,789 |\n| 2052 | 03 82P05 15164 | 28.848 | 95.320 | Dibang | E(o) | 21.89 | 3,913 |\n| 2053 | 03 82P05 15166 | 28.831 | 95.350 | Dibang | E(c) | 17.90 | 4,046 |\n| 2054 | 03 82P06 15169 | 28.746 | 95.398 | Dibang | E(o) | 11.06 | 3,787 |\n| 2055 | 03 82P13 15195 | 29.005 | 95.905 | Dibang | E(o) | 54.45 | 3,598 |\n| 2056 | 03 82P13 15198 | 28.979 | 95.861 | Dibang | E(c) | 19.07 | 3,622 |\n| 2057 | 03 91C03 15203 | 29.338 | 96.082 | Dibang | M(o) | 27.72 | 4,290 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 5962, "line_end": 6111, "token_count_estimate": 1572, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["14998", "15000", "15012", "15018", "15020", "15024", "15049", "15052", "15072", "15078", "15082", "15084", "15087", "15096", "15098", "15103", "15105", "15108", "15113", "15124", "15126", "15129", "15133", "15143", "15145", "15151", "15152", "15153", "15157", "15164", "15166", "15169", "15195", "15198", "15203", "82O08", "82O12", "82O15", "82O16", "82P05", "82P06", "82P13", "91C03"]}}
{"id": "0ed4f6f7b94e2219", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2058 | 03 91C03 15204 | 29.335 | 96.008 | Dibang | E(c) | 28.30 | 3,985 |\n| 2059 | 03 91C03 15206 | 29.325 | 96.114 | Dibang | E(o) | 11.06 | 3,962 |\n| 2060 | 03 91C03 15208 | 29.321 | 96.117 | Dibang | E(o) | 10.94 | 3,975 |\n| 2061 | 03 91C03 15211 | 29.315 | 96.125 | Dibang | E(o) | 11.69 | 3,996 |\n| 2062 | 03 91C03 15218 | 29.309 | 96.136 | Dibang | E(o) | 26.81 | 4,204 |\n| 2063 | 03 91C03 15221 | 29.304 | 96.142 | Dibang | E(o) | 27.95 | 4,221 |\n| 2064 | 03 91C03 15223 | 29.302 | 96.082 | Dibang | E(o) | 119.59 | 4,274 |\n| 2065 | 03 91C03 15235 | 29.292 | 96.001 | Dibang | E(o) | 14.80 | 4,079 |\n| 2066 | 03 91C03 15240 | 29.288 | 96.134 | Dibang | E(o) | 10.04 | 4,045 |\n| 2067 | 03 91C03 15243 | 29.283 | 96.091 | Dibang | E(o) | 12.82 | 4,204 |\n| 2068 | 03 91C03 15250 | 29.269 | 96.157 | Dibang | E(o) | 102.66 | 3,991 |\n| 2069 | 03 91C03 15257 | 29.257 | 96.138 | Dibang | E(o) | 23.81 | 3,766 |\n| 2070 | 03 91C03 15259 | 29.256 | 96.209 | Dibang | E(c) | 43.26 | 4,623 |\n| 2071 | 03 91C04 15262 | 29.249 | 96.028 | Dibang | E(o) | 11.97 | 4,133 |\n| 2072 | 03 91C04 15268 | 29.241 | 96.074 | Dibang | E(o) | 12.88 | 4,199 |\n| 2073 | 03 91C04 15278 | 29.229 | 96.192 | Dibang | E(o) | 106.39 | 3,473 |\n| 2074 | 03 91C04 15282 | 29.226 | 96.072 | Dibang | E(c) | 30.15 | 3,992 |\n| 2075 | 03 91C04 15283 | 29.226 | 96.160 | Dibang | E(o) | 57.36 | 3,313 |\n| 2076 | 03 91C04 15289 | 29.211 | 96.069 | Dibang | E(o) | 31.43 | 3,816 |\n| 2077 | 03 91C04 15295 | 29.197 | 96.192 | Dibang | E(c) | 15.74 | 4,321 |\n| 2078 | 03 91C04 15296 | 29.196 | 96.203 | Dibang | E(o) | 64.06 | 4,246 |\n| 2079 | 03 91C04 15301 | 29.191 | 96.112 | Dibang | E(c) | 26.85 | 3,775 |\n| 2080 | 03 91C04 15303 | 29.185 | 96.216 | Dibang | E(o) | 12.55 | 3,965 |\n| 2081 | 03 91C04 15304 | 29.183 | 96.087 | Dibang | E(c) | 23.74 | 4,263 |\n| 2082 | 03 91C04 15305 | 29.176 | 96.175 | Dibang | E(o) | 19.66 | 3,523 |\n| 2083 | 03 91C04 15313 | 29.155 | 96.069 | Dibang | E(o) | 26.03 | 3,588 |\n| 2084 | 03 91C04 15315 | 29.147 | 96.017 | Dibang | E(c) | 18.77 | 3,758 |\n| 2085 | 03 91C04 15316 | 29.145 | 96.066 | Dibang | E(c) | 30.82 | 3,949 |\n| 2086 | 03 91C04 15318 | 29.141 | 96.036 | Dibang | E(c) | 29.50 | 4,139 |\n| 2087 | 03 91C04 15328 | 29.107 | 96.230 | Dibang | E(o) | 11.56 | 4,333 |\n| 2088 | 03 91C04 15330 | 29.100 | 96.173 | Dibang | E(o) | 21.89 | 3,949 |\n| 2089 | 03 91C04 15332 | 29.096 | 96.223 | Dibang | E(c) | 15.85 | 4,355 |\n| 2090 | 03 91C04 15333 | 29.096 | 95.997 | Dibang | E(c) | 22.23 | 3,346 |\n| 2091 | 03 91C04 15335 | 29.092 | 96.243 | Dibang | E(c) | 26.44 | 4,117 |\n| 2092 | 03 91C04 15336 | 29.091 | 96.211 | Dibang | E(o) | 100.77 | 4,188 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 5962, "line_end": 6111, "token_count_estimate": 1612, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["15204", "15206", "15208", "15211", "15218", "15221", "15223", "15235", "15240", "15243", "15250", "15257", "15259", "15262", "15268", "15278", "15282", "15283", "15289", "15295", "15296", "15301", "15303", "15304", "15305", "15313", "15315", "15316", "15318", "15328", "15330", "15332", "15333", "15335", "15336", "91C03", "91C04"]}}
{"id": "97944826064c3288", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2093 | 03 91C04 15337 | 29.091 | 96.162 | Dibang | E(o) | 10.98 | 3,822 |\n| 2094 | 03 91C04 15341 | 29.083 | 96.201 | Dibang | E(o) | 10.69 | 4,049 |\n| 2095 | 03 91C04 15344 | 29.079 | 96.145 | Dibang | E(c) | 86.02 | 3,945 |\n| 2096 | 03 91C04 15345 | 29.077 | 96.230 | Dibang | E(o) | 24.43 | 4,217 |\n| 2097 | 03 91C04 15353 | 29.069 | 96.147 | Dibang | E(c) | 12.17 | 4,031 |\n| 2098 | 03 91C04 15358 | 29.061 | 96.220 | Dibang | E(o) | 16.61 | 4,451 |\n| 2099 | 03 91C04 15360 | 29.051 | 96.144 | Dibang | O | 79.93 | 3,602 |\n| 2100 | 03 91C04 15362 | 29.044 | 96.193 | Dibang | E(c) | 44.26 | 4,164 |\n| 2101 | 03 91C04 15363 | 29.042 | 96.220 | Dibang | E(o) | 39.66 | 4,317 |\n| 2102 | 03 91C04 15365 | 29.036 | 96.178 | Dibang | E(o) | 19.74 | 3,944 |\n| 2103 | 03 91C04 15367 | 29.030 | 96.223 | Dibang | E(c) | 44.32 | 4,199 |\n| 2104 | 03 91C04 15368 | 29.030 | 96.193 | Dibang | E(o) | 24.55 | 4,235 |\n| 2105 | 03 91C04 15371 | 29.026 | 96.044 | Dibang | E(o) | 11.17 | 3,564 |\n| 2106 | 03 91C04 15374 | 29.024 | 96.178 | Dibang | E(o) | 24.34 | 4,067 |\n| 2107 | 03 91C04 15378 | 29.018 | 96.062 | Dibang | E(o) | 15.99 | 3,757 |\n| 2108 | 03 91C04 15384 | 29.009 | 96.084 | Dibang | E(c) | 14.55 | 4,018 |\n| 2109 | 03 91C04 15386 | 29.008 | 96.218 | Dibang | E(o) | 47.18 | 3,664 |\n| 2110 | 03 91C04 15388 | 29.008 | 96.182 | Dibang | E(o) | 10.34 | 3,914 |\n| 2111 | 03 91C08 15390 | 29.244 | 96.245 | Dibang | E(o) | 50.88 | 4,360 |\n| 2112 | 03 91C08 15393 | 29.094 | 96.260 | Dibang | E(c) | 39.01 | 4,001 |\n| 2113 | 03 91C08 15396 | 29.060 | 96.303 | Dibang | E(c) | 11.53 | 4,152 |\n| 2114 | 03 91C08 15397 | 29.050 | 96.331 | Dibang | E(c) | 23.00 | 4,385 |\n| 2115 | 03 91C08 15402 | 29.040 | 96.304 | Dibang | E(o) | 10.97 | 3,986 |\n| 2116 | 03 91C08 15403 | 29.031 | 96.318 | Dibang | E(c) | 16.99 | 4,139 |\n| 2117 | 03 91C08 15405 | 29.023 | 96.311 | Dibang | E(c) | 14.87 | 4,253 |\n| 2118 | 03 91C08 15406 | 29.022 | 96.297 | Dibang | E(c) | 14.98 | 4,165 |\n| 2119 | 03 91D01 15410 | 29.001 | 96.184 | Dibang | E(c) | 11.95 | 4,009 |\n| 2120 | 03 91D01 15418 | 28.989 | 96.188 | Dibang | E(o) | 11.30 | 4,316 |\n| 2121 | 03 91D01 15419 | 28.987 | 96.069 | Dibang | E(c) | 37.26 | 3,627 |\n| 2122 | 03 91D01 15421 | 28.985 | 96.197 | Dibang | E(o) | 16.06 | 4,033 |\n| 2123 | 03 91D01 15427 | 28.952 | 96.038 | Dibang | E(c) | 20.31 | 3,634 |\n| 2124 | 03 91D01 15429 | 28.948 | 96.168 | Dibang | E(c) | 17.21 | 3,886 |\n| 2125 | 03 91D01 15431 | 28.941 | 96.020 | Dibang | E(o) | 11.66 | 3,449 |\n| 2126 | 03 91D01 15437 | 28.923 | 96.190 | Dibang | E(c) | 18.07 | 4,001 |\n| 2127 | 03 91D01 15438 | 28.913 | 96.178 | Dibang | E(o) | 27.91 | 3,802 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 5962, "line_end": 6111, "token_count_estimate": 1598, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["15337", "15341", "15344", "15345", "15353", "15358", "15360", "15362", "15363", "15365", "15367", "15368", "15371", "15374", "15378", "15384", "15386", "15388", "15390", "15393", "15396", "15397", "15402", "15403", "15405", "15406", "15410", "15418", "15419", "15421", "15427", "15429", "15431", "15437", "15438", "91C04", "91C08", "91D01"]}}
{"id": "304b3f48acae02ef", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2128 | 03 91D01 15440 | 28.889 | 96.151 | Dibang | E(c) | 22.47 | 4,079 |\n| 2129 | 03 91D01 15441 | 28.887 | 96.197 | Dibang | E(o) | 43.53 | 3,255 |\n| 2130 | 03 91D01 15442 | 28.886 | 96.137 | Dibang | E(o) | 12.38 | 3,720 |\n| 2131 | 03 91D01 15447 | 28.852 | 96.171 | Dibang | E(o) | 17.47 | 3,976 |\n| 2132 | 03 91D01 15450 | 28.846 | 96.127 | Dibang | E(o) | 11.19 | 3,697 |\n| 2133 | 03 91D01 15463 | 28.828 | 96.244 | Dibang | E(o) | 12.78 | 3,740 |\n| 2134 | 03 91D01 15465 | 28.821 | 96.121 | Dibang | E(o) | 39.49 | 4,060 |\n| 2135 | 03 91D01 15466 | 28.820 | 96.145 | Dibang | E(c) | 14.12 | 3,723 |\n| 2136 | 03 91D01 15467 | 28.814 | 96.122 | Dibang | E(c) | 13.42 | 4,115 |\n| 2137 | 03 91D01 15473 | 28.803 | 96.155 | Dibang | E(c) | 17.21 | 3,866 |\n| 2138 | 03 91D01 15478 | 28.793 | 96.055 | Dibang | E(c) | 10.25 | 3,948 |\n| 2139 | 03 91D01 15479 | 28.792 | 96.148 | Dibang | E(o) | 32.99 | 3,676 |\n| 2140 | 03 91D02 15486 | 28.542 | 96.125 | Dibang | E(o) | 10.62 | 4,031 |\n| 2141 | 03 91D05 15494 | 28.992 | 96.257 | Dibang | E(c) | 15.12 | 4,077 |\n| 2142 | 03 91D05 15511 | 28.944 | 96.371 | Dibang | E(o) | 12.61 | 4,236 |\n| 2143 | 03 91D05 15517 | 28.935 | 96.369 | Dibang | E(o) | 18.54 | 4,001 |\n| 2144 | 03 91D05 15522 | 28.928 | 96.339 | Dibang | E(o) | 48.39 | 4,011 |\n| 2145 | 03 91D05 15526 | 28.924 | 96.311 | Dibang | E(o) | 15.64 | 3,851 |\n| 2146 | 03 91D05 15527 | 28.924 | 96.368 | Dibang | E(c) | 10.38 | 4,109 |\n| 2147 | 03 91D05 15529 | 28.919 | 96.383 | Dibang | O | 48.87 | 3,302 |\n| 2148 | 03 91D05 15534 | 28.890 | 96.494 | Dibang | E(o) | 17.29 | 3,366 |\n| 2149 | 03 91D05 15539 | 28.879 | 96.372 | Dibang | E(o) | 27.50 | 4,090 |\n| 2150 | 03 91D05 15540 | 28.877 | 96.477 | Dibang | E(o) | 34.69 | 3,166 |\n| 2151 | 03 91D05 15542 | 28.875 | 96.394 | Dibang | O | 41.33 | 3,119 |\n| 2152 | 03 91D05 15543 | 28.874 | 96.436 | Dibang | O | 16.76 | 3,220 |\n| 2153 | 03 91D05 15547 | 28.862 | 96.440 | Dibang | O | 11.42 | 2,979 |\n| 2154 | 03 91D05 15548 | 28.861 | 96.249 | Dibang | E(c) | 11.21 | 3,847 |\n| 2155 | 03 91D05 15554 | 28.847 | 96.355 | Dibang | E(o) | 14.96 | 3,760 |\n| 2156 | 03 91D05 15561 | 28.832 | 96.250 | Dibang | E(o) | 15.75 | 3,835 |\n| 2157 | 03 91D05 15566 | 28.823 | 96.359 | Dibang | E(c) | 19.15 | 3,941 |\n| 2158 | 03 91D05 15570 | 28.809 | 96.459 | Dibang | E(c) | 35.25 | 4,078 |\n| 2159 | 03 91D05 15572 | 28.802 | 96.440 | Dibang | E(o) | 30.97 | 4,114 |\n| 2160 | 03 91D05 15573 | 28.801 | 96.484 | Dibang | E(o) | 34.81 | 3,819 |\n| 2161 | 03 91D05 15576 | 28.765 | 96.386 | Dibang | E(o) | 13.52 | 3,855 |\n| 2162 | 03 91D06 15578 | 28.749 | 96.375 | Dibang | E(o) | 25.74 | 3,784 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 5962, "line_end": 6111, "token_count_estimate": 1579, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["15440", "15441", "15442", "15447", "15450", "15463", "15465", "15466", "15467", "15473", "15478", "15479", "15486", "15494", "15511", "15517", "15522", "15526", "15527", "15529", "15534", "15539", "15540", "15542", "15543", "15547", "15548", "15554", "15561", "15566", "15570", "15572", "15573", "15576", "15578", "91D01", "91D02", "91D05", "91D06"]}}
{"id": "4d1c6142171bb225", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2163 | 03 91D06 15579 | 28.748 | 96.353 | Dibang | E(o) | 34.00 | 3,618 |\n| 2164 | 03 91D06 15582 | 28.740 | 96.413 | Dibang | E(c) | 12.04 | 4,105 |\n| 2165 | 03 91D06 15586 | 28.729 | 96.446 | Dibang | E(o) | 21.19 | 4,086 |\n| 2166 | 03 91D06 15588 | 28.727 | 96.485 | Dibang | E(c) | 28.63 | 4,084 |\n| 2167 | 03 91D06 15589 | 28.727 | 96.392 | Dibang | E(c) | 19.81 | 4,176 |\n| 2168 | 03 91D06 15590 | 28.725 | 96.422 | Dibang | E(o) | 16.86 | 4,218 |\n| 2169 | 03 91D06 15596 | 28.721 | 96.405 | Dibang | E(o) | 17.45 | 4,097 |\n| 2170 | 03 91D06 15605 | 28.666 | 96.426 | Dibang | E(c) | 20.84 | 4,238 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 5962, "line_end": 6111, "token_count_estimate": 439, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["15579", "15582", "15586", "15588", "15589", "15590", "15596", "15605", "91D06"]}}
{"id": "5519ca6a896ec23c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6112, "line_end": 6117, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c6764f42cc8b4922", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2171 | 03 91D06 15612 | 28.660 | 96.486 | Dibang | E(c) | 30.67 | 4,125 |\n| 2172 | 03 91D06 15617 | 28.617 | 96.439 | Dibang | E(c) | 10.66 | 3,914 |\n| 2173 | 03 91D06 15618 | 28.614 | 96.312 | Dibang | E(o) | 11.33 | 3,968 |\n| 2174 | 03 91D06 15620 | 28.608 | 96.320 | Dibang | E(c) | 23.55 | 4,239 |\n| 2175 | 03 91D06 15629 | 28.565 | 96.408 | Dibang | E(o) | 14.11 | 4,124 |\n| 2176 | 03 91D06 15631 | 28.563 | 96.355 | Dibang | E(o) | 10.39 | 4,152 |\n| 2177 | 03 91D06 15632 | 28.563 | 96.366 | Dibang | E(c) | 10.25 | 3,897 |\n| 2178 | 03 91D06 15645 | 28.545 | 96.443 | Dibang | E(o) | 12.55 | 4,297 |\n| 2179 | 03 91D06 15649 | 28.539 | 96.394 | Dibang | E(o) | 11.72 | 4,010 |\n| 2180 | 03 91D06 15654 | 28.524 | 96.424 | Dibang | E(c) | 14.09 | 4,096 |\n| 2181 | 03 91D06 15657 | 28.522 | 96.444 | Dibang | E(c) | 10.03 | 4,157 |\n| 2182 | 03 91D06 15658 | 28.521 | 96.250 | Dibang | E(o) | 16.14 | 3,636 |\n| 2183 | 03 91D06 15660 | 28.519 | 96.398 | Dibang | E(c) | 15.49 | 4,247 |\n| 2184 | 03 91D06 15664 | 28.513 | 96.398 | Dibang | E(c) | 26.12 | 4,345 |\n| 2185 | 03 91D06 15667 | 28.505 | 96.391 | Dibang | E(o) | 21.90 | 4,560 |\n| 2186 | 03 91D07 15670 | 28.421 | 96.377 | Dibang | E(c) | 12.36 | 3,763 |\n| 2187 | 03 91D09 15673 | 28.923 | 96.515 | Dibang | E(o) | 13.69 | 4,091 |\n| 2188 | 03 91D09 15674 | 28.872 | 96.510 | Dibang | E(o) | 18.70 | 3,926 |\n| 2189 | 03 91D09 15680 | 28.838 | 96.497 | Dibang | E(o) | 21.00 | 3,388 |\n| 2190 | 03 91D09 15682 | 28.829 | 96.532 | Dibang | E(o) | 37.63 | 3,584 |\n| 2191 | 03 91D09 15687 | 28.807 | 96.538 | Dibang | E(c) | 17.48 | 4,187 |\n| 2192 | 03 91D09 15688 | 28.801 | 96.512 | Dibang | E(o) | 26.17 | 4,344 |\n| 2193 | 03 91D09 15690 | 28.776 | 96.531 | Dibang | O | 101.67 | 3,510 |\n| 2194 | 03 91D09 15695 | 28.764 | 96.517 | Dibang | O | 14.33 | 3,502 |\n| 2195 | 03 91D10 15700 | 28.745 | 96.511 | Dibang | O | 24.68 | 3,219 |\n| 2196 | 03 91D10 15702 | 28.707 | 96.505 | Dibang | E(o) | 26.84 | 4,018 |\n| 2197 | 03 91D10 15705 | 28.697 | 96.572 | Dibang | E(c) | 20.56 | 4,133 |\n| 2198 | 03 91D10 15707 | 28.694 | 96.551 | Dibang | E(c) | 33.52 | 4,220 |\n| 2199 | 03 91D10 15710 | 28.687 | 96.584 | Dibang | E(c) | 21.37 | 4,132 |\n| 2200 | 03 91D10 15712 | 28.685 | 96.633 | Dibang | E(o) | 12.10 | 4,299 |\n| 2201 | 03 91D10 15715 | 28.676 | 96.510 | Dibang | E(o) | 20.27 | 4,310 |\n| 2202 | 03 91D10 15716 | 28.668 | 96.611 | Dibang | E(o) | 22.91 | 4,492 |\n| 2203 | 03 91D10 15718 | 28.666 | 96.632 | Dibang | E(o) | 17.58 | 4,217 |\n| 2204 | 03 91C03 15738 | 29.365 | 96.124 | Lohit | M(e) | 25.07 | 3,995 |\n| 2205 | 03 91C03 15741 | 29.329 | 96.195 | Lohit | E(o) | 25.27 | 4,286 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6118, "line_end": 6267, "token_count_estimate": 1573, "basins": [], "subbasins": ["Dibang", "Lohit"], "countries": [], "lake_ids": ["15612", "15617", "15618", "15620", "15629", "15631", "15632", "15645", "15649", "15654", "15657", "15658", "15660", "15664", "15667", "15670", "15673", "15674", "15680", "15682", "15687", "15688", "15690", "15695", "15700", "15702", "15705", "15707", "15710", "15712", "15715", "15716", "15718", "15738", "15741", "91C03", "91D06", "91D07", "91D09", "91D10"]}}
{"id": "7b4d6683e5e29efb", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2206 | 03 91C03 15744 | 29.324 | 96.209 | Lohit | E(o) | 25.02 | 4,333 |\n| 2207 | 03 91C03 15752 | 29.305 | 96.156 | Lohit | E(c) | 21.00 | 4,233 |\n| 2208 | 03 91C03 15759 | 29.264 | 96.245 | Lohit | M(o) | 17.37 | 4,408 |\n| 2209 | 03 91C03 15760 | 29.257 | 96.246 | Lohit | M(o) | 89.30 | 4,432 |\n| 2210 | 03 91C07 15761 | 29.387 | 96.375 | Lohit | M(e) | 29.13 | 4,997 |\n| 2211 | 03 91C08 15774 | 29.224 | 96.279 | Lohit | M(o) | 66.27 | 4,207 |\n| 2212 | 03 91C08 15782 | 29.175 | 96.346 | Lohit | E(c) | 27.74 | 4,298 |\n| 2213 | 03 91C08 15783 | 29.175 | 96.327 | Lohit | M(e) | 46.20 | 4,379 |\n| 2214 | 03 91C08 15786 | 29.130 | 96.324 | Lohit | M(o) | 26.67 | 4,392 |\n| 2215 | 03 91C08 15787 | 29.127 | 96.399 | Lohit | M(o) | 10.09 | 4,066 |\n| 2216 | 03 91C10 15816 | 29.597 | 96.617 | Lohit | M(e) | 10.44 | 5,104 |\n| 2217 | 03 91C10 15818 | 29.589 | 96.674 | Lohit | M(e) | 19.10 | 4,631 |\n| 2218 | 03 91C10 15826 | 29.561 | 96.630 | Lohit | M(e) | 12.40 | 4,919 |\n| 2219 | 03 91C11 15833 | 29.491 | 96.701 | Lohit | O | 610.17 | 3,916 |\n| 2220 | 03 91C11 15840 | 29.421 | 96.627 | Lohit | M(e) | 31.33 | 4,079 |\n| 2221 | 03 91C11 15843 | 29.406 | 96.681 | Lohit | M(o) | 11.75 | 4,834 |\n| 2222 | 03 91C12 15863 | 29.124 | 96.660 | Lohit | E(c) | 17.71 | 4,329 |\n| 2223 | 03 91C14 15953 | 29.502 | 96.977 | Lohit | M(e) | 12.44 | 5,145 |\n| 2224 | 03 91C15 15967 | 29.462 | 96.787 | Lohit | O | 518.00 | 3,916 |\n| 2225 | 03 91C15 15982 | 29.397 | 96.828 | Lohit | E(o) | 401.62 | 3,917 |\n| 2226 | 03 91C15 15998 | 29.298 | 96.816 | Lohit | M(e) | 292.36 | 3,954 |\n| 2227 | 03 91C15 16000 | 29.295 | 96.835 | Lohit | M(l) | 95.04 | 4,013 |\n| 2228 | 03 91C15 16004 | 29.268 | 96.837 | Lohit | E(o) | 108.04 | 4,119 |\n| 2229 | 03 91C15 16008 | 29.263 | 96.938 | Lohit | M(e) | 12.30 | 4,676 |\n| 2230 | 03 91C15 16011 | 29.254 | 96.965 | Lohit | M(o) | 10.15 | 4,631 |\n| 2231 | 03 91C16 16015 | 29.248 | 96.966 | Lohit | M(e) | 25.60 | 4,673 |\n| 2232 | 03 91C16 16019 | 29.238 | 96.826 | Lohit | M(o) | 209.76 | 4,219 |\n| 2233 | 03 91C16 16020 | 29.230 | 96.805 | Lohit | M(o) | 144.00 | 4,266 |\n| 2234 | 03 91C16 16024 | 29.221 | 96.814 | Lohit | M(e) | 52.51 | 4,279 |\n| 2235 | 03 91C16 16029 | 29.179 | 96.857 | Lohit | M(e) | 13.26 | 4,412 |\n| 2236 | 03 91C16 16031 | 29.171 | 96.867 | Lohit | M(o) | 12.68 | 4,458 |\n| 2237 | 03 91C16 16048 | 29.071 | 96.799 | Lohit | M(o) | 38.92 | 2,466 |\n| 2238 | 03 91D05 16058 | 28.952 | 96.493 | Lohit | M(o) | 15.31 | 4,493 |\n| 2239 | 03 91D06 16060 | 28.518 | 96.421 | Lohit | M(e) | 16.11 | 4,086 |\n| 2240 | 03 91D07 16069 | 28.345 | 96.334 | Lohit | O | 12.55 | 2,990 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6118, "line_end": 6267, "token_count_estimate": 1585, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["15744", "15752", "15759", "15760", "15761", "15774", "15782", "15783", "15786", "15787", "15816", "15818", "15826", "15833", "15840", "15843", "15863", "15953", "15967", "15982", "15998", "16000", "16004", "16008", "16011", "16015", "16019", "16020", "16024", "16029", "16031", "16048", "16058", "16060", "16069", "91C03", "91C07", "91C08", "91C10", "91C11", "91C12", "91C14", "91C15", "91C16", "91D05", "91D06", "91D07"]}}
{"id": "d7df6b5ced8732f5", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2241 | 03 91D09 16085 | 28.954 | 96.561 | Lohit | E(o) | 15.69 | 4,277 |\n| 2242 | 03 91D09 16094 | 28.939 | 96.740 | Lohit | E(o) | 10.73 | 4,352 |\n| 2243 | 03 91D09 16102 | 28.924 | 96.564 | Lohit | E(o) | 12.74 | 4,447 |\n| 2244 | 03 91D09 16105 | 28.921 | 96.553 | Lohit | E(o) | 25.07 | 4,463 |\n| 2245 | 03 91D09 16114 | 28.908 | 96.525 | Lohit | E(c) | 14.85 | 4,295 |\n| 2246 | 03 91D09 16120 | 28.898 | 96.550 | Lohit | O | 10.31 | 3,712 |\n| 2247 | 03 91D09 16129 | 28.810 | 96.606 | Lohit | E(c) | 10.76 | 4,362 |\n| 2248 | 03 91D09 16135 | 28.777 | 96.606 | Lohit | E(o) | 15.99 | 4,601 |\n| 2249 | 03 91D09 16137 | 28.770 | 96.617 | Lohit | E(c) | 21.18 | 4,255 |\n| 2250 | 03 91D10 16161 | 28.647 | 96.645 | Lohit | E(c) | 18.36 | 4,068 |\n| 2251 | 03 91D10 16163 | 28.644 | 96.589 | Lohit | E(c) | 24.51 | 4,368 |\n| 2252 | 03 91D10 16166 | 28.637 | 96.685 | Lohit | E(c) | 14.50 | 4,418 |\n| 2253 | 03 91D10 16171 | 28.628 | 96.533 | Lohit | E(c) | 10.94 | 4,260 |\n| 2254 | 03 91D10 16174 | 28.627 | 96.565 | Lohit | O | 16.84 | 3,282 |\n| 2255 | 03 91D10 16179 | 28.613 | 96.522 | Lohit | E(c) | 10.32 | 4,213 |\n| 2256 | 03 91D10 16180 | 28.611 | 96.723 | Lohit | E(o) | 14.21 | 4,107 |\n| 2257 | 03 91D10 16186 | 28.586 | 96.652 | Lohit | E(o) | 30.13 | 4,068 |\n| 2258 | 03 91D10 16189 | 28.580 | 96.681 | Lohit | E(o) | 32.17 | 3,999 |\n| 2259 | 03 91D10 16194 | 28.565 | 96.636 | Lohit | E(o) | 30.27 | 3,368 |\n| 2260 | 03 91D10 16196 | 28.562 | 96.715 | Lohit | E(o) | 16.48 | 4,019 |\n| 2261 | 03 91D10 16197 | 28.559 | 96.692 | Lohit | O | 11.58 | 3,483 |\n| 2262 | 03 91D10 16202 | 28.541 | 96.618 | Lohit | E(c) | 42.11 | 4,268 |\n| 2263 | 03 91D10 16203 | 28.541 | 96.602 | Lohit | E(c) | 25.61 | 4,487 |\n| 2264 | 03 91D10 16204 | 28.534 | 96.644 | Lohit | E(o) | 29.38 | 4,311 |\n| 2265 | 03 91D10 16206 | 28.530 | 96.622 | Lohit | E(o) | 23.40 | 3,907 |\n| 2266 | 03 91D10 16207 | 28.527 | 96.650 | Lohit | E(o) | 14.17 | 4,125 |\n| 2267 | 03 91D10 16210 | 28.516 | 96.699 | Lohit | E(o) | 299.20 | 3,330 |\n| 2268 | 03 91D10 16211 | 28.512 | 96.502 | Lohit | E(o) | 27.13 | 4,027 |\n| 2269 | 03 91D11 16215 | 28.492 | 96.692 | Lohit | E(c) | 20.67 | 4,046 |\n| 2270 | 03 91D13 16288 | 28.864 | 96.830 | Lohit | E(o) | 10.83 | 4,306 |\n| 2271 | 03 91D13 16293 | 28.850 | 96.928 | Lohit | E(c) | 14.85 | 4,299 |\n| 2272 | 03 91D13 16298 | 28.843 | 96.818 | Lohit | E(o) | 24.08 | 4,625 |\n| 2273 | 03 91D13 16303 | 28.832 | 96.820 | Lohit | E(o) | 20.11 | 4,305 |\n| 2274 | 03 91D13 16304 | 28.829 | 96.789 | Lohit | O | 31.87 | 3,683 |\n| 2275 | 03 91D14 16350 | 28.576 | 96.756 | Lohit | E(o) | 17.36 | 3,646 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6118, "line_end": 6267, "token_count_estimate": 1589, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["16085", "16094", "16102", "16105", "16114", "16120", "16129", "16135", "16137", "16161", "16163", "16166", "16171", "16174", "16179", "16180", "16186", "16189", "16194", "16196", "16197", "16202", "16203", "16204", "16206", "16207", "16210", "16211", "16215", "16288", "16293", "16298", "16303", "16304", "16350", "91D09", "91D10", "91D11", "91D13", "91D14"]}}
{"id": "769a38017e960a40", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2276 | 03 91D14 16352 | 28.536 | 96.766 | Lohit | E(o) | 25.91 | 3,918 |\n| 2277 | 03 91D14 16355 | 28.510 | 96.800 | Lohit | E(c) | 14.77 | 4,075 |\n| 2278 | 03 91D14 16356 | 28.504 | 96.833 | Lohit | E(c) | 15.18 | 4,058 |\n| 2279 | 03 91D14 16357 | 28.503 | 96.843 | Lohit | E(o) | 12.74 | 3,945 |\n| 2280 | 03 91D14 16358 | 28.503 | 96.860 | Lohit | E(o) | 17.34 | 3,522 |\n| 2281 | 03 91D15 16363 | 28.470 | 96.882 | Lohit | E(o) | 23.66 | 3,849 |\n| 2282 | 03 91D15 16366 | 28.447 | 96.885 | Lohit | E(o) | 18.22 | 4,113 |\n| 2283 | 03 91D15 16370 | 28.432 | 96.924 | Lohit | E(c) | 36.83 | 4,384 |\n| 2284 | 03 91D15 16372 | 28.425 | 96.872 | Lohit | E(o) | 36.05 | 3,980 |\n| 2285 | 03 91D15 16378 | 28.403 | 96.837 | Lohit | E(o) | 16.14 | 4,171 |\n| 2286 | 03 91D15 16387 | 28.394 | 96.869 | Lohit | E(o) | 11.69 | 4,215 |\n| 2287 | 03 91D15 16388 | 28.392 | 96.858 | Lohit | E(c) | 30.18 | 4,390 |\n| 2288 | 03 91D15 16389 | 28.381 | 96.859 | Lohit | E(o) | 19.10 | 4,049 |\n| 2289 | 03 91D15 16399 | 28.315 | 96.817 | Lohit | E(o) | 19.89 | 3,879 |\n| 2290 | 03 91D15 16407 | 28.253 | 96.819 | Lohit | M(o) | 16.22 | 4,030 |\n| 2291 | 03 91D16 16409 | 28.245 | 96.832 | Lohit | E(o) | 42.50 | 3,900 |\n| 2292 | 03 91D16 16415 | 28.214 | 96.885 | Lohit | E(c) | 21.24 | 3,960 |\n| 2293 | 03 91D16 16419 | 28.202 | 96.898 | Lohit | E(o) | 67.30 | 3,731 |\n| 2294 | 03 91D16 16423 | 28.165 | 96.830 | Lohit | E(c) | 20.69 | 3,890 |\n| 2295 | 03 91D16 16425 | 28.159 | 96.848 | Lohit | E(c) | 16.81 | 3,926 |\n| 2296 | 03 91G02 16450 | 29.701 | 97.002 | Lohit | M(o) | 12.26 | 5,122 |\n| 2297 | 03 91G02 16461 | 29.609 | 97.075 | Lohit | M(o) | 13.92 | 5,279 |\n| 2298 | 03 91G02 16474 | 29.507 | 97.061 | Lohit | M(o) | 13.60 | 5,275 |\n| 2299 | 03 91G03 16480 | 29.496 | 97.104 | Lohit | M(e) | 25.92 | 5,219 |\n| 2300 | 03 91G03 16495 | 29.472 | 97.035 | Lohit | M(e) | 14.95 | 5,238 |\n| 2301 | 03 91G03 16498 | 29.460 | 97.085 | Lohit | M(e) | 15.56 | 4,994 |\n| 2302 | 03 91G03 16519 | 29.403 | 97.012 | Lohit | M(o) | 10.40 | 5,156 |\n| 2303 | 03 91G03 16524 | 29.392 | 97.022 | Lohit | M(e) | 20.49 | 5,007 |\n| 2304 | 03 91G04 16550 | 29.228 | 97.029 | Lohit | M(e) | 22.63 | 3,952 |\n| 2305 | 03 91G04 16561 | 29.087 | 97.155 | Lohit | M(o) | 25.64 | 4,222 |\n| 2306 | 03 91G04 16585 | 29.039 | 97.095 | Lohit | M(o) | 10.02 | 4,430 |\n| 2307 | 03 91G04 16608 | 29.015 | 97.249 | Lohit | E(o) | 15.19 | 4,409 |\n| 2308 | 03 91G04 16619 | 29.004 | 97.134 | Lohit | E(o) | 12.94 | 4,401 |\n| 2309 | 03 91G07 16631 | 29.466 | 97.376 | Lohit | M(o) | 18.87 | 5,011 |\n| 2310 | 03 91G08 16680 | 29.229 | 97.333 | Lohit | M(o) | 13.94 | 4,761 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 140, "line_start": 6118, "line_end": 6267, "token_count_estimate": 1587, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["16352", "16355", "16356", "16357", "16358", "16363", "16366", "16370", "16372", "16378", "16387", "16388", "16389", "16399", "16407", "16409", "16415", "16419", "16423", "16425", "16450", "16461", "16474", "16480", "16495", "16498", "16519", "16524", "16550", "16561", "16585", "16608", "16619", "16631", "16680", "91D14", "91D15", "91D16", "91G02", "91G03", "91G04", "91G07", "91G08"]}}
{"id": "81c6d03a1b23696b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2311 | 03 91G08 16685 | 29.216 | 97.378 | Lohit | E(o) | 14.43 | 4,303 |\n| 2312 | 03 91G08 16692 | 29.200 | 97.370 | Lohit | M(o) | 16.42 | 4,623 |\n| 2313 | 03 91G12 16726 | 29.066 | 97.536 | Lohit | E(o) | 12.18 | 4,795 |\n| 2314 | 03 91G12 16731 | 29.032 | 97.512 | Lohit | E(o) | 14.82 | 4,141 |\n| 2315 | 03 91H01 16738 | 28.995 | 97.231 | Lohit | E(o) | 14.62 | 4,597 |\n| 2316 | 03 91H01 16757 | 28.977 | 97.215 | Lohit | E(o) | 63.42 | 4,092 |\n| 2317 | 03 91H01 16776 | 28.956 | 97.195 | Lohit | E(o) | 13.06 | 4,271 |\n| 2318 | 03 91H01 16783 | 28.947 | 97.100 | Lohit | E(o) | 47.64 | 4,412 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 141, "table_row_end": 148, "line_start": 6118, "line_end": 6267, "token_count_estimate": 436, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["16685", "16692", "16726", "16731", "16738", "16757", "16776", "16783", "91G08", "91G12", "91H01"]}}
{"id": "13991f91eb95115a", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6268, "line_end": 6272, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6cb8b7d5bb711083", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2319 | 03 91H01 16802 | 28.916 | 97.153 | Lohit | E(o) | 15.22 | 4,513 |\n| 2320 | 03 91H01 16808 | 28.904 | 97.149 | Lohit | E(o) | 33.69 | 4,319 |\n| 2321 | 03 91H01 16812 | 28.898 | 97.165 | Lohit | E(o) | 15.17 | 4,713 |\n| 2322 | 03 91H01 16837 | 28.848 | 97.092 | Lohit | M(o) | 14.66 | 4,448 |\n| 2323 | 03 91H01 16838 | 28.846 | 97.130 | Lohit | E(o) | 42.05 | 4,446 |\n| 2324 | 03 91H01 16843 | 28.835 | 97.148 | Lohit | E(o) | 18.10 | 4,392 |\n| 2325 | 03 91H01 16856 | 28.821 | 97.152 | Lohit | E(o) | 12.28 | 4,288 |\n| 2326 | 03 91H01 16862 | 28.813 | 97.170 | Lohit | E(o) | 19.35 | 4,401 |\n| 2327 | 03 91H01 16868 | 28.808 | 97.126 | Lohit | E(o) | 17.39 | 4,189 |\n| 2328 | 03 91H01 16888 | 28.796 | 97.183 | Lohit | E(o) | 16.32 | 4,457 |\n| 2329 | 03 91H01 16896 | 28.786 | 97.219 | Lohit | M(o) | 22.91 | 4,270 |\n| 2330 | 03 91H01 16898 | 28.783 | 97.153 | Lohit | E(o) | 83.11 | 3,712 |\n| 2331 | 03 91H01 16907 | 28.767 | 97.101 | Lohit | E(o) | 12.38 | 4,326 |\n| 2332 | 03 91H01 16908 | 28.763 | 97.058 | Lohit | O | 39.04 | 3,285 |\n| 2333 | 03 91H01 16916 | 28.753 | 97.137 | Lohit | E(o) | 20.19 | 4,176 |\n| 2334 | 03 91H02 16921 | 28.747 | 97.097 | Lohit | E(o) | 10.90 | 4,438 |\n| 2335 | 03 91H02 16922 | 28.744 | 97.217 | Lohit | E(o) | 21.99 | 4,318 |\n| 2336 | 03 91H02 16935 | 28.702 | 97.142 | Lohit | E(o) | 16.78 | 4,378 |\n| 2337 | 03 91H03 16992 | 28.351 | 97.157 | Lohit | E(o) | 15.17 | 4,079 |\n| 2338 | 03 91H03 17002 | 28.303 | 97.234 | Lohit | E(o) | 23.39 | 4,279 |\n| 2339 | 03 91H03 17005 | 28.291 | 97.218 | Lohit | E(o) | 15.73 | 4,071 |\n| 2340 | 03 91H03 17013 | 28.264 | 97.225 | Lohit | E(o) | 12.77 | 4,167 |\n| 2341 | 03 91H03 17014 | 28.262 | 97.232 | Lohit | E(c) | 26.62 | 4,183 |\n| 2342 | 03 91H04 17026 | 28.226 | 97.175 | Lohit | E(o) | 11.64 | 3,898 |\n| 2343 | 03 91H04 17029 | 28.220 | 97.120 | Lohit | E(o) | 11.79 | 3,986 |\n| 2344 | 03 91H04 17041 | 28.200 | 97.236 | Lohit | E(o) | 29.04 | 3,900 |\n| 2345 | 03 91H04 17073 | 28.087 | 97.240 | Lohit | E(o) | 21.98 | 4,507 |\n| 2346 | 03 91H04 17079 | 28.084 | 97.215 | Lohit | E(o) | 18.46 | 4,023 |\n| 2347 | 03 91H04 17094 | 28.058 | 97.226 | Lohit | E(o) | 18.41 | 4,113 |\n| 2348 | 03 91H04 17095 | 28.054 | 97.166 | Lohit | E(o) | 13.67 | 4,131 |\n| 2349 | 03 91H04 17105 | 28.041 | 97.193 | Lohit | E(o) | 11.33 | 4,217 |\n| 2350 | 03 91H04 17106 | 28.041 | 97.218 | Lohit | E(o) | 38.26 | 3,936 |\n| 2351 | 03 91H04 17107 | 28.040 | 97.126 | Lohit | E(o) | 35.71 | 3,969 |\n| 2352 | 03 91H04 17110 | 28.035 | 97.132 | Lohit | E(o) | 13.21 | 3,977 |\n| 2353 | 03 91H04 17111 | 28.034 | 97.241 | Lohit | E(o) | 18.18 | 4,299 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6273, "line_end": 6387, "token_count_estimate": 1593, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["16802", "16808", "16812", "16837", "16838", "16843", "16856", "16862", "16868", "16888", "16896", "16898", "16907", "16908", "16916", "16921", "16922", "16935", "16992", "17002", "17005", "17013", "17014", "17026", "17029", "17041", "17073", "17079", "17094", "17095", "17105", "17106", "17107", "17110", "17111", "91H01", "91H02", "91H03", "91H04"]}}
{"id": "37e779c8e092c342", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2354 | 03 91H04 17119 | 28.025 | 97.186 | Lohit | E(o) | 14.89 | 4,227 |\n| 2355 | 03 91H04 17123 | 28.024 | 97.175 | Lohit | E(o) | 13.28 | 4,037 |\n| 2356 | 03 91H04 17124 | 28.020 | 97.194 | Lohit | E(o) | 11.00 | 4,040 |\n| 2357 | 03 91H04 17127 | 28.016 | 97.215 | Lohit | E(o) | 10.80 | 4,303 |\n| 2358 | 03 91H04 17133 | 28.008 | 97.176 | Lohit | E(o) | 15.42 | 4,240 |\n| 2359 | 03 91H05 17139 | 28.989 | 97.268 | Lohit | E(o) | 14.17 | 4,416 |\n| 2360 | 03 91H05 17147 | 28.973 | 97.327 | Lohit | E(o) | 16.29 | 4,541 |\n| 2361 | 03 91H05 17154 | 28.958 | 97.335 | Lohit | E(o) | 14.08 | 4,595 |\n| 2362 | 03 91H05 17161 | 28.947 | 97.320 | Lohit | E(o) | 29.34 | 4,607 |\n| 2363 | 03 91H05 17163 | 28.944 | 97.304 | Lohit | M(o) | 43.16 | 4,732 |\n| 2364 | 03 91H05 17167 | 28.940 | 97.262 | Lohit | E(o) | 93.63 | 4,412 |\n| 2365 | 03 91H05 17181 | 28.915 | 97.350 | Lohit | E(o) | 43.77 | 4,016 |\n| 2366 | 03 91H05 17188 | 28.892 | 97.336 | Lohit | E(o) | 13.58 | 4,429 |\n| 2367 | 03 91H05 17192 | 28.877 | 97.355 | Lohit | M(o) | 44.22 | 4,569 |\n| 2368 | 03 91H06 17226 | 28.691 | 97.337 | Lohit | E(c) | 12.00 | 4,422 |\n| 2369 | 03 91H06 17241 | 28.595 | 97.439 | Lohit | E(o) | 14.46 | 4,271 |\n| 2370 | 03 91H06 17280 | 28.548 | 97.366 | Lohit | E(o) | 14.71 | 4,306 |\n| 2371 | 03 91H06 17282 | 28.548 | 97.393 | Lohit | E(o) | 12.59 | 4,331 |\n| 2372 | 03 91H06 17332 | 28.514 | 97.493 | Lohit | O | 20.47 | 4,469 |\n| 2373 | 03 91H07 17380 | 28.482 | 97.322 | Lohit | E(o) | 13.10 | 4,348 |\n| 2374 | 03 91H07 17399 | 28.472 | 97.380 | Lohit | E(o) | 12.22 | 4,388 |\n| 2375 | 03 91H07 17413 | 28.464 | 97.409 | Lohit | E(o) | 10.72 | 4,420 |\n| 2376 | 03 91H07 17428 | 28.432 | 97.253 | Lohit | E(o) | 12.87 | 4,315 |\n| 2377 | 03 91H07 17433 | 28.426 | 97.401 | Lohit | E(o) | 14.03 | 4,289 |\n| 2378 | 03 91H07 17452 | 28.412 | 97.465 | Lohit | E(o) | 56.61 | 4,300 |\n| 2379 | 03 91H07 17456 | 28.411 | 97.406 | Lohit | E(o) | 11.28 | 4,443 |\n| 2380 | 03 91H07 17478 | 28.400 | 97.393 | Lohit | E(o) | 10.29 | 4,472 |\n| 2381 | 03 91H07 17524 | 28.369 | 97.446 | Lohit | M(o) | 15.15 | 4,054 |\n| 2382 | 03 91H07 17528 | 28.340 | 97.369 | Lohit | E(o) | 14.89 | 4,230 |\n| 2383 | 03 91H07 17541 | 28.313 | 97.343 | Lohit | E(o) | 16.19 | 4,146 |\n| 2384 | 03 91H07 17548 | 28.274 | 97.253 | Lohit | E(c) | 20.87 | 4,290 |\n| 2385 | 03 91H07 17560 | 28.243 | 97.331 | Lohit | E(o) | 11.86 | 4,333 |\n| 2386 | 03 91H08 17563 | 28.240 | 97.250 | Lohit | E(o) | 11.79 | 3,980 |\n| 2387 | 03 91H08 17567 | 28.227 | 97.269 | Lohit | E(o) | 16.83 | 4,145 |\n| 2388 | 03 91H08 17570 | 28.223 | 97.285 | Lohit | E(o) | 32.29 | 4,189 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 36, "table_row_end": 70, "line_start": 6273, "line_end": 6387, "token_count_estimate": 1591, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["17119", "17123", "17124", "17127", "17133", "17139", "17147", "17154", "17161", "17163", "17167", "17181", "17188", "17192", "17226", "17241", "17280", "17282", "17332", "17380", "17399", "17413", "17428", "17433", "17452", "17456", "17478", "17524", "17528", "17541", "17548", "17560", "17563", "17567", "17570", "91H04", "91H05", "91H06", "91H07", "91H08"]}}
{"id": "c5e7378eb9941b8b", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2389 | 03 91H08 17576 | 28.214 | 97.318 | Lohit | E(c) | 12.12 | 4,373 |\n| 2390 | 03 91H08 17587 | 28.174 | 97.329 | Lohit | E(o) | 14.32 | 4,307 |\n| 2391 | 03 91H08 17599 | 28.162 | 97.320 | Lohit | E(o) | 10.73 | 4,038 |\n| 2392 | 03 91H08 17618 | 28.149 | 97.274 | Lohit | E(o) | 14.13 | 3,953 |\n| 2393 | 03 91H08 17620 | 28.140 | 97.290 | Lohit | E(o) | 26.03 | 4,055 |\n| 2394 | 03 91H08 17621 | 28.136 | 97.306 | Lohit | E(o) | 21.99 | 4,225 |\n| 2395 | 03 91H08 17622 | 28.136 | 97.321 | Lohit | E(o) | 17.77 | 4,276 |\n| 2396 | 03 91H08 17624 | 28.116 | 97.300 | Lohit | E(o) | 17.74 | 4,029 |\n| 2397 | 03 91H08 17626 | 28.107 | 97.311 | Lohit | E(o) | 21.72 | 4,343 |\n| 2398 | 03 91H08 17627 | 28.106 | 97.320 | Lohit | E(o) | 12.40 | 4,358 |\n| 2399 | 03 91H08 17629 | 28.101 | 97.270 | Lohit | E(c) | 14.99 | 4,169 |\n| 2400 | 03 91H08 17635 | 28.096 | 97.289 | Lohit | E(o) | 53.06 | 3,762 |\n| 2401 | 03 91H08 17639 | 28.080 | 97.304 | Lohit | E(o) | 23.71 | 4,304 |\n| 2402 | 03 91H08 17649 | 28.061 | 97.357 | Lohit | E(c) | 15.82 | 4,344 |\n| 2403 | 03 91H08 17651 | 28.054 | 97.330 | Lohit | E(o) | 25.72 | 4,424 |\n| 2404 | 03 91H08 17652 | 28.053 | 97.281 | Lohit | M(o) | 10.51 | 4,499 |\n| 2405 | 03 91H08 17656 | 28.045 | 97.293 | Lohit | E(o) | 10.43 | 4,472 |\n| 2406 | 03 91H09 17669 | 28.991 | 97.549 | Lohit | M(e) | 17.75 | 4,390 |\n| 2407 | 03 91H09 17672 | 28.938 | 97.522 | Lohit | E(c) | 21.47 | 4,392 |\n| 2408 | 03 91H09 17695 | 28.863 | 97.612 | Lohit | E(o) | 11.56 | 4,558 |\n| 2409 | 03 91H09 17706 | 28.852 | 97.631 | Lohit | E(o) | 12.69 | 4,523 |\n| 2410 | 03 91H10 17809 | 28.556 | 97.548 | Lohit | E(o) | 12.30 | 4,363 |\n| 2411 | 03 91H10 17824 | 28.536 | 97.621 | Lohit | E(o) | 15.42 | 4,616 |\n| 2412 | 03 91H10 17838 | 28.518 | 97.527 | Lohit | E(o) | 24.32 | 4,449 |\n| 2413 | 03 92A09 17844 | 27.816 | 96.707 | Lohit | E(o) | 10.89 | 3,488 |\n| 2414 | 03 92A13 17852 | 27.759 | 96.878 | Lohit | E(c) | 10.40 | 3,857 |\n| 2415 | 03 92A14 17856 | 27.742 | 96.845 | Lohit | E(o) | 18.26 | 3,778 |\n| 2416 | 03 92A14 17859 | 27.732 | 96.871 | Lohit | E(o) | 12.07 | 4,012 |\n| 2417 | 03 92A14 17860 | 27.726 | 96.848 | Lohit | E(o) | 18.83 | 3,669 |\n| 2418 | 03 92A14 17861 | 27.719 | 96.877 | Lohit | E(o) | 20.25 | 3,664 |\n| 2419 | 03 92A14 17866 | 27.712 | 96.938 | Lohit | E(o) | 12.92 | 3,751 |\n| 2420 | 03 92A14 17882 | 27.690 | 96.860 | Lohit | E(o) | 49.45 | 3,373 |\n| 2421 | 03 92A14 17896 | 27.646 | 96.878 | Lohit | E(o) | 15.08 | 3,263 |\n| 2422 | 03 92A14 17900 | 27.637 | 96.916 | Lohit | E(c) | 10.10 | 3,689 |\n| 2423 | 03 92E01 17907 | 27.996 | 97.187 | Lohit | E(c) | 40.63 | 4,091 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 71, "table_row_end": 105, "line_start": 6273, "line_end": 6387, "token_count_estimate": 1585, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["17576", "17587", "17599", "17618", "17620", "17621", "17622", "17624", "17626", "17627", "17629", "17635", "17639", "17649", "17651", "17652", "17656", "17669", "17672", "17695", "17706", "17809", "17824", "17838", "17844", "17852", "17856", "17859", "17860", "17861", "17866", "17882", "17896", "17900", "17907", "91H08", "91H09", "91H10", "92A09", "92A13", "92A14", "92E01"]}}
{"id": "3915806903d488d6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: table\nTable\n\n| S.No. | Glacial Lake ID No | Lat | Long | Subbasin | GL Type | Area (ha) | Elev (m) |\n|---|---|---|---|---|---|---|---|\n| 2424 | 03 92E01 17909 | 27.990 | 97.098 | Lohit | E(o) | 12.28 | 3,687 |\n| 2425 | 03 92E01 17933 | 27.959 | 97.105 | Lohit | E(o) | 17.42 | 3,831 |\n| 2426 | 03 92E01 17937 | 27.949 | 97.108 | Lohit | E(o) | 12.82 | 3,874 |\n| 2427 | 03 92E01 17938 | 27.948 | 97.131 | Lohit | E(c) | 11.08 | 3,819 |\n| 2428 | 03 92E05 17957 | 27.994 | 97.316 | Lohit | E(o) | 13.95 | 4,270 |\n| 2429 | 03 92E05 17959 | 27.989 | 97.369 | Lohit | E(o) | 51.93 | 4,188 |\n| 2430 | 03 92E05 17994 | 27.893 | 97.358 | Lohit | E(o) | 21.52 | 3,931 |\n| 2431 | 03 92E05 18001 | 27.879 | 97.360 | Lohit | E(o) | 40.18 | 4,158 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID No", "Lat", "Long", "Subbasin", "GL Type", "Area (ha)", "Elev (m)"], "table_row_start": 106, "table_row_end": 113, "line_start": 6273, "line_end": 6387, "token_count_estimate": 430, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["17909", "17933", "17937", "17938", "17957", "17959", "17994", "18001", "92E01", "92E05"]}}
{"id": "32f03af9ed3fb941", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - II: List of Glacial Lakes\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - II: List of Glacial Lakes", "section_headings": ["Annexure - II: List of Glacial Lakes"], "chunk_type": "text", "line_start": 6388, "line_end": 6392, "token_count_estimate": 47, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0507a9895cc2d8ce", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: text\n\nThere are 299 lakes having an area ≥ 50 ha, which is just 1.07% of the total glacial lake count, but covers total of 31.67% of the total glacial lakes area. Spatial distribution of these very large sized lakes i.e. ≥ 50 ha in area has been represented below in Figure 85 and details of these are given in Table 69, along with its area, type, geographic as well as hydrological location, and elevation at which they are situated. Among these 299 lakes, 176, 94 and 29 lakes are in the lake area range of < 100 ha, 100-250 ha and > 250 ha respectively. Out of these 299 large lakes, majority (101) is other glacier erosion lakes followed by end moraine-dammed glacial lakes (97) and few are glacial trough valley erosion lakes (7) and lateral moraine-dammed glacial lake (5).\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "text", "line_start": 6396, "line_end": 6407, "token_count_estimate": 264, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba8e80568ee5c403", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable: Table 69: List of glacial lakes with area ≥ 50 ha\n\n| S.No. | Glacial Lake ID Number | | | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | 01 | 42H09 | 00200 | 36.879 | 73.704 | Gilgit | O | 262.57 | 4,286 |\n| 2 | 01 | 42H10 | 00208 | 36.644 | 73.646 | Gilgit | O | 105.07 | 3,821 |\n| 3 | 01 | 43A09 | 00583 | 35.994 | 72.613 | Gilgit | O | 201.58 | 3,622 |\n| 4 | 01 | 43A09 | 00591 | 35.944 | 72.595 | Gilgit | O | 96.35 | 3,761 |\n| 5 | 01 | 43E05 | 00769 | 35.949 | 73.289 | Gilgit | E(o) | 54.23 | 4,228 |\n| 6 | 01 | 43E05 | 00774 | 35.945 | 73.365 | Gilgit | E(c) | 67.34 | 4,162 |\n| 7 | 01 | 43E09 | 00941 | 35.865 | 73.746 | Gilgit | E(o) | 84.31 | 4,140 |\n| 8 | 01 | 43J01 | 01363 | 34.829 | 74.062 | Jhelum | E(c) | 93.90 | 3,681 |\n| 9 | 01 | 43J09 | 01397 | 34.920 | 74.521 | Jhelum | M(e) | 60.60 | 4,041 |\n| 10 | 01 | 43J13 | 01449 | 34.845 | 74.809 | Indus Middle | E(c) | 50.44 | 3,992 |\n| 11 | 01 | 43J15 | 01489 | 34.432 | 74.924 | Jhelum | E(c) | 161.04 | 3,571 |\n| 12 | 01 | 43K10 | 01561 | 33.519 | 74.584 | Jhelum | E(o) | 70.83 | 3,934 |\n| 13 | 01 | 43K14 | 01588 | 33.512 | 74.769 | Jhelum | O | 128.54 | 3,486 |\n| 14 | 01 | 43N01 | 01839 | 34.991 | 75.236 | Indus Upper | O | 129.62 | 4,138 |\n| 15 | 01 | 43N02 | 01885 | 34.697 | 75.137 | Jhelum | E(o) | 64.95 | 4,103 |\n| 16 | 01 | 43N02 | 01897 | 34.666 | 75.179 | Jhelum | E(o) | 74.54 | 4,234 |\n| 17 | 01 | 43N04 | 01975 | 34.140 | 75.148 | Jhelum | E(c) | 82.93 | 3,780 |\n| 18 | 01 | 43N08 | 02065 | 34.094 | 75.498 | Jhelum | O | 52.27 | 3,575 |\n| 19 | 01 | 52C16 | 03156 | 33.159 | 76.984 | Indus Upper | M(e) | 59.78 | 4,479 |\n| 20 | 01 | 52H02 | 03651 | 32.526 | 77.220 | Chenab | M(e) | 77.59 | 4,069 |\n| 21 | 01 | 52H11 | 03771 | 32.499 | 77.547 | Chenab | M(e) | 128.69 | 4,150 |\n| 22 | 01 | 52J03 | 03811 | 34.457 | 78.136 | Shyok | M(o) | 95.68 | 5,295 |\n| 23 | 01 | 52J08 | 03836 | 34.233 | 78.426 | Shyok | O | 64.78 | 5,350 |\n| 24 | 01 | 52J12 | 03868 | 34.151 | 78.553 | Shyok | E(o) | 65.02 | 5,566 |\n| 25 | 01 | 52K07 | 03939 | 33.455 | 78.498 | Shyok | O | 147.89 | 5,308 |\n| 26 | 01 | 52K07 | 03940 | 33.427 | 78.488 | Shyok | O | 177.73 | 5,284 |\n| 27 | 01 | 61B15 | 04757 | 34.316 | 80.858 | Shyok | I(d) | 232.34 | 5,709 |\n| 28 | 01 | 61D15 | 04804 | 32.423 | 80.865 | Indus Upper | O | 58.84 | 4,452 |\n| 29 | 01 | 61F03 | 04813 | 34.299 | 81.202 | Shyok | O | 61.15 | 5,274 |\n| 30 | 01 | 61F07 | 04829 | 34.341 | 81.257 | Shyok | E(o) | 51.89 | 5,298 |\n| 31 | 01 | 62E04 | 05008 | 31.182 | 81.195 | Satluj | E(o) | 52.48 | 5,413 |\n| 32 | 01 | 62E11 | 05114 | 31.274 | 81.595 | Indus Upper | O | 156.89 | 5,229 |\n| 33 | 01 | 62F07 | 05179 | 30.431 | 81.433 | Satluj | E(o) | 192.62 | 5,484 |\n| 34 | 01 | 62F15 | 05295 | 30.385 | 81.930 | Satluj | M(e) | 59.79 | 5,224 |\n| 35 | 02 | 62F16 | 00708 | 30.129 | 81.781 | Humla | M(e) | 75.65 | 5,015 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": "Table 69: List of glacial lakes with area ≥ 50 ha", "columns": ["S.No.", "Glacial Lake ID Number", "", "", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6408, "line_end": 6452, "token_count_estimate": 1781, "basins": ["Indus"], "subbasins": ["Chenab", "Gilgit", "Indus Middle", "Indus Upper", "Jhelum", "Satluj", "Shyok"], "countries": [], "lake_ids": ["00200", "00208", "00583", "00591", "00708", "00769", "00774", "00941", "01363", "01397", "01449", "01489", "01561", "01588", "01839", "01885", "01897", "01975", "02065", "03156", "03651", "03771", "03811", "03836", "03868", "03939", "03940", "04757", "04804", "04813", "04829", "05008", "05114", "05179", "05295", "42H09", "42H10", "43A09", "43E05", "43E09", "43J01", "43J09", "43J13", "43J15", "43K10", "43K14", "43N01", "43N02", "43N04", "43N08"]}}
{"id": "75e6f3bf55d02e49", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable: Table 69: List of glacial lakes with area ≥ 50 ha\n\n| S.No. | Glacial Lake ID Number | | | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 36 | 02 | 62J04 | 00880 | 30.067 | 82.127 | Humla | M(l) | 62.33 | 4,829 |\n| 37 | 02 | 62P14 | 01803 | 28.691 | 83.852 | Marsyangdi | M(o) | 340.21 | 4,910 |\n| 38 | 02 | 71D07 | 01882 | 28.488 | 84.486 | Marsyangdi | M(e) | 89.44 | 4,038 |\n| 39 | 02 | 71H06 | 02059 | 28.644 | 85.491 | Bum Chu | M(e) | 50.98 | 4,985 |\n| 40 | 02 | 71H10 | 02153 | 28.623 | 85.510 | Bum Chu | M(e) | 118.78 | 5,127 |\n| 41 | 02 | 71H10 | 02154 | 28.616 | 85.527 | Bum Chu | M(e) | 103.58 | 5,113 |\n| 42 | 02 | 71H10 | 02162 | 28.562 | 85.602 | Bum Chu | M(e) | 129.19 | 5,361 |\n| 43 | 02 | 71H10 | 02165 | 28.532 | 85.609 | Bum Chu | M(e) | 540.55 | 5,352 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": "Table 69: List of glacial lakes with area ≥ 50 ha", "columns": ["S.No.", "Glacial Lake ID Number", "", "", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 43, "line_start": 6408, "line_end": 6452, "token_count_estimate": 517, "basins": [], "subbasins": [], "countries": [], "lake_ids": ["00880", "01803", "01882", "02059", "02153", "02154", "02162", "02165", "62J04", "62P14", "71D07", "71H06", "71H10"]}}
{"id": "2bda491705c5e3af", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "text", "line_start": 6453, "line_end": 6458, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1bdd538f7aebe0dc", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | | | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 44 | 02 | 71H10 | 02177 | 28.494 | 85.636 | Bum Chu | M(e) | 490.68 | 5,278 |\n| 45 | 02 | 71H11 | 02183 | 28.485 | 85.736 | Bum Chu | M(e) | 51.99 | 5,335 |\n| 46 | 02 | 71H15 | 02391 | 28.360 | 85.871 | Sun Kosi | M(e) | 463.78 | 5,212 |\n| 47 | 02 | 71H15 | 02402 | 28.329 | 85.869 | Sun Kosi | M(o) | 213.52 | 5,167 |\n| 48 | 02 | 71H15 | 02405 | 28.322 | 85.838 | Sun Kosi | M(e) | 540.35 | 5,067 |\n| 49 | 02 | 71H16 | 02493 | 28.211 | 85.847 | Sun Kosi | M(e) | 61.34 | 4,374 |\n| 50 | 02 | 71L03 | 02551 | 28.347 | 86.225 | Sun Kosi | M(e) | 55.90 | 5,348 |\n| 51 | 02 | 71L03 | 02555 | 28.335 | 86.192 | Sun Kosi | M(e) | 55.00 | 5,422 |\n| 52 | 02 | 71L03 | 02557 | 28.303 | 86.157 | Sun Kosi | M(e) | 59.05 | 5,307 |\n| 53 | 02 | 71L07 | 02736 | 28.374 | 86.305 | Bum Chu | M(e) | 391.50 | 5,346 |\n| 54 | 02 | 71L07 | 02788 | 28.199 | 86.582 | Bum Chu | M(e) | 134.64 | 5,094 |\n| 55 | 02 | 71L08 | 02932 | 28.033 | 86.500 | Tama Kosi | M(e) | 60.86 | 5,057 |\n| 56 | 02 | 71L09 | 02947 | 28.887 | 86.514 | Bum Chu | E(o) | 98.23 | 5,098 |\n| 57 | 02 | 71L12 | 03013 | 28.185 | 86.532 | Tama Kosi | M(e) | 67.68 | 5,025 |\n| 58 | 02 | 71L12 | 03032 | 28.135 | 86.531 | Tama Kosi | M(e) | 97.85 | 4,984 |\n| 59 | 02 | 71L12 | 03072 | 28.044 | 86.514 | Tama Kosi | M(l) | 57.94 | 5,241 |\n| 60 | 02 | 71P04 | 03182 | 28.208 | 87.101 | Arun Kosi | E(o) | 101.93 | 4,852 |\n| 61 | 02 | 71P04 | 03183 | 28.205 | 87.052 | Arun Kosi | O | 65.11 | 4,980 |\n| 62 | 02 | 71P04 | 03201 | 28.152 | 87.158 | Arun Kosi | O | 94.74 | 5,141 |\n| 63 | 02 | 71P04 | 03232 | 28.068 | 87.047 | Arun Kosi | M(e) | 78.93 | 5,589 |\n| 64 | 02 | 71P07 | 03342 | 28.393 | 86.379 | Bum Chu | M(e) | 100.11 | 5,482 |\n| 65 | 02 | 71P08 | 03345 | 28.213 | 87.470 | Arun Kosi | O | 131.39 | 4,781 |\n| 66 | 02 | 71P09 | 03420 | 28.858 | 86.519 | Bum Chu | E(o) | 81.24 | 5,254 |\n| 67 | 02 | 71P09 | 03422 | 28.832 | 86.522 | Bum Chu | E(o) | 281.32 | 5,319 |\n| 68 | 02 | 71P10 | 03475 | 28.694 | 87.534 | Bum Chu | E(o) | 60.74 | 5,158 |\n| 69 | 02 | 71P12 | 03514 | 28.230 | 87.591 | Arun Kosi | M(e) | 78.90 | 5,410 |\n| 70 | 02 | 71P12 | 03527 | 28.178 | 87.563 | Arun Kosi | M(e) | 104.19 | 5,011 |\n| 71 | 02 | 71P12 | 03561 | 28.114 | 87.655 | Arun Kosi | M(e) | 146.34 | 4,954 |\n| 72 | 02 | 71P12 | 03566 | 28.093 | 87.637 | Arun Kosi | M(e) | 72.47 | 5,178 |\n| 73 | 02 | 72I05 | 03634 | 27.947 | 86.446 | Tama Kosi | M(e) | 156.76 | 5,046 |\n| 74 | 02 | 72I05 | 03648 | 27.861 | 86.476 | Tama Kosi | M(e) | 158.40 | 4,550 |\n| 75 | 02 | 72I09 | 03725 | 27.975 | 86.681 | Dudh Kosi | M(l) | 57.83 | 4,834 |\n| 76 | 02 | 72I09 | 03801 | 27.779 | 86.612 | Dudh Kosi | M(e) | 117.31 | 4,831 |\n| 77 | 02 | 72I13 | 03932 | 27.924 | 86.786 | Dudh Kosi | M(l) | 54.85 | 4,512 |\n| 78 | 02 | 72I13 | 03950 | 27.898 | 86.925 | Dudh Kosi | M(e) | 139.77 | 5,003 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "", "", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6459, "line_end": 6504, "token_count_estimate": 1811, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["02177", "02183", "02391", "02402", "02405", "02493", "02551", "02555", "02557", "02736", "02788", "02932", "02947", "03013", "03032", "03072", "03182", "03183", "03201", "03232", "03342", "03345", "03420", "03422", "03475", "03514", "03527", "03561", "03566", "03634", "03648", "03725", "03801", "03932", "03950", "71H10", "71H11", "71H15", "71H16", "71L03", "71L07", "71L08", "71L09", "71L12", "71P04", "71P07", "71P08", "71P09", "71P10", "71P12"]}}
{"id": "f2b4732cf5d8e4ba", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | | | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 79 | 02 | 72I13 | 04014 | 27.783 | 86.957 | Dudh Kosi | M(e) | 87.28 | 5,198 |\n| 80 | 02 | 72I13 | 04032 | 27.755 | 86.958 | Dudh Kosi | M(o) | 86.50 | 4,927 |\n| 81 | 02 | 72M01 | 04125 | 27.798 | 87.092 | Arun Kosi | M(e) | 182.16 | 4,543 |\n| 82 | 02 | 72M13 | 04484 | 27.952 | 87.908 | Yeru Chu | M(e) | 64.79 | 5,165 |\n| 83 | 02 | 72M13 | 04486 | 27.950 | 87.930 | Yeru Chu | M(e) | 83.66 | 5,106 |\n| 84 | 02 | 72M13 | 04495 | 27.928 | 88.002 | Yeru Chu | M(e) | 113.22 | 5,348 |\n| 85 | 02 | 72M13 | 04496 | 27.926 | 87.771 | Arun Kosi | M(e) | 97.66 | 4,913 |\n| 86 | 02 | 72M13 | 04515 | 27.869 | 87.866 | Tamur Kosi | M(e) | 68.12 | 4,910 |\n| 87 | 02 | 77D08 | 04614 | 28.054 | 88.427 | Yeru Chu | O | 101.66 | 4,888 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "", "", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 44, "line_start": 6459, "line_end": 6504, "token_count_estimate": 547, "basins": [], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["04014", "04032", "04125", "04484", "04486", "04495", "04496", "04515", "04614", "72I13", "72M01", "72M13", "77D08"]}}
{"id": "92e55748f1c746e9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "text", "line_start": 6505, "line_end": 6512, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b6f6723640dc67c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | | | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 88 | 02 | 77D08 | 04622 | 28.022 | 88.355 | Yeru Chu | M(e) | 56.29 | 5,195 |\n| 89 | 02 | 77D08 | 04624 | 28.017 | 88.288 | Yeru Chu | M(e) | 50.43 | 5,268 |\n| 90 | 02 | 77D08 | 04627 | 28.009 | 88.259 | Yeru Chu | M(e) | 59.70 | 5,256 |\n| 91 | 02 | 78A01 | 04637 | 27.946 | 88.075 | Yeru Chu | M(e) | 148.59 | 5,488 |\n| 92 | 02 | 78A01 | 04639 | 27.933 | 88.066 | Yeru Chu | M(e) | 83.35 | 5,563 |\n| 93 | 03 | 62J03 | 00044 | 30.468 | 82.060 | Upper Yarlung Tsangpo | O | 378.80 | 5,180 |\n| 94 | 03 | 62J03 | 00151 | 30.398 | 82.192 | Upper Yarlung Tsangpo | E(o) | 86.53 | 5,203 |\n| 95 | 03 | 62J03 | 00163 | 30.362 | 82.055 | Upper Yarlung Tsangpo | M(e) | 56.83 | 5,283 |\n| 96 | 03 | 62J03 | 00201 | 30.255 | 82.209 | Upper Yarlung Tsangpo | M(e) | 128.70 | 5,057 |\n| 97 | 03 | 62J07 | 00243 | 30.432 | 82.362 | Upper Yarlung Tsangpo | O | 152.61 | 4,882 |\n| 98 | 03 | 62J07 | 00244 | 30.419 | 82.302 | Upper Yarlung Tsangpo | O | 901.40 | 4,931 |\n| 99 | 03 | 62J07 | 00289 | 30.342 | 82.271 | Upper Yarlung Tsangpo | O | 69.03 | 4,993 |\n| 100 | 03 | 62J07 | 00296 | 30.329 | 82.270 | Upper Yarlung Tsangpo | O | 58.67 | 4,990 |\n| 101 | 03 | 62J08 | 00344 | 30.103 | 82.270 | Upper Yarlung Tsangpo | M(e) | 203.15 | 4,875 |\n| 102 | 03 | 62J08 | 00354 | 30.079 | 82.343 | Upper Yarlung Tsangpo | M(e) | 91.73 | 4,849 |\n| 103 | 03 | 62J13 | 00456 | 30.881 | 82.859 | Upper Yarlung Tsangpo | O | 146.23 | 5,446 |\n| 104 | 03 | 62K09 | 00585 | 29.985 | 82.535 | Upper Yarlung Tsangpo | E(o) | 392.72 | 4,829 |\n| 105 | 03 | 62K13 | 00646 | 29.840 | 82.782 | Upper Yarlung Tsangpo | M(e) | 291.26 | 5,058 |\n| 106 | 03 | 62K13 | 00664 | 29.796 | 82.853 | Upper Yarlung Tsangpo | M(e) | 53.83 | 5,160 |\n| 107 | 03 | 62K14 | 00680 | 29.735 | 82.974 | Upper Yarlung Tsangpo | M(e) | 80.86 | 5,337 |\n| 108 | 03 | 62N10 | 00819 | 30.591 | 83.519 | Upper Yarlung Tsangpo | O | 270.94 | 5,227 |\n| 109 | 03 | 62N15 | 00896 | 30.465 | 83.984 | Upper Yarlung Tsangpo | E(o) | 84.88 | 5,450 |\n| 110 | 03 | 62N15 | 00912 | 30.431 | 83.996 | Upper Yarlung Tsangpo | E(o) | 206.16 | 5,429 |\n| 111 | 03 | 62O02 | 00938 | 29.726 | 83.105 | Upper Yarlung Tsangpo | O | 113.22 | 5,010 |\n| 112 | 03 | 62O02 | 00944 | 29.689 | 83.190 | Upper Yarlung Tsangpo | O | 54.50 | 5,007 |\n| 113 | 03 | 62O06 | 01004 | 29.604 | 83.376 | Upper Yarlung Tsangpo | O | 145.71 | 4,889 |\n| 114 | 03 | 62O06 | 01010 | 29.582 | 83.355 | Upper Yarlung Tsangpo | O | 118.74 | 4,888 |\n| 115 | 03 | 62O06 | 01021 | 29.511 | 83.444 | Upper Yarlung Tsangpo | O | 211.06 | 4,959 |\n| 116 | 03 | 62O07 | 01027 | 29.499 | 83.428 | Upper Yarlung Tsangpo | O | 59.80 | 4,959 |\n| 117 | 03 | 62O15 | 01246 | 29.470 | 83.764 | Upper Yarlung Tsangpo | O | 77.62 | 5,282 |\n| 118 | 03 | 71P09 | 01500 | 28.832 | 87.560 | Upper Yarlung Tsangpo | O | 140.16 | 5,296 |\n| 119 | 03 | 71C09 | 01990 | 29.845 | 84.676 | Upper Yarlung Tsangpo | M(e) | 51.54 | 5,536 |\n| 120 | 03 | 71G14 | 02213 | 29.558 | 85.880 | Upper Yarlung Tsangpo | O | 56.48 | 5,186 |\n| 121 | 03 | 71O02 | 02336 | 29.556 | 87.028 | Upper Yarlung Tsangpo | O | 119.59 | 4,729 |\n| 122 | 03 | 77B12 | 02407 | 30.168 | 88.620 | Upper Yarlung Tsangpo | E(o) | 50.13 | 5,029 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "", "", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6513, "line_end": 6558, "token_count_estimate": 1891, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["00044", "00151", "00163", "00201", "00243", "00244", "00289", "00296", "00344", "00354", "00456", "00585", "00646", "00664", "00680", "00819", "00896", "00912", "00938", "00944", "01004", "01010", "01021", "01027", "01246", "01500", "01990", "02213", "02336", "02407", "04622", "04624", "04627", "04637", "04639", "62J03", "62J07", "62J08", "62J13", "62K09", "62K13", "62K14", "62N10", "62N15", "62O02", "62O06", "62O07", "62O15", "71C09", "71G14"]}}
{"id": "ccffc922677021af", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | | | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 123 | 03 | 77B12 | 02408 | 30.148 | 88.627 | Upper Yarlung Tsangpo | E(o) | 213.67 | 5,011 |\n| 124 | 03 | 77D12 | 02625 | 28.026 | 88.710 | Teesta | M(e) | 113.46 | 5,148 |\n| 125 | 03 | 77D12 | 02636 | 28.008 | 88.698 | Teesta | M(e) | 99.32 | 5,209 |\n| 126 | 03 | 77D12 | 02640 | 28.006 | 88.713 | Teesta | M(e) | 119.15 | 5,238 |\n| 127 | 03 | 77D16 | 02649 | 28.011 | 88.756 | Teesta | M(o) | 108.12 | 5,094 |\n| 128 | 03 | 78A01 | 02664 | 27.920 | 88.159 | Teesta | M(e) | 83.63 | 5,441 |\n| 129 | 03 | 78A01 | 02665 | 27.913 | 88.196 | Teesta | M(e) | 128.14 | 5,194 |\n| 130 | 03 | 78A05 | 02815 | 27.947 | 88.332 | Teesta | M(o) | 60.49 | 5,034 |\n| 131 | 03 | 78A05 | 02874 | 27.822 | 88.249 | Teesta | M(e) | 70.94 | 5,414 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "", "", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 44, "line_start": 6513, "line_end": 6558, "token_count_estimate": 548, "basins": [], "subbasins": ["Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["02408", "02625", "02636", "02640", "02649", "02664", "02665", "02815", "02874", "77B12", "77D12", "77D16", "78A01", "78A05"]}}
{"id": "32c0308378823f96", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "text", "line_start": 6559, "line_end": 6565, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2324c94e3ef57fdb", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 220 | 03 | 82G06 | 10094 | 29.541 | 93.345 | Lower Yarlung Tsangpo | E(o) | 100.03 | 4,631 |\n| 221 | 03 | 82G10 | 10253 | 29.513 | 93.620 | Lower Yarlung Tsangpo | O | 79.97 | 4,362 |\n| 222 | 03 | 82G11 | 10262 | 29.477 | 93.631 | Lower Yarlung Tsangpo | E(o) | 82.79 | 4,369 |\n| 223 | 03 | 82G14 | 10427 | 29.542 | 93.830 | Lower Yarlung Tsangpo | E(o) | 56.22 | 4,419 |\n| 224 | 03 | 82G14 | 10455 | 29.502 | 93.937 | Lower Yarlung Tsangpo | E(o) | 56.29 | 4,444 |\n| 225 | 03 | 82J04 | 10483 | 30.126 | 94.090 | Lower Yarlung Tsangpo | E(o) | 276.68 | 3,802 |\n| 226 | 03 | 82J04 | 10484 | 30.115 | 94.188 | Lower Yarlung Tsangpo | M(e) | 94.40 | 3,905 |\n| 227 | 03 | 82J04 | 10489 | 30.046 | 94.157 | Lower Yarlung Tsangpo | E(o) | 107.17 | 4,294 |\n| 228 | 03 | 82J08 | 10499 | 30.099 | 94.270 | Lower Yarlung Tsangpo | M(e) | 68.10 | 3,924 |\n| 229 | 03 | 82K02 | 10661 | 29.545 | 94.067 | Lower Yarlung Tsangpo | E(o) | 50.77 | 4,299 |\n| 230 | 03 | 82K02 | 10688 | 29.526 | 94.057 | Lower Yarlung Tsangpo | E(o) | 78.00 | 4,533 |\n| 231 | 03 | 82K02 | 10696 | 29.518 | 94.121 | Lower Yarlung Tsangpo | E(o) | 118.34 | 4,501 |\n| 232 | 03 | 82K02 | 10703 | 29.505 | 94.133 | Lower Yarlung Tsangpo | E(o) | 101.69 | 4,577 |\n| 233 | 03 | 82K03 | 10730 | 29.472 | 94.236 | Lower Yarlung Tsangpo | E(o) | 50.80 | 4,509 |\n| 234 | 03 | 82K05 | 10788 | 29.915 | 94.280 | Lower Yarlung Tsangpo | E(c) | 183.14 | 4,385 |\n| 235 | 03 | 82K05 | 10845 | 29.828 | 94.462 | Lower Yarlung Tsangpo | E(o) | 57.27 | 4,133 |\n| 236 | 03 | 82K05 | 10852 | 29.813 | 94.433 | Lower Yarlung Tsangpo | O | 228.43 | 4,083 |\n| 237 | 03 | 82K09 | 10962 | 29.808 | 94.501 | Lower Yarlung Tsangpo | O | 55.81 | 4,305 |\n| 238 | 03 | 82G08 | 11911 | 29.240 | 93.276 | Lower Yarlung Tsangpo | E(o) | 58.59 | 4,914 |\n| 239 | 03 | 82G11 | 12006 | 29.405 | 93.708 | Lower Yarlung Tsangpo | E(o) | 74.32 | 4,505 |\n| 240 | 03 | 82G11 | 12065 | 29.287 | 93.736 | Lower Yarlung Tsangpo | E(o) | 59.16 | 4,562 |\n| 241 | 03 | 82G16 | 12326 | 29.035 | 93.836 | Lower Yarlung Tsangpo | O | 67.89 | 4,116 |\n| 242 | 03 | 82H13 | 12506 | 28.856 | 94.000 | Lower Yarlung Tsangpo | E(o) | 73.89 | 3,868 |\n| 243 | 03 | 82K14 | 12810 | 29.545 | 94.965 | Lower Yarlung Tsangpo | E(c) | 91.95 | 4,300 |\n| 244 | 03 | 82L05 | 12837 | 28.986 | 94.270 | Lower Yarlung Tsangpo | E(o) | 50.66 | 3,478 |\n| 245 | 03 | 82H03 | 13095 | 28.342 | 93.092 | Subansiri | E(o) | 62.44 | 4,251 |\n| 246 | 03 | 82H03 | 13097 | 28.320 | 93.047 | Subansiri | E(o) | 72.68 | 4,255 |\n| 247 | 03 | 83A09 | 13285 | 27.980 | 92.651 | Subansiri | M(e) | 52.21 | 4,988 |\n| 248 | 03 | 82D16 | 13318 | 28.116 | 92.951 | Subansiri | E(c) | 55.58 | 4,648 |\n| 249 | 03 | 82J08 | 13621 | 30.174 | 94.346 | Lower Yarlung Tsangpo | E(v) | 180.80 | 3,654 |\n| 250 | 03 | 82J08 | 13634 | 30.073 | 94.464 | Lower Yarlung Tsangpo | E(o) | 90.19 | 4,110 |\n| 251 | 03 | 82J08 | 13655 | 30.013 | 94.472 | Lower Yarlung Tsangpo | E(c) | 66.38 | 4,327 |\n| 252 | 03 | 82J08 | 13657 | 30.005 | 94.384 | Lower Yarlung Tsangpo | E(o) | 59.09 | 4,020 |\n| 253 | 03 | 82K05 | 13679 | 29.959 | 94.292 | Lower Yarlung Tsangpo | E(v) | 134.02 | 4,282 |\n| 254 | 03 | 82K05 | 13686 | 29.947 | 94.358 | Lower Yarlung Tsangpo | E(o) | 109.97 | 4,148 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6566, "line_end": 6611, "token_count_estimate": 1907, "basins": [], "subbasins": ["Lower Yarlung Tsangpo", "Subansiri"], "countries": [], "lake_ids": ["10094", "10253", "10262", "10427", "10455", "10483", "10484", "10489", "10499", "10661", "10688", "10696", "10703", "10730", "10788", "10845", "10852", "10962", "11911", "12006", "12065", "12326", "12506", "12810", "12837", "13095", "13097", "13285", "13318", "13621", "13634", "13655", "13657", "13679", "13686", "82D16", "82G06", "82G08", "82G10", "82G11", "82G14", "82G16", "82H03", "82H13", "82J04", "82J08", "82K02", "82K03", "82K05", "82K09"]}}
{"id": "7d5faf0adf342849", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 255 | 03 | 82K05 | 13714 | 29.896 | 94.461 | Lower Yarlung Tsangpo | E(o) | 85.25 | 4,346 |\n| 256 | 03 | 82K09 | 13791 | 29.890 | 94.569 | Lower Yarlung Tsangpo | E(v) | 158.02 | 4,149 |\n| 257 | 03 | 82K09 | 13855 | 29.829 | 94.633 | Lower Yarlung Tsangpo | E(v) | 60.20 | 4,231 |\n| 258 | 03 | 82K09 | 13886 | 29.779 | 94.601 | Lower Yarlung Tsangpo | E(v) | 184.22 | 4,146 |\n| 259 | 03 | 82N02 | 14031 | 30.603 | 95.182 | Lower Yarlung Tsangpo | M(e) | 110.12 | 4,278 |\n| 260 | 03 | 82N10 | 14193 | 30.473 | 95.575 | Lower Yarlung Tsangpo | E(o) | 56.47 | 4,866 |\n| 261 | 03 | 82N11 | 14301 | 30.251 | 95.604 | Lower Yarlung Tsangpo | M(o) | 134.45 | 4,442 |\n| 262 | 03 | 82N12 | 14308 | 30.221 | 95.584 | Lower Yarlung Tsangpo | M(e) | 85.49 | 4,342 |\n| 263 | 03 | 91C02 | 14526 | 29.598 | 96.141 | Lower Yarlung Tsangpo | M(o) | 65.09 | 4,025 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 44, "line_start": 6566, "line_end": 6611, "token_count_estimate": 593, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["13714", "13791", "13855", "13886", "14031", "14193", "14301", "14308", "14526", "82K05", "82K09", "82N02", "82N10", "82N11", "82N12", "91C02"]}}
{"id": "500b79bf7cce881d", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "text", "line_start": 6612, "line_end": 6618, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a0d7adb484ca7234", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 264 | 03 | 91C05 | 14550 | 29.823 | 96.351 | Lower Yarlung Tsangpo | M(o) | 101.06 | 4,870 |\n| 265 | 03 | 82O08 | 14709 | 29.180 | 95.486 | Dihang | E(o) | 93.81 | 3,533 |\n| 266 | 03 | 82O11 | 14834 | 29.304 | 95.640 | Dihang | E(v) | 70.17 | 3,322 |\n| 267 | 03 | 82O15 | 14903 | 29.371 | 95.873 | Dihang | E(o) | 103.75 | 4,344 |\n| 268 | 03 | 82O08 | 14974 | 29.129 | 95.439 | Dibang | E(o) | 53.88 | 3,284 |\n| 269 | 03 | 82O16 | 15129 | 29.011 | 95.885 | Dibang | E(o) | 55.15 | 3,778 |\n| 270 | 03 | 82P13 | 15195 | 29.005 | 95.905 | Dibang | E(o) | 54.45 | 3,598 |\n| 271 | 03 | 91C03 | 15223 | 29.302 | 96.082 | Dibang | E(o) | 119.59 | 4,274 |\n| 272 | 03 | 91C03 | 15250 | 29.269 | 96.157 | Dibang | E(o) | 102.66 | 3,991 |\n| 273 | 03 | 91C04 | 15278 | 29.229 | 96.192 | Dibang | E(o) | 106.39 | 3,473 |\n| 274 | 03 | 91C04 | 15283 | 29.226 | 96.160 | Dibang | E(o) | 57.36 | 3,313 |\n| 275 | 03 | 91C04 | 15296 | 29.196 | 96.203 | Dibang | E(o) | 64.06 | 4,246 |\n| 276 | 03 | 91C04 | 15336 | 29.091 | 96.211 | Dibang | E(o) | 100.77 | 4,188 |\n| 277 | 03 | 91C04 | 15344 | 29.079 | 96.145 | Dibang | E(c) | 86.02 | 3,945 |\n| 278 | 03 | 91C04 | 15360 | 29.051 | 96.144 | Dibang | O | 79.93 | 3,602 |\n| 279 | 03 | 91C08 | 15390 | 29.244 | 96.245 | Dibang | E(o) | 50.88 | 4,360 |\n| 280 | 03 | 91D09 | 15690 | 28.776 | 96.531 | Dibang | O | 101.67 | 3,510 |\n| 281 | 03 | 91C03 | 15760 | 29.257 | 96.246 | Lohit | M(o) | 89.30 | 4,432 |\n| 282 | 03 | 91C08 | 15774 | 29.224 | 96.279 | Lohit | M(o) | 66.27 | 4,207 |\n| 283 | 03 | 91C11 | 15833 | 29.491 | 96.701 | Lohit | O | 610.17 | 3,916 |\n| 284 | 03 | 91C15 | 15967 | 29.462 | 96.787 | Lohit | O | 518.00 | 3,916 |\n| 285 | 03 | 91C15 | 15982 | 29.397 | 96.828 | Lohit | E(o) | 401.62 | 3,917 |\n| 286 | 03 | 91C15 | 15998 | 29.298 | 96.816 | Lohit | M(e) | 292.36 | 3,954 |\n| 287 | 03 | 91C15 | 16000 | 29.295 | 96.835 | Lohit | M(l) | 95.04 | 4,013 |\n| 288 | 03 | 91C15 | 16004 | 29.268 | 96.837 | Lohit | E(o) | 108.04 | 4,119 |\n| 289 | 03 | 91C16 | 16019 | 29.238 | 96.826 | Lohit | M(o) | 209.76 | 4,219 |\n| 290 | 03 | 91C16 | 16020 | 29.230 | 96.805 | Lohit | M(o) | 144.00 | 4,266 |\n| 291 | 03 | 91C16 | 16024 | 29.221 | 96.814 | Lohit | M(e) | 52.51 | 4,279 |\n| 292 | 03 | 91D10 | 16210 | 28.516 | 96.699 | Lohit | E(o) | 299.20 | 3,330 |\n| 293 | 03 | 91D16 | 16419 | 28.202 | 96.898 | Lohit | E(o) | 67.30 | 3,731 |\n| 294 | 03 | 91H01 | 16757 | 28.977 | 97.215 | Lohit | E(o) | 63.42 | 4,092 |\n| 295 | 03 | 91H01 | 16898 | 28.783 | 97.153 | Lohit | E(o) | 83.11 | 3,712 |\n| 296 | 03 | 91H05 | 17167 | 28.940 | 97.262 | Lohit | E(o) | 93.63 | 4,412 |\n| 297 | 03 | 91H07 | 17452 | 28.412 | 97.465 | Lohit | E(o) | 56.61 | 4,300 |\n| 298 | 03 | 91H08 | 17635 | 28.096 | 97.289 | Lohit | E(o) | 53.06 | 3,762 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 6619, "line_end": 6656, "token_count_estimate": 1772, "basins": [], "subbasins": ["Dibang", "Dihang", "Lohit", "Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["14550", "14709", "14834", "14903", "14974", "15129", "15195", "15223", "15250", "15278", "15283", "15296", "15336", "15344", "15360", "15390", "15690", "15760", "15774", "15833", "15967", "15982", "15998", "16000", "16004", "16019", "16020", "16024", "16210", "16419", "16757", "16898", "17167", "17452", "17635", "82O08", "82O11", "82O15", "82O16", "82P13", "91C03", "91C04", "91C05", "91C08", "91C11", "91C15", "91C16", "91D09", "91D10", "91D16"]}}
{"id": "030bf4a53fe81740", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 299 | 03 | 92E05 | 17959 | 27.989 | 97.369 | Lohit | E(o) | 51.93 | 4,188 |", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 36, "line_start": 6619, "line_end": 6656, "token_count_estimate": 160, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": ["17959", "92E05"]}}
{"id": "cd1e956e8e5ec99f", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - III > Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Indian Himalayan River Basins with area ≥ 50 ha"], "chunk_type": "text", "line_start": 6657, "line_end": 6662, "token_count_estimate": 58, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "efbd0b856022ad1c", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - IV: Glossary\nType: text\n\n**Ablation:** The process that reduce the mass of the glacier (Cogley et al., 2011).\n\n**Ablation area/zone:** The part of the glacier where ablation exceeds accumulation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is negative. The extent of the ablation zone can vary strongly from year to year (Cogley et al., 2011).\n\n**Accumulation:** The process that add to the mass of the glacier (Cogley et al., 2011).\n\n**Accumulation area/zone:** The part of the glacier where accumulation exceeds ablation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is positive. The extent of the accumulation zone can vary strongly from year to year. The accumulation zone is not the same as the firn area (Cogley et al., 2011).\n\n**Altitude:** The vertical distance of a point above a datum, which is usually an estimate of mean sea level. Altitude and elevation are synonyms in common usage (Cogley et al., 2011).\n\n**Aspect:** The compass direction towards which a slope faces; measured clockwise in degrees from the North.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Avalanche:** A slide or flow of a mass of snow, firn or ice that becomes detached abruptly, often entraining additional material such as snow, debris and vegetation as it descends. The duration of an avalanche is typically seconds to minutes (Cogley et al., 2011).\n\n**Band:** One layer of multispectral image representing data values for a specific range of the electromagnetic spectrum of reflected light or heat.\n\n**Climate:** Climate is usually defined as the average weather or as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years. The relevant quantities are most often surface variables such as temperature, precipitation and wind (Pandey, 2019).\n\n**Climate change:** Climate change refers to a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. UNFCCC defines climate change as: 'a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods'. (Pandey, 2019).\n\n**Climate variability:** Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all spatial and temporal scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability) (Pandey, 2019).", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - IV: Glossary", "section_headings": ["Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 6664, "line_end": 6737, "token_count_estimate": 817, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2fee4273f75fadc9", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - IV: Glossary\nType: text\n\nor indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods ' . ( Pandey , 2019 ) . * * Climate variability : * * Climate variability refers to variations in the mean state and other statistics ( such as standard deviations , the occurrence of extremes , etc . ) of the climate on all spatial and temporal scales beyond that of individual weather events . Variability may be due to natural internal processes within the climate system ( internal variability ) , or to variations in natural or anthropogenic external forcing ( external variability ) ( Pandey , 2019 ) .\n\n**Cryosphere:** The cryosphere is the part of the Earth system that contains ice, for example snow on the ground, glaciers, ice sheets, lake ice, river ice, sea ice, seasonally and perennially frozen ground (GCW 2016).\n\n**Database:** An organized, integrated collection of data related by a common fact or purpose.\n\n***\n\nGLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\n\n**Debris-covered glacier:** A glacier that is covered at its tongue with supra-glacial debris across its full width (Kirkbride, 2011). In the accumulation zone any deposited debris is buried by later snowfalls, but in the ablation zone debris remains at the surface and englacial debris is added to the surface layer from beneath as ice ablates away. The debris cover affects the rate of ablation, with very thin debris resulting in accelerated melt and debris thicker than a few tens of millimetres reducing the melting rate (Cogley et al., 2011).\n\n**Digital Elevation Model (DEM):** An array of numbers representing the elevation of part or all of the Earth's surface as samples or averages at fixed spacing in two horizontal coordinate directions (Cogley et al., 2011).\n\n**Disaster:** A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNISDR 2017).\n\n**Disaster risk:** The potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity (UNISDR 2017).\n\n**Early warning system:** The set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare to act promptly and appropriately to reduce the possibility of harm or loss (Pandey, 2019).\n\n**Electromagnetic spectrum:** The spectrum of wavelengths of electromagnetic radiation.\n\n**Englacial:** Pertaining to the interior of the glacier, between the summer surface and the bed (Cogley et al., 2011).\n\n**Exposure:** The presence or situation of people, livelihoods, species, ecosystems, environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings, and other tangible human assets located in hazard-prone areas that could be adversely affected (UNISDR, 2017; Pandey, 2019).\n\n**Feature:** A real-world phenomenon, often used in cartography to name classes of elements shown on a map.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - IV: Glossary", "section_headings": ["Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 6664, "line_end": 6737, "token_count_estimate": 864, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7681e5cbb501a04", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - IV: Glossary\nType: text\n\n* * Pertaining to the interior of the glacier , between the summer surface and the bed ( Cogley et al . , 2011 ) . * * Exposure : * * The presence or situation of people , livelihoods , species , ecosystems , environmental functions , services , and resources , infrastructure , or economic , social , or cultural assets in places and settings , and other tangible human assets located in hazard - prone areas that could be adversely affected ( UNISDR , 2017 ; Pandey , 2019 ) . * * Feature : * * A real - world phenomenon , often used in cartography to name classes of elements shown on a map .\n\n**Firn:** Snow (in which the pore space is at least partially interconnected, allowing air and water to circulate) that has survived at least one ablation season but has not been transformed to glacier ice (Cogley et al., 2011).\n\n**Flood:** The overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas not normally submerged. Floods include river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods and glacial lake outburst floods (Pandey, 2019).\n\n**Format:** The pattern into which data are systematically arranged for use on a computer.\n\n**Geographic Information System (GIS):** A set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world for a particular set of circumstances.\n\n**Glacial Lake Outburst Flood (GLOF):** Flood caused by the outburst of a glacial lake due to rapid accumulation of water in it, resulting to extreme damage in loss of lives and infrastructure in the downstream area.\n\n**Glacial Lake:** As a result of glacier thinning and retreating, melt water gets accumulated at terminal moraines or on it covered by glacier ice, is known as glacial lake.\n\n**Glacier Erosion Lake:** These are the water bodies formed in a depression after the glacier has retreated in a form of cirque or trough valley, might be isolated and far away from the present glaciated area, and mostly stable in nature.", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - IV: Glossary", "section_headings": ["Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 6664, "line_end": 6737, "token_count_estimate": 572, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ffb1e6056e5ad3af", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - IV: Glossary > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: text\n\n**Glacier:** A perennial mass of ice, and possibly firn and snow, originating on the land surface by their crystallization of snow or other forms of solid precipitation and showing evidence of past or present flow (Cogley et al., 2011).\n\n**Global Positioning System (GPS):** A GPS is a position-fixing system that uses the time taken for signals to travel from at least three GPS satellites in a known orbit to a receiver on the ground.\n\n**Hazard:** The potential occurrence of a natural or human-induced physical event or trend or physical impact that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources (Pandey, 2019).\n\n**Ice-dammed Lake:** An Ice-dammed Lake is produced on the side(s) of a glacier, when an advancing glacier happens to intercept a tributary/tributaries pouring into a main glacier valley.\n\n**Impacts:** The term impacts is used primarily to refer to the effects on natural and human systems of extreme weather and climate events and of climate change. Impacts generally refer to effects on lives, livelihoods, health, ecosystems, economies, societies, cultures, services and infrastructure due to the interaction of climate changes or hazardous climate events occurring within a specific time period and the vulnerability of an exposed society or system. Impacts are also referred to as consequences and outcomes. The impacts of climate change on geophysical systems, including floods, droughts and sea level rise, are a subset of impacts called physical impacts (Pandey, 2019).\n\n**Latitude:** Angle measured in a north-south direction from the Earth’s center to locations on the Earth’s surface.\n\n**Longitude:** Angle measured in an east-west direction from the Earth’s center to locations on the Earth’s surface.\n\n**Layer:** Usually represents a theme or a feature type within the database.\n\n**Map:** An abstract representation of the physical features of a portion of the Earth’s surface graphically displayed on a planar surface. Map display signs, symbols and spatial relationships among the features.\n\n**Melt water:** The liquid resulting from melting of ice, firn or snow (Cogley et al., 2011).\n\n**Moraine-dammed Lake:** In the retreating process of a glacier, ice tends to melt in the lowest part of the glacier surrounded by Lateral-moraines and End-moraines, and forms into a lake known as Moraine-dammed Lake or Proglacial Lake.\n\n**Pixel:** Smallest discrete element that makes up an image, generally represents either a small square or portion of the Earth’s surface, scanned by satellite or aircraft.\n\n**Precipitation:** Liquid or solid products of the condensation of water vapour that fall from clouds or are deposited from the air onto the surface (Cogley et al., 2011).\n\n**Remote sensing:** The technique of obtaining data about the environment and surface of the earth from a distance, e.g. from an aircraft or satellite.\n\n**Resolution:** It is the accuracy at which a given map scale can depict the location and shape of geographic features.\n\n**Retreat:** Decrease of the length of a flow line (in case of glacier which is its terminus), measured from a fixed point. Advance is the opposite of retreat, that is, advance of the terminus (Cogley et al., 2011).\n\n***", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - IV: Glossary > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["Annexure - IV: Glossary", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "text", "line_start": 6739, "line_end": 6776, "token_count_estimate": 892, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f4f095ff4d9f6ba6", "text": "Document: IHR GlacialLake Atlas (1)\nSection: Annexure - IV: Glossary > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS\nType: text\n\n**Risk:** The potential for consequences where something of value is at stake and where the outcome is uncertain, recognizing the diversity of values. Risk is often represented as probability or likelihood of occurrence of hazardous events or trends multiplied by the impacts if these events or trends occur. In this report, the term risk is often used to refer to the potential, when the outcome is uncertain, for adverse consequences on lives, livelihoods, health, ecosystems and species, economic, social and cultural assets, services (including environmental services) and infrastructure (Pandey, 2019).\n\n**Scale:** The ratio or fraction between the distance on a map, chart or photograph and the corresponding distance on the surface of the Earth.\n\n**Slope:** A measure of change on surface value over distance, expressed in degrees or as a percentage.\n\n**Snow:** Solid precipitation in the form of ice crystals, chiefly in complex branched hexagonal form and often agglomerated into snowflakes; or an accumulation of the same on the Earth’s surface. It is also known as solid precipitation that has accumulated on the summer surface on a glacier and that transforms to firn at the end of the mass-balance year (Cogley et al., 2011).\n\n**Subglacial:** Pertaining to the glacier bed or to the material below the bed (Cogley et al., 2011).\n\n**Supra-glacial Lake:** Water bodies develop within the ice mass in any position of the glacier, but away from the terminal moraines are known as Supra-glacial lakes. Its basic characteristics are shifting, merging, and draining.\n\n**Terminus:** The lowest end of a glacier, also called glacier snout, glacier front or glacier toe (Cogley et al., 2011).\n\n**Tongue:** The lower, elongate part of a valley glacier or outlet glacier or a floating extension of a glacier or ice stream, laterally unconfined but markedly longer than wide (Cogley et al., 2011).\n\n**Topographic Map:** A map showing the features that describes the surface of a particular place or region. It contains contours indicating lines of equal surface elevation (relief), often referred to a topo maps.\n\n**Vulnerability:** The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt (Pandey, 2019).\n\nPrepared under: National Hydrology Project\n\nNational Remote Sensing Centre\nIndian Space Research Organisation\nDepartment of Space, Government of India\nHyderabad - 500 037", "metadata": {"source_file": "data/IHR_GlacialLake_Atlas (1)_gemini.md", "document_title": "IHR GlacialLake Atlas (1)", "section_path": "Annexure - IV: Glossary > GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS", "section_headings": ["Annexure - IV: Glossary", "GLACIAL LAKE ATLAS OF INDIAN HIMALAYAN RIVER BASINS"], "chunk_type": "text", "line_start": 6778, "line_end": 6809, "token_count_estimate": 670, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "242deec8eabb243d", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nThe Himalayan Region (HR) is facing important challenges in coping withthe adverse effects of climate change. Physically, the shrinking of mountain glaciers and expansion of Glacial Lakes are amongst the most recognizable and dynamic impacts of climate warming in this environment. In combination with this, altered stability of surrounding rock and ice walls, the potential threat from Glacial Lake Outburst Flood (GLOF) is evolving over time. Therefore, under such changing environment, a close watch on the relative change in water spread area of even smaller lakes has become very crucial in this region.\n\nAnalysis of worldwide literature on the outburst of glacial lakes and the field and theoretical experience have led to the conclusion that it is not feasible to make a reliable prediction of a specific occurrence on the basis of our existing knowledge. As direct predictions cannot be made, there is an urgent need to monitor a careful selection of prioritized lakes on a regular basis. This should be carried out in collaboration with other institutions, both nationally and internationally.\n\nThe work of monitoring of Glacial Lakes/Water Bodies (GLs/WBs) using remote sensing technique was taken up by CWC, DoWR, RD&DR, Ministry of Jal Shakti, during XI Plan period in the year 2009 under DWRIS Plan scheme. The inventory of GLs/WBs was published in August, 2011 in association with National Remote Sensing Centre (NRSC), Hyderabad based on the satellite data of Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing Satellite, Resourcesat-1 collected from May-Nov, 2009. This inventory is therefore hereafter referred as *Inventory of Glacial Lakes & Water Bodies (2011)*. As per this inventory, there are 2028 GLs/WBs with size more than 10 ha in the Himalayan Region draining towards India. The country wise & basin wise details of the inventory are given in **Table ES.1**.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 4, "line_end": 12, "token_count_estimate": 465, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "19ecca3dca9038a9", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: table\nTable: Table ES.1: Country wise & Basin wise Distribution of Glacial lakes and Water bodies above 10 Ha(in Nos.)\n\n| Country-wise Distribution | | | | Basin-wise Distribution | | | |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| **Country** | **Glacial Lakes (>10 Ha)** | **Water Bodies (>10 Ha)** | **Total (>10 Ha)** | **Basin Name** | **Glacial Lakes** | **Water Bodies** | **Total** |\n| India | 60 | 448 | 508 | Brahmaputra | 294 | 1099 | 1393 |\n| Bhutan | 77 | 124 | 201 | Ganga | 178 | 105 | 283 |\n| Nepal | 57 | 45 | 102 | Indus | 31 | 321 | 352 |\n| China | 309 | 904 | 1213 | **Total** | **503** | **1525** | **2028** |\n| Myanmar | - | 4 | 4 | | | | |\n| **Total** | **503** | **1525** | **2028** | | | | |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "table", "table_caption": "Table ES.1: Country wise & Basin wise Distribution of Glacial lakes and Water bodies above 10 Ha(in Nos.)", "columns": ["Country-wise Distribution", "", "", "", "Basin-wise Distribution", "", "", ""], "table_row_start": 1, "table_row_end": 7, "line_start": 13, "line_end": 21, "token_count_estimate": 372, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "d85cdaa97d3254ce", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nNRSC has published Glacial Lake Atlas of Indian Himalayan River Basins in the year 2023. The atlas depicts distribution of 28,043 glacial lakes of size greater than 0.25 ha mapped using high resolution Resourcesat-2 LISS4 MX satellite data of 2016-21.\n\nThe abstract of Glacial Lakes as per Glacial Atlas of IHR of NRSC 2023 is given in following **Table No. ES.2** .", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 22, "line_end": 31, "token_count_estimate": 112, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d8e71f21938ca8e8", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: table\nTable: Table ES.2: Abstract of Glacial Lakes as per Glacial Lake Atlas of IHR of NRSC 2023\n\n| Size(Ha) | No of Glacial Lakes - India | No of Glacial Lakes - Transboundary | No of Glacial Lakes - Total |\n|---|---|---|---|\n| 0.25-1 | 3342 | 9194 | 12536 |\n| 1-5 | 2862 | 7769 | 10631 |\n| 5-10 | 712 | 1733 | 2445 |\n| 10-50 | 596 | 1536 | 2132 |\n| >50 | 58 | 241 | 299 |\n| **Total** | **7570** | **20473** | **28043** |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "table", "table_caption": "Table ES.2: Abstract of Glacial Lakes as per Glacial Lake Atlas of IHR of NRSC 2023", "columns": ["Size(Ha)", "No of Glacial Lakes - India", "No of Glacial Lakes - Transboundary", "No of Glacial Lakes - Total"], "table_row_start": 1, "table_row_end": 6, "line_start": 32, "line_end": 39, "token_count_estimate": 208, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["10631", "12536", "20473", "28043"]}}
{"id": "606aee331b120293", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nMonitoring of 477 GLs/WBs with size more than 50 ha, sourced from Glacial Lake Inventory 2011, for change in water spread area, was carried out during monsoon season (August to October) every year since 2011. The monitoring activity initiated in NRSC was continued till 2015. CWC has taken up monitoring during 2016 and the work was undertaken by downloading and manually digitising Advanced Wide Field Sensor (AWiFS) Satellite imageries procured/ downloaded from NRSC and processing them in Arc GIS. This continued till 2021. From 2022, monitoring of additional 425 GLs with sizes of 10ha to 50ha was also included. This includes 385 Glacial Lakes with water spread area between 10-50 Ha from Glacial Lake Inventory (2011) and 40 high priority Glacial Lakes identified by Swiss Agency for Development and Cooperation (SDC) for NDMA. This adds up to a total of 902 GLs/WBs.\n\nMonitoring of Glacial lakes is further expanded in the year 2025 to include additional 1941 Glacial lakes of water spread area greater than 10 Ha,as per Glacial Lake Atlas of Indian River Basins of NRSC published in the year 2023. This includes 581 Glacial lakes located within India and 1360 Glacial Lakes located in the transboundary region. Hence CWC monitors a total of 2843 Glacial Lakes and Water Bodies of water spread area greater than 10 Ha of the Indian Himalayan Rivier Basin. The abstract of Glacial Lakes and Water Bodies taken up for monitoring by CWC is given in **Table ES.3.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 40, "line_end": 46, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b6503085e4000cb8", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: table\nTable: Table No ES.3: Abstract of Glacial Lakes and Water Bodies taken up for monitoring by CWC\n\n| Sl. No. | No. of GLs/WBs | Water Spread Area | Monitored Since | Inventory Reference |\n|---|---|---|---|---|\n| 1. | 477 GLs & WBs | > 50 Ha | 2011 | Glacial Lake Inventory, NRSC, 2011 |\n| 2 | 425 GLs | 10-50 Ha | 2022 | Glacial Lake Inventory, NRSC, 2011 (385 Nos.) & SDC Report (40 Nos.) |\n| 3. | 1941 Gls | >10 Ha | 2025 | Glacial Lake Atlas of Indian River Basins, NRSC, 2023 |\n| **Total No. of GLs & WBs Monitored = 2843 ( 2485 GLs & 358 WBs)** | | | | |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "table", "table_caption": "Table No ES.3: Abstract of Glacial Lakes and Water Bodies taken up for monitoring by CWC", "columns": ["Sl. No.", "No. of GLs/WBs", "Water Spread Area", "Monitored Since", "Inventory Reference"], "table_row_start": 1, "table_row_end": 4, "line_start": 47, "line_end": 52, "token_count_estimate": 248, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "24ca96a761cec4c7", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nThe state-wise and basin-wise details of 902 GLs & WBsbeing monitored by CWC is given in the Table. ES.4.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 53, "line_end": 57, "token_count_estimate": 49, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb70310a33d5883b", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: table\nTable: Table ES.4: Abstract of 902 GLs/WBs\n\n| Country/Area | State/Union Territory | No of Glacial Lakes - Indus Basin | No of Glacial Lakes - Ganga Basin | No of Glacial Lakes - Brahmaputra Basin | No of Glacial Lakes - Total | No of Water Bodies - Indus Basin | No of Water Bodies - Ganga Basin | No of Water Bodies - Brahmaputra Basin | No of Water Bodies - Total | Grand Total |\n|---|---|---|---|---|---|---|---|---|---|---|\n| India | Ladakh | 15 | 0 | 0 | 15 | 26 | 0 | 0 | 26 | 41 |\n| India | Jammu & Kashmir | 15 | 0 | 0 | 15 | 16 | 0 | 0 | 16 | 31 |\n| India | Himachal Pradesh | 10 | 0 | 0 | 10 | 5 | 0 | 0 | 5 | 15 |\n| India | Uttarakhand | 0 | 9 | 0 | 9 | 0 | 6 | 0 | 6 | 15 |\n| India | Sikkim | 0 | 0 | 42 | 42 | 0 | 0 | 1 | 1 | 43 |\n| India | Arunachal Pradesh | 0 | 0 | 9 | 9 | 0 | 0 | 25 | 25 | 34 |\n| India | **Total** | **40** | **9** | **51** | **100** | **47** | **6** | **26** | **79** | **179** |\n| India | **India Total** | | | | **100** | | | | **79** | **179** |\n| Transboundary | China | 12 | 110 | 187 | 309 | 49 | 19 | 191 | 259 | 568 |\n| Transboundary | Bhutan | 0 | 0 | 71 | 71 | 0 | 0 | 11 | 11 | 82 |\n| Transboundary | Nepal | 0 | 64 | 0 | 64 | 0 | 9 | 0 | 9 | 73 |\n| Transboundary | **Total** | **12** | **174** | **258** | **444** | **49** | **28** | **202** | **279** | **723** |\n| Transboundary | **Transboundary Total** | | | | **444** | | | | **279** | **723** |\n| **Grand Total** | | | | | **544** | | | | **358** | **902** |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "table", "table_caption": "Table ES.4: Abstract of 902 GLs/WBs", "columns": ["Country/Area", "State/Union Territory", "No of Glacial Lakes - Indus Basin", "No of Glacial Lakes - Ganga Basin", "No of Glacial Lakes - Brahmaputra Basin", "No of Glacial Lakes - Total", "No of Water Bodies - Indus Basin", "No of Water Bodies - Ganga Basin", "No of Water Bodies - Brahmaputra Basin", "No of Water Bodies - Total", "Grand Total"], "table_row_start": 1, "table_row_end": 14, "line_start": 58, "line_end": 73, "token_count_estimate": 747, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "2bf13e8a4b2ead66", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nThe state-wise and bassin-wise details of newly monitored 1941 GLs as per Glacial Lake Atlas 2023, being monitored by CWC is given in the **Table. ES.5.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 74, "line_end": 78, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcc051f89ac8819f", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: table\nTable: Table ES.5: Abstract of 1941 Glacial Lakes as per Glacial Lake Inventory 2023\n\n| Country/Area | State/UT | Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023 - Indus Basin (Nos.) | Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023 - Ganga Basin (Nos.) | Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023 - Brahmaputra Basin (Nos.) | Grand Total (Nos.) |\n|---|---|---|---|---|---|\n| India | Ladakh | 164 | 0 | 0 | 164 |\n| India | Jammu & Kashmir | 61 | 0 | 0 | 61 |\n| India | Himachal Pradesh | 5 | 2 | 0 | 7 |\n| India | Uttarakhand | 0 | 4 | 0 | 4 |\n| India | Sikkim | 0 | 0 | 30 | 30 |\n| India | Arunachal Pradesh | 0 | 0 | 315 | 315 |\n| India | **Total** | **230** | **6** | **345** | **581** |\n| India | **India Total** | | | | **581** |\n| Transboundary | Transboundary | 49 | 185 | 1126 | 1360 |\n| Transboundary | **Total Transboundary** | | | | **1360** |\n| **Grand Total** | | | | | **1941** |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "table", "table_caption": "Table ES.5: Abstract of 1941 Glacial Lakes as per Glacial Lake Inventory 2023", "columns": ["Country/Area", "State/UT", "Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023 - Indus Basin (Nos.)", "Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023 - Ganga Basin (Nos.)", "Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023 - Brahmaputra Basin (Nos.)", "Grand Total (Nos.)"], "table_row_start": 1, "table_row_end": 11, "line_start": 79, "line_end": 91, "token_count_estimate": 439, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e3ff74f30ea7a049", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nHigh resolution multi-spectral and microwave (SAR) images of foreign satellites at 10 m resolution have been processed and analysed in open-source cloud computing platform Google Earth Engine using\n\nautomatic algorithm which has been developed in-house. Visual inspection & manual digitisation has been used to supplement the automatic algorithm to complete the task. The Monthly Monitoring Report is shared with all stakeholders through email for further necessary action. The reports are also e-published on CWC website for any time access by the concerned (https://cwc.gov.in/glacial-lakeswater-bodies-himalayan-region)\n\n**Map of Study Area showing Glacial Lakes and Water Bodies being monitored by CWC**\n\n**Limitations and Assumptions:**\n\n**Limitations:**\n\n*   Glacial lake identification can be done either using visual interpretation or automatic mapping methods. The automatic mapping procedures have limitations due to varying terrain conditions such as lakes situated in the shadow portions of mountains, presence of snow cover, cloud cover, lakes being partly frozen, etc. As lake water absorbs the incident radiation making it appear in darker tone and colour in the standard FCC of satellite data, similar response also prevails over shadow region of clouds or mountains on surface, which may lead to incorrect mapping. Moreover, a mountain shadow covering a lake partly/completely within its vicinity, also makes it difficult to accurately map the lake boundary.\n*   A few Glacial lakes could not be mapped owing to the constraints such as Glacial lakes being under frozen condition, presence of snow or cloud cover over the lakes, lakes under mountain shadow or lakes in dried up condition.\n\n**Assumptions:**\n\n*   Inclusion or exclusion of water pixels near lake boundaries depending on more than or less than certain fraction of its area falling within the lake boundary.\n\nThis document presents the analysis and results of monitoring of 902 GL&WBs for August 2025. The lakes are analysed for change in water spread area with respect to area of Inventory 2011 and are categorized into 5 classes.\n\n(i) increase in water spread area greater than 40%\n(ii) increase in water spread area up to 40%\n(iii) no change in water spread area\n(iv) decrease in water spread area\n(v) change detection not performed due to reasons such as frozen condition, dried up condition, cloud cover etc.\n\nThe change detection in water spread area of 477 GLs & WBs greater than 50 Ha have been calculated for the following three cases.\n\n*   Difference between the current area of lake and base year area(2011)\n*   Difference between the current area of lake and Last five years average area(2020-2024)\n*   Difference between the current area of lake and Last ten years average area(2015-2024)\n\nThe minimum of change observed from the above three cases has been adopted to identify increase, decrease and no change in water spread area.\n\nAs the monitoring of 385 GLs with water spread area between 10 Ha & 50 Ha was initiated in 2022, the change detection in water spread area has been calculated for the following two cases\n\n*   Difference between the current area of lake and base year area(2011)\n*   Difference between the current area of lake and last three years average area(2022-2024)\n\nThe minimum of change observed from the above two cases has been adopted to identify increase, decrease and no change in water spread area.\n\nFor the remaining 40 GLs, as the inventory details (base year 2011) are not available and monitoring data being available only since 2022, the change detection in water spread area has been calculated as the\n\n*   Difference between the current area of lake and last three years average area(2022-2024)", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 92, "line_end": 165, "token_count_estimate": 853, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e9ae5152f7f97dfc", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\ntwo cases * Difference between the current area of lake and base year area ( 2011 ) * Difference between the current area of lake and last three years average area ( 2022 - 2024 ) The minimum of change observed from the above two cases has been adopted to identify increase , decrease and no change in water spread area . For the remaining 40 GLs , as the inventory details ( base year 2011 ) are not available and monitoring data being available only since 2022 , the change detection in water spread area has been calculated as the * Difference between the current area of lake and last three years average area ( 2022 - 2024 )\n\nFor the 1941 Glacial Lakes being newly monitored from the year 2025, the change detection in water spread area has been calculated as the\n\n* Difference between the current area of lake and base year area (2021; as per Glacial Lake Atlas 2023)\n\nThe number of lakes in each class has been identified. The lakes showing an increase in water spread area greater than 40% have been identified as those requiring vigorous monitoring for disaster purpose.\n\n**Results:**\n\n**Outcome of Monitoring of 902 GLs & WBs, August 2025**\n\n**Outcome of Monitoring of 1941 GLs as per Glacial Lake Atlas 2023, August 2025**\n\n**Combined Outcome of Monitoring total 2843 GLs & WBs, August 2025**\n\n**Conclusions:**\n\n**Conclusion of Monitoring of 902 GLs & WBs of Indian Himalayan Region**\n\n* **12 Water Bodies and 1 Glacial Lake** (>50Ha area) show increase in area greater than 40% when change detection was carried out with respect to base year area(2011), average area of last 5 years(2020-2024) & average area of last 10 years(2015-2024). These Glacial lake and Water Bodies are located in China.\n\n* 26 nos. of Glacial Lakes & Water Bodies have been merged to 13 nos. of Glacial Lakes & Water Bodies & combined area of merged glacial lakes and water bodies has been shown against respective glacial lakes and water bodies. However, merging and demerging of lakes is a dynamic process; hence figure of 902 Glacial Lakes & Water Bodies has been kept intact for analysis part. Details of merged Glacial Lakes & Water Bodies are as under.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 92, "line_end": 165, "token_count_estimate": 544, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "f197cd6aeb2cf0f7", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: table\nTable\n\n| Sl. No. | ID | GL/WB | Location | Remarks |\n|---|---|---|---|---|\n| 1 | 03_71G_008 | WB | China | Merged with nearby lake not in inventory 2011 |\n| 2 | 03_71K_011 | WB | China | Merged with nearby lake not in inventory 2011 |\n| 3 | 03_82N_032 | GL | China | Merged with nearby lake not in inventory 2011 |\n| 4 | 03_62O_040 | WB | China | Merged with nearby lake not in inventory 2011 |\n| 5 | 01_61C_014 | WB | China | Merged with each other |\n| | 01_61C_015 | WB | | |\n| 6 | 03_78E_009 | WB | China | Merged with each other |\n| | 03_78E_010 | WB | | |\n| 7 | 03_62O_041 | WB | China | Merged with each other |\n| | 03_62O_042 | WB | | |\n| 8 | 03_71K_007 | WB | China | Merged with each other |\n| | 03_71K_009 | WB | | |\n| 9 | 03_91C_035 | GL | China | Merged with each other |\n| | 03_91C_036 | GL | | |\n| 10 | 02_71P_018 | WB | China | Merged with each other |\n| | 02_71P_019 | GL | | |\n| | 02_71P_020 | GL | | |\n| 11 | 03_77L_048 | GL | China | Merged with each other |\n| | 03_77L_053 | GL | | |\n| 12 | 01_61C_002 | WB | China | Merged with each other |\n| | 01_61C_004 | WB | | |\n| | 01_61C_005 | WB | | |\n| | 01_61C_010 | WB | | |\n| | 01_61C_011 | WB | | |\n| 13 | 01_52H_003 | GL | India (Himachal Pradesh) | Merged with each other |\n| | 01_52H_004 | GL | | |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID", "GL/WB", "Location", "Remarks"], "table_row_start": 1, "table_row_end": 26, "line_start": 166, "line_end": 193, "token_count_estimate": 695, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": ["01_52H_003", "01_52H_004", "01_61C_002", "01_61C_004", "01_61C_005", "01_61C_010", "01_61C_011", "01_61C_014", "01_61C_015", "02_71P_018", "02_71P_019", "02_71P_020", "03_62O_040", "03_62O_041", "03_62O_042", "03_71G_008", "03_71K_007", "03_71K_009", "03_71K_011", "03_77L_048", "03_77L_053", "03_78E_009", "03_78E_010", "03_82N_032", "03_91C_035", "03_91C_036"]}}
{"id": "5177802843880638", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\n*   **9 Glacial Lakes (10 ha-50 Ha area)** show increase in area greater than 40% when change detection was carried out with respect to base year area (2011), average area of last 3 years (2022-2024). 7 GLs are located in China. The remaining 2 Glacial lakes are located in India **(Jammu & Kashmir- 1 No. & Sikkim – 1 No.).**\n*   The total Inventory area of **Glacial Lakes and Water Bodies** was 5,30,654 Ha during the year 2011 which has increased to 5,79,700 Ha during the year 2025 (August). There is a **9.24%** increase in area. *(Out of 902 GsL&WBs, only 798 GLs&WBs were considered for this interpretation. The remaining lakes include 40 SDC lakes which have no inventory details as well as GLs/WBs which were not analyzed/have been merged during the month of August, 2025.)*\n*   The total Inventory area of **Glacial Lakes** was 19,848 Ha during the year 2011 which has increased to 23,536 Ha during the year 2025 (August). There is a **18.58%** increase in area. *(Out of 544 GLs, only 465 GLs were considered for this interpretation. The remaining lakes include 40 SDC lakes which have no inventory details as well as lakes which were not analyzed/ have been merged during the month of August, 2025.).*\n\n*   The total Inventory area of **Glacial Lakes within India** was 1,995 Ha during the year 2011 which has increased to 2,445 Ha during the year 2025 (August). There is a **22.56%** increase in area. *(Out of 100GLs, only 55 GLs were considered for this interpretation. The remaining lakes include 40 SDC lakes which have no inventory details as well as lakes which were not analysed/have been merged during the month of August, 2025.).*\n*   **54 Glacial Lakes** (out of 100) located within India, as shown below, display increase in water spread area during the month of August 2025, and hence **demand vigorous monitoring for disaster purpose** *(Ladhak-5, Jammu & Kashmir-6, Himachal Pradesh-10, Uttarakhand- 4, Sikkim – 24 & Arunachal Pradesh-5).*\n\n**Conclusion of Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)**\n\n*   The total Inventory area of newly monitored **Glacial Lakes within India as per Glacial Lake Atlas, 2023** was 12,939 Ha in the year 2021 which has increased to 13,260 Ha during the year 2025 (August). There is a **5.26%** increase in area. *(Out of 581GLs, only 518 GLs were considered for this interpretation. The remaining lakes were not able to be analysed during the month of August, 2025.).*\n*   The total Inventory area of newly monitored **Glacial Lakes located in transboundary region as per Glacial Lake Atlas, 2023** was 43,723 Ha in the year 2021 which has increased to 45,862 Ha during the year 2025 (August). There is a **4.89%** increase in area. *(Out of 1360 GLs, only 1247 GLs were considered for this interpretation. The remaining lakes were not able to be analysed during the month of August, 2025.).*", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 194, "line_end": 221, "token_count_estimate": 799, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "2bea3d1a97db32dc", "text": "Document: Monitoring Report Aug 2025\nSection: Executive Summary\nType: text\n\nremaining lakes were not able to be analysed during the month of August , 2025 . ) . * * The total Inventory area of newly monitored * * Glacial Lakes located in transboundary region as per Glacial Lake Atlas , 2023 * * was 43 , 723 Ha in the year 2021 which has increased to 45 , 862 Ha during the year 2025 ( August ) . There is a * * 4 . 89 % * * increase in area . * ( Out of 1360 GLs , only 1247 GLs were considered for this interpretation . The remaining lakes were not able to be analysed during the month of August , 2025 . ) . *\n\n*   **374 Glacial Lakes** (out of 581) located within India as per Glacial Lake Atlas 2023, as shown below, display **increase in water spread area during the month of August 2025**, and hence **demand vigorous monitoring for disaster purpose** *(Ladhak-128, Jammu & Kashmir-44, Himachal Pradesh-3, Uttarakhand- 3, Sikkim – 20 & Arunachal Pradesh-176)*.\n\n**Conclusion on Combined Monitoring of 2843 Glacial lakes being monitored by CWC**\n\n*   **428 Glacial Lakes** (out of 681) located within India as per Glacial Lake Atlas 2023, as shown below, display **increase in water spread area during the month of August 2025**, and hence **demand vigorous monitoring for disaster purpose** *(Ladhak-133, Jammu & Kashmir-50, Himachal Pradesh-13, Uttarakhand- 7, Sikkim – 44 & Arunachal Pradesh-181)*.\n\n***", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "Executive Summary", "section_headings": ["Executive Summary"], "chunk_type": "text", "line_start": 194, "line_end": 221, "token_count_estimate": 400, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b248516fc951a9d6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.1 Glacial Lakes and Water Bodies\nType: text\n\nA glacial lake is a body of water with origins from a glacier. It is formed when a glacier erodes the surface before melting and the melt water fills the resulting depression. The water in Glacial Lakes accumulates behind loose naturally formed 'glacial/moraine dams' made of ice, sand, pebbles and ice residue as the glaciers melt. Various types of lakes may have different levels of hazard potential depending upon many factors such as the nature of damming materials, position of the lake, volume of the water, the nature and position of the associated mother glacier, physical and topographical conditions, and other physical conditions of the surroundings. Interaction between the risk factors and triggering processes such as ice avalanches, debris flows, rock fall, earthquake or landslides reaching a lake strongly affect the risk of a lake outburst. Moraine-dammed lakes located at the snout of a glacier have a high probability of breaching with high hazard potential and can breach suddenly leading to catastrophic floods. Such outburst floods are known as Glacial Lake Outburst Flood (GLOF).\n\nA water Body referred in this report is the body of water retained permanently due to obstruction created naturally or artificially but not directly associated with Glaciers.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.1 Glacial Lakes and Water Bodies", "section_headings": ["1. Introduction", "1.1 Glacial Lakes and Water Bodies"], "chunk_type": "text", "line_start": 225, "line_end": 229, "token_count_estimate": 334, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9baba46522483d4c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.2 Glacial Lakes in Indian Himalayan Region\nType: text\n\nThe Indian Himalayan Region (IHR) contains the world’s largest number of glaciers and snow outside the Polar Regions and are aptly called Third Pole of the world. It consists of three major river systems, ie, Indus, Ganga and Brahmaputra stretching over five countries viz. India, China, Nepal, Myanmar and Bhutan.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.2 Glacial Lakes in Indian Himalayan Region", "section_headings": ["1. Introduction", "1.2 Glacial Lakes in Indian Himalayan Region"], "chunk_type": "text", "line_start": 231, "line_end": 233, "token_count_estimate": 105, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "33f2c37289a63ff7", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe work of monitoring of Glacial Lakes/Water Bodies (GLs/WBs) was taken up by CWC, DoWR, RD&GR, Ministry of Jal Shakti, during XI Plan period in the year 2009, under DWRIS Plan scheme. The inventory of glacial lakes and water bodies of the Himalayan region of Indian river basins published in August, 2011 was done in association with National Remote Sensing Centre (NRSC), Hyderabad based on the satellite data of Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing Satellite, Resourcesat-1 collected from May to November, 2009. The inventory consisted of a total of 2028 Glacial Lakes and Water Bodies with water spread area greater than 10 Ha. The country-wise and basin-wise details of the Inventory are furnished in **Table No. 1.1** and **Table No. 1.2**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 235, "line_end": 242, "token_count_estimate": 229, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db4a9298e3a89214", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: table\nTable: Table 1.1: Country-wise details of Glacial Lakes & Water Bodies of Inventory (2011)\n\n| **Country** | **Glacial Lakes >10 Ha (Nos.)** | **Water Bodies >10 Ha (Nos.)** | **Total >10 Ha (Nos.)** |\n| :--- | :--- | :--- | :--- |\n| India | 60 | 448 | 508 |\n| Bhutan | 77 | 124 | 201 |\n| Nepal | 57 | 45 | 102 |\n| China | 309 | 904 | 1213 |\n| Myanmar | - | 4 | 4 |\n| **Total** | **503** | **1525** | **2028** |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "table", "table_caption": "Table 1.1: Country-wise details of Glacial Lakes & Water Bodies of Inventory (2011)", "columns": ["Country", "Glacial Lakes >10 Ha (Nos.)", "Water Bodies >10 Ha (Nos.)", "Total >10 Ha (Nos.)"], "table_row_start": 1, "table_row_end": 6, "line_start": 243, "line_end": 250, "token_count_estimate": 228, "basins": [], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "2ab578c74f9fff26", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: table\nTable: Table 1.2: Basin-wise details of Glacial Lakes & Water Bodies of Inventory (2011)\n\n| **Basin Name** | **Glacial Lakes (Nos.)** | **Water Bodies (Nos.)** | **Total (Nos.)** |\n| :--- | :--- | :--- | :--- |\n| Brahmaputra | 294 | 1099 | 1393 |\n| Ganga | 178 | 105 | 283 |\n| Indus | 31 | 321 | 352 |\n| **Total** | **503** | **1525** | **2028** |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "table", "table_caption": "Table 1.2: Basin-wise details of Glacial Lakes & Water Bodies of Inventory (2011)", "columns": ["Basin Name", "Glacial Lakes (Nos.)", "Water Bodies (Nos.)", "Total (Nos.)"], "table_row_start": 1, "table_row_end": 4, "line_start": 254, "line_end": 259, "token_count_estimate": 197, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a121fe028c497c8", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n**1.4 Glacial Lake Atlas of Indian Himalayan River Basins in 2023**\n\nNRSC has published Glacial Lake Atlas of Indian Himalayan River Basins in the year 2023. The atlas depicts distribution of 28,043 glacial lakes of size greater than 0.25 ha mapped using high resolution Resourcesat-2 LISS4 MX satellite data of 2016-17. The atlas presents the details of glacial lakes in terms of area, type and elevation and administrative unit wise for all three river basins i.e. Indus, Ganga and Brahmaputra. Ten different types of glacial lakes are identified and categorized into four major classes, viz., Moraine-dammed Lake, Ice-dammed lake, Glacier Erosion lake, and Other Glacial lake.\n\nThe abstract of Glacial Lakes as per Glacial Atlas of IHR of NRSC 2023 is given in following **Table No. 1.3**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 260, "line_end": 268, "token_count_estimate": 234, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "203344e31a7f636f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: table\nTable: Table 1.3: Abstract of Glacial Lakes as per Glacial Lake Atlas of IHR of NRSC 2023\n\n| **Size(Ha)** | **No of Glacial Lakes: India** | **No of Glacial Lakes: Transboundary** | **Total** |\n| :--- | :--- | :--- | :--- |\n| 0.25-1 | 3342 | 9194 | 12536 |\n| 1-5 | 2862 | 7769 | 10631 |\n| 5-10 | 712 | 1733 | 2445 |\n| 10-50 | 596 | 1536 | 2132 |\n| >50 | 58 | 241 | 299 |\n| **Total** | **7570** | **20473** | **28043** |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "table", "table_caption": "Table 1.3: Abstract of Glacial Lakes as per Glacial Lake Atlas of IHR of NRSC 2023", "columns": ["Size(Ha)", "No of Glacial Lakes: India", "No of Glacial Lakes: Transboundary", "Total"], "table_row_start": 1, "table_row_end": 6, "line_start": 269, "line_end": 276, "token_count_estimate": 231, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": ["10631", "12536", "20473", "28043"]}}
{"id": "a3548f1ae1dec1f8", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n**1.5 Objectives**\n\nThe broad objectives of the study are\n*   To monitor the spatial extent in terms of water spread area of the Glacial Lakes &Water Bodies from the inventory on monthly basis during August to October.\n*   To detect temporal changes in water spread area of Glacial Lakes & Water Bodies.\n*   To share the report with concerned stakeholders including National Disaster Management Authority / State Disaster Management Authority for suitable action.\n\n**1.6 Limitations and Assumption**\n\n**Limitations**\n*   Glacial lake identification can be done either using visual interpretation or automatic mapping methods. The automatic mapping procedures have limitations due to varying terrain conditions such as lakes being situated in the shadow portions of mountains, presence of snow cover, cloud cover, lakes being partly frozen, etc. As lake water absorbs the incident radiation making it appear in darker tone and colour in the standard FCC of satellite data, similar response also prevails over shadow region of clouds or mountains on surface, which may lead to incorrect mapping. Moreover, a mountain shadow covering a lake partly/completely within its vicinity, also makes it difficult to accurately map the lake boundary.\n*   A few Glacial lakes could not be mapped owing to the constraints such as they being under frozen condition, presence of snow or cloud cover over the lakes, lakes under mountain shadow or lakes in dried-up condition.\n\n**Assumptions:**\n*   Inclusion or exclusion of water pixels near lake boundaries depending on more than or less than certain fraction of its area falling within the lake boundary.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 277, "line_end": 296, "token_count_estimate": 401, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5748aa6f5882f541", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: table\nTable: Table 2.4: Abstract of State-wise & Basin-wise details of 902 GLs&WBs being monitored monthly by CWC\n\n| Country/Area | State/ Union Territory | No of Glacial Lakes: Indus Basin | No of Glacial Lakes: Ganga Basin | No of Glacial Lakes: Brahma-putra Basin | No of Glacial Lakes: Total | No of Water Bodies: Indus Basin | No of Water Bodies: Ganga Basin | No of Water Bodies: Brahma-putra Basin | No of Water Bodies: Total | Grand Total |\n|---|---|---|---|---|---|---|---|---|---|---|\n| India | Ladakh | 15 | 0 | 0 | 15 | 26 | 0 | 0 | 26 | 41 |\n| | Jammu & Kashmir | 15 | 0 | 0 | 15 | 16 | 0 | 0 | 16 | 31 |\n| | Himachal Pradesh | 10 | 0 | 0 | 10 | 5 | 0 | 0 | 5 | 15 |\n| | Uttarakhand | 0 | 9 | 0 | 9 | 0 | 6 | 0 | 6 | 15 |\n| | Sikkim | 0 | 0 | 42 | 42 | 0 | 0 | 1 | 1 | 43 |\n| | Arunachal Pradesh | 0 | 0 | 9 | 9 | 0 | 0 | 25 | 25 | 34 |\n| | Total | 40 | 9 | 51 | 100 | 47 | 6 | 26 | 79 | 179 |\n| | India Total | | | | 100 | | | | 79 | 179 |\n| Transboundary | China | 12 | 110 | 187 | 309 | 49 | 19 | 191 | 259 | 568 |\n| | Bhutan | 0 | 0 | 71 | 71 | 0 | 0 | 11 | 11 | 82 |\n| | Nepal | 0 | 64 | 0 | 64 | 0 | 9 | 0 | 9 | 73 |\n| | Total | 12 | 174 | 258 | 444 | 49 | 28 | 202 | 279 | 723 |\n| | Transboundary Total | | | | 444 | | | | 279 | 723 |\n| Grand Total | | | | | 544 | | | | 358 | 902 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "table", "table_caption": "Table 2.4: Abstract of State-wise & Basin-wise details of 902 GLs&WBs being monitored monthly by CWC", "columns": ["Country/Area", "State/ Union Territory", "No of Glacial Lakes: Indus Basin", "No of Glacial Lakes: Ganga Basin", "No of Glacial Lakes: Brahma-putra Basin", "No of Glacial Lakes: Total", "No of Water Bodies: Indus Basin", "No of Water Bodies: Ganga Basin", "No of Water Bodies: Brahma-putra Basin", "No of Water Bodies: Total", "Grand Total"], "table_row_start": 1, "table_row_end": 14, "line_start": 297, "line_end": 312, "token_count_estimate": 703, "basins": ["Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "0d0eca323b51f0cc", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n(iii) **1941** Glacial Lakes **being monitored from 2025** with water spread area above 10 Ha, sourced from the Glacial Lake Atlas of Indian Himalayan River Basins published by National Remote Sensing Centre, March 2023. This includes 180 Glacial Lakes with water spread area above 50 Ha and 1761 Glacial Lakes with water spread area between 10 Ha to 50 Ha. 581 Glacial lakes are located within India whereas the remaining 1360 Glacial Lakes are located transboundary.\n\nThe abstract of state-wise and basin-wise details of the 1941 GLs being monitored from 2025 as per Glacial Lake Atlas 2023, are furnished in **Table no. 2.5.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 313, "line_end": 319, "token_count_estimate": 193, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "747f20292304fcaf", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: table\nTable: Table 2.5: Abstract of State-wise & Basin-wise details of 1941GLs as per Glacial Lakes Atlas being monitored monthly by CWC\n\n| Country/Area | State/UT | Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023: Indus Basin (Nos.) | Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023: Ganga Basin (Nos.) | Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023: Brahmaputra Basin (Nos.) | Grand Total (Nos.) |\n|---|---|---|---|---|---|\n| India | Ladakh | 164 | 0 | 0 | 164 |\n| | Jammu & Kashmir | 61 | 0 | 0 | 61 |\n| | Himachal Pradesh | 5 | 2 | 0 | 7 |\n| | Uttarakhand | 0 | 4 | 0 | 4 |\n| | Sikkim | 0 | 0 | 30 | 30 |\n| | Arunachal Pradesh | 0 | 0 | 315 | 315 |\n| | Total | 230 | 6 | 345 | 581 |\n| | India Total | | 581 | | |\n| Transboundary | Transboundary | 49 | 185 | 1126 | 1360 |\n| | Total Transboundary | | 1360 | | |\n| Grand Total | | | 1941 | | |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "table", "table_caption": "Table 2.5: Abstract of State-wise & Basin-wise details of 1941GLs as per Glacial Lakes Atlas being monitored monthly by CWC", "columns": ["Country/Area", "State/UT", "Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023: Indus Basin (Nos.)", "Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023: Ganga Basin (Nos.)", "Glacial Lakes of size greater than 10Ha as per Glacial Lakes Atlas, 2023: Brahmaputra Basin (Nos.)", "Grand Total (Nos.)"], "table_row_start": 1, "table_row_end": 11, "line_start": 320, "line_end": 332, "token_count_estimate": 434, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8d8828da2c742c3b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nHence, CWC monitors a total of 2843 Glacial Lakes and Water Bodies of Indian Himalayan Region. This includes 2485 Glacial lakes and 358 Water Bodies. The break-up of 2843 GLs & WBs is shown in **Figure 2.2.**\n\nThe index map of the study area is shown in **Figure. 2.3**, and the location map of the study area showing the glacial lakes and Water Bodies being monitored by CWC is shown in **Figure.2.4**.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 333, "line_end": 338, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d91f5d5c0e4f7abf", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.3: Index Map of Study Area\n\n**Figure 2.3: Index Map of Study Area**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.3: Index Map of Study Area", "line_start": 339, "line_end": 339, "token_count_estimate": 55, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b3e52b2012b4c614", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.4: Map of Study Area showing Glacial Lakes and Water Bodies being monitored by CWC\n\n**Figure 2.4: Map of Study Area showing Glacial Lakes and Water Bodies being monitored by CWC**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.4: Map of Study Area showing Glacial Lakes and Water Bodies being monitored by CWC", "line_start": 343, "line_end": 343, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2599b584d6e32b78", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe GLs & WBs are mostly located at an elevation range of 3000m to 5500m. A few of them are located above elevation of 5500m and some below 3000m. The elevation of Waterbodies range from 200 m to 5000m. This can be visualized by comparing the location map of study area (**Figure 2.4**) with the relief map of the study area shown in **Figure 2.5**. The elevation range of GLs & WBs being monitored by CWC is shown in **Figure 2.6** & **Figure 2.7**.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 344, "line_end": 346, "token_count_estimate": 162, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31ee3fca35166f84", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.5: Relief Map of the Study Area\n\n**Figure 2.5: Relief Map of the Study Area**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.5: Relief Map of the Study Area", "line_start": 347, "line_end": 347, "token_count_estimate": 59, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bb6d7d259bfef67b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.6: Elevation Range of 902 GLs & WBs within Indian Himalayan Region being monitored by CWC\n\n**Figure 2.6: Elevation Range of 902 GLs & WBs within Indian Himalayan Region being monitored by CWC**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.6: Elevation Range of 902 GLs & WBs within Indian Himalayan Region being monitored by CWC", "line_start": 352, "line_end": 352, "token_count_estimate": 91, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "03da33a0eb17ed46", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.7: Elevation Range of total 2843 GLs & WBs within Indian Himalayan Region being monitored by CWC(as per Glacial Lake Atlas 2023)\n\n**Figure 2.7: Elevation Range of total 2843 GLs & WBs within Indian Himalayan Region being monitored by CWC(as per Glacial Lake Atlas 2023)**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.7: Elevation Range of total 2843 GLs & WBs within Indian Himalayan Region being monitored by CWC(as per Glacial Lake Atlas 2023)", "line_start": 354, "line_end": 354, "token_count_estimate": 115, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c752971289ca7262", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n***\n\nThe country-wise distribution of Glacial Lakes & Water Bodies being monitored by CWC is shown in **Figure 2.8 & Figure 2.9**.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 355, "line_end": 361, "token_count_estimate": 69, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e95a41b62b7b5c4b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.8: Country-wise distribution of 902 GLs & WBs in Indian Himalayan Region being monitored by CWC\n\n**Figure 2.8: Country-wise distribution of 902 GLs & WBs in Indian Himalayan Region being monitored by CWC**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.8: Country-wise distribution of 902 GLs & WBs in Indian Himalayan Region being monitored by CWC", "line_start": 362, "line_end": 362, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6898ab80d0a261e4", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 2.9: Country-wise distribution of total 2843 GLs & WBs in Indian Himalayan Region being monitored by CWC(as per Glacial Lake Atlas 2023)\n\n**Figure 2.9: Country-wise distribution of total 2843 GLs & WBs in Indian Himalayan Region being monitored by CWC(as per Glacial Lake Atlas 2023)**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 2.9: Country-wise distribution of total 2843 GLs & WBs in Indian Himalayan Region being monitored by CWC(as per Glacial Lake Atlas 2023)", "line_start": 364, "line_end": 364, "token_count_estimate": 117, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ac657f4857384e07", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe monitoring was done by downloading and manually digitising Advanced Wide Field Sensor (AWiFS) Satellite imageries procured/ downloaded from NRSC till 2021. High resolution SENTINEL-2 Multi-Spectral Imagery (MSI) and *Sentinel-1* Synthetic Aperture Radar (SAR) data (Microwave Imagery) have been utilized for the study, thereafter in Google Earth Engine platform.\n\n**2.2.1 Sentinel-2 Multi Spectral Imagery**\n\nThe Sentinal-2 mission comprises of a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. It is a wide-swath, high-resolution, multi-spectral imaging mission for monitoring of vegetation, soil and water cover, inland waterways and coastal areas. The SENTINEL-2 Multi-Spectral Instrument (MSI) has visible, near infrared and shortwave infrared sensors sampling 13 spectral bands - 4 bands at 10 m, 6 bands at 20 m and 3 bands at 60 m spatial resolution with a swath width of 290 km. The revisit frequency of each single SENTINEL-2 satellite is 10 days and the combined constellation revisit is 5 days. The Green, Red and NIR bands have been utilized for this study.\n\n**2.2.2 Sentinel-1 Synthetic Aperture Radar (Microwave Imagery)**\n\nThe *Sentinel-1* mission comprises a constellation of two polar-orbiting *satellites*, *Sentinel-1A and Sentinel-1B, sharing the same orbital plane*. It has C-band synthetic aperture radar (SAR) active sensor. Synthetic Aperture Radar (SAR) has the advantage of operating at wavelengths not impeded by cloud cover or a lack of illumination and can acquire data over a site during day or night time under all weather conditions. SAR actively transmits microwave signals towards the Earth and receives a portion of transmitted energy as backscatter from the ground. The SAR instrument provides radar backscatter measurements influenced by the terrain structure and surface roughness. Generally, the more roughness or structure on the ground, the greater the backscatter. Rough surfaces will scatter the energy and return a significant amount back to the antenna resulting in a bright feature. The C-band imaging operates in four exclusive imaging modes with different resolution (down to 5 m) and coverage (up to 400 km). It provides dual polarisation capability, very short revisit times and rapid product delivery. It can transmit a signal in either horizontal (H) or vertical (V) polarisation, and then receive in both H and V polarisations. For each observation, precise measurements of spacecraft position and altitude are available. The repeat orbit cycle of each Sentinel-1 satellite is 12-day. The backscatter intensity of vertical transmit-vertical receive (X) band (VV band) data has been utilized for the study.\n\n**3. Methodology**\n\nGoogle Earth Engine(GEE) has been used to process the Multispectral and Microwave Sentinel image data for the monitoring of Glacial Lakes & Water Bodies. Google Earth Engine (GEE) is a cloud-based geospatial analysis platform that enables users to visualize and analyze satellite images. The Microwave and Multispectral Satellite works on different principle, and hence separate methodology has been adopted to compute the water spread area of GL&WBs in an automatic manner.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 365, "line_end": 438, "token_count_estimate": 845, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dbc1b9088b761872", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n12 - day . The backscatter intensity of vertical transmit - vertical receive ( X ) band ( VV band ) data has been utilized for the study . * * 3 . Methodology * * Google Earth Engine ( GEE ) has been used to process the Multispectral and Microwave Sentinel image data for the monitoring of Glacial Lakes & Water Bodies . Google Earth Engine ( GEE ) is a cloud - based geospatial analysis platform that enables users to visualize and analyze satellite images . The Microwave and Multispectral Satellite works on different principle , and hence separate methodology has been adopted to compute the water spread area of GL & WBs in an automatic manner .\n\nMultispectral data consist of visible and infrared bands. The spectral combination of NIR, red & green bands is used to generate false colour composite (FCC). The Normalised Difference Water Index (NDWI) is computed using green and NIR band. The process of calculation of NDWI and FCC is repeated for each GL&WB. The OTSU algorithm is further used to identify the threshold of NDWI for segregating water pixels from other types of features. The detected water pixels are further summed to calculate water spread area in the region of interest.\n\nMicrowave data of Sentinel-1 is a phase-preserving dual polarisation SAR system. The backscatter intensity of vertical transmit vertical receive (X) band has been used to distinguish water pixels from other types of features. The OTSU algorithm is further used to identify the threshold of backscatter intensity for segregation. The water spread area of each lake has been calculated by summation of water pixels in the region of interest.\n\nThe automated area of the GLs&WBs are then verified manually in GEE. The lakes which show discrepancy in automated area extraction are required to be delineated manually based on the visual interpretation. This is required as the region being monitored has rugged terrain with high mountains and deep valleys, which may lead to effects like foreshortening, layover, mountain shadows etc in the microwave/SAR data. Cloud cover hinders the performance of Multispectral Satellite images.\n\nThe change detection in water spread area of 477 GLs & WBs greater than 50 Ha have been calculated for the following three cases.\n* Difference between the current area of lake and base year area(2011)\n* Difference between the current area of lake and Last five years average area(2020-2024)\n* Difference between the current area of lake and Last ten years average area(2015-2024)\n\nThe minimum of change observed from the above three cases has been adopted to identify increase, decrease and no change in water spread area.\n\nAs the monitoring of 385 GLs with waterspread area between 10 Ha & 50 Ha was initiated in 2022, the change detection in water spread area has been calculated for the following two cases\n\n* Difference between the current area of lake and base year area(2011)\n* Difference between the current area of lake and last three years average area(2022-2024)\n\nThe minimum of change observed from the above two cases has been adopted to identify increase, decrease and no change in water spread area.\n\nFor the remaining 40 GLs, as the inventory details (base year 2011) are not available and monitoring data being available only since 2022, the change detection in water spread area has been calculated as the\n\n* Difference between the current area of lake and last three years average area(2022-2024)\n\nFor the 1941 Glacial Lakes being newly monitored from the year 2025, the change detection in water spread area has been calculated as the", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 365, "line_end": 438, "token_count_estimate": 857, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "621b5d1bad855ebb", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nand last three years average area ( 2022 - 2024 ) The minimum of change observed from the above two cases has been adopted to identify increase , decrease and no change in water spread area . For the remaining 40 GLs , as the inventory details ( base year 2011 ) are not available and monitoring data being available only since 2022 , the change detection in water spread area has been calculated as the * Difference between the current area of lake and last three years average area ( 2022 - 2024 ) For the 1941 Glacial Lakes being newly monitored from the year 2025 , the change detection in water spread area has been calculated as the\n\n* Difference between the current area of lake and base year area (2021; as per Glacial Lake Atlas 2023)\n\nThereafter the GLs & WBs are categorized as those with increase in water spread area greater than 40%, increase in water spread area up to 40%, no change in water spread area, decrease in water spread area and analysis not performed due to limitations in remote sensing technology such as cloud cover, frozen condition, dried up condition etc.\n\nThe detailed flow-chart on methodology for automatic monitoring of Glacial Lakes and Water Bodies using satellite images is given below in Figure 3.1\n\n**4. Results**\n\n**4.1 Results of Monitoring of 902 Glacial Lakes & Water Bodies**\n\nThe water spread area of 902 Glacial Lakes & Water Bodies was calculated for the month of August 2025 in an automatic manner and manually digitized wherever required using the methodology described above. It includes **477 GL & WBs** with water spread area greater than 50ha which are being monitored since the year 2011 and **425 GLs** with water spread area between 10 Ha to 50 ha being monitored from the year 2022.\n\nThe results of change detection in water spread area of 477 GL & WBs are shown in **Table 4.1** to **Table 4.5.**\n\nIt is observed that out of **477 GL&WBs**,\n\ni. **13** show increase in water spread area greater than 40%\n\nii. **218** show increase in water spread area but less than 40%\n\niii. **15** show no change in water spread area\n\niv. **211** show decrease in water spread area\n\nv. change detection for remaining **20** could not be performed due to reasons such as like frozen condition, dried up condition, cloud cover etc.\n\nThe outcome of monitoring of **477 GLs & WBs (being monitored since 2011)** is shown in **Figure.4.1.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 365, "line_end": 438, "token_count_estimate": 600, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d51b88d4fea1fa2a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.1: Outcome of Monitoring of 477 GLs & WBs (being monitored since 2011), August 2025\n\n**Figure 4.1: Outcome of Monitoring of 477 GLs & WBs (being monitored since 2011), August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.1: Outcome of Monitoring of 477 GLs & WBs (being monitored since 2011), August 2025", "line_start": 439, "line_end": 439, "token_count_estimate": 93, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26191241406952bc", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nIt was observed that out of **425 Glacial Lakes**,\n\ni. **9** shows increase in water spread area greater than 40%\n\nii. **194** show increase in water spread area but less than 40%\n\niii. **8** show no change in water spread area\n\niv. **180** show decrease in water spread area\n\nv. change detection for remaining **34** could not be performed due to reasons such as like frozen condition, dried up condition, cloud cover etc.\n\nThe outcome of monitoring of **425 GLs (being monitored since 2022)** is shown in **Figure.4.2.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 440, "line_end": 454, "token_count_estimate": 169, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a71338269a332ef", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.2: Outcome of Monitoring of 425 GLs (being monitored since 2022), August 2025\n\n**Figure 4.2: Outcome of Monitoring of 425 GLs (being monitored since 2022), August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.2: Outcome of Monitoring of 425 GLs (being monitored since 2022), August 2025", "line_start": 455, "line_end": 455, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3240ff85cc76ba56", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe results of change detection in water spread area of remaining **425 GLs** are shown in **Table 4.6** and **Table 4.7.**\n\nThe combined outcome of monitoring of 902 GLs & WBs is shown in **Figure.4.3.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 456, "line_end": 461, "token_count_estimate": 92, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8d951c40f7d25b08", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.3: Combined Outcome of Monitoring of 902 GLs & WBs, August 2025\n\n**Figure 4.3: Combined Outcome of Monitoring of 902 GLs & WBs, August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.3: Combined Outcome of Monitoring of 902 GLs & WBs, August 2025", "line_start": 462, "line_end": 462, "token_count_estimate": 83, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2bd8c261b552709f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n**4.2 100 Glacial Lakes located in India requiring vigorous monitoring (Out of 902 GLs&WBs)**\n\nOut of the 902 GLs & WBs monitored, 100 Glacial Lakes (15 GLs>50 Ha & 85GLs – 10 to 50 Ha) are located in India. The analysis indicates that\n\nOut of 15 GLs\n\n(i) **8** show increase in water spread area\n(ii) **6** show decrease in water spread area\n(iii) change detection of **1** GL could not be performed\n\nOut of 85 GLs\n\n(i) **46** show increase in water spread area\n(ii) **30** show decrease in water spread area\n(iii) **2** shows no change in water spread area\n(iv) change detection of **7** GL could not be performed\n\nThe results of change detection in water spread area of 15 GLs (>50Ha) and 85 GLs (10ha-50ha) are shown in **Table 4.8** and **Table 4.9** respectively.\n\nThe state-wise distribution of Glacial Lakes located in India analyzed for the month of August 2025 is shown in **figure 4.4. The lakes showing increase in water spread demand vigorous monitoring.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 463, "line_end": 484, "token_count_estimate": 302, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "ee28de158aac599e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.4: State-wise distribution of Outcome of monitoring of 100 GLs within India (out of 902 GLs &WBs ), August 2025\n\n**Figure 4.4: State-wise distribution of Outcome of monitoring of 100 GLs within India (out of 902 GLs &WBs ), August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.4: State-wise distribution of Outcome of monitoring of 100 GLs within India (out of 902 GLs &WBs ), August 2025", "line_start": 485, "line_end": 485, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "986037f88e787843", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n**4.3 Results of Monitoring of 1941 Glacial Lakes as per Glacial Lake Atlas 2023**\n\n**4.3.1 Results of 581 Glacial Lakes located in India (Glacial Lake Atlas 2023)**\n\nIt is observed that out of 581GLs located in India,\n\ni. **14** show increase in water spread area greater than 40%\n\nii. **360** show increase in water spread area but less than 40%\n\niii. **25** show no change in water spread area\n\niv. **119** show decrease in water spread area\n\nv. change detection for remaining **63** could not be performed due to reasons such as like frozen condition, dried up condition, cloud cover etc.\n\nThe outcome of monitoring of **581 GLs located in India (being monitored from 2025)** is shown in **Figure.4.5.**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 486, "line_end": 505, "token_count_estimate": 216, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "d52e75074ef0305c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.5: Outcome of Monitoring of 581 GLs located in India (being monitored from 2025) for the month of August 2025\n\n**Figure 4.5: Outcome of Monitoring of 581 GLs located in India (being monitored from 2025) for the month of August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.5: Outcome of Monitoring of 581 GLs located in India (being monitored from 2025) for the month of August 2025", "line_start": 506, "line_end": 506, "token_count_estimate": 99, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5337b33027f682f2", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe results of change detection in water spread area of remaining **581 GLs** located in India are shown in **Table 4.10**.\n\n**4.3.2 Results of 1360 Glacial Lakes located in India (Glacial Lake Atlas 2023)**\n\nIt is observed that out of 1360 GLs located in Transboundary Region,\n\ni. **46** show increase in water spread area greater than 40%\nii. **673** show increase in water spread area but less than 40%\niii. **71** show no change in water spread area\niv. **457** show decrease in water spread area\nv. change detection for remaining **113** could not be performed due to reasons such as like frozen condition, dried up condition, cloud cover etc\n\nThe outcome of monitoring of **1360 GLs located transboundary (being monitored from 2025)** is shown in Figure.4.6.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 507, "line_end": 523, "token_count_estimate": 227, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "db4d2959827a53c7", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.6: Outcome of Monitoring of 1360 GLs located transboundary (being monitored from 2025) for the month of August 2025\n\n**Figure 4.6: Outcome of Monitoring of 1360 GLs located transboundary (being monitored from 2025) for the month of August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.6: Outcome of Monitoring of 1360 GLs located transboundary (being monitored from 2025) for the month of August 2025", "line_start": 524, "line_end": 524, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9cee2964690b4ccf", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe combined outcome of monitoring of 1941 GLs as per Glacial lake Atlas 2023, being monitored from 2025 is shown in Figure.4.7.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 525, "line_end": 527, "token_count_estimate": 63, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8a621de76665fad5", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.7: Combined Outcome of Monitoring of 1941 GLs as per Glacial Lake Atlas 2023 for the month of August 2025\n\n**Figure 4.7: Combined Outcome of Monitoring of 1941 GLs as per Glacial Lake Atlas 2023 for the month of August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.7: Combined Outcome of Monitoring of 1941 GLs as per Glacial Lake Atlas 2023 for the month of August 2025", "line_start": 528, "line_end": 528, "token_count_estimate": 97, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "596312a7f6e25b73", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe results of change detection in water spread area of remaining 1360 GLs located transboundary are shown in Table 4.11.\n\n**4.4 Glacial Lakes located in India requiring vigorous monitoring as per Glacial lake Atlas 2023**\n\nOut of the 1941 Glacial Lakes monitored as per Glacial Lake Atlas 2023, 581GLs are located in India. The state-wise distribution of these Glacial Lakes analyzed for the month of August 2025 is shown in figure 4.8. The lakes showing increase in water spread demand vigorous monitoring.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 529, "line_end": 538, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "aeda6c6ede9158b6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.8: State-wise distribution of Outcome of monitoring of 581 GLs within India (as per Glacial Lake Atlas 2023), August 2025\n\n**Figure 4.8: State-wise distribution of Outcome of monitoring of 581 GLs within India (as per Glacial Lake Atlas 2023), August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.8: State-wise distribution of Outcome of monitoring of 581 GLs within India (as per Glacial Lake Atlas 2023), August 2025", "line_start": 539, "line_end": 539, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8ae61836435d2abd", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe results of state-wise change detection in water spread area of 581 GLs located in India are shown in Table 4.12.\n\n**4.5 Results of total 2843 GLs & WBs monitored by CWC**\n\nOut of 2843 GLs & WBs monitored, 1435 show increase in area, 1008 show decrease in area, 108 show no change in area and 292 were no able to be anaysed from remote sensing data, during the month of August 2025.\n\nThe combined outcome of monitoring 2843 GLs & WBs is shown in Figure. 4.9.\n\n**4.6 Combined State-wise Results of total 681 Glacial lakes located in India**\n\nThe state-wise distribution of combined outcome of monitoring of 681 Glacial Lakes located in India is shown in Figure. 4.10.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 540, "line_end": 552, "token_count_estimate": 207, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "15ecf797ceb8cd2a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.9: Combined Outcome of Monitoring total 2843 GLs & WBs, August 2025\n\n**Figure 4.9: Combined Outcome of Monitoring total 2843 GLs & WBs, August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.9: Combined Outcome of Monitoring total 2843 GLs & WBs, August 2025", "line_start": 553, "line_end": 553, "token_count_estimate": 85, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3f32c1a8dc67fde", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: figure\nFigure: Figure 4.10: State-wise distribution of Combined Outcome of Monitoring of total 681 GLs located in India, August 2025\n\n**Figure 4.10: State-wise distribution of Combined Outcome of Monitoring of total 681 GLs located in India, August 2025**", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "figure", "figure_caption": "Figure 4.10: State-wise distribution of Combined Outcome of Monitoring of total 681 GLs located in India, August 2025", "line_start": 555, "line_end": 555, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "51186a9cd516c54b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\n**5.0 Conclusion**\n\n**5.1. Conclusion on Monitoring of 902 GLs & WBs of Indian Himalayan Region**\n* **12 Water Bodies and 1 Glacial Lake** (>50Ha area) show increase in area greater than 40% when change detection was carried out with respect to base year area(2011), average area of last 5 years(2020-2024) & average area of last 10 years(2015-2024). These Glacial lake and Water Bodies are located in China.\n* 26 nos. of Glacial Lakes & Water Bodies have been merged to 13 nos. of Glacial Lakes & Water Bodies & combined area of merged glacial lakes and water bodies has been shown against respective glacial lakes and water bodies. However, merging and demerging of lakes is a dynamic process; hence figure of 902 Glacial Lakes & Water Bodies has been kept intact for analysis part. Details of merged Glacial Lakes & Water Bodies are as under.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 556, "line_end": 565, "token_count_estimate": 254, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": []}}
{"id": "681bb23f99665054", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: table\nTable\n\n| Sl. No. | ID | GL/WB | Location | Remarks |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 03_71G_008 | WB | China | Merged with nearby lake not in inventory 2011 |\n| 2 | 03_71K_011 | WB | China | Merged with nearby lake not in inventory 2011 |\n| 3 | 03_82N_032 | GL | China | Merged with nearby lake not in inventory 2011 |\n| 4 | 03_62O_040 | WB | China | Merged with nearby lake not in inventory 2011 |\n| 5 | 01_61C_014 | WB | China | Merged with each other |\n| | 01_61C_015 | WB | | |\n| 6 | 03_78E_009 | WB | China | Merged with each other |\n| | 03_78E_010 | WB | | |\n| 7 | 03_62O_041 | WB | China | Merged with each other |\n| | 03_62O_042 | WB | | |\n| 8 | 03_71K_007 | WB | China | Merged with each other |\n| | 03_71K_009 | WB | | |\n| 9 | 03_91C_035 | GL | China | Merged with each other |\n| | 03_91C_036 | GL | | |\n| 10 | 02_71P_018 | WB | China | Merged with each other |\n| | 02_71P_019 | GL | | |\n| | 02_71P_020 | GL | | |\n| 11 | 03_77L_048 | GL | China | Merged with each other |\n| | 03_77L_053 | GL | | |\n| 12 | 01_61C_002 | WB | China | Merged with each other |\n| | 01_61C_004 | WB | | |\n| | 01_61C_005 | WB | | |\n| | 01_61C_010 | WB | | |\n| | 01_61C_011 | WB | | |\n| 13 | 01_52H_003 | GL | India (Himachal Pradesh) | Merged with each other |\n| | 01_52H_004 | GL | | |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID", "GL/WB", "Location", "Remarks"], "table_row_start": 1, "table_row_end": 26, "line_start": 566, "line_end": 593, "token_count_estimate": 721, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": ["01_52H_003", "01_52H_004", "01_61C_002", "01_61C_004", "01_61C_005", "01_61C_010", "01_61C_011", "01_61C_014", "01_61C_015", "02_71P_018", "02_71P_019", "02_71P_020", "03_62O_040", "03_62O_041", "03_62O_042", "03_71G_008", "03_71K_007", "03_71K_009", "03_71K_011", "03_77L_048", "03_77L_053", "03_78E_009", "03_78E_010", "03_82N_032", "03_91C_035", "03_91C_036"]}}
{"id": "0e4f2b6e7b444ae5", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011\nType: text\n\nThe details of these lakes are given as footnote under Table Nos 4.1 to 4.6.\n\n* **9 Glacial Lakes (10 ha-50 Ha area)** show increase in area greater than 40% when change detection was carried out with respect to base year area (2011), average area of last 3 years (2022-2024). 7 GLs are located in China. The remaining 2 Glacial lakes are located in India **(Jammu & Kashmir- 1 No. & Sikkim – 1 No.)**.\n* The total Inventory area of **Glacial Lakes and Water Bodies** was 5,30,654 Ha during the year 2011 which has increased to 5,79,700 Ha during the year 2025 (August). There is a **9.24%** increase in area. *(Out of 902 GsL&WBs, only 798 GLs&WBs were considered for this interpretation. The remaining lakes include 40 SDC lakes which have no inventory details as well as GLs/WBs which were not analyzed/have been merged during the month of August, 2025.)* This is shown in Figure below.\n\n* The total Inventory area of **Glacial Lakes** was 19,848 Ha during the year 2011 which has increased to 23,536 Ha during the year 2025 (August). There is a **18.58%** increase in area. *(Out of 544 GLs, only 465 GLs were considered for this interpretation. The remaining lakes include 40 SDC lakes which have no inventory details as well as lakes which were not analyzed/ have been merged during the month of August, 2025.)*. This is shown in Figure below.\n\n* The total Inventory area of **Glacial Lakes within India** was 1,995 Ha during the year 2011 which has increased to 2,445 Ha during the year 2025 (August). There is a **22.56%** increase in area. *(Out of 100GLs, only 55 GLs were considered for this interpretation. The remaining lakes include 40 SDC lakes which have no inventory details as well as lakes which were not analysed/have been merged during the month of August, 2025.)*. This is shown in Figure below.\n\n* **54 Glacial Lakes** (out of 100) located within India, as shown below, display increase in water spread area during the month of August 2025, and hence **demand vigorous monitoring for disaster purpose** *(Ladhak-5, Jammu & Kashmir-6, Himachal Pradesh-10, Uttarakhand- 4, Sikkim – 24 & Arunachal Pradesh-5)*.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011"], "chunk_type": "text", "line_start": 594, "line_end": 610, "token_count_estimate": 609, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "de551f7372e1cc3e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n* The total Inventory area of newly monitored **Glacial Lakes within India as per Glacial Lake Atlas, 2023** was 12,939 Ha in the year 2021 which has increased to 13,260 Ha during the year 2025 (August). There is a **5.26%** increase in area. *(Out of 581GLs, only 518 GLs were considered for this interpretation. The remaining lakes were not able to be analysed during the month of August, 2025.)*. This is shown in Figure below.\n\n* The total Inventory area of newly monitored **Glacial Lakes located in transboundary region as per Glacial Lake Atlas, 2023** was 43,723 Ha in the year 2021 which has increased to 45,862 Ha during the year 2025 (August). There is a **4.89%** increase in area. *(Out of 1360 GLs, only 1247 GLs were considered for this interpretation. The remaining lakes were not able to be analysed during the month of August, 2025.)*. This is shown in Figure below.\n\n* **374 Glacial Lakes** (out of 581) located within India as per Glacial Lake Atlas 2023, as shown below, display increase in water spread area during the month of August 2025, and hence **demand vigorous monitoring for disaster purpose** *(Ladhak-128, Jammu & Kashmir-44, Himachal Pradesh-3, Uttarakhand- 3, Sikkim – 20 & Arunachal Pradesh-176)*.\n\n**5.3 Conclusion on Combined Monitoring of 2843 Glacial lakes being monitored by CWC**\n\n* **428 Glacial Lakes** (out of 681) located within India as per Glacial Lake Atlas 2023, as shown below, display **increase in water spread area during the month of August 2025**, and hence **demand vigorous monitoring for disaster purpose** *(Ladhak-133, Jammu & Kashmir-50, Himachal Pradesh-13, Uttarakhand- 7, Sikkim – 44 & Arunachal Pradesh-181)*.\n* Use of a combination of Microwave satellite images in conjunction with multispectral satellite images (MSI) has largely overcome the short-comings due to obscurity from cloud cover and this has led to almost all-time and all-weather monitoring of all Glacial lakes & Water Bodies. This has increased availability of satellite images at shorter frequency interval and will facilitate in reducing the monitoring interval in future.\n* The use of Sentinel satellite images has brought the improvement of spatial resolution from 56m to10m leading to enhancement of monitoring accuracy. Sentinel images have also aided in improving temporal resolution.\n* Most of GLs & WBs exhibiting 40% or more increase in water spread area, are located in transboundary region.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 612, "line_end": 633, "token_count_estimate": 701, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "33560bac1a2700d1", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.1: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “More than 40% Increase” in area\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 03_71K_007 | NRSC | CH_430 | 4752 | WB | 29°34'46.2\" | 86°15'39.6\" | Brahmaputra | | China | 907 | 99 | 163 | 123 | 455 |\n| 2 | 03_78E_010 | NRSC | CH_606 | 4582 | WB | 27°57'48.96\" | 89°24'45.72\" | Brahmaputra | | China | 235 | 49 | 50 | 47 | 366 |\n| 3 | 01_61C_010 | NRSC | CH_38 | 4495 | WB | 33°43'28.92\" | 80°41'25.08\" | Indus | Indus | China | 3140 | 88 | 725 | 446 | 333 |\n| 4 | 02_71P_019 | NRSC | CH_207 | 4199 | GL | 28°21'8.64\" | 87°52'30.36\" | Ganga | Arun Kosi | China | 299 | 48 | 79 | 73 | 281 |\n| 5 | 03_71K_009 | NRSC | CH_432 | 4750 | WB | 29°33'26.28\" | 86°15'58.68\" | Brahmaputra | | China | 907 | 230 | 271 | 240 | 235 |\n| 6 | 01_61C_005 | NRSC | CH_33 | 4495 | WB | 33°44'54.96\" | 80°38'29.76\" | Indus | Indus | China | 3140 | 139 | 970 | 622 | 224 |\n| 7 | 01_61C_014 | NRSC | CH_42 | 4279 | WB | 33°29'57.12\" | 80°20'60\" | Indus | Indus | China | 1110 | 286 | 360 | 333 | 208 |\n| 8 | 03_62O_042 | NRSC | CH_387 | 4964 | WB | 29°29'56.04\" | 83°25'40.44\" | Brahmaputra | | China | 294 | 57 | 96 | 77 | 205 |\n| 9 | 01_61C_011 | NRSC | CH_39 | 4494 | WB | 33°43'13.44\" | 80°43'16.68\" | Indus | Indus | China | 3140 | 403 | 1091 | 790 | 188 |\n| 10 | 01_61C_002 | NRSC | CH_30 | 4494 | WB | 33°45'3.96\" | 80°35'51.72\" | Indus | Indus | China | 3140 | 685 | 1287 | 1045 | 144 |\n| 11 | 02_71P_018 | NRSC | CH_206 | 4199 | WB | 28°21'27.72\" | 87°53'6.72\" | Ganga | Arun Kosi | China | 299 | 51 | 125 | 91 | 139 |\n| 12 | 03_71K_011 | NRSC | CH_434 | 4761 | WB | 29°28'32.88\" | 86°13'50.88\" | Brahmaputra | | China | 678 | 387 | 381 | 387 | 75 |\n| 13 | 01_61D_001 | NRSC | CH_53 | 5593 | WB | 32°48'5.4\" | 80°29'0.96\" | Indus | Indus | China | 93 | 63 | 46 | 58 | 47 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.1: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “More than 40% Increase” in area", "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 13, "line_start": 634, "line_end": 648, "token_count_estimate": 1321, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi"], "countries": ["China"], "lake_ids": ["01_61C_002", "01_61C_005", "01_61C_010", "01_61C_011", "01_61C_014", "01_61D_001", "02_71P_018", "02_71P_019", "03_62O_042", "03_71K_007", "03_71K_009", "03_71K_011", "03_78E_010"]}}
{"id": "900f3cb35eb66ad3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n*A Water Body of China of Lake ID: 03_71K_011 has have merged with a nearby lake. The combined area has been shown against the lake.*\n*A Water Body of China of Lake ID: 03_62O_040 has have merged with a nearby lake. The combined area has been shown against the lake.*\n*The Waterbodies of China of Lake ID : 01_61C_002, 01_61C_004, 01_61C_005, 01_61C_010 & 01_61C_011 have merged with each other and combined area has been shown against each lake.*\n*The Waterbodies of China of Lake ID : 02_71P_018 has merged with nearby Glacial lakes of Lake ID: 02_71P_019 & Lake ID: 02_71P_020 and combined area has been shown against each lake.*\n*The Waterbodies of China of Lake ID : 03_71K_007 &03_71K_009 have merged with each other and combined area has been shown against each lake*\n*The Waterbodies of China of Lake ID : 03_62O_041 &03_62O_042 have merged with each other and combined area has been shown against each lake*\n*The Waterbodies of China of Lake ID : 01_61C_014 &01_61C_015 have merged with each other and combined area has been shown against each lake*", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 649, "line_end": 662, "token_count_estimate": 423, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["01_61C_002", "01_61C_004", "01_61C_005", "01_61C_010", "01_61C_011", "01_61C_014", "01_61C_015", "02_71P_018", "02_71P_019", "02_71P_020", "03_62O_040", "03_62O_041", "03_62O_042", "03_71K_007", "03_71K_009", "03_71K_011"]}}
{"id": "c28f37e793f8f78f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.2: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “Increase in area upto 40%”\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 01_53A_001 | NRSC | HP_9 | 409 | WB | 31°59'21.84\" | 76°3'1.44\" | Indus | Beas | India | 25131 | 16946 | 17824 | 18029 | 39 |\n| 2 | 02_71L_034 | NRSC | CH_188 | 5095 | GL | 28°2'0.96\" | 86°29'46.32\" | Ganga | Sun Kosi | China | 87 | 46 | 62 | 64 | 37 |\n| 3 | 03_62O_041 | NRSC | CH_386 | 4963 | WB | 29°30'39.6\" | 83°26'39.48\" | Brahmaputra | | China | 294 | 206 | 217 | 217 | 35 |\n| 4 | 03_78E_009 | NRSC | CH_605 | 4580 | WB | 27°57'37.08\" | 89°23'47.04\" | Brahmaputra | | China | 235 | 175 | 175 | 171 | 34 |\n| 5 | 01_61C_015 | NRSC | CH_43 | 4280 | WB | 33°29'16.44\" | 80°18'58.32\" | Indus | Indus | China | 1110 | 742 | 838 | 812 | 33 |\n| 6 | 03_62O_032 | NRSC | CH_377 | 5012 | WB | 29°41'21.48\" | 83°11'24.36\" | Brahmaputra | | China | 70 | 49 | 52 | 53 | 32 |\n| 7 | 01_52D_001 | NRSC | HP_1 | 780 | WB | 32°36'52.92\" | 76°1'53.76\" | Indus | Ravi | India | 970 | 725 | 694 | 752 | 29 |\n| 8 | 02_53K_001 | NRSC | UK_1 | 355 | WB | 29°34'10.2\" | 78°45'46.8\" | Ganga | Ramganga | India | 6720 | 3880 | 5368 | 5272 | 25 |\n| 9 | 01_53A_002 | NRSC | HP_10 | 495 | WB | 31°23'7.8\" | 76°32'6\" | Indus | Sutlej | India | 14450 | 10256 | 10429 | 11637 | 24 |\n| 10 | 03_78A_021 | NRSC | SK_26 | 5431 | GL | 27°49'28.2\" | 88°14'57.12\" | Brahmaputra | Teesta | India | 100 | 56 | 81 | 62 | 23 |\n| 11 | 03_77L_008 | NRSC | CH_524 | 4448 | WB | 28°49'31.8\" | 90°41'11.04\" | Brahmaputra | | China | 88 | 71 | 59 | 72 | 23 |\n| 12 | 01_43G_001 | NRSC | JK_67 | 346 | WB | 33°12'47.16\" | 73°42'41.76\" | Indus | Jhelum | India | 27870 | 14989 | 21287 | 22652 | 23 |\n| 13 | 02_72M_016 | NRSC | NP_92 | 4572 | GL | 27°47'54.6\" | 87°5'33.36\" | Ganga | Arun Kosi | Nepal | 258 | 161 | 211 | 185 | 22 |\n| 14 | 02_71P_025 | NRSC | CH_213 | 4807 | WB | 28°12'51.12\" | 87°28'5.88\" | Ganga | Arun Kosi | China | 164 | 104 | 135 | 135 | 22 |\n| 15 | 03_71G_001 | NRSC | CH_410 | 5163 | WB | 29°53'34.08\" | 85°14'49.56\" | Brahmaputra | | China | 906 | 720 | 747 | 744 | 21 |\n| 16 | 03_82O_047 | NRSC | CH_1039 | 3574 | WB | 29°9'46.08\" | 95°29'27.6\" | Brahmaputra | Dihang | China | 58 | 44 | 48 | 43 | 21 |\n| 17 | 01_43M_003 | NRSC | JK_120 | 2663 | WB | 35°13'54.84\" | 75°37'49.44\" | Indus | Shigar (Indus) | India | 268 | 187 | 218 | 223 | 20 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.2: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “Increase in area upto 40%”", "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 663, "line_end": 681, "token_count_estimate": 1630, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Beas", "Dihang", "Jhelum", "Kosi", "Ravi", "Teesta"], "countries": ["China", "India", "Nepal"], "lake_ids": ["01_43G_001", "01_43M_003", "01_52D_001", "01_53A_001", "01_53A_002", "01_61C_015", "02_53K_001", "02_71L_034", "02_71P_025", "02_72M_016", "03_62O_032", "03_62O_041", "03_71G_001", "03_77L_008", "03_78A_021", "03_78E_009", "03_82O_047", "10256", "10429", "11637", "14450", "14989", "16946", "17824", "18029", "21287", "22652", "25131", "27870"]}}
{"id": "5574daedc47154fe", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2023 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 18 | 02_71L_004 | NRSC | CH_159 | 5518 | GL | 28°23'40.92\" | 86°22'45.12\" | Ganga | Arun Kosi | China | 142 | 79 | 118 | 114 | 20 |\n| 19 | 02_53P_003 | NRSC | UK_11 | 207 | WB | 28°54'3.6\" | 79°37'22.8\" | Ganga | Ramganga | India | 1298 | 1078 | 850 | 867 | 20 |\n| 20 | 03_62O_030 | NRSC | CH_375 | 5013 | WB | 29°43'34.68\" | 83°6'16.56\" | Brahmaputra | | China | 124 | 97 | 101 | 104 | 19 |\n| 21 | 03_78E_007 | NRSC | BH_60 | 5008 | GL | 27°56'29.04\" | 89°55'48\" | Brahmaputra | Puna Tsang Chhu | Bhutan | 74 | 61 | 62 | 61 | 19 |\n| 22 | 03_77L_009 | NRSC | CH_525 | 4515 | WB | 28°47'21.12\" | 90°53'38.76\" | Brahmaputra | | China | 650 | 522 | 539 | 549 | 18 |\n| 23 | 03_77D_005 | NRSC/ SDC | SK_5 | 5249 | GL | 28°0'32.76\" | 88°41'52.44\" | Brahmaputra | Teesta | India | 118 | 88 | 100 | 91 | 18 |\n| 24 | 03_77H_018 | NRSC | CH_488 | 4699 | WB | 28°10'50.52\" | 89°32'3.84\" | Brahmaputra | | China | 98 | 80 | 83 | 83 | 18 |\n| 25 | 03_62K_009 | NRSC | CH_313 | 5079 | GL | 29°50'25.8\" | 82°47'0.6\" | Brahmaputra | | China | 348 | 250 | 290 | 297 | 17 |\n| 26 | 01_52J_009 | NRSC | JK_205 | 5576 | WB | 34°9'2.16\" | 78°33'11.52\" | Indus | Shyok | India | 67 | 57 | 57 | 57 | 17 |\n| 27 | 03_77D_003 | NRSC | SK_3 | 5098 | WB | 28°0'47.52\" | 88°45'20.88\" | Brahmaputra | Teesta | India | 113 | 84 | 98 | 95 | 16 |\n| 28 | 01_43J_004 | NRSC | JK_82 | 4078 | WB | 34°55'15.24\" | 74°31'14.88\" | Indus | Jhelum | India | 78 | 59 | 66 | 67 | 16 |\n| 29 | 03_78I_023 | NRSC | BH_104 | 5055 | GL | 27°56'22.56\" | 90°32'5.28\" | Brahmaputra | Manas Chhu & Mangde Chhu | Bhutan | 62 | 51 | 54 | 50 | 16 |\n| 30 | 01_52K_014 | NRSC | JK_222 | 4535 | WB | 33°15'6.84\" | 78°2'34.44\" | Indus | Indus | India | 492 | 405 | 427 | 422 | 15 |\n| 31 | 03_82O_016 | NRSC | CH_1023 | 4374 | WB | 29°22'19.56\" | 95°52'18.48\" | Brahmaputra | Dihang | China | 105 | 91 | 92 | 81 | 15 |\n| 32 | 02_78A_004 | NRSC | CH_270 | 5603 | GL | 27°55'58.08\" | 88°4'0.48\" | Ganga | Arun Kosi | China | 124 | 84 | 109 | 108 | 14 |\n| 33 | 03_77P_023 | NRSC | CH_593 | 4235 | WB | 28°1'55.56\" | 91°0'6.12\" | Brahmaputra | Kuri Chhu | China | 81 | 45 | 71 | 65 | 14 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2023 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 684, "line_end": 701, "token_count_estimate": 1482, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dihang", "Jhelum", "Kosi", "Manas", "Shyok", "Teesta"], "countries": ["Bhutan", "China", "India"], "lake_ids": ["01_43J_004", "01_52J_009", "01_52K_014", "02_53P_003", "02_71L_004", "02_78A_004", "03_62K_009", "03_62O_030", "03_77D_003", "03_77D_005", "03_77H_018", "03_77L_009", "03_77P_023", "03_78E_007", "03_78I_023", "03_82O_016"]}}
{"id": "b37b7d6ab359593a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2023 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 34 | 01_61F_004 | NRSC | CH_61 | 4814 | WB | 34°1'19.92\" | 81°36'47.88\" | Indus | Indus | China | 43854 | 36392 | 38538 | 38466 | 14 |\n| 35 | 02_71P_043 | NRSC | CH_231 | 5206 | GL | 28°5'36.6\" | 87°38'15\" | Ganga | Arun Kosi | China | 89 | 67 | 79 | 77 | 13 |\n| 36 | 03_82O_061 | NRSC | AP_54 | 3811 | WB | 29°0'40.32\" | 95°53'5.64\" | Brahmaputra | Dibang | India | 64 | 54 | 56 | 52 | 13 |\n| 37 | 01_61C_016 | NRSC | CH_44 | 4289 | WB | 33°25'58.44\" | 80°27'59.76\" | Indus | Indus | China | 410 | 344 | 362 | 366 | 12 |\n| 38 | 02_72I_004 | NRSC | CH_244 | 5074 | GL | 27°56'45.96\" | 86°26'47.4\" | Ganga | Sun Kosi | China | 217 | 121 | 190 | 193 | 12 |\n| 39 | 01_52H_004 | NRSC | HP_5 | 4155 | GL | 32°29'47.04\" | 77°33'5.76\" | Indus | Chenab | India | 173 | 46 | 154 | 146 | 12 |\n| 40 | 02_62F_019 | NRSC | NP_12 | 5039 | WB | 30°7'46.56\" | 81°46'44.76\" | Ganga | Karnali | Nepal | 74 | 58 | 64 | 66 | 12 |\n| 41 | 01_43J_022 | NRSC | JK_100 | 1583 | WB | 34°7'11.28\" | 74°49'50.52\" | Indus | Jhelum | India | 70 | 60 | 63 | 62 | 12 |\n| 42 | 02_71L_010 | NRSC | CH_165 | 5387 | GL | 28°20'54.96\" | 86°13'30\" | Ganga | Sun Kosi | China | 66 | 47 | 59 | 56 | 12 |\n| 43 | 03_78E_002 | NRSC | BH_57 | 5110 | GL | 27°58'21\" | 89°55'47.64\" | Brahmaputra | Puna Tsang Chhu | Bhutan | 65 | 58 | 52 | 55 | 12 |\n| 44 | 01_52J_005 | NRSC | JK_201 | 5430 | WB | 34°11'9.96\" | 78°30'28.08\" | Indus | Shyok | India | 49 | 44 | 43 | 43 | 12 |\n| 45 | 01_61H_001 | NRSC | CH_66 | 4619 | WB | 32°7'7.68\" | 81°16'9.84\" | Indus | Indus | China | 330 | 282 | 278 | 298 | 11 |\n| 46 | 03_62J_031 | NRSC | CH_303 | 4897 | GL | 30°6'14.04\" | 82°16'10.56\" | Brahmaputra | | China | 246 | 160 | 221 | 215 | 11 |\n| 47 | 03_77L_010 | NRSC | CH_526 | 4457 | WB | 28°48'40.68\" | 90°29'34.44\" | Brahmaputra | | China | 52 | 47 | 45 | 44 | 11 |\n| 48 | 01_52O_005 | NRSC | CH_8 | 4358 | WB | 33°23'25.08\" | 79°22'1.2\" | Indus | Indus | China | 877 | 780 | 794 | 776 | 10 |\n| 49 | 01_61C_023 | NRSC | CH_51 | 4350 | WB | 33°5'57.48\" | 80°10'38.64\" | Indus | Indus | China | 705 | 623 | 644 | 618 | 10 |\n| 50 | 02_78A_003 | NRSC | CH_269 | 5522 | GL | 27°56'46.68\" | 88°4'30.72\" | Ganga | Arun Kosi | China | 178 | 124 | 162 | 152 | 10 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2023 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 705, "line_end": 723, "token_count_estimate": 1558, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Chenab", "Dibang", "Jhelum", "Kosi", "Shyok"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_43J_022", "01_52H_004", "01_52J_005", "01_52O_005", "01_61C_016", "01_61C_023", "01_61F_004", "01_61H_001", "02_62F_019", "02_71L_010", "02_71P_043", "02_72I_004", "02_78A_003", "03_62J_031", "03_77L_010", "03_78E_002", "03_82O_061", "36392", "38466", "38538", "43854"]}}
{"id": "51c36e735970001e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 51 | 03_77D_002 | NRSC | SK_2 | 5156 | GL | 28°1'33.96\" | 88°42'36\" | Brahmaputra | Teesta | India | 116 | 104 | 105 | 100 | 10 |\n| 52 | 02_71L_028 | NRSC | CH_183 | 5027 | GL | 28°8'8.88\" | 86°31'45.48\" | Ganga | Sun Kosi | China | 112 | 79 | 102 | 101 | 10 |\n| 53 | 02_71D_004 | NRSC | NP_45 | 4064 | GL | 28°29'19.68\" | 84°29'8.52\" | Ganga | Trishuli | Nepal | 109 | 74 | 99 | 98 | 10 |\n| 54 | 01_43K_010 | NRSC | JK_111 | 3946 | WB | 33°31'8.4\" | 74°35'1.32\" | Indus | Jhelum | India | 73 | 66 | 67 | 65 | 10 |\n| 55 | 02_62K_012 | NRSC | NP_30 | 3653 | WB | 29°11'47.76\" | 82°56'54.6\" | Ganga | Bheri | Nepal | 520 | 469 | 478 | 476 | 9 |\n| 56 | 02_71H_012 | NRSC | CH_132 | 5379 | GL | 28°33'49.68\" | 85°36'14.76\" | Ganga | Arun Kosi | China | 141 | 89 | 127 | 129 | 9 |\n| 57 | 03_82N_004 | NRSC | CH_975 | 4290 | GL | 30°36'3.96\" | 95°10'59.16\" | Brahmaputra | | China | 137 | 92 | 126 | 118 | 9 |\n| 58 | 02_71P_029 | NRSC | CH_217 | 5045 | GL | 28°10'42.24\" | 87°33'41.4\" | Ganga | Arun Kosi | China | 114 | 80 | 103 | 105 | 9 |\n| 59 | 02_72M_007 | NRSC | CH_253 | 4950 | GL | 27°55'35.04\" | 87°46'11.64\" | Ganga | Arun Kosi | China | 108 | 94 | 99 | 96 | 9 |\n| 60 | 02_71P_047 | NRSC | CH_235 | 5614 | GL | 28°4'9.48\" | 87°2'53.88\" | Ganga | Arun Kosi | China | 98 | 80 | 90 | 90 | 9 |\n| 61 | 02_62J_003 | NRSC | NP_19 | 4854 | WB | 30°4'4.08\" | 82°7'35.04\" | Ganga | Karnali | Nepal | 62 | 49 | 56 | 57 | 9 |\n| 62 | 03_62J_026 | NRSC | CH_298 | 5078 | GL | 30°15'21.6\" | 82°12'34.2\" | Brahmaputra | | China | 144 | 103 | 134 | 126 | 8 |\n| 63 | 01_52H_002 | NRSC/SDC | HP_3 | 4101 | GL | 32°31'28.92\" | 77°13'5.88\" | Indus | Chenab | India | 106 | 62 | 98 | 94 | 8 |\n| 64 | 03_91H_025 | NRSC | CH_1190 | 3741 | WB | 28°46'58.8\" | 97°9'6.84\" | Brahmaputra | Lohit | China | 92 | 85 | 81 | 85 | 8 |\n| 65 | 02_72I_023 | NRSC | NP_76 | 5232 | GL | 27°46'59.16\" | 86°57'24.84\" | Ganga | Sun Kosi | Nepal | 88 | 81 | 70 | 74 | 8 |\n| 66 | 03_91C_069 | NRSC | AP_101 | 3245 | WB | 29°3'3.6\" | 96°8'40.2\" | Brahmaputra | Dibang | India | 84 | 78 | 76 | 76 | 8 |\n| 67 | 02_72M_005 | NRSC | CH_251 | 5141 | GL | 27°56'57.12\" | 87°55'51.96\" | Ganga | Arun Kosi | China | 80 | 71 | 74 | 72 | 8 |\n| 68 | 03_82J_005 | NRSC | CH_835 | 4134 | GL | 30°37'34.68\" | 94°26'42\" | Brahmaputra | | China | 80 | 67 | 74 | 72 | 8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 730, "line_end": 749, "token_count_estimate": 1646, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Chenab", "Dibang", "Jhelum", "Kosi", "Lohit", "Teesta"], "countries": ["China", "India", "Nepal"], "lake_ids": ["01_43K_010", "01_52H_002", "02_62J_003", "02_62K_012", "02_71D_004", "02_71H_012", "02_71L_028", "02_71P_029", "02_71P_047", "02_72I_023", "02_72M_005", "02_72M_007", "03_62J_026", "03_77D_002", "03_82J_005", "03_82N_004", "03_91C_069", "03_91H_025"]}}
{"id": "1a844c1481cc9642", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 69 | 01_62F_010 | NRSC | CH_101 | 5250 | GL | 30°23'11.04\" | 81°55'47.64\" | Indus | Sutlej | China | 71 | 45 | 66 | 64 | 8 |\n| 70 | 03_82J_025 | NRSC | CH_855 | 4038 | WB | 30°0'17.64\" | 94°23'1.68\" | Brahmaputra | | China | 64 | 59 | 57 | 56 | 8 |\n| 71 | 03_78E_028 | NRSC | BH_72 | 2161 | WB | 27°38'21.12\" | 89°44'24.36\" | Brahmaputra | Puna Tsang Chhu | Bhutan | 51 | 47 | 43 | 44 | 8 |\n| 72 | 01_52H_005 | NRSC | HP_6 | 4286 | WB | 32°28'53.76\" | 77°36'52.56\" | Indus | Chenab | India | 49 | 45 | 45 | 44 | 8 |\n| 73 | 03_62J_013 | NRSC | CH_285 | 4934 | WB | 30°25'8.04\" | 82°18'7.92\" | Brahmaputra | | China | 986 | 854 | 926 | 926 | 7 |\n| 74 | 02_71L_003 | NRSC | CH_158 | 5324 | WB | 28°49'55.92\" | 86°31'21\" | Ganga | Arun Kosi | China | 288 | 258 | 268 | 270 | 7 |\n| 75 | 03_77L_051 | NRSC | BH_22 | 4548 | GL | 28°5'31.2\" | 90°17'60\" | Brahmaputra | Puna Tsang Chhu | Bhutan | 169 | 143 | 157 | 158 | 7 |\n| 76 | 01_62E_003 | NRSC | CH_78 | 5104 | WB | 31°27'30.24\" | 81°5'26.52\" | Indus | Indus | China | 164 | 136 | 152 | 153 | 7 |\n| 77 | 03_62J_001 | NRSC | CH_273 | 5449 | WB | 30°52'49.8\" | 82°51'33.12\" | Brahmaputra | | China | 157 | 147 | 140 | 142 | 7 |\n| 78 | 03_62J_032 | NRSC | CH_304 | 4857 | GL | 30°4'42.6\" | 82°20'32.28\" | Brahmaputra | | China | 94 | 81 | 88 | 88 | 7 |\n| 79 | 03_82G_023 | NRSC | CH_784 | 4377 | WB | 29°30'45\" | 93°37'11.64\" | Brahmaputra | | China | 90 | 84 | 80 | 81 | 7 |\n| 80 | 03_78A_013 | NRSC | SK_19 | 5470 | GL | 27°55'7.68\" | 88°9'39.6\" | Brahmaputra | Teesta | India | 89 | 67 | 78 | 83 | 7 |\n| 81 | 02_72I_027 | NRSC | NP_80 | 4977 | GL | 27°45'17.28\" | 86°57'28.8\" | Ganga | Sun Kosi | Nepal | 88 | 82 | 78 | 76 | 7 |\n| 82 | 03_82J_019 | NRSC | CH_849 | 3944 | GL | 30°5'49.56\" | 94°16'10.92\" | Brahmaputra | | China | 88 | 45 | 83 | 76 | 7 |\n| 83 | 03_82K_020 | NRSC | CH_876 | 4364 | WB | 29°53'47.76\" | 94°27'41.4\" | Brahmaputra | | China | 87 | 77 | 81 | 80 | 7 |\n| 84 | 03_82G_017 | NRSC | CH_778 | 4437 | WB | 29°32'32.28\" | 93°49'49.44\" | Brahmaputra | | China | 57 | 53 | 50 | 51 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 753, "line_end": 770, "token_count_estimate": 1496, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Chenab", "Kosi", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52H_005", "01_62E_003", "01_62F_010", "02_71L_003", "02_72I_027", "03_62J_001", "03_62J_013", "03_62J_032", "03_77L_051", "03_78A_013", "03_78E_028", "03_82G_017", "03_82G_023", "03_82J_019", "03_82J_025", "03_82K_020"]}}
{"id": "dcfeaa8d9feb2426", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n| 85 | 03_91D_009 | NRSC | AP_108 | 4037 | WB | 28°55'40.44\" | 96°20'19.68\" | Brahmaputra | Dibang | India | 50 | 47 | 47 | 44 | 7", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 771, "line_end": 775, "token_count_estimate": 136, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": ["India"], "lake_ids": ["03_91D_009"]}}
{"id": "73f3695f7b73689b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 86 | 01_61C_018 | NRSC | CH_46 | 4291 | WB | 33°22'1.2\" | 80°33'11.16\" | Indus | Indus | China | 2073 | 1779 | 1934 | 1947 | 6 |\n| 87 | 03_77L_011 | NRSC | CH_527 | 4533 | WB | 28°45'34.92\" | 90°50'49.2\" | Brahmaputra | | China | 1285 | 1209 | 1173 | 1171 | 6 |\n| 88 | 01_61D_004 | NRSC | CH_56 | 4991 | WB | 32°9'24.84\" | 80°18'11.88\" | Indus | Indus | China | 580 | 550 | 532 | 527 | 6 |\n| 89 | 03_62O_039 | NRSC | CH_384 | 4555 | WB | 29°35'21.48\" | 83°59'19.68\" | Brahmaputra | | China | 308 | 236 | 290 | 278 | 6 |\n| 90 | 03_77L_043 | NRSC | CH_552 | 5200 | GL | 28°5'21.84\" | 90°47'18.6\" | Brahmaputra | Kuri Chhu | China | 251 | 181 | 237 | 228 | 6 |\n| 91 | 03_82J_014 | NRSC | CH_844 | 3703 | WB | 30°10'24.6\" | 94°20'44.52\" | Brahmaputra | | China | 195 | 183 | 179 | 178 | 6 |\n| 92 | 01_62E_013 | NRSC | CH_88 | 5345 | WB | 31°14'29.4\" | 81°41'9.96\" | Indus | Indus | China | 176 | 166 | 158 | 160 | 6 |\n| 93 | 03_71P_001 | NRSC | CH_448 | 5302 | WB | 28°49'56.64\" | 87°33'36\" | Brahmaputra | | China | 139 | 112 | 129 | 131 | 6 |\n| 94 | 03_77D_004 | NRSC/ SDC | SK_4 | 5287 | GL | 28°0'25.56\" | 88°42'46.08\" | Brahmaputra | Teesta | India | 123 | 106 | 116 | 116 | 6 |\n| 95 | 03_82F_007 | NRSC | CH_732 | 4801 | GL | 30°31'13.8\" | 93°26'41.28\" | Brahmaputra | | China | 123 | 115 | 114 | 116 | 6 |\n| 96 | 03_71O_006 | NRSC | CH_442 | 4738 | WB | 29°33'21.6\" | 87°1'39\" | Brahmaputra | | China | 121 | 104 | 113 | 114 | 6 |\n| 97 | 03_77K_015 | NRSC | CH_517 | 4455 | WB | 29°7'3.36\" | 90°20'9.24\" | Brahmaputra | | China | 119 | 108 | 110 | 112 | 6 |\n| 98 | 03_77L_030 | NRSC | BH_12 | 5305 | GL | 28°16'43.32\" | 90°13'32.88\" | Brahmaputra | | Bhutan | 92 | 79 | 86 | 86 | 6 |\n| 99 | 03_62J_015 | NRSC | CH_287 | 5207 | WB | 30°23'52.08\" | 82°11'32.28\" | Brahmaputra | | China | 88 | 70 | 80 | 83 | 6 |\n| 100 | 03_77L_067 | NRSC | BH_35 | 5231 | GL | 28°2'17.88\" | 90°21'50.4\" | Brahmaputra | Manas Chhu & Mangde Chhu | Bhutan | 85 | 78 | 80 | 77 | 6 |\n| 101 | 03_82B_015 | NRSC | CH_641 | 5124 | WB | 30°20'56.4\" | 92°44'7.08\" | Brahmaputra | | China | 84 | 75 | 80 | 79 | 6 |\n| 102 | 03_77L_017 | NRSC | CH_533 | 5340 | WB | 28°23'8.52\" | 90°19'9.12\" | Brahmaputra | | China | 84 | 74 | 79 | 74 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 776, "line_end": 794, "token_count_estimate": 1545, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Manas", "Teesta"], "countries": ["Bhutan", "China", "India"], "lake_ids": ["01_61C_018", "01_61D_004", "01_62E_013", "03_62J_015", "03_62O_039", "03_71O_006", "03_71P_001", "03_77D_004", "03_77K_015", "03_77L_011", "03_77L_017", "03_77L_030", "03_77L_043", "03_77L_067", "03_82B_015", "03_82F_007", "03_82J_014"]}}
{"id": "52b32e3157413368", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 103 | 03_62N_017 | NRSC | CH_334 | 5454 | WB | 30°27'55.44\" | 83°59'4.2\" | Brahmaputra | | China | 83 | 77 | 77 | 79 | 6 |\n| 104 | 03_78M_020 | NRSC | BH_195 | 4157 | WB | 27°50'15.72\" | 91°36'18.36\" | Brahmaputra | Dangme Chhu | Bhutan | 70 | 65 | 66 | 66 | 6 |\n| 105 | 02_77D_008 | NRSC | CH_263 | 5285 | GL | 28°1'6.24\" | 88°17'14.28\" | Ganga | Arun Kosi | China | 50 | 45 | 47 | 47 | 6 |\n| 106 | 03_82E_002 | NRSC | CH_720 | 5008 | WB | 31°7'53.4\" | 93°10'36.48\" | Brahmaputra | | China | 719 | 659 | 683 | 688 | 5 |\n| 107 | 01_61B_003 | NRSC | CH_28 | 5074 | WB | 34°14'5.64\" | 80°30'20.88\" | Indus | Indus | China | 235 | 224 | 209 | 199 | 5 |\n| 108 | 03_62N_021 | NRSC | CH_338 | 5432 | WB | 30°25'50.88\" | 83°59'48.84\" | Brahmaputra | | China | 208 | 197 | 182 | 189 | 5 |\n| 109 | 01_62E_010 | NRSC | CH_85 | 5233 | WB | 31°16'26.76\" | 81°35'41.64\" | Indus | Indus | China | 164 | 156 | 147 | 152 | 5 |\n| 110 | 02_71P_040 | NRSC | CH_228 | 4962 | WB | 28°6'50.04\" | 87°39'19.08\" | Ganga | Arun Kosi | China | 151 | 126 | 143 | 139 | 5 |\n| 111 | 03_78I_051 | NRSC | BH_132 | 5074 | GL | 27°53'26.16\" | 90°17'24.36\" | Brahmaputra | Manas Chhu & Mangde Chhu | Bhutan | 124 | 103 | 118 | 111 | 5 |\n| 112 | 03_77P_009 | NRSC | CH_580 | 5086 | WB | 28°32'46.68\" | 91°31'31.8\" | Brahmaputra | | China | 111 | 94 | 106 | 105 | 5 |\n| 113 | 03_82G_045 | NRSC | CH_806 | 4523 | WB | 29°24'19.44\" | 93°42'28.44\" | Brahmaputra | | China | 75 | 71 | 71 | 69 | 5 |\n| 114 | 03_77L_042 | NRSC | CH_551 | 5057 | GL | 28°5'56.4\" | 90°44'23.28\" | Brahmaputra | Kuri Chhu | China | 73 | 57 | 68 | 69 | 5 |\n| 115 | 01_52J_002 | NRSC | JK_198 | 5359 | WB | 34°13'59.16\" | 78°25'34.32\" | Indus | Shyok | India | 70 | 67 | 60 | 59 | 5 |\n| 116 | 01_61F_002 | NRSC | CH_59 | 5279 | WB | 34°17'55.32\" | 81°12'5.4\" | Indus | Indus | China | 62 | 59 | 52 | 51 | 5 |\n| 117 | 03_82O_054 | NRSC | CH_1046 | 3311 | WB | 29°7'41.88\" | 95°26'17.88\" | Brahmaputra | Dibang | China | 54 | 51 | 48 | 49 | 5 |\n| 118 | 03_91C_078 | NRSC | CH_1106 | 3694 | WB | 29°0'30.24\" | 96°13'4.44\" | Brahmaputra | Dibang | China | 50 | 48 | 45 | 44 | 5 |\n| 119 | 01_61C_001 | NRSC | CH_29 | 4526 | WB | 33°57'12.6\" | 80°54'12.96\" | Indus | Indus | China | 12221 | 11154 | 11727 | 11563 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 796, "line_end": 814, "token_count_estimate": 1562, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Kosi", "Manas", "Shyok"], "countries": ["Bhutan", "China", "India"], "lake_ids": ["01_52J_002", "01_61B_003", "01_61C_001", "01_61F_002", "01_62E_010", "02_71P_040", "02_77D_008", "03_62N_017", "03_62N_021", "03_77L_042", "03_77P_009", "03_78I_051", "03_78M_020", "03_82E_002", "03_82G_045", "03_82O_054", "03_91C_078", "11154", "11563", "11727", "12221"]}}
{"id": "3167bab2342f40a5", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.3: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “No Change” in area\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 03_77L_001 | NRSC | CH_520 | 4443 | WB | 28°57'20.52\" | 90°42'39.6\" | Brahmaputra | | China | 55987 | 55435 | 55940 | 54927 | 0 |\n| 2 | 02_71H_001 | NRSC | CH_121 | 4580 | WB | 28°53'32.28\" | 85°35'8.52\" | Ganga | Arun Kosi | China | 27166 | 26825 | 27085 | 26958 | 0 |\n| 3 | 02_71H_017 | NRSC | CH_137 | 5314 | GL | 28°29'43.44\" | 85°38'9.24\" | Ganga | Arun Kosi | China | 495 | 493 | 489 | 483 | 0 |\n| 4 | 03_77H_001 | NRSC | CH_476 | 4275 | WB | 28°49'46.92\" | 89°51'6.48\" | Brahmaputra | | China | 444 | 442 | 374 | 357 | 0 |\n| 5 | 01_61C_012 | NRSC | CH_40 | 4282 | WB | 33°32'45.24\" | 80°9'2.16\" | Indus | Indus | China | 315 | 290 | 312 | 315 | 0 |\n| 6 | 03_91C_029 | NRSC | CH_1078 | 4229 | WB | 29°14'15.72\" | 96°49'25.32\" | Brahmaputra | | China | 218 | 216 | 212 | 217 | 0 |\n| 7 | 01_43A_001 | NRSC | JK_22 | 3641 | WB | 35°59'42\" | 72°36'45.36\" | Indus | Gilgit | India | 207 | 203 | 207 | 203 | 0 |\n| 8 | 03_82B_007 | NRSC | CH_633 | 4964 | WB | 30°53'40.92\" | 92°57'2.52\" | Brahmaputra | | China | 205 | 199 | 205 | 205 | 0 |\n| 9 | 03_82C_010 | NRSC | CH_665 | 4921 | WB | 29°46'44.04\" | 92°23'17.16\" | Brahmaputra | | China | 153 | 153 | 150 | 148 | 0 |\n| 10 | 03_91C_034 | NRSC | AP_84 | 4288 | WB | 29°18'6.48\" | 96°4'55.92\" | Brahmaputra | Dibang | India | 146 | 134 | 146 | 136 | 0 |\n| 11 | 03_62O_040 | NRSC | CH_385 | 4896 | WB | 29°34'56.64\" | 83°21'20.16\" | Brahmaputra | | China | 138 | 107 | 138 | 130 | 0 |\n| 12 | 01_43E_023 | NRSC | JK_47 | 4155 | WB | 35°51'54\" | 73°44'42.72\" | Indus | Gilgit | India | 86 | 86 | 82 | 85 | 0 |\n| 13 | 02_71L_026 | NRSC | CH_181 | 5057 | GL | 28°11'8.52\" | 86°31'54.12\" | Ganga | Sun Kosi | China | 65 | 59 | 64 | 65 | 0 |\n| 14 | 03_82G_060 | NRSC | CH_821 | 4577 | WB | 29°17'13.92\" | 93°44'10.68\" | Brahmaputra | | China | 59 | 59 | 55 | 56 | 0 |\n| 15 | 03_71C_003 | NRSC | CH_396 | 5412 | GL | 29°51'59.76\" | 84°37'26.4\" | Brahmaputra | | China | 50 | 47 | 48 | 50 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.3: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “No Change” in area", "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 15, "line_start": 823, "line_end": 839, "token_count_estimate": 1460, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Gilgit", "Kosi"], "countries": ["China", "India"], "lake_ids": ["01_43A_001", "01_43E_023", "01_61C_012", "02_71H_001", "02_71H_017", "02_71L_026", "03_62O_040", "03_71C_003", "03_77H_001", "03_77L_001", "03_82B_007", "03_82C_010", "03_82G_060", "03_91C_029", "03_91C_034", "26825", "26958", "27085", "27166", "54927", "55435", "55940", "55987"]}}
{"id": "30a350b11cc30b87", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 840, "line_end": 848, "token_count_estimate": 95, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b6c69609dc8f16ef", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.4: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “Decrease” in area\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 03_77L_012 | NRSC | CH_528 | 5014 | WB | 28°33'58.68\" | 90°23'47.04\" | Brahmaputra | | China | 29078 | 28771 | 29398 | 29140 | -1 |\n| 2 | 01_52L_001 | NRSC | JK_225 | 4523 | WB | 32°53'48.12\" | 78°18'48.6\" | Indus | Sutlej | India | 13965 | 14139 | 13957 | 14058 | -1 |\n| 3 | 01_62J_001 | NRSC | CH_102 | 4784 | WB | 30°38'15.72\" | 82°8'6.36\" | Indus | Sutlej | China | 5753 | 5571 | 5817 | 5692 | -1 |\n| 4 | 03_82J_020 | NRSC | CH_850 | 3852 | WB | 30°3'1.08\" | 94°14'53.52\" | Brahmaputra | | China | 434 | 439 | 414 | 422 | -1 |\n| 5 | 03_62J_011 | NRSC | CH_283 | 5181 | WB | 30°28'6.6\" | 82°3'33.12\" | Brahmaputra | | China | 398 | 401 | 380 | 373 | -1 |\n| 6 | 03_78M_003 | NRSC | CH_614 | 4459 | WB | 27°54'3.96\" | 91°53'48.84\" | Brahmaputra | Dangme Chhu | China | 211 | 207 | 208 | 213 | -1 |\n| 7 | 02_72I_014 | NRSC | NP_67 | 4574 | GL | 27°51'41.04\" | 86°28'35.04\" | Ganga | Sun Kosi | Nepal | 168 | 134 | 170 | 168 | -1 |\n| 8 | 01_43N_001 | NRSC | JK_128 | 4142 | WB | 34°59'28.32\" | 75°14'9.96\" | Indus | Shingo (Indus) | India | 126 | 127 | 124 | 125 | -1 |\n| 9 | 03_82K_075 | NRSC | CH_931 | 4511 | WB | 29°31'3.36\" | 94°7'14.88\" | Brahmaputra | | China | 117 | 118 | 115 | 117 | -1 |\n| 10 | 03_91C_059 | NRSC | CH_1089 | 4303 | WB | 29°5'30.12\" | 96°12'39.24\" | Brahmaputra | Dibang | China | 97 | 98 | 94 | 95 | -1 |\n| 11 | 03_82F_020 | NRSC | CH_745 | 4110 | GL | 30°16'3\" | 93°27'22.68\" | Brahmaputra | | China | 70 | 71 | 69 | 69 | -1 |\n| 12 | 02_72M_009 | NRSC | NP_86 | 4932 | GL | 27°52'13.08\" | 87°52'3.36\" | Ganga | Tamor Kosi | Nepal | 64 | 65 | 63 | 65 | -1 |\n| 13 | 01_43N_032 | NRSC | JK_159 | 3595 | WB | 34°5'37.32\" | 75°29'52.44\" | Indus | Jhelum | India | 56 | 49 | 55 | 57 | -1 |\n| 14 | 03_77L_014 | NRSC | CH_530 | 5289 | WB | 28°26'19.32\" | 90°10'24.96\" | Brahmaputra | | China | 47 | 48 | 42 | 46 | -1 |\n| 15 | 03_82O_042 | NRSC | AP_49 | 3093 | WB | 29°10'36.48\" | 95°36'56.16\" | Brahmaputra | Dibang | India | 44 | 44 | 37 | 40 | -1 |\n| 16 | 01_62F_001 | NRSC | CH_92 | 4571 | WB | 30°41'19.68\" | 81°13'55.2\" | Indus | Sutlej | China | 25313 | 25486 | 25086 | 24522 | -1 |\n| 17 | 03_77P_006 | NRSC | CH_577 | 4616 | WB | 28°39'46.44\" | 91°40'46.56\" | Brahmaputra | | China | 4764 | 4566 | 4350 | 4868 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.4: Results of Analysis of GLs & WBs with water spread area greater than 50 ha showing “Decrease” in area", "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 849, "line_end": 867, "token_count_estimate": 1632, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Jhelum", "Kosi"], "countries": ["China", "India", "Nepal"], "lake_ids": ["01_43N_001", "01_43N_032", "01_52L_001", "01_62F_001", "01_62J_001", "02_72I_014", "02_72M_009", "03_62J_011", "03_77L_012", "03_77L_014", "03_77P_006", "03_78M_003", "03_82F_020", "03_82J_020", "03_82K_075", "03_82O_042", "03_91C_059", "13957", "13965", "14058", "14139", "24522", "25086", "25313", "25486", "28771", "29078", "29140", "29398"]}}
{"id": "2ca03f721da89d60", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 18 | 03_82F_004 | NRSC | CH_729 | 4508 | WB | 30°37'16.32\" | 93°10'49.8\" | Brahmaputra | | China | 687 | 692 | 700 | 698 | -2 |\n| 19 | 03_77L_013 | NRSC | CH_529 | 5191 | WB | 28°26'56.04\" | 90°15'24.84\" | Brahmaputra | | China | 343 | 319 | 348 | 327 | -2 |\n| 20 | 03_82J_017 | NRSC | CH_847 | 3829 | WB | 30°7'33.24\" | 94°5'24\" | Brahmaputra | | China | 277 | 282 | 276 | 278 | -2 |\n| 21 | 01_52K_009 | NRSC | JK_217 | 4921 | WB | 33°27'51.48\" | 78°36'39.24\" | Indus | Shyok | India | 200 | 204 | 193 | 195 | -2 |\n| 22 | 01_62F_004 | NRSC | CH_95 | 5493 | WB | 30°25'50.88\" | 81°25'58.44\" | Indus | Sutlej | China | 192 | 196 | 183 | 185 | -2 |\n| 23 | 03_77P_005 | NRSC | CH_576 | 4619 | WB | 28°45'55.08\" | 91°40'30\" | Brahmaputra | | China | 115 | 112 | 118 | 114 | -2 |\n| 24 | 03_82P_010 | NRSC | AP_67 | 1676 | WB | 28°8'53.16\" | 95°56'35.88\" | Brahmaputra | Dibang | India | 97 | 99 | 91 | 98 | -2 |\n| 25 | 03_82A_003 | NRSC | CH_622 | 4896 | WB | 31°6'33.12\" | 92°57'7.2\" | Brahmaputra | | China | 97 | 99 | 90 | 91 | -2 |\n| 26 | 03_82E_003 | NRSC | CH_721 | 5027 | WB | 31°6'12.96\" | 93°8'36.6\" | Brahmaputra | | China | 96 | 98 | 92 | 96 | -2 |\n| 27 | 03_82A_007 | NRSC | CH_626 | 4911 | WB | 31°2'10.32\" | 92°47'12.84\" | Brahmaputra | | China | 93 | 85 | 95 | 94 | -2 |\n| 28 | 03_82N_033 | NRSC | CH_1004 | 4357 | GL | 30°13'16.68\" | 95°35'0.24\" | Brahmaputra | | China | 87 | 89 | 84 | 84 | -2 |\n| 29 | 03_82O_029 | NRSC | JK_197 | 5311 | GL | 29°18'17.64\" | 95°38'20.4\" | Indus | Shyok | India | 72 | 68 | 73 | 67 | -2 |\n| 30 | 03_77L_035 | NRSC | BH_14 | 5486 | GL | 28°14'58.92\" | 90°11'13.56\" | Brahmaputra | | Bhutan | 67 | 68 | 60 | 59 | -2 |\n| 31 | 01_43P_002 | NRSC | JK_167 | 669 | WB | 32°41'48.84\" | 75°8'44.16\" | Indus | Ravi | India | 55 | 52 | 54 | 56 | -2 |\n| 32 | 03_78M_019 | NRSC | BH_194 | 4697 | WB | 27°50'49.92\" | 91°34'59.88\" | Brahmaputra | Dangme Chhu | Bhutan | 54 | 55 | 52 | 54 | -2 |\n| 33 | 01_62E_015 | NRSC | CH_90 | 5415 | WB | 31°10'56.28\" | 81°11'40.2\" | Indus | Sutlej | China | 50 | 51 | 47 | 47 | -2 |\n| 34 | 03_77L_029 | NRSC | CH_545 | 5451 | GL | 28°16'22.8\" | 90°35'24.36\" | Brahmaputra | Kuri Chhu | China | 48 | 45 | 44 | 49 | -2 |\n| 35 | 03_82O_064 | NRSC | AP_57 | 3689 | WB | 29°3'41.76\" | 95°15'45\" | Brahmaputra | Dihang | India | 45 | 44 | 46 | 44 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 872, "line_end": 891, "token_count_estimate": 1624, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Dihang", "Ravi", "Shyok"], "countries": ["Bhutan", "China", "India"], "lake_ids": ["01_43P_002", "01_52K_009", "01_62E_015", "01_62F_004", "03_77L_013", "03_77L_029", "03_77L_035", "03_77P_005", "03_78M_019", "03_82A_003", "03_82A_007", "03_82E_003", "03_82F_004", "03_82J_017", "03_82N_033", "03_82O_029", "03_82O_064", "03_82P_010"]}}
{"id": "7ec00bd9d500f78e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 36 | 03_91H_017 | NRSC | CH_1182 | 4590 | WB | 28°52'37.2\" | 97°21'19.44\" | Brahmaputra | Lohit | China | 45 | 46 | 40 | 37 | -2 |\n| 37 | 03_77L_003 | NRSC | CH_521 | 4434 | WB | 28°56'57.48\" | 90°31'1.2\" | Brahmaputra | | China | 3945 | 4065 | 3920 | 3968 | -3 |\n| 38 | 03_77P_017 | NRSC | CH_588 | 4751 | WB | 28°17'49.92\" | 91°56'44.52\" | Brahmaputra | Dangme Chhu | China | 2268 | 2345 | 2234 | 2193 | -3 |\n| 39 | 03_77H_008 | NRSC | CH_482 | 4570 | WB | 28°13'37.92\" | 89°38'17.52\" | Brahmaputra | | China | 1225 | 1268 | 1249 | 1198 | -3 |\n| 40 | 03_62O_024 | NRSC | CH_369 | 4637 | WB | 29°51'26.64\" | 83°15'5.76\" | Brahmaputra | | China | 860 | 721 | 887 | 853 | -3 |\n| 41 | 02_62P_003 | NRSC | NP_36 | 4937 | GL | 28°41'31.92\" | 83°51'9\" | Ganga | Trishuli | Nepal | 335 | 315 | 344 | 305 | -3 |\n| 42 | 01_42H_001 | NRSC | JK_1 | 4292 | WB | 36°52'50.16\" | 73°42'4.68\" | Indus | Gilgit | India | 267 | 276 | 269 | 276 | -3 |\n| 43 | 01_52K_012 | NRSC | JK_220 | 4695 | WB | 33°18'46.8\" | 78°28'41.16\" | Indus | Indus | India | 161 | 166 | 161 | 160 | -3 |\n| 44 | 02_78A_005 | NRSC | CH_271 | 5376 | GL | 27°55'41.16\" | 88°0'10.08\" | Ganga | Arun Kosi | China | 118 | 89 | 107 | 121 | -3 |\n| 45 | 01_52J_006 | NRSC | JK_202 | 5401 | WB | 34°10'23.88\" | 78°26'16.08\" | Indus | Shyok | India | 107 | 110 | 104 | 100 | -3 |\n| 46 | 03_82O_044 | NRSC | CH_1037 | 3552 | WB | 29°10'46.92\" | 95°29'6.72\" | Brahmaputra | Dihang | China | 89 | 92 | 89 | 88 | -3 |\n| 47 | 01_43N_030 | NRSC | JK_157 | 3799 | WB | 34°8'21.12\" | 75°8'50.64\" | Indus | Jhelum | India | 84 | 86 | 80 | 83 | -3 |\n| 48 | 03_91D_107 | NRSC | AP_163 | 3769 | WB | 28°12'8.64\" | 96°53'51.72\" | Brahmaputra | Lohit | India | 65 | 67 | 64 | 63 | -3 |\n| 49 | 03_91D_107 | NRSC | CH_1102 | 4258 | GL | 28°12'8.64\" | 96°53'51.72\" | Brahmaputra | Dibang | China | 65 | 67 | 64 | 63 | -3 |\n| 50 | 03_83A_012 | NRSC | AP_77 | 4287 | WB | 27°31'6.6\" | 92°2'2.4\" | Brahmaputra | Dangme Chhu | India | 61 | 63 | 57 | 56 | -3 |\n| 51 | 03_78A_009 | NRSC | SK_16 | 5044 | GL | 27°56'51.72\" | 88°19'52.68\" | Brahmaputra | Teesta | India | 60 | 55 | 61 | 62 | -3 |\n| 52 | 02_77D_007 | NRSC | CH_262 | 5215 | GL | 28°1'23.88\" | 88°21'16.2\" | Ganga | Arun Kosi | China | 57 | 55 | 55 | 59 | -3 |\n| 53 | 01_52C_003 | NRSC | JK_187 | 4512 | GL | 33°9'26.28\" | 76°59'3.48\" | Indus | Indus | India | 56 | 45 | 56 | 58 | -3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 893, "line_end": 912, "token_count_estimate": 1656, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Dihang", "Gilgit", "Jhelum", "Kosi", "Lohit", "Shyok", "Teesta"], "countries": ["China", "India", "Nepal"], "lake_ids": ["01_42H_001", "01_43N_030", "01_52C_003", "01_52J_006", "01_52K_012", "02_62P_003", "02_77D_007", "02_78A_005", "03_62O_024", "03_77H_008", "03_77L_003", "03_77P_017", "03_78A_009", "03_82O_044", "03_83A_012", "03_91D_107", "03_91H_017"]}}
{"id": "5da3973c0f691378", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 54 | 02_71L_032 | NRSC | CH_187 | 5250 | GL | 28°2'40.2\" | 86°30'49.32\" | Ganga | Sun Kosi | China | 56 | 58 | 52 | 52 | -3 |\n| 55 | 03_91C_070 | NRSC | CH_1098 | 4252 | WB | 29°2'37.32\" | 96°11'36.6\" | Brahmaputra | Dibang | China | 55 | 57 | 54 | 52 | -3 |\n| 56 | 02_71P_027 | NRSC | CH_215 | 5389 | GL | 28°11'40.2\" | 87°38'26.52\" | Ganga | Arun Kosi | China | 51 | 49 | 49 | 52 | -3 |\n| 57 | 03_62O_027 | NRSC | CH_372 | 4575 | WB | 29°48'47.16\" | 83°39'15.48\" | Brahmaputra | | China | 45 | 47 | 38 | 40 | -3 |\n| 58 | 03_82G_050 | NRSC | CH_811 | 4734 | WB | 29°22'57.36\" | 93°38'25.08\" | Brahmaputra | | China | 43 | 44 | 39 | 39 | -3 |\n| 59 | 03_77L_006 | NRSC | CH_522 | 4533 | WB | 28°53'40.2\" | 90°24'19.44\" | Brahmaputra | | China | 42 | 44 | 28 | 33 | -3 |\n| 60 | 01_61C_022 | NRSC | CH_50 | 4339 | WB | 33°5'51.36\" | 80°23'34.08\" | Indus | Indus | China | 1570 | 1420 | 1635 | 1503 | -4 |\n| 61 | 02_71P_015 | NRSC | CH_203 | 4153 | WB | 28°34'35.76\" | 87°32'38.76\" | Ganga | Arun Kosi | China | 1021 | 838 | 1067 | 991 | -4 |\n| 62 | 01_52K_011 | NRSC | JK_219 | 5291 | WB | 33°25'38.64\" | 78°29'16.44\" | Indus | Shyok | India | 178 | 186 | 173 | 173 | -4 |\n| 63 | 01_52K_010 | NRSC | JK_218 | 5313 | WB | 33°27'17.64\" | 78°29'54.24\" | Indus | Shyok | India | 147 | 152 | 143 | 140 | -4 |\n| 64 | 03_78M_016 | NRSC | CH_617 | 4647 | WB | 27°50'30.84\" | 91°53'34.44\" | Brahmaputra | Dangme Chhu | China | 142 | 142 | 147 | 147 | -4 |\n| 65 | 02_71H_007 | NRSC | CH_127 | 5149 | GL | 28°37'25.68\" | 85°30'33.84\" | Ganga | Arun Kosi | China | 120 | 125 | 116 | 120 | -4 |\n| 66 | 03_82B_004 | NRSC | CH_630 | 4893 | WB | 30°56'56.04\" | 92°53'22.56\" | Brahmaputra | | China | 99 | 93 | 101 | 103 | -4 |\n| 67 | 03_77J_003 | NRSC | CH_499 | 5039 | WB | 30°28'45.48\" | 90°57'58.32\" | Brahmaputra | | China | 85 | 89 | 84 | 85 | -4 |\n| 68 | 03_82K_002 | NRSC | CH_858 | 3998 | WB | 29°59'14.64\" | 94°26'7.44\" | Brahmaputra | | China | 74 | 75 | 77 | 75 | -4 |\n| 69 | 03_77H_012 | NRSC | CH_483 | 4723 | GL | 28°14'25.44\" | 89°41'41.28\" | Brahmaputra | | China | 73 | 76 | 69 | 75 | -4 |\n| 70 | 03_82E_007 | NRSC | CH_725 | 5043 | WB | 31°0'14.4\" | 93°5'16.08\" | Brahmaputra | | China | 68 | 71 | 64 | 68 | -4 |\n| 71 | 03_91H_011 | NRSC | CH_1176 | 4494 | WB | 28°56'43.44\" | 97°5'53.16\" | Brahmaputra | Lohit | China | 59 | 50 | 62 | 54 | -4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 919, "line_end": 938, "token_count_estimate": 1685, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Kosi", "Lohit", "Shyok"], "countries": ["China", "India"], "lake_ids": ["01_52K_010", "01_52K_011", "01_61C_022", "02_71H_007", "02_71L_032", "02_71P_015", "02_71P_027", "03_62O_027", "03_77H_012", "03_77J_003", "03_77L_006", "03_78M_016", "03_82B_004", "03_82E_007", "03_82G_050", "03_82K_002", "03_91C_070", "03_91H_011"]}}
{"id": "2596a93d31c5323c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 72 | 03_82N_019 | NRSC | CH_990 | 4877 | WB | 30°28'24.6\" | 95°34'30.36\" | Brahmaputra | | China | 53 | 55 | 52 | 49 | -4 |\n| 73 | 03_82K_006 | NRSC | CH_862 | 4523 | WB | 29°56'25.8\" | 94°35'18.24\" | Brahmaputra | | China | 50 | 52 | 46 | 47 | -4 |\n| 74 | 03_77H_013 | NRSC | CH_484 | 4950 | GL | 28°12'32.04\" | 89°44'42.72\" | Brahmaputra | | China | 46 | 48 | 44 | 47 | -4 |\n| 75 | 01_61C_021 | NRSC | CH_49 | 4349 | WB | 33°6'16.56\" | 80°17'10.32\" | Indus | Indus | China | 1094 | 1155 | 1150 | 1111 | -5 |\n| 76 | 03_71G_013 | NRSC | CH_422 | 4543 | WB | 29°6'7.56\" | 85°5'49.56\" | Brahmaputra | | China | 242 | 244 | 244 | 254 | -5 |\n| 77 | 02_72I_011 | NRSC | NP_64 | 5034 | GL | 27°53'58.2\" | 86°55'15.96\" | Ganga | Sun Kosi | Nepal | 158 | 107 | 167 | 153 | -5 |\n| 78 | 03_78E_023 | NRSC | CH_612 | 5291 | GL | 27°51'17.64\" | 89°15'59.76\" | Brahmaputra | | China | 60 | 38 | 54 | 63 | -5 |\n| 79 | 03_71C_005 | NRSC | CH_398 | 5551 | GL | 29°50'43.8\" | 84°40'32.16\" | Brahmaputra | | China | 54 | 57 | 49 | 53 | -5 |\n| 80 | 03_78E_026 | NRSC | CH_613 | 5161 | GL | 27°48'31.32\" | 89°13'37.2\" | Brahmaputra | Amo Chhu | China | 52 | 36 | 51 | 55 | -5 |\n| 81 | 03_78E_029 | NRSC | BH_73 | 4250 | WB | 27°38'37.68\" | 89°27'39.96\" | Brahmaputra | Puna Tsang Chhu | Bhutan | 43 | 45 | 38 | 36 | -5 |\n| 82 | 01_52G_003 | NRSC | JK_191 | 4533 | WB | 33°18'38.52\" | 77°59'49.2\" | Indus | Indus | India | 1380 | 1473 | 1129 | 1230 | -6 |\n| 83 | 03_62N_004 | NRSC | CH_321 | 5168 | WB | 30°40'5.16\" | 83°37'30.72\" | Brahmaputra | | China | 835 | 878 | 869 | 888 | -6 |\n| 84 | 02_71H_027 | NRSC | CH_147 | 5242 | GL | 28°21'40.32\" | 85°52'12.36\" | Ganga | Sun Kosi | China | 473 | 501 | 451 | 435 | -6 |\n| 85 | 02_62P_004 | NRSC | NP_37 | 807 | WB | 28°13'1.2\" | 83°56'43.8\" | Ganga | Trishuli | Nepal | 381 | 406 | 381 | 395 | -6 |\n| 86 | 03_82D_004 | NRSC | CH_710 | 4481 | WB | 28°52'54.84\" | 92°9'5.4\" | Brahmaputra | | China | 367 | 390 | 371 | 376 | -6 |\n| 87 | 03_78A_014 | NRSC/ SDC | SK_20 | 5234 | GL | 27°54'42.84\" | 88°11'54.96\" | Brahmaputra | Teesta | India | 140 | 123 | 150 | 141 | -6 |\n| 88 | 02_71L_023 | NRSC | CH_178 | 5106 | GL | 28°11'50.64\" | 86°34'54.12\" | Ganga | Arun Kosi | China | 120 | 116 | 126 | 127 | -6 |\n| 89 | 03_91C_045 | NRSC | AP_91 | 3493 | WB | 29°13'44.4\" | 96°11'29.4\" | Brahmaputra | Dibang | India | 106 | 113 | 104 | 107 | -6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 942, "line_end": 961, "token_count_estimate": 1694, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Kosi", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52G_003", "01_61C_021", "02_62P_004", "02_71H_027", "02_71L_023", "02_72I_011", "03_62N_004", "03_71C_005", "03_71G_013", "03_77H_013", "03_78A_014", "03_78E_023", "03_78E_026", "03_78E_029", "03_82D_004", "03_82K_006", "03_82N_019", "03_91C_045"]}}
{"id": "db57255e835455ef", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2005 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last 5 Years (ha) (ii) | Average Area of Last 10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 90 | 02_77D_006 | NRSC | CH_261 | 4894 | GL | 28°3'21.6\" | 88°25'35.4\" | Ganga | Arun Kosi | China | 94 | 80 | 87 | 100 | -6 |\n| 91 | 02_77D_006 | NRSC | CH_1032 | 3345 | WB | 28°3'21.6\" | 88°25'35.4\" | Brahmaputra | Dihang | China | 94 | 80 | 87 | 100 | -6 |\n| 92 | 03_91C_040 | NRSC | AP_87 | 4450 | WB | 29°15'19.08\" | 96°14'40.92\" | Brahmaputra | Lohit | India | 89 | 94 | 86 | 82 | -6 |\n| 93 | 03_82K_074 | NRSC | CH_930 | 4553 | WB | 29°31'33.96\" | 94°3'26.28\" | Brahmaputra | | China | 83 | 88 | 77 | 81 | -6 |\n| 94 | 01_43E_006 | NRSC | JK_30 | 4186 | WB | 35°56'43.08\" | 73°21'52.56\" | Indus | Gilgit | India | 67 | 71 | 66 | 68 | -6 |\n| 95 | 03_82G_062 | NRSC | CH_823 | 4925 | WB | 29°14'25.08\" | 93°16'33.6\" | Brahmaputra | | China | 54 | 58 | 55 | 56 | -6 |\n| 96 | 03_78M_010 | NRSC | BH_188 | 4496 | WB | 27°52'37.92\" | 91°38'1.68\" | Brahmaputra | Dangme Chhu | Bhutan | 47 | 50 | 41 | 42 | -6 |\n| 97 | 03_82B_020 | NRSC | CH_646 | 4986 | WB | 30°12'59.04\" | 92°30'59.76\" | Brahmaputra | | China | 46 | 49 | 45 | 49 | -6 |\n| 98 | 03_82F_014 | NRSC | CH_739 | 4691 | GL | 30°20'52.08\" | 93°30'24.12\" | Brahmaputra | | China | 46 | 49 | 43 | 44 | -6 |\n| 99 | 01_52G_001 | NRSC | JK_189 | 5008 | WB | 33°59'57.12\" | 77°58'44.04\" | Indus | Shyok | India | 42 | 45 | 44 | 44 | -6 |\n| 100 | 03_82J_004 | NRSC | CH_834 | 3957 | GL | 30°39'37.8\" | 94°29'7.8\" | Brahmaputra | | China | 502 | 356 | 542 | 533 | -7 |\n| 101 | 01_52L_002 | NRSC | JK_226 | 4986 | WB | 32°58'54.84\" | 78°35'43.44\" | Indus | Indus | India | 412 | 442 | 423 | 419 | -7 |\n| 102 | 02_71H_028 | NRSC | CH_148 | 5174 | WB | 28°19'49.08\" | 85°52'7.32\" | Ganga | Sun Kosi | China | 186 | 200 | 193 | 197 | -7 |\n| 103 | 03_62N_022 | NRSC | CH_339 | 4599 | WB | 30°12'15.12\" | 83°14'31.92\" | Brahmaputra | | China | 184 | 198 | 186 | 187 | -7 |\n| 104 | 01_43J_017 | NRSC | JK_95 | 3580 | WB | 34°25'55.56\" | 74°55'27.12\" | Indus | Jhelum | India | 153 | 164 | 157 | 157 | -7 |\n| 105 | 01_52O_002 | NRSC | CH_5 | 5262 | WB | 33°58'49.08\" | 79°32'35.52\" | Indus | Indus | China | 125 | 135 | 96 | 105 | -7 |\n| 106 | 03_91D_041 | NRSC | AP_135 | 3526 | WB | 28°46'32.52\" | 96°31'53.4\" | Brahmaputra | Dibang | India | 120 | 115 | 129 | 119 | -7 |\n| 107 | 03_91C_038 | NRSC | AP_85 | 4002 | WB | 29°16'8.4\" | 96°9'24.12\" | Brahmaputra | Dibang | India | 105 | 113 | 95 | 93 | -7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2005 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last 5 Years (ha) (ii)", "Average Area of Last 10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 964, "line_end": 983, "token_count_estimate": 1675, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Dihang", "Gilgit", "Jhelum", "Kosi", "Lohit", "Shyok"], "countries": ["Bhutan", "China", "India"], "lake_ids": ["01_43E_006", "01_43J_017", "01_52G_001", "01_52L_002", "01_52O_002", "02_71H_028", "02_77D_006", "03_62N_022", "03_78M_010", "03_82B_020", "03_82F_014", "03_82G_062", "03_82J_004", "03_82K_074", "03_91C_038", "03_91C_040", "03_91D_041"]}}
{"id": "d6737a9bbe9fa267", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2005 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last 5 Years (ha) (ii) | Average Area of Last 10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 108 | 03_91C_025 | NRSC | CH_1076 | 4022 | GL | 29°17'40.2\" | 96°50'3.84\" | Brahmaputra | | China | 104 | 95 | 111 | 111 | -7 |\n| 109 | 02_71P_035 | NRSC | CH_223 | 5146 | WB | 28°9'7.2\" | 87°9'27\" | Ganga | Arun Kosi | China | 100 | 107 | 90 | 98 | -7 |\n| 110 | 02_71L_013 | NRSC | CH_168 | 5324 | GL | 28°18'12.24\" | 86°9'27.36\" | Ganga | Sun Kosi | China | 59 | 64 | 55 | 58 | -7 |\n| 111 | 03_71G_008 | NRSC | CH_417 | 5187 | WB | 29°33'30.96\" | 85°52'50.52\" | Brahmaputra | | China | 56 | 60 | 52 | 58 | -7 |\n| 112 | 03_82G_019 | NRSC | CH_780 | 4460 | WB | 29°30'9\" | 93°56'12.12\" | Brahmaputra | | China | 55 | 59 | 54 | 50 | -7 |\n| 113 | 03_77D_008 | NRSC | SK_8 | 5039 | GL | 28°0'26.28\" | 88°29'41.64\" | Brahmaputra | Teesta | India | 43 | 46 | 40 | 44 | -7 |\n| 114 | 03_91D_080 | NRSC | CH_1135 | 4295 | WB | 28°32'29.76\" | 96°37'3.36\" | Brahmaputra | Lohit | China | 42 | 45 | 41 | 40 | -7 |\n| 115 | 01_52L_003 | NRSC | JK_227 | 4985 | WB | 32°55'14.88\" | 78°36'0.72\" | Indus | Indus | India | 595 | 649 | 513 | 558 | -8 |\n| 116 | 03_71G_010 | NRSC | CH_419 | 4491 | WB | 29°20'49.2\" | 85°4'58.8\" | Brahmaputra | | China | 280 | 304 | 197 | 250 | -8 |\n| 117 | 03_82K_042 | NRSC | CH_898 | 4364 | WB | 29°46'44.76\" | 94°36'2.88\" | Brahmaputra | | China | 188 | 205 | 186 | 179 | -8 |\n| 118 | 03_92A_006 | NRSC | AP_204 | 1178 | WB | 27°41'50.28\" | 96°27'7.2\" | Brahmaputra | Lohit | India | 76 | 83 | 74 | 79 | -8 |\n| 119 | 03_77H_030 | NRSC | CH_495 | 4802 | WB | 28°1'32.16\" | 89°25'37.56\" | Brahmaputra | | China | 61 | 66 | 59 | 58 | -8 |\n| 120 | 03_91C_046 | NRSC | AP_92 | 3353 | WB | 29°13'32.52\" | 96°9'36\" | Brahmaputra | Dibang | India | 56 | 61 | 54 | 54 | -8 |\n| 121 | 01_43N_027 | NRSC | JK_154 | 3683 | WB | 34°23'17.16\" | 75°7'6.6\" | Indus | Jhelum | India | 44 | 48 | 45 | 44 | -8 |\n| 122 | 02_53P_001 | NRSC | UK_9 | 210 | WB | 28°57'29.88\" | 79°50'32.64\" | Ganga | Ganga | India | 1862 | 2054 | 1495 | 1580 | -9 |\n| 123 | 03_71O_010 | NRSC | CH_446 | 4296 | WB | 29°12'14.4\" | 87°23'29.04\" | Brahmaputra | | China | 880 | 813 | 967 | 954 | -9 |\n| 124 | 03_77B_002 | NRSC | CH_453 | 5019 | WB | 30°8'51.72\" | 88°37'36.12\" | Brahmaputra | | China | 206 | 227 | 205 | 190 | -9 |\n| 125 | 03_82K_007 | NRSC | CH_863 | 4294 | WB | 29°57'31.68\" | 94°17'30.48\" | Brahmaputra | | China | 127 | 130 | 126 | 139 | -9 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2005 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last 5 Years (ha) (ii)", "Average Area of Last 10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 987, "line_end": 1006, "token_count_estimate": 1661, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Jhelum", "Kosi", "Lohit", "Teesta"], "countries": ["China", "India"], "lake_ids": ["01_43N_027", "01_52L_003", "02_53P_001", "02_71L_013", "02_71P_035", "03_71G_008", "03_71G_010", "03_71O_010", "03_77B_002", "03_77D_008", "03_77H_030", "03_82G_019", "03_82K_007", "03_82K_042", "03_91C_025", "03_91C_046", "03_91D_080", "03_92A_006"]}}
{"id": "dc8e869aee13b996", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 162 | 03_82K_039 | NRSC | CH_895 | 4128 | WB | 29°48'45.72\" | 94°25'57\" | Brahmaputra | | China | 183 | 224 | 196 | 205 | -18 |\n| 163 | 02_72I_002 | NRSC | NP_58 | 4854 | GL | 27°58'30.72\" | 86°40'52.32\" | Ganga | Sun Kosi | Nepal | 56 | 68 | 51 | 56 | -18 |\n| 164 | 02_71L_011 | NRSC | CH_166 | 5439 | GL | 28°20'7.44\" | 86°11'30.12\" | Ganga | Sun Kosi | China | 52 | 64 | 52 | 53 | -19 |\n| 165 | 03_77P_013 | NRSC | CH_584 | 5155 | WB | 28°31'48.36\" | 91°33'42.84\" | Brahmaputra | | China | 49 | 60 | 44 | 48 | -19 |\n| 166 | 03_82E_004 | NRSC | CH_722 | 5049 | WB | 31°3'52.92\" | 93°17'32.64\" | Brahmaputra | | China | 46 | 57 | 44 | 46 | -19 |\n| 167 | 01_61D_003 | NRSC | CH_55 | 4453 | WB | 32°25'23.52\" | 80°51'55.08\" | Indus | Indus | China | 55 | 69 | 47 | 55 | -20 |\n| 168 | 03_78E_017 | NRSC | CH_609 | 5253 | GL | 27°52'35.76\" | 89°17'45.96\" | Brahmaputra | | China | 52 | 65 | 44 | 50 | -20 |\n| 169 | 03_82G_048 | NRSC | CH_809 | 4663 | WB | 29°25'15.6\" | 93°17'27.6\" | Brahmaputra | | China | 44 | 55 | 43 | 44 | -20 |\n| 170 | 03_82K_103 | NRSC | CH_959 | 3964 | WB | 29°17'42.36\" | 94°12'6.12\" | Brahmaputra | | China | 40 | 50 | 40 | 38 | -20 |\n| 171 | 01_43J_021 | NRSC | JK_99 | 1582 | WB | 34°7'6.24\" | 74°51'39.6\" | Indus | Jhelum | India | 975 | 1238 | 967 | 972 | -21 |\n| 172 | 03_62O_002 | NRSC | CH_347 | 4587 | WB | 29°57'38.52\" | 83°16'11.64\" | Brahmaputra | | China | 46 | 58 | 41 | 44 | -21 |\n| 173 | 01_62E_002 | NRSC | CH_77 | 5139 | WB | 31°36'58.32\" | 81°1'0.48\" | Indus | Indus | China | 154 | 161 | 199 | 178 | -22 |\n| 174 | 03_82C_016 | NRSC | CH_671 | 4679 | WB | 29°39'59.76\" | 92°23'36.6\" | Brahmaputra | | China | 42 | 54 | 46 | 49 | -22 |\n| 175 | 03_71C_010 | NRSC | CH_403 | 4561 | WB | 29°18'39.6\" | 84°25'49.44\" | Brahmaputra | | China | 38 | 49 | 28 | 43 | -23 |\n| 176 | 03_71G_014 | NRSC | CH_423 | 4606 | WB | 29°5'1.68\" | 85°11'22.56\" | Brahmaputra | | China | 161 | 60 | 212 | 196 | -24 |\n| 177 | 01_52I_003 | NRSC | JK_195 | 5159 | WB | 35°24'37.8\" | 78°17'3.84\" | Indus | Shyok | India | 143 | 180 | 177 | 189 | -24 |\n| 178 | 03_82D_003 | NRSC | CH_709 | 4408 | WB | 28°53'37.32\" | 92°7'43.32\" | Brahmaputra | | China | 38 | 50 | 42 | 44 | -24 |\n| 179 | 01_42H_005 | NRSC | JK_5 | 2237 | WB | 36°14'56.76\" | 73°21'41.4\" | Indus | Gilgit | India | 54 | 73 | 58 | 57 | -26 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1011, "line_end": 1030, "token_count_estimate": 1622, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Gilgit", "Jhelum", "Kosi", "Shyok"], "countries": ["China", "India", "Nepal"], "lake_ids": ["01_42H_005", "01_43J_021", "01_52I_003", "01_61D_003", "01_62E_002", "02_71L_011", "02_72I_002", "03_62O_002", "03_71C_010", "03_71G_014", "03_77P_013", "03_78E_017", "03_82C_016", "03_82D_003", "03_82E_004", "03_82G_048", "03_82K_039", "03_82K_103"]}}
{"id": "a19b66ab9e86c7f3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 180 | 03_77K_009 | NRSC | CH_511 | 3937 | WB | 29°28'1.2\" | 90°10'20.28\" | Brahmaputra | | China | 51 | 70 | 59 | 65 | -27 |\n| 181 | 03_78A_018 | NRSC | CH_598 | 4880 | WB | 27°51'19.44\" | 88°56'41.28\" | Brahmaputra | Amo Chhu | China | 49 | 67 | 46 | 45 | -27 |\n| 182 | 03_91C_014 | NRSC | CH_1065 | 4033 | GL | 29°35'56.4\" | 96°8'28.68\" | Brahmaputra | | China | 47 | 65 | 48 | 48 | -28 |\n| 183 | 01_52O_003 | NRSC | CH_6 | 4252 | WB | 33°33'43.56\" | 79°57'46.8\" | Indus | Indus | China | 207 | 290 | 200 | 192 | -29 |\n| 184 | 03_77P_018 | NRSC | CH_589 | 4707 | WB | 28°6'5.76\" | 91°56'34.44\" | Brahmaputra | Dangme Chhu | China | 108 | 154 | 113 | 121 | -30 |\n| 185 | 02_53K_002 | NRSC | UK_2 | 260 | WB | 29°19'9.84\" | 78°55'13.08\" | Ganga | Ramganga | India | 1110 | 1597 | 883 | 968 | -31 |\n| 186 | 03_71G_009 | NRSC | CH_418 | 5032 | WB | 29°31'32.88\" | 85°38'37.32\" | Brahmaputra | | China | 120 | 178 | 119 | 136 | -32 |\n| 187 | 03_77H_004 | NRSC | CH_479 | 4428 | WB | 28°19'37.56\" | 89°25'43.68\" | Brahmaputra | | China | 135 | 201 | 142 | 150 | -33 |\n| 188 | 03_62N_009 | NRSC | CH_326 | 5241 | WB | 30°35'26.88\" | 83°31'7.32\" | Brahmaputra | | China | 188 | 288 | 274 | 273 | -35 |\n| 189 | 03_82F_010 | NRSC | CH_735 | 5030 | GL | 30°28'13.08\" | 93°31'59.52\" | Brahmaputra | | China | 28 | 44 | 26 | 23 | -36 |\n| 190 | 03_77O_001 | NRSC | CH_564 | 3879 | WB | 29°55'7.68\" | 91°5'22.2\" | Brahmaputra | | China | 114 | 181 | 145 | 154 | -37 |\n| 191 | 03_78A_001 | NRSC /SDC | SK_9 | 5371 | GL | 27°59'30.12\" | 88°48'55.8\" | Brahmaputra | Teesta | India | 175 | 156 | 182 | 284 | -38 |\n| 192 | 01_62B_001 | NRSC | CH_73 | 4526 | WB | 30°49'22.8\" | 80°44'34.8\" | Indus | Sutlej | China | 265 | 440 | 237 | 281 | -40 |\n| 193 | 03_71O_002 | NRSC | CH_438 | 4909 | WB | 29°42'16.92\" | 87°1'0.84\" | Brahmaputra | | China | 29 | 48 | 38 | 45 | -40 |\n| 194 | 02_72E_001 | NRSC | NP_57 | 1554 | WB | 27°36'6.48\" | 85°9'25.2\" | Ganga | Bagmati | Nepal | 94 | 158 | 146 | 158 | -41 |\n| 195 | 03_77P_012 | NRSC | CH_583 | 4975 | WB | 28°31'43.32\" | 91°39'54.36\" | Brahmaputra | | China | 53 | 91 | 60 | 59 | -42 |\n| 196 | 03_77P_016 | NRSC | CH_587 | 4749 | WB | 28°19'48.72\" | 91°57'47.88\" | Brahmaputra | Dangme Chhu | China | 143 | 251 | 179 | 210 | -43 |\n| 197 | 03_77O_002 | NRSC | CH_565 | 3806 | WB | 29°53'58.56\" | 91°10'0.12\" | Brahmaputra | | China | 52 | 91 | 61 | 74 | -43 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1034, "line_end": 1053, "token_count_estimate": 1636, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Teesta"], "countries": ["China", "India", "Nepal"], "lake_ids": ["01_52O_003", "01_62B_001", "02_53K_002", "02_72E_001", "03_62N_009", "03_71G_009", "03_71O_002", "03_77H_004", "03_77K_009", "03_77O_001", "03_77O_002", "03_77P_012", "03_77P_016", "03_77P_018", "03_78A_001", "03_78A_018", "03_82F_010", "03_91C_014"]}}
{"id": "8dab0a1e0194e711", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 198 | 03_77H_007 | NRSC | CH_481 | 4424 | WB | 28°16'25.68\" | 89°20'44.52\" | Brahmaputra | | China | 477 | 866 | 569 | 671 | -45 |\n| 199 | 03_82F_016 | NRSC | CH_741 | 4632 | WB | 30°19'7.68\" | 93°20'32.64\" | Brahmaputra | | China | 47 | 49 | 46 | 91 | -48 |\n| 200 | 03_77H_020 | NRSC | CH_490 | 4473 | WB | 28°8'59.64\" | 89°20'58.92\" | Brahmaputra | | China | 2325 | 4588 | 4565 | 4573 | -49 |\n| 201 | 01_52I_004 | NRSC | JK_196 | 5141 | WB | 35°23'27.96\" | 78°13'7.68\" | Indus | Shyok | India | 59 | 124 | 70 | 82 | -52 |\n| 202 | 03_82D_010 | NRSC | CH_716 | 5043 | WB | 28°11'29.4\" | 92°2'34.8\" | Brahmaputra | Dangme Chhu | China | 36 | 76 | 34 | 48 | -53 |\n| 203 | 02_77D_003 | NRSC | CH_258 | 4364 | WB | 28°18'33.12\" | 88°19'31.08\" | Ganga | Arun Kosi | China | 62 | 119 | 66 | 144 | -57 |\n| 204 | 03_78A_003 | NRSC/ SDC | SK_11 | 4977 | GL | 27°58'31.08\" | 88°36'59.04\" | Brahmaputra | Teesta | India | 35 | 58 | 52 | 85 | -59 |\n| 205 | 03_62O_028 | NRSC | CH_373 | 4577 | WB | 29°47'40.92\" | 83°33'20.88\" | Brahmaputra | | China | 350 | 887 | 336 | 542 | -61 |\n| 206 | 02_62B_001 | NRSC | CH_106 | 5216 | WB | 30°37'4.8\" | 80°37'49.44\" | Ganga | Karnali | China | 26 | 67 | 31 | 39 | -61 |\n| 207 | 02_77D_001 | NRSC | CH_256 | 4423 | WB | 28°24'16.2\" | 88°13'42.96\" | Ganga | Arun Kosi | China | 2229 | 5831 | 2988 | 3112 | -62 |\n| 208 | 01_61G_001 | NRSC | CH_62 | 4973 | WB | 33°49'12.72\" | 81°38'40.56\" | Indus | Indus | China | 24 | 85 | 52 | 64 | -72 |\n| 209 | 01_61G_003 | NRSC | CH_64 | 4864 | WB | 33°37'59.88\" | 81°23'14.64\" | Indus | Indus | China | 23 | 85 | 63 | 66 | -73 |\n| 210 | 03_62N_003 | NRSC | CH_320 | 5208 | WB | 30°42'38.16\" | 83°36'30.96\" | Brahmaputra | | China | 12 | 57 | 17 | 34 | -79 |\n| 211 | 01_52L_008 | NRSC | CH_1 | 3873 | WB | 32°19'35.04\" | 78°43'25.68\" | Indus | Sutlej | China | 15 | 50 | 78 | 90 | -83 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 14, "line_start": 1060, "line_end": 1075, "token_count_estimate": 1393, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Shyok", "Teesta"], "countries": ["China", "India"], "lake_ids": ["01_52I_004", "01_52L_008", "01_61G_001", "01_61G_003", "02_62B_001", "02_77D_001", "02_77D_003", "03_62N_003", "03_62O_028", "03_77H_007", "03_77H_020", "03_78A_003", "03_82D_010", "03_82F_016"]}}
{"id": "fc482b9a380b9698", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n*A Water Body of China of Lake ID: 03_71G_008 has have merged with a nearby lake. The combined area has been shown against the lake.*\n\n***", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 1076, "line_end": 1083, "token_count_estimate": 134, "basins": [], "subbasins": [], "countries": ["China"], "lake_ids": ["03_71G_008"]}}
{"id": "cc274c695423d4ef", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.5: GLs & WBs with water spread area greater than 50 ha “Not Analysed”\n\n| Sl. No. | Lake ID (as per GLI 2011) | Inventory Developed by | UID | Elevation (m) | Lake Type | Lattitude(N) | Longitude(E) | Basin | River | Country | Area of August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 1 | 03_91C_052 | NRSC | CH_1085 | 4591 | WB | 29°10'28.2\" | 96°19'32.16\" | Brahmaputra | Lohit | China | # | 64 | 35 | 42 | # |\n| 2 | 03_82K_080 | NRSC | CH_936 | 4530 | WB | 29°28'21.72\" | 94°14'10.68\" | Brahmaputra | | China | # | 47 | 49 | 48 | # |\n| 3 | 01_52E_001 | NRSC | JK_188 | 5116 | GL | 35°25'4.8\" | 77°36'16.56\" | Indus | Shyok | India | # | 51 | 6 | 19 | # |\n| 4 | 03_82K_045 | NRSC | CH_901 | 4572 | WB | 29°49'0.12\" | 94°7'58.8\" | Brahmaputra | | China | # | 49 | 46 | 45 | # |\n| 5 | 03_82J_024 | NRSC | CH_854 | 4362 | WB | 30°0'46.44\" | 94°28'17.76\" | Brahmaputra | | China | # | 67 | 65 | 64 | # |\n| 6 | 03_91C_024 | NRSC | CH_1075 | 3977 | GL | 29°17'53.16\" | 96°48'59.04\" | Brahmaputra | | China | # | 262 | 300 | 296 | # |\n| 7 | 02_72I_025 | NRSC | NP_78 | 4884 | GL | 27°46'44.4\" | 86°36'48.96\" | Ganga | Sun Kosi | Nepal | # | 108 | 137 | 123 | # |\n| 8 | 03_78I_048 | NRSC | BH_129 | 4169 | WB | 27°52'0.84\" | 90°48'58.32\" | Brahmaputra | Manas Chhu & Mangde Chhu | Bhutan | # | 52 | 52 | 50 | # |\n| 9 | 03_62K_012 | NRSC | CH_316 | 5368 | GL | 29°44'7.8\" | 82°58'26.04\" | Brahmaputra | | China | # | 73 | 87 | 80 | # |\n| 10 | 03_62O_038 | NRSC | CH_383 | 4893 | WB | 29°36'16.92\" | 83°22'38.28\" | Brahmaputra | | China | # | 124 | 132 | 134 | # |\n| 11 | 02_77D_009 | NRSC | CH_264 | 5296 | GL | 28°0'37.08\" | 88°15'29.52\" | Ganga | Arun Kosi | China | # | 58 | 59 | 61 | # |\n| 12 | 03_77P_020 | NRSC | CH_591 | 4649 | WB | 28°5'16.44\" | 91°15'25.92\" | Brahmaputra | Kuri Chhu | China | # | 63 | 56 | 57 | # |\n| 13 | 03_91D_081 | NRSC | CH_1136 | 3356 | WB | 28°30'58.32\" | 96°41'54.24\" | Brahmaputra | Lohit | China | # | 304 | 316 | 314 | # |\n| 14 | 03_91D_010 | NRSC | AP_109 | 3323 | WB | 28°55'8.4\" | 96°22'58.8\" | Brahmaputra | Dibang | India | # | 46 | 49 | 51 | # |\n| 15 | 03_91C_044 | NRSC | AP_90 | 4230 | WB | 29°13'23.16\" | 96°16'41.16\" | Brahmaputra | Lohit | India | # | 63 | 65 | 66 | # |\n| 16 | 02_63M_002 | NRSC | NP_41 | 112 | WB | 27°37'15.96\" | 83°6'6.12\" | Ganga | Rapti | Nepal | # | 153 | 76 | 99 | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.5: GLs & WBs with water spread area greater than 50 ha “Not Analysed”", "columns": ["Sl. No.", "Lake ID (as per GLI 2011)", "Inventory Developed by", "UID", "Elevation (m)", "Lake Type", "Lattitude(N)", "Longitude(E)", "Basin", "River", "Country", "Area of August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1084, "line_end": 1101, "token_count_estimate": 1547, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Kosi", "Lohit", "Manas", "Shyok"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52E_001", "02_63M_002", "02_72I_025", "02_77D_009", "03_62K_012", "03_62O_038", "03_77P_020", "03_78I_048", "03_82J_024", "03_82K_045", "03_82K_080", "03_91C_024", "03_91C_044", "03_91C_052", "03_91D_010", "03_91D_081"]}}
{"id": "9cecb5932b17609a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 18 | 03_91D_075 | NRSC | 4274 | 28°36'28.8\" | 96°19'14.16\" | Dibang | Brahmaputra | India | 32 | 23 | 26 | 24 |\n| 19 | 03_62O_031 | NRSC | 5381 | 29°41'40.2\" | 83°1'33.96\" | | Brahmaputra | China | 40 | 28 | 32 | 23 |\n| 20 | 03_62J_025 | NRSC | 5362 | 30°16'55.92\" | 82°10'2.64\" | | Brahmaputra | China | 26 | 19 | 21 | 21 |\n| 21 | 02_71H_025 | NRSC | 5303 | 28°24'23.4\" | 85°35'16.08\" | Trishuli | Ganga | China | 21 | 12 | 17 | 21 |\n| 22 | 01_53M_003 | NRSC | 5511 | 31°56'16.08\" | 79°59'39.84\" | Indus | Indus | China | 15 | 12 | 11 | 21 |\n| 23 | 01_52H_003 | NRSC | 4165 | 32°29'54.6\" | 77°32'37.32\" | Chenab | Indus | India | 173 | 28 | 144 | 20 |\n| 24 | 03_77L_057 | NRSC | 4897 | 28°3'35.28\" | 90°36'12.24\" | Kuri Chhu | Brahmaputra | | 55 | 36 | 46 | 20 |\n| 25 | 02_71L_035 | NRSC | 5091 | 28°1'2.28\" | 86°43'14.16\" | Sun Kosi | Ganga | Nepal | 23 | 19 | 17 | 20 |\n| 26 | 03_83A_005 | NRSC | 4994 | 27°45'20.52\" | 92°24'2.52\" | Dangme Chhu | Brahmaputra | India | 15 | 13 | 12 | 19 |\n| 27 | 03_77J_002 | NRSC | 5254 | 30°29'57.12\" | 90°56'52.8\" | | Brahmaputra | China | 14 | 12 | 12 | 19 |\n| 28 | 03_77L_053 | NRSC | 4793 | 28°3'12.96\" | 90°54'8.28\" | Kuri Chhu | Brahmaputra | China | 60 | 25 | 51 | 18 |\n| 29 | 03_78I_037 | NRSC | 5159 | 27°55'10.2\" | 90°24'25.92\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 18 | 11 | 15 | 18 |\n| 30 | 03_78I_058 | NRSC | 5041 | 27°52'34.32\" | 90°16'50.52\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 31 | 16 | 26 | 17 |\n| 31 | 02_71H_032 | NRSC | 5116 | 28°17'55.32\" | 85°49'8.4\" | Sun Kosi | Ganga | China | 30 | 22 | 26 | 17 |\n| 32 | 02_62K_001 | NRSC | 4404 | 29°59'35.88\" | 82°11'49.2\" | Karnali | Ganga | Nepal | 30 | 26 | 25 | 17 |\n| 33 | 02_71L_017 | NRSC | 5211 | 28°15'11.16\" | 86°6'10.44\" | Sun Kosi | Ganga | China | 17 | 15 | 14 | 17 |\n| 34 | 03_78A_011 | NRSC | 5168 | 27°53'60\" | 88°55'45.84\" | Amo Chhu | Brahmaputra | China | 17 | 14 | 14 | 17 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 1108, "line_end": 1126, "token_count_estimate": 1323, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Chenab", "Dibang", "Kosi", "Manas"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52H_003", "01_53M_003", "02_62K_001", "02_71H_025", "02_71H_032", "02_71L_017", "02_71L_035", "03_62J_025", "03_62O_031", "03_77J_002", "03_77L_053", "03_77L_057", "03_78A_011", "03_78I_037", "03_78I_058", "03_83A_005", "03_91D_075"]}}
{"id": "a53d253f82a0e994", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 35 | 02_72I_015 | NRSC | 5416 | 27°51'0\" | 86°55'42.96\" | Sun Kosi | Ganga | Nepal | 53 | 36 | 46 | 16 |\n| 36 | 02_71H_031 | NRSC | 5268 | 28°18'54\" | 85°56'50.28\" | Sun Kosi | Ganga | China | 34 | 20 | 29 | 16 |\n| 37 | 02_71P_001 | NRSC | 5498 | 28°50'26.88\" | 87°30'28.08\" | Arun Kosi | Ganga | China | 28 | 24 | 20 | 16 |\n| 38 | 02_71P_034 | NRSC | 5259 | 28°9'18\" | 87°36'46.44\" | Arun Kosi | Ganga | China | 26 | 23 | 22 | 15 |\n| 39 | 03_78I_020 | NRSC | 5331 | 27°58'13.8\" | 90°19'49.8\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 25 | 18 | 22 | 15 |\n| 40 | 02_72I_013 | NRSC | 5497 | 27°51'24.84\" | 86°56'13.56\" | Sun Kosi | Ganga | Nepal | 22 | 18 | 19 | 15 |\n| 41 | 02_71L_025 | NRSC | 5357 | 28°11'33.72\" | 86°21'1.8\" | Sun Kosi | Ganga | China | 22 | 16 | 19 | 15 |\n| 42 | 03_78I_026 | NRSC | 5233 | 27°56'26.88\" | 90°23'49.2\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 20 | 17 | 17 | 15 |\n| 43 | 03_91H_001 | NRSC | 4429 | 28°59'30.84\" | 97°32'54.24\" | Lohit | Brahmaputra | China | 19 | 13 | 17 | 15 |\n| 44 | 03_78E_008 | NRSC | 5045 | 27°56'27.6\" | 89°54'20.88\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 14 | 12 | 12 | 15 |\n| 45 | 03_77J_001 | NRSC | 5354 | 30°30'7.2\" | 90°54'46.08\" | | Brahmaputra | China | 30 | 26 | 26 | 14 |\n| 46 | 03_77L_056 | NRSC | 4963 | 28°2'46.32\" | 90°55'6.96\" | Kuri Chhu | Brahmaputra | China | 18 | 16 | 15 | 14 |\n| 47 | 03_91G_007 | NRSC | 4785 | 29°13'47.28\" | 97°19'55.92\" | Lohit | Brahmaputra | China | 14 | 11 | 12 | 14 |\n| 48 | 02_71H_023 | NRSC | 5595 | 28°26'42.36\" | 85°46'46.92\" | Arun Kosi | Ganga | China | 68 | 41 | 60 | 13 |\n| 49 | 02_62F_013 | NRSC | 5252 | 30°15'56.88\" | 81°20'51\" | Karnali | Ganga | China | 52 | 24 | 46 | 13 |\n| 50 | 03_78I_028 | NRSC | 4792 | 27°55'32.88\" | 90°33'17.64\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 30 | 24 | 26 | 13 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1130, "line_end": 1147, "token_count_estimate": 1268, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Kosi", "Lohit", "Manas"], "countries": ["Bhutan", "China", "Nepal"], "lake_ids": ["02_62F_013", "02_71H_023", "02_71H_031", "02_71L_025", "02_71P_001", "02_71P_034", "02_72I_013", "02_72I_015", "03_77J_001", "03_77L_056", "03_78E_008", "03_78I_020", "03_78I_026", "03_78I_028", "03_91G_007", "03_91H_001"]}}
{"id": "cb3edaba520c78ce", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 51 | 03_78A_017 | NRSC | 5545 | 27°53'34.8\" | 88°11'31.92\" | Teesta | Brahmaputra | India | 30 | 19 | 27 | 13 |\n| 52 | 03_62K_005 | NRSC | 4999 | 29°58'10.2\" | 82°29'39.84\" | | Brahmaputra | China | 25 | 21 | 22 | 13 |\n| 53 | 03_77L_028 | NRSC | 4632 | 28°16'15.24\" | 90°43'19.2\" | Kuri Chhu | Brahmaputra | China | 15 | 12 | 13 | 13 |\n| 54 | 03_77H_032 | NRSC | 5056 | 28°1'3.36\" | 89°26'59.64\" | | Brahmaputra | China | 12 | 11 | 8 | 13 |\n| 55 | 03_82J_006 | NRSC | 3657 | 30°32'8.88\" | 94°45'38.16\" | | Brahmaputra | China | 63 | 41 | 56 | 12 |\n| 56 | 01_53I_002 | NRSC/ SDC | 4273 | 31°39'38.52\" | 78°10'1.92\" | Sutlej | Indus | India | 34 | 23 | 30 | 12 |\n| 57 | 03_82J_003 | NRSC | 4161 | 30°41'4.2\" | 94°19'25.32\" | | Brahmaputra | China | 32 | 22 | 29 | 12 |\n| 58 | 02_62K_011 | NRSC | 4673 | 29°14'57.12\" | 82°33'49.68\" | Bheri | Ganga | Nepal | 30 | 26 | 27 | 12 |\n| 59 | 02_62F_007 | NRSC | 5179 | 30°20'18.96\" | 81°54'39.96\" | Karnali | Ganga | Nepal | 28 | 25 | 22 | 12 |\n| 60 | 02_53N_001 | NRSC | 4688 | 30°54'7.92\" | 79°45'12.6\" | Ganga | Ganga | India | 25 | 21 | 22 | 12 |\n| 61 | 02_78A_001 | NRSC | 5201 | 27°59'46.68\" | 88°24'7.2\" | Arun Kosi | Ganga | China | 23 | 16 | 21 | 12 |\n| 62 | 03_91D_098 | NRSC | 4197 | 28°24'10.44\" | 96°50'11.76\" | Lohit | Brahmaputra | China | 15 | 13 | 13 | 12 |\n| 63 | 03_91H_003 | NRSC | 4439 | 28°59'22.56\" | 97°16'4.08\" | Lohit | Brahmaputra | China | 14 | 10 | 12 | 12 |\n| 64 | 03_91C_002 | NRSC | 4691 | 29°53'36.96\" | 96°22'40.44\" | | Brahmaputra | China | 38 | 23 | 34 | 11 |\n| 65 | 03_71P_002 | NRSC | 5537 | 28°48'13.32\" | 87°37'28.2\" | | Brahmaputra | China | 18 | 13 | 16 | 11 |\n| 66 | 03_82L_006 | NRSC | 4147 | 28°52'48.36\" | 94°2'22.92\" | | Brahmaputra | China | 16 | 13 | 14 | 11 |\n| 67 | 03_77K_003 | NRSC | 5303 | 29°52'22.08\" | 90°0'28.08\" | | Brahmaputra | China | 16 | 14 | 13 | 11 |\n| 68 | 03_78I_005 | NRSC | 5338 | 27°59'47.04\" | 90°17'17.16\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 48 | 40 | 44 | 10 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1152, "line_end": 1171, "token_count_estimate": 1358, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Lohit", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_53I_002", "02_53N_001", "02_62F_007", "02_62K_011", "02_78A_001", "03_62K_005", "03_71P_002", "03_77H_032", "03_77K_003", "03_77L_028", "03_78A_017", "03_78I_005", "03_82J_003", "03_82J_006", "03_82L_006", "03_91C_002", "03_91D_098", "03_91H_003"]}}
{"id": "ae5697deb8d34d95", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 69 | 03_78E_011 | NRSC | 4952 | 27°55'48.72\" | 89°54'2.88\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 22 | 13 | 20 | 10 |\n| 70 | 03_78I_006 | NRSC | 5158 | 27°59'43.08\" | 90°15'38.16\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 21 | 16 | 19 | 10 |\n| 71 | 02_62G_002 | NRSC | 4822 | 29°55'17.76\" | 81°1'50.52\" | Karnali | Ganga | Nepal | 19 | 16 | 17 | 10 |\n| 72 | 03_78I_015 | NRSC | 5116 | 27°58'55.2\" | 90°14'38.76\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 18 | 16 | 16 | 10 |\n| 73 | 02_71L_024 | NRSC | 5263 | 28°11'37.68\" | 86°18'51.12\" | Sun Kosi | Ganga | China | 28 | 23 | 26 | 9 |\n| 74 | 03_77D_006 | NRSC/ SDC | 5084 | 28°0'51.84\" | 88°33'41.76\" | Teesta | Brahmaputra | India | 26 | 22 | 24 | 9 |\n| 75 | 03_82K_109 | NRSC | 4356 | 29°3'7.2\" | 94°5'49.2\" | | Brahmaputra | China | 24 | 22 | 22 | 9 |\n| 76 | 03_78I_054 | NRSC | 5138 | 27°52'59.88\" | 90°17'53.16\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 17 | 14 | 16 | 9 |\n| 77 | 03_77L_073 | NRSC | 5166 | 28°0'23.04\" | 90°34'21.36\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 14 | 12 | 13 | 9 |\n| 78 | 03_78A_006 | NRSC | 5004 | 27°58'15.6\" | 88°25'45.84\" | Teesta | Brahmaputra | India | 14 | 11 | 13 | 9 |\n| 79 | 03_78I_038 | NRSC | 5143 | 27°55'28.56\" | 90°15'30.6\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 12 | 11 | 10 | 9 |\n| 80 | 03_62K_010 | NRSC | 5181 | 29°47'45.96\" | 82°51'10.08\" | | Brahmaputra | China | 71 | 41 | 66 | 8 |\n| 81 | 03_77L_047 | NRSC | 4364 | 28°6'1.44\" | 90°13'49.08\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 47 | 23 | 44 | 8 |\n| 82 | 03_82F_005 | NRSC | 4762 | 30°32'6.36\" | 93°31'2.28\" | | Brahmaputra | China | 45 | 17 | 42 | 8 |\n| 83 | 03_77L_045 | NRSC | 5224 | 28°5'7.8\" | 90°36'17.64\" | Kuri Chhu | Brahmaputra | China | 35 | 32 | 32 | 8 |\n| 84 | 03_62K_006 | NRSC | 5101 | 29°57'47.52\" | 82°30'27\" | | Brahmaputra | China | 27 | 21 | 25 | 8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1173, "line_end": 1190, "token_count_estimate": 1270, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Kosi", "Manas", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["02_62G_002", "02_71L_024", "03_62K_006", "03_62K_010", "03_77D_006", "03_77L_045", "03_77L_047", "03_77L_073", "03_78A_006", "03_78E_011", "03_78I_006", "03_78I_015", "03_78I_038", "03_78I_054", "03_82F_005", "03_82K_109"]}}
{"id": "4a8f144f86ef750c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 85 | 03_78I_067 | NRSC | 4918 | 27°50'44.16\" | 90°18'9\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 25 | 20 | 23 | 8 |\n| 86 | 03_62J_027 | NRSC | 4781 | 30°15'23.76\" | 82°35'21.12\" | | Brahmaputra | China | 24 | 19 | 22 | 8 |\n| 87 | 02_71L_030 | NRSC | 5242 | 28°4'22.8\" | 86°31'12.72\" | Sun Kosi | Ganga | China | 24 | 19 | 22 | 8 |\n| 88 | 03_62J_004 | NRSC | 5556 | 30°48'25.56\" | 82°44'58.92\" | | Brahmaputra | China | 15 | 14 | 14 | 8 |\n| 89 | 02_72M_015 | NRSC | 4969 | 27°47'34.08\" | 87°56'1.32\" | Tamor Kosi | Ganga | Nepal | 14 | 13 | 12 | 8 |\n| 90 | 02_62F_009 | NRSC | 5586 | 30°18'7.2\" | 81°23'57.12\" | Karnali | Ganga | China | 12 | 11 | 10 | 8 |\n| 91 | 02_72I_008 | NRSC | 5040 | 27°55'44.4\" | 86°26'0.6\" | Sun Kosi | Ganga | | 36 | 32 | 34 | 7 |\n| 92 | 03_77L_079 | NRSC | 5386 | 28°0'21.24\" | 90°19'40.08\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 36 | 30 | 34 | 7 |\n| 93 | 01_52L_007 | NRSC | 5498 | 32°24'36.36\" | 78°53'56.4\" | Indus | Indus | India | 35 | 32 | 33 | 7 |\n| 94 | 02_72I_022 | NRSC | 5344 | 27°47'33\" | 86°50'21.12\" | Sun Kosi | Ganga | Nepal | 33 | 16 | 31 | 7 |\n| 95 | 02_72M_014 | NRSC | 5217 | 27°47'44.16\" | 87°58'27.48\" | Tamor Kosi | Ganga | Nepal | 24 | 21 | 23 | 7 |\n| 96 | 03_82F_009 | NRSC | 4712 | 30°29'36.6\" | 93°21'27.72\" | | Brahmaputra | China | 23 | 20 | 21 | 7 |\n| 97 | 03_78I_001 | NRSC | 5129 | 27°59'52.44\" | 90°35'33\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 16 | 15 | 11 | 7 |\n| 98 | 03_78A_020 | NRSC | 5219 | 27°52'49.44\" | 88°15'4.68\" | Teesta | Brahmaputra | India | 15 | 14 | 14 | 7 |\n| 99 | 03_77H_015 | NRSC | 4801 | 28°12'10.44\" | 89°42'46.8\" | | Brahmaputra | China | 15 | 12 | 14 | 7 |\n| 100 | 03_82F_021 | NRSC | 4487 | 30°14'58.56\" | 93°36'49.32\" | | Brahmaputra | China | 12 | 11 | 11 | 7 |\n| 101 | 02_71P_036 | NRSC | 5121 | 28°8'51.36\" | 87°28'6.96\" | Arun Kosi | Ganga | China | 40 | 32 | 38 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 1195, "line_end": 1213, "token_count_estimate": 1316, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Manas", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52L_007", "02_62F_009", "02_71L_030", "02_71P_036", "02_72I_008", "02_72I_022", "02_72M_014", "02_72M_015", "03_62J_004", "03_62J_027", "03_77H_015", "03_77L_079", "03_78A_020", "03_78I_001", "03_78I_067", "03_82F_009", "03_82F_021"]}}
{"id": "06c5f254575e9a86", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 102 | 03_78A_027 | NRSC/SDC | 4888 | 27°32'0.6\" | 88°5'8.52\" | Teesta | Brahmaputra | India | 38 | 33 | 36 | 6 |\n| 103 | 03_78A_010 | NRSC | 5078 | 27°57'0.72\" | 88°18'16.92\" | Teesta | Brahmaputra | India | 38 | 36 | 34 | 6 |\n| 104 | 03_78E_001 | NRSC | 5157 | 27°58'54.12\" | 89°53'47.4\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 35 | 26 | 33 | 6 |\n| 105 | 02_62G_003 | NRSC | 3603 | 29°53'50.64\" | 81°34'43.68\" | Karnali | Ganga | Nepal | 35 | 17 | 33 | 6 |\n| 106 | 03_62K_007 | NRSC | 4911 | 29°56'22.56\" | 82°36'7.56\" | | Brahmaputra | China | 30 | 25 | 28 | 6 |\n| 107 | 01_62B_002 | NRSC | 4998 | 30°33'9.72\" | 80°24'6.48\" | Sutlej | Indus | China | 27 | 14 | 26 | 6 |\n| 108 | 02_72I_028 | NRSC | 4408 | 27°44'33.36\" | 86°50'39.48\" | Sun Kosi | Ganga | Nepal | 26 | 21 | 25 | 6 |\n| 109 | 03_77L_071 | NRSC | 5228 | 28°1'41.52\" | 90°16'13.44\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 24 | 21 | 23 | 6 |\n| 110 | 03_77L_034 | NRSC | 5500 | 28°14'31.2\" | 90°30'23.76\" | Kuri Chhu | Brahmaputra | China | 22 | 21 | 20 | 6 |\n| 111 | 02_71L_005 | NRSC | 5524 | 28°23'33.72\" | 86°24'52.56\" | Arun Kosi | Ganga | China | 19 | 18 | 18 | 6 |\n| 112 | 02_72I_006 | NRSC | 4741 | 27°56'32.28\" | 86°41'55.32\" | Sun Kosi | Ganga | Nepal | 19 | 16 | 18 | 6 |\n| 113 | 03_77L_074 | NRSC | 5324 | 28°0'55.44\" | 90°21'9.36\" | Manas Chhu &MangdeChhu | Brahmaputra | Bhutan | 19 | 18 | 16 | 6 |\n| 114 | 03_82G_003 | NRSC | 4936 | 29°47'24.36\" | 93°29'17.88\" | | Brahmaputra | China | 18 | 13 | 17 | 6 |\n| 115 | 03_82L_007 | NRSC | 4163 | 28°50'15\" | 94°27'5.04\" | Ding | Brahmaputra | India | 17 | 16 | 16 | 6 |\n| 116 | 03_91G_009 | NRSC | 4637 | 29°12'2.88\" | 97°22'8.4\" | Lohit | Brahmaputra | China | 17 | 16 | 16 | 6 |\n| 117 | 03_78I_072 | NRSC | 4788 | 27°49'7.32\" | 90°23'39.12\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 13 | 11 | 12 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1217, "line_end": 1234, "token_count_estimate": 1259, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Lohit", "Manas", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_62B_002", "02_62G_003", "02_71L_005", "02_72I_006", "02_72I_028", "03_62K_007", "03_77L_034", "03_77L_071", "03_77L_074", "03_78A_010", "03_78A_027", "03_78E_001", "03_78I_072", "03_82G_003", "03_82L_007", "03_91G_009"]}}
{"id": "9fc784767cef4124", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 118 | 03_78A_005 | NRSC | 5201 | 27°58'31.44\" | 88°25'20.64\" | Teesta | Brahmaputra | India | 12 | 11 | 10 | 6 |\n| 119 | 03_82F_023 | NRSC | 4354 | 30°13'57\" | 93°34'35.76\" | | Brahmaputra | China | 12 | 11 | 11 | 6 |\n| 120 | 03_77L_062 | NRSC | 5295 | 28°2'50.64\" | 90°21'16.92\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 49 | 42 | 46 | 5 |\n| 121 | 02_62K_003 | NRSC | 4571 | 29°55'50.16\" | 82°12'22.68\" | Karnali | Ganga | Nepal | 45 | 43 | 42 | 5 |\n| 122 | 03_82N_029 | NRSC | 4492 | 30°16'4.8\" | 95°36'21.6\" | | Brahmaputra | China | 42 | 35 | 40 | 5 |\n| 123 | 03_78I_004 | NRSC | 5194 | 27°59'28.32\" | 90°25'6.24\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 38 | 36 | 33 | 5 |\n| 124 | 03_77L_058 | NRSC | 5016 | 28°2'53.88\" | 90°35'49.2\" | Kuri Chhu | Brahmaputra | | 34 | 28 | 33 | 5 |\n| 125 | 03_91H_007 | NRSC | 4635 | 28°56'52.08\" | 97°19'11.64\" | Lohit | Brahmaputra | China | 29 | 27 | 28 | 5 |\n| 126 | 03_71B_001 | NRSC | 5692 | 30°34'48\" | 84°4'3.72\" | | Brahmaputra | China | 28 | 27 | 27 | 5 |\n| 127 | 03_78E_003 | NRSC | 5152 | 27°58'26.4\" | 89°53'44.88\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 25 | 21 | 24 | 5 |\n| 128 | 03_78I_019 | NRSC | 5224 | 27°58'7.68\" | 90°24'42.48\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 24 | 18 | 23 | 5 |\n| 129 | 03_91H_036 | NRSC | 4457 | 28°31'5.16\" | 97°31'35.76\" | Lohit | Brahmaputra | China | 22 | 19 | 21 | 5 |\n| 130 | 03_91G_003 | NRSC | 5018 | 29°28'1.2\" | 97°22'29.28\" | Lohit | Brahmaputra | China | 20 | 15 | 19 | 5 |\n| 131 | 02_62B_005 | NRSC | 4314 | 30°26'44.52\" | 80°23'16.08\" | Sarda | Ganga | India | 13 | 12 | 10 | 5 |\n| 132 | 03_82F_025 | NRSC | 4253 | 30°12'29.52\" | 93°30'44.28\" | | Brahmaputra | China | 12 | 11 | 11 | 5 |\n| 133 | 03_78A_025 | NRSC | 4888 | 27°38'10.32\" | 88°48'57.96\" | Amo Chhu | Brahmaputra | | 11 | 10 | 11 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1239, "line_end": 1256, "token_count_estimate": 1262, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Lohit", "Manas", "Sarda", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["02_62B_005", "02_62K_003", "03_71B_001", "03_77L_058", "03_77L_062", "03_78A_005", "03_78A_025", "03_78E_003", "03_78I_004", "03_78I_019", "03_82F_023", "03_82F_025", "03_82N_029", "03_91G_003", "03_91H_007", "03_91H_036"]}}
{"id": "f94384ec9ca435ef", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 134 | 03_91H_008 | NRSC | 4755 | 28°56'41.28\" | 97°18'12.6\" | Lohit | Brahmaputra | China | 47 | 40 | 45 | 4 |\n| 135 | 03_77L_039 | NRSC | 5457 | 28°12'19.44\" | 90°23'7.08\" | Kuri Chhu | Brahmaputra | China | 44 | 38 | 42 | 4 |\n| 136 | 03_78A_012 | NRSC | 5130 | 27°54'4.32\" | 88°46'54.84\" | Teesta | Brahmaputra | India | 28 | 26 | 27 | 4 |\n| 137 | 03_91C_015 | NRSC | 4421 | 29°34'14.88\" | 96°22'26.04\" | | Brahmaputra | China | 27 | 26 | 20 | 4 |\n| 138 | 03_77H_027 | NRSC | 4927 | 28°5'14.28\" | 89°28'50.16\" | | Brahmaputra | China | 22 | 21 | 21 | 4 |\n| 139 | 03_78I_046 | NRSC | 5168 | 27°54'21.96\" | 90°16'32.16\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 22 | 20 | 21 | 4 |\n| 140 | 01_62F_007 | NRSC | 5344 | 30°25'36.48\" | 81°52'13.44\" | Sutlej | Indus | China | 22 | 16 | 21 | 4 |\n| 141 | 02_71P_042 | NRSC | 5524 | 28°7'46.56\" | 87°4'55.56\" | Arun Kosi | Ganga | China | 21 | 20 | 20 | 4 |\n| 142 | 03_78E_027 | NRSC | 4808 | 27°41'13.92\" | 89°24'29.88\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 19 | 13 | 18 | 4 |\n| 143 | 03_82C_011 | NRSC | 5242 | 29°45'0.72\" | 92°46'40.8\" | | Brahmaputra | China | 15 | 12 | 14 | 4 |\n| 144 | 03_82F_026 | NRSC | 4607 | 30°10'21\" | 93°43'5.52\" | | Brahmaputra | China | 14 | 13 | 12 | 4 |\n| 145 | 01_52C_002 | NRSC | 4092 | 33°52'10.2\" | 76°7'9.48\" | Chenab | Indus | India | 44 | 26 | 43 | 3 |\n| 146 | 03_71D_002 | NRSC | 5574 | 28°54'30.6\" | 84°30'25.56\" | | Brahmaputra | China | 36 | 30 | 35 | 3 |\n| 147 | 02_71H_011 | NRSC | 4509 | 28°34'9.48\" | 85°27'24.12\" | Trishuli | Ganga | China | 27 | 19 | 26 | 3 |\n| 148 | 02_71P_046 | NRSC | 4898 | 28°4'9.84\" | 87°8'1.32\" | Arun Kosi | Ganga | China | 27 | 25 | 26 | 3 |\n| 149 | 03_78I_009 | NRSC | 5108 | 27°59'6.36\" | 90°26'13.56\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 26 | 20 | 25 | 3 |\n| 150 | 02_71P_030 | NRSC | 5329 | 28°10'21.36\" | 87°28'44.76\" | Arun Kosi | Ganga | China | 23 | 18 | 22 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 1260, "line_end": 1278, "token_count_estimate": 1320, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Chenab", "Kosi", "Lohit", "Manas", "Teesta"], "countries": ["Bhutan", "China", "India"], "lake_ids": ["01_52C_002", "01_62F_007", "02_71H_011", "02_71P_030", "02_71P_042", "02_71P_046", "03_71D_002", "03_77H_027", "03_77L_039", "03_78A_012", "03_78E_027", "03_78I_009", "03_78I_046", "03_82C_011", "03_82F_026", "03_91C_015", "03_91H_008"]}}
{"id": "415e0139eb61f895", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 185 | 03_91C_071 | NRSC | 4339 | 29°2'31.2\" | 96°13'12\" | Dibang | Brahmaputra | China | 36 | 35 | 37 | -1 |\n| 186 | 03_82N_008 | NRSC | 4546 | 30°34'19.2\" | 95°15'15.48\" | | Brahmaputra | China | 33 | 18 | 33 | -1 |\n| 187 | 02_72I_018 | NRSC | 5370 | 27°49'57.72\" | 86°55'1.92\" | Sun Kosi | Ganga | Nepal | 31 | 31 | 31 | -1 |\n| 188 | 03_77L_063 | NRSC | 5183 | 28°2'6.36\" | 90°37'29.28\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 30 | 30 | 26 | -1 |\n| 189 | 02_71H_004 | NRSC | 5239 | 28°39'46.08\" | 85°28'31.8\" | Arun Kosi | Ganga | China | 28 | 19 | 28 | -1 |\n| 190 | 02_71L_022 | NRSC | 5554 | 28°12'26.28\" | 86°37'45.84\" | Arun Kosi | Ganga | China | 26 | 24 | 26 | -1 |\n| 191 | 02_71D_001 | NRSC | 4111 | 28°39'46.44\" | 84°28'17.76\" | Trishuli | Ganga | Nepal | 23 | 20 | 23 | -1 |\n| 192 | 03_78I_011 | NRSC | 5239 | 27°58'54.48\" | 90°22'52.32\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 20 | 19 | 20 | -1 |\n| 193 | 03_78I_064 | NRSC | 4976 | 27°51'41.04\" | 90°17'42.36\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 20 | 19 | 20 | -1 |\n| 194 | 02_72I_021 | NRSC | 5276 | 27°47'38.04\" | 86°54'38.52\" | Sun Kosi | Ganga | Nepal | 19 | 18 | 19 | -1 |\n| 195 | 03_82F_018 | NRSC | 4554 | 30°17'15.72\" | 93°28'45.12\" | | Brahmaputra | China | 17 | 17 | 16 | -1 |\n| 196 | 03_78E_016 | NRSC | 5004 | 27°53'2.04\" | 89°21'2.52\" | | Brahmaputra | China | 16 | 16 | 16 | -1 |\n| 197 | 03_91C_016 | NRSC | 4813 | 29°32'36.6\" | 96°36'57.96\" | | Brahmaputra | China | 13 | 13 | 13 | -1 |\n| 198 | 03_82F_011 | NRSC | 4720 | 30°26'26.16\" | 93°37'45.84\" | | Brahmaputra | China | 12 | 12 | 11 | -1 |\n| 199 | 03_78A_026 | NRSC | 4736 | 27°33'44.28\" | 88°7'24.96\" | Teesta | Brahmaputra | India | 11 | 11 | 11 | -1 |\n| 200 | 03_78I_057 | NRSC | 5060 | 27°52'24.24\" | 90°18'11.88\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 43 | 33 | 44 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1285, "line_end": 1302, "token_count_estimate": 1265, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Dibang", "Kosi", "Manas", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["02_71D_001", "02_71H_004", "02_71L_022", "02_72I_018", "02_72I_021", "03_77L_063", "03_78A_026", "03_78E_016", "03_78I_011", "03_78I_057", "03_78I_064", "03_82F_011", "03_82F_018", "03_82N_008", "03_91C_016", "03_91C_071"]}}
{"id": "a5f80735bcf05c58", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 201 | 02_72M_011 | NRSC | 4865 | 27°50'39.48\" | 87°4'50.88\" | Arun Kosi | Ganga | Nepal | 42 | 38 | 43 | -2 |\n| 202 | 03_77L_049 | NRSC | 4716 | 28°6'44.28\" | 90°1'35.04\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 38 | 39 | 33 | -2 |\n| 203 | 02_77D_010 | NRSC | 5127 | 28°0'23.76\" | 88°19'10.92\" | Arun Kosi | Ganga | China | 37 | 34 | 38 | -2 |\n| 204 | 02_72I_031 | NRSC | 4777 | 27°41'15\" | 86°51'29.52\" | Sun Kosi | Ganga | Nepal | 31 | 32 | 30 | -2 |\n| 205 | 03_91D_099 | NRSC | 4406 | 28°23'31.2\" | 96°51'28.44\" | Lohit | Brahmaputra | China | 30 | 30 | 29 | -2 |\n| 206 | 02_71L_020 | NRSC | 5348 | 28°14'23.28\" | 86°21'55.44\" | Sun Kosi | Ganga | China | 29 | 30 | 27 | -2 |\n| 207 | 02_62F_011 | NRSC | 5524 | 30°17'49.2\" | 81°23'16.8\" | Karnali | Ganga | China | 27 | 27 | 26 | -2 |\n| 208 | 03_77D_007 | NRSC/ SDC | 5015 | 28°0'26.28\" | 88°34'18.48\" | Teesta | Brahmaputra | India | 24 | 24 | 24 | -2 |\n| 209 | 02_71P_039 | NRSC | 5489 | 28°8'32.64\" | 87°6'19.08\" | Arun Kosi | Ganga | China | 18 | 15 | 18 | -2 |\n| 210 | 02_71L_027 | NRSC | 5234 | 28°9'2.88\" | 86°32'7.08\" | Sun Kosi | Ganga | China | 18 | 18 | 18 | -2 |\n| 211 | 03_91H_006 | NRSC | 4620 | 28°57'28.8\" | 97°20'3.84\" | Lohit | Brahmaputra | China | 17 | 17 | 17 | -2 |\n| 212 | 02_78A_002 | NRSC | 5397 | 27°59'21.48\" | 88°13'15.96\" | Arun Kosi | Ganga | China | 17 | 17 | 14 | -2 |\n| 213 | 01_42H_002 | NRSC | 2763 | 36°38'34.8\" | 73°24'26.64\" | Gilgit | Indus | India | 16 | 13 | 16 | -2 |\n| 214 | 02_71P_026 | NRSC | 5340 | 28°12'23.04\" | 87°33'37.8\" | Arun Kosi | Ganga | China | 16 | 16 | 15 | -2 |\n| 215 | 03_71C_004 | NRSC | 5575 | 29°51'22.68\" | 84°37'56.28\" | | Brahmaputra | China | 15 | 15 | 13 | -2 |\n| 216 | 03_77H_009 | NRSC | 5150 | 28°14'54.24\" | 89°51'5.76\" | | Brahmaputra | China | 15 | 15 | 15 | -2 |\n| 217 | 02_71H_030 | NRSC | 5411 | 28°19'28.56\" | 85°54'24.84\" | Sun Kosi | Ganga | China | 15 | 15 | 14 | -2 |\n| 218 | 03_78I_036 | NRSC | 5028 | 27°55'51.96\" | 90°12'32.76\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 12 | 11 | 12 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1308, "line_end": 1327, "token_count_estimate": 1380, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Gilgit", "Kosi", "Lohit", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_42H_002", "02_62F_011", "02_71H_030", "02_71L_020", "02_71L_027", "02_71P_026", "02_71P_039", "02_72I_031", "02_72M_011", "02_77D_010", "02_78A_002", "03_71C_004", "03_77D_007", "03_77H_009", "03_77L_049", "03_78I_036", "03_91D_099", "03_91H_006"]}}
{"id": "2d6be6539a7fedb9", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 219 | 02_72M_013 | NRSC | 5233 | 27°49'44.76\" | 87°5'41.64\" | Arun Kosi | Ganga | Nepal | 12 | 12 | 11 | -2 |\n| 220 | 03_78A_023 | NRSC | 4547 | 27°40'17.04\" | 88°30'46.44\" | Teesta | Brahmaputra | India | 32 | 33 | 28 | -3 |\n| 221 | 03_77L_023 | NRSC | 5489 | 28°18'3.6\" | 90°38'48.84\" | Kuri Chhu | Brahmaputra | China | 32 | 33 | 30 | -3 |\n| 222 | 02_72I_016 | NRSC | 5231 | 27°50'18.6\" | 86°56'7.8\" | Sun Kosi | Ganga | Nepal | 29 | 30 | 28 | -3 |\n| 223 | 03_91G_004 | NRSC | 5262 | 29°29'48.48\" | 97°6'10.8\" | Lohit | Brahmaputra | China | 27 | 21 | 28 | -3 |\n| 224 | 02_71P_031 | NRSC | 5395 | 28°10'3.36\" | 87°37'23.16\" | Arun Kosi | Ganga | China | 21 | 22 | 20 | -3 |\n| 225 | 03_71D_001 | NRSC | 5454 | 28°55'44.76\" | 84°18'2.52\" | | Brahmaputra | China | 20 | 21 | 19 | -3 |\n| 226 | 03_78A_007 | NRSC/SDC | 4977 | 27°57'38.88\" | 88°38'57.48\" | Teesta | Brahmaputra | India | 17 | 17 | 18 | -3 |\n| 227 | 02_71L_014 | NRSC | 5364 | 28°17'43.08\" | 86°9'2.88\" | Sun Kosi | Ganga | China | 17 | 18 | 16 | -3 |\n| 228 | 03_77H_019 | NRSC | 4804 | 28°10'21.36\" | 89°41'3.48\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 10 | 10 | 9 | -3 |\n| 229 | 03_62J_028 | NRSC | 5603 | 30°13'18.48\" | 82°13'58.44\" | | Brahmaputra | China | 41 | 37 | 43 | -4 |\n| 230 | 03_62K_008 | NRSC | 4968 | 29°55'26.76\" | 82°37'4.44\" | | Brahmaputra | China | 40 | 36 | 41 | -4 |\n| 231 | 02_71P_038 | NRSC | 5483 | 28°8'33.36\" | 87°6'42.12\" | Arun Kosi | Ganga | China | 27 | 23 | 28 | -4 |\n| 232 | 02_78A_008 | NRSC | 5032 | 27°32'44.88\" | 88°2'57.84\" | Tamor Kosi | Ganga | Nepal | 27 | 28 | 25 | -4 |\n| 233 | 03_77H_029 | NRSC | 5049 | 28°0'35.64\" | 89°53'0.96\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 22 | 21 | 23 | -4 |\n| 234 | 03_71C_006 | NRSC | 5482 | 29°49'4.8\" | 84°41'27.96\" | | Brahmaputra | China | 21 | 22 | 20 | -4 |\n| 235 | 03_91H_034 | NRSC | 4629 | 28°32'13.2\" | 97°37'15.6\" | Lohit | Brahmaputra | China | 13 | 13 | 14 | -4 |\n| 236 | 03_91H_015 | NRSC | 4553 | 28°51'10.08\" | 97°37'50.88\" | Lohit | Brahmaputra | China | 13 | 14 | 13 | -4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1332, "line_end": 1351, "token_count_estimate": 1402, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Kosi", "Lohit", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["02_71L_014", "02_71P_031", "02_71P_038", "02_72I_016", "02_72M_013", "02_78A_008", "03_62J_028", "03_62K_008", "03_71C_006", "03_71D_001", "03_77H_019", "03_77H_029", "03_77L_023", "03_78A_007", "03_78A_023", "03_91G_004", "03_91H_015", "03_91H_034"]}}
{"id": "34c61895cd98e548", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 237 | 03_82N_034 | NRSC | 4181 | 30°13'23.52\" | 95°32'32.64\" | | Brahmaputra | China | 13 | 13 | 14 | -4 |\n| 238 | 03_77J_005 | NRSC | 5766 | 30°4'29.64\" | 90°9'24.48\" | | Brahmaputra | China | 12 | 12 | 13 | -4 |\n| 239 | 02_62B_006 | NRSC | 5106 | 30°24'8.28\" | 80°47'4.92\" | Karnali | Ganga | China | 40 | 42 | 40 | -5 |\n| 240 | 03_82N_001 | NRSC | 5055 | 30°35'27.96\" | 95°33'3.24\" | | Brahmaputra | China | 36 | 38 | 35 | -5 |\n| 241 | 02_71H_010 | NRSC | 5481 | 28°34'32.16\" | 85°34'59.52\" | Arun Kosi | Ganga | China | 26 | 27 | 25 | -5 |\n| 242 | 01_52A_002 | NRSC | 4537 | 35°5'48.12\" | 76°14'0.6\" | Shyok | Indus | India | 22 | 23 | 21 | -5 |\n| 243 | 03_78I_040 | NRSC | 5167 | 27°55'13.44\" | 90°15'46.44\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 21 | 22 | 22 | -5 |\n| 244 | 03_91C_004 | NRSC | 4137 | 29°52'26.76\" | 96°19'29.28\" | | Brahmaputra | China | 20 | 21 | 18 | -5 |\n| 245 | 03_83A_007 | NRSC | 5028 | 27°43'39.36\" | 92°26'12.48\" | Jia Brali | Brahmaputra | India | 14 | 14 | 15 | -5 |\n| 246 | 03_78A_019 | NRSC/SDC | 4809 | 27°51'52.2\" | 88°51'46.44\" | Teesta | Brahmaputra | India | 14 | 15 | 12 | -5 |\n| 247 | 02_71L_019 | NRSC | 5378 | 28°14'56.04\" | 86°9'2.16\" | Sun Kosi | Ganga | China | 13 | 14 | 13 | -5 |\n| 248 | 01_62B_003 | NRSC | 5288 | 30°28'36.48\" | 80°35'35.16\" | Sutlej | Indus | India | 12 | 12 | 13 | -5 |\n| 249 | 03_82N_037 | NRSC | 4691 | 30°0'30.96\" | 95°54'54.36\" | | Brahmaputra | China | 12 | 13 | 11 | -5 |\n| 250 | 01_52A_004 | NRSC/SDC | 4619 | 35°4'28.2\" | 76°17'33.72\" | Shyok | Indus | India | 10 | 11 | 10 | -5 |\n| 251 | 01_52C_001 | NRSC | 4394 | 33°56'44.52\" | 76°13'53.76\" | Shingo (Indus) | Indus | India | 49 | 36 | 52 | -6 |\n| 252 | 02_72M_004 | NRSC | 5293 | 27°57'46.44\" | 87°48'42.12\" | Arun Kosi | Ganga | China | 48 | 35 | 51 | -6 |\n| 253 | 02_71L_031 | NRSC | 4682 | 28°4'4.8\" | 86°3'56.16\" | Sun Kosi | Ganga | China | 31 | 33 | 30 | -6 |\n| 254 | 02_62O_002 | NRSC | 5495 | 29°12'3.24\" | 83°41'2.76\" | Kali Gandak | Ganga | Nepal | 23 | 25 | 21 | -6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1355, "line_end": 1374, "token_count_estimate": 1404, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Gandak", "Kosi", "Shyok", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52A_002", "01_52A_004", "01_52C_001", "01_62B_003", "02_62B_006", "02_62O_002", "02_71H_010", "02_71L_019", "02_71L_031", "02_72M_004", "03_77J_005", "03_78A_019", "03_78I_040", "03_82N_001", "03_82N_034", "03_82N_037", "03_83A_007", "03_91C_004"]}}
{"id": "9efa462f201d8ac6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 255 | 03_78I_014 | NRSC | 5087 | 27°59'13.2\" | 90°7'48.72\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 20 | 21 | 18 | -6 |\n| 256 | 01_62E_016 | NRSC | 5528 | 31°10'42.6\" | 81°9'6.84\" | Sutlej | Indus | China | 20 | 21 | 20 | -6 |\n| 257 | 02_78A_006 | NRSC | 5743 | 27°55'39\" | 88°1'11.64\" | Arun Kosi | Ganga | China | 16 | 16 | 17 | -6 |\n| 258 | 02_71L_033 | NRSC | 5369 | 28°2'18.96\" | 86°42'34.56\" | Sun Kosi | Ganga | Nepal | 16 | 17 | 15 | -6 |\n| 259 | 03_77L_025 | NRSC | 5370 | 28°18'0.72\" | 90°36'29.52\" | Kuri Chhu | Brahmaputra | China | 14 | 15 | 15 | -6 |\n| 260 | 03_78A_031 | NRSC | 4305 | 27°26'15\" | 88°5'0.96\" | Teesta | Brahmaputra | India | 13 | 14 | 12 | -6 |\n| 261 | 03_91G_005 | NRSC | 5170 | 29°24'7.56\" | 97°0'32.4\" | Lohit | Brahmaputra | China | 13 | 14 | 10 | -6 |\n| 262 | 03_82N_018 | NRSC | 4333 | 30°31'44.4\" | 95°6'23.4\" | | Brahmaputra | China | 10 | 11 | 10 | -6 |\n| 263 | 02_71L_008 | NRSC | 5577 | 28°22'31.08\" | 86°15'27\" | Sun Kosi | Ganga | China | 36 | 24 | 39 | -7 |\n| 264 | 03_82N_025 | NRSC | 4764 | 30°22'51.24\" | 95°39'12.96\" | | Brahmaputra | China | 25 | 27 | 24 | -7 |\n| 265 | 02_71H_034 | NRSC | 4745 | 28°17'32.28\" | 85°10'12.72\" | Trishuli | Ganga | Nepal | 20 | 21 | 17 | -7 |\n| 266 | 03_83A_004 | NRSC | 5109 | 27°45'47.16\" | 92°25'29.64\" | Dangme Chhu | Brahmaputra | India | 17 | 17 | 18 | -7 |\n| 267 | 03_91D_070 | NRSC | 4126 | 28°36'36.36\" | 96°43'19.56\" | Lohit | Brahmaputra | China | 13 | 12 | 14 | -7 |\n| 268 | 03_71P_004 | NRSC | 5637 | 28°47'55.68\" | 87°36'12.24\" | | Brahmaputra | China | 11 | 12 | 11 | -7 |\n| 269 | 02_71L_009 | NRSC | 5546 | 28°20'53.16\" | 86°29'35.16\" | Arun Kosi | Ganga | China | 35 | 38 | 33 | -8 |\n| 270 | 02_71L_015 | NRSC | 5261 | 28°17'38.76\" | 86°7'52.32\" | Sun Kosi | Ganga | China | 25 | 27 | 23 | -8 |\n| 271 | 03_77H_017 | NRSC | 4537 | 28°10'19.2\" | 89°50'54.24\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 23 | 25 | 24 | -8 |\n| 272 | 03_91C_010 | NRSC | 4712 | 29°39'49.32\" | 96°33'8.64\" | | Brahmaputra | China | 21 | 23 | 21 | -8 |\n| 273 | 03_91C_012 | NRSC | 4663 | 29°35'18.6\" | 96°40'18.84\" | | Brahmaputra | China | 19 | 21 | 19 | -8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 19, "line_start": 1379, "line_end": 1399, "token_count_estimate": 1494, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Lohit", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_62E_016", "02_71H_034", "02_71L_008", "02_71L_009", "02_71L_015", "02_71L_033", "02_78A_006", "03_71P_004", "03_77H_017", "03_77L_025", "03_78A_031", "03_78I_014", "03_82N_018", "03_82N_025", "03_83A_004", "03_91C_010", "03_91C_012", "03_91D_070", "03_91G_005"]}}
{"id": "6df7d9d66dffe2af", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 274 | 01_52B_010 | NRSC/ SDC | 5122 | 34°3'6.48\" | 76°43'5.16\" | Indus | Indus | India | 17 | 18 | 16 | -8 |\n| 275 | 03_82N_031 | NRSC | 4409 | 30°14'17.88\" | 95°36'8.28\" | | Brahmaputra | China | 16 | 17 | 15 | -8 |\n| 276 | 01_62J_004 | NRSC | 5504 | 30°22'33.96\" | 82°1'6.24\" | Sutlej | Indus | China | 11 | 12 | 10 | -8 |\n| 277 | 02_62F_010 | NRSC | 5502 | 30°18'25.56\" | 81°51'55.44\" | Karnali | Ganga | Nepal | 10 | 11 | 10 | -8 |\n| 278 | 02_71H_006 | NRSC | 5167 | 28°38'33.72\" | 85°28'22.8\" | Arun Kosi | Ganga | China | 35 | 38 | 34 | -9 |\n| 279 | 03_91C_021 | NRSC | 4093 | 29°25'15.96\" | 96°37'30.72\" | | Brahmaputra | China | 32 | 35 | 32 | -9 |\n| 280 | 03_91H_073 | NRSC | 4481 | 28°3'15.48\" | 97°19'47.64\" | Lohit | Brahmaputra | India | 23 | 25 | 25 | -9 |\n| 281 | 03_77L_031 | NRSC | 4698 | 28°14'52.08\" | 90°42'43.2\" | Kuri Chhu | Brahmaputra | China | 19 | 21 | 17 | -9 |\n| 282 | 02_71L_007 | NRSC | 5576 | 28°22'54.84\" | 86°23'3.84\" | Arun Kosi | Ganga | China | 14 | 15 | 13 | -9 |\n| 283 | 03_71D_003 | NRSC | 5362 | 28°54'33.84\" | 84°20'51.72\" | | Brahmaputra | China | 10 | 11 | 10 | -9 |\n| 284 | 02_62J_001 | NRSC | 5182 | 30°11'46.68\" | 82°7'5.52\" | Karnali | Ganga | Nepal | 10 | 11 | 7 | -9 |\n| 285 | 03_77H_016 | NRSC | 4929 | 28°11'10.32\" | 89°35'51\" | | Brahmaputra | China | 34 | 38 | 35 | -10 |\n| 286 | 02_71H_009 | NRSC | 5448 | 28°34'50.16\" | 85°35'41.28\" | Arun Kosi | Ganga | China | 28 | 31 | 25 | -10 |\n| 287 | 03_77L_019 | NRSC | 5681 | 28°22'45.84\" | 90°5'41.28\" | | Brahmaputra | China | 13 | 13 | 14 | -10 |\n| 288 | 02_71L_016 | NRSC | 5345 | 28°16'12.36\" | 86°11'12.12\" | Sun Kosi | Ganga | China | 12 | 13 | 11 | -10 |\n| 289 | 03_71C_002 | NRSC | 5663 | 29°53'15\" | 84°32'13.2\" | | Brahmaputra | China | 11 | 12 | 9 | -10 |\n| 290 | 03_77H_025 | NRSC | 4312 | 28°6'19.44\" | 89°53'53.16\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 23 | 26 | 24 | -11 |\n| 291 | 01_43J_003 | NRSC | 3954 | 34°55'36.12\" | 74°9'19.44\" | Jhelum | Indus | India | 18 | 20 | 19 | -11 |\n| 292 | 01_52B_012 | NRSC | 5137 | 34°0'19.8\" | 76°47'12.84\" | Indus | Indus | India | 15 | 17 | 14 | -11 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 19, "line_start": 1401, "line_end": 1421, "token_count_estimate": 1492, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Jhelum", "Kosi", "Lohit"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_43J_003", "01_52B_010", "01_52B_012", "01_62J_004", "02_62F_010", "02_62J_001", "02_71H_006", "02_71H_009", "02_71L_007", "02_71L_016", "03_71C_002", "03_71D_003", "03_77H_016", "03_77H_025", "03_77L_019", "03_77L_031", "03_82N_031", "03_91C_021", "03_91H_073"]}}
{"id": "779a1493f8e3cc85", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 329 | 03_82G_007 | NRSC | 4994 | 29°39'28.08\" | 93°16'30\" | | Brahmaputra | China | 13 | 16 | 13 | -21 |\n| 330 | 02_71D_003 | NRSC | 3668 | 28°35'46.68\" | 84°37'39.72\" | Trishuli | Ganga | Nepal | 25 | 32 | 25 | -22 |\n| 331 | 03_71C_001 | NRSC | 5543 | 29°54'51.84\" | 84°36'2.88\" | | Brahmaputra | China | 9 | 11 | 8 | -22 |\n| 332 | 03_91G_006 | NRSC | 5028 | 29°23'30.48\" | 97°1'8.76\" | Lohit | Brahmaputra | China | 21 | 27 | 20 | -23 |\n| 333 | 02_71L_012 | NRSC | 5570 | 28°19'15.24\" | 86°9'30.96\" | Sun Kosi | Ganga | China | 19 | 25 | 20 | -23 |\n| 334 | 03_82J_001 | NRSC | 4775 | 30°49'51.6\" | 94°0'3.24\" | | Brahmaputra | China | 23 | 31 | 31 | -26 |\n| 335 | 03_78E_018 | NRSC | 5164 | 27°52'45.12\" | 89°19'28.2\" | | Brahmaputra | China | 18 | 24 | 17 | -26 |\n| 336 | 02_62K_006 | NRSC | 5053 | 29°49'18.48\" | 82°42'41.4\" | Karnali | Ganga | Nepal | 16 | 18 | 22 | -27 |\n| 337 | 03_82F_024 | NRSC | 4197 | 30°13'39.36\" | 93°38'11.04\" | | Brahmaputra | China | 19 | 17 | 27 | -29 |\n| 338 | 01_52A_003 | NRSC | 4586 | 35°5'33.36\" | 76°15'7.2\" | Shyok | Indus | India | 17 | 24 | 18 | -29 |\n| 339 | 03_82N_016 | NRSC | 5017 | 30°32'24.36\" | 95°22'30.36\" | | Brahmaputra | China | 8 | 11 | 6 | -29 |\n| 340 | 03_78M_013 | NRSC | 4232 | 27°53'43.08\" | 91°14'54.96\" | Kuri Chhu | Brahmaputra | Bhutan | 8 | 11 | 8 | -29 |\n| 341 | 02_72I_020 | NRSC | 5436 | 27°47'56.04\" | 86°57'56.52\" | Sun Kosi | Ganga | Nepal | 20 | 29 | 20 | -31 |\n| 342 | 03_62K_013 | NRSC | 5101 | 29°41'17.88\" | 82°59'2.4\" | | Brahmaputra | China | 50 | 37 | 73 | -32 |\n| 343 | 02_72M_001 | NRSC | 5675 | 27°59'21.48\" | 87°52'5.16\" | Arun Kosi | Ganga | China | 7 | 10 | 7 | -32 |\n| 344 | 02_72I_005 | NRSC | 4715 | 27°56'35.88\" | 86°42'40.68\" | Sun Kosi | Ganga | Nepal | 15 | 19 | 22 | -33 |\n| 345 | 03_82N_015 | NRSC | 5090 | 30°32'44.88\" | 95°20'35.52\" | | Brahmaputra | China | 7 | 10 | 6 | -33 |\n| 346 | 02_77D_005 | NRSC | 5738 | 28°3'52.92\" | 88°32'38.04\" | Arun Kosi | Ganga | China | 7 | 11 | 7 | -34 |\n| 347 | 02_62O_005 | NRSC | 5450 | 29°2'46.32\" | 83°40'27.48\" | Kali Gandak | Ganga | Nepal | 10 | 15 | 12 | -35 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 19, "line_start": 1428, "line_end": 1448, "token_count_estimate": 1457, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Gandak", "Kosi", "Lohit", "Shyok"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52A_003", "02_62K_006", "02_62O_005", "02_71D_003", "02_71L_012", "02_72I_005", "02_72I_020", "02_72M_001", "02_77D_005", "03_62K_013", "03_71C_001", "03_78E_018", "03_78M_013", "03_82F_024", "03_82G_007", "03_82J_001", "03_82N_015", "03_82N_016", "03_91G_006"]}}
{"id": "dd49c93746fc664e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Lattitude (E) | Longitude (N) | River | Basin | Country | Area of August 2025 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 348 | 02_62F_014 | NRSC | 5481 | 30°14'26.88\" | 81°19'53.4\" | Karnali | Ganga | China | 7 | 12 | 6 | -43 |\n| 349 | 02_62F_016 | NRSC | 5359 | 30°13'0.48\" | 81°48'5.04\" | Karnali | Ganga | Nepal | 15 | 29 | 15 | -48 |\n| 350 | 03_91C_043 | NRSC | 4429 | 29°10'44.04\" | 96°51'12.96\" | | Brahmaputra | China | 12 | 26 | 11 | -53 |\n| 351 | 03_77L_038 | NRSC | 5521 | 28°13'29.64\" | 90°15'26.64\" | | Brahmaputra | China | 14 | 30 | 14 | -54 |\n| 352 | 03_78A_008 | NRSC | 4998 | 27°57'3.24\" | 88°21'15.48\" | Teesta | Brahmaputra | India | 18 | 44 | 17 | -59 |\n| 353 | 03_77L_054 | NRSC | 4717 | 28°5'15\" | 90°19'33.24\" | Puna Tsang Chhu | Brahmaputra | Bhutan | 7 | 17 | 5 | -59 |\n| 354 | 03_77L_061 | NRSC | 5038 | 28°2'29.4\" | 90°32'15.72\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | 18 | 15 | 57 | -68 |\n| 355 | 03_91C_006 | NRSC | 5057 | 29°45'11.16\" | 96°27'48.96\" | | Brahmaputra | China | 4 | 14 | 4 | -71 |\n| 356 | 02_72I_024 | NRSC | 5165 | 27°47'23.28\" | 86°37'11.64\" | Sun Kosi | Ganga | Nepal | # | 35 | 34 | # |\n| 357 | 03_91C_023 | NRSC | 4811 | 29°23'8.88\" | 96°22'22.08\" | Lohit | Brahmaputra | China | # | 30 | 27 | # |\n| 358 | 03_78A_016 | NRSC | 5451 | 27°53'33.72\" | 88°12'47.16\" | Teesta | Brahmaputra | India | # | 14 | 12 | # |\n| 359 | 03_77L_040 | NRSC | 4515 | 28°9'14.76\" | 90°8'54.6\" | Puna Tsang Chhu | Brahmaputra | Bhutan | # | 12 | # | # |\n| 360 | 01_52P_004 | NRSC | 5470 | 32°23'7.08\" | 79°40'43.68\" | Indus | Indus | China | # | 14 | 0 | # |\n| 361 | 02_62B_007 | NRSC | 4839 | 30°16'42.96\" | 80°7'49.8\" | Sarda | Ganga | India | # | 19 | # | # |\n| 362 | 02_72I_009 | NRSC | 5292 | 27°55'2.64\" | 86°27'59.04\" | Sun Kosi | Ganga | Nepal | # | 11 | 18 | # |\n| 363 | 03_91D_096 | NRSC | 3794 | 28°25'56.64\" | 96°55'32.52\" | Lohit | Brahmaputra | China | # | 38 | 41 | # |\n| 364 | 02_62J_002 | NRSC | 5021 | 30°8'56.04\" | 82°9'42.12\" | Karnali | Ganga | Nepal | # | 16 | 15 | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Lattitude (E)", "Longitude (N)", "River", "Basin", "Country", "Area of August 2025 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 17, "line_start": 1452, "line_end": 1470, "token_count_estimate": 1338, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Lohit", "Manas", "Sarda", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_52P_004", "02_62B_007", "02_62F_014", "02_62F_016", "02_62J_002", "02_72I_009", "02_72I_024", "03_77L_038", "03_77L_040", "03_77L_054", "03_77L_061", "03_78A_008", "03_78A_016", "03_91C_006", "03_91C_023", "03_91C_043", "03_91D_096"]}}
{"id": "194c09cdd5c91bd3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Latitude (N) | Longitude (E) | River | Basin | Country | Area of August 2023 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 365 | 01_53M_001 | NRSC | 5576 | 31°59'0.96\" | 79°57'30.96\" | Indus | Indus | China | # | 11 | 16 | # |\n| 366 | 03_71P_003 | NRSC | 5360 | 28°47'47.76\" | 87°38'26.52\" | | Brahmaputra | China | # | 23 | 26 | # |\n| 367 | 03_82L_008 | NRSC | 4342 | 28°52'12.36\" | 94°1'5.88\" | | Brahmaputra | China | # | 12 | 11 | # |\n| 368 | 02_77D_011 | NRSC | 5305 | 28°0'19.08\" | 88°14'26.88\" | Arun Kosi | Ganga | China | # | 39 | 45 | # |\n| 369 | 03_91C_035 | NRSC | 4283 | 29°13'20.64\" | 96°48'34.2\" | | Brahmaputra | China | # | 24 | 51 | # |\n| 370 | 03_78A_002 | NRSC/ SDC | 4952 | 27°58'56.28\" | 88°30'28.08\" | Teesta | Brahmaputra | India | # | 22 | 37 | # |\n| 371 | 03_91C_036 | NRSC | 4298 | 29°13'6.96\" | 96°48'52.2\" | | Brahmaputra | China | # | 16 | 54 | # |\n| 372 | 03_77L_065 | NRSC | 5025 | 28°2'18.24\" | 90°32'47.76\" | Manas Chhu & Mangde Chhu | Brahmaputra | Bhutan | # | 17 | 16 | # |\n| 373 | 03_77H_021 | NRSC | 5135 | 28°8'37.68\" | 89°50'25.8\" | Puna Tsang Chhu | Brahmaputra | Bhutan | # | 15 | 14 | # |\n| 374 | 03_91D_082 | NRSC | 4550 | 28°32'28.68\" | 96°36'5.04\" | Lohit | Brahmaputra | China | # | 31 | 29 | # |\n| 375 | 02_72I_001 | NRSC | 5333 | 27°59'55.32\" | 86°50'8.16\" | Sun Kosi | Ganga | Nepal | # | 12 | 12 | # |\n| 376 | 03_62J_020 | NRSC | 5603 | 30°20'25.8\" | 82°8'26.16\" | | Brahmaputra | China | # | 18 | 13 | # |\n| 377 | 03_82F_012 | NRSC | 4454 | 30°21'27.36\" | 93°37'52.68\" | | Brahmaputra | China | # | 39 | 20 | # |\n| 378 | 03_62J_024 | NRSC | 5548 | 30°18'35.64\" | 82°11'58.92\" | | Brahmaputra | China | # | 31 | 19 | # |\n| 379 | 01_62E_007 | NRSC | 5641 | 31°17'6.36\" | 81°1'53.04\" | Sutlej | Indus | China | # | 11 | 14 | # |\n| 380 | 02_72I_026 | NRSC | 5188 | 27°46'39.72\" | 86°38'31.92\" | Sun Kosi | Ganga | Nepal | # | 30 | 27 | # |\n| 381 | 02_72I_010 | NRSC | 5125 | 27°54'57.96\" | 86°28'39\" | Sun Kosi | Ganga | Nepal | # | 14 | 14 | # |\n| 382 | 02_71D_002 | NRSC | 4063 | 28°39'24.48\" | 84°27'28.8\" | Trishuli | Ganga | Nepal | # | 10 | 6 | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Latitude (N)", "Longitude (E)", "River", "Basin", "Country", "Area of August 2023 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 18, "line_start": 1475, "line_end": 1494, "token_count_estimate": 1384, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi", "Lohit", "Manas", "Teesta"], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": ["01_53M_001", "01_62E_007", "02_71D_002", "02_72I_001", "02_72I_010", "02_72I_026", "02_77D_011", "03_62J_020", "03_62J_024", "03_71P_003", "03_77H_021", "03_77L_065", "03_78A_002", "03_82F_012", "03_82L_008", "03_91C_035", "03_91C_036", "03_91D_082"]}}
{"id": "4678c27a5b7cb206", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Elevation (m) | Latitude (N) | Longitude (E) | River | Basin | Country | Area of August 2023 (Ha) | Inventory Area 2011(Ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 383 | 03_91C_008 | NRSC | 4899 | 29°42'21.6\" | 96°18'24.84\" | | Brahmaputra | China | # | 23 | 22 | # |\n| 384 | 02_72I_017 | NRSC | 5018 | 27°50'45.96\" | 86°27'49.32\" | Sun Kosi | Ganga | Nepal | # | 14 | 11 | # |\n| 385 | 02_71H_019 | NRSC | 4674 | 28°30'36.36\" | 85°26'44.52\" | Trishuli | Ganga | China | # | 16 | 13 | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Elevation (m)", "Latitude (N)", "Longitude (E)", "River", "Basin", "Country", "Area of August 2023 (Ha)", "Inventory Area 2011(Ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 3, "line_start": 1498, "line_end": 1502, "token_count_estimate": 401, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Kosi"], "countries": ["China", "Nepal"], "lake_ids": ["02_71H_019", "02_72I_017", "03_91C_008"]}}
{"id": "c161a69560819699", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n\n- GLs/WBs with increase in Area > 40%\n- GLs/WBs with increase in Area – 0% to 40%\n- GLs/WBs with no change in Area\n- GLs/WBs with decrease in Area\n- GLs/WBs not analysed\n\nA Glacial Lake of China of Lake ID: 03_82N_032 has have merged with a nearby lake. The combined area has been shown against the lake.\nThe Glacial Lakes of China of Lake ID: 03_91C_035 & Lake ID: 03_91C_036 have merged with each other and combined area has been shown against each lake.\nThe Glacial Lakes of China of Lake ID: 03_77L_048 & Lake ID: 03_77L_053 have merged with each other and combined area has been shown against each lake.\nThe Waterbodies of China of Lake ID : 02_71P_018 has merged with nearby Glacial lakes of Lake ID: 02_71P_019 & Lake ID: 02_71P_020 and combined area has been shown against each lake.\nThe Glacial Lakes of India (Himachal Pradesh) of Lake ID: 01_52H_003 & Lake ID: 01_52H_004 have merged with each other and combined area has been shown against each lake.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 1503, "line_end": 1522, "token_count_estimate": 394, "basins": [], "subbasins": [], "countries": ["China", "India"], "lake_ids": ["01_52H_003", "01_52H_004", "02_71P_018", "02_71P_019", "02_71P_020", "03_77L_048", "03_77L_053", "03_82N_032", "03_91C_035", "03_91C_036"]}}
{"id": "f4f0708e6b427eff", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.7: Results of analysis of GLs identified by SDC with water spread area between 10ha - 50 ha\n\n| Sl.No. | Lake ID | Inventory Developed by | Rank of Vulnerability | Elevation | Latitude(N) | Longitude (E) | State | Country | Area of August 2020 (ha) | Base Area (Avg area of last 3 years (ha) | Change in Area (%) w.r.t Base Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 180 | SDC | Very High Risk | 4442 | 34° 21' 10.8\" | 76° 4' 37.2\" | JK | India | 17 | 11 | 52 |\n| 2 | 1805 | SDC | Very High Risk/81I | 4775 | 32° 45' 43.2\" | 77° 11' 42\" | HP | India | 6 | 4 | 40 |\n| 3 | 1936 | SDC | Very High Risk/321I | 4606 | 32° 15' 21.6\" | 76° 46' 37.2\" | HP | India | 4 | 3 | 37 |\n| 4 | 129 | SDC | Very High Risk | 4895 | 27°46'24.165\" | 92°19'1.10\" | AP | India | 13 | 10 | 32 |\n| 5 | 295 | SDC | Very High Risk | 4850 | 27° 55' 12\" | 88° 40' 19.2\" | SK | India | 10 | 8 | 32 |\n| 6 | 293 | SDC | Very High Risk | 5048 | 27° 57' 3.6\" | 88° 42' 18\" | SK | India | 3 | 2 | 29 |\n| 7 | 1360 | SDC | Very High Risk | 4667 | 35° 1' 37.2\" | 75° 43' 30\" | JK | India | 14 | 11 | 27 |\n| 8 | 298 | SDC | Very High Risk | 4508 | 27° 52' 22.8\" | 88° 38' 16.8\" | SK | India | 8 | 6 | 27 |\n| 9 | 292 | SDC | Medium Risk | 5577 | 28° 0' 21.6\" | 88° 39' 18\" | SK | India | 5 | 4 | 24 |\n| 10 | 951 | SDC | Very High Risk | 3762 | 34° 4' 1.2\" | 75° 28' 30\" | JK | India | 20 | 17 | 19 |\n| 11 | 173 | SDC | Medium Risk | 5150 | 34° 45' 54\" | 76° 42' 36\" | JK | India | 10 | 8 | 18 |\n| 12 | 958 | SDC | Very High Risk | 4103 | 34° 8' 16.8\" | 75° 24' 57.6\" | JK | India | 8 | 7 | 17 |\n| 13 | 515 | SDC | Medium Risk | 5063 | 27° 51' 14.4\" | 88° 48' 21.6\" | SK | India | 10 | 9 | 17 |\n| 14 | 976 | SDC | High Risk/15I | 4314 | 34° 11' 6\" | 75° 22' 19.2\" | JK | India | 20 | 17 | 17 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.7: Results of analysis of GLs identified by SDC with water spread area between 10ha - 50 ha", "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Rank of Vulnerability", "Elevation", "Latitude(N)", "Longitude (E)", "State", "Country", "Area of August 2020 (ha)", "Base Area (Avg area of last 3 years (ha)", "Change in Area (%) w.r.t Base Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1523, "line_end": 1538, "token_count_estimate": 965, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "c113a7baaea070d9", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Rank of Vulnerability | Elevation | Latitude(N) | Longitude (E) | State | Country | Area of August 2020 (ha) | Base Area (Avg area of last 3 years (ha) | Change in Area (%) w.r.t Base Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 15 | 2108 | SDC | Very High Risk/347G | 5587 | 30° 58' 33.6\" | 79° 27' 32.4\" | UK | India | 21 | 18 | 16 |\n| 16 | 260 | SDC | Medium Risk | 5253 | 27° 53' 38.4\" | 88° 45' 39.6\" | SK | India | 46 | 41 | 11 |\n| 17 | 1847 | SDC | Very High Risk | 4570 | 31° 54' 54\" | 77° 31' 37.2\" | HP | India | 15 | 14 | 11 |\n| 18 | 256 | SDC | High risk | 4615 | 27° 48' 57.6\" | 88° 39' 25.2\" | SK | India | 15 | 14 | 10 |\n| 19 | 1998 | SDC | Very High Risk | 3857 | 32° 19' 12\" | 76° 54' 28.8\" | HP | India | 1 | 1 | 9 |\n| 20 | 569 | SDC | Medium Risk | 5450 | 28° 0' 7.2\" | 88° 38' 24\" | SK | India | 32 | 30 | 8 |\n| 21 | 963 | SDC | Medium Risk | 3725 | 34° 8' 20.4\" | 75° 22' 33.6\" | JK | India | 32 | 30 | 7 |\n| 22 | 345 | SDC | Medium Risk | 5108 | 27° 51' 50.4\" | 88° 44' 49.2\" | SK | India | 19 | 18 | 7 |\n| 23 | 2031 | SDC | Very High Risk | 4702 | 31° 20' 20.4\" | 78° 15' 10.8\" | HP | India | 12 | 11 | 7 |\n| 24 | 1774 | SDC | Very High Risk | 4593 | 32° 13' 15.6\" | 76° 47' 16.8\" | HP | India | 8 | 8 | 6 |\n| 25 | 931 | SDC | Very High Risk | 4082 | 33° 55' 44.4\" | 75° 23' 20.4\" | JK | India | 20 | 19 | 5 |\n| 26 | 227 | SDC | Very High Risk | 5176 | 27° 59' 34.8\" | 88° 32' 49.2\" | SK | India | 63 | 61 | 3 |\n| 27 | 1032 | SDC | Very High Risk | 4007 | 34° 23' 9.6\" | 75° 3' 50.4\" | JK | India | 1 | 1 | 0 |\n| 28 | 237 | SDC | Very Low Risk | 5322 | 27° 59' 34.8\" | 88° 48' 3.6\" | SK | India | 8 | 8 | -1 |\n| 29 | 1037 | SDC | Medium Risk/27I | 3603 | 34° 25' 19.2\" | 75° 3' 28.8\" | JK | India | 38 | 38 | -1 |\n| 30 | 993 | SDC | Very High Risk | 4148 | 34° 13' 37.2\" | 75° 13' 19.2\" | JK | India | 6 | 6 | -3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Rank of Vulnerability", "Elevation", "Latitude(N)", "Longitude (E)", "State", "Country", "Area of August 2020 (ha)", "Base Area (Avg area of last 3 years (ha)", "Change in Area (%) w.r.t Base Area"], "table_row_start": 1, "table_row_end": 16, "line_start": 1542, "line_end": 1559, "token_count_estimate": 1052, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9f8b338d69a362f8", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl.No. | Lake ID | Inventory Developed by | Rank of Vulnerability | Elevation | Latitude(N) | Longitude (E) | State | Country | Area of August 2025 (ha) | Base Area (Avg area of last 3 years (ha) | Change in Area (%) w.r.t Base Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 31 | 2207 | SDC | Very High Risk | 4707 | 30° 54' 43.2\" | 78° 57' 28.8\" | UK | India | 10 | 10 | -4 |\n| 32 | 312 | SDC | Medium Risk | 5137 | 27° 42' 3.6\" | 88° 30' 50.4\" | SK | India | 7 | 8 | -8 |\n| 33 | 182 | SDC | Very High Risk | 4304 | 34° 14' 2.4\" | 75° 19' 30\" | JK | India | 7 | 8 | -10 |\n| 34 | 1014 | SDC | Very High Risk | 3989 | 34° 17' 56.4\" | 75° 3' 36\" | JK | India | 3 | 3 | -11 |\n| 35 | 938 | SDC | Very High Risk | 3683 | 33° 57' 10.8\" | 75° 22' 40.8\" | JK | India | 17 | 19 | -11 |\n| 36 | 27 | SDC | Very High Risk | 3775 | 34° 22' 51.6\" | 74° 52' 33.6\" | JK | India | 11 | 12 | -12 |\n| 37 | 98 | SDC | High Risk | 4103 | 34° 23' 31.2\" | 75° 5' 6\" | JK | India | # | 4 | # |\n| 38 | 2147 | SDC | Medium Risk | 5688 | 30° 58' 48\" | 79° 29' 13.2\" | UK | India | 1 | # | # |\n| 39 | 2299 | SDC | Very High Risk | 4490 | 30° 11' 2.4\" | 79° 52' 48\" | UK | India | # | # | # |\n| 40 | 599 | SDC | Very High Risk | 4251 | 27° 41' 42\" | 88° 42' 57.6\" | SK | India | # | 8 | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl.No.", "Lake ID", "Inventory Developed by", "Rank of Vulnerability", "Elevation", "Latitude(N)", "Longitude (E)", "State", "Country", "Area of August 2025 (ha)", "Base Area (Avg area of last 3 years (ha)", "Change in Area (%) w.r.t Base Area"], "table_row_start": 1, "table_row_end": 10, "line_start": 1564, "line_end": 1575, "token_count_estimate": 720, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "afafd8d53e304d2f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes.*\n\n- GLs/WBs with increase in Area > 40%\n- GLs/WBs with increase in Area – 0% to 40%\n- GLs/WBs with decrease in Area\n- GLs/WBs with no change in Area\n- GLs/WBs not analysed", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 1576, "line_end": 1588, "token_count_estimate": 137, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0883efaf090ddb57", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.8: Results of analysis of 15 GLs of size greater than 50 Ha located in India\n\n| Sl. No. | Lake ID | Agency | UID | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area 2009 (Ha) | Lake Area August 2025 (Ha) | Area of Base Year of 2011 (ha) (i) | Average Area of Last 5 Years (ha) (ii) | Average Area of Last 10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 01_52C_003 | NRSC | JK_187 | 4512 | 33° 9' 26.28\" | 76° 59' 3.48\" | Indus | Indus | Ladakh | 45 | 56 | 45 | 56 | 58 | -3 |\n| 2 | 01_52E_001 | NRSC | JK_188 | 5116 | 35° 25' 4.8\" | 77° 36' 16.56\" | Indus | Shyok | Ladakh | 51 | # | 51 | 6 | 19 | # |\n| 3 | 01_52J_001 | NRSC | JK_197 | 5311 | 34° 27' 27.72\" | 78° 8' 6.36\" | Indus | Shyok | Ladakh | 97 | 100 | 65 | 97 | 95 | 3 |\n| 4 | 01_52H_004 | NRSC | HP_5 | 4155 | 32° 29' 47.04\" | 77° 33' 5.76\" | Indus | Chenab | Himachal Pradesh | 46 | 173 | 46 | 154 | 146 | 12 |\n| 5 | 01_52H_002 | NRSC/ SDC | HP_3 | 4101 | 32° 31' 28.92\" | 77° 13' 5.88\" | Indus | Chenab | Himachal Pradesh | 62 | 106 | 62 | 98 | 94 | 8 |\n| 6 | 03_77D_002 | NRSC | SK_2 | 5156 | 28° 1' 33.96\" | 88° 42' 36\" | Brahma-putra | Teesta | Sikkim | 105 | 116 | 104 | 105 | 100 | 10 |\n| 7 | 03_77D_004 | NRSC/ SDC | SK_4 | 5287 | 28° 0' 25.56\" | 88° 42' 46.08\" | Brahma-putra | Teesta | Sikkim | 106 | 123 | 106 | 116 | 116 | 6 |\n| 8 | 03_77D_005 | NRSC/ SDC | SK_5 | 5249 | 28° 0' 32.76\" | 88° 41' 52.44\" | Brahma-putra | Teesta | Sikkim | 79 | 118 | 88 | 100 | 91 | 18 |\n| 9 | 03_77D_008 | NRSC | SK_8 | 5039 | 28° 0' 26.28\" | 88° 29' 41.64\" | Brahma-putra | Teesta | Sikkim | 46 | 43 | 46 | 40 | 44 | -7 |\n| 10 | 03_78A_001 | NRSC/ SDC | SK_9 | 5371 | 27° 59' 30.12\" | 88° 48' 55.8\" | Brahma-putra | Teesta | Sikkim | 156 | 175 | 156 | 182 | 284 | -38 |\n| 11 | 03_78A_003 | NRSC/ SDC | SK_11 | 4977 | 27° 58' 31.08\" | 88° 36' 59.04\" | Brahma-putra | Teesta | Sikkim | 58 | 35 | 58 | 52 | 85 | -59 |\n| 12 | 03_78A_009 | NRSC | SK_16 | 5044 | 27° 56' 51.72\" | 88° 19' 52.68\" | Brahma-putra | Teesta | Sikkim | 54 | 60 | 55 | 61 | 62 | -3 |\n| 13 | 03_78A_013 | NRSC | SK_19 | 5470 | 27° 55' 7.68\" | 88° 9' 39.6\" | Brahma-putra | Teesta | Sikkim | 63 | 89 | 67 | 78 | 83 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.8: Results of analysis of 15 GLs of size greater than 50 Ha located in India", "columns": ["Sl. No.", "Lake ID", "Agency", "UID", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area 2009 (Ha)", "Lake Area August 2025 (Ha)", "Area of Base Year of 2011 (ha) (i)", "Average Area of Last 5 Years (ha) (ii)", "Average Area of Last 10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 13, "line_start": 1589, "line_end": 1603, "token_count_estimate": 1283, "basins": ["Indus"], "subbasins": ["Chenab", "Shyok", "Teesta"], "countries": ["India"], "lake_ids": ["01_52C_003", "01_52E_001", "01_52H_002", "01_52H_004", "01_52J_001", "03_77D_002", "03_77D_004", "03_77D_005", "03_77D_008", "03_78A_001", "03_78A_003", "03_78A_009", "03_78A_013"]}}
{"id": "c02d8e2ac02c8d19", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Agency | UID | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area 2009 (Ha) | Lake Area August 2025 (Ha) | Area of Base Yearof 2011 (ha) (i) | Average Area of Last 5 Years (ha) (ii) | Average Area of Last10 years (ha) (iii) | Change in Area (%) w.r.t maximum of (i),(ii)&(iii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 14 | 03_78A_014 | NRSC/ SDC | SK_20 | 5234 | 27° 54' 42.84\" | 88° 11' 54.96\" | Brahma-putra | Teesta | Sikkim | 94 | 140 | 123 | 150 | 141 | -6 |\n| 15 | 03_78A_021 | NRSC | SK_26 | 5431 | 27° 49' 28.2\" | 88° 14' 57.12\" | Brahma-putra | Teesta | Sikkim | 56 | 100 | 56 | 81 | 62 | 23 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Agency", "UID", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area 2009 (Ha)", "Lake Area August 2025 (Ha)", "Area of Base Yearof 2011 (ha) (i)", "Average Area of Last 5 Years (ha) (ii)", "Average Area of Last10 years (ha) (iii)", "Change in Area (%) w.r.t maximum of (i),(ii)&(iii)"], "table_row_start": 1, "table_row_end": 2, "line_start": 1608, "line_end": 1611, "token_count_estimate": 401, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": ["03_78A_014", "03_78A_021"]}}
{"id": "69ccf69c6caf1a10", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n\n- GLs displaying increase in area", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 1612, "line_end": 1620, "token_count_estimate": 103, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e561285f32b0a7a1", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.9: Results of analysis of 85 GLs with size between 10ha to 50ha located in India\n\n| Sl. No. | Lake ID | Agency | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area August 2025 (Ha) | Inventory Area 2011 (ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 01_42H_002 | NRSC | 2763 | 36° 38' 34.8\" | 73° 24' 26.64\" | Indus | Gilgit | Ladakh | 16 | 13 | 16 | -2 |\n| 2 | 01_52A_002 | NRSC | 4537 | 35° 5' 48.12\" | 76° 14' 0.6\" | Indus | Shyok | Ladakh | 22 | 23 | 21 | -5 |\n| 3 | 01_52A_003 | NRSC | 4586 | 35° 5' 33.36\" | 76° 15' 7.2\" | Indus | Shyok | Ladakh | 17 | 24 | 18 | -29 |\n| 4 | 01_52A_004 | NRSC/ SDC | 4619 | 35° 4' 28.2\" | 76° 17' 33.72\" | Indus | Shyok | Ladakh | 10 | 11 | 10 | -5 |\n| 5 | 01_52B_010 | NRSC/ SDC | 5122 | 34° 3' 6.48\" | 76° 43' 5.16\" | Indus | Indus | Ladakh | 17 | 18 | 16 | -8 |\n| 6 | 01_52B_012 | NRSC | 5137 | 34° 0' 19.8\" | 76° 47' 12.84\" | Indus | Indus | Ladakh | 15 | 17 | 14 | -11 |\n| 7 | 01_52C_001 | NRSC | 4394 | 33° 56' 44.52\" | 76° 13' 53.76\" | Indus | Shingo (Indus) | Ladakh | 49 | 36 | 52 | -6 |\n| 8 | 01_52L_006 | NRSC | 5727 | 32° 26' 27.24\" | 78° 55' 29.28\" | Indus | Indus | Ladakh | 11 | 12 | 10 | -12 |\n| 9 | 01_52L_007 | NRSC | 5498 | 32° 24' 36.36\" | 78° 53' 56.4\" | Indus | Indus | Ladakh | 35 | 32 | 33 | 7 |\n| 10 | 173 | SDC | 5150 | 34° 45' 54\" | 76° 42' 36\" | Indus | | Ladakh | 10 | - | 12 | 18 |\n| 11 | 180 | SDC | 4442 | 34° 21' 10.8\" | 76° 4' 37.2\" | Indus | | Ladakh | 17 | - | 10 | 52 |\n| 12 | 1360 | SDC | 4667 | 35° 1' 37.2\" | 75° 43' 30\" | Indus | | Ladakh | 14 | - | 10 | 27 |\n| 13 | 01_43J_003 | NRSC | 3954 | 34° 55' 36.12\" | 74° 9' 19.44\" | Indus | Jhelum | Jammu & Kashmir | 18 | 20 | 19 | -11 |\n| 14 | 01_52C_002 | NRSC | 4092 | 33° 52' 10.2\" | 76° 7' 9.48\" | Indus | Chenab | Jammu & Kashmir | 44 | 26 | 43 | 3 |\n| 15 | 27 | SDC | 3775 | 34° 22' 51.6\" | 74° 52' 33.6\" | Indus | | Jammu & Kashmir | 11 | - | 12 | -12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.9: Results of analysis of 85 GLs with size between 10ha to 50ha located in India", "columns": ["Sl. No.", "Lake ID", "Agency", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area August 2025 (Ha)", "Inventory Area 2011 (ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1621, "line_end": 1637, "token_count_estimate": 1185, "basins": ["Indus"], "subbasins": ["Chenab", "Gilgit", "Jhelum", "Shyok"], "countries": ["India"], "lake_ids": ["01_42H_002", "01_43J_003", "01_52A_002", "01_52A_003", "01_52A_004", "01_52B_010", "01_52B_012", "01_52C_001", "01_52C_002", "01_52L_006", "01_52L_007"]}}
{"id": "6bf805fd7c51276b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Agency | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area August 2025 (Ha) | Inventory Area 2011 (ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 16 | 98 | SDC | 4103 | 34° 23' 31.2\" | 75° 5' 6\" | Indus | | Jammu & Kashmir | # | | 4 | # |\n| 17 | 182 | SDC | 4304 | 34° 14' 2.4\" | 75° 19' 30\" | Indus | | Jammu & Kashmir | 7 | - | 8 | -10 |\n| 18 | 931 | SDC | 4082 | 33° 55' 44.4\" | 75° 23' 20.4\" | Indus | | Jammu & Kashmir | 20 | - | 19 | 5 |\n| 19 | 938 | SDC | 3683 | 33° 57' 10.8\" | 75° 22' 40.8\" | Indus | | Jammu & Kashmir | 17 | - | 19 | -11 |\n| 20 | 951 | SDC | 3762 | 34° 4' 1.2\" | 75° 28' 30\" | Indus | | Jammu & Kashmir | 20 | - | 17 | 19 |\n| 21 | 958 | SDC | 4103 | 34° 8' 16.8\" | 75° 24' 57.6\" | Indus | | Jammu & Kashmir | 8 | - | 7 | 17 |\n| 22 | 963 | SDC | 3725 | 34° 8' 20.4\" | 75° 22' 33.6\" | Indus | | Jammu & Kashmir | 32 | - | 30 | 7 |\n| 23 | 976 | SDC | 4314 | 34° 11' 6\" | 75° 22' 19.2\" | Indus | | Jammu & Kashmir | 20 | - | 17 | 17 |\n| 24 | 993 | SDC | 4148 | 34° 13' 37.2\" | 75° 13' 19.2\" | Indus | | Jammu & Kashmir | 6 | - | 6 | -3 |\n| 25 | 1014 | SDC | 3989 | 34° 17' 56.4\" | 75° 3' 36\" | Indus | | Jammu & Kashmir | 3 | - | 3 | -11 |\n| 26 | 1032 | SDC | 4007 | 34° 23' 9.6\" | 75° 3' 50.4\" | Indus | | Jammu & Kashmir | 1 | - | 1 | 0 |\n| 27 | 1037 | SDC | 3603 | 34° 25' 19.2\" | 75° 3' 28.8\" | Indus | | Jammu & Kashmir | 38 | - | 38 | -1 |\n| 28 | 01_52H_003 | NRSC | 4165 | 32° 29' 54.6\" | 77° 32' 37.32\" | Indus | Chenab | Himachal Pradesh | 173 | 28 | 144 | 20 |\n| 29 | 01_53I_002 | NRSC/SDC | 4273 | 31° 39' 38.52\" | 78° 10' 1.92\" | Indus | Sutlej | Himachal Pradesh | 34 | 23 | 30 | 12 |\n| 30 | 1774 | SDC | 4593 | 32° 13' 15.6\" | 76° 47' 16.8\" | Indus | | Himachal Pradesh | 8 | - | 8 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Agency", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area August 2025 (Ha)", "Inventory Area 2011 (ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1642, "line_end": 1658, "token_count_estimate": 1099, "basins": ["Indus"], "subbasins": ["Chenab"], "countries": [], "lake_ids": ["01_52H_003", "01_53I_002"]}}
{"id": "1a30ef99e20cc93d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Agency | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area August 2025 (Ha) | Inventory Area 2011 (ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 31 | 1805 | SDC | 4775 | 32° 45' 43.2\" | 77° 11' 42\" | Indus | | Himachal Pradesh | 6 | - | 4 | 40 |\n| 32 | 1847 | SDC | 4570 | 31° 54' 54\" | 77° 31' 37.2\" | Indus | | Himachal Pradesh | 15 | - | 14 | 11 |\n| 33 | 1936 | SDC | 4606 | 32° 15' 21.6\" | 76° 46' 37.2\" | Indus | | Himachal Pradesh | 4 | - | 3 | 37 |\n| 34 | 1998 | SDC | 3857 | 32° 19' 12\" | 76° 54' 28.8\" | Indus | | Himachal Pradesh | 1 | - | 1 | 9 |\n| 35 | 2031 | SDC | 4702 | 31° 20' 20.4\" | 78° 15' 10.8\" | Indus | | Himachal Pradesh | 12 | - | 11 | 7 |\n| 36 | 01_62B_003 | NRSC | 5288 | 30° 28' 36.48\" | 80° 35' 35.16\" | Indus | Sutlej | Uttarakhand | 12 | 12 | 13 | -5 |\n| 37 | 02_53N_001 | NRSC | 4688 | 30° 54' 7.92\" | 79° 45' 12.6\" | Ganga | Ganga | Uttarakhand | 25 | 21 | 22 | 12 |\n| 38 | 02_62B_004 | NRSC | 4918 | 30° 33' 52.2\" | 80° 10' 41.16\" | Ganga | Sarda | Uttarakhand | 29 | 19 | 21 | 35 |\n| 39 | 02_62B_005 | NRSC | 4314 | 30° 26' 44.52\" | 80° 23' 16.08\" | Ganga | Sarda | Uttarakhand | 13 | 12 | 10 | 5 |\n| 40 | 02_62B_007 | NRSC | 4839 | 30° 16' 42.96\" | 80° 7' 49.8\" | Ganga | Sarda | Uttarakhand | # | 19 | # | # |\n| 41 | 2108 | SDC | 5587 | 30° 58' 33.6\" | 79° 27' 32.4\" | Ganga | | Uttarakhand | 21 | - | 18 | 16 |\n| 42 | 2147 | SDC | 5688 | 30° 58' 48\" | 79° 29' 13.2\" | Ganga | | Uttarakhand | 1 | - | 0 | # |\n| 43 | 2207 | SDC | 4707 | 30° 54' 43.2\" | 78° 57' 28.8\" | Ganga | | Uttarakhand | 10 | - | 10 | -4 |\n| 44 | 2299 | SDC | 4490 | 30° 11' 2.4\" | 79° 52' 48\" | Ganga | | Uttarakhand | # | - | # | # |\n| 45 | 03_77D_006 | NRSC/SDC | 5084 | 28° 0' 51.84\" | 88° 33' 41.76\" | Brahma-putra | Teesta | Sikkim | 26 | 22 | 24 | 9 |\n| 46 | 03_77D_007 | NRSC/SDC | 5015 | 28° 0' 26.28\" | 88° 34' 18.48\" | Brahma-putra | Teesta | Sikkim | 24 | 24 | 24 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Agency", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area August 2025 (Ha)", "Inventory Area 2011 (ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 16, "line_start": 1662, "line_end": 1679, "token_count_estimate": 1188, "basins": ["Ganga", "Indus"], "subbasins": ["Sarda", "Teesta"], "countries": [], "lake_ids": ["01_62B_003", "02_53N_001", "02_62B_004", "02_62B_005", "02_62B_007", "03_77D_006", "03_77D_007"]}}
{"id": "4a9318f59c69fe11", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Agency | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area August 2023 (Ha) | Inventory Area 2011 (ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 47 | 03_78A_002 | NRSC/SDC | 4952 | 27° 58' 56.28\" | 88° 30' 28.08\" | Brahma-putra | Teesta | Sikkim | # | 22 | 37 | # |\n| 48 | 03_78A_005 | NRSC | 5201 | 27° 58' 31.44\" | 88° 25' 20.64\" | Brahma-putra | Teesta | Sikkim | 12 | 11 | 10 | 6 |\n| 49 | 03_78A_006 | NRSC | 5004 | 27° 58' 15.6\" | 88° 25' 45.84\" | Brahma-putra | Teesta | Sikkim | 14 | 11 | 13 | 9 |\n| 50 | 03_78A_007 | NRSC/SDC | 4977 | 27° 57' 38.88\" | 88° 38' 57.48\" | Brahma-putra | Teesta | Sikkim | 17 | 17 | 18 | -3 |\n| 51 | 03_78A_008 | NRSC | 4998 | 27° 57' 3.24\" | 88° 21' 15.48\" | Brahma-putra | Teesta | Sikkim | 18 | 44 | 17 | -59 |\n| 52 | 03_78A_010 | NRSC | 5078 | 27° 57' 0.72\" | 88° 18' 16.92\" | Brahma-putra | Teesta | Sikkim | 38 | 36 | 34 | 6 |\n| 53 | 03_78A_012 | NRSC | 5130 | 27° 54' 4.32\" | 88° 46' 54.84\" | Brahma-putra | Teesta | Sikkim | 28 | 26 | 27 | 4 |\n| 54 | 03_78A_015 | NRSC/SDC | 4970 | 27° 52' 23.88\" | 88° 47' 22.2\" | Brahma-putra | Teesta | Sikkim | 10 | 12 | 9 | -16 |\n| 55 | 03_78A_016 | NRSC | 5451 | 27° 53' 33.72\" | 88° 12' 47.16\" | Brahma-putra | Teesta | Sikkim | # | 14 | 12 | # |\n| 56 | 03_78A_017 | NRSC | 5545 | 27° 53' 34.8\" | 88° 11' 31.92\" | Brahma-putra | Teesta | Sikkim | 30 | 19 | 27 | 13 |\n| 57 | 03_78A_019 | NRSC/SDC | 4809 | 27° 51' 52.2\" | 88° 51' 46.44\" | Brahma-putra | Teesta | Sikkim | 14 | 15 | 12 | -5 |\n| 58 | 03_78A_020 | NRSC | 5219 | 27° 52' 49.44\" | 88° 15' 4.68\" | Brahma-putra | Teesta | Sikkim | 15 | 14 | 14 | 7 |\n| 59 | 03_78A_023 | NRSC | 4547 | 27° 40' 17.04\" | 88° 30' 46.44\" | Brahma-putra | Teesta | Sikkim | 32 | 33 | 28 | -3 |\n| 60 | 03_78A_026 | NRSC | 4736 | 27° 33' 44.28\" | 88° 7' 24.96\" | Brahma-putra | Teesta | Sikkim | 11 | 11 | 11 | -1 |\n| 61 | 03_78A_027 | NRSC/SDC | 4888 | 27° 32' 0.6\" | 88° 5' 8.52\" | Brahma-putra | Teesta | Sikkim | 38 | 33 | 36 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Agency", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area August 2023 (Ha)", "Inventory Area 2011 (ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1684, "line_end": 1700, "token_count_estimate": 1256, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": ["03_78A_002", "03_78A_005", "03_78A_006", "03_78A_007", "03_78A_008", "03_78A_010", "03_78A_012", "03_78A_015", "03_78A_016", "03_78A_017", "03_78A_019", "03_78A_020", "03_78A_023", "03_78A_026", "03_78A_027"]}}
{"id": "89a85d0ea6e45bc9", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Agency | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area August 2023 (Ha) | Inventory Area 2011 (ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)(&(ii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 62 | 03_78A_031 | NRSC | 4305 | 27° 26' 15\" | 88° 5' 9.6\" | Brahma-putra | Teesta | Sikkim | 13 | 14 | 12 | -6 |\n| 63 | 03_78A_035 | NRSC | 4998 | 27° 57' 3.24\" | 88° 21' 15.48\" | Brahma-putra | Teesta | Sikkim | 25 | - | 14 | 75 |\n| 64 | 227 | SDC | 5176 | 27° 59' 34.8\" | 88° 32' 49.2\" | Brahma-putra | | Sikkim | 63 | - | 61 | 3 |\n| 65 | 237 | SDC | 5322 | 27° 59' 34.8\" | 88° 48' 3.6\" | Brahma-putra | | Sikkim | 8 | - | 8 | -1 |\n| 66 | 256 | SDC | 4615 | 27° 48' 57.6\" | 88° 39' 25.2\" | Brahma-putra | | Sikkim | 15 | - | 14 | 10 |\n| 67 | 260 | SDC | 5253 | 27° 53' 38.4\" | 88° 45' 39.6\" | Brahma-putra | | Sikkim | 46 | - | 41 | 11 |\n| 68 | 292 | SDC | 5577 | 28° 0' 21.6\" | 88° 39' 18\" | Brahma-putra | | Sikkim | 5 | - | 4 | 24 |\n| 69 | 293 | SDC | 5048 | 27° 57' 3.6\" | 88° 42' 18\" | Brahma-putra | | Sikkim | 3 | - | 2 | 29 |\n| 70 | 295 | SDC | 4850 | 27° 55' 12\" | 88° 40' 19.2\" | Brahma-putra | | Sikkim | 10 | - | 8 | 32 |\n| 71 | 298 | SDC | 4508 | 27° 52' 22.8\" | 88° 38' 16.8\" | Brahma-putra | | Sikkim | 8 | - | 6 | 27 |\n| 72 | 312 | SDC | 5137 | 27° 42' 3.6\" | 88° 30' 50.4\" | Brahma-putra | | Sikkim | 7 | - | 8 | -8 |\n| 73 | 345 | SDC | 5108 | 27° 51' 50.4\" | 88° 44' 49.2\" | Brahma-putra | | Sikkim | 19 | - | 18 | 7 |\n| 74 | 515 | SDC | 5063 | 27° 51' 14.4\" | 88° 48' 21.6\" | Brahma-putra | | Sikkim | 10 | - | 9 | 17 |\n| 75 | 569 | SDC | 5450 | 28° 0' 7.2\" | 88° 38' 24\" | Brahma-putra | | Sikkim | 32 | - | 30 | 8 |\n| 76 | 599 | SDC | 4251 | 27° 41' 42\" | 88° 42' 57.6\" | Brahma-putra | | Sikkim | # | - | 8 | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Agency", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area August 2023 (Ha)", "Inventory Area 2011 (ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)(&(ii)"], "table_row_start": 1, "table_row_end": 15, "line_start": 1704, "line_end": 1720, "token_count_estimate": 1132, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": ["03_78A_031", "03_78A_035"]}}
{"id": "9f928015a20e82a7", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Agency | Elevation (m) | Latitude (N) | Longitude (E) | Basin | River | State/UT | Lake Area August 2025 (Ha) | Inventory Area 2011 (ha) (i) | Base Area Average (area of last 3 years) (ha) (ii) | Change in Area (%) w.r.t maximum of (i)&(ii) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 77 | 03_82L_007 | NRSC | 4163 | 28° 50' 15\" | 94° 27' 5.04\" | Brahma-putra | Ding | Arunachal Pradesh | 17 | 16 | 16 | 6 |\n| 78 | 03_83A_003 | NRSC | 5188 | 27° 46' 12.72\" | 92° 25' 56.64\" | Brahma-putra | Dangme Chhu | Arunachal Pradesh | 86 | 24 | 86 | 0 |\n| 79 | 03_83A_004 | NRSC | 5109 | 27° 45' 47.16\" | 92° 25' 29.64\" | Brahma-putra | Dangme Chhu | Arunachal Pradesh | 17 | 17 | 18 | -7 |\n| 80 | 03_83A_005 | NRSC | 4994 | 27° 45' 20.52\" | 92° 24' 2.52\" | Brahma-putra | Dangme Chhu | Arunachal Pradesh | 15 | 13 | 12 | 19 |\n| 81 | 03_83A_007 | NRSC | 5028 | 27° 43' 39.36\" | 92° 26' 12.48\" | Brahma-putra | Jia Brali | Arunachal Pradesh | 14 | 14 | 15 | -5 |\n| 82 | 03_91C_026 | NRSC | 4305 | 29° 20' 18.24\" | 96° 4' 57.72\" | Brahma-putra | Dibang | Arunachal Pradesh | 28 | 28 | 27 | 1 |\n| 83 | 03_91D_075 | NRSC | 4274 | 28° 36' 28.8\" | 96° 19' 14.16\" | Brahma-putra | Dibang | Arunachal Pradesh | 32 | 23 | 26 | 24 |\n| 84 | 03_91H_073 | NRSC | 4481 | 28° 3' 15.48\" | 97° 19' 47.64\" | Brahma-putra | Lohit | Arunachal Pradesh | 23 | 25 | 25 | -9 |\n| 85 | 129 | SDC | 4895 | 27°46'24.165\" | 92°19'1.10\" | Brahma-putra | | Arunachal Pradesh | 13 | - | 10 | 32 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Agency", "Elevation (m)", "Latitude (N)", "Longitude (E)", "Basin", "River", "State/UT", "Lake Area August 2025 (Ha)", "Inventory Area 2011 (ha) (i)", "Base Area Average (area of last 3 years) (ha) (ii)", "Change in Area (%) w.r.t maximum of (i)&(ii)"], "table_row_start": 1, "table_row_end": 9, "line_start": 1727, "line_end": 1737, "token_count_estimate": 845, "basins": [], "subbasins": ["Dibang", "Lohit"], "countries": [], "lake_ids": ["03_82L_007", "03_83A_003", "03_83A_004", "03_83A_005", "03_83A_007", "03_91C_026", "03_91D_075", "03_91H_073"]}}
{"id": "839b419ff9ff6ef9", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. “-“ indicates Inventory Area not available. Inventory Area (in Ha) has been rounded off.*\n\n- *GLs displaying increase in area*\n\n***", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 1738, "line_end": 1748, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71aee23b28286ff2", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.10: Results of analysis of newly monitored 581 GLs within India as per Glacial lake Atlas 2023 with water spread area greater than 10ha\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 03477D120008 | 28.05860 | 88.63120 | Teesta River | 4917 | Brahmaputra | North Sikkim | Sikkim | 15 | 46 | 211 |\n| 2 | 0143N1602259 | 34.04020 | 75.84410 | Nagmithong Lungpa | 4094 | Indus | Kargil | Ladakh | 25 | 57 | 126 |\n| 3 | 03391D060647 | 28.72070 | 96.40540 | Thangkung Chu | 4097 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 35 | 101 |\n| 4 | 0152J0203796 | 34.51980 | 78.10090 | | 5265 | Indus | Leh | Ladakh | 10 | 20 | 95 |\n| 5 | 03478A090378 | 27.95720 | 88.71390 | Laehung Chu | 5078 | Brahmaputra | North Sikkim | Sikkim | 26 | 46 | 74 |\n| 6 | 03391D010514 | 28.82840 | 96.24390 | Thangkung Chu | 3740 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 22 | 72 |\n| 7 | 0143E0600854 | 35.73870 | 73.25630 | | 4016 | Indus | Leh | Ladakh | 19 | 30 | 59 |\n| 8 | 03391D010491 | 28.88890 | 96.15140 | Dri Chu | 4079 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | 35 | 56 |\n| 9 | 0253J1400068 | 30.74580 | 78.98700 | | 4734 | Ganga | Tehri Garhwal | Uttarakhand | 26 | 39 | 53 |\n| 10 | 0262B1100358 | 30.39170 | 80.53180 | | 4753 | Ganga | Pithoragarh | Uttarakhand | 11 | 17 | 52 |\n| 11 | 0152F0903545 | 34.90480 | 77.61570 | | 5125 | Indus | Leh | Ladakh | 15 | 21 | 45 |\n| 12 | 0143J1501473 | 34.45720 | 74.98510 | Satsar N | 3748 | Indus | Bandipore | Jammu & Kashmir | 10 | 15 | 45 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.10: Results of analysis of newly monitored 581 GLs within India as per Glacial lake Atlas 2023 with water spread area greater than 10ha", "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 12, "line_start": 1749, "line_end": 1762, "token_count_estimate": 890, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Teesta"], "countries": ["India"], "lake_ids": ["0143E0600854", "0143J1501473", "0143N1602259", "0152F0903545", "0152J0203796", "0253J1400068", "0262B1100358", "03391D010491", "03391D010514", "03391D060647", "03477D120008", "03478A090378", "04020", "05860", "10090", "15140", "24390", "25630", "39170", "40540", "45720", "51980", "53180", "61570", "63120", "71390", "72070", "73870", "74580", "82840", "84410", "88890", "90480", "95720", "98510", "98700"]}}
{"id": "9d66d4ede82395af", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 13 | 03591H041391 | 28.03430 | 97.24110 | | 4299 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 26 | 43 |\n| 14 | 0152J0303815 | 34.44560 | 78.14310 | Kunzang Lungpa | 5311 | Indus | Leh | Ladakh | 21 | 29 | 41 |\n| 15 | 0143N0902149 | 34.80780 | 75.54480 | | 4687 | Indus | Kargil | Ladakh | 13 | 18 | 40 |\n| 16 | 03478A100477 | 27.72300 | 88.69020 | Rabom Chu | 4472 | Brahmaputra | North Sikkim | Sikkim | 12 | 17 | 40 |\n| 17 | 03391C040410 | 29.05440 | 96.10230 | Dri Chu | 4103 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 14 | 40 |\n| 18 | 03382O150125 | 29.27040 | 95.92500 | Aison Chu | 2605 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 22 | 37 |\n| 19 | 03591H081898 | 28.14860 | 97.27350 | | 3953 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | 19 | 34 |\n| 20 | 03082K150028 | 29.25640 | 94.76970 | Nugong Chu | 3294 | Brahmaputra | Upper Siang | Arunachal Pradesh | 13 | 17 | 34 |\n| 21 | 0142D1500009 | 36.41210 | 72.90110 | Hurguji Gol | 4256 | Indus | Leh | Ladakh | 11 | 15 | 34 |\n| 22 | 0143J0901397 | 34.92000 | 74.52110 | Kishanganga | 4041 | Indus | Muzaffarabad | Jammu & Kashmir | 61 | 80 | 32 |\n| 23 | 03382O160179 | 29.01110 | 95.88510 | Matuni | 3778 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 55 | 73 | 32 |\n| 24 | 0152C0102942 | 33.94210 | 76.01890 | Fariabad | 4197 | Indus | Kishtwar | Jammu & Kashmir | 24 | 31 | 29 |\n| 25 | 0143I1101289 | 35.39950 | 74.67560 | Buldar Gah | 3386 | Indus | Leh | Ladakh | 14 | 18 | 29 |\n| 26 | 03391D010478 | 28.95210 | 96.03760 | Dri Chu | 3634 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 26 | 28 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1765, "line_end": 1780, "token_count_estimate": 977, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["01110", "0142D1500009", "0143I1101289", "0143J0901397", "0143N0902149", "0152C0102942", "0152J0303815", "01890", "03082K150028", "03382O150125", "03382O160179", "03391C040410", "03391D010478", "03430", "03478A100477", "03591H041391", "03591H081898", "03760", "05440", "10230", "14310", "14860", "24110", "25640", "27040", "27350", "39950", "41210", "44560", "52110", "54480", "67560", "69020", "72300", "76970", "80780", "88510", "90110", "92000", "92500", "94210", "95210"]}}
{"id": "525c6d1bdb320be2", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 27 | 03391D050605 | 28.84680 | 96.35500 | Thangkung Chu | 3760 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 19 | 27 |\n| 28 | 0143N0401967 | 34.20250 | 75.20500 | | 3682 | Indus | Anantnag | Jammu & Kashmir | 16 | 20 | 26 |\n| 29 | 0143M1201758 | 35.17190 | 75.52070 | | 4551 | Indus | Leh | Ladakh | 10 | 13 | 26 |\n| 30 | 03478A130538 | 27.93580 | 88.78430 | Lachung Chu | 5332 | Brahmaputra | North Sikkim | Sikkim | 10 | 13 | 26 |\n| 31 | 03391D050599 | 28.86120 | 96.24860 | Thangkung Chu | 3847 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 14 | 25 |\n| 32 | 03391C030274 | 29.30220 | 96.08210 | Jairu Chu | 4274 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 120 | 148 | 24 |\n| 33 | 03591H081904 | 28.11610 | 97.30010 | | 4029 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 22 | 24 |\n| 34 | 0143N1302218 | 34.85900 | 75.94180 | | 4796 | Indus | Kargil | Ladakh | 14 | 17 | 24 |\n| 35 | 0152J0303821 | 34.40060 | 78.07860 | | 5307 | Indus | Leh | Ladakh | 20 | 25 | 23 |\n| 36 | 03478A140609 | 27.73000 | 88.83270 | Teesta River | 4446 | Brahmaputra | North Sikkim | Sikkim | 15 | 18 | 23 |\n| 37 | 03478A090357 | 27.98810 | 88.73620 | Teesta River | 5188 | Brahmaputra | North Sikkim | Sikkim | 11 | 13 | 23 |\n| 38 | 03391D010524 | 28.80340 | 96.15510 | Thangkung Chu | 3866 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 21 | 22 |\n| 39 | 03478A050185 | 27.97580 | 88.41760 | Nakul Chu | 5224 | Brahmaputra | North Sikkim | Sikkim | 16 | 20 | 22 |\n| 40 | 0143E0600858 | 35.64340 | 73.35230 | Bolono Gah | 3747 | Indus | Leh | Ladakh | 14 | 17 | 22 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1784, "line_end": 1799, "token_count_estimate": 970, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Teesta"], "countries": [], "lake_ids": ["0143E0600858", "0143M1201758", "0143N0401967", "0143N1302218", "0152J0303821", "03391C030274", "03391D010524", "03391D050599", "03391D050605", "03478A050185", "03478A090357", "03478A130538", "03478A140609", "03591H081904", "07860", "08210", "11610", "15510", "17190", "20250", "20500", "24860", "30010", "30220", "35230", "35500", "40060", "41760", "52070", "64340", "73000", "73620", "78430", "80340", "83270", "84680", "85900", "86120", "93580", "94180", "97580", "98810"]}}
{"id": "e7a355f86f141a12", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 41 | 03082O150269 | 29.38680 | 95.98740 | Chendruk Chu | 4251 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 43 | 51 | 20 |\n| 42 | 03382O160144 | 29.23150 | 95.98110 | Aison Chu | 4167 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 37 | 20 |\n| 43 | 03082O110214 | 29.28310 | 95.73980 | Rirung Chu | 4168 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 24 | 20 |\n| 44 | 03591H041306 | 28.22630 | 97.17530 | | 3898 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 14 | 20 |\n| 45 | 03391D010482 | 28.94110 | 96.01970 | Dri Chu | 3449 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 14 | 20 |\n| 46 | 03391D090743 | 28.77560 | 96.53110 | Thangkung Chu | 3510 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 102 | 121 | 19 |\n| 47 | 03391D050621 | 28.80920 | 96.45900 | Thangkung Chu | 4078 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 35 | 42 | 19 |\n| 48 | 03391C040364 | 29.15530 | 96.06930 | Dri Chu | 3588 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 26 | 31 | 19 |\n| 49 | 03478A130530 | 27.96870 | 88.79690 | Laehung Chu | 5361 | Brahmaputra | North Sikkim | Sikkim | 18 | 22 | 19 |\n| 50 | 03391D010518 | 28.81370 | 96.12160 | Thangkung Chu | 4115 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 16 | 19 |\n| 51 | 03391D050562 | 28.94410 | 96.37140 | Thangkung Chu | 4236 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 15 | 19 |\n| 52 | 0152F1503582 | 34.39790 | 77.98260 | | 5344 | Indus | Leh | Ladakh | 28 | 33 | 18 |\n| 53 | 03391C030308 | 29.25650 | 96.13790 | Jairu Chu | 3766 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 24 | 28 | 18 |\n| 54 | 03382P050203 | 28.87340 | 95.34980 | Emra River | 3647 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 23 | 27 | 18 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1804, "line_end": 1819, "token_count_estimate": 996, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0152F1503582", "01970", "03082O110214", "03082O150269", "03382O160144", "03382P050203", "03391C030308", "03391C040364", "03391D010482", "03391D010518", "03391D050562", "03391D050621", "03391D090743", "03478A130530", "03591H041306", "06930", "12160", "13790", "15530", "17530", "22630", "23150", "25650", "28310", "34980", "37140", "38680", "39790", "45900", "53110", "73980", "77560", "79690", "80920", "81370", "87340", "94110", "94410", "96870", "98110", "98260", "98740"]}}
{"id": "4aefb946beafe4e6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 55 | 03591D160694 | 28.21430 | 96.88530 | Dzayul chu | 3960 | Brahmaputra | Anjaw | Arunachal Pradesh | 21 | 25 | 18 |\n| 56 | 03591H071840 | 28.24310 | 97.33060 | | 4333 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 14 | 18 |\n| 57 | 03391C040388 | 29.09100 | 96.16230 | Dri Chu | 3822 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 13 | 18 |\n| 58 | 03391C030255 | 29.33500 | 96.00830 | Aison Chu | 3985 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 33 | 17 |\n| 59 | 0152D0703190 | 32.33590 | 76.33200 | Ravi River | 3971 | Indus | Chamba | Himachal Pradesh | 26 | 30 | 17 |\n| 60 | 03391C040384 | 29.09550 | 95.99670 | Dri Chu | 3346 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | 26 | 17 |\n| 61 | 0143E0500837 | 35.81680 | 73.29750 | | 2981 | Indus | Leh | Ladakh | 15 | 18 | 17 |\n| 62 | 03591H041375 | 28.05390 | 97.16570 | | 4131 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | 16 | 17 |\n| 63 | 0143N0401973 | 34.15610 | 75.11300 | Zajmara | 3941 | Indus | Anantnag | Jammu & Kashmir | 10 | 12 | 17 |\n| 64 | 0143N1302207 | 34.88830 | 75.89210 | Pohultakish Nar | 4882 | Indus | Kargil | Ladakh | 10 | 12 | 17 |\n| 65 | 0143E0600856 | 35.65200 | 73.35730 | Bolono Gah | 3735 | Indus | Leh | Ladakh | 19 | 22 | 16 |\n| 66 | 03082O120227 | 29.23380 | 95.59560 | Rirung Chu | 3750 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 17 | 16 |\n| 67 | 03391C040422 | 29.02640 | 96.04450 | Dri Chu | 3564 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 13 | 16 |\n| 68 | 03382O160163 | 29.15290 | 95.77190 | Aison Chu | 3864 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 12 | 16 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1823, "line_end": 1838, "token_count_estimate": 983, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Ravi"], "countries": [], "lake_ids": ["00830", "0143E0500837", "0143E0600856", "0143N0401973", "0143N1302207", "0152D0703190", "02640", "03082O120227", "03382O160163", "03391C030255", "03391C040384", "03391C040388", "03391C040422", "03591D160694", "03591H041375", "03591H071840", "04450", "05390", "09100", "09550", "11300", "15290", "15610", "16230", "16570", "21430", "23380", "24310", "29750", "33060", "33200", "33500", "33590", "35730", "59560", "65200", "77190", "81680", "88530", "88830", "89210", "99670"]}}
{"id": "ae2f3d816f5b898f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 69 | 0152B1202881 | 34.00470 | 76.72150 | Photang | 5050 | Indus | Leh | Ladakh | 18 | 21 | 15 |\n| 70 | 03591H041385 | 28.04120 | 97.19250 | | 4217 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 13 | 15 |\n| 71 | 03391D090735 | 28.82920 | 96.53160 | Thangkung Chu | 3584 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 38 | 43 | 14 |\n| 72 | 03591H041321 | 28.20020 | 97.23560 | | 3900 | Brahmaputra | Anjaw | Arunachal Pradesh | 29 | 33 | 14 |\n| 73 | 03591H041353 | 28.08660 | 97.24010 | | 4507 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | 25 | 14 |\n| 74 | 03391D010498 | 28.85200 | 96.17120 | Thangkung Chu | 3976 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 20 | 14 |\n| 75 | 03683A020774 | 27.51360 | 92.04850 | Mago Chu | 4088 | Brahmaputra | West Kameng | Arunachal Pradesh | 14 | 16 | 14 |\n| 76 | 0143K1001558 | 33.52970 | 74.57390 | Rupu Nala | 3955 | Indus | Kulgam | Jammu & Kashmir | 12 | 14 | 14 |\n| 77 | 0143E0500795 | 35.91770 | 73.37120 | Shiobat Gah | 4502 | Indus | Leh | Ladakh | 12 | 14 | 14 |\n| 78 | 0143K1001568 | 33.50490 | 74.59370 | Rupu Nala | 4089 | Indus | Kulgam | Jammu & Kashmir | 11 | 13 | 14 |\n| 79 | 03678M140521 | 27.61960 | 91.77950 | Dangme Chu | 3978 | Brahmaputra | Tawang | Arunachal Pradesh | 11 | 13 | 14 |\n| 80 | 03391C040333 | 29.22620 | 96.07220 | Matuni | 3992 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 30 | 34 | 13 |\n| 81 | 03391D100753 | 28.74540 | 96.51130 | Thangkung Chu | 3219 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 25 | 28 | 13 |\n| 82 | 0143K1001544 | 33.55880 | 74.52620 | Rimbiara River | 3851 | Indus | Kulgam | Jammu & Kashmir | 23 | 26 | 13 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1845, "line_end": 1860, "token_count_estimate": 980, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["00470", "0143E0500795", "0143K1001544", "0143K1001558", "0143K1001568", "0152B1202881", "03391C040333", "03391D010498", "03391D090735", "03391D100753", "03591H041321", "03591H041353", "03591H041385", "03678M140521", "03683A020774", "04120", "04850", "07220", "08660", "17120", "19250", "20020", "22620", "23560", "24010", "37120", "50490", "51130", "51360", "52620", "52970", "53160", "55880", "57390", "59370", "61960", "72150", "74540", "77950", "82920", "85200", "91770"]}}
{"id": "45bb67169c5f9495", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 83 | 03591D090416 | 28.77010 | 96.61670 | Dzayul chu | 4255 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 24 | 13 |\n| 84 | 03591H081902 | 28.13570 | 97.32110 | | 4276 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 20 | 13 |\n| 85 | 03391C040429 | 29.01790 | 96.06170 | Dri Chu | 3757 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 18 | 13 |\n| 86 | 03591D090414 | 28.77650 | 96.60620 | Dzayul chu | 4601 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 18 | 13 |\n| 87 | 0143I1501320 | 35.33020 | 74.78580 | | 3475 | Indus | Leh | Ladakh | 15 | 17 | 13 |\n| 88 | 0152K1103987 | 33.47370 | 78.50230 | Kok Lungpa | 5312 | Indus | Leh | Ladakh | 15 | 17 | 13 |\n| 89 | 03591H081909 | 28.10140 | 97.26980 | Sertik chu | 4169 | Brahmaputra | Anjaw | Arunachal Pradesh | 15 | 17 | 13 |\n| 90 | 0143E0100726 | 35.82940 | 73.22790 | | 4195 | Indus | Leh | Ladakh | 12 | 14 | 13 |\n| 91 | 0143J0901417 | 34.83980 | 74.67560 | Sakmai N | 3868 | Indus | Leh | Ladakh | 11 | 12 | 13 |\n| 92 | 0142H0800185 | 36.02370 | 73.32760 | | 4559 | Indus | Leh | Ladakh | 11 | 12 | 13 |\n| 93 | 0152J0203794 | 34.52130 | 78.09040 | | 5270 | Indus | Leh | Ladakh | 11 | 12 | 13 |\n| 94 | 03678M090228 | 27.83390 | 91.55320 | Kulong Chu | 4521 | Brahmaputra | Tawang | Arunachal Pradesh | 67 | 75 | 12 |\n| 95 | 03391C040366 | 29.14660 | 96.01690 | Matuni | 3758 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 21 | 12 |\n| 96 | 03382O160135 | 29.23920 | 95.98000 | Aison Chu | 4155 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 19 | 12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1864, "line_end": 1879, "token_count_estimate": 964, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H0800185", "0143E0100726", "0143I1501320", "0143J0901417", "0152J0203794", "0152K1103987", "01690", "01790", "02370", "03382O160135", "03391C040366", "03391C040429", "03591D090414", "03591D090416", "03591H081902", "03591H081909", "03678M090228", "06170", "09040", "10140", "13570", "14660", "22790", "23920", "26980", "32110", "32760", "33020", "47370", "50230", "52130", "55320", "60620", "61670", "67560", "77010", "77650", "78580", "82940", "83390", "83980", "98000"]}}
{"id": "f9bb440ab18b1743", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 97 | 0143K0501527 | 33.82910 | 74.43530 | Sinwar | 4043 | Indus | Badgam | Jammu & Kashmir | 15 | 17 | 12 |\n| 98 | 0143N0101856 | 34.91940 | 75.18870 | Sar Sangri | 4225 | Indus | Leh | Ladakh | 14 | 16 | 12 |\n| 99 | 03478A060326 | 27.72280 | 88.45260 | | 4199 | Brahmaputra | North Sikkim | Sikkim | 14 | 16 | 12 |\n| 100 | 03391C040354 | 29.18450 | 96.21600 | Dri Chu | 3965 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 14 | 12 |\n| 101 | 03477D120024 | 28.01440 | 88.65190 | Teesta River | 5522 | Brahmaputra | North Sikkim | Sikkim | 12 | 13 | 12 |\n| 102 | 03591H081879 | 28.16210 | 97.32040 | Depuchu | 4038 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 12 | 12 |\n| 103 | 0143A0900591 | 35.94430 | 72.59470 | | 3761 | Indus | Leh | Ladakh | 96 | 107 | 11 |\n| 104 | 03391C040347 | 29.19650 | 96.20260 | Dri Chu | 4246 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 64 | 71 | 11 |\n| 105 | 03678M090217 | 27.84720 | 91.58260 | Dozam Chu | 4656 | Brahmaputra | Tawang | Arunachal Pradesh | 49 | 55 | 11 |\n| 106 | 03391C030310 | 29.25570 | 96.20920 | Jairu Chu | 4623 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 43 | 48 | 11 |\n| 107 | 0142H1500357 | 36.26470 | 73.95500 | Shukur Gah | 3706 | Indus | Leh | Ladakh | 28 | 31 | 11 |\n| 108 | 0143N1302212 | 34.87820 | 75.87070 | | 4687 | Indus | Kargil | Ladakh | 19 | 21 | 11 |\n| 109 | 0143E0500757 | 35.96210 | 73.39730 | Gugalo Gah | 4349 | Indus | Leh | Ladakh | 19 | 21 | 11 |\n| 110 | 03683A020783 | 27.50520 | 92.04450 | Mago Chu | 4170 | Brahmaputra | West Kameng | Arunachal Pradesh | 11 | 12 | 11 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1884, "line_end": 1899, "token_count_estimate": 969, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Teesta"], "countries": [], "lake_ids": ["0142H1500357", "0143A0900591", "0143E0500757", "0143K0501527", "0143N0101856", "0143N1302212", "01440", "03391C030310", "03391C040347", "03391C040354", "03477D120024", "03478A060326", "03591H081879", "03678M090217", "03683A020783", "04450", "16210", "18450", "18870", "19650", "20260", "20920", "21600", "25570", "26470", "32040", "39730", "43530", "45260", "50520", "58260", "59470", "65190", "72280", "82910", "84720", "87070", "87820", "91940", "94430", "95500", "96210"]}}
{"id": "15b4c657bfe4a091", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 111 | 03678M090226 | 27.83770 | 91.60480 | Dangme Chu | 4125 | Brahmaputra | Tawang | Arunachal Pradesh | 66 | 73 | 10 |\n| 112 | 03591H041386 | 28.04090 | 97.21830 | | 3936 | Brahmaputra | Anjaw | Arunachal Pradesh | 38 | 42 | 10 |\n| 113 | 0152K0303890 | 33.28150 | 78.22970 | Indus River | 5646 | Indus | Leh | Ladakh | 35 | 38 | 10 |\n| 114 | 03082O150279 | 29.33480 | 95.87080 | Chendruk Chu | 4025 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 30 | 10 |\n| 115 | 0142H1200327 | 36.01050 | 73.55770 | Hangrus Gah | 4232 | Indus | Leh | Ladakh | 24 | 27 | 10 |\n| 116 | 03591H081906 | 28.10680 | 97.31080 | | 4343 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | 24 | 10 |\n| 117 | 03591C030027 | 29.30460 | 96.15620 | Kangri Karpo chu | 4233 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 23 | 10 |\n| 118 | 0142H1100230 | 36.35070 | 73.51380 | Kurkuhi Bar | 4420 | Indus | Leh | Ladakh | 17 | 19 | 10 |\n| 119 | 0143K0501515 | 33.86560 | 74.41660 | | 3968 | Indus | Badgam | Jammu & Kashmir | 14 | 15 | 10 |\n| 120 | 0152K1003986 | 33.51720 | 78.51920 | | 5404 | Indus | Leh | Ladakh | 14 | 15 | 10 |\n| 121 | 03591H031293 | 28.26360 | 97.22540 | Depuchu | 4167 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 14 | 10 |\n| 122 | 0143M0801728 | 35.14330 | 75.26120 | | 4659 | Indus | Leh | Ladakh | 12 | 13 | 10 |\n| 123 | 03591H041309 | 28.22000 | 97.11970 | | 3986 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 13 | 10 |\n| 124 | 03478A050232 | 27.92000 | 88.31350 | Khara Chu | 5114 | Brahmaputra | North Sikkim | Sikkim | 10 | 11 | 10 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1903, "line_end": 1918, "token_count_estimate": 963, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["01050", "0142H1100230", "0142H1200327", "0143K0501515", "0143M0801728", "0152K0303890", "0152K1003986", "03082O150279", "03478A050232", "03591C030027", "03591H031293", "03591H041309", "03591H041386", "03591H081906", "03678M090226", "04090", "10680", "11970", "14330", "15620", "21830", "22000", "22540", "22970", "26120", "26360", "28150", "30460", "31080", "31350", "33480", "35070", "41660", "51380", "51720", "51920", "55770", "60480", "83770", "86560", "87080", "92000"]}}
{"id": "302a412e2744d260", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 125 | 03391C040411 | 29.05090 | 96.14450 | Dri Chu | 3602 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 80 | 87 | 9 |\n| 126 | 0152J1203868 | 34.15100 | 78.55280 | | 5566 | Indus | Leh | Ladakh | 65 | 71 | 9 |\n| 127 | 03592A142163 | 27.68970 | 96.86030 | | 3373 | Brahmaputra | Anjaw | Arunachal Pradesh | 49 | 54 | 9 |\n| 128 | 03391D010516 | 28.82120 | 96.12080 | Thangkung Chu | 4060 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 39 | 43 | 9 |\n| 129 | 03591H031294 | 28.26200 | 97.23230 | Depuchu | 4183 | Brahmaputra | Anjaw | Arunachal Pradesh | 27 | 29 | 9 |\n| 130 | 0143N0501997 | 34.82460 | 75.38250 | | 4315 | Indus | Leh | Ladakh | 23 | 25 | 9 |\n| 131 | 03591H081901 | 28.13570 | 97.30630 | | 4225 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | 24 | 9 |\n| 132 | 03592A142142 | 27.71900 | 96.87660 | | 3664 | Brahmaputra | Anjaw | Arunachal Pradesh | 20 | 22 | 9 |\n| 133 | 0143N0902078 | 34.96510 | 75.54520 | | 4412 | Indus | Leh | Ladakh | 14 | 15 | 9 |\n| 134 | 03478A130533 | 27.96350 | 88.79660 | Laehung Chu | 5350 | Brahmaputra | North Sikkim | Sikkim | 12 | 13 | 9 |\n| 135 | 0143M1201776 | 35.09600 | 75.57940 | | 4375 | Indus | Leh | Ladakh | 11 | 12 | 9 |\n| 136 | 0143E0900919 | 35.88920 | 73.69210 | Balres Gah | 3995 | Indus | Leh | Ladakh | 11 | 12 | 9 |\n| 137 | 0253N0900232 | 30.90420 | 79.74740 | | 4683 | Ganga | Chamoli | Uttarakhand | 11 | 12 | 9 |\n| 138 | 0143I0101159 | 35.77290 | 74.19600 | Bal Gah | 4368 | Indus | Leh | Ladakh | 11 | 12 | 9 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1925, "line_end": 1940, "token_count_estimate": 926, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0143E0900919", "0143I0101159", "0143M1201776", "0143N0501997", "0143N0902078", "0152J1203868", "0253N0900232", "03391C040411", "03391D010516", "03478A130533", "03591H031294", "03591H081901", "03592A142142", "03592A142163", "05090", "09600", "12080", "13570", "14450", "15100", "19600", "23230", "26200", "30630", "38250", "54520", "55280", "57940", "68970", "69210", "71900", "74740", "77290", "79660", "82120", "82460", "86030", "87660", "88920", "90420", "96350", "96510"]}}
{"id": "6d3c03e618148330", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 139 | 03592A142181 | 27.63660 | 96.91560 | | 3689 | Brahmaputra | Changlang | Arunachal Pradesh | 10 | 11 | 9 |\n| 140 | 03391C040334 | 29.22590 | 96.15980 | Jairu Chu | 3313 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 57 | 62 | 8 |\n| 141 | 03591C080058 | 29.17460 | 96.32720 | Kangri Karpo chu | 4379 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 46 | 50 | 8 |\n| 142 | 03591H081850 | 28.22340 | 97.28510 | | 4189 | Brahmaputra | Anjaw | Arunachal Pradesh | 32 | 35 | 8 |\n| 143 | 03082O150281 | 29.32650 | 95.84670 | Chendruk Chu | 4290 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 30 | 8 |\n| 144 | 03591C080057 | 29.17530 | 96.34630 | Kangri Karpo chu | 4298 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 30 | 8 |\n| 145 | 03591H081900 | 28.13960 | 97.29010 | | 4055 | Brahmaputra | Anjaw | Arunachal Pradesh | 26 | 28 | 8 |\n| 146 | 03591C030013 | 29.36500 | 96.12370 | Keli chu | 3995 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 25 | 27 | 8 |\n| 147 | 0142H1200253 | 36.12860 | 73.50500 | Hakis Gah | 4482 | Indus | Leh | Ladakh | 23 | 25 | 8 |\n| 148 | 03478A110508 | 27.46610 | 88.75100 | Chakung Chu | 4052 | Brahmaputra | North Sikkim | Sikkim | 22 | 24 | 8 |\n| 149 | 0143E1301057 | 35.85180 | 73.87730 | Shaturhao Gah | 4166 | Indus | Leh | Ladakh | 19 | 21 | 8 |\n| 150 | 03082O040056 | 29.06870 | 95.24230 | Dihang | 3823 | Brahmaputra | Upper Siang | Arunachal Pradesh | 19 | 20 | 8 |\n| 151 | 03391D050568 | 28.93490 | 96.36880 | Thangkung Chu | 4001 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 20 | 8 |\n| 152 | 03391D050612 | 28.83220 | 96.24960 | Thangkung Chu | 3835 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 17 | 8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1944, "line_end": 1959, "token_count_estimate": 969, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Dihang"], "countries": [], "lake_ids": ["0142H1200253", "0143E1301057", "03082O040056", "03082O150281", "03391C040334", "03391D050568", "03391D050612", "03478A110508", "03591C030013", "03591C080057", "03591C080058", "03591H081850", "03591H081900", "03592A142181", "06870", "12370", "12860", "13960", "15980", "17460", "17530", "22340", "22590", "24230", "24960", "28510", "29010", "32650", "32720", "34630", "36500", "36880", "46610", "50500", "63660", "75100", "83220", "84670", "85180", "87730", "91560", "93490"]}}
{"id": "470f7d27278be6dc", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 153 | 03182H140416 | 28.54860 | 93.88370 | Subansiri | 4070 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 16 | 17 | 8 |\n| 154 | 0143K1101582 | 33.44440 | 74.61370 | Panch Gabbar Nala | 3580 | Indus | Rajauri | Jammu & Kashmir | 15 | 16 | 8 |\n| 155 | 03591D090393 | 28.90790 | 96.52490 | Kangri Karpo chu | 4295 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 8 |\n| 156 | 03391C030286 | 29.29210 | 96.00070 | Aison Chu | 4079 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 8 |\n| 157 | 0142H1200249 | 36.15390 | 73.61640 | Saro Gah | 4545 | Indus | Leh | Ladakh | 14 | 15 | 8 |\n| 158 | 0143K1001559 | 33.52830 | 74.56110 | Rupu Nala | 4096 | Indus | Kulgam | Jammu & Kashmir | 11 | 12 | 8 |\n| 159 | 03383A130220 | 27.90100 | 92.78650 | Kameng | 4248 | Brahmaputra | East Kameng | Arunachal Pradesh | 10 | 11 | 8 |\n| 160 | 03683A010646 | 27.75300 | 92.04070 | Tsona Chu | 4084 | Brahmaputra | Tawang | Arunachal Pradesh | 10 | 11 | 8 |\n| 161 | 0143K1401588 | 33.51230 | 74.76930 | Konsarnag Nala | 3486 | Indus | Kulgam | Jammu & Kashmir | 129 | 138 | 7 |\n| 162 | 03391C040329 | 29.22920 | 96.19160 | Jairu Chu | 3473 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 106 | 114 | 7 |\n| 163 | 03683A020770 | 27.51850 | 92.03330 | Mago Chu | 4274 | Brahmaputra | Tawang | Arunachal Pradesh | 61 | 65 | 7 |\n| 164 | 0143E0500769 | 35.94920 | 73.28880 | Pakhtari Gol | 4228 | Indus | Leh | Ladakh | 54 | 58 | 7 |\n| 165 | 0143N0802065 | 34.09380 | 75.49780 | Gratabal Nar (East Liddar River) | 3575 | Indus | Anantnag | Jammu & Kashmir | 52 | 56 | 7 |\n| 166 | 0143K1001567 | 33.50900 | 74.62470 | Rupu Nala | 3937 | Indus | Kulgam | Jammu & Kashmir | 34 | 36 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1964, "line_end": 1979, "token_count_estimate": 1002, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["00070", "0142H1200249", "0143E0500769", "0143K1001559", "0143K1001567", "0143K1101582", "0143K1401588", "0143N0802065", "03182H140416", "03330", "03383A130220", "03391C030286", "03391C040329", "03591D090393", "03683A010646", "03683A020770", "04070", "09380", "15390", "19160", "22920", "28880", "29210", "44440", "49780", "50900", "51230", "51850", "52490", "52830", "54860", "56110", "61370", "61640", "62470", "75300", "76930", "78650", "88370", "90100", "90790", "94920"]}}
{"id": "e99f22a39936b623", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 167 | 0143E0900928 | 35.87990 | 73.57670 | Mashado Gah | 4059 | Indus | Leh | Ladakh | 31 | 33 | 7 |\n| 168 | 03382O160132 | 29.24950 | 95.99340 | Aison Chu | 4204 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 29 | 31 | 7 |\n| 169 | 0143E0600855 | 35.67510 | 73.34420 | Gacher Gah | 3428 | Indus | Leh | Ladakh | 25 | 27 | 7 |\n| 170 | 0142H0800166 | 36.13050 | 73.38420 | Khatbari Gah | 4503 | Indus | Leh | Ladakh | 23 | 25 | 7 |\n| 171 | 0142H0800174 | 36.10810 | 73.46460 | | 4488 | Indus | Leh | Ladakh | 22 | 23 | 7 |\n| 172 | 0142L0400386 | 36.23920 | 74.08420 | Naltar river | 3453 | Indus | Leh | Ladakh | 21 | 22 | 7 |\n| 173 | 0143M0801730 | 35.13250 | 75.32120 | | 4526 | Indus | Leh | Ladakh | 20 | 21 | 7 |\n| 174 | 03391C040356 | 29.17560 | 96.17470 | Dri Chu | 3523 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 21 | 7 |\n| 175 | 03683A020734 | 27.56610 | 92.21550 | Mago Chu | 4348 | Brahmaputra | West Kameng | Arunachal Pradesh | 20 | 21 | 7 |\n| 176 | 03478A090374 | 27.96180 | 88.74340 | Laehung Chu | 5065 | Brahmaputra | North Sikkim | Sikkim | 20 | 21 | 7 |\n| 177 | 03591D160704 | 28.15880 | 96.84750 | | 3926 | Brahmaputra | Anjaw | Arunachal Pradesh | 17 | 18 | 7 |\n| 178 | 03591H081847 | 28.22730 | 97.26850 | Depuchu | 4145 | Brahmaputra | Anjaw | Arunachal Pradesh | 17 | 18 | 7 |\n| 179 | 0143N0902126 | 34.87290 | 75.71590 | | 4664 | Indus | Kargil | Ladakh | 16 | 17 | 7 |\n| 180 | 0143I0201186 | 35.70700 | 74.24330 | Pahot Gah | 4318 | Indus | Leh | Ladakh | 16 | 17 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 1983, "line_end": 1998, "token_count_estimate": 969, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H0800166", "0142H0800174", "0142L0400386", "0143E0600855", "0143E0900928", "0143I0201186", "0143M0801730", "0143N0902126", "03382O160132", "03391C040356", "03478A090374", "03591D160704", "03591H081847", "03683A020734", "08420", "10810", "13050", "13250", "15880", "17470", "17560", "21550", "22730", "23920", "24330", "24950", "26850", "32120", "34420", "38420", "46460", "56610", "57670", "67510", "70700", "71590", "74340", "84750", "87290", "87990", "96180", "99340"]}}
{"id": "30db066747439f7f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2001 | Lake Area August 2021 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 181 | 03082O150283 | 29.32290 | 95.85740 | Chendruk Chu | 4169 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 7 |\n| 182 | 0142H0800179 | 36.08570 | 73.46060 | | 4577 | Indus | Leh | Ladakh | 14 | 15 | 7 |\n| 183 | 0142H0800198 | 35.99970 | 73.31240 | | 4615 | Indus | Leh | Ladakh | 13 | 14 | 7 |\n| 184 | 03678M140440 | 27.69820 | 91.90270 | Towang Chu | 4047 | Brahmaputra | Tawang | Arunachal Pradesh | 13 | 14 | 7 |\n| 185 | 03591H081856 | 28.21400 | 97.31780 | | 4373 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 13 | 7 |\n| 186 | 0143K1101571 | 33.49570 | 74.56480 | Paniul | 3812 | Indus | Punch | Jammu & Kashmir | 11 | 12 | 7 |\n| 187 | 0142H0800173 | 36.11200 | 73.44920 | | 4427 | Indus | Leh | Ladakh | 11 | 12 | 7 |\n| 188 | 0143M0401638 | 35.23060 | 75.18240 | | 4547 | Indus | Leh | Ladakh | 11 | 12 | 7 |\n| 189 | 03082P050298 | 28.94310 | 95.29780 | Yang Sang Chu | 3780 | Brahmaputra | Upper Siang | Arunachal Pradesh | 11 | 12 | 7 |\n| 190 | 03391D010501 | 28.84650 | 96.12720 | Dri Chu | 3697 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 12 | 7 |\n| 191 | 03082O110216 | 29.27270 | 95.73870 | Rirung Chu | 4023 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 12 | 7 |\n| 192 | 0143E0500742 | 35.98670 | 73.31310 | Bashkar Gah | 4583 | Indus | Leh | Ladakh | 10 | 11 | 7 |\n| 193 | 0152K0403898 | 33.13680 | 78.19710 | Zoboshisha Nala | 5733 | Indus | Leh | Ladakh | 10 | 11 | 7 |\n| 194 | 03391D010529 | 28.79320 | 96.05470 | Dri Chu | 3948 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2001", "Lake Area August 2021 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2001, "line_end": 2016, "token_count_estimate": 944, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H0800173", "0142H0800179", "0142H0800198", "0143E0500742", "0143K1101571", "0143M0401638", "0152K0403898", "03082O110216", "03082O150283", "03082P050298", "03391D010501", "03391D010529", "03591H081856", "03678M140440", "05470", "08570", "11200", "12720", "13680", "18240", "19710", "21400", "23060", "27270", "29780", "31240", "31310", "31780", "32290", "44920", "46060", "49570", "56480", "69820", "73870", "79320", "84650", "85740", "90270", "94310", "98670", "99970"]}}
{"id": "2c8fd7d9f61f8216", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2001 | Lake Area August 2021 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 195 | 03382P050207 | 28.86290 | 95.34020 | Emra River | 3789 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 7 |\n| 196 | 03591C080049 | 29.22410 | 96.27900 | Kangri Karpo chu | 4207 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 66 | 70 | 6 |\n| 197 | 03591H081915 | 28.09610 | 97.28890 | Sertik chu | 3762 | Brahmaputra | Anjaw | Arunachal Pradesh | 53 | 56 | 6 |\n| 198 | 03591H041387 | 28.04010 | 97.12570 | | 3969 | Brahmaputra | Anjaw | Arunachal Pradesh | 36 | 38 | 6 |\n| 199 | 0143E1300983 | 35.97220 | 73.91120 | Thapas Gah | 4426 | Indus | Leh | Ladakh | 29 | 31 | 6 |\n| 200 | 0152J1203865 | 34.20010 | 78.51610 | | 5386 | Indus | Leh | Ladakh | 24 | 26 | 6 |\n| 201 | 0142H0800171 | 36.11650 | 73.42310 | Gupis Gah | 4437 | Indus | Leh | Ladakh | 22 | 23 | 6 |\n| 202 | 0143F1301107 | 34.83050 | 73.98470 | | 3873 | Indus | Muzaffarabad | Jammu & Kashmir | 21 | 22 | 6 |\n| 203 | 03391D060656 | 28.66610 | 96.42640 | Thangkung Chu | 4238 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 22 | 6 |\n| 204 | 0142H1200295 | 36.03850 | 73.59210 | | 4274 | Indus | Leh | Ladakh | 19 | 20 | 6 |\n| 205 | 03683A020778 | 27.50940 | 92.10150 | Mago Chu | 4148 | Brahmaputra | West Kameng | Arunachal Pradesh | 19 | 20 | 6 |\n| 206 | 0142H1200250 | 36.14520 | 73.63940 | | 4484 | Indus | Leh | Ladakh | 18 | 19 | 6 |\n| 207 | 0143N0902074 | 34.98680 | 75.55350 | Tukziwai Lungma | 4269 | Indus | Leh | Ladakh | 18 | 19 | 6 |\n| 208 | 0143I0201192 | 35.68620 | 74.23000 | Pahot Gah | 4336 | Indus | Leh | Ladakh | 14 | 15 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2001", "Lake Area August 2021 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2020, "line_end": 2035, "token_count_estimate": 944, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H0800171", "0142H1200250", "0142H1200295", "0143E1300983", "0143F1301107", "0143I0201192", "0143N0902074", "0152J1203865", "03382P050207", "03391D060656", "03591C080049", "03591H041387", "03591H081915", "03683A020778", "03850", "04010", "09610", "10150", "11650", "12570", "14520", "20010", "22410", "23000", "27900", "28890", "34020", "42310", "42640", "50940", "51610", "55350", "59210", "63940", "66610", "68620", "83050", "86290", "91120", "97220", "98470", "98680"]}}
{"id": "498e3e3185499dce", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 209 | 0143J1501480 | 34.45180 | 74.93240 | Kankanaz N | 3889 | Indus | Ganderbal | Jammu & Kashmir | 12 | 13 | 6 |\n| 210 | 0142H1100220 | 36.43130 | 73.56600 | | 4030 | Indus | Leh | Ladakh | 12 | 13 | 6 |\n| 211 | 0143E0900961 | 35.78340 | 73.50740 | | 3918 | Indus | Leh | Ladakh | 12 | 13 | 6 |\n| 212 | 03182H140402 | 28.59740 | 93.80450 | Subansiri | 4163 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | 13 | 6 |\n| 213 | 03478A050278 | 27.81710 | 88.26090 | Rhuling Chu | 5487 | Brahmaputra | North Sikkim | Sikkim | 12 | 13 | 6 |\n| 214 | 03683A060875 | 27.71520 | 92.38580 | Gorjo Chu | 5117 | Brahmaputra | Tawang | Arunachal Pradesh | 12 | 13 | 6 |\n| 215 | 03391D060670 | 28.61420 | 96.31160 | Thangkung Chu | 3968 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 12 | 6 |\n| 216 | 0143N0401966 | 34.20740 | 75.14730 | | 3699 | Indus | Ganderbal | Jammu & Kashmir | 10 | 11 | 6 |\n| 217 | 03391D060683 | 28.56340 | 96.35490 | Thangkung Chu | 4152 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 6 |\n| 218 | 03592A132133 | 27.75870 | 96.87790 | | 3857 | Brahmaputra | Anjaw | Arunachal Pradesh | 10 | 11 | 6 |\n| 219 | 03382P050183 | 28.97710 | 95.26340 | Emra River | 3769 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 6 |\n| 220 | 0143A0900583 | 35.99440 | 72.61300 | | 3622 | Indus | Leh | Ladakh | 202 | 212 | 5 |\n| 221 | 03477D160043 | 28.01110 | 88.75600 | Teesta River | 5094 | Brahmaputra | North Sikkim | Sikkim | 108 | 113 | 5 |\n| 222 | 03382P130245 | 29.00480 | 95.90550 | Matuni | 3598 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 54 | 57 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2040, "line_end": 2055, "token_count_estimate": 948, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri", "Teesta"], "countries": [], "lake_ids": ["00480", "01110", "0142H1100220", "0143A0900583", "0143E0900961", "0143J1501480", "0143N0401966", "03182H140402", "03382P050183", "03382P130245", "03391D060670", "03391D060683", "03477D160043", "03478A050278", "03592A132133", "03683A060875", "14730", "20740", "26090", "26340", "31160", "35490", "38580", "43130", "45180", "50740", "56340", "56600", "59740", "61300", "61420", "71520", "75600", "75870", "78340", "80450", "81710", "87790", "90550", "93240", "97710", "99440"]}}
{"id": "56a6956139773140", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 223 | 03391D050573 | 28.92780 | 96.33900 | Thangkung Chu | 4011 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 48 | 51 | 5 |\n| 224 | 0152G1303615 | 33.99890 | 77.97940 | Sherchu Lungpa | 4991 | Indus | Leh | Ladakh | 44 | 46 | 5 |\n| 225 | 0143E1301036 | 35.88250 | 73.76320 | Singal Gah | 4104 | Indus | Leh | Ladakh | 41 | 43 | 5 |\n| 226 | 03678M140485 | 27.67300 | 91.87500 | Towang Chu | 4222 | Brahmaputra | Tawang | Arunachal Pradesh | 33 | 35 | 5 |\n| 227 | 03678M090216 | 27.85080 | 91.60420 | Dangme Chu | 4509 | Brahmaputra | Tawang | Arunachal Pradesh | 31 | 33 | 5 |\n| 228 | 0143J1401458 | 34.53670 | 74.82770 | Patalwan Nala | 3922 | Indus | Bandipore | Jammu & Kashmir | 31 | 32 | 5 |\n| 229 | 0143K0501528 | 33.82030 | 74.45090 | | 3997 | Indus | Badgam | Jammu & Kashmir | 29 | 31 | 5 |\n| 230 | 03082O120239 | 29.20240 | 95.54520 | Rirung Chu | 3838 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | 23 | 5 |\n| 231 | 0143M0301632 | 35.26350 | 75.19350 | | 4551 | Indus | Leh | Ladakh | 19 | 20 | 5 |\n| 232 | 03382P130248 | 28.97900 | 95.86080 | Matuni | 3622 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 20 | 5 |\n| 233 | 03082O150271 | 29.37480 | 95.95110 | Chendruk Chu | 4296 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 18 | 5 |\n| 234 | 03082K081988 | 29.07700 | 94.27130 | Neyul pu chu | 3682 | Brahmaputra | Upper Siang | Arunachal Pradesh | 17 | 18 | 5 |\n| 235 | 0143E1301053 | 35.85830 | 73.86940 | Shaturhao Gah | 4211 | Indus | Leh | Ladakh | 15 | 16 | 5 |\n| 236 | 0143N0101842 | 34.97200 | 75.05050 | | 4265 | Indus | Leh | Ladakh | 14 | 15 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2059, "line_end": 2074, "token_count_estimate": 959, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0143E1301036", "0143E1301053", "0143J1401458", "0143K0501528", "0143M0301632", "0143N0101842", "0152G1303615", "03082K081988", "03082O120239", "03082O150271", "03382P130248", "03391D050573", "03678M090216", "03678M140485", "05050", "07700", "19350", "20240", "26350", "27130", "33900", "37480", "45090", "53670", "54520", "60420", "67300", "76320", "82030", "82770", "85080", "85830", "86080", "86940", "87500", "88250", "92780", "95110", "97200", "97900", "97940", "99890"]}}
{"id": "78c6f69f976db268", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 237 | 0143N0101852 | 34.92180 | 75.24980 | | 4152 | Indus | Leh | Ladakh | 14 | 15 | 5 |\n| 238 | 0143M0801717 | 35.18230 | 75.42230 | | 4530 | Indus | Leh | Ladakh | 14 | 15 | 5 |\n| 239 | 03683A020736 | 27.56460 | 92.06510 | Mago Chu | 4326 | Brahmaputra | Tawang | Arunachal Pradesh | 14 | 15 | 5 |\n| 240 | 03591H081867 | 28.17430 | 97.32860 | | 4307 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | 15 | 5 |\n| 241 | 03391D090748 | 28.76440 | 96.51710 | Thangkung Chu | 3502 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 15 | 5 |\n| 242 | 0143N0902124 | 34.88310 | 75.68000 | | 4627 | Indus | Kargil | Ladakh | 13 | 14 | 5 |\n| 243 | 0152B0502772 | 34.85390 | 76.36760 | Rumboka | 4821 | Indus | Kargil | Ladakh | 13 | 14 | 5 |\n| 244 | 0143K1001560 | 33.52190 | 74.60480 | Rupu Nala | 3844 | Indus | Kulgam | Jammu & Kashmir | 12 | 13 | 5 |\n| 245 | 0143I0101158 | 35.77910 | 74.20530 | Bal Gah | 4304 | Indus | Leh | Ladakh | 12 | 13 | 5 |\n| 246 | 03391D010493 | 28.88640 | 96.13650 | Dri Chu | 3720 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 13 | 5 |\n| 247 | 0143E1301013 | 35.91640 | 73.99240 | | 4312 | Indus | Leh | Ladakh | 11 | 12 | 5 |\n| 248 | 0143E0500790 | 35.92540 | 73.27400 | | 4227 | Indus | Leh | Ladakh | 11 | 12 | 5 |\n| 249 | 0143E0500781 | 35.93600 | 73.26860 | | 4362 | Indus | Leh | Ladakh | 11 | 12 | 5 |\n| 250 | 0143J1501467 | 34.46970 | 74.91280 | Shonshpahi N | 3852 | Indus | Bandipore | Jammu & Kashmir | 10 | 11 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2079, "line_end": 2094, "token_count_estimate": 959, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0143E0500781", "0143E0500790", "0143E1301013", "0143I0101158", "0143J1501467", "0143K1001560", "0143M0801717", "0143N0101852", "0143N0902124", "0152B0502772", "03391D010493", "03391D090748", "03591H081867", "03683A020736", "06510", "13650", "17430", "18230", "20530", "24980", "26860", "27400", "32860", "36760", "42230", "46970", "51710", "52190", "56460", "60480", "68000", "76440", "77910", "85390", "88310", "88640", "91280", "91640", "92180", "92540", "93600", "99240"]}}
{"id": "982be99baa397c40", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 251 | 0142H1200302 | 36.03020 | 73.71360 | | 4320 | Indus | Leh | Ladakh | 10 | 11 | 5 |\n| 252 | 0142H1200262 | 36.09640 | 73.66800 | Gakuch Gah | 4423 | Indus | Leh | Ladakh | 10 | 11 | 5 |\n| 253 | 0143E1601096 | 35.18360 | 73.95800 | | 4120 | Indus | Leh | Ladakh | 10 | 11 | 5 |\n| 254 | 03383A060157 | 27.60170 | 92.38530 | Sangli Chu | 4227 | Brahmaputra | West Kameng | Arunachal Pradesh | 10 | 11 | 5 |\n| 255 | 0142H0900200 | 36.87920 | 73.70430 | | 4286 | Indus | Leh | Ladakh | 263 | 272 | 4 |\n| 256 | 0143E0900941 | 35.86450 | 73.74600 | Balres Gah | 4140 | Indus | Leh | Ladakh | 84 | 88 | 4 |\n| 257 | 0143N0401975 | 34.13960 | 75.14790 | | 3780 | Indus | Anantnag | Jammu & Kashmir | 83 | 86 | 4 |\n| 258 | 0143E0500774 | 35.94500 | 73.36500 | Gugalo Gah | 4162 | Indus | Leh | Ladakh | 67 | 70 | 4 |\n| 259 | 03391D050580 | 28.91870 | 96.38320 | Thangkung Chu | 3302 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 49 | 51 | 4 |\n| 260 | 03382O120065 | 29.17620 | 95.61690 | Andra Chu | 3063 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 41 | 43 | 4 |\n| 261 | 0152K0803965 | 33.05490 | 78.46960 | | 5745 | Indus | Leh | Ladakh | 36 | 38 | 4 |\n| 262 | 0143J1501493 | 34.41820 | 74.93550 | Kankanaz N | 3503 | Indus | Ganderbal | Jammu & Kashmir | 37 | 38 | 4 |\n| 263 | 03391D050591 | 28.87680 | 96.47710 | Thangkung Chu | 3166 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 35 | 36 | 4 |\n| 264 | 0152B1402898 | 34.67090 | 76.75770 | Zagainakchan Lungpa | 4965 | Indus | Leh | Ladakh | 30 | 31 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2098, "line_end": 2113, "token_count_estimate": 973, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H0900200", "0142H1200262", "0142H1200302", "0143E0500774", "0143E0900941", "0143E1601096", "0143J1501493", "0143N0401975", "0152B1402898", "0152K0803965", "03020", "03382O120065", "03383A060157", "03391D050580", "03391D050591", "05490", "09640", "13960", "14790", "17620", "18360", "36500", "38320", "38530", "41820", "46960", "47710", "60170", "61690", "66800", "67090", "70430", "71360", "74600", "75770", "86450", "87680", "87920", "91870", "93550", "94500", "95800"]}}
{"id": "31e3ce2ec3d82e5c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 265 | 03391C030272 | 29.30390 | 96.14210 | Jairu Chu | 4221 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 29 | 4 |\n| 266 | 03391D100755 | 28.70740 | 96.50540 | Thangkung Chu | 4018 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 28 | 4 |\n| 267 | 03391C040352 | 29.19130 | 96.11160 | Dri Chu | 3775 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 28 | 4 |\n| 268 | 0143N0902089 | 34.94680 | 75.59210 | | 4558 | Indus | Leh | Ladakh | 25 | 26 | 4 |\n| 269 | 0143E0900962 | 35.78210 | 73.57190 | | 4082 | Indus | Leh | Ladakh | 22 | 23 | 4 |\n| 270 | 0143N0802045 | 34.23410 | 75.27510 | Basmai Nar | 3830 | Indus | Anantnag | Jammu & Kashmir | 18 | 19 | 4 |\n| 271 | 03592A142137 | 27.74250 | 96.84480 | | 3778 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 19 | 4 |\n| 272 | 0152A0802526 | 35.01060 | 76.32010 | Indus River | 4753 | Indus | Kargil | Ladakh | 15 | 16 | 4 |\n| 273 | 0143I0101122 | 35.96220 | 74.01910 | | 4285 | Indus | Leh | Ladakh | 15 | 16 | 4 |\n| 274 | 03591D050336 | 28.95220 | 96.49290 | Kangri Karpo chu | 4493 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 4 |\n| 275 | 03478A150681 | 27.36810 | 88.82620 | Rungpo Chu | 3930 | Brahmaputra | East Sikkim | Sikkim | 13 | 14 | 4 |\n| 276 | 03591D070347 | 28.34490 | 96.33410 | | 2990 | Brahmaputra | Lohit | Arunachal Pradesh | 13 | 13 | 4 |\n| 277 | 03678M140405 | 27.73390 | 91.87090 | Tsona Chu | 4456 | Brahmaputra | Tawang | Arunachal Pradesh | 13 | 13 | 4 |\n| 278 | 0152A0102354 | 35.95420 | 76.03020 | | 4276 | Indus | Leh | Ladakh | 11 | 11 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2118, "line_end": 2133, "token_count_estimate": 976, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Lohit"], "countries": [], "lake_ids": ["01060", "0143E0900962", "0143I0101122", "0143N0802045", "0143N0902089", "0152A0102354", "0152A0802526", "01910", "03020", "03391C030272", "03391C040352", "03391D100755", "03478A150681", "03591D050336", "03591D070347", "03592A142137", "03678M140405", "11160", "14210", "19130", "23410", "27510", "30390", "32010", "33410", "34490", "36810", "49290", "50540", "57190", "59210", "70740", "73390", "74250", "78210", "82620", "84480", "87090", "94680", "95220", "95420", "96220"]}}
{"id": "1a34ed402ef7ecb6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 279 | 0152B0502808 | 34.77160 | 76.49980 | Yaldor | 4761 | Indus | Leh | Ladakh | 11 | 11 | 4 |\n| 280 | 0142H1000208 | 36.64400 | 73.64640 | | 3821 | Indus | Leh | Ladakh | 105 | 108 | 3 |\n| 281 | 0152J0803836 | 34.23300 | 78.42640 | | 5350 | Indus | Leh | Ladakh | 65 | 67 | 3 |\n| 282 | 0143K0501525 | 33.84110 | 74.42790 | Sinwar | 3954 | Indus | Badgam | Jammu & Kashmir | 45 | 47 | 3 |\n| 283 | 03082O150276 | 29.35390 | 95.92650 | Chendruk Chu | 4357 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 38 | 39 | 3 |\n| 284 | 03383A130229 | 27.87730 | 92.79990 | Kameng | 3943 | Brahmaputra | East Kameng | Arunachal Pradesh | 36 | 37 | 3 |\n| 285 | 0152L1004049 | 32.73570 | 78.72610 | Hanle River | 5626 | Indus | Leh | Ladakh | 35 | 36 | 3 |\n| 286 | 0143J1501486 | 34.44420 | 74.89190 | Salnai N | 3846 | Indus | Bandipore | Jammu & Kashmir | 34 | 35 | 3 |\n| 287 | 03391D010530 | 28.79150 | 96.14800 | Thangkung Chu | 3676 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 33 | 34 | 3 |\n| 288 | 03382O120096 | 29.09170 | 95.56590 | Andra Chu | 3247 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 29 | 30 | 3 |\n| 289 | 0143E1301054 | 35.85560 | 73.77520 | Khanbari Nala | 3962 | Indus | Leh | Ladakh | 28 | 29 | 3 |\n| 290 | 0143J1501499 | 34.39240 | 74.87360 | | 3556 | Indus | Bandipore | Jammu & Kashmir | 27 | 28 | 3 |\n| 291 | 03478A150677 | 27.37460 | 88.76290 | Rangpo Chu | 3744 | Brahmaputra | East Sikkim | Sikkim | 27 | 28 | 3 |\n| 292 | 0143N1302194 | 34.94700 | 75.76250 | | 4778 | Indus | Leh | Ladakh | 25 | 26 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2137, "line_end": 2152, "token_count_estimate": 969, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H1000208", "0143E1301054", "0143J1501486", "0143J1501499", "0143K0501525", "0143N1302194", "0152B0502808", "0152J0803836", "0152L1004049", "03082O150276", "03382O120096", "03383A130229", "03391D010530", "03478A150677", "09170", "14800", "23300", "35390", "37460", "39240", "42640", "42790", "44420", "49980", "56590", "64400", "64640", "72610", "73570", "76250", "76290", "77160", "77520", "79150", "79990", "84110", "85560", "87360", "87730", "89190", "92650", "94700"]}}
{"id": "d24b18d35e9c9433", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2000 | Lake Area August 2020 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 293 | 0143K1001564 | 33.51010 | 74.56430 | Paniul | 3909 | Indus | Punch | Jammu & Kashmir | 25 | 26 | 3 |\n| 294 | 0152K1003984 | 33.55770 | 78.50560 | | 5665 | Indus | Leh | Ladakh | 25 | 26 | 3 |\n| 295 | 0143E0100689 | 35.90570 | 73.15480 | Kanu Gol | 4328 | Indus | Leh | Ladakh | 24 | 25 | 3 |\n| 296 | 0143E0100682 | 35.91990 | 73.06420 | Rani Gol | 4303 | Indus | Leh | Ladakh | 21 | 22 | 3 |\n| 297 | 0143N0201889 | 34.68360 | 75.14050 | Burzil Nala | 4133 | Indus | Leh | Ladakh | 21 | 22 | 3 |\n| 298 | 03591H041359 | 28.08420 | 97.21540 | | 4023 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 19 | 3 |\n| 299 | 03478A150691 | 27.34620 | 88.81870 | Rangpo Chu | 3546 | Brahmaputra | East Sikkim | Sikkim | 18 | 19 | 3 |\n| 300 | 0143E1301045 | 35.86790 | 73.92890 | Shaturhao Gah | 4064 | Indus | Leh | Ladakh | 18 | 18 | 3 |\n| 301 | 03592E012215 | 27.95870 | 97.10460 | | 3831 | Brahmaputra | Anjaw | Arunachal Pradesh | 17 | 18 | 3 |\n| 302 | 03382O160151 | 29.21860 | 95.74920 | Aison Chu | 3951 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 18 | 3 |\n| 303 | 0152K0703937 | 33.49550 | 78.49750 | | 5428 | Indus | Leh | Ladakh | 17 | 17 | 3 |\n| 304 | 0143J1501485 | 34.44820 | 74.90850 | Kankanaz N | 3979 | Indus | Ganderbal | Jammu & Kashmir | 15 | 16 | 3 |\n| 305 | 0143E0900929 | 35.87680 | 73.61490 | Mashado Gah | 3993 | Indus | Leh | Ladakh | 15 | 15 | 3 |\n| 306 | 0143N0101854 | 34.92040 | 75.17720 | Sar Sangri | 4257 | Indus | Leh | Ladakh | 15 | 15 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2000", "Lake Area August 2020 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2157, "line_end": 2172, "token_count_estimate": 946, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0143E0100682", "0143E0100689", "0143E0900929", "0143E1301045", "0143J1501485", "0143K1001564", "0143N0101854", "0143N0201889", "0152K0703937", "0152K1003984", "03382O160151", "03478A150691", "03591H041359", "03592E012215", "06420", "08420", "10460", "14050", "15480", "17720", "21540", "21860", "34620", "44820", "49550", "49750", "50560", "51010", "55770", "56430", "61490", "68360", "74920", "81870", "86790", "87680", "90570", "90850", "91990", "92040", "92890", "95870"]}}
{"id": "be24a3aa347b0f22", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2000 | Lake Area August 2020 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 307 | 0143E0500791 | 35.92490 | 73.34230 | 'Salil Gah | 4396 | Indus | Leh | Ladakh | 12 | 12 | 3 |\n| 308 | 03082K081989 | 29.07460 | 94.29620 | Neyul pu chu | 4377 | Brahmaputra | Upper Siang | Arunachal Pradesh | 12 | 12 | 3 |\n| 309 | 0253I0400008 | 31.23080 | 78.21090 | | 4264 | Ganga | Shimla | Himachal Pradesh | 11 | 11 | 3 |\n| 310 | 03383A130208 | 27.92040 | 92.79340 | Kameng | 4291 | Brahmaputra | East Kameng | Arunachal Pradesh | 11 | 11 | 3 |\n| 311 | 0143J1501489 | 34.43190 | 74.92450 | Kankanaz N | 3571 | Indus | Ganderbal | Jammu & Kashmir | 161 | 164 | 2 |\n| 312 | 0143K1001561 | 33.51890 | 74.58370 | Rupu Nala | 3934 | Indus | Kulgam | Jammu & Kashmir | 71 | 72 | 2 |\n| 313 | 0143N0201885 | 34.69660 | 75.13710 | Burzil Nala | 4103 | Indus | Leh | Ladakh | 65 | 66 | 2 |\n| 314 | 0143J1501462 | 34.49280 | 74.92120 | Lachhan Pinjan Nala | 3881 | Indus | Bandipore | Jammu & Kashmir | 38 | 39 | 2 |\n| 315 | 03391D010470 | 28.98680 | 96.06880 | Dri Chu | 3627 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 37 | 38 | 2 |\n| 316 | 0143J1401457 | 34.54790 | 74.81910 | Patalwan Nala | 3872 | Indus | Bandipore | Jammu & Kashmir | 31 | 32 | 2 |\n| 317 | 03391C040340 | 29.21120 | 96.06900 | Matuni | 3816 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 32 | 2 |\n| 318 | 0143N0301932 | 34.39700 | 75.10260 | Raman Nala | 3812 | Indus | Bandipore | Jammu & Kashmir | 30 | 30 | 2 |\n| 319 | 03382O150119 | 29.29420 | 95.88650 | Aison Chu | 3517 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 29 | 2 |\n| 320 | 0143E0200735 | 35.72980 | 73.21570 | Kachila Gah | 3970 | Indus | Leh | Ladakh | 26 | 27 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2000", "Lake Area August 2020 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2176, "line_end": 2191, "token_count_estimate": 980, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0143E0200735", "0143E0500791", "0143J1401457", "0143J1501462", "0143J1501489", "0143K1001561", "0143N0201885", "0143N0301932", "0253I0400008", "03082K081989", "03382O150119", "03383A130208", "03391C040340", "03391D010470", "06880", "06900", "07460", "10260", "13710", "21090", "21120", "21570", "23080", "29420", "29620", "34230", "39700", "43190", "49280", "51890", "54790", "58370", "69660", "72980", "79340", "81910", "88650", "92040", "92120", "92450", "92490", "98680"]}}
{"id": "ed4e0c0d818a9438", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 321 | 0143E0100728 | 35.82480 | 73.21140 | | 4271 | Indus | Leh | Ladakh | 26 | 27 | 2 |\n| 322 | 0143J1301450 | 34.83960 | 74.76100 | Reat | 3979 | Indus | Leh | Ladakh | 25 | 26 | 2 |\n| 323 | 0143J0901405 | 34.90840 | 74.67990 | Mir Malik Gah | 3864 | Indus | Leh | Ladakh | 23 | 24 | 2 |\n| 324 | 0143J0101360 | 34.85750 | 74.07690 | | 3680 | Indus | Muzaffarabad | Jammu & Kashmir | 21 | 21 | 2 |\n| 325 | 0143E1301005 | 35.93910 | 73.98530 | | 4258 | Indus | Leh | Ladakh | 20 | 20 | 2 |\n| 326 | 03082K160033 | 29.22160 | 94.77970 | Nugong Chu | 3754 | Brahmaputra | Upper Siang | Arunachal Pradesh | 20 | 20 | 2 |\n| 327 | 03382O160176 | 29.01890 | 95.90950 | Dri Chu | 3671 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 18 | 18 | 2 |\n| 328 | 03678M140452 | 27.69270 | 91.85690 | Dangme Chu | 4283 | Brahmaputra | Tawang | Arunachal Pradesh | 18 | 18 | 2 |\n| 329 | 0143I0101141 | 35.81720 | 74.08340 | | 4269 | Indus | Leh | Ladakh | 17 | 17 | 2 |\n| 330 | 03182H060297 | 28.65010 | 93.46210 | Subansiri | 4046 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 17 | 17 | 2 |\n| 331 | 03391D050577 | 28.92430 | 96.31080 | Thangkung Chu | 3851 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 16 | 2 |\n| 332 | 03591H031285 | 28.29080 | 97.21800 | Depuchu | 4071 | Brahmaputra | Anjaw | Arunachal Pradesh | 16 | 16 | 2 |\n| 333 | 0143K1401591 | 33.50250 | 74.85020 | Zali Nar | 3623 | Indus | Kulgam | Jammu & Kashmir | 15 | 15 | 2 |\n| 334 | 03182H110383 | 28.48860 | 93.55490 | Subansiri | 4647 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 14 | 14 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2196, "line_end": 2211, "token_count_estimate": 984, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0143E0100728", "0143E1301005", "0143I0101141", "0143J0101360", "0143J0901405", "0143J1301450", "0143K1401591", "01890", "03082K160033", "03182H060297", "03182H110383", "03382O160176", "03391D050577", "03591H031285", "03678M140452", "07690", "08340", "21140", "21800", "22160", "29080", "31080", "46210", "48860", "50250", "55490", "65010", "67990", "69270", "76100", "77970", "81720", "82480", "83960", "85020", "85690", "85750", "90840", "90950", "92430", "93910", "98530"]}}
{"id": "3eddb521985348cf", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 335 | 03391D090726 | 28.92330 | 96.51520 | Thangkung Chu | 4091 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 14 | 2 |\n| 336 | 0143M0401679 | 35.04870 | 75.19820 | | 4398 | Indus | Leh | Ladakh | 13 | 13 | 2 |\n| 337 | 03082O120226 | 29.23730 | 95.65910 | Rirung Chu | 4016 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 13 | 2 |\n| 338 | 03182H150426 | 28.26980 | 93.78060 | Subansiri | 3537 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 13 | 13 | 2 |\n| 339 | 0143I1501319 | 35.33190 | 74.94140 | | 4427 | Indus | Leh | Ladakh | 12 | 12 | 2 |\n| 340 | 03591H081843 | 28.23990 | 97.25040 | | 3980 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 12 | 2 |\n| 341 | 0143I0601249 | 35.72180 | 74.25560 | | 4254 | Indus | Leh | Ladakh | 11 | 11 | 2 |\n| 342 | 0143N0502010 | 34.77510 | 75.48030 | | 4626 | Indus | Kargil | Ladakh | 11 | 11 | 2 |\n| 343 | 03591H041407 | 28.01640 | 97.21490 | | 4303 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 11 | 2 |\n| 344 | 0152K0703939 | 33.45510 | 78.49820 | Kok Lungpa | 5308 | Indus | Leh | Ladakh | 148 | 149 | 1 |\n| 345 | 0143N0101839 | 34.99110 | 75.23630 | | 4138 | Indus | Leh | Ladakh | 130 | 131 | 1 |\n| 346 | 03082O150273 | 29.37140 | 95.87290 | Chendruk Chu | 4344 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 104 | 105 | 1 |\n| 347 | 03391C030301 | 29.26920 | 96.15700 | Jairu Chu | 3991 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 103 | 104 | 1 |\n| 348 | 03282D160026 | 28.11590 | 92.95130 | | 4648 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 56 | 56 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2215, "line_end": 2230, "token_count_estimate": 975, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0143I0601249", "0143I1501319", "0143M0401679", "0143N0101839", "0143N0502010", "0152K0703939", "01640", "03082O120226", "03082O150273", "03182H150426", "03282D160026", "03391C030301", "03391D090726", "03591H041407", "03591H081843", "04870", "11590", "15700", "19820", "21490", "23630", "23730", "23990", "25040", "25560", "26920", "26980", "33190", "37140", "45510", "48030", "49820", "51520", "65910", "72180", "77510", "78060", "87290", "92330", "94140", "95130", "99110"]}}
{"id": "fd4f9984ebdc84aa", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 349 | 0143J1301449 | 34.84530 | 74.80880 | Reat | 3992 | Indus | Leh | Ladakh | 50 | 51 | 1 |\n| 350 | 03082L052049 | 28.98550 | 94.27010 | Neyul pu chu | 3478 | Brahmaputra | West Siang | Arunachal Pradesh | 51 | 51 | 1 |\n| 351 | 0152H1103772 | 32.48290 | 77.61480 | Chandra River | 4276 | Indus | Lahul & Spiti | Himachal Pradesh | 47 | 48 | 1 |\n| 352 | 0143N0401974 | 34.14410 | 75.11010 | Dagwan Nar | 3810 | Indus | Srinagar | Jammu & Kashmir | 43 | 44 | 1 |\n| 353 | 0152A0402407 | 35.17110 | 76.20510 | | 3301 | Indus | Leh | Ladakh | 31 | 31 | 1 |\n| 354 | 0143J0101345 | 34.96220 | 74.10040 | | 3603 | Indus | Muzaffarabad | Jammu & Kashmir | 30 | 30 | 1 |\n| 355 | 03778A150012 | 27.33030 | 88.84630 | | 3901 | Brahmaputra | East Sikkim | Sikkim | 28 | 28 | 1 |\n| 356 | 03391C030269 | 29.30910 | 96.13550 | Jairu Chu | 4204 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 27 | 1 |\n| 357 | 03591H081919 | 28.07990 | 97.30390 | | 4304 | Brahmaputra | Anjaw | Arunachal Pradesh | 24 | 24 | 1 |\n| 358 | 0142H0300089 | 36.26320 | 73.04760 | | 4618 | Indus | Leh | Ladakh | 22 | 22 | 1 |\n| 359 | 0143K1001551 | 33.54000 | 74.56480 | Rupu Nala | 3967 | Indus | Kulgam | Jammu & Kashmir | 22 | 22 | 1 |\n| 360 | 03592A142141 | 27.72560 | 96.84840 | | 3669 | Brahmaputra | Anjaw | Arunachal Pradesh | 19 | 19 | 1 |\n| 361 | 0143N0401978 | 34.09340 | 75.16070 | Lokul Chhumanai | 3919 | Indus | Anantnag | Jammu & Kashmir | 18 | 18 | 1 |\n| 362 | 03382P050216 | 28.83120 | 95.34960 | Emra River | 4046 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 18 | 18 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2235, "line_end": 2250, "token_count_estimate": 983, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0142H0300089", "0143J0101345", "0143J1301449", "0143K1001551", "0143N0401974", "0143N0401978", "0152A0402407", "0152H1103772", "03082L052049", "03382P050216", "03391C030269", "03591H081919", "03592A142141", "03778A150012", "04760", "07990", "09340", "10040", "11010", "13550", "14410", "16070", "17110", "20510", "26320", "27010", "30390", "30910", "33030", "34960", "48290", "54000", "56480", "61480", "72560", "80880", "83120", "84530", "84630", "84840", "96220", "98550"]}}
{"id": "9b45ee8d7bce3983", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 363 | 0152A0802513 | 35.05580 | 76.26100 | Indus River | 4881 | Indus | Leh | Ladakh | 17 | 17 | 1 |\n| 364 | 03382O160158 | 29.17340 | 95.82600 | Aison Chu | 3790 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 15 | 1 |\n| 365 | 0152B0902845 | 34.81070 | 76.50220 | | 4762 | Indus | Leh | Ladakh | 13 | 13 | 1 |\n| 366 | 03382O160174 | 29.07400 | 95.94640 | Matuni | 4165 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 13 | 1 |\n| 367 | 03592E012219 | 27.94950 | 97.10750 | | 3874 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 13 | 1 |\n| 368 | 03282D160031 | 28.10610 | 92.97010 | | 4340 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 13 | 13 | 1 |\n| 369 | 03592A142147 | 27.71210 | 96.93840 | | 3751 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 13 | 1 |\n| 370 | 0143J1501464 | 34.48900 | 74.90620 | Lachhan Pinjan Nala | 4097 | Indus | Bandipore | Jammu & Kashmir | 12 | 12 | 1 |\n| 371 | 03382P050201 | 28.87500 | 95.37730 | Emra River | 3504 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 12 | 1 |\n| 372 | 0143O0502294 | 33.75390 | 75.46980 | | 3921 | Indus | Anantnag | Jammu & Kashmir | 11 | 11 | 1 |\n| 373 | 0143M0401682 | 35.01640 | 75.00090 | Darle Gah | 3931 | Indus | Leh | Ladakh | 11 | 11 | 1 |\n| 374 | 03391C030259 | 29.32130 | 96.11750 | Jairu Chu | 3975 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 11 | 1 |\n| 375 | 0152K0703940 | 33.42740 | 78.48760 | Kok Lungpa | 5284 | Indus | Leh | Ladakh | 178 | 178 | 0 |\n| 376 | 03391C040395 | 29.07940 | 96.14480 | Dri Chu | 3945 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 86 | 86 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2254, "line_end": 2269, "token_count_estimate": 983, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["00090", "0143J1501464", "0143M0401682", "0143O0502294", "0152A0802513", "0152B0902845", "0152K0703940", "01640", "03282D160031", "03382O160158", "03382O160174", "03382P050201", "03391C030259", "03391C040395", "03592A142147", "03592E012219", "05580", "07400", "07940", "10610", "10750", "11750", "14480", "17340", "26100", "32130", "37730", "42740", "46980", "48760", "48900", "50220", "71210", "75390", "81070", "82600", "87500", "90620", "93840", "94640", "94950", "97010"]}}
{"id": "f3dfa8678d6591a0", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 377 | 03591D160698 | 28.20220 | 96.89800 | Dzayul chu | 3731 | Brahmaputra | Anjaw | Arunachal Pradesh | 67 | 67 | 0 |\n| 378 | 03082O080107 | 29.06180 | 95.26310 | Dihang | 3668 | Brahmaputra | Upper Siang | Arunachal Pradesh | 48 | 48 | 0 |\n| 379 | 0143K1001554 | 33.53630 | 74.55470 | Rupu Nala | 4006 | Indus | Kulgam | Jammu & Kashmir | 32 | 32 | 0 |\n| 380 | 0142H1200268 | 36.08000 | 73.64610 | Suku Gah | 4344 | Indus | Leh | Ladakh | 29 | 29 | 0 |\n| 381 | 03682L010077 | 28.75260 | 94.00100 | Siyom | 4019 | Brahmaputra | West Siang | Arunachal Pradesh | 23 | 23 | 0 |\n| 382 | 03382P050214 | 28.84810 | 95.32010 | Emra River | 3913 | Brahmaputra | Upper Siang | Arunachal Pradesh | 22 | 22 | 0 |\n| 383 | 03391D090733 | 28.83790 | 96.49740 | Thangkung Chu | 3388 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 21 | 0 |\n| 384 | 0143A1300645 | 35.86870 | 72.93970 | Anari Gol | 4378 | Indus | Leh | Ladakh | 20 | 20 | 0 |\n| 385 | 0143J0101350 | 34.94800 | 74.13850 | | 4011 | Indus | Muzaffarabad | Jammu & Kashmir | 20 | 20 | 0 |\n| 386 | 03382O120071 | 29.16580 | 95.67410 | Andra Chu | 3771 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 19 | 0 |\n| 387 | 0143E0900912 | 35.90450 | 73.56830 | Doba Gah | 4109 | Indus | Leh | Ladakh | 18 | 18 | 0 |\n| 388 | 0152E1203425 | 35.03170 | 77.69980 | | 5161 | Indus | Leh | Ladakh | 18 | 18 | 0 |\n| 389 | 03082O120228 | 29.23330 | 95.65410 | Rirung Chu | 3915 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 17 | 0 |\n| 390 | 0143K1001546 | 33.54970 | 74.54260 | Rimbiara River | 3912 | Indus | Kulgam | Jammu & Kashmir | 16 | 16 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2274, "line_end": 2289, "token_count_estimate": 968, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Dihang"], "countries": [], "lake_ids": ["00100", "0142H1200268", "0143A1300645", "0143E0900912", "0143J0101350", "0143K1001546", "0143K1001554", "0152E1203425", "03082O080107", "03082O120228", "03170", "03382O120071", "03382P050214", "03391D090733", "03591D160698", "03682L010077", "06180", "08000", "13850", "16580", "20220", "23330", "26310", "32010", "49740", "53630", "54260", "54970", "55470", "56830", "64610", "65410", "67410", "69980", "75260", "83790", "84810", "86870", "89800", "90450", "93970", "94800"]}}
{"id": "3f99e572b893d534", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 391 | 0143E1301021 | 35.90580 | 73.92150 | | 4223 | Indus | Leh | Ladakh | 16 | 16 | 0 |\n| 392 | 03383A100182 | 27.74150 | 92.51540 | Pachuk | 3863 | Brahmaputra | East Kameng | Arunachal Pradesh | 13 | 13 | 0 |\n| 393 | 0143N0902115 | 34.89570 | 75.61670 | | 4547 | Indus | Kargil | Ladakh | 12 | 12 | 0 |\n| 394 | 0143I0401224 | 35.07320 | 74.17650 | | 4042 | Indus | Leh | Ladakh | 12 | 12 | 0 |\n| 395 | 0143I0501239 | 35.82080 | 74.29400 | | 4313 | Indus | Leh | Ladakh | 12 | 12 | 0 |\n| 396 | 03591H041404 | 28.02050 | 97.19390 | | 4040 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 11 | 0 |\n| 397 | 0152H0603708 | 32.72180 | 77.41300 | Bharatpur Nala | 5128 | Indus | Lahul & Spiti | Himachal Pradesh | 10 | 10 | 0 |\n| 398 | 03082O040061 | 29.05800 | 95.23680 | Dihang | 3743 | Brahmaputra | Upper Siang | Arunachal Pradesh | 10 | 10 | 0 |\n| 399 | 03382P050213 | 28.85160 | 95.36850 | Emra River | 3192 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 10 | 0 |\n| 400 | 0143J0101363 | 34.82900 | 74.06200 | | 3681 | Indus | Muzaffarabad | Jammu & Kashmir | 94 | 93 | -1 |\n| 401 | 0143N0201897 | 34.66610 | 75.17940 | Badogam Nar | 4234 | Indus | Leh | Ladakh | 75 | 74 | -1 |\n| 402 | 0143N0301937 | 34.38780 | 75.11850 | Raman Nala | 3663 | Indus | Bandipore | Jammu & Kashmir | 47 | 47 | -1 |\n| 403 | 03082O120243 | 29.19520 | 95.59170 | Rirung Chu | 3708 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 43 | 43 | -1 |\n| 404 | 03182H100356 | 28.59450 | 93.72570 | Subansiri | 4208 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 25 | 25 | -1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2293, "line_end": 2308, "token_count_estimate": 962, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Dihang", "Subansiri"], "countries": [], "lake_ids": ["0143E1301021", "0143I0401224", "0143I0501239", "0143J0101363", "0143N0201897", "0143N0301937", "0143N0902115", "0152H0603708", "02050", "03082O040061", "03082O120243", "03182H100356", "03382P050213", "03383A100182", "03591H041404", "05800", "06200", "07320", "11850", "17650", "17940", "19390", "19520", "23680", "29400", "36850", "38780", "41300", "51540", "59170", "59450", "61670", "66610", "72180", "72570", "74150", "82080", "82900", "85160", "89570", "90580", "92150"]}}
{"id": "7c0cf0e0e4c12005", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 405 | 03391D060637 | 28.72910 | 96.44640 | Thangkung Chu | 4086 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 21 | -1 |\n| 406 | 03383A090169 | 27.83470 | 92.68540 | Kameng | 3951 | Brahmaputra | East Kameng | Arunachal Pradesh | 18 | 18 | -1 |\n| 407 | 03182H100371 | 28.56770 | 93.56270 | Subansiri | 4074 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 16 | 16 | -1 |\n| 408 | 0253I0700018 | 31.26000 | 78.25480 | | 4403 | Ganga | Shimla | Himachal Pradesh | 15 | 15 | -1 |\n| 409 | 03592A142177 | 27.64640 | 96.87790 | | 3263 | Brahmaputra | Changlang | Arunachal Pradesh | 15 | 15 | -1 |\n| 410 | 03391D060681 | 28.56520 | 96.40780 | Thangkung Chu | 4124 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 14 | -1 |\n| 411 | 03391D010517 | 28.81980 | 96.14460 | Thangkung Chu | 3723 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 14 | -1 |\n| 412 | 03683A020714 | 27.58850 | 92.22210 | Mago Chu | 4455 | Brahmaputra | West Kameng | Arunachal Pradesh | 13 | 13 | -1 |\n| 413 | 03082K160032 | 29.22580 | 94.78320 | Nugong Chu | 3683 | Brahmaputra | Upper Siang | Arunachal Pradesh | 13 | 13 | -1 |\n| 414 | 03678M140443 | 27.69720 | 91.87320 | Towang Chu | 4467 | Brahmaputra | Tawang | Arunachal Pradesh | 13 | 13 | -1 |\n| 415 | 0143E1301012 | 35.91660 | 73.94550 | | 4178 | Indus | Leh | Ladakh | 12 | 12 | -1 |\n| 416 | 03382P050195 | 28.91120 | 95.33350 | Emra River | 3789 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 12 | -1 |\n| 417 | 0152J0303822 | 34.39040 | 78.08860 | | 5226 | Indus | Leh | Ladakh | 11 | 11 | -1 |\n| 418 | 0152G1003609 | 33.55560 | 77.66410 | Kuam Lungpa | 5222 | Indus | Leh | Ladakh | 10 | 10 | -1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2313, "line_end": 2328, "token_count_estimate": 963, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0143E1301012", "0152G1003609", "0152J0303822", "0253I0700018", "03082K160032", "03182H100371", "03382P050195", "03383A090169", "03391D010517", "03391D060637", "03391D060681", "03592A142177", "03678M140443", "03683A020714", "08860", "14460", "22210", "22580", "25480", "26000", "33350", "39040", "40780", "44640", "55560", "56270", "56520", "56770", "58850", "64640", "66410", "68540", "69720", "72910", "78320", "81980", "83470", "87320", "87790", "91120", "91660", "94550"]}}
{"id": "625f15cc45646722", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 419 | 03478A090364 | 27.97080 | 88.59330 | Teesta River | 4748 | Brahmaputra | North Sikkim | Sikkim | 10 | 10 | -1 |\n| 420 | 03382O120067 | 29.17230 | 95.73640 | Yonggyap Chu | 3652 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 10 | -1 |\n| 421 | 0143J0901414 | 34.84900 | 74.52290 | Janwai N | 4016 | Indus | Muzaffarabad | Jammu & Kashmir | 23 | 22 | -2 |\n| 422 | 03282D160039 | 28.08720 | 92.93740 | Kameng River | 4328 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 21 | 21 | -2 |\n| 423 | 03082P050304 | 28.89910 | 95.31270 | Yang Sang Chu | 3788 | Brahmaputra | Upper Siang | Arunachal Pradesh | 20 | 20 | -2 |\n| 424 | 03678M090223 | 27.84140 | 91.56500 | Dangme Chu | 4558 | Brahmaputra | Tawang | Arunachal Pradesh | 16 | 16 | -2 |\n| 425 | 0143N1102173 | 34.49110 | 75.64910 | Sando Nala | 4521 | Indus | Kargil | Ladakh | 15 | 15 | -2 |\n| 426 | 03591H041390 | 28.03460 | 97.13190 | | 3977 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 13 | -2 |\n| 427 | 03591H041403 | 28.02410 | 97.17500 | | 4037 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 13 | -2 |\n| 428 | 0143J1501500 | 34.38520 | 74.94420 | Kankanaz N | 3722 | Indus | Ganderbal | Jammu & Kashmir | 11 | 11 | -2 |\n| 429 | 0152B0502787 | 34.81820 | 76.40900 | Rumboka | 4816 | Indus | Kargil | Ladakh | 10 | 10 | -2 |\n| 430 | 03391D060684 | 28.56310 | 96.36600 | Thangkung Chu | 3897 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 10 | -2 |\n| 431 | 03383A090174 | 27.82460 | 92.64680 | Pachuk | 4609 | Brahmaputra | East Kameng | Arunachal Pradesh | 50 | 48 | -3 |\n| 432 | 03391D060630 | 28.74760 | 96.35320 | Thangkung Chu | 3618 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 34 | 33 | -3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2332, "line_end": 2347, "token_count_estimate": 980, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Teesta"], "countries": [], "lake_ids": ["0143J0901414", "0143J1501500", "0143N1102173", "0152B0502787", "02410", "03082P050304", "03282D160039", "03382O120067", "03383A090174", "03391D060630", "03391D060684", "03460", "03478A090364", "03591H041390", "03591H041403", "03678M090223", "08720", "13190", "17230", "17500", "31270", "35320", "36600", "38520", "40900", "49110", "52290", "56310", "56500", "59330", "64680", "64910", "73640", "74760", "81820", "82460", "84140", "84900", "89910", "93740", "94420", "97080"]}}
{"id": "eccdc91a7373d269", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 433 | 03382O120099 | 29.08420 | 95.50130 | Emra River | 3480 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 26 | -3 |\n| 434 | 03282D160045 | 28.06170 | 92.88370 | Kameng River | 4365 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 24 | 23 | -3 |\n| 435 | 03383A060129 | 27.63690 | 92.40740 | Sangli Chu | 4325 | Brahmaputra | West Kameng | Arunachal Pradesh | 22 | 21 | -3 |\n| 436 | 03082O150277 | 29.35250 | 95.90180 | Chendruk Chu | 4212 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 20 | -3 |\n| 437 | 03391D090740 | 28.80680 | 96.53790 | Thangkung Chu | 4187 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 17 | -3 |\n| 438 | 0152C0603061 | 33.50670 | 76.44980 | Hagshu | 4219 | Indus | Kishtwar | Jammu & Kashmir | 16 | 16 | -3 |\n| 439 | 03678M100268 | 27.70220 | 91.63430 | Dangme Chu | 4319 | Brahmaputra | Tawang | Arunachal Pradesh | 16 | 16 | -3 |\n| 440 | 03678A150140 | 27.49630 | 88.79100 | Tangka Chu | 4807 | Brahmaputra | North Sikkim | Sikkim | 16 | 15 | -3 |\n| 441 | 0143J0501374 | 34.87590 | 74.47710 | Kishanganga | 3592 | Indus | Muzaffarabad | Jammu & Kashmir | 12 | 12 | -3 |\n| 442 | 03591H081907 | 28.10650 | 97.31990 | | 4358 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 12 | -3 |\n| 443 | 0143K1001550 | 33.54010 | 74.52310 | Paniul | 3812 | Indus | Punch | Jammu & Kashmir | 10 | 10 | -3 |\n| 444 | 0152B0502780 | 34.83340 | 76.35180 | Gavis | 4818 | Indus | Kargil | Ladakh | 10 | 10 | -3 |\n| 445 | 03592E052241 | 27.98950 | 97.36910 | | 4188 | Brahmaputra | Anjaw | Arunachal Pradesh | 52 | 50 | -4 |\n| 446 | 03592E012189 | 27.99650 | 97.18700 | | 4091 | Brahmaputra | Anjaw | Arunachal Pradesh | 41 | 39 | -4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2352, "line_end": 2367, "token_count_estimate": 1005, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["0143J0501374", "0143K1001550", "0152B0502780", "0152C0603061", "03082O150277", "03282D160045", "03382O120099", "03383A060129", "03391D090740", "03591H081907", "03592E012189", "03592E052241", "03678A150140", "03678M100268", "06170", "08420", "10650", "18700", "31990", "35180", "35250", "36910", "40740", "44980", "47710", "49630", "50130", "50670", "52310", "53790", "54010", "63430", "63690", "70220", "79100", "80680", "83340", "87590", "88370", "90180", "98950", "99650"]}}
{"id": "be87400059391518", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 447 | 0142P1100571 | 36.43960 | 75.68600 | | 4709 | Indus | Leh | Ladakh | 28 | 27 | -4 |\n| 448 | 03182H070345 | 28.29710 | 93.41940 | Yu me chu | 3841 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 21 | 20 | -4 |\n| 449 | 0152B0502784 | 34.82740 | 76.45580 | | 4648 | Indus | Leh | Ladakh | 20 | 19 | -4 |\n| 450 | 03391D060640 | 28.72700 | 96.39150 | Thangkung Chu | 4176 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 19 | -4 |\n| 451 | 03478A100500 | 27.66210 | 88.68950 | Rabom Chu | 4298 | Brahmaputra | North Sikkim | Sikkim | 16 | 15 | -4 |\n| 452 | 03182H060324 | 28.51990 | 93.37710 | Yu me chu | 4247 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 14 | 13 | -4 |\n| 453 | 03391D050627 | 28.76510 | 96.38640 | Thangkung Chu | 3855 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 13 | -4 |\n| 454 | 0143I1601334 | 35.07460 | 74.95810 | | 4413 | Indus | Leh | Ladakh | 10 | 10 | -4 |\n| 455 | 03592E052283 | 27.87930 | 97.36050 | | 4158 | Brahmaputra | Anjaw | Arunachal Pradesh | 40 | 38 | -5 |\n| 456 | 0143I1601333 | 35.08220 | 74.96120 | Thue Gah | 4317 | Indus | Leh | Ladakh | 31 | 29 | -5 |\n| 457 | 03391D060663 | 28.65960 | 96.48590 | Thangkung Chu | 4125 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 29 | -5 |\n| 458 | 03391D050590 | 28.87860 | 96.37180 | Thangkung Chu | 4090 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 26 | -5 |\n| 459 | 03082P050294 | 28.96410 | 95.25200 | Yang Sang Chu | 3717 | Brahmaputra | Upper Siang | Arunachal Pradesh | 20 | 19 | -5 |\n| 460 | 03391D060641 | 28.72500 | 96.42220 | Thangkung Chu | 4218 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 16 | -5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2371, "line_end": 2386, "token_count_estimate": 1003, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0142P1100571", "0143I1601333", "0143I1601334", "0152B0502784", "03082P050294", "03182H060324", "03182H070345", "03391D050590", "03391D050627", "03391D060640", "03391D060641", "03391D060663", "03478A100500", "03592E052283", "07460", "08220", "25200", "29710", "36050", "37180", "37710", "38640", "39150", "41940", "42220", "43960", "45580", "48590", "51990", "65960", "66210", "68600", "68950", "72500", "72700", "76510", "82740", "87860", "87930", "95810", "96120", "96410"]}}
{"id": "a8241a68ac9edf25", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 461 | 0143K1001538 | 33.73460 | 74.52350 | Sainmarg Nar | 4064 | Indus | Badgam | Jammu & Kashmir | 14 | 13 | -5 |\n| 462 | 03391D060639 | 28.72730 | 96.48540 | Thangkung Chu | 4084 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 29 | 27 | -6 |\n| 463 | 0143E1601082 | 35.24700 | 73.79950 | | 3988 | Indus | Leh | Ladakh | 17 | 16 | -6 |\n| 464 | 03082K120012 | 29.17670 | 94.52570 | Rigong Chu | 4258 | Brahmaputra | Upper Siang | Arunachal Pradesh | 15 | 14 | -6 |\n| 465 | 03391C030294 | 29.28330 | 96.09110 | Jairu Chu | 4204 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 12 | -6 |\n| 466 | 0143O0502270 | 33.95200 | 75.41070 | Sangam Nala | 4082 | Indus | Kishtwar | Jammu & Kashmir | 27 | 25 | -7 |\n| 467 | 0143N0902117 | 34.89300 | 75.71250 | | 4589 | Indus | Kargil | Ladakh | 19 | 18 | -7 |\n| 468 | 03682L050088 | 28.95330 | 94.38290 | Siyom | 3858 | Brahmaputra | Upper Siang | Arunachal Pradesh | 19 | 18 | -7 |\n| 469 | 03082K112004 | 29.30770 | 94.63690 | Lushar pu chu | 4142 | Brahmaputra | Upper Siang | Arunachal Pradesh | 15 | 14 | -7 |\n| 470 | 03382P050193 | 28.91680 | 95.33970 | Emra River | 3901 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 10 | -7 |\n| 471 | 03678M140517 | 27.62470 | 91.80340 | Towang Chu | 4329 | Brahmaputra | Tawang | Arunachal Pradesh | 11 | 10 | -7 |\n| 472 | 03591H041374 | 28.05830 | 97.22590 | | 4113 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 17 | -8 |\n| 473 | 03182H060310 | 28.54110 | 93.37990 | Subansiri | 4307 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 16 | 15 | -8 |\n| 474 | 0143J1301448 | 34.84640 | 74.78660 | Reat | 3883 | Indus | Leh | Ladakh | 14 | 13 | -8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2393, "line_end": 2408, "token_count_estimate": 990, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0143E1601082", "0143J1301448", "0143K1001538", "0143N0902117", "0143O0502270", "03082K112004", "03082K120012", "03182H060310", "03382P050193", "03391C030294", "03391D060639", "03591H041374", "03678M140517", "03682L050088", "05830", "09110", "17670", "22590", "24700", "28330", "30770", "33970", "37990", "38290", "41070", "48540", "52350", "52570", "54110", "62470", "63690", "71250", "72730", "73460", "78660", "79950", "80340", "84640", "89300", "91680", "95200", "95330"]}}
{"id": "bd0197f492f7270e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 475 | 0152B0502816 | 34.76200 | 76.46860 | Yaldor | 4895 | Indus | Leh | Ladakh | 18 | 16 | -9 |\n| 476 | 03082P050299 | 28.92240 | 95.32290 | Yang Sang Chu | 3841 | Brahmaputra | Upper Siang | Arunachal Pradesh | 16 | 15 | -9 |\n| 477 | 03391D060633 | 28.73990 | 96.41340 | Thangkung Chu | 4105 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 11 | -9 |\n| 478 | 0143M0401664 | 35.11900 | 75.22790 | | 4586 | Indus | Leh | Ladakh | 18 | 16 | -10 |\n| 479 | 03383A130216 | 27.90420 | 92.80980 | Kameng | 4306 | Brahmaputra | East Kameng | Arunachal Pradesh | 12 | 11 | -10 |\n| 480 | 03592E012220 | 27.94750 | 97.13110 | | 3819 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 10 | -10 |\n| 481 | 03382P060219 | 28.74640 | 95.39840 | Chichi River | 3787 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 10 | -10 |\n| 482 | 03182H140401 | 28.61620 | 93.81990 | Subansiri | 3590 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 46 | 41 | -11 |\n| 483 | 03682H130005 | 28.76280 | 93.98650 | | 3957 | Brahmaputra | West Siang | Arunachal Pradesh | 20 | 18 | -11 |\n| 484 | 0143K1001537 | 33.74020 | 74.52580 | Sainmarg Nar | 4018 | Indus | Badgam | Jammu & Kashmir | 10 | 9 | -11 |\n| 485 | 0143J0101353 | 34.90170 | 74.04840 | | 3896 | Indus | Muzaffarabad | Jammu & Kashmir | 22 | 19 | -12 |\n| 486 | 0152B0502771 | 34.85410 | 76.38270 | Rumboka | 4688 | Indus | Kargil | Ladakh | 14 | 12 | -12 |\n| 487 | 03182H140408 | 28.58270 | 93.81160 | Subansiri | 3799 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 11 | 10 | -12 |\n| 488 | 03391D050598 | 28.86240 | 96.44010 | Thangkung Chu | 2979 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 10 | -12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2410, "line_end": 2425, "token_count_estimate": 981, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0143J0101353", "0143K1001537", "0143M0401664", "0152B0502771", "0152B0502816", "03082P050299", "03182H140401", "03182H140408", "03382P060219", "03383A130216", "03391D050598", "03391D060633", "03592E012220", "03682H130005", "04840", "11900", "13110", "22790", "32290", "38270", "39840", "41340", "44010", "46860", "52580", "58270", "61620", "73990", "74020", "74640", "76200", "76280", "80980", "81160", "81990", "85410", "86240", "90170", "90420", "92240", "94750", "98650"]}}
{"id": "c05c7cfa73709435", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 489 | 03391D010492 | 28.88670 | 96.19690 | Dri Chu | 3255 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 44 | 38 | -13 |\n| 490 | 03391D050623 | 28.80180 | 96.44020 | Thangkung Chu | 4114 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 27 | -13 |\n| 491 | 0152B0902844 | 34.81260 | 76.51720 | | 4771 | Indus | Leh | Ladakh | 21 | 18 | -13 |\n| 492 | 03382O160153 | 29.20220 | 95.79670 | Aison Chu | 4054 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 13 | -13 |\n| 493 | 03591H041399 | 28.02540 | 97.18630 | | 4227 | Brahmaputra | Anjaw | Arunachal Pradesh | 15 | 13 | -13 |\n| 494 | 03382O120059 | 29.20770 | 95.74160 | Aison Chu | 3913 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 9 | -14 |\n| 495 | 03382O160130 | 29.25130 | 95.95710 | Aison Chu | 3738 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 9 | -14 |\n| 496 | 03682L050087 | 28.96320 | 94.37220 | Siyom | 4145 | Brahmaputra | Upper Siang | Arunachal Pradesh | 19 | 16 | -16 |\n| 497 | 03082H101688 | 28.67210 | 93.72350 | Nelung phu chu | 4361 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | 10 | -17 |\n| 498 | 03682L010076 | 28.75390 | 94.13350 | Siyom | 4057 | Brahmaputra | West Siang | Arunachal Pradesh | 11 | 9 | -18 |\n| 499 | 03391C040367 | 29.14530 | 96.06630 | Dri Chu | 3949 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 25 | -19 |\n| 500 | 03391D060711 | 28.52130 | 96.24980 | Thangkung Chu | 3636 | Brahmaputra | Lower Dibang Valley | Arunachal Pradesh | 16 | 13 | -19 |\n| 501 | 03391D010469 | 28.98940 | 96.18810 | Dri Chu | 4316 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 9 | -20 |\n| 502 | 0253N0900235 | 30.90100 | 79.74620 | | 4689 | Ganga | Chamoli | Uttarakhand | 11 | 9 | -21 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2430, "line_end": 2445, "token_count_estimate": 1018, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["0152B0902844", "0253N0900235", "02540", "03082H101688", "03382O120059", "03382O160130", "03382O160153", "03391C040367", "03391D010469", "03391D010492", "03391D050623", "03391D060711", "03591H041399", "03682L010076", "03682L050087", "06630", "13350", "14530", "18630", "18810", "19690", "20220", "20770", "24980", "25130", "37220", "44020", "51720", "52130", "67210", "72350", "74160", "74620", "75390", "79670", "80180", "81260", "88670", "90100", "95710", "96320", "98940"]}}
{"id": "856785ff4375b844", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 503 | 03182H110392 | 28.45770 | 93.57910 | Subansiri | 3922 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 48 | 37 | -22 |\n| 504 | 03382P050202 | 28.87460 | 95.33410 | Emra River | 3756 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 16 | -22 |\n| 505 | 03391D050617 | 28.82330 | 96.35900 | Thangkung Chu | 3941 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 15 | -22 |\n| 506 | 03591H081932 | 28.05280 | 97.28130 | | 4499 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 8 | -24 |\n| 507 | 03391D060669 | 28.61730 | 96.43940 | Thangkung Chu | 3914 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 8 | -25 |\n| 508 | 0152L0704043 | 32.36300 | 78.27230 | | 5357 | Indus | Lahul & Spiti | Himachal Pradesh | 14 | 10 | -26 |\n| 509 | 03591H041413 | 28.00820 | 97.17630 | | 4240 | Brahmaputra | Anjaw | Arunachal Pradesh | 15 | 11 | -29 |\n| 510 | 03391C040319 | 29.24140 | 96.07370 | Jairu Chu | 4199 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 9 | -30 |\n| 511 | 03591H081936 | 28.04450 | 97.29260 | | 4472 | Brahmaputra | Anjaw | Arunachal Pradesh | 10 | 7 | -33 |\n| 512 | 03478A100496 | 27.66800 | 88.68670 | Teesta River | 4117 | Brahmaputra | North Sikkim | Sikkim | 15 | 9 | -39 |\n| 513 | 03182H070344 | 28.30470 | 93.43720 | Yu me chu | 3952 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 35 | 20 | -43 |\n| 514 | 03082O150278 | 29.35240 | 95.91530 | Chendruk Chu | 4357 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 46 | 25 | -46 |\n| 515 | 0152E1603441 | 35.06000 | 77.85600 | | 4679 | Indus | Leh | Ladakh | 33 | 17 | -48 |\n| 516 | 0152A0602485 | 35.72050 | 76.37520 | | 4140 | Indus | Leh | Ladakh | 12 | 6 | -51 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2449, "line_end": 2464, "token_count_estimate": 1004, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang", "Subansiri", "Teesta"], "countries": [], "lake_ids": ["00820", "0152A0602485", "0152E1603441", "0152L0704043", "03082O150278", "03182H070344", "03182H110392", "03382P050202", "03391C040319", "03391D050617", "03391D060669", "03478A100496", "03591H041413", "03591H081932", "03591H081936", "04450", "05280", "06000", "07370", "17630", "24140", "27230", "28130", "29260", "30470", "33410", "35240", "35900", "36300", "37520", "43720", "43940", "45770", "57910", "61730", "66800", "68670", "72050", "82330", "85600", "87460", "91530"]}}
{"id": "ccb433aebda3a822", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2023 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 517 | 0143I1501322 | 35.31510 | 74.93680 | | 4197 | Indus | Leh | Ladakh | 20 | 8 | -60 |\n| 518 | 0143N0802049 | 34.19270 | 75.32050 | | 3734 | Indus | Anantnag | Jammu & Kashmir | 11 | 2 | -81 |\n| 519 | 0142P0400481 | 36.13720 | 75.13630 | Hunza | 3697 | Indus | Leh | Ladakh | 25 | # | # |\n| 520 | 0142P0400492 | 36.12640 | 75.13970 | Hunza | 3725 | Indus | Leh | Ladakh | 13 | # | # |\n| 521 | 0152C0503044 | 33.84360 | 76.37530 | Pholohiow | 4116 | Indus | Kargil | Ladakh | 18 | # | # |\n| 522 | 0152L1504075 | 32.49150 | 78.85240 | Handle River | 5706 | Indus | Leh | Ladakh | 11 | # | # |\n| 523 | 0152A0602465 | 35.72980 | 76.41010 | | 4161 | Indus | Leh | Ladakh | 13 | # | # |\n| 524 | 0143N0401979 | 34.06290 | 75.21560 | Tson Nar | 3691 | Indus | Anantnag | Jammu & Kashmir | 13 | # | # |\n| 525 | 0143O0602296 | 33.61860 | 75.53680 | Zarnag Nala | 3852 | Indus | Kishtwar | Jammu & Kashmir | 11 | # | # |\n| 526 | 0152E1103389 | 35.47590 | 77.51350 | | 5342 | Indus | Leh | Ladakh | 22 | # | # |\n| 527 | 0153I1404650 | 31.55360 | 78.75110 | | 5276 | Indus | Kinnaur | Himachal Pradesh | 11 | # | # |\n| 528 | 03391C040435 | 29.00930 | 96.08370 | Dri Chu | 4018 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | # | # |\n| 529 | 03391D020537 | 28.54180 | 96.12500 | Thangkung Chu | 4031 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | # | # |\n| 530 | 03391D050624 | 28.80140 | 96.48430 | Thangkung Chu | 3819 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 35 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2023 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2469, "line_end": 2484, "token_count_estimate": 947, "basins": ["Brahmaputra", "Indus"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["00930", "0142P0400481", "0142P0400492", "0143I1501322", "0143N0401979", "0143N0802049", "0143O0602296", "0152A0602465", "0152C0503044", "0152E1103389", "0152L1504075", "0153I1404650", "03391C040435", "03391D020537", "03391D050624", "06290", "08370", "12500", "12640", "13630", "13720", "13970", "19270", "21560", "31510", "32050", "37530", "41010", "47590", "48430", "49150", "51350", "53680", "54180", "55360", "61860", "72980", "75110", "80140", "84360", "85240", "93680"]}}
{"id": "fd3ae26fb0324850", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2023 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 531 | 03391D090727 | 28.87230 | 96.51030 | Thangkung Chu | 3926 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | # | # |\n| 532 | 03391D090741 | 28.80060 | 96.51250 | Thangkung Chu | 4344 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 26 | # | # |\n| 533 | 03182H110391 | 28.45860 | 93.60540 | Subansiri | 3957 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 534 | 03182H140406 | 28.58680 | 93.83940 | Subansiri | 4296 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 11 | # | # |\n| 535 | 03591D150678 | 28.31460 | 96.81730 | Dzayul chu | 3879 | Brahmaputra | Anjaw | Arunachal Pradesh | 20 | # | # |\n| 536 | 03591D150686 | 28.25300 | 96.81940 | Dzayul chu | 4030 | Brahmaputra | Anjaw | Arunachal Pradesh | 16 | # | # |\n| 537 | 03591D160702 | 28.16450 | 96.83050 | Gediu chu | 3890 | Brahmaputra | Anjaw | Arunachal Pradesh | 21 | # | # |\n| 538 | 03592E012191 | 27.98990 | 97.09780 | | 3687 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | # | # |\n| 539 | 03082K081990 | 29.06890 | 94.30030 | Neyul pu chu | 4435 | Brahmaputra | Upper Siang | Arunachal Pradesh | 13 | # | # |\n| 540 | 03391C040346 | 29.19740 | 96.19240 | Dri Chu | 4321 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | # | # |\n| 541 | 03391C030291 | 29.28790 | 96.13440 | Jairu Chu | 4045 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | # | # |\n| 542 | 03182H110385 | 28.48300 | 93.60300 | Subansiri | 3987 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 543 | 03382O160146 | 29.22890 | 95.96160 | Aison Chu | 4328 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | # | # |\n| 544 | 03391C040355 | 29.18290 | 96.08690 | Dri Chu | 4263 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 24 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2023 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2488, "line_end": 2503, "token_count_estimate": 993, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["03082K081990", "03182H110385", "03182H110391", "03182H140406", "03382O160146", "03391C030291", "03391C040346", "03391C040355", "03391D090727", "03391D090741", "03591D150678", "03591D150686", "03591D160702", "03592E012191", "06890", "08690", "09780", "13440", "16450", "18290", "19240", "19740", "22890", "25300", "28790", "30030", "31460", "45860", "48300", "51030", "51250", "58680", "60300", "60540", "80060", "81730", "81940", "83050", "83940", "87230", "96160", "98990"]}}
{"id": "1f37bf61e52cfe84", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 545 | 03182H070342 | 28.31310 | 93.39150 | Yu me chu | 4066 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 20 | # | # |\n| 546 | 03282H070072 | 28.29400 | 93.35480 | | 3597 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 10 | # | # |\n| 547 | 03391C040369 | 29.14140 | 96.03630 | Dri Chu | 4139 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 30 | # | # |\n| 548 | 03678M090241 | 27.80160 | 91.64290 | Dangme Chu | 4504 | Brahmaputra | Tawang | Arunachal Pradesh | 12 | # | # |\n| 549 | 03391C030262 | 29.31530 | 96.12530 | Jairu Chu | 3996 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | # | # |\n| 550 | 03478A060337 | 27.67790 | 88.37600 | Lumthul Chu | 3915 | Brahmaputra | North Sikkim | Sikkim | 18 | # | # |\n| 551 | 03391D050585 | 28.89040 | 96.49440 | Thangkung Chu | 3366 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | # | # |\n| 552 | 03391C040313 | 29.24930 | 96.02800 | Aison Chu | 4133 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | # | # |\n| 553 | 03478A010083 | 27.85180 | 88.23980 | Bhuthang Chu | 5190 | Brahmaputra | North Sikkim | Sikkim | 31 | # | # |\n| 554 | 03182H060321 | 28.52390 | 93.39400 | Yu me chu | 4249 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 555 | 03592E052239 | 27.99360 | 97.31550 | | 4270 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | # | # |\n| 556 | 03082P050307 | 28.87560 | 95.31600 | Yang Sang Chu | 3847 | Brahmaputra | Upper Siang | Arunachal Pradesh | 15 | # | # |\n| 557 | 03592A092125 | 27.81640 | 96.70680 | | 3488 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | # | # |\n| 558 | 03592E052276 | 27.89270 | 97.35850 | | 3931 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2508, "line_end": 2523, "token_count_estimate": 1010, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["02800", "03082P050307", "03182H060321", "03182H070342", "03282H070072", "03391C030262", "03391C040313", "03391C040369", "03391D050585", "03478A010083", "03478A060337", "03592A092125", "03592E052239", "03592E052276", "03630", "03678M090241", "12530", "14140", "23980", "24930", "29400", "31310", "31530", "31550", "31600", "35480", "35850", "37600", "39150", "39400", "49440", "52390", "64290", "67790", "70680", "80160", "81640", "85180", "87560", "89040", "89270", "99360"]}}
{"id": "94a2748da7b7951e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 559 | 03592A142140 | 27.73240 | 96.87060 | | 4012 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | # | # |\n| 560 | 03391D050593 | 28.87540 | 96.39450 | Thangkung Chu | 3119 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 41 | # | # |\n| 561 | 03391D050594 | 28.87410 | 96.43580 | Thangkung Chu | 3220 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | # | # |\n| 562 | 03082P020293 | 28.72040 | 95.16010 | Dihang | 3640 | Brahmaputra | Upper Siang | Arunachal Pradesh | 22 | # | # |\n| 563 | 03478A150633 | 27.44210 | 88.75240 | Bakcha Chu | 4055 | Brahmaputra | North Sikkim | Sikkim | 11 | # | # |\n| 564 | 03391D060629 | 28.74900 | 96.37490 | Thangkung Chu | 3784 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 26 | # | # |\n| 565 | 03478A150630 | 27.47720 | 88.77570 | Glong Chu | 4633 | Brahmaputra | North Sikkim | Sikkim | 11 | # | # |\n| 566 | 03478A090356 | 27.99060 | 88.60250 | Teesta River | 4778 | Brahmaputra | North Sikkim | Sikkim | 10 | # | # |\n| 567 | 03182H100359 | 28.58530 | 93.74300 | Subansiri | 3758 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 11 | # | # |\n| 568 | 03182H100368 | 28.57570 | 93.57830 | Subansiri | 3842 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 37 | # | # |\n| 569 | 03678M090251 | 27.75720 | 91.65040 | Dangme Chu | 4152 | Brahmaputra | Tawang | Arunachal Pradesh | 17 | # | # |\n| 570 | 03682H140058 | 28.56230 | 93.88680 | | 3938 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 13 | # | # |\n| 571 | 03182H100349 | 28.66780 | 93.73410 | Subansiri | 4016 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 20 | # | # |\n| 572 | 03182H100360 | 28.58470 | 93.71210 | Subansiri | 3873 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 14 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 14, "line_start": 2527, "line_end": 2542, "token_count_estimate": 1017, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Dihang", "Subansiri", "Teesta"], "countries": [], "lake_ids": ["03082P020293", "03182H100349", "03182H100359", "03182H100360", "03182H100368", "03391D050593", "03391D050594", "03391D060629", "03478A090356", "03478A150630", "03478A150633", "03592A142140", "03678M090251", "03682H140058", "16010", "37490", "39450", "43580", "44210", "47720", "56230", "57570", "57830", "58470", "58530", "60250", "65040", "66780", "71210", "72040", "73240", "73410", "74300", "74900", "75240", "75720", "77570", "87060", "87410", "87540", "88680", "99060"]}}
{"id": "06a5406aac95b74c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation (m) | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 573 | 03082L050034 | 28.97820 | 94.39780 | Pite | 3111 | Brahmaputra | Upper Siang | Arunachal Pradesh | 25 | # | # |\n| 574 | 03682H140047 | 28.60610 | 93.84530 | | 4022 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 575 | 03682L010072 | 28.75690 | 94.15250 | Siyom | 3875 | Brahmaputra | West Siang | Arunachal Pradesh | 13 | # | # |\n| 576 | 03282D160025 | 28.11710 | 92.96890 | | 4214 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 18 | # | # |\n| 577 | 03478A050302 | 27.75880 | 88.48350 | Zemu Chu | 4467 | Brahmaputra | North Sikkim | Sikkim | 14 | # | # |\n| 578 | 03391C040381 | 29.10000 | 96.17310 | Dri Chu | 3949 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | # | # |\n| 579 | 03391D050578 | 28.92400 | 96.36800 | Thangkung Chu | 4109 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | # | # |\n| 580 | 03591D160688 | 28.24510 | 96.83170 | Dzayul chu | 3900 | Brahmaputra | Anjaw | Arunachal Pradesh | 43 | # | # |\n| 581 | 03391C030257 | 29.32490 | 96.11400 | Jairu Chu | 3962 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation (m)", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 9, "line_start": 2547, "line_end": 2557, "token_count_estimate": 716, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["03082L050034", "03282D160025", "03391C030257", "03391C040381", "03391D050578", "03478A050302", "03591D160688", "03682H140047", "03682L010072", "10000", "11400", "11710", "15250", "17310", "24510", "32490", "36800", "39780", "48350", "60610", "75690", "75880", "83170", "84530", "92400", "96890", "97820"]}}
{"id": "1c94ee78d1859c56", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n\n- GLs/WBs with increase in Area > 40%\n- GLs/WBs with increase in Area – 0% to 40%\n- GLs/WBs with no change in Area\n- GLs/WBs with decrease in Area\n- GLs/WBs not analysed", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 2558, "line_end": 2570, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "da38e715f33da876", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.11: Results of analysis of newly monitored 1360 GLs in Trans-boundary region as per Glacial lake Atlas 2023 with water spread area greater than 10ha\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0272I1304024 | 27.7661 | 86.8705 | | 4931 | Ganga | Transboundary | 14 | 70 | 412 |\n| 2 | 0271H0602078 | 28.5589 | 85.3330 | | 4973 | Ganga | Transboundary | 13 | 57 | 352 |\n| 3 | 0271L1102963 | 28.2832 | 86.5024 | | 5157 | Ganga | Transboundary | 23 | 72 | 218 |\n| 4 | 0272I0503642 | 27.9271 | 86.4196 | | 5020 | Ganga | Transboundary | 16 | 43 | 174 |\n| 5 | 03882N030334 | 30.4817 | 95.0322 | Yigrong chu | 4335 | Brahmaputra | Transboundary | 15 | 37 | 149 |\n| 6 | 03982F160664 | 30.2000 | 93.7983 | Wortse chu | 4195 | Brahmaputra | Transboundary | 20 | 48 | 135 |\n| 7 | 03982F011318 | 30.9027 | 93.0181 | Tsenrak chu | 4957 | Brahmaputra | Transboundary | 11 | 26 | 129 |\n| 8 | 0271H1602501 | 28.1506 | 85.9053 | | 4495 | Ganga | Transboundary | 13 | 28 | 122 |\n| 9 | 03991C020517 | 29.5794 | 96.1613 | Nagong Chu | 3874 | Brahmaputra | Transboundary | 13 | 28 | 115 |\n| 10 | 0262K0100917 | 29.9282 | 82.2345 | | 4236 | Ganga | Transboundary | 12 | 24 | 96 |\n| 11 | 03882N030347 | 30.3950 | 95.0819 | Yigrong chu | 4151 | Brahmaputra | Transboundary | 12 | 24 | 96 |\n| 12 | 03982N110267 | 30.3261 | 95.6114 | Poto Chu | 4916 | Brahmaputra | Transboundary | 17 | 32 | 85 |\n| 13 | 03982F150661 | 30.2609 | 93.7635 | Wortse chu | 4019 | Brahmaputra | Transboundary | 65 | 119 | 84 |\n| 14 | 03177L120127 | 28.0785 | 90.7431 | Longdo Chu | 5172 | Brahmaputra | Transboundary | 18 | 31 | 74 |\n| 15 | 03591C150243 | 29.4622 | 96.7869 | Zo chu | 3916 | Brahmaputra | Transboundary | 518 | 882 | 70 |\n| 16 | 03582K050072 | 29.9544 | 94.3203 | Numphu chu | 4232 | Brahmaputra | Transboundary | 10 | 17 | 70 |\n| 17 | 0262F1100543 | 30.2543 | 81.6894 | | 4264 | Ganga | Transboundary | 28 | 47 | 69 |\n| 18 | 03362J070254 | 30.4065 | 82.3549 | | 4870 | Brahmaputra | Transboundary | 23 | 39 | 68 |\n| 19 | 0271L1102952 | 28.3209 | 86.5109 | | 5321 | Ganga | Transboundary | 11 | 18 | 67 |\n| 20 | 03082O080081 | 29.1632 | 95.4906 | Shumo Chu | 3544 | Brahmaputra | Transboundary | 35 | 58 | 67 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.11: Results of analysis of newly monitored 1360 GLs in Trans-boundary region as per Glacial lake Atlas 2023 with water spread area greater than 10ha", "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 20, "line_start": 2571, "line_end": 2592, "token_count_estimate": 1233, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262F1100543", "0262K0100917", "0271H0602078", "0271H1602501", "0271L1102952", "0271L1102963", "0272I0503642", "0272I1304024", "03082O080081", "03177L120127", "03362J070254", "03582K050072", "03591C150243", "03882N030334", "03882N030347", "03982F011318", "03982F150661", "03982F160664", "03982N110267", "03991C020517"]}}
{"id": "171c20ee574689f3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 21 | 0271P1003474 | 28.7121 | 87.5483 | | 5188 | Ganga | Transboundary | 41 | 68 | 65 |\n| 22 | 0262F0700489 | 30.3189 | 81.4446 | | 5497 | Ganga | Transboundary | 12 | 20 | 64 |\n| 23 | 0271H1502460 | 28.2605 | 85.9146 | | 5106 | Ganga | Transboundary | 16 | 26 | 63 |\n| 24 | 03982C060243 | 29.6006 | 92.4900 | Kam chu | 4951 | Brahmaputra | Transboundary | 16 | 26 | 63 |\n| 25 | 0162E0304961 | 31.4421 | 81.1568 | | 5240 | Indus | Transboundary | 13 | 21 | 61 |\n| 26 | 0162J0205311 | 30.5917 | 82.0839 | | 5382 | Indus | Transboundary | 10 | 16 | 58 |\n| 27 | 03177L160250 | 28.0213 | 90.8398 | | 4985 | Brahmaputra | Transboundary | 16 | 25 | 58 |\n| 28 | 0278A0104638 | 27.9428 | 88.0410 | | 5821 | Ganga | Transboundary | 10 | 16 | 55 |\n| 29 | 03882J070276 | 30.4313 | 94.4548 | Yigrong chu | 3902 | Brahmaputra | Transboundary | 13 | 20 | 54 |\n| 30 | 03591C140229 | 29.5016 | 96.9773 | Tsengo chu | 5145 | Brahmaputra | Transboundary | 12 | 19 | 53 |\n| 31 | 0271H1002160 | 28.5732 | 85.6593 | | 5238 | Ganga | Transboundary | 11 | 16 | 51 |\n| 32 | 03991C060601 | 29.6256 | 96.4703 | Nagong Chu | 4364 | Brahmaputra | Transboundary | 13 | 19 | 51 |\n| 33 | 03882J030243 | 30.2619 | 94.1342 | Yigrong chu | 4161 | Brahmaputra | Transboundary | 13 | 19 | 51 |\n| 34 | 03982B110877 | 30.4944 | 92.6456 | Tsenrak chu | 4817 | Brahmaputra | Transboundary | 98 | 147 | 50 |\n| 35 | 03082G121325 | 29.0249 | 93.5673 | Yarlung tsangpo | 4553 | Brahmaputra | Transboundary | 11 | 16 | 50 |\n| 36 | 03362J030049 | 30.4605 | 82.1079 | | 5177 | Brahmaputra | Transboundary | 17 | 25 | 49 |\n| 37 | 03362O021576 | 29.6321 | 83.1514 | | 5367 | Brahmaputra | Transboundary | 21 | 31 | 48 |\n| 38 | 03982N100155 | 30.5139 | 95.6695 | Yu Chu | 4761 | Brahmaputra | Transboundary | 16 | 24 | 48 |\n| 39 | 03678M090138 | 27.9959 | 91.5108 | Yombu Chu | 5319 | Brahmaputra | Transboundary | 12 | 18 | 48 |\n| 40 | 03582J080013 | 30.1417 | 94.3013 | Nunkhu Phu chu | 4405 | Brahmaputra | Transboundary | 11 | 17 | 48 |\n| 41 | 03178M050623 | 27.9962 | 91.2871 | | 4522 | Brahmaputra | Transboundary | 18 | 26 | 46 |\n| 42 | 03878I050884 | 27.9920 | 90.2993 | Pho Chu | 5345 | Brahmaputra | Transboundary | 12 | 17 | 45 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2599, "line_end": 2622, "token_count_estimate": 1265, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0162E0304961", "0162J0205311", "0262F0700489", "0271H1002160", "0271H1502460", "0271P1003474", "0278A0104638", "03082G121325", "03177L160250", "03178M050623", "03362J030049", "03362O021576", "03582J080013", "03591C140229", "03678M090138", "03878I050884", "03882J030243", "03882J070276", "03982B110877", "03982C060243", "03982N100155", "03991C060601"]}}
{"id": "0016b128a7d2f25f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 43 | 03591C160326 | 29.0712 | 96.7985 | Yamda chu | 2466 | Brahmaputra | Transboundary | 39 | 56 | 44 |\n| 44 | 03082O120240 | 29.2000 | 95.5049 | Shumo Chu | 4104 | Brahmaputra | Transboundary | 24 | 35 | 44 |\n| 45 | 0262F0700453 | 30.4047 | 81.4273 | | 5472 | Ganga | Transboundary | 11 | 16 | 43 |\n| 46 | 0153I1304639 | 31.9187 | 78.7839 | | 5351 | Indus | Transboundary | 13 | 19 | 41 |\n| 47 | 03077L110243 | 28.4536 | 90.6218 | Yidam Chu | 5286 | Brahmaputra | Transboundary | 19 | 27 | 40 |\n| 48 | 0152P1104322 | 32.3991 | 79.6221 | Indus River | 5058 | Indus | Transboundary | 24 | 33 | 39 |\n| 49 | 03177L120130 | 28.0777 | 90.6608 | Longdo Chu | 5074 | Brahmaputra | Transboundary | 10 | 14 | 39 |\n| 50 | 0262F1200548 | 30.3783 | 81.5742 | | 5097 | Ganga | Transboundary | 22 | 31 | 38 |\n| 51 | 03582J080008 | 30.1754 | 94.4085 | Nunkhu Phu chu | 3566 | Brahmaputra | Transboundary | 28 | 38 | 36 |\n| 52 | 03082O080097 | 29.1437 | 95.4930 | Shumo Chu | 3673 | Brahmaputra | Transboundary | 18 | 24 | 36 |\n| 53 | 03077P130226 | 28.8019 | 91.9368 | Yarlung tsangpo | 5207 | Brahmaputra | Transboundary | 12 | 16 | 36 |\n| 54 | 0162A1004888 | 31.6937 | 80.6644 | Indus River | 5532 | Indus | Transboundary | 14 | 19 | 35 |\n| 55 | 0162F1505243 | 30.4625 | 81.9930 | | 5161 | Indus | Transboundary | 21 | 28 | 35 |\n| 56 | 03177P060376 | 28.5178 | 91.4870 | | 5125 | Brahmaputra | Transboundary | 16 | 22 | 35 |\n| 57 | 03878I010683 | 27.9850 | 90.1490 | Pho Chu | 4911 | Brahmaputra | Transboundary | 14 | 19 | 35 |\n| 58 | 0262P0101651 | 28.9590 | 83.1872 | | 5071 | Ganga | Transboundary | 28 | 37 | 34 |\n| 59 | 03878I050886 | 27.9635 | 90.2590 | Pho Chu | 5226 | Brahmaputra | Transboundary | 34 | 45 | 34 |\n| 60 | 03362O112320 | 29.2681 | 83.6176 | | 5168 | Brahmaputra | Transboundary | 31 | 41 | 34 |\n| 61 | 03591H071828 | 28.2741 | 97.2534 | | 4290 | Brahmaputra | Transboundary | 21 | 28 | 34 |\n| 62 | 0271P0803345 | 28.2132 | 87.4704 | | 4781 | Ganga | Transboundary | 131 | 175 | 33 |\n| 63 | 0271P1003461 | 28.7470 | 87.6246 | | 5545 | Ganga | Transboundary | 17 | 23 | 33 |\n| 64 | 03177P030309 | 28.3512 | 91.0787 | Lhodrak Nub chu | 4787 | Brahmaputra | Transboundary | 77 | 103 | 33 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2626, "line_end": 2649, "token_count_estimate": 1264, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0152P1104322", "0153I1304639", "0162A1004888", "0162F1505243", "0262F0700453", "0262F1200548", "0262P0101651", "0271P0803345", "0271P1003461", "03077L110243", "03077P130226", "03082O080097", "03082O120240", "03177L120130", "03177P030309", "03177P060376", "03362O112320", "03582J080008", "03591C160326", "03591H071828", "03878I010683", "03878I050886"]}}
{"id": "ec09551df9989861", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 65 | 03362J070303 | 30.3287 | 82.2704 | | 4990 | Brahmaputra | Transboundary | 59 | 78 | 33 |\n| 66 | 03362J140556 | 30.5718 | 82.9057 | | 5553 | Brahmaputra | Transboundary | 14 | 19 | 33 |\n| 67 | 03982K011503 | 29.8340 | 94.1605 | Nyang chu | 4390 | Brahmaputra | Transboundary | 13 | 17 | 33 |\n| 68 | 03082O030045 | 29.4830 | 95.0384 | Dihang | 3897 | Brahmaputra | Transboundary | 40 | 53 | 32 |\n| 69 | 0271P0303177 | 28.2703 | 87.2460 | | 5166 | Ganga | Transboundary | 15 | 20 | 31 |\n| 70 | 03682D040545 | 28.2297 | 92.0423 | Tsona Chu | 5107 | Brahmaputra | Transboundary | 16 | 21 | 31 |\n| 71 | 0262K1101320 | 29.2968 | 82.7052 | | 5003 | Ganga | Transboundary | 10 | 13 | 30 |\n| 72 | 03591C110109 | 29.4908 | 96.7008 | Zo chu | 3916 | Brahmaputra | Transboundary | 610 | 795 | 30 |\n| 73 | 03362J070313 | 30.3110 | 82.2593 | | 4997 | Brahmaputra | Transboundary | 25 | 33 | 30 |\n| 74 | 03277G090115 | 29.8769 | 89.5443 | Lhabu chu | 5475 | Brahmaputra | Transboundary | 12 | 16 | 30 |\n| 75 | 03977G091357 | 29.8869 | 89.5924 | Nanggung chu | 5407 | Brahmaputra | Transboundary | 10 | 13 | 30 |\n| 76 | 03391C040413 | 29.0444 | 96.1934 | Dri Chu | 4164 | Brahmaputra | Transboundary | 44 | 57 | 29 |\n| 77 | 03678E050357 | 27.8033 | 89.3498 | Paro Chu | 4349 | Brahmaputra | Transboundary | 12 | 16 | 29 |\n| 78 | 03278I090656 | 27.8046 | 90.5772 | Dur Chu | 4704 | Brahmaputra | Transboundary | 10 | 13 | 29 |\n| 79 | 0271H0802119 | 28.1244 | 85.4686 | Trisuli River | 4787 | Ganga | Transboundary | 14 | 18 | 28 |\n| 80 | 03678E060389 | 27.6440 | 89.4619 | | 4220 | Brahmaputra | Transboundary | 35 | 45 | 28 |\n| 81 | 03878E130473 | 27.9878 | 89.8920 | | 5130 | Brahmaputra | Transboundary | 11 | 14 | 28 |\n| 82 | 03982F070481 | 30.3266 | 93.3505 | Drukla chu | 4549 | Brahmaputra | Transboundary | 24 | 30 | 27 |\n| 83 | 03877L040240 | 28.0657 | 90.2314 | Pho Chu | 4739 | Brahmaputra | Transboundary | 11 | 14 | 27 |\n| 84 | 03991C050525 | 29.9044 | 96.3989 | Moru Lung Pa Chu | 4892 | Brahmaputra | Transboundary | 10 | 13 | 27 |\n| 85 | 0262F1600679 | 30.2045 | 81.8776 | | 5518 | Ganga | Transboundary | 13 | 16 | 26 |\n| 86 | 0271P1203562 | 28.1067 | 87.5842 | | 4968 | Ganga | Transboundary | 16 | 20 | 26 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2652, "line_end": 2675, "token_count_estimate": 1290, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Dihang"], "countries": [], "lake_ids": ["0262F1600679", "0262K1101320", "0271H0802119", "0271P0303177", "0271P1203562", "03082O030045", "03277G090115", "03278I090656", "03362J070303", "03362J070313", "03362J140556", "03391C040413", "03591C110109", "03678E050357", "03678E060389", "03682D040545", "03877L040240", "03878E130473", "03977G091357", "03982F070481", "03982K011503", "03991C050525"]}}
{"id": "2bf01f8e194dec2d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 87 | 03362N151451 | 30.4609 | 83.9728 | | 5448 | Brahmaputra | Transboundary | 19 | 24 | 26 |\n| 88 | 03082C140566 | 29.5814 | 92.7906 | Gyab Pucong chu | 4884 | Brahmaputra | Transboundary | 15 | 19 | 26 |\n| 89 | 03977G141478 | 29.5089 | 89.8818 | Nyemo ma chu | 5420 | Brahmaputra | Transboundary | 12 | 15 | 26 |\n| 90 | 0272I1303881 | 27.9746 | 86.8035 | | 5183 | Ganga | Transboundary | 25 | 31 | 25 |\n| 91 | 03082G121361 | 29.0031 | 93.5659 | Yarlung tsangpo | 4824 | Brahmaputra | Transboundary | 14 | 18 | 25 |\n| 92 | 03177L120074 | 28.2441 | 90.7431 | Lhodrak nub chu | 4657 | Brahmaputra | Transboundary | 12 | 15 | 25 |\n| 93 | 03971P091189 | 28.8472 | 87.5682 | Mangkar chu | 5288 | Brahmaputra | Transboundary | 10 | 13 | 25 |\n| 94 | 0262K0901258 | 29.7982 | 82.6711 | | 4654 | Ganga | Transboundary | 14 | 17 | 24 |\n| 95 | 0272I1404052 | 27.6963 | 86.7916 | | 4951 | Ganga | Transboundary | 13 | 16 | 24 |\n| 96 | 03982C051117 | 29.7945 | 92.3893 | Medroma chu | 4976 | Brahmaputra | Transboundary | 18 | 22 | 24 |\n| 97 | 0262K0200945 | 29.7021 | 82.2452 | | 4671 | Ganga | Transboundary | 11 | 13 | 23 |\n| 98 | 03591C150260 | 29.3971 | 96.8278 | Zo chu | 3917 | Brahmaputra | Transboundary | 402 | 492 | 23 |\n| 99 | 03082G121340 | 29.0169 | 93.7128 | Guchu chu | 4411 | Brahmaputra | Transboundary | 25 | 31 | 23 |\n| 100 | 03177L120124 | 28.0813 | 90.6523 | Longdo Chu | 4999 | Brahmaputra | Transboundary | 19 | 23 | 23 |\n| 101 | 03591H071679 | 28.4717 | 97.3803 | | 4388 | Brahmaputra | Transboundary | 12 | 15 | 23 |\n| 102 | 03971O141166 | 29.5844 | 87.9160 | Zecho Puchu | 5424 | Brahmaputra | Transboundary | 12 | 15 | 23 |\n| 103 | 03178I130455 | 27.9880 | 90.8169 | Lhodrak Chu | 4905 | Brahmaputra | Transboundary | 11 | 13 | 23 |\n| 104 | 03362J070296 | 30.3415 | 82.2709 | | 4993 | Brahmaputra | Transboundary | 69 | 84 | 22 |\n| 105 | 03082O110180 | 29.3558 | 95.6641 | Rirung Chu | 4091 | Brahmaputra | Transboundary | 16 | 20 | 22 |\n| 106 | 03971C120949 | 29.1223 | 84.7444 | | 5049 | Brahmaputra | Transboundary | 15 | 18 | 22 |\n| 107 | 03982J041395 | 30.1325 | 94.1228 | Drukla chu | 4318 | Brahmaputra | Transboundary | 12 | 15 | 22 |\n| 108 | 03177L120131 | 28.0775 | 90.5840 | Longdo Chu | 5312 | Brahmaputra | Transboundary | 11 | 13 | 22 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2679, "line_end": 2702, "token_count_estimate": 1300, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262K0200945", "0262K0901258", "0272I1303881", "0272I1404052", "03082C140566", "03082G121340", "03082G121361", "03082O110180", "03177L120074", "03177L120124", "03177L120131", "03178I130455", "03362J070296", "03362N151451", "03591C150260", "03591H071679", "03971C120949", "03971O141166", "03971P091189", "03977G141478", "03982C051117", "03982J041395"]}}
{"id": "a10ddb537e72ab59", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2005 | Lake Area August 2020 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 109 | 0162E0304979 | 31.2604 | 81.1327 | | 5530 | Indus | Transboundary | 12 | 15 | 21 |\n| 110 | 03982B130996 | 30.9558 | 92.8667 | Tsenrak chu | 4890 | Brahmaputra | Transboundary | 23 | 28 | 21 |\n| 111 | 03982G101086 | 29.6444 | 93.5482 | Nyang chu | 4738 | Brahmaputra | Transboundary | 20 | 24 | 21 |\n| 112 | 03182D160192 | 28.2160 | 92.7676 | Chayul chu | 5164 | Brahmaputra | Transboundary | 13 | 16 | 21 |\n| 113 | 0262O1201512 | 29.2184 | 83.7017 | | 5426 | Ganga | Transboundary | 42 | 51 | 20 |\n| 114 | 03362O021552 | 29.6739 | 83.1824 | | 5011 | Brahmaputra | Transboundary | 25 | 30 | 20 |\n| 115 | 03982G141364 | 29.5059 | 93.8057 | Nyang chu | 4504 | Brahmaputra | Transboundary | 22 | 26 | 20 |\n| 116 | 03877H160190 | 28.0047 | 89.9556 | | 4622 | Brahmaputra | Transboundary | 13 | 15 | 20 |\n| 117 | 03278I090704 | 27.7570 | 90.6242 | | 4410 | Brahmaputra | Transboundary | 12 | 15 | 20 |\n| 118 | 03982G060983 | 29.6026 | 93.4268 | Banang chu | 4512 | Brahmaputra | Transboundary | 12 | 14 | 20 |\n| 119 | 03878I010799 | 27.8479 | 90.2459 | Pho Chu | 4803 | Brahmaputra | Transboundary | 11 | 13 | 20 |\n| 120 | 0271H0802127 | 28.0827 | 85.4135 | Trisuli Khola | 4393 | Ganga | Transboundary | 13 | 15 | 19 |\n| 121 | 0272M1004375 | 27.6811 | 87.5172 | | 4472 | Ganga | Transboundary | 10 | 12 | 19 |\n| 122 | 0271P0403202 | 28.1517 | 87.1744 | | 5075 | Ganga | Transboundary | 11 | 13 | 19 |\n| 123 | 0262K1101321 | 29.2695 | 82.5903 | | 4335 | Ganga | Transboundary | 10 | 12 | 19 |\n| 124 | 0162E0104958 | 31.9852 | 81.0633 | | 4734 | Indus | Transboundary | 12 | 14 | 19 |\n| 125 | 0271H1002164 | 28.5480 | 85.6156 | | 5443 | Ganga | Transboundary | 22 | 26 | 19 |\n| 126 | 0262F1500620 | 30.3355 | 81.9203 | | 5130 | Ganga | Transboundary | 10 | 12 | 19 |\n| 127 | 03362O021544 | 29.6891 | 83.1902 | | 5007 | Brahmaputra | Transboundary | 55 | 65 | 19 |\n| 128 | 03082C100406 | 29.5241 | 92.5107 | Loyul chu | 5266 | Brahmaputra | Transboundary | 15 | 18 | 19 |\n| 129 | 0262O0701459 | 29.3767 | 83.3901 | | 5175 | Ganga | Transboundary | 12 | 14 | 18 |\n| 130 | 0271P0503278 | 28.7542 | 87.4271 | | 5511 | Ganga | Transboundary | 14 | 16 | 18 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2005", "Lake Area August 2020 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2707, "line_end": 2730, "token_count_estimate": 1271, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0162E0104958", "0162E0304979", "0262F1500620", "0262K1101321", "0262O0701459", "0262O1201512", "0271H0802127", "0271H1002164", "0271P0403202", "0271P0503278", "0272M1004375", "03082C100406", "03182D160192", "03278I090704", "03362O021544", "03362O021552", "03877H160190", "03878I010799", "03982B130996", "03982G060983", "03982G101086", "03982G141364"]}}
{"id": "3770cd7e576a1d69", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2005 | Lake Area August 2020 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 131 | 0271H1602503 | 28.1385 | 85.9193 | | 4865 | Ganga | Transboundary | 10 | 12 | 18 |\n| 132 | 03591H011187 | 28.7635 | 97.0581 | Trigo chu | 3285 | Brahmaputra | Transboundary | 39 | 46 | 18 |\n| 133 | 03982G101115 | 29.5936 | 93.7308 | Nyang chu | 4549 | Brahmaputra | Transboundary | 34 | 40 | 18 |\n| 134 | 03362J030085 | 30.4309 | 82.1806 | | 5000 | Brahmaputra | Transboundary | 32 | 38 | 18 |\n| 135 | 03877L040278 | 28.0108 | 90.1868 | Pho Chu | 5144 | Brahmaputra | Transboundary | 20 | 23 | 18 |\n| 136 | 03082O110168 | 29.3725 | 95.7122 | Rirung Chu | 4042 | Brahmaputra | Transboundary | 14 | 16 | 18 |\n| 137 | 03178I130508 | 27.9392 | 90.9445 | Lhodrak Chu | 4504 | Brahmaputra | Transboundary | 12 | 14 | 18 |\n| 138 | 03082G081142 | 29.1896 | 93.2752 | Anartang chu | 5021 | Brahmaputra | Transboundary | 11 | 13 | 18 |\n| 139 | 03082K031785 | 29.4668 | 94.1306 | Sungkar chu | 4487 | Brahmaputra | Transboundary | 11 | 13 | 18 |\n| 140 | 0262K0901222 | 29.8933 | 82.5137 | | 5031 | Ganga | Transboundary | 11 | 13 | 17 |\n| 141 | 03362J070250 | 30.4316 | 82.3619 | | 4882 | Brahmaputra | Transboundary | 153 | 178 | 17 |\n| 142 | 03362J030175 | 30.3522 | 82.2379 | | 5106 | Brahmaputra | Transboundary | 30 | 35 | 17 |\n| 143 | 03277G090125 | 29.8499 | 89.5338 | Lhabu chu | 5298 | Brahmaputra | Transboundary | 26 | 30 | 17 |\n| 144 | 03382O080010 | 29.1410 | 95.4370 | Emra River | 3322 | Brahmaputra | Transboundary | 21 | 25 | 17 |\n| 145 | 03678M090154 | 27.9122 | 91.5766 | Nyamjang Chu | 4628 | Brahmaputra | Transboundary | 14 | 16 | 17 |\n| 146 | 03591H071708 | 28.4320 | 97.2532 | | 4315 | Brahmaputra | Transboundary | 13 | 15 | 17 |\n| 147 | 03982G141317 | 29.5684 | 93.9019 | Nyang chu | 4610 | Brahmaputra | Transboundary | 12 | 14 | 17 |\n| 148 | 0272I0503652 | 27.8500 | 86.3561 | | 4424 | Ganga | Transboundary | 11 | 13 | 16 |\n| 149 | 0152P0404252 | 32.2096 | 79.0435 | | 5272 | Indus | Transboundary | 22 | 25 | 16 |\n| 150 | 0262F0700464 | 30.3645 | 81.3760 | | 5756 | Ganga | Transboundary | 10 | 12 | 16 |\n| 151 | 03982B150119 | 30.2970 | 92.9246 | Nyang chu | 4831 | Brahmaputra | Transboundary | 29 | 34 | 16 |\n| 152 | 03082G111189 | 29.4617 | 93.5555 | Pulung chu | 4719 | Brahmaputra | Transboundary | 22 | 25 | 16 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2005", "Lake Area August 2020 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2734, "line_end": 2757, "token_count_estimate": 1296, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0152P0404252", "0262F0700464", "0262K0901222", "0271H1602503", "0272I0503652", "03082G081142", "03082G111189", "03082K031785", "03082O110168", "03178I130508", "03277G090125", "03362J030085", "03362J030175", "03362J070250", "03382O080010", "03591H011187", "03591H071708", "03678M090154", "03877L040278", "03982B150119", "03982G101115", "03982G141317"]}}
{"id": "ef69cd17e1fff36d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 153 | 03082O080099 | 29.1399 | 95.4045 | Dihang | 3765 | Brahmaputra | Transboundary | 21 | 24 | 16 |\n| 154 | 03082O030051 | 29.2543 | 95.2274 | Dihang | 1441 | Brahmaputra | Transboundary | 21 | 24 | 16 |\n| 155 | 03582J080021 | 30.0982 | 94.4831 | Numphu chu | 3289 | Brahmaputra | Transboundary | 16 | 18 | 16 |\n| 156 | 03591H011116 | 28.8479 | 97.0922 | Trigo chu | 4448 | Brahmaputra | Transboundary | 15 | 17 | 16 |\n| 157 | 03982K061838 | 29.5281 | 94.3428 | Nyang chu | 4312 | Brahmaputra | Transboundary | 15 | 17 | 16 |\n| 158 | 03082C060300 | 29.5259 | 92.4908 | Gochumu Chu | 5279 | Brahmaputra | Transboundary | 11 | 13 | 16 |\n| 159 | 03278I090541 | 27.9437 | 90.7429 |  | 4848 | Brahmaputra | Transboundary | 11 | 13 | 16 |\n| 160 | 03177P080432 | 28.0116 | 91.2923 |  | 4622 | Brahmaputra | Transboundary | 11 | 13 | 16 |\n| 161 | 0271H1502387 | 28.3710 | 85.8904 |  | 5242 | Ganga | Transboundary | 27 | 31 | 15 |\n| 162 | 0162A0104839 | 31.8808 | 79.9993 | Indus River | 5511 | Indus | Transboundary | 14 | 16 | 15 |\n| 163 | 0162E1105115 | 31.2719 | 81.5184 |  | 5172 | Indus | Transboundary | 29 | 33 | 15 |\n| 164 | 03082H131716 | 28.8406 | 93.7518 | Nelung phu chu | 4120 | Brahmaputra | Transboundary | 31 | 36 | 15 |\n| 165 | 03362J080323 | 30.2169 | 82.4859 |  | 4791 | Brahmaputra | Transboundary | 25 | 29 | 15 |\n| 166 | 03591H031282 | 28.3033 | 97.2337 |  | 4279 | Brahmaputra | Transboundary | 23 | 27 | 15 |\n| 167 | 03082G121314 | 29.0340 | 93.6017 | Yarlung tsangpo | 4602 | Brahmaputra | Transboundary | 22 | 25 | 15 |\n| 168 | 03362O021536 | 29.7308 | 83.0734 |  | 5086 | Brahmaputra | Transboundary | 22 | 25 | 15 |\n| 169 | 03362N101369 | 30.5511 | 83.5119 |  | 5239 | Brahmaputra | Transboundary | 17 | 19 | 15 |\n| 170 | 03362J070308 | 30.3184 | 82.2724 |  | 4996 | Brahmaputra | Transboundary | 16 | 18 | 15 |\n| 171 | 03877L040295 | 28.0018 | 90.1388 | Pho Chu | 5063 | Brahmaputra | Transboundary | 16 | 18 | 15 |\n| 172 | 03982K021585 | 29.5432 | 94.0862 | Bhezhung chu | 4631 | Brahmaputra | Transboundary | 15 | 17 | 15 |\n| 173 | 03177L150152 | 28.3528 | 90.8512 | Lhodrak Nub chu | 4624 | Brahmaputra | Transboundary | 15 | 17 | 15 |\n| 174 | 03982F120617 | 30.1627 | 93.7287 | Draksum chu | 4178 | Brahmaputra | Transboundary | 14 | 16 | 15 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2762, "line_end": 2785, "token_count_estimate": 1308, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dihang"], "countries": [], "lake_ids": ["0162A0104839", "0162E1105115", "0271H1502387", "03082C060300", "03082G121314", "03082H131716", "03082O030051", "03082O080099", "03177L150152", "03177P080432", "03278I090541", "03362J070308", "03362J080323", "03362N101369", "03362O021536", "03582J080021", "03591H011116", "03591H031282", "03877L040295", "03982F120617", "03982K021585", "03982K061838"]}}
{"id": "c7e0482f91e12a2b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 175 | 03082O100143 | 29.5962 | 95.6910 | Chendruk Chu | 4314 | Brahmaputra | Transboundary | 11 | 13 | 15 |\n| 176 | 03082O110187 | 29.3472 | 95.7320 | Rirung Chu | 4140 | Brahmaputra | Transboundary | 11 | 13 | 15 |\n| 177 | 03878I010673 | 27.9967 | 90.2334 | Pho Chu | 4990 | Brahmaputra | Transboundary | 11 | 13 | 15 |\n| 178 | 03082K031762 | 29.4871 | 94.0994 | Lapu chu | 4733 | Brahmaputra | Transboundary | 10 | 12 | 15 |\n| 179 | 0262K1001286 | 29.6961 | 82.5614 |  | 4830 | Ganga | Transboundary | 11 | 13 | 14 |\n| 180 | 0271D0701880 | 28.4966 | 84.2556 |  | 4987 | Ganga | Transboundary | 11 | 13 | 14 |\n| 181 | 03177L120138 | 28.0715 | 90.6559 | Longdo Chu | 5020 | Brahmaputra | Transboundary | 40 | 46 | 14 |\n| 182 | 03982F040452 | 30.0278 | 93.1971 | Jya chu | 5014 | Brahmaputra | Transboundary | 33 | 37 | 14 |\n| 183 | 03082G161535 | 29.0287 | 93.9817 | Nelung phu chu | 4072 | Brahmaputra | Transboundary | 32 | 36 | 14 |\n| 184 | 03177L150185 | 28.2810 | 90.7504 | Lhodrak Nub Chu | 4722 | Brahmaputra | Transboundary | 31 | 35 | 14 |\n| 185 | 03082G161525 | 29.0379 | 93.7616 | Guchu chu | 4242 | Brahmaputra | Transboundary | 22 | 25 | 14 |\n| 186 | 03391D100771 | 28.6661 | 96.6320 | Thangkung Chu | 4217 | Brahmaputra | Transboundary | 18 | 20 | 14 |\n| 187 | 03082K031844 | 29.3956 | 94.0662 | Lapu chu | 4523 | Brahmaputra | Transboundary | 17 | 19 | 14 |\n| 188 | 03982B120087 | 30.0057 | 92.7423 | Nyang chu | 5108 | Brahmaputra | Transboundary | 14 | 16 | 14 |\n| 189 | 03982C100317 | 29.5394 | 92.6357 | Kam chu | 5179 | Brahmaputra | Transboundary | 14 | 16 | 14 |\n| 190 | 03082G161539 | 29.0214 | 93.8153 | Yarlung tsangpo | 4729 | Brahmaputra | Transboundary | 12 | 14 | 14 |\n| 191 | 03982B110881 | 30.4381 | 92.6787 | Tsenrak chu | 5111 | Brahmaputra | Transboundary | 11 | 13 | 14 |\n| 192 | 03177L160232 | 28.0404 | 90.9074 |  | 4924 | Brahmaputra | Transboundary | 11 | 13 | 14 |\n| 193 | 03977K071644 | 29.4374 | 90.4948 |  | 5373 | Brahmaputra | Transboundary | 11 | 13 | 14 |\n| 194 | 0271D1501962 | 28.3799 | 84.7798 |  | 4656 | Ganga | Transboundary | 17 | 19 | 13 |\n| 195 | 0271P1203556 | 28.1306 | 87.5989 |  | 5261 | Ganga | Transboundary | 14 | 16 | 13 |\n| 196 | 0262F0700494 | 30.3122 | 81.4092 |  | 5634 | Ganga | Transboundary | 13 | 15 | 13 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2789, "line_end": 2812, "token_count_estimate": 1295, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262F0700494", "0262K1001286", "0271D0701880", "0271D1501962", "0271P1203556", "03082G161525", "03082G161535", "03082G161539", "03082K031762", "03082K031844", "03082O100143", "03082O110187", "03177L120138", "03177L150185", "03177L160232", "03391D100771", "03878I010673", "03977K071644", "03982B110881", "03982B120087", "03982C100317", "03982F040452"]}}
{"id": "45e278d858aa4ff6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 197 | 0271P0603299 | 28.7350 | 87.4398 | | 5577 | Ganga | Transboundary | 24 | 27 | 13 |\n| 198 | 0162F1505256 | 30.4190 | 81.8678 | | 5356 | Indus | Transboundary | 14 | 16 | 13 |\n| 199 | 03177L110059 | 28.2731 | 90.7361 | Lhodrak Nub Chu | 4510 | Brahmaputra | Transboundary | 167 | 189 | 13 |\n| 200 | 03591H011062 | 28.9469 | 97.0996 | Trigo chu | 4412 | Brahmaputra | Transboundary | 48 | 54 | 13 |\n| 201 | 03591C160293 | 29.2476 | 96.9656 | Zo chu | 4673 | Brahmaputra | Transboundary | 26 | 29 | 13 |\n| 202 | 03391C080448 | 29.0496 | 96.3309 | Thangkung Chu | 4385 | Brahmaputra | Transboundary | 23 | 26 | 13 |\n| 203 | 03082K031767 | 29.4838 | 94.0343 | Lapu chu | 4470 | Brahmaputra | Transboundary | 22 | 25 | 13 |\n| 204 | 03962N150128 | 30.4565 | 83.8606 | | 5710 | Brahmaputra | Transboundary | 18 | 20 | 13 |\n| 205 | 03591D100453 | 28.6273 | 96.5648 | Maog chu | 3282 | Brahmaputra | Transboundary | 17 | 19 | 13 |\n| 206 | 03082G161540 | 29.0212 | 93.7556 | Guchu chu | 4696 | Brahmaputra | Transboundary | 15 | 17 | 13 |\n| 207 | 03678M090231 | 27.8152 | 91.5945 | Kulong Chu | 4678 | Brahmaputra | Transboundary | 14 | 16 | 13 |\n| 208 | 03177L150172 | 28.3068 | 90.8524 | Lhodrak Nub chu | 4648 | Brahmaputra | Transboundary | 14 | 16 | 13 |\n| 209 | 03082K031822 | 29.4169 | 94.1497 | Sungkar chu | 4438 | Brahmaputra | Transboundary | 12 | 13 | 13 |\n| 210 | 03278I050365 | 27.8133 | 90.2543 | | 4815 | Brahmaputra | Transboundary | 11 | 13 | 13 |\n| 211 | 03362O021578 | 29.6274 | 83.1312 | | 5366 | Brahmaputra | Transboundary | 11 | 12 | 13 |\n| 212 | 03877H160158 | 28.0296 | 89.8933 | | 5036 | Brahmaputra | Transboundary | 11 | 12 | 13 |\n| 213 | 0271L1203105 | 28.0260 | 86.6817 | | 5149 | Ganga | Transboundary | 19 | 21 | 12 |\n| 214 | 0271H0802130 | 28.0802 | 85.4073 | Trisuli Khola | 4327 | Ganga | Transboundary | 15 | 17 | 12 |\n| 215 | 0162A1504911 | 31.4987 | 80.9480 | | 5238 | Indus | Transboundary | 20 | 22 | 12 |\n| 216 | 0262F1200560 | 30.2839 | 81.5737 | | 5077 | Ganga | Transboundary | 14 | 16 | 12 |\n| 217 | 0262G1000816 | 29.6483 | 81.5555 | | 4551 | Ganga | Transboundary | 12 | 13 | 12 |\n| 218 | 0153M1304741 | 31.8781 | 79.9836 | Indus River | 5514 | Indus | Transboundary | 28 | 31 | 12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2817, "line_end": 2840, "token_count_estimate": 1285, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0153M1304741", "0162A1504911", "0162F1505256", "0262F1200560", "0262G1000816", "0271H0802130", "0271L1203105", "0271P0603299", "03082G161540", "03082K031767", "03082K031822", "03177L110059", "03177L150172", "03278I050365", "03362O021578", "03391C080448", "03591C160293", "03591D100453", "03591H011062", "03678M090231", "03877H160158", "03962N150128"]}}
{"id": "8ddc3569a5640adc", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 219 | 0271H0101972 | 28.9614 | 85.0837 | | 5388 | Ganga | Transboundary | 17 | 19 | 12 |\n| 220 | 03362J070251 | 30.4189 | 82.3017 | | 4931 | Brahmaputra | Transboundary | 901 | 1007 | 12 |\n| 221 | 03362O021538 | 29.7255 | 83.1047 | | 5010 | Brahmaputra | Transboundary | 113 | 127 | 12 |\n| 222 | 03077P100416 | 28.5461 | 91.5251 | Rong Chu | 5083 | Brahmaputra | Transboundary | 102 | 114 | 12 |\n| 223 | 03577H120121 | 28.1806 | 89.5354 | | 4694 | Brahmaputra | Transboundary | 82 | 92 | 12 |\n| 224 | 03362O072027 | 29.4989 | 83.4278 | | 4959 | Brahmaputra | Transboundary | 60 | 67 | 12 |\n| 225 | 03082K031789 | 29.4640 | 94.0447 | Lapu chu | 4509 | Brahmaputra | Transboundary | 40 | 45 | 12 |\n| 226 | 03178I130555 | 27.8962 | 90.9966 | Lhodrak Chu | 4361 | Brahmaputra | Transboundary | 33 | 37 | 12 |\n| 227 | 03982B040766 | 30.1924 | 92.0093 | Zhorong chu | 5203 | Brahmaputra | Transboundary | 27 | 30 | 12 |\n| 228 | 03278I090499 | 27.9723 | 90.5083 | Kungong Chu | 5101 | Brahmaputra | Transboundary | 25 | 28 | 12 |\n| 229 | 03278I050359 | 27.8188 | 90.2719 | | 4898 | Brahmaputra | Transboundary | 21 | 24 | 12 |\n| 230 | 03182H020201 | 28.7210 | 93.2174 | Subansiri | 4474 | Brahmaputra | Transboundary | 17 | 19 | 12 |\n| 231 | 03977O100526 | 29.5560 | 91.6377 | Sephuk chu | 5276 | Brahmaputra | Transboundary | 17 | 19 | 12 |\n| 232 | 03391C080454 | 29.0311 | 96.3177 | Thangkung Chu | 4139 | Brahmaputra | Transboundary | 17 | 19 | 12 |\n| 233 | 03982G141258 | 29.6572 | 93.7517 | Nyang chu | 4490 | Brahmaputra | Transboundary | 15 | 17 | 12 |\n| 234 | 03591H071804 | 28.3688 | 97.4464 | Dupuchu | 4054 | Brahmaputra | Transboundary | 15 | 17 | 12 |\n| 235 | 03591D140635 | 28.5037 | 96.8325 | Dzayul chu | 4058 | Brahmaputra | Transboundary | 15 | 17 | 12 |\n| 236 | 03082G151463 | 29.3368 | 93.7576 | Sungkar chu | 4540 | Brahmaputra | Transboundary | 14 | 16 | 12 |\n| 237 | 03977G151486 | 29.4954 | 89.8314 | | 5493 | Brahmaputra | Transboundary | 13 | 15 | 12 |\n| 238 | 03982B040772 | 30.1704 | 92.0020 | Zhorong chu | 5027 | Brahmaputra | Transboundary | 13 | 15 | 12 |\n| 239 | 03278I090573 | 27.9213 | 90.5026 | Mangde Chu | 5082 | Brahmaputra | Transboundary | 13 | 15 | 12 |\n| 240 | 03982G141357 | 29.5237 | 93.9810 | Bhezhung chu | 4276 | Brahmaputra | Transboundary | 12 | 13 | 12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2844, "line_end": 2867, "token_count_estimate": 1315, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": ["0271H0101972", "03077P100416", "03082G151463", "03082K031789", "03178I130555", "03182H020201", "03278I050359", "03278I090499", "03278I090573", "03362J070251", "03362O021538", "03362O072027", "03391C080454", "03577H120121", "03591D140635", "03591H071804", "03977G151486", "03977O100526", "03982B040766", "03982B040772", "03982G141258", "03982G141357"]}}
{"id": "10b9727958282151", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 241 | 03878E090440 | 27.8400 | 89.6001 | | 4180 | Brahmaputra | Transboundary | 12 | 13 | 12 |\n| 242 | 03982N110250 | 30.3864 | 95.7445 | Yu Chu | 4257 | Brahmaputra | Transboundary | 11 | 12 | 12 |\n| 243 | 03391C040392 | 29.0831 | 96.2010 | Dri Chu | 4049 | Brahmaputra | Transboundary | 11 | 12 | 12 |\n| 244 | 03591H071693 | 28.4639 | 97.4091 | | 4420 | Brahmaputra | Transboundary | 11 | 12 | 12 |\n| 245 | 03591D090373 | 28.9394 | 96.7404 | Kangri Karpo chu | 4352 | Brahmaputra | Transboundary | 11 | 12 | 12 |\n| 246 | 0152P1104368 | 32.2982 | 79.6619 | Indus River | 5690 | Indus | Transboundary | 17 | 19 | 11 |\n| 247 | 0271P0403233 | 28.0645 | 87.1929 | | 4837 | Ganga | Transboundary | 11 | 12 | 11 |\n| 248 | 0272M0504150 | 27.9830 | 87.3448 | | 3728 | Ganga | Transboundary | 26 | 29 | 11 |\n| 249 | 0271P0403181 | 28.2260 | 87.0534 | | 5050 | Ganga | Transboundary | 17 | 19 | 11 |\n| 250 | 0271L1203139 | 28.0056 | 86.6821 | Dudh Kosi River | 4947 | Ganga | Transboundary | 17 | 19 | 11 |\n| 251 | 03982G101165 | 29.5126 | 93.6200 | Nyang chu | 4362 | Brahmaputra | Transboundary | 80 | 89 | 11 |\n| 252 | 03877L040269 | 28.0155 | 90.2104 | Pho Chu | 5127 | Brahmaputra | Transboundary | 54 | 60 | 11 |\n| 253 | 03077P100420 | 28.5303 | 91.5616 | Rong Chu | 5153 | Brahmaputra | Transboundary | 45 | 50 | 11 |\n| 254 | 03982K011509 | 29.8168 | 94.1328 | Nyang chu | 4558 | Brahmaputra | Transboundary | 45 | 50 | 11 |\n| 255 | 03362J030123 | 30.4132 | 82.1712 | | 5002 | Brahmaputra | Transboundary | 38 | 42 | 11 |\n| 256 | 03082C070370 | 29.2868 | 92.4529 | Yarlung tsangpo | 4890 | Brahmaputra | Transboundary | 23 | 26 | 11 |\n| 257 | 03591D100485 | 28.5299 | 96.6217 | Dulai chu | 3907 | Brahmaputra | Transboundary | 23 | 26 | 11 |\n| 258 | 03982G101108 | 29.6047 | 93.5281 | Banang chu | 4995 | Brahmaputra | Transboundary | 21 | 23 | 11 |\n| 259 | 03991C050524 | 29.9075 | 96.2574 | Cho Dzong Chu | 4578 | Brahmaputra | Transboundary | 20 | 22 | 11 |\n| 260 | 03177L160255 | 28.0183 | 90.8432 | | 4957 | Brahmaputra | Transboundary | 19 | 21 | 11 |\n| 261 | 03982G141304 | 29.5890 | 93.9287 | Bhezhung chu | 4669 | Brahmaputra | Transboundary | 19 | 21 | 11 |\n| 262 | 03882J110303 | 30.3933 | 94.6534 | Yigrong chu | 4007 | Brahmaputra | Transboundary | 19 | 21 | 11 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2872, "line_end": 2895, "token_count_estimate": 1295, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Kosi"], "countries": [], "lake_ids": ["0152P1104368", "0271L1203139", "0271P0403181", "0271P0403233", "0272M0504150", "03077P100420", "03082C070370", "03177L160255", "03362J030123", "03391C040392", "03591D090373", "03591D100485", "03591H071693", "03877L040269", "03878E090440", "03882J110303", "03982G101108", "03982G101165", "03982G141304", "03982K011509", "03982N110250", "03991C050524"]}}
{"id": "b0bdeb03ced8b860", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 263 | 03177L160257 | 28.0161 | 90.9293 | | 4801 | Brahmaputra | Transboundary | 18 | 20 | 11 |\n| 264 | 03982C021100 | 29.5703 | 92.0431 | Medroma chu | 5312 | Brahmaputra | Transboundary | 14 | 15 | 11 |\n| 265 | 0272I1404056 | 27.6809 | 86.8532 | | 4694 | Ganga | Transboundary | 12 | 13 | 10 |\n| 266 | 03362O062010 | 29.5822 | 83.3553 | | 4888 | Brahmaputra | Transboundary | 119 | 131 | 10 |\n| 267 | 03082K142018 | 29.5450 | 94.9650 | Yarlung tsangpo | 4300 | Brahmaputra | Transboundary | 92 | 101 | 10 |\n| 268 | 03582K090246 | 29.8294 | 94.6326 | Rong chu | 4231 | Brahmaputra | Transboundary | 60 | 66 | 10 |\n| 269 | 03391C040437 | 29.0083 | 96.2182 | Dri Chu | 3664 | Brahmaputra | Transboundary | 47 | 52 | 10 |\n| 270 | 03977K010205 | 29.7915 | 90.2462 | Lhorong chu | 5120 | Brahmaputra | Transboundary | 34 | 37 | 10 |\n| 271 | 03982G060966 | 29.6502 | 93.3375 | Banang chu | 4738 | Brahmaputra | Transboundary | 26 | 29 | 10 |\n| 272 | 03362O021546 | 29.6865 | 83.0972 | | 5040 | Brahmaputra | Transboundary | 25 | 28 | 10 |\n| 273 | 03082G111245 | 29.3293 | 93.7485 | Sungkar chu | 4528 | Brahmaputra | Transboundary | 25 | 27 | 10 |\n| 274 | 03577H120119 | 28.1855 | 89.6612 | | 4886 | Brahmaputra | Transboundary | 22 | 24 | 10 |\n| 275 | 03982F120634 | 30.1116 | 93.5011 | Drukla chu | 4516 | Brahmaputra | Transboundary | 16 | 18 | 10 |\n| 276 | 03082G111219 | 29.3893 | 93.6095 | Pulung chu | 4772 | Brahmaputra | Transboundary | 15 | 17 | 10 |\n| 277 | 03982B110903 | 30.2918 | 92.6816 | Tsenrak chu | 5168 | Brahmaputra | Transboundary | 15 | 16 | 10 |\n| 278 | 03591D100445 | 28.6371 | 96.6846 | Dzayul chu | 4418 | Brahmaputra | Transboundary | 14 | 16 | 10 |\n| 279 | 03278I050381 | 27.7924 | 90.4086 | Mangde Chu | 4759 | Brahmaputra | Transboundary | 15 | 16 | 10 |\n| 280 | 03591G020753 | 29.5069 | 97.0605 | | 5275 | Brahmaputra | Transboundary | 14 | 15 | 10 |\n| 281 | 03591D090381 | 28.9240 | 96.5637 | Kangri Karpo chu | 4447 | Brahmaputra | Transboundary | 13 | 14 | 10 |\n| 282 | 03982J041409 | 30.0191 | 94.1233 | Nangu chu | 4356 | Brahmaputra | Transboundary | 12 | 13 | 10 |\n| 283 | 03982C051135 | 29.7777 | 92.4571 | Medroma chu | 4996 | Brahmaputra | Transboundary | 12 | 13 | 10 |\n| 284 | 03982F080555 | 30.0715 | 93.3568 | Drukla chu | 5065 | Brahmaputra | Transboundary | 12 | 13 | 10 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2899, "line_end": 2922, "token_count_estimate": 1307, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0272I1404056", "03082G111219", "03082G111245", "03082K142018", "03177L160257", "03278I050381", "03362O021546", "03362O062010", "03391C040437", "03577H120119", "03582K090246", "03591D090381", "03591D100445", "03591G020753", "03977K010205", "03982B110903", "03982C021100", "03982C051135", "03982F080555", "03982F120634", "03982G060966", "03982J041409"]}}
{"id": "7ec3c4e7de656708", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 285 | 03082G121303 | 29.0464 | 93.7407 | Guchu chu | 4201 | Brahmaputra | Transboundary | 12 | 13 | 10 |\n| 286 | 03977J110071 | 30.4510 | 90.6513 | Lha chu | 5597 | Brahmaputra | Transboundary | 12 | 13 | 10 |\n| 287 | 03591H021200 | 28.7474 | 97.0972 | Trigo chu | 4438 | Brahmaputra | Transboundary | 11 | 12 | 10 |\n| 288 | 03391D060710 | 28.5220 | 96.4439 | Thangkung Chu | 4157 | Brahmaputra | Transboundary | 10 | 11 | 10 |\n| 289 | 0271D1101929 | 28.3721 | 84.5793 | | 4430 | Ganga | Transboundary | 11 | 12 | 9 |\n| 290 | 0262K0400994 | 29.0838 | 82.1864 | | 4242 | Ganga | Transboundary | 11 | 12 | 9 |\n| 291 | 0271P0303176 | 28.2741 | 87.1505 | | 5012 | Ganga | Transboundary | 12 | 13 | 9 |\n| 292 | 0272M0504175 | 27.9480 | 87.3486 | | 3991 | Ganga | Transboundary | 10 | 11 | 9 |\n| 293 | 0272M1004429 | 27.6389 | 87.6333 | | 4289 | Ganga | Transboundary | 16 | 18 | 9 |\n| 294 | 0262K0100916 | 29.9286 | 82.2065 | | 4550 | Ganga | Transboundary | 33 | 36 | 9 |\n| 295 | 0271H0802141 | 28.0606 | 85.3889 | | 4205 | Ganga | Transboundary | 16 | 18 | 9 |\n| 296 | 03362O062021 | 29.5110 | 83.4441 | | 4959 | Brahmaputra | Transboundary | 211 | 229 | 9 |\n| 297 | 03582J080009 | 30.1735 | 94.3458 | Nunkhu Phu chu | 3654 | Brahmaputra | Transboundary | 181 | 197 | 9 |\n| 298 | 03278I090613 | 27.8614 | 90.5906 | Kungong Chu | 4770 | Brahmaputra | Transboundary | 75 | 82 | 9 |\n| 299 | 03182H030281 | 28.3417 | 93.0923 | Char chu | 4251 | Brahmaputra | Transboundary | 62 | 68 | 9 |\n| 300 | 03082G111223 | 29.3829 | 93.6402 | Pulung chu | 4717 | Brahmaputra | Transboundary | 40 | 43 | 9 |\n| 301 | 03982K021591 | 29.5375 | 94.1134 | Nyang chu | 4520 | Brahmaputra | Transboundary | 37 | 40 | 9 |\n| 302 | 03982O090387 | 29.7962 | 95.5120 | Po Tsangpo | 4133 | Brahmaputra | Transboundary | 29 | 32 | 9 |\n| 303 | 03591C080061 | 29.1305 | 96.3236 | Kangri Karpo chu | 4392 | Brahmaputra | Transboundary | 27 | 29 | 9 |\n| 304 | 03371B032391 | 30.3909 | 84.0962 | | 5396 | Brahmaputra | Transboundary | 25 | 27 | 9 |\n| 305 | 03677P120027 | 28.1430 | 91.6641 | Tsukke Chu | 5139 | Brahmaputra | Transboundary | 19 | 21 | 9 |\n| 306 | 03591C030034 | 29.2635 | 96.2454 | Kangri Karpo chu | 4408 | Brahmaputra | Transboundary | 17 | 19 | 9 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2929, "line_end": 2952, "token_count_estimate": 1312, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262K0100916", "0262K0400994", "0271D1101929", "0271H0802141", "0271P0303176", "0272M0504175", "0272M1004429", "03082G111223", "03082G121303", "03182H030281", "03278I090613", "03362O062021", "03371B032391", "03391D060710", "03582J080009", "03591C030034", "03591C080061", "03591H021200", "03677P120027", "03977J110071", "03982K021591", "03982O090387"]}}
{"id": "d2f5398b5084d5db", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n***", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 2953, "line_end": 2957, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c54f0e5f51bfe294", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 307 | 03082C070331 | 29.3547 | 92.4533 | Dekimu chu | 5072 | Brahmaputra | Transboundary | 13 | 14 | 9 |\n| 308 | 03082O110167 | 29.3744 | 95.7182 | Rirung Chu | 4046 | Brahmaputra | Transboundary | 13 | 14 | 9 |\n| 309 | 03177P080425 | 28.0425 | 91.2939 | | 4780 | Brahmaputra | Transboundary | 13 | 14 | 9 |\n| 310 | 03278I090602 | 27.8835 | 90.7123 | Chakje Chu | 4875 | Brahmaputra | Transboundary | 13 | 14 | 9 |\n| 311 | 03082O110166 | 29.3744 | 95.5582 | Rirung Chu | 4049 | Brahmaputra | Transboundary | 13 | 14 | 9 |\n| 312 | 03391D010461 | 29.0010 | 96.1841 | Dri Chu | 4009 | Brahmaputra | Transboundary | 12 | 13 | 9 |\n| 313 | 03362K050618 | 29.9836 | 82.4792 | | 5223 | Brahmaputra | Transboundary | 12 | 13 | 9 |\n| 314 | 03082G121327 | 29.0238 | 93.7313 | Guchu chu | 4214 | Brahmaputra | Transboundary | 11 | 12 | 9 |\n| 315 | 03977N160433 | 30.1775 | 91.9954 | Zhorong chu | 5098 | Brahmaputra | Transboundary | 10 | 11 | 9 |\n| 316 | 03077L030144 | 28.2566 | 90.1916 | Kya Chu | 5451 | Brahmaputra | Transboundary | 10 | 11 | 9 |\n| 317 | 03982K011487 | 29.8563 | 94.1546 | Nyang chu | 4320 | Brahmaputra | Transboundary | 10 | 11 | 9 |\n| 318 | 03878I010740 | 27.9202 | 90.0751 | Pho Chu | 4326 | Brahmaputra | Transboundary | 10 | 11 | 9 |\n| 319 | 03278I130851 | 27.7837 | 90.8113 | Panggyetangka | 4126 | Brahmaputra | Transboundary | 10 | 11 | 9 |\n| 320 | 0162F1505269 | 30.3995 | 81.8527 | | 5722 | Indus | Transboundary | 14 | 15 | 8 |\n| 321 | 0271D1401959 | 28.5053 | 84.8024 | Buri Gandaki | 3684 | Ganga | Transboundary | 21 | 23 | 8 |\n| 322 | 0153M1304727 | 31.9248 | 79.8648 | | 5356 | Indus | Transboundary | 31 | 34 | 8 |\n| 323 | 0277D0804617 | 28.0328 | 88.4610 | | 5156 | Ganga | Transboundary | 14 | 15 | 8 |\n| 324 | 0271P0403235 | 28.0394 | 87.1675 | | 4739 | Ganga | Transboundary | 24 | 26 | 8 |\n| 325 | 0162E1105111 | 31.2838 | 81.6310 | | 5259 | Indus | Transboundary | 26 | 28 | 8 |\n| 326 | 0271P0403201 | 28.1517 | 87.1579 | | 5141 | Ganga | Transboundary | 95 | 102 | 8 |\n| 327 | 0272M1004440 | 27.6287 | 87.7057 | | 4319 | Ganga | Transboundary | 13 | 14 | 8 |\n| 328 | 03082G161527 | 29.0354 | 93.8358 | Yarlung tsangpo | 4116 | Brahmaputra | Transboundary | 68 | 73 | 8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2958, "line_end": 2981, "token_count_estimate": 1304, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0153M1304727", "0162E1105111", "0162F1505269", "0271D1401959", "0271P0403201", "0271P0403235", "0272M1004440", "0277D0804617", "03077L030144", "03082C070331", "03082G121327", "03082G161527", "03082O110166", "03082O110167", "03177P080425", "03278I090602", "03278I130851", "03362K050618", "03391D010461", "03878I010740", "03977N160433", "03982K011487"]}}
{"id": "c199ba4ecde1d93b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 329 | 03082O090110 | 29.7634 | 95.5457 | Dihang | 3474 | Brahmaputra | Transboundary | 47 | 51 | 8 |\n| 330 | 03982G060986 | 29.6010 | 93.4650 | Banang chu | 4604 | Brahmaputra | Transboundary | 32 | 35 | 8 |\n| 331 | 03362N020755 | 30.5381 | 83.0437 | | 5375 | Brahmaputra | Transboundary | 27 | 29 | 8 |\n| 332 | 03591D090384 | 28.9210 | 96.5525 | Kangri Karpo chu | 4463 | Brahmaputra | Transboundary | 25 | 27 | 8 |\n| 333 | 03082O140254 | 29.5387 | 95.7948 | Chendruk Chu | 3895 | Brahmaputra | Transboundary | 22 | 24 | 8 |\n| 334 | 03882J020229 | 30.7057 | 94.1177 | Nok chu | 4741 | Brahmaputra | Transboundary | 18 | 19 | 8 |\n| 335 | 03391C040409 | 29.0613 | 96.2197 | Dri Chu | 4451 | Brahmaputra | Transboundary | 17 | 18 | 8 |\n| 336 | 03682D040559 | 28.0676 | 92.1473 | Tsona Chu | 4973 | Brahmaputra | Transboundary | 17 | 18 | 8 |\n| 337 | 03082G040968 | 29.1573 | 93.0686 | Yarlung tsangpo | 4461 | Brahmaputra | Transboundary | 16 | 17 | 8 |\n| 338 | 03082K031913 | 29.2907 | 94.1460 | Sungkar chu | 4468 | Brahmaputra | Transboundary | 16 | 17 | 8 |\n| 339 | 03982G111188 | 29.4433 | 93.7149 | Nyang chu | 4414 | Brahmaputra | Transboundary | 15 | 16 | 8 |\n| 340 | 03362J090386 | 30.8316 | 82.5257 | | 5307 | Brahmaputra | Transboundary | 15 | 16 | 8 |\n| 341 | 03591D140634 | 28.5098 | 96.7998 | Dzayul chu | 4075 | Brahmaputra | Transboundary | 15 | 16 | 8 |\n| 342 | 03591G121010 | 29.0321 | 97.5125 | | 4141 | Brahmaputra | Transboundary | 15 | 16 | 8 |\n| 343 | 03678M090161 | 27.9007 | 91.6196 | Nyamjang Chu | 4938 | Brahmaputra | Transboundary | 15 | 16 | 8 |\n| 344 | 03977K011568 | 29.7727 | 90.1510 | Nyemo ma chu | 5271 | Brahmaputra | Transboundary | 14 | 15 | 8 |\n| 345 | 03362N141390 | 30.6616 | 83.7627 | | 5192 | Brahmaputra | Transboundary | 14 | 15 | 8 |\n| 346 | 03178I130481 | 27.9561 | 90.9546 | Lhodrak Chu | 4606 | Brahmaputra | Transboundary | 12 | 13 | 8 |\n| 347 | 03471G060443 | 29.7403 | 85.3141 | Nazhung Tsangpo | 5307 | Brahmaputra | Transboundary | 12 | 13 | 8 |\n| 348 | 03982K031684 | 29.4261 | 94.1864 | Nyang chu | 4487 | Brahmaputra | Transboundary | 10 | 11 | 8 |\n| 349 | 03278I130849 | 27.7903 | 90.7837 | Chamkar Chu | 4263 | Brahmaputra | Transboundary | 10 | 11 | 8 |\n| 350 | 0272I1404048 | 27.7191 | 86.9099 | | 4989 | Ganga | Transboundary | 15 | 16 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 2986, "line_end": 3009, "token_count_estimate": 1327, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Dihang"], "countries": [], "lake_ids": ["0272I1404048", "03082G040968", "03082K031913", "03082O090110", "03082O140254", "03178I130481", "03278I130849", "03362J090386", "03362N020755", "03362N141390", "03391C040409", "03471G060443", "03591D090384", "03591D140634", "03591G121010", "03678M090161", "03682D040559", "03882J020229", "03977K011568", "03982G060986", "03982G111188", "03982K031684"]}}
{"id": "f2c368cdab1387a4", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 351 | 0262K0701167 | 29.3914 | 82.3943 | | 3954 | Ganga | Transboundary | 17 | 18 | 7 |\n| 352 | 0153M1304738 | 31.9001 | 79.9863 | Indus River | 5663 | Indus | Transboundary | 10 | 11 | 7 |\n| 353 | 0271P0803384 | 28.0643 | 87.3537 | | 4190 | Ganga | Transboundary | 13 | 14 | 7 |\n| 354 | 0271P0803413 | 28.0124 | 87.2965 | | 4416 | Ganga | Transboundary | 12 | 13 | 7 |\n| 355 | 0271H1502406 | 28.3212 | 85.9301 | | 5298 | Ganga | Transboundary | 11 | 12 | 7 |\n| 356 | 03362J030044 | 30.4685 | 82.0596 | | 5180 | Brahmaputra | Transboundary | 379 | 407 | 7 |\n| 357 | 03362J130474 | 30.8805 | 82.8592 | | 5446 | Brahmaputra | Transboundary | 146 | 157 | 7 |\n| 358 | 03177P080405 | 28.0880 | 91.2571 | | 4630 | Brahmaputra | Transboundary | 56 | 60 | 7 |\n| 359 | 03982G141369 | 29.5023 | 93.9368 | Bhezhung chu | 4444 | Brahmaputra | Transboundary | 56 | 60 | 7 |\n| 360 | 03362K090632 | 29.9799 | 82.5888 | | 4853 | Brahmaputra | Transboundary | 49 | 52 | 7 |\n| 361 | 03678M090186 | 27.8785 | 91.5173 | Kulong Chu | 4248 | Brahmaputra | Transboundary | 33 | 35 | 7 |\n| 362 | 03882F060085 | 30.5268 | 93.4187 | Arsashung chu | 4758 | Brahmaputra | Transboundary | 32 | 34 | 7 |\n| 363 | 03882F140185 | 30.6585 | 93.8944 | Nok chu | 4791 | Brahmaputra | Transboundary | 25 | 27 | 7 |\n| 364 | 03882J070273 | 30.4871 | 94.2664 | Po Yigrong chu | 4456 | Brahmaputra | Transboundary | 25 | 27 | 7 |\n| 365 | 03591C030016 | 29.3294 | 96.1950 | Kangri Karpo chu | 4286 | Brahmaputra | Transboundary | 25 | 27 | 7 |\n| 366 | 03391C040425 | 29.0238 | 96.1781 | Dri Chu | 4067 | Brahmaputra | Transboundary | 24 | 26 | 7 |\n| 367 | 03082G121311 | 29.0366 | 93.7352 | Guchu chu | 4202 | Brahmaputra | Transboundary | 22 | 24 | 7 |\n| 368 | 03082H011572 | 28.7569 | 93.0792 | Yarlung tsangpo | 4161 | Brahmaputra | Transboundary | 22 | 24 | 7 |\n| 369 | 03362K090631 | 29.9803 | 82.5666 | | 4845 | Brahmaputra | Transboundary | 20 | 21 | 7 |\n| 370 | 03977K011559 | 29.8177 | 90.1832 | Nyemo ma chu | 5356 | Brahmaputra | Transboundary | 19 | 20 | 7 |\n| 371 | 03982B090868 | 30.7541 | 92.7449 | Me chu | 4872 | Brahmaputra | Transboundary | 19 | 20 | 7 |\n| 372 | 03982B120939 | 30.2169 | 92.5688 | Zhorong chu | 5037 | Brahmaputra | Transboundary | 18 | 19 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3013, "line_end": 3036, "token_count_estimate": 1315, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0153M1304738", "0262K0701167", "0271H1502406", "0271P0803384", "0271P0803413", "03082G121311", "03082H011572", "03177P080405", "03362J030044", "03362J130474", "03362K090631", "03362K090632", "03391C040425", "03591C030016", "03678M090186", "03882F060085", "03882F140185", "03882J070273", "03977K011559", "03982B090868", "03982B120939", "03982G141369"]}}
{"id": "e6ec4e9f3324e1bf", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 373 | 03982G141367 | 29.5039 | 93.8877 | Bhezhung chu | 4619 | Brahmaputra | Transboundary | 18 | 19 | 7 |\n| 374 | 03082G121294 | 29.0560 | 93.6936 | Yarlung tsangpo | 4576 | Brahmaputra | Transboundary | 17 | 18 | 7 |\n| 375 | 03982K061802 | 29.7437 | 94.4606 | Drrakchi chu | 4534 | Brahmaputra | Transboundary | 17 | 18 | 7 |\n| 376 | 03982C011073 | 29.8657 | 92.2080 | Medroma chu | 5227 | Brahmaputra | Transboundary | 17 | 18 | 7 |\n| 377 | 03391C040383 | 29.0959 | 96.2225 | Dri Chu | 4355 | Brahmaputra | Transboundary | 16 | 17 | 7 |\n| 378 | 03591H071808 | 28.3399 | 97.3687 | Dupuchu | 4230 | Brahmaputra | Transboundary | 15 | 16 | 7 |\n| 379 | 03082G151387 | 29.4738 | 93.8635 | Sungkar chu | 4422 | Brahmaputra | Transboundary | 14 | 15 | 7 |\n| 380 | 03082K041943 | 29.0544 | 94.0024 | Nelung phu chu | 4966 | Brahmaputra | Transboundary | 13 | 14 | 7 |\n| 381 | 03982G020870 | 29.5197 | 93.1768 | Banang chu | 5210 | Brahmaputra | Transboundary | 13 | 14 | 7 |\n| 382 | 03082O110177 | 29.3586 | 95.6560 | Rirung Chu | 3951 | Brahmaputra | Transboundary | 13 | 14 | 7 |\n| 383 | 03278I090485 | 27.9826 | 90.5935 | Kungong Chu | 5028 | Brahmaputra | Transboundary | 12 | 13 | 7 |\n| 384 | 03977C021229 | 29.6315 | 88.0070 | Rong chu | 5410 | Brahmaputra | Transboundary | 12 | 13 | 7 |\n| 385 | 03982N030018 | 30.2694 | 95.2165 | Poto Chu | 3938 | Brahmaputra | Transboundary | 12 | 13 | 7 |\n| 386 | 03982K031653 | 29.4701 | 94.1616 | Nyang chu | 4602 | Brahmaputra | Transboundary | 12 | 13 | 7 |\n| 387 | 03082H011566 | 28.7726 | 93.1716 | Yarlung tsangpo | 4522 | Brahmaputra | Transboundary | 11 | 12 | 7 |\n| 388 | 03982G141285 | 29.6133 | 93.9105 | Nyang chu | 4622 | Brahmaputra | Transboundary | 11 | 12 | 7 |\n| 389 | 03982K021622 | 29.5071 | 94.0280 | Bhezhung chu | 4737 | Brahmaputra | Transboundary | 11 | 12 | 7 |\n| 390 | 03582K090163 | 29.9096 | 94.7110 | Rong chu | 4411 | Brahmaputra | Transboundary | 10 | 11 | 7 |\n| 391 | 0271P0403196 | 28.1661 | 87.1218 | | 5219 | Ganga | Transboundary | 12 | 13 | 6 |\n| 392 | 0262F1200551 | 30.3279 | 81.5122 | | 5221 | Ganga | Transboundary | 12 | 13 | 6 |\n| 393 | 0262P0101643 | 28.9926 | 83.1735 | | 4821 | Ganga | Transboundary | 16 | 17 | 6 |\n| 394 | 0271P0603293 | 28.7373 | 87.4790 | | 5415 | Ganga | Transboundary | 24 | 25 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3039, "line_end": 3062, "token_count_estimate": 1329, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262F1200551", "0262P0101643", "0271P0403196", "0271P0603293", "03082G121294", "03082G151387", "03082H011566", "03082K041943", "03082O110177", "03278I090485", "03391C040383", "03582K090163", "03591H071808", "03977C021229", "03982C011073", "03982G020870", "03982G141285", "03982G141367", "03982K021622", "03982K031653", "03982K061802", "03982N030018"]}}
{"id": "cc060d270018df8b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 395 | 0272M0104106 | 27.9063 | 87.1884 | | 4490 | Ganga | Transboundary | 33 | 35 | 6 |\n| 396 | 0161F0704829 | 34.3407 | 81.2568 | | 5298 | Indus | Transboundary | 52 | 55 | 6 |\n| 397 | 0262F0700461 | 30.3784 | 81.4208 | | 5659 | Ganga | Transboundary | 26 | 27 | 6 |\n| 398 | 0272M0904259 | 27.9003 | 87.6987 | | 5226 | Ganga | Transboundary | 11 | 12 | 6 |\n| 399 | 0272M1304544 | 27.7571 | 87.7770 | | 4708 | Ganga | Transboundary | 25 | 27 | 6 |\n| 400 | 0262K1001282 | 29.7233 | 82.6443 | | 4978 | Ganga | Transboundary | 19 | 20 | 6 |\n| 401 | 0262F1200585 | 30.1621 | 81.7478 | | 5046 | Ganga | Transboundary | 16 | 17 | 6 |\n| 402 | 03962O150222 | 29.4702 | 83.7636 | | 5282 | Brahmaputra | Transboundary | 78 | 82 | 6 |\n| 403 | 03391C080441 | 29.2435 | 96.2450 | Jairu Chu | 4360 | Brahmaputra | Transboundary | 51 | 54 | 6 |\n| 404 | 03362J080348 | 30.1058 | 82.4043 | | 4912 | Brahmaputra | Transboundary | 48 | 51 | 6 |\n| 405 | 03362N101365 | 30.5632 | 83.5184 | | 5240 | Brahmaputra | Transboundary | 42 | 45 | 6 |\n| 406 | 03082O100150 | 29.5128 | 95.6931 | Chendruk Chu | 3922 | Brahmaputra | Transboundary | 38 | 40 | 6 |\n| 407 | 03277G090109 | 29.8990 | 89.6388 | Lhabu chu | 5260 | Brahmaputra | Transboundary | 34 | 36 | 6 |\n| 408 | 03982K021606 | 29.5287 | 94.0013 | Bhezhung chu | 4262 | Brahmaputra | Transboundary | 31 | 33 | 6 |\n| 409 | 03082G111207 | 29.4043 | 93.6503 | Pulung chu | 4624 | Brahmaputra | Transboundary | 31 | 33 | 6 |\n| 410 | 03982G101158 | 29.5195 | 93.6728 | Nyang chu | 4549 | Brahmaputra | Transboundary | 30 | 32 | 6 |\n| 411 | 03582K090191 | 29.8769 | 94.6300 | | 4490 | Brahmaputra | Transboundary | 23 | 24 | 6 |\n| 412 | 03082K031799 | 29.4536 | 94.0061 | Lapu chu | 4391 | Brahmaputra | Transboundary | 21 | 22 | 6 |\n| 413 | 03082K112003 | 29.3111 | 94.6113 | Lushar pu chu | 4253 | Brahmaputra | Transboundary | 21 | 22 | 6 |\n| 414 | 03982K021541 | 29.6007 | 94.0762 | Bhezhung chu | 4730 | Brahmaputra | Transboundary | 21 | 22 | 6 |\n| 415 | 03082O140255 | 29.5348 | 95.8044 | Chendruk Chu | 4065 | Brahmaputra | Transboundary | 21 | 22 | 6 |\n| 416 | 03878E130566 | 27.8939 | 89.9442 | | 4858 | Brahmaputra | Transboundary | 21 | 22 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3066, "line_end": 3089, "token_count_estimate": 1295, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0161F0704829", "0262F0700461", "0262F1200585", "0262K1001282", "0272M0104106", "0272M0904259", "0272M1304544", "03082G111207", "03082K031799", "03082K112003", "03082O100150", "03082O140255", "03277G090109", "03362J080348", "03362N101365", "03391C080441", "03582K090191", "03878E130566", "03962O150222", "03982G101158", "03982K021541", "03982K021606"]}}
{"id": "ce81cd0f9363d6e1", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2001 | Lake Area August 2020 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 417 | 03678M130358 | 27.7952 | 91.8384 | Tsona Chu | 4667 | Brahmaputra | Transboundary | 20 | 21 | 1 |\n| 418 | 03671N120003 | 30.0884 | 87.6970 | Langna Chu | 4731 | Brahmaputra | Transboundary | 20 | 21 | 1 |\n| 419 | 03982B110916 | 30.2563 | 92.5959 | Tsenrak chu | 5021 | Brahmaputra | Transboundary | 18 | 19 | 1 |\n| 420 | 03982G101107 | 29.6058 | 93.5883 | Nyang chu | 4734 | Brahmaputra | Transboundary | 18 | 19 | 1 |\n| 421 | 03982K051795 | 29.7666 | 94.4821 | Nyang chu | 4417 | Brahmaputra | Transboundary | 18 | 19 | 1 |\n| 422 | 03082G081140 | 29.1964 | 93.3275 | Yarlung tsangpo | 4728 | Brahmaputra | Transboundary | 17 | 18 | 1 |\n| 423 | 03082L012038 | 28.8878 | 94.0302 | Nelung phu chu | 4104 | Brahmaputra | Transboundary | 17 | 18 | 1 |\n| 424 | 03391D010472 | 28.9848 | 96.1970 | Dri Chu | 4033 | Brahmaputra | Transboundary | 16 | 17 | 1 |\n| 425 | 03591H031272 | 28.3508 | 97.1567 | | 4079 | Brahmaputra | Transboundary | 15 | 16 | 1 |\n| 426 | 03982G020778 | 29.7467 | 93.1489 | Banang chu | 4943 | Brahmaputra | Transboundary | 15 | 16 | 1 |\n| 427 | 03391D060707 | 28.5245 | 96.4243 | Thangkung Chu | 4096 | Brahmaputra | Transboundary | 14 | 15 | 1 |\n| 428 | 03362J070255 | 30.4064 | 82.2561 | | 4936 | Brahmaputra | Transboundary | 14 | 15 | 1 |\n| 429 | 03982C100290 | 29.5897 | 92.7156 | Kam chu | 5381 | Brahmaputra | Transboundary | 13 | 14 | 1 |\n| 430 | 03277G090130 | 29.8349 | 89.5465 | Lhabu chu | 5394 | Brahmaputra | Transboundary | 12 | 13 | 1 |\n| 431 | 03982B110906 | 30.2881 | 92.5114 | Tsenrak chu | 5182 | Brahmaputra | Transboundary | 11 | 12 | 1 |\n| 432 | 03971C160970 | 29.0761 | 84.8387 | | 5434 | Brahmaputra | Transboundary | 11 | 12 | 1 |\n| 433 | 03082O110192 | 29.3358 | 95.7414 | Rirung Chu | 4111 | Brahmaputra | Transboundary | 11 | 12 | 1 |\n| 434 | 03982B120047 | 30.0895 | 92.5326 | Nyang chu | 5084 | Brahmaputra | Transboundary | 11 | 12 | 1 |\n| 435 | 03471C100049 | 29.7509 | 84.6716 | | 5554 | Brahmaputra | Transboundary | 10 | 11 | 1 |\n| 436 | 03982C130337 | 29.7771 | 92.9254 | Nyang chu | 5093 | Brahmaputra | Transboundary | 10 | 11 | 1 |\n| 437 | 03278I090640 | 27.8275 | 90.7014 | Chamkar Chu | 4363 | Brahmaputra | Transboundary | 10 | 11 | 1 |\n| 438 | 03982N100138 | 30.5331 | 95.5654 | Yu Chu | 4918 | Brahmaputra | Transboundary | 10 | 11 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2001", "Lake Area August 2020 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3094, "line_end": 3117, "token_count_estimate": 1311, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082G081140", "03082L012038", "03082O110192", "03277G090130", "03278I090640", "03362J070255", "03391D010472", "03391D060707", "03471C100049", "03591H031272", "03671N120003", "03678M130358", "03971C160970", "03982B110906", "03982B110916", "03982B120047", "03982C100290", "03982C130337", "03982G020778", "03982G101107", "03982K051795", "03982N100138"]}}
{"id": "7ae79e74c6c5c402", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2001 | Lake Area August 2020 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 439 | 03082G061002 | 29.5031 | 93.4970 | Pulung chu | 4822 | Brahmaputra | Transboundary | 10 | 11 | 1 |\n| 440 | 0161B1504757 | 34.3157 | 80.8579 | | 5709 | Indus | Transboundary | 232 | 243 | 11 |\n| 441 | 0271H0602053 | 28.6756 | 85.4098 | | 5447 | Ganga | Transboundary | 21 | 22 | 1 |\n| 442 | 0272M0904233 | 27.9973 | 87.5223 | | 4720 | Ganga | Transboundary | 12 | 13 | 1 |\n| 443 | 0271P0603311 | 28.7282 | 87.4777 | | 5384 | Ganga | Transboundary | 25 | 26 | 1 |\n| 444 | 0271P0903420 | 28.8579 | 86.5194 | Bum Chu | 5254 | Ganga | Transboundary | 81 | 85 | 4 |\n| 445 | 0162E1105114 | 31.2740 | 81.5949 | | 5229 | Indus | Transboundary | 157 | 164 | 7 |\n| 446 | 0143A1300649 | 35.8552 | 72.9804 | Anari Gol | 4325 | Indus | Transboundary | 13 | 14 | 1 |\n| 447 | 0271P0403245 | 28.0075 | 87.0276 | | 5509 | Ganga | Transboundary | 14 | 15 | 1 |\n| 448 | 0262K0801187 | 29.1185 | 82.2562 | | 4430 | Ganga | Transboundary | 13 | 14 | 1 |\n| 449 | 0262G1000819 | 29.6097 | 81.5438 | | 4467 | Ganga | Transboundary | 13 | 14 | 1 |\n| 450 | 0162F1505296 | 30.3849 | 81.8408 | | 5571 | Indus | Transboundary | 12 | 13 | 1 |\n| 451 | 0162E0304970 | 31.2848 | 81.1757 | | 5171 | Indus | Transboundary | 18 | 19 | 1 |\n| 452 | 03582K090182 | 29.8895 | 94.5691 | | 4149 | Brahmaputra | Transboundary | 158 | 166 | 8 |\n| 453 | 03582K050077 | 29.9470 | 94.3578 | Numphu chu | 4148 | Brahmaputra | Transboundary | 110 | 116 | 6 |\n| 454 | 03582J080043 | 30.0128 | 94.4717 | Numphu chu | 4327 | Brahmaputra | Transboundary | 66 | 70 | 4 |\n| 455 | 03582J080045 | 30.0050 | 94.3837 | Numphu chu | 4020 | Brahmaputra | Transboundary | 59 | 62 | 3 |\n| 456 | 03982K051767 | 29.8278 | 94.4623 | Drrakchi chu | 4133 | Brahmaputra | Transboundary | 57 | 60 | 3 |\n| 457 | 03082G111243 | 29.3310 | 93.7207 | Sungkar chu | 4610 | Brahmaputra | Transboundary | 50 | 52 | 2 |\n| 458 | 03882J110304 | 30.3519 | 94.7325 | Yigrong chu | 2698 | Brahmaputra | Transboundary | 36 | 38 | 2 |\n| 459 | 03982B120943 | 30.2102 | 92.5732 | Zhorong chu | 5037 | Brahmaputra | Transboundary | 32 | 34 | 2 |\n| 460 | 03082G111188 | 29.4660 | 93.5900 | Pulung chu | 4615 | Brahmaputra | Transboundary | 30 | 32 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2001", "Lake Area August 2020 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3121, "line_end": 3144, "token_count_estimate": 1281, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0143A1300649", "0161B1504757", "0162E0304970", "0162E1105114", "0162F1505296", "0262G1000819", "0262K0801187", "0271H0602053", "0271P0403245", "0271P0603311", "0271P0903420", "0272M0904233", "03082G061002", "03082G111188", "03082G111243", "03582J080043", "03582J080045", "03582K050077", "03582K090182", "03882J110304", "03982B120943", "03982K051767"]}}
{"id": "aa09370c79228aa3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 461 | 03178I130558 | 27.8909 | 90.9659 | Lhodrak Chu | 4359 | Brahmaputra | Transboundary | 26 | 27 | 5 |\n| 462 | 03082C060312 | 29.5097 | 92.4589 | Gochumu Chu | 5077 | Brahmaputra | Transboundary | 24 | 25 | 5 |\n| 463 | 03982K051784 | 29.7800 | 94.4484 | Drrakchi chu | 4412 | Brahmaputra | Transboundary | 24 | 25 | 5 |\n| 464 | 03982N100104 | 30.5643 | 95.6440 | Yu Chu | 5073 | Brahmaputra | Transboundary | 18 | 19 | 5 |\n| 465 | 03391D010480 | 28.9481 | 96.1681 | Dri Chu | 3886 | Brahmaputra | Transboundary | 17 | 18 | 5 |\n| 466 | 03278I060417 | 27.7490 | 90.2935 | | 4569 | Brahmaputra | Transboundary | 17 | 18 | 5 |\n| 467 | 03982K011504 | 29.8330 | 94.1349 | Nyang chu | 4723 | Brahmaputra | Transboundary | 16 | 17 | 5 |\n| 468 | 03977J110075 | 30.4464 | 90.6489 | Lha chu | 5576 | Brahmaputra | Transboundary | 16 | 17 | 5 |\n| 469 | 03878I010791 | 27.8570 | 90.1913 | Pho Chu | 4733 | Brahmaputra | Transboundary | 16 | 17 | 5 |\n| 470 | 03082K031871 | 29.3608 | 94.1470 | Sungkar chu | 4551 | Brahmaputra | Transboundary | 16 | 17 | 5 |\n| 471 | 03591H071821 | 28.3134 | 97.3430 | | 4146 | Brahmaputra | Transboundary | 16 | 17 | 5 |\n| 472 | 03982F011317 | 30.9086 | 93.0160 | Tsenrak chu | 4958 | Brahmaputra | Transboundary | 15 | 16 | 5 |\n| 473 | 03982G111181 | 29.4581 | 93.6205 | Nyang chu | 4856 | Brahmaputra | Transboundary | 15 | 16 | 5 |\n| 474 | 03082G071075 | 29.2960 | 93.4235 | Pulung chu | 4648 | Brahmaputra | Transboundary | 15 | 16 | 5 |\n| 475 | 03977C021228 | 29.7217 | 88.2355 | Yarlung tsangpo | 5422 | Brahmaputra | Transboundary | 14 | 15 | 5 |\n| 476 | 03582K090260 | 29.8146 | 94.5723 | Rong chu | 4400 | Brahmaputra | Transboundary | 13 | 14 | 5 |\n| 477 | 03982F030407 | 30.2957 | 93.2160 | Nyem chu | 4942 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 478 | 03391D070723 | 28.4211 | 96.3765 | Ithun River | 3763 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 479 | 03882F070096 | 30.4625 | 93.3903 | Nyewo chu | 4788 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 480 | 03278I090695 | 27.7637 | 90.6360 | Dur Chu | 4614 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 481 | 03982B131012 | 30.8931 | 92.8600 | Tsenrak chu | 4921 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 482 | 03362N050915 | 30.7512 | 83.3273 | | 5631 | Brahmaputra | Transboundary | 12 | 13 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3149, "line_end": 3172, "token_count_estimate": 1339, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082C060312", "03082G071075", "03082K031871", "03178I130558", "03278I060417", "03278I090695", "03362N050915", "03391D010480", "03391D070723", "03582K090260", "03591H071821", "03878I010791", "03882F070096", "03977C021228", "03977J110075", "03982B131012", "03982F011317", "03982F030407", "03982G111181", "03982K011504", "03982K051784", "03982N100104"]}}
{"id": "aa4e00314f19dca6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 483 | 03177P040367 | 28.0095 | 91.0273 | | 4251 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 484 | 03082O110172 | 29.3671 | 95.6959 | Rirung Chu | 4282 | Brahmaputra | Transboundary | 12 | 13 | 5 |\n| 485 | 03971O141161 | 29.6418 | 87.9248 | Rong chu | 5282 | Brahmaputra | Transboundary | 11 | 12 | 5 |\n| 486 | 03177P040356 | 28.0231 | 91.0359 | | 4260 | Brahmaputra | Transboundary | 11 | 12 | 5 |\n| 487 | 03982G010773 | 29.7578 | 93.1227 | Banang chu | 5099 | Brahmaputra | Transboundary | 11 | 11 | 5 |\n| 488 | 03082G151469 | 29.3112 | 93.8059 | Sungkar chu | 4623 | Brahmaputra | Transboundary | 11 | 11 | 5 |\n| 489 | 0271P1003475 | 28.6942 | 87.5338 | | 5158 | Ganga | Transboundary | 61 | 63 | 4 |\n| 490 | 0271P1203561 | 28.1139 | 87.6547 | | 4954 | Ganga | Transboundary | 146 | 152 | 4 |\n| 491 | 0272M0204132 | 27.6909 | 87.2058 | | 4088 | Ganga | Transboundary | 11 | 12 | 4 |\n| 492 | 0272M1304523 | 27.8485 | 87.8312 | | 4437 | Ganga | Transboundary | 33 | 35 | 4 |\n| 493 | 0272M1004416 | 27.6513 | 87.7025 | | 4460 | Ganga | Transboundary | 14 | 15 | 4 |\n| 494 | 0262K0601085 | 29.7137 | 82.2504 | | 4651 | Ganga | Transboundary | 12 | 12 | 4 |\n| 495 | 03882F020048 | 30.6209 | 93.1806 | Arsashung chu | 4499 | Brahmaputra | Transboundary | 690 | 720 | 4 |\n| 496 | 03982J041397 | 30.1257 | 94.0897 | Drukla chu | 3802 | Brahmaputra | Transboundary | 277 | 289 | 4 |\n| 497 | 03582J080022 | 30.0732 | 94.4644 | Nunkhu Phu chu | 4110 | Brahmaputra | Transboundary | 90 | 94 | 4 |\n| 498 | 03982G111174 | 29.4767 | 93.6314 | Nyang chu | 4369 | Brahmaputra | Transboundary | 83 | 86 | 4 |\n| 499 | 03982A160597 | 31.1120 | 92.8154 | | 4905 | Brahmaputra | Transboundary | 68 | 71 | 4 |\n| 500 | 03577H120126 | 28.1375 | 89.5344 | | 5292 | Brahmaputra | Transboundary | 50 | 52 | 4 |\n| 501 | 03077L010088 | 28.9284 | 90.2228 | Rong Chu | 5109 | Brahmaputra | Transboundary | 48 | 50 | 4 |\n| 502 | 03582K090132 | 29.9406 | 94.5888 | Numphu chu | 4509 | Brahmaputra | Transboundary | 48 | 50 | 4 |\n| 503 | 03982F070483 | 30.3187 | 93.3426 | Drukla chu | 4613 | Brahmaputra | Transboundary | 47 | 49 | 4 |\n| 504 | 03082K041934 | 29.1009 | 94.1244 | Yuisumpu chu | 4562 | Brahmaputra | Transboundary | 45 | 47 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3176, "line_end": 3199, "token_count_estimate": 1316, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262K0601085", "0271P1003475", "0271P1203561", "0272M0204132", "0272M1004416", "0272M1304523", "03077L010088", "03082G151469", "03082K041934", "03082O110172", "03177P040356", "03177P040367", "03577H120126", "03582J080022", "03582K090132", "03882F020048", "03971O141161", "03982A160597", "03982F070483", "03982G010773", "03982G111174", "03982J041397"]}}
{"id": "b92bb80dd384cc3f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 505 | 03982G101095 | 29.6299 | 93.5610 | Nyang chu | 4563 | Brahmaputra | Transboundary | 44 | 46 | 4 |\n| 506 | 03878I010782 | 27.8753 | 90.2070 | Pho Chu | 4913 | Brahmaputra | Transboundary | 41 | 42 | 4 |\n| 507 | 03177L160229 | 28.0487 | 90.9533 | | 4642 | Brahmaputra | Transboundary | 41 | 42 | 4 |\n| 508 | 03082C140549 | 29.6658 | 92.7995 | Gyab Pucong chu | 5126 | Brahmaputra | Transboundary | 37 | 38 | 4 |\n| 509 | 03982G141305 | 29.5871 | 93.8970 | Nyang chu | 4786 | Brahmaputra | Transboundary | 34 | 35 | 4 |\n| 510 | 03878I010821 | 27.8273 | 90.2217 | Pho Chu | 4657 | Brahmaputra | Transboundary | 31 | 32 | 4 |\n| 511 | 03582K090186 | 29.8849 | 94.6460 | | 4308 | Brahmaputra | Transboundary | 28 | 29 | 4 |\n| 512 | 03591D130577 | 28.8430 | 96.8182 | Kangri Karpo chu | 4625 | Brahmaputra | Transboundary | 24 | 25 | 4 |\n| 513 | 03878I010674 | 27.9948 | 90.2219 | Pho Chu | 4936 | Brahmaputra | Transboundary | 23 | 24 | 4 |\n| 514 | 03183A050471 | 27.8310 | 92.3450 | Loro Nakpo chu | 5032 | Brahmaputra | Transboundary | 22 | 23 | 4 |\n| 515 | 03591H011195 | 28.7533 | 97.1370 | Dzayul chu | 4176 | Brahmaputra | Transboundary | 20 | 21 | 4 |\n| 516 | 03278I090660 | 27.7983 | 90.6204 | Chamkar Chu | 4574 | Brahmaputra | Transboundary | 18 | 19 | 4 |\n| 517 | 03591D140637 | 28.5029 | 96.8603 | Dzayul chu | 3522 | Brahmaputra | Transboundary | 17 | 18 | 4 |\n| 518 | 03982G141282 | 29.6210 | 93.7789 | Nyang chu | 4409 | Brahmaputra | Transboundary | 15 | 16 | 4 |\n| 519 | 03582K090170 | 29.9005 | 94.5327 | Numphu chu | 4615 | Brahmaputra | Transboundary | 15 | 16 | 4 |\n| 520 | 03082G111184 | 29.4974 | 93.5023 | Pulung chu | 4702 | Brahmaputra | Transboundary | 15 | 16 | 4 |\n| 521 | 03878I010850 | 27.7608 | 90.1720 | | 4333 | Brahmaputra | Transboundary | 15 | 16 | 4 |\n| 522 | 03591H061521 | 28.5946 | 97.4391 | | 4271 | Brahmaputra | Transboundary | 14 | 15 | 4 |\n| 523 | 03982K021559 | 29.5783 | 94.1127 | Nyang chu | 4509 | Brahmaputra | Transboundary | 14 | 15 | 4 |\n| 524 | 03982K031647 | 29.4752 | 94.1510 | Nyang chu | 4632 | Brahmaputra | Transboundary | 14 | 15 | 4 |\n| 525 | 03982K021605 | 29.5290 | 94.0759 | Bhezhung chu | 4691 | Brahmaputra | Transboundary | 13 | 14 | 4 |\n| 526 | 03082G151376 | 29.4882 | 93.8258 | Sungkar chu | 4741 | Brahmaputra | Transboundary | 13 | 14 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3206, "line_end": 3229, "token_count_estimate": 1335, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082C140549", "03082G111184", "03082G151376", "03177L160229", "03183A050471", "03278I090660", "03582K090170", "03582K090186", "03591D130577", "03591D140637", "03591H011195", "03591H061521", "03878I010674", "03878I010782", "03878I010821", "03878I010850", "03982G101095", "03982G141282", "03982G141305", "03982K021559", "03982K021605", "03982K031647"]}}
{"id": "73aacd45eb67a722", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 527 | 03278I090631 | 27.8441 | 90.6182 | Kungong Chu | 4767 | Brahmaputra | Transboundary | 13 | 13 | 4 |\n| 528 | 03362N141389 | 30.6693 | 83.7644 | | 5193 | Brahmaputra | Transboundary | 13 | 13 | 4 |\n| 529 | 03591D100476 | 28.5587 | 96.6916 | Dulai chu | 3483 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 530 | 03591H091975 | 28.8630 | 97.6116 | | 4558 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 531 | 03982G141327 | 29.5579 | 93.8790 | Nyang chu | 4479 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 532 | 03982N110179 | 30.4964 | 95.6496 | Yu Chu | 4954 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 533 | 03082C100413 | 29.5168 | 92.6811 | Gyab Pucong chu | 5276 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 534 | 03278I140894 | 27.7073 | 90.9601 | | 3952 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 535 | 03082O110188 | 29.3465 | 95.6949 | Rirung Chu | 4101 | Brahmaputra | Transboundary | 12 | 12 | 4 |\n| 536 | 03878I010797 | 27.8499 | 90.2126 | Pho Chu | 4855 | Brahmaputra | Transboundary | 11 | 11 | 4 |\n| 537 | 03582K090169 | 29.9019 | 94.5280 | Numphu chu | 4591 | Brahmaputra | Transboundary | 11 | 11 | 4 |\n| 538 | 03277L080051 | 28.0021 | 90.3109 | Mangde Chu | 5461 | Brahmaputra | Transboundary | 11 | 11 | 4 |\n| 539 | 0162F1505285 | 30.3904 | 81.8185 | | 5544 | Indus | Transboundary | 15 | 15 | 3 |\n| 540 | 0262F1200591 | 30.1402 | 81.6852 | | 4655 | Ganga | Transboundary | 21 | 22 | 3 |\n| 541 | 0262K0200959 | 29.6664 | 82.2031 | | 4388 | Ganga | Transboundary | 19 | 20 | 3 |\n| 542 | 0262K0501015 | 29.8784 | 82.3830 | | 4967 | Ganga | Transboundary | 13 | 13 | 3 |\n| 543 | 0262P0901759 | 28.8859 | 83.5273 | | 5578 | Ganga | Transboundary | 31 | 32 | 3 |\n| 544 | 0262L1301400 | 28.8189 | 82.8671 | | 4429 | Ganga | Transboundary | 18 | 19 | 3 |\n| 545 | 0162F1105228 | 30.3929 | 81.5325 | Pilchung Lung | 5128 | Indus | Transboundary | 17 | 18 | 3 |\n| 546 | 03982F160687 | 30.0202 | 93.9675 | Draksum chu | 3475 | Brahmaputra | Transboundary | 2658 | 2746 | 3 |\n| 547 | 03982E041228 | 31.1315 | 93.1770 | | 5007 | Brahmaputra | Transboundary | 692 | 714 | 3 |\n| 548 | 03591C160297 | 29.2382 | 96.8263 | Zo chu | 4219 | Brahmaputra | Transboundary | 210 | 217 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3233, "line_end": 3256, "token_count_estimate": 1310, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0162F1105228", "0162F1505285", "0262F1200591", "0262K0200959", "0262K0501015", "0262L1301400", "0262P0901759", "03082C100413", "03082O110188", "03277L080051", "03278I090631", "03278I140894", "03362N141389", "03582K090169", "03591C160297", "03591D100476", "03591H091975", "03878I010797", "03982E041228", "03982F160687", "03982G141327", "03982N110179"]}}
{"id": "b9c300e8df6ada1d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 549 | 03582K090277 | 29.7794 | 94.6013 | Rong chu | 4146 | Brahmaputra | Transboundary | 184 | 190 | 3 |\n| 550 | 03971P091194 | 28.8321 | 87.5600 | Mangkar chu | 5296 | Brahmaputra | Transboundary | 140 | 145 | 3 |\n| 551 | 03982B131003 | 30.9341 | 92.7745 | Tsenrak chu | 4835 | Brahmaputra | Transboundary | 122 | 126 | 3 |\n| 552 | 03982A160651 | 31.0361 | 92.7869 | | 4908 | Brahmaputra | Transboundary | 94 | 97 | 3 |\n| 553 | 03591H051447 | 28.9395 | 97.2616 | Nechen Kora chu | 4412 | Brahmaputra | Transboundary | 94 | 96 | 3 |\n| 554 | 03982K021609 | 29.5263 | 94.0570 | Bhezhung chu | 4533 | Brahmaputra | Transboundary | 78 | 80 | 3 |\n| 555 | 03878I010836 | 27.8002 | 90.2307 | | 4758 | Brahmaputra | Transboundary | 75 | 77 | 3 |\n| 556 | 03082O110203 | 29.3040 | 95.6403 | Rirung Chu | 3322 | Brahmaputra | Transboundary | 70 | 72 | 3 |\n| 557 | 03082G111265 | 29.2870 | 93.7364 | Tsepa chu | 4562 | Brahmaputra | Transboundary | 59 | 61 | 3 |\n| 558 | 03982N100172 | 30.4734 | 95.5754 | Yu Chu | 4866 | Brahmaputra | Transboundary | 56 | 58 | 3 |\n| 559 | 03362N151439 | 30.4799 | 83.9082 | | 5521 | Brahmaputra | Transboundary | 42 | 43 | 3 |\n| 560 | 03982G141362 | 29.5061 | 93.9108 | Bhezhung chu | 4555 | Brahmaputra | Transboundary | 41 | 42 | 3 |\n| 561 | 03082G111204 | 29.4170 | 93.6911 | Sungkar chu | 4557 | Brahmaputra | Transboundary | 39 | 40 | 3 |\n| 562 | 03277L120073 | 28.0344 | 90.6639 | | 4715 | Brahmaputra | Transboundary | 31 | 32 | 3 |\n| 563 | 03278I130822 | 27.8730 | 90.8033 | Chamkar Chu | 4453 | Brahmaputra | Transboundary | 27 | 28 | 3 |\n| 564 | 03582K050081 | 29.9424 | 94.2874 | Numphu chu | 4296 | Brahmaputra | Transboundary | 27 | 28 | 3 |\n| 565 | 03362N020759 | 30.5295 | 83.0727 | | 5609 | Brahmaputra | Transboundary | 27 | 28 | 3 |\n| 566 | 03077P100417 | 28.5379 | 91.5973 | Rong Chu | 5070 | Brahmaputra | Transboundary | 26 | 27 | 3 |\n| 567 | 03082G141368 | 29.5067 | 93.8399 | Sungkar chu | 4449 | Brahmaputra | Transboundary | 24 | 25 | 3 |\n| 568 | 03582K090197 | 29.8686 | 94.6872 | Rong chu | 4378 | Brahmaputra | Transboundary | 24 | 25 | 3 |\n| 569 | 03582K090138 | 29.9329 | 94.7204 | | 4372 | Brahmaputra | Transboundary | 23 | 24 | 3 |\n| 570 | 03278I090606 | 27.8782 | 90.5705 | Kungong Chu | 4664 | Brahmaputra | Transboundary | 23 | 24 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3259, "line_end": 3282, "token_count_estimate": 1346, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03077P100417", "03082G111204", "03082G111265", "03082G141368", "03082O110203", "03277L120073", "03278I090606", "03278I130822", "03362N020759", "03362N151439", "03582K050081", "03582K090138", "03582K090197", "03582K090277", "03591H051447", "03878I010836", "03971P091194", "03982A160651", "03982B131003", "03982G141362", "03982K021609", "03982N100172"]}}
{"id": "8e5457aa346f588e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 571 | 03982F080572 | 30.0246 | 93.4058 | Drukla chu | 4772 | Brahmaputra | Transboundary | 21 | 22 | 3 |\n| 572 | 03391D100763 | 28.6868 | 96.5844 | Thangkung Chu | 4132 | Brahmaputra | Transboundary | 21 | 22 | 3 |\n| 573 | 03977J110065 | 30.4726 | 90.7023 | Lha chu | 5534 | Brahmaputra | Transboundary | 20 | 21 | 3 |\n| 574 | 03591H061612 | 28.5138 | 97.4927 | | 4469 | Brahmaputra | Transboundary | 20 | 21 | 3 |\n| 575 | 03982K011431 | 29.9482 | 94.1606 | Penam chu | 4052 | Brahmaputra | Transboundary | 19 | 20 | 3 |\n| 576 | 03878I010677 | 27.9914 | 90.2157 | Pho Chu | 4936 | Brahmaputra | Transboundary | 18 | 19 | 3 |\n| 577 | 03982B130955 | 30.9993 | 92.9898 | | 5010 | Brahmaputra | Transboundary | 18 | 19 | 3 |\n| 578 | 03878I010719 | 27.9410 | 90.1895 | Pho Chu | 4626 | Brahmaputra | Transboundary | 18 | 18 | 3 |\n| 579 | 03982K061811 | 29.6821 | 94.4945 | Nyang chu | 4529 | Brahmaputra | Transboundary | 18 | 18 | 3 |\n| 580 | 03982G131224 | 29.8907 | 93.9925 | Nyang chu | 4552 | Brahmaputra | Transboundary | 17 | 17 | 3 |\n| 581 | 03082K031802 | 29.4499 | 94.1552 | Sungkar chu | 4610 | Brahmaputra | Transboundary | 17 | 17 | 3 |\n| 582 | 03982K021520 | 29.6583 | 94.2121 | Nyang chu | 4583 | Brahmaputra | Transboundary | 16 | 17 | 3 |\n| 583 | 03177P130009 | 28.7756 | 91.9814 | Sikung chu | 5013 | Brahmaputra | Transboundary | 16 | 17 | 3 |\n| 584 | 03991C050537 | 29.8672 | 96.3695 | Moru Lung Pa Chu | 4775 | Brahmaputra | Transboundary | 15 | 16 | 3 |\n| 585 | 03982N100100 | 30.5756 | 95.5756 | Poto Chu | 4924 | Brahmaputra | Transboundary | 15 | 16 | 3 |\n| 586 | 03082O110175 | 29.3625 | 95.5984 | Rirung Chu | 4150 | Brahmaputra | Transboundary | 16 | 16 | 3 |\n| 587 | 03977G091381 | 29.8434 | 89.7178 | Nanggung chu | 5296 | Brahmaputra | Transboundary | 14 | 14 | 3 |\n| 588 | 03982K031628 | 29.4989 | 94.1549 | Nyang chu | 4720 | Brahmaputra | Transboundary | 14 | 14 | 3 |\n| 589 | 03582K090180 | 29.8922 | 94.7274 | Rong chu | 4288 | Brahmaputra | Transboundary | 14 | 14 | 3 |\n| 590 | 03982C011039 | 29.9895 | 92.1911 | Zhorong chu | 5123 | Brahmaputra | Transboundary | 14 | 14 | 3 |\n| 591 | 03471G020385 | 29.7284 | 85.2107 | Nazhung Tsangpo | 5374 | Brahmaputra | Transboundary | 14 | 14 | 3 |\n| 592 | 03082K031902 | 29.3213 | 94.1187 | Sungkar chu | 4553 | Brahmaputra | Transboundary | 13 | 13 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3288, "line_end": 3311, "token_count_estimate": 1354, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082K031802", "03082K031902", "03082O110175", "03177P130009", "03391D100763", "03471G020385", "03582K090180", "03591H061612", "03878I010677", "03878I010719", "03977G091381", "03977J110065", "03982B130955", "03982C011039", "03982F080572", "03982G131224", "03982K011431", "03982K021520", "03982K031628", "03982K061811", "03982N100100", "03991C050537"]}}
{"id": "4f08b7bc903303cb", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 593 | 03591D150666 | 28.3944 | 96.8694 | Dzayul chu | 4215 | Brahmaputra | Transboundary | 12 | 12 | 3 |\n| 594 | 03982N150339 | 30.2877 | 95.8707 | Yu Chu | 4724 | Brahmaputra | Transboundary | 12 | 12 | 3 |\n| 595 | 03077P020321 | 28.5922 | 91.1153 | Yidam Chu | 5362 | Brahmaputra | Transboundary | 12 | 12 | 3 |\n| 596 | 03082K041952 | 29.0253 | 94.0661 | Yuisumpu chu | 4289 | Brahmaputra | Transboundary | 12 | 12 | 3 |\n| 597 | 03882J050250 | 30.8418 | 94.3533 | Wok chu | 4973 | Brahmaputra | Transboundary | 12 | 12 | 3 |\n| 598 | 03582J080041 | 30.0165 | 94.4440 | Numphu chu | 4609 | Brahmaputra | Transboundary | 12 | 12 | 3 |\n| 599 | 03982B080829 | 30.1988 | 92.4134 | Zhorong chu | 4955 | Brahmaputra | Transboundary | 11 | 11 | 3 |\n| 600 | 03977J070022 | 30.3655 | 90.4572 | Lha chu | 5598 | Brahmaputra | Transboundary | 11 | 11 | 3 |\n| 601 | 0162F1105218 | 30.4296 | 81.7135 | | 5184 | Indus | Transboundary | 23 | 24 | 2 |\n| 602 | 0262F0700475 | 30.3427 | 81.4126 | | 5734 | Ganga | Transboundary | 30 | 31 | 2 |\n| 603 | 0271D0101825 | 28.8256 | 84.1499 | | 5406 | Ganga | Transboundary | 11 | 11 | 2 |\n| 604 | 0272M1004425 | 27.6449 | 87.5731 | | 3860 | Ganga | Transboundary | 12 | 12 | 2 |\n| 605 | 0271P0503275 | 28.7597 | 87.4166 | | 5458 | Ganga | Transboundary | 16 | 16 | 2 |\n| 606 | 0152L1604178 | 32.0294 | 78.8446 | Pare Chu | 5621 | Indus | Transboundary | 16 | 16 | 2 |\n| 607 | 0271L0902949 | 28.8154 | 86.5292 | | 5326 | Ganga | Transboundary | 32 | 33 | 2 |\n| 608 | 03982G061005 | 29.5406 | 93.3451 | Banang chu | 4631 | Brahmaputra | Transboundary | 100 | 102 | 2 |\n| 609 | 03362J030151 | 30.3975 | 82.1922 | | 5203 | Brahmaputra | Transboundary | 87 | 88 | 2 |\n| 610 | 03082G111206 | 29.4053 | 93.7079 | Sungkar chu | 4505 | Brahmaputra | Transboundary | 74 | 76 | 2 |\n| 611 | 03382O080021 | 29.1286 | 95.4387 | Emra River | 3284 | Brahmaputra | Transboundary | 54 | 55 | 2 |\n| 612 | 03878I010758 | 27.9009 | 90.1733 | Pho Chu | 4671 | Brahmaputra | Transboundary | 45 | 46 | 2 |\n| 613 | 03082O100134 | 29.6328 | 95.6130 | Dihang | 4320 | Brahmaputra | Transboundary | 39 | 40 | 2 |\n| 614 | 03278I090574 | 27.9192 | 90.5367 | Kungong Chu | 4935 | Brahmaputra | Transboundary | 38 | 39 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3318, "line_end": 3341, "token_count_estimate": 1291, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Dihang"], "countries": [], "lake_ids": ["0152L1604178", "0162F1105218", "0262F0700475", "0271D0101825", "0271L0902949", "0271P0503275", "0272M1004425", "03077P020321", "03082G111206", "03082K041952", "03082O100134", "03278I090574", "03362J030151", "03382O080021", "03582J080041", "03591D150666", "03878I010758", "03882J050250", "03977J070022", "03982B080829", "03982G061005", "03982N150339"]}}
{"id": "4867eb67556ecfff", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 615 | 03278I050369 | 27.8097 | 90.4058 | Mangde Chu | 4701 | Brahmaputra | Transboundary | 33 | 34 | 2 |\n| 616 | 03582K090279 | 29.7772 | 94.5601 | Rong chu | 4161 | Brahmaputra | Transboundary | 32 | 33 | 2 |\n| 617 | 03591D100473 | 28.5651 | 96.6364 | Maog chu | 3368 | Brahmaputra | Transboundary | 30 | 31 | 2 |\n| 618 | 03982K051726 | 29.8862 | 94.2657 | Nezhi chu | 4265 | Brahmaputra | Transboundary | 27 | 27 | 2 |\n| 619 | 03362N141398 | 30.6168 | 83.7540 | | 5181 | Brahmaputra | Transboundary | 25 | 26 | 2 |\n| 620 | 03177L120142 | 28.0641 | 90.6483 | Longdo Chu | 5105 | Brahmaputra | Transboundary | 25 | 25 | 2 |\n| 621 | 03878I010796 | 27.8503 | 90.1912 | Pho Chu | 4770 | Brahmaputra | Transboundary | 25 | 25 | 2 |\n| 622 | 03591D100442 | 28.6438 | 96.5891 | Maog chu | 4368 | Brahmaputra | Transboundary | 25 | 25 | 2 |\n| 623 | 03982B120930 | 30.2314 | 92.5627 | Zhorong chu | 5048 | Brahmaputra | Transboundary | 24 | 24 | 2 |\n| 624 | 03591G040829 | 29.2283 | 97.0293 | Dzayulchu | 3952 | Brahmaputra | Transboundary | 23 | 23 | 2 |\n| 625 | 03082C100400 | 29.5324 | 92.5207 | Loyul chu | 5249 | Brahmaputra | Transboundary | 22 | 23 | 2 |\n| 626 | 03982G050885 | 29.9995 | 93.3854 | Drukla chu | 4873 | Brahmaputra | Transboundary | 21 | 21 | 2 |\n| 627 | 03082K041953 | 29.0172 | 94.0386 | Yeul pu chu | 4107 | Brahmaputra | Transboundary | 20 | 20 | 2 |\n| 628 | 03982N150318 | 30.3414 | 95.8557 | Yu Chu | 4851 | Brahmaputra | Transboundary | 19 | 19 | 2 |\n| 629 | 03082C020249 | 29.5357 | 92.0197 | Yarlung tsangpo | 5130 | Brahmaputra | Transboundary | 19 | 19 | 2 |\n| 630 | 03582K090188 | 29.8821 | 94.5817 | | 4418 | Brahmaputra | Transboundary | 19 | 19 | 2 |\n| 631 | 03982K091882 | 29.8487 | 94.5187 | Drrakchi chu | 4594 | Brahmaputra | Transboundary | 18 | 18 | 2 |\n| 632 | 03082K031798 | 29.4541 | 94.0152 | Lapu chu | 4391 | Brahmaputra | Transboundary | 17 | 17 | 2 |\n| 633 | 03082G111202 | 29.4188 | 93.7393 | Sungkar chu | 4594 | Brahmaputra | Transboundary | 15 | 15 | 2 |\n| 634 | 03878E090376 | 27.9703 | 89.6118 | | 4679 | Brahmaputra | Transboundary | 15 | 15 | 2 |\n| 635 | 03591H061560 | 28.5484 | 97.3663 | | 4306 | Brahmaputra | Transboundary | 15 | 15 | 2 |\n| 636 | 03982K051765 | 29.8290 | 94.2818 | Nezhi chu | 4403 | Brahmaputra | Transboundary | 15 | 15 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3345, "line_end": 3368, "token_count_estimate": 1316, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082C020249", "03082C100400", "03082G111202", "03082K031798", "03082K041953", "03177L120142", "03278I050369", "03362N141398", "03582K090188", "03582K090279", "03591D100442", "03591D100473", "03591G040829", "03591H061560", "03878E090376", "03878I010796", "03982B120930", "03982G050885", "03982K051726", "03982K051765", "03982K091882", "03982N150318"]}}
{"id": "2e95071579090040", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 637 | 03362N151423 | 30.5000 | 83.9634 | | 5497 | Brahmaputra | Transboundary | 15 | 15 | 2 |\n| 638 | 03082D050754 | 28.9763 | 92.2526 | Changra chu | 4824 | Brahmaputra | Transboundary | 15 | 15 | 2 |\n| 639 | 03178M010605 | 27.9561 | 91.0564 | Lhodrak Chu | 4055 | Brahmaputra | Transboundary | 14 | 14 | 2 |\n| 640 | 03982B080845 | 30.0806 | 92.3902 | Zhorong chu | 4971 | Brahmaputra | Transboundary | 14 | 14 | 2 |\n| 641 | 03982G060968 | 29.6481 | 93.2526 | Banang chu | 5137 | Brahmaputra | Transboundary | 12 | 12 | 2 |\n| 642 | 03082G151422 | 29.4139 | 93.7827 | Sungkar chu | 4291 | Brahmaputra | Transboundary | 12 | 12 | 2 |\n| 643 | 03082G151430 | 29.4045 | 93.7751 | Sungkar chu | 4666 | Brahmaputra | Transboundary | 12 | 12 | 2 |\n| 644 | 03391D060701 | 28.5394 | 96.3939 | Thangkung Chu | 4010 | Brahmaputra | Transboundary | 12 | 12 | 2 |\n| 645 | 03982K021614 | 29.5217 | 94.0981 | Nyang chu | 4520 | Brahmaputra | Transboundary | 12 | 12 | 2 |\n| 646 | 03591C110119 | 29.4056 | 96.6806 | Zo chu | 4834 | Brahmaputra | Transboundary | 12 | 12 | 2 |\n| 647 | 03591D130567 | 28.8640 | 96.8297 | Kangri Karpo chu | 4306 | Brahmaputra | Transboundary | 11 | 11 | 2 |\n| 648 | 03082O080077 | 29.1833 | 95.4513 | Shumo Chu | 3922 | Brahmaputra | Transboundary | 11 | 11 | 2 |\n| 649 | 03982N070073 | 30.3924 | 95.4190 | Poto Chu | 4317 | Brahmaputra | Transboundary | 11 | 11 | 2 |\n| 650 | 03582K090128 | 29.9428 | 94.7169 | | 4554 | Brahmaputra | Transboundary | 11 | 11 | 2 |\n| 651 | 03182D120098 | 28.2064 | 92.7423 | Chayul chu | 5176 | Brahmaputra | Transboundary | 11 | 11 | 2 |\n| 652 | 0271P0803352 | 28.1646 | 87.4178 | | 4818 | Ganga | Transboundary | 11 | 11 | 1 |\n| 653 | 0262G0900775 | 29.8983 | 81.7467 | | 4692 | Ganga | Transboundary | 13 | 13 | 1 |\n| 654 | 0162A1504913 | 31.4948 | 80.9854 | | 5386 | Indus | Transboundary | 25 | 25 | 1 |\n| 655 | 0271P0903422 | 28.8319 | 86.5221 | Bum Chu | 5319 | Ganga | Transboundary | 281 | 284 | 1 |\n| 656 | 0271P0603290 | 28.7423 | 87.3824 | | 5501 | Ganga | Transboundary | 42 | 42 | 1 |\n| 657 | 0262K0701165 | 29.4077 | 82.4296 | | 4415 | Ganga | Transboundary | 20 | 20 | 1 |\n| 658 | 0262F0700447 | 30.4168 | 81.4266 | | 5496 | Ganga | Transboundary | 18 | 18 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3373, "line_end": 3396, "token_count_estimate": 1300, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0162A1504913", "0262F0700447", "0262G0900775", "0262K0701165", "0271P0603290", "0271P0803352", "0271P0903422", "03082D050754", "03082G151422", "03082G151430", "03082O080077", "03178M010605", "03182D120098", "03362N151423", "03391D060701", "03582K090128", "03591C110119", "03591D130567", "03982B080845", "03982G060968", "03982K021614", "03982N070073"]}}
{"id": "640b381c0ce1df05", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 659 | 0161F0304813 | 34.2987 | 81.2018 | | 5274 | Indus | Transboundary | 61 | 62 | 1 |\n| 660 | 03982B130991 | 30.9763 | 92.9414 | Tsenrak chu | 4904 | Brahmaputra | Transboundary | 442 | 447 | 1 |\n| 661 | 03982B131001 | 30.9345 | 92.8285 | Tsenrak chu | 4886 | Brahmaputra | Transboundary | 223 | 226 | 1 |\n| 662 | 03362N151463 | 30.4308 | 83.9965 | | 5429 | Brahmaputra | Transboundary | 206 | 209 | 1 |\n| 663 | 03982K051709 | 29.9154 | 94.2804 | Nezhi chu | 4385 | Brahmaputra | Transboundary | 183 | 185 | 1 |\n| 664 | 03578E050199 | 27.9593 | 89.3970 | | 4576 | Brahmaputra | Transboundary | 182 | 184 | 1 |\n| 665 | 03982B131009 | 30.9063 | 92.8174 | Tsenrak chu | 4960 | Brahmaputra | Transboundary | 177 | 178 | 1 |\n| 666 | 03982C051134 | 29.7788 | 92.3881 | Medroma chu | 4916 | Brahmaputra | Transboundary | 155 | 156 | 1 |\n| 667 | 03982K021617 | 29.5176 | 94.1207 | Nyang chu | 4501 | Brahmaputra | Transboundary | 118 | 119 | 1 |\n| 668 | 03982E041274 | 31.1035 | 93.1436 | | 5024 | Brahmaputra | Transboundary | 101 | 102 | 1 |\n| 669 | 03591C030035 | 29.2568 | 96.2459 | Kangri Karpo chu | 4432 | Brahmaputra | Transboundary | 89 | 90 | 1 |\n| 670 | 03977J150171 | 30.4790 | 90.9656 | Lha chu | 5035 | Brahmaputra | Transboundary | 86 | 87 | 1 |\n| 671 | 03177P040351 | 28.0339 | 91.0032 | | 4200 | Brahmaputra | Transboundary | 77 | 78 | 1 |\n| 672 | 03671G140093 | 29.5580 | 85.8798 | Raga tsangpo | 5186 | Brahmaputra | Transboundary | 56 | 57 | 1 |\n| 673 | 03578E050197 | 27.9628 | 89.4127 | | 4576 | Brahmaputra | Transboundary | 45 | 46 | 1 |\n| 674 | 03678M140387 | 27.7455 | 91.8703 | Tsona Chu | 4345 | Brahmaputra | Transboundary | 45 | 45 | 1 |\n| 675 | 03591H051461 | 28.9153 | 97.3501 | | 4016 | Brahmaputra | Transboundary | 44 | 44 | 1 |\n| 676 | 03278I090662 | 27.7958 | 90.6342 | Chamkar Chu | 4462 | Brahmaputra | Transboundary | 41 | 41 | 1 |\n| 677 | 03178M050643 | 27.9314 | 91.2697 | | 4732 | Brahmaputra | Transboundary | 40 | 40 | 1 |\n| 678 | 03977G091364 | 29.8728 | 89.5961 | Nanggung chu | 5342 | Brahmaputra | Transboundary | 40 | 40 | 1 |\n| 679 | 03278I130807 | 27.9067 | 90.8122 | Chamkar Chu | 4804 | Brahmaputra | Transboundary | 36 | 36 | 1 |\n| 680 | 03982C061148 | 29.6392 | 92.2528 | Medroma chu | 4862 | Brahmaputra | Transboundary | 35 | 36 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3398, "line_end": 3421, "token_count_estimate": 1302, "basins": ["Brahmaputra", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0161F0304813", "03177P040351", "03178M050643", "03278I090662", "03278I130807", "03362N151463", "03578E050197", "03578E050199", "03591C030035", "03591H051461", "03671G140093", "03678M140387", "03977G091364", "03977J150171", "03982B130991", "03982B131001", "03982B131009", "03982C051134", "03982C061148", "03982E041274", "03982K021617", "03982K051709"]}}
{"id": "53069ca06f124e81", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 681 | 03082O110176 | 29.3594 | 95.5833 | Rirung Chu | 4193 | Brahmaputra | Transboundary | 34 | 34 | 1 |\n| 682 | 03982F080532 | 30.1164 | 93.2743 | Drukla chu | 4630 | Brahmaputra | Transboundary | 33 | 33 | 1 |\n| 683 | 03278I050385 | 27.7917 | 90.4210 | Mangde Chu | 4527 | Brahmaputra | Transboundary | 28 | 28 | 1 |\n| 684 | 03982K021537 | 29.6107 | 94.1541 | Nyang chu | 4690 | Brahmaputra | Transboundary | 26 | 26 | 1 |\n| 685 | 03982B160150 | 30.1971 | 92.9758 | Nyem chu | 4995 | Brahmaputra | Transboundary | 25 | 25 | 1 |\n| 686 | 03678E100432 | 27.5830 | 89.4993 | | 4421 | Brahmaputra | Transboundary | 25 | 25 | 1 |\n| 687 | 03982A160563 | 31.1538 | 92.8170 | | 5030 | Brahmaputra | Transboundary | 24 | 24 | 1 |\n| 688 | 03977K071635 | 29.4750 | 90.4140 | | 5325 | Brahmaputra | Transboundary | 21 | 21 | 1 |\n| 689 | 03082K031861 | 29.3750 | 94.0187 | Sungkar chu | 4418 | Brahmaputra | Transboundary | 20 | 20 | 1 |\n| 690 | 03878I010853 | 27.7569 | 90.1955 | | 4442 | Brahmaputra | Transboundary | 20 | 20 | 1 |\n| 691 | 03082K041949 | 29.0313 | 94.0554 | Yuisumpu chu | 4240 | Brahmaputra | Transboundary | 19 | 19 | 1 |\n| 692 | 03278I090603 | 27.8812 | 90.5665 | Kungong Chu | 4666 | Brahmaputra | Transboundary | 19 | 19 | 1 |\n| 693 | 03177P030314 | 28.3052 | 91.0697 | Lhodrak Nub chu | 4727 | Brahmaputra | Transboundary | 19 | 19 | 1 |\n| 694 | 03982A120553 | 31.0858 | 92.7237 | | 5025 | Brahmaputra | Transboundary | 18 | 18 | 1 |\n| 695 | 03982F080563 | 30.0595 | 93.2774 | Drukla chu | 4928 | Brahmaputra | Transboundary | 18 | 18 | 1 |\n| 696 | 03591H081929 | 28.0610 | 97.3569 | | 4344 | Brahmaputra | Transboundary | 16 | 16 | 1 |\n| 697 | 03982F160679 | 30.0753 | 93.9745 | Ortse chu | 4615 | Brahmaputra | Transboundary | 16 | 16 | 1 |\n| 698 | 03582K090193 | 29.8751 | 94.6156 | | 4588 | Brahmaputra | Transboundary | 16 | 16 | 1 |\n| 699 | 03977J110067 | 30.4675 | 90.7081 | Lha chu | 5512 | Brahmaputra | Transboundary | 16 | 16 | 1 |\n| 700 | 03082O100142 | 29.5993 | 95.7399 | Chendruk Chu | 4010 | Brahmaputra | Transboundary | 15 | 15 | 1 |\n| 701 | 03082G151475 | 29.3050 | 93.8346 | Sungkar chu | 4614 | Brahmaputra | Transboundary | 14 | 14 | 1 |\n| 702 | 03982N110189 | 30.4695 | 95.5876 | Yu Chu | 4876 | Brahmaputra | Transboundary | 14 | 14 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3426, "line_end": 3449, "token_count_estimate": 1307, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082G151475", "03082K031861", "03082K041949", "03082O100142", "03082O110176", "03177P030314", "03278I050385", "03278I090603", "03582K090193", "03591H081929", "03678E100432", "03878I010853", "03977J110067", "03977K071635", "03982A120553", "03982A160563", "03982B160150", "03982F080532", "03982F080563", "03982F160679", "03982K021537", "03982N110189"]}}
{"id": "22ccfd2ca70d39e1", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 703 | 03591G020740 | 29.6091 | 97.0748 | Tsengo chu | 5279 | Brahmaputra | Transboundary | 14 | 14 | 1 |\n| 704 | 03977N160429 | 30.2087 | 91.9663 | Tsenrak chu | 4993 | Brahmaputra | Transboundary | 13 | 13 | 1 |\n| 705 | 03082C110427 | 29.4882 | 92.6443 | Loyul chu | 5225 | Brahmaputra | Transboundary | 13 | 13 | 1 |\n| 706 | 03977K071633 | 29.4840 | 90.4361 | | 5332 | Brahmaputra | Transboundary | 13 | 13 | 1 |\n| 707 | 03678A140086 | 27.6163 | 88.8811 | Khangphu Chu | 4670 | Brahmaputra | Transboundary | 13 | 13 | 1 |\n| 708 | 03877H120008 | 28.1391 | 89.7417 | | 4842 | Brahmaputra | Transboundary | 13 | 13 | 1 |\n| 709 | 03982O130412 | 29.8703 | 95.9819 | Po Tsangpo | 4707 | Brahmaputra | Transboundary | 12 | 12 | 1 |\n| 710 | 03278I090617 | 27.8569 | 90.7397 | Panggyetangka | 4406 | Brahmaputra | Transboundary | 12 | 12 | 1 |\n| 711 | 03878I020863 | 27.7377 | 90.1596 | | 4447 | Brahmaputra | Transboundary | 12 | 12 | 1 |\n| 712 | 03182H020220 | 28.6577 | 93.1439 | Subansiri | 4518 | Brahmaputra | Transboundary | 12 | 12 | 1 |\n| 713 | 03178I130456 | 27.9845 | 90.8268 | Lhodrak Chu | 4737 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 714 | 03977K100285 | 29.6027 | 90.6567 | Tolung chu | 5451 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 715 | 03591D100450 | 28.6283 | 96.5333 | Maog chu | 4260 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 716 | 03082G081139 | 29.1968 | 93.3456 | Yarlung tsangpo | 4841 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 717 | 03877L040287 | 28.0067 | 90.1062 | Pho Chu | 4544 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 718 | 03182D120154 | 28.0013 | 92.6408 | Lhanga chu | 5269 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 719 | 03678M090230 | 27.8238 | 91.5594 | Kulong Chu | 4674 | Brahmaputra | Transboundary | 11 | 11 | 1 |\n| 720 | 0262P1401797 | 28.7117 | 83.9202 | | 4986 | Ganga | Transboundary | 11 | 11 | 0 |\n| 721 | 0262O0401431 | 29.1082 | 83.0256 | | 4884 | Ganga | Transboundary | 13 | 13 | 0 |\n| 722 | 0262F1600708 | 30.1290 | 81.7814 | | 5015 | Ganga | Transboundary | 76 | 76 | 0 |\n| 723 | 0262F0700485 | 30.3217 | 81.3755 | | 5542 | Ganga | Transboundary | 14 | 14 | 0 |\n| 724 | 0143E0100708 | 35.8709 | 73.0732 | Anari Gol | 4617 | Indus | Transboundary | 16 | 16 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3453, "line_end": 3476, "token_count_estimate": 1290, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": ["0143E0100708", "0262F0700485", "0262F1600708", "0262O0401431", "0262P1401797", "03082C110427", "03082G081139", "03178I130456", "03182D120154", "03182H020220", "03278I090617", "03591D100450", "03591G020740", "03678A140086", "03678M090230", "03877H120008", "03877L040287", "03878I020863", "03977K071633", "03977K100285", "03977N160429", "03982O130412"]}}
{"id": "b71b2f712d1f6a5e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 725 | 0262K0701172 | 29.3843 | 82.4238 | | 4434 | Ganga | Transboundary | 47 | 47 | 0 |\n| 726 | 0262P1401790 | 28.7257 | 83.8901 | | 5103 | Ganga | Transboundary | 15 | 15 | 0 |\n| 727 | 0262G0900799 | 29.7729 | 81.5268 | | 4576 | Ganga | Transboundary | 50 | 50 | 0 |\n| 728 | 0153M1304734 | 31.9093 | 79.9875 | Indus River | 5558 | Indus | Transboundary | 14 | 14 | 0 |\n| 729 | 0271H0202020 | 28.5853 | 85.0219 | | 5047 | Ganga | Transboundary | 12 | 12 | 0 |\n| 730 | 0162A1504919 | 31.4745 | 80.9919 | | 5408 | Indus | Transboundary | 27 | 27 | 0 |\n| 731 | 0153I1304615 | 31.9925 | 78.8450 | | 5613 | Indus | Transboundary | 21 | 21 | 0 |\n| 732 | 0162A0104838 | 31.9005 | 80.0013 | Indus River | 5476 | Indus | Transboundary | 20 | 20 | 0 |\n| 733 | 0262O1201533 | 29.1749 | 83.7481 | | 5360 | Ganga | Transboundary | 17 | 17 | 0 |\n| 734 | 0271P1103497 | 28.2683 | 87.6337 | | 5674 | Ganga | Transboundary | 10 | 10 | 0 |\n| 735 | 03362K090630 | 29.9853 | 82.5355 | | 4829 | Brahmaputra | Transboundary | 393 | 394 | 0 |\n| 736 | 03591D100489 | 28.5164 | 96.6988 | Dulai chu | 3330 | Brahmaputra | Transboundary | 299 | 300 | 0 |\n| 737 | 03578E050203 | 27.9414 | 89.3886 | | 4572 | Brahmaputra | Transboundary | 279 | 279 | 0 |\n| 738 | 03678M130326 | 27.8415 | 91.8919 | Nyamjang Chu | 4638 | Brahmaputra | Transboundary | 146 | 146 | 0 |\n| 739 | 03577H070058 | 28.3268 | 89.4303 | | 4426 | Brahmaputra | Transboundary | 140 | 140 | 0 |\n| 740 | 03391C040387 | 29.0912 | 96.2112 | Dri Chu | 4188 | Brahmaputra | Transboundary | 101 | 101 | 0 |\n| 741 | 03082O080078 | 29.1799 | 95.4857 | Shumo Chu | 3533 | Brahmaputra | Transboundary | 94 | 94 | 0 |\n| 742 | 03882F020058 | 30.5348 | 93.0584 | Arsashung chu | 4817 | Brahmaputra | Transboundary | 87 | 87 | 0 |\n| 743 | 03177L160272 | 28.0033 | 90.9055 | | 4754 | Brahmaputra | Transboundary | 87 | 87 | 0 |\n| 744 | 03591H011177 | 28.7826 | 97.1528 | Dzayul chu | 3712 | Brahmaputra | Transboundary | 83 | 83 | 0 |\n| 745 | 03982K091885 | 29.8077 | 94.5008 | Drrakchi chu | 4305 | Brahmaputra | Transboundary | 56 | 56 | 0 |\n| 746 | 03982B080012 | 30.0492 | 92.4431 | Nyang chu | 4993 | Brahmaputra | Transboundary | 53 | 53 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3481, "line_end": 3504, "token_count_estimate": 1280, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0153I1304615", "0153M1304734", "0162A0104838", "0162A1504919", "0262G0900799", "0262K0701172", "0262O1201533", "0262P1401790", "0271H0202020", "0271P1103497", "03082O080078", "03177L160272", "03362K090630", "03391C040387", "03577H070058", "03578E050203", "03591D100489", "03591H011177", "03678M130326", "03882F020058", "03982B080012", "03982K091885"]}}
{"id": "f868e6c13e31e38c", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 747 | 03982K021582 | 29.5446 | 94.0669 | Bhezhung chu | 4299 | Brahmaputra | Transboundary | 51 | 51 | 0 |\n| 748 | 03082K031912 | 29.2956 | 94.2021 | Yarlung tsangpo | 3931 | Brahmaputra | Transboundary | 43 | 43 | 0 |\n| 749 | 03982N110214 | 30.4358 | 95.6018 | Yu Chu | 5002 | Brahmaputra | Transboundary | 34 | 34 | 0 |\n| 750 | 03277L120133 | 28.0029 | 90.7054 | | 4927 | Brahmaputra | Transboundary | 33 | 33 | 0 |\n| 751 | 03591D130583 | 28.8295 | 96.7894 | Kangri Karpo chu | 3683 | Brahmaputra | Transboundary | 32 | 32 | 0 |\n| 752 | 03278I060420 | 27.7199 | 90.3256 | | 4231 | Brahmaputra | Transboundary | 31 | 31 | 0 |\n| 753 | 03391D010489 | 28.9134 | 96.1778 | Dri Chu | 3802 | Brahmaputra | Transboundary | 28 | 28 | 0 |\n| 754 | 03082G151461 | 29.3407 | 93.7885 | Sungkar chu | 4497 | Brahmaputra | Transboundary | 27 | 27 | 0 |\n| 755 | 03277B120003 | 30.1052 | 88.6178 | Lengra chu | 4903 | Brahmaputra | Transboundary | 27 | 27 | 0 |\n| 756 | 03582J080003 | 30.2140 | 94.3834 | Nunkhu Phu chu | 4715 | Brahmaputra | Transboundary | 27 | 27 | 0 |\n| 757 | 03982C051109 | 29.8077 | 92.3949 | Medroma chu | 5052 | Brahmaputra | Transboundary | 25 | 25 | 0 |\n| 758 | 03082O110200 | 29.3240 | 95.5902 | Rirung Chu | 4016 | Brahmaputra | Transboundary | 25 | 25 | 0 |\n| 759 | 03591C030019 | 29.3236 | 96.2094 | Kangri Karpo chu | 4333 | Brahmaputra | Transboundary | 25 | 25 | 0 |\n| 760 | 03982N070071 | 30.4204 | 95.2958 | Poto Chu | 4404 | Brahmaputra | Transboundary | 22 | 22 | 0 |\n| 761 | 03177L160261 | 28.0125 | 90.9836 | | 4277 | Brahmaputra | Transboundary | 21 | 21 | 0 |\n| 762 | 03382O080047 | 29.0719 | 95.4048 | Emra River | 3864 | Brahmaputra | Transboundary | 19 | 19 | 0 |\n| 763 | 03278I130836 | 27.8298 | 90.8113 | Chamkar Chu | 4367 | Brahmaputra | Transboundary | 18 | 18 | 0 |\n| 764 | 03982B130995 | 30.9566 | 92.8252 | Tsenrak chu | 4920 | Brahmaputra | Transboundary | 17 | 17 | 0 |\n| 765 | 03082C060289 | 29.5466 | 92.4794 | Gochumu Chu | 5077 | Brahmaputra | Transboundary | 16 | 16 | 0 |\n| 766 | 03982K031632 | 29.4931 | 94.1782 | Nyang chu | 4512 | Brahmaputra | Transboundary | 16 | 16 | 0 |\n| 767 | 03878I010763 | 27.8942 | 90.1804 | Pho Chu | 4812 | Brahmaputra | Transboundary | 16 | 16 | 0 |\n| 768 | 03082C100397 | 29.5405 | 92.6869 | Gyab Pucong chu | 5120 | Brahmaputra | Transboundary | 16 | 16 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3508, "line_end": 3531, "token_count_estimate": 1326, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082C060289", "03082C100397", "03082G151461", "03082K031912", "03082O110200", "03177L160261", "03277B120003", "03277L120133", "03278I060420", "03278I130836", "03382O080047", "03391D010489", "03582J080003", "03591C030019", "03591D130583", "03878I010763", "03982B130995", "03982C051109", "03982K021582", "03982K031632", "03982N070071", "03982N110214"]}}
{"id": "aa28a7fe17366ab9", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 769 | 03082O080076 | 29.1881 | 95.4532 | Shumo Chu | 3866 | Brahmaputra | Transboundary | 15 | 15 | 0 |\n| 770 | 03977C151305 | 29.5001 | 88.9498 | Yarlung tsangpo | 5391 | Brahmaputra | Transboundary | 15 | 15 | 0 |\n| 771 | 03982C011086 | 29.8304 | 92.2423 | Medroma chu | 5179 | Brahmaputra | Transboundary | 15 | 15 | 0 |\n| 772 | 03982G061001 | 29.5554 | 93.2686 | Banang chu | 4784 | Brahmaputra | Transboundary | 14 | 14 | 0 |\n| 773 | 03982E041289 | 31.0810 | 93.1627 | | 5071 | Brahmaputra | Transboundary | 14 | 14 | 0 |\n| 774 | 03591H071713 | 28.4262 | 97.4014 | | 4289 | Brahmaputra | Transboundary | 14 | 14 | 0 |\n| 775 | 03982C011061 | 29.9285 | 92.0683 | Mangra chu | 4939 | Brahmaputra | Transboundary | 14 | 14 | 0 |\n| 776 | 03177L160274 | 28.0011 | 90.9323 | | 4663 | Brahmaputra | Transboundary | 14 | 14 | 0 |\n| 777 | 03591H011055 | 28.9559 | 97.1953 | Nechen Kora chu | 4271 | Brahmaputra | Transboundary | 13 | 13 | 0 |\n| 778 | 03082G060980 | 29.5548 | 93.4544 | Pulung chu | 4911 | Brahmaputra | Transboundary | 13 | 13 | 0 |\n| 779 | 03382O080026 | 29.1173 | 95.4796 | Emra River | 3706 | Brahmaputra | Transboundary | 13 | 13 | 0 |\n| 780 | 03982K031636 | 29.4913 | 94.1313 | Nyang chu | 4608 | Brahmaputra | Transboundary | 12 | 12 | 0 |\n| 781 | 03082K031786 | 29.4651 | 94.1029 | Lapu chu | 4516 | Brahmaputra | Transboundary | 12 | 12 | 0 |\n| 782 | 03591H061506 | 28.6912 | 97.3365 | | 4422 | Brahmaputra | Transboundary | 12 | 12 | 0 |\n| 783 | 03582K090254 | 29.8244 | 94.5985 | Rong chu | 4564 | Brahmaputra | Transboundary | 12 | 12 | 0 |\n| 784 | 03678E030342 | 27.3252 | 89.1395 | Yak Chu | 4092 | Brahmaputra | Transboundary | 11 | 11 | 0 |\n| 785 | 03682H140020 | 28.7276 | 93.7951 | | 4205 | Brahmaputra | Transboundary | 11 | 11 | 0 |\n| 786 | 03977K061627 | 29.5039 | 90.4388 | | 5352 | Brahmaputra | Transboundary | 11 | 11 | 0 |\n| 787 | 03982B080005 | 30.0722 | 92.4574 | Nyang chu | 5096 | Brahmaputra | Transboundary | 11 | 11 | 0 |\n| 788 | 03278I050397 | 27.7804 | 90.3997 | Mangde Chu | 4532 | Brahmaputra | Transboundary | 11 | 11 | 0 |\n| 789 | 03082K031921 | 29.2709 | 94.1669 | Yarlung tsangpo | 4523 | Brahmaputra | Transboundary | 10 | 10 | 0 |\n| 790 | 03178M010610 | 27.9017 | 91.2418 | | 4180 | Brahmaputra | Transboundary | 10 | 10 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3538, "line_end": 3561, "token_count_estimate": 1328, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082G060980", "03082K031786", "03082K031921", "03082O080076", "03177L160274", "03178M010610", "03278I050397", "03382O080026", "03582K090254", "03591H011055", "03591H061506", "03591H071713", "03678E030342", "03682H140020", "03977C151305", "03977K061627", "03982B080005", "03982C011061", "03982C011086", "03982E041289", "03982G061001", "03982K031636"]}}
{"id": "3057a85bf411c471", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 791 | 0272M0504182 | 27.9317 | 87.2928 | | 4373 | Ganga | Transboundary | 12 | 12 | -1 |\n| 792 | 0262K0200953 | 29.6753 | 82.1930 | | 4223 | Ganga | Transboundary | 15 | 15 | -1 |\n| 793 | 0272M0504192 | 27.9182 | 87.3064 | | 4416 | Ganga | Transboundary | 11 | 11 | -1 |\n| 794 | 0161B1504760 | 34.3072 | 80.9820 | | 5729 | Indus | Transboundary | 43 | 42 | -1 |\n| 795 | 0262J0400880 | 30.0674 | 82.1270 | Lurupua Khola | 4829 | Ganga | Transboundary | 62 | 62 | -1 |\n| 796 | 0272M1004438 | 27.6296 | 87.6955 | | 4179 | Ganga | Transboundary | 14 | 14 | -1 |\n| 797 | 0271D1401952 | 28.6166 | 84.9120 | | 4683 | Ganga | Transboundary | 10 | 10 | -1 |\n| 798 | 03582K050069 | 29.9587 | 94.2919 | Numphu chu | 4282 | Brahmaputra | Transboundary | 134 | 133 | -1 |\n| 799 | 03982K021624 | 29.5045 | 94.1329 | Nyang chu | 4577 | Brahmaputra | Transboundary | 102 | 101 | -1 |\n| 800 | 03577H080090 | 28.0251 | 89.4278 | | 4791 | Brahmaputra | Transboundary | 63 | 63 | -1 |\n| 801 | 03082G081111 | 29.2404 | 93.2762 | Yarlung tsangpo | 4914 | Brahmaputra | Transboundary | 59 | 58 | -1 |\n| 802 | 03591H071732 | 28.4118 | 97.4649 | | 4300 | Brahmaputra | Transboundary | 57 | 56 | -1 |\n| 803 | 03082G111230 | 29.3702 | 93.6938 | Sungkar chu | 4725 | Brahmaputra | Transboundary | 49 | 48 | -1 |\n| 804 | 03982A120552 | 31.1024 | 92.6988 | | 5003 | Brahmaputra | Transboundary | 49 | 48 | -1 |\n| 805 | 03982B120947 | 30.1889 | 92.5406 | Zhorong chu | 4955 | Brahmaputra | Transboundary | 42 | 42 | -1 |\n| 806 | 03982G101167 | 29.5120 | 93.7135 | Nyang chu | 4344 | Brahmaputra | Transboundary | 39 | 39 | -1 |\n| 807 | 03982A160647 | 31.0513 | 92.8115 | | 4930 | Brahmaputra | Transboundary | 35 | 35 | -1 |\n| 808 | 03582K050098 | 29.9133 | 94.4992 | Numphu chu | 4443 | Brahmaputra | Transboundary | 34 | 34 | -1 |\n| 809 | 03082G111246 | 29.3282 | 93.6774 | Pulung chu | 4628 | Brahmaputra | Transboundary | 33 | 33 | -1 |\n| 810 | 03362J030022 | 30.4776 | 82.1728 | | 5250 | Brahmaputra | Transboundary | 31 | 31 | -1 |\n| 811 | 03982K011463 | 29.8958 | 94.2090 | Nezhi chu | 4444 | Brahmaputra | Transboundary | 28 | 28 | -1 |\n| 812 | 03671O010006 | 29.8236 | 87.0162 | Lewa Tsangpo | 4954 | Brahmaputra | Transboundary | 26 | 26 | -1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3565, "line_end": 3588, "token_count_estimate": 1309, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0161B1504760", "0262J0400880", "0262K0200953", "0271D1401952", "0272M0504182", "0272M0504192", "0272M1004438", "03082G081111", "03082G111230", "03082G111246", "03362J030022", "03577H080090", "03582K050069", "03582K050098", "03591H071732", "03671O010006", "03982A120552", "03982A160647", "03982B120947", "03982G101167", "03982K011463", "03982K021624"]}}
{"id": "13377d33b0bd61a5", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 813 | 03982K051770 | 29.8195 | 94.3582 | Zha chu | 4439 | Brahmaputra | Transboundary | 24 | 24 | -1 |\n| 814 | 03877H160185 | 28.0067 | 89.8635 | | 4846 | Brahmaputra | Transboundary | 23 | 23 | -1 |\n| 815 | 03878I010694 | 27.9636 | 90.1466 | Pho Chu | 4882 | Brahmaputra | Transboundary | 23 | 23 | -1 |\n| 816 | 03082C110491 | 29.2997 | 92.6508 | Yarlung tsangpo | 5149 | Brahmaputra | Transboundary | 22 | 22 | -1 |\n| 817 | 03982G141336 | 29.5454 | 93.9100 | Bhezhung chu | 4840 | Brahmaputra | Transboundary | 21 | 21 | -1 |\n| 818 | 03391D100768 | 28.6755 | 96.5105 | Thangkung Chu | 4310 | Brahmaputra | Transboundary | 20 | 20 | -1 |\n| 819 | 03591D130582 | 28.8324 | 96.8204 | Kangri Karpo chu | 4305 | Brahmaputra | Transboundary | 20 | 20 | -1 |\n| 820 | 03082G151397 | 29.4653 | 93.9961 | Lapu chu | 4471 | Brahmaputra | Transboundary | 20 | 20 | -1 |\n| 821 | 03082G111266 | 29.2867 | 93.7012 | Yarlung tsangpo | 4643 | Brahmaputra | Transboundary | 18 | 18 | -1 |\n| 822 | 03278I090478 | 27.9871 | 90.5054 | Kungong Chu | 5199 | Brahmaputra | Transboundary | 18 | 18 | -1 |\n| 823 | 03278I130872 | 27.7569 | 90.8075 | Panggyetangka | 4181 | Brahmaputra | Transboundary | 18 | 18 | -1 |\n| 824 | 03982K031685 | 29.4241 | 94.1969 | Nyang chu | 4316 | Brahmaputra | Transboundary | 18 | 18 | -1 |\n| 825 | 03591D150645 | 28.4475 | 96.8854 | Dzayul chu | 4113 | Brahmaputra | Transboundary | 18 | 18 | -1 |\n| 826 | 03082G121362 | 29.0028 | 93.6677 | Nelung phu chu | 4558 | Brahmaputra | Transboundary | 17 | 17 | -1 |\n| 827 | 03878I010839 | 27.7931 | 90.2000 | | 4556 | Brahmaputra | Transboundary | 17 | 17 | -1 |\n| 828 | 03082G111253 | 29.3125 | 93.6274 | Pulung chu | 4835 | Brahmaputra | Transboundary | 16 | 16 | -1 |\n| 829 | 03082G151405 | 29.4541 | 93.9785 | Sungkar chu | 4670 | Brahmaputra | Transboundary | 16 | 16 | -1 |\n| 830 | 03582K050107 | 29.8839 | 94.4464 | Numphu chu | 4632 | Brahmaputra | Transboundary | 16 | 16 | -1 |\n| 831 | 03591G040887 | 29.0152 | 97.2488 | | 4409 | Brahmaputra | Transboundary | 15 | 15 | -1 |\n| 832 | 03382O080001 | 29.1761 | 95.4471 | Emra River | 3906 | Brahmaputra | Transboundary | 14 | 14 | -1 |\n| 833 | 03982B110026 | 30.3267 | 92.7201 | Nyang chu | 5213 | Brahmaputra | Transboundary | 14 | 14 | -1 |\n| 834 | 03591H071660 | 28.4818 | 97.3223 | | 4348 | Brahmaputra | Transboundary | 13 | 13 | -1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3593, "line_end": 3616, "token_count_estimate": 1320, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082C110491", "03082G111253", "03082G111266", "03082G121362", "03082G151397", "03082G151405", "03278I090478", "03278I130872", "03382O080001", "03391D100768", "03582K050107", "03591D130582", "03591D150645", "03591G040887", "03591H071660", "03877H160185", "03878I010694", "03878I010839", "03982B110026", "03982G141336", "03982K031685", "03982K051770"]}}
{"id": "e3ac8736c7b07c07", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 835 | 03082G030905 | 29.2839 | 93.1167 | Yarlung tsangpo | 4765 | Brahmaputra | Transboundary | 13 | 13 | -1 |\n| 836 | 03678M130368 | 27.7848 | 91.9478 | Tsona Chu | 4484 | Brahmaputra | Transboundary | 13 | 13 | -1 |\n| 837 | 03277G090111 | 29.8944 | 89.6200 | Lhabu chu | 5460 | Brahmaputra | Transboundary | 12 | 12 | -1 |\n| 838 | 03082O110204 | 29.3038 | 95.7246 | Rirung Chu | 3952 | Brahmaputra | Transboundary | 12 | 12 | -1 |\n| 839 | 03382O080024 | 29.1201 | 95.3968 | Emra River | 3914 | Brahmaputra | Transboundary | 11 | 11 | -1 |\n| 840 | 03982G111185 | 29.4516 | 93.6202 | Nyang chu | 4894 | Brahmaputra | Transboundary | 11 | 11 | -1 |\n| 841 | 03278I090577 | 27.9152 | 90.5594 | Kungong Chu | 4939 | Brahmaputra | Transboundary | 11 | 11 | -1 |\n| 842 | 03982G010738 | 29.8086 | 93.0179 | Nyang chu | 5360 | Brahmaputra | Transboundary | 11 | 11 | -1 |\n| 843 | 03278I050364 | 27.8140 | 90.3625 | | 4776 | Brahmaputra | Transboundary | 11 | 11 | -1 |\n| 844 | 03982F080546 | 30.0786 | 93.3594 | Drukla chu | 4950 | Brahmaputra | Transboundary | 10 | 10 | -1 |\n| 845 | 03591C080062 | 29.1275 | 96.3994 | Kangri Karpo chu | 4066 | Brahmaputra | Transboundary | 10 | 10 | -1 |\n| 846 | 03591C150289 | 29.2537 | 96.9651 | Zo chu | 4631 | Brahmaputra | Transboundary | 10 | 10 | -1 |\n| 847 | 03278I090657 | 27.8031 | 90.5849 | Dur Chu | 4647 | Brahmaputra | Transboundary | 10 | 10 | -1 |\n| 848 | 03678E100446 | 27.5568 | 89.5651 | | 4189 | Brahmaputra | Transboundary | 10 | 10 | -1 |\n| 849 | 0271H1502430 | 28.2934 | 85.8304 | | 5023 | Ganga | Transboundary | 29 | 28 | -2 |\n| 850 | 0262K0701173 | 29.3823 | 82.4101 | | 4287 | Ganga | Transboundary | 10 | 10 | -2 |\n| 851 | 0271P0803407 | 28.0227 | 87.2699 | | 4636 | Ganga | Transboundary | 10 | 10 | -2 |\n| 852 | 0162F1005203 | 30.5147 | 81.7015 | | 4818 | Indus | Transboundary | 10 | 10 | -2 |\n| 853 | 0271P0503279 | 28.7503 | 87.4551 | | 5680 | Ganga | Transboundary | 32 | 31 | -2 |\n| 854 | 0272M1004374 | 27.6812 | 87.6945 | | 4633 | Ganga | Transboundary | 13 | 13 | -2 |\n| 855 | 0262K1201334 | 29.1851 | 82.5629 | | 4597 | Ganga | Transboundary | 16 | 16 | -2 |\n| 856 | 0262K0100909 | 29.9711 | 82.2495 | | 4881 | Ganga | Transboundary | 17 | 17 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3620, "line_end": 3643, "token_count_estimate": 1275, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0162F1005203", "0262K0100909", "0262K0701173", "0262K1201334", "0271H1502430", "0271P0503279", "0271P0803407", "0272M1004374", "03082G030905", "03082O110204", "03277G090111", "03278I050364", "03278I090577", "03278I090657", "03382O080024", "03591C080062", "03591C150289", "03678E100446", "03678M130368", "03982F080546", "03982G010738", "03982G111185"]}}
{"id": "df38b9455e81bf7b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 857 | 0262G0900762 | 29.9356 | 81.6737 | | 4569 | Ganga | Transboundary | 11 | 11 | -2 |\n| 858 | 0271P1203559 | 28.1177 | 87.6146 | | 5052 | Ganga | Transboundary | 36 | 35 | -2 |\n| 859 | 0271P0903438 | 28.7731 | 87.5532 | | 5319 | Ganga | Transboundary | 26 | 25 | -2 |\n| 860 | 0272I1303952 | 27.8938 | 86.9133 | | 4986 | Ganga | Transboundary | 11 | 11 | -2 |\n| 861 | 0271P0603305 | 28.7343 | 87.4064 | | 5600 | Ganga | Transboundary | 18 | 18 | -2 |\n| 862 | 03982B131010 | 30.8962 | 92.9098 | Tsenrak chu | 4923 | Brahmaputra | Transboundary | 267 | 262 | -2 |\n| 863 | 03982E041302 | 31.0040 | 93.0882 | | 5040 | Brahmaputra | Transboundary | 71 | 70 | -2 |\n| 864 | 03982G141340 | 29.5423 | 93.8304 | Nyang chu | 4419 | Brahmaputra | Transboundary | 56 | 55 | -2 |\n| 865 | 03582K090280 | 29.7755 | 94.5730 | Rong chu | 4161 | Brahmaputra | Transboundary | 48 | 47 | -2 |\n| 866 | 03982F080581 | 30.0080 | 93.4373 | Drukla chu | 4696 | Brahmaputra | Transboundary | 41 | 40 | -2 |\n| 867 | 03582K090189 | 29.8794 | 94.5412 | | 4266 | Brahmaputra | Transboundary | 38 | 37 | -2 |\n| 868 | 03082O110162 | 29.3773 | 95.6301 | Rirung Chu | 3750 | Brahmaputra | Transboundary | 34 | 33 | -2 |\n| 869 | 03977J140144 | 30.5180 | 90.8280 | Lha chu | 5352 | Brahmaputra | Transboundary | 28 | 27 | -2 |\n| 870 | 03591G040840 | 29.0871 | 97.1551 | | 4222 | Brahmaputra | Transboundary | 26 | 25 | -2 |\n| 871 | 03682D040551 | 28.1928 | 92.0395 | Tsona Chu | 5036 | Brahmaputra | Transboundary | 26 | 25 | -2 |\n| 872 | 03982K051721 | 29.8943 | 94.2836 | Nezhi chu | 4288 | Brahmaputra | Transboundary | 25 | 24 | -2 |\n| 873 | 03082G071039 | 29.3991 | 93.3187 | Namo chu | 4757 | Brahmaputra | Transboundary | 24 | 24 | -2 |\n| 874 | 03391C040419 | 29.0299 | 96.1931 | Dri Chu | 4235 | Brahmaputra | Transboundary | 25 | 24 | -2 |\n| 875 | 03391C040396 | 29.0773 | 96.2304 | Dri Chu | 4217 | Brahmaputra | Transboundary | 24 | 24 | -2 |\n| 876 | 03362N151428 | 30.4921 | 83.8423 | | 5633 | Brahmaputra | Transboundary | 23 | 23 | -2 |\n| 877 | 03382O080002 | 29.1670 | 95.4498 | Emra River | 3945 | Brahmaputra | Transboundary | 21 | 21 | -2 |\n| 878 | 03082C140552 | 29.6563 | 92.8347 | Gyab Pucong chu | 4997 | Brahmaputra | Transboundary | 20 | 20 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3650, "line_end": 3673, "token_count_estimate": 1292, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262G0900762", "0271P0603305", "0271P0903438", "0271P1203559", "0272I1303952", "03082C140552", "03082G071039", "03082O110162", "03362N151428", "03382O080002", "03391C040396", "03391C040419", "03582K090189", "03582K090280", "03591G040840", "03682D040551", "03977J140144", "03982B131010", "03982E041302", "03982F080581", "03982G141340", "03982K051721"]}}
{"id": "04f4c5b37fcda507", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 879 | 03077O150159 | 29.4305 | 91.9708 | On chu | 5197 | Brahmaputra | Transboundary | 20 | 20 | -2 |\n| 880 | 03591H011141 | 28.8127 | 97.1700 | Dzayul chu | 4401 | Brahmaputra | Transboundary | 19 | 19 | -2 |\n| 881 | 03982N110246 | 30.3911 | 95.6569 | Yu Chu | 4734 | Brahmaputra | Transboundary | 18 | 18 | -2 |\n| 882 | 03082G081146 | 29.1797 | 93.2721 | Anartang chu | 4932 | Brahmaputra | Transboundary | 17 | 17 | -2 |\n| 883 | 03082C120518 | 29.2257 | 92.6251 | Yarlung tsangpo | 5115 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 884 | 03082H011569 | 28.7671 | 93.0733 | Yarlung tsangpo | 4149 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 885 | 03278I130816 | 27.8856 | 90.8299 | Panggyetangka | 4845 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 886 | 03878E130618 | 27.8430 | 89.9101 | | 4317 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 887 | 03591H051427 | 28.9733 | 97.3267 | | 4541 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 888 | 03582K090168 | 29.9021 | 94.5613 | | 4495 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 889 | 03578E050189 | 27.9922 | 89.4244 | | 4713 | Brahmaputra | Transboundary | 16 | 16 | -2 |\n| 890 | 03982K051764 | 29.8291 | 94.3627 | Zha chu | 4612 | Brahmaputra | Transboundary | 15 | 15 | -2 |\n| 891 | 03178M050634 | 27.9627 | 91.4260 | | 4858 | Brahmaputra | Transboundary | 13 | 13 | -2 |\n| 892 | 03982N100133 | 30.5373 | 95.5482 | Poto Chu | 4811 | Brahmaputra | Transboundary | 13 | 13 | -2 |\n| 893 | 03878I010855 | 27.7532 | 90.1643 | | 4226 | Brahmaputra | Transboundary | 13 | 13 | -2 |\n| 894 | 03982K021564 | 29.5732 | 94.1395 | Nyang chu | 4673 | Brahmaputra | Transboundary | 12 | 12 | -2 |\n| 895 | 03178I130497 | 27.9467 | 90.9743 | Lhodrak Chu | 4426 | Brahmaputra | Transboundary | 11 | 11 | -2 |\n| 896 | 03678M050111 | 27.8792 | 91.4486 | Kulong Chu | 4229 | Brahmaputra | Transboundary | 10 | 10 | -2 |\n| 897 | 03878E090403 | 27.9476 | 89.6489 | | 4310 | Brahmaputra | Transboundary | 10 | 10 | -2 |\n| 898 | 03582K090131 | 29.9407 | 94.6131 | Numphu chu | 4546 | Brahmaputra | Transboundary | 10 | 10 | -2 |\n| 899 | 03278I060418 | 27.7376 | 90.2956 | | 4389 | Brahmaputra | Transboundary | 10 | 10 | -2 |\n| 900 | 0262K0200957 | 29.6686 | 82.2215 | | 4577 | Ganga | Transboundary | 11 | 11 | -3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3677, "line_end": 3700, "token_count_estimate": 1294, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262K0200957", "03077O150159", "03082C120518", "03082G081146", "03082H011569", "03178I130497", "03178M050634", "03278I060418", "03278I130816", "03578E050189", "03582K090131", "03582K090168", "03591H011141", "03591H051427", "03678M050111", "03878E090403", "03878E130618", "03878I010855", "03982K021564", "03982K051764", "03982N100133", "03982N110246"]}}
{"id": "8ee0a2e86b3f0f04", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 901 | 0161D1504804 | 32.4229 | 80.8651 | | 4452 | Indus | Transboundary | 59 | 57 | -3 |\n| 902 | 0162F1505286 | 30.3904 | 81.8947 | | 5441 | Indus | Transboundary | 10 | 10 | -3 |\n| 903 | 0262K0501073 | 29.7539 | 82.4147 | | 4692 | Ganga | Transboundary | 42 | 41 | -3 |\n| 904 | 03678M130291 | 27.9008 | 91.8959 | Nyamjang Chu | 4452 | Brahmaputra | Transboundary | 217 | 212 | -3 |\n| 905 | 03982B110894 | 30.3495 | 92.7350 | Tsenrak chu | 5112 | Brahmaputra | Transboundary | 87 | 85 | -3 |\n| 906 | 03578E050194 | 27.9688 | 89.3789 | | 4568 | Brahmaputra | Transboundary | 67 | 65 | -3 |\n| 907 | 03982B131014 | 30.8786 | 92.8808 | Tsenrak chu | 4982 | Brahmaputra | Transboundary | 52 | 51 | -3 |\n| 908 | 03678A130021 | 27.8541 | 88.9448 | Khangphu Chu | 4874 | Brahmaputra | Transboundary | 49 | 48 | -3 |\n| 909 | 03982B120940 | 30.2163 | 92.5166 | Zhorong chu | 4981 | Brahmaputra | Transboundary | 48 | 47 | -3 |\n| 910 | 03278I130810 | 27.8968 | 90.8165 | Chamkar Chu | 4712 | Brahmaputra | Transboundary | 45 | 43 | -3 |\n| 911 | 03591H051472 | 28.8765 | 97.3554 | Nechen Kora chu | 4569 | Brahmaputra | Transboundary | 44 | 43 | -3 |\n| 912 | 03982K061805 | 29.7318 | 94.4829 | Drrakchi chu | 4552 | Brahmaputra | Transboundary | 37 | 36 | -3 |\n| 913 | 03082G081122 | 29.2200 | 93.3061 | Yarlung tsangpo | 4887 | Brahmaputra | Transboundary | 35 | 34 | -3 |\n| 914 | 03182D120101 | 28.1851 | 92.6962 | Chayul chu | 5162 | Brahmaputra | Transboundary | 28 | 27 | -3 |\n| 915 | 03982K091887 | 29.7851 | 94.5161 | Drrakchi chu | 4497 | Brahmaputra | Transboundary | 28 | 27 | -3 |\n| 916 | 03577H080065 | 28.1364 | 89.4019 | | 4474 | Brahmaputra | Transboundary | 27 | 26 | -3 |\n| 917 | 03591D150642 | 28.4700 | 96.8823 | Dzayul chu | 3849 | Brahmaputra | Transboundary | 24 | 23 | -3 |\n| 918 | 03362J100427 | 30.6084 | 82.6515 | | 5088 | Brahmaputra | Transboundary | 21 | 20 | -3 |\n| 919 | 03391D100758 | 28.6966 | 96.5723 | Thangkung Chu | 4133 | Brahmaputra | Transboundary | 21 | 20 | -3 |\n| 920 | 03362N151450 | 30.4613 | 83.8984 | | 5536 | Brahmaputra | Transboundary | 19 | 18 | -3 |\n| 921 | 03982K021576 | 29.5563 | 94.2423 | Nyang chu | 4598 | Brahmaputra | Transboundary | 18 | 17 | -3 |\n| 922 | 03982K021530 | 29.6315 | 94.0985 | Nyang chu | 4711 | Brahmaputra | Transboundary | 17 | 17 | -3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3703, "line_end": 3726, "token_count_estimate": 1338, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0161D1504804", "0162F1505286", "0262K0501073", "03082G081122", "03182D120101", "03278I130810", "03362J100427", "03362N151450", "03391D100758", "03577H080065", "03578E050194", "03591D150642", "03591H051472", "03678A130021", "03678M130291", "03982B110894", "03982B120940", "03982B131014", "03982K021530", "03982K021576", "03982K061805", "03982K091887"]}}
{"id": "1e74d8d3b0a7cd84", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 923 | 03877L040267 | 28.0222 | 90.1792 | Pho Chu | 5081 | Brahmaputra | Transboundary | 18 | 17 | -3 |\n| 924 | 03683A010618 | 27.8512 | 92.1606 | Tsona Chu | 4824 | Brahmaputra | Transboundary | 17 | 16 | -3 |\n| 925 | 03591D100475 | 28.5625 | 96.7148 | Dulai chu | 4019 | Brahmaputra | Transboundary | 16 | 16 | -3 |\n| 926 | 03678M090243 | 27.7977 | 91.5925 | Kulong Chu | 4066 | Brahmaputra | Transboundary | 16 | 16 | -3 |\n| 927 | 03982C061144 | 29.7340 | 92.3868 | Medroma chu | 4746 | Brahmaputra | Transboundary | 17 | 16 | -3 |\n| 928 | 03082K140015 | 29.5042 | 94.9687 | Doshung Chu | 4451 | Brahmaputra | Transboundary | 16 | 15 | -3 |\n| 929 | 03591G080964 | 29.2164 | 97.3776 | | 4303 | Brahmaputra | Transboundary | 14 | 14 | -3 |\n| 930 | 03178I130542 | 27.9126 | 90.8446 | Lhodrak Chu | 4509 | Brahmaputra | Transboundary | 14 | 14 | -3 |\n| 931 | 03878E110456 | 27.2660 | 89.7262 | | 4420 | Brahmaputra | Transboundary | 14 | 14 | -3 |\n| 932 | 03282H030052 | 28.3387 | 93.1219 | | 4086 | Brahmaputra | Transboundary | 14 | 14 | -3 |\n| 933 | 03362N141400 | 30.6119 | 83.7635 | | 5180 | Brahmaputra | Transboundary | 13 | 13 | -3 |\n| 934 | 03982K051794 | 29.7682 | 94.4649 | Nyang chu | 4463 | Brahmaputra | Transboundary | 13 | 13 | -3 |\n| 935 | 03082O100116 | 29.7226 | 95.5281 | Dihang | 3065 | Brahmaputra | Transboundary | 13 | 13 | -3 |\n| 936 | 03278I050317 | 27.8487 | 90.3326 | | 5050 | Brahmaputra | Transboundary | 13 | 13 | -3 |\n| 937 | 03082C060313 | 29.5051 | 92.4641 | Gochumu Chu | 5103 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 938 | 03278I090591 | 27.8948 | 90.5984 | Kungong Chu | 4640 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 939 | 03982N100140 | 30.5266 | 95.6931 | Yu Chu | 4988 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 940 | 03882J010216 | 30.7893 | 94.1174 | Wok chu | 4656 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 941 | 03982K021580 | 29.5508 | 94.1110 | Nyang chu | 4578 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 942 | 03982K051728 | 29.8815 | 94.3773 | Zha chu | 4575 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 943 | 03591H011186 | 28.7671 | 97.1008 | Trigo chu | 4326 | Brahmaputra | Transboundary | 12 | 12 | -3 |\n| 944 | 03591H071736 | 28.4105 | 97.4060 | | 4443 | Brahmaputra | Transboundary | 11 | 11 | -3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3730, "line_end": 3753, "token_count_estimate": 1327, "basins": ["Brahmaputra"], "subbasins": ["Dihang"], "countries": [], "lake_ids": ["03082C060313", "03082K140015", "03082O100116", "03178I130542", "03278I050317", "03278I090591", "03282H030052", "03362N141400", "03591D100475", "03591G080964", "03591H011186", "03591H071736", "03678M090243", "03683A010618", "03877L040267", "03878E110456", "03882J010216", "03982C061144", "03982K021580", "03982K051728", "03982K051794", "03982N100140"]}}
{"id": "d21adb21a695fbec", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 945 | 03082G071003 | 29.4942 | 93.3025 | Pulung chu | 4971 | Brahmaputra | Transboundary | 11 | 11 | -3 |\n| 946 | 03678E100439 | 27.5680 | 89.5236 | | 4450 | Brahmaputra | Transboundary | 11 | 11 | -3 |\n| 947 | 03591H071758 | 28.3996 | 97.3932 | | 4472 | Brahmaputra | Transboundary | 10 | 10 | -3 |\n| 948 | 03591D090399 | 28.8981 | 96.5499 | Kangri Karpo chu | 3712 | Brahmaputra | Transboundary | 10 | 10 | -3 |\n| 949 | 03982N060051 | 30.5311 | 95.4869 | Poto Chu | 4783 | Brahmaputra | Transboundary | 10 | 10 | -3 |\n| 950 | 03591D100458 | 28.6126 | 96.5223 | Maog chu | 4213 | Brahmaputra | Transboundary | 10 | 10 | -3 |\n| 951 | 03977G061340 | 29.5626 | 89.3539 | Yarlung tsangpo | 5427 | Brahmaputra | Transboundary | 10 | 10 | -3 |\n| 952 | 0262K0200972 | 29.6538 | 82.2228 | | 4492 | Ganga | Transboundary | 14 | 13 | -4 |\n| 953 | 0262F1600728 | 30.0585 | 81.9555 | | 4440 | Ganga | Transboundary | 11 | 11 | -4 |\n| 954 | 0271L0902947 | 28.8870 | 86.5137 | | 5098 | Ganga | Transboundary | 98 | 94 | -4 |\n| 955 | 0271P0503264 | 28.7749 | 87.4371 | | 5553 | Ganga | Transboundary | 22 | 21 | -4 |\n| 956 | 03577H120113 | 28.2279 | 89.6381 | | 4568 | Brahmaputra | Transboundary | 1274 | 1224 | -4 |\n| 957 | 03982A160586 | 31.1204 | 92.8328 | | 4902 | Brahmaputra | Transboundary | 389 | 373 | -4 |\n| 958 | 03671O020355 | 29.5560 | 87.0276 | Raga tsangpo | 4729 | Brahmaputra | Transboundary | 120 | 115 | -4 |\n| 959 | 03582K050105 | 29.8962 | 94.4615 | Numphu chu | 4346 | Brahmaputra | Transboundary | 85 | 82 | -4 |\n| 960 | 03082G060988 | 29.5253 | 93.4702 | Pulung chu | 4686 | Brahmaputra | Transboundary | 46 | 44 | -4 |\n| 961 | 03082G071035 | 29.4208 | 93.2905 | Namo chu | 4644 | Brahmaputra | Transboundary | 45 | 43 | -4 |\n| 962 | 03082D020737 | 28.7054 | 92.1111 | Si chu | 4780 | Brahmaputra | Transboundary | 39 | 37 | -4 |\n| 963 | 03982B120926 | 30.2368 | 92.5491 | Zhorong chu | 5061 | Brahmaputra | Transboundary | 33 | 32 | -4 |\n| 964 | 03591D100465 | 28.5865 | 96.6518 | Maog chu | 4068 | Brahmaputra | Transboundary | 30 | 29 | -4 |\n| 965 | 03591D100490 | 28.5120 | 96.5025 | Maog chu | 4027 | Brahmaputra | Transboundary | 27 | 26 | -4 |\n| 966 | 03082G071056 | 29.3218 | 93.2654 | Pulung chu | 4951 | Brahmaputra | Transboundary | 26 | 25 | -4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3758, "line_end": 3781, "token_count_estimate": 1327, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262F1600728", "0262K0200972", "0271L0902947", "0271P0503264", "03082D020737", "03082G060988", "03082G071003", "03082G071035", "03082G071056", "03577H120113", "03582K050105", "03591D090399", "03591D100458", "03591D100465", "03591D100490", "03591H071758", "03671O020355", "03678E100439", "03977G061340", "03982A160586", "03982B120926", "03982N060051"]}}
{"id": "65df5c9fad66b5ee", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 967 | 03591D140631 | 28.5360 | 96.7663 | Dzayul chu | 3918 | Brahmaputra | Transboundary | 26 | 25 | -4 |\n| 968 | 03982K021581 | 29.5497 | 94.1203 | Nyang chu | 4579 | Brahmaputra | Transboundary | 25 | 24 | -4 |\n| 969 | 03982B110917 | 30.2533 | 92.5029 | Tsenrak chu | 5056 | Brahmaputra | Transboundary | 22 | 21 | -4 |\n| 970 | 03382O080038 | 29.0850 | 95.4380 | Emra River | 3706 | Brahmaputra | Transboundary | 21 | 20 | -4 |\n| 971 | 03082D020749 | 28.6479 | 92.1599 | Si chu | 4976 | Brahmaputra | Transboundary | 20 | 19 | -4 |\n| 972 | 03082O080084 | 29.1610 | 95.4637 | Shumo Chu | 3826 | Brahmaputra | Transboundary | 20 | 19 | -4 |\n| 973 | 03591G030777 | 29.4597 | 97.0855 | | 4994 | Brahmaputra | Transboundary | 16 | 15 | -4 |\n| 974 | 03591D090364 | 28.9537 | 96.5614 | Kangri Karpo chu | 4277 | Brahmaputra | Transboundary | 16 | 15 | -4 |\n| 975 | 03082K031833 | 29.4020 | 94.1993 | Yarlung tsangpo | 4616 | Brahmaputra | Transboundary | 15 | 14 | -4 |\n| 976 | 03878E130533 | 27.9200 | 89.8098 | | 4260 | Brahmaputra | Transboundary | 15 | 14 | -4 |\n| 977 | 03878I010733 | 27.9312 | 90.0905 | Pho Chu | 4521 | Brahmaputra | Transboundary | 15 | 14 | -4 |\n| 978 | 03591H011017 | 28.9949 | 97.2311 | Nechen Kora chu | 4597 | Brahmaputra | Transboundary | 15 | 14 | -4 |\n| 979 | 03982C050230 | 29.8164 | 92.3986 | Kam chu | 5068 | Brahmaputra | Transboundary | 13 | 13 | -4 |\n| 980 | 03178I130547 | 27.9031 | 90.9802 | Lhodrak Chu | 4290 | Brahmaputra | Transboundary | 14 | 13 | -4 |\n| 981 | 03591H051468 | 28.8917 | 97.3365 | Nechen Kora chu | 4429 | Brahmaputra | Transboundary | 14 | 13 | -4 |\n| 982 | 03982K021534 | 29.6216 | 94.1628 | Nyang chu | 4716 | Brahmaputra | Transboundary | 12 | 12 | -4 |\n| 983 | 03982G101112 | 29.5984 | 93.5245 | Banang chu | 4945 | Brahmaputra | Transboundary | 12 | 12 | -4 |\n| 984 | 03391D060697 | 28.5445 | 96.4432 | Thangkung Chu | 4297 | Brahmaputra | Transboundary | 13 | 12 | -4 |\n| 985 | 03982K021578 | 29.5546 | 94.0873 | Bhezhung chu | 4751 | Brahmaputra | Transboundary | 13 | 12 | -4 |\n| 986 | 03982B120938 | 30.2178 | 92.5358 | Zhorong chu | 5017 | Brahmaputra | Transboundary | 12 | 12 | -4 |\n| 987 | 03878I010834 | 27.8024 | 90.2386 | | 4762 | Brahmaputra | Transboundary | 12 | 12 | -4 |\n| 988 | 03982B080001 | 30.0931 | 92.4850 | Nyang chu | 5158 | Brahmaputra | Transboundary | 11 | 11 | -4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3785, "line_end": 3808, "token_count_estimate": 1352, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082D020749", "03082K031833", "03082O080084", "03178I130547", "03382O080038", "03391D060697", "03591D090364", "03591D140631", "03591G030777", "03591H011017", "03591H051468", "03878E130533", "03878I010733", "03878I010834", "03982B080001", "03982B110917", "03982B120938", "03982C050230", "03982G101112", "03982K021534", "03982K021578", "03982K021581"]}}
{"id": "06703c74ad1c1b78", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 989 | 03382O080045 | 29.0738 | 95.4272 | Emra River | 3670 | Brahmaputra | Transboundary | 11 | 11 | -4 |\n| 990 | 03177P040335 | 28.0751 | 91.2342 | | 4686 | Brahmaputra | Transboundary | 11 | 11 | -4 |\n| 991 | 03582K050059 | 29.9835 | 94.3280 | Numphu chu | 4515 | Brahmaputra | Transboundary | 11 | 11 | -4 |\n| 992 | 03678M090206 | 27.8610 | 91.6285 | Nyamjang Chu | 4581 | Brahmaputra | Transboundary | 10 | 10 | -4 |\n| 993 | 03082G081160 | 29.1255 | 93.3261 | Yarlung tsangpo | 4886 | Brahmaputra | Transboundary | 10 | 10 | -4 |\n| 994 | 03077O110092 | 29.4624 | 91.6459 | Yarlung tsangpo | 5296 | Brahmaputra | Transboundary | 10 | 10 | -4 |\n| 995 | 03177L120109 | 28.1291 | 90.6044 | Longdo Chu | 5199 | Brahmaputra | Transboundary | 10 | 10 | -4 |\n| 996 | 03082G071096 | 29.2698 | 93.2851 | Yarlung tsangpo | 5066 | Brahmaputra | Transboundary | 10 | 10 | -4 |\n| 997 | 0262K0200958 | 29.6676 | 82.1940 | | 4379 | Ganga | Transboundary | 13 | 12 | -5 |\n| 998 | 0262K0701181 | 29.3092 | 82.4743 | | 4081 | Ganga | Transboundary | 16 | 15 | -5 |\n| 999 | 0262F0700441 | 30.4241 | 81.3302 | | 5807 | Ganga | Transboundary | 13 | 12 | -5 |\n| 1000 | 03277B120002 | 30.1478 | 88.6265 | Lengra chu | 5011 | Brahmaputra | Transboundary | 214 | 204 | -5 |\n| 1001 | 03982B131011 | 30.8941 | 92.9508 | Tsenrak chu | 4958 | Brahmaputra | Transboundary | 206 | 195 | -5 |\n| 1002 | 03982C010184 | 29.9176 | 92.1797 | Kam chu | 5161 | Brahmaputra | Transboundary | 38 | 36 | -5 |\n| 1003 | 03082C110447 | 29.3902 | 92.5488 | Loyul chu | 4947 | Brahmaputra | Transboundary | 34 | 32 | -5 |\n| 1004 | 03082G060987 | 29.5328 | 93.3901 | Pulung chu | 4799 | Brahmaputra | Transboundary | 26 | 25 | -5 |\n| 1005 | 03982K021594 | 29.5369 | 94.0337 | Bhezhung chu | 4509 | Brahmaputra | Transboundary | 25 | 24 | -5 |\n| 1006 | 03982C010185 | 29.9050 | 92.1924 | Kam chu | 4992 | Brahmaputra | Transboundary | 25 | 24 | -5 |\n| 1007 | 03178I130461 | 27.9667 | 90.8936 | Lhodrak Chu | 4579 | Brahmaputra | Transboundary | 22 | 21 | -5 |\n| 1008 | 03082C060287 | 29.5595 | 92.4514 | Gochumu Chu | 4921 | Brahmaputra | Transboundary | 21 | 20 | -5 |\n| 1009 | 03582K090172 | 29.8992 | 94.7010 | Rong chu | 4500 | Brahmaputra | Transboundary | 21 | 20 | -5 |\n| 1010 | 03471B160005 | 30.0397 | 84.9257 | | 5730 | Brahmaputra | Transboundary | 20 | 19 | -5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3815, "line_end": 3838, "token_count_estimate": 1332, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262F0700441", "0262K0200958", "0262K0701181", "03077O110092", "03082C060287", "03082C110447", "03082G060987", "03082G071096", "03082G081160", "03177L120109", "03177P040335", "03178I130461", "03277B120002", "03382O080045", "03471B160005", "03582K050059", "03582K090172", "03678M090206", "03982B131011", "03982C010184", "03982C010185", "03982K021594"]}}
{"id": "b92949de44e17033", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 1011 | 03977N160431 | 30.1868 | 91.9882 | Zhorong chu | 5159 | Brahmaputra | Transboundary | 19 | 18 | -5 |\n| 1012 | 03982K091888 | 29.7817 | 94.5270 | Nyang chu | 4585 | Brahmaputra | Transboundary | 19 | 18 | -5 |\n| 1013 | 03582K090215 | 29.8606 | 94.5313 | | 4592 | Brahmaputra | Transboundary | 18 | 17 | -5 |\n| 1014 | 03278I090607 | 27.8757 | 90.5809 | Kungong Chu | 4599 | Brahmaputra | Transboundary | 17 | 16 | -5 |\n| 1015 | 03082G111215 | 29.3961 | 93.6820 | Sungkar chu | 4888 | Brahmaputra | Transboundary | 16 | 15 | -5 |\n| 1016 | 03082C150626 | 29.2763 | 92.8023 | Serpulung chu | 5207 | Brahmaputra | Transboundary | 16 | 15 | -5 |\n| 1017 | 03982N100129 | 30.5414 | 95.7033 | Yu Chu | 5053 | Brahmaputra | Transboundary | 16 | 15 | -5 |\n| 1018 | 03678M090253 | 27.7563 | 91.5293 | Kulong Chu | 3868 | Brahmaputra | Transboundary | 16 | 15 | -5 |\n| 1019 | 03178M010609 | 27.9095 | 91.2412 | | 4347 | Brahmaputra | Transboundary | 15 | 14 | -5 |\n| 1020 | 03678E030327 | 27.4022 | 89.1562 | Yak Chu | 4180 | Brahmaputra | Transboundary | 14 | 13 | -5 |\n| 1021 | 03982K011477 | 29.8807 | 94.2072 | Nezhi chu | 4448 | Brahmaputra | Transboundary | 14 | 13 | -5 |\n| 1022 | 03982K021529 | 29.6360 | 94.1691 | Nyang chu | 4808 | Brahmaputra | Transboundary | 13 | 12 | -5 |\n| 1023 | 03591H061562 | 28.5475 | 97.3928 | | 4331 | Brahmaputra | Transboundary | 13 | 12 | -5 |\n| 1024 | 03182D040038 | 28.0456 | 92.1331 | Loro Nakpo chu | 5113 | Brahmaputra | Transboundary | 13 | 12 | -5 |\n| 1025 | 03082C060314 | 29.5028 | 92.4703 | Gochumu Chu | 5161 | Brahmaputra | Transboundary | 12 | 11 | -5 |\n| 1026 | 03278I140877 | 27.7478 | 90.8214 | Panggyetangka | 4438 | Brahmaputra | Transboundary | 12 | 11 | -5 |\n| 1027 | 03082G151415 | 29.4202 | 93.8637 | Sungkar chu | 4611 | Brahmaputra | Transboundary | 12 | 11 | -5 |\n| 1028 | 03391C080447 | 29.0597 | 96.3030 | Thangkung Chu | 4152 | Brahmaputra | Transboundary | 12 | 11 | -5 |\n| 1029 | 03671O020358 | 29.5140 | 87.1082 | Raga tsangpo | 5432 | Brahmaputra | Transboundary | 12 | 11 | -5 |\n| 1030 | 03678M050124 | 27.8597 | 91.4948 | Kulong Chu | 3913 | Brahmaputra | Transboundary | 11 | 10 | -5 |\n| 1031 | 03077P020323 | 28.5867 | 91.1549 | Yidam Chu | 5563 | Brahmaputra | Transboundary | 10 | 10 | -5 |\n| 1032 | 03982K061821 | 29.5737 | 94.2759 | Nyang chu | 4609 | Brahmaputra | Transboundary | 10 | 10 | -5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3842, "line_end": 3865, "token_count_estimate": 1347, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03077P020323", "03082C060314", "03082C150626", "03082G111215", "03082G151415", "03178M010609", "03182D040038", "03278I090607", "03278I140877", "03391C080447", "03582K090215", "03591H061562", "03671O020358", "03678E030327", "03678M050124", "03678M090253", "03977N160431", "03982K011477", "03982K021529", "03982K061821", "03982K091888", "03982N100129"]}}
{"id": "7a5c12cd0cd067dd", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1077 | 03982K051701 | 29.9651 | 94.2509 | Penam chu | 4289 | Brahmaputra | Transboundary | 19 | 18 | -7 |\n| 1078 | 03591D100440 | 28.6473 | 96.6454 | Maog chu | 4068 | Brahmaputra | Transboundary | 18 | 17 | -7 |\n| 1079 | 03878E130642 | 27.8152 | 89.9389 | | 4146 | Brahmaputra | Transboundary | 17 | 16 | -7 |\n| 1080 | 03882J080291 | 30.1811 | 94.2882 | Yigrong chu | 4363 | Brahmaputra | Transboundary | 16 | 15 | -7 |\n| 1081 | 03591D060338 | 28.5179 | 96.4212 | Tali chu | 4086 | Brahmaputra | Transboundary | 16 | 15 | -7 |\n| 1082 | 03391D050545 | 28.9924 | 96.2567 | Thangkung Chu | 4077 | Brahmaputra | Transboundary | 15 | 14 | -7 |\n| 1083 | 03682D040547 | 28.2234 | 92.0434 | Tsona Chu | 5099 | Brahmaputra | Transboundary | 15 | 14 | -7 |\n| 1084 | 03178M010620 | 27.8026 | 91.2397 | | 4077 | Brahmaputra | Transboundary | 15 | 14 | -7 |\n| 1085 | 03982A160570 | 31.1384 | 92.9984 | | 4894 | Brahmaputra | Transboundary | 14 | 13 | -7 |\n| 1086 | 03982B160136 | 30.2185 | 92.9408 | Nyang chu | 5208 | Brahmaputra | Transboundary | 13 | 12 | -7 |\n| 1087 | 03591G040898 | 29.0038 | 97.1344 | Trigo chu | 4401 | Brahmaputra | Transboundary | 13 | 12 | -7 |\n| 1088 | 03982A160646 | 31.0518 | 92.9611 | | 4922 | Brahmaputra | Transboundary | 13 | 12 | -7 |\n| 1089 | 03878E130563 | 27.8959 | 89.8846 | Pho Chu | 4573 | Brahmaputra | Transboundary | 12 | 11 | -7 |\n| 1090 | 03082G071025 | 29.4444 | 93.4117 | Namo chu | 4797 | Brahmaputra | Transboundary | 11 | 10 | -7 |\n| 1091 | 03278I090693 | 27.7649 | 90.6249 | Dur Chu | 4338 | Brahmaputra | Transboundary | 11 | 10 | -7 |\n| 1092 | 03671K050160 | 29.9354 | 86.4222 | Kam chu | 5477 | Brahmaputra | Transboundary | 11 | 10 | -7 |\n| 1093 | 0262K0400982 | 29.1158 | 82.2363 | | 4270 | Ganga | Transboundary | 11 | 10 | -8 |\n| 1094 | 03982E041292 | 31.0785 | 93.0497 | | 4950 | Brahmaputra | Transboundary | 40 | 37 | -8 |\n| 1095 | 03591D150651 | 28.4247 | 96.8723 | Dulai chu | 3980 | Brahmaputra | Transboundary | 36 | 33 | -8 |\n| 1096 | 03591H011087 | 28.9042 | 97.1489 | Nechen Kora chu | 4319 | Brahmaputra | Transboundary | 34 | 31 | -8 |\n| 1097 | 03591D100483 | 28.5340 | 96.6443 | Dulai chu | 4311 | Brahmaputra | Transboundary | 29 | 27 | -8 |\n| 1098 | 03082C100412 | 29.5172 | 92.7385 | Gyab Pucong chu | 4930 | Brahmaputra | Transboundary | 24 | 22 | -8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3868, "line_end": 3891, "token_count_estimate": 1346, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262K0400982", "03082C100412", "03082G071025", "03178M010620", "03278I090693", "03391D050545", "03591D060338", "03591D100440", "03591D100483", "03591D150651", "03591G040898", "03591H011087", "03671K050160", "03682D040547", "03878E130563", "03878E130642", "03882J080291", "03982A160570", "03982A160646", "03982B160136", "03982E041292", "03982K051701"]}}
{"id": "e19c7d4b1af5c54e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1099 | 03878I010819 | 27.8290 | 90.1570 | Pho Chu | 4428 | Brahmaputra | Transboundary | 24 | 22 | -8 |\n| 1100 | 03391D100769 | 28.6680 | 96.6106 | Thangkung Chu | 4492 | Brahmaputra | Transboundary | 23 | 21 | -8 |\n| 1101 | 03591H011175 | 28.7859 | 97.2192 | Nechen Kora chu | 4270 | Brahmaputra | Transboundary | 23 | 21 | -8 |\n| 1102 | 03678M130357 | 27.7956 | 91.9254 | Tsona Chu | 4499 | Brahmaputra | Transboundary | 23 | 21 | -8 |\n| 1103 | 03678E060400 | 27.5692 | 89.3785 | Paro Chu | 4239 | Brahmaputra | Transboundary | 23 | 21 | -8 |\n| 1104 | 03982E081312 | 31.0544 | 93.3006 | | 5055 | Brahmaputra | Transboundary | 21 | 19 | -8 |\n| 1105 | 03591D140629 | 28.5761 | 96.7564 | Dzayul chu | 3646 | Brahmaputra | Transboundary | 17 | 16 | -8 |\n| 1106 | 03591H011147 | 28.8081 | 97.1257 | Nechen Kora chu | 4189 | Brahmaputra | Transboundary | 17 | 16 | -8 |\n| 1107 | 03082G030841 | 29.4367 | 93.1729 | Anartang chu | 5034 | Brahmaputra | Transboundary | 16 | 15 | -8 |\n| 1108 | 03591H011167 | 28.7958 | 97.1834 | Dzayul chu | 4457 | Brahmaputra | Transboundary | 16 | 15 | -8 |\n| 1109 | 03878E130628 | 27.8327 | 89.9601 | | 4127 | Brahmaputra | Transboundary | 16 | 15 | -8 |\n| 1110 | 03591H011081 | 28.9160 | 97.1532 | Nechen Kora chu | 4513 | Brahmaputra | Transboundary | 15 | 14 | -8 |\n| 1111 | 03082C110499 | 29.2830 | 92.6561 | Yarlung tsangpo | 5050 | Brahmaputra | Transboundary | 15 | 14 | -8 |\n| 1112 | 03591D100486 | 28.5273 | 96.6502 | Dulai chu | 4125 | Brahmaputra | Transboundary | 14 | 13 | -8 |\n| 1113 | 03082G071030 | 29.4388 | 93.2540 | Namo chu | 4755 | Brahmaputra | Transboundary | 14 | 13 | -8 |\n| 1114 | 03878I010779 | 27.8771 | 90.1852 | Pho Chu | 4828 | Brahmaputra | Transboundary | 14 | 13 | -8 |\n| 1115 | 03878E090428 | 27.9110 | 89.6510 | | 4226 | Brahmaputra | Transboundary | 13 | 12 | -8 |\n| 1116 | 03982G151373 | 29.4910 | 93.9632 | Bhezhung chu | 4704 | Brahmaputra | Transboundary | 13 | 12 | -8 |\n| 1117 | 03982B130983 | 30.9878 | 92.8887 | Tsenrak chu | 4956 | Brahmaputra | Transboundary | 13 | 12 | -8 |\n| 1118 | 03982K021526 | 29.6447 | 94.1851 | Nyang chu | 4503 | Brahmaputra | Transboundary | 12 | 11 | -8 |\n| 1119 | 03082C160661 | 29.2017 | 92.9844 | Kucha chu | 5013 | Brahmaputra | Transboundary | 12 | 11 | -8 |\n| 1120 | 03278I050414 | 27.7565 | 90.2977 | | 4630 | Brahmaputra | Transboundary | 11 | 10 | -8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3895, "line_end": 3918, "token_count_estimate": 1361, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082C110499", "03082C160661", "03082G030841", "03082G071030", "03278I050414", "03391D100769", "03591D100486", "03591D140629", "03591H011081", "03591H011147", "03591H011167", "03591H011175", "03678E060400", "03678M130357", "03878E090428", "03878E130628", "03878I010779", "03878I010819", "03982B130983", "03982E081312", "03982G151373", "03982K021526"]}}
{"id": "53a2c68eef849abe", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 1165 | 03671G140055 | 29.7334 | 85.7832 | Raga tsangpo | 5384 | Brahmaputra | Transboundary | 16 | 14 | -11 |\n| 1166 | 03082C070338 | 29.3344 | 92.3729 | Yarlung tsangpo | 5065 | Brahmaputra | Transboundary | 12 | 11 | -11 |\n| 1167 | 03982G020865 | 29.5320 | 93.2364 | Banang chu | 5018 | Brahmaputra | Transboundary | 11 | 10 | -11 |\n| 1168 | 03082K031834 | 29.4009 | 94.2080 | Yarlung tsangpo | 4485 | Brahmaputra | Transboundary | 10 | 9 | -11 |\n| 1169 | 0262B1500379 | 30.3474 | 80.8864 | | 5253 | Ganga | Transboundary | 12 | 11 | -12 |\n| 1170 | 03982B130998 | 30.9487 | 92.8897 | Tsenrak chu | 4892 | Brahmaputra | Transboundary | 119 | 104 | -12 |\n| 1171 | 03982E081310 | 31.0646 | 93.2924 | | 5047 | Brahmaputra | Transboundary | 51 | 45 | -12 |\n| 1172 | 03678E060396 | 27.5837 | 89.4868 | | 4313 | Brahmaputra | Transboundary | 46 | 41 | -12 |\n| 1173 | 03082G151451 | 29.3590 | 93.8229 | Sungkar chu | 4518 | Brahmaputra | Transboundary | 22 | 19 | -12 |\n| 1174 | 03678A140094 | 27.5993 | 88.8583 | Tangka Chu | 4581 | Brahmaputra | Transboundary | 12 | 11 | -12 |\n| 1175 | 03278I050374 | 27.8027 | 90.3979 | Mangde Chu | 4825 | Brahmaputra | Transboundary | 11 | 10 | -12 |\n| 1176 | 03577L030160 | 28.3536 | 90.0983 | | 5624 | Brahmaputra | Transboundary | 10 | 9 | -12 |\n| 1177 | 03678M130302 | 27.8736 | 91.8329 | Nyamjang Chu | 4340 | Brahmaputra | Transboundary | 10 | 9 | -12 |\n| 1178 | 0262K1201333 | 29.1936 | 82.5456 | | 4693 | Ganga | Transboundary | 36 | 31 | -13 |\n| 1179 | 0271H1502402 | 28.3291 | 85.8690 | Tsongdupu Chu | 5167 | Ganga | Transboundary | 214 | 185 | -13 |\n| 1180 | 03977C011222 | 29.7545 | 88.2402 | Rong chu | 5600 | Brahmaputra | Transboundary | 30 | 26 | -13 |\n| 1181 | 03678A150154 | 27.4640 | 88.8287 | Torsa River | 4531 | Brahmaputra | Transboundary | 22 | 19 | -13 |\n| 1182 | 03982C051138 | 29.7701 | 92.4420 | Medroma chu | 4924 | Brahmaputra | Transboundary | 15 | 13 | -13 |\n| 1183 | 03678M050119 | 27.8713 | 91.4589 | Kulong Chu | 3832 | Brahmaputra | Transboundary | 13 | 11 | -13 |\n| 1184 | 03178I130562 | 27.8745 | 90.9994 | Lhodrak Chu | 4203 | Brahmaputra | Transboundary | 12 | 10 | -13 |\n| 1185 | 03982K051713 | 29.9061 | 94.3652 | Zha chu | 4391 | Brahmaputra | Transboundary | 12 | 10 | -13 |\n| 1186 | 03582K050073 | 29.9539 | 94.4861 | Numphu chu | 4550 | Brahmaputra | Transboundary | 10 | 9 | -13 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3923, "line_end": 3946, "token_count_estimate": 1331, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262B1500379", "0262K1201333", "0271H1502402", "03082C070338", "03082G151451", "03082K031834", "03178I130562", "03278I050374", "03577L030160", "03582K050073", "03671G140055", "03678A140094", "03678A150154", "03678E060396", "03678M050119", "03678M130302", "03977C011222", "03982B130998", "03982C051138", "03982E081310", "03982G020865", "03982K051713"]}}
{"id": "1e190b2ed3707213", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 1187 | 03678E110481 | 27.2672 | 89.7063 | Wong Chu | 4447 | Brahmaputra | Transboundary | 10 | 9 | -13 |\n| 1188 | 0272M1404551 | 27.7447 | 87.7823 | | 4775 | Ganga | Transboundary | 20 | 17 | -14 |\n| 1189 | 0272M0904267 | 27.8640 | 87.7374 | | 5337 | Ganga | Transboundary | 14 | 12 | -14 |\n| 1190 | 03677P160082 | 28.1018 | 91.9419 | Tsona Chu | 4705 | Brahmaputra | Transboundary | 118 | 101 | -14 |\n| 1191 | 03591H011117 | 28.8457 | 97.1299 | Dzayul chu | 4446 | Brahmaputra | Transboundary | 42 | 36 | -14 |\n| 1192 | 03678M130271 | 27.9917 | 91.9520 | Tsona Chu | 4354 | Brahmaputra | Transboundary | 41 | 35 | -14 |\n| 1193 | 03082G071010 | 29.4761 | 93.2992 | Pulung chu | 4846 | Brahmaputra | Transboundary | 21 | 18 | -14 |\n| 1194 | 03082G040932 | 29.2377 | 93.0382 | Yarlung tsangpo | 4976 | Brahmaputra | Transboundary | 14 | 12 | -14 |\n| 1195 | 03177P040349 | 28.0446 | 91.0038 | | 4085 | Brahmaputra | Transboundary | 13 | 11 | -14 |\n| 1196 | 03878I010813 | 27.8360 | 90.1859 | Pho Chu | 4573 | Brahmaputra | Transboundary | 12 | 10 | -14 |\n| 1197 | 03177P040365 | 28.0125 | 91.0436 | | 4421 | Brahmaputra | Transboundary | 10 | 9 | -14 |\n| 1198 | 0262L1301402 | 28.7902 | 82.9807 | | 4796 | Ganga | Transboundary | 11 | 9 | -15 |\n| 1199 | 03878E130551 | 27.9031 | 89.8980 | Pho Chu | 4784 | Brahmaputra | Transboundary | 13 | 11 | -15 |\n| 1200 | 0162E0405027 | 31.1531 | 81.2434 | | 5651 | Indus | Transboundary | 16 | 13 | -16 |\n| 1201 | 03982C061147 | 29.6669 | 92.3935 | Medroma chu | 4677 | Brahmaputra | Transboundary | 54 | 45 | -16 |\n| 1202 | 03982F120620 | 30.1526 | 93.5268 | Drukla chu | 4847 | Brahmaputra | Transboundary | 16 | 13 | -16 |\n| 1203 | 03362O112294 | 29.3223 | 83.6457 | | 5159 | Brahmaputra | Transboundary | 16 | 13 | -16 |\n| 1204 | 03982K031648 | 29.4751 | 94.1941 | Nyang chu | 4420 | Brahmaputra | Transboundary | 14 | 12 | -16 |\n| 1205 | 03278I130819 | 27.8797 | 90.7905 | Panggyetangka | 4807 | Brahmaputra | Transboundary | 11 | 9 | -16 |\n| 1206 | 0272M0104093 | 27.9593 | 87.2467 | | 4457 | Ganga | Transboundary | 12 | 10 | -17 |\n| 1207 | 0271H1602493 | 28.2108 | 85.8472 | | 4374 | Ganga | Transboundary | 61 | 51 | -17 |\n| 1208 | 03678M090190 | 27.8771 | 91.6335 | Nyamjang Chu | 4480 | Brahmaputra | Transboundary | 44 | 37 | -17 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3950, "line_end": 3973, "token_count_estimate": 1308, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0162E0405027", "0262L1301402", "0271H1602493", "0272M0104093", "0272M0904267", "0272M1404551", "03082G040932", "03082G071010", "03177P040349", "03177P040365", "03278I130819", "03362O112294", "03591H011117", "03677P160082", "03678E110481", "03678M090190", "03678M130271", "03878E130551", "03878I010813", "03982C061147", "03982F120620", "03982K031648"]}}
{"id": "44eafc400a1416e6", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1209 | 0162E0404993 | 31.2340 | 81.1375 | Trokpo Shar Chu | 5562 | Indus | Transboundary | 11 | 9 | -18 |\n| 1210 | 03678M090202 | 27.8686 | 91.6708 | Nyamjang Chu | 4054 | Brahmaputra | Transboundary | 45 | 37 | -18 |\n| 1211 | 03082H011560 | 28.8713 | 93.2238 | Yarlung tsangpo | 4801 | Brahmaputra | Transboundary | 22 | 18 | -18 |\n| 1212 | 03678A150198 | 27.2738 | 88.9436 | Torsa River | 3897 | Brahmaputra | Transboundary | 16 | 13 | -18 |\n| 1213 | 0271P0403237 | 28.0324 | 87.1904 | | 5013 | Ganga | Transboundary | 27 | 22 | -19 |\n| 1214 | 03982K051774 | 29.8126 | 94.4326 | Drrakchi chu | 4083 | Brahmaputra | Transboundary | 228 | 185 | -19 |\n| 1215 | 03982B141027 | 30.7361 | 92.8753 | Me chu | 4911 | Brahmaputra | Transboundary | 21 | 17 | -19 |\n| 1216 | 03362J140516 | 30.6540 | 82.8798 | | 5322 | Brahmaputra | Transboundary | 12 | 10 | -19 |\n| 1217 | 03962N150130 | 30.4534 | 83.8458 | | 5580 | Brahmaputra | Transboundary | 23 | 18 | -20 |\n| 1218 | 03082C140551 | 29.6564 | 92.7834 | Gyab Pucong chu | 5123 | Brahmaputra | Transboundary | 15 | 12 | -20 |\n| 1219 | 03177P080424 | 28.0449 | 91.2609 | | 4600 | Brahmaputra | Transboundary | 12 | 10 | -20 |\n| 1220 | 0272M1004445 | 27.6201 | 87.7117 | | 4454 | Ganga | Transboundary | 13 | 10 | -21 |\n| 1221 | 03278I130842 | 27.8096 | 90.8636 | Tang Chu | 4112 | Brahmaputra | Transboundary | 23 | 18 | -21 |\n| 1222 | 03982G060952 | 29.7162 | 93.4273 | Banang chu | 5001 | Brahmaputra | Transboundary | 13 | 10 | -21 |\n| 1223 | 03982C061149 | 29.6187 | 92.3270 | Medroma chu | 4974 | Brahmaputra | Transboundary | 12 | 9 | -22 |\n| 1224 | 03982G050915 | 29.9479 | 93.2502 | Nyang chu | 5069 | Brahmaputra | Transboundary | 10 | 8 | -23 |\n| 1225 | 03577H010054 | 28.8047 | 89.1550 | Gyenkar zhung Chu | 4219 | Brahmaputra | Transboundary | 68 | 51 | -25 |\n| 1226 | 03982B160132 | 30.2290 | 92.9676 | Nyem chu | 4851 | Brahmaputra | Transboundary | 11 | 8 | -25 |\n| 1227 | 0272M1404553 | 27.7382 | 87.7782 | | 4705 | Ganga | Transboundary | 22 | 16 | -27 |\n| 1228 | 0152P1104326 | 32.3887 | 79.6591 | Indus River | 5720 | Indus | Transboundary | 12 | 9 | -27 |\n| 1229 | 03082K150023 | 29.3156 | 94.9221 | Tangong Chu | 3657 | Brahmaputra | Transboundary | 31 | 23 | -27 |\n| 1230 | 03982B130961 | 30.9963 | 92.8316 | Tsenrak chu | 4830 | Brahmaputra | Transboundary | 23 | 17 | -27 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 3980, "line_end": 4003, "token_count_estimate": 1357, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0152P1104326", "0162E0404993", "0271P0403237", "0272M1004445", "0272M1404553", "03082C140551", "03082H011560", "03082K150023", "03177P080424", "03278I130842", "03362J140516", "03577H010054", "03678A150198", "03678M090202", "03962N150130", "03982B130961", "03982B141027", "03982B160132", "03982C061149", "03982G050915", "03982G060952", "03982K051774"]}}
{"id": "6ffb67650a7ed674", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n***", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 4004, "line_end": 4008, "token_count_estimate": 62, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "846c30b1c51c62d8", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1231 | 0271L0902948 | 28.8405 | 86.5745 | | 5465 | Ganga | Transboundary | 26 | 19 | -28 |\n| 1232 | 03882F090120 | 30.8026 | 93.6825 | Sa chu | 5130 | Brahmaputra | Transboundary | 11 | 8 | -28 |\n| 1233 | 03362N101360 | 30.5905 | 83.5186 | | 5227 | Brahmaputra | Transboundary | 271 | 186 | -31 |\n| 1234 | 0152L1504109 | 32.3201 | 78.9790 | | 5498 | Indus | Transboundary | 11 | 7 | -33 |\n| 1235 | 0272M1004447 | 27.6168 | 87.5430 | | 4471 | Ganga | Transboundary | 11 | 7 | -36 |\n| 1236 | 03982K031627 | 29.4990 | 94.1918 | Nyang chu | 4490 | Brahmaputra | Transboundary | 11 | 7 | -36 |\n| 1237 | 03678E020262 | 27.5361 | 89.1676 | Ha Chu | 4462 | Brahmaputra | Transboundary | 31 | 18 | -41 |\n| 1238 | 03977J070024 | 30.3511 | 90.4981 | Lha chu | 5507 | Brahmaputra | Transboundary | 12 | 7 | -44 |\n| 1239 | 03682D040546 | 28.2278 | 92.0230 | Tsona Chu | 5115 | Brahmaputra | Transboundary | 12 | 6 | -48 |\n| 1240 | 03882J080292 | 30.1765 | 94.2821 | Yigrong chu | 4607 | Brahmaputra | Transboundary | 14 | 7 | -50 |\n| 1241 | 03362J150611 | 30.1162 | 82.7555 | | 4842 | Brahmaputra | Transboundary | 13 | 6 | -53 |\n| 1242 | 03678E100437 | 27.5743 | 89.5251 | | 4344 | Brahmaputra | Transboundary | 11 | 5 | -53 |\n| 1243 | 03977K100273 | 29.6149 | 90.6046 | Tolung chu | 5248 | Brahmaputra | Transboundary | 22 | 10 | -55 |\n| 1244 | 03671N070001 | 30.2514 | 87.4282 | Dranak Chu | 4878 | Brahmaputra | Transboundary | 20 | 8 | -61 |\n| 1245 | 03391C040439 | 29.0077 | 96.1818 | Dri Chu | 3914 | Brahmaputra | Transboundary | 10 | 4 | -61 |\n| 1246 | 0153I1304640 | 31.9144 | 78.8404 | | 5583 | Indus | Transboundary | 18 | 6 | -67 |\n| 1247 | 03682D040543 | 28.2389 | 92.0335 | Tsona Chu | 5130 | Brahmaputra | Transboundary | 12 | 2 | -83 |\n| 1248 | 0271P0403222 | 28.1113 | 87.0649 | | 5468 | Ganga | Transboundary | 31 | # | # |\n| 1249 | 0161B1504755 | 34.3237 | 80.8399 | | 5742 | Indus | Transboundary | 13 | # | # |\n| 1250 | 0271H1102228 | 28.4041 | 85.6046 | | 5265 | Ganga | Transboundary | 12 | # | # |\n| 1251 | 0262F1600678 | 30.2139 | 81.7579 | | 5368 | Ganga | Transboundary | 12 | # | # |\n| 1252 | 0262K1001276 | 29.7432 | 82.7048 | | 5346 | Ganga | Transboundary | 11 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 4009, "line_end": 4032, "token_count_estimate": 1317, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0152L1504109", "0153I1304640", "0161B1504755", "0262F1600678", "0262K1001276", "0271H1102228", "0271L0902948", "0271P0403222", "0272M1004447", "03362J150611", "03362N101360", "03391C040439", "03671N070001", "03678E020262", "03678E100437", "03682D040543", "03682D040546", "03882F090120", "03882J080292", "03977J070024", "03977K100273", "03982K031627"]}}
{"id": "462354cc9a419eaf", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 1253 | 0262K0701169 | 29.3869 | 82.4156 | | 4447 | Ganga | Transboundary | 13 | # | # |\n| 1254 | 0262F1500614 | 30.3452 | 81.8309 | | 5520 | Ganga | Transboundary | 10 | # | # |\n| 1255 | 0271H1102252 | 28.3581 | 85.5383 | | 4390 | Ganga | Transboundary | 11 | # | # |\n| 1256 | 0262K0701158 | 29.4322 | 82.3609 | | 4398 | Ganga | Transboundary | 27 | # | # |\n| 1257 | 0262K0601094 | 29.6978 | 82.3731 | | 4842 | Ganga | Transboundary | 13 | # | # |\n| 1258 | 0271P0803353 | 28.1608 | 87.4872 | | 5298 | Ganga | Transboundary | 10 | # | # |\n| 1259 | 0271L0402665 | 28.1831 | 86.2255 | | 5223 | Ganga | Transboundary | 12 | # | # |\n| 1260 | 03391C040379 | 29.1071 | 96.2295 | Dri Chu | 4333 | Brahmaputra | Transboundary | 12 | # | # |\n| 1261 | 03391C040386 | 29.0924 | 96.2426 | Dri Chu | 4117 | Brahmaputra | Transboundary | 26 | # | # |\n| 1262 | 03391D060717 | 28.5131 | 96.3978 | Thangkung Chu | 4345 | Brahmaputra | Transboundary | 26 | # | # |\n| 1263 | 03391D060720 | 28.5045 | 96.3914 | Thangkung Chu | 0 | Brahmaputra | Transboundary | 22 | # | # |\n| 1264 | 03082K150021 | 29.3296 | 94.8158 | Nugong Chu | 4151 | Brahmaputra | Transboundary | 27 | # | # |\n| 1265 | 03682H140009 | 28.7415 | 93.9151 | | 4150 | Brahmaputra | Transboundary | 10 | # | # |\n| 1266 | 03591D100468 | 28.5805 | 96.6805 | Maog chu | 3999 | Brahmaputra | Transboundary | 32 | # | # |\n| 1267 | 03591G040864 | 29.0391 | 97.0946 | Trigo chu | 4430 | Brahmaputra | Transboundary | 10 | # | # |\n| 1268 | 03982N110276 | 30.3001 | 95.5483 | Poto Chu | 4419 | Brahmaputra | Transboundary | 13 | # | # |\n| 1269 | 03882J050249 | 30.8493 | 94.3435 | Wok chu | 5015 | Brahmaputra | Transboundary | 22 | # | # |\n| 1270 | 03677P150046 | 28.3553 | 91.9599 | Tsona Chu | 4774 | Brahmaputra | Transboundary | 20 | # | # |\n| 1271 | 03178M050659 | 27.8838 | 91.2812 | | 4802 | Brahmaputra | Transboundary | 13 | # | # |\n| 1272 | 03878I010764 | 27.8927 | 90.2187 | Pho Chu | 5013 | Brahmaputra | Transboundary | 16 | # | # |\n| 1273 | 03577H080063 | 28.1445 | 89.3964 | Tochu | 4473 | Brahmaputra | Transboundary | 62 | # | # |\n| 1274 | 03591C150286 | 29.2629 | 96.9378 | Zo chu | 4676 | Brahmaputra | Transboundary | 12 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 4035, "line_end": 4058, "token_count_estimate": 1278, "basins": ["Brahmaputra", "Ganga"], "subbasins": [], "countries": [], "lake_ids": ["0262F1500614", "0262K0601094", "0262K0701158", "0262K0701169", "0271H1102252", "0271L0402665", "0271P0803353", "03082K150021", "03178M050659", "03391C040379", "03391C040386", "03391D060717", "03391D060720", "03577H080063", "03591C150286", "03591D100468", "03591G040864", "03677P150046", "03682H140009", "03878I010764", "03882J050249", "03982N110276"]}}
{"id": "ce504f0bce652f76", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 1275 | 03677P120038 | 28.0243 | 91.5975 | Yombu Chu | 4804 | Brahmaputra | Transboundary | 37 | # | # |\n| 1276 | 03582K090233 | 29.8520 | 94.5831 | | 4688 | Brahmaputra | Transboundary | 13 | # | # |\n| 1277 | 03982K031674 | 29.4471 | 94.1684 | Nyang chu | 4730 | Brahmaputra | Transboundary | 11 | # | # |\n| 1278 | 03177P080439 | 28.0012 | 91.2623 | | 4464 | Brahmaputra | Transboundary | 18 | # | # |\n| 1279 | 03591H021201 | 28.7442 | 97.2170 | Dzayul chu | 4318 | Brahmaputra | Transboundary | 22 | # | # |\n| 1280 | 03178M010597 | 27.9855 | 91.2149 | | 4311 | Brahmaputra | Transboundary | 16 | # | # |\n| 1281 | 03678M090240 | 27.8057 | 91.5896 | Kulong Chu | 4406 | Brahmaputra | Transboundary | 14 | # | # |\n| 1282 | 03678M090189 | 27.8773 | 91.7172 | Nyamjang Chu | 4533 | Brahmaputra | Transboundary | 18 | # | # |\n| 1283 | 03362J070281 | 30.3706 | 82.2732 | | 4966 | Brahmaputra | Transboundary | 11 | # | # |\n| 1284 | 03878I010747 | 27.9154 | 90.2442 | Pho Chu | 5171 | Brahmaputra | Transboundary | 13 | # | # |\n| 1285 | 03591C160309 | 29.1706 | 96.8667 | Zo chu | 4458 | Brahmaputra | Transboundary | 13 | # | # |\n| 1286 | 03982G131203 | 29.9240 | 93.8895 | Draksum chu | 4453 | Brahmaputra | Transboundary | 12 | # | # |\n| 1287 | 03278I050214 | 27.9455 | 90.3776 | Mangde Chu | 5273 | Brahmaputra | Transboundary | 10 | # | # |\n| 1288 | 03982B141024 | 30.7392 | 92.7752 | Me chu | 4864 | Brahmaputra | Transboundary | 12 | # | # |\n| 1289 | 03591C100092 | 29.5974 | 96.6175 | Zo chu | 5104 | Brahmaputra | Transboundary | 10 | # | # |\n| 1290 | 03391D060713 | 28.5194 | 96.3981 | Thangkung Chu | 4247 | Brahmaputra | Transboundary | 15 | # | # |\n| 1291 | 03082K031894 | 29.3427 | 94.1608 | Yarlung tsangpo | 4564 | Brahmaputra | Transboundary | 11 | # | # |\n| 1292 | 03882F110141 | 30.4614 | 93.5089 | Nyewo chu | 4785 | Brahmaputra | Transboundary | 12 | # | # |\n| 1293 | 03391C040404 | 29.0691 | 96.1467 | Dri Chu | 4031 | Brahmaputra | Transboundary | 12 | # | # |\n| 1294 | 03982F160682 | 30.0593 | 93.7806 | Draksum chu | 3751 | Brahmaputra | Transboundary | 20 | # | # |\n| 1295 | 03882J020223 | 30.7320 | 94.2105 | Wok chu | 4550 | Brahmaputra | Transboundary | 11 | # | # |\n| 1296 | 03391C080457 | 29.0223 | 96.2968 | Thangkung Chu | 4165 | Brahmaputra | Transboundary | 15 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 4062, "line_end": 4085, "token_count_estimate": 1308, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082K031894", "03177P080439", "03178M010597", "03278I050214", "03362J070281", "03391C040404", "03391C080457", "03391D060713", "03582K090233", "03591C100092", "03591C160309", "03591H021201", "03677P120038", "03678M090189", "03678M090240", "03878I010747", "03882F110141", "03882J020223", "03982B141024", "03982F160682", "03982G131203", "03982K031674"]}}
{"id": "acf1cd42654b0c60", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1297 | 03682H140024 | 28.7070 | 93.7924 | | 3793 | Brahmaputra | Transboundary | 18 | # | # |\n| 1298 | 03982G020853 | 29.5800 | 93.1993 | Banang chu | 5180 | Brahmaputra | Transboundary | 17 | # | # |\n| 1299 | 03082H131726 | 28.7849 | 93.9231 | Nelung phu chu | 4147 | Brahmaputra | Transboundary | 13 | # | # |\n| 1300 | 03278I130832 | 27.8410 | 90.8312 | Tang Chu | 4568 | Brahmaputra | Transboundary | 18 | # | # |\n| 1301 | 03582K090252 | 29.8251 | 94.5505 | | 4275 | Brahmaputra | Transboundary | 13 | # | # |\n| 1302 | 03677P120022 | 28.1641 | 91.5107 | Tsukke Chu | 5221 | Brahmaputra | Transboundary | 12 | # | # |\n| 1303 | 03582J080032 | 30.0281 | 94.3957 | Numphu chu | 4664 | Brahmaputra | Transboundary | 11 | # | # |\n| 1304 | 03391C080456 | 29.0233 | 96.3109 | Thangkung Chu | 4253 | Brahmaputra | Transboundary | 15 | # | # |\n| 1305 | 03591D130572 | 28.8498 | 96.9282 | Trigo chu | 4299 | Brahmaputra | Transboundary | 15 | # | # |\n| 1306 | 03278I130825 | 27.8666 | 90.8162 | Tang Chu | 4135 | Brahmaputra | Transboundary | 54 | # | # |\n| 1307 | 03982F080534 | 30.0991 | 93.2720 | Drukla chu | 4704 | Brahmaputra | Transboundary | 34 | # | # |\n| 1308 | 03082K071959 | 29.3969 | 94.3038 | Yarlung tsangpo | 4321 | Brahmaputra | Transboundary | 11 | # | # |\n| 1309 | 03082H131713 | 28.8470 | 93.7840 | Nelung phu chu | 4057 | Brahmaputra | Transboundary | 24 | # | # |\n| 1310 | 03982F080559 | 30.0652 | 93.3338 | Drukla chu | 4888 | Brahmaputra | Transboundary | 10 | # | # |\n| 1311 | 03178I130565 | 27.8658 | 90.8504 | Lhodrak Chu | 4600 | Brahmaputra | Transboundary | 10 | # | # |\n| 1312 | 03591H011122 | 28.8355 | 97.1481 | Dzayul chu | 4392 | Brahmaputra | Transboundary | 18 | # | # |\n| 1313 | 03582K050087 | 29.9324 | 94.3879 | Numphu chu | 4238 | Brahmaputra | Transboundary | 13 | # | # |\n| 1314 | 03082K031794 | 29.4574 | 94.0520 | Lapu chu | 4692 | Brahmaputra | Transboundary | 13 | # | # |\n| 1315 | 03678E050369 | 27.7664 | 89.3686 | Paro Chu | 4396 | Brahmaputra | Transboundary | 10 | # | # |\n| 1316 | 03582K050057 | 29.9850 | 94.3419 | Numphu chu | 4407 | Brahmaputra | Transboundary | 11 | # | # |\n| 1317 | 03678M050099 | 27.9161 | 91.4502 | Kulong Chu | 4422 | Brahmaputra | Transboundary | 13 | # | # |\n| 1318 | 03982G101120 | 29.5849 | 93.7458 | Nyang chu | 4699 | Brahmaputra | Transboundary | 11 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 22, "line_start": 4090, "line_end": 4113, "token_count_estimate": 1341, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082H131713", "03082H131726", "03082K031794", "03082K071959", "03178I130565", "03278I130825", "03278I130832", "03391C080456", "03582J080032", "03582K050057", "03582K050087", "03582K090252", "03591D130572", "03591H011122", "03677P120022", "03678E050369", "03678M050099", "03682H140024", "03982F080534", "03982F080559", "03982G020853", "03982G101120"]}}
{"id": "18fd23143e9420e3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1319 | 03582J080002 | 30.2187 | 94.3361 | Nunkhu Phu chu | 4565 | Brahmaputra | Transboundary | 12 | # | # |\n| 1320 | 03982K031651 | 29.4722 | 94.2362 | Nyang chu | 4509 | Brahmaputra | Transboundary | 51 | # | # |\n| 1321 | 03678E030288 | 27.4925 | 89.1491 | Ha Chu | 4064 | Brahmaputra | Transboundary | 28 | # | # |\n| 1322 | 03982G131198 | 29.9501 | 93.9718 | Draksum chu | 4239 | Brahmaputra | Transboundary | 22 | # | # |\n| 1323 | 03178M050664 | 27.8430 | 91.2708 | | 3980 | Brahmaputra | Transboundary | 20 | # | # |\n| 1324 | 03982K011437 | 29.9406 | 94.0932 | Penam chu | 4059 | Brahmaputra | Transboundary | 17 | # | # |\n| 1325 | 03678M090178 | 27.8875 | 91.5959 | Kulong Chu | 4626 | Brahmaputra | Transboundary | 17 | # | # |\n| 1326 | 03591D090408 | 28.8097 | 96.6055 | Dzayul chu | 4362 | Brahmaputra | Transboundary | 11 | # | # |\n| 1327 | 03178M050652 | 27.9045 | 91.2900 | | 4625 | Brahmaputra | Transboundary | 32 | # | # |\n| 1328 | 03391C040416 | 29.0364 | 96.1781 | Dri Chu | 3944 | Brahmaputra | Transboundary | 20 | # | # |\n| 1329 | 03082H091666 | 28.7709 | 93.6310 | Nelung phu chu | 4688 | Brahmaputra | Transboundary | 18 | # | # |\n| 1330 | 03391C080444 | 29.0939 | 96.2597 | Dri Chu | 4001 | Brahmaputra | Transboundary | 39 | # | # |\n| 1331 | 03582J080027 | 30.0526 | 94.3577 | Numphu chu | 4730 | Brahmaputra | Transboundary | 26 | # | # |\n| 1332 | 03982G010765 | 29.7642 | 93.1150 | Banang chu | 5076 | Brahmaputra | Transboundary | 24 | # | # |\n| 1333 | 03678M090236 | 27.8113 | 91.5008 | Kulong Chu | 4213 | Brahmaputra | Transboundary | 17 | # | # |\n| 1334 | 03082H131722 | 28.7897 | 93.9123 | Nelung phu chu | 4226 | Brahmaputra | Transboundary | 17 | # | # |\n| 1335 | 03982K031652 | 29.4718 | 94.2132 | Nyang chu | 4432 | Brahmaputra | Transboundary | 16 | # | # |\n| 1336 | 03391D100760 | 28.6935 | 96.5512 | Thangkung Chu | 4220 | Brahmaputra | Transboundary | 34 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 18, "line_start": 4117, "line_end": 4136, "token_count_estimate": 1124, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03082H091666", "03082H131722", "03178M050652", "03178M050664", "03391C040416", "03391C080444", "03391D100760", "03582J080002", "03582J080027", "03591D090408", "03678E030288", "03678M090178", "03678M090236", "03982G010765", "03982G131198", "03982K011437", "03982K031651", "03982K031652"]}}
{"id": "b26bf7632f5749a0", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1337 | 03977G151502 | 29.4768 | 89.9316 | Nyemo ma chu | 5408 | Brahmaputra | Transboundary | 13 | # | # |\n| 1338 | 03982E041208 | 31.1477 | 93.0966 | | 4994 | Brahmaputra | Transboundary | 10 | # | # |\n| 1339 | 03591C120139 | 29.1245 | 96.6598 | Sangyi chu | 4329 | Brahmaputra | Transboundary | 18 | # | # |\n| 1340 | 03077L070212 | 28.2645 | 90.2757 | Kya Chu | 5829 | Brahmaputra | Transboundary | 11 | # | # |\n| 1341 | 03882F150201 | 30.4893 | 93.8233 | Po Yigrong chu | 4933 | Brahmaputra | Transboundary | 10 | # | # |\n| 1342 | 03591D100481 | 28.5414 | 96.6177 | Dulai chu | 4268 | Brahmaputra | Transboundary | 42 | # | # |\n| 1343 | 03678M090238 | 27.8085 | 91.5487 | Kulong Chu | 4297 | Brahmaputra | Transboundary | 19 | # | # |\n| 1344 | 03582J080001 | 30.2483 | 94.3564 | Nunkhu Phu chu | 4853 | Brahmaputra | Transboundary | 15 | # | # |\n| 1345 | 03591C150282 | 29.2677 | 96.8369 | Zo chu | 4119 | Brahmaputra | Transboundary | 108 | # | # |\n| 1346 | 03878E130630 | 27.8290 | 89.9437 | | 4456 | Brahmaputra | Transboundary | 37 | # | # |\n| 1347 | 03178M050644 | 27.9294 | 91.2999 | | 4371 | Brahmaputra | Transboundary | 20 | # | # |\n| 1348 | 03591D110494 | 28.4924 | 96.6919 | Dulai chu | 4046 | Brahmaputra | Transboundary | 21 | # | # |\n| 1349 | 03591H011091 | 28.8984 | 97.1650 | Nechen Kora chu | 4713 | Brahmaputra | Transboundary | 15 | # | # |\n| 1350 | 03982J041401 | 30.0638 | 94.1891 | Penam chu | 4616 | Brahmaputra | Transboundary | 35 | # | # |\n| 1351 | 03678M090213 | 27.8544 | 91.5001 | Kulong Chu | 4049 | Brahmaputra | Transboundary | 32 | # | # |\n| 1352 | 03591H021214 | 28.7020 | 97.1423 | Trigo chu | 4378 | Brahmaputra | Transboundary | 17 | # | # |\n| 1353 | 03678E030284 | 27.4981 | 89.0931 | Torsa River | 4569 | Brahmaputra | Transboundary | 11 | # | # |\n| 1354 | 03678A130026 | 27.8442 | 88.9397 | Khangphu Chu | 4863 | Brahmaputra | Transboundary | 28 | # | # |\n| 1355 | 03878E090424 | 27.9215 | 89.6356 | | 4269 | Brahmaputra | Transboundary | 11 | # | # |\n| 1356 | 03671K060197 | 29.6716 | 86.3801 | Dongmo chu | 5512 | Brahmaputra | Transboundary | 11 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 20, "line_start": 4143, "line_end": 4164, "token_count_estimate": 1228, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03077L070212", "03178M050644", "03582J080001", "03591C120139", "03591C150282", "03591D100481", "03591D110494", "03591H011091", "03591H021214", "03671K060197", "03678A130026", "03678E030284", "03678M090213", "03678M090238", "03878E090424", "03878E130630", "03882F150201", "03977G151502", "03982E041208", "03982J041401"]}}
{"id": "2ebdae14069e3861", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | ID_No | Latitude | Longitude | River | Elevation | Basin | Region | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1357 | 03182H030283 | 28.3199 | 93.0469 | Char chu | 4255 | Brahmaputra | Transboundary | 73 | # | # |\n| 1358 | 03982G131212 | 29.9071 | 93.8971 | Draksum chu | 4492 | Brahmaputra | Transboundary | 13 | # | # |\n| 1359 | 03178M010613 | 27.8772 | 91.0137 | Lhodrak Chu | 4162 | Brahmaputra | Transboundary | 12 | # | # |\n| 1360 | 03362N060936 | 30.5802 | 83.3650 | | 5719 | Brahmaputra | Transboundary | 11 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "ID_No", "Latitude", "Longitude", "River", "Elevation", "Basin", "Region", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 4, "line_start": 4166, "line_end": 4171, "token_count_estimate": 390, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": ["03178M010613", "03182H030283", "03362N060936", "03982G131212"]}}
{"id": "841b4715f3de8535", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n\n- GLs/WBs with increase in Area > 40%\n- GLs/WBs with increase in Area – 0% to 40%\n- GLs/WBs with no change in Area\n- GLs/WBs with decrease in Area\n- GLs/WBs not analysed", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 4172, "line_end": 4185, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7045fa4d657a9496", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable: Table 4.12: Results of State-wise change detection in water spread area of 581 GLs within India as per Glacial lake Atlas 2023.\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0143N1602259 | 34.04020 | 75.84410 | Nagmithong Lungpa | 4094 | Indus | Kargil | Ladakh | 25 | 57 | 126 |\n| 2 | 0152J0203796 | 34.51980 | 78.10090 | | 5265 | Indus | Leh | Ladakh | 10 | 20 | 95 |\n| 3 | 0143E0600854 | 35.73870 | 73.25630 | | 4016 | Indus | Leh | Ladakh | 19 | 30 | 59 |\n| 4 | 0152F0903545 | 34.90480 | 77.61570 | | 5125 | Indus | Leh | Ladakh | 15 | 21 | 45 |\n| 5 | 0152J0303815 | 34.44560 | 78.14310 | Kunzang Lungpa | 5311 | Indus | Leh | Ladakh | 21 | 29 | 41 |\n| 6 | 0143N0902149 | 34.80780 | 75.54480 | | 4687 | Indus | Kargil | Ladakh | 13 | 18 | 40 |\n| 7 | 0142D1500009 | 36.41210 | 72.90110 | Hurguji Gol | 4256 | Indus | Leh | Ladakh | 11 | 15 | 34 |\n| 8 | 0143I1101289 | 35.39950 | 74.67560 | Buldar Gah | 3386 | Indus | Leh | Ladakh | 14 | 18 | 29 |\n| 9 | 0143M1201758 | 35.17190 | 75.52070 | | 4551 | Indus | Leh | Ladakh | 10 | 13 | 26 |\n| 10 | 0143N1302218 | 34.85900 | 75.94180 | | 4796 | Indus | Kargil | Ladakh | 14 | 17 | 24 |\n| 11 | 0152J0303821 | 34.40060 | 78.07860 | | 5307 | Indus | Leh | Ladakh | 20 | 25 | 23 |\n| 12 | 0143E0600858 | 35.64340 | 73.35230 | Bolono Gah | 3747 | Indus | Leh | Ladakh | 14 | 17 | 22 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": "Table 4.12: Results of State-wise change detection in water spread area of 581 GLs within India as per Glacial lake Atlas 2023.", "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 12, "line_start": 4186, "line_end": 4199, "token_count_estimate": 859, "basins": ["Indus"], "subbasins": [], "countries": ["India"], "lake_ids": ["0142D1500009", "0143E0600854", "0143E0600858", "0143I1101289", "0143M1201758", "0143N0902149", "0143N1302218", "0143N1602259", "0152F0903545", "0152J0203796", "0152J0303815", "0152J0303821", "04020", "07860", "10090", "14310", "17190", "25630", "35230", "39950", "40060", "41210", "44560", "51980", "52070", "54480", "61570", "64340", "67560", "73870", "80780", "84410", "85900", "90110", "90480", "94180"]}}
{"id": "52e259510abde36e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 13 | 0152F1503582 | 34.39790 | 77.98260 | | 5344 | Indus | Leh | Ladakh | 28 | 33 | 18 |\n| 14 | 0143E0500837 | 35.81680 | 73.29750 | | 2981 | Indus | Leh | Ladakh | 15 | 18 | 17 |\n| 15 | 0143N1302207 | 34.88830 | 75.89210 | Pohultakish Nar | 4882 | Indus | Kargil | Ladakh | 10 | 12 | 17 |\n| 16 | 0143E0600856 | 35.65200 | 73.35730 | Bolono Gah | 3735 | Indus | Leh | Ladakh | 19 | 22 | 16 |\n| 17 | 0152B1202881 | 34.00470 | 76.72150 | Photang | 5050 | Indus | Leh | Ladakh | 18 | 21 | 15 |\n| 18 | 0143E0500795 | 35.91770 | 73.37120 | Shiobat Gah | 4502 | Indus | Leh | Ladakh | 12 | 14 | 14 |\n| 19 | 0143I1501320 | 35.33020 | 74.78580 | | 3475 | Indus | Leh | Ladakh | 15 | 17 | 13 |\n| 20 | 0152K1103987 | 33.47370 | 78.50230 | Kok Lungpa | 5312 | Indus | Leh | Ladakh | 15 | 17 | 13 |\n| 21 | 0143E0100726 | 35.82940 | 73.22790 | | 4195 | Indus | Leh | Ladakh | 12 | 14 | 13 |\n| 22 | 0143J0901417 | 34.83980 | 74.67560 | Sakmai N | 3868 | Indus | Leh | Ladakh | 11 | 12 | 13 |\n| 23 | 0142H0800185 | 36.02370 | 73.32760 | | 4559 | Indus | Leh | Ladakh | 11 | 12 | 13 |\n| 24 | 0152J0203794 | 34.52130 | 78.09040 | | 5270 | Indus | Leh | Ladakh | 11 | 12 | 13 |\n| 25 | 0143N0101856 | 34.91940 | 75.18870 | Sar Sangri | 4225 | Indus | Leh | Ladakh | 14 | 16 | 12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4203, "line_end": 4217, "token_count_estimate": 879, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["00470", "0142H0800185", "0143E0100726", "0143E0500795", "0143E0500837", "0143E0600856", "0143I1501320", "0143J0901417", "0143N0101856", "0143N1302207", "0152B1202881", "0152F1503582", "0152J0203794", "0152K1103987", "02370", "09040", "18870", "22790", "29750", "32760", "33020", "35730", "37120", "39790", "47370", "50230", "52130", "65200", "67560", "72150", "78580", "81680", "82940", "83980", "88830", "89210", "91770", "91940", "98260"]}}
{"id": "a43acdb9eaadb802", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 26 | 0143A0900591 | 35.94430 | 72.59470 | | 3761 | Indus | Leh | Ladakh | 96 | 107 | 11 |\n| 27 | 0142H1500357 | 36.26470 | 73.95500 | Shukur Gah | 3706 | Indus | Leh | Ladakh | 28 | 31 | 11 |\n| 28 | 0143N1302212 | 34.87820 | 75.87070 | | 4687 | Indus | Kargil | Ladakh | 19 | 21 | 11 |\n| 29 | 0143E0500757 | 35.96210 | 73.39730 | Gugalo Gah | 4349 | Indus | Leh | Ladakh | 19 | 21 | 11 |\n| 30 | 0152K0303890 | 33.28150 | 78.22970 | Indus River | 5646 | Indus | Leh | Ladakh | 35 | 38 | 10 |\n| 31 | 0142H1200327 | 36.01050 | 73.55770 | Hangrus Gah | 4232 | Indus | Leh | Ladakh | 24 | 27 | 10 |\n| 32 | 0142H1100230 | 36.35070 | 73.51380 | Kurkuhi Bar | 4420 | Indus | Leh | Ladakh | 17 | 19 | 10 |\n| 33 | 0152K1003986 | 33.51720 | 78.51920 | | 5404 | Indus | Leh | Ladakh | 14 | 15 | 10 |\n| 34 | 0143M0801728 | 35.14330 | 75.26120 | | 4659 | Indus | Leh | Ladakh | 12 | 13 | 10 |\n| 35 | 0152J1203868 | 34.15100 | 78.55280 | | 5566 | Indus | Leh | Ladakh | 65 | 71 | 9 |\n| 36 | 0143N0501997 | 34.82460 | 75.38250 | | 4315 | Indus | Leh | Ladakh | 23 | 25 | 9 |\n| 37 | 0143N0902078 | 34.96510 | 75.54520 | | 4412 | Indus | Leh | Ladakh | 14 | 15 | 9 |\n| 38 | 0143M1201776 | 35.09600 | 75.57940 | | 4375 | Indus | Leh | Ladakh | 11 | 12 | 9 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4222, "line_end": 4236, "token_count_estimate": 871, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["01050", "0142H1100230", "0142H1200327", "0142H1500357", "0143A0900591", "0143E0500757", "0143M0801728", "0143M1201776", "0143N0501997", "0143N0902078", "0143N1302212", "0152J1203868", "0152K0303890", "0152K1003986", "09600", "14330", "15100", "22970", "26120", "26470", "28150", "35070", "38250", "39730", "51380", "51720", "51920", "54520", "55280", "55770", "57940", "59470", "82460", "87070", "87820", "94430", "95500", "96210", "96510"]}}
{"id": "af6929ef48897f8d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 39 | 0143E0900919 | 35.88920 | 73.69210 | Balres Gah | 3995 | Indus | Leh | Ladakh | 11 | 12 | 9 |\n| 40 | 0143I0101159 | 35.77290 | 74.19600 | Bal Gah | 4368 | Indus | Leh | Ladakh | 11 | 12 | 9 |\n| 41 | 0142H1200253 | 36.12860 | 73.50500 | Hakis Gah | 4482 | Indus | Leh | Ladakh | 23 | 25 | 8 |\n| 42 | 0143E1301057 | 35.85180 | 73.87730 | Shaturhao Gah | 4166 | Indus | Leh | Ladakh | 19 | 21 | 8 |\n| 43 | 0142H1200249 | 36.15390 | 73.61640 | Saro Gah | 4545 | Indus | Leh | Ladakh | 14 | 15 | 8 |\n| 44 | 0143E0500769 | 35.94920 | 73.28880 | Pakhtari Gol | 4228 | Indus | Leh | Ladakh | 54 | 58 | 7 |\n| 45 | 0143E0900928 | 35.87990 | 73.57670 | Mashado Gah | 4059 | Indus | Leh | Ladakh | 31 | 33 | 7 |\n| 46 | 0143E0600855 | 35.67510 | 73.34420 | Gacher Gah | 3428 | Indus | Leh | Ladakh | 25 | 27 | 7 |\n| 47 | 0142H0800166 | 36.13050 | 73.38420 | Khatbari Gah | 4503 | Indus | Leh | Ladakh | 23 | 25 | 7 |\n| 48 | 0142H0800174 | 36.10810 | 73.46460 | | 4488 | Indus | Leh | Ladakh | 22 | 23 | 7 |\n| 49 | 0142L0400386 | 36.23920 | 74.08420 | Naltar river | 3453 | Indus | Leh | Ladakh | 21 | 22 | 7 |\n| 50 | 0143M0801730 | 35.13250 | 75.32120 | | 4526 | Indus | Leh | Ladakh | 20 | 21 | 7 |\n| 51 | 0143N0902126 | 34.87290 | 75.71590 | | 4664 | Indus | Kargil | Ladakh | 16 | 17 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4240, "line_end": 4254, "token_count_estimate": 900, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142H0800166", "0142H0800174", "0142H1200249", "0142H1200253", "0142L0400386", "0143E0500769", "0143E0600855", "0143E0900919", "0143E0900928", "0143E1301057", "0143I0101159", "0143M0801730", "0143N0902126", "08420", "10810", "12860", "13050", "13250", "15390", "19600", "23920", "28880", "32120", "34420", "38420", "46460", "50500", "57670", "61640", "67510", "69210", "71590", "77290", "85180", "87290", "87730", "87990", "88920", "94920"]}}
{"id": "ea28e48f39da9934", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 52 | 0143I0201186 | 35.70700 | 74.24330 | Pahot Gah | 4318 | Indus | Leh | Ladakh | 16 | 17 | 7 |\n| 53 | 0142H0800179 | 36.08570 | 73.46060 | | 4577 | Indus | Leh | Ladakh | 14 | 15 | 7 |\n| 54 | 0142H0800198 | 35.99970 | 73.31240 | | 4615 | Indus | Leh | Ladakh | 13 | 14 | 7 |\n| 55 | 0142H0800173 | 36.11200 | 73.44920 | | 4427 | Indus | Leh | Ladakh | 11 | 12 | 7 |\n| 56 | 0143M0401638 | 35.23060 | 75.18240 | | 4547 | Indus | Leh | Ladakh | 11 | 12 | 7 |\n| 57 | 0143E0500742 | 35.98670 | 73.31310 | Bashkar Gah | 4583 | Indus | Leh | Ladakh | 10 | 11 | 7 |\n| 58 | 0152K0403898 | 33.13680 | 78.19710 | Zoboshisha Nala | 5733 | Indus | Leh | Ladakh | 10 | 11 | 7 |\n| 59 | 0143E1300983 | 35.97220 | 73.91120 | Thapas Gah | 4426 | Indus | Leh | Ladakh | 29 | 31 | 6 |\n| 60 | 0152J1203865 | 34.20010 | 78.51610 | | 5386 | Indus | Leh | Ladakh | 24 | 26 | 6 |\n| 61 | 0142H0800171 | 36.11650 | 73.42310 | Gupis Gah | 4437 | Indus | Leh | Ladakh | 22 | 23 | 6 |\n| 62 | 0142H1200295 | 36.03850 | 73.59210 | | 4274 | Indus | Leh | Ladakh | 19 | 20 | 6 |\n| 63 | 0142H1200250 | 36.14520 | 73.63940 | | 4484 | Indus | Leh | Ladakh | 18 | 19 | 6 |\n| 64 | 0143N0902074 | 34.98680 | 75.55350 | Tukziwai Lungma | 4269 | Indus | Leh | Ladakh | 18 | 19 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4259, "line_end": 4273, "token_count_estimate": 881, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142H0800171", "0142H0800173", "0142H0800179", "0142H0800198", "0142H1200250", "0142H1200295", "0143E0500742", "0143E1300983", "0143I0201186", "0143M0401638", "0143N0902074", "0152J1203865", "0152K0403898", "03850", "08570", "11200", "11650", "13680", "14520", "18240", "19710", "20010", "23060", "24330", "31240", "31310", "42310", "44920", "46060", "51610", "55350", "59210", "63940", "70700", "91120", "97220", "98670", "98680", "99970"]}}
{"id": "b6930055e0ea2cf4", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 65 | 0143I0201192 | 35.68620 | 74.23000 | Pahot Gah | 4336 | Indus | Leh | Ladakh | 14 | 15 | 6 |\n| 66 | 0142H1100220 | 36.43130 | 73.56600 | | 4030 | Indus | Leh | Ladakh | 12 | 13 | 6 |\n| 67 | 0143E0900961 | 35.78340 | 73.50740 | | 3918 | Indus | Leh | Ladakh | 12 | 13 | 6 |\n| 68 | 0143A0900583 | 35.99440 | 72.61300 | | 3622 | Indus | Leh | Ladakh | 202 | 212 | 5 |\n| 69 | 0152G1303615 | 33.99890 | 77.97940 | Sherchu Lungpa | 4991 | Indus | Leh | Ladakh | 44 | 46 | 5 |\n| 70 | 0143E1301036 | 35.88250 | 73.76320 | Singal Gah | 4104 | Indus | Leh | Ladakh | 41 | 43 | 5 |\n| 71 | 0143M0301632 | 35.26350 | 75.19350 | | 4551 | Indus | Leh | Ladakh | 19 | 20 | 5 |\n| 72 | 0143E1301053 | 35.85830 | 73.86940 | Shaturhao Gah | 4211 | Indus | Leh | Ladakh | 15 | 16 | 5 |\n| 73 | 0143N0101842 | 34.97200 | 75.05050 | | 4265 | Indus | Leh | Ladakh | 14 | 15 | 5 |\n| 74 | 0143N0101852 | 34.92180 | 75.24980 | | 4152 | Indus | Leh | Ladakh | 14 | 15 | 5 |\n| 75 | 0143M0801717 | 35.18230 | 75.42230 | | 4530 | Indus | Leh | Ladakh | 14 | 15 | 5 |\n| 76 | 0143N0902124 | 34.88310 | 75.68000 | | 4627 | Indus | Kargil | Ladakh | 13 | 14 | 5 |\n| 77 | 0152B0502772 | 34.85390 | 76.36760 | Rumboka | 4821 | Indus | Kargil | Ladakh | 13 | 14 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4277, "line_end": 4291, "token_count_estimate": 875, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142H1100220", "0143A0900583", "0143E0900961", "0143E1301036", "0143E1301053", "0143I0201192", "0143M0301632", "0143M0801717", "0143N0101842", "0143N0101852", "0143N0902124", "0152B0502772", "0152G1303615", "05050", "18230", "19350", "23000", "24980", "26350", "36760", "42230", "43130", "50740", "56600", "61300", "68000", "68620", "76320", "78340", "85390", "85830", "86940", "88250", "88310", "92180", "97200", "97940", "99440", "99890"]}}
{"id": "c1ed00a3dc8e2750", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 78 | 0143I0101158 | 35.77910 | 74.20530 | Bal Gah | 4304 | Indus | Leh | Ladakh | 12 | 13 | 5 |\n| 79 | 0143E1301013 | 35.91640 | 73.99240 | | 4312 | Indus | Leh | Ladakh | 11 | 12 | 5 |\n| 80 | 0143E0500790 | 35.92540 | 73.27400 | | 4227 | Indus | Leh | Ladakh | 11 | 12 | 5 |\n| 81 | 0143E0500781 | 35.93600 | 73.26860 | | 4362 | Indus | Leh | Ladakh | 11 | 12 | 5 |\n| 82 | 0142H1200302 | 36.03020 | 73.71360 | | 4320 | Indus | Leh | Ladakh | 10 | 11 | 5 |\n| 83 | 0142H1200262 | 36.09640 | 73.66800 | Gakuch Gah | 4423 | Indus | Leh | Ladakh | 10 | 11 | 5 |\n| 84 | 0143E1601096 | 35.18360 | 73.95800 | | 4120 | Indus | Leh | Ladakh | 10 | 11 | 5 |\n| 85 | 0142H0900200 | 36.87920 | 73.70430 | | 4286 | Indus | Leh | Ladakh | 263 | 272 | 4 |\n| 86 | 0143E0900941 | 35.86450 | 73.74600 | Balres Gah | 4140 | Indus | Leh | Ladakh | 84 | 88 | 4 |\n| 87 | 0143E0500774 | 35.94500 | 73.36500 | Gugalo Gah | 4162 | Indus | Leh | Ladakh | 67 | 70 | 4 |\n| 88 | 0152K0803965 | 33.05490 | 78.46960 | Luglung | 5745 | Indus | Leh | Ladakh | 36 | 38 | 4 |\n| 89 | 0152B1402898 | 34.67090 | 76.75770 | Zagainakchan Lungpa | 4965 | Indus | Leh | Ladakh | 30 | 31 | 4 |\n| 90 | 0143N0902089 | 34.94680 | 75.59210 | | 4558 | Indus | Leh | Ladakh | 25 | 26 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4296, "line_end": 4310, "token_count_estimate": 882, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142H0900200", "0142H1200262", "0142H1200302", "0143E0500774", "0143E0500781", "0143E0500790", "0143E0900941", "0143E1301013", "0143E1601096", "0143I0101158", "0143N0902089", "0152B1402898", "0152K0803965", "03020", "05490", "09640", "18360", "20530", "26860", "27400", "36500", "46960", "59210", "66800", "67090", "70430", "71360", "74600", "75770", "77910", "86450", "87920", "91640", "92540", "93600", "94500", "94680", "95800", "99240"]}}
{"id": "9e405d534622554b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 91 | 0143E0900962 | 35.78210 | 73.57190 | | 4082 | Indus | Leh | Ladakh | 22 | 23 | 4 |\n| 92 | 0152A0802526 | 35.01060 | 76.32010 | Indus River | 4753 | Indus | Kargil | Ladakh | 15 | 16 | 4 |\n| 93 | 0143I0101122 | 35.96220 | 74.01910 | | 4285 | Indus | Leh | Ladakh | 15 | 16 | 4 |\n| 94 | 0152A0102354 | 35.95420 | 76.03020 | | 4276 | Indus | Leh | Ladakh | 11 | 11 | 4 |\n| 95 | 0152B0502808 | 34.77160 | 76.49980 | Yaldor | 4761 | Indus | Leh | Ladakh | 11 | 11 | 4 |\n| 96 | 0142H1000208 | 36.64400 | 73.64640 | | 3821 | Indus | Leh | Ladakh | 105 | 108 | 3 |\n| 97 | 0152J0803836 | 34.23300 | 78.42640 | | 5350 | Indus | Leh | Ladakh | 65 | 67 | 3 |\n| 98 | 0152L1004049 | 32.73570 | 78.72610 | Hanle River | 5626 | Indus | Leh | Ladakh | 35 | 36 | 3 |\n| 99 | 0143E1301054 | 35.85560 | 73.77520 | Khanbari Nala | 3962 | Indus | Leh | Ladakh | 28 | 29 | 3 |\n| 100 | 0143N1302194 | 34.94700 | 75.76250 | | 4778 | Indus | Leh | Ladakh | 25 | 26 | 3 |\n| 101 | 0152K1003984 | 33.55770 | 78.50560 | | 5665 | Indus | Leh | Ladakh | 25 | 26 | 3 |\n| 102 | 0143E0100689 | 35.90570 | 73.15480 | Kanu Gol | 4328 | Indus | Leh | Ladakh | 24 | 25 | 3 |\n| 103 | 0143E0100682 | 35.91990 | 73.06420 | Rani Gol | 4303 | Indus | Leh | Ladakh | 21 | 22 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4314, "line_end": 4328, "token_count_estimate": 872, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["01060", "0142H1000208", "0143E0100682", "0143E0100689", "0143E0900962", "0143E1301054", "0143I0101122", "0143N1302194", "0152A0102354", "0152A0802526", "0152B0502808", "0152J0803836", "0152K1003984", "0152L1004049", "01910", "03020", "06420", "15480", "23300", "32010", "42640", "49980", "50560", "55770", "57190", "64400", "64640", "72610", "73570", "76250", "77160", "77520", "78210", "85560", "90570", "91990", "94700", "95420", "96220"]}}
{"id": "9adb4203f0c9eb3d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 104 | 0143N0201889 | 34.68360 | 75.14050 | Burzil Nala | 4133 | Indus | Leh | Ladakh | 21 | 22 | 3 |\n| 105 | 0143E1301045 | 35.86790 | 73.92890 | Shaturhao Gah | 4064 | Indus | Leh | Ladakh | 18 | 18 | 3 |\n| 106 | 0152K0703937 | 33.49550 | 78.49750 | | 5428 | Indus | Leh | Ladakh | 17 | 17 | 3 |\n| 107 | 0143E0900929 | 35.87680 | 73.61490 | Mashado Gah | 3993 | Indus | Leh | Ladakh | 15 | 15 | 3 |\n| 108 | 0143N0101854 | 34.92040 | 75.17720 | Sar Sangri | 4257 | Indus | Leh | Ladakh | 15 | 15 | 3 |\n| 109 | 0143E0500791 | 35.92490 | 73.34230 | 'Salil Gah | 4396 | Indus | Leh | Ladakh | 12 | 12 | 3 |\n| 110 | 0143N0201885 | 34.69660 | 75.13710 | Burzil Nala | 4103 | Indus | Leh | Ladakh | 65 | 66 | 2 |\n| 111 | 0143E0200735 | 35.72980 | 73.21570 | Kachila Gah | 3970 | Indus | Leh | Ladakh | 26 | 27 | 2 |\n| 112 | 0143E0100728 | 35.82480 | 73.21140 | | 4271 | Indus | Leh | Ladakh | 26 | 27 | 2 |\n| 113 | 0143J1301450 | 34.83960 | 74.76100 | Reat | 3979 | Indus | Leh | Ladakh | 25 | 26 | 2 |\n| 114 | 0143J0901405 | 34.90840 | 74.67990 | Mir Malik Gah | 3864 | Indus | Leh | Ladakh | 23 | 24 | 2 |\n| 115 | 0143E1301005 | 35.93910 | 73.98530 | | 4258 | Indus | Leh | Ladakh | 20 | 20 | 2 |\n| 116 | 0143I0101141 | 35.81720 | 74.08340 | | 4269 | Indus | Leh | Ladakh | 17 | 17 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4333, "line_end": 4347, "token_count_estimate": 894, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0143E0100728", "0143E0200735", "0143E0500791", "0143E0900929", "0143E1301005", "0143E1301045", "0143I0101141", "0143J0901405", "0143J1301450", "0143N0101854", "0143N0201885", "0143N0201889", "0152K0703937", "08340", "13710", "14050", "17720", "21140", "21570", "34230", "49550", "49750", "61490", "67990", "68360", "69660", "72980", "76100", "81720", "82480", "83960", "86790", "87680", "90840", "92040", "92490", "92890", "93910", "98530"]}}
{"id": "098831c73eff071b", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 117 | 0143M0401679 | 35.04870 | 75.19820 | | 4398 | Indus | Leh | Ladakh | 13 | 13 | 2 |\n| 118 | 0143I1501319 | 35.33190 | 74.94140 | | 4427 | Indus | Leh | Ladakh | 12 | 12 | 2 |\n| 119 | 0143I0601249 | 35.72180 | 74.25560 | | 4254 | Indus | Leh | Ladakh | 11 | 11 | 2 |\n| 120 | 0143N0502010 | 34.77510 | 75.48030 | | 4626 | Indus | Kargil | Ladakh | 11 | 11 | 2 |\n| 121 | 0152K0703939 | 33.45510 | 78.49820 | Kok Lungpa | 5308 | Indus | Leh | Ladakh | 148 | 149 | 1 |\n| 122 | 0143N0101839 | 34.99110 | 75.23630 | | 4138 | Indus | Leh | Ladakh | 130 | 131 | 1 |\n| 123 | 0143J1301449 | 34.84530 | 74.80880 | Reat | 3992 | Indus | Leh | Ladakh | 50 | 51 | 1 |\n| 124 | 0152A0402407 | 35.17110 | 76.20510 | | 3301 | Indus | Leh | Ladakh | 31 | 31 | 1 |\n| 125 | 0142H0300089 | 36.26320 | 73.04760 | | 4618 | Indus | Leh | Ladakh | 22 | 22 | 1 |\n| 126 | 0152A0802513 | 35.05580 | 76.26100 | Indus River | 4881 | Indus | Leh | Ladakh | 17 | 17 | 1 |\n| 127 | 0152B0902845 | 34.81070 | 76.50220 | | 4762 | Indus | Leh | Ladakh | 13 | 13 | 1 |\n| 128 | 0143M0401682 | 35.01640 | 75.00090 | Darle Gah | 3931 | Indus | Leh | Ladakh | 11 | 11 | 1 |\n| 129 | 0152K0703940 | 33.42740 | 78.48760 | Kok Lungpa | 5284 | Indus | Leh | Ladakh | 178 | 178 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4351, "line_end": 4365, "token_count_estimate": 872, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["00090", "0142H0300089", "0143I0601249", "0143I1501319", "0143J1301449", "0143M0401679", "0143M0401682", "0143N0101839", "0143N0502010", "0152A0402407", "0152A0802513", "0152B0902845", "0152K0703939", "0152K0703940", "01640", "04760", "04870", "05580", "17110", "19820", "20510", "23630", "25560", "26100", "26320", "33190", "42740", "45510", "48030", "48760", "49820", "50220", "72180", "77510", "80880", "81070", "84530", "94140", "99110"]}}
{"id": "9741ad81e9496cff", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 130 | 0142H1200268 | 36.08000 | 73.64610 | Suku Gah | 4344 | Indus | Leh | Ladakh | 29 | 29 | 0 |\n| 131 | 0143A1300645 | 35.86870 | 72.93970 | Anari Gol | 4378 | Indus | Leh | Ladakh | 20 | 20 | 0 |\n| 132 | 0143E0900912 | 35.90450 | 73.56830 | Doba Gah | 4109 | Indus | Leh | Ladakh | 18 | 18 | 0 |\n| 133 | 0152E1203425 | 35.03170 | 77.69980 | | 5161 | Indus | Leh | Ladakh | 18 | 18 | 0 |\n| 134 | 0143E1301021 | 35.90580 | 73.92150 | | 4223 | Indus | Leh | Ladakh | 16 | 16 | 0 |\n| 135 | 0143N0902115 | 34.89570 | 75.61670 | | 4547 | Indus | Kargil | Ladakh | 12 | 12 | 0 |\n| 136 | 0143I0401224 | 35.07320 | 74.17650 | | 4042 | Indus | Leh | Ladakh | 12 | 12 | 0 |\n| 137 | 0143I0501239 | 35.82080 | 74.29400 | | 4313 | Indus | Leh | Ladakh | 12 | 12 | 0 |\n| 138 | 0143N0201897 | 34.66610 | 75.17940 | Badogam Nar | 4234 | Indus | Leh | Ladakh | 75 | 74 | -1 |\n| 139 | 0143E1301012 | 35.91660 | 73.94550 | | 4178 | Indus | Leh | Ladakh | 12 | 12 | -1 |\n| 140 | 0152J0303822 | 34.39040 | 78.08860 | | 5226 | Indus | Leh | Ladakh | 11 | 11 | -1 |\n| 141 | 0152G1003609 | 33.55560 | 77.66410 | Kuam Lungpa | 5222 | Indus | Leh | Ladakh | 10 | 10 | -1 |\n| 142 | 0143N1102173 | 34.49110 | 75.64910 | Sando Nala | 4521 | Indus | Kargil | Ladakh | 15 | 15 | -2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4370, "line_end": 4384, "token_count_estimate": 880, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142H1200268", "0143A1300645", "0143E0900912", "0143E1301012", "0143E1301021", "0143I0401224", "0143I0501239", "0143N0201897", "0143N0902115", "0143N1102173", "0152E1203425", "0152G1003609", "0152J0303822", "03170", "07320", "08000", "08860", "17650", "17940", "29400", "39040", "49110", "55560", "56830", "61670", "64610", "64910", "66410", "66610", "69980", "82080", "86870", "89570", "90450", "90580", "91660", "92150", "93970", "94550"]}}
{"id": "60bd1cc5d6a69006", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 143 | 0152B0502787 | 34.81820 | 76.40900 | Rumboka | 4816 | Indus | Kargil | Ladakh | 10 | 10 | -2 |\n| 144 | 0152B0502780 | 34.83340 | 76.35180 | Gavis | 4818 | Indus | Kargil | Ladakh | 10 | 10 | -3 |\n| 145 | 0142P1100571 | 36.43960 | 75.68600 | | 4709 | Indus | Leh | Ladakh | 28 | 27 | -4 |\n| 146 | 0152B0502784 | 34.82740 | 76.45580 | | 4648 | Indus | Leh | Ladakh | 20 | 19 | -4 |\n| 147 | 0143I1601334 | 35.07460 | 74.95810 | | 4413 | Indus | Leh | Ladakh | 10 | 10 | -4 |\n| 148 | 0143I1601333 | 35.08220 | 74.96120 | Thue Gah | 4317 | Indus | Leh | Ladakh | 31 | 29 | -5 |\n| 149 | 0143E1601082 | 35.24700 | 73.79950 | | 3988 | Indus | Leh | Ladakh | 17 | 16 | -6 |\n| 150 | 0143N0902117 | 34.89300 | 75.71250 | | 4589 | Indus | Kargil | Ladakh | 19 | 18 | -7 |\n| 151 | 0143J1301448 | 34.84640 | 74.78660 | Reat | 3883 | Indus | Leh | Ladakh | 14 | 13 | -8 |\n| 152 | 0152B0502816 | 34.76200 | 76.46860 | Yaldor | 4895 | Indus | Leh | Ladakh | 18 | 16 | -9 |\n| 153 | 0143M0401664 | 35.11900 | 75.22790 | | 4586 | Indus | Leh | Ladakh | 18 | 16 | -10 |\n| 154 | 0152B0502771 | 34.85410 | 76.38270 | Rumboka | 4688 | Indus | Kargil | Ladakh | 14 | 12 | -12 |\n| 155 | 0152B0902844 | 34.81260 | 76.51720 | | 4771 | Indus | Leh | Ladakh | 21 | 18 | -13 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4388, "line_end": 4402, "token_count_estimate": 884, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142P1100571", "0143E1601082", "0143I1601333", "0143I1601334", "0143J1301448", "0143M0401664", "0143N0902117", "0152B0502771", "0152B0502780", "0152B0502784", "0152B0502787", "0152B0502816", "0152B0902844", "07460", "08220", "11900", "22790", "24700", "35180", "38270", "40900", "43960", "45580", "46860", "51720", "68600", "71250", "76200", "78660", "79950", "81260", "81820", "82740", "83340", "84640", "85410", "89300", "95810", "96120"]}}
{"id": "31431e3270c355b9", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 156 | 0152E1603441 | 35.06000 | 77.85600 | | 4679 | Indus | Leh | Ladakh | 33 | 17 | -48 |\n| 157 | 0152A0602485 | 35.72050 | 76.37520 | | 4140 | Indus | Leh | Ladakh | 12 | 6 | -51 |\n| 158 | 0143I1501322 | 35.31510 | 74.93680 | | 4197 | Indus | Leh | Ladakh | 20 | 8 | -60 |\n| 159 | 0142P0400481 | 36.13720 | 75.13630 | Hunza | 3697 | Indus | Leh | Ladakh | 25 | # | # |\n| 160 | 0142P0400492 | 36.12640 | 75.13970 | Hunza | 3725 | Indus | Leh | Ladakh | 13 | # | # |\n| 161 | 0152C0503044 | 33.84360 | 76.37530 | Pholohiow | 4116 | Indus | Kargil | Ladakh | 18 | # | # |\n| 162 | 0152L1504075 | 32.49150 | 78.85240 | Handle River | 5706 | Indus | Leh | Ladakh | 11 | # | # |\n| 163 | 0152A0602465 | 35.72980 | 76.41010 | | 4161 | Indus | Leh | Ladakh | 13 | # | # |\n| 164 | 0152E1103389 | 35.47590 | 77.51350 | | 5342 | Indus | Leh | Ladakh | 22 | # | # |\n| 165 | 0143J1501473 | 34.45720 | 74.98510 | Satsar N | 3748 | Indus | Bandipore | Jammu & Kashmir | 10 | 15 | 45 |\n| 166 | 0143J0901397 | 34.92000 | 74.52110 | Kishanganga | 4041 | Indus | Muzaffarabad | Jammu & Kashmir | 61 | 80 | 32 |\n| 167 | 0152C0102942 | 33.94210 | 76.01890 | Fariabad | 4197 | Indus | Kishtwar | Jammu & Kashmir | 24 | 31 | 29 |\n| 168 | 0143N0401967 | 34.20250 | 75.20500 | | 3682 | Indus | Anantnag | Jammu & Kashmir | 16 | 20 | 26 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4407, "line_end": 4421, "token_count_estimate": 890, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0142P0400481", "0142P0400492", "0143I1501322", "0143J0901397", "0143J1501473", "0143N0401967", "0152A0602465", "0152A0602485", "0152C0102942", "0152C0503044", "0152E1103389", "0152E1603441", "0152L1504075", "01890", "06000", "12640", "13630", "13720", "13970", "20250", "20500", "31510", "37520", "37530", "41010", "45720", "47590", "49150", "51350", "52110", "72050", "72980", "84360", "85240", "85600", "92000", "93680", "94210", "98510"]}}
{"id": "21c0b7cd2c2aa5f5", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 169 | 0143N0401973 | 34.15610 | 75.11300 | Zajmara | 3941 | Indus | Anantnag | Jammu & Kashmir | 10 | 12 | 17 |\n| 170 | 0143K1001558 | 33.52970 | 74.57390 | Rupu Nala | 3955 | Indus | Kulgam | Jammu & Kashmir | 12 | 14 | 14 |\n| 171 | 0143K1001568 | 33.50490 | 74.59370 | Rupu Nala | 4089 | Indus | Kulgam | Jammu & Kashmir | 11 | 13 | 14 |\n| 172 | 0143K1001544 | 33.55880 | 74.52620 | Rimbiara River | 3851 | Indus | Kulgam | Jammu & Kashmir | 23 | 26 | 13 |\n| 173 | 0143K0501527 | 33.82910 | 74.43530 | Sinwar | 4043 | Indus | Badgam | Jammu & Kashmir | 15 | 17 | 12 |\n| 174 | 0143K0501515 | 33.86560 | 74.41660 | | 3968 | Indus | Badgam | Jammu & Kashmir | 14 | 15 | 10 |\n| 175 | 0143K1101582 | 33.44440 | 74.61370 | Panch Gabbar Nala | 3580 | Indus | Rajauri | Jammu & Kashmir | 15 | 16 | 8 |\n| 176 | 0143K1001559 | 33.52830 | 74.56110 | Rupu Nala | 4096 | Indus | Kulgam | Jammu & Kashmir | 11 | 12 | 8 |\n| 177 | 0143K1401588 | 33.51230 | 74.76930 | Konsarnag Nala | 3486 | Indus | Kulgam | Jammu & Kashmir | 129 | 138 | 7 |\n| 178 | 0143N0802065 | 34.09380 | 75.49780 | Gratabal Nar (East Liddar River) | 3575 | Indus | Anantnag | Jammu & Kashmir | 52 | 56 | 7 |\n| 179 | 0143K1001567 | 33.50900 | 74.62470 | Rupu Nala | 3937 | Indus | Kulgam | Jammu & Kashmir | 34 | 36 | 7 |\n| 180 | 0143K1101571 | 33.49570 | 74.56480 | Paniul | 3812 | Indus | Punch | Jammu & Kashmir | 11 | 12 | 7 |\n| 181 | 0143F1301107 | 34.83050 | 73.98470 | | 3873 | Indus | Muzaffarabad | Jammu & Kashmir | 21 | 22 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4425, "line_end": 4439, "token_count_estimate": 931, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0143F1301107", "0143K0501515", "0143K0501527", "0143K1001544", "0143K1001558", "0143K1001559", "0143K1001567", "0143K1001568", "0143K1101571", "0143K1101582", "0143K1401588", "0143N0401973", "0143N0802065", "09380", "11300", "15610", "41660", "43530", "44440", "49570", "49780", "50490", "50900", "51230", "52620", "52830", "52970", "55880", "56110", "56480", "57390", "59370", "61370", "62470", "76930", "82910", "83050", "86560", "98470"]}}
{"id": "6c8555582dc92a0d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 182 | 0143J1501480 | 34.45180 | 74.93240 | Kankanaz N | 3889 | Indus | Ganderbal | Jammu & Kashmir | 12 | 13 | 6 |\n| 183 | 0143N0401966 | 34.20740 | 75.14730 | | 3699 | Indus | Ganderbal | Jammu & Kashmir | 10 | 11 | 6 |\n| 184 | 0143J1401458 | 34.53670 | 74.82770 | Patalwan Nala | 3922 | Indus | Bandipore | Jammu & Kashmir | 31 | 32 | 5 |\n| 185 | 0143K0501528 | 33.82030 | 74.45090 | | 3997 | Indus | Badgam | Jammu & Kashmir | 29 | 31 | 5 |\n| 186 | 0143K1001560 | 33.52190 | 74.60480 | Rupu Nala | 3844 | Indus | Kulgam | Jammu & Kashmir | 12 | 13 | 5 |\n| 187 | 0143J1501467 | 34.46970 | 74.91280 | Shonshpahi N | 3852 | Indus | Bandipore | Jammu & Kashmir | 10 | 11 | 5 |\n| 188 | 0143N0401975 | 34.13960 | 75.14790 | | 3780 | Indus | Anantnag | Jammu & Kashmir | 83 | 86 | 4 |\n| 189 | 0143J1501493 | 34.41820 | 74.93550 | Kankanaz N | 3503 | Indus | Ganderbal | Jammu & Kashmir | 37 | 38 | 4 |\n| 190 | 0143N0802045 | 34.23410 | 75.27510 | Basmai Nar | 3830 | Indus | Anantnag | Jammu & Kashmir | 18 | 19 | 4 |\n| 191 | 0143K0501525 | 33.84110 | 74.42790 | Sinwar | 3954 | Indus | Badgam | Jammu & Kashmir | 45 | 47 | 3 |\n| 192 | 0143J1501486 | 34.44420 | 74.89190 | Salnai N | 3846 | Indus | Bandipore | Jammu & Kashmir | 34 | 35 | 3 |\n| 193 | 0143J1501499 | 34.39240 | 74.87360 | | 3556 | Indus | Bandipore | Jammu & Kashmir | 27 | 28 | 3 |\n| 194 | 0143K1001564 | 33.51010 | 74.56430 | Paniul | 3909 | Indus | Punch | Jammu & Kashmir | 25 | 26 | 3 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4444, "line_end": 4458, "token_count_estimate": 918, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0143J1401458", "0143J1501467", "0143J1501480", "0143J1501486", "0143J1501493", "0143J1501499", "0143K0501525", "0143K0501528", "0143K1001560", "0143K1001564", "0143N0401966", "0143N0401975", "0143N0802045", "13960", "14730", "14790", "20740", "23410", "27510", "39240", "41820", "42790", "44420", "45090", "45180", "46970", "51010", "52190", "53670", "56430", "60480", "82030", "82770", "84110", "87360", "89190", "91280", "93240", "93550"]}}
{"id": "875c6cf574fc4aa8", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 195 | 0143J1501485 | 34.44820 | 74.90850 | Kankanaz N | 3979 | Indus | Ganderbal | Jammu & Kashmir | 15 | 16 | 3 |\n| 196 | 0143J1501489 | 34.43190 | 74.92450 | Kankanaz N | 3571 | Indus | Ganderbal | Jammu & Kashmir | 161 | 164 | 2 |\n| 197 | 0143K1001561 | 33.51890 | 74.58370 | Rupu Nala | 3934 | Indus | Kulgam | Jammu & Kashmir | 71 | 72 | 2 |\n| 198 | 0143J1501462 | 34.49280 | 74.92120 | Lachhan Pinjan Nala | 3881 | Indus | Bandipore | Jammu & Kashmir | 38 | 39 | 2 |\n| 199 | 0143J1401457 | 34.54790 | 74.81910 | Patalwan Nala | 3872 | Indus | Bandipore | Jammu & Kashmir | 31 | 32 | 2 |\n| 200 | 0143N0301932 | 34.39700 | 75.10260 | Raman Nala | 3812 | Indus | Bandipore | Jammu & Kashmir | 30 | 30 | 2 |\n| 201 | 0143J0101360 | 34.85750 | 74.07690 | | 3680 | Indus | Muzaffarabad | Jammu & Kashmir | 21 | 21 | 2 |\n| 202 | 0143K1401591 | 33.50250 | 74.85020 | Zali Nar | 3623 | Indus | Kulgam | Jammu & Kashmir | 15 | 15 | 2 |\n| 203 | 0143N0401974 | 34.14410 | 75.11010 | Dagwan Nar | 3810 | Indus | Srinagar | Jammu & Kashmir | 43 | 44 | 1 |\n| 204 | 0143J0101345 | 34.96220 | 74.10040 | | 3603 | Indus | Muzaffarabad | Jammu & Kashmir | 30 | 30 | 1 |\n| 205 | 0143K1001551 | 33.54000 | 74.56480 | Rupu Nala | 3967 | Indus | Kulgam | Jammu & Kashmir | 22 | 22 | 1 |\n| 206 | 0143N0401978 | 34.09340 | 75.16070 | Lokul Chhumanai | 3919 | Indus | Anantnag | Jammu & Kashmir | 18 | 18 | 1 |\n| 207 | 0143J1501464 | 34.48900 | 74.90620 | Lachhan Pinjan Nala | 4097 | Indus | Bandipore | Jammu & Kashmir | 12 | 12 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4462, "line_end": 4476, "token_count_estimate": 936, "basins": ["Indus"], "subbasins": [], "countries": [], "lake_ids": ["0143J0101345", "0143J0101360", "0143J1401457", "0143J1501462", "0143J1501464", "0143J1501485", "0143J1501489", "0143K1001551", "0143K1001561", "0143K1401591", "0143N0301932", "0143N0401974", "0143N0401978", "07690", "09340", "10040", "10260", "11010", "14410", "16070", "39700", "43190", "44820", "48900", "49280", "50250", "51890", "54000", "54790", "56480", "58370", "81910", "85020", "85750", "90620", "90850", "92120", "92450", "96220"]}}
{"id": "11ad283e9b3178be", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 234 | 0262B1100358 | 30.39170 | 80.53180 | | 4753 | Ganga | Pithoragarh | Uttarakhand | 11 | 17 | 52 |\n| 235 | 0253N0900232 | 30.90420 | 79.74740 | | 4683 | Ganga | Chamoli | Uttarakhand | 11 | 12 | 9 |\n| 236 | 0253N0900235 | 30.90100 | 79.74620 | | 4689 | Ganga | Chamoli | Uttarakhand | 11 | 9 | -21 |\n| 237 | 03477D120008 | 28.05860 | 88.63120 | Teesta River | 4917 | Brahmaputra | North Sikkim | Sikkim | 15 | 46 | 211 |\n| 238 | 03478A090378 | 27.95720 | 88.71390 | Laehung Chu | 5078 | Brahmaputra | North Sikkim | Sikkim | 26 | 46 | 74 |\n| 239 | 03478A100477 | 27.72300 | 88.69020 | Rabom Chu | 4472 | Brahmaputra | North Sikkim | Sikkim | 12 | 17 | 40 |\n| 240 | 03478A130538 | 27.93580 | 88.78430 | Lachung Chu | 5332 | Brahmaputra | North Sikkim | Sikkim | 10 | 13 | 26 |\n| 241 | 03478A140609 | 27.73000 | 88.83270 | Teesta River | 4446 | Brahmaputra | North Sikkim | Sikkim | 15 | 18 | 23 |\n| 242 | 03478A090357 | 27.98810 | 88.73620 | Teesta River | 5188 | Brahmaputra | North Sikkim | Sikkim | 11 | 13 | 23 |\n| 243 | 03478A050185 | 27.97580 | 88.41760 | Nakul Chu | 5224 | Brahmaputra | North Sikkim | Sikkim | 16 | 20 | 22 |\n| 244 | 03478A130530 | 27.96870 | 88.79690 | Laehung Chu | 5361 | Brahmaputra | North Sikkim | Sikkim | 18 | 22 | 19 |\n| 245 | 03478A060326 | 27.72280 | 88.45260 | | 4199 | Brahmaputra | North Sikkim | Sikkim | 14 | 16 | 12 |\n| 246 | 03477D120024 | 28.01440 | 88.65190 | Teesta River | 5522 | Brahmaputra | North Sikkim | Sikkim | 12 | 13 | 12 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4481, "line_end": 4495, "token_count_estimate": 927, "basins": ["Brahmaputra", "Ganga"], "subbasins": ["Teesta"], "countries": [], "lake_ids": ["01440", "0253N0900232", "0253N0900235", "0262B1100358", "03477D120008", "03477D120024", "03478A050185", "03478A060326", "03478A090357", "03478A090378", "03478A100477", "03478A130530", "03478A130538", "03478A140609", "05860", "39170", "41760", "45260", "53180", "63120", "65190", "69020", "71390", "72280", "72300", "73000", "73620", "74620", "74740", "78430", "79690", "83270", "90100", "90420", "93580", "95720", "96870", "97580", "98810"]}}
{"id": "6bd43cc6555335ca", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 247 | 03478A050232 | 27.92000 | 88.31350 | Khara Chu | 5114 | Brahmaputra | North Sikkim | Sikkim | 10 | 11 | 10 |\n| 248 | 03478A130533 | 27.96350 | 88.79660 | Laehung Chu | 5350 | Brahmaputra | North Sikkim | Sikkim | 12 | 13 | 9 |\n| 249 | 03478A110508 | 27.46610 | 88.75100 | Chakung Chu | 4052 | Brahmaputra | North Sikkim | Sikkim | 22 | 24 | 8 |\n| 250 | 03478A090374 | 27.96180 | 88.74340 | Laehung Chu | 5065 | Brahmaputra | North Sikkim | Sikkim | 20 | 21 | 7 |\n| 251 | 03478A050278 | 27.81710 | 88.26090 | Rhuling Chu | 5487 | Brahmaputra | North Sikkim | Sikkim | 12 | 13 | 6 |\n| 252 | 03477D160043 | 28.01110 | 88.75600 | Teesta River | 5094 | Brahmaputra | North Sikkim | Sikkim | 108 | 113 | 5 |\n| 253 | 03478A150681 | 27.36810 | 88.82620 | Rungpo Chu | 3930 | Brahmaputra | East Sikkim | Sikkim | 13 | 14 | 4 |\n| 254 | 03478A150677 | 27.37460 | 88.76290 | Rangpo Chu | 3744 | Brahmaputra | East Sikkim | Sikkim | 27 | 28 | 3 |\n| 255 | 03478A150691 | 27.34620 | 88.81870 | Rangpo Chu | 3546 | Brahmaputra | East Sikkim | Sikkim | 18 | 19 | 3 |\n| 256 | 03778A150012 | 27.33030 | 88.84630 | | 3901 | Brahmaputra | East Sikkim | Sikkim | 28 | 28 | 1 |\n| 257 | 03478A090364 | 27.97080 | 88.59330 | Teesta River | 4748 | Brahmaputra | North Sikkim | Sikkim | 10 | 10 | -1 |\n| 258 | 03678A150140 | 27.49630 | 88.79100 | Tangka Chu | 4807 | Brahmaputra | North Sikkim | Sikkim | 16 | 15 | -3 |\n| 259 | 03478A100500 | 27.66210 | 88.68950 | Rabom Chu | 4298 | Brahmaputra | North Sikkim | Sikkim | 16 | 15 | -4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4499, "line_end": 4513, "token_count_estimate": 942, "basins": ["Brahmaputra"], "subbasins": ["Teesta"], "countries": [], "lake_ids": ["01110", "03477D160043", "03478A050232", "03478A050278", "03478A090364", "03478A090374", "03478A100500", "03478A110508", "03478A130533", "03478A150677", "03478A150681", "03478A150691", "03678A150140", "03778A150012", "26090", "31350", "33030", "34620", "36810", "37460", "46610", "49630", "59330", "66210", "68950", "74340", "75100", "75600", "76290", "79100", "79660", "81710", "81870", "82620", "84630", "92000", "96180", "96350", "97080"]}}
{"id": "db9e17c45e3571ca", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 260 | 03478A100496 | 27.66800 | 88.68670 | Teesta River | 4117 | Brahmaputra | North Sikkim | Sikkim | 15 | 9 | -39 |\n| 261 | 03478A060337 | 27.67790 | 88.37600 | Lumthul Chu | 3915 | Brahmaputra | North Sikkim | Sikkim | 18 | # | # |\n| 262 | 03478A010083 | 27.85180 | 88.23980 | Bhuthang Chu | 5190 | Brahmaputra | North Sikkim | Sikkim | 31 | # | # |\n| 263 | 03478A150633 | 27.44210 | 88.75240 | Bakcha Chu | 4055 | Brahmaputra | North Sikkim | Sikkim | 11 | # | # |\n| 264 | 03478A150630 | 27.47720 | 88.77570 | Glong Chu | 4633 | Brahmaputra | North Sikkim | Sikkim | 11 | # | # |\n| 265 | 03478A090356 | 27.99060 | 88.60250 | Teesta River | 4778 | Brahmaputra | North Sikkim | Sikkim | 10 | # | # |\n| 266 | 03478A050302 | 27.75880 | 88.48350 | Zemu Chu | 4467 | Brahmaputra | North Sikkim | Sikkim | 14 | # | # |\n| 267 | 03391D060647 | 28.72070 | 96.40540 | Thangkung Chu | 4097 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 35 | 101 |\n| 268 | 03391D010514 | 28.82840 | 96.24390 | Thangkung Chu | 3740 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 22 | 72 |\n| 269 | 03391D010491 | 28.88890 | 96.15140 | Dri Chu | 4079 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | 35 | 56 |\n| 270 | 03591H041391 | 28.03430 | 97.24110 | | 4299 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 26 | 43 |\n| 271 | 03391C040410 | 29.05440 | 96.10230 | Dri Chu | 4103 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 14 | 40 |\n| 272 | 03382O150125 | 29.27040 | 95.92500 | Aison Chu | 2605 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 22 | 37 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4520, "line_end": 4534, "token_count_estimate": 920, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Teesta"], "countries": [], "lake_ids": ["03382O150125", "03391C040410", "03391D010491", "03391D010514", "03391D060647", "03430", "03478A010083", "03478A050302", "03478A060337", "03478A090356", "03478A100496", "03478A150630", "03478A150633", "03591H041391", "05440", "10230", "15140", "23980", "24110", "24390", "27040", "37600", "40540", "44210", "47720", "48350", "60250", "66800", "67790", "68670", "72070", "75240", "75880", "77570", "82840", "85180", "88890", "92500", "99060"]}}
{"id": "c736acbacc80603f", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 273 | 03591H081898 | 28.14860 | 97.27350 | | 3953 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | 19 | 34 |\n| 274 | 03082K150028 | 29.25640 | 94.76970 | Nugong Chu | 3294 | Brahmaputra | Upper Siang | Arunachal Pradesh | 13 | 17 | 34 |\n| 275 | 03382O160179 | 29.01110 | 95.88510 | Matuni | 3778 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 55 | 73 | 32 |\n| 276 | 03391D010478 | 28.95210 | 96.03760 | Dri Chu | 3634 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 26 | 28 |\n| 277 | 03391D050605 | 28.84680 | 96.35500 | Thangkung Chu | 3760 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 19 | 27 |\n| 278 | 03391D050599 | 28.86120 | 96.24860 | Thangkung Chu | 3847 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 14 | 25 |\n| 279 | 03391C030274 | 29.30220 | 96.08210 | Jairu Chu | 4274 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 120 | 148 | 24 |\n| 280 | 03591H081904 | 28.11610 | 97.30010 | | 4029 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 22 | 24 |\n| 281 | 03391D010524 | 28.80340 | 96.15510 | Thangkung Chu | 3866 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 21 | 22 |\n| 282 | 03082O150269 | 29.38680 | 95.98740 | Chendruk Chu | 4251 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 43 | 51 | 20 |\n| 283 | 03382O160144 | 29.23150 | 95.98110 | Aison Chu | 4167 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 37 | 20 |\n| 284 | 03082O110214 | 29.28310 | 95.73980 | Rirung Chu | 4168 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 24 | 20 |\n| 285 | 03591H041306 | 28.22630 | 97.17530 | | 3898 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 14 | 20 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4538, "line_end": 4552, "token_count_estimate": 918, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["01110", "03082K150028", "03082O110214", "03082O150269", "03382O160144", "03382O160179", "03391C030274", "03391D010478", "03391D010524", "03391D050599", "03391D050605", "03591H041306", "03591H081898", "03591H081904", "03760", "08210", "11610", "14860", "15510", "17530", "22630", "23150", "24860", "25640", "27350", "28310", "30010", "30220", "35500", "38680", "73980", "76970", "80340", "84680", "86120", "88510", "95210", "98110", "98740"]}}
{"id": "414499d02c286b1a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 286 | 03391D010482 | 28.94110 | 96.01970 | Dri Chu | 3449 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 14 | 20 |\n| 287 | 03391D090743 | 28.77560 | 96.53110 | Thangkung Chu | 3510 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 102 | 121 | 19 |\n| 288 | 03391D050621 | 28.80920 | 96.45900 | Thangkung Chu | 4078 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 35 | 42 | 19 |\n| 289 | 03391C040364 | 29.15530 | 96.06930 | Dri Chu | 3588 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 26 | 31 | 19 |\n| 290 | 03391D010518 | 28.81370 | 96.12160 | Thangkung Chu | 4115 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 16 | 19 |\n| 291 | 03391D050562 | 28.94410 | 96.37140 | Thangkung Chu | 4236 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 15 | 19 |\n| 292 | 03391C030308 | 29.25650 | 96.13790 | Jairu Chu | 3766 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 24 | 28 | 18 |\n| 293 | 03382P050203 | 28.87340 | 95.34980 | Emra River | 3647 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 23 | 27 | 18 |\n| 294 | 03591D160694 | 28.21430 | 96.88530 | Dzayul chu | 3960 | Brahmaputra | Anjaw | Arunachal Pradesh | 21 | 25 | 18 |\n| 295 | 03591H071840 | 28.24310 | 97.33060 | | 4333 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 14 | 18 |\n| 296 | 03391C040388 | 29.09100 | 96.16230 | Dri Chu | 3822 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 13 | 18 |\n| 297 | 03391C030255 | 29.33500 | 96.00830 | Aison Chu | 3985 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 33 | 17 |\n| 298 | 03391C040384 | 29.09550 | 95.99670 | Dri Chu | 3346 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | 26 | 17 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4555, "line_end": 4569, "token_count_estimate": 927, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["00830", "01970", "03382P050203", "03391C030255", "03391C030308", "03391C040364", "03391C040384", "03391C040388", "03391D010482", "03391D010518", "03391D050562", "03391D050621", "03391D090743", "03591D160694", "03591H071840", "06930", "09100", "09550", "12160", "13790", "15530", "16230", "21430", "24310", "25650", "33060", "33500", "34980", "37140", "45900", "53110", "77560", "80920", "81370", "87340", "88530", "94110", "94410", "99670"]}}
{"id": "8ae3cc1e4be830e0", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 299 | 03591H041375 | 28.05390 | 97.16570 | | 4131 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | 16 | 17 |\n| 300 | 03082O120227 | 29.23380 | 95.59560 | Rirung Chu | 3750 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 17 | 16 |\n| 301 | 03391C040422 | 29.02640 | 96.04450 | Dri Chu | 3564 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 13 | 16 |\n| 302 | 03382O160163 | 29.15290 | 95.77190 | Aison Chu | 3864 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 12 | 16 |\n| 303 | 03591H041385 | 28.04120 | 97.19250 | | 4217 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 13 | 15 |\n| 304 | 03391D090735 | 28.82920 | 96.53160 | Thangkung Chu | 3584 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 38 | 43 | 14 |\n| 305 | 03591H041321 | 28.20020 | 97.23560 | | 3900 | Brahmaputra | Anjaw | Arunachal Pradesh | 29 | 33 | 14 |\n| 306 | 03591H041353 | 28.08660 | 97.24010 | | 4507 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | 25 | 14 |\n| 307 | 03391D010498 | 28.85200 | 96.17120 | Thangkung Chu | 3976 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 20 | 14 |\n| 308 | 03683A020774 | 27.51360 | 92.04850 | Mago Chu | 4088 | Brahmaputra | West Kameng | Arunachal Pradesh | 14 | 16 | 14 |\n| 309 | 03678M140521 | 27.61960 | 91.77950 | Dangme Chu | 3978 | Brahmaputra | Tawang | Arunachal Pradesh | 11 | 13 | 14 |\n| 310 | 03391C040333 | 29.22620 | 96.07220 | Matuni | 3992 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 30 | 34 | 13 |\n| 311 | 03391D100753 | 28.74540 | 96.51130 | Thangkung Chu | 3219 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 25 | 28 | 13 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4573, "line_end": 4587, "token_count_estimate": 908, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["02640", "03082O120227", "03382O160163", "03391C040333", "03391C040422", "03391D010498", "03391D090735", "03391D100753", "03591H041321", "03591H041353", "03591H041375", "03591H041385", "03678M140521", "03683A020774", "04120", "04450", "04850", "05390", "07220", "08660", "15290", "16570", "17120", "19250", "20020", "22620", "23380", "23560", "24010", "51130", "51360", "53160", "59560", "61960", "74540", "77190", "77950", "82920", "85200"]}}
{"id": "6080a99fbeec5923", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 312 | 03591D090416 | 28.77010 | 96.61670 | Dzayul chu | 4255 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 24 | 13 |\n| 313 | 03591H081902 | 28.13570 | 97.32110 | | 4276 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 20 | 13 |\n| 314 | 03391C040429 | 29.01790 | 96.06170 | Dri Chu | 3757 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 18 | 13 |\n| 315 | 03591D090414 | 28.77650 | 96.60620 | Dzayul chu | 4601 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 18 | 13 |\n| 316 | 03591H081909 | 28.10140 | 97.26980 | Sertik chu | 4169 | Brahmaputra | Anjaw | Arunachal Pradesh | 15 | 17 | 13 |\n| 317 | 03678M090228 | 27.83390 | 91.55320 | Kulong Chu | 4521 | Brahmaputra | Tawang | Arunachal Pradesh | 67 | 75 | 12 |\n| 318 | 03391C040366 | 29.14660 | 96.01690 | Matuni | 3758 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 21 | 12 |\n| 319 | 03382O160135 | 29.23920 | 95.98000 | Aison Chu | 4155 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 19 | 12 |\n| 320 | 03391C040354 | 29.18450 | 96.21600 | Dri Chu | 3965 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 14 | 12 |\n| 321 | 03591H081879 | 28.16210 | 97.32040 | Depuchu | 4038 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 12 | 12 |\n| 322 | 03391C040347 | 29.19650 | 96.20260 | Dri Chu | 4246 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 64 | 71 | 11 |\n| 323 | 03678M090217 | 27.84720 | 91.58260 | Dozam Chu | 4656 | Brahmaputra | Tawang | Arunachal Pradesh | 49 | 55 | 11 |\n| 324 | 03391C030310 | 29.25570 | 96.20920 | Jairu Chu | 4623 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 43 | 48 | 11 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4592, "line_end": 4606, "token_count_estimate": 955, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["01690", "01790", "03382O160135", "03391C030310", "03391C040347", "03391C040354", "03391C040366", "03391C040429", "03591D090414", "03591D090416", "03591H081879", "03591H081902", "03591H081909", "03678M090217", "03678M090228", "06170", "10140", "13570", "14660", "16210", "18450", "19650", "20260", "20920", "21600", "23920", "25570", "26980", "32040", "32110", "55320", "58260", "60620", "61670", "77010", "77650", "83390", "84720", "98000"]}}
{"id": "c22837aad4ac9d56", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 325 | 03683A020783 | 27.50520 | 92.04450 | Mago Chu | 4170 | Brahmaputra | West Kameng | Arunachal Pradesh | 11 | 12 | 11 |\n| 326 | 03678M090226 | 27.83770 | 91.60480 | Dangme Chu | 4125 | Brahmaputra | Tawang | Arunachal Pradesh | 66 | 73 | 10 |\n| 327 | 03591H041386 | 28.04090 | 97.21830 | | 3936 | Brahmaputra | Anjaw | Arunachal Pradesh | 38 | 42 | 10 |\n| 328 | 03082O150279 | 29.33480 | 95.87080 | Chendruk Chu | 4025 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 30 | 10 |\n| 329 | 03591H081906 | 28.10680 | 97.31080 | | 4343 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | 24 | 10 |\n| 330 | 03591C030027 | 29.30460 | 96.15620 | Kangri Karpo chu | 4233 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 23 | 10 |\n| 331 | 03591H031293 | 28.26360 | 97.22540 | Depuchu | 4167 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 14 | 10 |\n| 332 | 03591H041309 | 28.22000 | 97.11970 | | 3986 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 13 | 10 |\n| 333 | 03391C040411 | 29.05090 | 96.14450 | Dri Chu | 3602 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 80 | 87 | 9 |\n| 334 | 03592A142163 | 27.68970 | 96.86030 | | 3373 | Brahmaputra | Anjaw | Arunachal Pradesh | 49 | 54 | 9 |\n| 335 | 03391D010516 | 28.82120 | 96.12080 | Thangkung Chu | 4060 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 39 | 43 | 9 |\n| 336 | 03591H031294 | 28.26200 | 97.23230 | Depuchu | 4183 | Brahmaputra | Anjaw | Arunachal Pradesh | 27 | 29 | 9 |\n| 337 | 03591H081901 | 28.13570 | 97.30630 | | 4225 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | 24 | 9 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4612, "line_end": 4626, "token_count_estimate": 945, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["03082O150279", "03391C040411", "03391D010516", "03591C030027", "03591H031293", "03591H031294", "03591H041309", "03591H041386", "03591H081901", "03591H081906", "03592A142163", "03678M090226", "03683A020783", "04090", "04450", "05090", "10680", "11970", "12080", "13570", "14450", "15620", "21830", "22000", "22540", "23230", "26200", "26360", "30460", "30630", "31080", "33480", "50520", "60480", "68970", "82120", "83770", "86030", "87080"]}}
{"id": "4872e71153cb8cab", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 338 | 03592A142142 | 27.71900 | 96.87660 | | 3664 | Brahmaputra | Anjaw | Arunachal Pradesh | 20 | 22 | 9 |\n| 339 | 03592A142181 | 27.63660 | 96.91560 | | 3689 | Brahmaputra | Changlang | Arunachal Pradesh | 10 | 11 | 9 |\n| 340 | 03391C040334 | 29.22590 | 96.15980 | Jairu Chu | 3313 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 57 | 62 | 8 |\n| 341 | 03591C080058 | 29.17460 | 96.32720 | Kangri Karpo chu | 4379 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 46 | 50 | 8 |\n| 342 | 03591H081850 | 28.22340 | 97.28510 | | 4189 | Brahmaputra | Anjaw | Arunachal Pradesh | 32 | 35 | 8 |\n| 343 | 03082O150281 | 29.32650 | 95.84670 | Chendruk Chu | 4290 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 30 | 8 |\n| 344 | 03591C080057 | 29.17530 | 96.34630 | Kangri Karpo chu | 4298 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 30 | 8 |\n| 345 | 03591H081900 | 28.13960 | 97.29010 | | 4055 | Brahmaputra | Anjaw | Arunachal Pradesh | 26 | 28 | 8 |\n| 346 | 03591C030013 | 29.36500 | 96.12370 | Keli chu | 3995 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 25 | 27 | 8 |\n| 347 | 03082O040056 | 29.06870 | 95.24230 | Dihang | 3823 | Brahmaputra | Upper Siang | Arunachal Pradesh | 19 | 20 | 8 |\n| 348 | 03391D050568 | 28.93490 | 96.36880 | Thangkung Chu | 4001 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 20 | 8 |\n| 349 | 03391D050612 | 28.83220 | 96.24960 | Thangkung Chu | 3835 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 17 | 8 |\n| 350 | 03182H140416 | 28.54860 | 93.88370 | Subansiri | 4070 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 16 | 17 | 8 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4633, "line_end": 4647, "token_count_estimate": 920, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Dihang", "Subansiri"], "countries": [], "lake_ids": ["03082O040056", "03082O150281", "03182H140416", "03391C040334", "03391D050568", "03391D050612", "03591C030013", "03591C080057", "03591C080058", "03591H081850", "03591H081900", "03592A142142", "03592A142181", "06870", "12370", "13960", "15980", "17460", "17530", "22340", "22590", "24230", "24960", "28510", "29010", "32650", "32720", "34630", "36500", "36880", "54860", "63660", "71900", "83220", "84670", "87660", "88370", "91560", "93490"]}}
{"id": "4d6beb8cd7d78145", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 351 | 03591D090393 | 28.90790 | 96.52490 | Kangri Karpo chu | 4295 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 8 |\n| 352 | 03391C030286 | 29.29210 | 96.00070 | Aison Chu | 4079 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 8 |\n| 353 | 03383A130220 | 27.90100 | 92.78650 | Kameng | 4248 | Brahmaputra | East Kameng | Arunachal Pradesh | 10 | 11 | 8 |\n| 354 | 03683A010646 | 27.75300 | 92.04070 | Tsona Chu | 4084 | Brahmaputra | Tawang | Arunachal Pradesh | 10 | 11 | 8 |\n| 355 | 03391C040329 | 29.22920 | 96.19160 | Jairu Chu | 3473 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 106 | 114 | 7 |\n| 356 | 03683A020770 | 27.51850 | 92.03330 | Mago Chu | 4274 | Brahmaputra | Tawang | Arunachal Pradesh | 61 | 65 | 7 |\n| 357 | 03382O160132 | 29.24950 | 95.99340 | Aison Chu | 4204 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 29 | 31 | 7 |\n| 358 | 03391C040356 | 29.17560 | 96.17470 | Dri Chu | 3523 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 20 | 21 | 7 |\n| 359 | 03683A020734 | 27.56610 | 92.21550 | Mago Chu | 4348 | Brahmaputra | West Kameng | Arunachal Pradesh | 20 | 21 | 7 |\n| 360 | 03591D160704 | 28.15880 | 96.84750 | | 3926 | Brahmaputra | Anjaw | Arunachal Pradesh | 17 | 18 | 7 |\n| 361 | 03591H081847 | 28.22730 | 97.26850 | Depuchu | 4145 | Brahmaputra | Anjaw | Arunachal Pradesh | 17 | 18 | 7 |\n| 362 | 03082O150283 | 29.32290 | 95.85740 | Chendruk Chu | 4169 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 7 |\n| 363 | 03678M140440 | 27.69820 | 91.90270 | Towang Chu | 4047 | Brahmaputra | Tawang | Arunachal Pradesh | 13 | 14 | 7 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4651, "line_end": 4665, "token_count_estimate": 918, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["00070", "03082O150283", "03330", "03382O160132", "03383A130220", "03391C030286", "03391C040329", "03391C040356", "03591D090393", "03591D160704", "03591H081847", "03678M140440", "03683A010646", "03683A020734", "03683A020770", "04070", "15880", "17470", "17560", "19160", "21550", "22730", "22920", "24950", "26850", "29210", "32290", "51850", "52490", "56610", "69820", "75300", "78650", "84750", "85740", "90100", "90270", "90790", "99340"]}}
{"id": "0db5075c127713fb", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 364 | 03591H081856 | 28.21400 | 97.31780 | | 4373 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 13 | 7 |\n| 365 | 03082P050298 | 28.94310 | 95.29780 | Yang Sang Chu | 3780 | Brahmaputra | Upper Siang | Arunachal Pradesh | 11 | 12 | 7 |\n| 366 | 03391D010501 | 28.84650 | 96.12720 | Dri Chu | 3697 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 12 | 7 |\n| 367 | 03082O110216 | 29.27270 | 95.73870 | Rirung Chu | 4023 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 12 | 7 |\n| 368 | 03391D010529 | 28.79320 | 96.05470 | Dri Chu | 3948 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 7 |\n| 369 | 03382P050207 | 28.86290 | 95.34020 | Emra River | 3789 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 7 |\n| 370 | 03591C080049 | 29.22410 | 96.27900 | Kangri Karpo chu | 4207 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 66 | 70 | 6 |\n| 371 | 03591H081915 | 28.09610 | 97.28890 | Sertik chu | 3762 | Brahmaputra | Anjaw | Arunachal Pradesh | 53 | 56 | 6 |\n| 372 | 03591H041387 | 28.04010 | 97.12570 | | 3969 | Brahmaputra | Anjaw | Arunachal Pradesh | 36 | 38 | 6 |\n| 373 | 03391D060656 | 28.66610 | 96.42640 | Thangkung Chu | 4238 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 22 | 6 |\n| 374 | 03683A020778 | 27.50940 | 92.10150 | Mago Chu | 4148 | Brahmaputra | West Kameng | Arunachal Pradesh | 19 | 20 | 6 |\n| 375 | 03182H140402 | 28.59740 | 93.80450 | Subansiri | 4163 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | 13 | 6 |\n| 376 | 03683A060875 | 27.71520 | 92.38580 | Gorjo Chu | 5117 | Brahmaputra | Tawang | Arunachal Pradesh | 12 | 13 | 6 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4670, "line_end": 4684, "token_count_estimate": 953, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["03082O110216", "03082P050298", "03182H140402", "03382P050207", "03391D010501", "03391D010529", "03391D060656", "03591C080049", "03591H041387", "03591H081856", "03591H081915", "03683A020778", "03683A060875", "04010", "05470", "09610", "10150", "12570", "12720", "21400", "22410", "27270", "27900", "28890", "29780", "31780", "34020", "38580", "42640", "50940", "59740", "66610", "71520", "73870", "79320", "80450", "84650", "86290", "94310"]}}
{"id": "f7c85294b3e2ff6d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 377 | 03391D060670 | 28.61420 | 96.31160 | Thangkung Chu | 3968 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 12 | 6 |\n| 378 | 03391D060683 | 28.56340 | 96.35490 | Thangkung Chu | 4152 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 6 |\n| 379 | 03592A132133 | 27.75870 | 96.87790 | | 3857 | Brahmaputra | Anjaw | Arunachal Pradesh | 10 | 11 | 6 |\n| 380 | 03382P050183 | 28.97710 | 95.26340 | Emra River | 3769 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 11 | 6 |\n| 381 | 03382P130245 | 29.00480 | 95.90550 | Matuni | 3598 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 54 | 57 | 5 |\n| 382 | 03391D050573 | 28.92780 | 96.33900 | Thangkung Chu | 4011 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 48 | 51 | 5 |\n| 383 | 03678M140485 | 27.67300 | 91.87500 | Towang Chu | 4222 | Brahmaputra | Tawang | Arunachal Pradesh | 33 | 35 | 5 |\n| 384 | 03678M090216 | 27.85080 | 91.60420 | Dangme Chu | 4509 | Brahmaputra | Tawang | Arunachal Pradesh | 31 | 33 | 5 |\n| 385 | 03082O120239 | 29.20240 | 95.54520 | Rirung Chu | 3838 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | 23 | 5 |\n| 386 | 03382P130248 | 28.97900 | 95.86080 | Matuni | 3622 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 20 | 5 |\n| 387 | 03082O150271 | 29.37480 | 95.95110 | Chendruk Chu | 4296 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 18 | 5 |\n| 388 | 03082K081988 | 29.07700 | 94.27130 | Neyul pu chu | 3682 | Brahmaputra | Upper Siang | Arunachal Pradesh | 17 | 18 | 5 |\n| 389 | 03683A020736 | 27.56460 | 92.06510 | Mago Chu | 4326 | Brahmaputra | Tawang | Arunachal Pradesh | 14 | 15 | 5 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4686, "line_end": 4700, "token_count_estimate": 944, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["00480", "03082K081988", "03082O120239", "03082O150271", "03382P050183", "03382P130245", "03382P130248", "03391D050573", "03391D060670", "03391D060683", "03592A132133", "03678M090216", "03678M140485", "03683A020736", "06510", "07700", "20240", "26340", "27130", "31160", "33900", "35490", "37480", "54520", "56340", "56460", "60420", "61420", "67300", "75870", "85080", "86080", "87500", "87790", "90550", "92780", "95110", "97710", "97900"]}}
{"id": "d2e099e007aa9c56", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 390 | 03591H081867 | 28.17430 | 97.32860 | | 4307 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | 15 | 5 |\n| 391 | 03391D090748 | 28.76440 | 96.51710 | Thangkung Chu | 3502 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 15 | 5 |\n| 392 | 03391D010493 | 28.88640 | 96.13650 | Dri Chu | 3720 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 13 | 5 |\n| 393 | 03383A060157 | 27.60170 | 92.38530 | Sangli Chu | 4227 | Brahmaputra | West Kameng | Arunachal Pradesh | 10 | 11 | 5 |\n| 394 | 03391D050580 | 28.91870 | 96.38320 | Thangkung Chu | 3302 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 49 | 51 | 4 |\n| 395 | 03382O120065 | 29.17620 | 95.61690 | Andra Chu | 3063 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 41 | 43 | 4 |\n| 396 | 03391D050591 | 28.87680 | 96.47710 | Thangkung Chu | 3166 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 35 | 36 | 4 |\n| 397 | 03391C030272 | 29.30390 | 96.14210 | Jairu Chu | 4221 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 29 | 4 |\n| 398 | 03391D100755 | 28.70740 | 96.50540 | Thangkung Chu | 4018 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 28 | 4 |\n| 399 | 03391C040352 | 29.19130 | 96.11160 | Dri Chu | 3775 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 28 | 4 |\n| 400 | 03592A142137 | 27.74250 | 96.84480 | | 3778 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 19 | 4 |\n| 401 | 03591D050336 | 28.95220 | 96.49290 | Kangri Karpo chu | 4493 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 16 | 4 |\n| 402 | 03591D070347 | 28.34490 | 96.33410 | | 2990 | Brahmaputra | Lohit | Arunachal Pradesh | 13 | 13 | 4 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4705, "line_end": 4719, "token_count_estimate": 938, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Lohit"], "countries": [], "lake_ids": ["03382O120065", "03383A060157", "03391C030272", "03391C040352", "03391D010493", "03391D050580", "03391D050591", "03391D090748", "03391D100755", "03591D050336", "03591D070347", "03591H081867", "03592A142137", "11160", "13650", "14210", "17430", "17620", "19130", "30390", "32860", "33410", "34490", "38320", "38530", "47710", "49290", "50540", "51710", "60170", "61690", "70740", "74250", "76440", "84480", "87680", "88640", "91870", "95220"]}}
{"id": "eceed67e92648de3", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 403 | 03678M140405 | 27.73390 | 91.87090 | Tsona Chu | 4456 | Brahmaputra | Tawang | Arunachal Pradesh | 13 | 13 | 4 |\n| 404 | 03082O150276 | 29.35390 | 95.92650 | Chendruk Chu | 4357 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 38 | 39 | 3 |\n| 405 | 03383A130229 | 27.87730 | 92.79990 | Kameng | 3943 | Brahmaputra | East Kameng | Arunachal Pradesh | 36 | 37 | 3 |\n| 406 | 03391D010530 | 28.79150 | 96.14800 | Thangkung Chu | 3676 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 33 | 34 | 3 |\n| 407 | 03382O120096 | 29.09170 | 95.56590 | Andra Chu | 3247 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 29 | 30 | 3 |\n| 408 | 03591H041359 | 28.08420 | 97.21540 | | 4023 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 19 | 3 |\n| 409 | 03592E012215 | 27.95870 | 97.10460 | | 3831 | Brahmaputra | Anjaw | Arunachal Pradesh | 17 | 18 | 3 |\n| 410 | 03382O160151 | 29.21860 | 95.74920 | Aison Chu | 3951 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 18 | 3 |\n| 411 | 03082K081989 | 29.07460 | 94.29620 | Neyul pu chu | 4377 | Brahmaputra | Upper Siang | Arunachal Pradesh | 12 | 12 | 3 |\n| 412 | 03383A130208 | 27.92040 | 92.79340 | Kameng | 4291 | Brahmaputra | East Kameng | Arunachal Pradesh | 11 | 11 | 3 |\n| 413 | 03391D010470 | 28.98680 | 96.06880 | Dri Chu | 3627 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 37 | 38 | 2 |\n| 414 | 03391C040340 | 29.21120 | 96.06900 | Matuni | 3816 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 32 | 2 |\n| 415 | 03382O150119 | 29.29420 | 95.88650 | Aison Chu | 3517 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 28 | 29 | 2 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4723, "line_end": 4737, "token_count_estimate": 943, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["03082K081989", "03082O150276", "03382O120096", "03382O150119", "03382O160151", "03383A130208", "03383A130229", "03391C040340", "03391D010470", "03391D010530", "03591H041359", "03592E012215", "03678M140405", "06880", "06900", "07460", "08420", "09170", "10460", "14800", "21120", "21540", "21860", "29420", "29620", "35390", "56590", "73390", "74920", "79150", "79340", "79990", "87090", "87730", "88650", "92040", "92650", "95870", "98680"]}}
{"id": "88e5387f67f82c11", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 416 | 03082K160033 | 29.22160 | 94.77970 | Nugong Chu | 3754 | Brahmaputra | Upper Siang | Arunachal Pradesh | 20 | 20 | 2 |\n| 417 | 03382O160176 | 29.01890 | 95.90950 | Dri Chu | 3671 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 18 | 18 | 2 |\n| 418 | 03678M140452 | 27.69270 | 91.85690 | Dangme Chu | 4283 | Brahmaputra | Tawang | Arunachal Pradesh | 18 | 18 | 2 |\n| 419 | 03182H060297 | 28.65010 | 93.46210 | Subansiri | 4046 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 17 | 17 | 2 |\n| 420 | 03391D050577 | 28.92430 | 96.31080 | Thangkung Chu | 3851 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | 16 | 2 |\n| 421 | 03591H031285 | 28.29080 | 97.21800 | Depuchu | 4071 | Brahmaputra | Anjaw | Arunachal Pradesh | 16 | 16 | 2 |\n| 422 | 03182H110383 | 28.48860 | 93.55490 | Subansiri | 4647 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 14 | 14 | 2 |\n| 423 | 03391D090726 | 28.92330 | 96.51520 | Thangkung Chu | 4091 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 14 | 2 |\n| 424 | 03082O120226 | 29.23730 | 95.65910 | Rirung Chu | 4016 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 13 | 2 |\n| 425 | 03182H150426 | 28.26980 | 93.78060 | Subansiri | 3537 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 13 | 13 | 2 |\n| 426 | 03591H081843 | 28.23990 | 97.25040 | | 3980 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | 12 | 2 |\n| 427 | 03591H041407 | 28.01640 | 97.21490 | | 4303 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 11 | 2 |\n| 428 | 03082O150273 | 29.37140 | 95.87290 | Chendruk Chu | 4344 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 104 | 105 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4742, "line_end": 4756, "token_count_estimate": 928, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["01640", "01890", "03082K160033", "03082O120226", "03082O150273", "03182H060297", "03182H110383", "03182H150426", "03382O160176", "03391D050577", "03391D090726", "03591H031285", "03591H041407", "03591H081843", "03678M140452", "21490", "21800", "22160", "23730", "23990", "25040", "26980", "29080", "31080", "37140", "46210", "48860", "51520", "55490", "65010", "65910", "69270", "77970", "78060", "85690", "87290", "90950", "92330", "92430"]}}
{"id": "dc1ae4962f6913ed", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 429 | 03391C030301 | 29.26920 | 96.15700 | Jairu Chu | 3991 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 103 | 104 | 1 |\n| 430 | 03282D160026 | 28.11590 | 92.95130 | | 4648 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 56 | 56 | 1 |\n| 431 | 03082L052049 | 28.98550 | 94.27010 | Neyul pu chu | 3478 | Brahmaputra | West Siang | Arunachal Pradesh | 51 | 51 | 1 |\n| 432 | 03391C030269 | 29.30910 | 96.13550 | Jairu Chu | 4204 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 27 | 27 | 1 |\n| 433 | 03591H081919 | 28.07990 | 97.30390 | | 4304 | Brahmaputra | Anjaw | Arunachal Pradesh | 24 | 24 | 1 |\n| 434 | 03592A142141 | 27.72560 | 96.84840 | | 3669 | Brahmaputra | Anjaw | Arunachal Pradesh | 19 | 19 | 1 |\n| 435 | 03382P050216 | 28.83120 | 95.34960 | Emra River | 4046 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 18 | 18 | 1 |\n| 436 | 03382O160158 | 29.17340 | 95.82600 | Aison Chu | 3790 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 15 | 1 |\n| 437 | 03382O160174 | 29.07400 | 95.94640 | Matuni | 4165 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 13 | 1 |\n| 438 | 03592E012219 | 27.94950 | 97.10750 | | 3874 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 13 | 1 |\n| 439 | 03282D160031 | 28.10610 | 92.97010 | | 4340 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 13 | 13 | 1 |\n| 440 | 03592A142147 | 27.71210 | 96.93840 | | 3751 | Brahmaputra | Anjaw | Arunachal Pradesh | 13 | 13 | 1 |\n| 441 | 03382P050201 | 28.87500 | 95.37730 | Emra River | 3504 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 12 | 1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4760, "line_end": 4774, "token_count_estimate": 908, "basins": ["Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": ["03082L052049", "03282D160026", "03282D160031", "03382O160158", "03382O160174", "03382P050201", "03382P050216", "03391C030269", "03391C030301", "03591H081919", "03592A142141", "03592A142147", "03592E012219", "07400", "07990", "10610", "10750", "11590", "13550", "15700", "17340", "26920", "27010", "30390", "30910", "34960", "37730", "71210", "72560", "82600", "83120", "84840", "87500", "93840", "94640", "94950", "95130", "97010", "98550"]}}
{"id": "6db190680dc8708e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 442 | 03391C030259 | 29.32130 | 96.11750 | Jairu Chu | 3975 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 11 | 1 |\n| 443 | 03391C040395 | 29.07940 | 96.14480 | Dri Chu | 3945 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 86 | 86 | 0 |\n| 444 | 03591D160698 | 28.20220 | 96.89800 | Dzayul chu | 3731 | Brahmaputra | Anjaw | Arunachal Pradesh | 67 | 67 | 0 |\n| 445 | 03082O080107 | 29.06180 | 95.26310 | Dihang | 3668 | Brahmaputra | Upper Siang | Arunachal Pradesh | 48 | 48 | 0 |\n| 446 | 03682L010077 | 28.75260 | 94.00100 | Siyom | 4019 | Brahmaputra | West Siang | Arunachal Pradesh | 23 | 23 | 0 |\n| 447 | 03382P050214 | 28.84810 | 95.32010 | Emra River | 3913 | Brahmaputra | Upper Siang | Arunachal Pradesh | 22 | 22 | 0 |\n| 448 | 03391D090733 | 28.83790 | 96.49740 | Thangkung Chu | 3388 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 21 | 0 |\n| 449 | 03382O120071 | 29.16580 | 95.67410 | Andra Chu | 3771 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 19 | 0 |\n| 450 | 03082O120228 | 29.23330 | 95.65410 | Rirung Chu | 3915 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | 17 | 0 |\n| 451 | 03383A100182 | 27.74150 | 92.51540 | Pachuk | 3863 | Brahmaputra | East Kameng | Arunachal Pradesh | 13 | 13 | 0 |\n| 452 | 03591H041404 | 28.02050 | 97.19390 | | 4040 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 11 | 0 |\n| 453 | 03082O040061 | 29.05800 | 95.23680 | Dihang | 3743 | Brahmaputra | Upper Siang | Arunachal Pradesh | 10 | 10 | 0 |\n| 454 | 03382P050213 | 28.85160 | 95.36850 | Emra River | 3192 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 10 | 0 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4781, "line_end": 4795, "token_count_estimate": 950, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Dihang"], "countries": [], "lake_ids": ["00100", "02050", "03082O040061", "03082O080107", "03082O120228", "03382O120071", "03382P050213", "03382P050214", "03383A100182", "03391C030259", "03391C040395", "03391D090733", "03591D160698", "03591H041404", "03682L010077", "05800", "06180", "07940", "11750", "14480", "16580", "19390", "20220", "23330", "23680", "26310", "32010", "32130", "36850", "49740", "51540", "65410", "67410", "74150", "75260", "83790", "84810", "85160", "89800"]}}
{"id": "aa4c298650321499", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 455 | 03082O120243 | 29.19520 | 95.59170 | Rirung Chu | 3708 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 43 | 43 | -1 |\n| 456 | 03182H100356 | 28.59450 | 93.72570 | Subansiri | 4208 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 25 | 25 | -1 |\n| 457 | 03391D060637 | 28.72910 | 96.44640 | Thangkung Chu | 4086 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 21 | -1 |\n| 458 | 03383A090169 | 27.83470 | 92.68540 | Kameng | 3951 | Brahmaputra | East Kameng | Arunachal Pradesh | 18 | 18 | -1 |\n| 459 | 03182H100371 | 28.56770 | 93.56270 | Subansiri | 4074 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 16 | 16 | -1 |\n| 460 | 03592A142177 | 27.64640 | 96.87790 | | 3263 | Brahmaputra | Changlang | Arunachal Pradesh | 15 | 15 | -1 |\n| 461 | 03391D060681 | 28.56520 | 96.40780 | Thangkung Chu | 4124 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 14 | -1 |\n| 462 | 03391D010517 | 28.81980 | 96.14460 | Thangkung Chu | 3723 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | 14 | -1 |\n| 463 | 03683A020714 | 27.58850 | 92.22210 | Mago Chu | 4455 | Brahmaputra | West Kameng | Arunachal Pradesh | 13 | 13 | -1 |\n| 464 | 03082K160032 | 29.22580 | 94.78320 | Nugong Chu | 3683 | Brahmaputra | Upper Siang | Arunachal Pradesh | 13 | 13 | -1 |\n| 465 | 03678M140443 | 27.69720 | 91.87320 | Towang Chu | 4467 | Brahmaputra | Tawang | Arunachal Pradesh | 13 | 13 | -1 |\n| 466 | 03382P050195 | 28.91120 | 95.33350 | Emra River | 3789 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 12 | -1 |\n| 467 | 03382O120067 | 29.17230 | 95.73640 | Yonggyap Chu | 3652 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 10 | -1 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4799, "line_end": 4813, "token_count_estimate": 949, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["03082K160032", "03082O120243", "03182H100356", "03182H100371", "03382O120067", "03382P050195", "03383A090169", "03391D010517", "03391D060637", "03391D060681", "03592A142177", "03678M140443", "03683A020714", "14460", "17230", "19520", "22210", "22580", "33350", "40780", "44640", "56270", "56520", "56770", "58850", "59170", "59450", "64640", "68540", "69720", "72570", "72910", "73640", "78320", "81980", "83470", "87320", "87790", "91120"]}}
{"id": "245dd74e2463c664", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 494 | 03391D060639 | 28.72730 | 96.48540 | Thangkung Chu | 4084 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 29 | 27 | -6 |\n| 495 | 03082K120012 | 29.17670 | 94.52570 | Rigong Chu | 4258 | Brahmaputra | Upper Siang | Arunachal Pradesh | 15 | 14 | -6 |\n| 496 | 03391C030294 | 29.28330 | 96.09110 | Jairu Chu | 4204 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 12 | -6 |\n| 497 | 03682L050088 | 28.95330 | 94.38290 | Siyom | 3858 | Brahmaputra | Upper Siang | Arunachal Pradesh | 19 | 18 | -7 |\n| 498 | 03082K112004 | 29.30770 | 94.63690 | Lushar pu chu | 4142 | Brahmaputra | Upper Siang | Arunachal Pradesh | 15 | 14 | -7 |\n| 499 | 03382P050193 | 28.91680 | 95.33970 | Emra River | 3901 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 10 | -7 |\n| 500 | 03678M140517 | 27.62470 | 91.80340 | Towang Chu | 4329 | Brahmaputra | Tawang | Arunachal Pradesh | 11 | 10 | -7 |\n| 501 | 03591H041374 | 28.05830 | 97.22590 | | 4113 | Brahmaputra | Anjaw | Arunachal Pradesh | 18 | 17 | -8 |\n| 502 | 03182H060310 | 28.54110 | 93.37990 | Subansiri | 4307 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 16 | 15 | -8 |\n| 503 | 03082P050299 | 28.92240 | 95.32290 | Yang Sang Chu | 3841 | Brahmaputra | Upper Siang | Arunachal Pradesh | 16 | 15 | -9 |\n| 504 | 03391D060633 | 28.73990 | 96.41340 | Thangkung Chu | 4105 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | 11 | -9 |\n| 505 | 03383A130216 | 27.90420 | 92.80980 | Kameng | 4306 | Brahmaputra | East Kameng | Arunachal Pradesh | 12 | 11 | -10 |\n| 506 | 03592E012220 | 27.94750 | 97.13110 | | 3819 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 10 | -10 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4816, "line_end": 4830, "token_count_estimate": 941, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["03082K112004", "03082K120012", "03082P050299", "03182H060310", "03382P050193", "03383A130216", "03391C030294", "03391D060633", "03391D060639", "03591H041374", "03592E012220", "03678M140517", "03682L050088", "05830", "09110", "13110", "17670", "22590", "28330", "30770", "32290", "33970", "37990", "38290", "41340", "48540", "52570", "54110", "62470", "63690", "72730", "73990", "80340", "80980", "90420", "91680", "92240", "94750", "95330"]}}
{"id": "813e6ec04635a982", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 507 | 03382P060219 | 28.74640 | 95.39840 | Chichi River | 3787 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 10 | -10 |\n| 508 | 03182H140401 | 28.61620 | 93.81990 | Subansiri | 3590 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 46 | 41 | -11 |\n| 509 | 03682H130005 | 28.76280 | 93.98650 | | 3957 | Brahmaputra | West Siang | Arunachal Pradesh | 20 | 18 | -11 |\n| 510 | 03182H140408 | 28.58270 | 93.81160 | Subansiri | 3799 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 11 | 10 | -12 |\n| 511 | 03391D050598 | 28.86240 | 96.44010 | Thangkung Chu | 2979 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 10 | -12 |\n| 512 | 03391D010492 | 28.88670 | 96.19690 | Dri Chu | 3255 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 44 | 38 | -13 |\n| 513 | 03391D050623 | 28.80180 | 96.44020 | Thangkung Chu | 4114 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 27 | -13 |\n| 514 | 03382O160153 | 29.20220 | 95.79670 | Aison Chu | 4054 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | 13 | -13 |\n| 515 | 03591H041399 | 28.02540 | 97.18630 | | 4227 | Brahmaputra | Anjaw | Arunachal Pradesh | 15 | 13 | -13 |\n| 516 | 03382O120059 | 29.20770 | 95.74160 | Aison Chu | 3913 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 9 | -14 |\n| 517 | 03382O160130 | 29.25130 | 95.95710 | Aison Chu | 3738 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | 9 | -14 |\n| 518 | 03682L050087 | 28.96320 | 94.37220 | Siyom | 4145 | Brahmaputra | Upper Siang | Arunachal Pradesh | 19 | 16 | -16 |\n| 519 | 03082H101688 | 28.67210 | 93.72350 | Nelung phu chu | 4361 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | 10 | -17 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4834, "line_end": 4848, "token_count_estimate": 941, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["02540", "03082H101688", "03182H140401", "03182H140408", "03382O120059", "03382O160130", "03382O160153", "03382P060219", "03391D010492", "03391D050598", "03391D050623", "03591H041399", "03682H130005", "03682L050087", "18630", "19690", "20220", "20770", "25130", "37220", "39840", "44010", "44020", "58270", "61620", "67210", "72350", "74160", "74640", "76280", "79670", "80180", "81160", "81990", "86240", "88670", "95710", "96320", "98650"]}}
{"id": "3ec4aec3e89fcfce", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 520 | 03682L010076 | 28.75390 | 94.13350 | Siyom | 4057 | Brahmaputra | West Siang | Arunachal Pradesh | 11 | 9 | -18 |\n| 521 | 03391C040367 | 29.14530 | 96.06630 | Dri Chu | 3949 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 31 | 25 | -19 |\n| 522 | 03391D060711 | 28.52130 | 96.24980 | Thangkung Chu | 3636 | Brahmaputra | Lower Dibang Valley | Arunachal Pradesh | 16 | 13 | -19 |\n| 523 | 03391D010469 | 28.98940 | 96.18810 | Dri Chu | 4316 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 9 | -20 |\n| 524 | 03182H110392 | 28.45770 | 93.57910 | Subansiri | 3922 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 48 | 37 | -22 |\n| 525 | 03382P050202 | 28.87460 | 95.33410 | Emra River | 3756 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 21 | 16 | -22 |\n| 526 | 03391D050617 | 28.82330 | 96.35900 | Thangkung Chu | 3941 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | 15 | -22 |\n| 527 | 03591H081932 | 28.05280 | 97.28130 | | 4499 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | 8 | -24 |\n| 528 | 03391D060669 | 28.61730 | 96.43940 | Thangkung Chu | 3914 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | 8 | -25 |\n| 529 | 03591H041413 | 28.00820 | 97.17630 | | 4240 | Brahmaputra | Anjaw | Arunachal Pradesh | 15 | 11 | -29 |\n| 530 | 03391C040319 | 29.24140 | 96.07370 | Jairu Chu | 4199 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 13 | 9 | -30 |\n| 531 | 03591H081936 | 28.04450 | 97.29260 | | 4472 | Brahmaputra | Anjaw | Arunachal Pradesh | 10 | 7 | -33 |\n| 532 | 03182H070344 | 28.30470 | 93.43720 | Yu me chu | 3952 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 35 | 20 | -43 |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4853, "line_end": 4867, "token_count_estimate": 958, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["00820", "03182H070344", "03182H110392", "03382P050202", "03391C040319", "03391C040367", "03391D010469", "03391D050617", "03391D060669", "03391D060711", "03591H041413", "03591H081932", "03591H081936", "03682L010076", "04450", "05280", "06630", "07370", "13350", "14530", "17630", "18810", "24140", "24980", "28130", "29260", "30470", "33410", "35900", "43720", "43940", "45770", "52130", "57910", "61730", "75390", "82330", "87460", "98940"]}}
{"id": "89abc5660cfebb5e", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 533 | 03082O150278 | 29.35240 | 95.91530 | Chendruk Chu | 4357 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 46 | 25 | -46 |\n| 534 | 03391C040435 | 29.00930 | 96.08370 | Dri Chu | 4018 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 15 | # | # |\n| 535 | 03391D020537 | 28.54180 | 96.12500 | Thangkung Chu | 4031 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | # | # |\n| 536 | 03391D050624 | 28.80140 | 96.48430 | Thangkung Chu | 3819 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 35 | # | # |\n| 537 | 03391D090727 | 28.87230 | 96.51030 | Thangkung Chu | 3926 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 19 | # | # |\n| 538 | 03391D090741 | 28.80060 | 96.51250 | Thangkung Chu | 4344 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 26 | # | # |\n| 539 | 03182H110391 | 28.45860 | 93.60540 | Subansiri | 3957 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 540 | 03182H140406 | 28.58680 | 93.83940 | Subansiri | 4296 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 11 | # | # |\n| 541 | 03591D150678 | 28.31460 | 96.81730 | Dzayul chu | 3879 | Brahmaputra | Anjaw | Arunachal Pradesh | 20 | # | # |\n| 542 | 03591D150686 | 28.25300 | 96.81940 | Dzayul chu | 4030 | Brahmaputra | Anjaw | Arunachal Pradesh | 16 | # | # |\n| 543 | 03591D160702 | 28.16450 | 96.83050 | Gediu chu | 3890 | Brahmaputra | Anjaw | Arunachal Pradesh | 21 | # | # |\n| 544 | 03592E012191 | 27.98990 | 97.09780 | | 3687 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | # | # |\n| 545 | 03082K081990 | 29.06890 | 94.30030 | Neyul pu chu | 4435 | Brahmaputra | Upper Siang | Arunachal Pradesh | 13 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4871, "line_end": 4885, "token_count_estimate": 953, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["00930", "03082K081990", "03082O150278", "03182H110391", "03182H140406", "03391C040435", "03391D020537", "03391D050624", "03391D090727", "03391D090741", "03591D150678", "03591D150686", "03591D160702", "03592E012191", "06890", "08370", "09780", "12500", "16450", "25300", "30030", "31460", "35240", "45860", "48430", "51030", "51250", "54180", "58680", "60540", "80060", "80140", "81730", "81940", "83050", "83940", "87230", "91530", "98990"]}}
{"id": "b62f562df681ee1d", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 546 | 03391C040346 | 29.19740 | 96.19240 | Dri Chu | 4321 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 16 | # | # |\n| 547 | 03391C030291 | 29.28790 | 96.13440 | Jairu Chu | 4045 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | # | # |\n| 548 | 03182H110385 | 28.48300 | 93.60300 | Subansiri | 3987 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 549 | 03382O160146 | 29.22890 | 95.96160 | Aison Chu | 4328 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 14 | # | # |\n| 550 | 03391C040355 | 29.18290 | 96.08690 | Dri Chu | 4263 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 24 | # | # |\n| 551 | 03182H070342 | 28.31310 | 93.39150 | Yu me chu | 4066 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 20 | # | # |\n| 552 | 03282H070072 | 28.29400 | 93.35480 | | 3597 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 10 | # | # |\n| 553 | 03391C040369 | 29.14140 | 96.03630 | Dri Chu | 4139 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 30 | # | # |\n| 554 | 03678M090241 | 27.80160 | 91.64290 | Dangme Chu | 4504 | Brahmaputra | Tawang | Arunachal Pradesh | 12 | # | # |\n| 555 | 03391C030262 | 29.31530 | 96.12530 | Jairu Chu | 3996 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | # | # |\n| 556 | 03391D050585 | 28.89040 | 96.49440 | Thangkung Chu | 3366 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | # | # |\n| 557 | 03391C040313 | 29.24930 | 96.02800 | Aison Chu | 4133 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 12 | # | # |\n| 558 | 03182H060321 | 28.52390 | 93.39400 | Yu me chu | 4249 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4890, "line_end": 4904, "token_count_estimate": 934, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["02800", "03182H060321", "03182H070342", "03182H110385", "03282H070072", "03382O160146", "03391C030262", "03391C030291", "03391C040313", "03391C040346", "03391C040355", "03391C040369", "03391D050585", "03630", "03678M090241", "08690", "12530", "13440", "14140", "18290", "19240", "19740", "22890", "24930", "28790", "29400", "31310", "31530", "35480", "39150", "39400", "48300", "49440", "52390", "60300", "64290", "80160", "89040", "96160"]}}
{"id": "f46f8247a00edcf8", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 559 | 03592E052239 | 27.99360 | 97.31550 | | 4270 | Brahmaputra | Anjaw | Arunachal Pradesh | 14 | # | # |\n| 560 | 03082P050307 | 28.87560 | 95.31600 | Yang Sang Chu | 3847 | Brahmaputra | Upper Siang | Arunachal Pradesh | 15 | # | # |\n| 561 | 03592A092125 | 27.81640 | 96.70680 | | 3488 | Brahmaputra | Anjaw | Arunachal Pradesh | 11 | # | # |\n| 562 | 03592E052276 | 27.89270 | 97.35850 | | 3931 | Brahmaputra | Anjaw | Arunachal Pradesh | 22 | # | # |\n| 563 | 03592A142140 | 27.73240 | 96.87060 | | 4012 | Brahmaputra | Anjaw | Arunachal Pradesh | 12 | # | # |\n| 564 | 03391D050593 | 28.87540 | 96.39450 | Thangkung Chu | 3119 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 41 | # | # |\n| 565 | 03391D050594 | 28.87410 | 96.43580 | Thangkung Chu | 3220 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 17 | # | # |\n| 566 | 03082P020293 | 28.72040 | 95.16010 | Dihang | 3640 | Brahmaputra | Upper Siang | Arunachal Pradesh | 22 | # | # |\n| 567 | 03391D060629 | 28.74900 | 96.37490 | Thangkung Chu | 3784 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 26 | # | # |\n| 568 | 03182H100359 | 28.58530 | 93.74300 | Subansiri | 3758 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 11 | # | # |\n| 569 | 03182H100368 | 28.57570 | 93.57830 | Subansiri | 3842 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 37 | # | # |\n| 570 | 03678M090251 | 27.75720 | 91.65040 | Dangme Chu | 4152 | Brahmaputra | Tawang | Arunachal Pradesh | 17 | # | # |\n| 571 | 03682H140058 | 28.56230 | 93.88680 | | 3938 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 13 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 13, "line_start": 4908, "line_end": 4922, "token_count_estimate": 918, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Dihang", "Subansiri"], "countries": [], "lake_ids": ["03082P020293", "03082P050307", "03182H100359", "03182H100368", "03391D050593", "03391D050594", "03391D060629", "03592A092125", "03592A142140", "03592E052239", "03592E052276", "03678M090251", "03682H140058", "16010", "31550", "31600", "35850", "37490", "39450", "43580", "56230", "57570", "57830", "58530", "65040", "70680", "72040", "73240", "74300", "74900", "75720", "81640", "87060", "87410", "87540", "87560", "88680", "89270", "99360"]}}
{"id": "f9c87135f00c790a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: table\nTable\n\n| Sl. No. | Lake ID | Latitude | Longitude | River | Elevation | Basin | District | State | Inventory Area (Ha) 2021 | Lake Area August 2025 (Ha) | Change in Area w.r.t. Inventory Area |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 572 | 03182H100349 | 28.66780 | 93.73410 | Subansiri | 4016 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 20 | # | # |\n| 573 | 03182H100360 | 28.58470 | 93.71210 | Subansiri | 3873 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 14 | # | # |\n| 574 | 03082L050034 | 28.97820 | 94.39780 | Pite | 3111 | Brahmaputra | Upper Siang | Arunachal Pradesh | 25 | # | # |\n| 575 | 03682H140047 | 28.60610 | 93.84530 | | 4022 | Brahmaputra | Upper Subansiri | Arunachal Pradesh | 12 | # | # |\n| 576 | 03682L010072 | 28.75690 | 94.15250 | Siyom | 3875 | Brahmaputra | West Siang | Arunachal Pradesh | 13 | # | # |\n| 577 | 03282D160025 | 28.11710 | 92.96890 | | 4214 | Brahmaputra | Kurung Kumey | Arunachal Pradesh | 18 | # | # |\n| 578 | 03391C040381 | 29.10000 | 96.17310 | Dri Chu | 3949 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 22 | # | # |\n| 579 | 03391D050578 | 28.92400 | 96.36800 | Thangkung Chu | 4109 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 10 | # | # |\n| 580 | 03591D160688 | 28.24510 | 96.83170 | Dzayul chu | 3900 | Brahmaputra | Anjaw | Arunachal Pradesh | 43 | # | # |\n| 581 | 03391C030257 | 29.32490 | 96.11400 | Jairu Chu | 3962 | Brahmaputra | Dibang Valley | Arunachal Pradesh | 11 | # | # |", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "table", "table_caption": null, "columns": ["Sl. No.", "Lake ID", "Latitude", "Longitude", "River", "Elevation", "Basin", "District", "State", "Inventory Area (Ha) 2021", "Lake Area August 2025 (Ha)", "Change in Area w.r.t. Inventory Area"], "table_row_start": 1, "table_row_end": 10, "line_start": 4927, "line_end": 4938, "token_count_estimate": 753, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Subansiri"], "countries": [], "lake_ids": ["03082L050034", "03182H100349", "03182H100360", "03282D160025", "03391C030257", "03391C040381", "03391D050578", "03591D160688", "03682H140047", "03682L010072", "10000", "11400", "11710", "15250", "17310", "24510", "32490", "36800", "39780", "58470", "60610", "66780", "71210", "73410", "75690", "83170", "84530", "92400", "96890", "97820"]}}
{"id": "f34d4f94ccd0567a", "text": "Document: Monitoring Report Aug 2025\nSection: 1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)\nType: text\n\n*Note: “#” indicates frozen/ dried/cloud covered lakes. Inventory Area (in Ha) has been rounded off.*\n\n- GLs displaying increase in area\n\n**6. References**\n\n1. NRSC, July 2011. *Final Report of \"Inventory and Monitoring of Glacial Lakes / Water Bodies in the Himalayan Region of Indian River Basins\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.\n\n2. NRSC, April 2012. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins during 2011\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.\n\n3. NRSC, March 2013. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins during 2012\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.\n\n4. NRSC, December 2013. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins during 2013\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.\n\n5. NRSC, December 2014. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins during 2014\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.\n\n6. NRSC, December 2015. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins during 2015\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.\n\n7. CWC, February 2017. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins for 2016\"*, Technical Report Published by Climate Change & IAD Directorate, CWC, New Delhi.\n\n8. CWC, March 2018. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins for 2017\"*, Technical Report Published by Morphology & Climate Change Directorate, CWC, New Delhi.\n\n9. CWC, January 2019. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins for 2018\"*, Technical Report Published by Morphology & Climate Change Directorate, CWC, New Delhi.\n\n10. CWC, February 2020. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins for 2019\"*, Technical Report Published by Morphology & Climate Change Directorate, CWC, New Delhi.\n\n11. CWC, December 2020. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins for 2020\"*, Technical Report Published by Morphology & Climate Change Directorate, CWC, New Delhi.\n\n12. CWC, December 2021. *Report on \"Monitoring of Glacial Lakes/Water Bodies in the Himalayan Region of Indian River Basins for 2021\"*, Technical Report Published by Morphology & Climate Change Directorate, CWC, New Delhi.\n\n13. Gorelick, N. a. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. doi: 10.1016/j.rse.2017.06.031\n\n14. NRSC, March 2023. *Final Report of \"Glacial Lake Atlas of Indian Himalayan River Basins\"*, Technical Report Published by National Remote Sensing Centre, Hyderabad.", "metadata": {"source_file": "data/Monitoring_Report_Aug_2025_gemini.md", "document_title": "Monitoring Report Aug 2025", "section_path": "1. Introduction > 1.3 Inventory of Glacial Lakes & Water Bodies 2011 > 5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)", "section_headings": ["1. Introduction", "1.3 Inventory of Glacial Lakes & Water Bodies 2011", "5.2 Conclusion on Monitoring of 1941 Glacial lakes being monitored from 2025 (as per Glacial Lake Atlas, 2023)"], "chunk_type": "text", "line_start": 4939, "line_end": 4980, "token_count_estimate": 875, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1eeb5ebc38f0b25b", "text": "Document: output vertex chunked\nSection: SUMMARY\nType: text\n\nNational Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Hyderabad as one of the Implementing Agency under the National Hydrology Project (NHP), is carrying out hydrological studies using satellite data and geospatial techniques. As part of this, detailed glacial lake inventory, prioritization for Glacial Lake Outburst Flood (GLOF) risk, and simulation of GLOF for selected lakes are taken up for entire catchment of Indian Himalayan Rivers covering Indus, Ganga, and Brahmaputra River Basins. Under this activity, an updated inventory of glacial lakes using high resolution satellite data was prepared for the Indus and Ganga River basins and published in December 2020 (NRSC-RSAA-WRG-WRAD-Nov2020-TR-0001702-V1.0) and June 2021 (NRSC-RSAA-WRG-WRAD-Mar2021-TR-0001818-V1.0) respectively, and currently an updated inventory of glacial lakes has been prepared for the Brahmaputra River basin. The present glacial lake atlas is based on the inventoried glacial lakes in part of Brahmaputra River basin from its origin to foothills of Himalayas covering a catchment area of 3,99,833 Km².\n\nThe study portion of Brahmaputra River basin covers part of India and transboundary region. Brahmaputra River basin has been divided into 12 subbasins on the basis of confluence of major rivers contributing into the system viz., Amo chu, Dibang, Dihang, Jia Bharali, Lhasa Tsangpo, Lohit, Lower Yarlung Tsangpo, Manas, Puna Tsang Chu, Subansiri, Teesta and Upper Yarlung Tsangpo. Elevation in the river basin varies from the minimum 450 m to the maximum 8,352 m above mean sea level (amsl). In India, Brahmaputra River basin extends in two states viz., Arunachal Pradesh and Sikkim.\n\nIn the present study, glacial lakes with water spread area ≥ 0.25 ha have been mapped using Resourcesat-2 (RS-2) Linear Imaging Self Scanning Sensor-IV (LISS-IV) satellite data using visual interpretation techniques. Based on its process of lake formation, location, and type of damming material, glacial lakes are identified in all ten different types, majorly grouped into four categories viz., Moraine-dammed, Ice-dammed, Glacier Erosion, and Other glacial lakes.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SUMMARY", "section_headings": ["SUMMARY"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 587, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": ["Amo chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": ["0001702", "0001818"]}}
{"id": "0b4c3352f0560526", "text": "Document: output vertex chunked\nSection: SUMMARY\nType: text\n\nlevel ( amsl ) . In India , Brahmaputra River basin extends in two states viz . , Arunachal Pradesh and Sikkim . In the present study , glacial lakes with water spread area ≥ 0 . 25 ha have been mapped using Resourcesat - 2 ( RS - 2 ) Linear Imaging Self Scanning Sensor - IV ( LISS - IV ) satellite data using visual interpretation techniques . Based on its process of lake formation , location , and type of damming material , glacial lakes are identified in all ten different types , majorly grouped into four categories viz . , Moraine - dammed , Ice - dammed , Glacier Erosion , and Other glacial lakes .\n\nA total of 18,001 glacial lakes have been mapped in the Brahmaputra River basin using a total of 187 high resolution multispectral RS-2 LISS-IV images, with a total lake water spread area of 92,990.74 ha. Each glacial lake has been given a 12 alpha-numeric unique glacial lake ID, along with several attributes that include hydrological, geometrical, geographical, and topographical characteristics. About 14,499 (80.55%) lakes are with < 5 ha lake area contributing to 21.92% of total lake area. The remaining lakes with > 5 ha in size are 3,502 (19.45%) contributing to 78.08% of total lake area in the basin. There are only 207 glacial lakes in the Brahmaputra River basin having an area of greater than 50 ha. Other Glacial Erosion lake type are found to be the maximum with 11,846 (65.81%) occupying a total lake extent of 48,368.91 ha at 52.01% in the basin. Majority (i.e. 93.34%) of the lakes are situated above the high altitude range of greater than 4,000 m amsl and dominated by Other Glacial Erosion lake type i.e., 65.31%.\n\nGlacial lakes are predominantly distributed in Lower Yarlung Tsangpo subbasin (27.66%) followed by Upper Yarlung Tsangpo subbasin (16.11%), with a total lake extent of 26,371.81 ha and 16,088.67 ha at 28.36% and 17.30% respectively in the entire basin. In terms of very large size lakes i.e. >50 ha Lower Yarlung Tsangpo subbasin has majority i.e. 54 out of 207 large lakes within it. Very large size glacial lakes of one each are present in Amo chu and Dibang subbasins. Other Glacial Erosion lakes, which are dominant lake type in Brahmaputra\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nRiver basin distributed in all subbasins, and found maximum in count in Lower Yarlung Tsangpo subbasin. However, two Lateral Moraine Dammed lakes with Ice are present in the entire Brahmaputra River basin and one each is located in Lower Yarlung Tsangpo and Upper Yarlung Tsangpo subbasins. Teesta subbasin consists of higher number of Supra-glacial Lakes in the entire Brahmaputra River basin, whereas UpperYarlung Tsangpo subbasin contains higher number of Other Moraine Dammed lakes.\n\nA total of 2,921 (i.e. 16.23%) glacial lakes lies within Indian region covering 16.95% of the total lake area, whereas remaining 83.77% of lakes are located in transboundary region with a 83.05% of the total lake area.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SUMMARY", "section_headings": ["SUMMARY"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 841, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo chu", "Dibang", "Lower Yarlung Tsangpo", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "1af0da7233c2afab", "text": "Document: output vertex chunked\nSection: SUMMARY\nType: text\n\n, two Lateral Moraine Dammed lakes with Ice are present in the entire Brahmaputra River basin and one each is located in Lower Yarlung Tsangpo and Upper Yarlung Tsangpo subbasins . Teesta subbasin consists of higher number of Supra - glacial Lakes in the entire Brahmaputra River basin , whereas UpperYarlung Tsangpo subbasin contains higher number of Other Moraine Dammed lakes . A total of 2 , 921 ( i . e . 16 . 23 % ) glacial lakes lies within Indian region covering 16 . 95 % of the total lake area , whereas remaining 83 . 77 % of lakes are located in transboundary region with a 83 . 05 % of the total lake area .\n\nIn Indian region, majority of glacial lakes are of Other Glacial Erosion Lake type (73.60%), followed by Cirque Erosion lakes (11.23%) and Other Moraine Dammed lakes (7.91%). Arunachal Pradesh and Sikkim States share 74.91% and 25.09% of lake count with a total lake area of 79.27% and 20.73% respectively. Majority of lakes in Arunachal Pradesh and Sikkim are predominantly of lake area < 5 ha, but lying in high (4,001 - 5,000 m) and very high altitude range (> 5,000 m) respectively. Lakes in Sikkim are only situated above 3,000 m elevation.\n\nIn this atlas, map sheets (plates) are prepared on the basis of the Survey of India (SOI) toposheet index (1: 250,000 scale) which are 65 in number covering the entire Brahmaputra River basin. Out of 65 plates, only 54 plates have glacial lakes and corresponding plates are incorporated in atlas. The map sheets are arranged in such a way that glacial lake map is on the right page and its corresponding satellite image is on the left page. At the end of the atlas, an annexure is provided containing list of glacial lakes of size ≥ 5 ha inventoried in the Brahmaputra River basin with their unique glacial lake ID, latitude, longitude, subbasin, glacial lake type, area (ha), and elevation (m). Glacial Lake ID number of 12 alpha-numeric character has 3 characters bold with dark red colour depicting the corresponding toposheet number of the SOI of 1:250,000 scale.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SUMMARY", "section_headings": ["SUMMARY"], "chunk_type": "text", "line_start": 4, "line_end": 32, "token_count_estimate": 591, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Lower Yarlung Tsangpo", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "aea64d7ee2d8a211", "text": "Document: output vertex chunked\nSection: 1. INTRODUCTION > 1.1 About Project\nType: text\n\nThe National Hydrology Project (NHP) sponsored by Department of Water Resources, River Development and Ganga Rejuvenation (DoWR, RD&GR), Ministry of Jal Shakti, Government of India (GOI) with financial aid from the World Bank. The objective of the project is to improve the extent and accessibility of water resources information and strengthen institutional capacity to enable improved water resources planning and management across India. The mission is to establish an effective and sound hydrologic database and Hydrological Information System (HIS), together with the development of consistent and scientifically based tools and design aids, to assist in the effective water resources planning and management of the implementing agencies.\n\nNHP is intended for setting up of a system for timely and reliable water resources data acquisition, storage, collation and management. It will also provide tools/systems for informed decision making through Decision Support Systems (DSS) for water resources assessment, flood management, reservoir operations, drought management, etc. NHP also seeks to build capacity of the State and Central sector organisations in water resources management through the use of Information Systems and adoption of State-of-the-art technologies like Remote Sensing. NHP will improve and expand hydrology data and information systems, strengthen water resources operation and planning systems, and enhance institutional capacity for water resources management. NHP will contribute to the GOI Digital India initiative by integrating water resources information across State and Central agencies.\n\nNational Remote Sensing Centre (NRSC), as one of the Implementing Agency under NHP, is engaged with generation of geo-spatial products & services pertaining to water resources sector, generation of high resolution Digital Elevation Models (DEM), development of flood early warning systems, decision support system development for irrigation water management, modelling & dissemination of hydrological products to support water resources management and capacity building to NHP stakeholders. The satellite data based geo-spatial products & services, mainly encompassing the following:\n\n* Satellite Data/Geo-Spatial Data Hosting & Services through Bhuvan Web Portal\n* Water Resources Information Products & Services (Satellite/Model derived – Bhuvan/India- Water Resources Information System (India-WRIS)/National Water Informatics Centre (NWIC))\n* Customized Applications Development (Flood Forecasting, Irrigation Water Management)\n* Hydro-conditioned Digital Elevation Model (Satellite & Aerial)\n* Capacity Building (Customized Training & Hand Holding)\n\nAs part of various NHP technical studies carried out, NRSC has taken up “Glacial Lake Outburst Flood (GLOF) Risk Assessment of Glacial Lakes in the Himalayan Region of Indian River Basins”. In this activity, it was proposed to prepare an updated inventory of glacial lakes, prioritization and selection of critical glacial lakes based on certain characteristics (such as glacial lake, glacier, topography and others), GLOF modelling and flood inundation simulation for selected few lakes using high resolution Digital Elevation Model (DEM) for downstream of the lakes along their river reach, and to assess GLOF risk.\n\nAs a result of initial outcome of this activity, an updated inventory of glacial lakes in Indus and Ganga River basins was generated using multispectral (MX) high resolution satellite data of Resourcesat-2 (RS-2) Linear Imaging\n\n***\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 3\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "1. INTRODUCTION > 1.1 About Project", "section_headings": ["1. INTRODUCTION", "1.1 About Project"], "chunk_type": "text", "line_start": 36, "line_end": 60, "token_count_estimate": 806, "basins": ["BRAHMAPUTRA", "Ganga", "Indus"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2100ec2ef2bd574f", "text": "Document: output vertex chunked\nSection: 1. INTRODUCTION > 1.1 About Project\nType: text\n\nof critical glacial lakes based on certain characteristics ( such as glacial lake , glacier , topography and others ) , GLOF modelling and flood inundation simulation for selected few lakes using high resolution Digital Elevation Model ( DEM ) for downstream of the lakes along their river reach , and to assess GLOF risk . As a result of initial outcome of this activity , an updated inventory of glacial lakes in Indus and Ganga River basins was generated using multispectral ( MX ) high resolution satellite data of Resourcesat - 2 ( RS - 2 ) Linear Imaging * * * National Remote Sensing Centre , ISRO , Hyderabad | 3 GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSelf Scanning Sensor-IV (LISS-IV) for mapping lakes with size ≥ 0.25 ha. The geo-spatial database of glacial lakes has been used to publish “Glacial Lake Atlas of Indus River Basin” (NRSC-RSAA-WRG-WRAD-Nov2020-TR-0001702-V1.0), “Glacial Lake Atlas of Ganga River Basin” (NRSC-RSAA-WRG-WRAD-Mar2021-TR-0001818-V1.0) and presently the atlas of “Glacial Lake Atlas of Brahmaputra River Basin”.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "1. INTRODUCTION > 1.1 About Project", "section_headings": ["1. INTRODUCTION", "1.1 About Project"], "chunk_type": "text", "line_start": 36, "line_end": 60, "token_count_estimate": 331, "basins": ["BRAHMAPUTRA", "Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0001702", "0001818"]}}
{"id": "a39282e9ed72a33e", "text": "Document: output vertex chunked\nSection: 1. INTRODUCTION > 1.2 Glacial Lakes\nType: text\n\nIndian Himalayan Region (IHR) contains the world’s largest number of glaciers and snow outside the Polar Regions and are aptly called third pole of the world. Many studies undertaken globally showed that glaciers around the world have been retreating since the industrial revolution, which began around eighteenth century. As the glaciers are thinning and retreating, resulting in associated glacier melt water lakes are expanding in size and new lakes continue to form. The lakes receiving melt water from glaciers are generally known as glacial lakes. A glacial lake is defined as water mass existing in a sufficient amount and extending with a free surface in, under, beside, and/or in front of a glacier and originating from glacier activities and/or retreating processes of a glacier. As glaciers retreat, the formation of glacial lakes takes place behind moraine or ice ‘dam’. These damming materials are generally weak and can breach suddenly due to various triggering factors, leading to catastrophic floods. Such outburst floods are known as GLOF.\n\nGLOFs are characterized by extreme peak discharges, with an exceptional erosion/transport potential; therefore, they can turn into flow-type movements (Emmer, 2017). Failure of such lake happens due to many factors which include erosion process, increase in water pressure, merging of an avalanche/rock into lake, nature of the damming materials etc., and this may lead to a GLOF event which could be highly disastrous in nature and create long-term degradation in the valleys, both physically and socio-economically (Mool et al., 2001b). Accordingly, Emmer et al., (2016) showed an annual nonlinear increase in the number of scientific publications focusing on GLOFs recently. Hence, monitoring of glacier associated lakes is very useful in the IHR to identify critical glacial lakes, for which a detailed inventory of glacial lakes and its type is required. According to their position relative to the glacier and damming mechanism, these glacial lakes can be classified into several types (Panda et al., 2014).\n\nInventorying glacial lakes located in these remote mountain areas with rugged terrain and inclement weather by traditional means is very tedious and difficult, hence Remote Sensing (RS) data plays a greater role in generating information on glacial lakes (Kulkarni, 1991; Berither et al., 2007; Wagnon et al., 2007; Raj, 2010; Cogley et al., 2011; Pratap et al., 2016; Gupta et al., 2019, Guru et al., 2019). Satellites with high spatial, spectral and temporal resolution sensors are useful in deriving lake information with better accuracy and repeatedly.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "1. INTRODUCTION > 1.2 Glacial Lakes", "section_headings": ["1. INTRODUCTION", "1.2 Glacial Lakes"], "chunk_type": "text", "line_start": 62, "line_end": 68, "token_count_estimate": 662, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe1f66e179118ae4", "text": "Document: output vertex chunked\nSection: 1. INTRODUCTION > 1.3 Previous Studies\nType: text\n\nSeveral studies have been taken up in the past to assess the glacial lake distribution in the Hindu Kush Himalayas (HKH), covering parts of eight countries viz., Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan, and lies within five river basins of Amu Darya, Indus, Ganga, Brahmaputra, and Irrawaddy (Komori, 2007; Gardelle et al., 2011; Wang et al., 2011; Wang et al., 2012; Nie et al., 2013; Raj et al., 2013; Wang et al., 2013; Worni et al., 2013; Che et al., 2014; Bambari et al., 2015; Zhang et al., 2015; Nie et al., 2017; Rounce et al., 2017; Nagai et al., 2017; Gupta et al., 2019; Guru et al., 2019; Shugar et al., 2020). But only few glacial lake inventories are available in public domain, amongst which the first inventory was prepared by the International Centre for Integrated Mountain Development (ICIMOD), Nepal, for the entire HKH region (covering\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nthe entire IHR within it), using satellite data of the Land Observation Satellite (Landsat) Thematic Mapper (TM) of the United States Geological Survey (USGS) and the Indian Remote Sensing satellite (IRS-1D) Linear Imaging and Self-scanning Sensor-III (LISS-III) during 1999-2005, along with topographic maps published between the 1950s and 1982 (Mool et al., 2001a; Mool et al., 2001b; Mool et al., 2003; Bhagat et al., 2004; Roohi et al., 2005; Sah et al., 2005; Wu et al., 2005, Ives et al., 2010). This inventory has been revised in 2018 using Landsat TM and Enhanced Thematic Mapper Plus (ETM+) data of years 2004-07 ± 3 (Maharjan et al., 2018). Both glacial lake inventories prepared by the ICIMOD, have mapped lakes with size > 0.3 ha. Second inventory of glacial lakes and water bodies in the IHR (within India only) was carried out by the NRSC, Hyderabad in collaboration with the Central Water Commission (CWC), New Delhi (NRSC-RS&GISAA-WRG-CWC-Lakes-May2011-TR255). Glacial lakes and water bodies located in all three major basins of Indus, Ganga, and Brahmaputra, of size > 10 ha were mapped using Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data for the year 2009 (Hakeem et al., 2011). Subsequently, monthly monitoring of these lakes (> 50 ha) was carried out using satellite data for the months of June to October during the years 2011 to 2015.\n\nThird latest glacial lake inventory is prepared by the Space Application Centre (SAC), Ahmedabad i.e. “National Wetland Atlas: High Altitude Lakes of India”, using IRS-P6 LISS-III, comprising high altitude lake information of the IHR, within Indian administrative region only (Panigrahy et al., 2012). In this atlas, wetlands of size > 2.25 ha were mapped as a polygons and less than that were mapped as a points, using satellite data for the period of 2006-08.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "1. INTRODUCTION > 1.3 Previous Studies", "section_headings": ["1. INTRODUCTION", "1.3 Previous Studies"], "chunk_type": "text", "line_start": 70, "line_end": 80, "token_count_estimate": 810, "basins": ["BRAHMAPUTRA", "Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Myanmar", "Nepal"], "lake_ids": []}}
{"id": "7f51e82cd65f1978", "text": "Document: output vertex chunked\nSection: 1. INTRODUCTION > 1.4 Highlights of the Atlas\nType: text\n\n**The highlights of the present atlas:**\n* The present atlas is first of its kind depicting spatial distribution of glacial lakes of size ≥ 0.25 ha in Brahmaputra River basin mapped using high resolution satellite data\n* The atlas provides the details of all the glacial lakes in entire catchment of Brahmaputra River basin, both within Indian and transboundary region\n* The atlas contain details of area range-wise glacial lakes along with 10 categories of types. Further, the atlas present the distribution of glacial lakes in terms of area vs. type, elevation, area vs. elevation and type vs. elevation, at basin, subbasin, administrative and transboundary regions\n* The atlas also provides comprehensive list of all glacial lakes with unique ID considering hydrological, geometrical, geographical, topographical attribute information\n\n**The expected utility of the atlas:**\n* The atlas provides a comprehensive & systematic glacial lake database for Brahmaputra River basin with size ≥ 0.25 ha\n* In the context of climate change impact analysis, the atlas can be used as reference data for carrying out change analysis, both with respect to historical and future time periods\n* The atlas also provides authentic database for regular or periodic monitoring changes in spatial extent (expansion/shrinkage), and formation of new lakes\n* The atlas can also be used in conjunction with glacier information for their retreat and climate impact studies\n* The information on glacial lakes like their type, hydrological, topographical, and associated glaciers are useful in identifying the potential critical glacial lakes and consequent GLOF risk\n* Central and State Disaster Management Authorities can make use of the atlas for disaster mitigation planning and related programs\n* This glacial lake atlas can be used in Detailed Project Report (DPR) preparation for new hydropower/multi purpose projects\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nLuggye Tsho & Raphstreng Tsho at the snout of Glacier in Puna Tsang Chu Subbasin, Located in Bhutan as seen in FCC Satellite image\n\nSatellite: Resourcesat – 2\nSensor: LISS-IV MX\nDate of Image: 16.12.2016\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "1. INTRODUCTION > 1.4 Highlights of the Atlas", "section_headings": ["1. INTRODUCTION", "1.4 Highlights of the Atlas"], "chunk_type": "text", "line_start": 82, "line_end": 114, "token_count_estimate": 533, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Puna Tsang Chu"], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "e67dea63831a7ae4", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.1 Overview\nType: text\n\nThe IHR consists of three major river systems of Indus, Ganga, and Brahmaputra stretches over four countries viz., India, China, Nepal and Bhutan, and on the basis of physiography it has been divided into four mountain regions viz., Eastern Himalayas, Central Himalayas, Western Himalayas, and the Karakoram Mountain range. The Brahmaputra basin spreads over countries of Tibet (China), India, Bhutan and Bangladesh covering drainage area of 5,80,000 Sq.Km. In India it has a share of about 33.52% which is 1,94,413 Sq.Km cutting across the states Arunachal Pradesh, Assam, West Bengal, Meghalaya, Nagaland and Sikkim.\n\nTopographically River Brahmaputra is unique in terms of its diverse environment as it has cold plateau in Tibet, rainy Himalayan region, alluvial lands of Assam and the large deltaic plains of Bangladesh (Figure 1). The Brahmaputra River also known as Yarlung Tsangpo (in Tibet) originates in the glacier mass from the Kailash ranges of Himalayas at an altitude of 5,150 m south of the lake ’Konggyu Tsho’. The higher elevation zones in the basin causes snow fall mostly over the northern region. The river flows through a length of 2,900 Km out of which 916 Km in the Indian Territory and joins finally in the Bay of Bengal. The catchment area receives number of tributaries at its north and south banks especially in Indian region. The catchment receives heavy rainfall with significant spatial variability. The land use/cover of the basin consist extensive forest cover, plantations, crop fields and swampy water lands, the northern part of the basin (outside India) covers mostly snow. There are number of hydraulic structures constructed across the tributaries in terms of weirs, barrages for the purpose of Irrigation and Dams for the hydro power utilization.\n\nThe Brahmaputra River basin from its origin to foothills of Himalayas with a catchment area of 3,99,833 Sq.Km is considered in the present study, which extends from latitude 26.70 N to 3.270 N and from longitude 820 E to 97.770 E (Figure 2).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.1 Overview", "section_headings": ["2. STUDY AREA", "2.1 Overview"], "chunk_type": "text", "line_start": 118, "line_end": 124, "token_count_estimate": 546, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": ["Bhutan", "China", "India", "Nepal"], "lake_ids": []}}
{"id": "e81ce414c9c0f21a", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: text\n\nThe entire Brahmaputra River basin is sub divided into three reaches namely upper, middle and lower. In the upper reach the river is fed by glaciers. In the lower and middle reaches it is joined by number of tributaries. The\n\n***\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 7\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nprincipal tributaries of the Brahmaputra River joining from right are the Lohit, the Dibang, the Subansiri, the Jia bharali, the Dhansiri, the Manas, the Torsa, the Sankosh and the Teesta whereas the Burhidihing, the Desang, the Dikhow, the Dhansiri and the Kopili joins it from left. Considering hydrological settings of the above said reaches and the present study reach, Brahmaputra River basin is divided in 12 subbasins viz., Upper Yarlung Tsangpo, Lhasa Tsangpo, Lower Yarlung Tsangpo, Dihang, Dibang, Lohit, Subansiri, Jia Bharali, Manas, Amo Chu, Puna Tsang Chu, and Teesta. Figure 2 shows the location of the study area with Resourcesat-2 (RS-2) Linear Imaging Self Scanner (LISS-IV) satellite images. Table 1 shows the catchment area of each of the above subbasins.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "text", "line_start": 126, "line_end": 136, "token_count_estimate": 339, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Jia bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "1690642b086438bb", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: figure\nFigure: Figure 2: Location of Brahmaputra River basin showing RS-2 LISS-IV images\n\n**Figure 2: Location of Brahmaputra River basin showing RS-2 LISS-IV images**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "figure", "figure_caption": "Figure 2: Location of Brahmaputra River basin showing RS-2 LISS-IV images", "line_start": 137, "line_end": 137, "token_count_estimate": 69, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "aad628c8935480b7", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: table\nTable: Table 1: Details of subbasins of Brahmaputra River basin\n\n| S. No. | Subbasin | Area (Km²) | Area (%) |\n|---|---|---|---|\n| 1 | Amo Chu | 9,829 | 2.46 |\n| 2 | Dibang | 12,238 | 3.06 |\n| 3 | Dihang | 22,158 | 5.54 |\n| 4 | Jia Bharali | 13,084 | 3.27 |\n| 5 | Lhasa Tsangpo | 32,896 | 8.23 |\n| 6 | Lohit | 25,799 | 6.45 |\n| 7 | Lower Yarlung Tsangpo | 74,334 | 18.59 |\n| 8 | Manas | 32,166 | 8.04 |\n| 9 | Puna Tsang Chu | 10,204 | 2.55 |\n| 10 | Subansiri | 30,644 | 7.66 |\n| 11 | Teesta | 8,555 | 2.14 |\n| 12 | Upper Yarlung Tsangpo | 1,27,926 | 31.99 |\n| | **Total** | **3,99,833** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "table", "table_caption": "Table 1: Details of subbasins of Brahmaputra River basin", "columns": ["S. No.", "Subbasin", "Area (Km²)", "Area (%)"], "table_row_start": 1, "table_row_end": 13, "line_start": 141, "line_end": 155, "token_count_estimate": 332, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "578859ba53bd4233", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.2 Hydrological Divide\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.2 Hydrological Divide", "section_headings": ["2. STUDY AREA", "2.2 Hydrological Divide"], "chunk_type": "text", "line_start": 156, "line_end": 162, "token_count_estimate": 42, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "52e7d327f657fde7", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.3 Hydrology\nType: text\n\nThe Upper reach of the River Brahmaputra flows through 1,625 Km from the source of origination point to the Indo-China border through Tibetan plateau and enters India at Kobo, Arunachal Pradesh, Upper reach of the River is mostly fed by snow and glaciers. In the middle reach between India and Bangladesh border it flows through a length of 916 Km where it has numerous riverine islands because of its low gradient. The entire lower reach falls within the Bangladesh flows about a length of 337 Km and drains into Bay of Bengal. The river flows are at low in winter season, gradually increases in summer season (due to melting of snow and glaciers in upper reaches) and reaches peak in monsoon season. The average water resources potential (In India) of the basin is 537.24 BCM out of which utilizable surface water resource is 24 BCM (Brahmaputra Basin Report, 2014).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.3 Hydrology", "section_headings": ["2. STUDY AREA", "2.3 Hydrology"], "chunk_type": "text", "line_start": 164, "line_end": 166, "token_count_estimate": 233, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["China", "India"], "lake_ids": []}}
{"id": "eeaa591d5200b9f7", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.4 Topography\nType: text\n\nThe study area mainly resides in the middle and eastern Himalayan region, which is also known as one of the main topographic division of the Indian subcontinent. The Himalayas comprises the Himalayan ranges including their numerous snow peaks and each of these peaks is surrounded by snow fields and glaciers. The elevation of the study area ranges between 450 m and 8,352 m amsl, where glaciers and glacial lakes are mostly distributed in the higher altitude region. The mean elevation of the study area is about 4,048 m amsl. Hypsometric curve is a graph which shows the proportion of land area that exists at various elevations by plotting relative area against relative height, as shown in Figure 3 for the study area.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.4 Topography", "section_headings": ["2. STUDY AREA", "2.4 Topography"], "chunk_type": "text", "line_start": 168, "line_end": 175, "token_count_estimate": 208, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c918c2622113feee", "text": "Document: output vertex chunked\nSection: 2. STUDY AREA > 2.5 Climate\nType: text\n\nThe climate of the Brahmaputra river basin varies from the harsh cold, and dry conditions found in Tibet to the generally hot and humid conditions prevailing in Assam state. Tibetan winters are severely cold, with average temperatures below 0°C, while summers are mild and sunny. The upper river valley lies in the rain shadow of the Himalayas, and precipitation there is relatively light (Lhasa receives about 400 mm annually). Climate over the Brahmaputra River basin is mainly experiences four seasons in a year namely winter, summer, and monsoon and post monsoon. The winter season begins in December and continues to the end of February. From March onwards, the hot weather starts and continues up to the last week of May. The monsoon begins in the last week of May or in early June and the basin receives heavy rainfall spatially distributed over the basin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "2. STUDY AREA > 2.5 Climate", "section_headings": ["2. STUDY AREA", "2.5 Climate"], "chunk_type": "text", "line_start": 177, "line_end": 183, "token_count_estimate": 245, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a897b426c316be07", "text": "Document: output vertex chunked\nSection: 3. DATA USED\nType: text\n\nEarth observation satellites capture the data repeatedly in various spectral ranges and at different spatial and radiometric resolutions. For inventorying glacial lakes, high to medium resolution datasets are proved to be useful by many research studies (Bolch et al., 2010; Mergili et al., 2013; Wang et al., 2013; Zhang et al., 2015; Gupta et al., 2019, Guru et al., 2019). Data captured between September and December were mostly used because the presence of snow or cloud cover during this period is minimum. USGS satellite data of Landsat 5 and 7 (TM and ETM+) has been used widely for mapping glacial lakes due to free accessibility. Whereas, IRS satellite data from sensors of AWiFS, LISS-III and LISS-IV has also been used for such inventory.\n\nIn the present study, high resolution Resourcesat-2 LISS-IV satellite images with spatial resolution of 5.8 m covering a swath of 70 × 70 Km have been used for inventorying glacial lakes. Maximum of the images used for inventorying were of 2016-2021 (74%) and remaining images procured were of previous years due to non-availability of cloud-free and snow-free images for the recent years. Majority of images were of September and December months (73%) due to less snow and cloud cover, and rest 27% images of other months. Figure 4 shows the layout of the RS-2 LISS-IV scenes (path-wise) procured for the Brahmaputra River basin along with its details in Table 2. The layout of satellite scenes is divided into paths (shown in separate colours) and rows (row numbers shown in the layout).\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 185, "line_end": 195, "token_count_estimate": 424, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c7d7214f70c2c4a7", "text": "Document: output vertex chunked\nSection: 3. DATA USED\nType: table\nTable: Table 2: Details of satellite scenes used for inventory\n\n| | Other Months | Sep - Dec | Total |\n| :--- | :---: | :---: | :---: |\n| Prior to 2016 | 16 | 33 | 49 |\n| 2016-21 | 34 | 104 | 138 |\n| **Total** | **50** | **137** | **187** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "table", "table_caption": "Table 2: Details of satellite scenes used for inventory", "columns": ["", "Other Months", "Sep - Dec", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 196, "line_end": 200, "token_count_estimate": 123, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "086289976b56ef5e", "text": "Document: output vertex chunked\nSection: 3. DATA USED\nType: text\n\nDigital Elevation Model (DEM) of Cartosat satellite with 10 m spatial resolution has been used for topographic information and watershed boundary generation. Figure 5 shows elevation range map of the study area i.e. Brahmaputra River basin. Other information like names of lakes and rivers has been gathered from digital toposheets available from University of Texas - Toposheet Library at 1:250,000 scale and Tibet Map Institute at 1:100,000 scale (U.S. Army Map Service 1955; Andre 2017).\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "3. DATA USED", "section_headings": ["3. DATA USED"], "chunk_type": "text", "line_start": 201, "line_end": 208, "token_count_estimate": 149, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1495ccb066be362", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation\nType: text\n\nThe spectral reflectance curve of water in the visible spectrum starts with a low in Blue region (0.4 to 0.5 μm), reaches peak in Green region (0.5 to 0.6 μm), decreases in Red region (0.6 to 0.7 μm) and probably the most distinctive characteristic is the energy absorption at Near InfraRed (NIR) wavelengths. Identifying and delineating water bodies with remote sensing data are carried out easily in near infrared wavelengths because of this absorption property in IR region. However, various physical conditions of water bodies (water depth, turbidity, chlorophyll content, etc.) manifest spectral changes. As a result of various conditions of lakes, the water in satellite images in False Colour Composite (FCC) ranges in appearance from light to dark blue to black. In the case of frozen lakes, it appears white.\n\nGlacial lake sizes are generally small, having circular, semi-circular, or elongated shapes with very fine texture and are generally associated with glaciers in high altitude areas. Certain types of glacial lakes, like erosion and cirque lakes are not necessarily associated with glaciers. Knowledge of the physical characteristics of the glacial lakes, and their associated features is always essential for the interpretation of the images.\n\nSatellite data interpretation can be done using visual image interpretation keys such as colour, size, tone, texture, pattern, association, shape, shadow, and orientation. A number of remote sensing methods had been developed for glacial lake detection and mapping or development of inventory (Kääb 2000; Mool et al., 2001a; Huggel et al., 2002; Huggel et al., 2006; Ives et al., 2010). Manual or automated lake mapping methods have certain difficulties in identifying the lakes, which are described in the following section. An attempt was made to study the accuracy of mapping of glacial lakes using multiple automated methods along with visual interpretation, the details of which are given in Annexure-1. From this study, it was concluded that visual interpretation method was best accurate method. Hence, in the present study glacial lakes and their different types are identified and mapped using RS-2 LISS-IV multispectral images using visual interpretation method.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation"], "chunk_type": "text", "line_start": 212, "line_end": 218, "token_count_estimate": 544, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d93887dd1839000c", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:\nType: text\n\nGlacial lake identification can be done either using visual interpretation or automatic mapping methods. The automatic mapping procedures have limitations due to varying terrain conditions like lakes situated in the shadow portions of mountains, presence of snow cover, cloud cover, and partly frozen lakes, etc. In the presence of snow cover on the glacier tongue or glacier’s ablation area where many Supra-glacial lakes may present, both methods have limitations and difficulties.\n\nAs lake water absorbs the incident radiation making it appear in darker tone and colour in the standard FCC of satellite data, similar response also prevails over shadow region of clouds or mountains on surface, which may lead to incorrect mapping. In fact, a mountain shadow covering a lake partly/completely within its vicinity, making it difficult to accurately map the lake boundary.\n\nMany lakes due to inclement terrain condition, can be under shadow of high peaks and will get missed in both ways of mapping. On the contrary, a lake can also present in white colour while it is in frozen form due to cold\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nweather conditions over the area, then definitely it will not get classified while automatic mapping. Whereas, frozen lakes can be identified and mapped using visual interpretation to some extent.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Difficulties in Lake Identification:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Difficulties in Lake Identification:"], "chunk_type": "text", "line_start": 220, "line_end": 232, "token_count_estimate": 334, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "797f61d7c8dbda02", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Challenges in Automatic Mapping:\nType: text\n\nIn the IHR, due to high and inclement terrain surface and due to near vertical acquisition of satellite images, some lakes get covered with shadows of mountains, which create problems in identifying glacial lakes. Also identification of lakes with high turbidity, partial ice covered lakes and the lakes in shadow areas are misclassified by automatic methods. Glacial lake mapping is always a semi-automatic approach because even after applying any of that method, it should always be followed by the post processing i.e. correcting the errors using visual interpretation. Even in all cases, automatic mapping will never give the exact and accurate boundary of the lake, leading to necessary manual corrections.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Challenges in Automatic Mapping:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Challenges in Automatic Mapping:"], "chunk_type": "text", "line_start": 234, "line_end": 236, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4bb37fe5bc3676d5", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Reasoning for Visual Interpretation:\nType: text\n\nAlthough automatic mapping methods can speed up the detection of glacial lakes, but these methods could not be applied to the entire Himalayan region due to lot of variations in satellite scenes (seasons/years) and problems mentioned above. For example, if lakes are frozen or covered with snow or cloud and lies in a shadow area, they cannot be detected using these automatic methods. In such cases, the manual interpretation method will be helpful to map these lakes. Thus, any mapping of glacial lakes can be automated up to a certain extent only. So, visual image interpretation keys and technique will give accurate results and avoids misclassification. Therefore, in this present study, glacial lakes and its type identification, and its mapping for the entire Brahmaputra River basin (within IHR) has been done manually using visual interpretation. High resolution satellite data available on Bhuvan/Google Earth has been used on need basis in finalizing various features of glacial lake database.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Reasoning for Visual Interpretation:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Reasoning for Visual Interpretation:"], "chunk_type": "text", "line_start": 238, "line_end": 240, "token_count_estimate": 252, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b1a58ccd4800f2f", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.1 Satellite Data Interpretation > Limitations:\nType: text\n\nThe RS-2 LISS-IV MX data used for glacial lake database preparation sporadically covered with cloud and seasonal/permanent snow. Also, the Himalayan region being highly varying topography with steep slopes, the satellite data has hill shadows. Thus few glacial lakes would not have been mapped owing to the following constraints:\n* Presence of snow or cloud over the glacial lakes\n* Glacial lakes under frozen condition\n* Glacial lakes under mountain shadow", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.1 Satellite Data Interpretation > Limitations:", "section_headings": ["4. METHODOLOGY", "4.1 Satellite Data Interpretation", "Limitations:"], "chunk_type": "text", "line_start": 242, "line_end": 247, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7388de3236d68760", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: text\n\nVarious researchers have proposed glacial lakes classification schemes based on dam type, process of lake formation, topographic feature, and geographical position (Hewitt 1982; Liu and Sharma 1988; Clague and Evans 2000; Mool et al., 2001a, 2001b). Lakes located on the glacier surface can be mapped using satellite data, but there are englacial and subglacial lakes that may also exist, but cannot be mapped from aerial/optical satellite images, requires ground based instrument (Yao et al., 2018). Majorly surface glacial lakes are classified in 4 classes and 10 subclasses, i.e. Moraine-dammed lake, Ice-dammed lake, Glacier Erosion lake (also known as Bed-rock lake), and Other Glacial lake. Two character symbol has been used for glacial lake classification, in\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nwhich first letter (uppercase) represents lake type and second letter (lowercase) within brackets represents lake subtype, for example, M(e) for End-moraine dammed lake. Details of types of lakes are given in Table 3 and their appearance in satellite images are shown in Figure 6.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "text", "line_start": 249, "line_end": 260, "token_count_estimate": 314, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "37fccd1732bb2a16", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: table\nTable: Table 3: Glacial lake types and their identification keys\n\n| S.No. | Lake Type | Lake Subtype | Code | Identification Keys |\n|---|---|---|---|---|\n| 1 | Moraine-dammed Lake | End-moraine Dammed Lake | M(e) | Lake dammed by end (terminal) moraines |\n| 2 | Moraine-dammed Lake | Lateral Moraine Dammed Lake | M(l) | Lake dammed by lateral moraine(s) not in contact with glacial ice |\n| 3 | Moraine-dammed Lake | Lateral Moraine Dammed Lake (with Ice) | M(lg) | Lake dammed by lateral moraine(s) in contact with glacial ice |\n| 4 | Moraine-dammed Lake | Other Moraine Dammed Lake | M(o) | Lake dammed by other moraines |\n| 5 | Ice-dammed Lake | Supra-glacial Lake | I(s) | Pond or lake on the surface of a glacier |\n| 6 | Ice-dammed Lake | Glacier Ice-dammed Lake | I(d) | Lake dammed by glacier ice with no lateral moraines |\n| 7 | Glacier Erosion Lake | Cirque Erosion Lake | E(c) | A small pond occupying a cirque |\n| 8 | Glacier Erosion Lake | Glacier Trough Valley Erosion Lake | E(v) | Lakes formed in the glacier trough as a result of the glacier erosion process |\n| 9 | Glacier Erosion Lake | Other Glacial Erosion Lake | E(o) | Bodies of water occupying depressions formed by the glacial erosion process |\n| 10 | Other Glacial Lake | Other Glacial Lake | O | Lakes formed in a glaciated valley, and fed by glacial melt, but damming material not directly part of the glacial process |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "table", "table_caption": "Table 3: Glacial lake types and their identification keys", "columns": ["S.No.", "Lake Type", "Lake Subtype", "Code", "Identification Keys"], "table_row_start": 1, "table_row_end": 10, "line_start": 261, "line_end": 272, "token_count_estimate": 530, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "36c00a091aee0421", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.2 Types of Glacial Lake\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.2 Types of Glacial Lake", "section_headings": ["4. METHODOLOGY", "4.2 Types of Glacial Lake"], "chunk_type": "text", "line_start": 273, "line_end": 277, "token_count_estimate": 44, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "294fe168ad83815f", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: text\n\nA total of 22 attributes has been given to all mapped lake features in the geodatabase, which are broadly consisting information grouped in five different categories as shows in Table 4.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 279, "line_end": 283, "token_count_estimate": 64, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f7f1b1374ff8e10a", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: table\nTable: Table 4: Details of glacial lake attributes\n\n| S.No. | Category | Attribute |\n| :--- | :--- | :--- |\n| 1 | Hydrological | Basin, subbasin, river, lake name |\n| 2 | Geometrical | Maximum length, mean width, surface area |\n| 3 | Geographical | Latitude, longitude, region, state, district, toposheet 250k, toposheet 50k |\n| 4 | Topographical | Elevation, aspect |\n| 5 | Lake Information | Feature types, glacial lake type, lake ID |\n| 6 | Data Source Information | Source of database, source of elevation, date of pass |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 4: Details of glacial lake attributes", "columns": ["S.No.", "Category", "Attribute"], "table_row_start": 1, "table_row_end": 6, "line_start": 284, "line_end": 291, "token_count_estimate": 205, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6ce9b0b20d080ac", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: text\n\nTypically, lake ID is given in 12 alpha-numeric character format like “0378A0115656”, where first two digits ‘03’ refers to Basin code which is Brahmaputra (01-Indus and 02-Ganga), next five characters ‘78A01’ refers to the 1:250,000 (78A) and 1:50,000 (78A01) scale SOI Toposheet number, and the last five digits refers to the sequential number of each lake sorted from top left to bottom right. A typical example of the glacial lake database generated is given below in Table 5 along with fields and format.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 292, "line_end": 296, "token_count_estimate": 163, "basins": ["Brahmaputra", "Ganga", "Indus"], "subbasins": [], "countries": [], "lake_ids": ["0378A0115656", "78A01"]}}
{"id": "a6773ae1783d51e6", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: table\nTable: Table 5: Typical example of glacial lake attribute database\n\n| S.No. | Database Fields | Type | Format / Unit | Lake Attribute |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | ID No | String | Text | 0378A0115656 |\n| 2 | Toposheet 250K | String | Text | 78A |\n| 3 | Toposheet 50K | String | Text | 78A01 |\n| 4 | Latitude* | Float | Decimal Degree | 27.913 |\n| 5 | Longitude* | Float | Decimal Degree | 88.195 |\n| 6 | Basin | String | Text | Brahmaputra |\n| 7 | Subbasin | String | Text | Teesta |\n| 8 | River | String | Text | Teesta River |\n| 9 | Type (GL/WB) | String | Text | Glacial Lake |\n| 10 | Name | String | Text | South Lhonak |\n| 11 | Glacial Lake Type | String | Text | M(e): End-moraine Dammed Lake |\n| 12 | Surface Area | Float | ha | 128.13 |\n| 13 | Length | Float | Km | 2.45 |\n| 14 | Mean Width | Float | Km | 0.66 |\n| 15 | Elevation | Integer | m (amsl) | 5,194 |\n| 16 | Aspect | String | Text | NE |\n| 17 | Source of Database | String | Text | RS-2 LISS-IV |\n| 18 | Date of Pass | Date | DDMMYYYY | 30122016 |\n| 19 | Source of Elevation | String | Text | Cartosat DEM |\n| 20 | Region | String | Text | India |\n| 21 | State | String | Text | Sikkim |\n| 22 | District | String | Text | North Sikkim |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "table", "table_caption": "Table 5: Typical example of glacial lake attribute database", "columns": ["S.No.", "Database Fields", "Type", "Format / Unit", "Lake Attribute"], "table_row_start": 1, "table_row_end": 22, "line_start": 297, "line_end": 320, "token_count_estimate": 595, "basins": ["Brahmaputra"], "subbasins": ["Teesta"], "countries": ["India"], "lake_ids": ["0378A0115656", "30122016", "78A01"]}}
{"id": "5b130609c73b9131", "text": "Document: output vertex chunked\nSection: 4. METHODOLOGY > 4.3 Lake Attribute Information\nType: text\n\n\\* Latitude, longitude, and elevation has been taken at the centroid of the lake\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "4. METHODOLOGY > 4.3 Lake Attribute Information", "section_headings": ["4. METHODOLOGY", "4.3 Lake Attribute Information"], "chunk_type": "text", "line_start": 321, "line_end": 328, "token_count_estimate": 63, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a1ce9e7170b79974", "text": "Document: output vertex chunked\nSection: 5. RESULTS\nType: text\n\nThe mapped glacial lakes are analyzed for their distribution in terms of area, type, and elevation, at basin, subbasin, administrative and transboundary level. Area of mapped glacial lakes is ranging from a minimum of 0.25 ha to a maximum of 2,658.49 ha. Details of glacial lakes of size ≥ 5 ha inventoried for the Brahmaputra River basin is given in Annexure-II. The results are discussed in subsequent sections:", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS", "section_headings": ["5. RESULTS"], "chunk_type": "text", "line_start": 330, "line_end": 332, "token_count_estimate": 125, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1a88f9b7ab5e27a3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics\nType: text\n\n**Area range-wise Distribution**\n\nA total of 18,001 glacial lakes (≥ 0.25 ha) were identified and mapped using RS-2 LISS-IV images for the entire Brahmaputra River basin, with a total lake water spread area of 92,990.74 ha. Table 6 and Figure 7 shows the area range-wise distribution of glacial lakes for the entire basin. About 14,499 (80.55%) lakes are with < 5 ha lake area contributing to 21.92% of total lake area. The remaining lakes with > 5 ha in size are 3,502 (19.45%) contributing to 78.08% of total lake area in the basin. Details of lakes > 50 ha is given in Annexure-III.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics"], "chunk_type": "text", "line_start": 334, "line_end": 340, "token_count_estimate": 190, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6739eb08c9ae479d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics\nType: table\nTable: Table 6: Area range-wise distribution of Glacial Lakes (GL) in Brahmaputra River basin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 3,627 | 1,294.83 | 1.39 |\n| 2 | 0.5 - 1 | 3,856 | 2,771.84 | 2.98 |\n| 3 | 1 - 5 | 7,016 | 16,315.71 | 17.55 |\n| 4 | 5 - 10 | 1,749 | 12,348.79 | 13.28 |\n| 5 | 10 - 50 | 1,546 | 30,040.25 | 32.31 |\n| 6 | > 50 | 207 | 30,219.32 | 32.50 |\n| | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics"], "chunk_type": "table", "table_caption": "Table 6: Area range-wise distribution of Glacial Lakes (GL) in Brahmaputra River basin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 341, "line_end": 349, "token_count_estimate": 292, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "18eac480237cdf7f", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics"], "chunk_type": "text", "line_start": 350, "line_end": 354, "token_count_estimate": 42, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3dec101821562d8", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the entire Brahmaputra River basin is given in Table 7 and Figure 8. Out of 10 types of lakes described above, all types of lakes are present in the basin. Out of 10 types of glacial lakes, Other Glacial Erosion lakes are found to be the maximum with 11,846 (65.81%) occupying a total lake extent of 48,368.91 ha at 52.01% in the basin. Two other types of lake, namely, Other Moraine Dammed and Other Glacial lakes are 3,019 (16.77%) and 1,481 (8.23%), extend over an area of 9,457.52 ha (10.17%) and 12,049.76 ha (12.96%) respectively.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 356, "line_end": 360, "token_count_estimate": 200, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2eb20acfa46dbb92", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 7: Type-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 391 | 10,620.51 | 11.42 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 38 | 259.67 | 0.28 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 2 | 0.93 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 3,019 | 9,457.52 | 10.17 |\n| 5 | I(s) | Supra-glacial Lake | 272 | 263.13 | 0.28 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 2 | 2.50 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 943 | 8,521.90 | 9.16 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 7 | 3,445.91 | 3.71 |\n| 9 | E(o) | Other Glacial Erosion Lake | 11,846 | 48,368.91 | 52.01 |\n| 10 | O | Other Glacial Lake | 1,481 | 12,049.76 | 12.96 |\n| | | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 7: Type-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 361, "line_end": 373, "token_count_estimate": 484, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1556dd276c869be8", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 8: Type-wise distribution of GL in Brahmaputra River basin\n\n**Figure 8: Type-wise distribution of GL in Brahmaputra River basin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 8: Type-wise distribution of GL in Brahmaputra River basin", "line_start": 375, "line_end": 375, "token_count_estimate": 71, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af9c9f070bade796", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 8 and Figure 9. The lakes with < 5 ha in size (80.55%) are dominant with Other Glacial Erosion lake type (67.58%) followed by Other Moraine Dammed (18.02%) and Other Glacial lake (8.66%). The lakes with > 5 ha (19.45%) are dominated by Other Glacial Erosion lakes (58.45%) followed by Cirque Erosion lake (14.85%) and Other Moraine Dammed lake (11.62%). All types of Moraine-dammed glacial lakes, which constitute about 18.99% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 376, "line_end": 387, "token_count_estimate": 209, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "457cad73a87677b0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution\nType: table\nTable: Table 8: Area range-wise vs. Type-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Lake Area Range (ha) | Types of Glacial Lake | | | | | | | | | | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | | **M(e)** | **M(l)** | **M(lg)** | **M(o)** | **I(s)** | **I(d)** | **E(c)** | **E(v)** | **E(o)** | **O** | |\n| 1 | 0.25 - 0.5 | 6 | 6 | 1 | 718 | 153 | 0 | 13 | 0 | 2,324 | 406 | 3,627 |\n| 2 | 0.5 - 1 | 12 | 6 | 1 | 743 | 75 | 1 | 46 | 0 | 2,636 | 336 | 3,856 |\n| 3 | 1 - 5 | 93 | 17 | 0 | 1,151 | 38 | 1 | 364 | 0 | 4,839 | 513 | 7,016 |\n| 4 | 5 - 10 | 78 | 3 | 0 | 236 | 4 | 0 | 250 | 0 | 1,092 | 86 | 1,749 |\n| 5 | 10 - 50 | 152 | 5 | 0 | 156 | 2 | 0 | 262 | 0 | 868 | 101 | 1,546 |\n| 6 | > 50 | 50 | 1 | 0 | 15 | 0 | 0 | 8 | 7 | 87 | 39 | 207 |\n| | **Total** | **391** | **38** | **2** | **3,019** | **272** | **2** | **943** | **7** | **11,846** | **1,481** | **18,001** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 8: Area range-wise vs. Type-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Types of Glacial Lake", "", "", "", "", "", "", "", "", "", "Total"], "table_row_start": 1, "table_row_end": 8, "line_start": 388, "line_end": 397, "token_count_estimate": 565, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d633c25c6961c2ca", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 398, "line_end": 403, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "670557d929ddbd5f", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nElevation ranges over the entire study area has been classified into four different categories viz., low altitude (up to 3,000 m), medium altitude (3,001 - 4,000 m), high altitude (4,001 - 5,000 m), and very high altitude (> 5,000 m). Table 9 and Figure 10 shows the distribution of the glacial lakes in the Brahmaputra basin as per elevation range-wise. Majority of glacial lakes are situated above 4,000 m elevation i.e. 16,802 (93.34%) with total lake area of 81,290.89 ha (87.41%) and remaining 6.66% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 405, "line_end": 409, "token_count_estimate": 189, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4a041f6dd8ad165b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 9: Elevation range-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :---: | :---: | :---: |\n| 1 | up to 3,000 | 11 | 152.21 | 0.16 |\n| 2 | 3,001 - 4,000 | 1,188 | 11,547.28 | 12.42 |\n| 3 | 4,001 - 5,000 | 9,670 | 52,525.25 | 56.48 |\n| 4 | > 5,000 | 7,132 | 28,765.99 | 30.93 |\n| | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 9: Elevation range-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 410, "line_end": 416, "token_count_estimate": 256, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0d1c5dc1f8a54ea", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\n20 | National Remote Sensing Centre, ISRO, Hyderabad\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 10 and Figure 11. It is noted that, about 39.62% of glacial lakes (7,132) are situated in very high altitude range i.e. > 5,000 m amsl, which also constitutes total lake area within that range i.e. 30.93%. However, very few glacial lakes (11) lies below 3,000 m amsl, has maximum of its lakes with 10 - 50 ha lake area range. Figure 11 shows that maximum of lakes lying in very high altitude range is of size ranging 1 - 5 ha (i.e. 2,728), followed by lakes in high altitude range within in 1 - 5 ha (i.e. 3,772).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 417, "line_end": 428, "token_count_estimate": 254, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb6fd72ba72c2012", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 10: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000 - No. of lakes | Elevation Range (m) up to 3,000 - Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000 - No. of lakes | Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000 - No. of lakes | Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) > 5,000 - No. of lakes | Elevation Range (m) > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 98 | 36.60 | 1,824 | 655.23 | 1,705 | 603.00 | 3,627 | 1,294.83 |\n| 2 | 0.5 - 1 | 1 | 0.64 | 124 | 90.74 | 1,966 | 1,417.19 | 1,765 | 1,263.30 | 3,856 | 2,771.84 |\n| 3 | 1 - 5 | 3 | 10.07 | 513 | 1,299.91 | 3,772 | 8,855.14 | 2,728 | 6,150.52 | 7,016 | 16,315.71 |\n| 4 | 5 - 10 | 1 | 5.77 | 219 | 1,576.08 | 1,035 | 7,306.45 | 494 | 3,460.51 | 1,749 | 12,348.79 |\n| 5 | 10 - 50 | 6 | 135.75 | 209 | 4,288.89 | 952 | 18,351.30 | 379 | 7,264.24 | 1,546 | 30,040.25 |\n| 6 | > 50 | 0 | 0.00 | 25 | 4,255.00 | 121 | 15,939.70 | 61 | 10,024.36 | 207 | 30,219.32 |\n| Total | | 11 | 152.21 | 1,188 | 11,547.28 | 9,670 | 52,525.25 | 7,132 | 28,765.99 | 18,001 | 92,990.74 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 10: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000 - No. of lakes", "Elevation Range (m) up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000 - No. of lakes", "Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000 - No. of lakes", "Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) > 5,000 - No. of lakes", "Elevation Range (m) > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 429, "line_end": 437, "token_count_estimate": 683, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ab8cbac20ea3321", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 11: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin\n\n**Figure 11: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 11: Area range-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin", "line_start": 439, "line_end": 439, "token_count_estimate": 89, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "731976cfd07eed73", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 440, "line_end": 445, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6538fb3e11f54294", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per type-wise vs. elevation range-wise, given in Table 11 and Figure 12. The dominant lake type in the basin i.e., Other Glacial Erosion lakes (65.81%) are predominantly located in the elevation range of 4,001 - 5,000 m (60.20%). The two other dominant lake types, namely, Other Moraine Dammed and Other Glacial lakes are mostly distributed in both 4,001 - 5,000 m and > 5,000 m elevation ranges. 72.98% of Moraine-dammed glacial lakes, which constitute 13.98% of the total lakes, lies in the very high altitude range of > 5,000 m amsl. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 13.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 447, "line_end": 451, "token_count_estimate": 223, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "34a1cf37e2c39b70", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 11: Type-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin\n\n| S.No. | Elevation Range (m) | Types of Glacial Lake   M(e) | Types of Glacial Lake   M(l) | Types of Glacial Lake   M(lg) | Types of Glacial Lake   M(o) | Types of Glacial Lake   I(s) | Types of Glacial Lake   I(d) | Types of Glacial Lake   E(c) | Types of Glacial Lake   E(v) | Types of Glacial Lake   E(o) | Types of Glacial Lake   O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 4 | 3 | 11 |\n| 2 | 3,001 - 4,000 | 11 | 0 | 0 | 26 | 16 | 0 | 158 | 2 | 869 | 106 | 1,188 |\n| 3 | 4,001 - 5,000 | 147 | 25 | 1 | 718 | 159 | 0 | 687 | 4 | 7,131 | 798 | 9,670 |\n| 4 | > 5,000 | 233 | 13 | 1 | 2,271 | 97 | 2 | 98 | 1 | 3,842 | 574 | 7,132 |\n| | **Total** | **391** | **38** | **2** | **3,019** | **272** | **2** | **943** | **7** | **11,846** | **1,481** | **18,001** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 11: Type-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin", "columns": ["S.No.", "Elevation Range (m)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 452, "line_end": 458, "token_count_estimate": 503, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff9e425fba987645", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 459, "line_end": 463, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d0ae5b4497068569", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 13: Elevation range-Type-wise spatial distribution of GL in Brahmaputra River basin\n\nFigure 13: Elevation range-Type-wise spatial distribution of GL in Brahmaputra River basin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 13: Elevation range-Type-wise spatial distribution of GL in Brahmaputra River basin", "line_start": 464, "line_end": 464, "token_count_estimate": 87, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5b95cafe6bc6c49f", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.1 Brahmaputra Basin Level Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.1 Brahmaputra Basin Level Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 465, "line_end": 470, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "503412e73d7c5461", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nThe Amo Chu subbasin of the Brahmaputra River basin is the eleventh largest subbasin amongst all, covering a total area of 9,829 Km² i.e. 2.46% of the total basin area (Figure 14). The Amo Chu as known as Torsa River originates in Tibet with two main tributaries, the Torsa river, and the Wong Chu, all flowing south. It drains about 16,000 sq.km of Tibetan territory before entering Bhutan, where it flows southeast for about 170 km and then enters the Indian flood plains of West Bengal near Phuntsholing (a border town of Bhutan). A total of 513 glacial lakes has been mapped, covering a total area of 1,565.46 ha i.e. 0.15% of the total area of the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 474, "line_end": 476, "token_count_estimate": 224, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu"], "countries": ["Bhutan"], "lake_ids": []}}
{"id": "3719960c5e1006cd", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: figure\nFigure: Figure 14: Location map of the Amo Chu subbasin\n\n**Figure 14: Location map of the Amo Chu subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 14: Location map of the Amo Chu subbasin", "line_start": 477, "line_end": 477, "token_count_estimate": 68, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "462c6b72620533b8", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Amo Chu subbasin, glacial lakes has been distributed in all 6 different classes of area ranges viz., 0.25 - 0.5 ha, 0.5 - 1 ha, 1 - 5 ha, 5 - 10 ha, 10 - 50 ha and > 50 ha. Table 12 and Figure 15 shows the area-wise distribution of glacial lakes for the Amo Chu subbasin. About 437 (85.19%) lakes are with < 5 ha lake area contributing to 36.78% of total lake area. The remaining lakes with > 5 ha in size are only 76 (14.81%) but contributing to 63.22% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 478, "line_end": 489, "token_count_estimate": 206, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "4b4da3345fcf620d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: table\nTable: Table 12: Area range-wise distribution of GL in Amo Chu subbasin\n\n| S.No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 111 | 37.82 | 2.42 |\n| 2 | 0.5 - 1 | 130 | 94.31 | 6.02 |\n| 3 | 1 - 5 | 196 | 443.69 | 28.34 |\n| 4 | 5 - 10 | 43 | 297.44 | 19.00 |\n| 5 | 10 - 50 | 32 | 639.52 | 40.85 |\n| 6 | > 50 | 1 | 52.68 | 3.37 |\n| | **Total** | **513** | **1,565.46** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "table", "table_caption": "Table 12: Area range-wise distribution of GL in Amo Chu subbasin", "columns": ["S.No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 490, "line_end": 498, "token_count_estimate": 277, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "fcc854e2cc416964", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Amo Chu subbasin is given in Table 13 and Figure 16. Out of 10, 7 types of glacial lakes are present in the Amo Chu subbasin, where Other Glacial Erosion lakes are found to be the maximum with 412 (80.31%) occupying a total lake extent of 943.57 ha at 60.27% in the subbasin. Cirque Erosion Lake are second majority of lakes i.e. 61 (11.89%) and extend over an area of 467.50 ha (29.86%).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 499, "line_end": 509, "token_count_estimate": 187, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "b3e563081bda44f3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: table\nTable: Table 13: Type-wise distribution of GL in Amo Chu subbasin\n\n| S.No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :---: | :--- | :---: | :---: | :---: |\n| 1 | M(e) | End-moraine Dammed Lake | 2 | 65.08 | 4.16 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 4 | 8.37 | 0.53 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 27 | 75.33 | 4.81 |\n| 5 | I(s) | Supra-glacial Lake | 5 | 3.31 | 0.21 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 61 | 467.50 | 29.86 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 412 | 943.57 | 60.27 |\n| 10 | O | Other Glacial Lake | 2 | 2.30 | 0.15 |\n| | | **Total** | **513** | **1,565.46** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "table", "table_caption": "Table 13: Type-wise distribution of GL in Amo Chu subbasin", "columns": ["S.No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 510, "line_end": 522, "token_count_estimate": 468, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "e54ac6b9367a6a9e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 14 and Figure 17. The lakes with < 5 ha in size (85.19%) are dominated by Other Glacial Erosion lake (80.31%) followed by Cirque Erosion lake type (11.89%). Lakes with > 5 ha (14.81%) are dominated by Other Glacial Erosion lake (48.68%) followed by Cirque Erosion lake type (44.74%). All types of Moraine-dammed lakes, which constitute about 6.43%, are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 523, "line_end": 534, "token_count_estimate": 191, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "5051e4b70870d1bd", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: table\nTable: Table 14: Area range-wise vs. Type-wise distribution of GL in Amo Chu subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 106 | 0 | 111 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 6 | 2 | 0 | 5 | 0 | 116 | 0 | 130 |\n| 3 | 1 - 5 | 0 | 3 | 0 | 15 | 1 | 0 | 22 | 0 | 153 | 2 | 196 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 19 | 0 | 23 | 0 | 43 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 2 | 0 | 0 | 15 | 0 | 14 | 0 | 32 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| **Total** | | **2** | **4** | **0** | **27** | **5** | **0** | **61** | **0** | **412** | **2** | **513** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "table", "table_caption": "Table 14: Area range-wise vs. Type-wise distribution of GL in Amo Chu subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 535, "line_end": 543, "token_count_estimate": 512, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "d3f03ca3ed57db93", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Amo Chu subbasin has been shown in Table 15 and Figure 18. Majority of glacial lakes are situated above 4,000 m elevation i.e. 496 (96.69%) with total lake area of 1,460.94 ha (93.31%) and remaining 3.31% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 544, "line_end": 555, "token_count_estimate": 147, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "a664419392d487a5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: table\nTable: Table 15: Elevation range-wise distribution of GL in Amo Chu subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :---: | :---: | :---: |\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 17 | 104.52 | 6.68 |\n| 3 | 4,001 - 5,000 | 462 | 1,350.92 | 86.28 |\n| 4 | > 5,000 | 34 | 110.02 | 7.03 |\n| | **Total** | **513** | **1,565.46** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "table", "table_caption": "Table 15: Elevation range-wise distribution of GL in Amo Chu subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 556, "line_end": 562, "token_count_estimate": 244, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "819882b5babe81fc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 16 and Figure 19. It is noted that, 96.69% of glacial lakes (496) are situated above high altitude range i.e. > 4,000 m amsl, which constitutes 93.32% of total lake area. However, few glacial lakes (17) lies below 4,001 m, which are within 0.25 - 50 ha lake area range. Maximum of lakes lying in very high altitude range is of size ranging 1 - 5 ha (i.e. 196), followed by lakes of size 0.5 - 1 ha (i.e. 130).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 563, "line_end": 574, "token_count_estimate": 214, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "eedf143458cae7a6", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: table\nTable: Table 16: Area range-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000 - No. of lakes | Elevation Range (m) up to 3,000 - Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000 - No. of lakes | Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000 - No. of lakes | Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) > 5,000 - No. of lakes | Elevation Range (m) > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 1 | 0.35 | 99 | 33.84 | 11 | 3.63 | 111 | 37.82 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 0 | 0.00 | 122 | 88.40 | 8 | 5.91 | 130 | 94.31 |\n| 3 | 1 - 5 | 0 | 0.00 | 9 | 25.18 | 175 | 393.48 | 12 | 25.04 | 196 | 443.69 |\n| 4 | 5 - 10 | 0 | 0.00 | 5 | 35.39 | 37 | 255.69 | 1 | 6.36 | 43 | 297.44 |\n| 5 | 10 - 50 | 0 | 0.00 | 2 | 43.60 | 29 | 579.51 | 1 | 16.40 | 32 | 639.52 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 1 | 52.68 | 1 | 52.68 |\n| Total | | 0 | 0.00 | 17 | 104.52 | 462 | 1,350.92 | 34 | 110.02 | 513 | 1,565.46 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "table", "table_caption": "Table 16: Area range-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000 - No. of lakes", "Elevation Range (m) up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000 - No. of lakes", "Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000 - No. of lakes", "Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) > 5,000 - No. of lakes", "Elevation Range (m) > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 575, "line_end": 583, "token_count_estimate": 612, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "1854ddd3014ae0e4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: figure\nFigure: Figure 19: Area range-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin\n\n**Figure 19: Area range-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 19: Area range-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin", "line_start": 585, "line_end": 585, "token_count_estimate": 90, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "4e6bb7baf346d2be", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 17 and Figure 20. The dominant lake types in the basin i.e., Other Glacial Erosion lakes (80.31%) are predominantly located in the elevation range of > 4,000 m (96.69%). The other dominant lake type, namely, Cirque Erosion lakes are also distributed in > 4,000 m elevation range which constitutes 11.89% of the total lakes. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 21.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 586, "line_end": 596, "token_count_estimate": 198, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "1387682e3934c3ba", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: table\nTable: Table 17: Type-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 14 | 0 | 17 |\n| 3 | 4,001 - 5,000 | 1 | 4 | 0 | 17 | 3 | 0 | 55 | 0 | 380 | 2 | 462 |\n| 4 | > 5,000 | 1 | 0 | 0 | 10 | 2 | 0 | 3 | 0 | 18 | 0 | 34 |\n| | **Total** | **2** | **4** | **0** | **27** | **5** | **0** | **61** | **0** | **412** | **2** | **513** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "table", "table_caption": "Table 17: Type-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 597, "line_end": 603, "token_count_estimate": 435, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "17066b50a243a2c0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: figure\nFigure: Figure 20: Type-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin\n\n**Figure 20: Type-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 20: Type-wise vs. Elevation range-wise distribution of GL in Amo Chu subbasin", "line_start": 605, "line_end": 605, "token_count_estimate": 88, "basins": [], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "0b93187c761f6741", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.1 Amo Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.1 Amo Chu Subbasin"], "chunk_type": "text", "line_start": 606, "line_end": 615, "token_count_estimate": 68, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu"], "countries": [], "lake_ids": []}}
{"id": "2cdf4f2719689708", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin\nType: text\n\nThe Dibang subbasin is the Ninth Smallest subbasin of the Brahmaputra basin covering a total area of 12,238 Km² i.e. 3.06% of the total basin area (Figure 22). The Sisar, Mathun, Tangon, Dri, Ithun and Emra are the major tributaries of the Dibang. It is an upstream tributary river of the Brahmaputra that originates and flows through the Mishmi Hills from the state of Arunachal Pradesh and Nyingchi Prefecture in the Tibet Autonomous Region. A total of 772 glacial lakes has been mapped, covering a total area of 6,566.10 ha i.e. 0.53% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Dibang subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 18 and Figure 23 shows the area range-wise distribution of glacial lakes for the Dibang subbasin. About 440 (56.99%) lakes are with < 5 ha lake area contributing to 14.78% of total lake area. The remaining lakes with > 5 ha in size are only 332 (43.01%) contributing to 85.22% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.2 Dibang Subbasin"], "chunk_type": "text", "line_start": 617, "line_end": 630, "token_count_estimate": 340, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "914987dae47d1620", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin\nType: table\nTable: Table 18: Area range-wise distribution of GL in Dibang subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :---: | :---: | :---: |\n| 1 | 0.25 - 0.5 | 24 | 9.90 | 0.15 |\n| 2 | 0.5 - 1 | 82 | 60.95 | 0.93 |\n| 3 | 1 - 5 | 334 | 899.70 | 13.70 |\n| 4 | 5 - 10 | 144 | 1,030.44 | 15.69 |\n| 5 | 10 - 50 | 175 | 3,532.30 | 53.80 |\n| 6 | > 50 | 13 | 1,032.81 | 15.73 |\n| | **Total** | **772** | **6,566.10** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.2 Dibang Subbasin"], "chunk_type": "table", "table_caption": "Table 18: Area range-wise distribution of GL in Dibang subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 631, "line_end": 639, "token_count_estimate": 281, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "9c53c62eafded7ba", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin\nType: text\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Dibang subbasin is given in Table 19 and Figure 24. Out of 10 types of glacial lakes, 5 types of lake are present in the Dibang subbasin, where Other Glacial Erosion lakes are found to be the maximum with 601 (77.85%) occupying a total lake extent of 4,319.31 ha at 65.78% in the subbasin. After that, Other Cirque Erosion lakes are in majority with 141 (18.26%) and extend over a total area of 1,750.15 ha (26.65%). All Moraine Dammed lakes covers about 0.49% of entire area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.2 Dibang Subbasin"], "chunk_type": "text", "line_start": 640, "line_end": 650, "token_count_estimate": 215, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "0885864ecc6ecfb5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin\nType: table\nTable: Table 19: Type-wise distribution of GL in Dibang subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 1 | 4.29 | 0.07 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 0 | 0.00 | 0.00 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 1 | 27.72 | 0.42 |\n| 5 | I(s) | Supra-glacial Lake | 0 | 0.00 | 0.00 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 141 | 1,750.15 | 26.65 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 601 | 4,319.31 | 65.78 |\n| 10 | O | Other Glacial Lake | 28 | 464.63 | 7.08 |\n| | | **Total** | **772** | **6,566.10** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.2 Dibang Subbasin"], "chunk_type": "table", "table_caption": "Table 19: Type-wise distribution of GL in Dibang subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 651, "line_end": 663, "token_count_estimate": 464, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "6be4f9280af2188b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.2 Dibang Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.2 Dibang Subbasin"], "chunk_type": "text", "line_start": 664, "line_end": 669, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "e918d800933e1acc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 20 and Figure 25. The lakes with < 5 ha in size (56.99%) are dominant with Other Glacial Erosion Lakes (87.50%) and Cirque Erosion lakes (8.86%). Lakes with > 5 ha (43.01%) are dominated by Other Glacial Erosion Lakes (65.06%). Out of 772, 742 are all types of Glacier Erosion lakes contributes to about 96.11%.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 671, "line_end": 675, "token_count_estimate": 148, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ce94d7c32ae89f3b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 20: Area range-wise vs. Type-wise distribution of GL in Dibang subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(c) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 20 | 3 | 24 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 80 | 1 | 82 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 285 | 11 | 334 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 0 | 103 | 2 | 144 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 62 | 0 | 103 | 9 | 175 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10 | 2 | 13 |\n| | **Total** | **1** | **0** | **0** | **1** | **0** | **0** | **141** | **0** | **601** | **28** | **772** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 20: Area range-wise vs. Type-wise distribution of GL in Dibang subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(c)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 676, "line_end": 684, "token_count_estimate": 511, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "817b9751fdbd19a0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 25: Area range-wise vs. Type-wise distribution of GL in Dibang subbasin\n\n**Figure 25: Area range-wise vs. Type-wise distribution of GL in Dibang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 25: Area range-wise vs. Type-wise distribution of GL in Dibang subbasin", "line_start": 686, "line_end": 686, "token_count_estimate": 84, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "9bc4648808fec4d4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Dibang subbasin has been shown in Table 21 and Figure 26. Majority of glacial lakes are situated above 3,000 m elevation range i.e. 770 (99.74%) with total lake area of 6,538.64 ha (99.58%) and remaining only 0.26% glacial lakes are below 3,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 687, "line_end": 697, "token_count_estimate": 148, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "f5ca9f5a55181801", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 21: Elevation range-wise distribution of GL in Dibang subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 2 | 27.46 | 0.42 |\n| 2 | 3,001 - 4,000 | 405 | 3,586.02 | 54.61 |\n| 3 | 4,001 - 5,000 | 365 | 2,952.62 | 44.97 |\n| 4 | > 5,000 | 0 | 0.00 | 0.00 |\n| | **Total** | **772** | **6,566.10** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 21: Elevation range-wise distribution of GL in Dibang subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 698, "line_end": 704, "token_count_estimate": 231, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "1a955885fbeec176", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 705, "line_end": 710, "token_count_estimate": 51, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f5bf355b5e6932fc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 22 and Figure 27. It is noted that, 52.46% of glacial lakes (405) are situated in altitude range of 3,001 - 4,000 m amsl, which constitutes a total lake area of 54.61%. However, majority of glacial lakes (334) of size < 5 ha lies in the range of 3,001 - 4,000 m. It has been further noticed that, no lakes are lying at very high altitude of > 5,000 m.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 712, "line_end": 716, "token_count_estimate": 165, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d2584cf073a648ce", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Dibang subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of Lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of Lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of Lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of Lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of Lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 13 | 5.34 | 11 | 4.56 | 0 | 0.00 | 24 | 9.90 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 34 | 25.03 | 48 | 35.92 | 0 | 0.00 | 82 | 60.95 |\n| 3 | 1 - 5 | 0 | 0.00 | 178 | 485.06 | 156 | 414.64 | 0 | 0.00 | 334 | 899.70 |\n| 4 | 5 - 10 | 0 | 0.00 | 81 | 594.52 | 63 | 435.92 | 0 | 0.00 | 144 | 1,030.44 |\n| 5 | 10 - 50 | 2 | 27.46 | 90 | 1,778.56 | 83 | 1,726.28 | 0 | 0.00 | 175 | 3,532.30 |\n| 6 | > 50 | 0 | 0.00 | 9 | 698.00 | 4 | 335.00 | 0 | 0.00 | 13 | 1,032.81 |\n| | **Total** | **2** | **27.46** | **405** | **3,586.02** | **365** | **2,952.62** | **0** | **0.00** | **772** | **6,566.10** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 22: Area range-wise vs. Elevation range-wise distribution of GL in Dibang subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of Lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of Lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of Lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of Lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of Lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 717, "line_end": 725, "token_count_estimate": 646, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "22c5a669a2b6c045", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Dibang subbasin\n\n**Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Dibang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 27: Area range-wise vs. Elevation range-wise distribution of GL in Dibang subbasin", "line_start": 727, "line_end": 727, "token_count_estimate": 90, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "1f5ad82df9a8b506", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 23 and Figure 28. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (601) with 77.85% are predominantly located in the elevation range of 3,001 - 4,000 m (53.58%). The other dominant lake type, Cirque Erosion Lakes are distributed predominantly in 3,001 - 5,000 m and 4,001 - 5,000 m elevation ranges with 40.43% and 59.57% respectively. Only 2 lakes of total Other Moraine-dammed Lakes lie above 4,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 29.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 728, "line_end": 738, "token_count_estimate": 239, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "51c27614c7be9328", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 23: Type-wise vs. Elevation range-wise distribution of GL in Dibang subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake M(e) | Types of Glacial Lake M(l) | Types of Glacial Lake M(lg) | Types of Glacial Lake M(o) | Types of Glacial Lake I(s) | Types of Glacial Lake I(d) | Types of Glacial Lake E(c) | Types of Glacial Lake E(v) | Types of Glacial Lake E(o) | Types of Glacial Lake O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 322 | 26 | 405 |\n| 3 | 4,001 - 5,000 | 1 | 0 | 0 | 1 | 0 | 0 | 84 | 0 | 279 | 0 | 365 |\n| 4 | > 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **1** | **0** | **0** | **1** | **0** | **0** | **141** | **0** | **601** | **28** | **772** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 23: Type-wise vs. Elevation range-wise distribution of GL in Dibang subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 739, "line_end": 745, "token_count_estimate": 506, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "f9e9f4eaa102ba9b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Dibang subbasin\n\n**Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Dibang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 28: Type-wise vs. Elevation range-wise distribution of GL in Dibang subbasin", "line_start": 747, "line_end": 747, "token_count_estimate": 88, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "f526c1c6a0b009cb", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 748, "line_end": 753, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c37f5329de9b3615", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 29: Elevation range-Type-wise spatial distribution of GL in Dibang subbasin\n\n**Figure 29: Elevation range-Type-wise spatial distribution of GL in Dibang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 29: Elevation range-Type-wise spatial distribution of GL in Dibang subbasin", "line_start": 754, "line_end": 754, "token_count_estimate": 88, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "e941de04f4ac2412", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 755, "line_end": 759, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8a17c97ac7ee875", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Dihang Subbasin\nType: text\n\nThe Dihang subbasin is the Seventh largest subbasin of the Brahmaputra River basin covering a total area of 22,158 Km² i.e. 5.54% of the total basin area (Figure 30). It is formed by the confluence of two small rivers, Namphuk and Namchik, which originate in the Patkai hills, part of the Eastern Himalayan ranges in Arunachal Pradesh. Shyom River is one of the main tributary of Dihang River. A total of 433 glacial lakes has been mapped, covering a total area of 2,923.66 ha i.e. 0.13% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.3 Dihang Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.3 Dihang Subbasin"], "chunk_type": "text", "line_start": 761, "line_end": 767, "token_count_estimate": 206, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "a71a8121688f021a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nIn Dihang subbasin, glacial lakes have been distributed in all 6 classes of area ranges. Table 24 and Figure 31 shows the area range-wise distribution of glacial lakes for the Dihang subbasin. About 275 (63.51%) lakes are with < 5 ha lake area contributing to 17.44% of total lake area. The remaining lakes with > 5 ha in size are only 158 (36.49%) but contributing to 82.56% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 769, "line_end": 773, "token_count_estimate": 143, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "5b84be6e9fa96bab", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 24: Area range-wise distribution of GL in Dihang subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 39 | 13.89 | 0.48 |\n| 2 | 0.5 - 1 | 60 | 43.21 | 1.48 |\n| 3 | 1 - 5 | 176 | 452.83 | 15.49 |\n| 4 | 5 - 10 | 78 | 543.84 | 18.60 |\n| 5 | 10 - 50 | 77 | 1,602.17 | 54.80 |\n| 6 | > 50 | 3 | 267.72 | 9.16 |\n| | **Total** | **433** | **2,923.66** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 24: Area range-wise distribution of GL in Dihang subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 774, "line_end": 782, "token_count_estimate": 273, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "f045c2d40f657f65", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Dihang subbasin is given in Table 25 and Figure 32. Out of 10 types of glacial lakes, 7 types of lake are present in the Dihang subbasin, where Other Glacial Erosion lakes are found to be the maximum with 301 (69.52%) occupying a total lake extent of 1,696.49 ha at 58.03% in the subbasin. After that, Cirque Erosion Lake are in majority with 108 (24.94%) extend over a total lake area of 922.66 ha (31.56%).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 783, "line_end": 793, "token_count_estimate": 182, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "1bec4b87d83f6de1", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 25: Type-wise distribution of GL in Dihang subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :--- | :---: | :---: | :---: |\n| 1 | M(e) | End-moraine Dammed Lake | 1 | 6.82 | 0.23 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 0 | 0.00 | 0.00 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 18 | 213.54 | 7.30 |\n| 5 | I(s) | Supra-glacial Lake | 3 | 1.31 | 0.04 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 108 | 922.66 | 31.56 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 1 | 70.17 | 2.40 |\n| 9 | E(o) | Other Glacial Erosion Lake | 301 | 1,696.49 | 58.03 |\n| 10 | O | Other Glacial Lake | 1 | 12.67 | 0.43 |\n| | | **Total** | **433** | **2,923.66** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 25: Type-wise distribution of GL in Dihang subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 794, "line_end": 806, "token_count_estimate": 464, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "5c415b512fb4bd87", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 807, "line_end": 812, "token_count_estimate": 49, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d15a55cc8ca9bf6e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 26 and Figure 33. The lakes with < 5 ha in size (63.51%) are dominant with Other Glacial Erosion (78.91%) and Cirque Erosion lake (16.36%). Lakes with > 5 ha (36.49%) are dominated by Other Glacial Erosion lakes (53.16%). All types of Moraine Dammed lakes, which constitute about 4.39% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 814, "line_end": 818, "token_count_estimate": 146, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1207f2eec214192c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 26: Area range-wise vs. Type-wise distribution of GL in Dihang subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 35 | 0 | 39 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 1 | 0 | 4 | 0 | 53 | 0 | 60 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 7 | 0 | 0 | 40 | 0 | 129 | 0 | 176 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 3 | 0 | 0 | 34 | 0 | 40 | 0 | 78 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 5 | 0 | 0 | 29 | 0 | 42 | 1 | 77 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 3 |\n| | **Total** | **1** | **0** | **0** | **18** | **3** | **0** | **108** | **1** | **301** | **1** | **433** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 26: Area range-wise vs. Type-wise distribution of GL in Dihang subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 819, "line_end": 827, "token_count_estimate": 483, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "4b6b71902203c516", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 33: Area range-wise vs. Type-wise distribution of GL in Dihang subbasin\n\n**Figure 33: Area range-wise vs. Type-wise distribution of GL in Dihang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 33: Area range-wise vs. Type-wise distribution of GL in Dihang subbasin", "line_start": 829, "line_end": 829, "token_count_estimate": 84, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "b2a56d085e3ca2c3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Dihang subbasin has been shown in Table 27 and Figure 34. Majority of glacial lakes are situated below 4,000 m elevation range i.e. 229 (52.89%) with total lake area of 1,640.37 ha (56.11%) and remaining 47.11% glacial lakes are above 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 830, "line_end": 840, "token_count_estimate": 148, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "22a30da44b2b5d68", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 27: Elevation range-wise distribution of GL in Dihang subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 2 | 26.48 | 0.91 |\n| 2 | 3,001 - 4,000 | 227 | 1,613.89 | 55.20 |\n| 3 | 4,001 - 5,000 | 204 | 1,283.29 | 43.89 |\n| 4 | > 5,000 | 0 | 0.00 | 0.00 |\n| | **Total** | **433** | **2,923.66** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 27: Elevation range-wise distribution of GL in Dihang subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 841, "line_end": 847, "token_count_estimate": 240, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "08e73ab9c4efc7d4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 34: Elevation range-wise distribution of GL in Dihang subbasin\n\n**Figure 34: Elevation range-wise distribution of GL in Dihang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 34: Elevation range-wise distribution of GL in Dihang subbasin", "line_start": 849, "line_end": 849, "token_count_estimate": 76, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "260438d02c864791", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 850, "line_end": 855, "token_count_estimate": 51, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "caec9eafa85fed2e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 28 and Figure 35. It is noted that, 47.11% of glacial lakes (204) and 52.42% of glacial lakes (227) are situated in the range of 4,001 – 5,000 m and 3,001 – 4,000 m amsl respectively, which also constitutes total lake area of 2,897.18 ha (99.09%). However, no glacial lakes lies above 5,000 m. Lakes in the elevation of range of 3,001 – 4,001 m and 4,001 – 5,000 m are well distributed in all the lake area ranges.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 857, "line_end": 861, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b414f50cee03bac", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Dihang subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 17 | 5.88 | 22 | 8.01 | 0 | 0.00 | 39 | 13.89 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 26 | 18.53 | 34 | 24.68 | 0 | 0.00 | 60 | 43.21 |\n| 3 | 1 - 5 | 0 | 0.00 | 95 | 246.22 | 81 | 206.61 | 0 | 0.00 | 176 | 452.83 |\n| 4 | 5 - 10 | 1 | 5.76 | 49 | 336.78 | 28 | 201.30 | 0 | 0.00 | 78 | 543.84 |\n| 5 | 10 - 50 | 1 | 20.72 | 38 | 842.51 | 38 | 738.94 | 0 | 0.00 | 77 | 1,602.17 |\n| 6 | > 50 | 0 | 0.00 | 2 | 164.00 | 1 | 104.00 | 0 | 0.00 | 3 | 267.72 |\n| **Total** | | **2** | **26.48** | **227** | **1,613.89** | **204** | **1,283.29** | **0** | **0.00** | **433** | **2,923.66** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 28: Area range-wise vs. Elevation range-wise distribution of GL in Dihang subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 862, "line_end": 870, "token_count_estimate": 662, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "9a3436dd79906632", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: figure\nFigure: Figure 35: Area range-wise vs. Elevation range-wise distribution of GL in Dihang subbasin\n\n**Figure 35: Area range-wise vs. Elevation range-wise distribution of GL in Dihang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 35: Area range-wise vs. Elevation range-wise distribution of GL in Dihang subbasin", "line_start": 872, "line_end": 872, "token_count_estimate": 90, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "f3a690698702cbc8", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 873, "line_end": 878, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bf9da86835a9c216", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 29 and Figure 36. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (69.51%) are predominantly located in the elevation range of 3,001 – 4,000 m (54.82%). Other Moraine Dammed are distributed predominantly in the elevation range of 4,001 – 5,000 m i.e. 94.44%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 37.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 880, "line_end": 884, "token_count_estimate": 171, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5c32ec9fdb1c02d7", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 29: Type-wise vs. Elevation range-wise distribution of GL in Dihang subbasin\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 1 | 3 | 0 | 56 | 1 | 165 | 1 | 227 |\n| 3 | 4,001 - 5,000 | 1 | 0 | 0 | 17 | 0 | 0 | 52 | 0 | 134 | 0 | 204 |\n| 4 | > 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | **Total** | **1** | **0** | **0** | **18** | **3** | **0** | **108** | **1** | **301** | **1** | **433** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 29: Type-wise vs. Elevation range-wise distribution of GL in Dihang subbasin", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 885, "line_end": 891, "token_count_estimate": 407, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "06a70ffe2db3f9a4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 36: Type-wise vs. Elevation range-wise distribution of GL in Dihang subbasin\n\n**Figure 36: Type-wise vs. Elevation range-wise distribution of GL in Dihang subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 36: Type-wise vs. Elevation range-wise distribution of GL in Dihang subbasin", "line_start": 893, "line_end": 893, "token_count_estimate": 88, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "fc9e7a38f14834b2", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 894, "line_end": 899, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7750f9e16fbd46de", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 37: Elevation range-Type-wise spatial distribution of GL in Dihang subbasin\n\nFigure 37: Elevation range-Type-wise spatial distribution of GL in Dihang subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 37: Elevation range-Type-wise spatial distribution of GL in Dihang subbasin", "line_start": 900, "line_end": 900, "token_count_estimate": 85, "basins": [], "subbasins": ["Dihang"], "countries": [], "lake_ids": []}}
{"id": "fba495d87c1ee6d8", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 901, "line_end": 905, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ebd49efa80ed051c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Jia Bharali Subbasin\nType: text\n\nThe Jia Bharali subbasin is the eighth largest subbasin of the Brahmaputra River basin covering a total area of 13,084 Km² i.e. 3.27% of the total basin area (Figure 38). Bhareli River and Bichom River are the two main tributaries. The Bhareli river originates in the hills of Arunachal Pradesh and flows through the heart of Tezpur before its confluence with the Brahmaputra River. A total of 234 glacial lakes has been mapped, covering a total area of 640.12 ha i.e. 0.04% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Jia Bharali subbasin, glacial lakes have been distributed in 5 classes of area ranges except > 50 ha range. Table 30 and Figure 39 shows the area range-wise distribution of glacial lakes for the Jia Bharali subbasin. About 200 (85.47%) lakes are with < 5 ha lake area contributing to 43.05% of total lake area. The remaining lakes with > 5 ha in size are only 34 (14.53%) contributing to 56.95% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Jia Bharali Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.4 Jia Bharali Subbasin"], "chunk_type": "text", "line_start": 907, "line_end": 919, "token_count_estimate": 325, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "5a87a16edd2b91a4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Jia Bharali Subbasin\nType: table\nTable: Table 30: Area range-wise distribution of GL in Jia Bharali subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 43 | 16.00 | 2.50 |\n| 2 | 0.5 - 1 | 63 | 43.82 | 6.85 |\n| 3 | 1 - 5 | 94 | 215.77 | 33.71 |\n| 4 | 5 - 10 | 24 | 168.51 | 26.32 |\n| 5 | 10 - 50 | 10 | 196.02 | 30.62 |\n| 6 | > 50 | 0 | 0.00 | 0.00 |\n| | **Total** | **234** | **640.12** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Jia Bharali Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.4 Jia Bharali Subbasin"], "chunk_type": "table", "table_caption": "Table 30: Area range-wise distribution of GL in Jia Bharali subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 920, "line_end": 928, "token_count_estimate": 273, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "d00576a9b027e4cb", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Jia Bharali Subbasin\nType: text\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 49\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.4 Jia Bharali Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.4 Jia Bharali Subbasin"], "chunk_type": "text", "line_start": 929, "line_end": 933, "token_count_estimate": 65, "basins": ["BRAHMAPUTRA"], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "d23e6e6799bf6521", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Jia Bharali subbasin is given in Table 31 and Figure 40. Out of 10 types of glacial lakes, 5 types of lake are present in the Jia Bharali subbasin, where Other Glacial Erosion Lakes are found to be the maximum with 192 (82.05%) occupying a total lake extent of 520.46 ha at 81.31% in the subbasin. After that, Other Moraine Dammed Lake and Cirque Erosion lakes are in majority with 26 (11.11%) and 13 (5.56%) and extend over a total lake area of 46.05 ha (7.19%) and 51.5 ha (8.05%) respectively.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 935, "line_end": 939, "token_count_estimate": 186, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "0f93df01790c821b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 31: Type-wise distribution of GL in Jia Bharali subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :--- | :---: | :---: | :---: |\n| 1 | M(e) | End-moraine Dammed Lake | 0 | 0.00 | 0.00 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 1 | 13.80 | 2.16 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 26 | 46.05 | 7.19 |\n| 5 | I(s) | Supra-glacial Lake | 0 | 0.00 | 0.00 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 13 | 51.50 | 8.05 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 192 | 520.46 | 81.31 |\n| 10 | O | Other Glacial Lake | 2 | 8.31 | 1.30 |\n| | | **Total** | **234** | **640.12** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 31: Type-wise distribution of GL in Jia Bharali subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 940, "line_end": 952, "token_count_estimate": 461, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "f0745bf9c0a646e3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 953, "line_end": 957, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "987257f7d145eed0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 32 and Figure 41. The lakes with < 5 ha in size (85.47%) are dominant with Other Glacial Erosion lakes (83%) and Other Moraine Dammed (11.50%). Lakes with > 5 ha (14.53%) are dominated by Other Glacial Erosion Lake (76.47%). All types of Moraine Dammed lakes, which constitute about 85.18% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 959, "line_end": 963, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1ebf3a26c760f4ef", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 32: Area range-wise vs. Type-wise distribution of GL in Jia Bharali subbasin\n\n| S. No. | Lake Area Range (ha) | Types of Glacial Lake | | | | | | | | | | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | | **M(e)** | **M(l)** | **M(lg)** | **M(o)** | **I(s)** | **I(d)** | **E(c)** | **E(v)** | **E(o)** | **O** | |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 37 | 0 | 43 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 10 | 0 | 0 | 1 | 0 | 51 | 1 | 63 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 8 | 0 | 0 | 8 | 0 | 78 | 0 | 94 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 18 | 1 | 24 |\n| 5 | 10 - 50 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 0 | 10 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| **Total** | | **0** | **1** | **0** | **26** | **0** | **0** | **13** | **0** | **192** | **2** | **234** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 32: Area range-wise vs. Type-wise distribution of GL in Jia Bharali subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Types of Glacial Lake", "", "", "", "", "", "", "", "", "", "Total"], "table_row_start": 1, "table_row_end": 8, "line_start": 964, "line_end": 973, "token_count_estimate": 542, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "01b39961e427db28", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Jai Bharali subbasin has been shown in Table 33 and Figure 42. Majority of glacial lakes are situated in 4,001 - 5,000 m elevation range i.e. 195 (83.33%) with total lake area of 500.45 ha (78.18%) and remaining 23 glacial lakes constituting area of 44.00 ha (6.87%) are in > 5000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 974, "line_end": 985, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff7a3dff310684be", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 33: Elevation range-wise distribution of GL in Jia Bharali subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 16 | 95.67 | 14.95 |\n| 3 | 4,001 - 5,000 | 195 | 500.45 | 78.18 |\n| 4 | > 5,000 | 23 | 44.00 | 6.87 |\n| | **Total** | **234** | **640.12** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 33: Elevation range-wise distribution of GL in Jia Bharali subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 986, "line_end": 992, "token_count_estimate": 237, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "d4fe5c44e8025c9b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 42: Elevation range-wise distribution of GL in Jia Bharali subbasin\n\n**Figure 42: Elevation range-wise distribution of GL in Jia Bharali subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 42: Elevation range-wise distribution of GL in Jia Bharali subbasin", "line_start": 994, "line_end": 994, "token_count_estimate": 80, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "bdcd7ee6400d2834", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 34 and Figure 43. It is noted that, 83.33% of glacial lakes (195) are situated in high altitude range i.e. 4,001 - 5,000 m amsl out of total of 234 lakes. However, only 23 glacial lakes lies above the 5,000 m elevation, predominantly having size of < 5ha. It has been further noticed that, 82.35% of lakes > 5 ha are lying within in high altitude range i.e. 4,001 - 5,000 m, with majority in the ranges of 5 - 10 ha.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 995, "line_end": 1006, "token_count_estimate": 217, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "09e3207d2cd01901", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 34: Area range-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000: No. of lakes | Elevation Range (m) up to 3,000: Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000: No. of lakes | Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000: No. of lakes | Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha) | Elevation Range (m) > 5,000: No. of lakes | Elevation Range (m) > 5,000: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 1 | 0.43 | 37 | 13.89 | 5 | 1.68 | 43 | 16.00 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 1 | 0.87 | 55 | 38.15 | 7 | 4.80 | 63 | 43.82 |\n| 3 | 1 - 5 | 0 | 0.00 | 11 | 27.12 | 75 | 169.77 | 8 | 18.88 | 94 | 215.77 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 21 | 149.87 | 3 | 18.64 | 24 | 168.51 |\n| 5 | 10 - 50 | 0 | 0.00 | 3 | 67.25 | 7 | 128.77 | 0 | 0.00 | 10 | 196.02 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **16** | **95.67** | **195** | **500.45** | **23** | **44.00** | **234** | **640.12** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 34: Area range-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000: No. of lakes", "Elevation Range (m) up to 3,000: Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000: No. of lakes", "Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000: No. of lakes", "Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha)", "Elevation Range (m) > 5,000: No. of lakes", "Elevation Range (m) > 5,000: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1007, "line_end": 1015, "token_count_estimate": 622, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "74ca7f804537e16d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 35 and Figure 44. The dominant lake type in the subbasin i.e., Other Glacial Erosion Lakes (82.05%) are predominantly located in the elevation range of 4,001 - 5,000 m (93.22%). The other dominant lake type, namely, Other Moraine Dammed Lakes are distributed predominantly in very high altitude elevation range of > 5,000 m. All (100%) of all types of Moraine Dammed lakes lies above 4,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 45.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1016, "line_end": 1027, "token_count_estimate": 225, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c51ccf1fe8aaca6a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 35: Type-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake M(e) | Types of Glacial Lake M(l) | Types of Glacial Lake M(lg) | Types of Glacial Lake M(o) | Types of Glacial Lake I(s) | Types of Glacial Lake I(d) | Types of Glacial Lake E(c) | Types of Glacial Lake E(c) | Types of Glacial Lake E(v) | Types of Glacial Lake O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 13 | 0 | 16 |\n| 3 | 4,001 - 5,000 | 0 | 1 | 0 | 3 | 0 | 0 | 10 | 0 | 179 | 2 | 195 |\n| 4 | > 5,000 | 0 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 23 |\n| | **Total** | **0** | **1** | **0** | **26** | **0** | **0** | **13** | **0** | **192** | **2** | **234** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 35: Type-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1028, "line_end": 1034, "token_count_estimate": 479, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "13b5759588c4919b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 44: Type-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin\n\n**Figure 44: Type-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 44: Type-wise vs. Elevation range-wise distribution of GL in Jia Bharali subbasin", "line_start": 1036, "line_end": 1036, "token_count_estimate": 90, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "32be62f09a0caa8b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1037, "line_end": 1042, "token_count_estimate": 51, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "27f5bf52a54121a9", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 45: Elevation range-Type-wise spatial distribution of GL in Jia Bharali subbasin\n\nFigure 45: Elevation range-Type-wise spatial distribution of GL in Jia Bharali subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 45: Elevation range-Type-wise spatial distribution of GL in Jia Bharali subbasin", "line_start": 1043, "line_end": 1043, "token_count_estimate": 87, "basins": [], "subbasins": ["Jia Bharali"], "countries": [], "lake_ids": []}}
{"id": "76d14293cff06708", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1044, "line_end": 1048, "token_count_estimate": 51, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a8bb046ff48c28bf", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Lhasa Tsangpo Subbasin\nType: text\n\nThe Lhasa Tsangpo subbasin is the third largest subbasin of the Brahmaputra River basin covering a total area of 32,896 Km² i.e. 8.23% of the total basin area (Figure 46). The Lhasa River is the longest of the Yarlung Tsangpo tributaries. It flows through the south of the Tibet Autonomous Region of China, and is a left tributary of the Yarlung Tsangpo which is about 450 kilometers long. Tsenrak Chu is one of the main tributaries of the Lhasa Tsangpo River. A total of 1,225 lakes were mapped, covering a total area of 6,980.94 ha i.e. 0.21% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.5 Lhasa Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.5 Lhasa Tsangpo Subbasin"], "chunk_type": "text", "line_start": 1050, "line_end": 1057, "token_count_estimate": 223, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Lhasa Tsangpo"], "countries": ["China"], "lake_ids": []}}
{"id": "e4db244541c91cd2", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nIn Lhasa Tsangpo subbasin, glacial lakes have been distributed in all area ranges. Table 36 and Figure 47 shows the area range-wise distribution of glacial lakes for the Lhasa Tsangpo subbasin. About 1,048 (85.55%) lakes are with < 5 ha lake area contributing to 19.33% of total lake area. The remaining lakes with > 5 ha in size are only 177 (14.45%) contributing to 80.67% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1059, "line_end": 1063, "token_count_estimate": 146, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "8ab4a43e97c4dcbe", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 36: Area range-wise distribution of GL in Lhasa Tsangpo subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 267 | 98.10 | 1.41 |\n| 2 | 0.5 - 1 | 294 | 209.19 | 3.00 |\n| 3 | 1 - 5 | 487 | 1,041.65 | 14.92 |\n| 4 | 5 - 10 | 84 | 567.46 | 8.13 |\n| 5 | 10 - 50 | 72 | 1,413.21 | 20.24 |\n| 6 | > 50 | 21 | 3,651.33 | 52.30 |\n| | **Total** | **1,225** | **6,980.94** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 36: Area range-wise distribution of GL in Lhasa Tsangpo subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1064, "line_end": 1072, "token_count_estimate": 272, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "72fb6a8c3b5b7040", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1073, "line_end": 1077, "token_count_estimate": 49, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "940804d6e1474602", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Lhasa Tsangpo subbasin is given in Table 37 and Figure 48. Out of 10 types of glacial lakes, only 7 types of lake are present in the Lhasa Tsangpo subbasin, where Other Glacial Erosion Lakes are found to be the maximum with 673 (54.94%) occupying a total lake area extent of 3,824.79 ha at 54.79% in the subbasin. Two other types of lake, namely, Other Moraine Dammed and Other Glacier lakes are 126 (10.29%) and 389 (31.76%) and extend over lake area of 220.78 ha (3.16%) and 2,759.67 ha (39.53%) respectively.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1079, "line_end": 1083, "token_count_estimate": 196, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "e1891fa066842003", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 37: Type-wise distribution of GL in Lhasa Tsangpo subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :---: | :--- | ---: | ---: | ---: |\n| 1 | M(e) | End-moraine Dammed Lake | 9 | 70.26 | 1.01 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 1 | 20.39 | 0.29 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 126 | 220.78 | 3.16 |\n| 5 | I(s) | Supra-glacial Lake | 4 | 2.16 | 0.03 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 23 | 82.90 | 1.19 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 673 | 3,824.79 | 54.79 |\n| 10 | O | Other Glacial Lake | 389 | 2,759.67 | 39.53 |\n| | | **Total** | **1,225** | **6,980.94** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 37: Type-wise distribution of GL in Lhasa Tsangpo subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1084, "line_end": 1096, "token_count_estimate": 467, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "73e97a72ac598eda", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 38 and Figure 49. The lakes with < 5 ha in size (85.55%) are dominant with Other Glacial Erosion Lake (55.05%) and Other Glacial lake (31.29%). Lakes with > 5 ha (14.45%) are equally by Other Glacial Erosion Lake (54.23%) and Other Glacial lake (34.46%). All types of Moraine Dammed lakes, which constitutes about 11.10% are predominantly with < 5 ha in water spread", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1097, "line_end": 1108, "token_count_estimate": 181, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0a3d5b9e5ee28819", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 38: Area range-wise vs. Type-wise distribution of GL in Lhasa Tsangpo subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 28 | 2 | 0 | 1 | 0 | 132 | 104 | 267 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 39 | 2 | 0 | 2 | 0 | 167 | 84 | 294 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 50 | 0 | 0 | 15 | 0 | 278 | 140 | 487 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 6 | 0 | 0 | 4 | 0 | 40 | 32 | 84 |\n| 5 | 10 - 50 | 3 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 42 | 22 | 72 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 7 | 21 |\n| | **Total** | **9** | **1** | **0** | **126** | **4** | **0** | **23** | **0** | **673** | **389** | **1,225** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 38: Area range-wise vs. Type-wise distribution of GL in Lhasa Tsangpo subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1109, "line_end": 1117, "token_count_estimate": 488, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "9dee3b34552d415a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Lhasa Tsangpo subbasin has been shown in Table 39 and Figure 50. Except one, all glacial lakes are situated above 4,000 m elevation range i.e. 1,224 (99.92%) with total lake area of 6,980.46 ha (99.99%) and remaining 1 glacial lake is in the range of 3,001 - 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1118, "line_end": 1128, "token_count_estimate": 154, "basins": ["BRAHMAPUTRA"], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "f0c2132b0c8ead14", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 39: Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :---: | :---: | :---: |\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 1 | 0.49 | 0.01 |\n| 3 | 4,001 - 5,000 | 376 | 3,684.05 | 52.77 |\n| 4 | > 5,000 | 848 | 3,296.40 | 47.22 |\n| | **Total** | **1,225** | **6,980.94** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 39: Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1129, "line_end": 1135, "token_count_estimate": 247, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "9c761e115251d0f5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 50: Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin\n\n**Figure 50: Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 50: Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin", "line_start": 1137, "line_end": 1137, "token_count_estimate": 79, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "e187be1efad6d721", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1138, "line_end": 1143, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5721f0186917a5d3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 40 and Figure 51. It is noted that, 30.69% of glacial lakes (376) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which also constitutes maximum total lake area within that range i.e. 52.77%. However, 848 glacial lakes lies above 5,000 m, has majority of its lakes are < 5 ha i.e. 87.38%. Maximum lakes lying in high altitude range is of size ranging 1 - 5 ha (i.e. 138), followed by lakes of size 0.25 – 0.5 ha (i.e. 92). It has been further noticed that, 18.61% of lakes > 5 ha are lying within in high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size ranging of 10 - 50 ha.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1145, "line_end": 1149, "token_count_estimate": 255, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "52834d91f554b4be", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 40: Area range-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 1 | 0.49 | 92 | 34.98 | 174 | 62.63 | 267 | 98.10 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 0 | 0.00 | 76 | 54.79 | 218 | 154.40 | 294 | 209.19 |\n| 3 | 1 - 5 | 0 | 0.00 | 0 | 0.00 | 138 | 284.08 | 349 | 757.57 | 487 | 1,041.65 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 26 | 181.03 | 58 | 386.43 | 84 | 567.46 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 29 | 566.75 | 43 | 846.46 | 72 | 1,413.21 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 15 | 2,562.00 | 6 | 1,089.00 | 21 | 3,651.33 |\n| **Total** | | **0** | **0.00** | **1** | **0.49** | **376** | **3,684.05** | **848** | **3,296.40** | **1,225** | **6,980.94** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 40: Area range-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1150, "line_end": 1158, "token_count_estimate": 660, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c231f86a1c45b417", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1159, "line_end": 1163, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "56713ccc9742e76e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 41 and Figure 52. The dominant lake types in the subbasin i.e., Other Glacial Erosion Lakes (54.93%) are predominantly located in the very high elevation range of > 5,000 m (69.22%). The other dominant lake type, namely, Other Moraine Dammed and Other Glacial lakes are also seen > 5,000 m elevation range i.e. 14.85% and 11.43% respectively. All types of Moraine-dammed lakes, lie above 5,000 m elevation. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 53.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1165, "line_end": 1169, "token_count_estimate": 201, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c1fde84c5f310988", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: table\nTable: Table 41: Type-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin\n\n| S. No. | Elevation Range (m) | Types of Glacial Lake M(e) | Types of Glacial Lake M(l) | Types of Glacial Lake M(lg) | Types of Glacial Lake M(o) | Types of Glacial Lake I(s) | Types of Glacial Lake I(d) | Types of Glacial Lake E(c) | Types of Glacial Lake E(c) | Types of Glacial Lake E(o) | Types of Glacial Lake O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| 3 | 4,001 - 5,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 291 | 376 |\n| 4 | > 5,000 | 9 | 1 | 0 | 126 | 4 | 0 | 23 | 0 | 588 | 97 | 848 |\n| | **Total** | **9** | **1** | **0** | **126** | **4** | **0** | **23** | **0** | **673** | **389** | **1,225** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 41: Type-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin", "columns": ["S. No.", "Elevation Range (m)", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1170, "line_end": 1176, "token_count_estimate": 511, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c34adb3ea1f72c78", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 52: Type-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin\n\n**Figure 52: Type-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 52: Type-wise vs. Elevation range-wise distribution of GL in Lhasa Tsangpo subbasin", "line_start": 1178, "line_end": 1178, "token_count_estimate": 94, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "4941596fb8fc1d4d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1179, "line_end": 1185, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "49b091ab488de5a6", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: figure\nFigure: Figure 53: Elevation range-Type-wise spatial distribution of GL in Lhasa Tsangpo subbasin\n\n**Figure 53: Elevation range-Type-wise spatial distribution of GL in Lhasa Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 53: Elevation range-Type-wise spatial distribution of GL in Lhasa Tsangpo subbasin", "line_start": 1186, "line_end": 1186, "token_count_estimate": 94, "basins": [], "subbasins": ["Lhasa Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "2a53b700a347049b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1187, "line_end": 1191, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "662f22bec97d4a41", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.6 Lohit Subbasin\nType: text\n\nThe Lohit subbasin is the sixth largest subbasin of the Brahmaputra River basin covering a total area of 25,799 Km² i.e. 6.45% of the total basin area (Figure 54). Lohit River is a river in Arunachal Pradesh in India. It is a tributary to the Brahmaputra River. It originates in eastern Tibet, in the Zayal Chu range and surges through Arunachal Pradesh for 200 km, before reaching in the plains of Assam. The river flows through Mishmi hills to meet the Siang at the head of the Brahmaputra valley. Kangri Karpo Chu and Zang Chu are two main tributaries of Lohit River. A total of 2,276 glacial lakes has been mapped, covering a total area of 11,529.23 ha i.e. 0.44% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Lohit subbasin, glacial lakes have been distributed in all area ranges. Table 42 and Figure 55 shows the area range-wise distribution of glacial lakes for the Lohit subbasin. About 1,819 (79.92%) lakes are with < 5 ha lake area contributing to 22.28% of total lake area. The remaining lakes with > 5 ha in size are 457 (20.08%) contributing to 77.72% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.6 Lohit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.6 Lohit Subbasin"], "chunk_type": "text", "line_start": 1193, "line_end": 1206, "token_count_estimate": 369, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Lohit"], "countries": ["India"], "lake_ids": []}}
{"id": "6be9ee0215004e9c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.6 Lohit Subbasin\nType: table\nTable: Table 42: Area range-wise distribution of GL in Lohit subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 466 | 165.05 | 1.43 |\n| 2 | 0.5 - 1 | 496 | 360.40 | 3.13 |\n| 3 | 1 - 5 | 857 | 2,043.43 | 17.73 |\n| 4 | 5 - 10 | 229 | 1,615.47 | 14.01 |\n| 5 | 10 - 50 | 209 | 3,989.56 | 34.62 |\n| 6 | > 50 | 19 | 3,355.32 | 29.12 |\n| | **Total** | **2,276** | **11,529.23** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.6 Lohit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.6 Lohit Subbasin"], "chunk_type": "table", "table_caption": "Table 42: Area range-wise distribution of GL in Lohit subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1207, "line_end": 1215, "token_count_estimate": 285, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "cc46cdedd579320f", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.6 Lohit Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.6 Lohit Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.6 Lohit Subbasin"], "chunk_type": "text", "line_start": 1216, "line_end": 1221, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "7f20a0b905c1ff40", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Lohit subbasin is given in Table 43 and Figure 56. Out of 10 types of glacial lakes, only 7 types of lake are present in the Lohit subbasin, where Other Glacial lakes are found to be the maximum with 1,572 (69.06%) occupying a total lake extent of 6,632.45 ha at 57.53% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 386 (16.95%) and extend over a total area of 1,589.65 ha at 13.79%. in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1223, "line_end": 1227, "token_count_estimate": 171, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "75c204ea359c9132", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 43: Type-wise distribution of GL in Lohit subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 59 | 850.32 | 7.38 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 5 | 102.81 | 0.89 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 386 | 1,589.65 | 13.79 |\n| 5 | I(s) | Supra-glacial Lake | 12 | 11.00 | 0.10 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 82 | 879.15 | 7.63 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 1,572 | 6,632.45 | 57.53 |\n| 10 | O | Other Glacial Lake | 160 | 1,463.85 | 12.70 |\n| | | **Total** | **2,276** | **11,529.23** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 43: Type-wise distribution of GL in Lohit subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1228, "line_end": 1240, "token_count_estimate": 458, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "49a6e4b30da4b263", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 44 and Figure 57. The lakes with < 5 ha in size (79.92%) are dominant with Other Glacial Erosion (70.20%) and Other Moraine Dammed lakes (17.70%). Lakes with > 5 ha (20.08%) are also dominated by Other Glacial Erosion lakes (64.55%). All types of Glacier Erosion lakes, which constitute about 72.67% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1241, "line_end": 1252, "token_count_estimate": 176, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "32a5defe988c5edc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 44: Area range-wise vs. Type-wise distribution of GL in Lohit subbasin\n\n| S. No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(e) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 3 | 1 | 0 | 92 | 5 | 0 | 0 | 0 | 310 | 55 | 466 |\n| 2 | 0.5 - 1 | 2 | 0 | 0 | 102 | 3 | 0 | 1 | 0 | 348 | 40 | 496 |\n| 3 | 1 - 5 | 24 | 3 | 0 | 128 | 4 | 0 | 27 | 0 | 619 | 52 | 857 |\n| 4 | 5 - 10 | 10 | 0 | 0 | 36 | 0 | 0 | 21 | 0 | 158 | 4 | 229 |\n| 5 | 10 - 50 | 18 | 0 | 0 | 24 | 0 | 0 | 33 | 0 | 127 | 7 | 209 |\n| 6 | > 50 | 2 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 10 | 2 | 19 |\n| | **Total** | **59** | **5** | **0** | **386** | **12** | **0** | **82** | **0** | **1,572** | **160** | **2,276** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 44: Area range-wise vs. Type-wise distribution of GL in Lohit subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(e)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1253, "line_end": 1261, "token_count_estimate": 516, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "94035177edfec55c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nNational Remote Sensing Centre, ISRO, Hyderabad | 67\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Lohit subbasin has been shown in Table 45 and Figure 58. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 1,990 (87.43%) with total lake area of 7,575.13 ha (65.70%) and remaining 12.57% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1262, "line_end": 1272, "token_count_estimate": 163, "basins": ["BRAHMAPUTRA"], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "22c9d567d5be3d91", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 45: Elevation range-wise distribution of GL in Lohit subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :--- | :---: | :---: | :---: |\n| 1 | up to 3,000 | 5 | 58.10 | 0.50 |\n| 2 | 3,001 - 4,000 | 281 | 3,896.04 | 33.79 |\n| 3 | 4,001 - 5,000 | 1,735 | 7,025.16 | 60.93 |\n| 4 | > 5,000 | 255 | 549.93 | 4.77 |\n| | **Total** | **2,276** | **11,529.23** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 45: Elevation range-wise distribution of GL in Lohit subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1273, "line_end": 1279, "token_count_estimate": 250, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "2980ef1da051abb3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 58: Elevation range-wise distribution of GL in Lohit subbasin\n\n**Figure 58: Elevation range-wise distribution of GL in Lohit subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 58: Elevation range-wise distribution of GL in Lohit subbasin", "line_start": 1281, "line_end": 1281, "token_count_estimate": 73, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "8a4abbc1f88de7cd", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 46 and Figure 59. It is noted that, 76.20% of glacial lakes (1,735) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 60.91%. It has been further noticed that, 71.49% of lakes > 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 5 ha. However, 5 glacial lakes lie below 3,000 m and 255 lakes above 5,000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1282, "line_end": 1292, "token_count_estimate": 225, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72c7b6b527019f0e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 46: Area range-wise vs. Elevation range-wise distribution of GL in Lohit subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000: No. of lakes | Elevation Range (m) up to 3,000: Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000: No. of lakes | Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000: No. of lakes | Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha) | Elevation Range (m) > 5,000: No. of lakes | Elevation Range (m) > 5,000: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 29 | 10.75 | 365 | 129.20 | 72 | 25.10 | 466 | 165.05 |\n| 2 | 0.5 - 1 | 1 | 0.62 | 34 | 25.47 | 395 | 286.49 | 66 | 47.82 | 496 | 360.40 |\n| 3 | 1 - 5 | 2 | 6.01 | 118 | 275.41 | 648 | 1,559.14 | 89 | 202.87 | 857 | 2,043.43 |\n| 4 | 5 - 10 | 0 | 0.00 | 49 | 355.34 | 162 | 1,139.26 | 18 | 120.87 | 229 | 1,615.47 |\n| 5 | 10 - 50 | 2 | 51.47 | 43 | 904.24 | 154 | 2,880.58 | 10 | 153.27 | 209 | 3,989.56 |\n| 6 | > 50 | 0 | 0.00 | 8 | 2,325.00 | 11 | 1,030.00 | 0 | 0.00 | 19 | 3,355.32 |\n| | **Total** | **5** | **58.10** | **281** | **3,896.04** | **1,735** | **7,025.16** | **255** | **549.93** | **2,276** | **11,529.23** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 46: Area range-wise vs. Elevation range-wise distribution of GL in Lohit subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000: No. of lakes", "Elevation Range (m) up to 3,000: Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000: No. of lakes", "Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000: No. of lakes", "Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha)", "Elevation Range (m) > 5,000: No. of lakes", "Elevation Range (m) > 5,000: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1293, "line_end": 1301, "token_count_estimate": 661, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "3d5d53d976109bc8", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 59: Area range-wise vs. Elevation range-wise distribution of GL in Lohit subbasin\n\n**Figure 59: Area range-wise vs. Elevation range-wise distribution of GL in Lohit subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 59: Area range-wise vs. Elevation range-wise distribution of GL in Lohit subbasin", "line_start": 1303, "line_end": 1303, "token_count_estimate": 85, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "14ec6f66e9f37303", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 47 and Figure 60. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (69.06%) are predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Supra-glacial lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 16.95% and 0.52%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 61.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1304, "line_end": 1315, "token_count_estimate": 208, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c44ac6e3504e7fe", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 47: Type-wise vs. Elevation range-wise distribution of GL in Lohit subbasin\n\n| S. No. | Elevation Range (m) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 5 |\n| 2 | 3,001 - 4,000 | 3 | 0 | 0 | 3 | 4 | 0 | 16 | 0 | 205 | 50 | 281 |\n| 3 | 4,001 - 5,000 | 29 | 3 | 0 | 176 | 8 | 0 | 65 | 0 | 1,345 | 109 | 1,735 |\n| 4 | > 5,000 | 27 | 2 | 0 | 204 | 0 | 0 | 1 | 0 | 21 | 0 | 255 |\n| | **Total** | **59** | **5** | **0** | **386** | **12** | **0** | **82** | **0** | **1,572** | **160** | **2,276** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 47: Type-wise vs. Elevation range-wise distribution of GL in Lohit subbasin", "columns": ["S. No.", "Elevation Range (m)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1316, "line_end": 1322, "token_count_estimate": 435, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "4166dae5ce0ffce9", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 60: Type-wise vs. Elevation range-wise distribution of GL in Lohit subbasin\n\n**Figure 60: Type-wise vs. Elevation range-wise distribution of GL in Lohit subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 60: Type-wise vs. Elevation range-wise distribution of GL in Lohit subbasin", "line_start": 1324, "line_end": 1324, "token_count_estimate": 83, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "067a17d3c686e5d7", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1325, "line_end": 1329, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11e3a9519527da0c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: figure\nFigure: Figure 61: Elevation range-Type-wise spatial distribution of GL in Lohit subbasin\n\nFigure 61: Elevation range-Type-wise spatial distribution of GL in Lohit subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 61: Elevation range-Type-wise spatial distribution of GL in Lohit subbasin", "line_start": 1330, "line_end": 1330, "token_count_estimate": 80, "basins": [], "subbasins": ["Lohit"], "countries": [], "lake_ids": []}}
{"id": "b55089b2d36b6b3a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1331, "line_end": 1335, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3a19dab6d59267e5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin\nType: text\n\nThe Lower Yarlung Tsangpo subbasin is the second largest subbasin of the Brahmaputra River basin covering a total area of 74,334 Km² i.e. 18.59% of the total basin area (Figure 62). Major tributaries of Yarlung Tsangpo include Nyangchu River, Lhasa River, Nyang River, and Parlung Tsangpo. It originates at Angsi Glacier in western Tibet, southeast of Mount Kailash and Lake Manasarovar, it later forms the South Tibet Valley and Yarlung Tsangpo Grand Canyon before passing into the state of Arunachal Pradesh, India. A total of 4,979 glacial lakes has been mapped, covering a total area of 26,371.81 ha i.e. 0.35% of the total area of the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.7 Lower Yarlung Tsangpo Subbasin"], "chunk_type": "text", "line_start": 1337, "line_end": 1339, "token_count_estimate": 225, "basins": ["Brahmaputra"], "subbasins": ["Lower Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "b02d2c8be1a69f2b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin\nType: figure\nFigure: Figure 62: Location map of the Lower Yarlung Tsangpo subbasin\n\n**Figure 62: Location map of the Lower Yarlung Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.7 Lower Yarlung Tsangpo Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 62: Location map of the Lower Yarlung Tsangpo subbasin", "line_start": 1340, "line_end": 1340, "token_count_estimate": 81, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c7c47da256e45a70", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-wise Distribution**\n\nIn Lower Yarlung Tsangpo subbasin, glacial lakes have been distributed in all area ranges. Table 48 and Figure 63 shows the area range-wise distribution of glacial lakes for the Lower Yarlung Tsangpo subbasin. About 3,967 (79.67%) lakes are with < 5 ha lake area contributing to 22.12% of total lake area. The remaining lakes with > 5 ha in size are 1,012 (20.33%) contributing to 77.88% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.7 Lower Yarlung Tsangpo Subbasin"], "chunk_type": "text", "line_start": 1341, "line_end": 1352, "token_count_estimate": 184, "basins": ["BRAHMAPUTRA"], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "fb35f37037c982fb", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin\nType: table\nTable: Table 48: Area range-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n| S. No. | Subbasin | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 904 | 328.04 | 1.24 |\n| 2 | 0.5 - 1 | 1,024 | 735.02 | 2.79 |\n| 3 | 1 - 5 | 2,039 | 4,769.34 | 18.08 |\n| 4 | 5 - 10 | 529 | 3,724.79 | 14.12 |\n| 5 | 10 - 50 | 429 | 8,081.12 | 30.64 |\n| 6 | > 50 | 54 | 8,733.50 | 33.12 |\n| | **Total** | **4,979** | **26,371.81** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.7 Lower Yarlung Tsangpo Subbasin"], "chunk_type": "table", "table_caption": "Table 48: Area range-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "columns": ["S. No.", "Subbasin", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1353, "line_end": 1361, "token_count_estimate": 297, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "60182341128b9574", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.7 Lower Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.7 Lower Yarlung Tsangpo Subbasin"], "chunk_type": "text", "line_start": 1362, "line_end": 1366, "token_count_estimate": 56, "basins": ["BRAHMAPUTRA"], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "22c634def41ba77e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Lower Yarlung Tsangpo subbasin is given in Table 49 and Figure 64. Out of 10 types of glacial lakes, 9 types of lake are present in the Lower Yarlung Tsangpo subbasin, where Other Glacial Erosion lakes are found to be the maximum with 3,832 (76.96%) occupying a total lake extent of 15,111.18 ha at 57.30% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 694 (13.93%) and extend over a total area of 2,362.46 ha at 8.96% in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1368, "line_end": 1372, "token_count_estimate": 180, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "565cdc5b5ac10195", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 49: Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 96 | 2,355.92 | 8.93 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 11 | 27.05 | 0.10 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 1 | 0.33 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 694 | 2,362.46 | 8.96 |\n| 5 | I(s) | Supra-glacial Lake | 25 | 13.59 | 0.05 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 114 | 1,294.92 | 4.91 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 6 | 3,375.74 | 12.80 |\n| 9 | E(o) | Other Glacial Erosion Lake | 3,832 | 15,111.18 | 57.30 |\n| 10 | O | Other Glacial Lake | 200 | 1,830.62 | 6.94 |\n| | | **Total** | **4,979** | **26,371.81** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 49: Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1373, "line_end": 1385, "token_count_estimate": 477, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "29a6650060cfdc5c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1386, "line_end": 1391, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d5030fbeb958210c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 50 and Figure 65. The lakes with < 5 ha in size (79.67%) are dominant with Other Glacial Erosion (78.32%) and Other Moraine Dammed lakes (14.99%). Lakes with > 5 ha (20.33%) are also dominated by Other Glacial Erosion lakes (71.64%). All types of Glacier Erosion lakes, which constitute about 79.37% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1393, "line_end": 1397, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f58b325e2ce5e1a2", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 50: Area range-wise vs. Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n| S. No. | Subbasin | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 3 | 1 | 137 | 14 | 0 | 2 | 0 | 700 | 44 | 904 |\n| 2 | 0.5 - 1 | 6 | 4 | 0 | 164 | 8 | 0 | 6 | 0 | 798 | 38 | 1,024 |\n| 3 | 1 - 5 | 23 | 2 | 0 | 294 | 3 | 0 | 38 | 0 | 1,609 | 70 | 2,039 |\n| 4 | 5 - 10 | 21 | 1 | 0 | 56 | 0 | 0 | 33 | 0 | 396 | 22 | 529 |\n| 5 | 10 - 50 | 32 | 1 | 0 | 38 | 0 | 0 | 32 | 0 | 305 | 21 | 429 |\n| 6 | > 50 | 11 | 0 | 0 | 5 | 0 | 0 | 3 | 6 | 24 | 5 | 54 |\n| | **Total** | **96** | **11** | **1** | **694** | **25** | **0** | **114** | **6** | **3,832** | **200** | **4,979** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 50: Area range-wise vs. Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "columns": ["S. No.", "Subbasin", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1398, "line_end": 1406, "token_count_estimate": 499, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c128993cebcae6cf", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 65: Area range-wise vs. Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n**Figure 65: Area range-wise vs. Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 65: Area range-wise vs. Type-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "line_start": 1408, "line_end": 1408, "token_count_estimate": 94, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "7c03bc7a0cdbf36a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\n***\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1409, "line_end": 1413, "token_count_estimate": 54, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bee8860e08dd7fd5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Lower Yarlung Tsangpo subbasin has been shown in Table 51 and Figure 66. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 4,878 (97.97%) with total lake area of 24,875.68 ha (94.32%) and remaining 2.03% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1415, "line_end": 1419, "token_count_estimate": 127, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "5e4b2ef054743eac", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 51: Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 2 | 40.11 | 0.15 |\n| 2 | 3,001 - 4,000 | 99 | 1,456.03 | 5.52 |\n| 3 | 4,001 - 5,000 | 3,056 | 16,552.09 | 62.76 |\n| 4 | > 5,000 | 1,822 | 8,323.58 | 31.56 |\n| | **Total** | **4,979** | **26,371.81** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 51: Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1420, "line_end": 1426, "token_count_estimate": 241, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "7320246feff2161a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 66: Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n**Figure 66: Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 66: Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "line_start": 1428, "line_end": 1428, "token_count_estimate": 85, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "3ea474d20e86d839", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1429, "line_end": 1432, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c81dcf974fe5e491", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 52 and Figure 67. It is noted that, 61.37% of glacial lakes (3,056) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 62.76%. It has been further noticed that, 58.85% of lakes > 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 5 ha. However, 2 glacial lakes lie below 3,000 m and 1,822 lakes above 5,000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1434, "line_end": 1438, "token_count_estimate": 202, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "abe13c8d14d80886", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: table\nTable: Table 52: Area range-wise vs. Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 20 | 7.64 | 514 | 188.22 | 370 | 132.18 | 904 | 328.04 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 17 | 11.67 | 600 | 433.46 | 407 | 289.89 | 1,024 | 735.02 |\n| 3 | 1 - 5 | 1 | 4.04 | 36 | 77.62 | 1,220 | 2,890.09 | 782 | 1,797.59 | 2,039 | 4,769.34 |\n| 4 | 5 - 10 | 0 | 0.00 | 9 | 66.53 | 369 | 2,592.23 | 151 | 1,066.03 | 529 | 3,724.79 |\n| 5 | 10 - 50 | 1 | 36.07 | 11 | 223.71 | 312 | 6,017.32 | 105 | 1,804.01 | 429 | 8,081.11 |\n| 6 | > 50 | 0 | 0.00 | 6 | 1,069.00 | 41 | 4,431.00 | 7 | 3,233.88 | 54 | 8,733.51 |\n| **Total** | | **2** | **40.11** | **99** | **1,456.03** | **3,056** | **16,552.09** | **1,822** | **8,323.58** | **4,979** | **26,371.81** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 52: Area range-wise vs. Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1439, "line_end": 1447, "token_count_estimate": 715, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "5e4582487ceb6a7d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1448, "line_end": 1455, "token_count_estimate": 54, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e736b56c2bbeb696", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 53 and Figure 68. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (76.96%) are predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Cirque Erosion Lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 48.70% and 77.19%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 69.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range wise Distribution"], "chunk_type": "text", "line_start": 1457, "line_end": 1461, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5af6827be81cb193", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range wise Distribution\nType: table\nTable: Table 53: Type-wise vs. Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |\n| 2 | 3,001 - 4,000 | 7 | 0 | 0 | 22 | 8 | 0 | 1 | 1 | 40 | 20 | 99 |\n| 3 | 4,001 - 5,000 | 57 | 9 | 1 | 338 | 11 | 0 | 88 | 4 | 2,411 | 137 | 3,056 |\n| 4 | > 5,000 | 32 | 2 | 0 | 333 | 6 | 0 | 25 | 1 | 1,380 | 43 | 1,822 |\n| **Total** | | **96** | **11** | **1** | **694** | **25** | **0** | **114** | **6** | **3,832** | **200** | **4,979** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range wise Distribution"], "chunk_type": "table", "table_caption": "Table 53: Type-wise vs. Elevation range-wise distribution of GL in Lower Yarlung Tsangpo subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1462, "line_end": 1468, "token_count_estimate": 527, "basins": [], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "73a7626388e1ad46", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-Elevation range wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-Elevation range wise Distribution"], "chunk_type": "text", "line_start": 1469, "line_end": 1479, "token_count_estimate": 70, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "08d71569b546c49e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\nThe Manas subbasin is the fourth largest subbasin of the Brahmaputra River basin covering a total area of 32,166 Km² i.e. 8.04% of the total basin area (Figure 70). The Manas River is formed by the joining of two rivers of similar catchment area, the Mangde Chu and the Dangme Chu. It is a transboundary river in the Himalayan foothills between southern Bhutan and India. Kuru Chu, Bumtang, and Mangde Chu are the main tributaries of Manas River. A total of 2,526 glacial lakes has been mapped, covering a total area of 11,318.96 ha i.e. 0.35% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-range-wise Distribution**\n\nIn Manas subbasin, glacial lakes have been distributed in all area ranges. Table 54 and Figure 71 shows the area range-wise distribution of glacial lakes for the Manas subbasin. About 2,037 (80.64%) lakes are with < 5 ha lake area contributing to 24.85% of total lake area. The remaining lakes with > 5 ha in size are 489 (19.36%) contributing to 75.15% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1481, "line_end": 1493, "token_count_estimate": 338, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Manas"], "countries": ["Bhutan", "India"], "lake_ids": []}}
{"id": "c9940c63b4da0cd0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: table\nTable: Table 54: Area range-wise distribution of GL in Manas subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 582 | 204.74 | 1.81 |\n| 2 | 0.5 - 1 | 492 | 352.31 | 3.11 |\n| 3 | 1 - 5 | 963 | 2,256.31 | 19.93 |\n| 4 | 5 - 10 | 239 | 1,694.77 | 15.03 |\n| 5 | 10 - 50 | 224 | 4,221.54 | 37.26 |\n| 6 | > 50 | 26 | 2,589.31 | 22.85 |\n| | **Total** | **2,526** | **11,318.96** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "table", "table_caption": "Table 54: Area range-wise distribution of GL in Manas subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1494, "line_end": 1502, "token_count_estimate": 285, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "45eb89d43b0c6f60", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Manas subbasin is given in Table 55 and Figure 72. Out of 10 types of glacial lakes, 7 types of lake are present in the Manas subbasin, where Other Glacial Erosion lakes are found to be the maximum with 1,719 (68.05%) occupying a total lake extent of 5,554.27 ha at 49.07% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 410 (16.23%) and extend over a total area of 1,430.07 ha at 12.63% in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1503, "line_end": 1515, "token_count_estimate": 197, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "900aa943acca75b0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: table\nTable: Table 55: Type-wise distribution of GL in Manas subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 47 | 1,674.77 | 14.80 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 3 | 56.95 | 0.50 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 410 | 1,430.07 | 12.63 |\n| 5 | I(s) | Supra-glacial Lake | 67 | 64.47 | 0.57 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 243 | 1,976.10 | 17.46 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 1,719 | 5,554.27 | 49.07 |\n| 10 | O | Other Glacial Lake | 37 | 562.33 | 4.97 |\n| | | **Total** | **2,526** | **11,318.96** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "table", "table_caption": "Table 55: Type-wise distribution of GL in Manas subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1516, "line_end": 1528, "token_count_estimate": 470, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "78154c340076e38a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 56 and Figure 73. The lakes with < 5 ha in size (80.64%) are dominant with Other Glacial Erosion (71.91%) and Other Moraine Dammed lakes (16.93%). Lakes with > 5 ha (19.36%) are also dominated by Other Glacial Erosion lakes (51.94%). All types of Glacier Erosion lakes, which constitute about 77.76% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1529, "line_end": 1539, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "77968755ba8c91ee", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: table\nTable: Table 56: Area range-wise vs. Type-wise distribution of GL in Manas subbasin\n\n| S. No. | Subbasin | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 114 | 43 | 0 | 2 | 0 | 416 | 7 | 582 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 91 | 20 | 0 | 9 | 0 | 364 | 8 | 492 |\n| 3 | 1 - 5 | 7 | 1 | 0 | 140 | 2 | 0 | 113 | 0 | 685 | 15 | 963 |\n| 4 | 5 - 10 | 8 | 0 | 0 | 29 | 1 | 0 | 61 | 0 | 139 | 1 | 239 |\n| 5 | 10 - 50 | 22 | 2 | 0 | 34 | 1 | 0 | 56 | 0 | 106 | 3 | 224 |\n| 6 | > 50 | 10 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 9 | 3 | 26 |\n| **Total** | | **47** | **3** | **0** | **410** | **67** | **0** | **243** | **0** | **1,719** | **37** | **2,526** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "table", "table_caption": "Table 56: Area range-wise vs. Type-wise distribution of GL in Manas subbasin", "columns": ["S. No.", "Subbasin", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1540, "line_end": 1548, "token_count_estimate": 490, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "74a33fecb79eedde", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Manas subbasin has been shown in Table 57 and Figure 74. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 2,474 (97.94%) with total lake area of 11,087.29 ha (97.95%) and remaining 2.05% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1549, "line_end": 1561, "token_count_estimate": 151, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "f1e550235a2fc5e0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: table\nTable: Table 57: Elevation range-wise distribution of GL in Manas subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 52 | 231.67 | 2.05 |\n| 3 | 4,001 - 5,000 | 1,656 | 8,137.45 | 71.89 |\n| 4 | > 5,000 | 818 | 2,949.84 | 26.06 |\n| **Total** | | **2,526** | **11,318.96** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "table", "table_caption": "Table 57: Elevation range-wise distribution of GL in Manas subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1562, "line_end": 1568, "token_count_estimate": 234, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "eb29c676a687316b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 58 and Figure 75. It is noted that, 65.55% of glacial lakes (1,656) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 71.89%. It has been further noticed that, 63.32% of lakes < 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 5 ha. However, no glacial lakes lie below 3,000 m and 818 lakes above 5,000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1569, "line_end": 1579, "token_count_estimate": 228, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "36553814bdea0b3c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: table\nTable: Table 58: Area range-wise vs. Elevation range-wise distribution of GL in Manas subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000: No. of lakes | Elevation Range (m) up to 3,000: Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000: No. of lakes | Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000: No. of lakes | Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha) | Elevation Range (m) > 5,000: No. of lakes | Elevation Range (m) > 5,000: Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 5 | 2.12 | 337 | 119.12 | 240 | 83.50 | 582 | 204.74 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 3 | 2.20 | 282 | 201.74 | 207 | 148.37 | 492 | 352.31 |\n| 3 | 1 - 5 | 0 | 0.00 | 28 | 71.57 | 671 | 1,584.98 | 264 | 599.76 | 963 | 2,256.31 |\n| 4 | 5 - 10 | 0 | 0.00 | 10 | 74.33 | 182 | 1,291.23 | 47 | 329.20 | 239 | 1,694.77 |\n| 5 | 10 - 50 | 0 | 0.00 | 6 | 81.45 | 166 | 3,089.22 | 52 | 1,050.86 | 224 | 4,221.54 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 18 | 1,851 | 8 | 738.15 | 26 | 2,589.31 |\n| | **Total** | **0** | **0.00** | **52** | **231.67** | **1,656** | **8,137.45** | **818** | **2,949.84** | **2,526** | **11,318.96** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "table", "table_caption": "Table 58: Area range-wise vs. Elevation range-wise distribution of GL in Manas subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000: No. of lakes", "Elevation Range (m) up to 3,000: Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000: No. of lakes", "Elevation Range (m) 3,001 - 4,000: Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000: No. of lakes", "Elevation Range (m) 4,001 - 5,000: Total Lake Area (ha)", "Elevation Range (m) > 5,000: No. of lakes", "Elevation Range (m) > 5,000: Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1580, "line_end": 1588, "token_count_estimate": 683, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "92cf075681507c70", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 59 and Figure 76. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (68.05%) are predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Cirque Erosion Lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 17.07% and 90.94%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 77.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1589, "line_end": 1601, "token_count_estimate": 213, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "e56cae16eeb57f5d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: table\nTable: Table 59: Type-wise vs. Elevation range-wise distribution of GL in Manas subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 38 | 2 | 52 |\n| 3 | 4,001 - 5,000 | 15 | 1 | 0 | 70 | 35 | 0 | 221 | 0 | 1,280 | 34 | 1,656 |\n| 4 | > 5,000 | 32 | 2 | 0 | 340 | 32 | 0 | 10 | 0 | 401 | 1 | 818 |\n| | **Total** | **47** | **3** | **0** | **410** | **67** | **0** | **243** | **0** | **1,719** | **37** | **2,526** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "table", "table_caption": "Table 59: Type-wise vs. Elevation range-wise distribution of GL in Manas subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1602, "line_end": 1608, "token_count_estimate": 515, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "8ab619e2e378ea20", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1609, "line_end": 1613, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "db64ed654245dfdc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: figure\nFigure: Figure 77: Elevation range-Type-wise spatial distribution of GL in Manas subbasin\n\nFigure 77: Elevation range-Type-wise spatial distribution of GL in Manas subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 77: Elevation range-Type-wise spatial distribution of GL in Manas subbasin", "line_start": 1614, "line_end": 1614, "token_count_estimate": 82, "basins": [], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "a980dd544eef053e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.8 Manas Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.8 Manas Subbasin"], "chunk_type": "text", "line_start": 1615, "line_end": 1619, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": ["Manas"], "countries": [], "lake_ids": []}}
{"id": "5b55baa3578e752a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.9 Puna Tsang Chu Subbasin\nType: text\n\nThe Puna Tsang Chu subbasin is the tenth largest subbasin of the Brahmaputra River basin covering a total area of 10,204 Km² i.e. 2.55% of the total basin area (Figure 78). The Puna Tshang Chu Basin has been formed by the joining of the Mo Chu and Pho Chu Rivers. The Mo Chu originates from the northeastern slope of Chomolhari and the Pho Chu from the western slope of Kula Gangri. Mo Chu and Pho Chu are the two main tributaries of Sankosh River. A total of 921 glacial lakes has been mapped, covering a total area of 3,880.79 ha i.e. 0.38% of the total area of the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.9 Puna Tsang Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.9 Puna Tsang Chu Subbasin"], "chunk_type": "text", "line_start": 1621, "line_end": 1623, "token_count_estimate": 205, "basins": ["Brahmaputra"], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "23afcb2eebaf51ea", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.9 Puna Tsang Chu Subbasin\nType: figure\nFigure: Figure 78: Location map of the Puna Tsang Chu subbasin\n\nFigure 78: Location map of the Puna Tsang Chu subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.9 Puna Tsang Chu Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.9 Puna Tsang Chu Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 78: Location map of the Puna Tsang Chu subbasin", "line_start": 1624, "line_end": 1624, "token_count_estimate": 69, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "57960a2c51c3642c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\nIn Puna Tsang Chu subbasin, glacial lakes have been distributed in all area ranges. Table 60 and Figure 79 shows the area range-wise distribution of glacial lakes for the Puna Tsang Chu subbasin. About 737 (80.02%) lakes are with < 5 ha lake area contributing to 26.70% of total lake area. The remaining lakes with > 5 ha in size are 184 (19.98%) contributing to 73.30% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1629, "line_end": 1633, "token_count_estimate": 141, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "f5facdfcf6baf6ca", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: table\nTable: Table 60: Area range-wise distribution of GL in Puna Tsang Chu subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 180 | 64.82 | 1.67 |\n| 2 | 0.5 - 1 | 187 | 134.37 | 3.46 |\n| 3 | 1 - 5 | 370 | 836.81 | 21.56 |\n| 4 | 5 - 10 | 93 | 668.05 | 17.21 |\n| 5 | 10 - 50 | 84 | 1,556.92 | 40.12 |\n| 6 | > 50 | 7 | 619.82 | 15.97 |\n| | **Total** | **921** | **3,880.79** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 60: Area range-wise distribution of GL in Puna Tsang Chu subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1634, "line_end": 1642, "token_count_estimate": 279, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "0605bfdffca963ef", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution\nType: text\n\n***", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 1643, "line_end": 1645, "token_count_estimate": 33, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "003cd43292daaee0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Puna Tsang Chu subbasin is given in Table 61 and Figure 80. Out of 10 types of glacial lakes, 6 types of lake are present in the Puna Tsang Chu subbasin, where Other Glacial Erosion lakes are found to be the maximum with 590 (64.06%) occupying a total lake extent of 1,845.71 ha at 47.56% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 172 (18.68%) and extend over a total area of 641.47 ha at 16.53%. in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1647, "line_end": 1651, "token_count_estimate": 173, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "1cbfc51c25fd8dfa", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 61: Type-wise distribution of GL in Puna Tsang Chu subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 34 | 802.08 | 20.67 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 2 | 10.66 | 0.27 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 172 | 641.47 | 16.53 |\n| 5 | I(s) | Supra-glacial Lake | 61 | 76.49 | 1.97 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 62 | 504.38 | 13.00 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 590 | 1,845.71 | 47.56 |\n| 10 | O | Other Glacial Lake | 0 | 0.00 | 0.00 |\n| | | **Total** | **921** | **3,880.79** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 61: Type-wise distribution of GL in Puna Tsang Chu subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1652, "line_end": 1664, "token_count_estimate": 463, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "0fb30a9254570b3a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1665, "line_end": 1667, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bde73ca7fe3863b4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 62 and Figure 81. The lakes with < 5 ha in size (80.02%) are dominant with Other Glacial Erosion (68.11%) and Other Moraine Dammed lakes (19.40%). Lakes with > 5 ha (19.97%) are also dominated by Other Glacial Erosion lakes (47.82%). All types of Glacier Erosion lakes, which constitute about 70.79% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1669, "line_end": 1673, "token_count_estimate": 150, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "06a47cd5e017c63a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 62: Area range-wise vs. Type-wise distribution of GL in Puna Tsang Chu subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 34 | 26 | 0 | 0 | 0 | 120 | 0 | 180 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 41 | 17 | 0 | 5 | 0 | 124 | 0 | 187 |\n| 3 | 1 - 5 | 6 | 1 | 0 | 68 | 16 | 0 | 21 | 0 | 258 | 0 | 370 |\n| 4 | 5 - 10 | 7 | 1 | 0 | 17 | 2 | 0 | 17 | 0 | 49 | 0 | 93 |\n| 5 | 10 - 50 | 19 | 0 | 0 | 10 | 0 | 0 | 18 | 0 | 37 | 0 | 84 |\n| 6 | > 50 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 7 |\n| **Total** | | **34** | **2** | **0** | **172** | **61** | **0** | **62** | **0** | **590** | **0** | **921** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 62: Area range-wise vs. Type-wise distribution of GL in Puna Tsang Chu subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1674, "line_end": 1682, "token_count_estimate": 492, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "b2acf5ee1c7e3e7e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: figure\nFigure: Figure 81: Area range-wise vs. Type-wise distribution of GL in Puna Tsang Chu subbasin\n\n**Figure 81: Area range-wise vs. Type-wise distribution of GL in Puna Tsang Chu subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 81: Area range-wise vs. Type-wise distribution of GL in Puna Tsang Chu subbasin", "line_start": 1684, "line_end": 1684, "token_count_estimate": 88, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "015ba7fcbfbb0577", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 1685, "line_end": 1691, "token_count_estimate": 52, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31b8282d7343c6e3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Puna Tsang Chu subbasin has been shown in Table 63 and Figure 82. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 912 (99.02%) with total lake area of 3,855.93 ha (99.36%) and remaining 0.98% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1693, "line_end": 1697, "token_count_estimate": 127, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "1bc18b71734f41eb", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 63: Elevation range-wise distribution of GL in Puna Tsang Chu subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 9 | 24.85 | 0.64 |\n| 3 | 4,001 - 5,000 | 668 | 2,720.80 | 70.11 |\n| 4 | > 5,000 | 244 | 1,135.14 | 29.25 |\n| **Total** | | **921** | **3,880.79** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 63: Elevation range-wise distribution of GL in Puna Tsang Chu subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1698, "line_end": 1704, "token_count_estimate": 233, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "2f6859462e6335e9", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: figure\nFigure: Figure 82: Elevation range-wise distribution of GL in Puna Tsang Chu subbasin\n\n**Figure 82: Elevation range-wise distribution of GL in Puna Tsang Chu subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 82: Elevation range-wise distribution of GL in Puna Tsang Chu subbasin", "line_start": 1706, "line_end": 1706, "token_count_estimate": 79, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "5d35a1c1c353772d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 64 and Figure 83. It is noted that, 72.52% of glacial lakes (668) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 70.10%. It has been further noticed that, 72.72% of lakes < 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 5 ha. However, no glacial lakes lie below 3,000 m and 244 lakes above 5,000 m elevation range", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1707, "line_end": 1718, "token_count_estimate": 227, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dcbe1bcd199d2a78", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 64: Area range-wise vs. Elevation range-wise distribution of GL in Puna Tsang Chu subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 1 | 0.32 | 132 | 48.00 | 47 | 16.71 | 180 | 64.82 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 1 | 0.71 | 132 | 94.00 | 54 | 39.43 | 187 | 134.37 |\n| 3 | 1 - 5 | 0 | 0.00 | 6 | 18.01 | 272 | 608.00 | 92 | 211.29 | 370 | 836.81 |\n| 4 | 5 - 10 | 0 | 0.00 | 1 | 5.81 | 69 | 498.00 | 23 | 164.32 | 93 | 668.05 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 60 | 1,113.00 | 24 | 444.32 | 84 | 1,556.92 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 3 | 361.00 | 4 | 259.07 | 7 | 619.82 |\n| | **Total** | **0** | **0.00** | **9** | **24.85** | **668** | **2,720.80** | **244** | **1,135.14** | **921** | **3,880.79** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 64: Area range-wise vs. Elevation range-wise distribution of GL in Puna Tsang Chu subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1719, "line_end": 1727, "token_count_estimate": 663, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "9c1cd5e179dd164e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 65 and Figure 84. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (64.06%) are predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Cirque Erosion Lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 20.93% and 93.54%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 85.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1728, "line_end": 1741, "token_count_estimate": 212, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "43cf99b00501e415", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 65: Type-wise vs. Elevation range-wise distribution of GL in Puna Tsang Chu subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 0 | 9 |\n| 3 | 4,001 - 5,000 | 17 | 2 | 0 | 36 | 60 | 0 | 58 | 0 | 495 | 0 | 668 |\n| 4 | > 5,000 | 17 | 0 | 0 | 136 | 1 | 0 | 3 | 0 | 87 | 0 | 244 |\n| | **Total** | **34** | **2** | **0** | **172** | **61** | **0** | **62** | **0** | **590** | **0** | **921** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 65: Type-wise vs. Elevation range-wise distribution of GL in Puna Tsang Chu subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1742, "line_end": 1748, "token_count_estimate": 513, "basins": [], "subbasins": ["Puna Tsang Chu"], "countries": [], "lake_ids": []}}
{"id": "7fcc36903f9fcb4e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1749, "line_end": 1759, "token_count_estimate": 68, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a133ad501e1d4c39", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\nThe Subansiri subbasin is the fifth largest subbasin of the Brahmaputra River basin covering a total area of 30,644 Km² i.e. 7.66% of the total basin area (Figure 86). The Subansiri River originates in the Himalayas near Mount Porom in the Tibet. It enters India near the town of Taksing and flows east and southeast through Miri Hills, then south to the Assam Valley at Dulangmukh in Dhemaji district, where it joins the Brahmaputra River at Jamurighat in Lakhimpur district. Small tributaries of the Subansiri include Rangandi, Dikrong and Kamala. Chyumi Chu and Kamla are the two main tributaries of Subansiri River. A total of 539 glacial lakes has been mapped, covering a total area of 2,004.24 ha i.e. 0.06% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-range-wise Distribution**\n\nIn Subansiri subbasin, glacial lakes have been distributed in all area ranges. Table 66 and Figure 87 shows the area range-wise distribution of glacial lakes for the Subansiri subbasin. About 435 (80.71%) lakes are with < 5 ha lake area contributing to 30.93% of total lake area. The remaining lakes with > 5 ha in size are 104 (19.29%) contributing to 69.07% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1761, "line_end": 1774, "token_count_estimate": 384, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Subansiri"], "countries": ["India"], "lake_ids": []}}
{"id": "ec461bb181945b69", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: table\nTable: Table 66: Area range-wise distribution of GL in Subansiri subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 96 | 34.94 | 1.74 |\n| 2 | 0.5 - 1 | 121 | 88.12 | 4.40 |\n| 3 | 1 - 5 | 218 | 496.80 | 24.79 |\n| 4 | 5 - 10 | 62 | 445.09 | 22.21 |\n| 5 | 10 - 50 | 38 | 696.38 | 34.75 |\n| 6 | > 50 | 4 | 242.91 | 12.12 |\n| | **Total** | **539** | **2,004.24** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "table", "table_caption": "Table 66: Area range-wise distribution of GL in Subansiri subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1775, "line_end": 1783, "token_count_estimate": 265, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "4a274f3414ab15ab", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Subansiri subbasin is given in Table 67 and Figure 88. Out of 10 types of glacial lakes, 8 types of lake are present in the Subansiri subbasin, where Other Glacial Erosion lakes are found to be the maximum with 346 (64.19%) occupying a total lake extent of 1,232.77 ha at 61.51% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 89 (16.51%) and extend over a total area of 159.91 ha at 7.98% in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1784, "line_end": 1797, "token_count_estimate": 197, "basins": ["BRAHMAPUTRA"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "8b9ccbffe938958d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: table\nTable: Table 67: Type-wise distribution of GL in Subansiri subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 33 | 293.18 | 14.63 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 1 | 1.74 | 0.09 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 89 | 159.91 | 7.98 |\n| 5 | I(s) | Supra-glacial Lake | 6 | 6.31 | 0.31 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 2 | 2.50 | 0.12 |\n| 7 | E(c) | Cirque Erosion Lake | 31 | 235.25 | 11.74 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 346 | 1,232.77 | 61.51 |\n| 10 | O | Other Glacial Lake | 31 | 72.58 | 3.62 |\n| | | **Total** | **539** | **2,004.24** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "table", "table_caption": "Table 67: Type-wise distribution of GL in Subansiri subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1798, "line_end": 1810, "token_count_estimate": 452, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "44e821e4ca1c6dc5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 68 and Figure 89. The lakes with < 5 ha in size (80.71%) are dominant with Other Glacial Erosion (65.05%) and Other Moraine Dammed lakes (19.31%). Lakes with > 5 ha (19.29%) are also dominated by Other Glacial Erosion lakes (60.57%). All types of Glacier Erosion lakes, which constitute about 69.94% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1811, "line_end": 1821, "token_count_estimate": 182, "basins": ["BRAHMAPUTRA"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "a2cc4968bf9313ab", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: table\nTable: Table 68: Area range-wise vs. Type-wise distribution of GL in Subansiri subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 23 | 2 | 0 | 1 | 0 | 58 | 12 | 96 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 23 | 3 | 1 | 2 | 0 | 81 | 10 | 121 |\n| 3 | 1 - 5 | 12 | 1 | 0 | 38 | 1 | 1 | 15 | 0 | 144 | 6 | 218 |\n| 4 | 5 - 10 | 13 | 0 | 0 | 3 | 0 | 0 | 8 | 0 | 36 | 2 | 62 |\n| 5 | 10 - 50 | 6 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 25 | 1 | 38 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 4 |\n| Total | | 33 | 1 | 0 | 89 | 6 | 2 | 31 | 0 | 346 | 31 | 539 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "table", "table_caption": "Table 68: Area range-wise vs. Type-wise distribution of GL in Subansiri subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1822, "line_end": 1830, "token_count_estimate": 540, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "e0be4f5a94e325db", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: figure\nFigure: Figure 89: Area range-wise vs. Type-wise distribution of GL in Subansiri subbasin\n\n**Figure 89: Area range-wise vs. Type-wise distribution of GL in Subansiri subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 89: Area range-wise vs. Type-wise distribution of GL in Subansiri subbasin", "line_start": 1832, "line_end": 1832, "token_count_estimate": 87, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "9d293849c769cbd1", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Subansiri subbasin has been shown in Table 69 and Figure 90. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 482 (89.42%) with total lake area of 1,580.4 ha (78.85%) and remaining 10.58% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1833, "line_end": 1845, "token_count_estimate": 154, "basins": ["BRAHMAPUTRA"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "6a99fe47f3de3ab1", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: table\nTable: Table 69: Elevation range-wise distribution of GL in Subansiri subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 57 | 423.83 | 21.15 |\n| 3 | 4,001 - 5,000 | 279 | 1,131.38 | 56.45 |\n| 4 | > 5,000 | 203 | 449.02 | 22.40 |\n| Total | | 539 | 2,004.24 | 100.00 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "table", "table_caption": "Table 69: Elevation range-wise distribution of GL in Subansiri subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1846, "line_end": 1852, "token_count_estimate": 223, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "0bd0d83f9afbe632", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: figure\nFigure: Figure 90: Elevation range-wise distribution of GL in Subansiri subbasin\n\n**Figure 90: Elevation range-wise distribution of GL in Subansiri subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 90: Elevation range-wise distribution of GL in Subansiri subbasin", "line_start": 1854, "line_end": 1854, "token_count_estimate": 79, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "9493b870289d049b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 70 and Figure 91. It is noted that, 51.76% of glacial lakes (279) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 56.44%. It has been further noticed that, 51.26% of lakes < 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 5 ha. However, no glacial lakes lie below 3,000 m and 203 lakes above 5,000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1855, "line_end": 1865, "token_count_estimate": 227, "basins": ["BRAHMAPUTRA"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "776db0498e36fb79", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: table\nTable: Table 70: Area range-wise vs. Elevation range-wise distribution of GL in Subansiri subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range: up to 3,000 - No. of lakes | Elevation Range: up to 3,000 - Total Lake Area (ha) | Elevation Range: 3,001 - 4,000 - No. of lakes | Elevation Range: 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range: 4,001 - 5,000 - No. of lakes | Elevation Range: 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range: > 5,000 - No. of lakes | Elevation Range: > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 3 | 0.78 | 43 | 16.27 | 50 | 17.89 | 96 | 34.94 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 6 | 4.69 | 61 | 44.19 | 54 | 39.24 | 121 | 88.12 |\n| 3 | 1 - 5 | 0 | 0.00 | 23 | 56.46 | 119 | 277.90 | 76 | 162.44 | 218 | 496.80 |\n| 4 | 5 - 10 | 0 | 0.00 | 13 | 91.55 | 33 | 238.06 | 16 | 115.48 | 62 | 445.09 |\n| 5 | 10 - 50 | 0 | 0.00 | 12 | 270.35 | 19 | 312.05 | 7 | 113.98 | 38 | 696.38 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 4 | 243.00 | 0 | 0.00 | 4 | 242.91 |\n| Total | | 0 | 0.00 | 57 | 423.83 | 279 | 1,131.38 | 203 | 449.02 | 539 | 2,004.24 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "table", "table_caption": "Table 70: Area range-wise vs. Elevation range-wise distribution of GL in Subansiri subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range: up to 3,000 - No. of lakes", "Elevation Range: up to 3,000 - Total Lake Area (ha)", "Elevation Range: 3,001 - 4,000 - No. of lakes", "Elevation Range: 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range: 4,001 - 5,000 - No. of lakes", "Elevation Range: 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range: > 5,000 - No. of lakes", "Elevation Range: > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1866, "line_end": 1874, "token_count_estimate": 601, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "9f94738bd53b19a6", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 71 and Figure 92. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (64.19%) are predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Cirque Erosion Lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 12.35% and 67.74%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 93.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1875, "line_end": 1885, "token_count_estimate": 213, "basins": ["BRAHMAPUTRA"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "57265277ab7f47dc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: table\nTable: Table 71: Type-wise vs. Elevation range-wise distribution of GL in Subansiri subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 43 | 5 | 57 |\n| 3 | 4,001 - 5,000 | 7 | 0 | 0 | 11 | 0 | 0 | 21 | 0 | 222 | 18 | 279 |\n| 4 | > 5,000 | 26 | 1 | 0 | 78 | 6 | 2 | 1 | 0 | 81 | 8 | 203 |\n| Total | | 33 | 1 | 0 | 89 | 6 | 2 | 31 | 0 | 346 | 31 | 539 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "table", "table_caption": "Table 71: Type-wise vs. Elevation range-wise distribution of GL in Subansiri subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 1886, "line_end": 1892, "token_count_estimate": 463, "basins": [], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "d57a967ff164963d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.10 Subansiri Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.10 Subansiri Subbasin"], "chunk_type": "text", "line_start": 1893, "line_end": 1901, "token_count_estimate": 69, "basins": ["BRAHMAPUTRA"], "subbasins": ["Subansiri"], "countries": [], "lake_ids": []}}
{"id": "8a4d796d1a502019", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.11 Teesta Subbasin\nType: text\n\nThe Teesta subbasin is the smallest subbasin of the Brahmaputra River basin covering a total area of 8,555 Km² i.e. 2.14% of the total basin area (Figure 94). The river originates from Cholamo Lake at an elevation of 5,330 m above sea level in the Himalayas. It is fed by the glaciers viz. Zemu, Changame Khanpu, Talung etc. Zenu Chu and Rangit are the two main tributaries of the Teesta River. A total of 683 glacial lakes has been mapped, covering a total area of 3,120.76 ha i.e. 0.36% of the total area of the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.11 Teesta Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.11 Teesta Subbasin"], "chunk_type": "text", "line_start": 1903, "line_end": 1907, "token_count_estimate": 191, "basins": ["Brahmaputra"], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "9e07a6e767eaccd9", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range-wise Distribution\nType: text\n\nIn Teesta subbasin, glacial lakes have been distributed in all area ranges. Table 72 and Figure 95 shows the area range-wise distribution of glacial lakes for the Teesta subbasin. About 575 (84.19%) lakes are with < 5 ha lake area contributing to 22.48% of total lake area. The remaining lakes with > 5 ha in size are 108 (15.81%) contributing to 77.52% of total lake area in the subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range-wise Distribution"], "chunk_type": "text", "line_start": 1909, "line_end": 1913, "token_count_estimate": 138, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "636ecdb7930c2b56", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range-wise Distribution\nType: table\nTable: Table 72: Area range-wise distribution of GL in Teesta subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 196 | 66.61 | 2.13 |\n| 2 | 0.5 - 1 | 153 | 111.22 | 3.56 |\n| 3 | 1 - 5 | 226 | 523.58 | 16.78 |\n| 4 | 5 - 10 | 47 | 353.24 | 11.32 |\n| 5 | 10 - 50 | 50 | 982.43 | 31.48 |\n| 6 | > 50 | 11 | 1,083.68 | 34.72 |\n| | **Total** | **683** | **3,120.76** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area-range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 72: Area range-wise distribution of GL in Teesta subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1914, "line_end": 1922, "token_count_estimate": 265, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "74fd79220df3755d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Teesta subbasin is given in Table 73 and Figure 96. Out of 10 types of glacial lakes, 7 types of lake are present in the Teesta subbasin, where Other Glacial Erosion lakes are found to be the maximum with 382 (55.93%) occupying a total lake extent of 765.76 ha at 24.53% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 148 (21.67%) and extend over a total area of 633.39 ha at 20.29%. in the subbasin", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1925, "line_end": 1929, "token_count_estimate": 168, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "aca2f73dddbc675b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 73: Type-wise distribution of GL in Teesta subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 31 | 1,374.46 | 44.03 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 5 | 8.08 | 0.26 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 148 | 633.39 | 20.29 |\n| 5 | I(s) | Supra-glacial Lake | 72 | 48.75 | 1.56 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 37 | 258.17 | 8.27 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 382 | 765.76 | 24.53 |\n| 10 | O | Other Glacial Lake | 8 | 33.15 | 1.06 |\n| | | **Total** | **683** | **3,121.76** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 73: Type-wise distribution of GL in Teesta subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 1930, "line_end": 1942, "token_count_estimate": 451, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "ce02e15f84a12493", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 74 and Figure 97. The lakes with < 5 ha in size (84.19%) are dominant with Other Glacial Erosion (60.34%) and Other Moraine Dammed lakes (21.39%). Lakes with > 5 ha (15.81%) are also dominated by Other Glacial Erosion lakes (32.40%). All types of Glacier Erosion lakes, which constitute about 61.34% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1943, "line_end": 1951, "token_count_estimate": 178, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "68c8aca79728424d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 74: Area range-wise vs. Type-wise distribution of GL in Teesta subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake   M(e) | Types of Glacial Lake   M(l) | Types of Glacial Lake   M(lg) | Types of Glacial Lake   M(o) | Types of Glacial Lake   I(s) | Types of Glacial Lake   I(d) | Types of Glacial Lake   E(c) | Types of Glacial Lake   E(v) | Types of Glacial Lake   E(o) | Types of Glacial Lake   O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 37 | 50 | 0 | 0 | 0 | 108 | 1 | 196 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 35 | 13 | 0 | 2 | 0 | 100 | 2 | 153 |\n| 3 | 1 - 5 | 1 | 4 | 0 | 51 | 8 | 0 | 19 | 0 | 139 | 4 | 226 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 12 | 1 | 0 | 7 | 0 | 23 | 0 | 47 |\n| 5 | 10 - 50 | 17 | 0 | 0 | 11 | 0 | 0 | 9 | 0 | 12 | 1 | 50 |\n| 6 | > 50 | 9 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |\n| **Total** | | **31** | **5** | **0** | **148** | **72** | **0** | **37** | **0** | **382** | **8** | **683** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 74: Area range-wise vs. Type-wise distribution of GL in Teesta subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake M(e)", "Types of Glacial Lake M(l)", "Types of Glacial Lake M(lg)", "Types of Glacial Lake M(o)", "Types of Glacial Lake I(s)", "Types of Glacial Lake I(d)", "Types of Glacial Lake E(c)", "Types of Glacial Lake E(v)", "Types of Glacial Lake E(o)", "Types of Glacial Lake O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 1952, "line_end": 1960, "token_count_estimate": 548, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "2a358041790179c7", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Teesta subbasin has been shown in Table 75 and Figure 98. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 661 (96.78%) with total lake area of 3,011.61 ha (96.50%) and remaining 3.22% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1961, "line_end": 1971, "token_count_estimate": 147, "basins": ["BRAHMAPUTRA"], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "88db402be1cb6a9e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: table\nTable: Table 75: Elevation range-wise distribution of GL in Teesta subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area   (ha) | Total Lake Area   (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 22 | 109.14 | 3.50 |\n| 3 | 4,001 - 5,000 | 375 | 1,057.08 | 33.87 |\n| 4 | > 5,000 | 286 | 1,954.54 | 62.63 |\n| **Total** | | **683** | **3,120.76** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 75: Elevation range-wise distribution of GL in Teesta subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 1972, "line_end": 1978, "token_count_estimate": 227, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "d69b9a4f4dea851c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type-wise Distribution"], "chunk_type": "text", "line_start": 1979, "line_end": 1983, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "727762773ff42f46", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 76 and Figure 99. It is noted that, 54.82% of glacial lakes (375) are situated in high altitude range i.e. 4,001 - 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 33.86%. It has been further noticed that, 56.59% of lakes < 5 ha are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 5 ha. However, no glacial lakes lie below 3,000 m and 286 lakes above 5,000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1985, "line_end": 1989, "token_count_estimate": 199, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0262a84ccd840c8d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area Elevation range-wise Distribution\nType: table\nTable: Table 76: Area range-wise vs. Elevation range-wise distribution of GL in Teesta subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) up to 3,000 - No. of lakes | Elevation Range (m) up to 3,000 - Total Lake Area (ha) | Elevation Range (m) 3,001 - 4,000 - No. of lakes | Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) 4,001 - 5,000 - No. of lakes | Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) > 5,000 - No. of lakes | Elevation Range (m) > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 7 | 2.47 | 109 | 37.00 | 80 | 27.14 | 196 | 66.61 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 2 | 1.58 | 90 | 65.00 | 61 | 44.15 | 153 | 111.22 |\n| 3 | 1 - 5 | 0 | 0.00 | 7 | 12.04 | 127 | 288.00 | 92 | 223.48 | 226 | 523.58 |\n| 4 | 5 - 10 | 0 | 0.00 | 2 | 15.83 | 23 | 164.00 | 22 | 173.62 | 47 | 353.24 |\n| 5 | 10 - 50 | 0 | 0.00 | 4 | 77.23 | 25 | 445.00 | 21 | 460.33 | 50 | 982.43 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 1 | 58.00 | 10 | 1,025.81 | 11 | 1,083.68 |\n| | **Total** | **0** | **0.00** | **22** | **109.14** | **375** | **1,057.08** | **286** | **1,954.54** | **683** | **3,120.76** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 76: Area range-wise vs. Elevation range-wise distribution of GL in Teesta subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) up to 3,000 - No. of lakes", "Elevation Range (m) up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) 3,001 - 4,000 - No. of lakes", "Elevation Range (m) 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) 4,001 - 5,000 - No. of lakes", "Elevation Range (m) 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) > 5,000 - No. of lakes", "Elevation Range (m) > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 1990, "line_end": 1998, "token_count_estimate": 631, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "ae1133d70b338733", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 1999, "line_end": 2007, "token_count_estimate": 52, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6ddcb319e626247b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 77 and Figure 100. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (55.92%) are predominantly located in the elevation range of 4,001 - 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Cirque Erosion Lakes are distributed in high altitude range (4,001 - 5,000 m), i.e. 16.89% and 83.78%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 101.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2009, "line_end": 2013, "token_count_estimate": 182, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "19e8555f79398ce1", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type Elevation range-wise Distribution\nType: table\nTable: Table 77: Type-wise vs. Elevation range-wise distribution of GL in Teesta subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 19 | 1 | 22 |\n| 3 | 4,001 - 5,000 | 7 | 5 | 0 | 25 | 42 | 0 | 31 | 0 | 263 | 2 | 375 |\n| 4 | > 5,000 | 23 | 0 | 0 | 123 | 29 | 0 | 6 | 0 | 100 | 5 | 286 |\n| | **Total** | **31** | **5** | **0** | **148** | **72** | **0** | **37** | **0** | **382** | **8** | **683** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 77: Type-wise vs. Elevation range-wise distribution of GL in Teesta subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 2014, "line_end": 2020, "token_count_estimate": 485, "basins": [], "subbasins": ["Teesta"], "countries": [], "lake_ids": []}}
{"id": "083396e74ed692ff", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Type Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Type Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Type Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2021, "line_end": 2027, "token_count_estimate": 68, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a3ea520072e7f6a2", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: text\n\nThe Upper Yarlung Tsangpo subbasin is the largest subbasin of the Brahmaputra River basin covering a total area of 1,27,926 Km² i.e. 31.99% of the total basin area (Figure 102). Major tributaries of Yarlung Tsangpo include Nyang chu River, Shang Chu, Nyang River, Mu Chu, and Parlung Tsangpo. It originates at Angsi Glacier in western Tibet, southeast of Mount Kailash and Lake Manasarovar, it later forms the South Tibet Valley and Yarlung Tsangpo Grand Canyon before passing into the state of Arunachal Pradesh, India. A total of 2,900 glacial lakes has been mapped, covering a total area of 16,088.66 ha i.e. 0.12% of the total area of the subbasin.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area-range-wise Distribution**\n\nIn Upper Yarlung Tsangpo subbasin, glacial lakes have been distributed in all area ranges. Table 78 and Figure 103 shows the area range-wise distribution of glacial lakes for the Upper Yarlung Tsangpo subbasin. About 2,529 (87.21%) lakes are with < 5 ha lake area contributing to 19.45% of total lake area. The remaining lakes with > 5 ha in size are 371 (12.79%) contributing to 80.55% of total lake area in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "text", "line_start": 2029, "line_end": 2041, "token_count_estimate": 371, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "a2beb0e6aae636fc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: table\nTable: Table 78: Area range-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n| S. No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 719 | 255.25 | 1.59 |\n| 2 | 0.5 - 1 | 754 | 538.93 | 3.35 |\n| 3 | 1 - 5 | 1,056 | 2,335.77 | 14.52 |\n| 4 | 5 - 10 | 177 | 1,239.70 | 7.71 |\n| 5 | 10 - 50 | 146 | 3,129.09 | 19.45 |\n| 6 | > 50 | 48 | 8,589.93 | 53.39 |\n| | **Total** | **2,900** | **16,088.67** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "table", "table_caption": "Table 78: Area range-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2042, "line_end": 2050, "token_count_estimate": 295, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "30c0e3b2bc61cf2a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: figure\nFigure: Figure 103: Area range-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n**Figure 103: Area range-wise distribution of GL in Upper Yarlung Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 103: Area range-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "line_start": 2052, "line_end": 2052, "token_count_estimate": 89, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "fba94b8c79a69aa5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Upper Yarlung Tsangpo subbasin is given in Table 79 and Figure 104. Out of 10 types of glacial lakes, 8 types of lake are present in the Upper Yarlung Tsangpo subbasin, where Other Glacial Erosion lakes are found to be the maximum with 1,226 (42.28%) occupying a total lake extent of 5,923.31 ha at 36.82% in the subbasin. After that, Other Moraine Dammed lakes are in majority with 922 (31.79%) and extend over a total area of 2,057.14 ha at 12.79%. in the subbasin.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "text", "line_start": 2053, "line_end": 2067, "token_count_estimate": 213, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "7cc9231d080943fb", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: table\nTable: Table 79: Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n| S. No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | M(e) | End-moraine Dammed Lake | 78 | 3,123.41 | 19.41 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 5 | 9.81 | 0.06 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 1 | 0.60 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 922 | 2,057.14 | 12.79 |\n| 5 | I(s) | Supra-glacial Lake | 17 | 35.73 | 0.22 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 28 | 99.22 | 0.62 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 1,226 | 5,923.31 | 36.82 |\n| 10 | O | Other Glacial Lake | 623 | 4,839.45 | 30.08 |\n| | | **Total** | **2,900** | **16,088.67** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "table", "table_caption": "Table 79: Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "columns": ["S. No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 2068, "line_end": 2080, "token_count_estimate": 486, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "2df791195acb0844", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: figure\nFigure: Figure 104: Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n**Figure 104: Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "figure", "figure_caption": "Figure 104: Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "line_start": 2082, "line_end": 2082, "token_count_estimate": 87, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "16fab09a2c43cfef", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > 5.2.12 Upper Yarlung Tsangpo Subbasin", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "5.2.12 Upper Yarlung Tsangpo Subbasin"], "chunk_type": "text", "line_start": 2083, "line_end": 2085, "token_count_estimate": 56, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "36c9b47073873268", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 80 and Figure 105. The lakes with < 5 ha in size (87.21%) are dominant with Other Glacial Erosion (43.41%) and Other Moraine Dammed lakes (32.66%). Lakes with > 5 ha (12.79%) are also dominated by Other Glacial Erosion lakes (34.50%). All types of Glacier Erosion lakes, which constitute about 43.24% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 2087, "line_end": 2091, "token_count_estimate": 151, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "30a22f0e6eee70d0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: table\nTable: Table 80: Area range-wise vs. Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n| S. No. | Subbasin | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 2 | 0 | 244 | 7 | 0 | 4 | 0 | 282 | 180 | 719 |\n| 2 | 0.5 - 1 | 3 | 0 | 1 | 230 | 6 | 0 | 8 | 0 | 354 | 152 | 754 |\n| 3 | 1 - 5 | 15 | 2 | 0 | 352 | 3 | 0 | 9 | 0 | 462 | 213 | 1,056 |\n| 4 | 5 - 10 | 12 | 1 | 0 | 70 | 0 | 0 | 5 | 0 | 67 | 22 | 177 |\n| 5 | 10 - 50 | 34 | 0 | 0 | 26 | 1 | 0 | 2 | 0 | 47 | 36 | 146 |\n| 6 | > 50 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 20 | 48 |\n| | **Total** | **78** | **5** | **1** | **922** | **17** | **0** | **28** | **0** | **1,226** | **623** | **2,900** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 80: Area range-wise vs. Type-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "columns": ["S. No.", "Subbasin", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2092, "line_end": 2100, "token_count_estimate": 498, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c9e10b017483ee1d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Area range-Type-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Area range-Type-wise Distribution"], "chunk_type": "text", "line_start": 2101, "line_end": 2107, "token_count_estimate": 51, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "31c1f2048d113df2", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Upper Yarlung Tsangpo subbasin has been shown in Table 81 and Figure 106. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 2,898 (99.93%) with total lake area of 16,083.48 ha (99.96%) and remaining 0.07% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2109, "line_end": 2113, "token_count_estimate": 128, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "4cc62cbf64c25760", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 81: Elevation range-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.00 | 0.00 |\n| 2 | 3,001 - 4,000 | 2 | 5.20 | 0.03 |\n| 3 | 4,001 - 5,000 | 299 | 6,130.29 | 38.10 |\n| 4 | > 5,000 | 2,599 | 9,953.18 | 61.86 |\n| | **Total** | **2,900** | **16,088.67** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 81: Elevation range-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2114, "line_end": 2120, "token_count_estimate": 238, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "66fdd770e1ec637a", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 82 and Figure 107. It is noted that, 89.62% of glacial lakes (2,599) are situated in very high altitude range i.e. > 5,000 m amsl, which constitutes maximum share of total lake area within that range i.e. 61.86%. It has been further noticed that, 10.31% of lakes are lying within high altitude range i.e. 4,001 - 5,000 m, majority of them falling in size range of 1 - 10 ha. However, no glacial lakes lie below 3,000 m elevation range.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2121, "line_end": 2131, "token_count_estimate": 212, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fcac86131a352f71", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 82: Area range-wise vs. Elevation range-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m) - up to 3,000 - No. of lakes | Elevation Range (m) - up to 3,000 - Total Lake Area (ha) | Elevation Range (m) - 3,001 - 4,000 - No. of lakes | Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m) - 4,001 - 5,000 - No. of lakes | Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m) - > 5,000 - No. of lakes | Elevation Range (m) - > 5,000 - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 0 | 0.00 | 63 | 22.71 | 656 | 232.54 | 719 | 255.25 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 0 | 0.00 | 71 | 49.64 | 683 | 489.29 | 754 | 538.93 |\n| 3 | 1 - 5 | 0 | 0.00 | 2 | 5.20 | 90 | 178.89 | 964 | 2,151.68 | 1,056 | 2,335.77 |\n| 4 | 5 - 10 | 0 | 0.00 | 0 | 0.00 | 22 | 160.15 | 155 | 1,079.55 | 177 | 1,239.70 |\n| 5 | 10 - 50 | 0 | 0.00 | 0 | 0.00 | 30 | 754.51 | 116 | 2,374.58 | 146 | 3,129.09 |\n| 6 | > 50 | 0 | 0.00 | 0 | 0.00 | 23 | 4,964.39 | 25 | 3,625.54 | 48 | 8,589.93 |\n| | **Total** | **0** | **0.00** | **2** | **5.20** | **299** | **6,130.29** | **2,599** | **9,953.18** | **2,900** | **16,088.67** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 82: Area range-wise vs. Elevation range-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m) - up to 3,000 - No. of lakes", "Elevation Range (m) - up to 3,000 - Total Lake Area (ha)", "Elevation Range (m) - 3,001 - 4,000 - No. of lakes", "Elevation Range (m) - 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m) - 4,001 - 5,000 - No. of lakes", "Elevation Range (m) - 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m) - > 5,000 - No. of lakes", "Elevation Range (m) - > 5,000 - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2132, "line_end": 2140, "token_count_estimate": 669, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "3bc493cbcac4b908", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 83 and Figure 108. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (42.27%) are predominantly located in the elevation above 5,000 m. The other dominant lake type, namely, Other Moraine Dammed and Other Glacial Lakes are prominently seen in the elevation above 5,000 m i.e. 31.79% and 21.48%. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 109.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2141, "line_end": 2155, "token_count_estimate": 201, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "47fc699a3eeca058", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: table\nTable: Table 83: Type-wise vs. Elevation range-wise distribution of GL in Upper Yarlung Tsangpo subbasin\n\n| S. No. | Subbasin | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 3,001 - 4,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| 3 | 4,001 - 5,000 | 12 | 0 | 0 | 24 | 0 | 0 | 2 | 0 | 58 | 203 | 299 |\n| 4 | > 5,000 | 66 | 5 | 1 | 898 | 17 | 0 | 26 | 0 | 1,166 | 420 | 2,599 |\n| | **Total** | **78** | **5** | **1** | **922** | **17** | **0** | **28** | **0** | **1,226** | **623** | **2,900** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 83: Type-wise vs. Elevation range-wise distribution of GL in Upper Yarlung Tsangpo subbasin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 2156, "line_end": 2162, "token_count_estimate": 494, "basins": [], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "d4ed2254b81cbc9f", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.2 Subbasin-Wise Statistics > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.2 Subbasin-Wise Statistics", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2163, "line_end": 2171, "token_count_estimate": 67, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3e8b19187d538ca", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins\nType: text\n\nGlacial lakes in all 12 subbasins of Brahmaputra River basin are compared for number of glacial lakes, total lake area, lake types and their elevation ranges in the following sections.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins"], "chunk_type": "text", "line_start": 2173, "line_end": 2175, "token_count_estimate": 70, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c3e99bff87d76e7d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: text\n\nTable 84 and Figure 110 shows the subbasin-wise distribution of number of glacial lakes and their water spread area for the Brahmaputra River basin. Lakes are predominantly distributed in Lower Yarlung Tsangpo (27.66%) followed by Upper Yarlung Tsangpo subbasin (16.11%), occupying a total lake extent of 26,371.81 ha and 16,088.67 ha at 28.36% and 17.30% respectively in the entire basin. However, minimum glacial lakes are present in Jia Bharali subbasin (1.30%) followed by Dihang subbasin (2.40%), covering a total lake extent of 0.69% and 3.14% respectively.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "text", "line_start": 2177, "line_end": 2181, "token_count_estimate": 187, "basins": ["Brahmaputra"], "subbasins": ["Dihang", "Jia Bharali", "Lower Yarlung Tsangpo", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "0cf376c8ca16789f", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: table\nTable: Table 84: Subbasin-wise distribution of GL in Brahmaputra River basin\n\n| S.No. | Subbasin | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | Amo Chu | 513 | 1,565.46 | 1.68 |\n| 2 | Dibang | 772 | 6,566.10 | 7.06 |\n| 3 | Dihang | 433 | 2,923.66 | 3.14 |\n| 4 | Jia Bharali | 234 | 640.12 | 0.69 |\n| 5 | Lhasa Tsangpo | 1,225 | 6,980.94 | 7.51 |\n| 6 | Lohit | 2,276 | 11,529.23 | 12.40 |\n| 7 | Lower Yarlung Tsangpo | 4,979 | 26,371.81 | 28.36 |\n| 8 | Manas | 2,526 | 11,318.96 | 12.17 |\n| 9 | Puna Tsang Chu | 921 | 3,880.79 | 4.17 |\n| 10 | Subansiri | 539 | 2,004.24 | 2.16 |\n| 11 | Teesta | 683 | 3,120.76 | 3.36 |\n| 12 | Upper Yarlung Tsangpo | 2,900 | 16,088.67 | 17.30 |\n| | **Total** | **18,001** | **92,990.74** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "table", "table_caption": "Table 84: Subbasin-wise distribution of GL in Brahmaputra River basin", "columns": ["S.No.", "Subbasin", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 13, "line_start": 2182, "line_end": 2196, "token_count_estimate": 431, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "6c435f0d88840412", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Subbasin-Area range-wise Distribution**\n\nGlacial lakes have been distributed in all subbasins for 6 classes of area ranges. Table 85 and Figure 111 shows subbasin-area range-wise distribution of glacial lakes for the Brahmaputra River basin. All subbasins contain glacial lakes in all area ranges except Jia Bharali, where lakes are not present in the area range of > 50 ha. Lower Yarlung Tsangpo is the subbasin which has majority of lakes > 50 ha i.e. 26.08%.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "text", "line_start": 2197, "line_end": 2209, "token_count_estimate": 178, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Jia Bharali", "Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "38d646372ba448f3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: table\nTable: Table 85: Subbasin-wise vs. Area range-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Sub basin | 0.25 - 0.5 ha (No. of Lakes) | 0.25 - 0.5 ha (Total Lake Area (ha)) | 0.5 - 1 ha (No. of Lakes) | 0.5 - 1 ha (Total Lake Area (ha)) | 1 - 5 ha (No. of Lakes) | 1 - 5 ha (Total Lake Area (ha)) | 5 - 10 ha (No. of Lakes) | 5 - 10 ha (Total Lake Area (ha)) | 10 - 50 ha (No. of Lakes) | 10 - 50 ha (Total Lake Area (ha)) | > 50 ha (No. of Lakes) | > 50 ha (Total Lake Area (ha)) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Amo Chu | 111 | 37.82 | 130 | 94.31 | 196 | 443.69 | 43 | 297.44 | 32 | 639.52 | 1 | 52.68 |\n| 2 | Dibang | 24 | 9.90 | 82 | 60.95 | 334 | 899.70 | 144 | 1,030.44 | 175 | 3,532.30 | 13 | 1,032.81 |\n| 3 | Dihang | 39 | 13.89 | 60 | 43.21 | 176 | 452.83 | 78 | 543.84 | 77 | 1,602.17 | 3 | 267.72 |\n| 4 | Jia Bharali | 43 | 16.00 | 63 | 43.82 | 94 | 215.77 | 24 | 168.51 | 10 | 196.02 | 0 | 0.00 |\n| 5 | Lhasa Tsangpo | 267 | 98.10 | 294 | 209.19 | 487 | 1,041.65 | 84 | 567.46 | 72 | 1,413.21 | 21 | 3,651.33 |\n| 6 | Lohit | 466 | 165.05 | 496 | 360.40 | 857 | 2,043.43 | 229 | 1,615.47 | 209 | 3,989.56 | 19 | 3,355.32 |\n| 7 | Lower Yarlung Tsangpo | 904 | 328.04 | 1,024 | 735.02 | 2,039 | 4,769.34 | 529 | 3,724.79 | 429 | 8,081.12 | 54 | 8,733.50 |\n| 8 | Manas | 582 | 204.74 | 492 | 352.31 | 963 | 2,256.31 | 239 | 1,694.77 | 224 | 4,221.54 | 26 | 2,589.31 |\n| 9 | Puna Tsang Chu | 180 | 64.82 | 187 | 134.37 | 370 | 836.81 | 93 | 668.05 | 84 | 1,556.92 | 7 | 619.82 |\n| 10 | Subansiri | 96 | 34.94 | 121 | 88.12 | 218 | 496.80 | 62 | 445.09 | 38 | 696.38 | 4 | 242.91 |\n| 11 | Teesta | 196 | 66.61 | 153 | 111.22 | 226 | 523.58 | 47 | 353.24 | 50 | 982.43 | 11 | 1,083.68 |\n| 12 | Upper Yarlung Tsangpo | 719 | 255.25 | 754 | 538.93 | 1,056 | 2,335.77 | 177 | 1,239.70 | 146 | 3,129.09 | 48 | 8,589.93 |\n| | **Total** | **3,627** | **1,294.83** | **3,856** | **2,771.84** | **7,016** | **16,315.71** | **1,749** | **12,348.79** | **1,546** | **30,040.25** | **207** | **30,219.32** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "table", "table_caption": "Table 85: Subbasin-wise vs. Area range-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Sub basin", "0.25 - 0.5 ha (No. of Lakes)", "0.25 - 0.5 ha (Total Lake Area (ha))", "0.5 - 1 ha (No. of Lakes)", "0.5 - 1 ha (Total Lake Area (ha))", "1 - 5 ha (No. of Lakes)", "1 - 5 ha (Total Lake Area (ha))", "5 - 10 ha (No. of Lakes)", "5 - 10 ha (Total Lake Area (ha))", "10 - 50 ha (No. of Lakes)", "10 - 50 ha (Total Lake Area (ha))", "> 50 ha (No. of Lakes)", "> 50 ha (Total Lake Area (ha))"], "table_row_start": 1, "table_row_end": 13, "line_start": 2210, "line_end": 2224, "token_count_estimate": 1134, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "0c4979cdf68bcb8d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Subbasin-Type-wise Distribution**\n\nGlacial lake distribution by subbasin vs. type-wise is given in Table 86 and Figure 112. It has been observed that, in descending order of total lake count, 3 types of lakes viz., Other Glacial Erosion, Other Moraine Dammed, and Cirque Erosion lakes are distributed in all subbasins, and Other Glacial Erosion lakes were found predominantly in Lower Yarlung Tsangpo (51.77%), Manas (26.77%), Lohit (13.26%), and Upper Yarlung Tsangpo (6.27%) respectively. Lower Yarlung Tsangpo subbasin consists higher number of End Moraine Dammed lakes i.e. 24.55%. Lateral Moraine Dammed lakes are present in all subbasins except Dibang and Dihang.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "text", "line_start": 2225, "line_end": 2237, "token_count_estimate": 245, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dibang", "Dihang", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c0d260bbb6fe0af0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: table\nTable: Table 86: Subbasin-wise vs. Type-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Amo Chu | 2 | 4 | 0 | 27 | 5 | 0 | 61 | 0 | 412 | 2 | 513 |\n| 2 | Dibang | 1 | 0 | 0 | 1 | 0 | 0 | 141 | 0 | 601 | 28 | 772 |\n| 3 | Dihang | 1 | 0 | 0 | 18 | 3 | 0 | 108 | 1 | 301 | 1 | 433 |\n| 4 | Jai Bharali | 0 | 1 | 0 | 26 | 0 | 0 | 13 | 0 | 192 | 2 | 234 |\n| 5 | Lhasa Tsangpo | 9 | 1 | 0 | 126 | 4 | 0 | 23 | 0 | 673 | 389 | 1,225 |\n| 6 | Lohit | 59 | 5 | 0 | 386 | 12 | 0 | 82 | 0 | 1,572 | 160 | 2,276 |\n| 7 | Lower Yarlung Tsangpo | 96 | 11 | 1 | 694 | 25 | 0 | 114 | 6 | 3,832 | 200 | 4,979 |\n| 8 | Manas | 47 | 3 | 0 | 410 | 67 | 0 | 243 | 0 | 1,719 | 37 | 2,526 |\n| 9 | Puna Tsang Chu | 34 | 2 | 0 | 172 | 61 | 0 | 62 | 0 | 590 | 0 | 921 |\n| 10 | Subansiri | 33 | 1 | 0 | 89 | 6 | 2 | 31 | 0 | 346 | 31 | 539 |\n| 11 | Teesta | 31 | 5 | 0 | 148 | 72 | 0 | 37 | 0 | 382 | 8 | 683 |\n| 12 | Upper Yarlung Tsangpo | 78 | 5 | 1 | 922 | 17 | 0 | 28 | 0 | 1,226 | 623 | 2,900 |\n| | **Total** | **391** | **38** | **2** | **3,019** | **272** | **2** | **943** | **7** | **11,846** | **1,481** | **18,001** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "table", "table_caption": "Table 86: Subbasin-wise vs. Type-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 13, "line_start": 2238, "line_end": 2252, "token_count_estimate": 860, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "b9e95325cecd17a0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Subbasin-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per subbasin vs. elevation-range wise, given in Table 87 and Figure 113. Majority of glacial lakes are situated in all subbasins in high altitude range i.e. 4,001 - 5,000 m. After that, majority of glacial lakes in all subbasins are located in very high and medium altitude range i.e. > 5,000 m and 3,001 - 4,000 m. Only 11 lakes are located in all the subbasins in the elevation range < 3,000 m.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "text", "line_start": 2253, "line_end": 2261, "token_count_estimate": 191, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f9605517b2986bdf", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: table\nTable: Table 87: Subbasin-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin\n\n| S. No. | Subbasin | up to 3,000 - No. of lakes | up to 3,000 - Total Lake Area (ha) | 3,001 - 4,000 - No. of lakes | 3,001 - 4,000 - Total Lake Area (ha) | 4,001 - 5,000 - No. of lakes | 4,001 - 5,000 - Total Lake Area (ha) | > 5,000 - No. of lakes | > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Amo Chu | 0 | 0.00 | 17 | 104.52 | 462 | 1,350.92 | 34 | 110.02 |\n| 2 | Dibang | 2 | 27.46 | 405 | 3,586.02 | 365 | 2,952.62 | 0 | 0.00 |\n| 3 | Dihang | 2 | 26.48 | 227 | 1,613.89 | 204 | 1,283.29 | 0 | 0.00 |\n| 4 | Jai Bharali | 0 | 0.00 | 16 | 95.67 | 195 | 500.45 | 23 | 44.00 |\n| 5 | Lhasa Tsangpo | 0 | 0.00 | 1 | 0.49 | 376 | 3,684.05 | 848 | 3,296.40 |\n| 6 | Lohit | 5 | 58.10 | 281 | 3,896.04 | 1,735 | 7,025.16 | 255 | 549.93 |\n| 7 | Lower Yarlung Tsangpo | 2 | 40.11 | 99 | 1,456.03 | 3,056 | 16,552.09 | 1,822 | 8,323.58 |\n| 8 | Manas | 0 | 0.00 | 52 | 231.67 | 1,656 | 8,137.45 | 818 | 2,949.84 |\n| 9 | Puna Tsang Chu | 0 | 0.00 | 9 | 24.85 | 668 | 2,720.80 | 244 | 1,135.14 |\n| 10 | Subansiri | 0 | 0.00 | 57 | 423.83 | 279 | 1,131.38 | 203 | 449.02 |\n| 11 | Teesta | 0 | 0.00 | 22 | 109.14 | 375 | 1,057.08 | 286 | 1,954.54 |\n| 12 | Upper Yarlung Tsangpo | 0 | 0.00 | 2 | 5.20 | 299 | 6,130.29 | 2,599 | 9,953.18 |\n| | **Total** | **11** | **152.21** | **1,188** | **11,547.28** | **9,670** | **52,525.55** | **7,132** | **28,765.99** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "table", "table_caption": "Table 87: Subbasin-wise vs. Elevation range-wise distribution of GL in Brahmaputra River basin", "columns": ["S. No.", "Subbasin", "up to 3,000 - No. of lakes", "up to 3,000 - Total Lake Area (ha)", "3,001 - 4,000 - No. of lakes", "3,001 - 4,000 - Total Lake Area (ha)", "4,001 - 5,000 - No. of lakes", "4,001 - 5,000 - Total Lake Area (ha)", "> 5,000 - No. of lakes", "> 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 13, "line_start": 2262, "line_end": 2276, "token_count_estimate": 812, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "aa64a638d383c5ea", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**5.4 India Level Statistics**\n\nBrahmaputra River basin covers part of India and transboundary region, where in India it is covering a total area of 81,590 Km² i.e. 20.40% of the basin area. In India, basin area has been spread in three states viz., Arunachal Pradesh, Sikkim, and West Bengal. Both states of Arunachal Pradesh and Sikkim covers a total of 94.48% of the basin area lies within India and remaining 5.52% in West Bengal which does not contain any glacial lake. A total of 2,921 glacial lakes lies within Indian region, covering a total area of 15,758.05 ha i.e. 3.93% of the total area of the Brahmaputra River basin.\n\n**Area range-wise Distribution**\n\nIn Indian region, glacial lakes have been distributed in all 6 classes of area ranges. Table 88 and Figure 114 shows the area range-wise distribution of glacial lakes for the Indian region. About 2,143 (73.37%) lakes are with < 5 ha lake area contributing to 21.56% of total lake area. The remaining lakes with > 5 ha in size are 778 (26.63%) but contributing to 78.44% of total lake area in the region.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "text", "line_start": 2277, "line_end": 2297, "token_count_estimate": 339, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f65d034fd4456b29", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: table\nTable: Table 88: Area range-wise distribution of GL in India\n\n| S.No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 458 | 162.29 | 1.03 |\n| 2 | 0.5 - 1 | 509 | 369.74 | 2.35 |\n| 3 | 1 - 5 | 1,176 | 2,866.10 | 18.19 |\n| 4 | 5 - 10 | 391 | 2,792.42 | 17.72 |\n| 5 | 10 - 50 | 355 | 6,958.06 | 44.16 |\n| 6 | > 50 | 32 | 2,609.44 | 16.56 |\n| | **Total** | **2,921** | **15,758.05** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "table", "table_caption": "Table 88: Area range-wise distribution of GL in India", "columns": ["S.No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2298, "line_end": 2306, "token_count_estimate": 272, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "59ddcedd6f1129d7", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Subbasin-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Subbasin-wise Distribution"], "chunk_type": "text", "line_start": 2307, "line_end": 2309, "token_count_estimate": 49, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "72b61ca274b4c222", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the Indian region is given in Table 89 and Figure 115. Out of all 10 types of glacial lakes, 7 types of lakes are present in the Indian region, where Other Glacial Erosion lakes are found to be the maximum with 2,150 (73.60%) occupying a total lake extent of 9,349.53 ha at 59.33% in the region. After that, Cirque Erosion Lakes and Other Moraine Dammed Lakes are in majority with 328 (11.23%) and 231 (7.91%) and extend over a total area of 2,947.28 ha (18.70%) and 1,194.07 ha (7.58%) respectively.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Type-wise Distribution"], "chunk_type": "text", "line_start": 2311, "line_end": 2315, "token_count_estimate": 185, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "120ecb23b5ffb988", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution\nType: table\nTable: Table 89: Type-wise distribution of GL in India\n\n| S.No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :---: | :---: | :--- | :---: | :---: | :---: |\n| 1 | M(e) | End-moraine Dammed Lake | 38 | 1,477.67 | 9.38 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 6 | 21.87 | 0.14 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 0 | 0.00 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 231 | 1,194.07 | 7.58 |\n| 5 | I(s) | Supra-glacial Lake | 72 | 48.75 | 0.31 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 0 | 0.00 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 328 | 2,947.28 | 18.70 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 0 | 0.00 | 0.00 |\n| 9 | E(o) | Other Glacial Erosion Lake | 2,150 | 9,349.53 | 59.33 |\n| 10 | O | Other Glacial Lake | 96 | 718.88 | 4.56 |\n| | | **Total** | **2,921** | **15,758.05** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 89: Type-wise distribution of GL in India", "columns": ["S.No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 2316, "line_end": 2328, "token_count_estimate": 472, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "73c682bc93370bc6", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Type-wise Distribution"], "chunk_type": "text", "line_start": 2329, "line_end": 2335, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5267580b2620c549", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area range Type-wise Distribution\nType: text\n\nGlacial lake distribution by area range vs. type-wise is given in Table 90 and Figure 116. The lakes with < 5 ha in size (73.37%) are dominant with Other Glacial Erosion Lakes (77.55%) and Other Moraine Dammed lakes (8.49%). Lakes with > 5 ha (26.63%) are dominated by Other Glacial Erosion Lakes (62.72%). All types of Moraine-dammed lakes, which constitute about 9.41% are predominantly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area range Type-wise Distribution"], "chunk_type": "text", "line_start": 2337, "line_end": 2341, "token_count_estimate": 153, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "801e94fae5ab1086", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area range Type-wise Distribution\nType: table\nTable: Table 90: Area range-wise vs. Type-wise distribution of GL in India\n\n| S.No. | Lake Area Range (ha) | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n| :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 50 | 50 | 0 | 2 | 0 | 338 | 18 | 458 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 56 | 13 | 0 | 9 | 0 | 412 | 18 | 509 |\n| 3 | 1 - 5 | 3 | 4 | 0 | 76 | 8 | 0 | 136 | 0 | 912 | 37 | 1,176 |\n| 4 | 5 - 10 | 7 | 0 | 0 | 21 | 1 | 0 | 92 | 0 | 264 | 6 | 391 |\n| 5 | 10 - 50 | 19 | 1 | 0 | 24 | 0 | 0 | 86 | 0 | 211 | 14 | 355 |\n| 6 | > 50 | 9 | 0 | 0 | 4 | 0 | 0 | 3 | 0 | 13 | 3 | 32 |\n| | **Total** | **38** | **6** | **0** | **231** | **72** | **0** | **328** | **0** | **2,150** | **96** | **2,921** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area range Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 90: Area range-wise vs. Type-wise distribution of GL in India", "columns": ["S.No.", "Lake Area Range (ha)", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2342, "line_end": 2350, "token_count_estimate": 531, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "85601d627a1e1c2d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area range Type-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area range Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area range Type-wise Distribution"], "chunk_type": "text", "line_start": 2351, "line_end": 2355, "token_count_estimate": 49, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "007a37fee1bdbe7d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the Indian region has been shown in Table 91 and Figure 117. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 2,153 (73.71%) with total lake area of 9,963.6 ha (63.23%) and remaining 26.29% glacial lakes are below 4,000 m elevation.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2357, "line_end": 2361, "token_count_estimate": 118, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0b69f587c61bccf4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution\nType: table\nTable: Table 91: Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 4 | 44.73 | 0.28 |\n| 2 | 3,001 - 4,000 | 764 | 5,749.71 | 36.49 |\n| 3 | 4,001 - 5,000 | 1,783 | 7,780.92 | 49.38 |\n| 4 | > 5,000 | 370 | 2,182.68 | 13.85 |\n| **Total** | | **2,921** | **15,758.05** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 91: Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2362, "line_end": 2368, "token_count_estimate": 232, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8121d8cee549a664", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2369, "line_end": 2373, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ff118dc86764f5d4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 92 and Figure 118. It is noted that, 61.04% of glacial lakes (1,783) are situated in high altitude range i.e. 4,001 - 5,000 m, which also constitutes majority of total lake area within that range i.e. 49.37%. However, 4 glacial lakes lies below 3,000 m. 82.97% of lakes lying in very high altitude range are < 5 ha, predominantly of size ranging 1 – 5 ha (i.e. 123), followed by lakes of size 0.25 – 0.5 ha (i.e. 102).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2375, "line_end": 2379, "token_count_estimate": 192, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba43a86f46ac466e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 92: Area range-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | Elevation Range (m): up to 3,000 - No. of lakes | Elevation Range (m): up to 3,000 - Total Lake Area (ha) | Elevation Range (m): 3,001 - 4,000 - No. of lakes | Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha) | Elevation Range (m): 4,001 - 5,000 - No. of lakes | Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha) | Elevation Range (m): > 5,000 - No. of lakes | Elevation Range (m): > 5,000 - Total Lake Area (ha) | Total: No. of lakes | Total: Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 46 | 17.18 | 310 | 110.33 | 102 | 34.78 | 458 | 162.29 |\n| 2 | 0.5 - 1 | 0 | 0.00 | 77 | 58.31 | 350 | 252.82 | 82 | 58.61 | 509 | 369.74 |\n| 3 | 1 - 5 | 1 | 4.71 | 335 | 841.70 | 717 | 1,725.87 | 123 | 293.82 | 1,176 | 2,866.10 |\n| 4 | 5 - 10 | 0 | 0.00 | 149 | 1,078.74 | 213 | 1,491.57 | 29 | 222.11 | 391 | 2,792.42 |\n| 5 | 10 - 50 | 3 | 40.02 | 146 | 2,938.78 | 183 | 3,487.28 | 23 | 491.98 | 355 | 6,958.06 |\n| 6 | > 50 | 0 | 0.00 | 11 | 815.00 | 10 | 713.06 | 11 | 1,081.38 | 32 | 2,609.44 |\n| **Total** | | **4** | **44.73** | **764** | **5,749.71** | **1,783** | **7,780.92** | **370** | **2,182.68** | **2,921** | **15,758.05** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 92: Area range-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range (m): up to 3,000 - No. of lakes", "Elevation Range (m): up to 3,000 - Total Lake Area (ha)", "Elevation Range (m): 3,001 - 4,000 - No. of lakes", "Elevation Range (m): 3,001 - 4,000 - Total Lake Area (ha)", "Elevation Range (m): 4,001 - 5,000 - No. of lakes", "Elevation Range (m): 4,001 - 5,000 - Total Lake Area (ha)", "Elevation Range (m): > 5,000 - No. of lakes", "Elevation Range (m): > 5,000 - Total Lake Area (ha)", "Total: No. of lakes", "Total: Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2380, "line_end": 2388, "token_count_estimate": 663, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "eab60bf55017ed8c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 93 and Figure 119. The dominant lake type in the region i.e., Other Glacial Erosion Lakes (73.60%) are predominantly located in the elevation range of 4,001 - 5,000 m (79.47%). The other dominant lake type, namely, Cirque Erosion Lakes and Other Moraine Dammed lakes are distributed predominantly in altitude range of 4,001 - 5,000 m and > 5,000 m elevation range respectively, i.e. 63.10% and 69.26%. Majority i.e. 99.27% of all types of Moraine-dammed lakes lies above 4,000 m.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2389, "line_end": 2397, "token_count_estimate": 233, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2bdf4f56c8865478", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 93: Type-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Subbasin | Types of Glacial Lake: M(e) | Types of Glacial Lake: M(l) | Types of Glacial Lake: M(lg) | Types of Glacial Lake: M(o) | Types of Glacial Lake: I(s) | Types of Glacial Lake: I(d) | Types of Glacial Lake: E(c) | Types of Glacial Lake: E(v) | Types of Glacial Lake: E(o) | Types of Glacial Lake: O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 4 |\n| 2 | 3,001 - 4,000 | 2 | 0 | 0 | 0 | 1 | 0 | 112 | 0 | 588 | 61 | 764 |\n| 3 | 4,001 - 5,000 | 13 | 6 | 0 | 71 | 42 | 0 | 207 | 0 | 1,417 | 27 | 1,783 |\n| 4 | > 5,000 | 23 | 0 | 0 | 160 | 29 | 0 | 9 | 0 | 144 | 5 | 370 |\n| | **Total** | **38** | **6** | **0** | **231** | **72** | **0** | **328** | **0** | **2,150** | **96** | **2,921** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 93: Type-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Subbasin", "Types of Glacial Lake: M(e)", "Types of Glacial Lake: M(l)", "Types of Glacial Lake: M(lg)", "Types of Glacial Lake: M(o)", "Types of Glacial Lake: I(s)", "Types of Glacial Lake: I(d)", "Types of Glacial Lake: E(c)", "Types of Glacial Lake: E(v)", "Types of Glacial Lake: E(o)", "Types of Glacial Lake: O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 2398, "line_end": 2404, "token_count_estimate": 488, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "69e85aafe73dec48", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**5.5 Indian State’s Statistics**\n\nGlacial lakes located in 2 states of Indian region are compared for lake count, total lake area, lake types and their elevation ranges in the following sections.\n\n**State-wise Distribution**\n\nTable 94 and Figure 120 shows the State-wise distribution of glacial lakes of Indian region. Lakes are predominantly distributed in Arunachal Pradesh with 2,188 (74.91%) occupying a total lake extent of 12,490.77 ha at 79.27% in the region. Sikkim contains 733 glacial lakes (25.09%) extend over an area of 3,267.28 ha (20.73%).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2405, "line_end": 2421, "token_count_estimate": 197, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7d52ba5ed7c788b6", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 94: State-wise distribution of GL in India\n\n| S.No. | Code | State | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | AP | Arunachal Pradesh | 2,188 | 12,490.77 | 79.27 |\n| 2 | SK | Sikkim | 733 | 3,267.28 | 20.73 |\n| | | **Total** | **2,921** | **15,758.05** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 94: State-wise distribution of GL in India", "columns": ["S.No.", "Code", "State", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 3, "line_start": 2422, "line_end": 2426, "token_count_estimate": 188, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f1f2225f635a5628", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**State-Area range-wise Distribution**\n\nGlacial lakes have been distributed in both states for all classes of area range. Table 95 and Figure 121 shows the State -area range-wise distribution of glacial lakes for the Indian region. It has been observed that, glacial lakes in Arunachal Pradesh (AP) are predominantly < 5 ha (69.69%), majority of which are within 1 - 5 ha in size i.e. 60.85%, followed by lakes of 0.5 - 1 ha in size i.e. 22.62%. Not only in Arunachal Pradesh (AP), maximum number of lakes < 5 ha are (84.31%) located in Sikkim.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2427, "line_end": 2437, "token_count_estimate": 202, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "84ed31ebccc71aa4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 95: State-wise vs. Area range-wise distribution of GL in India\n\n| S. No. | Lake Area Range (ha) | State - Arunachala Pradesh - No. of lakes | State - Arunachala Pradesh - Total Lake Area (ha) | State - Sikkim - No. of lakes | State - Sikkim - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 252 | 92.35 | 206 | 69.95 | 458 | 162.30 |\n| 2 | 0.5 - 1 | 345 | 250.31 | 164 | 119.43 | 509 | 369.74 |\n| 3 | 1 - 5 | 928 | 2,303.41 | 248 | 562.68 | 1,176 | 2,866.09 |\n| 4 | 5 - 10 | 341 | 2,418.08 | 50 | 374.34 | 391 | 2,792.42 |\n| 5 | 10 - 50 | 301 | 5,900.86 | 54 | 1,057.19 | 355 | 6,958.06 |\n| 6 | > 50 | 21 | 1,525.76 | 11 | 1,083.69 | 32 | 2,609.44 |\n| | **Total** | **2,188** | **12,490.77** | **733** | **3,267.28** | **2,921** | **15,758.05** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 95: State-wise vs. Area range-wise distribution of GL in India", "columns": ["S. No.", "Lake Area Range (ha)", "State - Arunachala Pradesh - No. of lakes", "State - Arunachala Pradesh - Total Lake Area (ha)", "State - Sikkim - No. of lakes", "State - Sikkim - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2438, "line_end": 2446, "token_count_estimate": 446, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e7b41c2ccd915869", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**State-Type-wise Distribution**\n\nGlacial lake distribution by State vs. type-wise is given in Table 96 and Figure 122. It has been observed that, Arunachal Pradesh and Sikkim contains same types of glacial lakes except Supra-glacial Lake in Sikkim, with majority of Other Glacial Erosion Lakes i.e. 73.60%, followed by Cirque Erosion Lakes i.e. 11.23%. All types of moraine dammed lakes in Sikkim are 184 with 67.15%.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2447, "line_end": 2459, "token_count_estimate": 173, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "068e0a7f8fe5f1cb", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 96: State-wise vs. Type-wise distribution of GL in India\n\n| S. No. | State | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Arunachal Pradesh | 7 | 1 | 0 | 83 | 0 | 0 | 282 | 0 | 1,727 | 88 | 2,188 |\n| 2 | Sikkim | 31 | 5 | 0 | 148 | 72 | 0 | 46 | 0 | 423 | 8 | 733 |\n| | **Total** | **38** | **6** | **0** | **231** | **72** | **0** | **328** | **0** | **2,150** | **96** | **2,921** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 96: State-wise vs. Type-wise distribution of GL in India", "columns": ["S. No.", "State", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total"], "table_row_start": 1, "table_row_end": 3, "line_start": 2460, "line_end": 2464, "token_count_estimate": 394, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "e1e22937ac572030", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**State-Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per State vs. elevation-range wise, given in Table 97 and Figure 123. It has been observed that, majority of glacial lakes (61.04%) are located in high altitude range i.e. 4,001 - 5,000 m in both states. This is followed by medium altitude range i.e. 3,001 - 4,000 m in both states (26.15%).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2465, "line_end": 2476, "token_count_estimate": 159, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c88f3cc752fb407", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 97: State-wise vs. Elevation range-wise distribution of GL in India\n\n| S. No. | Elevation Range (m) | State: Arunachal Pradesh - No. of lakes | State: Arunachal Pradesh - Total Lake Area (ha) | State: Sikkim - No. of lakes | State: Sikkim - Total Lake Area (ha) | Total - No. of lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 4 | 44.73 | 0 | 0 | 4 | 44.73 |\n| 2 | 3,001 - 4,000 | 739 | 5,610.27 | 25 | 139.44 | 764 | 5,749.71 |\n| 3 | 4,001 - 5,000 | 1,363 | 6,610.21 | 420 | 1,170.71 | 1783 | 7,780.92 |\n| 4 | > 5,000 | 82 | 225.56 | 288 | 1,957.14 | 370 | 2,182.69 |\n| | **Total** | **2,188** | **12,490.77** | **733** | **3,267.28** | **2,921** | **15,758.05** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 97: State-wise vs. Elevation range-wise distribution of GL in India", "columns": ["S. No.", "Elevation Range (m)", "State: Arunachal Pradesh - No. of lakes", "State: Arunachal Pradesh - Total Lake Area (ha)", "State: Sikkim - No. of lakes", "State: Sikkim - Total Lake Area (ha)", "Total - No. of lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2477, "line_end": 2483, "token_count_estimate": 376, "basins": [], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "4097d6ad406c9245", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**5.5.1 District Level Statistics of Arunachal Pradesh**\n\nArunachal Pradesh is the largest state covering area of Brahmaputra River basin, contains glacial lakes only in fourteen districts. Amongst which, Dibang Valley covers the majority of the total lake area.\n\n**Area-range-wise Distribution**\n\nGlacial lakes have been distributed in both districts for 6 classes of area ranges, and area range-wise distribution for both districts has been shown in Table 98 and Figure 124. Glacial lakes in Dibang Valley district are found to be the maximum with 669 (30.58%) occupying a total lake extent of 5,746.19 ha at 46%. About 1,525 (69.69%) lakes are with < 5 ha lake area contributing to 21.18% of total lake area in the district.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2484, "line_end": 2502, "token_count_estimate": 236, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "dfdfea10c371547e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: table\nTable: Table 98: Area range-wise distribution of GL in Districts of Arunachal Pradesh\n\n| S. No. | District | Lake Area Range (ha): 0.25 - 0.5 - No. of Lakes | Lake Area Range (ha): 0.25 - 0.5 - Total Lake Area (ha) | Lake Area Range (ha): 0.5 - 1 - No. of Lakes | Lake Area Range (ha): 0.5 - 1 - Total Lake Area (ha) | Lake Area Range (ha): 1 - 5 - No. of Lakes | Lake Area Range (ha): 1 - 5 - Total Lake Area (ha) | Lake Area Range (ha): 5 - 10 - No. of Lakes | Lake Area Range (ha): 5 - 10 - Total Lake Area (ha) | Lake Area Range (ha): 10 - 50 - No. of Lakes | Lake Area Range (ha): 10 - 50 - Total Lake Area (ha) | Lake Area Range (ha): > 50 - No. of Lakes | Lake Area Range (ha): > 50 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anjaw | 52 | 18.60 | 75 | 56.11 | 193 | 451.54 | 65 | 471.47 | 61 | 1,149.56 | 3 | 172.29 |\n| 2 | Changlang | 4 | 1.59 | 0 | 0.00 | 1 | 1.93 | 2 | 15.25 | 2 | 25.18 | 0 | 0.00 |\n| 3 | Dibang Valley | 30 | 11.97 | 76 | 55.56 | 282 | 747.80 | 124 | 891.21 | 145 | 3042.37 | 12 | 997.29 |\n| 4 | East Kameng | 5 | 1.78 | 11 | 8.02 | 28 | 71.67 | 12 | 86.32 | 7 | 150.02 | 0 | 0.00 |\n| 5 | Kra Daadi | 1 | 0.25 | 0 | 0.00 | 1 | 1.19 | 2 | 17.19 | 0 | 0.00 | 0 | 0.00 |\n| 6 | Kurung Kumey | 9 | 3.31 | 20 | 15.55 | 33 | 73.39 | 7 | 52.67 | 5 | 86.20 | 1 | 55.58 |\n| 7 | Lohit | 0 | 0.00 | 0 | 0.00 | 2 | 7.52 | 0 | 0.00 | 1 | 12.55 | 0 | 0.00 |\n| 8 | Lower Dibang Valley | 0 | 0.00 | 0 | 0.00 | 3 | 13.81 | 2 | 14.26 | 1 | 16.14 | 0 | 0.00 |\n| 9 | Siang | 2 | 0.69 | 2 | 1.65 | 6 | 17.88 | 2 | 14.06 | 1 | 18.47 | 0 | 0.00 |\n| 10 | Tawang | 95 | 34.09 | 77 | 54.43 | 196 | 466.16 | 53 | 362.67 | 18 | 317.63 | 4 | 249.94 |\n| 11 | Upper Siang | 1 | 0.48 | 9 | 6.29 | 36 | 91.11 | 20 | 141.17 | 21 | 383.11 | 0 | 0.00 |\n| 12 | Upper Subansiri | 9 | 3.33 | 21 | 14.02 | 64 | 172.45 | 34 | 230.52 | 26 | 490.47 | 0 | 0.00 |\n| 13 | West Kameng | 42 | 15.47 | 45 | 32.11 | 63 | 142.52 | 14 | 91.60 | 9 | 141.81 | 0 | 0.00 |\n| 14 | West Siang | 2 | 0.78 | 9 | 6.58 | 20 | 44.44 | 4 | 29.69 | 4 | 67.35 | 1 | 50.66 |\n| | **Total** | **252** | **92.35** | **345** | **250.31** | **928** | **2,303.41** | **341** | **2,418.08** | **301** | **5,900.86** | **21** | **1,525.76** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 98: Area range-wise distribution of GL in Districts of Arunachal Pradesh", "columns": ["S. No.", "District", "Lake Area Range (ha): 0.25 - 0.5 - No. of Lakes", "Lake Area Range (ha): 0.25 - 0.5 - Total Lake Area (ha)", "Lake Area Range (ha): 0.5 - 1 - No. of Lakes", "Lake Area Range (ha): 0.5 - 1 - Total Lake Area (ha)", "Lake Area Range (ha): 1 - 5 - No. of Lakes", "Lake Area Range (ha): 1 - 5 - Total Lake Area (ha)", "Lake Area Range (ha): 5 - 10 - No. of Lakes", "Lake Area Range (ha): 5 - 10 - Total Lake Area (ha)", "Lake Area Range (ha): 10 - 50 - No. of Lakes", "Lake Area Range (ha): 10 - 50 - Total Lake Area (ha)", "Lake Area Range (ha): > 50 - No. of Lakes", "Lake Area Range (ha): > 50 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 15, "line_start": 2503, "line_end": 2519, "token_count_estimate": 1169, "basins": [], "subbasins": ["Dibang", "Lohit", "Subansiri"], "countries": [], "lake_ids": []}}
{"id": "d658cb7104de7052", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2520, "line_end": 2522, "token_count_estimate": 50, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58a70e805a9b81f3", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution\nType: text\n\nDistribution of different types of glacial lakes in the districts of Arunachal Pradesh is given in Table 99 and Figure 125. It has been observed that, Other Glacial Erosion lakes are maximum with 489 (73.09%) in the Dibang Valley district, followed by Cirque Erosion Lake with 117 (17.48%). Total Glacial Erosion Lakes are 1,727 (78.93%) out of total lakes in the Arunachal Pradesh", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Type-wise Distribution"], "chunk_type": "text", "line_start": 2524, "line_end": 2528, "token_count_estimate": 130, "basins": [], "subbasins": ["Dibang"], "countries": [], "lake_ids": []}}
{"id": "9d564c3fcd9d935b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution\nType: table\nTable: Table 99: Type-wise distribution of GL in Districts of Arunachal Pradesh\n\n| S. No. | District | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | No. of Lakes | Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anjaw | 0 | 0 | 0 | 3 | 0 | 0 | 28 | 0 | 393 | 25 | 449 | 2,319.57 |\n| 2 | Changlang | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 6 | 1 | 9 | 43.95 |\n| 3 | Dibang Valley | 4 | 0 | 0 | 29 | 0 | 0 | 117 | 0 | 489 | 30 | 669 | 5,746.20 |\n| 4 | East Kameng | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 59 | 0 | 63 | 317.81 |\n| 5 | Kra Daadi | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 4 | 18.63 |\n| 6 | Kurung Kumey | 2 | 0 | 0 | 9 | 0 | 0 | 14 | 0 | 50 | 0 | 75 | 286.70 |\n| 7 | Lohit | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 20.07 |\n| 8 | Lower Dibang Valley | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 4 | 0 | 6 | 44.21 |\n| 9 | Siang | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 11 | 0 | 13 | 52.75 |\n| 10 | Tawang | 0 | 0 | 0 | 14 | 0 | 0 | 50 | 0 | 365 | 14 | 443 | 1,484.92 |\n| 11 | Upper Siang | 0 | 0 | 0 | 1 | 0 | 0 | 22 | 0 | 62 | 2 | 87 | 622.16 |\n| 12 | Upper Subansiri | 1 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 125 | 7 | 154 | 910.79 |\n| 13 | West Kameng | 0 | 1 | 0 | 27 | 0 | 0 | 9 | 0 | 132 | 4 | 173 | 423.51 |\n| 14 | West Siang | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 27 | 4 | 40 | 199.50 |\n| | **Total** | **7** | **1** | **0** | **83** | **0** | **0** | **282** | **0** | **1,727** | **88** | **2,188** | **12,490.77** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Type-wise Distribution"], "chunk_type": "table", "table_caption": "Table 99: Type-wise distribution of GL in Districts of Arunachal Pradesh", "columns": ["S. No.", "District", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "No. of Lakes", "Lake Area (ha)"], "table_row_start": 1, "table_row_end": 15, "line_start": 2529, "line_end": 2545, "token_count_estimate": 921, "basins": [], "subbasins": ["Dibang", "Lohit", "Subansiri"], "countries": [], "lake_ids": []}}
{"id": "04cbb3e8c9556a2e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Type-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Type-wise Distribution"], "chunk_type": "text", "line_start": 2546, "line_end": 2550, "token_count_estimate": 48, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c04841bb8e797b60", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution\nType: text\n\nElevation range-wise distribution of the glacial lakes in the districts of Arunachal Pradesh has been shown in Table 100 and Figure 126. Majority of glacial lakes are situated in the elevation range of 4,001 – 5,000 i.e. 1,363 with total lake area of 6,610.22 ha. Tawang district contains maximum number of glacial lakes in the elevation range of > 5,000 m i.e. 56. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 127.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2552, "line_end": 2556, "token_count_estimate": 154, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "737fd0b4491f3ed1", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution\nType: table\nTable: Table 100: Elevation range-wise distribution of GL in Districts of Arunachal Pradesh\n\n| S. No. | District | up to 3,000: No. of Lakes | up to 3,000: Total Lake Area (ha) | 3,001 - 4,000: No. of Lakes | 3,001 - 4,000: Total Lake Area (ha) | 4,001 - 5,000: No. of Lakes | 4,001 - 5,000: Total Lake Area (ha) | > 5,000: No. of Lakes | > 5,000: Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Anjaw | 0 | 0.00 | 174 | 1,049.58 | 275 | 1,269.99 | 0 | 0.00 |\n| 2 | Changlang | 0 | 0.00 | 9 | 43.95 | 0 | 0.00 | 0 | 0.00 |\n| 3 | Dibang Valley | 2 | 27.47 | 345 | 3,151.43 | 322 | 2,567.30 | 0 | 0.00 |\n| 4 | East Kameng | 0 | 0.00 | 12 | 86.34 | 51 | 231.47 | 0 | 0.00 |\n| 5 | Kra Daadi | 0 | 0.00 | 3 | 11.43 | 1 | 7.20 | 0 | 0.00 |\n| 6 | Kurung Kumey | 0 | 0.00 | 10 | 28.21 | 65 | 258.49 | 0 | 0.00 |\n| 7 | Lohit | 2 | 17.27 | 1 | 2.80 | 0 | 0.00 | 0 | 0.00 |\n| 8 | Lower Dibang Valley | 0 | 0.00 | 5 | 40.00 | 1 | 4.21 | 0 | 0.00 |\n| 9 | Siang | 0 | 0.00 | 9 | 27.28 | 4 | 25.47 | 0 | 0.00 |\n| 10 | Tawang | 0 | 0.00 | 14 | 69.60 | 373 | 1,254.93 | 56 | 160.39 |\n| 11 | Upper Siang | 0 | 0.00 | 63 | 495.71 | 24 | 126.45 | 0 | 0.00 |\n| 12 | Upper Subansiri | 0 | 0.00 | 63 | 454.02 | 91 | 456.77 | 0 | 0.00 |\n| 13 | West Kameng | 0 | 0.00 | 3 | 6.39 | 144 | 351.94 | 26 | 65.16 |\n| 14 | West Siang | 0 | 0.00 | 28 | 143.50 | 12 | 56.00 | 0 | 0.00 |\n| | **Total** | **4** | **45.73** | **739** | **5,610.27** | **1,363** | **6,610.21** | **82** | **226.56** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Elevation range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 100: Elevation range-wise distribution of GL in Districts of Arunachal Pradesh", "columns": ["S. No.", "District", "up to 3,000: No. of Lakes", "up to 3,000: Total Lake Area (ha)", "3,001 - 4,000: No. of Lakes", "3,001 - 4,000: Total Lake Area (ha)", "4,001 - 5,000: No. of Lakes", "4,001 - 5,000: Total Lake Area (ha)", "> 5,000: No. of Lakes", "> 5,000: Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 15, "line_start": 2557, "line_end": 2573, "token_count_estimate": 814, "basins": [], "subbasins": ["Dibang", "Lohit", "Subansiri"], "countries": [], "lake_ids": []}}
{"id": "575608d2d8a9c2a4", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Elevation range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Elevation range-wise Distribution"], "chunk_type": "text", "line_start": 2574, "line_end": 2576, "token_count_estimate": 49, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af9e7964598ec55c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > 5.5.2 District Level Statistics of Sikkim\nType: text\n\nSikkim is the second largest state covering area of Brahmaputra River basin, contains glacial lakes in four districts viz., North Sikkim, South Sikkim, West Sikkim, and East Sikkim. Amongst which, North Sikkim has the majority of glacial lakes covering 88.89% of the total lake area in the state.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > 5.5.2 District Level Statistics of Sikkim", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "5.5.2 District Level Statistics of Sikkim"], "chunk_type": "text", "line_start": 2578, "line_end": 2580, "token_count_estimate": 119, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5f82981ae876d7ef", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: text\n\nGlacial lakes has been distributed in 4 districts of Sikkim for 6 classes of area ranges, and area range-wise distribution for those has been shown in Table 101 and Figure 128. Glacial lakes in North Sikkim district are found to be the maximum with 589 (80.35%) occupying a total lake extent of 2,904.40 ha at 88.89%. About 618 (84.31%) lakes are with < 5 ha lake area contributing to 23.02% of total lake area in the district. Whereas, remaining lakes in the district with > 5 ha in size are only 15.69%, predominantly of 5 - 50 ha in size.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "text", "line_start": 2582, "line_end": 2586, "token_count_estimate": 180, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6592714443e50346", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: table\nTable: Table 101: Area range-wise distribution of GL in Districts of Sikkim\n\n| S. No. | District | No. of Lakes (0.25 - 0.5 ha) | Total Lake Area (ha) (0.25 - 0.5 ha) | No. of Lakes (0.5 - 1 ha) | Total Lake Area (ha) (0.5 - 1 ha) | No. of Lakes (1 - 5 ha) | Total Lake Area (ha) (1 - 5 ha) | No. of Lakes (5 - 10 ha) | Total Lake Area (ha) (5 - 10 ha) | No. of Lakes (10 - 50 ha) | Total Lake Area (ha) (10 - 50 ha) | No. of Lakes (> 50 ha) | Total Lake Area (ha) (> 50 ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | North Sikkim | 165 | 56.86 | 126 | 90.55 | 200 | 462.75 | 40 | 301.17 | 47 | 909.39 | 11 | 1,083.69 |\n| 2 | South Sikkim | 1 | 0.44 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 |\n| 3 | West Sikkim | 18 | 5.85 | 18 | 14.22 | 17 | 34.61 | 3 | 22.22 | 3 | 60.83 | 0 | 0.00 |\n| 4 | East Sikkim | 22 | 6.80 | 20 | 14.66 | 31 | 65.32 | 7 | 50.96 | 4 | 86.98 | 0 | 0.00 |\n| | **Total** | **206** | **69.95** | **164** | **119.43** | **248** | **562.68** | **50** | **374.34** | **54** | **1,057.19** | **11** | **1,083.69** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 101: Area range-wise distribution of GL in Districts of Sikkim", "columns": ["S. No.", "District", "No. of Lakes (0.25 - 0.5 ha)", "Total Lake Area (ha) (0.25 - 0.5 ha)", "No. of Lakes (0.5 - 1 ha)", "Total Lake Area (ha) (0.5 - 1 ha)", "No. of Lakes (1 - 5 ha)", "Total Lake Area (ha) (1 - 5 ha)", "No. of Lakes (5 - 10 ha)", "Total Lake Area (ha) (5 - 10 ha)", "No. of Lakes (10 - 50 ha)", "Total Lake Area (ha) (10 - 50 ha)", "No. of Lakes (> 50 ha)", "Total Lake Area (ha) (> 50 ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2587, "line_end": 2593, "token_count_estimate": 556, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3203a380fd40e37d", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the districts of Sikkim is given in Table 102 and Figure 129. It has been observed that, only 7 types of glacial lakes are distributed in the state, where Other Glacial Erosion Lakes are found to be the maximum with 423 (57.70%) in the state, followed by Other Moraine Dammed lakes with 148 (20.19%). North Sikkim district contains maximum number of glacial lakes in comparison with other districts in the state, with majority of Other Glacial Erosion Lakes (51.78%), followed by Other Moraine Dammed lakes i.e. 23.93%.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "text", "line_start": 2594, "line_end": 2602, "token_count_estimate": 205, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb588d671c85f458", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: table\nTable: Table 102: Type-wise distribution of GL in Districts of Sikkim\n\n| S. No. | District | Types of Glacial Lake - M(e) | Types of Glacial Lake - M(l) | Types of Glacial Lake - M(lg) | Types of Glacial Lake - M(o) | Types of Glacial Lake - I(s) | Types of Glacial Lake - I(d) | Types of Glacial Lake - E(c) | Types of Glacial Lake - E(v) | Types of Glacial Lake - E(o) | Types of Glacial Lake - O | Total - No. of Lakes | Total - Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | North Sikkim | 30 | 2 | 0 | 141 | 68 | 0 | 36 | 0 | 305 | 7 | 589 | 2,904.39 |\n| 2 | South Sikkim | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0.44 |\n| 3 | West Sikkim | 1 | 3 | 0 | 7 | 4 | 0 | 2 | 0 | 42 | 0 | 59 | 137.73 |\n| 4 | East Sikkim | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 75 | 1 | 84 | 224.72 |\n| | **Total** | **31** | **5** | **0** | **148** | **72** | **0** | **46** | **0** | **423** | **8** | **733** | **3,267.28** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 102: Type-wise distribution of GL in Districts of Sikkim", "columns": ["S. No.", "District", "Types of Glacial Lake - M(e)", "Types of Glacial Lake - M(l)", "Types of Glacial Lake - M(lg)", "Types of Glacial Lake - M(o)", "Types of Glacial Lake - I(s)", "Types of Glacial Lake - I(d)", "Types of Glacial Lake - E(c)", "Types of Glacial Lake - E(v)", "Types of Glacial Lake - E(o)", "Types of Glacial Lake - O", "Total - No. of Lakes", "Total - Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2603, "line_end": 2609, "token_count_estimate": 520, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1909faa9d21d4715", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the districts of Sikkim has been shown in Table 103 and Figure 130. Majority of glacial lakes (57.29%) are situated in high altitude i.e. 4,001 - 5,000 m elevation range with total lake area of 1,170.72 ha (35.83%). This is followed by glacial lakes in very high altitude elevation range with 39.29%. North Sikkim district contains maximum number of glacial lakes above 4,000 m elevation in comparison with any other district in the state, with majority of them falling in high altitude range i.e. 4,001 - 5,000 m. Elevation range-type-wise distribution of glacial lakes has been represented in Figure 131.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "text", "line_start": 2610, "line_end": 2623, "token_count_estimate": 234, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "840c8792860822ed", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: table\nTable: Table 103: Elevation range-wise distribution of GL in Districts of Sikkim\n\n| S. No. | District | Lake Area Range (ha) - up to 3,000 - No. of Lakes | Lake Area Range (ha) - up to 3,000 - Total Lake Area (ha) | Lake Area Range (ha) - 3,001 - 4,000 - No. of Lakes | Lake Area Range (ha) - 3,001 - 4,000 - Total Lake Area (ha) | Lake Area Range (ha) - 4,001 - 5,000 - No. of Lakes | Lake Area Range (ha) - 4,001 - 5,000 - Total Lake Area (ha) | Lake Area Range (ha) - > 5,000 - No. of Lakes | Lake Area Range (ha) - > 5,000 - Total Lake Area (ha) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | North Sikkim | 0 | 0.00 | 10 | 33.76 | 293 | 914.45 | 286 | 1,956.18 |\n| 2 | South Sikkim | 0 | 0.00 | 0 | 0.00 | 1 | 0.44 | 0 | 0.00 |\n| 3 | West Sikkim | 0 | 0.00 | 0 | 0.00 | 57 | 136.79 | 2 | 0.94 |\n| 4 | East Sikkim | 0 | 0.00 | 15 | 105.68 | 69 | 119.04 | 0 | 0.00 |\n| | **Total** | **0** | **0.00** | **25** | **139.44** | **420** | **1,170.72** | **288** | **1,957.14** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 103: Elevation range-wise distribution of GL in Districts of Sikkim", "columns": ["S. No.", "District", "Lake Area Range (ha) - up to 3,000 - No. of Lakes", "Lake Area Range (ha) - up to 3,000 - Total Lake Area (ha)", "Lake Area Range (ha) - 3,001 - 4,000 - No. of Lakes", "Lake Area Range (ha) - 3,001 - 4,000 - Total Lake Area (ha)", "Lake Area Range (ha) - 4,001 - 5,000 - No. of Lakes", "Lake Area Range (ha) - 4,001 - 5,000 - Total Lake Area (ha)", "Lake Area Range (ha) - > 5,000 - No. of Lakes", "Lake Area Range (ha) - > 5,000 - Total Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2624, "line_end": 2630, "token_count_estimate": 472, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f2cf34303e62928", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.3 Inter Comparison of Subbasins > Area-range-wise Distribution", "section_headings": ["5. RESULTS", "5.3 Inter Comparison of Subbasins", "Area-range-wise Distribution"], "chunk_type": "text", "line_start": 2631, "line_end": 2642, "token_count_estimate": 66, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cf2bb0ea014d2309", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics\nType: text\n\nApart from India, Brahmaputra River basin also covers part of transboundary region which has a total area of 3,18,243 Km² i.e. 79.59% of the total river basin area. This transboundary region covers majority part of it in China and little in Bhutan. A total of 15,080 glacial lakes lies within transboundary region, covering a total area of 77,232.69 ha i.e. 0.24% of the total area of the Brahmaputra River basin under transboundary region.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics"], "chunk_type": "text", "line_start": 2644, "line_end": 2646, "token_count_estimate": 146, "basins": ["Brahmaputra"], "subbasins": [], "countries": ["Bhutan", "China", "India"], "lake_ids": []}}
{"id": "f571954b486252e5", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\nIn Transboundary region, glacial lakes have been distributed in all 6 classes of area ranges. Table 68 and Figure 84 shows the area range-wise distribution of glacial lakes for the Transboundary region. About 12,356 (81.93%) lakes are with < 5 ha lake area contributing to 21.99% of total lake area. The remaining lakes with > 5 ha in size are only 2,724 (18.06%) but contributing to 78.01% of total lake area in the region.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2648, "line_end": 2652, "token_count_estimate": 141, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bc4692feb9b63be7", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 104: Area range-wise distribution of GL in Transboundary region\n\n| S.No. | Lake Area Range (ha) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n| :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 3,169 | 1,132.53 | 1.47 |\n| 2 | 0.5 - 1 | 3,347 | 2,402.14 | 3.11 |\n| 3 | 1 - 5 | 5,840 | 13,449.61 | 17.41 |\n| 4 | 5 - 10 | 1,358 | 9,556.37 | 12.37 |\n| 5 | 10 - 50 | 1,191 | 23,082.29 | 29.89 |\n| 6 | > 50 | 175 | 27,609.75 | 35.75 |\n| | **Total** | **15,080** | **77,232.69** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 104: Area range-wise distribution of GL in Transboundary region", "columns": ["S.No.", "Lake Area Range (ha)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2653, "line_end": 2661, "token_count_estimate": 287, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "99d5238134edf02e", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\n**Type-wise Distribution**\n\nDistribution of different types of glacial lakes in the Transboundary region is given in Table 105 and Figure 133. All types of glacial lakes are present in the Transboundary region, where Other Glacial Erosion lakes are found to be the maximum with 9,696 (64.30%) occupying a total lake extent of 39,019.48 ha at 50.52% in the region. After that, Other Moraine Dammed and Other Glacial lakes are in majority with 2,788 (18.49%) and 1,385 (9.18%) and extend over a total area of 8,263.44 ha (10.70%) and 11,330.78 ha (14.67%) respectively.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2662, "line_end": 2668, "token_count_estimate": 191, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fd6f9d2483ff5840", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 105: Type-wise distribution of GL in Transboundary region\n\n| S.No. | Code | Types of Glacial Lake | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|---|\n| 1 | M(e) | End-moraine Dammed Lake | 353 | 9,142.85 | 11.84 |\n| 2 | M(l) | Lateral Moraine Dammed Lake | 32 | 237.80 | 0.31 |\n| 3 | M(lg) | Lateral Moraine Dammed Lake with Ice | 2 | 0.93 | 0.00 |\n| 4 | M(o) | Other Moraine Dammed Lake | 2,788 | 8,263.44 | 10.70 |\n| 5 | I(s) | Supra-glacial Lake | 200 | 214.38 | 0.28 |\n| 6 | I(d) | Glacier Ice-dammed Lake | 2 | 2.50 | 0.00 |\n| 7 | E(c) | Cirque Erosion Lake | 615 | 5,574.62 | 7.22 |\n| 8 | E(v) | Glacier Trough Valley Erosion Lake | 7 | 3,445.91 | 4.46 |\n| 9 | E(o) | Other Glacial Erosion Lake | 9,696 | 39,019.48 | 50.52 |\n| 10 | O | Other Glacial Lake | 1,385 | 11,330.78 | 14.67 |\n| | | **Total** | **15,080** | **77,232.69** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 105: Type-wise distribution of GL in Transboundary region", "columns": ["S.No.", "Code", "Types of Glacial Lake", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 11, "line_start": 2669, "line_end": 2681, "token_count_estimate": 472, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8ec183fab9c3b15b", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\n**Area range-Type-wise Distribution**\n\nGlacial lake distribution by area range vs. type-wise is given in Table 106 and Figure 134. The lakes with < 5 ha in size (81.93%) are dominant with Other Glacial Erosion (65.85%) and Other Moraine Dammed lakes (19.66%). Lakes with > 5 ha (18.06%) are also dominated by Other Glacial Erosion lakes (57.23%). All types of Moraine-dammed lakes, which constitute about 21.05%, are majorly with < 5 ha in water spread.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2682, "line_end": 2688, "token_count_estimate": 161, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8f7c29b0ce1bcdc0", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 106: Area range-wise vs. Type-wise distribution of GL in Transboundary region\n\n| S. No. | Subbasin | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 6 | 6 | 1 | 668 | 103 | 0 | 11 | 0 | 1,986 | 388 | 3,169 |\n| 2 | 0.5 - 1 | 12 | 5 | 1 | 687 | 62 | 1 | 37 | 0 | 2,224 | 318 | 3,347 |\n| 3 | 1 - 5 | 90 | 13 | 0 | 1,075 | 30 | 1 | 228 | 0 | 3,927 | 476 | 5,840 |\n| 4 | 5 - 10 | 71 | 3 | 0 | 215 | 3 | 0 | 158 | 0 | 828 | 80 | 1,358 |\n| 5 | 10 - 50 | 133 | 4 | 0 | 132 | 2 | 0 | 176 | 0 | 657 | 87 | 1,191 |\n| 6 | > 50 | 41 | 1 | 0 | 11 | 0 | 0 | 5 | 7 | 74 | 36 | 175 |\n| | **Total** | **353** | **32** | **2** | **2,788** | **200** | **2** | **615** | **7** | **9,696** | **1,385** | **15,080** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 106: Area range-wise vs. Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Subbasin", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2689, "line_end": 2697, "token_count_estimate": 505, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "321ba2e18984c486", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Elevation range-wise Distribution**\n\nElevation range-wise distribution of the glacial lakes in the Transboundary region has been shown in Table 107 and Figure 135. Majority of glacial lakes are situated above 4,000 m elevation range i.e. 14,649 (97.14%) with total lake area of 71,327.42 ha, contributing 92.35% of lake area.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2698, "line_end": 2706, "token_count_estimate": 137, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b8420c8bc6b29609", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 107: Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Elevation Range (m) | No. of Lakes | Total Lake Area (ha) | Total Lake Area (%) |\n|---|---|---|---|---|\n| 1 | up to 3,000 | 7 | 107.41 | 0.14 |\n| 2 | 3,001 - 4,000 | 424 | 5,797.86 | 7.51 |\n| 3 | 4,001 - 5,000 | 7,887 | 44,743.89 | 57.93 |\n| 4 | > 5,000 | 6,762 | 26,583.53 | 34.42 |\n| | **Total** | **15,080** | **77,232.69** | **100.00** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 107: Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Elevation Range (m)", "No. of Lakes", "Total Lake Area (ha)", "Total Lake Area (%)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2707, "line_end": 2713, "token_count_estimate": 237, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "efa6cbd984cab708", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\n***\n\n146 GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Area Elevation range-wise Distribution**\n\nGlacial lake distribution has been analyzed as per area range vs. elevation range-wise, given in Table 108 and Figure 136. It is noted that, 52.30% of glacial lakes (7,887) are situated in high altitude range i.e. 4,001 - 5,000 m, which also constitutes majority of total lake area within that range i.e. 57.93%. However, 7 glacial lakes lies below 3,000 m, has 50% of its lakes < 5 ha in size. 87% of lakes lying in very high altitude range are < 5 ha, majorly of size ranging 1 - 5 ha (i.e. 2,605), followed by lakes of size 0.5 - 1 ha (i.e. 1,683). It has been further noticed that, 12.88% of lakes > 5 ha are lying within in the very high altitude range, majority of them falling in size ranging of 5 - 10 ha.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2714, "line_end": 2726, "token_count_estimate": 272, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54ff2b4667410b62", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 108: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region\n\n| S. No. | Lake Area Range (ha) | Elevation Range: up to 3,000 (No. of lakes) | Elevation Range: up to 3,000 (Total Lake Area ha) | Elevation Range: 3,001 - 4,000 (No. of lakes) | Elevation Range: 3,001 - 4,000 (Total Lake Area ha) | Elevation Range: 4,001 - 5,000 (No. of lakes) | Elevation Range: 4,001 - 5,000 (Total Lake Area ha) | Elevation Range: > 5,000 (No. of lakes) | Elevation Range: > 5,000 (Total Lake Area ha) | Total (No. of lakes) | Total (Lake Area ha) |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0.00 | 52 | 19.42 | 1,514 | 544.89 | 1,603 | 568.22 | 3,169 | 1,132.53 |\n| 2 | 0.5 - 1 | 1 | 0.62 | 47 | 32.57 | 1,616 | 1,163.96 | 1,683 | 1,204.99 | 3,347 | 2,402.14 |\n| 3 | 1 - 5 | 2 | 5.34 | 178 | 458.10 | 3,055 | 7,129.38 | 2,605 | 5,856.79 | 5,840 | 13,449.61 |\n| 4 | 5 - 10 | 1 | 5.75 | 70 | 497.14 | 822 | 5,814.98 | 465 | 3,238.50 | 1,358 | 9,556.37 |\n| 5 | 10 - 50 | 3 | 95.70 | 63 | 1,350.01 | 769 | 14,864.32 | 356 | 6,772.26 | 1,191 | 23,082.29 |\n| 6 | > 50 | 0 | 0.00 | 14 | 3,440.62 | 111 | 15,226.36 | 50 | 8,942.77 | 175 | 27,609.75 |\n| | **Total** | **7** | **107.41** | **424** | **5,797.86** | **7,887** | **44,743.89** | **6,762** | **26,583.53** | **15,080** | **77,232.69** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 108: Area range-wise vs. Elevation range-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Lake Area Range (ha)", "Elevation Range: up to 3,000 (No. of lakes)", "Elevation Range: up to 3,000 (Total Lake Area ha)", "Elevation Range: 3,001 - 4,000 (No. of lakes)", "Elevation Range: 3,001 - 4,000 (Total Lake Area ha)", "Elevation Range: 4,001 - 5,000 (No. of lakes)", "Elevation Range: 4,001 - 5,000 (Total Lake Area ha)", "Elevation Range: > 5,000 (No. of lakes)", "Elevation Range: > 5,000 (Total Lake Area ha)", "Total (No. of lakes)", "Total (Lake Area ha)"], "table_row_start": 1, "table_row_end": 7, "line_start": 2727, "line_end": 2735, "token_count_estimate": 679, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5f0395ca624b661c", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Type Elevation range-wise Distribution**\n\nGlacial lake distribution has also been analyzed as per type-wise vs. elevation range-wise, given in Table 109 and Figure 137. The dominant lake type in the subbasin i.e., Other Glacial Erosion lakes (64.29%) are predominantly located in the elevation range 4,001 - 5,000 m (58.93%). The other dominant lake type, namely, Other Moraine Dammed and End-Moraine Dammed Lakes are also majorly distributed in very high altitude range > 5,000 m elevation range, i.e. 75.71% and 59.49%. Majority i.e. 73.54% of all types of Moraine-dammed lakes lies in > 5,000 m.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2736, "line_end": 2746, "token_count_estimate": 222, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1b8e20044a6b39dc", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: table\nTable: Table 109: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region\n\n| S. No. | Subbasin | M(e) | M(l) | M(lg) | M(o) | I(s) | I(d) | E(c) | E(v) | E(o) | O | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 3 | 0 | 7 |\n| 2 | 3,001 - 4,000 | 9 | 0 | 0 | 26 | 15 | 0 | 46 | 2 | 281 | 45 | 424 |\n| 3 | 4,001 - 5,000 | 134 | 19 | 1 | 647 | 117 | 0 | 480 | 4 | 5,714 | 771 | 7,887 |\n| 4 | > 5,000 | 210 | 13 | 1 | 2,111 | 68 | 2 | 89 | 1 | 3,698 | 569 | 6,762 |\n| **Total** | | **353** | **32** | **2** | **2,788** | **200** | **2** | **615** | **7** | **9,696** | **1,385** | **15,080** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "table", "table_caption": "Table 109: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region", "columns": ["S. No.", "Subbasin", "M(e)", "M(l)", "M(lg)", "M(o)", "I(s)", "I(d)", "E(c)", "E(v)", "E(o)", "O", "Total"], "table_row_start": 1, "table_row_end": 5, "line_start": 2747, "line_end": 2753, "token_count_estimate": 421, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "943aae41c2e50035", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: figure\nFigure: Figure 137: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region\n\n**Figure 137: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "figure", "figure_caption": "Figure 137: Elevation range-wise vs. Type-wise distribution of GL in Transboundary region", "line_start": 2755, "line_end": 2755, "token_count_estimate": 81, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "667d40354dd78c96", "text": "Document: output vertex chunked\nSection: 5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "5. RESULTS > 5.6 Transboundary Region Statistics > Area range-wise Distribution", "section_headings": ["5. RESULTS", "5.6 Transboundary Region Statistics", "Area range-wise Distribution"], "chunk_type": "text", "line_start": 2756, "line_end": 2765, "token_count_estimate": 49, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "96e2e19eeac07793", "text": "Document: output vertex chunked\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 138 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Brahmaputra River basin. A total of 65 toposheets covered the entire study area, of which 54 toposheets contain glacial lakes.\n\nFigure 138 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Brahmaputra River basin. A total of 65 toposheets covered the entire study area, of which 54 toposheets contain glacial lakes.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 138 shows the layout map representing SOI 250K toposheets overlaid on satellite image acquisition year layer covering the Brahmaputra River basin. A total of 65 toposheets covered the entire study area, of which 54 toposheets contain glacial lakes.", "line_start": 2768, "line_end": 2768, "token_count_estimate": 146, "basins": ["Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "feb7ab32924452f1", "text": "Document: output vertex chunked\nSection: 6. INDEX OF MAP SHEETS\nType: figure\nFigure: Figure 138: Layout of SOI 250K Toposheets and year of satellite data used\n\nFigure 138: Layout of SOI 250K Toposheets and year of satellite data used", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "figure", "figure_caption": "Figure 138: Layout of SOI 250K Toposheets and year of satellite data used", "line_start": 2770, "line_end": 2770, "token_count_estimate": 66, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "45239977d32f260e", "text": "Document: output vertex chunked\nSection: 6. INDEX OF MAP SHEETS\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "6. INDEX OF MAP SHEETS", "section_headings": ["6. INDEX OF MAP SHEETS"], "chunk_type": "text", "line_start": 2771, "line_end": 2779, "token_count_estimate": 39, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e7c90277494fa939", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nTransboundary Region | Map 1 | Plate No: 62J\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 2781, "line_end": 2787, "token_count_estimate": 78, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b21d94a231df6f4d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 59 | 1 | 0 | 0 | 0 | 20 | 111 | 192 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 44 | 2 | 0 | 1 | 0 | 10 | 94 | 151 |\n| 3 | 1 - 5 | 1 | 1 | 0 | 46 | 0 | 0 | 0 | 0 | 8 | 106 | 162 |\n| 4 | 5 - 10 | 2 | 1 | 0 | 13 | 0 | 0 | 1 | 0 | 1 | 10 | 28 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 2 | 16 | 27 |\n| 6 | > 50 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 11 |\n| **Total** | | **11** | **3** | **0** | **167** | **3** | **0** | **2** | **0** | **42** | **343** | **571** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2788, "line_end": 2796, "token_count_estimate": 536, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f9b2e501b3b125f4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 2\nPlate No: 62J\nUpper Yarlung Tsangpo\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nLegend\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 2797, "line_end": 2860, "token_count_estimate": 344, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "f488548963d0c72b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 133 | 1,858.8 |\n| 4 | > 5,000 | 438 | 1,716.9 |\n| | Total | 571 | 3,575.6 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2861, "line_end": 2867, "token_count_estimate": 175, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0ba143d5361f8e37", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 3\nPlate No: 62K\nUpper Yarlung Tsangpo\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 2868, "line_end": 2899, "token_count_estimate": 191, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "926703392cc783e5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 17 | 1 | 0 | 0 | 0 | 1 | 15 | 35 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 6 | 9 | 23 |\n| 3 | 1 - 5 | 1 | 1 | 0 | 15 | 0 | 0 | 0 | 0 | 4 | 18 | 39 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| 5 | 10 - 50 | 6 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 9 |\n| 6 | > 50 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| | Total | 11 | 2 | 0 | 44 | 1 | 0 | 0 | 0 | 14 | 42 | 114 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 2900, "line_end": 2908, "token_count_estimate": 537, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d88d94549dc3342b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 4\nPlate No: 62K\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 2909, "line_end": 2971, "token_count_estimate": 309, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "1abec63e37700529", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 31 | 602.4 |\n| 4 | > 5,000 | 83 | 639.0 |\n| | Total | 114 | 1,241.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 2972, "line_end": 2978, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a10d6b0069726fa4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 5\nPlate No: 62M\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 2979, "line_end": 3013, "token_count_estimate": 218, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "06a109020bc23537", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 2 | 0 | 7 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3014, "line_end": 3022, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "35f69edabc7ff3f5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 6\nPlate No: 62M\n\nUpper Yarlung Tsangpo\n\n0 5 10 20 Km\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3023, "line_end": 3077, "token_count_estimate": 277, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "fa8e1294e65fb1af", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 7 | 13.4 |\n| | Total | 7 | 13.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3078, "line_end": 3084, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dace22cf2337cd52", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 7\nPlate No: 62N\n\nUpper Yarlung Tsangpo\n\n0 5 10 20 Km\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3085, "line_end": 3133, "token_count_estimate": 250, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "ec166c7a9d9f1607", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 49 | 0 | 0 | 0 | 0 | 26 | 11 | 86 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 28 | 4 | 72 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 58 | 0 | 0 | 1 | 0 | 31 | 18 | 112 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 2 | 1 | 13 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 6 | 6 | 17 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 |\n| | Total | 11 | 0 | 0 | 155 | 0 | 0 | 1 | 0 | 95 | 41 | 303 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3134, "line_end": 3142, "token_count_estimate": 512, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "be9789e8377e9fba", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 8\nPlate No: 62N\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3143, "line_end": 3195, "token_count_estimate": 277, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "d4e14c6b68f8ae9a", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 303 | 1,337.0 |\n| Total | | 303 | 1,337.0 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3196, "line_end": 3202, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "18838c5f6c2b4d68", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 9\nPlate No: 62O\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3203, "line_end": 3249, "token_count_estimate": 249, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "ce0fbdf18e4e8b4b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 21 | 2 | 0 | 1 | 0 | 30 | 9 | 63 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 21 | 1 | 0 | 0 | 0 | 20 | 5 | 47 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 35 | 0 | 0 | 1 | 0 | 55 | 8 | 99 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 10 | 2 | 21 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 3 | 9 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 7 |\n| Total | | 1 | 0 | 0 | 88 | 3 | 0 | 2 | 0 | 118 | 34 | 246 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3250, "line_end": 3258, "token_count_estimate": 602, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5840ab495f5fbfa3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3259, "line_end": 3276, "token_count_estimate": 110, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6e7b28f1d054087", "text": "Document: output vertex chunked\nSection: GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nType: text\n\nTransboundary Region\nMap 10\nPlate No: 62O\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins:\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN", "section_headings": ["GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3278, "line_end": 3312, "token_count_estimate": 175, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "a07b9e88d18c5a0d", "text": "Document: output vertex chunked\nSection: GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 6 | 537.0 |\n| 4 | > 5,000 | 240 | 895.3 |\n| | Total | 246 | 1,432.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN", "section_headings": ["GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3313, "line_end": 3319, "token_count_estimate": 166, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62e9447479459c98", "text": "Document: output vertex chunked\nSection: GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\nElevation Range (m)\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN", "section_headings": ["GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3320, "line_end": 3359, "token_count_estimate": 212, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "fc89533df6858c3c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nTransboundary Region\nMap 11\nPlate No: 71B\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3361, "line_end": 3372, "token_count_estimate": 84, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "39be34e65fa895e1", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 2 | 2 | 9 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 7 | 0 | 10 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 5 | 1 | 0 | 1 | 0 | 4 | 7 | 18 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 1 | 0 | 0 | 15 | 2 | 0 | 2 | 0 | 13 | 11 | 44 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3373, "line_end": 3381, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44ebdc573273d527", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 12\nPlate No: 71B\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\n\nBrahmaputra Basin\nIndia\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3382, "line_end": 3435, "token_count_estimate": 298, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "29cc3922842e4d6c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 44 | 157.9 |\n| | Total | 44 | 157.9 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3436, "line_end": 3442, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dda82474fa3a8818", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 13\nPlate No: 71C\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3443, "line_end": 3488, "token_count_estimate": 266, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "c9562deb5c23e119", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 24 | 0 | 0 | 0 | 0 | 18 | 3 | 45 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 26 | 1 | 0 | 0 | 0 | 27 | 0 | 54 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 33 | 1 | 0 | 1 | 0 | 29 | 0 | 67 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 5 | 0 | 10 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 7 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| | Total | 7 | 0 | 0 | 90 | 2 | 0 | 1 | 0 | 81 | 3 | 184 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3489, "line_end": 3497, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d6a7159de4e492c7", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 14\nPlate No: 71C\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nLocation Map\nBrahmaputra Basin\nIndia\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3498, "line_end": 3567, "token_count_estimate": 381, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "6c4b005c437934bc", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 1 | 3.1 |\n| 4 | > 5,000 | 183 | 466.6 |\n| | Total | 184 | 469.6 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3568, "line_end": 3574, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "12fa07ab9c3f1a53", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 15\nPlate No: 71D\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3575, "line_end": 3608, "token_count_estimate": 195, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "baff172fe2538365", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 3 | 1 | 9 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 7 | 0 | 0 | 1 | 0 | 5 | 1 | 14 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 4 | 1 | 11 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 3 | 0 | 0 | 19 | 0 | 0 | 1 | 0 | 13 | 3 | 39 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3609, "line_end": 3617, "token_count_estimate": 601, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a6f81db012747d8e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 16\nPlate No: 71D\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3618, "line_end": 3671, "token_count_estimate": 284, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "1c1d91f8f238e09d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 39 | 118.3 |\n| | Total | 39 | 118.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3672, "line_end": 3678, "token_count_estimate": 163, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "d40c6e7a52f28954", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 17\nPlate No: 71F\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3679, "line_end": 3725, "token_count_estimate": 252, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "5ecb64d5cfcaac52", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 4 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 6 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 5 | 0 | 11 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 14 | 0 | 21 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3726, "line_end": 3734, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9120212f283d1fce", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Map 18**\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nPlate No: 71F\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution\n\n**Subbasins**\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3735, "line_end": 3775, "token_count_estimate": 221, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "71eccab14cf0d7b3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 21 | 26.4 |\n| | Total | 21 | 26.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3776, "line_end": 3782, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3f3c8e833b4f61aa", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Map 19**\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nPlate No: 71G\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3783, "line_end": 3816, "token_count_estimate": 210, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "67df4c0408f5ba74", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes | | | | | | | | | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | | **Moraine Dammed Lake** | ** ** | ** ** | ** ** | **Ice Dammed Lake** | ** ** | **Erosion Lake** | ** ** | ** ** | | |\n| | | **End-moraine Dammed Lake** | **Lateral Moraine Dammed Lake** | **Lateral Moraine Dammed Lake with Ice** | **Other Moraine Dammed Lake** | **Supra-glacial Lake** | **Glacier Ice-dammed Lake** | **Cirque Erosion Lake** | **Glacier Trough Valley Erosion Lake** | **Other Glacial Erosion Lake** | | |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 16 | 6 | 30 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 11 | 0 | 0 | 3 | 0 | 21 | 6 | 42 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 19 | 0 | 0 | 1 | 0 | 35 | 13 | 69 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 4 | 4 | 11 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| | **Total** | **2** | **0** | **0** | **41** | **0** | **0** | **4** | **0** | **79** | **30** | **156** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes", "", "", "", "", "", "", "", "", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 9, "line_start": 3817, "line_end": 3827, "token_count_estimate": 611, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91cb1ad97de92387", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\n**Map 20**\n**Plate No: 71G**\nTransboundary Region\nUpper Yarlung Tsangpo\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3828, "line_end": 3848, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "54a86df53e5dac94", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 3 | 2.6 |\n| 4 | > 5,000 | 153 | 366.5 |\n| **Total** | | **156** | **369.1** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3849, "line_end": 3855, "token_count_estimate": 176, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f8028aa1b1d8a600", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\n**Map 21**\n**Plate No: 71H**\nTransboundary Region\nUpper Yarlung Tsangpo\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3856, "line_end": 3891, "token_count_estimate": 221, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "876dcb4330d2e2c1", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 7 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 5 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| **Total** | | **1** | **0** | **0** | **3** | **0** | **0** | **0** | **0** | **12** | **0** | **16** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3892, "line_end": 3900, "token_count_estimate": 561, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5a4a5bf6b31abe69", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nMap 22\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nPlate No: 71H\nUpper Yarlung Tsangpo\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3901, "line_end": 3922, "token_count_estimate": 174, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "910db72255b8deb5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 16 | 23.1 |\n| | Total | 16 | 23.1 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 3923, "line_end": 3929, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "393fc1e2b1b660fe", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nMap 23\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nPlate No: 71K\nUpper Yarlung Tsangpo\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 3930, "line_end": 3998, "token_count_estimate": 346, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "35b6407958f1594c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 16 | 1 | 37 |\n| 2 | 0.5 - 1 | 0 | 0 | 1 | 17 | 0 | 0 | 0 | 0 | 22 | 6 | 46 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 29 | 0 | 0 | 0 | 0 | 22 | 4 | 55 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 2 | 0 | 6 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 1 | 69 | 0 | 0 | 1 | 0 | 63 | 12 | 146 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 3999, "line_end": 4007, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f08cc9c85d5158d5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 24\nPlate No: 71K\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution\n\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4008, "line_end": 4065, "token_count_estimate": 287, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "07a0594f9b960110", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 1 | 0.7 |\n| 4 | > 5,000 | 145 | 228.7 |\n| | Total | 146 | 229.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4066, "line_end": 4072, "token_count_estimate": 164, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "54c40f75396302fb", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 25\nPlate No: 71L\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4073, "line_end": 4111, "token_count_estimate": 212, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "e92ef382ccf04ee4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4112, "line_end": 4120, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e006bd8e08140813", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nTransboundary Region\nMap 26\nPlate No: 71L\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4121, "line_end": 4141, "token_count_estimate": 158, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11a7b659433f06a8", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 5 | 8.7 |\n| | Total | 5 | 8.7 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4142, "line_end": 4148, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "16c90e2ffcc2a659", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nTransboundary Region\nMap 27\nPlate No: 71N\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4149, "line_end": 4181, "token_count_estimate": 206, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0de39900c07f336f", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 5 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4182, "line_end": 4190, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9bebfc7632daddec", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\n\nMap 28\n\nPlate No: 71N\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4191, "line_end": 4222, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "445f948ca7f4acf9", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 2 | 40.1 |\n| 4 | > 5,000 | 3 | 4.1 |\n| | Total | 5 | 44.2 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4223, "line_end": 4229, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "854ed5e194a03c55", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nBrahmaputra Basin\n\nIndia\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\n\nMap 29\n\nPlate No: 71O\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4230, "line_end": 4306, "token_count_estimate": 372, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "6773228759fe8b73", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 8 | 0 | 10 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 13 | 2 | 18 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 18 | 0 | 19 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 5 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| | Total | 0 | 0 | 0 | 6 | 0 | 0 | 1 | 0 | 47 | 4 | 58 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4307, "line_end": 4315, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad37dce38c8d38d1", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 30\nPlate No: 71O\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4316, "line_end": 4369, "token_count_estimate": 296, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "d0857cffd534e9b5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 6 | 176.4 |\n| 4 | > 5,000 | 52 | 107.3 |\n| | **Total** | **58** | **283.7** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4370, "line_end": 4376, "token_count_estimate": 169, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6c7c71ca97c6d165", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\nElevation Range (m)\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 31\nPlate No: 71P\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4377, "line_end": 4424, "token_count_estimate": 263, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "b73e3e3d575e99c1", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | End-moraine Dammed Lake | Lateral Moraine Dammed Lake | Lateral Moraine Dammed Lake with Ice | Other Moraine Dammed Lake | Supra-glacial Lake | Glacier Ice-dammed Lake | Cirque Erosion Lake | Glacier Trough Valley Erosion Lake | Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 5 | 0 | 8 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 5 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 5 | 2 | 15 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| | **Total** | **2** | **0** | **0** | **16** | **0** | **0** | **0** | **0** | **14** | **4** | **36** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "End-moraine Dammed Lake", "Lateral Moraine Dammed Lake", "Lateral Moraine Dammed Lake with Ice", "Other Moraine Dammed Lake", "Supra-glacial Lake", "Glacier Ice-dammed Lake", "Cirque Erosion Lake", "Glacier Trough Valley Erosion Lake", "Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4425, "line_end": 4433, "token_count_estimate": 489, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9eddc5b2af395ce4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 32\nPlate No: 71P\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types**\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\n**Location Map**\nBrahmaputra Basin\nIndia\n\nSubbasins:\n*   Amo Chu\n*   Dibang\n*   Dihang\n*   Jia Bharali\n*   Lhasa Tsangpo\n*   Lohit\n*   Lower Yarlung Tsangpo\n*   Manas\n*   Puna Tsang Chu\n*   Subansiri\n*   Teesta\n*   Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4434, "line_end": 4484, "token_count_estimate": 306, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "3a91061a80c26806", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 36 | 258.9 |\n| | Total | 36 | 258.9 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4485, "line_end": 4491, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3c151ae7f5029b00", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 33\nPlate No: 77B\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4492, "line_end": 4539, "token_count_estimate": 271, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "04fc4deb0816e7bd", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| Total | | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 11 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4540, "line_end": 4548, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "96cec8ef622661c9", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 34\nPlate No: 77B\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4549, "line_end": 4604, "token_count_estimate": 284, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "8ab4e258a5edd24d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 4 | 34.4 |\n| 4 | > 5,000 | 7 | 274.4 |\n| Total | | 11 | 308.8 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4605, "line_end": 4611, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "db49337304ff9289", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 35\nPlate No: 77C\n\nUpper Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4612, "line_end": 4661, "token_count_estimate": 249, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "a110eead90d3dda7", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes   Moraine Dammed Lake   End-moraine Dammed Lake | Types of Glacial Lakes   Moraine Dammed Lake   Lateral Moraine Dammed Lake | Types of Glacial Lakes   Moraine Dammed Lake   Lateral Moraine Dammed Lake with ice | Types of Glacial Lakes   Moraine Dammed Lake   Other Moraine Dammed Lake | Types of Glacial Lakes   Ice Dammed Lake   Supra-glacial Lake | Types of Glacial Lakes   Ice Dammed Lake   Glacier Ice-dammed Lake | Types of Glacial Lakes   Erosion Lake   Cirque Erosion Lake | Types of Glacial Lakes   Erosion Lake   Glacier Trough Valley Erosion Lake | Types of Glacial Lakes   Erosion Lake   Other Glacial Erosion Lake | Types of Glacial Lakes   Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 12 | 0 | 16 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 6 | 0 | 0 | 1 | 0 | 19 | 1 | 27 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 29 | 2 | 44 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 6 | 0 | 11 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 0 | 0 | 0 | 27 | 0 | 0 | 2 | 0 | 70 | 3 | 102 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4662, "line_end": 4670, "token_count_estimate": 583, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f12b651221ab8bd4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Map 36**\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nPlate No: 77C\nData Source: Resourcesat-2 LISS-IV 0 5 10 20 Km\nUpper Yarlung Tsangpo\n\n**Distribution of Glacial Lake Types**\n**Location Map**\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4671, "line_end": 4699, "token_count_estimate": 192, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "ecc43290fab452b3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 1 | 1.2 |\n| 3 | 4,001 - 5,000 | 4 | 5.4 |\n| 4 | > 5,000 | 97 | 266.2 |\n| | Total | 102 | 272.8 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4700, "line_end": 4706, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "149c10eb34143a04", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nBrahmaputra Basin\nIndia\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Map 37**\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nState: Sikkim\nPlate No: 77D\nUpper Yarlung Tsangpo\nNorth Sikkim\nTeesta\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5) 0 5 10 20 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4707, "line_end": 4780, "token_count_estimate": 396, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "0dc320d4c8d4d5d6", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 28 | 1 | 32 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 22 | 2 | 28 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 7 | 0 | 0 | 3 | 0 | 32 | 4 | 46 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |\n| 5 | 10 - 50 | 3 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 6 |\n| 6 | > 50 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |\n| | Total | 6 | 0 | 0 | 17 | 1 | 0 | 4 | 0 | 84 | 7 | 119 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4781, "line_end": 4789, "token_count_estimate": 574, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1cf9a2ca54303da3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nState: Sikkim\nMap 38\nPlate No: 77D\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nBrahmaputra Basin\nIndia\n\nSubbasins:\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4790, "line_end": 4837, "token_count_estimate": 295, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "47b05e9b9fe84248", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 8 | 46.7 |\n| 4 | > 5,000 | 111 | 710.8 |\n| Total | | 119 | 757.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4838, "line_end": 4844, "token_count_estimate": 164, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "958f8a290b87c90f", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 39\nPlate No: 77F\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4845, "line_end": 4890, "token_count_estimate": 249, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "4ac7799d2f050362", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 2 | 0 | 7 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 6 | 0 | 9 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 8 | 0 | 12 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 0 | 0 | 0 | 9 | 2 | 0 | 1 | 0 | 16 | 0 | 28 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4891, "line_end": 4899, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "26d7e3e59ad9ef63", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region &nbsp;&nbsp;&nbsp;&nbsp; Map 40 &nbsp;&nbsp;&nbsp;&nbsp; Plate No: 77F\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4900, "line_end": 4922, "token_count_estimate": 198, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c5a73db91e30b5ed", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 28 | 25.5 |\n| Total | | 28 | 25.5 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4923, "line_end": 4929, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a14e180b2ffa5d31", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region &nbsp;&nbsp;&nbsp;&nbsp; Map 41 &nbsp;&nbsp;&nbsp;&nbsp; Plate No: 77G\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4930, "line_end": 4962, "token_count_estimate": 230, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "3d891103cd762bd3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes   Moraine Dammed Lake   End-moraine Dammed Lake | Types of Glacial Lakes   Moraine Dammed Lake   Lateral Moraine Dammed Lake | Types of Glacial Lakes   Moraine Dammed Lake   Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes   Moraine Dammed Lake   Other Moraine Dammed Lake | Types of Glacial Lakes   Ice Dammed Lake   Supra-glacial Lake | Types of Glacial Lakes   Ice Dammed Lake   Glacier Ice-dammed Lake | Types of Glacial Lakes   Erosion Lake   Cirque Erosion Lake | Types of Glacial Lakes   Erosion Lake   Glacier Trough Valley Erosion Lake | Types of Glacial Lakes   Erosion Lake   Other Glacial Erosion Lake | Types of Glacial Lakes   Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 6 | 0 | 0 | 2 | 0 | 59 | 2 | 69 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 6 | 0 | 0 | 1 | 0 | 77 | 3 | 88 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 18 | 0 | 0 | 1 | 0 | 90 | 3 | 113 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 16 | 2 | 22 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 11 | 1 | 13 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 3 | 0 | 0 | 34 | 0 | 0 | 4 | 0 | 253 | 11 | 305 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 4963, "line_end": 4971, "token_count_estimate": 583, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fbac52b4a17f153d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 42\nPlate No: 77G\n\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4972, "line_end": 4988, "token_count_estimate": 150, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "07595f7c1b3c158e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 2 | 4.8 |\n| 4 | > 5,000 | 303 | 734.6 |\n| | Total | 305 | 739.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 4989, "line_end": 4995, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e1d7505d841cc4e8", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 43\nPlate No: 77H\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 8)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 4996, "line_end": 5024, "token_count_estimate": 181, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "848efd4d244ff428", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 14 | 3 | 0 | 0 | 0 | 26 | 3 | 46 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 18 | 2 | 0 | 1 | 0 | 27 | 9 | 57 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 31 | 2 | 0 | 1 | 0 | 61 | 12 | 110 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 10 | 0 | 0 | 3 | 0 | 6 | 2 | 24 |\n| 5 | 10 - 50 | 8 | 0 | 0 | 7 | 1 | 0 | 4 | 0 | 2 | 1 | 23 |\n| 6 | > 50 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 8 |\n| | Total | 15 | 0 | 0 | 80 | 8 | 0 | 9 | 0 | 127 | 29 | 268 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5025, "line_end": 5033, "token_count_estimate": 512, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2e8d11a6129a8c5f", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nTransboundary Region\nMap 44\nPlate No: 77H\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5034, "line_end": 5064, "token_count_estimate": 175, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "27aaaa52b1ef1f81", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 1 | 4.0 |\n| 3 | 4,001 - 5,000 | 171 | 2,587.3 |\n| 4 | > 5,000 | 96 | 333.2 |\n| | Total | 268 | 2,924.5 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5065, "line_end": 5071, "token_count_estimate": 166, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "dfe2300d206ff528", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\n**Elevation Range (m)**\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\n**Subbasins**\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nTransboundary Region\nMap 45\nPlate No: 77J\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5072, "line_end": 5134, "token_count_estimate": 319, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "53626960c0c58805", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 13 | 2 | 0 | 0 | 0 | 18 | 2 | 35 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 19 | 2 | 0 | 0 | 0 | 18 | 3 | 42 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 42 | 0 | 0 | 1 | 0 | 26 | 2 | 72 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 4 | 1 | 10 |\n| 5 | 10 - 50 | 3 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 2 | 1 | 10 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| | Total | 5 | 1 | 0 | 81 | 4 | 0 | 1 | 0 | 69 | 9 | 170 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5135, "line_end": 5143, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "151d60ee87081a47", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 46\nPlate No: 77J\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5144, "line_end": 5164, "token_count_estimate": 163, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "48b285593a3e9a3a", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 0 | 0.0 |\n| 4 | > 5,000 | 170 | 514.1 |\n| Total | | 170 | 514.1 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5165, "line_end": 5171, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7ebbe122621bc2ae", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 47\nPlate No: 77K\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5172, "line_end": 5203, "token_count_estimate": 206, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f75ef4cfb6189e1f", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 37 | 4 | 54 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 71 | 1 | 93 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 18 | 0 | 0 | 1 | 0 | 88 | 6 | 116 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 4 | 0 | 0 | 2 | 0 | 8 | 0 | 16 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 2 | 11 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 7 | 0 | 0 | 56 | 0 | 0 | 3 | 0 | 211 | 13 | 290 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5204, "line_end": 5212, "token_count_estimate": 601, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4322978408e3ac75", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 48\nPlate No: 77K\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5213, "line_end": 5257, "token_count_estimate": 229, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "90e8ea8121ec9c5d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 4 | 2.8 |\n| 4 | > 5,000 | 286 | 639.0 |\n| | Total | 290 | 641.8 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5258, "line_end": 5264, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5ba7febf2510cb51", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 49\nPlate No: 77L\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5265, "line_end": 5310, "token_count_estimate": 265, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "9b2f71f117861c8b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 79 | 62 | 0 | 0 | 0 | 64 | 4 | 209 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 58 | 31 | 0 | 0 | 0 | 39 | 2 | 130 |\n| 3 | 1 - 5 | 7 | 2 | 0 | 69 | 14 | 0 | 2 | 0 | 69 | 5 | 168 |\n| 4 | 5 - 10 | 10 | 0 | 0 | 15 | 3 | 0 | 3 | 0 | 14 | 0 | 45 |\n| 5 | 10 - 50 | 24 | 0 | 0 | 18 | 1 | 0 | 4 | 0 | 13 | 0 | 60 |\n| 6 | > 50 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 17 |\n| | Total | 55 | 2 | 0 | 239 | 111 | 0 | 9 | 0 | 202 | 11 | 629 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5311, "line_end": 5319, "token_count_estimate": 594, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f73f0e0fb6fb746e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region Map 50 Plate No: 77L\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nBrahmaputra Basin\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5320, "line_end": 5388, "token_count_estimate": 356, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "4c1769c0302a7179", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 223 | 1,700.1 |\n| 4 | > 5,000 | 406 | 2,744.4 |\n| | Total | 629 | 4,444.5 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5389, "line_end": 5395, "token_count_estimate": 170, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2996fd8569b2c03b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region Map 51 Plate No: 77N\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5396, "line_end": 5412, "token_count_estimate": 129, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fe17f75693a2ea1c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 18 | 1 | 22 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 26 | 0 | 32 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 4 | 0 | 0 | 3 | 0 | 40 | 2 | 49 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 4 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 13 | 0 | 0 | 5 | 0 | 89 | 3 | 110 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5413, "line_end": 5421, "token_count_estimate": 592, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f1330b7deed54ac6", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 52\nPlate No: 77N\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake | Ice Dammed Lake | Erosion Lake | Other Glacial Lake\n\n**Location Map**\nBrahmaputra Basin, India\n\nSubbasins:\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5422, "line_end": 5463, "token_count_estimate": 271, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "40ed6f2d7da4efc7", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 9 | 25.5 |\n| 4 | > 5,000 | 101 | 166.8 |\n| | Total | 110 | 192.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5464, "line_end": 5470, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91417a1484e098d8", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\nElevation Range (m):\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 53\nPlate No: 77O\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5471, "line_end": 5516, "token_count_estimate": 292, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "06de49695c664eb9", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 1 | 13 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 0 | 19 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 57 | 0 | 59 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 1 | 10 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 100 | 2 | 104 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5517, "line_end": 5525, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c2b3cc1b349f4001", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region Map 54 Plate No: 77O\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5526, "line_end": 5546, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7985db62c8495e73", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 1 | 0.5 |\n| 3 | 4,001 - 5,000 | 1 | 1.7 |\n| 4 | > 5,000 | 102 | 288.2 |\n| | Total | 104 | 290.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5547, "line_end": 5553, "token_count_estimate": 161, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "131985e3409f82ef", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region Map 55 Plate No: 77P\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5554, "line_end": 5580, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "8438cd6f6bccc7bb", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 18 | 1 | 0 | 0 | 0 | 42 | 7 | 68 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 21 | 1 | 0 | 2 | 0 | 53 | 3 | 80 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 45 | 0 | 0 | 6 | 0 | 66 | 9 | 129 |\n| 4 | 5 - 10 | 3 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 12 | 0 | 29 |\n| 5 | 10 - 50 | 5 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 11 | 3 | 22 |\n| 6 | > 50 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 1 | 7 |\n| | Total | 12 | 0 | 0 | 93 | 2 | 0 | 17 | 0 | 188 | 23 | 335 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5581, "line_end": 5589, "token_count_estimate": 575, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "491df81d054aed65", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 56\nPlate No: 77P\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5590, "line_end": 5633, "token_count_estimate": 232, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "3232a3f5b584e805", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| --- | --- | --- | --- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 3 | 8.9 |\n| 3 | 4,001 - 5,000 | 127 | 1,079.2 |\n| 4 | > 5,000 | 205 | 613.5 |\n| | Total | 335 | 1,701.7 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5634, "line_end": 5640, "token_count_estimate": 172, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "723f6f7e084db3f9", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Sikkim\nMap 57\nPlate No: 78A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5641, "line_end": 5689, "token_count_estimate": 271, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "bdbc5b4c67d20a62", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Types of Glacial Lakes Other Glacial Lake | Total |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 38 | 51 | 0 | 0 | 0 | 148 | 1 | 238 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 34 | 13 | 0 | 4 | 0 | 151 | 0 | 203 |\n| 3 | 1 - 5 | 1 | 5 | 0 | 51 | 8 | 0 | 30 | 0 | 202 | 2 | 299 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 11 | 1 | 0 | 14 | 0 | 32 | 0 | 62 |\n| 5 | 10 - 50 | 15 | 0 | 0 | 11 | 0 | 0 | 18 | 0 | 14 | 1 | 59 |\n| 6 | > 50 | 6 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |\n| Total | | 26 | 6 | 0 | 146 | 73 | 0 | 66 | 0 | 547 | 4 | 868 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5690, "line_end": 5698, "token_count_estimate": 598, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "50ee0f8cc3b917a2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Sikkim\nMap 58\nPlate No: 78A\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\nBrahmaputra Basin\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5699, "line_end": 5745, "token_count_estimate": 221, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "2867bdc33df4bb5e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 31 | 172.0 |\n| 3 | 4,001 - 5,000 | 565 | 1,568.6 |\n| 4 | > 5,000 | 272 | 1,396.7 |\n| | Total | 868 | 3,137.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5746, "line_end": 5752, "token_count_estimate": 170, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6f930f9c5d9cfad2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 59\nPlate No: 78E\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5753, "line_end": 5801, "token_count_estimate": 265, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "46281a495359c705", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes - Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 123 | 6 | 133 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 11 | 3 | 0 | 5 | 0 | 128 | 9 | 157 |\n| 3 | 1 - 5 | 1 | 2 | 0 | 29 | 3 | 0 | 21 | 0 | 220 | 9 | 285 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 5 | 0 | 0 | 21 | 0 | 34 | 0 | 60 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 6 | 0 | 0 | 12 | 0 | 26 | 0 | 48 |\n| 6 | > 50 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 3 | 0 | 7 |\n| | Total | 7 | 3 | 0 | 55 | 7 | 0 | 60 | 0 | 534 | 24 | 690 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes - Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes - Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes - Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes - Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes - Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes - Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes - Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes - Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5802, "line_end": 5810, "token_count_estimate": 604, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3fca29a97ee841e4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 60\nPlate No: 78E\n\nUpper Yarlung Tsangpo\nPuna Tsang Chu\nAmo Chu\n\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5811, "line_end": 5834, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu", "Puna Tsang Chu", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "0cfb16cb5836b2ef", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 16 | 65.8 |\n| 3 | 4,001 - 5,000 | 610 | 2,341.0 |\n| 4 | > 5,000 | 64 | 525.9 |\n| | Total | 690 | 2,932.7 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5835, "line_end": 5841, "token_count_estimate": 177, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "195d2d8be8101106", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\n* Brahmaputra Basin\n* India\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\n**GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN**\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 61\nPlate No: 78I\n\nPuna Tsang Chu\nManas\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5842, "line_end": 5919, "token_count_estimate": 406, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "d62dd3a2c0fc806c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 44 | 4 | 0 | 0 | 0 | 166 | 0 | 214 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 52 | 3 | 0 | 7 | 0 | 157 | 0 | 219 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 77 | 1 | 0 | 60 | 0 | 330 | 0 | 469 |\n| 4 | 5 - 10 | 3 | 1 | 0 | 20 | 0 | 0 | 33 | 0 | 59 | 0 | 116 |\n| 5 | 10 - 50 | 11 | 2 | 0 | 21 | 0 | 0 | 32 | 0 | 59 | 0 | 125 |\n| 6 | > 50 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 3 | 0 | 6 |\n| | Total | 16 | 3 | 0 | 215 | 8 | 0 | 133 | 0 | 774 | 0 | 1149 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 5920, "line_end": 5928, "token_count_estimate": 540, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3efa512a256f1056", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 62\nPlate No: 78I\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\nBrahmaputra Basin\nIndia\n\nSubbasins\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5929, "line_end": 5979, "token_count_estimate": 303, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "2ac7e999f635d6e4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 9 | 31.6 |\n| 3 | 4,001 - 5,000 | 780 | 3,426.2 |\n| 4 | > 5,000 | 360 | 1,404.6 |\n| | Total | 1149 | 4,862.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 5980, "line_end": 5986, "token_count_estimate": 178, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "71ed251f0f6c3935", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nState: Arunachal Pradesh\nMap 63\nPlate No: 78M\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 5987, "line_end": 6033, "token_count_estimate": 268, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "2f4f8250493e06d0", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 82 | 3 | 86 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 88 | 6 | 98 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 196 | 11 | 244 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 51 | 1 | 72 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 25 | 0 | 32 | 1 | 59 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 4 |\n| | Total | 0 | 0 | 0 | 2 | 0 | 0 | 87 | 0 | 449 | 25 | 563 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6034, "line_end": 6042, "token_count_estimate": 630, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ba3b399a1bb45eeb", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped.\n\nNHP\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 64\nPlate No: 78M\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6043, "line_end": 6095, "token_count_estimate": 283, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "62ce75568d0ce5f2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 38 | 185.8 |\n| 3 | 4,001 - 5,000 | 522 | 2,681.4 |\n| 4 | > 5,000 | 3 | 17.9 |\n| | Total | 563 | 2,885.1 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6096, "line_end": 6102, "token_count_estimate": 169, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ae4872c46a9e4b0e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nNHP\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 65\nPlate No: 82A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 2)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6103, "line_end": 6150, "token_count_estimate": 245, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "96965368c069d4c3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 35 | 37 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 28 | 30 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 40 | 49 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 13 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 6 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 15 | 123 | 139 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6151, "line_end": 6159, "token_count_estimate": 601, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "fb68cc9b18b1e3c0", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nTransboundary Region\nMap 66\nPlate No: 82A\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6160, "line_end": 6183, "token_count_estimate": 158, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ed4c570323e58ed4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 125 | 920.8 |\n| 4 | > 5,000 | 14 | 115.8 |\n| Total | | 139 | 1,036.6 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6184, "line_end": 6190, "token_count_estimate": 171, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cac531145389d2c4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nTransboundary Region\nMap 67\nPlate No: 82B\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6191, "line_end": 6223, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "f660fa5ea9622869", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 65 | 22 | 92 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 89 | 24 | 114 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 3 | 0 | 0 | 8 | 0 | 202 | 32 | 246 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 34 | 4 | 39 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 12 | 38 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 2 | 11 |\n| Total| | 1 | 0 | 0 | 8 | 0 | 0 | 10 | 0 | 425 | 96 | 540 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6224, "line_end": 6232, "token_count_estimate": 601, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cbbb32e8f4b1402d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 68\nPlate No: 82B\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6233, "line_end": 6297, "token_count_estimate": 313, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "5001b2952fef6ead", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 115 | 2,154.4 |\n| 4 | > 5,000 | 425 | 1,370.9 |\n| | Total | 540 | 3,525.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6298, "line_end": 6304, "token_count_estimate": 168, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "62288c4d5a9f029d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 69\nPlate No: 82C\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6305, "line_end": 6335, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "5e7c1a152a339212", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 2 | 0 | 0 | 33 | 1 | 0 | 0 | 0 | 109 | 8 | 153 |\n| 2 | 0.5 - 1 | 2 | 0 | 0 | 26 | 1 | 0 | 1 | 0 | 118 | 6 | 154 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 37 | 1 | 0 | 4 | 0 | 264 | 8 | 315 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 8 | 0 | 0 | 2 | 0 | 59 | 5 | 78 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 42 | 2 | 51 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 |\n| | Total | 13 | 0 | 0 | 105 | 3 | 0 | 9 | 0 | 594 | 29 | 753 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6336, "line_end": 6344, "token_count_estimate": 514, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "14c9d9fd184d5dd2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 70\nPlate No: 82C\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6345, "line_end": 6365, "token_count_estimate": 158, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "91540db8feb576f6", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 158 | 871.0 |\n| 4 | > 5,000 | 595 | 1,705.1 |\n| | Total | 753 | 2,576.0 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6366, "line_end": 6372, "token_count_estimate": 168, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3b5c6b169414bdee", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 71\nPlate No: 82D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6373, "line_end": 6402, "token_count_estimate": 181, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0f59bbe4dd25bab9", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 19 | 1 | 0 | 0 | 0 | 47 | 9 | 76 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 22 | 0 | 1 | 0 | 0 | 61 | 1 | 86 |\n| 3 | 1 - 5 | 7 | 0 | 0 | 33 | 0 | 1 | 1 | 0 | 69 | 7 | 118 |\n| 4 | 5 - 10 | 10 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 11 | 0 | 26 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 10 | 2 | 18 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n| | Total | 22 | 0 | 0 | 78 | 1 | 2 | 5 | 0 | 198 | 19 | 325 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6403, "line_end": 6411, "token_count_estimate": 601, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad6a638035370d64", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 72\nPlate No: 82D\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6412, "line_end": 6458, "token_count_estimate": 268, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "22de058c0c812ec7", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 96 | 389.0 |\n| 4 | > 5,000 | 229 | 512.8 |\n| | Total | 325 | 901.8 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6459, "line_end": 6465, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5fc00d7f1d96d914", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\nElevation Range (m)\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 73\nPlate No: 82E\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6466, "line_end": 6510, "token_count_estimate": 249, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "cf9acf184a15050c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 40 | 45 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 30 | 39 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 62 | 71 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 11 | 12 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 6 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 4 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 148 | 177 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6511, "line_end": 6519, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3d177e6a582b0b71", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\n**Transboundary Region**\n**Map 74**\n**Plate No: 82E**\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6520, "line_end": 6545, "token_count_estimate": 179, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "cb3edb36c784cf6e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 104 | 198.8 |\n| 4 | > 5,000 | 73 | 1,120.3 |\n| | **Total** | **177** | **1,319.1** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6546, "line_end": 6552, "token_count_estimate": 182, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "48effa9bb407c5fb", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\n**Transboundary Region**\n**Map 75**\n**Plate No: 82F**\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 7)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6553, "line_end": 6602, "token_count_estimate": 280, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "c207d415845c8a1c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 1 | 23 | 1 | 0 | 0 | 0 | 56 | 2 | 83 |\n| 2 | 0.5 - 1 | 2 | 0 | 0 | 27 | 2 | 0 | 0 | 0 | 67 | 0 | 98 |\n| 3 | 1 - 5 | 7 | 1 | 0 | 77 | 1 | 0 | 3 | 0 | 128 | 0 | 217 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 13 | 0 | 0 | 5 | 0 | 21 | 1 | 45 |\n| 5 | 10 - 50 | 11 | 1 | 0 | 7 | 0 | 0 | 2 | 0 | 20 | 2 | 43 |\n| 6 | > 50 | 3 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 1 | 8 |\n| | **Total** | **28** | **2** | **1** | **149** | **4** | **0** | **10** | **1** | **293** | **6** | **494** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6603, "line_end": 6611, "token_count_estimate": 562, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "58af55ee89bb2518", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**DISCLAIMER:**\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6612, "line_end": 6616, "token_count_estimate": 85, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "42650abc5cc06549", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 1\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 76\nPlate No: 82F\n\nLhasa Tsangpo\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 1", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 1"], "chunk_type": "text", "line_start": 6618, "line_end": 6666, "token_count_estimate": 220, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "692a358a69ee4fa4", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 1\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 1 | 1.5 |\n| 3 | 4,001 - 5,000 | 184 | 2,041.3 |\n| 4 | > 5,000 | 309 | 3,579.3 |\n| | Total | 494 | 5,622.1 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 1", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 1"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6667, "line_end": 6673, "token_count_estimate": 173, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "da5d5de60e8e1e64", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 1\nType: text\n\nLegend\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 1", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 1"], "chunk_type": "text", "line_start": 6674, "line_end": 6704, "token_count_estimate": 163, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "7a49ef7be392dc65", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 77\nPlate No: 82G\n\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "text", "line_start": 6706, "line_end": 6724, "token_count_estimate": 120, "basins": ["BRAHMAPUTRA"], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "dc7b20bfbb96735c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Types of Glacial Lakes Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 1 | 0 | 0 | 23 | 1 | 0 | 1 | 0 | 224 | 8 | 258 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 29 | 0 | 0 | 2 | 0 | 249 | 6 | 286 |\n| 3 | 1 - 5 | 4 | 0 | 0 | 54 | 0 | 0 | 9 | 0 | 522 | 15 | 604 |\n| 4 | 5 - 10 | 6 | 0 | 0 | 6 | 0 | 0 | 7 | 0 | 142 | 6 | 167 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 4 | 0 | 0 | 11 | 0 | 96 | 5 | 117 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 2 | 9 |\n| Total | | 12 | 0 | 0 | 116 | 1 | 0 | 30 | 0 | 1240 | 42 | 1441 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6725, "line_end": 6733, "token_count_estimate": 591, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "38c28175933f6c79", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: text\n\nLegend\n\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Administrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region Map 78 Plate No: 82G\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "text", "line_start": 6734, "line_end": 6753, "token_count_estimate": 160, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1b13938894f59aec", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 2 | 1.6 |\n| 3 | 4,001 - 5,000 | 1186 | 5,262.5 |\n| 4 | > 5,000 | 253 | 510.8 |\n| Total | | 1441 | 5,774.9 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6754, "line_end": 6760, "token_count_estimate": 172, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "8dea9444d131a842", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh Map 79 Plate No: 82H\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "text", "line_start": 6761, "line_end": 6791, "token_count_estimate": 211, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "dd66adc175069771", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 7 | 0 | 0 | 1 | 0 | 61 | 8 | 77 |\n| 2 | 0.5 - 1 | 2 | 0 | 0 | 7 | 0 | 0 | 2 | 0 | 90 | 9 | 110 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 14 | 0 | 0 | 27 | 0 | 163 | 7 | 212 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 2 | 0 | 0 | 14 | 0 | 46 | 6 | 69 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 9 | 0 | 30 | 3 | 43 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 |\n| Total | | 4 | 0 | 0 | 31 | 0 | 0 | 53 | 0 | 393 | 33 | 514 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6792, "line_end": 6800, "token_count_estimate": 516, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a77e0bb9798118aa", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh | Map 80 | Plate No: 82H\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "text", "line_start": 6801, "line_end": 6822, "token_count_estimate": 167, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a727e20fe0c43e2c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 5.9 |\n| 2 | 3,001 - 4,000 | 82 | 629.7 |\n| 3 | 4,001 - 5,000 | 401 | 1,423.0 |\n| 4 | > 5,000 | 30 | 34.2 |\n| | Total | 514 | 2,092.8 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6823, "line_end": 6829, "token_count_estimate": 172, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "1200070b05f33173", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region | Map 81 | Plate No: 82J\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "text", "line_start": 6830, "line_end": 6860, "token_count_estimate": 213, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "0fd3286e474096c3", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 6 | 5 | 0 | 0 | 0 | 14 | 0 | 26 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 22 | 2 | 0 | 0 | 0 | 13 | 1 | 39 |\n| 3 | 1 - 5 | 1 | 1 | 0 | 29 | 1 | 0 | 4 | 0 | 37 | 0 | 73 |\n| 4 | 5 - 10 | 4 | 0 | 0 | 6 | 0 | 0 | 3 | 0 | 5 | 0 | 18 |\n| 5 | 10 - 50 | 4 | 0 | 0 | 8 | 0 | 0 | 2 | 0 | 11 | 1 | 26 |\n| 6 | > 50 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 0 | 12 |\n| | Total | 15 | 3 | 0 | 71 | 8 | 0 | 10 | 1 | 84 | 2 | 194 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake - End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake - Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake - Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake - Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake - Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake - Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6861, "line_end": 6869, "token_count_estimate": 604, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f015a962abf673d2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN > Page 2", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "Page 2"], "chunk_type": "text", "line_start": 6870, "line_end": 6878, "token_count_estimate": 102, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "44164e4a45765a36", "text": "Document: output vertex chunked\nSection: GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nType: text\n\nTransboundary Region\nMap 82\nPlate No: 82J\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN", "section_headings": ["GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6880, "line_end": 6891, "token_count_estimate": 76, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "2b666934980d616a", "text": "Document: output vertex chunked\nSection: GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 1 | 36.1 |\n| 2 | 3,001 - 4,000 | 19 | 1,020.6 |\n| 3 | 4,001 - 5,000 | 160 | 1,469.1 |\n| 4 | > 5,000 | 14 | 53.6 |\n| | Total | 194 | 2,579.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN", "section_headings": ["GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 6892, "line_end": 6898, "token_count_estimate": 169, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3035e9b039fc574a", "text": "Document: output vertex chunked\nSection: GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\nType: text\n\nBrahmaputra Basin\nIndia\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* > 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN", "section_headings": ["GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6899, "line_end": 6952, "token_count_estimate": 288, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "7915a47e10dda2f1", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nState: Arunachal Pradesh\nMap 83\nPlate No: 82K\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6954, "line_end": 6963, "token_count_estimate": 79, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "b214dd5fd319a86e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 6 | 2 | 0 | 1 | 0 | 151 | 11 | 171 |\n| 2 | 0.5 - 1 | 0 | 1 | 0 | 4 | 1 | 0 | 1 | 0 | 161 | 17 | 185 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 5 | 0 | 0 | 15 | 0 | 343 | 25 | 389 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 1 | 0 | 0 | 17 | 0 | 105 | 7 | 130 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 3 | 0 | 0 | 16 | 0 | 103 | 8 | 132 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 8 | 2 | 16 |\n| | Total | 3 | 1 | 0 | 19 | 3 | 0 | 52 | 4 | 871 | 70 | 1023 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 6964, "line_end": 6972, "token_count_estimate": 515, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "7274fbb72ecc65a2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh | Map 84 | Plate No: 82K\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\n**Location Map**\nBrahmaputra Basin, India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 6973, "line_end": 7033, "token_count_estimate": 383, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "a557f741f54e318a", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 39 | 275.1 |\n| 3 | 4,001 - 5,000 | 778 | 4,510.9 |\n| 4 | > 5,000 | 206 | 1,429.2 |\n| | Total | 1023 | 6,215.3 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7034, "line_end": 7040, "token_count_estimate": 171, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f06f7aefae202ac0", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh | Map 85 | Plate No: 82L\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7041, "line_end": 7066, "token_count_estimate": 192, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "4414306a77d97200", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 4 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 1 | 12 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 25 | 3 | 34 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 7 | 0 | 12 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 9 | 0 | 12 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| | Total | 0 | 0 | 0 | 1 | 0 | 0 | 13 | 0 | 57 | 4 | 75 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7067, "line_end": 7075, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "e0d143b47ca655da", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nState: Arunachal Pradesh\nMap 86\nPlate No: 82L\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n* Moraine Dammed Lake\n* Ice Dammed Lake\n* Erosion Lake\n* Other Glacial Lake\n\n**Location Map**\nBrahmaputra Basin\nIndia\n\nSubbasins\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7076, "line_end": 7138, "token_count_estimate": 378, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "c78a5b2a294f623a", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 49 | 233.0 |\n| 3 | 4,001 - 5,000 | 26 | 186.8 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 75 | 419.8 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7139, "line_end": 7145, "token_count_estimate": 164, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "381a325f8a13a9c7", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nTransboundary Region\nMap 87\nPlate No: 82N\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7146, "line_end": 7175, "token_count_estimate": 183, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "1cc4e8c45ae71572", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 1 | 0 | 25 | 3 | 0 | 0 | 0 | 64 | 5 | 98 |\n| 2 | 0.5 - 1 | 0 | 2 | 0 | 23 | 2 | 0 | 1 | 0 | 49 | 3 | 80 |\n| 3 | 1 - 5 | 3 | 0 | 0 | 35 | 0 | 0 | 2 | 0 | 95 | 3 | 138 |\n| 4 | 5 - 10 | 1 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 23 | 1 | 31 |\n| 5 | 10 - 50 | 5 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 18 | 1 | 32 |\n| 6 | > 50 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 4 |\n| | Total | 11 | 4 | 0 | 97 | 5 | 0 | 3 | 0 | 250 | 13 | 383 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7176, "line_end": 7184, "token_count_estimate": 512, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "eaab94f68a861116", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Subbasin Boundary\n* District Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 88\nPlate No: 82N\n\nLower Yarlung Tsangpo\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7185, "line_end": 7238, "token_count_estimate": 265, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "e230b25ef3a283af", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 15 | 26.0 |\n| 3 | 4,001 - 5,000 | 248 | 1,277.9 |\n| 4 | > 5,000 | 120 | 292.0 |\n| | Total | 383 | 1,595.9 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7239, "line_end": 7245, "token_count_estimate": 167, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c0e3ea7ebc055e99", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 89\nPlate No: 82O\n\nLower Yarlung Tsangpo\nDihang\nDibang Valley\nDibang\nUpper Siang\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7246, "line_end": 7303, "token_count_estimate": 280, "basins": ["BRAHMAPUTRA"], "subbasins": ["Dibang", "Dihang", "Lower Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "c768f177da6c1692", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 34 | 3 | 43 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 4 | 1 | 0 | 3 | 0 | 63 | 1 | 72 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 10 | 0 | 0 | 21 | 0 | 147 | 1 | 180 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 26 | 0 | 55 | 0 | 84 |\n| 5 | 10 - 50 | 2 | 0 | 0 | 6 | 0 | 0 | 22 | 0 | 51 | 3 | 84 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 5 |\n| | Total | 3 | 0 | 0 | 26 | 4 | 0 | 72 | 1 | 354 | 8 | 468 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7304, "line_end": 7312, "token_count_estimate": 513, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bbc9367ccd0f231e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 90\nPlate No: 82O\n\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7313, "line_end": 7361, "token_count_estimate": 272, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "2ed2cf7b93f11ae7", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 3 | 40.8 |\n| 2 | 3,001 - 4,000 | 277 | 1,927.8 |\n| 3 | 4,001 - 5,000 | 188 | 1,317.8 |\n| 4 | > 5,000 | 0 | 0.0 |\n| Total | | 468 | 3,286.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7362, "line_end": 7368, "token_count_estimate": 169, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "15fddf0d20ef5694", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 91\nPlate No: 82P\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7369, "line_end": 7414, "token_count_estimate": 245, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "bcb95f45a4d7fa3b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 3 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 10 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 42 | 0 | 48 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 17 | 0 | 21 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 12 | 0 | 17 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| Total | | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 84 | 0 | 100 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7415, "line_end": 7423, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ebe72465b7a20ded", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 92\nPlate No: 82P\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nLocation Map\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7424, "line_end": 7449, "token_count_estimate": 171, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "11b681ceac3d71ee", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 94 | 576.2 |\n| 3 | 4,001 - 5,000 | 6 | 38.2 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 100 | 614.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7450, "line_end": 7456, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3edbda17b8e67610", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 93\nPlate No: 83A\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7457, "line_end": 7521, "token_count_estimate": 319, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "ec9684efe7285794", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 16 | 1 | 0 | 3 | 0 | 145 | 3 | 168 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 19 | 2 | 0 | 3 | 0 | 134 | 1 | 159 |\n| 3 | 1 - 5 | 6 | 1 | 0 | 27 | 1 | 0 | 27 | 0 | 191 | 3 | 256 |\n| 4 | 5 - 10 | 2 | 0 | 0 | 4 | 0 | 0 | 6 | 0 | 47 | 0 | 59 |\n| 5 | 10 - 50 | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 0 | 17 | 1 | 24 |\n| 6 | > 50 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |\n| | Total | 10 | 2 | 0 | 69 | 4 | 0 | 41 | 0 | 535 | 8 | 669 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7522, "line_end": 7530, "token_count_estimate": 513, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6b90c3630b5f1722", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 94\nPlate No: 83A\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7531, "line_end": 7585, "token_count_estimate": 267, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "306bd256944d32b2", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 21 | 106.8 |\n| 3 | 4,001 - 5,000 | 465 | 1,260.4 |\n| 4 | > 5,000 | 183 | 387.4 |\n| Total | | 669 | 1,754.6 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7586, "line_end": 7592, "token_count_estimate": 178, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a225402b34cbddce", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 95\nPlate No: 91B\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 1)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7593, "line_end": 7638, "token_count_estimate": 238, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "194b6445849d7e38", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 16 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 23 | 0 | 25 |\n| 3 | 1 - 5 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 21 | 0 | 24 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 4 | 0 | 6 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 64 | 0 | 71 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7639, "line_end": 7647, "token_count_estimate": 537, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "af2cc2356196952d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region Map 96 Plate No: 91B\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nLocation Map\n\nElevation-wise Glacial Lake Distribution\n\n**Subbasins**\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7648, "line_end": 7702, "token_count_estimate": 306, "basins": ["BRAHMAPUTRA"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": []}}
{"id": "c48f46a4b657542b", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 0 | 0.0 |\n| 3 | 4,001 - 5,000 | 46 | 87.5 |\n| 4 | > 5,000 | 25 | 23.8 |\n| | Total | 71 | 111.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7703, "line_end": 7709, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "ad46d6025081bbcb", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh Map 97 Plate No: 91C\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 6)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7710, "line_end": 7737, "token_count_estimate": 180, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "b75857110ca46a8f", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 3 | 2 | 0 | 45 | 5 | 0 | 0 | 0 | 41 | 7 | 103 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 70 | 3 | 0 | 0 | 0 | 58 | 4 | 136 |\n| 3 | 1 - 5 | 16 | 1 | 0 | 87 | 4 | 0 | 10 | 0 | 126 | 2 | 246 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 31 | 0 | 0 | 10 | 0 | 33 | 2 | 81 |\n| 5 | 10 - 50 | 14 | 0 | 0 | 11 | 0 | 0 | 27 | 0 | 37 | 0 | 89 |\n| 6 | > 50 | 2 | 1 | 0 | 6 | 0 | 0 | 1 | 0 | 9 | 3 | 22 |\n| | Total | 41 | 4 | 0 | 250 | 12 | 0 | 48 | 0 | 304 | 18 | 677 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7738, "line_end": 7746, "token_count_estimate": 513, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "4ea88770435b8b65", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 98\nPlate No: 91C\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\n*   Moraine Dammed Lake\n*   Ice Dammed Lake\n*   Erosion Lake\n*   Other Glacial Lake\n\n**Location Map**\nBrahmaputra Basin\nIndia\n\n**Subbasins**\n*   Amo Chu\n*   Dibang\n*   Dihang\n*   Jia Bharali\n*   Lhasa Tsangpo\n*   Lohit\n*   Lower Yarlung Tsangpo\n*   Manas\n*   Puna Tsang Chu\n*   Subansiri\n*   Teesta\n*   Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\n**Legend**\n*   Glacial Lake\n*   River / Stream\n*   Basin Boundary\n*   Subbasin Boundary\n*   District Boundary\n*   State / UT Boundary\n*   International Boundary\n\n**Elevation Range (m)**\n*   up to 3,000\n*   3,001 - 4,000\n*   4,001 - 5,000\n*   > 5,000\n\n**Prepared By:**\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\n**Under:**\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7747, "line_end": 7824, "token_count_estimate": 450, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "d406b72b62700f07", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 3 | 40.8 |\n| 2 | 3,001 - 4,000 | 98 | 2,950.8 |\n| 3 | 4,001 - 5,000 | 429 | 3,456.7 |\n| 4 | > 5,000 | 147 | 292.4 |\n| | Total | 677 | 6,740.7 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7825, "line_end": 7831, "token_count_estimate": 172, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "6059bcac05a9c1fa", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 99\nPlate No: 91D\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 5)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7832, "line_end": 7850, "token_count_estimate": 110, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "70ab3b98f31ef4f8", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake | Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake | Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake | Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake | Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake | Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake | Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake | Types of Glacial Lakes: Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 57 | 8 | 70 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 6 | 0 | 0 | 2 | 0 | 73 | 8 | 89 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 5 | 0 | 0 | 26 | 0 | 234 | 27 | 292 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 3 | 0 | 0 | 32 | 0 | 82 | 2 | 119 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 2 | 0 | 0 | 51 | 0 | 75 | 11 | 140 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 |\n| | Total | 1 | 0 | 0 | 21 | 0 | 0 | 111 | 0 | 523 | 57 | 713 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes: Moraine Dammed Lake: End-moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Types of Glacial Lakes: Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes: Moraine Dammed Lake: Other Moraine Dammed Lake", "Types of Glacial Lakes: Ice Dammed Lake: Supra-glacial Lake", "Types of Glacial Lakes: Ice Dammed Lake: Glacier Ice-dammed Lake", "Types of Glacial Lakes: Erosion Lake: Cirque Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes: Erosion Lake: Other Glacial Erosion Lake", "Types of Glacial Lakes: Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7851, "line_end": 7859, "token_count_estimate": 604, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "80edbf7856d81d3c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n*   Subbasin Boundary\n*   District Boundary\n\n**DISCLAIMER:**\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\n\nMap 100\n\nPlate No: 91D\n\nData Source: Resourcesat-2 LISS-IV\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7860, "line_end": 7884, "token_count_estimate": 167, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "90d1b499645bc7a5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 3 | 28.7 |\n| 2 | 3,001 - 4,000 | 238 | 2,351.9 |\n| 3 | 4,001 - 5,000 | 472 | 2,629.6 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 713 | 5,010.2 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7885, "line_end": 7891, "token_count_estimate": 171, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "73a72aed637d84d5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\n\nMap 101\n\nPlate No: 91G\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 3)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7892, "line_end": 7929, "token_count_estimate": 206, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "17881907b05ac86c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake - End-moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake | Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake - Other Moraine Dammed Lake | Ice Dammed Lake - Supra-glacial Lake | Ice Dammed Lake - Glacier Ice-dammed Lake | Erosion Lake - Cirque Erosion Lake | Erosion Lake - Glacier Trough Valley Erosion Lake | Erosion Lake - Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 39 | 3 | 80 |\n| 2 | 0.5 - 1 | 1 | 0 | 0 | 31 | 0 | 0 | 0 | 0 | 25 | 0 | 57 |\n| 3 | 1 - 5 | 11 | 1 | 0 | 45 | 0 | 0 | 0 | 0 | 48 | 4 | 109 |\n| 4 | 5 - 10 | 5 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 6 | 0 | 21 |\n| 5 | 10 - 50 | 5 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 5 | 0 | 19 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| | Total | 22 | 1 | 0 | 133 | 0 | 0 | 0 | 0 | 123 | 7 | 286 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake - End-moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake", "Moraine Dammed Lake - Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake - Other Moraine Dammed Lake", "Ice Dammed Lake - Supra-glacial Lake", "Ice Dammed Lake - Glacier Ice-dammed Lake", "Erosion Lake - Cirque Erosion Lake", "Erosion Lake - Glacier Trough Valley Erosion Lake", "Erosion Lake - Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 7930, "line_end": 7938, "token_count_estimate": 512, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "916fefb2f98868ee", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nTransboundary Region\nMap 102\nPlate No: 91G\n\nData Source: Resourcesat-2 LISS-IV\n\nDistribution of Glacial Lake Types\n\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\n\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7939, "line_end": 7988, "token_count_estimate": 254, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "3a45ada3d2d4f33e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 5 | 39.6 |\n| 3 | 4,001 - 5,000 | 152 | 413.6 |\n| 4 | > 5,000 | 129 | 314.2 |\n| | Total | 286 | 767.4 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 7989, "line_end": 7995, "token_count_estimate": 166, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9c475ac6b3e68977", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 103\nPlate No: 91H\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 7996, "line_end": 8043, "token_count_estimate": 240, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "526b426866ad5c31", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 173 | 38 | 222 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 235 | 29 | 273 |\n| 3 | 1 - 5 | 0 | 1 | 0 | 16 | 0 | 0 | 17 | 0 | 359 | 26 | 419 |\n| 4 | 5 - 10 | 1 | 0 | 0 | 5 | 0 | 0 | 5 | 0 | 83 | 2 | 96 |\n| 5 | 10 - 50 | 1 | 0 | 0 | 6 | 0 | 0 | 7 | 0 | 77 | 2 | 93 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 |\n| | Total | 2 | 1 | 0 | 47 | 0 | 0 | 29 | 0 | 932 | 97 | 1108 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 8044, "line_end": 8052, "token_count_estimate": 516, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0300ef6d67b18153", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nGLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 104\nPlate No: 91H\nData Source: Resourcesat-2 LISS-IV\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\nLocation Map\nBrahmaputra Basin\nIndia\n\nSubbasins\nAmo Chu\nDibang\nDihang\nJia Bharali\nLhasa Tsangpo\nLohit\nLower Yarlung Tsangpo\nManas\nPuna Tsang Chu\nSubansiri\nTeesta\nUpper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nElevation-wise Glacial Lake Distribution", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 8053, "line_end": 8101, "token_count_estimate": 272, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "1d081d658592b97e", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 69 | 485.8 |\n| 3 | 4,001 - 5,000 | 1038 | 3,561.5 |\n| 4 | > 5,000 | 1 | 0.3 |\n| Total | | 1108 | 4,047.6 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 8102, "line_end": 8108, "token_count_estimate": 167, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3ea2feff284cc8ad", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nGlacial Lake\nRiver / Stream\nBasin Boundary\nSubbasin Boundary\nDistrict Boundary\nState / UT Boundary\nInternational Boundary\n\nElevation Range (m)\nup to 3,000\n3,001 - 4,000\n4,001 - 5,000\n> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nSATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\n\nState: Arunachal Pradesh\nMap 105\nPlate No: 92A\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n0 5 10 20 Km\n\nDistribution of Glacial Lake Types vs. Area-wise", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 8109, "line_end": 8155, "token_count_estimate": 244, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "22412aac88340297", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake | Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice | Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake | Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake | Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake | Types of Glacial Lakes Erosion Lake Cirque Erosion Lake | Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake | Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake | Types of Glacial Lakes Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 1 | 10 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 6 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 3 | 27 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 8 | 0 | 10 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 8 | 0 | 10 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| Total | | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 54 | 5 | 63 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Types of Glacial Lakes Moraine Dammed Lake End-moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake", "Types of Glacial Lakes Moraine Dammed Lake Lateral Moraine Dammed Lake with Ice", "Types of Glacial Lakes Moraine Dammed Lake Other Moraine Dammed Lake", "Types of Glacial Lakes Ice Dammed Lake Supra-glacial Lake", "Types of Glacial Lakes Ice Dammed Lake Glacier Ice-dammed Lake", "Types of Glacial Lakes Erosion Lake Cirque Erosion Lake", "Types of Glacial Lakes Erosion Lake Glacier Trough Valley Erosion Lake", "Types of Glacial Lakes Erosion Lake Other Glacial Erosion Lake", "Types of Glacial Lakes Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 8156, "line_end": 8164, "token_count_estimate": 582, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "914d7caab56b0fe5", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nLegend\nSubbasin Boundary\nDistrict Boundary\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nState: Arunachal Pradesh\nMap 106\nPlate No: 92A\n\nData Source: Resourcesat-2 LISS-IV\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 8165, "line_end": 8189, "token_count_estimate": 165, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "08fc4fbac68b57ef", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n|---|---|---|---|\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 48 | 294.2 |\n| 3 | 4,001 - 5,000 | 15 | 36.0 |\n| 4 | > 5,000 | 0 | 0.0 |\n| | Total | 63 | 330.1 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 8190, "line_end": 8196, "token_count_estimate": 162, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "369ed230a641b13d", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrolgy Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nPlate Nos. with GREY annotation does not have Glacial Lakes.\nCorresponding Mapsheets not provided.\n\nDISCLAIMER: The Adminitrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN**\n\nState: Arunachal Pradesh\nMap 107\nPlate No: 92E\n\nData Source: Resourcesat-2 LISS-IV (Total Nos of Scenes: 4)\n\n**Distribution of Glacial Lake Types vs. Area-wise**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 8197, "line_end": 8230, "token_count_estimate": 212, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}
{"id": "94d467a97a5e9785", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Glacial Lake Area Range (ha) | Moraine Dammed Lake: End-moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake | Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice | Moraine Dammed Lake: Other Moraine Dammed Lake | Ice Dammed Lake: Supra-glacial Lake | Ice Dammed Lake: Glacier Ice-dammed Lake | Erosion Lake: Cirque Erosion Lake | Erosion Lake: Glacier Trough Valley Erosion Lake | Erosion Lake: Other Glacial Erosion Lake | Other Glacial Lake | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 0.25 - 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 1 | 14 |\n| 2 | 0.5 - 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 14 |\n| 3 | 1 - 5 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 36 | 2 | 42 |\n| 4 | 5 - 10 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 15 | 0 | 17 |\n| 5 | 10 - 50 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 6 | 0 | 8 |\n| 6 | > 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |\n| | Total | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 85 | 3 | 96 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake Area Range (ha)", "Moraine Dammed Lake: End-moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake", "Moraine Dammed Lake: Lateral Moraine Dammed Lake with Ice", "Moraine Dammed Lake: Other Moraine Dammed Lake", "Ice Dammed Lake: Supra-glacial Lake", "Ice Dammed Lake: Glacier Ice-dammed Lake", "Erosion Lake: Cirque Erosion Lake", "Erosion Lake: Glacier Trough Valley Erosion Lake", "Erosion Lake: Other Glacial Erosion Lake", "Other Glacial Lake", "Total"], "table_row_start": 1, "table_row_end": 7, "line_start": 8231, "line_end": 8239, "token_count_estimate": 511, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "526bdbee529f44be", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\nDISCLAIMER:\n(a) The Adminitrative Boundaries shown are for scientific study and not for statutory purpose.\n(b) Satellite image depicts both Glacial Lakes and Water Bodies, but only glacial lakes were mapped.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**GLACIAL LAKES IN PART OF BRAHMAPUTRA BASIN**\n\nState: Arunachal Pradesh | Map 108 | Plate No: 92E\n--- | --- | ---\n\nData Source: Resourcesat-2 LISS-IV\n\n**Distribution of Glacial Lake Types**\nMoraine Dammed Lake\nIce Dammed Lake\nErosion Lake\nOther Glacial Lake\n\n**Location Map**\nBrahmaputra Basin\nIndia\n\n**Subbasins**\n* Amo Chu\n* Dibang\n* Dihang\n* Jia Bharali\n* Lhasa Tsangpo\n* Lohit\n* Lower Yarlung Tsangpo\n* Manas\n* Puna Tsang Chu\n* Subansiri\n* Teesta\n* Upper Yarlung Tsangpo\n\nPlate Nos. with GREY annotation does not have Glacial Lakes. Corresponding Mapsheets not provided.\n\n**Elevation-wise Glacial Lake Distribution**", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 8240, "line_end": 8285, "token_count_estimate": 293, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": ["Amo Chu", "Dibang", "Dihang", "Jia Bharali", "Lhasa Tsangpo", "Lohit", "Lower Yarlung Tsangpo", "Manas", "Puna Tsang Chu", "Subansiri", "Teesta", "Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "cddbc83de0c97b97", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: table\nTable\n\n| S.No. | Elevation Range (m) | No. of Glacial Lakes | Glacial Lake Area (ha) |\n| :--- | :--- | :--- | :--- |\n| 1 | up to 3,000 | 0 | 0.0 |\n| 2 | 3,001 - 4,000 | 31 | 157.2 |\n| 3 | 4,001 - 5,000 | 65 | 292.2 |\n| 4 | > 5,000 | 0 | 0.0 |\n| **Total** | | **96** | **449.4** |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Elevation Range (m)", "No. of Glacial Lakes", "Glacial Lake Area (ha)"], "table_row_start": 1, "table_row_end": 5, "line_start": 8286, "line_end": 8292, "token_count_estimate": 177, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "298c41592b25984c", "text": "Document: output vertex chunked\nSection: SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN\nType: text\n\n**Legend**\n* Glacial Lake\n* River / Stream\n* Basin Boundary\n* Subbasin Boundary\n* District Boundary\n* State / UT Boundary\n* International Boundary\n\n**Elevation Range (m)**\n* up to 3,000\n* 3,001 - 4,000\n* 4,001 - 5,000\n* \\> 5,000\n\nPrepared By:\nWater Resources Group\nNational Remote Sensing Centre, ISRO\nDepartment of Space, Government of India\n\nUnder:\nNational Hydrology Project\nDepartment of Water Resources, RD & GR\nMinistry of Jal Shakti, Government of India\n\nDISCLAIMER: The Administrative Boundaries shown are for scientific study and not for statutory purpose\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\nShiecuo and Shimocuo Glacial Lake at the snout of Glacier in Upper Yarlung Tsangpo Subbasin Located in Tibet as seen in FCC satellite image\n\nSatellite: Resourcesat – 2\nSensor: LISS-IVMX\nDate of Image: 16.12.2016\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN", "section_headings": ["SATELLITE IMAGE OF PART OF BRAHMAPUTRA BASIN"], "chunk_type": "text", "line_start": 8293, "line_end": 8340, "token_count_estimate": 277, "basins": ["BRAHMAPUTRA"], "subbasins": ["Upper Yarlung Tsangpo"], "countries": ["India"], "lake_ids": []}}
{"id": "af00cdbb4799f8c2", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: text\n\nThere are 207 lakes having an area greater than 50 ha, which is just 1.15% of the total glacial lake count, but covers total of 32.50% of the total glacial lakes area. Spatial distribution of these very large sized lakes i.e. > 50 ha in area has been represented below in Figure 141 and details of these are given in Table 111, along with its area, type, geographic as well as hydrological location, and elevation at which they are situated. Among these 207 lakes, 121, 65 and 21 lakes are in the lake area range of < 100 ha, 100-250 ha and > 250 ha respectively. Out of these 207 large lakes, majority (87) is glacier erosion lakes followed by moraine-dammed glacial lakes (50) and few are glacial trough valley erosion lakes (7) and lateral moraine-dammed glacial lake (1).\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "text", "line_start": 11998, "line_end": 12008, "token_count_estimate": 252, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "5081706831eddf52", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable: Table 111: List of glacial lakes with area > 50 ha\n\n| S.No. | Glacial Lake ID Number | Glacial Lake ID Number | Glacial Lake ID Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | 03 | 62J03 | 00044 | 30.468 | 82.060 | Upper Yarlung Tsangpo | O | 378.80 | 5,180 |\n| 2 | 03 | 62J03 | 00151 | 30.398 | 82.192 | Upper Yarlung Tsangpo | E(o) | 86.53 | 5,203 |\n| 3 | 03 | 62J03 | 00163 | 30.362 | 82.055 | Upper Yarlung Tsangpo | M(e) | 56.83 | 5,283 |\n| 4 | 03 | 62J03 | 00201 | 30.255 | 82.209 | Upper Yarlung Tsangpo | M(e) | 128.70 | 5,057 |\n| 5 | 03 | 62J07 | 00243 | 30.432 | 82.362 | Upper Yarlung Tsangpo | O | 152.61 | 4,882 |\n| 6 | 03 | 62J07 | 00244 | 30.419 | 82.302 | Upper Yarlung Tsangpo | O | 901.40 | 4,931 |\n| 7 | 03 | 62J07 | 00289 | 30.342 | 82.271 | Upper Yarlung Tsangpo | O | 69.03 | 4,993 |\n| 8 | 03 | 62J07 | 00296 | 30.329 | 82.270 | Upper Yarlung Tsangpo | O | 58.67 | 4,990 |\n| 9 | 03 | 62J08 | 00344 | 30.103 | 82.270 | Upper Yarlung Tsangpo | M(e) | 203.15 | 4,875 |\n| 10 | 03 | 62J08 | 00354 | 30.079 | 82.343 | Upper Yarlung Tsangpo | M(e) | 91.73 | 4,849 |\n| 11 | 03 | 62J13 | 00456 | 30.881 | 82.859 | Upper Yarlung Tsangpo | O | 146.23 | 5,446 |\n| 12 | 03 | 62K09 | 00585 | 29.985 | 82.535 | Upper Yarlung Tsangpo | E(o) | 392.72 | 4,829 |\n| 13 | 03 | 62K13 | 00646 | 29.840 | 82.782 | Upper Yarlung Tsangpo | M(e) | 291.26 | 5,058 |\n| 14 | 03 | 62K13 | 00664 | 29.796 | 82.853 | Upper Yarlung Tsangpo | M(e) | 53.83 | 5,160 |\n| 15 | 03 | 62K14 | 00680 | 29.735 | 82.974 | Upper Yarlung Tsangpo | M(e) | 80.86 | 5,337 |\n| 16 | 03 | 62N10 | 00819 | 30.591 | 83.519 | Upper Yarlung Tsangpo | O | 270.94 | 5,227 |\n| 17 | 03 | 62N15 | 00896 | 30.465 | 83.984 | Upper Yarlung Tsangpo | E(o) | 84.88 | 5,450 |\n| 18 | 03 | 62N15 | 00912 | 30.431 | 83.996 | Upper Yarlung Tsangpo | E(o) | 206.16 | 5,429 |\n| 19 | 03 | 62O02 | 00938 | 29.726 | 83.105 | Upper Yarlung Tsangpo | O | 113.22 | 5,010 |\n| 20 | 03 | 62O02 | 00944 | 29.689 | 83.190 | Upper Yarlung Tsangpo | O | 54.50 | 5,007 |\n| 21 | 03 | 62O06 | 01004 | 29.604 | 83.376 | Upper Yarlung Tsangpo | O | 145.71 | 4,889 |\n| 22 | 03 | 62O06 | 01010 | 29.582 | 83.355 | Upper Yarlung Tsangpo | O | 118.74 | 4,888 |\n| 23 | 03 | 62O06 | 01021 | 29.511 | 83.444 | Upper Yarlung Tsangpo | O | 211.06 | 4,959 |\n| 24 | 03 | 62O07 | 01027 | 29.499 | 83.428 | Upper Yarlung Tsangpo | O | 59.80 | 4,959 |\n| 25 | 03 | 62O15 | 01246 | 29.470 | 83.764 | Upper Yarlung Tsangpo | O | 77.62 | 5,282 |\n| 26 | 03 | 71P09 | 01500 | 28.832 | 87.560 | Upper Yarlung Tsangpo | O | 140.16 | 5,296 |\n| 27 | 03 | 71C09 | 01990 | 29.845 | 84.676 | Upper Yarlung Tsangpo | M(e) | 51.54 | 5,536 |\n| 28 | 03 | 71G14 | 02213 | 29.558 | 85.880 | Upper Yarlung Tsangpo | O | 56.48 | 5,186 |\n| 29 | 03 | 71O02 | 02336 | 29.556 | 87.028 | Upper Yarlung Tsangpo | O | 119.59 | 4,729 |\n| 30 | 03 | 77B12 | 02407 | 30.168 | 88.620 | Upper Yarlung Tsangpo | E(o) | 50.13 | 5,029 |\n| 31 | 03 | 77B12 | 02408 | 30.148 | 88.627 | Upper Yarlung Tsangpo | E(o) | 213.67 | 5,011 |\n| 32 | 03 | 77D12 | 02625 | 28.026 | 88.710 | Teesta | M(e) | 113.46 | 5,148 |\n| 33 | 03 | 77D12 | 02636 | 28.008 | 88.698 | Teesta | M(e) | 99.32 | 5,209 |\n| 34 | 03 | 77D12 | 02640 | 28.006 | 88.713 | Teesta | M(e) | 119.15 | 5,238 |\n| 35 | 03 | 77D16 | 02649 | 28.011 | 88.756 | Teesta | M(o) | 108.12 | 5,094 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": "Table 111: List of glacial lakes with area > 50 ha", "columns": ["S.No.", "Glacial Lake ID Number", "Glacial Lake ID Number", "Glacial Lake ID Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 12009, "line_end": 12053, "token_count_estimate": 1911, "basins": ["Brahmaputra"], "subbasins": ["Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["00044", "00151", "00163", "00201", "00243", "00244", "00289", "00296", "00344", "00354", "00456", "00585", "00646", "00664", "00680", "00819", "00896", "00912", "00938", "00944", "01004", "01010", "01021", "01027", "01246", "01500", "01990", "02213", "02336", "02407", "02408", "02625", "02636", "02640", "02649", "62J03", "62J07", "62J08", "62J13", "62K09", "62K13", "62K14", "62N10", "62N15", "62O02", "62O06", "62O07", "62O15", "71C09", "71G14"]}}
{"id": "0cdfb586058c5578", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable: Table 111: List of glacial lakes with area > 50 ha\n\n| S.No. | Glacial Lake ID Number | Glacial Lake ID Number | Glacial Lake ID Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|---|\n| 36 | 03 | 78A01 | 02664 | 27.920 | 88.159 | Teesta | M(e) | 83.63 | 5,441 |\n| 37 | 03 | 78A01 | 02665 | 27.913 | 88.196 | Teesta | M(e) | 128.14 | 5,194 |\n| 38 | 03 | 78A05 | 02815 | 27.947 | 88.332 | Teesta | M(o) | 60.49 | 5,034 |\n| 39 | 03 | 78A05 | 02874 | 27.822 | 88.249 | Teesta | M(e) | 70.94 | 5,414 |\n| 40 | 03 | 78A09 | 02955 | 27.992 | 88.545 | Teesta | M(e) | 68.28 | 5,161 |\n| 41 | 03 | 78A09 | 02961 | 27.975 | 88.616 | Teesta | M(e) | 57.86 | 4,960 |\n| 42 | 03 | 78A13 | 03118 | 27.990 | 88.816 | Teesta | M(e) | 174.29 | 5,303 |\n| 43 | 03 | 77H01 | 03295 | 28.805 | 89.155 | Upper Yarlung Tsangpo | O | 68.23 | 4,219 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": "Table 111: List of glacial lakes with area > 50 ha", "columns": ["S.No.", "Glacial Lake ID Number", "Glacial Lake ID Number", "Glacial Lake ID Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 43, "line_start": 12009, "line_end": 12053, "token_count_estimate": 521, "basins": ["Brahmaputra"], "subbasins": ["Teesta", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["02664", "02665", "02815", "02874", "02955", "02961", "03118", "03295", "77H01", "78A01", "78A05", "78A09", "78A13"]}}
{"id": "ef245e64f7021035", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "text", "line_start": 12054, "line_end": 12057, "token_count_estimate": 52, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "0c7a8f5ec0687dba", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 44 | 03 77H07 03298 | 28.327 | 89.430 | Upper Yarlung Tsangpo | E(o) | 140.50 | 4,426 |\n| 45 | 03 77H08 03302 | 28.144 | 89.396 | Upper Yarlung Tsangpo | O | 62.48 | 4,473 |\n| 46 | 03 77H08 03328 | 28.025 | 89.428 | Upper Yarlung Tsangpo | E(o) | 63.43 | 4,791 |\n| 47 | 03 77H12 03343 | 28.241 | 89.695 | Upper Yarlung Tsangpo | E(o) | 70.85 | 4,693 |\n| 48 | 03 77H12 03344 | 28.228 | 89.638 | Upper Yarlung Tsangpo | E(o) | 1273.72 | 4,568 |\n| 49 | 03 77H12 03352 | 28.181 | 89.535 | Upper Yarlung Tsangpo | E(o) | 82.15 | 4,694 |\n| 50 | 03 77H16 03368 | 28.230 | 89.887 | Upper Yarlung Tsangpo | M(e) | 151.09 | 4,921 |\n| 51 | 03 77L03 03394 | 28.264 | 90.068 | Upper Yarlung Tsangpo | M(e) | 188.07 | 5,149 |\n| 52 | 03 77L04 03395 | 28.236 | 90.104 | Upper Yarlung Tsangpo | M(e) | 585.02 | 5,126 |\n| 53 | 03 78E05 03424 | 27.969 | 89.379 | Upper Yarlung Tsangpo | E(o) | 66.73 | 4,568 |\n| 54 | 03 78E05 03429 | 27.959 | 89.397 | Upper Yarlung Tsangpo | E(o) | 181.69 | 4,576 |\n| 55 | 03 78E05 03433 | 27.941 | 89.389 | Upper Yarlung Tsangpo | E(o) | 279.30 | 4,572 |\n| 56 | 03 78E05 03454 | 27.878 | 89.312 | Upper Yarlung Tsangpo | M(e) | 61.73 | 5,001 |\n| 57 | 03 78E01 03664 | 27.809 | 89.230 | Amo Chu | M(e) | 52.68 | 5,137 |\n| 58 | 03 77L04 04187 | 28.107 | 90.247 | Puna Tsang Chu | M(e) | 132.27 | 4,369 |\n| 59 | 03 77L04 04243 | 28.016 | 90.210 | Puna Tsang Chu | E(o) | 54.12 | 5,127 |\n| 60 | 03 77L08 04272 | 28.092 | 90.301 | Puna Tsang Chu | M(e) | 153.89 | 4,513 |\n| 61 | 03 78E13 04457 | 27.973 | 89.930 | Puna Tsang Chu | E(c) | 65.00 | 5,076 |\n| 62 | 03 78E13 04481 | 27.940 | 89.930 | Puna Tsang Chu | M(o) | 72.67 | 5,002 |\n| 63 | 03 78I01 04658 | 27.977 | 90.233 | Puna Tsang Chu | M(o) | 67.28 | 5,072 |\n| 64 | 03 78I01 04807 | 27.800 | 90.231 | Puna Tsang Chu | E(o) | 74.59 | 4,758 |\n| 65 | 03 77J15 05060 | 30.479 | 90.966 | Lhasa Tsangpo | E(o) | 85.72 | 5,035 |\n| 66 | 03 82A16 05382 | 31.120 | 92.833 | Lhasa Tsangpo | O | 388.59 | 4,902 |\n| 67 | 03 82A16 05393 | 31.112 | 92.815 | Lhasa Tsangpo | O | 68.03 | 4,905 |\n| 68 | 03 82A16 05396 | 31.109 | 92.952 | Lhasa Tsangpo | O | 97.67 | 4,894 |\n| 69 | 03 82A16 05447 | 31.036 | 92.787 | Lhasa Tsangpo | O | 94.42 | 4,908 |\n| 70 | 03 82B11 05671 | 30.494 | 92.646 | Lhasa Tsangpo | E(o) | 98.02 | 4,817 |\n| 71 | 03 82B11 05688 | 30.350 | 92.735 | Lhasa Tsangpo | E(o) | 87.32 | 5,112 |\n| 72 | 03 82B13 05785 | 30.976 | 92.941 | Lhasa Tsangpo | E(o) | 441.76 | 4,904 |\n| 73 | 03 82B13 05792 | 30.949 | 92.890 | Lhasa Tsangpo | O | 118.54 | 4,892 |\n| 74 | 03 82B13 05795 | 30.935 | 92.828 | Lhasa Tsangpo | O | 222.71 | 4,886 |\n| 75 | 03 82B13 05797 | 30.934 | 92.775 | Lhasa Tsangpo | E(o) | 122.17 | 4,835 |\n| 76 | 03 82B13 05803 | 30.906 | 92.817 | Lhasa Tsangpo | E(o) | 177.10 | 4,960 |\n| 77 | 03 82B13 05804 | 30.896 | 92.910 | Lhasa Tsangpo | E(o) | 266.63 | 4,923 |\n| 78 | 03 82B13 05805 | 30.894 | 92.951 | Lhasa Tsangpo | E(o) | 205.95 | 4,958 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 12058, "line_end": 12103, "token_count_estimate": 1720, "basins": ["Brahmaputra"], "subbasins": ["Amo Chu", "Lhasa Tsangpo", "Puna Tsang Chu", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["03298", "03302", "03328", "03343", "03344", "03352", "03368", "03394", "03395", "03424", "03429", "03433", "03454", "03664", "04187", "04243", "04272", "04457", "04481", "04658", "04807", "05060", "05382", "05393", "05396", "05447", "05671", "05688", "05785", "05792", "05795", "05797", "05803", "05804", "05805", "77H07", "77H08", "77H12", "77H16", "77J15", "77L03", "77L04", "77L08", "78E01", "78E05", "78E13", "78I01", "82A16", "82B11", "82B13"]}}
{"id": "a50c8058bc5a38b5", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 79 | 03 82B13 05808 | 30.879 | 92.881 | Lhasa Tsangpo | E(o) | 52.44 | 4,982 |\n| 80 | 03 82C05 05928 | 29.779 | 92.388 | Lhasa Tsangpo | E(o) | 154.54 | 4,916 |\n| 81 | 03 82C06 05941 | 29.667 | 92.394 | Lhasa Tsangpo | E(o) | 53.86 | 4,677 |\n| 82 | 03 82E04 06022 | 31.132 | 93.177 | Lhasa Tsangpo | O | 692.16 | 5,007 |\n| 83 | 03 82E04 06068 | 31.103 | 93.144 | Lhasa Tsangpo | E(o) | 101.01 | 5,024 |\n| 84 | 03 82E04 06096 | 31.004 | 93.088 | Lhasa Tsangpo | E(o) | 71.50 | 5,040 |\n| 85 | 03 82E08 06104 | 31.065 | 93.292 | Lhasa Tsangpo | E(o) | 51.21 | 5,047 |\n| 86 | 03 77L03 06157 | 28.278 | 90.226 | Upper Yarlung Tsangpo | M(e) | 86.59 | 5,301 |\n| 87 | 03 77L04 06161 | 28.245 | 90.185 | Upper Yarlung Tsangpo | M(e) | 60.02 | 5,455 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 44, "line_start": 12058, "line_end": 12103, "token_count_estimate": 520, "basins": ["Brahmaputra"], "subbasins": ["Lhasa Tsangpo", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["05808", "05928", "05941", "06022", "06068", "06096", "06104", "06157", "06161", "77L03", "77L04", "82B13", "82C05", "82C06", "82E04", "82E08"]}}
{"id": "ced3e10533daa495", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "text", "line_start": 12104, "line_end": 12110, "token_count_estimate": 52, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "deb15ed4b4425c8d", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 88 | 03 77P10 06238 | 28.546 | 91.525 | Upper Yarlung Tsangpo | O | 101.76 | 5,083 |\n| 89 | 03 77L11 06298 | 28.273 | 90.736 | Manas | M(e) | 167.03 | 4,510 |\n| 90 | 03 77L12 06314 | 28.241 | 90.724 | Manas | M(e) | 88.56 | 4,654 |\n| 91 | 03 77L12 06349 | 28.123 | 90.566 | Manas | M(e) | 67.52 | 5,172 |\n| 92 | 03 77L12 06358 | 28.096 | 90.738 | Manas | M(e) | 67.74 | 5,004 |\n| 93 | 03 77L16 06450 | 28.086 | 90.787 | Manas | M(e) | 216.41 | 5,165 |\n| 94 | 03 77L16 06456 | 28.059 | 90.903 | Manas | E(o) | 52.05 | 4,768 |\n| 95 | 03 77L16 06503 | 28.003 | 90.905 | Manas | E(o) | 87.32 | 4,754 |\n| 96 | 03 77P03 06540 | 28.351 | 91.079 | Manas | M(e) | 77.20 | 4,787 |\n| 97 | 03 77P04 06580 | 28.034 | 91.003 | Manas | E(o) | 76.90 | 4,200 |\n| 98 | 03 77P08 06632 | 28.088 | 91.257 | Manas | E(o) | 56.11 | 4,630 |\n| 99 | 03 77L08 06904 | 28.037 | 90.364 | Manas | M(e) | 84.10 | 5,216 |\n| 100 | 03 77L08 06928 | 28.014 | 90.374 | Manas | M(e) | 72.68 | 5,182 |\n| 101 | 03 77L12 07005 | 28.022 | 90.709 | Manas | M(e) | 145.52 | 4,868 |\n| 102 | 03 78I05 07173 | 27.890 | 90.290 | Manas | M(e) | 114.20 | 5,059 |\n| 103 | 03 78I09 07447 | 27.939 | 90.535 | Manas | E(c) | 59.94 | 5,036 |\n| 104 | 03 78I09 07510 | 27.861 | 90.591 | Manas | E(o) | 74.98 | 4,770 |\n| 105 | 03 78I13 07712 | 27.867 | 90.816 | Manas | E(o) | 53.98 | 4,135 |\n| 106 | 03 77P08 07817 | 28.037 | 91.452 | Manas | M(o) | 56.24 | 4,737 |\n| 107 | 03 77P16 07886 | 28.102 | 91.942 | Manas | E(o) | 117.73 | 4,705 |\n| 108 | 03 77P16 07892 | 28.059 | 91.939 | Manas | E(o) | 240.03 | 4,631 |\n| 109 | 03 78M09 08030 | 27.838 | 91.605 | Manas | O | 66.35 | 4,125 |\n| 110 | 03 78M09 08032 | 27.834 | 91.553 | Manas | E(c) | 67.23 | 4,521 |\n| 111 | 03 78M13 08095 | 27.901 | 91.896 | Manas | O | 217.48 | 4,452 |\n| 112 | 03 78M13 08130 | 27.841 | 91.892 | Manas | O | 145.65 | 4,638 |\n| 113 | 03 83A02 08572 | 27.519 | 92.033 | Manas | E(o) | 60.79 | 4,274 |\n| 114 | 03 83A05 08628 | 27.771 | 92.435 | Manas | M(o) | 55.56 | 5,179 |\n| 115 | 03 82F02 08814 | 30.621 | 93.181 | Lower Yarlung Tsangpo | O | 689.79 | 4,499 |\n| 116 | 03 82F02 08824 | 30.535 | 93.058 | Lower Yarlung Tsangpo | E(o) | 86.74 | 4,817 |\n| 117 | 03 82F06 08851 | 30.521 | 93.445 | Lower Yarlung Tsangpo | M(o) | 114.42 | 4,780 |\n| 118 | 03 82F11 08921 | 30.355 | 93.632 | Lower Yarlung Tsangpo | M(e) | 54.25 | 4,442 |\n| 119 | 03 82J06 09026 | 30.654 | 94.492 | Lower Yarlung Tsangpo | M(e) | 530.64 | 3,942 |\n| 120 | 03 82J06 09027 | 30.626 | 94.444 | Lower Yarlung Tsangpo | M(e) | 66.04 | 4,095 |\n| 121 | 03 82J11 09055 | 30.452 | 94.603 | Lower Yarlung Tsangpo | M(e) | 181.75 | 3,998 |\n| 122 | 03 82J14 09067 | 30.537 | 94.759 | Lower Yarlung Tsangpo | M(e) | 51.12 | 3,631 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 12111, "line_end": 12156, "token_count_estimate": 1655, "basins": ["Brahmaputra"], "subbasins": ["Lower Yarlung Tsangpo", "Manas", "Upper Yarlung Tsangpo"], "countries": [], "lake_ids": ["06238", "06298", "06314", "06349", "06358", "06450", "06456", "06503", "06540", "06580", "06632", "06904", "06928", "07005", "07173", "07447", "07510", "07712", "07817", "07886", "07892", "08030", "08032", "08095", "08130", "08572", "08628", "08814", "08824", "08851", "08921", "09026", "09027", "09055", "09067", "77L08", "77L11", "77L12", "77L16", "77P03", "77P04", "77P08", "77P10", "77P16", "78I05", "78I09", "78I13", "78M09", "78M13", "82F02"]}}
{"id": "18f7c4e7d4c4acc1", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|\n| 123 | 03 82B08 09108 | 30.049 | 92.443 | Lower Yarlung Tsangpo | E(o) | 52.80 | 4,993 |\n| 124 | 03 82F07 09589 | 30.267 | 93.457 | Lower Yarlung Tsangpo | M(o) | 70.57 | 4,076 |\n| 125 | 03 82F12 09688 | 30.242 | 93.638 | Lower Yarlung Tsangpo | M(e) | 108.96 | 4,181 |\n| 126 | 03 82F15 09750 | 30.261 | 93.764 | Lower Yarlung Tsangpo | M(e) | 64.75 | 4,019 |\n| 127 | 03 82F16 09776 | 30.020 | 93.967 | Lower Yarlung Tsangpo | E(v) | 2658.49 | 3,475 |\n| 128 | 03 82G06 10094 | 29.541 | 93.345 | Lower Yarlung Tsangpo | E(o) | 100.03 | 4,631 |\n| 129 | 03 82G10 10253 | 29.513 | 93.620 | Lower Yarlung Tsangpo | O | 79.97 | 4,362 |\n| 130 | 03 82G11 10262 | 29.477 | 93.631 | Lower Yarlung Tsangpo | E(o) | 82.79 | 4,369 |\n| 131 | 03 82G14 10427 | 29.542 | 93.830 | Lower Yarlung Tsangpo | E(o) | 56.22 | 4,419 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 44, "line_start": 12111, "line_end": 12156, "token_count_estimate": 536, "basins": ["Brahmaputra"], "subbasins": ["Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["09108", "09589", "09688", "09750", "09776", "10094", "10253", "10262", "10427", "82B08", "82F07", "82F12", "82F15", "82F16", "82G06", "82G10", "82G11", "82G14"]}}
{"id": "cca4a1c918300d98", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "text", "line_start": 12157, "line_end": 12164, "token_count_estimate": 52, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "3c2602d6c6d2c40f", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|\n| 132 | 03 82G14 | 10455 | 29.502 | 93.937 | Lower Yarlung Tsangpo | E(o) | 56.29 | 4,444 |\n| 133 | 03 82J04 | 10483 | 30.126 | 94.090 | Lower Yarlung Tsangpo | E(o) | 276.68 | 3,802 |\n| 134 | 03 82J04 | 10484 | 30.115 | 94.188 | Lower Yarlung Tsangpo | M(e) | 94.40 | 3,905 |\n| 135 | 03 82J04 | 10489 | 30.046 | 94.157 | Lower Yarlung Tsangpo | E(o) | 107.17 | 4,294 |\n| 136 | 03 82J08 | 10499 | 30.099 | 94.270 | Lower Yarlung Tsangpo | M(e) | 68.10 | 3,924 |\n| 137 | 03 82K02 | 10661 | 29.545 | 94.067 | Lower Yarlung Tsangpo | E(o) | 50.77 | 4,299 |\n| 138 | 03 82K02 | 10688 | 29.526 | 94.057 | Lower Yarlung Tsangpo | E(o) | 78.00 | 4,533 |\n| 139 | 03 82K02 | 10696 | 29.518 | 94.121 | Lower Yarlung Tsangpo | E(o) | 118.34 | 4,501 |\n| 140 | 03 82K02 | 10703 | 29.505 | 94.133 | Lower Yarlung Tsangpo | E(o) | 101.69 | 4,577 |\n| 141 | 03 82K03 | 10730 | 29.472 | 94.236 | Lower Yarlung Tsangpo | E(o) | 50.80 | 4,509 |\n| 142 | 03 82K05 | 10788 | 29.915 | 94.280 | Lower Yarlung Tsangpo | E(c) | 183.14 | 4,385 |\n| 143 | 03 82K05 | 10845 | 29.828 | 94.462 | Lower Yarlung Tsangpo | E(o) | 57.27 | 4,133 |\n| 144 | 03 82K05 | 10852 | 29.813 | 94.433 | Lower Yarlung Tsangpo | O | 228.43 | 4,083 |\n| 145 | 03 82K09 | 10962 | 29.808 | 94.501 | Lower Yarlung Tsangpo | O | 55.81 | 4,305 |\n| 146 | 03 82G08 | 11911 | 29.240 | 93.276 | Lower Yarlung Tsangpo | E(o) | 58.59 | 4,914 |\n| 147 | 03 82G11 | 12006 | 29.405 | 93.708 | Lower Yarlung Tsangpo | E(o) | 74.32 | 4,505 |\n| 148 | 03 82G11 | 12065 | 29.287 | 93.736 | Lower Yarlung Tsangpo | E(o) | 59.16 | 4,562 |\n| 149 | 03 82G16 | 12326 | 29.035 | 93.836 | Lower Yarlung Tsangpo | O | 67.89 | 4,116 |\n| 150 | 03 82H13 | 12506 | 28.856 | 94.000 | Lower Yarlung Tsangpo | E(o) | 73.89 | 3,868 |\n| 151 | 03 82K14 | 12810 | 29.545 | 94.965 | Lower Yarlung Tsangpo | E(c) | 91.95 | 4,300 |\n| 152 | 03 82L05 | 12837 | 28.986 | 94.270 | Lower Yarlung Tsangpo | E(o) | 50.66 | 3,478 |\n| 153 | 03 82H03 | 13095 | 28.342 | 93.092 | Subansiri | E(o) | 62.44 | 4,251 |\n| 154 | 03 82H03 | 13097 | 28.320 | 93.047 | Subansiri | E(o) | 72.68 | 4,255 |\n| 155 | 03 83A09 | 13285 | 27.980 | 92.651 | Subansiri | M(e) | 52.21 | 4,988 |\n| 156 | 03 82D16 | 13318 | 28.116 | 92.951 | Subansiri | E(c) | 55.58 | 4,648 |\n| 157 | 03 82J08 | 13621 | 30.174 | 94.346 | Lower Yarlung Tsangpo | E(v) | 180.80 | 3,654 |\n| 158 | 03 82J08 | 13634 | 30.073 | 94.464 | Lower Yarlung Tsangpo | E(o) | 90.19 | 4,110 |\n| 159 | 03 82J08 | 13655 | 30.013 | 94.472 | Lower Yarlung Tsangpo | E(c) | 66.38 | 4,327 |\n| 160 | 03 82J08 | 13657 | 30.005 | 94.384 | Lower Yarlung Tsangpo | E(o) | 59.09 | 4,020 |\n| 161 | 03 82K05 | 13679 | 29.959 | 94.292 | Lower Yarlung Tsangpo | E(v) | 134.02 | 4,282 |\n| 162 | 03 82K05 | 13686 | 29.947 | 94.358 | Lower Yarlung Tsangpo | E(o) | 109.97 | 4,148 |\n| 163 | 03 82K05 | 13714 | 29.896 | 94.461 | Lower Yarlung Tsangpo | E(o) | 85.25 | 4,346 |\n| 164 | 03 82K09 | 13791 | 29.890 | 94.569 | Lower Yarlung Tsangpo | E(v) | 158.02 | 4,149 |\n| 165 | 03 82K09 | 13855 | 29.829 | 94.633 | Lower Yarlung Tsangpo | E(v) | 60.20 | 4,231 |\n| 166 | 03 82K09 | 13886 | 29.779 | 94.601 | Lower Yarlung Tsangpo | E(v) | 184.22 | 4,146 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 35, "line_start": 12165, "line_end": 12210, "token_count_estimate": 1819, "basins": ["Brahmaputra"], "subbasins": ["Lower Yarlung Tsangpo", "Subansiri"], "countries": [], "lake_ids": ["10455", "10483", "10484", "10489", "10499", "10661", "10688", "10696", "10703", "10730", "10788", "10845", "10852", "10962", "11911", "12006", "12065", "12326", "12506", "12810", "12837", "13095", "13097", "13285", "13318", "13621", "13634", "13655", "13657", "13679", "13686", "13714", "13791", "13855", "13886", "82D16", "82G08", "82G11", "82G14", "82G16", "82H03", "82H13", "82J04", "82J08", "82K02", "82K03", "82K05", "82K09", "82K14", "82L05"]}}
{"id": "346d682af445b1eb", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|\n| 167 | 03 82N02 | 14031 | 30.603 | 95.182 | Lower Yarlung Tsangpo | M(e) | 110.12 | 4,278 |\n| 168 | 03 82N10 | 14193 | 30.473 | 95.575 | Lower Yarlung Tsangpo | E(o) | 56.47 | 4,866 |\n| 169 | 03 82N11 | 14301 | 30.251 | 95.604 | Lower Yarlung Tsangpo | M(o) | 134.45 | 4,442 |\n| 170 | 03 82N12 | 14308 | 30.221 | 95.584 | Lower Yarlung Tsangpo | M(e) | 85.49 | 4,342 |\n| 171 | 03 91C02 | 14526 | 29.598 | 96.141 | Lower Yarlung Tsangpo | M(o) | 65.09 | 4,025 |\n| 172 | 03 91C05 | 14550 | 29.823 | 96.351 | Lower Yarlung Tsangpo | M(o) | 101.06 | 4,870 |\n| 173 | 03 82O08 | 14709 | 29.180 | 95.486 | Dihang | E(o) | 93.81 | 3,533 |\n| 174 | 03 82O11 | 14834 | 29.304 | 95.640 | Dihang | E(v) | 70.17 | 3,322 |\n| 175 | 03 82O15 | 14903 | 29.371 | 95.873 | Dihang | E(o) | 103.75 | 4,344 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 36, "table_row_end": 44, "line_start": 12165, "line_end": 12210, "token_count_estimate": 542, "basins": ["Brahmaputra"], "subbasins": ["Dihang", "Lower Yarlung Tsangpo"], "countries": [], "lake_ids": ["14031", "14193", "14301", "14308", "14526", "14550", "14709", "14834", "14903", "82N02", "82N10", "82N11", "82N12", "82O08", "82O11", "82O15", "91C02", "91C05"]}}
{"id": "7af216ef88800998", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: text\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "text", "line_start": 12211, "line_end": 12219, "token_count_estimate": 53, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "bd7c7fb611ac27ae", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: table\nTable\n\n| S.No. | Glacial Lake ID | Number | Latitude | Longitude | Subbasin | GL Type | Area (ha) | Elevation (m) |\n|---|---|---|---|---|---|---|---|---|\n| 176 | 03 82O08 | 14974 | 29.129 | 95.439 | Dibang | E(o) | 53.88 | 3,284 |\n| 177 | 03 82O16 | 15129 | 29.011 | 95.885 | Dibang | E(o) | 55.15 | 3,778 |\n| 178 | 03 82P13 | 15195 | 29.005 | 95.905 | Dibang | E(o) | 54.45 | 3,598 |\n| 179 | 03 91C03 | 15223 | 29.302 | 96.082 | Dibang | E(o) | 119.59 | 4,274 |\n| 180 | 03 91C03 | 15250 | 29.269 | 96.157 | Dibang | E(o) | 102.66 | 3,991 |\n| 181 | 03 91C04 | 15278 | 29.229 | 96.192 | Dibang | E(o) | 106.39 | 3,473 |\n| 182 | 03 91C04 | 15283 | 29.226 | 96.160 | Dibang | E(o) | 57.36 | 3,313 |\n| 183 | 03 91C04 | 15296 | 29.196 | 96.203 | Dibang | E(o) | 64.06 | 4,246 |\n| 184 | 03 91C04 | 15336 | 29.091 | 96.211 | Dibang | E(o) | 100.77 | 4,188 |\n| 185 | 03 91C04 | 15344 | 29.079 | 96.145 | Dibang | E(c) | 86.02 | 3,945 |\n| 186 | 03 91C04 | 15360 | 29.051 | 96.144 | Dibang | O | 79.93 | 3,602 |\n| 187 | 03 91C08 | 15390 | 29.244 | 96.245 | Dibang | E(o) | 50.88 | 4,360 |\n| 188 | 03 91D09 | 15690 | 28.776 | 96.531 | Dibang | O | 101.67 | 3,510 |\n| 189 | 03 91C03 | 15760 | 29.257 | 96.246 | Lohit | M(o) | 89.30 | 4,432 |\n| 190 | 03 91C08 | 15774 | 29.224 | 96.279 | Lohit | M(o) | 66.27 | 4,207 |\n| 191 | 03 91C11 | 15833 | 29.491 | 96.701 | Lohit | O | 610.17 | 3,916 |\n| 192 | 03 91C15 | 15967 | 29.462 | 96.787 | Lohit | O | 518.00 | 3,916 |\n| 193 | 03 91C15 | 15982 | 29.397 | 96.828 | Lohit | E(o) | 401.62 | 3,917 |\n| 194 | 03 91C15 | 15998 | 29.298 | 96.816 | Lohit | M(e) | 292.36 | 3,954 |\n| 195 | 03 91C15 | 16000 | 29.295 | 96.835 | Lohit | M(l) | 95.04 | 4,013 |\n| 196 | 03 91C15 | 16004 | 29.268 | 96.837 | Lohit | E(o) | 108.04 | 4,119 |\n| 197 | 03 91C16 | 16019 | 29.238 | 96.826 | Lohit | M(o) | 209.76 | 4,219 |\n| 198 | 03 91C16 | 16020 | 29.230 | 96.805 | Lohit | M(o) | 144.00 | 4,266 |\n| 199 | 03 91C16 | 16024 | 29.221 | 96.814 | Lohit | M(e) | 52.51 | 4,279 |\n| 200 | 03 91D10 | 16210 | 28.516 | 96.699 | Lohit | E(o) | 299.20 | 3,330 |\n| 201 | 03 91D16 | 16419 | 28.202 | 96.898 | Lohit | E(o) | 67.30 | 3,731 |\n| 202 | 03 91H01 | 16757 | 28.977 | 97.215 | Lohit | E(o) | 63.42 | 4,092 |\n| 203 | 03 91H01 | 16898 | 28.783 | 97.153 | Lohit | E(o) | 83.11 | 3,712 |\n| 204 | 03 91H05 | 17167 | 28.940 | 97.262 | Lohit | E(o) | 93.63 | 4,412 |\n| 205 | 03 91H07 | 17452 | 28.412 | 97.465 | Lohit | E(o) | 56.61 | 4,300 |\n| 206 | 03 91H08 | 17635 | 28.096 | 97.289 | Lohit | E(o) | 53.06 | 3,762 |\n| 207 | 03 92E05 | 17959 | 27.989 | 97.369 | Lohit | E(o) | 51.93 | 4,188 |", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "table", "table_caption": null, "columns": ["S.No.", "Glacial Lake ID", "Number", "Latitude", "Longitude", "Subbasin", "GL Type", "Area (ha)", "Elevation (m)"], "table_row_start": 1, "table_row_end": 32, "line_start": 12220, "line_end": 12253, "token_count_estimate": 1526, "basins": ["Brahmaputra"], "subbasins": ["Dibang", "Lohit"], "countries": [], "lake_ids": ["14974", "15129", "15195", "15223", "15250", "15278", "15283", "15296", "15336", "15344", "15360", "15390", "15690", "15760", "15774", "15833", "15967", "15982", "15998", "16000", "16004", "16019", "16020", "16024", "16210", "16419", "16757", "16898", "17167", "17452", "17635", "17959", "82O08", "82O16", "82P13", "91C03", "91C04", "91C08", "91C11", "91C15", "91C16", "91D09", "91D10", "91D16", "91H01", "91H05", "91H07", "91H08", "92E05"]}}
{"id": "8281ab64106be413", "text": "Document: output vertex chunked\nSection: Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha\nType: text\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Glacial Lakes of Brahmaputra River basin with area > 50 ha", "section_headings": ["Annexure - III", "Glacial Lakes of Brahmaputra River basin with area > 50 ha"], "chunk_type": "text", "line_start": 12254, "line_end": 12259, "token_count_estimate": 52, "basins": ["BRAHMAPUTRA", "Brahmaputra"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "a5c046e843c0f703", "text": "Document: output vertex chunked\nSection: Annexure - III > Annexure - IV: Glossary\nType: text\n\n**Ablation:** The process that reduce the mass of the glacier (Cogley et al., 2011).\n\n**Ablation area/zone:** The part of the glacier where ablation exceeds accumulation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is negative. The extent of the ablation zone can vary strongly from year to year (Cogley et al., 2011).\n\n**Accumulation:** The process that add to the mass of the glacier (Cogley et al., 2011).\n\n**Accumulation area/zone:** The part of the glacier where accumulation exceeds ablation in magnitude, that is, where the cumulative mass balance relative to the start of the mass-balance year is positive. The extent of the accumulation zone can vary strongly from year to year. The accumulation zone is not the same as the firn area (Cogley et al., 2011).\n\n**Altitude:** The vertical distance of a point above a datum, which is usually an estimate of mean sea level. Altitude and elevation are synonyms in common usage (Cogley et al., 2011).\n\n**Aspect:** The compass direction towards which a slope faces; measured clockwise in degrees from the North.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Attribute:** Non-spatial descriptive characteristics of a real-world phenomenon, often a measurement or value associated with spatial locations.\n\n**Avalanche:** A slide or flow of a mass of snow, firn or ice that becomes detached abruptly, often entraining additional material such as snow, debris and vegetation as it descends. The duration of an avalanche is typically seconds to minutes (Cogley et al., 2011).\n\n**Band:** One layer of multispectral image representing data values for a specific range of the electromagnetic spectrum of reflected light or heat.\n\n**Climate:** Climate is usually defined as the average weather or as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years. The relevant quantities are most often surface variables such as temperature, precipitation and wind (Pandey, 2019).\n\n**Climate change:** Climate change refers to a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. UNFCCC defines climate change as: ‘a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods’. (Pandey, 2019).\n\n**Climate variability:** Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all spatial and temporal scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability) (Pandey, 2019).", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Annexure - IV: Glossary", "section_headings": ["Annexure - III", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 12261, "line_end": 12404, "token_count_estimate": 819, "basins": [], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "88a6f7292bc9c8a1", "text": "Document: output vertex chunked\nSection: Annexure - III > Annexure - IV: Glossary\nType: text\n\nor indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods ’ . ( Pandey , 2019 ) . * * Climate variability : * * Climate variability refers to variations in the mean state and other statistics ( such as standard deviations , the occurrence of extremes , etc . ) of the climate on all spatial and temporal scales beyond that of individual weather events . Variability may be due to natural internal processes within the climate system ( internal variability ) , or to variations in natural or anthropogenic external forcing ( external variability ) ( Pandey , 2019 ) .\n\n**Cryosphere:** The cryosphere is the part of the Earth system that contains ice, for example snow on the ground, glaciers, ice sheets, lake ice, river ice, sea ice, seasonally and perennially frozen ground (GCW 2016).\n\n**Database:** An organized, integrated collection of data related by a common fact or purpose.\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Debris-covered glacier:** A glacier that is covered at its tongue with supra-glacial debris across its full width (Kirkbride, 2011). In the accumulation zone any deposited debris is buried by later snowfalls, but in the ablation zone debris remains at the surface and englacial debris is added to the surface layer from beneath as ice ablates away. The debris cover affects the rate of ablation, with very thin debris resulting in accelerated melt and debris thicker than a few tens of millimetres reducing the melting rate (Cogley et al., 2011).\n\n**Digital Elevation Model (DEM):** An array of numbers representing the elevation of part or all of the Earth’s surface as samples or averages at fixed spacing in two horizontal coordinate directions (Cogley et al., 2011).\n\n**Disaster:** A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNISDR 2017).\n\n**Disaster risk:** The potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity (UNISDR 2017).\n\n**Early warning system:** The set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare to act promptly and appropriately to reduce the possibility of harm or loss (Pandey, 2019).\n\n**Electromagnetic spectrum:** The spectrum of wavelengths of electromagnetic radiation.\n\n**Englacial:** Pertaining to the interior of the glacier, between the summer surface and the bed (Cogley et al., 2011).\n\n**Exposure:** The presence or situation of people, livelihoods, species, ecosystems, environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings, and other tangible human assets located in hazard-prone areas that could be adversely affected (UNISDR, 2017; Pandey, 2019).\n\n**Feature:** A real-world phenomenon, often used in cartography to name classes of elements shown on a map.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Annexure - IV: Glossary", "section_headings": ["Annexure - III", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 12261, "line_end": 12404, "token_count_estimate": 866, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "c72b500846a4c5bd", "text": "Document: output vertex chunked\nSection: Annexure - III > Annexure - IV: Glossary\nType: text\n\n* * Pertaining to the interior of the glacier , between the summer surface and the bed ( Cogley et al . , 2011 ) . * * Exposure : * * The presence or situation of people , livelihoods , species , ecosystems , environmental functions , services , and resources , infrastructure , or economic , social , or cultural assets in places and settings , and other tangible human assets located in hazard - prone areas that could be adversely affected ( UNISDR , 2017 ; Pandey , 2019 ) . * * Feature : * * A real - world phenomenon , often used in cartography to name classes of elements shown on a map .\n\n**Firn:** Snow (in which the pore space is at least partially interconnected, allowing air and water to circulate) that has survived at least one ablation season but has not been transformed to glacier ice (Cogley et al., 2011).\n\n**Flood:** The overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas not normally submerged. Floods include river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods and glacial lake outburst floods (Pandey, 2019).\n\n**Format:** The pattern into which data are systematically arranged for use on a computer.\n\n**Geographic Information System (GIS):** A set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world for a particular set of circumstances.\n\n**Glacial Lake Outburst Flood (GLOF):** Flood caused by the outburst of a glacial lake due to rapid accumulation of water in it, resulting to extreme damage in loss of lives and infrastructure in the downstream area.\n\n**Glacial Lake:** As a result of glacier thinning and retreating, melt water gets accumulated at terminal moraines or on it covered by glacier ice, is known as glacial lake.\n\n**Glacier Erosion Lake:** These are the water bodies formed in a depression after the glacier has retreated in a form of cirque or trough valley, might be isolated and far away from the present glaciated area, and mostly stable in nature.\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Glacier:** A perennial mass of ice, and possibly firn and snow, originating on the land surface by their crystallization of snow or other forms of solid precipitation and showing evidence of past or present flow (Cogley et al., 2011).\n\n**Global Positioning System (GPS):** A GPS is a position-fixing system that uses the time taken for signals to travel from at least three GPS satellites in a known orbit to a receiver on the ground.\n\n**Hazard:** The potential occurrence of a natural or human-induced physical event or trend or physical impact that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources (Pandey, 2019).\n\n**Ice-dammed Lake:** An Ice-dammed Lake is produced on the side(s) of a glacier, when an advancing glacier happens to intercept a tributary/tributaries pouring into a main glacier valley.", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Annexure - IV: Glossary", "section_headings": ["Annexure - III", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 12261, "line_end": 12404, "token_count_estimate": 832, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "9dcc466aa50c4cd8", "text": "Document: output vertex chunked\nSection: Annexure - III > Annexure - IV: Glossary\nType: text\n\nGPS satellites in a known orbit to a receiver on the ground . * * Hazard : * * The potential occurrence of a natural or human - induced physical event or trend or physical impact that may cause loss of life , injury , or other health impacts , as well as damage and loss to property , infrastructure , livelihoods , service provision , ecosystems and environmental resources ( Pandey , 2019 ) . * * Ice - dammed Lake : * * An Ice - dammed Lake is produced on the side ( s ) of a glacier , when an advancing glacier happens to intercept a tributary / tributaries pouring into a main glacier valley .\n\n**Impacts:** The term impacts is used primarily to refer to the effects on natural and human systems of extreme weather and climate events and of climate change. Impacts generally refer to effects on lives, livelihoods, health, ecosystems, economies, societies, cultures, services and infrastructure due to the interaction of climate changes or hazardous climate events occurring within a specific time period and the vulnerability of an exposed society or system. Impacts are also referred to as consequences and outcomes. The impacts of climate change on geophysical systems, including floods, droughts and sea level rise, are a subset of impacts called physical impacts (Pandey, 2019).\n\n**Latitude:** Angle measured in a north-south direction from the Earth’s center to locations on the Earth’s surface.\n\n**Longitude:** Angle measured in an east-west direction from the Earth’s center to locations on the Earth’s surface.\n\n**Layer:** Usually represents a theme or a feature type within the database.\n\n**Map:** An abstract representation of the physical features of a portion of the Earth’s surface graphically displayed on a planar surface. Map display signs, symbols and spatial relationships among the features.\n\n**Melt water:** The liquid resulting from melting of ice, firn or snow (Cogley et al., 2011).\n\n**Moraine-dammed Lake:** In the retreating process of a glacier, ice tends to melt in the lowest part of the glacier surrounded by Lateral-moraines and End-moraines, and forms into a lake known as Moraine-dammed Lake or Proglacial Lake.\n\n**Pixel:** Smallest discrete element that makes up an image, generally represents either a small square or portion of the Earth’s surface, scanned by satellite or aircraft.\n\n**Precipitation:** Liquid or solid products of the condensation of water vapour that fall from clouds or are deposited from the air onto the surface (Cogley et al., 2011).\n\n**Remote sensing:** The technique of obtaining data about the environment and surface of the earth from a distance, e.g. from an aircraft or satellite.\n\n**Resolution:** It is the accuracy at which a given map scale can depict the location and shape of geographic features.\n\n**Retreat:** Decrease of the length of a flow line (in case of glacier which is its terminus), measured from a fixed point. Advance is the opposite of retreat, that is, advance of the terminus (Cogley et al., 2011).\n\n***\n\nGLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Annexure - IV: Glossary", "section_headings": ["Annexure - III", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 12261, "line_end": 12404, "token_count_estimate": 808, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": [], "lake_ids": []}}
{"id": "f57882f832907813", "text": "Document: output vertex chunked\nSection: Annexure - III > Annexure - IV: Glossary\nType: text\n\n: * * The technique of obtaining data about the environment and surface of the earth from a distance , e . g . from an aircraft or satellite . * * Resolution : * * It is the accuracy at which a given map scale can depict the location and shape of geographic features . * * Retreat : * * Decrease of the length of a flow line ( in case of glacier which is its terminus ) , measured from a fixed point . Advance is the opposite of retreat , that is , advance of the terminus ( Cogley et al . , 2011 ) . * * * GLACIAL LAKE ATLAS OF BRAHMAPUTRA RIVER BASIN\n\n**Risk:** The potential for consequences where something of value is at stake and where the outcome is uncertain, recognizing the diversity of values. Risk is often represented as probability or likelihood of occurrence of hazardous events or trends multiplied by the impacts if these events or trends occur. In this report, the term risk is often used to refer to the potential, when the outcome is uncertain, for adverse consequences on lives, livelihoods, health, ecosystems and species, economic, social and cultural assets, services (including environmental services) and infrastructure (Pandey, 2019).\n\n**Scale:** The ratio or fraction between the distance on a map, chart or photograph and the corresponding distance on the surface of the Earth.\n\n**Slope:** A measure of change on surface value over distance, expressed in degrees or as a percentage.\n\n**Snow:** Solid precipitation in the form of ice crystals, chiefly in complex branched hexagonal form and often agglomerated into snowflakes; or an accumulation of the same on the Earth’s surface. It is also known as solid precipitation that has accumulated on the summer surface on a glacier and that transforms to firn at the end of the mass-balance year (Cogley et al., 2011).\n\n**Subglacial:** Pertaining to the glacier bed or to the material below the bed (Cogley et al., 2011).\n\n**Supra-glacial Lake:** Water bodies develop within the ice mass in any position of the glacier, but away from the terminal moraines are known as Supra-glacial lakes. Its basic characteristics are shifting, merging, and draining.\n\n**Terminus:** The lowest end of a glacier, also called glacier snout, glacier front or glacier toe (Cogley et al., 2011).\n\n**Tongue:** The lower, elongate part of a valley glacier or outlet glacier or a floating extension of a glacier or ice stream, laterally unconfined but markedly longer than wide (Cogley et al., 2011).\n\n**Topographic Map:** A map showing the features that describes the surface of a particular place or region. It contains contours indicating lines of equal surface elevation (relief), often referred to a topo maps.\n\n**Vulnerability:** The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt (Pandey, 2019).\n\nPrepared under: National Hydrology Project\n\nNational Remote Sensing Centre\nIndian Space Research Organisation\nDepartment of Space, Government of India\nHyderabad - 500 037", "metadata": {"source_file": "data/output_gemini_vertex_chunked.md", "document_title": "output vertex chunked", "section_path": "Annexure - III > Annexure - IV: Glossary", "section_headings": ["Annexure - III", "Annexure - IV: Glossary"], "chunk_type": "text", "line_start": 12261, "line_end": 12404, "token_count_estimate": 816, "basins": ["BRAHMAPUTRA"], "subbasins": [], "countries": ["India"], "lake_ids": []}}